Contaminated Ground Water and Sediment: Modeling for Management and Remediation

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Author: Calvin C. Chien | Jr. | Miguel A. Medina | George F. Pinder | Danny D. Reible | Brent Sleep | Chunmiao Zhe

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Contaminated Ground Water and Sediment Modeling for Management and Remediation

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Contaminated Ground Water and Sediment Modeling for Management and Remediation

Edited by

Calvin C. Chien Miguel A. Medina, Jr. George F. Pinder Danny D. Reible Brent E. Sleep Chunmiao Zheng

LEWIS PUBLISHERS A CRC Press Company Boca Raton London New York Washington, D.C.

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Library of Congress Cataloging-in-Publication Data Contaminated ground water and sediment : modeling for management and remediation/ edited by Calvin C. Chien … [et al.]. p. cm. Includes bibliographical references. ISBN 0-56670-667-X (alk. paper) 1. Ground water—Pollution—Mathematical models. 2. Contaminated sediments—Mathematical models. 3. Organochlorine compounds—Environmental aspects—Mathematical models. I. Chien, Calvin C. TD426.C657 2003 628.1¢68—dc22

2003061194

This book contains information obtained from authentic and highly regarded sources. Reprinted material is quoted with permission, and sources are indicated. A wide variety of references are listed. Reasonable efforts have been made to publish reliable data and information, but the author and the publisher cannot assume responsibility for the validity of all materials or for the consequences of their use. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microÞlming, and recording, or by any information storage or retrieval system, without prior permission in writing from the publisher. All rights reserved. Authorization to photocopy items for internal or personal use, or the personal or internal use of speciÞc clients, may be granted by CRC Press LLC, provided that $1.50 per page photocopied is paid directly to Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923 USA. The fee code for users of the Transactional Reporting Service is ISBN 0-56670-667X/04/$0.00+$1.50. The fee is subject to change without notice. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. The consent of CRC Press LLC does not extend to copying for general distribution, for promotion, for creating new works, or for resale. SpeciÞc permission must be obtained in writing from CRC Press LLC for such copying. Direct all inquiries to CRC Press LLC, 2000 N.W. Corporate Blvd., Boca Raton, Florida 33431. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identiÞcation and explanation, without intent to infringe.

Visit the CRC Press Web site at www.crcpress.com © 2004 by CRC Press LLC Lewis Publishers is an imprint of CRC Press LLC No claim to original U.S. Government works International Standard Book Number 0-56670-667-X Library of Congress Card Number 2003061194 Printed in the United States of America 1 2 3 4 5 6 7 8 9 0 Printed on acid-free paper

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Introduction The use of models to provide additional details on contaminant fate and transport has rapidly increased in the past 3 decades. The increasing global recognition of the potential risks associated with surface water or ground water contamination and speciÞc environmental regulations implemented after 1980 have demanded a more accurate understanding of these risks as they relate to human health and the environment. Modeling has become an invaluable tool in providing the necessary information to understand the risks associated with contaminants in complex environments with complicated environmental processes. Although improvements in computing power provided by modern personal computers and various new computational methods have allowed the development of more sophisticated environmental models, many technical issues and disagreements on particular modeling approaches and methods remain. Because the public, government agencies, and industry all have a high level of interest and stake in environmental protection and remediation, and because billions of dollars are spent every year for remediation, the need for a comprehensive review of the theory, practice, and future direction of modeling technology is becoming more urgent. The formation, requested by the U.S. Environmental Protection Agency (USEPA), of the Environmental Modeling Subcommittee of the Science Advisory Board in 2000, the effort ordered by the USEPA Administrator in 2003 to revitalize the agency’s Council for Regulatory Environmental Modeling (CREM), and a panel study on the same issue recently planned by the National Research Council (NRC) best explain the increasing urgency to better understand modeling technology development and application so that a more reasonable and defensible decision-making process for environmental issues can be achieved. The DuPont Company provided a forum and necessary support for this purpose. A workshop, Modeling and Management of Emerging Environmental Issues — Expert Workshop 2000, was planned, organized, and chaired by Calvin C. Chien, leader of environmental modeling technology and development for the DuPont Corporate Remediation Group (CRG). Approximately four dozen modeling experts from the U.S. and Canada were carefully selected and invited to participate in this effort. Four panels were formed, with each addressing one of the following primary environmental contamination and remediation issues involving modeling: (1) Mixing Zone: Discharge of Contaminated Ground water into Surface Water Bodies, (2) Contaminated Sediment: Its Fate and Transport, (3) Optimization Modeling for Remediation and Monitoring, and (4) Simulation of Halogenated Hydrocarbons in the Subsurface. Although the details of these issues vary, all involve technical and/or regulatory challenges and a high Þnancial stake. Each panel had a panel leader who worked with the CRG to select the panel members, outline the panel discussion, facilitate the discussion at the workshop, and

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help prepare the manuscripts for the chapters presented in this book. The workshop was held from July 25 to 27, 2000, at the campus of Penn State University Great Valley in Malvern, PA. An assistant panel leader supported each leader and took discussion notes, which helped panelists in the preparation of this book. A complete list of panelists and their afÞliations are provided in Appendix A. This book was prepared using the information generated from workshop discussions and additional materials provided by the panelists. The primary objectives of this book were to provide information on the state of the art and current practice and identify the research and development needs of the modeling technologies discussed. It should be noted that the discussions herein are based not only on technical analysis but also on regulatory acceptance and cost effectiveness. This book comprises four chapters. Each chapter addresses one of the four topic areas discussed at the workshop. In most cases, a section of each chapter was prepared by a panel member and, in some cases, includes materials offered by other members. The panel leaders either assembled the material submitted by the panelists or further edited the manuscripts prior to overall editing. During the editing process, original submitted materials were modiÞed, expanded, and reorganized. As a result, it is impossible to accurately allocate credits to individual contributors. However, those individuals who made signiÞcant contributions are mentioned. The person responsible for assembling and editing each chapter manuscript is listed at the beginning of the chapter, followed by the names of signiÞcant contributors. Calvin C. Chien was responsible for the overall planning, preparation, and publication of the book. DuPont and the book contributors want to express their deep appreciations to Penn State Great Valley and Elayna McReynolds, the conference coordinator, for the support provided during the workshop. Special credit must be offered to Kathleen O. Adams, DuPont contract technical writer, for her deep involvement, dedication, and signiÞcant contributions throughout the editing process.

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Editors Calvin C. Chien, a senior environmental fellow with the Corporate Remediation Group, joined DuPont in 1981 and transferred to DuPont Engineering in 1986. With an undergraduate degree in hydraulic engineering, he earned his Ph.D. from the State University of New York at Buffalo in 1974 with research in the modeling of hydrologic systems. In his near 30 years of practice, Dr. Chien has focused on performing ground water investigations and facilitating environmental remediation technology development. As a company leader for technology development, he has concentrated in the areas of environmental modeling and containment technology since 1986. Besides serving as the leader of the Groundwater Work Group of the Chemical Manufacturers Association (CMA, now American Chemistry Council) in the late 1990s, he was an appointed member of the U.S. Environmental Protection Agency (USEPA) Science Advisory Board for four terms and served on the Environmental Engineering Committee and Environmental Modeling Subcommittee from 1993 to 2000. He was also appointed to serve on its Science and Technology Achievement Award (STAA) Committee. Dr. Chien has served on many national ground water modeling technical and review committees. He has advocated for the collaboration among industry, university, and government agencies through a number of major national expert workshops in the past 10 years. Dr. Chien is recognized as the pioneer in a new approach in solving problems in environmental remediation and as one of the leading modelers in the industry. Miguel A. Medina, Jr. earned a Ph.D. degree in water resources and environmental engineering sciences from the University of Florida in 1976 and joined the Duke faculty thereafter. He is director of the International Honors Program of the School of Engineering and director of the Center for Hydrologic Sciences. He has been a registered professional hydrologist by the American Institute of Hydrology (Minnesota) since 1983 and was its vice president for institute development from 1998 to 2000. He was named External Evaluator of the UNESCO International Hydrological Programme from 2002 to 2003. Professor Medina has conducted funded research in hydrologic and water quality mathematical modeling for the U.S. Environmental Protection Agency (USEPA), the National Science Foundation, the OfÞce of Water Research and Technology, the U.S. Air Force, the U.S. Army Waterways Experiment Station, the Naval Oceanographic OfÞce, DuPont Engineering, the North Carolina Water Resources Research Institute, and the State of North Carolina. His current research focuses on ßow and solute transport surface/ground water interactions and he has published numerous articles on this topic in peer-reviewed journals.

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Dr. Medina is a former president of the Universities Council on Water Resources, Inc. and the North Carolina Section of the American Water Resources Association. He is a consultant to the USEPA, the World Health Organization, the Research Triangle Institute of North Carolina, the Inter-American Development Bank, the Pan American Health Organization, UNESCO, the Ministries of Water Resources in Venezuela and Spain, the Technical Advisory Service for Attorneys, and other private enterprises. He is a past chairman of the International Technical Advisory Committee of the International Ground Water Modeling Center (Colorado School of Mines, and Delft, the Netherlands). In 1989, the Governor of North Carolina appointed Dr. Medina to the Environmental Management Commission. George F. Pinder received his Bachelor of Science degree in geology at the University of Western Ontario (London) and his Ph.D. in geology, civil engineering, and agriculture at the University of Illinois at Urbana. After 4 years as a research hydrologist with the U.S. Geological Survey in Washington, he joined the Civil Engineering Department at Princeton University as an associate professor. He was promoted to full professor 5 years later. He served as chairman of the Department of Civil Engineering and Operations Research from 1980 to 1989. He served as dean of the College of Engineering and Mathematics at the University of Vermont from 1989 to 1996 and is currently head of the Research Center for Groundwater Remediation Design at the University of Vermont. Dr. Pinder has published more than 200 papers and reports in the area of quantitative ground water models. He has also published seven books. The latest, Groundwater Modeling Using Geographical Information Systems, was published in 2002 by John Wiley & Sons. In addition to his responsibilities as founding editor of the journals Advances in Water Resources and Numerical Methods for Partial Differential Equations, he is also on the editorial board of Applied Numerical Mathematics and Numerical Methods in Fluids. Dr. Pinder served as dean of the Division of Engineering, Mathematics, and Business Administration at the University of Vermont from 1992 to 1996; he was named a 1993–1994 University Scholar in recognition of his contributions to research and scholarship. The American Geophysical Union (AGU) presented their Horton Award to Dr. Pinder in 1969 and in 1993 invited him to become an AGU fellow. In 1975, The Geological Society of America presented him with the O.E. Meinzer Award for an outstanding contribution to the Þeld of hydrology. He received the Hinds medal of the American Society of Civil Engineers in 2002. Daniel D. Reible has provided national and international leadership on environmental matters to students, colleagues, and his profession. He is currently Chevron Professor of Chemical Engineering and director of the Hazardous Substance Research Center at Louisiana State University. He joined LSU after receiving a B.S. degree in chemical engineering from Lamar University (1977) and an M.S. and Ph.D. in chemical engineering from the California Institute of Technology (1979 and 1982, respectively). As a teacher he has developed several graduate-level courses in chemical engineering and remains active in teaching both undergraduate and advanced-level chemical engineering courses. His teaching efforts have also

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extended far beyond the university, for example, with his direction of Advanced Study Institutes in Prague with NATO support in 2001 and in Rio de Janeiro with NSF support in 2002. Both institutes involved more than 100 attendees focused on the current science of environmental assessment and remediation. He is the author of two books, Fundamentals of Environmental Engineering and Diffusion Models of Environmental Transport, which are widely used as both course texts and reference books. Dr. Reible has been active in both environmental research and its implications for policy. He has edited two books over the past 2 years on the state of the art in assessment and remediation of contaminated sites. He served on the National Research Council Committee on PCB-contaminated sediments, which has had a profound impact on the management of contaminated sediments in this country, and currently serves on the National Research Council Committee for Remediation of Navy Sites. He recently provided congressional testimony before the U.S. House Subcommittee on Water Resources and the Environment on strategies for the management of contaminated sediment sites. His leadership role in environmental research and its policy implications has been recognized by the American Institute of Chemical Engineers from whom Dr. Reible received the Lawrence K. Cecil Award in 2001. Brent E. Sleep is a professor in the Department of Civil Engineering at the University of Toronto, where he teaches courses in contaminant hydrogeology, environmental chemistry, and engineering mathematics. Dr. Sleep’s research interests and publications are in the area of remediation of organic contamination of ground water, including experimental studies and numerical modeling. Current projects include laboratory studies of anaerobic biodegradation of DNAPL source zones, in situ chemical oxidation and biodegradation of DNAPL source zones, biodegradation of mixtures of halogenated organic compounds, isotopic fractionation associated with biological processes, bioÞlm growth in fractures, and biological processes in low permeability media. Numerical modeling is focused on modeling nonisothermal multiphase ßow and multicomponent transport in the subsurface incorporating biological processes, parameter estimation, and optimization of remediation processes. Previous studies have included pilot-scale studies and numerical modeling of subsurface LNAPL and DNAPL transport, free-phase recovery, bioventing, air sparging, and bench-scale studies of vapor transport in soils, sequential anaerobic/aerobic biodegradation of chlorinated ethenes, and steam ßushing for DNAPL removal. Dr. Sleep holds a Ph.D. in civil engineering from the University of Waterloo. He also holds a B.A.Sc. and M.Eng. in chemical engineering from the University of Waterloo. Dr. Sleep is a member of the American Geophysical Union and the National Ground Water Association and an associate editor of Advances in Water Resources. Chunmiao Zheng is professor of hydrogeology in the Department of Geological Sciences at the University of Alabama. He holds a Ph.D. in hydrogeology from the University of Wisconsin–Madison. From 1988 to 1993, he was a senior hydrogeologist and director of software development at S.S. Papadopulos & Associates, Inc.,

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an environmental and water-resource consulting Þrm. Since 1993, he has been leading the interdisciplinary hydrogeology program at the University of Alabama. Dr. Zheng is developer of MT3D/MT3DMS, a widely used contaminant fate and transport simulation model, and co-author of the textbook Applied Contaminant Transport Modeling, published by John Wiley & Sons and currently in the second edition. Dr. Zheng has published over 50 papers and book chapters on both applied and theoretical aspects of hydrogeology, contaminant transport, and optimal ground water management. He is recipient of the 1998 John Hem Excellence in Science and Engineering Award from the National Ground Water Association for outstanding contributions to the understanding of ground water, and is a fellow of the Geological Society of America. Dr. Zheng serves on the Groundwater Committee of the American Geophysical Union, the Standing Committee on Hydrologic Information Systems of the Consortium of Universities for Advancement of Hydrologic Science, and the editorial boards of Ground Water and Hydrogeology Journal.

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Contributors CHAPTER 1 Miguel A. Medina, Jr. Chapter Editor Duke University

Mimi B. Dannel U.S. Environmental Protection Agency Headquarters Joseph V. DePinto Limno-Tech, Inc.

Robert L. Doneker Oregon Graduate Institute of Science and Technology

James A. Dyer DuPont Company

Nancy R. Grosso DuPont Company

Kevin J. Farley Manhattan College

D. Michael Johns Windward Environmental, LLC

Marcelo H. Garcia University of Illinois

Wu-Seng Lung University of Virginia

David Glaser Quantitative Environmental Analysis

Farrukh Mohsen Gannet Fleming, Inc.

John M. Hamrick Tetra Tech, Inc.

Aaron I. Packman Northwestern University

Richard H. Jensen DuPont Company

Philip J. Roberts Georgia Institute of Technology

Wilbert J. Lick University of California at Santa Barbara

CHAPTER 2

Robert A. Pastorok Exponent Environmental Group

Danny D. Reible Chapter Editor Louisiana State Univeristy Sam Bentley Louisiana State University

Richard F. Schwer DuPont Company C. Kirk Ziegler Quantitative Environmental Analysis

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CHAPTER 3

Donna M. Rizzo Subterranean Research, Inc.

George F. Pinder Chapter Editor University of Vermont

Brian J. Wagner U.S. Geological Survey

David E. Dougherty Subterranean Research, Inc.

Kathleen M. Yager U.S. Environmental Protection Agency, Technology Innovation OfÞce

Robert M. Greenwald GeoTrans, Inc. George P. Karatzas Technical University of Crete Peter K. Kitanidis Stanford University Hugo A. Loaiciga University of California at Santa Barbara Reed M. Maxwell Lawrence Livermore National Laboratory Alexander S. Mayer Michigan Technological University Dennis B. McLaughlin Massachusetts Institute of Technology Richard C. Peralta U.S. Air Force Reserve and Utah State University

William W.-G. Yeh University of California at Los Angeles

CHAPTER 4 Brent E. Sleep Chapter Editor University of Toronto Neal D. Durant Geotrans, Inc. Charles R. Faust GeoTrans, Inc. Joseph G. Guarnaccia CIBA-Geigy Specialty Chemicals Mark R. Harkness General Electric Corporation Jack C. Parker Oak Ridge National Laboratory Lily Sehayek URS Corporation Penn State Great Valley

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Contents Chapter 1

Surface Water–Ground Water Interactions and Modeling Applications..................................................................1

prepared by Miguel A. Medina, Jr. with contributions by Robert L. Doneker, Nancy R. Grosso, D. Michael Johns, Wu-Seng Lung, Farrukh Mohsen, Aaron I. Packman, Philip J. Roberts Chapter 2

The Role of Modeling in Managing Contaminated Sediments....................................................................61

prepared by Danny D. Reible with contributions by Sam Bentley, Mimi B. Dannel, Joseph V. DePinto, James A. Dyer, Kevin J. Farley, Marcelo H. Garcia, David Glaser, John M. Hamrick, Richard H. Jensen, Wilbert J. Lick, Robert A. Pastorok, Richard F. Schwer, C. Kirk Ziegler Chapter 3

Optimization and Modeling for Remediation and Monitoring........107

prepared by George F. Pinder with contributions by David E. Dougherty, Robert M. Greenwald, George P. Karatzas, Peter K. Kitanidis, Hugo A. Loaiciga, Reed M. Maxwell, Alexander S. Mayer, Dennis B. McLaughlin, Richard C. Peralta, Donna M. Rizzo, Brian J. Wagner, Kathleen M. Yager, William W.-G. Yeh Chapter 4

Modeling Fate and Transport of Chlorinated Organic Compounds in the Subsurface ...........................................179

prepared by Brent E. Sleep with contributions by Neal D. Durant, Charles R. Faust, Joseph G. Guarnaccia, Mark R. Harkness, Jack C. Parker, Lily Sehayek Appendix A: Workshop Panels..............................................................................259 Index ......................................................................................................................263

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1

Surface Water–Ground Water Interactions and Modeling Applications prepared by Miguel A. Medina, Jr. with contributions by Robert L. Doneker, Nancy R. Grosso, D. Michael Johns, Wu-Seng Lung, Farrukh Mohsen, Aaron I. Packman, Philip J. Roberts

CONTENTS 1.1

1.2

1.3

Introduction and Overview ..............................................................................2 1.1.1 Overview of Issues IdentiÞed ..............................................................3 1.1.2 Ground Water–Surface Water Interaction Technical Background.......6 The User’s Perspective.....................................................................................7 1.2.1 Point Source Discharge Regulations ...................................................7 1.2.1.1 The Zone of Initial Dilution (ZID) ......................................8 1.2.1.2 The Toxic Dilution Zone (TDZ) ..........................................9 1.2.2 National Pollutant Discharge Elimination System (NPDES) Permitting Technical Issues ...............................................................10 1.2.2.1 Two-Stage Mixing ..............................................................10 1.2.2.2 Federal Guidelines ..............................................................11 1.2.2.3 Acute Toxicity.....................................................................11 1.2.2.4 Dimensions of Regulatory Mixing Zones..........................11 1.2.3 Nonpoint Sources...............................................................................11 1.2.3.1 State of Michigan Mixing Zone Rules...............................13 Current State of Knowledge ..........................................................................14 1.3.1 Problem-Oriented Perspective............................................................14 1.3.1.1 Ecological and Health Risk Aspects ..................................14 1.3.1.2 Environment Boundaries and Scope ..................................17 1.3.1.3 Ground Water–Surface Water Connections........................18 1.3.1.4 Stream–Subsurface Exchange Processes............................18 1.3.1.5 Implications for Controlled and Uncontrolled Contaminant Discharges .....................................................21 1.3.2 Enabling Technologies Perspective — Simulation Models ..............23

0-56670-667-X/04/$0.00+$1.50 © 2004 by CRC Press LLC

1


2

Contaminated Ground Water and Sediment

1.3.2.1

Introduction and Policy Implications of Technological Limits ......................................................24 1.3.2.2 Modeling Stream–Subsurface Exchange Processes...........27 1.3.2.3 Tidal Exchanges and Oscillations ......................................29 1.3.2.4 Mathematical Formulation — Tidally Inßuenced Case.....29 1.3.2.5 Exit Concentration ..............................................................33 1.3.3 Emerging Technologies......................................................................37 1.3.3.1 Mathematical Models .........................................................39 1.3.3.2 New Laboratory Techniques...............................................40 1.3.3.3 New Field Techniques ........................................................44 1.3.4 Alternative Approaches ......................................................................46 1.3.4.1 InsigniÞcant Momentum-Induced Dilution........................46 1.3.4.2 Modeling Approach ............................................................46 1.3.4.3 Case Studies of Model Applications ..................................47 1.4 Acceptance of Methodology..........................................................................49 1.5 Summary, Conclusions, and Recommendations............................................49 Acknowledgments....................................................................................................53 References................................................................................................................53

1.1 INTRODUCTION AND OVERVIEW The interaction between surface water and ground water bodies traditionally has been idealized as a simple unidirectional transport process. More recent detailed examination has shown that ßow systems can be complicated. Complicated ßow and mixing patterns can have signiÞcant implications for physical, biogeochemical, and biological processes within the system and for contaminant transport. Ultimately, the effects of these complex processes on the risk to human health and the environment must be assessed. This panel examined the technical complexities of surface water and ground water interaction on a spatial and temporal scale. The regulatory framework of mixing zones was reviewed, and the policy implications of mixing zones on ground water and surface water interaction were discussed. The panel focused on mathematical modeling of these processes and reviewed the state-of-the-art technology in aqueous mixing simulation models. Advantages and disadvantages of different modeling approaches, time and spatial resolution disparities, and aggregation–disaggregation of data were also discussed. The U.S. Environmental Protection Agency (USEPA, 1998b) considers the primary exchange processes between the sediment and the overlying surface water to occur within the upper 2 in. of sediment deposits. Important elements in estimating the ground water contribution are distinguishing and characterizing the various inputs to the surface water–sediment system, which, in some cases, can be contaminated sediment. The speciÞc role of modeling in managing contaminated sediments is reviewed in Chapter 2. Until recently, methods for quantifying the local extent and quality of contaminated ground water discharges and their pollutant load to surface waters consisted primarily of hydrologic and physicochemical techniques (USEPA, 1998a). Promising new research is focusing on the use of biological indicators (organisms that


Surface Water–Ground Water Interactions and Modeling Applications

3

FIGURE 1.1 Atmospheric–surface water–ground water–sediment interactions. (ModiÞed after Minsker et al., 1998.)

spend all or part of their life cycle in contact with ground water) to characterize zones of ground water–surface water interaction, reviewed later in this chapter. This chapter attempts to present the best understanding of the underlying hydrodynamic, chemical, and biological processes required to describe contaminant transport between ground water and surface water and the limitations of numerical modeling. Figure 1.1 (Minsker et al., 1998) shows some of the interactions between ground water and surface water bodies, including atmospheric exchange and exchanges between ground water, sediment, the water column, and the larger surface water body.

1.1.1 OVERVIEW

OF ISSUES IDENTIFIED

The expert panel identiÞed several technical issues, including speciÞc modeling issues that deserve further discussion. Among the most salient technical issues that need resolution with regard to surface water–ground water interactions (i.e., the mixing zone) are as follows: •

•

DeÞning conceptual models of sufÞcient detail for aquifer, transition zone, and water column interactions (including biologic, geologic, hydrologic, and geochemical processes) DeÞning the relevance of the ecology in the transition zone (e.g., hyporheic zone, which is usually deÞned in terms of the biota only)


4


• • • • •

•

Locating a discharge area or upwelling Characterizing a discharge Locating and characterizing a plume Identifying and characterizing all other contaminant sources Identifying not only signiÞcant ground water–surface water interactions, exchanges, and processes but deÞning these within both spatial and temporal frameworks DeÞning data quality objectives (DQOs)

Closely associated with these technical issues are the following speciÞc modeling issues that are required to resolve the issues listed above adequately: •

• •

• • • • •

Formulate screening and tiered approaches using modeling. A tiered approach addresses two important technical issues. First, simple analytic solutions increasing in complexity allow for a greater understanding of the system. Second, less complex sites require less complex models, and the tiered approach allows the evaluation to stop at an appropriate level of complexity. Develop research and operational models for the mixing zones, transition zones, and interfaces (e.g., contaminated sediments layers). Develop linkage techniques to couple ground water and surface water models to address unique temporal and spatial scaling for ßow, transport, and biogeochemical processes. Apply veriÞcation procedures (including peer review), benchmarking, validation, and Þeld testing. Use and develop scientiÞc process models to deÞne various types of mixing zones and transition zones in support of conceptual model development. Obtain new data sets to develop and validate modeling approaches to address regulatory requirements. Establish feedback mechanisms between data collection, modeling, and resource decisions. Develop methods to account for uncertainty and heterogeneity.

For complex surface water sites where an unacceptable risk to human health and the environment is likely, sophisticated mathematical modeling may be necessary. A framework can be developed to achieve the following: •

•

Apply hydrodynamic modeling principles while incorporating key chemical and biological criteria to deÞne more quantitatively the mixing zone regulatory boundaries and target goals (e.g., ecological impacts on a localized scale, large-scale ecological or human health concerns). Evaluate alternative control or management strategies to achieve sound risk-based decisions even under conditions of uncertainty.

All major factors central to the transport and fate of contaminants (physical, chemical, biological) and ecological risk should be identiÞed properly in the model



Physical and hydrodynamic aspects

Ecological aspects

Biogeochemical ground water and surface water

Surface water system Ground water system Transition zone Mixing zones Streambed Bed sediment Hydraulic exchange

Toxicity Bioaccumulation Trophic transfer

Geochemical and biogeochemical reactions Bioavailability Bioaccessibility

5

FIGURE 1.2 Broad technical aspects.

for complex sites. In addition, these models should have the capability to address current and potential regulatory deÞnitions of the various types of mixing zones and transition zones. Accounting for parameter uncertainty can permit key regulatory policies to be addressed in the presence of technical uncertainty, perhaps encouraging a review of the policy or the granting of a variance. The broad technical aspects can be lumped into three major categories, as illustrated in Figure 1.2. For an improved understanding of the ecological relevance of the biological community in the transition zone, the following needs were identiÞed: • • • • • •

Compare site chemical data to the appropriate ecological benchmark criteria. Perform basic research in community structure, life histories, faunal structure, functional structure. Improve sampling techniques. Improve evaluation techniques, including community analysis and toxicity assessment. Incorporate into the risk paradigm. Evaluate risk presented by a discrete plume in terms of the risk posed to overall ecologic and environmental health of the system.

The policy and management issues below remain to be resolved by the regulatory agencies. Changes in policy can alter not only the regulatory landscape but also the technical analysis. • • •

Should the geometry of regulatory mixing zones be based on the hydrodynamic mixing zone? Under what conditions are mixing zones acceptable in terms of risk to ecological receptors? Can mixing zones be integrated into total maximum daily loads (TMDLs) such as storm water discharges?


6


• •

Are antidegradation policies consistent or overly conservative with respect to ecological risk? What are the policy implications of technological limits?

1.1.2 GROUND WATER–SURFACE WATER INTERACTION TECHNICAL BACKGROUND Winter (1995, 1998) and Winter et al. (1998) note that surface water bodies are integral parts of ground water ßow systems, and ground water interacts with surface water in nearly all landscapes — from small streams, lakes, and wetlands in headwater areas to major river valleys and seacoasts. On a relatively large scale, characterization of water mass transfer between ground water and surface water bodies is relatively well understood. For example, streams are either gaining or losing. However, the ßow system on a smaller scale near the interface of the surface water column and the sediment bed can be complicated. At this scale, ground water–surface water interactions are probably best thought of as a superposition of ßows that occur at a number of different spatial scales and often change seasonally or in response to a climatic event. Complex small-scale ßows can result from a variety of physical aspects and processes such as seasonally high surface water levels, evaporation and transpiration of ground water from around the perimeter of surface water bodies, rifße and pool dynamics in streams, tidal ßuctuations, limited hydraulic exchange due to impermeable sediment, and streamßow and velocity. Ground water and surface water interaction or mixing can be divided into the following zones (see Figure 1.1 [Minsker et al., 1998]): • • •

The surface water column (both near the discharge area and further out into the larger part of the surface water body) The bank storage zone or the shallow sediment section near the sediment bed, also referred to as the biologically active zone The zone of transition from ground water to surface water below the sediment–water interface but not into the aquifer proper

Within various surface water body environments, speciÞc processes can play a more signiÞcant role than in other environments. For instance, streams present a very special case of ground water and surface water interaction. Within streams, a portion of the biologically active zone and the transition zone is called the hyporheic zone based on biological environment. The process of water and solute exchange in both directions across a streambed is usually termed the hyporheic exchange. The direction of seepage through the streambed is commonly related to abrupt changes in bed slope or to meanders in the stream channel. The dimensions of the hyporheic zone depend on the type of sediment in the streambed and banks, streambed slope and variability, and hydraulic gradients. The hyporheic zone is a potentially significant zone of biological activity in aquatic systems. Because of ground water and surface water mixing within the hyporheic zone, the chemical and biological characteristics of water within the zone can differ considerably from those of adjacent surface water and ground water systems.



7

The ecological and health risk factors associated with mixing zones, whether in the surface water column or in the sediment bed and deeper, are reviewed in more detail later in this chapter. Examples of modeling the exchange of ground water and surface water are given for the case of a streambed and a tidal estuary.

1.2 THE USER’S PERSPECTIVE While mixing zones have been applied to point source discharges, application to nonpoint sources has not been widely addressed in regulatory management. Furthermore, the spatial deÞnition of a regulatory mixing zone and the actual physical mixing zone resulting from a hydrodynamic sequence of events are not usually the same. Thus, a distinction is made throughout this chapter between regulatory mixing zones and hydrodynamic mixing zones. The regulatory deÞnition of the mixing zone describes it as an allocated impact zone where numeric water quality criteria can be exceeded as long as acutely toxic conditions are prevented. Currently, the USEPA (2001) is conducting a review to consider a potential nationwide phase-out of mixing zones for the most persistent, toxic, and bioaccumulative chemicals of concern (BCCs) such as mercury, dichlorodiphenyltrichloroethane (DDT), polychlorinated biphenyls (PCBs), and dioxins. BCCs will be phased out of permitted discharges to the Great Lakes. Because mixing zone regulations have been applied primarily to point sources of contamination, that perspective is reviewed Þrst, even though it is the nonpoint source aspect of the problem (surface water–ground water interaction) that is of primary focus in this chapter. Although states have the Þnal say on mixing zones, the USEPA (1993) does provide some guidance that may be applicable to nonpoint sources. For example, the handbook (USEPA, 1993) does not explicitly exclude nonpoint sources in the mixing zone deÞnition. Furthermore, the handbook indicates that the mixing zone can be deÞned in terms of volume and that the location and shape should be deÞned using biological criteria. Some examples of the Michigan Department of Environmental Quality (MDEQ) mixing zone rules are presented in the following text because they provide some interpretation for nonpoint source application. A discussion of the nonpoint source regulatory framework as it applies to mixing zones is also presented.

1.2.1 POINT SOURCE DISCHARGE REGULATIONS The USEPA’s Water Quality Standards (WQS) regulation (40 CFR 131, Federal Register, Subpart B) allows states to adopt provisions authorizing mixing zones. Thus, individual state law and policy determine whether a mixing zone is permitted. The mixing zone is deÞned as an allocated impact zone where numeric water quality criteria can be exceeded as long as acutely toxic conditions are prevented. A mixing zone can be thought of as a limited area or volume where the initial dilution of a discharge occurs. Water quality standards apply at the boundary of the mixing zone, not within the mixing zone itself. The USEPA has published numerous documents providing guidance for determining mixing zones (e.g., USEPA, 1991 and 1993; USEPA Region VIII, 1994).


8


In terms of location, biologically important areas are identiÞed and protected (i.e., Þsh spawning areas) as well as all drinking water intakes. With regard to size, the area or volume of an individual zone or group of zones must be limited to the smallest practicable that will not interfere with the designated uses of the waterway. The shape is a simple conÞguration that is easy to locate in a body of water while avoiding impingement of biologically important areas. In general, a mixing zone should be free from the following: • • • • •

Acutely toxic conditions Materials that settle to form objectionable deposits Substances such as ßoating debris and oil that form nuisances Substances that produce objectionable color, odor, and taste Substances that produce undesirable aquatic life or result in a dominant nuisance species

The USEPA rules for mixing zones recognize that the state has discretion whether to adopt a mixing zone and to specify its dimensions. The USEPA allows the use of a mixing zone in permit applications except where one is prohibited in state regulations. State regulations addressing streams or rivers generally limit mixing zone widths or cross-sectional areas and allow lengths to be determined on a case-by-case basis. According to a report prepared for the Chemical Manufacturers Association by The Advent Group (1994), 23 states have a narrative mixing zone language and 27 states have speciÞc mixing zone area and/or volume deÞnitions in their regulations. SpeciÞc examples from the MDEQ Surface Water Quality Division administrative rules on mixing zones are presented in the section after point source regulations, for it is clear that in those regulations some thought was given to nonpoint source compliance as well. In the case of lakes, estuaries, and coastal waters, some states specify the surface area that can be affected by the discharge. The surface area limitation usually applies to the underlying water column and benthic area. In the absence of speciÞc mixing zone dimensions, the actual shape and size is determined on a case-by-case basis. 1.2.1.1 The Zone of Initial Dilution (ZID) Special mixing zone deÞnitions have been developed for the discharge of municipal wastewater into the coastal ocean, as regulated under Section 301(h) of the Clean Water Act. Frequently, these same deÞnitions are used for industrial and other discharges into coastal waters or large lakes, resulting in a plurality of terminology. For those discharges, the mixing zone is labeled as the ZID in which rapid mixing of the waste stream (usually the rising buoyant fresh water plume within the ambient saline water) occurs. The USEPA requires that the ZID be a regularly shaped area (e.g., circular or rectangular) surrounding the discharge structure (e.g., submerged pipe or diffuser line) and encompassing the regions of high (exceeding standards) pollutant concentrations under design conditions. In practice, limiting boundaries deÞned by dimensions equal to the water depth measured horizontally from any



9

point of the discharge structure is accepted by the USEPA, provided no other mixing zone restrictions are violated. The ZID is often denoted differently in common use and regulatory management. In common use, the ZID often refers to the initial dilution of a discharge. Initial dilution is the process of forced entrainment of ambient water into a discharge plume (ßow) through both momentum and buoyancy-induced turbulent and shear processes. As the discharge ßow propagates into the ambient water, it entrains water that dilutes the discharge. Through the entrainment process, the plume density approaches the ambient density (neutral buoyancy) and, depending on the location at which this occurs, the plume either reaches the surface or becomes trapped at some intermediate level. Therefore, the spatial extent of the mixing region is sometimes referred to as the ZID because it is where initial dilution is achieved. Beyond the ZID, ambient mixing processes tend to control further dilution of the plume. However, in a 1994 USEPA technical support document (USEPA, 1994), the ZID is deÞned as follows: The zone of initial dilution (ZID) is the region of initial mixing surrounding or adjacent to the end of the outfall pipe or diffuser ports and includes the underlying seabed. The ZID describes an area in which inhabitants, including the benthos, can be chronically exposed to concentrations of pollutants in violation of water quality standards and criteria or at least to concentrations more severe than those predicted for critical conditions. The ZID is not intended to describe the area bounding the entire mixing process for all conditions or the total area impacted by the sedimentation of settleable material.

1.2.1.2 The Toxic Dilution Zone (TDZ) The USEPA maintains the following two water quality criteria for the allowable concentration of toxic substances: a criterion maximum concentration (CMC) to protect against acute or lethal effects and a criterion continuous concentration (CCC) to protect against chronic effects (USEPA, 1991). The CMC value is greater than or equal to the CCC value and is usually more restrictive. The CCC must be met at the edge of the same regulatory mixing zone speciÞed for conventional and nonconventional discharges. Lethality to passing organisms within the mixing zone can be prevented in one of the following four ways: • •

The Þrst alternative is to meet the CMC criterion within the pipe itself. The second alternative is to meet the CMC within a short distance from the outfall. If dilution of the toxic discharge in the ambient environment is allowed, a TDZ that is usually more restrictive than the legal mixing zone for conventional and nonconventional pollutants can be used. The revised 1991 toxic technical support document (USEPA, 1991) recommends a minimum exit velocity for new discharges of 10 ft/s in order to allow sufÞciently rapid mixing that minimizes organism exposure time to toxic material. The document does not set a requirement in this regard, recognizing that the restrictions listed in the following paragraph can in many instances also be met by other designs, especially if the ambient velocity is large.


10


•

•

The third alternative is making the outfall design meet the most restrictive of the following geometric restrictions for a TDZ: • The CMC must be met within 10% of the distance from the edge of the outfall structure to the edge of the regulatory mixing zone in any spatial direction. • The CMC must be met within a distance of 50 times the discharge length scale in any spatial direction. The discharge length scale is deÞned as the square root of the cross-sectional area of any discharge outlet. This restriction is intended to ensure a dilution factor of at least 10 within this distance under all possible circumstances, including situations of severe bottom interaction and surface interaction. • The CMC must be met within a distance of Þve times the local water depth in any horizontal direction. The local water depth is deÞned as the natural water depth (existing prior to the installation of the discharge outlet) prevailing under mixing zone design condition (e.g., low ßow for rivers). This restriction prevents locating the discharge in very shallow environments or very close to shore, which results in signiÞcant surface and bottom concentrations. A fourth alternative is to show that a drifting organism would not be exposed more than 1 h to average concentrations exceeding the CMC.

1.2.2 NATIONAL POLLUTANT DISCHARGE ELIMINATION SYSTEM (NPDES) PERMITTING TECHNICAL ISSUES During the NPDES permit evaluation process, regulators assess many dischargers with stringent efßuent limits. When the efßuent concentration must meet the ambient water quality standard, no mixing zone is allowed for the discharge. On the other hand, many states allow mixing zones in rivers and streams under certain conditions. QuantiÞcation of mixing zones to comply with regulations is an urgent topic facing many regulatory staff, water quality engineers, and water quality management decision makers. More speciÞcally, determining how to combine the regulatory aspects with technical issues in quantifying mixing zones is the key to this water quality problem in receiving waters. There are a number of water quality constituents related to mixing zones, ranging from conventional pollutants (e.g., temperature, fecal coliform bacteria, viruses/pathogens) to nonconventional contaminants (e.g., metals, synthetic organics, chlorine residual, color, whole efßuent toxicity). 1.2.2.1 Two-Stage Mixing When wastewater is discharged into the receiving water, its transport can be divided into two stages with distinct mixing characteristics. The initial momentum of the discharge determines mixing and dilution in the Þrst stage. The design of the discharge outfall should provide ample momentum to dilute the concentrations in the immediate contact area as quickly as possible. (It should be noted that many existing outfalls with small ßows do not have sufÞcient momentum for initial



11

dilution.) The second stage of mixing covers a more extensive area in which the effect of initial momentum is diminished and the waste is mixed primarily by residual plume buoyancy and ambient turbulence. 1.2.2.2 Federal Guidelines For toxic discharges, the USEPA maintains two water quality criteria for allowable magnitude of toxic substances: a CMC to protect against acute or lethal effects and a CCC to protect against chronic effects. Thus, the CMC should be met at the edge of the zone of initial dilution, and the CCC should be met at the edge of the overall mixing zone. In some states, this zone of initial dilution is referred to as the allocated impact zone. In rivers or tidal rivers that have a persistent through-ßow in the downstream direction and do not exhibit signiÞcant natural density stratiÞcation, the 1-day, 10-year low ßow (1Q10) and 7-day, 10-year low ßow (7Q10) for the CMC and CCC, respectively, are recommended in steady-state mixing-zone modeling analysis (USEPA, 1991). 1.2.2.3 Acute Toxicity The CMC is used as a means to prevent lethality or other acute effects. It is deÞned as one half of the Þnal acute value (FAV) for speciÞc toxicants and 0.3 acute toxic unit (TUa) for whole efßuent toxicity (USEPA, 1991). The acute toxicity unit is expressed as TUa = 100/LC50, where LC50 is the percentage of efßuent that causes 50% of the organisms to die through the exposure period. For example, an efßuent that is found to have an LC50 of 5% is evaluated as 20 TUa. 1.2.2.4 Dimensions of Regulatory Mixing Zones The dimensions of a mixing zone in a river are usually determined by state regulations. Doneker and Jirka (1990) provide a summary of state mixing zone regulations. In general, regulatory mixing zones are speciÞed by a distance, area, or volume around the discharge point. For example, Virginia water quality standards state that the overall mixing zone shall not constitute more than one half of the width of the receiving watercourse or one third of the area of any cross section of the receiving watercourse (Lung, 1995). In addition, it shall not extend downstream for a distance more than Þve times the width of the receiving watercourse at the point of discharge. The dimensions of the allocated impact zone within which the CMC is met depend on the size of the overall mixing zone (Lung, 1995). It appears that the river width is the key factor determining the sizes of the allocated impact zone and the overall mixing zone in a river or tidal river system.

1.2.3 NONPOINT SOURCES The contribution of ground water to total streamßow varies widely among streams, but hydrologists estimate the average contribution to be between 40 and 50% in small- and medium-sized streams (Alley et al., 1999). Extrapolation of these numbers to large rivers is more complicated; however, the ground water contribution to all


12


streamßow can be as large as 40%. This does not include ground water contributions to lakes and wetlands. According to Tomassoni (2000), the ground water–surface water interaction zone is important from federal statutory, regulatory, and policy perspectives because 75% of Superfund and Resource Conservation and Recovery Act (RCRA) sites are located within a half mile of a surface water body. In 47% of these Superfund sites, there have been recorded impacts to surface water. Although progress has been achieved in controlling point sources within the past 25 years through the Clean Water Act, the USEPA now needs to consider nonpoint sources. The USEPA supports sound science and risk-based decision making (RBDM). RBDM requires a multidisciplinary approach; an understanding of requirements; and ßexibility in applicable statutes, regulations, and policies (Tomassoni, 2000). As noted earlier, there are many technical and policy issues regarding ground water–surface water interactions, and good policy depends on good technical information. Recently, greater attention has been placed by the USEPA (2000) on these interactions. The goal of Superfund is to return usable ground water to beneÞcial uses (current and future) where practical. When this is not practical, Superfund strives to prevent further migration and exposure and to evaluate opportunities for further risk reduction. Preliminary remedial goals are set at levels that protect resources, including surface waters that receive contaminated ground water, taking into account Clean Water Act requirements or state standards, whichever are more stringent. Final cleanup levels are attained throughout the plume and beyond the edge of any wastes left in place, where the point of compliance for a surface water body is where the release enters the surface water. Alternate concentration limits (ACLs) can be considered where contaminated ground water discharges to surface water, where contaminated ground water does not lead to increased contaminants in surface water, where enforceable measures are available to prevent exposure to ground water, or where restoring ground water is “not practicable.” Tomassoni (2000) further points out that RCRA has similar requirements to Superfund with respect to the following: returning usable ground water to beneÞcial uses, points of compliance for ground water and surface water, protection of surface water from contaminated ground water, and provisions for ACLs and treatment of principal threats. Therefore, if current human exposures are under control and no further migration of contaminated ground water is expected, primary near-term goals are established using two environmental indicators. Thus, surface water becomes the boundary if the discharge of contaminated ground water is within protective limits. It is estimated that the majority of contaminated sites have serious potential to affect surface waters. Although the federal framework allows for RBDM with respect to ground water–surface water interaction, the expectation of restoring ground water to beneÞcial use and ensuring that discharges of ground water to surface water are protective must be achieved. The following are among the key policy issues to ponder: • • • •

How to achieve short- and long-term protection Where, how, and how often to measure compliance Whether to restore ground water even if it has no impact on surface water How to address the diversity of surface bodies consistently



• •

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How to address cleanup goals in relation to the Clean Water Act’s NPDES approach How to account for, track, and translate TMDLs in watersheds

In theory, nonpoint sources could be managed within existing mixing zone deÞnitions and regulation. However, particularly for TDZs, some of the deÞnitions and control strategies that apply to point sources are not relevant to nonpoint sources. Furthermore, it is difÞcult to generalize the actual practice of implementing the mixing zone regulations given the large number and diverse types of jurisdictions and permit-granting authorities involved. By and large, however, current procedure falls into one of the following approaches or can involve a combination thereof: •

•

The mixing zone is deÞned by some numerical dimension. The applicant must demonstrate that the existing or proposed discharge meets all applicable standards for conventional pollutants or for the CCC of toxic pollutants at the edge of the speciÞed mixing zone. No numerical deÞnition for a mixing zone applies. In this case, the applicant proposes a mixing zone dimension. To do so, the applicant generally uses actual concentration measurements for existing discharges, dye dispersion tests, or model predictions to show at what plume distance, width, or region the applicable standard will be met. Further, ecologically or water use-oriented arguments are used to demonstrate that the size of the predicted region provides reasonable protection. The permitting authority calculates the proposal for a mixing zone. This approach resembles a negotiating process with the objective of providing optimal protection of the aquatic environment consistent with other uses.

1.2.3.1 State of Michigan Mixing Zone Rules The MDEQ Surface Water Quality Division (1999) contains language that allows nonpoint source mixing. The administrative rules deÞne a mixing zone as “the portion of a water body in which a point source discharge or venting ground water is mixed with the receiving water.” As a minimum restriction, the FAV for aquatic life shall not be exceeded when determining a wasteload allocation for acute aquatic life protection unless it is determined by the MDEQ that a higher level is acceptable or it can be demonstrated to the MDEQ that an acute mixing zone is acceptable consistent with subrule (7). Subrule (7) is quite detailed and intended for site-speciÞc investigations, including items such as whether overlapping mixing zones exist and the following: •

•

“A description of the amount of dilution occurring at the boundaries of the proposed mixing zone and the size, shape, and location of the area of mixing, including the manner in which diffusion and dispersion occur.” “The mixing zone demonstration shall be based on the assumption that environmental fate or other physical, chemical, or biological factors do not affect the concentration of the toxic substance in the water column within the proposed mixing zone.”


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Mixing zone boundaries should be determined on a case-by-case basis. With regard to surface runoff, “a watercourse or portions of a watercourse that without one or more point source discharges would have no ßow except during periods of surface runoff may be considered as a mixing zone for a point source discharge.” The Michigan administrative rules also have speciÞc provisions for temperature at the edge of the mixing zone: “monthly maximum temperatures, based on the ninetieth percentile occurrence of natural water temperatures plus the increase allowed at the edge of the mixing zone and in part on long-term physiological needs of Þsh, may be exceeded for short periods when natural water temperatures exceed the ninetieth percentile occurrence.” Of particular interest are the provisions made at the ground water–surface water interface: •

•

If a remedial action plan (RAP) allows for a mixing zone for discharges of ground water venting to a surface water, then the ground water discharge must comply with the same mixing zone rules as those of point source discharges. If a mixing zone is not provided in the RAP or permit, the ground water quality must meet the generic ground water–surface water interface (GSI) criteria.

GSI criteria can be summarized as follows: • • • •

Chronic criteria are calculated based on dilution and ambient surface water data in order to meet water quality criteria after mixing. Final acute criteria are calculated as maximum concentrations not to be exceeded at the GSI in order to prevent immediate harm to aquatic life. Mixing zones for BCCs are allowed for existing discharges until March 23, 2007. More stringent provisions apply to the Great Lakes throughout the administrative rules.

1.3 CURRENT STATE OF KNOWLEDGE 1.3.1 PROBLEM-ORIENTED PERSPECTIVE 1.3.1.1 Ecological and Health Risk Aspects The hyporheic zone represents a zone of transition from ground water to surface water and can extend up to approximately 40 in. (100 cm) below the sediment–water interface. Figure 1.3 shows the approximate position of the hyporheic zone during low ßow conditions (Williams, 2000). The size of the hyporheic zone can vary seasonally and in response to ßooding or drought. The relative contributions of ground water and surface water to this transition depend on the geologic characteristics within the zone and the prevailing hydraulic heads. While this zone represents an interesting hydrogeologic feature, it can also represent a potentially signiÞcant zone of biological activity in aquatic systems.



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Water table Water table

le

eab rm r Pe laye

Hyporheic zone

le eab m r pe er Im lay

Ground water

FIGURE 1.3 Position of the hyporheic zone during low river ßow (Williams, 2000).

Research has not determined how rapidly hyporheic zone organisms spread following the episodes of extensive surface water intrusion into ground water as a result of periods of ßooding or how rapidly the extent of the zone varies seasonally or in response to drought (USEPA, 1998a). However, the species richness and community structure of these organisms have been shown to change with alterations in ground water quality. Thus, the organisms living within the shallow ground water zone can serve as indicators of surface water–ground water interactions. 1.3.1.1.1 Hyporheic Zone Biological Community The structure and characteristics of the biological community that lives within the hyporheic zone is not well deÞned. This is due, in part, to the general difÞculties in obtaining good samples from this zone. Therefore, the zone has not been as well investigated as other components of aquatic systems. Traditionally, scientists have considered only the upper few inches (typically only the upper 6 in. or 15 cm) as the biologically active zone in most aquatic systems, but investigations have found a diverse community of organisms that inhabit substrate at depths greater than 6 in. or 15 cm (Williams and Hynes, 1974). Biological members of the hyporheic zone include permanent members, which complete their entire life cycle within the zone, and transient members, which spend only a portion of their life cycle within this zone. Permanent members include species of rotifers, oligocheates, copepods, ostracods, cladocerans, and other crustaceans. Transient members include species typically found as members of the streambed benthos that migrate as early instar larval stages to avoid disturbance (e.g., high river ßows) (Williams, 1987) or stress (e.g., temperature extremes) (Harper and Hynes, 1970). In addition to serving as habitat


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and refuge for groups of organisms, the hyporheic zone can also be an area of signiÞcant nutrient recycling within aquatic systems (Williams, 2000). 1.3.1.1.2 Hyporheic Community Assessments Traditional water quality and sediment assessments have focused on those portions of the aquatic community that are relatively well understood. One only has to open a general college textbook on ecology to obtain information about water column and macrofaunal communities. Testing procedures designed to assess the potential impacts of contaminants on these communities have been developed and are commonly used in environmental studies. This is in sharp contrast to both the understanding of the biological community that can be associated with the hyporheic zone and the role this community plays in the functioning of aquatic ecosystems. Studies attempting to characterize the functional role of the hyporheic zone suggest that it is an important site for the transformation and storage of nutrients (Triska et al., 1994) and it mediates the availability of N and P (Storey et al., 1999). Research on the hyporheic zone biological community has been hampered by the difÞculty of obtaining quantitative biological samples (Williams, 2000). As new sampling techniques are developed and old ones are improved, a better understanding of the role hyporheic zones play in aquatic ecosystems will emerge. The ecological risks associated with the discharge of contaminated ground water to surface water currently are evaluated only superÞcially in risk assessments (Burton and Greenberg, 2000). Although ground water can serve as a signiÞcant pathway of exposure to aquatic communities, the risks of such discharges are typically considered only for water column receptors (e.g., Þsh and submerged plants) or benthic communities (e.g., macrofaunal members of the benthic community that live in the upper 15 cm of sediment). The hyporheic community — whether one considers deeper dwelling macrobenthic organisms smaller organisms that comprise the meiofaunal community or the microbial community — is not considered in such assessments. The signiÞcance of omitting the hyporheic community from risk assessments is unknown. The National Oceanic and Atmospheric Administration (NOAA), in a recent review of 11 Superfund sites, found that 8 of the sites contained ground water with contaminant concentrations exceeding screening level concentrations (Matta and Dillon, 2000). Although the screening level concentrations used in the review were highly conservative, the results suggest that the potential for impacts to those organisms occupying the hyporheic zone may need to be considered at similar sites. At this time, there are severe limitations to assessing risk to the hyporheic zone realistically. These limitations include the following: •

•

•

Lack of understanding of the complex hydrogeologic structure and morphology of the surface water body and other variables that deÞne the existence and boundaries of the hyporheic zone Lack of understanding of the functional role of the biological community within the hyporheic zone in aquatic ecosystems and under what conditions the functional role is signiÞcant Lack of understanding of the biological effects upon the hyporheic zone community of the hydrogeologic structure of an area



• •

17

Lack of data on the sensitivity of the hyporheic zone community to contaminants Lack of test methods to assess potential impacts to the hyporheic zone community

1.3.1.2 Environment Boundaries and Scope The mixing behavior of a point source discharge is governed by the interplay of ambient conditions in the receiving water body and by the discharge characteristics. The ambient conditions in the receiving water body (e.g., stream, river, lake, reservoir, estuary, coastal waters) are described by the water body’s geometric and dynamic characteristics. Important geometric parameters include plan shape, vertical cross sections, and bathymetry, especially in the discharge vicinity. Dynamic characteristics are given by the velocity and density distribution in the water body, again primarily in the discharge vicinity. In many cases, these conditions can be taken as steady state with little variation because the time scale for the mixing processes is usually of the order of minutes up to perhaps 1 h. In some cases, particularly tidally inßuenced ßows, the ambient conditions can be highly transient and the assumption of steady-state conditions can be inappropriate. In this case, the effective dilution of the discharge plume can be reduced relative to that under steady-state conditions. A speciÞc example of a tidally inßuenced case is presented below. The discharge conditions relate to the geometric and ßux characteristics of the point source considered. For a single port discharge, the port diameter, its elevation above the bottom, and its orientation provide the geometry; for multiport diffuser installations, the arrangement of the individual ports along the diffuser line, the orientation of the diffuser line, and construction details represent additional geometric features; and for surface discharges, the cross section and orientation of the ßow entering the ambient watercourse are important. The ßux characteristics are given by the efßuent discharge ßow rate, by its momentum ßux, and by its buoyancy ßux. The buoyancy ßux represents the effect of the relative density difference between the efßuent discharge and the ambient conditions in combination with the gravitational acceleration. It is a measure of the tendency of the efßuent ßow to rise (i.e., positive buoyancy) or fall (i.e., negative buoyancy). In surface water–ground water interactions, the source characterization of the recharge area requires adequate description. In particular, the area of the recharge zone, the temperature (density) of the recharge water, the local bathymetry near the recharge area, the downstream ambient water body cross section, and the possible chemistry of the water–sediment interface must be described well for mixing zone analyses. The hydrodynamics of an efßuent continuously discharging into a receiving water body can be conceptualized as a mixing process occurring in two separate regions. In the Þrst region, the initial characteristics of discharge momentum ßux, buoyancy ßux, and source geometry inßuence the jet trajectory and mixing. This region is referred to as the near-Þeld and encompasses any surface, bottom, or terminal layer interaction. As the turbulent plume travels farther from the source, the source characteristics become less important. Conditions existing in the ambient


18


environment control trajectory and dilution of the turbulent plume through buoyant spreading motions and passive diffusion due to ambient turbulence. This region is referred to as the far-Þeld. It is stressed that the distinction between near-Þeld and far-Þeld is made purely on hydrodynamic grounds and is unrelated to any regulatory mixing zone deÞnitions. Information about the density distribution in the ambient water body is very important to correctly predict mixing zone characteristics. Ambient density (not temperature) is the controlling parameter in most mixing zone analyses. If the ambient water is fresh water and it is above 4ûC, the entering ambient temperature can be used to specify ambient density. If the ambient water body is below 4ûC or is brackish, then density must be speciÞed directly. As a practical guide, vertical variation in density of less than 0.1 kg/m3 or in temperature of less than 1ûC can be neglected for mixing zone analyses. For uniform conditions, the average ambient density or average temperature can be speciÞed. When in doubt about the speciÞcation of the ambient density values, it is reasonable to Þrst simplify as much as possible. The sensitivity of a given assumption can be explored in subsequent mixing zone simulations. 1.3.1.3 Ground Water–Surface Water Connections Recently, the interface between surface water and ground water has received increasing attention. Traditional hydrologic methods, which operationally separate surface water and ground water components, are focused only on relatively large-scale water ßuxes. The emphasis on contaminant transport and stream ecology requires a closer examination of ßow paths and processes because local-scale biogeochemical interactions often can control reactive substance transport. The interface between surface water and ground water is especially important in this regard because this region is characterized by strong gradients in physical, chemical, and ecological parameters. Thus, additional emphasis must be placed on understanding both the underlying heterogeneity of the system and the actual ßow paths that contaminants take through this heterogeneous system. These factors control the types of transformation reactions and microbial processes that contaminants are exposed to when a subsurface contaminant plume impinges either on a surface water body or downstream of a surface water release. 1.3.1.4 Stream–Subsurface Exchange Processes The large-scale ground water ßow system, often the only one considered in ground water modeling, provides the setting within which the stream ßows. Indeed, it is well known that ground water base ßow maintains perennial streams. Even at this scale, examining ground water ßow paths can show that stream–aquifer connections can be more complicated than expected. For example, upstream losing reaches can be directly linked to downstream gaining reaches. Seminal work by Hynes (1975, 1983) advanced the idea that large-scale stream–subsurface exchange is intimately linked to the ground water ßow system by periodic bedrock controls on subsurface ßow in steep mountain streams. Hynes elegantly described this regional stream–subsurface exchange pattern as a set of “beads on a string,” evoking visualization of



19

the periodic large-scale penetration of stream water into the subsurface and subsequent return to the stream. The same sort of ßow pattern also can develop at smaller spatial scales. Geomorphic evolution of stream channels produces a wide range of periodic topographical features. High-gradient streams are often characterized by pool-rifße sequences, which can produce stream–subsurface exchange ßows similar to those envisioned by Hynes (Harvey and Bencala, 1993; Wondzell and Swanson, 1996). Lower-gradient, alluvial rivers generally have large planform meanders which can generally be expected to admit a stream–subsurface exchange ßow. These meanders are subject to a difference in hydraulic head from the upstream to the downstream side, which yields an exchange ßow through the meander (Harvey and Bencala, 1993; Wroblicky et al., 1998). Additional stream–subsurface exchange can be induced whenever the stream changes orientation relative to the local ground water ßow system. These ßows can be inßuenced by some particular features of the local ground water ßow system such as preferential subsurface ßows through relict stream channels (Wondzell and Swanson, 1996). The interplay of hydrodynamics and sediment transport also produces characteristic, periodic topographical patterns on the streambed. These features, known as bedforms, then induce vertical stream–subsurface exchange ßows underneath the stream. Depending on the type of sediment and the size of the stream, duneshaped bedforms in loose alluvial material can have a length ranging from centimeters up to hundreds of meters (Vanoni, 1975; Raudkivi, 1998). Streamßow over bedforms produces drag, which serves as frictional resistance to the streamßow and induces a subsurface advective ßow through the bedform called pumping. This process has been examined in some detail in laboratory experiments and analyzed using process-based models that explicitly calculate the induced subsurface ßows for various stream and subsurface boundary conditions (Savant et al., 1987; Thibodeaux and Boyle, 1987; Elliott and Brooks, 1997a, 1997b; Packman, 1999; Packman et al., 2000b, 2000c). In addition, this pumping process can occur in many other natural systems, potentially whenever there is a ßow over a porous bed. This form of exchange is clearly important on the sea ßoor (Huettel et al., 1996, 1998), and wind pumping through snow packs also has been theorized (Colbeck, 1989, 1997). Bed sediment transport produces bedform migration, which also mixes stream and subsurface water. This exchange process has been termed turnover. Turnover is expected to be the dominant exchange process in streams with very Þne-grained sediments (Rutherford et al., 1993, 1995; Elliott and Brooks, 1997a, 1997b). In addition, turnover moderates the advective pumping process by causing the bedforms that induce advection to move downstream. The interplay of pumping and turnover has been analyzed by Elliott and Brooks (1997a, 1997b) and Packman and Brooks (2001). The near-surface structure of exchange ßows is expected to be particularly important for contaminant transport because this region is both highly heterogeneous and characterized by sharp gradients in biogeochemical parameters. Figure 1.4 shows Elliott’s observations of dye penetration from a stream into a streambed covered with bedforms. Under stable bed conditions (no bed sediment transport), there is


20


FIGURE 1.4 Observations of dye penetration fronts due to stream–subsurface with bedforms. (From Elliott, 1990.)

clearly a periodic variation of penetration and release under each bedform. Bed sediment transport tends to homogenize the interfacial region. Contaminant release from the bed would be a mirror-image of the inÞltration shown in Figure 1.4. In this case, the contaminant would be expected to be preferentially carried out of the bed at periodic release points on each bedform. A photograph of upward dye transport in a gravel bed can be found in Thibodeaux and Boyle (1987); Huettel et al. (1998) provide detailed microprobe measurements of metal transport out of a natural marine sediment, and numerical simulations of tracer release are given by Feng et al. (2000). Anecdotal information supports the picture developed from laboratory experiments: contaminant releases from streambeds are often found to have a high degree of spatial variability, and discharge of nutrient-rich ground water to streams has been observed to produce periodic algae growth on the streambed. The smaller-scale processes described above are expected to be particularly important for contaminant transport and ecologically relevant substances such as nutrients. These processes deÞne the most active region of stream water and ground water mixing in the sense of Triska and coworkers (1989). This relatively sharp transition between waters with surface water and ground water signatures (in the geochemical sense) has profound implications for the behavior of reactive substances. In particular, generally there is a complex transition region near the stream channel whose characteristics are determined by both the sediment heterogeneity and the various imposed exchange ßow patterns. Contaminant transfer from ground



21

TABLE 1.1 Stream–Subsurface Exchange Processes That Can Occur at Various Spatial Scales Relevant Spatial Scale

Representative Exchange Processes

Bed sediment grain scale

Turbulent stream–subsurface interactions Advective ßows induced around small-scale bed features Advective pumping due to streamßow over bedforms Turnover due to bed sediment transport Subsurface ßow induced at meanders or pool rifße sequences Exchange ßow due to orientation of stream relative to the local ground water table Interaction with regional ground water ßow system Periodic lateral subsurface ßow due to tidal variation in stream level

Bedform scale Channel scale

Reach scale and larger

water to surface water is expected to be highly mediated by those processes considered unique to this transition region. Downstream contaminant transport is expected to be similarly mediated by hyporheic processes due to the active exchange of water with the shallow subsurface. These factors will be examined in more detail in the next section. Table 1.1 summarizes stream–subsurface exchange processes that can occur at various spatial scales. Additional information on these processes can be found in reviews by Sharp (1988), Larkin and Sharp (1992), Winter (1995), and Winter et al. (1998). Additional analysis of stream–subsurface exchange processes and a directed discussion of the importance of these processes for stream ecology can be found in the recent book edited by Jones and Mulholland (1999) and the review by Brunke and Gonser (1997). 1.3.1.5 Implications for Controlled and Uncontrolled Contaminant Discharges Stream–subsurface exchange can be expected to be important for downstream contaminant transport whenever streambed sediments are permeable enough to admit a signiÞcant exchange ßux. Interactions with channel sediments are expected to modify both accidental discharges such as spills and long-term contaminant discharges. Downstream transport of a contaminant pulse is modiÞed by the exchange of some of the contaminant into the subsurface, where it is retained for some time and from where it returns to the streamßow. This process has been termed transient storage (Bencala and Walters, 1983) and is one of the processes responsible for the wellknown tailing of solute pulses in streams (Fischer et al., 1979). The key implication of this exchange for contaminant transport is that the contaminant is subject to considerably different biogeochemical processes in the subsurface. For example, sorption of chromium to streambed sediments caused chromium to be retained in the bed much longer than a conservative solute (Wörman, 1998; Wörman et al., 1998; Johannson et al., 2001). Thus, over the time scale of a contaminant pulse,


22


stream–subsurface exchange often can represent a sink for the contaminant, with release coming a considerable time after the passage of the main pulse. When the surface water contaminant release persists for a long period of time, be it a permitted discharge or an uncontrolled release, stream–subsurface exchange can cause the contaminant to be transported from the stream to the near-stream ground water ßow system. The net result often is the development of a subsurface reservoir of the introduced contaminant. This is particularly the case when the contaminant is persistent and has a high afÞnity for sediments. This effect has been seen most clearly for mine-derived contaminants such as metals and arsenic, which have produced large-scale contamination of ßoodplain sediments and near-stream surÞcial aquifers (Moore and Luoma, 1990; Broshears et al., 1996; Harvey and Fuller, 1998). In these cases, stream–subsurface exchange can cause these subsurface or off-channel sources to remain a source of stream contamination after the main contaminant discharge ceases. In other cases, stream–subsurface exchange could potentially be beneÞcial if it exposes contaminants to subsurface conditions that reduce their toxicity. However, these processes rarely have been examined, and so additional study is necessary to characterize the range of interactions that can be important for various contaminants. Stream–subsurface exchange also inßuences the discharge of ground water contaminants to streams by causing the development of a subsurface mixing region — the hyporheic zone — with water quality characteristics between those of the bulk surface water and ground water. The hyporheic zone can inßuence ground water contaminant discharges in two important ways. First, the contaminant plume can be subject to enhanced dilution just before it enters the stream. Second, the hyporheic region can have unique conditions that facilitate contaminant transformation. For the common case of a reduced, contaminated ground water discharging to an oxic surface water, the hyporheic zone often is characterized by at least partially aerobic conditions. In this sense, the hyporheic zone can be thought of as a relatively wellßushed sedimentary environment, and, as such, apparently often represents a region where there is the opportunity for relatively specialized microbial population growth (Baker et al., 1999; Findlay and Sobczak, 1999). Thus, the hyporheic zone can subject contaminants to transformation processes that cannot occur elsewhere in the stream–aquifer system (Nagorski and Moore, 1999). In some cases, these hyporheic processes can be highly beneÞcial, for example, by offering the opportunity for naturally enhanced in situ bioremediation of reduced, organic-rich plumes discharging to streams (Hynes, 1983; USEPA, 2000). When a reduced, organic-rich plume discharges to a stream, the hyporheic zone can thus provide a region of natural aerobic bioremediation. Knowledge of stream–subsurface exchange processes can be used to design improved assessment and monitoring strategies for contamination in the near-stream environment. Subsurface contaminant distributions, ßuxes from contaminated sediments, and ground water plume discharges are all expected to show a high degree of spatial heterogeneity. Further, streams are a dynamic sedimentary environment, and both streambed conditions and stream–subsurface exchange ßuxes are expected to change over time (Packman et al., 2000a). In particular, any ßood that rearranges channel sediments could potentially alter the local hydrodynamic conditions and



23

thus the resulting interfacial ßuxes. Sampling strategies should be designed to measure spatial variability so as to produce reasonable estimates of the average contaminant ßux to the stream in a given area and may also need to account for long-term variability in stream conditions. Another successful approach is to combine conservative tracer injections to probe stream–subsurface interactions with extensive measurement of contaminant concentrations in the stream (Kimball, 1997; Harvey and Wagner, 1999). Sampling strategies that do not adequately consider stream–subsurface exchange ßuxes are likely to produce contaminant ßux estimates with high uncertainty.

1.3.2 ENABLING TECHNOLOGIES PERSPECTIVE — SIMULATION MODELS An issue that is always present is to what degree are models used, certiÞed, or approved for speciÞc uses, including the regulatory process itself. For example, the CORMIX family of models has undergone extensive USEPA and scientiÞc journal peer review for modeling a wide range of discharge sources in mixing zone analysis. The CORMIX system has been distributed worldwide since 1990 with over 1200 existing user groups. That is a form of certiÞcation, although not a formal test subjected to known benchmarks of success. The CORMIX models are used by both regulators and applicants in the NPDES permit program for point source discharges. CORMIX models are used to assess mixing zone characteristics, optimize outfall design, and determine regulatory compliance and environmental impact. The primary impediment to model use regarding mixing zones of ground water–surface water interaction is the need to characterize the contamination sources. Existing CORMIX models can represent the source conditions that would be typical for these discharge sources. Federal agencies such as the USEPA have an important role in simulation model selection and utilization. This role should include, at a minimum, providing guidelines on acceptable models and measures of performance and validation. Such a document exists for ground water models (USEPA, 1988). General guidelines address three factors: objectives criteria, technical criteria, and implementation criteria. The role of federal agencies should also include providing leadership in model development through sponsored research at the appropriate program ofÞces. Medina et al. (1996) developed a ground water quality modeling advisory system for the U.S. Air Force for use in investigating remediation alternatives for subsurface contamination cleanup. The system is capable of accounting for uncertainty not only in the prediction of solute transport but also in the optimization of the remediation scheme through chance constraints. The system guides users in selecting the most appropriate transport models through an algorithm independently tested with machine learning codes (Reich et al., 1996). Currently, most pollutant fate and transport models do not integrate well over multiple space and time scales. This is certainly the case for mixing zone models. There is an obvious and persistent need within mixing zone models to link nearÞeld effects with far-Þeld effects; however, no general procedures or guidelines for accomplishing this linkage exist.


24


The criteria for technical implementation in mixing zone problems should, at a minimum, include the following: • • •

Correct simulation of mixing zone physical processes in ground water–surface water interaction Validation of proposed physical process models with available data sets Model guidance for correct simulation method application

The corresponding application objectives should include the following: • • •

Appropriate space and time scales of physical process simulation for ground water–surface water interaction Adequate ground water source characterization for mixing zone analysis Adequate availability of downstream site characteristics

1.3.2.1 Introduction and Policy Implications of Technological Limits The need to manage and understand uncertainties for both scientiÞc and regulatory applications in the subsurface environment has long been recognized by the National Research Council (NRC, 1990). This awareness has extended to international circles as well. The International Association of Hydrological Sciences published a guide on ground water contamination risk assessment in 1990 (Reichard et al., 1990) as a contribution of the U.S. to the UNESCO International Hydrological Programme. Over the past several decades, many different models for surface and subsurface contaminant transport (including sediment) under varying conditions and assumptions have been proposed and tested. These studies range from models based on very simple, one-dimensional analytical (closed form) solutions that assume a completely homogeneous and isotropic ßuid (or porous medium in the case of subsurface models) to complex, three-dimensional numerical codes that allow for complete speciÞcation of ßow and contaminant characteristics throughout a three-dimensional grid. Other examples besides CORMIX include the hydrodynamic-eutrophication model (HEM-3D) developed by Park et al. (1995) and the hydrodynamic and sediment transport model EFDC (Hamrick, 1992, 1996). In essence, HEM-3D is an integration of a water quality model with 21 state variables and the EFDC. Upon receiving the information of physical transport from EFDC, HEM-3D simulates the spatial and temporal distributions of water quality parameters (e.g., dissolved oxygen, suspended algae, carbon, nitrogen, phosphorus and silica cycles, coliform bacteria). Upon receiving the particulate organic matter deposited from the overlying water column, a sediment process model (also with 21 state variables) simulates their diagenesis and the resulting ßuxes of inorganic substances and sediment oxygen demand back to the water column. It is doubtful that a model with 42 state variables can ever be fully calibrated and veriÞed. For any complex set of models, two key questions are as follows:



• •

25

To what extent can this set of codes accurately predict all the essential complex physical, biological, and chemical phenomena? Given the accuracy of this set of codes, is it reasonable to make regulatory decisions based on either short- or long-term predictions?

Strictly mechanistic models, particularly those applied to simulate complex water quality processes, usually are inadequate and sometimes inappropriate for use as the basis for management decisions. A thorough uncertainty analysis has never been successfully undertaken for a model of the detail incorporated in HEM-3D, so this model may be completely incompatible with risk analysis without proper modiÞcation. Partial error propagation studies that have been completed for similar models (e.g., CE-QUAL-ICM) have always resulted in large prediction errors for variables other than hydrologic or hydrodynamic, salinity, and suspended sediment. These models are good learning tools; they assist the analyst in organizing and collecting the data needed to make decisions, and they express knowledge and relationships about processes and linkages. However, natural processes are vastly more complex and variable than are commonly represented in these models. Alternatives to strictly mechanistic models are discussed below in further detail. All contaminant transport models, regardless of the complexity of the solution method, require certain assumptions regarding the nature of the transport processes. As a result, they can provide only an approximation of the actual spread or deposition of a contaminant from a given site and the associated risk. This situation presents a familiar yet difÞcult problem to the analyst and decision-makers. SufÞcient data are rarely, if ever, available to apply the most complex, three-dimensional contaminant transport models to a monitored site. The analyst must, whether explicitly or implicitly, choose a transport model based on a trade-off between the presumed greater accuracy of complex models and the less onerous data requirements and easier application of simpler models. Even with the choice of an appropriate transport model, considerable uncertainty is likely to be present in the analysis of contamination risk. For example, in ground water solute transport models (which require estimating parameters that are difÞcult to measure and are spatially variable such as hydraulic conductivity and dispersivity), there is often good reason to doubt the accuracy of the input data. For instance, if an analytical model requires the spatial average of the hydraulic conductivity throughout the local area of the aquifer, and the available data consist of only one or two slug tests plus an expert opinion, there is good reason to doubt that the reported best estimate of the parameter accurately reßects the true mean value. Simply running the model in a deterministic mode using the best estimates of the parameters will not provide sufÞcient information for a decision because the uncertainty in the analysis has not been taken into account. For instance, if a deterministic application suggests no risk of contamination, no information is provided as to the certainty of this conclusion. The recommended alternative is to consider explicitly the uncertainty that is present in the analysis through the use of Monte Carlo analysis, Latin Hypercube Sampling, or other stochastic methods (Medina et al., 1995). Uncertainty enters the modeling process in the following three ways:


26


• •

Natural parameter variability Measurement error, which also introduces uncertainty in parameter estimation Model error, representing uncertainty introduced by the degree to which the simplifying assumptions used to develop a model fail to represent the actual physical, biological, and chemical processes at the site accurately

•

The Þrst two of these sources of uncertainty can be analyzed separately; however, the data are often insufÞcient. In such cases, the natural and measurement uncertainty can be combined into one source of uncertainty for the Monte Carlo type analysis by specifying the distribution of the parameter value. The third source of uncertainty in the analysis is due to the degree to which the transport model applied can misrepresent actual processes at the site. This source of uncertainty is very difÞcult to quantify and indeed may be impossible to quantify for speciÞc sites without extensive sampling and monitoring. The alternatives vary. One is simply to reduce process detail. Drawing somewhat on the analogy of statistical mechanics, simpler models can be developed to predict space–time aggregate behavior while making sure that these models are comprehensive enough to predict the important quantities. Alternatively, as noted above, the uncertainty in the complex models (e.g., through Monte Carlo methods) can be accounted for by combining probability distributions of the input parameters with a mechanistic deterministic model. Such Monte Carlo simulation experiments were conducted by Medina et al. (1988, 1989) with a two-dimensional numerical ground water solute transport model. The experimental design framework was conducted to ensure that the individual effects of each variable were isolated from the effects of the others. The effect on the probability distribution of the contaminant concentration at an observation point in the ßow Þeld due to variance in the input parameters is illustrated in Figure 1.5. There is only one concentration associated with a prob1 0.9

Deterministic solution

Probability

0.8 0.7 0.6 0.5

Variance in time to failure

0.4 0.3

Variance in hydraulic conductivity

0.2 0.1 0 0

500

1000 1500 2000 Contaminant Concentration, mg/l

2500

FIGURE 1.5 Uncertainty in contaminant concentration due to variance in model input parameters.



27

ability of 1.0 (the vertical line, with no variance) for the deterministic solution, whereas a spread in the probability distributions represents the uncertainty in the predictions where variability in the input parameters is allowed. Yet, the uncertainty in the predictions covers a much broader range for the deterministic solution for hydraulic conductivity variance than for variance in time to failure. This approach was used in modifying several deterministic analytical, semianalytical, and numerical solution ground water solute transport models into Monte Carlo versions to account for uncertainty in input parameters (Medina et al., 1996). Other applications accounting for natural and model parameter uncertainties are addressed by Cassiani and Medina (1997) and Liu et al. (2000). The policy implications of technological limits can be summarized by noting that key policies and cleanup goals should be addressed in the presence of technical uncertainty (e.g., complex interactions, model and parameter uncertainties). Periodic review of policy and the regulatory environment should be encouraged to recognize and incorporate new modeling knowledge and/or superior innovative remediation technologies. Among the important technological limitations of existing mixing zones are that these models generally do not account for chemical or biological reactions within the mixing zone. However, some simple Þrst-order decay approaches are available. Because the time scales are short within mixing zones, only biological and chemical reactions with relatively short time scales need to be considered. Aquifers tend to be characterized as large area or volume sources, whereas most mixing zones are much more limited in spatial extent. Time scales in aquifers also tend to be quite large, especially when compared with the time scales of most mixing zones in ground water–surface water interactions. An example modeling approach for the case of tidal exchanges and oscillations is presented later in this section. 1.3.2.2 Modeling Stream–Subsurface Exchange Processes Stream–subsurface interactions are not represented well in most hydrologic and hydraulic transport models. In large part, this occurs because of the traditional distinction between surface water and ground water modeling and the unique difÞculties in modeling each of the two hydrodynamic environments. Thus, the stream–subsurface interface is considered essentially as a boundary condition in major modeling packages such as MODFLOW and TOXIWASP. The main difÞculty is that stream–subsurface exchange ßux cannot be calculated using these models without Þrst specifying key hydrodynamic exchange parameters. In reality, the exchange ßux is determined by the hydrodynamic conditions on both sides of the interface, as well as local conditions such as sediment size and hydraulic conductivity. To adequately model the transport of contaminants across the surface–subsurface interface, it will be necessary to develop models with much greater level of detail. However, it appears that it will take a considerable amount of time to develop the requisite level of modeling sophistication in large part because all the individual hydrodynamic processes that drive exchange are still not understood. Beyond this, the contaminant transport issue is particularly difÞcult because the interplay of transport and transformation processes must also be understood. As described above,


28


contaminants are subject to a variety of biogeochemical transformations as they cross the hyporheic zone, and the net contaminant ßux highly depends on the way that contaminants are transported through this highly heterogeneous interfacial region. The net result is that, similar to the Þeld measurement of contaminant transport across the surface–subsurface interface, the complex nature of the hyporheic zone makes it difÞcult to obtain adequate averages of large-scale ßuxes based on the understanding of smaller-scale processes. The development of models for stream–subsurface exchange has taken three distinctly different directions. One approach, developed for Þeld-scale evaluations, has been to combine highly idealized exchange models with an appropriate method for Þeld evaluation of stream–subsurface exchange parameters in individual stream reaches. This approach was pioneered by the U.S. Geological Survey (USGS) groups working on acid mine drainage issues and has been applied over the past 20 years to both mine contamination problems and stream ecology (Bencala and Walters, 1983; Bencala et al., 1990; Kim et al., 1992; Tate et al., 1995; Broshears et al., 1996; Valett et al., 1996; Mulholland et al., 1997). The Transient Storage Model (Bencala and Walters, 1983) and its numerical implementation OTIS (Runkel, 1998; Runkel, 2000) has been used most widely for these applications, but alternative approaches include representing exchange as a diffusive or lumped dispersive process (Wörman, 1998, 2000; Lees et al., 2000). This method is most appropriate for determining net contaminant ßuxes from ground water to streams, as summarized in the USGS fact sheets prepared by Kimball (1997) and Runkel (2000). A second approach has been to model plan–form exchange ßows, such as those through meanders, by using ground water models such as MODFLOW (Harvey and Bencala, 1993; Wondzell and Swanson, 1996; Wroblicky et al., 1998). This type of model is very appropriate for modeling larger-scale exchange ßuxes but is limited by its inability to resolve the Þne-scale features and ßows that are likely to be very important for contaminant discharges. The Þnal approach has been to develop models for individual exchange processes and then to integrate these ßuxes to determine net transport. These models are advantageous because they address the contaminant transport problem in great detail, but are still very limited because only a few exchange processes have been examined, and upscaling to real environmental contaminant transport is problematic. Currently, models exist for isolated transport processes such as exchange induced by bedforms (Elliot and Brooks, 1997a, 1997b), obstacles (Hutchinson and Webster, 1998), and biogenic sediment mounds (Huettel et al., 1996, 1998); and for the physicochemical exchange of metals (Eylers et al., 1995; Jonsson and Wörman, 2001), surfactants (Forman, 1998), and colloids (Packman et al., 2000b, 2000c). All these process-level models have resulted from directed experimental study of a limited set of transport processes. Wörman et al. (2002) recently presented a new model that uses moment methods to apply process-level understanding to modeling net solute transport in streams. This approach provides an excellent framework for applying detailed process models at larger scales. Lin and Medina (2003) incorporated transient storage into conjunctive steam-aquifer ßow and solute transport modeling. Other papers on modeling hyporheic zone processes appear in Runkel et al. (2003). Many additional exchange processes still



29

need to be studied, including the interplay of physical transport and biogeochemical contaminant transformations. Thus, a considerable amount of effort will be required to develop a complete range of process descriptions and to integrate these into an overall model for stream–subsurface exchange. 1.3.2.3 Tidal Exchanges and Oscillations A quantitative understanding of ground water concentrations as ground water exits to surface water is an important aspect of the overall understanding of the hydrodynamics in the mixing zone. Ground water transport models provide a means to developing that understanding. The receiving surface water body usually can be treated as a boundary. Appropriate head and concentration conditions can be imposed at this boundary to simulate the transport. When the receiving surface water body is subject to tidal oscillations, the modeling becomes somewhat challenging. The problem, however, is of a high practical signiÞcance because a disproportionately large number of industries have developed on the coastal areas of this country. Physically, the problem is complicated because during high tides, surface water from the tidal water body invades the ground water aquifer to distances dictated by aquifer properties. Hence, 50% of the time there is no discharge. But assuming the absence of any contamination from upstream, clean surface water enters the aquifers and lowers contaminant concentrations. This tidal ßushing assists in lowering the exit concentration by diluting the ground water with surface water. The extent to which dilution occurs can be assessed quantitatively by an appropriate ground water model as discussed in the remainder of this subsection. Tides in coastal water and estuaries produce sinusoidal ground water ßow velocities in adjacent aquifers. The solution of the one-dimensional transient equation of ßow induced by an oscillating boundary condition has been solved by adapting a model of heat transfer in solid objects exposed to periodic temperature variations (Jacob, 1950; Todd, 1959). The mathematical relationships derived from such an analogy have been used widely by many investigators in characterizing the temporal and spatial conditions of ground water ßow regimes adjacent to coastal areas (Ferris, 1951; Gregg, 1966; Gieske and De Vries, 1985; Maas and De Lange, 1987; Serfes, 1987; Rowland and Hannoura, 1988). Using that solution to describe the ßuctuating velocity Þeld, Yim and Mohsen (1992) developed a numerical model to address the transport problem. Results of the model simulation are presented in the following section as an example of an enabling technologies perspective for simulation models and to illustrate transport dynamics. 1.3.2.4 Mathematical Formulation — Tidally Influenced Case Figure 1.6, part A, shows an aquifer bordering a tidal surface water body. This Þgure also shows the location of the water table at high and low tides. Figure 1.6, part B, is an idealization of the same situation to aid mathematical treatment. For the purposes of this discussion, it is assumed that the width of the aquifer is inÞnite normal to the plane of the Þgure. It is further assumed that no contaminating sources


30


FIGURE 1.6 Schematic of aquifer contamination on the bank of a tidal estuary.

are present upstream so that the surface water body (which travels much faster that the ground water) is essentially clean. The tidal ßuctuation in the surface water causes a sinusoidal ßuctuation of the ground water level. The response of ground water levels to a sinusoidal boundary condition in a homogeneous and isotropic conÞned aquifer has been studied by analogy to a one-dimensional heat-conduction equation (Todd, 1959) and is given by: È 2p t ù h ( x, t ) = ho exp ( - xb) sin Í - xbú Î to û

(1.1)

where h(x, t) is the rise and fall of the piezometric surface at distance x from the bank with respect to a mean surface-water level at time (t), ho is the amplitude of the tide, and to is the period of a complete tidal cycle. The term b represents the physical properties of the aquifer and is deÞned as: b = ( p S ) / (t o T )

(1.2)

where S is the storage coefÞcient, and T is the transmissivity deÞned by: T = KB

(1.3)



31

where K is the hydraulic conductivity and B is the thickness of the conÞned aquifer. It is assumed that the response of h (x, t) given in Equation 1.1 in a conÞned aquifer also can be applied to an unconÞned aquifer, provided that ho is sufÞciently smaller than the saturated thickness of the aquifer (Todd, 1959). For simplicity, the saturated depth is denoted by B as well. For unconÞned aquifers, S is the speciÞc yield rather than storage coefÞcient. The oscillating velocity Þeld, Vo, induced by the tides can be obtained by differentiating Equation 1.1. Further, it is assumed that superimposing the oscillating velocity Þeld on the regional ground water ßow (Vr) as follows may represent the total velocity Þeld: V = Vo + Vr = -

K ∂h K i ne ∂x ne r

(1.4)

where ne is the effective porosity of the aquifer, and ir is the average regional ground water gradient. The oscillating velocity component Vo of Equation 1.4 can be expressed as: Vo = ho

È 2p t K pù b exp ( - xb) 2 sin Í - xb + ú ne 4û Î to

(1.5)

Figure 1.7 shows the periodic variations of h (with respect to the mean surfacewater elevation in the tidal stream) and Vo (oscillating velocity) as a function of Head h 1.0

X = 4 ft

h/ho

0.5 0.0 −0.5 −1.0

X = 10 ft 0.0

0.5

1.0

1.5

Time (days) Velocity Vo Vone /hoKβ

1.0

X = 4 ft

0.5 0.0 −0.5 −1.0

X = 10 ft 0.0

0.5

1.0 Time (days)

FIGURE 1.7 Periodic variation of h and Vo at x = 4 ft and 10 ft.

1.5


32


time (t) at different distances (x) from the surface-water boundary. The Þgure shows a phase shift of the velocity oscillations from head oscillations, which is apparent when Equation 1.5 is compared with Equation 1.4. At any given location, the maximum oscillating velocity occurs at the time when h is halfway between the mean surface water level and its maximum. At this time, the ßuctuating head gradient (Mh/Mx) becomes the maximum, thereby inducing the maximum oscillating velocity. The one-dimensional differential equation describing the transport of mass by advection and dispersion is given by Bear (1979): ∂ ∂x

È ∂C ù ∂(VC) ∂C ÍÎ D ∂x úû - ∂x = ∂t

(1.6)

The coefÞcient of dispersion, D, is expressed as the product of dispersivity and velocity; that is: D=a V

(1.7)

where a is the constant longitudinal dispersivity. It should be noted that the dispersion coefÞcients also vary in accordance with the sinusoidal velocity term given in Equation 1.4. The governing Equation 1.6 is to be solved with D as deÞned by Equations 1.4 and 1.7. The boundary conditions under which the system of equations must be solved are difÞcult to establish. For the purpose of this study, the following conditions have been assumed. At the surfacewater interface [x = 0], oscillating boundary conditions are imposed as given below. C x=0 = 0 for V ≥ (surface water enters aquifer) ∂C ∂x

x =0

= 0 for V < 0 (ground water enters stream)

(1.8)

(1.9)

When V < 0, uncontaminated surface water from the tidal estuary inÞltrates the aquifer. For simplicity, C = 0 at x = 0, although a third-type boundary condition could be applied (i.e., D [∂C/∂x] + VC = 0). Yim and Mohsen (1992) assumed that ∂C/∂x ª 0 because the concentration levels over an inÞnitesimal distance inward from the interface are very close to the concentration levels in the stream. Because the surface water concentration is always assumed to be zero, setting C = 0 would mimic the third type of boundary condition. When V < 0, ground water from the aquifer discharges into the tidal stream. The main portion of the contaminant plume that discharges to the stream is the portion that has been diluted during the previous high-tide cycle. The concentration levels near x = 0 are not equal to zero; however, the concentration gradient over an inÞnitesimal distance near x = 0 is also assumed to be insigniÞcant. One way to treat this type of boundary condition is to set the concentration gradient equal to



33

1.0 0.9

0.7

tia

In i

0.6

lC on

0.5

dit ion

0.4

s

Concentration (ppm)

0.8

0.3 0.2 0.1 0.0 0

50

100

150

200

Distance (ft)

FIGURE 1.8 Initial distribution of contaminant concentration.

zero as in Equation 1.9. This results in a zero dispersive ßux between the aquifer and stream. (Yim and Mohsen [1992] performed additional tests to conÞrm this treatment.) The boundary condition imposed at the other end (i.e., at x = L) is: ∂C ∂x

x=L

=0

(1.10)

The condition (Equation 1.10) is applied assuming that the boundary at x = L is sufÞciently removed from the plume. An alternative boundary condition to Equation 1.10 is to set C = 0 at x = L, provided that the distance (L) is sufÞciently far from the upgradient end of the plume. The initial condition depends on the known information about contamination at t = 0. The Þrst example discussed here comprises a triangular initial distribution. As shown in Figure 1.8, a 160-ft long, triangular-shaped mass was placed on a 200-ft model domain with the peak concentration of 1 part per million (ppm) at x = 80 ft. From x = 160 to x = 200 ft, zero initial concentration from 160 to 200 ft was chosen to ensure that no mass escapes beyond the boundary of the space domain (x = 200 ft) by adverse dispersion against the direction of regional ground water ßow to the stream. The one-dimensional advection–dispersion Equations 1.6 and 1.7 were solved by a Þnite difference method using the Crank–Nicolson implicit algorithm. Yim and Mohsen (1992) exhaustively validated the computer code by comparing it with an analytical solution and conducting mass conservation studies. 1.3.2.5 Exit Concentration The results obtained from the base-case simulation are further used herein to provide a detailed discussion on exit concentration. Figure 1.9, part A, illustrates a net


34


(A) Head Variation at x = 2 ft

Head (ft)

1.00 0.50 0.00 −0.50 −1.00 0.0

1.0 Time (days)

2.0

Concentration (ppm)

(B) Concentration Variation at x = 2 ft 0.05 0.04 0.03 0.02 0.01 0.00 0.0

1.0 Time (days)

2.0

FIGURE 1.9 Head and concentration variations near the exit boundary.

sinusoidal variation of the ground water levels close to the exit boundary x = 2 ft. At this location, the total variation induced by 1-ft tidal amplitude at the stream is +/–0.75 ft. Figure 1.9, part B, illustrates the variation of the concentration levels at the same location. Although the total variation is relatively small (approximately 0.01 ppm), concentration levels respond to the ground water ßuctuation as shown in Figure 1.9, part A. For example, concentration levels become lowest at positive peaks of the ground water variation because of dilution during the inward ßow periods. On the contrary, the highest concentration levels correspond to negative peaks. The consistency of this relationship vanishes with increasing distance (x) from the stream, primarily because of the phase shift relationship between the sinusoidal head and the velocity variations described in Equations 1.1 and 1.5, respectively. Furthermore, concentration levels near the exit boundary remain low only up to the distance where tidal inßuence is relatively signiÞcant. The simulated concentration levels at x = 2 ft and x = 10 ft for a 3-year period are shown in Figure 1.10 by solid lines. At x = 2 ft, concentration levels are relatively invariant from the initial level of 0.025 ppm. Concentration levels at x = 10 ft also remain relatively invariant from the original concentration of 0.125 ppm. At distances beyond the reach of the tidal inßuence, however, concentration levels depend only on the prevailing regional gradient and the upgradient concentration levels. The two accompanying broken line curves shown in Figure 1.10 represent the variation in concentration levels at x = 2 ft and x = 10 ft without tidal ßuctuation at the stream. Concentration levels near the stream are signiÞcantly higher in the absence of the tide. Comparing Figure 1.11 (base case without tide) with Figure 1.12 (base



35

1.0 0.9

Concentration (ppm)

0.8

No Tide x = 10 ft x = 2 ft

0.7 0.6 0.5 0.4

With Tide x = 10 ft x = 2 ft

0.3 0.2 0.1 0.0 0.0

1.0

2.0

3.0

Time (years)

FIGURE 1.10 Comparison of the exit concentration with and without tide.

ir = –0.01 Without Tide 1.0 0.9

0.7

0.5

0.3

ns

2 years

0.4

itio

d on

lC

1 year

0.6

tia Ini

Concentration (ppm)

0.8

3 years

0.2 0.1 0.0 0

50

100 Distance (ft)

150

200

FIGURE 1.11 Results of the base-case simulation with zero tidal amplitude.


36


ir = –0.01 With Tide 1.0 0.9 Ini

0.7

on

lC

1 year

0.6

tia

0.5

0.3

s

2 years 0.4

i on

di t

Concentration (ppm)

0.8

3 years

0.2 0.1 0.0 0

50

100 Distance (ft)

150

200

FIGURE 1.12 Results of the base-case simulation with tidal amplitude = 1 ft.

case with tide) shows that predicted concentration distributions beyond 40 ft are nearly identical for all times. This phenomenon was observed in actual data obtained from an industrial site located adjacent to a major tidal river in New Jersey. Shallow ground water beneath the site discharges to the river where the stage level adjacent to the site undergoes a total tidal ßuctuation of 6 ft. Currently, the shallow ground water is found to be contaminated with arsenic, and the age of the arsenic plume is believed to be several decades. The highest arsenic level of 600 parts per billion (ppb) was detected at a few hundred feet inland from the riverbank, while arsenic levels obtained from river water samples collected both upstream and downstream of the site were consistently 12 to 18 ppb over a 4-year period. Several rounds of ground water samples taken from a monitoring well located approximately 30 ft from the bank also showed consistently low arsenic levels (around 20 ppb). Consequently, arsenic measurements obtained from the monitoring well were believed to be signiÞcantly affected by the river water. In this case, the area of clean zone resulting from the 6 ft of tidal ßuctuation is approximately 30 ft inland. The implication of the above analysis is that a zone of tidal inßuence should be delineated properly for any monitoring network to be installed in aquifers adjacent to tidal water bodies. Although one may assess the extent of tidal inßuence by employing the velocity Equation 1.5, the spatiotemporal behavior of the plume must be evaluated based on a numerical model similar to the one presented in this study. Yim and Mohsen (1992) also performed a series of sensitivity studies that revealed that the cleanup process is highly sensitive to the regional gradient. The tidal effects are present only at small gradients. At higher gradients, the streamward velocity overwhelms the tidal ßuctuations. The tidal ßushing also depends on hydraulic conductivity. With increasing hydraulic conductivity the tidal inßuence radiates



37

further inland, but with increasing hydraulic conductivity the regional velocity also increases, which lowers the tidal impact. Subsequent to the 1992 work by Yim and Mohsen, the model was modiÞed to include the following boundary condition: C n =L = CL

(1.11)

This enabled the code to be applied to a situation of a continuing source. This modiÞed code was applied to an actual facility on the banks of a tidal river. Johnson and Olsen (2000) presented the results of an extensive tracer study using 96 sampling piezometers in three circular clusters at the tidal banks of Aberdeen Proving Ground in Maryland. The objective of that study was to estimate ground water velocity and dispersivity. Measured concentrations compared signiÞcantly better with numerical predictions with tide than without.

1.3.3 EMERGING TECHNOLOGIES Mixing zones generally consist of two distinct regions, commonly called the nearand far-Þelds. The distinction between them is illustrated in Figure 1.13 for the case of a wastewater discharge from a multiport diffuser into a water environment ßowing with a speed u. The main physical mechanism responsible for mixing the discharge and reducing contaminant concentrations contained within the discharge is turbulence. The distinction between the two regimes lies in the source of this turbulence. In the near-Þeld, processes related to the discharge itself induce the turbulence, possibly due to shear resulting from the exit velocity (e.g., a high-velocity discharge from a diffuser nozzle) or the buoyancy of the discharge (e.g., a domestic sewage discharge into marine or estuarine waters). In the latter case, the discharge momentum may also be important. In the far-Þeld, the turbulence responsible for mixing Near-field: Self -induced turbulence

Far-field: Ambient turbulence

u

Mixing zones: • Regulatory • Hydrodynamic • ZID

FIGURE 1.13 The terminology of mixing zones.


38


FIGURE 1.14 Examples of mixing. (a) No excess source momentum (leaking source); (b) with excess source momentum ßux (jet).

is naturally present in the receiving environment (e.g., turbulence due to boundary shear in a ßowing river). The hydrodynamic mixing zone is generally considered to be synonymous with the near-Þeld. A regulatory mixing zone is speciÞed by some environmental regulatory agencies; water quality regulations must be met at the boundary of this zone. This concept therefore recognizes that rapid mixing can occur in the immediate vicinity of a wastewater discharge, with its concomitant dramatic reductions in contaminant concentrations and therefore environmental impact. The regulatory mixing zone may or may not coincide with the near- or far-Þelds; it may encompass only some of the near-Þeld or consist of the entire near-Þeld and extend into the far-Þeld. Indeed, a future issue is to reconcile regulatory deÞnitions and concepts of mixing zones, which have not kept up with recent advances in our understanding of the hydrodynamics of mixing zones. Examples of mixing are shown in Figure 1.14, which are photographs of discharges from a round nozzle into a coßowing turbulent stream. In the top photograph, the release is isokinetic (i.e., at the same velocity as the ambient ßow). There is no self-induced turbulence, so the mixing is entirely due to the ambient turbulence acting over a wide range of scales. The mixing in this photograph is therefore entirely in the far-Þeld. In the bottom photograph, the discharge velocity is higher than the ambient velocity. The excess source momentum ßux generates shear that causes turbulence. The scale of this turbulence is automatically the same scale as that of the mixing plume, leading to efÞcient mixing and rapid dilution. The mixing in this photograph is therefore initially in the near-Þeld. This self-induced turbulence decays with distance from the source, and eventually ambient turbulent mixing dominates. The mixing then moves into the far-Þeld.



Stationary

39

Crossflow

u Unstratified

Internal hydraulic jump

b) Dense effluent: Brine, industrial effluent

c) Surface thermal discharge: Power plant

u ρ(z)

Stratified

ρ(z)

a) Buoyant effluent: Sewage into estuaries or coastal waters, thermal discharges

d) Jet into stratified environment: Sewage discharge into lake, pumped storage flow

FIGURE 1.15 Mixing zone behaviors for various buoyancy-modiÞed discharges.

Most practical discharges are more complex than these simple situations. Examples are the buoyancy-modiÞed ßows sketched in Figure 1.15 in which a wide range of ßow possibilities exist. Complications arise because of interactions between discharges from multiple ports, arbitrary discharge angles, free surface and bottom boundary interactions, and the effects of ambient density stratiÞcation. Applications of point discharge modeling to ground water discharge may be limited because no signiÞcant momentum is associated with diffuse ground water discharge. Applications may exist, however, when ground water is expressed as discrete subaqueous springs. 1.3.3.1 Mathematical Models To predict mixing under these conditions, many mathematical models have been developed. Some of these have been previously mentioned. These models fall into three general types. First are entrainment models such as Fan and Brooks (1969). The rate of entrainment of ßow into the jet or plume is computed according to some assumed relationships that are typically functions of the local centerline velocity, the ambient velocity, the angle between them, and the local density differences. There are two types of entrainment models: Lagrangian and Eulerian. Lagrangian models follow a plume element along its trajectory; an example is UM3, which is available in the U.S. The USEPA interfaces Visual PLUMES (Frick et al., 2000). Eulerian models use a Þxed reference frame to track plume trajectory (e.g., CorJet within CORMIX, UDHKDEN within PLUMES [Frick et al., 2000; Doneker and Jirka, 2002]). In both Lagrangian and Eulerian approaches, the equations for conservation of mass, momentum, and energy are solved stepwise along the plume trajectory. The growth of each element is determined by an entrainment hypothesis. For multiport manifolds, the ßows begin as round buoyant jets issuing from one side of the diffuser that can merge to a plane buoyant jet. The model outputs consist of plume characteristics along its trajectory such as centerline dilution, width, and centerline height. Entrainment models work fairly well with relatively deep receiving


40


waters, but some caution may be needed in shallow waters with long diffusers where the supply of entraining water can be limited and boundary mixing can be signiÞcant. Jirka and Doneker (1991) provide a two-dimensional analysis to determine whether the ßow is deep or shallow, depending on the interplay of discharge and ambient conditions. Shallow water implies local reentrainment where the assumptions of integral models may not apply. CORMIX contains methods to identify and simulate both stable deep water conditions and unstable shallow water conditions for twodimensional ßows. The unstable results should be applied with caution to diffusers of Þnite length that result in three-dimensional effects where the entraining water can be supplied from the diffuser ends. These problems are overcome to some extent by semiempirical models based on dimensional analysis and length scale arguments. These models classify the ßows according to the relative magnitudes of characteristic ßow length scales and employ asymptotic solutions based on experimental data. Examples include RSB for marine sewage discharges (Roberts, 1999) and CORMIX, which classiÞes the ßow based on the relative magnitudes of length scales computed from the input information. RSB and CORMIX are also available from the USEPA. Length scale models can be unreliable when applied near transitions in ßow behavior and are not well suited to arbitrarily shaped density stratiÞcations and nonuniform ambient velocities. Finally, computational ßuid dynamics (CFD) models, in which the averaged Navier–Stokes equations (the Reynolds equations) are solved with some turbulence closure assumptions, are beginning to appear. These are difÞcult to apply to complex geometries, particularly with buoyancy effects. Furthermore, CFD models have not yet made signiÞcant inroads into the engineering predictions of mixing zones. 1.3.3.2 New Laboratory Techniques Recently developed laboratory and Þeld experimental techniques are now leading to rapid improvements in understanding these complex ßows and in the ability to model them. In the laboratory, laser-induced ßuorescence (LIF) has been widely used to study mixing processes in turbulent ßows, particularly jets and plumes. Many studies have been reported since the earliest ones by Owen (1976) and Dimotakis et al. (1983). The technique is illustrated in Figure 1.16. The beam from an argon ion laser is passed through cylindrical optics to create a thin collimated sheet that passes into the test tank. A small amount of ßuorescent dye is added to the inßow. The laser sheet causes the dye to ßuoresce, and a charge-coupled device (CCD) camera captures the emitted light. By suitable calibration, the images can be converted to instantaneous maps of the whole concentration Þeld. These can be averaged to obtain the mean concentration Þeld. Examples of a horizontal buoyant jet are shown in Figure 1.17. For details, see Ferrier et al. (1993). The ability to obtain high spatial and temporal resolution images that yield quantitative information on the instantaneous scalar concentration Þelds has proven very useful in understanding the mechanics of turbulent mixing processes. Most of these studies have been planar LIF (PLIF) in which



41

Inflow Laser sheet Argon Ion laser

Cylindrical lenses CCD camera

Density-stratified tank

Image processing system

FIGURE 1.16 Schematic of a planar laser-induced ßuorescence (PLIF) system.

FIGURE 1.17 Laser-induced ßuorescence images of horizontal buoyant jets. (a) Instantaneous image; (b) time-averaged image.

the information is obtained from a two-dimensional plane through the ßow. Even relatively simple jet and plume ßows are inherently three-dimensional, and PLIF cannot reveal this three-dimensionality. Three-dimensional LIF (3DLIF) systems that overcome these deÞciencies have recently begun to appear. In these systems, the laser sheet is swept through the ßow at high speed and images are captured with a synchronized camera. Through suitable postprocessing and calibration, the three-dimensional concentration Þeld can then be obtained. These systems have only recently become practical because of advances in instrumentation, especially optoelectronics, low-light high-speed cameras, high-speed scanning mirrors, image capture and processing, and mass storage devices. Furthermore, the use of LIF in stratiÞed ßows is now possible by use of refractive index matching techniques (Daviero et al., 2001). A schematic depiction of a 3DLIF system is shown in Figure 1.18. This system and experimental procedures are described in detail in Roberts and Tian (2000). The tank is glass-walled and has a tow carriage powered by a variable-speed DC electric motor carriage that runs on precision stainless-steel rods the length of the


42


Towing carriage

Plano convex lens Scanning mirrors

Inflow

Density-stratified towing tank

Tow

Laser sheets

y

Plume

Argon Ion laser y-mirror signal Camera sync signal z-mirror signal

Timing signal Images

Scanning mirror and timing control computer

High speed CCD camera

Image acquisition computer

FIGURE 1.18 Schematic of 3DLIF experimental arrangement.

tank. The efßuent (a mixture of water, salt, and ßuorescent dye) is supplied from a reservoir by a rotary pump. The tank can be linearly stratiÞed using a two-tank Þlling system. The 3DLIF system is controlled by two computers, one for overall timing control and one for image capture. The beam from an argon ion laser strikes two orthogonal fast galvanometer scanning mirrors that move the beam in the horizontal (y) and vertical (z) directions. The beam then strikes a large plano-convex lens so that it is always refracted parallel to the axis of the tow tank. The ßow images are captured by a high-speed CCD camera that is attached to the tow carriage and moves with it so that the discharge appears to be stationary relative to the camera. An input/output (I/O) board in the timing computer controls synchronization. It Þrst sends a transistor–transistor logic (TTL) signal to a frame grabber in the image acquisition computer. This board then sends a signal to the camera to begin image acquisition (exposure). Simultaneously, the I/O board begins sending an analog voltage to move the vertical (z) mirror. The beam makes one sweep down and back while the camera is exposing. A signal is then sent to the horizontal (y) mirror to move the beam a small distance horizontally. The cycle begins again with another TTL signal that downloads the previous frame, clears the camera buffer, and begins the next exposure. This is repeated so that multiple vertical slices through the ßow are obtained. After a predetermined number of slices, the beam returns to the starting point and the cycle starts again. For details see Roberts and Tian (2000). To illustrate the system, experiments were performed on vertical buoyant jets in unstratiÞed and stratiÞed crossßows with the conÞguration of Figure 1.18. (The results here are shown inverted to represent a positively buoyant jet discharging upwards.) Three-dimensional visualizations of the outer surface of the jets are shown in Figure 1.19. In these Þgures the surface threshold level is set just above zero.



43

FIGURE 1.19 Renderings of instantaneous threshold concentration of surface of vertical buoyant jets in crossßows. (a) UnstratiÞed; (b) stratiÞed.

FIGURE 1.20 Renderings of time-averaged threshold concentration surfaces of vertical buoyant jets in crossßows. (a) InstratiÞed; (b) stratiÞed.

Similar views of the time-averaged surfaces are shown in Figure 1.20. The unstratiÞed jet shows an expected shape and trajectory; the stratiÞed jet shows ßattening near its terminal rise height because of gravitational collapse under the inßuence of density stratiÞcation. A great amount of information can be extracted from these three-dimensional data. For example, Figure 1.21 shows vertical proÞles of tracer concentration at various distances from the source. Tracer levels are shown by color coding. The familiar kidney shapes to the proÞles are apparent, with the maximum concentrations appearing to the sides of the vertical plane through the jet centerline. Plume trajectories and variations of dilution along the trajectory have also been obtained, and information of this type should be of great value in improving mathematical plume models. The results were compared with generally accepted asymptotic solutions. They were found to agree closely to those of the buoyancy dominated far-Þeld regime deÞned by Wright (1984). Because stratiÞcation reduces vertical mixing, and the effect of stratiÞcation becomes more important with increasing distance from the nozzle, the jet follows almost the same trajectory and dilution in unstratiÞed and stratiÞed ßows near to the source but diverges at greater distances.


44


FIGURE 1.21 Vertical concentration proÞles through (a) unstratiÞed and (b) stratiÞed jets.

In stratiÞed ßows, the jet collapses at some point; it stops rising and the dilution becomes constant. In unstratiÞed crossßows, however, the jet keeps rising and the dilution keeps increasing until it reaches the water surface. For details, see Tian and Roberts (2001). 1.3.3.3 New Field Techniques Techniques for Þeld observations of mixing zones are also advancing rapidly. Examples of observations of ocean sewage outfalls in which dilution has been measured are in Faisst et al. (1990); Roberts and Wilson (1990); Davison et al. (1993); Wu et al. (1994); Petrenko et al. (1998); and Roldao et al. (2000). In these studies, dilution was measured either directly by added tracers such as ßuorescent dye or radioisotopes or indirectly by observing salinity variations. A typical Þeld technique is shown in Figure 1.22 (Roldao et al., 2000). In this study, ßuorescent dye was added to the sewage. These Þeld studies often show patchiness in the wasteÞelds that cannot be predicted by present mathematical models, although gross characteristics are reasonably represented. Mixing zone modeling in the Þeld is often complicated by the rapid spatial and temporal variations in physical conditions that can occur, for example, in coastal waters. Recent advances in oceanographic instrumentation hold the promise of much improved modeling under these conditions. Particularly valuable are acoustic doppler current proÞlers (ADCPs) and thermistor strings that allow continuous measurement of both velocity and density structure through the entire water column. The combination of long-time series of measurements from such instruments with suitable mathematical models can result in considerable insight into the statistical variation of mixing zone properties. An example is the modeling of the Mamala Bay, HI outfalls (Roberts, 1999). The combination of coupled near- and far-Þeld models, current meter data from ADCPs, and density stratiÞcation data from thermistor strings enabled predictions of bacterial dispersion around the outfalls such as that illustrated in Figure 1.23. Optical chemical sensors are now under development that should allow realtime in situ measurement of hydrocarbons and other naturally occurring species in



45

Digital radio antenna (DGPS) VHF antenna Tracer detector Pump

Radio phone

Depth meter

Graphic display

“Bip” Sample collecting Portable fluorometer Centrifugal pump Rubber hose for continuous pumping of samples

Data logger

Sample bottles

Sample collecting Ballast (100 kg)

Digital radio GPS

Laptop computer

Rubber hose for continuous pumping of samples

FIGURE 1.22 Field sampling method and tracer detection apparatus.

Percent 6 2 1

Honolulu

Pearl Harbor

Waikiki Beach

Meters 0

2,500

5,000

FIGURE 1.23 Predicted frequency with which a total coliforms level greater than 10,000 per 100 ml is exceeded (Roberts, 1999).

wastewaters (Mizaikoff, 1999). These would allow the measurement of mixing characteristics in complex surface water environments without the use of added tracers. For example, a recent major innovation for mid-infrared applications is the development of wavelength-deÞnable semiconductor quantum-cascade lasers, which involves infrared diagnostic systems that can monitor absorption at freely selectable wavelengths with exceptionally high sensitivity, allowing monitoring of speciÞc substances even in the presence of many other compounds.


46


1.3.4 ALTERNATIVE APPROACHES Traditionally, mixing zone modeling has focused on submerged outfalls with signiÞcant efßuent ßow rates. In recent years, many state regulatory agencies turned their attention to small wastewater ßow discharges from either municipal or industrial facilities. A typical small-ßow outfall is located on the bank of the river with the outlet elevation well above the river water surface. The wastewater ßow reaches the river through the pipe by gravity. Buoyancy-induced dilution is not being considered as many small, surface discharges have negligible density difference compared with that of the receiving water. 1.3.4.1 Insignificant Momentum-Induced Dilution The federal guideline requires that the acute toxicity impact be evaluated under the 1Q10 low ßow condition at the study site. The wastewater outfall for many existing facilities is located above the water surface under the 1Q10 low ßow condition in the river, resulting in zero initial dilution. Considering a momentum jet in a crossßow, one can evaluate the dilution ratio in the jet momentum-dominated regime (or momentum-dominated near Þeld) by using the following equation: s = 0.17

Mo y Qo

(1.12)

where Qo is the wastewater ßow rate, Mo is the momentum ßux (Qo uo), uo is the efßuent velocity, and y is the characteristic length. For the Blacksburg/Virginia Polytechnic Institute (VPI) wastewater treatment plant on the New River, the following data are used to calculate the momentuminduced dilution ratio: • • • •

Wastewater ßow rate: Qo = 6 million gal/day = 9.284 ft3/s Cross-sectional area of outfall pipe: A = 15.9 ft2 Efßuent velocity: uo = Qo/A = 0.584 ft/s Momentum ßux: Mo = 2 Qo uo = 115.9 ft4/s2

Equation 1.12 yields an initial dilution ratio at the edge of allocated impact (y = 28 ft) to be 0.846 which is below 1.0 (no dilution!). Therefore, the only dilution for the wastewater treatment plant’s discharge would be from the ambient turbulence in the river. 1.3.4.2 Modeling Approach The above calculation shows the insigniÞcance of the momentum-induced dilution. The modeling approach to quantifying the mixing zone is therefore based on a conservative consideration (i.e., not counting any momentum-induced dilution to meet the water quality standards at the edge of the allocated impact zone). For whole efßuent toxicity, this means that the CMC of 0.3 TUa must be met at the edge of



47

the allocated impact zone by relying on the ambient turbulence-induced mixing, not by momentum-induced mixing. It is viewed as conservative because momentuminduced mixing, if any, is not included in the calculation. A two-dimensional modeling framework is therefore used to compute the concentration pattern below the outfall on a riverine system as follows (Neely, 1982):

C( x , y ) =

(

M

du 4 pDy x u

)

12

e

- y 2u 4 Dy x

(1.13)

where C(x,y) is concentration at any given location below the outfall, M is the mass discharge rate, u is the average velocity in the river, Dy is the lateral dispersion coefÞcient, x is the distance below the outfall, y is the distance across the river, and d is the average water depth at the outfall. For estuarine and tidal systems, the follow equation is used (Hamrick and Neilson, 1989): 12 ux ù È M u Ê x 2 y2 ˆ ú 2 Dx Í C( x , y ) = e K0 + Á ˜ Í 2 Dx Ë Dx Dy ¯ ú pd Dx Dy û Î

(1.14)

where Dx is the longitudinal dispersion coefÞcient and K0 is the modiÞed Bessel function of the second kind of order zero. 1.3.4.3 Case Studies of Model Applications 1.3.4.3.1 A Single Pipe Surface Discharge into a Tidal River The Falling Creek wastewater treatment plant proposed to discharge its 10 mg/day ßow into the tidal James River below Richmond in 1992. The Virginia State Water Control Board (SWCB) originally proposed an acute whole efßuent toxicity (WET) limit of 1 TUa at the end of the pipe (i.e., allowing no dilution). Following the application of Equation 1.3 near the proposed outfall, the model results show that the 0.3 TUa acute toxicity limit can be met at the edge of the allocated mixing zone under the 1Q10 low ßow condition (Figure 1.24). This result was approved by the SWCB and USEPA Region III, thereby lifting the more stringent acute toxicity limit of 1 TUa (Lung, 1995). 1.3.4.3.2 Ammonia Toxicity in a Riverine System As demonstrated earlier, the Blacksburg/VPI plant (with an average ßow rate of 6 mg/day) lacks sufÞcient momentum to generate initial dilution. Equation 1.2 was applied to this plant on the New River, VA, to perform an ammonia mixing zone modeling analysis. Figure 1.25 shows that the acute ammonia limit of 1.35 mg/l developed by the state regulatory agency would be met at the edge of the allocated impact zone under the 1Q10 low ßow condition in the New River. The efßuent ammonia concentration is 7 mg/l.


48


200 180

(b) Whole Effluent Toxicity = 2 TUa

Across River (ft)

160 140

Ua

5T

0.0

120

Ua

6T

100

6 0.0

80 60

Ua

0T

0.1

40

Allocated Impact Zone 0.20 TUa 0.30 TUa

20 0 –200

–150

–100

–50 0 50 100 Distance (ft) along Shoreline

150

200

FIGURE 1.24 Model-calculated isopleths with WET limit of 2 Tua from Falling Creek plant.

50 45

Across River (ft)

40

Effluent Ammonia Conc. = 7 mg / l Wastewater Flow = 6 mgd

35

Allocated Impact Zone

30 25 20

1.17 mg/l 1.35 mg/l 1.75 mg/l 2.33 mg/l 3.50 mg/l

15 10 5 0 –50

–40

–30

–20 –10 0 10 20 Distance (ft) along Shoreline

30

40

50

FIGURE 1.25 Model-calculated ammonia concentration isopleths in the New River.

1.3.4.3.3 An Effluent Channel into a Tidal River A DuPont facility near Richmond, VA, discharges its efßuents into the tidal James River via a natural channel. In this study, Equation 1.3 was applied to quantify the dilution ratio in the overall mixing zone. The dilution ratios were then used to determine whether the CCCs for metals and other constituent concentrations can be met at the edge of the overall mixing zone under the 7Q10 low ßow condition (Lung, 1998). The model-calculated dilution isopleths are shown in Figure 1.26. A key technical issue in applying Equations 1.13 and 1.14 is the assignment of the longitudinal and lateral dispersion coefÞcients in the receiving water. At the present time, three approaches are being adopted by the modeling profession. The Þrst approach uses empirical equations to calculate the dispersion coefÞcients (Lung, 1995). The second approach is conducting a dye dispersion study to back-calculate



Ambient Velocity = 0.053 ft/sec Longitudinal Dispersion = 10 ft2/sec Lateral Dispersion = 2 ft2/sec James River ===>

49

Regulatory Mixing Zone 10:1

4:1 ratio = 3.33:1

6:1

8:1

–200–100 0 100 200 300 400 500 ft Effluent Flow = 40 mgd Effluent Flow = 1 mg /l Drainage Ditch

FIGURE 1.26 Model-calculated dilution isopleths in the overall mixing zone in the tidal James River following the discharge of DuPont facility near Richmond, VA.

the lateral dispersion coefÞcient (e.g., for Equation 1.13) (USEPA, 1991). The third option is to perform a hydrodynamic modeling analysis to determine the advective and dispersion transport in the receiving water.

1.4 ACCEPTANCE OF METHODOLOGY The use of benchmarking has been a common practice to conÞrm the adequacy of numerical models by comparing predicted results against known solutions with test data sets developed from analytical solutions or Þeld or laboratory experiments. Expert systems and rule-based decision support has had considerable success in technology transfer. Integrated systems that combine simulation, visualization, and tools for documentation and design optimization are particularly powerful. Public funding is necessary for academic research and basic methodology development to ensure impartiality and openness. However, once the methods and techniques are published openly, then there is undeniable pressure to expand and support the software systems created. Most public agencies are not able to make the steady and continuing investment of funds necessary to produce reliable and up-to-date software.

1.5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS The most salient technical issues that need resolution with regard to surface water–ground water interactions (the mixing zone) are as follows: •

•

DeÞning conceptual models for aquifer, transition zone, and water column interactions (including biologic, geologic, hydrologic, and geochemical processes) DeÞning the relevance of the ecology in the transition zone (e.g., hyporheic zone, which is usually deÞned in terms of the biota only)


50


• • • • •

•

Locating a discharge area or upwelling Characterizing a discharge Locating and characterizing a plume Identifying and characterizing all other contaminant sources Not only identifying signiÞcant ground water–surface water interactions, exchanges, and processes, but deÞning these within both spatial and temporal frameworks DeÞning DQOs

The following speciÞc modeling activities are recommended to resolve the above issues adequately: • • •

• • • • •

Formulate screening and tiered approaches using modeling. Develop research and operational models for the mixing zones, transition zones, and interfaces (e.g., contaminated sediments layers). Develop linkage techniques to couple ground water and surface water models to address unique temporal and spatial scaling for ßow, transport, and biogeochemical processes. Apply veriÞcation procedures (including peer review), benchmarking, validation, and Þeld testing. Use and develop scientiÞc process models to deÞne various types of mixing zones and transition zones in support of conceptual model development. Obtain new data sets to develop and validate modeling approaches to address regulatory requirements. Establish feedback mechanisms between data collection, modeling, and resource decisions. Develop methods to account for uncertainty and heterogeneity.

For complex surface water sites where an unacceptable risk to human health and the environment is likely, a modeling framework can be developed to achieve the following: •

•

Apply hydrodynamic modeling principles while incorporating key chemical and biological criteria to deÞne more quantitatively the mixing zone regulatory boundaries and target goals (e.g., ecological impacts on a localized scale, large-scale ecological or human health concerns). Evaluate alternative control or cleanup strategies to achieve sound remedial management decisions even under conditions of uncertainty.

As a result, all major factors central to the transport of contaminants (physical, chemical, biological) and risk to biota can be identiÞed properly for complex sites. These models also have the capability to address current and potential regulatory deÞnitions of the various types of mixing zones and transition zones. Accounting for parameter uncertainty also allows key regulatory policies to be addressed in the presence of technical uncertainty, perhaps encouraging a review of the policy or the granting of a variance.



51

The Þeld of stream–subsurface exchange modeling is still developing. A number of these models are currently available, but they all have somewhat limited applicability. Because of this, there has been a lack of propagation of other modeling methods to stream–subsurface exchange problems. System heterogeneity and uncertainty in model estimates have not been addressed in great detail, and tools such as optimization methods, inverse modeling, and sensitivity analysis have only rarely been applied to stream–subsurface exchange. Strictly mechanistic models, particularly those applied to simulate complex water quality processes, are usually inadequate and sometimes inappropriate for use as the basis for management decisions. Thus, any current analysis of contaminant transport across the surface water–ground water interface is likely to be fraught with uncertainty. Partial error propagation studies that have been completed for similar models have often resulted in large prediction errors for variables other than hydrologic or hydrodynamic, salinity, and suspended sediment. These models are good learning tools, they assist the analyst in organizing and collecting the data needed to ultimately make decisions, and they express knowledge and relationships about processes and linkages. However, natural processes are vastly more complex and variable than are commonly represented in these models. The available models may be best used together to address different aspects of a typical contaminant transport problem. Models such as OTIS and MODFLOW can be used to examine larger-scale ßuxes and bound the transport problem. The available process models can be employed to directly examine important smallerscale features (e.g., by contributing general understanding of the processes relevant to a particular type of contaminant, by providing guidelines for the design of Þeld sampling programs). Development of a new class of directly applicable models for contaminant transport between surface and ground waters will require considerable effort in a number of areas, as summarized in Table 1.2. Reviews on modeling stream–subsurface interactions can be found in Bencala et al. (1993) and Packman and Bencala (1999). Emerging technologies in CFD models are beginning to appear in which the averaged Navier–Stokes equations (the Reynolds equations) are solved with some turbulence closure assumptions. These are still difÞcult to apply to complex geometries, and CFD models have not yet made signiÞcant inroads into engineering predictions of mixing zones. However, recently developed laboratory and Þeld experimental techniques are now leading to rapid improvements in understanding these complex ßows and in the ability to model them. In the laboratory, LIF is used more widely to study mixing processes in turbulent ßows, particularly jets and plumes. The use of “benchmarking” could be of great use in comparing different approaches and would involve the simulation of a common data set where Þeld or laboratory data were available for comparison to model predictions. Distinct cases with different physical characteristics of the mixing zone should be evaluated (e.g., a small mountain stream, large piedmont rivers, estuary, and coastline). Multiday workshops have great potential for user education but are expensive and time consuming. An integration of Web-based tutorials and perhaps a yearly user-group/training session might be most effective. Academia clearly has a role to


52


TABLE 1.2 Important Directions for Future Development of Models for Contaminant Transport Across the Surface Water–Ground Water Interface Topic Exchange Hydrodynamics

Contaminant Transport

General Model Development

Needs for Future Model Development Examination of additional exchange mechanisms Integration of multiple individual process descriptions Linking existing large-scale models to process models Methods for averaging or up-scaling exchange parameters Improved consideration of long-term stream transport dynamics Development of integrated watershed models (i.e., that model surface waters and ground waters as a continuum) Improved characterization of heterogeneity in the underlying biogeochemical processes Better understanding of system determinants of biogeochemical parameters (for example, the effect of system geology and hydrology on microbial communities) Analysis of coupled physical and biogeochemical heterogeneity Investigation of the role of physical transport as a determinant of chemical and microbial heterogeneity in the hyporheic zone Improved understanding of importance of transport through the hyporheic zone for important classes of contaminants Better consideration of uncertainty, especially the propagation of uncertainty estimates across the stream–subsurface interface

play as an unbiased arbiter of good science. The basic techniques and mathematical formulations should be published in referred scientiÞc journals and available for public scrutiny and feedback. The policy implications of technological limits can be summarized by noting that both key policies and cleanup goals should be addressed in the presence of technical uncertainty (e.g., complex interactions; model and natural parameter uncertainties; technical limits in the measurement of physical, chemical, and biological variables). Periodic review of policy and the regulatory environment should be encouraged to recognize and incorporate new modeling knowledge and/or superior innovative remediation technologies. Differences between the hydrodynamic and regulatory mixing zone deÞnitions must be reconciled. Finally, nonpoint sources in theory could be managed within existing mixing zone deÞnitions and regulation. A case in point is the language provided by the MDEQ mixing zone administrative rules. However, particularly for TDZs, some of the deÞnitions and control strategies that apply to point sources are not relevant to nonpoint sources. Furthermore, it is difÞcult to generalize the actual practice of implementing the mixing zone regulations given the large number and diverse types of jurisdictions and permit-granting authorities involved. By and large, however, current procedure falls into one of the following approaches or can involve a combination thereof:



•

•

•

53

The mixing zone is deÞned by some numerical dimension. The applicant must demonstrate that the existing or proposed discharge meets all applicable standards for conventional pollutants or for the CCC of toxic pollutants at the edge of the speciÞed mixing zone. No numerical deÞnition for a mixing zone applies. In this case, the applicant may propose a mixing zone dimension. To do so the applicant generally uses actual concentration measurements for existing discharges, dye dispersion tests, or model predictions to show at what plume distance, width, or region the applicable standard will be met. Further, ecological or water use-oriented arguments can be used to demonstrate that the size of that predicted region provides reasonable protection. The permitting authority can calculate that proposal for a mixing zone. This approach resembles a negotiating process with the objective of providing optimal protection of the aquatic environment consistent with other uses. Chronic and Þnal acute criteria are developed at the ground water–surface water interface (e.g., MDEQ).

ACKNOWLEDGMENTS The authors wish to acknowledge the participation of other panel members: Henk Haitjema (Indiana University), Steven McCutcheon (USEPA, Athens), and also Bart Ruiter (DuPont Company). Helpful comments were also received by workshop participants, in particular, Stephen Johnson (Roy F. Weston), Amy Dembosky (WIK Associates), Jonathan Johnson (USGS), William Deutsch (URS Diamond), Glen Wyatt (Weyerhauser), and Kevin Garon (DuPont Corporate Remediation Group).

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Roldao, J., J.L.B. Carvalho, and P.J.W. Roberts. 2000. Field Observations of Dilution on the Ipanema Beach Outfall. First World Congress of the IWA, Paris, July 3–7, 2000. Rowland, M.J. and A.A. Hannoura. 1988. Regional ground water modeling and aquifer characterization. Proceedings of the Second National Outdoor Action Conference on Aquifer Restoration, Ground Water Monitoring and Geophysical Methods, Dublin, OH, May 23–26, 1988. Runkel, R.L. 1998. One-Dimensional Transport with Inßow and Storage (OTIS) — A Solute Transport Model for Streams and Rivers. U.S. Geological Survey, Water-Resources Investigations Report 98-4018, 73 p. Runkel, R.L. 2000. Using OTIS To Model Solute Transport in Streams and Rivers. U.S. Geological Survey, Fact Sheet FS-138-99. Runkel, R.L., D.M. McKnight, and H. Rajaram. 2003. Modeling hyporheic zone processes. Special Issue. Adv. Water Resour., 26(9), 901–1039. Rutherford, J.C., G.J. Latimer, and R.K. Smith. 1993. Bedform mobility and benthic oxygen uptake. Water Res., 27(10), 1545–1558. Rutherford, J.C., J.D. Boyle, A.H. Elliott, T.V.J. Hatherell, and T.W. Chiu. 1995. Modeling benthic oxygen uptake by pumping. J. Environ. Eng., 21(1), 84–95. Savant, S.A., D.D. Reible, and L.J. Thibodeaux. 1987. Convective transport within stable river sediments. Water Resour. Res., 23(9), 1763–1768. Serfes, M.E. 1987. Interpretation of tidally affected ground water ßow systems in pollution studies. Proceedings of Petroleum Hydrocarbons and Organic Chemicals in Ground Water: Prevention Detection and Restoration, November 17–19, 1987. Sharp, J.M., Jr., 1988. Alluvial aquifers along major rivers. In Hydrogeology: The Geology of North America, W. Back, J.S. Rosenshein, and P.R. Seabe (Eds.), Vol. O –2, pp. 273–282. Storey, R.C., R.R. Fulthorpe, and D.D. Williams. 1999. Perspectives and predictions on the microbial ecology of the hyporheic zone. Freshwater Biol., 41, 1–13. Tate, C.M., R.E. Broshears, and D.M. McKnight. 1995. Phosphate dynamics in an acidic mountain stream: interactions involving algal uptake, sorption by iron oxide and photoreduction. Limnol. Oceanogr., 40(5), 938–946. The Advent Group. September 1994. Evaluation of Technical, Environmental, and Economic BeneÞts of Mixing Zones and Zones of Initial Dilution. Prepared for the Chemical Manufacturers Association. Thibodeaux, L.J. and J.D. Boyle. 1987. Bedform-generated convective transport in bottom sediment. Nature, 325(22), 341–343. Tian, X. and P.J.W. Roberts. 2001. Application of Three-Dimensional Imaging to Plume Flows. XXIX IAHR Congress, Beijing, China, September 16–21, 2001. Todd, D.K. 1959. Ground Water Hydrology. John Wiley & Sons, London, pp. 163–167. Tomassoni, G. July 2000. A federal statutory/regulatory/policy perspective on remedial decision-making with respect to ground-water/surface-water interaction. In Proceedings of the Ground-Water/Surface-Water Interactions Workshop. EPA/542/R-00/007, OfÞce of Solid Waste and Emergency Response, Washington, D.C., pp. 13–14. Triska, F.J., V.C. Kennedy, R.J. Avanzino, G.W. Zellweger, and K.E. Bencala. 1989. Retention and transport of nutrients in a third-order stream in northwestern california: hyporheic Process. Ecology, 70, 1893–1905. Triska, F.J., A.P. Jackman, J.H. Duff, and R.J. Avanzino. 1994. Ammonium sorption to channel and riparian sediments: a transient storage pool for dissolved organic nitrogen. Biogeochemistry, 26, 67–83. USEPA. May 1988. Selection Criteria for Mathematical Models Used in Exposure Assessments. EPA/600/8-88.075.



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USEPA. 1991. Technical Support Document for Water Quality–Based Toxics Control. Report EPA 505/2-90-001, OfÞce of Water, Washington, D.C. USEPA. September 1993. Technical Support Document for Water Quality–Based Toxics Control, U.S. EPA Water Quality Standards Handbook, 2nd ed. EPA 823-B-93-002, OfÞce of Water, Washington, D.C. USEPA. 1994. Amended Section 301(H) Technical Support Document. EPA 842-B-94-007, Oceans and Coastal Protection Division, OfÞce of Wetlands, Oceans and Watersheds, Washington, D.C. USEPA Region VIII. December 1994. Mixing Zones and Dilution Policy. Water Management Division, Denver, CO. USEPA. 1998a. Biological Indicators of Ground Water–Surface Water Interaction: Update. EPA 816-R-98-018, OfÞce of Water, Washington, D.C. USEPA. 1998b. Technical Protocol For Evaluating Natural Attenuation of Chlorinated Solvents in Ground Water. EPA 600/R-98/128, OfÞce of Research and Development, Washington, D.C., p. 78. USEPA. 2000. Proceedings of the Ground-Water/Surface-Water Interactions Workshop. OfÞce of Solid Waste and Emergency Response, Washington, D.C. EPA 542/R-00/007. USEPA. 2001. Review of National Mixing Zone Phase Out for Bioaccumulative Chemicals of Concern. OfÞce of Water, Great Lakes Initiative Web Page. Valett, H.M., J.A. Morice, C.N. Dahm, and M.E. Campana. 1996. Parent lithology, surface–groundwater exchange, and nitrate retention in headwater streams. Limnol. Oceanogr., 41(2), 333–345. Vanoni, V.A. 1975. Sedimentation Engineering. ASCE — Manuals and Reports on Engineering Practice 54, American Society of Civil Engineers, New York. Williams, D.D. 1987. The Ecology of Temporary Waters. Timber Press, Portland, OR, 205 pp. Williams, D.D. 2000. Field technology and ecological characterization of the hyporheic zone. In Proceedings of the Ground-Water/Surface-Water Interaction Workshop. EPA 542/R-00/007, pp. 38–43. Williams, D.D. and H.B.N. Hynes. 1974. The occurrence of benthos deep in the substratum of a stream. Freshwat. Biol. 4, 233–256. Winter, T.C. 1995. Recent advances in understanding the interaction of groundwater and surface water. Rev. Geophys., 33, 985–994. Winter, T.C. 1999. Relation of streams, lakes, and wetlands to groundwater ßow systems. Hydrogeol. J., 7, 28–45. Winter, T.C., J.W. Harvey, O.H. Franke, and W.M. Alley. 1998. Ground Water and Surface Water: A Single Resource. U.S. Geological Survey, Circular 1139. Wondzell, S.M. and F.J. Swanson. 1996. Seasonal and storm dynamics of the hyporheic zone of a 4th-order mountain stream. I. Hydrologic processes. J. N. Am. Benthol. Soc., 15(1), 3–19. Wörman, A. 1998. Analytical solution and time scale for transport of reactive solutes in rivers and streams. Water Resour. Res., 34(10), 2703–2716. Wörman, A. 2000. Comparison of models for transient storage in small streams. Water Resour. Res., 36(2), 455–468. Wörman, A., J. Forsman, and H. Johansson. 1998. Modeling retention of sorbing solutes in streams based on a tracer experiment using 51Cr. J. Environ. Eng., 124(2), 122–130. Wörman, A., A.I. Packman, H. Johansson, and K. Jonsson. 2002. Effect of ßow-induced exchange in hyporheic zones on longitudinal transport of solutes in streams and rivers. Water Resour. Res., 38(1), 10. 129/2000WR000211, 15 pp.


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2

The Role of Modeling in Managing Contaminated Sediments prepared by Danny D. Reible with contributions by Sam Bentley, Mimi B. Dannel, Joseph V. DePinto, James A. Dyer, Kevin J. Farley, Marcelo H. Garcia, David Glaser, John M. Hamrick, Richard H. Jensen, Wilbert J. Lick, Robert A. Pastorok, Richard F. Schwer, C. Kirk Ziegler

CONTENTS 2.1

2.2

Introduction ....................................................................................................62 2.1.1 SigniÞcance and Objectives ...............................................................62 2.1.2 Status of Contaminated Sediment Management ...............................63 2.1.3 Contaminated Sediment Modeling Applications...............................65 2.1.3.1 Conceptual Site Model Development and Testing.............65 2.1.3.2 Baseline Risk Assessment ..................................................65 2.1.3.3 Evaluation of Total Maximum Daily Loads ......................66 2.1.3.4 Comparative Evaluation of Remedial Management Plans ..... 68 State of Knowledge and Practice...................................................................69 2.2.1 Relation between Sediment and Common Contaminants.................69 2.2.2 Sediment Transport Model Components ...........................................73 2.2.2.1 Sediment Erosion and Deposition Processes .....................73 2.2.2.2 Sediment Transport Model Minimum Requirements ........75 2.2.3 Contaminant Fate and Transport Model Components ......................76 2.2.3.1 Contaminant Fate and Transport Processes in Unstable Sediments.........................................................76 2.2.3.2 Contaminant Fate and Transport Processes in Stable Sediments.............................................................77 2.2.3.3 Contaminant Transference via Food Webs.........................83 2.2.3.4 Human and Ecological Risk Evaluation ............................85 2.2.4 Model Calibration and Uncertainty ...................................................88 2.2.4.1 Sources of Model Uncertainty ...........................................88

0-56670-667-X/04/$0.00+$1.50 © 2004 by CRC Press LLC

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62


2.2.4.2

Techniques for Calibrating a Model and Evaluating Uncertainty.................................................89 2.2.4.3 Measures of Model Acceptability ......................................90 2.3 Challenges and Emerging Issues ...................................................................90 2.3.1 Cohesive Sediment Erosion and Transport........................................91 2.3.2 Contaminant Release and Availability...............................................92 2.3.3 Advective Processes in the Hyperheic Zone .....................................94 2.3.4 Bioturbation as a Sediment and Contaminant Transport Mechanism.........................................................................95 2.3.5 Contaminant Bioaccumulation and Effects in Benthic and Higher Organisms .......................................................................98 2.4 Summary of Research and Data Needs.......................................................100 2.4.1 Sediment Transport Process Modeling ............................................100 2.4.2 Contaminant Process Modeling .......................................................101 2.4.3 Biological Process Modeling ...........................................................101 2.4.4 Metals Release and Availability.......................................................101 2.4.5 Hydrophobic Organic Contaminant Release and Availability.........102 Acknowledgments..................................................................................................102 References..............................................................................................................102

2.1 INTRODUCTION 2.1.1 SIGNIFICANCE

AND

OBJECTIVES

Contaminated sediment management poses some of the most difÞcult site remediation issues today. Contaminated sediments typically reside in spatially variable and dynamic systems subject to seasonal ßow variations and episodic storm events. The volume of sediments that must be managed often exceeds 1 million yd3, dwarÞng many contaminated soil sites. These sediments are also associated with equally daunting volumes of water, and efforts to remove the contamination typically entrains even more water. The National Sediment Quality Survey (United States Environmental Protection Agency [USEPA], 1998) classiÞed 26% of 21,000 freshwater and estuarine sediment sampling stations in the U.S. as Tier 1 (i.e., adverse effects on aquatic life or human health are probable) and 49% as Tier 2 (i.e., adverse effects on aquatic life or human health are possible but expected infrequently). The realization of these potential risks depends in part on the degree of conservatism built into the toxicological assumptions and in part on the processes controlling both contaminant release from the sediments and the transfer to benthic, aquatic, and land-based organisms. Observations of impairments in ecological or human health can indicate potential pollution problems; however, linking these adverse effects to contaminated sediments requires an understanding of the processes leading to exposure and uptake. In addition, the selection of cost-effective and environmentally protective remedial alternatives is dependent upon the ability to predict the risks during implementation and into the future. Conceptual models can establish hypotheses as to the links between current or potential exposure to contaminated sediments and the risk to human and ecological health. Testing these hypotheses, however, generally requires translation of the


The Role of Modeling in Managing Contaminated Sediments

63

conceptual model into quantitative form. Quantitative models can be used to answer such questions as the following: • • • • •

•

Is the observed exposure and risk consistent with identiÞed sources of that risk? What are the most important source areas and exposure processes and pathways? What data can be collected to characterize these important processes and pathways most accurately? What interventions can be most effective in responding to these processes and pathways? What are the future exposure and risks if the sediments are managed by • Natural processes? • In situ containment or treatment technologies? • Removal and ex situ treatment or disposal? How does uncertainty in processes and pathways and the parameters that characterize them translate into uncertainty in current and/or potential future risks?

In making decisions about contaminated sites, the use of quantitative modeling to answer these questions is a critical link between observing current exposure and risk (i.e., deÞning baseline risk) and comparing and selecting management approaches that effectively minimize or control that risk. This chapter summarizes applications of quantitative prognostic models of contaminant processes in sediments, assesses the state-of-the-art of these models with respect to accuracy and adequacy, and identiÞes research that can contribute to improvements in model development and their use in resolving sediment management challenges.

2.1.2 STATUS

OF

CONTAMINATED SEDIMENT MANAGEMENT

The goals for contaminated sediment management were identiÞed by the USEPA (1998) to include the following: • •

• •

Prevent further contamination of sediments that may cause unacceptable human health or ecological risks. When practical, clean up existing sediment contamination that adversely affects the nation’s waterbodies or their uses or that causes other signiÞcant effects on human health or the environment. Ensure that sediment dredging and the disposal of dredged material continue to be managed in an environmentally sound manner. Develop and consistently apply methodologies for analyzing contaminated sediments.

Sediment modeling can assist in achieving these goals by helping to quantify the importance of potential sources of sediment contaminants and by predicting sediment fate and transport processes that inßuence exposure and risk. SpeciÞcally,


64


models can be used to evaluate the effect of extreme events, the likelihood that existing sources can lead to sediment recontamination, and the contribution of sediments to the pollutant burden faced by the ecosystem. In addition, models can be used to compare the effectiveness of various sediment management approaches. Contaminated sediment sites are often poorly controlled, dynamic systems containing large volumes of moderately contaminated material. An analysis of Superfund Records of Decisions from 1982 to 1997 (USEPA, 1999) showed that the average contaminated soil site considered for ex situ treatment contained 38,000 yd3 of contaminated material; for in situ treatment, the total was approximately 105,000 yd3 of contaminated material. Contaminated sediment sites, however, often contain in excess of 1,000,000 yd3 of contaminated material and generally are not directly accessible. Soils can be removed in a relatively dry state for further processing, whereas sediments are removed as slurries with a high proportion of water that must be treated. The assessment and control of contaminant releases when removing submerged sediments is also much more difÞcult than when removing soil for ex situ treatment. This difÞculty is the result of limited control over the aquatic environment as well as the chemical and physical changes the sediment undergoes during removal (e.g., anaerobic to aerobic and wet to dry). Many of the potential technologies for contaminated sediment management were initially developed to manage contaminated soils. Unfortunately, many of these technologies are either difÞcult to apply or impose potentially unacceptable risks when applied to contaminated sediments. Identifying, comparing, and selecting remedial options for contaminated sediment is also complicated by the multiple technologies often involved. For example, ex situ treatment or sediment disposal typically introduces a complete train of technologies, including removing material by dredging, temporarily storing or pretreating to reduce water content or volume, treating or disposing of Þnal dredged material, and managing any residually contaminated materials. Large contaminated sediment sites generally require applying different options at different areas on-site, each containing multiple technologies. Therefore, identifying sediment management and remediation options must recognize the entire train of technologies that constitute each option so that a fair evaluation and comparison of these options can be accomplished. Risk reduction has been generally accepted as the metric by which various options are judged and selected. Use of this metric, however, places a premium on the quantitative modeling required to link the sediments to exposure and risk. Evaluating management or remedial options requires deÞning remedial action goals and objectives and developing a valid conceptual model of the sediment system to be remediated. At all but the most trivial sites, a sophisticated quantitative model can be helpful or necessary to develop and test the conceptual model and evaluate the effectiveness of various management options in meeting the remedial action goals and objectives. Large, complicated sites posing substantial risks and potentially large cleanup costs generally require the development of an extensive database and sophisticated prognostic models in order to compare management options and evaluate potential risk reduction adequately. There is no generally accepted option for managing contaminated sediments at all sites. Removal approaches typically have been focused on a portion of the



65

contaminated sites where contaminant levels are the highest and the potential success in terms of reduction of risk is limited to the extent that the “hot spot” contributes to the overall risk of the site. Due to incomplete removal, resuspension during removal, and the resulting residual contamination, the effectiveness of removal options is closely linked to the natural setting and processes inßuencing the sediment contaminants. Nonremoval options are also closely connected to natural fate and transport processes. Linking these processes to exposure and risk is dependent on modeling. Most sediment contaminants are relatively refractory at least below the upper few inches of sediment and, therefore, are unlikely to exhibit substantial attenuation by fate processes such as microbial degradation except over very long time scales. Thus, quantitatively predicting exposure and risks far into the future is often required. In addition, most sediment contaminants are strongly associated with the solid phase, and sediment transport can control contaminant fate. Therefore, modeling natural exposure and fate processes entails describing river hydraulics and sediment migration as well as contaminant transport. The sections below discuss some of the speciÞc applications of contaminated sediment modeling.

2.1.3 CONTAMINATED SEDIMENT MODELING APPLICATIONS 2.1.3.1 Conceptual Site Model Development and Testing A conceptual model is necessary to deÞne the fundamental relationship between contaminant levels in the sediment and levels of exposure and risk to human health and the environment. A conceptual model identiÞes any ongoing sources that can lead to sediment recontamination, mechanisms that can move contaminants from sediments to receptor organisms, and fate processes that can reduce available contaminant concentrations or their effects on receptor organisms. Some of the processes that deÞne exposure within a conceptual model are depicted in Figure 2.1. A valid conceptual model is necessary to identify which remedial options have the potential to address the most important contaminant processes effectively. The conceptual model is the foundation on which site management actions are identiÞed and implemented. Although a conceptual model need not be quantitative, comparison with a quantitative model can help identify and test a conceptual model. For example, the question as to whether all sources have been identiÞed can be answered by the ability to quantitatively predict the extent of contamination in water and biota based on the recognized sources. An inability to reproduce the observed patterns of contamination can suggest that additional sources exist. Similarly, an inability to predict contaminant ßux from sediment to water based on presumed mechanisms and processes can indicate that additional processes are operative. 2.1.3.2 Baseline Risk Assessment Within a risk-based, decision-making framework, the existing or baseline risks deÞne the signiÞcance of a contaminated sediment problem and the need for management or remediation. Although Þeld measurements can identify contaminant levels in the environment and body burdens in potentially affected species, establishing a cause-


66


Vaporization/Deposition Advection Overlying Water Particles

Dissolved

Colloidal

Dispersion

Advection

Erosion

Nonparticle Exchange Processes

Dispersion

Resuspension

Biologically Active Zone

Source Area

Fate Processes

Burial/Exposure Ground Water Exchange

Deep Sediments FIGURE 2.1 Potential water column and sediment processes inßuencing contaminant transport and fate in a river segment.

and-effect relationship between the two normally requires using a numerical model. Generally, it is not possible to relate sediment concentrations to risks without identifying and quantifying fate and transport processes that lead to exposure, assimilation, and effect. Quantitative modeling can be used to assess important pathways and processes and compare various contaminated sediment management approaches. The processes and their relative importance vary widely in different sediment environments as illustrated in Table 2.1. Current exposure and risk and the predicted attenuation of contaminants as a result of these natural processes serves as a baseline with which to compare active management approaches. Constitutive relationships and measurements of the parameters within those relationships are critical to the quantitative descriptions of these processes. 2.1.3.3 Evaluation of Total Maximum Daily Loads Contaminated sediments fall under the purview of several regulatory programs, including the total maximum daily load (TMDL) program established by 303(d) of the Clean Water Act. A TMDL is a calculation of the maximum amount of a pollutant that a waterbody can receive and still meet water quality standards. This TMDL must be allocated to the various sources of the pollutant. A TMDL must



67

TABLE 2.1 Sediment Processes and Their Relationship to Various Sediment Environments Environment

Environmental Characteristics

Key Fate and Transport Processes

Lacustrine

Low energy environment Generally depositional environment Ground water interaction decreasing away from shore Organic matter decreasing with distance from shore Often Þne-grained sediment

Riverine

Low to high energy environment Depositional or erosional environment Potential for signiÞcant ground water interaction Variable sediment characteristics (Þne to coarse grained)

Estuarine

Generally low-energy environment Generally depositional environment Generally Þne-grained sediment

Coastal marine

Relatively high-energy environment, decreasing with depth and distance from shore Often coarse sediments

Sediment deposition Water-side mass transfer limitations Ground water advection in near-shore area Bioturbation (especially in near-shore area) Diffusion in quiescent settings Metal sequestration Aerobic and anaerobic biotransformation of contaminants of concern (COCs) Biotransformation of organic matter Local and generalized ground water advection Sediment deposition and resuspension Aerobic biotransformation processes in surÞcial sediments (potentially anaerobic at depth) Bioturbation Bioturbation Sediment deposition Water-side mass transfer limitations Aerobic and anaerobic biotransformation of COCs Biotransformation of organic matter Uptake and biotransformation in plants Bioturbation Sediment erosion and deposition Localized advection processes

also include a margin of safety to account for uncertainty and must consider seasonal variation. Of the approximately 20,000 waterbodies currently slated for TMDL development, at least 300 are expected to be impaired speciÞcally by sediments. Many additional bodies of water can be more impaired as a result of the contribution of pollutants from contaminated sediments or through the effect of sediment processes (e.g., sediment oxygen demand) on overlying water quality. Predictive modeling is a key tool in establishing TMDLs because the model provides the needed link between pollutant loadings to a waterbody and the effect of these loadings on attaining water quality standards. Modeling is especially important in situations such as contaminated sediment sites where it is difÞcult to directly


68


measure how much of the pollutant loading can be attributed to speciÞc sources. However, most of the tools used to establish TMDLs, including the USEPA’s Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) system, do not include state-of-the-art sediment modeling capabilities. TMDLs can also be designed to eliminate sediment toxicity. Although sediment toxicity can be determined based on biological assays, the allocation of point or nonpoint contaminant sources can be related to sediment concentrations through only a numerical model. Numerical models must be able to deÞne contaminant access and availability and the assimilative capacity of the speciÞc receptor at risk. In addition, numerical modeling is required to determine the ability of management approaches (including natural attenuation and recovery) to eliminate sediment toxicity. Numerical models can also be used as a foundation for allocating allowable pollutant discharges to point and nonpoint sources. 2.1.3.4 Comparative Evaluation of Remedial Management Plans The primary goal of contaminated sediment management is to protect resources at risk such as human or ecological health, commercial or recreational Þshing stocks, or a particular endangered species. Ideally, management and remedial options that best protect affected resources or lead to resource recovery should be selected. Because measurements can only hope to indicate the current state, quantitative models must be used to allow comparison of the future effect of various scenarios. SigniÞcant questions remain regarding how best to use the forward projections in time. In principle, models provide concentrations or rates of exposure as a function of time and place. This information provides a basis on which risk and effects can be estimated. However, both the assessment of future concentrations and the rates of exposure, along with the assessment of future risks and effects, are subject to great uncertainty. An alternative to evaluating and comparing management options is to employ contaminant mass ßows as a surrogate measure of exposure and, ultimately, risk. That is, a technology can generally be assumed to pose less exposure and risk if it leaves less residual contamination and loses fewer contaminants to the air and water than does an alternative technology. The evaluation of contaminant mass ßows for each management option can be most useful in the comparative evaluation by providing a systematic screening tool. A comparative analysis of mass ßows can also help identify those components of an overall management strategy that largely control the overall exposure or risk and, therefore, should receive the most resources and effort for detailed evaluation. In this manner, screening sediment management alternatives can ensure that all needed components of any given option are included for subsequent evaluation. Even a comparative analysis of mass ßow generally requires sophisticated modeling of contaminated sediment fate and transport processes. Although contaminant mass ßow can be useful, exposure and risk to human and ecological health ultimately drives the need for remediation and the success or failure of any management or remedial option. In particular, contaminant mass



69

ßow is not very helpful in balancing short-term acute risks with long-term risks. Contaminated sediment removal, for example, tends to lead to increased risks in the short term in exchange for potential reduced long-term risk. It is important to note that in situ sediment management approaches are always subject to potential failure because of future events. Modeling can provide a basis for identifying the magnitude of the potential future exposures and risk that the various management options can pose.

2.2 STATE OF KNOWLEDGE AND PRACTICE A variety of models have been used to predict sediment and contaminant behavior, fate, and effects in ecosystems. Thoms et al. (1995) summarized the capabilities of some of these models and showed the variety of transport and fate pathways that they describe. This section summarizes some of the key processes that characterize the fate and transport pathways.

2.2.1 RELATION BETWEEN SEDIMENT CONTAMINANTS

AND

COMMON

Most priority pollutants and other contaminants of concern (COCs) are strongly associated with solids and therefore tend to accumulate in sediments. Contaminants that do not strongly associate with solids (e.g., polar organic compounds, soluble metals) rarely represent sediment contaminants in that they are efÞciently released to the overlying water. Historical industrial and municipal efßuents and runoff are often responsible for sediment contamination because only those contaminants that tend to partition strongly to the solid phase remain in their historical location. Soluble and volatile contaminants tend to be transported away by water movement or are released to the air via evaporative processes. Similarly, when more soluble and volatile contaminants initially contaminate sediments, their mobility ultimately allows them to migrate into more mobile phases, effectively eliminating them from the sediments. There are exceptions to these general rules when contaminants are continuing to be introduced to the sediments from ground waters or from active sources, but the bulk of sediment contaminants are those that strongly associate with the solid phase. The extent to which sediments are associated with the solid phase is deÞned by the effective sediment–water partition coefÞcient, Ksw. This coefÞcient is deÞned as the ratio of the concentration of contaminant on solids, Ws (milligrams per kilogram [mg/kg]), to the water concentration, Cw (milligrams per liter [mg/l]). Given a density of solids, rs (kg/l), the fraction of contaminant associated with the solid phase, fs, is given by the following equation: fs =

rs K sw 1 + rs K sw

(2.1)

For large Ksw values, as would be expected for most sediment contaminants, the fraction associated with solids approaches unity unless the density of solids is small.


70


For hydrophobic organic compounds, the commonly employed linear, reversible model is that the sediment–water partition coefÞcient is given by (Koc)(foc) where Koc is the organic carbon–based partition coefÞcient and a measure of the compound hydrophobicity, and foc is the fraction organic carbon that serves as the solution phase in the solid. A moderately hydrophobic sediment contaminant might be pyrene, a polycyclic aromatic hydrocarbon (PAH) with a Koc of approximately 105 l/kg. A typical sediment organic carbon is of the order of 1%, suggesting a Ksw of about 1,000 by this model. Thus, in the sediment bed, where rs is large (of the order of 1 kg/l), a Ksw of 1,000 l/kg suggests that about 99.9% of the contaminant would be associated with the solid phase. In the overlying water, however, if the suspended sediment concentration is low (perhaps 100 mg/l or less), the majority of the contaminant can be dissolved and not associated with the solid phase. Similarly, changes in redox conditions for resuspended sediment can cause metal releases that might normally be associated with sediments in a stable bed. The strong association of contaminants with the solid phase in a sediment bed, however, suggests that contaminant fate often is deÞned largely by sediment mobility and fate. This section examines some of the most important sediment contaminants and their physical and chemical characteristics that relate to fate and mobility in the environment. These contaminants are described below and include heavy metals, oxygen-demanding contaminants in sediments, undifferentiated oil and grease, pesticides, polychlorinated biphenyls (PCBs), and PAHs. •

Heavy Metals The toxic elements include antimony, arsenic, beryllium, cadmium, copper, lead, mercury, nickel, silver, thallium, and zinc. These pollutants are important in that they are nonbiodegradable, toxic in solution, and subject to biomagniÞcation. The chemistry of many of these compounds is complex in sediments. A portion is generally chemically Þxed and largely unavailable to Þsh and higher organisms without chemical changes in the sediment. Often a portion is ion exchangeable that can become available simply with the addition of a more strongly held contaminant. Finally, a portion is soluble, mobile, and directly available for uptake by organisms. Myers et al. (1996) indicate that the partition coefÞcient between the leachable fraction and the water is typically between 3 and 10, resulting in a leachable fraction of metals that is typically less than 10% and sometimes much less. The equilibrium state for metals and other elemental species depends on the chemical state of the water and sediment, particularly the pH and oxidation–reduction conditions. The ratio of sediment loading to equilibrium water concentration is often very large for metals, but only a small fraction of the metals are typically available. As a result of this variability, a site-speciÞc measurement of the sediment–water partition coefÞcient is preferred over any predictive approach. The ultimate or potential availability of many metals appears to be controlled by the presence of acid volatile sulÞdes (AVS) in the sediments in which they reside. The term “AVS” refers to the manner in which sulÞde presence is measured. If the



•

•

•

ratio of AVS is greater than the simultaneously extractable metal (SEM) content, most of the key metals of concern are bound to the sediment as sulÞdes. In this form, the metals appear to be largely unavailable to receptors from microorganisms up through humans. Because many marine sediments contain signiÞcant quantities of AVS, much of the metals may not be available to provide exposure and risk to organisms. Although high AVS/SEM ratios indicate that certain metals are bound in unavailable forms, low AVS/SEM ratios do not necessarily indicate available metals. Historically, metals-processing industries as well as urban and rural runoff provided the most signiÞcant sources of these elements. In addition, lead was widely distributed in the environment as a result of the use of tetraethyl lead in gasoline to control premature ignition (i.e., knocking). Although ongoing sources have been largely controlled, some sources of metals are controlled poorly and represent a continuing source (e.g., leaching from abandoned mining sites, urban runoff). Unlike oxygen-demanding contaminants, these pollutants are not easily neutralized by natural processes. Oxygen-Demanding Contaminants A variety of organic and inorganic compounds in sediments consume oxygen during chemical fate reactions. The cumulative effect of their presence is measured by sediment oxygen demand, a parameter similar in signiÞcance to oxygen-demanding measures in the overlying water. Sediment oxygen demand serves to reduce available oxygen in the sediment and encourage anaerobic conditions within the sediment. This can inßuence the rate of fate processes (e.g., biological contaminant degradation) and the chemical state of metals, inßuencing their mobility. In slow-moving water, the sediment oxygen demand can also impact oxygen levels in the overlying water. No speciÞc levels of oxygen-demanding constituents are considered problematic. Rather, the impact of these contaminants depends on the dynamics of the sediment and overlying water column. Undifferentiated Oil and Grease Long-chain nonpolar organic compounds, such as oil and grease, associate strongly with solids and sediments. Their presence in sediments is measured by oil and grease and total petroleum hydrocarbon (TPH) concentration. The sources of these compounds are generally TPH production or processing facilities or facilities that use or process signiÞcant amounts of these compounds. In addition, municipal and industrial wastewater treatment efßuents can lead to signiÞcant accumulation in sediments over time. Because many of these sources are much more carefully controlled than in the past, oil and grease or TPH levels in sediments often represent excellent indicators of historical pollution. Pesticides The pesticide priority pollutants are generally chlorinated hydrocarbons. They include compounds such as aldrin; dieldrin; 1,1,1-trichloro2,2-bis(p-chlorophenyl)ethane (DDT); 1,1-dichloro-2,2-bis(p-chlorophenyl)ethane (DDD); endosulfan; endrin; heptachlor; lindane; and

71


72


•

•

chlordane. They are readily assimilated by aquatic animals and bioaccumulate in body fats and are subject to biomagniÞcation. BiomagniÞcation refers to the tendency of these pollutants to increase in concentration at higher trophic levels. DDT is an excellent example of the potential problem associated with these compounds. Although it is not considered extremely toxic to people and its usage was decreasing during the 1960s, the use of DDT was effectively banned in 1972 as a result of its persistence, its potential for biomagniÞcation, and its effects on wildlife. Braune and Nordstrom (1989) measured a herring gull/alewife trophic transfer factor of 85 for DDE, the dominant persistent metabolite of DDT. Oliver and Niimi (1988) measured a biota–sediment accumulation factor of 3.7 (mg/g wet)/(mg/g dry) for DDE in sculpins in a Lake Ontario ecosystem. The biota–sediment accumulation factor is the accumulation in the organism normalized by the organism’s lipid content divided by the organic carbon–normalized sediment concentration. The magniÞcation at each level is dependent on the feeding habits and animal metabolism and, because the organochlorines tend to build up in the lipid or fat fraction of the body, the proportion of body fat. PCBs PCBs are complex mixtures of organochlorines that are extremely stable and are used widely in industry, especially as electrical capacitor and transformer oils. Unfortunately, the stability of PCBs also means that they are persistent in the environment. As with organochlorine pesticides, PCBs are readily assimilated by aquatic animals, soluble in body fats, and biomagnify in the food chain. Although the toxicity of many individual PCBs is relatively low, speciÞc isomers plus trace contamination with other chlorinated species raised signiÞcant health concerns. As a result, PCB production was banned in the U.S. in 1979. It should be emphasized that PCBs are a complex mixture of compounds and, in fact, are generally named only by the total percentage of chlorine in the mixture. SpeciÞc PCB mixtures are referred to as Aroclors. For example, Aroclor 1254 contains 54% chlorine and Aroclor 1260 contains 60% chlorine. Again, as a result of the material’s persistence, signiÞcant quantities remain in the environment. Industrialized harbor areas in the Great Lakes and northeastern U.S. represent the most signiÞcant repository of PCBs. Fish advisories exist in many of the Great Lakes as a result of health concerns from eating PCB-contaminated Þsh. Because of the potential for PCBs to sorb onto organic materials in sediments and Þsh lipids, such advisories are aimed primarily at fatty, bottom-feeding Þsh and top predators in which PCB concentrations are the highest. PAHs PAHs (e.g., naphthalene, ßuoranthene, pyrene, chrysene) are used as chemical intermediates and are present in fossil fuels. PAHs composed of two aromatic rings (e.g., naphthalene) tend to be the most volatile, soluble, and mobile. Solubility and volatility as well as degradability



73

under natural conditions tend to decrease as the number of rings increases. Although the monocyclic aromatics tend to be present in the lighter oil stocks, PAHs tend to be present in coal liquids and the heavier oil stocks as a result of their lesser volatility. Some PAHs have been found to be carcinogenic in animals and are assumed to be carcinogenic in humans. PAHs tend to be intermediate in persistence and have a bioaccumulation potential between the monocyclic aromatics/halogenated aliphatics and the PCBs. The use of PAHs during the combustion of industrial fuels and oils (e.g., diesel, coal liquids, heavy fuel oils) has resulted in their presence at old industrial sites where contamination levels can be especially high.

2.2.2 SEDIMENT TRANSPORT MODEL COMPONENTS 2.2.2.1 Sediment Erosion and Deposition Processes As outlined above, a critical component of any attempt to describe the behavior of sediment contaminants involves a need to describe the migration and fate of the sediments with which they are associated. The dominant characteristics that control the direct exposure of Þsh and higher animals to contaminated sediment are burial, vertical mixing of the sediment bed, and resuspension of particles from the sediment bed. Because most persistent sediment contaminants are associated with the solid phase, any mobilization of this phase dramatically increases contaminant mobility. As a result, contaminants can be distributed over large areas, and signiÞcantly increased water column concentrations can be observed relative to less active sediment–water transport. Erosion and resuspension conditions also eliminate natural recovery that might occur in less active environments due to deposition and burial of the contaminated sediment. Under high energy conditions in a stream, erosion of the sediment bed can occur, and individual sediment particles can be carried downstream either by sliding along the surface of the sediment or by being suspended in the stream. In a sandy sediment, the process normally results in the formation of dunelike structures that progress downstream by the process of erosion on the upstream face and deposition on the downstream face. The erosion process is depicted in Figure 2.2. During this overturning and migration process, sediment particles are exposed and either scoured and suspended in the stream or reburied by other sediment particles. During exposure to the stream water, contaminants sorbed onto the sediment particles can be desorbed, and contaminants in the adjacent pore water can be mixed into the overlying water. It should be noted that this process occurs in the sand and gravel bed where, typically, organic carbon and contaminant levels are relatively low. Once particles are set in motion, the following three types of movement can be recognized: • • •

Rolling and sliding along the bed surface (i.e., bed load transport) Suspension in the free stream (i.e., suspended load) A transitional motion characterized by saltation and particle jumps


74


SEPARATION POINT SUSPENDED PARTICLE AG

DR

BED LOAD PARTICLE ANGLE OF REPOSE

=1I STAGNATION POINT

FIGURE 2.2 Sediment movement due to erosion processes.

The ability to predict the onset of resuspension in a sediment remains largely limited to cohesionless, coarse-grained particles. Site-speciÞc measurements of bed and/or suspended load sediment transport are needed to characterize cohesive, Þnegrained sediment. The onset of particle motion in a cohesionless sediment is determined by the balance between the submerged weight of a particle and the lift associated with the water ßow over the particle. Raudkivi (1967) discusses the physics of the onset of the particle motion and shows that the onset of particle motion occurs when a critical threshold friction velocity is exceeded. u*c = b

r p - rw rw

gd p

(2.2)

Here u*c is the critical threshold friction velocity; rp and dp are the particle density and diameter, respectively; rw is the density of water; and b is a coefÞcient incorporating the angle of repose of the particle (i.e., the slope of the upstream face of the sediment dune) and the partial coverage by other sediment particles. The friction velocity is related to the surface shear stress, tb, by u*c = (tb/uw)1/2. This relationship simply emphasizes that sediment resuspension occurs when the lift caused by the overlying ßow overcomes the weight of the particle. The friction velocity is a parameter related to the surface friction that can be determined by velocity proÞle measurements. Raudkivi (1967) suggests that b is approximately 0.2. For cohesive sediments, the friction velocity required to produce particle motion is signiÞcantly larger for a particular particle size than would be suggested by the above relationship. The property of cohesiveness is a complicated function of particle size, bulk density, mineralogy, organic content, and salinity. These properties vary signiÞcantly with position and time. Often as a result of lack of sufÞcient data on the deposit properties with position and time, these variations are not fully incorporated in sediment transport models. The rate of erosion, E, is related to the local bed density, rs, and the probability of a particle becoming resuspended, which for a cohesive sediment is related to the bottom shear stress, tb.



E = Pero ( t b n )rs = At b n rs m

75

(2.3)

The exponent on the bottom shear stress depends on the bed properties but is typically between two and three for cohesive sediments. This implies that the erosion rate depends on the fourth to sixth power of stream velocity because bottom shear stress typically depends on the square of velocity. The strong dependence on stream velocity emphasizes that a critical component of any effort to model sediment dynamics is knowing the stream hydrodynamics. Although it is not yet possible to predict the relationship between erosion rate and shear stress for cohesive sediments, it is possible to make measurements from which the values of A, n, and m can be determined (McNeill et al., 1996). Net sediment transport is the difference between the erosion rate, deÞned above, and the deposition rate. In general, deposition can be modeled with a relationship of the form D = Pdep ws Cs

(2.4)

where Pdep is the probability of capture of the depositing particle, ws is the vertical settling velocity of the particles, and Cs is the suspended sediment concentration. The probability of deposition tends to decrease as the bed shear stress increases. The local particle concentration can be modeled as a decreasing exponential with height above the bed (Jones and Lick, 2000). There are also signiÞcant differences between cohesive (ßocculating) sediment and noncohesive (i.e., sandy) sediment. Sandy sediment deposition can be modeled employing the formulation of Cheng (1997). In cohesive sediments, deposition is affected by aggregation or disaggregation processes that are complex functions of sediment and stream conditions (Lick and Lick, 1988). The most easily measured quantity with which to calibrate sediment transport models is suspended sediment load in the overlying water column. Because the source of suspended sediment concentrations can be runoff from surrounding soil or transport from points upstream in addition to direct sediment bed erosion, such measurements are of limited usefulness in assessing or calibrating sediment bed erosion. Changes in suspended sediment load with location or with time in response to variations in ßow are a more sensitive indicator of local erosion and deposition. Changes in bathymetry also provide a more direct indication of sediment bed movement. Changes in water depth can be small or highly variable in space, suggesting that the uncertainty in measured changes can limit usefulness as a calibration tool. 2.2.2.2 Sediment Transport Model Minimum Requirements A hydrodynamic model must accurately describe the space and time variations in stream velocity because of the critical role velocity plays in sediment transport. A ßexible sediment transport model cannot rely on measured velocity Þelds because simulated conditions are likely to extend beyond the measurement database. Data requirements for such a hydrodynamic model include streambed geometry and


76


bathymetry, ßow rates, downstream water surface elevation (e.g., boundary conditions), and water and atmospheric properties (e.g., salinity, temperature, wind). Water surface elevation and current velocity measurements and water property documentation can be used to calibrate or validate the model. Data requirements for a sediment transport model include measured sediment loads (i.e., magnitude and composition) and bed properties including erosion potential (e.g., as measured via “sedßume”; McNeill et al., 1996). Measured suspended solid concentrations and sediment bed elevation changes provide calibration and validation data. For consistency, both hydrodynamic and sediment transport models should use the same numerical grid of sufÞcient resolution to resolve bathymetry and bed variability. The models employed for both hydrodynamics and sediment transport should be coupled prognostic, material balance–based models. The dimensionality required depends on the system as does the degree of sophistication required in the windforcing model. As emphasized above, the sediment transport model should be fundamental and mechanistic to aid extrapolation beyond the calibration validation database. At a minimum, the sediment transport model should separately track ßocculating clay and silt, and noncohesive sandy components. Models that simulate only a single size class of particles and do not capture the physics of resuspension and settling are not able to correlate sediment and contaminants adequately. Predicting contaminant dynamics requires unique identiÞcation of the sources and sinks of particular sediment deposits. Seemingly accurate reproduction of total suspended sediment loads does not necessarily predict contaminant dynamics. To describe sediment and contaminant dynamics, deposition should account for the effect of shear stress and, for cohesive sediments, ßocculation effects. Resuspension should account for site-speciÞc bed properties and, if necessary, the effects of bed armoring by consolidation or size gradation. Finally, it should be emphasized that sediment transport is not dependent upon contaminant fate. Sediment transport model calibration using contaminant fate and transport measurements is inappropriate. Instead, a sediment transport model provides an additional constraint upon the contaminant transport.

2.2.3 CONTAMINANT FATE

AND

TRANSPORT MODEL COMPONENTS

2.2.3.1 Contaminant Fate and Transport Processes in Unstable Sediments The conventional paradigm for deÞning the fate and behavior of hydrophobic and other contaminants that are strongly associated with the solid phase is that contaminant dynamics are largely deÞned by sediment dynamics. To the extent that contaminants remain associated with the solid phase, the description of sediment erosion and deposition also deÞne contaminant transport. As indicated previously, however, the extent to which contaminants are associated with the solid phase depends on the effective sediment–water partition coefÞcient and the local solidphase density. Suspended sediment loads in rivers rarely exceed 1 g/l even downstream of dredging operations. If the effective sediment–water partition coefÞcient is of the order of 1,000 l/kg, 1 g/l of suspended sediment suggests that the water



77

column contaminant is 50% dissolved and 50% associated with the particulate fraction. At lower suspended sediment concentrations or lower sediment–water partition coefÞcients, the contaminant is found predominantly in the dissolved state in the water. Under such conditions, contaminant fate is no longer tied to the fate of the sediment, and other processes, including vaporization and in-stream dispersion, dominate contaminant transport. The surface layer of the sediment bed provides the crucial link between sediments, water, and biota. Invertebrates that live in or on the sediment bed accumulate contaminants from the surface layer. The contaminant concentration in the surface layer is controlled by both sediment dynamics and chemical hydrophobicity. Deposition and resuspension rates control the rate at which contaminated surface sediments are buried, that is, are moved out of the active surface layer. In addition, the depth and extent of surface layer mixing controls the degree to which buried contaminants are kept available to the surface layer. Chemicals that are relatively more hydrophobic are more strongly associated with the particulate matter in the bed. This results in lower concentrations dissolved in the pore water and, therefore, lower concentrations available for diffusion and advection in the overlying water column. 2.2.3.2 Contaminant Fate and Transport Processes in Stable Sediments Under some conditions, contaminant dynamics can be decoupled from hydrodynamics and sediment erosion and deposition. A variety of processes, including diffusion, advection, and bioturbation, can be responsible for contaminant exchange under conditions where bulk sediment transport is unimportant. Under these conditions, contaminant exchange at the sediment–water interface serves as a boundary condition for a fate and transport model within the water column. Determining water column contaminant concentrations and the fate and transport of water-borne contaminants requires the deÞnition of the sediment boundary as a source or sink of contaminants. In general, the controlling resistance to contaminant mass transfer to or from stable (i.e., noneroding) sediments can be either the movement within the benthic boundary layer in the overlying water or the movement within the sediment bed. Because of the traditional focus of contaminated sediment models in accurately describing the sediment transport, simple lumped exchange coefÞcients are typically used to characterize contaminant release from the sediment. Lumping the individual transport processes in the overlying water and in the sediment bed with effective mass transfer coefÞcients ks and kw, with units of length and time, the ßux between the bulk sediment and the sediment–water interface (at concentration Wsi) can be written as follows: Flux = ks rs (Ws - Wsi )

(2.5)

Similarly, the ßux between the sediment–water interface (at concentration Cwi) and the overlying bulk water can be written as follows:


78


Flux = kw (Cwi - Cw )

(2.6)

DeÞned in this manner, the mass transfer coefÞcients, ks and kw, represent normalized ßuxes (i.e., the ßux normalized by the concentration driving force in the respective phase). The concentrations at the interface, Wsi and Csi, are generally assumed to be in quasi-steady equilibrium. Some fraction of the contaminants can be Þxed to the sediment phase and are unable to be released to the pore water or overlying water as discussed earlier. Regardless of the cause and ultimate extent of limited contaminant desorption from sediments, the ratio of the quantity effectively sorbed onto the solid phase at the interface, Wsi, to that which is in the adjacent aqueous phase, Csi (Ksw = Wsi/Csi), represents an effective partition coefÞcient. An overall mass transfer coefÞcient, K, can be deÞned using the difference in the bulk concentrations in each phase (Ws and Cw) as the driving force. Equating the different expressions for the ßux and assuming that the interfacial concentrations are at an apparent equilibrium (Ksw = Wsi/Cwi), the overall mass transfer coefÞcient can be related to the individual sediment and water coefÞcients as follows: 1 1 rK = + s sw K ks kw

(2.7)

The relative importance of the sediment and water-side mass transfer resistances thus depends on the relative magnitude of the individual side coefÞcients. However, this relationship indicates that the importance of water-side mass transfer resistances increases as the capacity for sorption onto the solid phase increases. The water-side coefÞcients are a function of ßow and chemical properties. Stream velocity and bottom roughness are characterized by the bottom friction velocity. The Schmidt number (Nsc = Dw/nw, where Dw is the chemical diffusivity in water and nw is the kinematic viscosity of water) characterizes the inßuence of the chemical. Density stratiÞcation and local variations in bottom roughness can inßuence water-side mass transfer coefÞcients, but the average coefÞcient in neutrally stratiÞed water bodies is often described by a relationship of the following form (Reible, 1999): kw =

1 k u N -2 / 3 2 * Sc

(2.8)

where k is the von Karman constant (0.4). For many organic compounds, the Schmidt number is the order of 1000, whereas the friction velocity is typically of the order centimeters per second (cm/sec). Thus, the water-side mass transfer coefÞcient is generally of the order of 100 cm/day. Ksw is of the order of 1000 l/kg or more for many hydrophobic contaminants, and sediment bulk density is of the order of 1 g/cm3 (1 kg/l). Thus, water-side mass transfer resistances are the same order as sediment-side exchange coefÞcients of the order of 0.1 cm/day. Higher sedimentside exchange coefÞcients imply control by water-side Quantitative Environmental



79

Analysis mass transfer resistances; lower sediment-side exchange coefÞcients mean water-side mass transfer resistances are of lesser importance. The various processes that make up sediment-side mass transfer resistances and their magnitudes are discussed below. •

Molecular Diffusion Molecular diffusion is the most basic and ubiquitous chemical transport process within a sediment bed. Random molecular motion in pore water results in contaminant molecule movement from regions of high porewater concentration to those of low concentration. The magnitude of the contaminant ßux is quantiÞed by Fick’s Þrst law and couples the concentration gradient to the diffusion coefÞcient. The diffusion coefÞcient in porous sediments is reduced by the Þnite porosity of the sediments and the tortuous path through the sediments. Tortuosity, t, the ratio of the actual diffusion path to the straight line distance, generally has been found to be a function of bed porosity, suggesting the following model for diffusivity: Dsw = Dw

e ª Dw e n t

(2.9)

Here Dw is the molecular diffusion coefÞcient of the contaminant in free water which is typically of the order of 0.5 to 1.5 cm2/day. The value of n is typically in the range 1.33 < n < 2 with sandy, granular sediments exhibiting lower values of n than cohesive sediments. Thus, Dsw is typically of the order of 0.1 cm2/day. The quasi-steady diffusion ßux from a bulk sediment concentration at depth, d, can be written as follows: Flux =

Dsw rs (W - Wsi ) d K sw s

ks,diff ª

Dsw rs d K sw

(2.10)

This relationship recognizes that diffusion is operative only on the desorbed fraction of contaminants. As a result, diffusion is intrinsically much slower than water-side mass transport (and therefore a controlling resistance to mass transfer) unless the diffusion path length is very small. Diffusive transport can be enhanced by processes that enhance contaminant release to the mobile water phase. Chief among these is the presence of colloidal organic carbon in the pore water. The colloidal organic carbon (often operationally deÞned as dissolved organic carbon) can migrate through the sediment pore space via Brownian diffusion, causing migration of associated contaminants. For hydrophobic organic compounds, the organic carbon–based partition coefÞcient (Koc = Ksw/foc, where foc is the fraction organic carbon) is


80


often used to approximate the partitioning to colloidal organic carbon. The enhancement of pore-water transport processes by colloidal organic carbon present at concentration, Doc, can then be estimated as follows: coll Dsw = 1 + Koc roc Dsw

•

kscoll = 1 + Koc roc ks

(2.11)

Advective Transport Advective transport at a superÞcial, or Darcy velocity, V, is also operative only on pore-water contaminants. Hydraulic exchange processes at the sediment–water interface occur at a variety of scales. At the sediment-grain scale, turbulent ßuctuations in the overlying water can enhance local transport from sediment to water. On the scale of the uneven bedforms that typically characterize the bottom surface, local pressure variations can enhance in-bed migration and contaminant transport (Savant et al., 1987). Similarly, cross-stream pressure variations at stream meanders can generate local advective ßows within the underlying sediment. Finally, the regional ground water gradients can drive net ground water movement into or out of the stream. Local variations in bed properties also can give rise to signiÞcant variability in the magnitude or even the direction of this transport. As with diffusion, however, the implementation of these processes in current models is generally limited to an average exchange coefÞcient, which, at steady state, is deÞned by the following relation: Flux =

V W e K sw s

ks,adv ª

V e K sw

(2.12)

Flows driven by local processes and even regional ground water ßows with signiÞcant spatial variability in bed permeability are not well represented by an average exchange coefÞcient. Spatial variations in sedimentation contribute to heterogeneous contaminant deposition and consequent ßow and transport variations. Bed dynamics such as siltation or coarsening over time further complicate the description of ßow and transport in the hyporheic zone. The local ratio of advective to diffusive processes is given by the following Peclet number: N Pe =

Vd Dw e n+1

(2.13)

Note that the Peclet number is not inßuenced by sorption because both processes are operative on pore-water contaminants. Both processes also are enhanced equally by colloidal or other mechanisms that in-



•

crease pore-water concentrations. It is important to note that the Peclet number typically exceeds unity (i.e., advection dominates) at low ground water seepage velocities. Over a characteristic length scale of 1 cm and with an effective diffusivity of 0.1 cm2/day, advection dominates transport if the seepage velocity exceeds 0.1 cm/day. Measuring ground water ßow velocities and, in particular, stream bed seepage velocities is difÞcult because these measurements must reßect the seasonal nature and spatial variability of ground water ßow. The use of seepage meters (i.e., containers covering a portion of the sediment bed that collect water that seeps through the sediment) is common. Net seepage measurements, however, do not fully describe contaminant transport in that the bulk of any observed seepage can be through sandy portions of the bed that contain little or no contamination. Chemical tracers in the overlying water have been used to estimate the net effect of seepage on contaminant transport (Bencala, 1984). Spatial variability is made more complex in that advection also can be driven by local variations in pressure on the uneven surface of the sediments. These ßows are not normally incorporated in current contaminated sediment models except as being a component of the sediment-side mass exchange coefÞcient. Rather than measuring velocity, measuring ground water ßow in the surrounding aquifer can be useful because this measurement represents an average inßow or outßow from the waterbody. The general direction of the ground water ßow can be measured by piezometers placed at different elevations below the bed of the waterbody. If the underlying water head is greater than the head in the stream, inßow occurs; outßow occurs in the reverse situation. In addition to deÞning direction, this information is used to estimate the ßow rate if the permeability of the medium can be measured. An alternative means of detecting slow vertical transport by ground water ßow is through tracers, as described by Cornett et al. (1989). Bioturbation The previous discussion largely considered sediment as a collection of sediment particles separated by water-Þlled pore spaces. In reality, a variety of plants and animals reside in sediments. Root systems and animal burrows can provide channels for preferential water ßow and contaminant transport. Even more important, the near-surface sediment often is mixed continuously by the activities of benthic organisms such as clams and worms. Sediment processing by animals residing in the upper layers includes burrowing, ingestion and defecation, tube building, and biodeposition. Taken together, these processes are termed bioturbation. A depiction of the type of animals that can be present and their interaction with sediments is provided in Figure 2.3. The net result of bioturbation is the vertical and horizontal movement of sediment particles and pore water. Contaminants on the particles or in the pore spaces likewise are trans-

81


82


FIGURE 2.3 Feeding types of benthic organisms. (After Rhoads, 1974.)

ported in the bioturbation process, which is especially important when transporting hydrophobic contaminants that are heavily retarded by pore-water processes. Worm tubes and other macroscopic animal burrows can signiÞcantly enhance contaminant transport by advection across the sediment–water interface. In addition, direct ingestion of sediment deposits can lead to rapid transport of sediment and associated contaminants to the surface. Bioturbation is not fully incorporated in most system-wide contaminated sediment models and is often lumped together with other processes as part of the sediment-side mass exchange coefÞcient or an effective biodiffusion coefÞcient. Effective biodiffusion or exchange coefÞcients are crude approximations at best because they do not separate the various modes of particle movement exhibited by an organism. In addition, because they are normally measured with strongly sorbing contaminants, pore-water pumping and the effect of bioturbation on other diffusive and advective processes (i.e., creation of secondary porosity, changes in texture or permeability) are not included. Most Þeld measurements of



83

bioturbation can be described by an effective diffusion coefÞcient of the order of 10–4 to 0.01 cm2/day or an effective mass transfer coefÞcient of 10–5 to 0.001 cm/day based on a 10-cm biologically active zone. As stated previously, effective molecular diffusion coefÞcients in sediments are of the order of 0.1 cm2/day. For a hydrophobic contaminant (e.g., pyrene) with an approximate partition coefÞcient, Ksw~1000 l/kg, bioturbation is expected to control contaminant migration in the upper layers of a stable sediment bed. Using 1 cm/year (0.003 cm/day) as an effective bioturbation exchange coefÞcient, the magnitude of the bioturbation and molecular diffusion coefÞcients are as follows: ks,diff =

0.1 cm / day = 0.0001 cm / day 1000

ks,bio = 0.003 cm / day

(2.14)

Thus, bioturbation-induced transport is of the order of 30 times larger than diffusive transport in this example. For some elemental species whose leachable fraction can partition only weakly into the solid phase, the enhancement by bioturbation may be minimal. However, pore-water pumping or other soluble transport processes that are not part of most biodiffusion coefÞcient measurements can be important for these species. One area in which very little information is known is hydrophobic contaminant transport via gas movement through sediments. The source of the gas is often biological (e.g., the production of hydrogen sulÞde or methane by anaerobic metabolic processes in microorganisms). Hydrophobic organic compounds tend to sorb preferentially at the gas–solid interface. The mass of contaminant moving in this manner can be relatively small but still may result in surface recontamination, and the bulk movement of the gas phase could conceivably alter the physical properties of the sediment bed. The dominance of transport by bioturbation and other biological processes is greatest near individual organism burrows. In general, bioturbation is the primary migration mechanism of sorbing contaminants in stable surÞcial sediments unless the physical character of the sediment or its level of contamination precludes signiÞcant colonization by benthic organisms or ground water seepage is such that advection dominates. As indicated previously, in most system-wide models of contaminant transport from sediments, the effect of bioturbation is lumped into an overall mass exchange coefÞcient with other sedimentside transport processes. 2.2.3.3 Contaminant Transference via Food Webs Ultimately, it is the portion of the contaminant that moves via natural processes into the water or food chain that is the source of exposure and potential risk of contaminated sediments to Þsh and higher animals. The exposure and risk depend on the


84


organism’s access to the contaminant, the contaminant’s availability, and the organism’s assimilative capacity. Contaminant assimilation and risk of effects in higher animals generally depend on exposure via one of the following pathways: •

• •

Direct exposure to Þsh and higher animals by contaminant release from resuspended contaminated sediment or by incidental ingestion of contaminated bed sediments Direct exposure to Þsh and higher animals from contaminant release in dissolved form from bed sediment to the overlying water Indirect exposure to higher animals by predation and plant harvesting and animals that ingest or otherwise assimilate sediment contaminants

For hydrophobic compounds strongly associated with particulate matter, the third pathway dominates. The analysis of contaminant transfer and exposure via the food web is complicated by species diversity and feeding behaviors and movement patterns. Often, the organism of primary interest is high in the food web with multiple levels separating the organism from the contamination source. The food webs vary with time, especially with season, and with space. Bioaccumulation can be analyzed in two ways. For organisms that exhibit relatively little movement and for which contaminant concentrations respond rapidly to changes in exposure concentrations, simple linear relationships between exposure sources and body burdens are appropriate. Organisms at the base of the food web, primarily benthic and water column invertebrates, are generally in this category. For example, a biota and sediment accumulation factor (BSAF) can be deÞned as the ratio of contaminant concentrations in the organism and local sediments. For hydrophobic compounds, the tendency of organisms to accumulate contaminants is often deÞned by the lipid content of the organism. Similarly, the bioavailability of the contaminant in sediment is controlled to a large degree by the organic carbon content of the sediment. Thus, the BSAF for hydrophobic compounds is generally deÞned as the ratio of the lipid-normalized accumulation in an organism and the organic carbon–normalized quantity in the sediment. BSAF =

Wb / fl Ws / foc

(2.15)

Here, the quantity Wb is the concentration of contaminant in the organism (mg/kg wet wt), fl is the fraction of lipids (g lipid/g wet wt), Ws is the concentration of contaminant in the sediment (mg/kg dry wt), and foc is the fraction organic carbon (OC) of the sediment (g OC/g dry wt). Note that the use of the BSAF does not imply chemical equilibrium, only a steady relationship between contaminant levels in sediment and organism. Organisms are not in chemical equilibrium with their environment; the contaminant level reached represents the outcome of uptake, loss, and dilution processes in the organism. BSAF values are for chemical- and species-speciÞc insofar as the bioavailability, assimilation, efÞciency, and depuration rates differ among chemicals and species. The appropriate units of the BSAF are also chemical speciÞc. For example, methyl mercury tends to associate with



85

protein and, therefore, lipid normalization of the organism body burden is not appropriate. In this case, the units of the numerator of the BSAF are more appropriately mg/kg dry wt. For organisms far removed from the sediment (e.g., Þsh in a pelagic food web), BSAFs cannot provide an accurate estimate of contaminant concentrations under changing conditions in sediments and local waters. In this circumstance, assuming a constant BSAF results in an inaccurate prediction of contaminant concentrations in a water column-based food web. To improve one’s ability of predicting exposure and accumulation at higher trophic levels, it is necessary to explicitly model contaminant transfer through the food web. Bioaccumulation models exist that can simulate the transfer of contaminants through complex, dynamic food webs over the entire life cycle of an organism, incorporating complex movement patterns in a dynamic (i.e., time variable) framework. There are several frameworks available, of which two in particular have been widely applied to real-world problems. The framework developed by Frank Gobas (Gobas, 1993; Gobas et al., 1995) has been used at several sites and in the development of methodologies for computing water quality criteria. The model framework developed by Thomann and Connolly (1984) has also been applied at multiple sites with multiple chemicals and has been used by the USEPA in developing water quality criteria. These models were recently compared and found to compute similar contaminant concentrations. (Another model whose development has been supported by the USEPA is Aquatox [USEPA, 2000].) As an illustration, Farley and Strauss (2000) employed the time-variable, age-dependent bioaccumulation model developed by Thomann and Connolly (1984) to predict PCB transport through a lower Hudson River food web composed of phytoplankton, zooplankton, white perch, and striped bass. Time-dependent diffusive uptake, ingestion, egestion and excretion, growth dilution, and metabolic processes at each of these trophic levels were modeled. Bioaccumulation modeling has developed sufÞciently to permit realistic calculations of the extent of trophic transfer of many chemicals in aquatic food webs, including hydrophobic organics and metals. Such models generally are limited by the availability of data to deÞne biological processes, in particular food web structure and the ultimate source of the contaminant to the food web (i.e., sediment vs. water column) and movement/migration patterns. Food web models can also be linked with chemical fate and transport models to give a uniÞed mass balance framework to help assess potential remediation strategies. Because of the advanced state of food web models, they can be used effectively to evaluate the sensitivity of model projections to uncertainty in key biological processes. In this way, the adequacy of linked chemical fate and food web models can be evaluated in light of the desired model objectives. 2.2.3.4 Human and Ecological Risk Evaluation DeÞning exposure and accumulation in receptor organisms does not complete the analysis. A risk-based approach to managing contaminated sediments ultimately requires translation of that exposure and uptake into effects. Estimates of relative


86


Growth Release of Chemical

Physiology Reproduction

Survival

Transport

Disease

v

Behavior Direct Contact Food

Bioaccumulation Desorption

FIGURE 2.4 Potential effects of contaminated sediments.

exposure can be useful in comparing management alternatives. The need for any management action, however, depends on deÞning the actual risks of no action. Ultimately, the effectiveness of any management action is also deÞned by the degree to which that action meets risk reduction goals. Note that historically little interaction exists between the hydraulic and sediment transport models and ecological effects models. As shown in Figure 2.4, the potential effects of contaminated sediments on the ecosystem are many. For estimating risk to ecological receptors, toxicological information is normally limited to estimates of effects threshold concentrations of a few responses in particular species. Intertaxon regression relationships can allow extrapolation of this information. Such threshold concentrations are usually applied in a conservative fashion. In addition, though they provide an indication of the potential for impacts on individuals, threshold values based on laboratory toxicity tests generally do not directly address the issue of population-level effects. For complex mixtures of endocrine disruptors, toxic equivalency factors are developed and are useful in that they allow screening level estimates of toxicological impacts. Toxic equivalency factors are useful, however, only for similar species exhibiting similar effects (i.e., when the mode of action and response are the same as the manner in which the toxic equivalency was deÞned). A key requirement is knowing the resulting uncertainty in dose–response or effects thresholds between species and among individuals within a species. A more sophisticated approach to ecological risk assessment involves use of a full dose–response relationship (instead of a single threshold concentration) combined with a population or ecosystem model. This is a growing Þeld, born of an interaction between ecology and environmental toxicology. Approaches include the use of scalar abundance models, life-history models, individual-based models, and metapopulation models using geographic information systems. An important part of ecological effects modeling is modeling the landscape or setting in which the ecosystem resides. Dynamic changes (e.g., seasonal) in the landscape clearly modify the ecosystem and its response to contaminants.



87

MODEL TYPE

Individual-Based Models

Age/Size Structured Models

Cape Sable Seaside Sparrow

Snail Kite

Manatees

White-Tailed Deer

Wading Birds

Crocodiles

Florida Panther

Freshwater Fish Functional Groups

Radio Tracking Tools

Reptiles and Amphibians

Estuarine Fish Functional Group

Alligators

Linked Cell Models

Process Models

Spatially Explicit Species Index Models

Abiotic Conditions Models

Lower Trophic Level Components Cape Sable Seaside Sparrow Snail Kite High Resolution Topography Disturbances

Vegetation

Long-Legged Wading Birds

High Resolution Freshwater Hydrology

Short-Legged Wading Birds

White-Tailed Deers Alligators

High Resolution SICS Hydrology and Salinity

FIGURE 2.5 Structure of Across Trophic Level System Simulation (ATLSS) Multimodel.

An example of the structure and capabilities of an ecosystem model can be found in the Everglades model of Fleming et al. (1994). Figure 2.5 illustrates the model components and their interrelationships. At low trophic levels, process models are employed in lieu of individual or species models. As one moves higher in the food web, more speciÞc models are employed. For the middle trophic levels, structured population models are employed; at high trophic levels, modeling is focused on the individual. Across trophic-level system simulation (ATLSS) was designed to describe transtrophic abundances of contaminants, intertrophic carbon ßow, and the abundance and diversity of keystone species. The primary COC was mercury. Due to the complex biogeochemical cycling of mercury in its various forms, mercury presents a great challenge for chemical fate modeling. In addition, the uncertainties related to food webs, population interactions, biological effects, and dose–response relationships are common to all ecosystem models. Human health risk assessment is generally performed using a single threshold dose, deÞned as the dose below which risks are acceptable. Depending on the chemical, critical doses are deÞned independently for cancer and noncancer effects. Human health risk assessment differs from ecological risk assessment in that the protection of individuals is the focus. In ecological risk assessment, the protection of populations is the focus, except for the case of endangered species. Key sources of uncertainty are the threshold doses and food consumption patterns and rates. In addition, people generally consume top predators so that the uncertainties associated with the calculation of bioaccumulation also contribute to overall uncertainty in human health risk assessment.


88


2.2.4 MODEL CALIBRATION

AND

UNCERTAINTY

2.2.4.1 Sources of Model Uncertainty Evaluating a model involves the following steps: • • •

SpeciÞcation of model objectives Selection/development of model SpeciÞcation of preconceptions and working assumptions (model parameterization) • Filter Þeld data to ensure consistency and accuracy. • Calibrate model parameters. • Evaluate uncertainty/conÞrm model calibration with additional data. • Perform model projections.

All models, no matter how sophisticated, are approximations of physical reality. It has been said that all models are wrong, but some are useful. All models exhibit uncertainty and error, which do not pose difÞculties if the uncertainty and error is minimal or associated with insigniÞcant issues relative to the questions being asked of a model. Models are designed to meet speciÞc objectives, and their appropriateness and suitability depends only on their ability to address those objectives. A critical component of all models is the assessment of uncertainty or error. Although the model evaluation steps identiÞed above are listed separately, they are not independent. Rarely is process knowledge sufÞciently complete and data of sufÞcient quantity and quality to ensure parameter uniqueness. Instead, different working assumptions and model formulations can result in different realizations of a model that do not vary signiÞcantly in their ability to reproduce data. The model projections forward in time may differ dramatically, and the conclusions drawn from them can be much different. The following three principle sources of error exist: • • •

SimpliÞcations and approximations in the model equations and their numerical solution Error and uncertainty in data and parameterizations of models Ignorance about future conditions and assumptions about future uses

Equation error can be attributed to either an inaccurate conceptualization or an inadequate translation of that conceptualization to a numerical model. Contaminant sources and sinks that are not identiÞed in a model are a major limitation to either describing current conditions or investigating sediment management approaches. Calibration of such a model to data can lead to incorrect assumptions about the existence or importance of other processes in a model and how best to respond to the risks those processes pose. Incorrect formulation of speciÞc processes can lead to similar problems. Comparing data trends vs. model predictions can identify errors and is discussed in the following text. Numerical implementation errors include spatial and time-grid discretization errors and use of algorithms that exhibit excessive numerical diffusion



89

or instability. Unlike process description errors, standard approaches exist to evaluate or minimize these problems. Parameterization error can arise from calibrating to data that are in error or inadequate for system characterization or use inaccurate parameters/parameterizations. Parameters and constitutive relationships that employ them are normally Þt to either laboratory or Þeld data over speciÞc ranges in conditions. Use of these parameters outside of these ranges is a source of error. Parameters cannot be independent, and calibration measurements reßect that covariance, thereby increasing the difÞculty and reducing the accuracy in calibrating parameters. Gaps and uncertainty in calibration data further limit parameter accuracy. To evaluate the effectiveness of natural recovery or other contaminated sediment management options, models are used to predict future conditions. Assumptions about baseline conditions necessary to support a model can be measured, but future conditions must be assumed. Errors in these assumptions do not constitute model errors but do limit prediction accuracy. In particular, future condition assumptions can incorrectly bias the conclusions of the modeling and inappropriately support or reject speciÞc sediment management options. 2.2.4.2 Techniques for Calibrating a Model and Evaluating Uncertainty The acceptability of a model depends on the questions being asked of it and the precision required of the answers. Often, the goal of the model is to provide a basis for choosing an appropriate environmental management strategy to maintain or improve water or sediment quality. Uncertainty can limit the ability of the model to differentiate among management approaches. Assuming the model is potentially an accurate representation of the system and that the future conditions that must be modeled are reasonably well deÞned, the controllable uncertainty is generally limited to the model parameters. Some parameter values can be deÞned with high precision on the basis of theory or controlled experimentation. Using fundamental and theoretically sound parameterizations or constitutive relationships increases the number of parameters likely to be speciÞed in this manner. The remaining parameters must be calibrated to Þeld observations. As a result, parameters that are the most uncertain are those to which the model is least sensitive under the conditions of the calibration. If the model later must be used to predict conditions outside the calibration range, this uncertainty can pose signiÞcant model limitations. The evaluation of model uncertainty normally proceeds by producing model results over a range of parameter values. If the distributions of the parameter values and the covariance between parameters are known, the distribution of the model results could, in principle, be evaluated through Bayesian analysis. Unfortunately, such an approach is essentially impossible with complex contaminated sediment models. The parameter distributions and their covariance are generally not known. Randomly selected parameter values are commonly used in Monte Carlo simulations of model uncertainty. Without knowledge of the parameter distributions or the covariance between parameters, however, such an approach can lead to misleading conclusions. A more feasible approach is to develop bounding parameter estimates by applying sound engineering judgment. These bounding estimates might be used


90


directly to examine the bounds of the model predictions or evaluate model sensitivity to the parameters. Bounding estimates must be used with caution to ensure that the results are not invalidated by unrecognized parameter covariance. The use of engineering judgment to deÞne parameter bounds necessarily introduces subjectivity. As a result, bounding estimates should be selected carefully, documented and supported adequately, and reviewed critically. Despite these cautions, bounding estimating often remains the only feasible means of exploring model uncertainty because of parameter uncertainty. An alternative approach to exploring model sensitivity when two or more conceptualizations of a system or management approach have been identiÞed is to attempt to support the alternative conceptualization or approach within reasonable ranges of parameter values. This approach is analogous to statistically proving or disproving the null hypothesis (i.e., “Can the model be used to support an alternative hypothesis employing reasonable parameter values?”). 2.2.4.3 Measures of Model Acceptability Model acceptance depends on the conÞdence placed in the conclusions drawn from it. There are a variety of measures of uncertainty that can arise from the analyses presented above. No single measure of uncertainty is always appropriate, but quantitative measures that can be useful exist. In particular, the root–mean squared error between model predictions and observations can be useful but also can be misleading as a single indicator of model accuracy. The root–mean squared error does not indicate bias in the model predictions nor agreement with data trends. Bias and agreement with spatial and temporal trends should be evaluated as additional indicators. Because fundamentally sound models are based on a material balance within the system, all models should provide closure of the material balance. Failure to close the material balance can indicate numerical or conceptual errors in the model formulation. Comparing the sensitivity of the model predictions with parameter values and with estimates of the uncertainty in those parameters also indicates the conÞdence with which the model results should be judged. Ultimately, however, the model acceptability in a particular application depends on the application and the degree to which the conclusions reached rely on the model predictions.

2.3 CHALLENGES AND EMERGING ISSUES Uncertainty analyses in a variety of situations and research into the various transport mechanisms responsible for exposure and risk have identiÞed several key processes that represent the biggest challenges to successfully modeling contaminated sediments. These processes include the following: • • • • •

Cohesive sediment erosion and transport Contaminant release and availability Advective processes in the hyperheic zone Bioturbation as a sediment and contaminant transport mechanism Contaminant bioaccumulation and effects in benthic and higher organisms



2.3.1 COHESIVE SEDIMENT EROSION

AND

91

TRANSPORT

The friction velocity required to produce particle motion for cohesive sediments is signiÞcantly larger for a particular particle size than for sandy sediments. As indicated previously, cohesiveness is a complicated function of particle size, bulk density, mineralogy, organic content, and salinity. These properties vary signiÞcantly with position and time. Incorporating these variations and improving the theoretical basis for predicting cohesive sediment transport is a current challenge faced by sediment transport models. Erosion in cohesive sediment beds depends on the second or third power of shear stress and the fourth to sixth power of water velocity. The strong dependence on stream velocity emphasizes the critical need for high-resolution modeling of stream hydraulics when trying to predict sediment transport. Even given the stream hydraulics, it is not yet possible to predict erosion without bed measurements. Deposition varies signiÞcantly with time and space due to the effects of bed shear stress (inversely related to deposition), cohesiveness of the depositing sediment, and ßocculation processes. Floc size and settling speed decrease as the ßuid shear, sediment concentration, and salinity increase. Flocs produced at lower ßuid shears and sediment concentrations tend to settle more slowly than do ßocs produced under different conditions. Flocs produced under differential settling conditions when ßuid shear is negligible tend to approach steady-state size distributions more slowly and settle more rapidly than do ßocs inßuenced by signiÞcant ßuid shear (Lick and Lick, 1988; Lick et al., 1993). These variations in settling and depositional dynamics from ßocculation are difÞcult to predict but necessary to describe sediment erosion and deposition adequately. Processes such as gas generation also inßuence sediment bed density and erodibility (Jepsen et al., 2000). Sediment bed properties also depend on time, as indicated by measurements involving Detroit River sediments over a 2- to 3-month period. The bulk density increased from 1.38 to 1.48 g/cm3, and the resulting erodibility changed by one to two orders of magnitude (McNeill et al., 1996). The resulting bed armoring also can be inßuenced by time due to changes in the character of depositing particles. Although a priori prediction is not yet possible, erodibility measurements with shear stress and sediment density can be obtained using experimental techniques such as Sedßume (McNeill et al., 1996). In the Sedßume apparatus, the sample to be evaluated is raised continuously to maintain a constant-level bottom surface as the sediment erodes. The fundamental constitutive relationships developed from such measurements can be used in realistic sediment transport models. Extensive measurements are needed to support high-resolution models of sediment transport that account for physical property variabilities of the sediment beds with space and time. The dependence of sediment erosion on bed properties and consolidation has led to the need for improved bed representations. In the models Recovery and WASP5, the bulk density is constant or constant within speciÞc layers. In SED2D, HSTCM, and CE-QUAL-ICTOX, the bulk density is allowed to vary. However, because the bed properties are deÞned independently, the pore-water ßow is not necessarily consistent with changes in the bed properties. The Environmental Fluid Dynamics Code developing through the USEPA is designed to employ a consistent framework for contaminant, hydrodynamic, and sediment transport modeling. Void


92


ratio is deÞned by a speciÞed depth-dependent proÞle in a manner similar to SED2D, but Þnite-strain consolidation is used to predict bed properties with time and depth. In this manner, the dynamic response of the bed properties to episodic ßow events can be estimated. The deÞnition of bed dynamics and bed response to episodic ßow events is critical when evaluating in situ contaminated sediment management options including natural recovery and in situ capping. Erosion and deposition behavior are also signiÞcantly inßuenced by high-frequency ßuctuations in ßow (e.g., turbulence). This inßuence has been observed in resuspension measurements by the motion of vessel trafÞc (Lopez and Garcia, 2001). Vessel movements inßuence the turbulent ßow structure which, in turn, affects the sediment resuspension. The average bed shear stress does not completely capture the inßuence of ßow on sediment resuspension. Admiraal et al. (2000) has shown that there is a particle–size dependent phase lag between the velocity ßuctuations and the bed erosion. In general, model predictions of erosion rates are not possible except when based on site-speciÞc measurements and correlated to bed and ßow properties using theoretically valid, physically based models. Only a sound theoretical basis provides any conÞdence that the model can be extrapolated beyond the range of collected measurements or used to predict future behavior. Improved models of cohesive sediment properties, their variations in space and time, and their relationship to overlying water hydraulics are needed to achieve this goal.

2.3.2 CONTAMINANT RELEASE

AND

AVAILABILITY

The extent to which contaminants desorb to the aqueous phase can be limited. Metals can be present in an essentially insoluble form, or organic compounds can be sorbed in a form that does not release or releases only very slowly. In many sediments and especially in marine sediments, the quantity of AVS present is sufÞciently large so that divalent metals (e.g., lead, cadmium) can be present in essentially an insoluble sulÞde form. In general, however, the cycling of metals in sediments involves a complicated set of processes (Bufße and Devitre, 1994). Phenomena that control metal speciation in sediment–water systems include the following: • • •

•

Metal hydrolysis, such as zinc hydrolysis: Zn2+ + 3 OH ´ Zn(OH3)Bulk precipitation of insoluble species such as Fe(OH)3, MnOx, Cd(OH)2, and metal sulÞdes Complexation with inorganic and organic ligands such as Cl-, SO42-, CO32-, EDTA, and the humic and fulvic acids or other materials that comprise colloidal organic material Oxidation–reduction reactions including arsenic (As[V] ´ As[III]), iron (Fe[III] ´ Fe[II]), and manganese (Mn[IV] ´ Mn[II])

Predicting these processes and evaluating the solubility and availability of the metal species is essentially limited to equilibrium calculations. Extensive databases exist for minerals, metal hydroxides, and metal complexes. At equilibrium, redox potential and pH can be used to describe speciation in metal species. Unfortunately, metal speciation



93

is rarely at equilibrium in the sediments or overlying water. Because some reactions can be at equilibrium while others are not, and due to the presence of important trace species that may not be considered in the equilibrium relationships, oxidation–reduction potential and pH measurements may not be very useful for predicting metal solubility and availability. The thermodynamic database for metal complexes with organic colloids is also not as well developed as with other species. The uncertainty in predicting metals availability is illustrated in Lindberg and Runnels (1984). The results of many laboratory and Þeld observations also indicate that a signiÞcant fraction of sediment-bound organic contaminants do not desorb linearly and reversibly as suggested by conventional partitioning models, are not biodegraded, and are difÞcult to remove by extraction with surfactants or cosolvents. For example, Pereira et al. (1988) found that the concentration of halogenated organic compounds in native water, suspended sediments, and biota was far below the values predicted with respect to concentrations in the contaminated bottom sediments collected from Bayou d’Inde, LA. Similarly, McGroddy and Farrington (1995) and Readman and Mantoura (1987) observed a fraction of PAHs in river sediments not available for partitioning. Ghosh et al. (2000) has related highly sorbed concentrations and highly effective partition coefÞcients to the presence of soot in sediments. In addition, it is common for hydrophobic compounds to remain persistent in sediments even when conditions are appropriate for biodegradation to occur (Connaughton et al., 1993; Kan et al., 1994; Hatzinger and Alexander, 1996). For most of these sediments, contamination had ceased for periods of many years, yet the sediment-bound contaminants persisted over decades without signiÞcant concentration reduction or hydrocarbon Þngerprint loss. Luthy et al. (1997) has summarized evidence for sequestration in soils and sediments. The sorption and desorption of organic chemicals to soils and sediments is a complex process given the diversity, magnitude, and activity of chemical species, phases, and interfaces commonly present in contaminated subsurface environments. A research group at Rice University has made substantial progress in establishing the concept of irreversible adsorption or hysteresis as a fate mechanism in natural sediments (e.g., Kan et al., 1994, 1997, 1998; Hunter et al., 1996). It has been postulated that the observed phenomenon can be due to the occlusion of the chemicals from desorption by cooperative conformational changes of the organic phase during the adsorption process. The conformational rearrangement of the solid organic matter in the presence of adsorbed chemicals could cause the chemical environment of the adsorbate to be different and, hence, be the source of desorption resistance and hysteresis. At least one hypothesis has stated that the quality of organic matter determines the degree of hysteresis in sorption–desorption experiments (Huang and Weber, 1997), suggesting that the size of the desorption-resistant compartment can be related to organic matter age. An alternative hypothesis is that an apparent hysteresis is only the result of slow desorption (Lick and Rapaka, 1996; Pignatello and Xing, 1996). Field sediments may have been contaminated for decades, allowing time for sorption into phases or portions of sediments that allow only equally slow desorption. Because the period of desorption can be very short compared with the period of sorption, the net effect is observation of an apparent hysteresis that does not reßect true equilibrium. Another


94


complicating factor when dealing with hydrophobic PAHs in these systems is the presence of high concentrations of colloidal dissolved organic carbon in the pore water (Reible et al., 1991). PAHs that have poor water solubility partition to this colloidal material, decreasing the apparent partition coefÞcient and giving rise to what appears to be hysteretic desorption. Further research is needed to resolve whether data supporting observations of desorption hysteresis or desorption resistance represent experimental artifacts or different phenomena. Whether due to slow sorption or to a hysteretic desorption-resistant fraction, it is most important to ascertain whether there is any effect of this phenomena on bioavailability and accumulation in receptor organisms.

2.3.3 ADVECTIVE PROCESSES

IN THE

HYPERHEIC ZONE

The hyperheic zone represents the upper portions of the sediments and marks the interface between the sediment–water interface and the local ground waters. A hydraulic gradient between the ground waters and the surface waterbody can give rise to a seep and advective transport. Even in the absence of a mean hydraulic gradient, an advective ßux can still be observed. Local pressure variations of the order of 100 to 1000 N/m2 can be observed between the upstream and downstream faces of the typically triangular-shaped, dunelike sediment structures that form at the sediment–water interface. The ßow is characterized by a simple turbulent shearing ßow on most of the upstream face and a recirculating wake on the downstream face that also inßuences a portion of the subsequent sediment dune. It is the weak and poorly organized ßow in the wake that results in the immobility of these sediment grains under bed load conditions. Thibodeaux and Boyle (1987) approximated the dunes as simple geometric shapes such as cylinders and showed that measured pressure data on those simple shapes are sufÞcient to generate a potentially significant in-bed ßow. Savant et al. (1987) used the pressure proÞle data generated by Vittal et al. (1977) to predict head distributions and, through Darcy’s law, velocity proÞles in triangular sediment dunes in a laboratory ßume. The relatively high pressure on the upstream face resulted in a ßow down and into the dune turning upward and out of the lower pressure downstream face. Savant et al. (1987) showed excellent agreement between the predicted and observed ßow patterns. More extensive analyses of these processes can be found in Elliot and Brooks (1997a, 1997b), Work et al. (2000), and Packman and Bencala (2000). The experiments and modeling indicate that the induced in-bed ßow could extend as much as four to Þve dune heights into the sediment. By using measured permeabilities and estimated dune geometries in several river systems, Savant et al. (1987) showed that this mechanism could lead to advectively dominated transport in these rivers. In-bed Peclet numbers, the nondimensional ratio of advective to diffusive transport, ranged from the order of 100 in the Nile (Egypt) and Red (Louisiana, U.S.) Rivers to 300 to 1700 in the Mississippi River (Louisiana, U.S.). This mechanism likely is important mostly in sediment beds subject to signiÞcant organism burrowing activity and in permeable, sandy sediments as might be observed on the continental shelf of the coastal U.S. Field investigations of contaminant levels in sediment beds typically involve analytical chemistry measurements performed on the sediment solids to measure total



95

sediment loading. The concentration proÞles can be averaged over sediment depth and reßect a mixed sample from a few inches to 1 ft or more of core length. ProÞles of concentration based on thin slices less than 1 cm (0.39 in.) in depth spanning the entire depth of contamination are rare. Concentration proÞle data usually are limited in that they generally represent a snapshot in time and seldom reßect trends over time. Typically, single proÞles for a particular year are available for hydrophobic organics and metals. These measurements are not useful in assessing the inßuence of diffusivity that is manifested over longer time periods and result in depleting contaminant levels over short distances. In addition, diffusion normally occurs at signiÞcant rates only within the pore-water phase, suggesting that pore-water concentrations can be much more useful. A better approach for both conceptual and quantitative fate and transport model development is the use of high-resolution coring with both total and speciated measurements via phase and constituent. In this manner, the fraction of the available contaminant dissolved in the pore water is identiÞed. For metals in particular, total sediment loading measurements are not useful. By discriminating between soluble and insoluble fractions of metals, a much better assessment of potential adverse effects is achieved. High-resolution coring enables a comparison with detailed mathematical models and can be useful in identifying the most important processes. For example, diffusional processes give rise to concentration proÞles that are quite different from advectively dominated transport processes.

2.3.4 BIOTURBATION AS A SEDIMENT TRANSPORT MECHANISM

AND

CONTAMINANT

Bioturbation has been identiÞed as a likely dominant sediment-side mass transfer process in stable sediments. The possibility of bioturbation as a transport mechanism has long been recognized. Boudreaux (1986a) points to references on the effect of biological activity on sediment composition and properties as old as Davison (1891). A wide variety of animal organisms that live on and in the upper sediment layer exist, and they interact with sediments in a variety of ways. If the scale of the individual mixing events is very small compared with the depth and area of the sediment (e.g., the depth and area of a box core sample of a sediment), then bioturbation gives the appearance of a diffusive process. Boudreaux (1986a) examined the conditions under which a diffusive model of bioturbation is appropriate. Because of the decrease in organism density and activity with depth in sediment, some investigators have speculated that a depth-dependent biodiffusion coefÞcient is appropriate. However, as Boudreaux (1986b) noted, it is often difÞcult to differentiate between a constant and depth-dependent biodiffusion coefÞcient on the basis of available data. Alternatively, the relatively rapid mixing of the biologically active sediment zone by bioturbation suggests that using a relatively well mixed, thin layer with an average exchange coefÞcient to layers below or the overlying layers above can be a reasonable approximation of the process. This approach is used in the U.S. Army Corps of Engineers (USACE) Recovery model. The primary difÞculty with either diffusive or exchange coefÞcient approaches is that parameters may not be applicable to other sites, even one nearby, if the density, distribution, and type of organisms are different.


96


Water Column

234

Bioturbation Styles, Depths, and Rates S70-S50, Eel Shelf

S60 x1 Th XS Excess activity (dpm/g) 0.1 1 0 Db = 19 cm2 /year 2

0 to 5 cm: Biodiffusive, Db = 5 to 30 cm2 /year

10

5 to 15 cm: Bioadvection, 5- to 70-year turnover

Depth (cm)

4 6 8 10 12 14 16 6 to 50 (?) cm: Slow advection, >100-year turnover

FIGURE 2.6 Example of depth-dependent bioturbation intensity associated with tiering of infauna from the Eel Shelf of Northern California. (Adapted from Bentley and Nittrouer, 2001.)

Despite this, biodiffusion and mass exchange coefÞcients and their operative depths can show surprising similarity between sites. More than 90% of the 240 observations of bioturbation mixing depths in both freshwater and saltwater reported by Thoms et al. (1995) were 15 cm or less and more than 80% were 10 cm or less. Occasionally, particular species or individual organisms can penetrate more deeply, but these incursions do not appear to affect areal average exchange rates. This is illustrated in Figure 2.6, which shows effective bioturbation diffusion coefÞcients as a function of depth in a marine environment. In this setting, speciÞc styles and rates of bioturbation are associated with the depth distribution of particular types of organisms. The upper few inches are turned over rapidly. This rapid diffusive mixing is associated with small polychaetes and mollusks that dwell in the upper 1 to 5 cm of the seabed. Deeper advective mixing occurs in the subjacent tier, produced primarily by head-down deposit-feeding polychaetes. The deepest and slowest mixing is associated with large open burrow networks, such as those produced by thallassinid shrimp. Thorium proÞles are used in a particular core to estimate an effective bioturbation diffusion coefÞcient of 17 cm2/year in the upper 3 cm. Although the example suggests that the upper layer in this proÞle may not be well mixed, the ßux can be described by either an effective diffusion coefÞcient or a mass transfer coefÞcient. The relationship between them can be written as follows:



ks,bio =

Dbio 17cm 2 / yr ª = 5.7cm / yr d bio 3cm

97

(2.16)

Similarly, Aller (1982) estimated an effective biodiffusion coefÞcient of 5 to 32 cm2/year in Narragansett Bay; Brownawell (1986) estimated a biodiffusion coefÞcient of 9.4 cm2/year in Buzzards Bay; and, Þnally, using the data of Spaulding (1987), Thibodeaux (1989) observed an essentially identical biodiffusion coefÞcient of 9 to 13 cm2/year in the Upper Estuary of New Bedford Harbor. Matisoff (1982) observed that more than two thirds of the available measurements in both freshwater and saltwater conditions suggest an effective particle diffusion coefÞcient of 0.3 to 30 cm2/year. This can be restated as effective mass transfer coefÞcients of 0.03 to 3 cm/year using an average effective layer depth of 10 cm. The vast majority of these measurements of effective bioturbation diffusivities were made by estimating particle reworking rates using strongly sorbed radionuclides associated with nuclear testing. The time and amounts of particular radionuclide release and their current distribution within sediment allow the measurement of the reworking rates in stable sediments. The measurement range of 10-4 to 0.01 cm2/day is consistent with the measurements of Reible et al. (1996) of effective bioturbation mass transfer coefÞcients of 0.001 to 0.01 cm/day in laboratory studies of tubiÞcid worms at Þeld densities in freshwater sediments. The tubiÞcid worms studied, Tubifex tubifex and Limnodrilus hoffmeisteri, were found at very high densities and represented the bulk of the biomass in all sediment bioassays conducted during the Assessment and Remediation of Contaminated Sediments (ARCS) Program, Great Lakes National Program OfÞce, in the Great Lakes (USEPA, 1993). These worms are head-down deposit feeders capable of processing 10 or more times their own weight in sediment every day. The secondary effects include the effect of the presence of burrows on permeability, the vertical redistribution of porosity, and the inßuences on bioÞlms on sediment cohesiveness and stability. For strongly sorbing contaminants, the primary effect of bioturbation is on particle movement. Thibodeaux et al. (2001) correlated both laboratory and Þeld measurements of effective transport coefÞcients with the partition coefÞcient of the contaminant. To improve the predictive capability of models relative to bioturbation, an improved understanding of the particular sediment–organism interactions is necessary. As shown in Figure 2.7, the speciÞc interactions of an organism with sediment and the resulting contaminant transport are complex. The Þgure shows direct measurements of particle egestion rate (by rate of fecal mound production) and fecal mound porosity (and derived estimate of sediment dilation) conducted in benthic mesocosms containing populations of Schizocardium at Þeld densities (300 to 600 individuals m-2) in natural muddy sediment. The use of average exchange coefÞcients is especially problematic with marine organisms because only a few individuals can be found per square meter, yet they tend to be larger and penetrate more deeply into the sediment than organisms in freshwater sediments. As with sediment erosion and deposition, only fundamental mechanistic models can be extrapolated to the future and to conditions outside the range of their development. Various organisms can be lumped into speciÞc modes of interaction, and these lumped modes are employed


98


Ingestion Rates for Natural Population Dry Sediment (mass): 7.2 x 2.4 g cm-2 year -1 Wet Sediment (volume): 7.6–25.1 cm3 cm-2 year-1 (porosity between 0.75 and 0.85) Turnover Period for 0–15 cm depth (average porosity 0.85) = 0.4–1.2 years H2O Volume Required to Dilute Sediment from Porosity of 0.8 (feeding depth) to 0.9 (surface porosity) = 14.1 x 4.6 cm3 year-1

FIGURE 2.7 Bioturbation and ßuid displacement rates for a natural population of estuarine acorn worms, Schizocardium sp., a common and locally dominant bioturbator in the northern Gulf of Mexico. (Adapted from Bentley and Richardson, 2001.)

in contaminant transport models. Mohanty et al. (1998) and Mohanty and Reible (2001) have attempted to provide mechanistically based descriptions of organism behavior. In the later paper, Mohanty and Reible use a statistical model, including Þnite-jump Levy ßights, to describe organism burrowing and foraging, and sediment ingestion and egestion for food. Currently these models are too complex to be used directly in a system-wide contaminated sediment model but can be used to predict average exchange coefÞcients.

2.3.5 CONTAMINANT BIOACCUMULATION AND EFFECTS IN BENTHIC AND HIGHER ORGANISMS Contaminant entry into ecological or human receptors normally requires satisfying the following three conditions: • • •

Opportunity — Exposure of the receptor to the matrix in which the contaminant resides Potential Availability — The fraction of the contaminant that is not irreversibly sequestered or bound to the matrix in which it resides Assimilative Capacity — The ability of an organism to assimilate the potentially available fraction of the contaminant

These conditions might be referred to as access, availability, and assimilative capacity. No bioavailability exists and, therefore, no contaminant exposure and risk occurs without satisfying all three of these conditions. That is, the organism must be exposed to the matrix in which the contaminant resides (or to which the contaminant can move), some fraction of the contaminant must be able to be released from that matrix in a form that can cause adverse effects, and the organism must have



99

some capacity to take up the contaminant in that form. As indicated previously, the contaminant access normally is limited to biological organisms present in the upper 10 to 15 cm of a stable sediment bed. Potential availability also can be limited, but additional research is needed to investigate the existence and signiÞcance of this phenomenon as outlined in the previous section. The Þnal condition that must be met before contaminants can be biologically available is to have an assimilative capacity in the organism of concern. Many examples of contaminants that are ingested (i.e., there is opportunity for exposure) exist for which no physicochemical limitations are apparent (i.e., potential availability is high) but for which uptake is still limited. The human body, for example, has a Þnite capability for assimilating most metals, with large fractions passing through the body without absorption or effect. In addition, speciÞc organic contaminants can be effectively metabolized by certain organisms, limiting the accumulation of toxic contaminants. The processes that lead to assimilation are contaminant and organism speciÞc. It is important to recognize that without mechanisms for assimilation, no bioavailability exists for the organism. Lu et al. (2003) measured accumulation in freshwater benthic organisms and correlated it to the apparent desorption partition coefÞcient. The apparent partition coefÞcients are consistent with the model of Tomson and Kan in which a desorptionresistant fraction exists with an apparently stronger association with the solid phase. The apparent partition coefÞcient could also be simply an operational deÞnition and represent a nonequilibrium measurement. A model of accumulation was developed assuming that the organism was able to achieve equilibrium with the apparent porewater concentration. The resulting predictions of observed accumulation in benthic worms correlated well with experimental observations as shown in Figure 2.8. The data represented in Figure 2.8 also included data in which the apparent contaminant partition coefÞcient was reduced by the presence of the contaminant in a separate pure phase (Millward et al., 2001) and a separate adsorbent phase. To improve the ability to predict exposure and accumulation at higher trophic levels, it is necessary to model contaminant transfer explicitly through the food web. (Examples were discussed previously.) One important difÞculty associated with predicting food web transfer is deÞning a food web that captures the essential features

BSAF Predicted

2

1 r 2 = 0.9 0

0

1 BSAF Observed

FIGURE 2.8 Effective partition coefÞcient as a predictor of BSAF.

2


100


v

Largemouth Bass

Centrarchids Bluefin Killifish

Least Killifish Mosquitofish

Crayfish

Sailfin Molly Flagfish v

Insects

Zooplankton

Fish Eggs

Periphyton and Plant Debris

FIGURE 2.9 Food web concept.

of the actual food web and is sufÞciently simple to allow characterization and parameterization. Figure 2.9 displays a conceptual food web indicating some of the relationships between a higher trophic level predator and organisms that may be in more direct contact with contaminated sediments. The other key difÞculty is specifying movement patterns of the biota, which determines where they are exposed. SpeciÞcation of the species-speciÞc accumulation and contaminant transfer, the seasonal variations in feeding habits of these organisms, and the spatial variability of particular species and contaminants is a daunting task that requires a combination of general biological knowledge and site-speciÞc information. Models will continue to need to incorporate even more sophisticated food web and effects components to develop and evaluate contaminated sediment management alternatives. When a riskbased approach is used to evaluate and compare management alternatives for contaminated sediments, food web models can provide an important link between the predictions of sediment transport and chemical fate models and the information needs of decision makers.

2.4 SUMMARY OF RESEARCH AND DATA NEEDS 2.4.1 SEDIMENT TRANSPORT PROCESS MODELING Hydrodynamic and sediment transport processes are the foundations on which contaminated sediment modeling lies. Critical problems in deÞning these foundations are associated with the spatial and temporal variability of sediment properties. Improvements in the resolution of measurement, calibration, and prediction of sediment properties and processes are required. In addition, there is a need under some conditions to model dynamical mechanical properties of sediment beds for



101

consolidation, pore-water extrusion, and response of bed to storm events. Also affecting the dynamics of the sediment bed are turbulent ßuctuations in the overlying water, even in quasi-steady ßows. All these processes must be described by constitutive relations in models that rely upon Þrst principles and are developed, to the extent possible, with reproducible, independent measurements. Further research is required in each of these areas to support improvements in modeling sediment properties and dynamics.

2.4.2 CONTAMINANT PROCESS MODELING There is a need to assess processes of contaminant release from sediments by mechanisms other than particle release in the laboratory and Þeld. Processes that are relatively uncertain at this time include the following: • • • •

Bioturbation Stream–ground water interaction Locally driven hyporheic zone ßows Gas generation and release

Developing an ability to readily assess the effects of these processes on contaminant transport in the Þeld is especially important. Field measurements need to be developed with laboratory testing under controlled conditions to validate the approach. Laboratory experimentation also is needed to improve the fundamental basis on which Þeld and laboratory data can be parameterized.

2.4.3 BIOLOGICAL PROCESS MODELING Many biological processes are lumped into overall exchange coefÞcients or simple accumulation measures such as the biota–sediment accumulation factor. Improving the ability to model contaminant fate processes in sediments requires a greater focus on individual processes and organisms to the extent possible. Understanding better the interactions of individual classes of organisms with sediments and developing models that better represent these interactions is needed. To improve accumulation estimates over simple BSAF calculations, dynamic food webs of important target species must be included in models. Ultimately, the modeling effort should move to expanding ecological risk assessments to the entire ecosystem. Because risk reduction is the fundamental principle behind managing contaminated sediments, developing better links between contaminant modeling and ecological dynamics and effects is required.

2.4.4 METALS RELEASE

AND

AVAILABILITY

In general, metals release and availability is much more complicated than hydrophobic organic processes. Much of current understanding is limited to the thermodynamics of a subset of the contaminants present in sediments. To address the situation, research tools should be applied to Þeld probes so that Þeld conditions can be better deÞned. There is also a need to move beyond equilibrium predictions to the kinetics of metal speciation, reaction, and transport. Multisolute, multisorbent


102


effects are especially difÞcult to characterize at this time. Metal speciation is often driven by biological processes, and metals release can be strongly affected by transitions between anaerobic and aerobic conditions. As a result, the link with biological and particularly microbial processes needs to be better understood, especially for mercury, which undergoes important biologically mediated reactions in the environment. AVS, which have been shown to play an important role in the availability of several metals (particularly divalent metal ions), also can play a role for arsenic and mercury. Additional research is needed to identify what role, if any, AVS may play in the availability of arsenic and mercury.

2.4.5 HYDROPHOBIC ORGANIC CONTAMINANT RELEASE AND AVAILABILITY Hydrophobic organic compounds (HOC) are not subject to the complexity of oxidation–reduction and other reactions faced by many metal species. Some research has suggested that a signiÞcant fraction of HOC contaminants may not desorb from sediments, or desorb only to a limited extent, or over long time scales. Research using different contaminants and sediment systems has yielded apparently contradictory results. There is a need to be able to predict the extent and rate of desorption from contaminated sediments, and further research is needed to develop those predictions. In addition, there is a need to determine if bimodal or rate-limited HOC desorption exists and to determine its inßuence on contaminant availability and uptake.

ACKNOWLEDGMENTS This chapter was prepared as a result of a workshop on modeling in the management of contaminated sediments held July 25 to 27, 2000, in Malvern, PA, with the primary support of DuPont. The efforts and encouragement of Calvin Chien (DuPont) were especially helpful in seeing this project through to conclusion. In addition to the named authors, the contributions of other workshop participants, including John Connolly (Quantitative Environmental Analysis); David Williams (Ciba Specialty Chemicals); Michael Kravitz (USEPA); Chris Reed (URS Corporation); Martin Lebo (Weyerhauser Company); Mel Skaggs (InDepth Environmental Associates); Marc Davies (Ballard, Spahr, Anderson, and Ingersoll); Cynthia Evanko (Geosyntec Consultants); and Ernie Watkins (USEPA) are gratefully acknowledged.

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Optimization and Modeling for Remediation and Monitoring prepared by George F. Pinder with contributions by David E. Dougherty, Robert M. Greenwald, George P. Karatzas, Peter K. Kitanidis, Hugo A. Loaiciga, Reed M. Maxwell, Alexander S. Mayer, Dennis B. McLaughlin, Richard C. Peralta, Donna M. Rizzo, Brian J. Wagner, Kathleen M. Yager, William W.-G. Yeh

CONTENTS 3.1 3.2

3.3

Introduction ..................................................................................................108 The User’s Persective...................................................................................109 3.2.1 The View from the U.S. Environmental Protection Agency (USEPA) .............................................................................109 3.2.2 The View from the U.S. Department of Energy (DOE) .................110 3.2.2.1 Application of Site Characterization and Monitoring Technologies...........................................111 3.2.2.2 Numerical and Optimization Models ...............................114 3.2.2.3 Innovative Technologies and the Regulatory Process......115 3.2.2.4 Technology Needs ............................................................116 3.2.3 The View from the U.S. Department of Defense (DoD) ................116 3.2.3.1 Optimization Efforts .........................................................117 3.2.3.2 Model Development Efforts .............................................119 3.2.3.3 Monitoring Efforts ............................................................119 3.2.4 The View from Industry...................................................................121 State of Knowledge and Practice.................................................................121 3.3.1 The Simulation Optimization Approach..........................................122 3.3.1.1 Gradient Control Remediation Technology .....................122 3.3.1.2 Concentration Constraints Remediation Technology.......124

0-56670-667-X/04/$0.00+$1.50 © 2004 by CRC Press LLC

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3.3.2

Stochastic Optimization to Accommodate Potential Design Failure ..................................................................................126 3.3.2.1 Chance-Constrained Ground Water Management Model ..........................................................127 3.3.2.2 Multiple Realization Ground Water Management Model ..........................................................129 3.3.2.3 Alternative Stochastic Optimization Methods .................131 3.3.3 Uncertainty .......................................................................................131 3.3.3.1 Sources..............................................................................132 3.3.3.2 Examples...........................................................................133 3.3.4 Design-Risk Cost Tradeoff ..............................................................141 3.3.5 Long-Term Ground Water Monitoring ............................................146 3.3.5.1 The Relationship between Remedy and Monitoring .......147 3.3.5.2 Performance Monitoring Problems ..................................148 3.3.5.3 Methods.............................................................................150 3.4 Gaining Acceptance .....................................................................................153 3.4.1 Remediation System Design Optimization Demonstrations ...........153 3.4.1.1 Dissolved TCE Cleanup at Central Base Area, Norton Air Force Base, California ...................................154 3.4.1.2 Model Calibration and TCE/PCE Plume Containment at March AFB, California.................................................157 3.4.1.3 Containment and Cleanup of TCE and DCE Plumes, Wurtsmith AFB, Michigan ...............................................158 3.4.1.4 Dissolved TCE Cleanup at Massachusetts Military Reservation .........................................................160 3.4.2 Long-Term Monitoring Field Studies..............................................162 3.4.3 Communication Improvements........................................................164 3.5 Challenges and Emerging Issues .................................................................166 3.5.1 Optimization Algorithmic Challenges IdentiÞed through Application Needs ..............................................................166 3.5.1.1 Natural Variability Over Space and Time ........................166 3.5.1.2 Multiple Constituents........................................................167 3.5.1.3 Multiple Phases.................................................................167 3.6 Summary ......................................................................................................168 Acknowledgments..................................................................................................169 References..............................................................................................................169

3.1 INTRODUCTION The focus of this chapter is optimization and modeling for remediation and monitoring. The goal is to provide the reader with insights into the optimization and modeling tools available for cost-effective resolution of environmental problems, especially as they pertain to ground water contamination and its long-term impacts. To achieve this goal, the technical and practical challenges inherent in this approach are presented as well as documented accomplishments. Utilizing this organizational approach, the reader should comprehend both the Þnancial beneÞts and the antici-


Optimization and Modeling for Remediation and Monitoring

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pated costs associated with using optimal design and modeling when resolving and managing problems addressable via this technology. The chapter is subdivided into the following three main topics: the user’s perspective, current state of knowledge, and gaining acceptance. Each topic is further subdivided to address many of the speciÞc issues of current importance to the professional ground water community. While the discussion of each speciÞc issue reßects the views of the authors, the issues have been deÞned in such a way as to provide an integrated discussion of the main topics. Nevertheless, the styles, formats, and levels of technical detail found in the various presentations are, by their nature, different.

3.2 THE USER’S PERSECTIVE 3.2.1 THE VIEW FROM THE U.S. ENVIRONMENTAL PROTECTION AGENCY (USEPA) Designing and maintaining effective remediation systems that satisfy all technical, regulatory, and social constraints is an extremely challenging task given the variety of hydrogeologic and contaminant settings of hazardous waste sites. The USEPA supports the use of the most efÞcient and effective tools available for all phases of site cleanup — from innovative, Þeld-based site characterization technologies and improved data management and visualization tools to innovative in situ and ex situ remediation technologies. One such promising innovative technology is mathematical optimization for the design and redesign of remediation and monitoring systems. However, as with many innovative technologies, the regulated community has been reluctant to adopt these approaches readily due to the lack of cost and performance data and concern over regulatory acceptance. In 1999, the USEPA completed a demonstration project applying hydraulic optimization techniques for pump-and-treat systems (Greenwald, 1999). The scope of this study included selecting three sites with existing pump-and-treat systems, screening the sites for optimization potential, and applying a hydraulic optimization code at each site. At two of the sites, pumping solutions were obtained that had the potential to yield millions of dollars in savings relative to current pumping rate costs. At the third site, no substantial improvement over the current design was identiÞed with optimization. The general conclusions from this study were that hydraulic optimization has the potential to improve operating pump-and-treat systems and that more complicated sites (i.e., large ground water plumes and many extraction and injections wells) are more likely to beneÞt from hydraulic optimization. It is important to note that there are many mathematical optimization algorithms available and that this study evaluated only one hydraulic optimization approach. Although this study conclusively determined that mathematical optimization can be beneÞcial at improving pump-and-treat system design, very few applications of this technology have been observed at USEPA or other state-led sites. This lack of application of optimization algorithms can be attributed to several factors, including lack of technology awareness, lack of well-trained optimization modelers in the consulting engineering community, and cost. Certainly the lack of awareness of


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optimization techniques in the remediation community is the primary factor contributing to low use. Although optimization algorithms are widespread in many industries, the remediation community has not adopted these techniques as standard practice for remediation. Furthermore, there are few trained users or real-world examples of their applications. For this reason, industry and the consulting engineering and government communities are not fully aware of the beneÞts of optimization algorithms and do not have personnel trained in these applications. Without the pull from problem holders requesting these techniques or the push from consulting engineers recommending their use, there is minimal demand for applying optimization algorithms in hazardous waste site cleanup. From a regulatory perspective, because few sites have requested the use of mathematical optimization algorithms, regulators have not been widely exposed to their applications. Another problem associated with the lack of use of mathematical optimization is cost or perceived cost. Many sites have developed simple ßow models based on limited site-characterization information. These models are generally used as one tool in the decision-making process for the site, but often are not adequate models for use with an optimization algorithm. In order to ensure a worthwhile optimization analysis, the base model(s) might need to be updated or completely redone, which is an additional (and sometimes unforeseen) cost. This additional step prior to an optimization analysis can discourage continuing with the optimization analysis. Although there are several reasons for the lack of use of optimization in the remediation Þeld, there remains a deÞnitive need to improve remediation systems using mathematical optimization or other approaches. The USEPA estimates that over 700 pump-and-treat systems are under construction or operating at Superfund sites across the country per the Records of Decisions (RODs) (USEPA, 1999). Many of these systems are anticipated to operate for years to decades at substantial cost to industry and the government. Furthermore, many of these systems were designed based on limited site information and limited knowledge of the capabilities of pumpand-treat systems. All stakeholders can beneÞt substantially by implementing mathematical optimization techniques in these cases. Other continuous improvement techniques such as periodically evaluating system performance, labor and monitoring practices, aboveground treatment components, and data management also should be considered. There is a tremendous need to ensure that pump-and-treat systems and other remedial systems are properly designed, maintained, and monitored; the remediation community should consider the use of optimization approaches to this end.

3.2.2 THE VIEW FROM THE U.S. DEPARTMENT OF ENERGY (DOE) The DOE is a major partner in managing the nation’s toxic substances in the subsurface. The DOE has administrative jurisdiction over several sites that contain remnants of radioactive and other toxic wastes generated during the Cold War’s nuclear race. Those sites include, but are not limited to, Hanford Reservation (Washington), Idaho National Environmental Engineering Laboratory (INEEL, Idaho), Oak Ridge National Laboratory (ORNL, Tennessee), Rocky Flats (Colorado), and the Savannah River site (SRS, South Carolina and Georgia). Taken in conjunction, these DOE sites represent perhaps the most signiÞcant repository of



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radioactive compounds in the subsurface environment in the U.S. The cost of containment, abatement, and remediation (i.e., environmental restoration) associated with DOE sites outranks that of any other agency, public or private, in the nation. The monitoring, characterization, and modeling of subsurface pollutants at DOE sites present enormous challenges due to the nature of the pollutants and the complexity and heterogeneity of the transport environment. On the other hand, the challenges present opportunities to use innovative optimization methods to help identify environmental restoration technologies at DOE sites. This section summarizes the results of a recent survey of subsurface characterization, environmental monitoring, and modeling technologies at DOE sites. Numerical modeling technologies included optimization models as well. Although the focus of this chapter is on optimization methods, information gathered on all subsurface characterization and environmental monitoring technologies are presented to demonstrate that the application of optimization methods at DOE sites cannot be examined in isolation from other technologies. In fact, optimization methods are only beginning to be tested at large-scale DOE sites. In this respect, their usefulness and effectiveness in large-scale and complex subsurface pollution situations at DOE sites is still experimental. It should be noted that the information and opinions presented in this section do not reßect the DOE’s ofÞcial position on subsurface contamination management at its sites. 3.2.2.1 Application of Site Characterization and Monitoring Technologies Table 3.1 shows a summary of technologies currently in use or those that have been used at INEEL, ORNL, and SRS in subsurface characterization, environmental monitoring, and modeling. This table also summarizes the survey responses obtained from the three sites. An “X” in Table 3.1 indicates that the technology is currently used. A blank space indicates neither current nor past use of a speciÞc technology. As seen in the table, a wide range of remote sensing, geophysical technologies, nuclear logging, drilling, ground water and vadose zone sampling, analytical technologies, and numerical/statistical technologies, as well as optimization methods, are currently in use or have been used at all three sites. INEEL and ORNL reported the use of 12 to 13 of the 30 listed analytical technologies. These two sites rely largely on off site analytical laboratories for sample analysis. Thus, many of the listed analytical technologies are not deployed as functional units within INEEL or ORNL. SRS, on the other hand, reported the application of 24 of the 30 listed analytical technologies. This reßects the fact that Westinghouse, the management and operation (M&O) contractor at SRS, maintains fully equipped and staffed analytical laboratories within the SRS boundaries, where many of the Þeld samples undergo analysis. Ecological monitoring, an aspect of site characterization, was overlooked initially and not mentioned in the survey. ORNL and SRS actively monitor vegetation, Þsh, mammals, and other biota, as well as surface water bodies. Living organisms are tested mostly for radionuclides and metals that accumulate in tissues (e.g., cesium and strontium isotopes, mercury). Ecological monitoring is performed by capturing


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TABLE 3.1 Summary of Site Characterization, Environmental Monitoring, and Modeling Technologies Used at Selected DOE Sites Technology

INEEL

ORNL

SRS

X

X

X

X X Past use X

X X X X X

X X X X X

Resistivity surveys Cross borehole tomography

X X

X

X X

Nuclear Logging Density logging Nuclear logging (natural gamma, neutron logging, gamma–gamma radiation)

X X

X X

X X

X

Remote Sensing Remote sensing/aerial photography Surface Geophysics Electrical resistivity Electromagnetic conductivity Seismic methods Ground-penetrating radar Magnetometer surveys Borehole Geophysics

Drilling Geoprobe®-type penetrometer Large SCAPS platform Standard methods (e.g., hollow-stem auger, rotary) Direct sonic drilling Rotosonic drilling Horizontal drilling

X Past use Past use Past use

X X X

X X X X X X

Ground Water Sampling Sampling (e.g., bladder, dedicated pumps) Sampling bailers (e.g., thief sampler)

X X

X X

X X

Soils Characterization Sampling technologies (e.g., discrete, continuous)

X

X

X

X

X

Vadose Zone Water and Gas Monitoring Lysimeter (e.g., suction, pressure/vacuum) X Electrical resistivity blocks Past use Soil–gas monitoring (e.g., probes, chambers) X Time-domain reßectometry X Electronic leak detection system Thermocouple psychrometers Tensiometers Past use Frequency-domain capacity probes Automatic VOC collection/gas chromatography

X



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TABLE 3.1 (CONTINUED) Summary of Site Characterization, Environmental Monitoring, and Modeling Technologies Used at Selected DOE Sites Technology

INEEL

ORNL

SRS

Analytical Technologies Gas chromatography X X High-performance liquid chromatography X Thin-layer chromatography Super-critical ßuid chromatography Gas chromatography/mass spectrometry X X X Mass spectrometry Past use X X Ion mobility spectrometry Atomic absorption spectrometry Past use X X Atomic emission spectrometry X X Laser-induced breakdown spectrometry Infrared spectrometry (e.g., fourier transform) Past use X X Near-IR reßectance/transmission spectrometry Raman spectroscopy UV-visible spectrometry (e.g., ßuorescence, synchronous X X luminescence) Fluorescence spectrometry X X X-ray ßuorescence Past use X Gamma spectrometry X X Radiation detectors (e.g., Geiger counter, solid/liquid X X X scintillator, semiconductor detector) Nuclear magnetic resonance X Photoionization detector X X X Electrical conductivity sensor X X Electrochemical techniques X Explosive sensor X Free-product sensor X Fiber-optics sensor (e.g., solid, porous) X Piezoelectric sensors X In situ chemical probes (e.g., chlorine, pH/ORP, TDS, DO) X X X Membrane-based testing devices (e.g., RDX, TNT, PCBs) X X Environmental test kits (color testing, titrimetric testing, X X X immunoassays) Detector tubes X X Numerical/Spatial/Statistical Models Geostatistical/statistical X X X Flow and transport and optimization models X X X Geographic/expert/decision support systems X X X Notes: X = current use of the technology at a DOE site; SCAPS = site characterization and analysis penetrometer system; VOC = volatile organic compound; IR = infrared spectroscopy; ORP = oxidation reduction potential; TDS = total dissolved solids; DO = dissolved oxygen; RDX = royal demolition explosive; TNT = trinitrotoluene; PCBs = polychlorinated biphenyls.


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and/or sampling specimens and testing parts or tissue in the laboratory according to standard protocols. The spreading of toxic wastes through living organisms highlights the complexity of pathways and exposure hazards associated with contaminants at DOE sites. The situation is worsened by the spatial scale over which contaminants and contaminant vectors (e.g., Þsh) operate. Therefore, to understand the seriousness of the environmental restoration challenge at DOE sites, one must realize that there are countless point and nonpoint sources of pollution within those sites and many agents of contamination spreading through soil, water, air, and living organisms. 3.2.2.2 Numerical and Optimization Models Environmental restoration has progressed from screening-level and deÞnitive-level characterization to risk analysis, containment, abatement, and remediation. As a result, models have become ßexible and useful tools for creating and analyzing a variety of scenarios in a cost-effective manner. For example, a mass transport numerical model can simulate the fate and transport of benzene in ground water that is being pumped, treated, and recharged according to a speciÞc pump-and-treat scheme. Or a vadose zone model such as SESOIL can be implemented to assess the effect of soil capping on long-term metal vertical migration in the vadose zone. Numerical, spatial, and statistical models are accepted and used for a wide range of applications at all three sites (Table 3.1). Modelers at DOE sites typically are part of the risk analysis groups at these sites. The risk analysis groups determine the likelihood of environmental harm caused by pollutants within DOE sites. By and large, they house most of the personnel qualiÞed to work with simulation and optimization models. The state-of-the-art of optimization modeling at DOE sites consists of heuristic search techniques based on ground water ßow and transport models. In this approach, the analyst implements ground water and transport models for a selected range of stress or remediation control variables (e.g., pumping rates, soil venting aeration, permeable treatment bed thickness). The measure of effectiveness of a particular control variable is then assessed. For example, the amount of a polar hydrocarbon retained in a permeable treatment bed is determined as a function of the bed’s thickness. Or the concentration of a chlorinated hydrocarbon remaining in solution is assessed as a function of the pumping rate in a pumpand-treat system. The analyst applies his experience and professional judgment in constraining the feasible range of the decision variables, while noting other important factors such as the cost of containment, abatement and remediation, the time required to achieve desired targets, and other regulatory constraints. Expert systems, also called decision support systems (DSS), have been developed to assist risk analysts in the search for the best environmental restoration alternatives in the heuristic approach (Loaiciga and Leipnik, 2000). The Þnal result of the heuristic search is a series of values of the measure of pollution-control effectiveness and related parameters needed to achieve it. An assessment of the uncertainty associated with each of the entertained pollution-control options can be issued also. The Þnal pollution-control decision, which can be a mixture of



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alternative restoration technologies, is arrived at through a consensus-building approach that involves contractors, DOE personnel, and regulators (state and federal). The implementation of restoration strategies relies heavily on real-time monitoring to make adjustments as needed while the restoration work progresses. In this sense, the restoration work relies on feedback and corrections to achieve pollution-control targets. The implementation of optimization modeling at DOE sites is a distant variation of the classical open-loop optimization prevalent in research literature. Classical open-loop optimization refers to optimizing a system that has no feedback control and primarily employs linear, nonlinear, and dynamic programming algorithms. Contaminant processes of varying degrees of complexity are imbedded in the mathematical formulation of the search algorithm, which yields a set of decision variables that maximizes or minimizes a prespeciÞed restoration beneÞt/cost (objective) function while satisfying a set of constraints imposed by the control, abatement, and remediation technologies; by resource and economic limitations; and by the intervening biological, chemical, and physical processes (Willis and Yeh, 1987). Because restoration strategies derived by the classical optimization approach have no feedback mechanism, they are best interpreted as plausible courses of action that need frequent updating to achieve desired goals. The greatest limitation of classical optimization is its ability to deal with the subtleties and complexities of real-world restoration problems at DOE sites. Another obstacle to its adoption by DOE is the high degree of specialization required by the users. These obstacles render classical optimization out of reach for DOE users and others. 3.2.2.3 Innovative Technologies and the Regulatory Process One of the key issues raised at all surveyed DOE sites is the role that state and federal regulations play in the application of new environmental restoration technologies. According to input received during interviews, state and federal regulators are generally risk-averse when approving new characterization, monitoring, and modeling technologies. Technical procedures for sample collection and analysis approved at each site rely on traditional and presumably well-tested technologies. Thus, for example, a split-spoon sampler is preferred over a Geoprobe® soil corer at INEEL because the latter has not been proven to regulators to yield samples of at least equal representativeness to those obtained by the former. This preference exists in spite of the fact that the Geoprobe soil corer yields shallow and deep soil cores that preserve the integrity of volatile organic compounds (VOCs) in the soil matrix — a most difÞcult task with split-spoon samplers. On the other hand, some examples justify the risk aversion of regulators toward new technologies. One is the case of a polychlorinated biphenyl (PCB) in situ immunoassay test kit that was used at SRS in an attempt to separate PCB debris at an old, weathered landÞll. About 50% of the in situ results were false positives. Debris separation ultimately relied on standard sample collection and laboratory analysis. DOE sites have so-called technology demonstration programs that seem ideal for testing new environmental restoration technologies. Such a program could be


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a natural framework under which to test a novel apparatus, technique, or model, and, if successful, approve it for Þeld deployment or application. The reality is somewhat different. Contractors work under strict federal facility compliance agreements (FFCAs) that stipulate the environmental restoration milestones and deadlines to be met under agreed-upon budgets. As a result, the contractors have limited funding, time, and resources to develop, test, and permit new equipment and simulation models. Alternatively, the new technology research and development could be undertaken by universities or other research centers and then transferred to DOE if proven successful in test trials. The latter avenue seems a necessity for optimization modeling, which requires signiÞcant mathematical and computational skills rarely found outside university laboratories. Yet, a considerable gap remains between the capabilities currently offered by optimization modeling and the realities and complexities of DOE environmental restoration. It is in this respect that pilot test projects are most needed to determine the potential contribution of optimization techniques to environmental restoration at DOE sites. 3.2.2.4 Technology Needs Finally, site-characterization technology users expressed consensus on the need for a few technologies that, if available, would greatly expedite environmental restoration efforts. First is a Þeld-deployable probe for radionuclide speciation with adequate quantitative accuracy. Such a device would bypass arduous and hazardous sampling, handling, testing, and disposal of radioactive materials. The other technology in the users’ wish lists is an accurate in situ analyzer for VOCs in soils and ground water. VOC loss during sampling is a major problem that biases analytical results, and VOCs represent the second most threatening contaminant after radionuclides at INEEL, ORNL, and SRS. Low priority was given to optimization model application, probably due to limited experience with applications at DOE sites and the lack of familiarity of DOE managers, regulators, and contractors with optimization models in general.

3.2.3 THE VIEW FROM THE U.S. DEPARTMENT OF DEFENSE (DOD) Congress established the Defense Environmental Restoration Program (DERP) in 1984 to remediate contamination at DoD sites. Since then, the DoD has spent almost $20 billion on the DERP through two accounts. About $5 billion has been spent through the Base Realignment and Closure Act (BRAC) account to remediate bases being closed and transferred to civilian use. The rest of the funds have been spent through the DERP account at bases remaining active. Funding limitations make it necessary to prioritize remedial activities and approaches. After all, some contamination poses less risk than others. Some remediation approaches cost less than others, but the cheaper alternative can take longer to achieve about the same result as the more expensive alternative. Selecting a remediation approach for a contaminated site can involve economic analysis and compromise between DoD and environmental regulatory agencies. The DoD has attempted to improve the efÞcacy and reduce the cost of remediation. Included actions have involved innovative technology demonstration projects,



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technology transfer, system operation evaluations, research, and development. In DoD parlance, optimization refers to any effort to improve a process. Optimization can involve reducing costs of construction, labor, energy, treatment, monitoring, analysis, reporting, documentation, data retrieval, or data archiving without endangering human health and safety or the environment. This section mentions some DoD optimization efforts in monitoring, analysis, and remediation. Most do not include formal mathematical optimization. All the major defense services and agencies support some efforts in optimization. 3.2.3.1 Optimization Efforts DoD agencies share information and methods with each other and other organizations. The Technology Transfer Division of the Air Force Center for Environmental Excellence (AFCEE/ERT) organizes an Annual Technology Transfer Conference highlighting new developments and lessons learned by DoD services and agencies, the U.S. Geological Survey (USGS), and the USEPA. Each military service has at least one center developing improvements in remediation technology, often in collaboration with other organizations. For example, the Naval Facilities Engineering Service Center (NAVFAC), in cooperation with the other services and the USEPA, currently leads a project demonstrating pump-andtreat optimization. The Army has several centers of expertise that apply and publish remediation guidance. This section does not mention all DoD centers or initiatives; however, it does discuss example demonstration and technology transfer initiatives promoting optimized methods. 3.2.3.1.1 AFCEE Pump-and-Treat Optimization AFCEE/ERC conceived and awarded a project in 1993 to demonstrate applying formal optimization to pump-and-treat or pump, treat, and reinject operations. (Hereinafter, pump-and-treat is used to refer to both types of systems.) Resulting efforts demonstrated that signiÞcant cost reductions could result from applying simulation optimization modeling to pump-and-treat system design and pumping-strategy development. Additional simulation optimization applications at Air Force and DoD sites followed the ERC project. 3.2.3.1.2 DoD Pump-and-Treat Operation Evaluation By 1996, DoD was operating 75 pump-and-treat systems as the primary remedy for sites having chlorinated solvent–contaminated ground water. Because of the large operation and maintenance (O&M) costs, the DoD OfÞce of the Inspector General decided to evaluate the cost and effectiveness of these systems. Some of the Þndings are as follows: • • • •

Annual pump-and-treat system costs reached $40 million by 1996. Many of the pump-and-treat systems were designed before more suitable technologies were available. Sometimes the achieved remediation using pump-and-treat was slow. Some pump-and-treat systems were not going to achieve required cleanup goals within a reasonable period.


118


• •

Many of the pump-and-treat systems had indeÞnite shutoff dates. Continuing the operations and monitoring of pump-and-treat systems would consume an increasing portion of the DERA (DoD, 1998).

The DoD recommended that military services and agencies evaluate the existing systems to determine whether replacing pump-and-treat with other technologies might improve performance or reduce cost. The Inspector General recommended that the military cooperate with the public, scientists, and environmental regulators to determine more effective alternate remediation methods. Respondents to the DoD evaluation in 1998 indicated that monitored natural attenuation was the preferred remediation approach and that this approach was being selected for new sites, if possible. Partially as a result of the DoD report, the military increased efforts to improve pump-and-treat management and use better and less costly approaches when appropriate. 3.2.3.1.3 Air Force/Defense Logistics Agency Remediation Process Optimization (RPO) RPO is a program-management tool developed by the AFCEE/ERT to provide a systematic iterative approach to evaluate all phases of remedial actions and update and optimize the effectiveness and efÞciency of efforts to achieve cleanup goals. RPO provides a mechanism to feed information back into the decision process so that goals can be updated (if necessary) and met. The objective of RPO is to utilize best practice technical and management approaches to protect human health and the environment (AFCEE, 1999). The Air Force Base Conversion Agency (AFBCA) is applying the RPO process through AFCEE/ERT and is responsible for remediation programs of bases being closed and converted to civilian use. Contaminated property cannot be transferred to civilian ownership until a remediation method approved by regulators is in place. Because Congress wants property ownership to proceed as quickly as possible, AFBCA remediation projects have a strong temporal component. The AFBCA is eager to get approved remedies in place as quickly as possible, within funding limits, so it can transfer property ownership. With this in mind, the AFBCA has initiated a program to periodically (usually every 5 years) reevaluate pump-andtreat operations and the need for existing remediation systems. One of the Þrst RPO reports concerned Operable Unit 1 (OU1) of AFBCA’s George Air Force Base. OU1 contains a pump-and-treat system to treat a trichloroethene (TCE) plume that originates in an upper unconÞned stratum and reaches a lower stratum. Per the ROD, the pump-and-treat system must contain the plume of dissolved TCE and reduce concentrations to below 5 ppb. The pump-and-treat system began operation in 1991 and was augmented in 1996. Treated water is injected into the upper stratum upgradient of the plume. Regulators fear that the injection increases contamination migration to the lower stratum. The Air Force and environmental regulators have not yet reached agreement on the site conceptual model and on how the contamination reaches the lower stratum. The RPO report states that the pump-and-treat system has been inefÞcient in reducing mass, and it is questionable whether this method will achieve cleanup goals within a reasonable period. RPO recommendations included the following:



• • • • • •

119

Ceasing pumping at 11 of the 18 extraction wells, reducing ßow by up to 50% Evaluating water treatment and disposal alternatives Reducing sampling frequency to annual from semiannual Reducing the number of sampled monitoring wells from 47 to 34 Pursuing alternative cleanup goals Fully evaluating other potential remediation measures (e.g., monitored natural attenuation, phytoremediation)

The RPO team considers that implementing short-term recommendations could reduce annual costs by more than $170,000, and long-term recommendations could save $5 million during the remaining 33-year project life. 3.2.3.2 Model Development Efforts In response to the need for improved integrated software to aid ground water cleanup, the DoD, in partnership with the DOE, USEPA, Cray Research, and 20 academic partners, has developed the DoD Ground Water Modeling System (GMS) (http://chl.wes.army.mil/software/gms/). The GMS is comprehensive, integrated software for simulating subsurface ßow and contaminant fate and transport. It includes many popular or public domain simulation models. GMS simpliÞes ground water ßow and transport modeling by making it easy to use an assemblage of computational tools. GMS provides tools for simulation, site characterization, model conceptualization, mesh and grid generation, geostatistical evaluation, visualization, and simulation. 3.2.3.3 Monitoring Efforts 3.2.3.3.1 Passive Diffusion Bag (PDB) Samplers Using PDB samplers (developed by Don Vroblesky of the USGS) can signiÞcantly reduce the cost of ground water sampling. PDB samplers can obtain representative VOC ground water concentrations from monitoring wells. A typical PDB sampler consists of a low-density polyethylene tube that is closed at both ends and lies ßat when empty. The tube is Þlled with deionized water and is positioned at the target location in the aquifer by attachment to a weighted line. The PDB samplers equilibrate within approximately 48 h for TCE and tetrachloroethene (PCE). Vinyl chloride and some chloroethanes can require between 96 and 168 h to equilibrate. The samplers remain in a well at least 2 weeks to allow the well water to restabilize after the disruption and absorption caused by the sampler. Recovery consists of removing the samplers from the well and immediately transferring the enclosed water to 40ml sampling vials for analysis. The samplers can help delineate contaminant stratiÞcation in wells having insigniÞcant vertical ßow, and multiple PDB samplers can be used to help identify chemically stratiÞed wells or wells with ßow pattern changes through the screen as a result of ground water pumping or seasonal ßuctuations. However, PDB samplers are ineffective for inorganic ions or for highly soluble organics such as methyl tert butyl ether (MTBE).


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Three years of intensive testing at Air Force and Navy sites indicate that sampling with PDBs produces data as accurate as those obtained through other presently approved sampling techniques. Using PDB samplers can result in cost savings of 50 to 70%. The AFCEE/ERT and USGS are evaluating a new passive sampler exclusively for inorganic and natural attenuation parameters and analytes. An interagency workgroup, including the Air Force, Army, Navy, Defense Logistics Agency (DLA), USEPA, Interstate Technology and Regulatory Cooperation (ITRC), and USGS, published a user’s guide for PDB samplers (USGS, 2001). The AFCEE/ERT sponsored development of the guidance and is implementing its use at 20 DoD installations. 3.2.3.3.2 Pneumatic Well Logging (PneuLog®) of Soil Vapor Extraction (SVE) Wells PneuLog (a product of Praxis Environmental Technologies) is an in-well instrument used to quickly deÞne the vertical distribution of contamination and soil permeability in SVE wells. PneuLog provides much greater vertical proÞling data than any other available technique optimizing or supporting closure of SVE systems. Under active vapor extraction, the PneuLog device is lowered and raised along a well screen using an automated cable reel while simultaneously recording the ßow rate and total vapor concentration. Flow can be attributed to speciÞc soil intervals from the measured changes in cumulative ßow. Additionally, this change in ßow over a depth interval effectively deÞnes its permeability. The contaminant vapor concentration is measured continuously through a Teßon® sampling tube located just above the ßow sensor and conveyed to the surface where it is analyzed using a photoionization detector (PID). A mass balance is used to determine a proÞle of the soil-gas contamination from the changes in cumulative ßow and total concentration measured in the well. In addition, vapor samples can be collected at discrete depths for compound-speciÞc analyses. Site conceptual models are improved by deÞning preferential ßow paths that bypass contaminated intervals and by identifying mass transfer limited soils that extend cleanup times. The data allow SVE to focus on the most contaminated intervals and avoid stagnation zones. A more detailed soil-gas concentration proÞle allows more accurate contaminant transport modeling to assess the risk from residual contamination. More accurate risk evaluation allows remedial managers to know when the vadose zone is sufÞciently clean to terminate SVE. This technology is applicable only to the screen intervals of active SVE wells. PneuLog has been utilized at numerous BRAC and active Air Force bases to improve conceptual site models, enhance SVE operations, and support closure of SVE systems. The AFCEE funded use of the technology in the initial characterization of the vadose zone at three sites and supported efforts to optimize SVE operations at seven sites using PneuLog. The optimization effort at the seven sites has saved the Air Force an estimated $300,000 to $500,000 to date. Additional cost savings will be achieved over time as these sites achieve closure based on the detailed data set provided by PneuLog. Further details of these site-speciÞc efforts are provided in the Þnal report submitted by Praxis to the AFCEE (Praxis Environmental Technologies, 2000).



3.2.4 THE VIEW

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FROM INDUSTRY

Industry is primarily interested in reducing long-term cost liability. Therefore, industry generally is willing to spend money up front for an optimization analysis (plus any subsequent costs associated with system modiÞcations) if it is considered likely that the total life-cycle cost will be reduced as a result. Making this assessment requires a site-speciÞc cost-beneÞt analysis prior to a full optimization evaluation that accounts for the expected cost of the optimization analysis, expected costs of system modiÞcations, and expected savings. Industry generally performs cost evaluations in terms of net present value (NPV), using a discount rate that adjusts future expenditures to their present value. (Money not spent today can generally be invested by industry at a rate that exceeds inßation; therefore, current dollars are worth more than future dollars.) Consequently, optimization analyses performed for industry should be performed with respect to NPV. Industry must gain the approval of regulatory agencies to implement or modify remedial strategies. Strategies that are derived by using mathematical optimization techniques linked to ground water simulation models are no different in this regard than strategies derived solely on the basis of ground water modeling, because the mathematical optimization algorithms simply perform a series of simulations with the ground water model in an efÞcient order. Therefore, regulatory issues should focus on the validity of the ground water model predictions. Once that is established (i.e., the simulation model is accepted as a valid design tool by the regulators), the linkage of mathematical optimization algorithms with the simulation model should not create additional regulatory concerns. Industry is keenly aware that new approaches to ground water remediation continue to evolve. In some cases, the evolution is because of new technology (e.g., in situ bioremediation, chemical oxidation, permeable reactive barriers), and in other cases, regulatory reform (e.g., monitored natural attenuation). The determination of whether to apply simulation optimization techniques must consider not only the potential beneÞts with respect to the current remediation strategy (e.g., pump and treat), but also whether resources are better spent pursuing alternative remedial approaches that might replace and/or augment the current remediation strategy. At many sites, long-term ground water monitoring costs can in fact be the greatest lifecycle cost component. At these sites, the optimization of ground water monitoring can represent the greatest opportunity for future cost savings.

3.3 STATE OF KNOWLEDGE AND PRACTICE The contamination of ground water supplies poses widespread and signiÞcant environmental problems. In the past few decades, different remediation strategies have been applied and a great deal of research is in progress. The most common ground water remediation techniques are as follows: • •

Pump and treat Bioremediation


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• • • •

Air stripping Vapor extraction Permeable walls (e.g., iron walls, biological barriers, chemical barriers) Phytoremediation

In recent years, optimization management models have been developed to design ground water remediation strategies. These models combine mathematical optimization techniques with ground water ßow and mass transport simulators to determine an optimal remedial design. Regarding the aforementioned remediation techniques, optimization management models have been presented to pump-and-treat (early 1970s) (Gorelick, 1983) and bioremediation (late 1990s). Another important area of research and development over the past few years is long-term ground water monitoring design optimization. The long-term ground water monitoring issue is signiÞcant because of the duration of monitoring programs, the need to verify remedies, and the potential for remedy modiÞcations if either the remedy or monitoring plan does not perform adequately. In addition, long-term ground water monitoring has received substantially less attention than remediation process design optimization, so a greater potential exists for signiÞcant impacts. Time-robust monitoring networks (i.e., monitoring networks that perform well for extended periods of time) have not been investigated extensively to date and are recommended as a research focus area.

3.3.1 THE SIMULATION OPTIMIZATION APPROACH The development of ground water simulation models in the early 1970s provided planners with quantitative techniques for analyzing alternative management strategies. In recent years, simulation models have been combined with optimization models to identify the best management alternatives while considering management objectives and constraints. Typical ground water remediation problems involve the design of the well Þeld, that is, the determination of the number, location, and pumping/recharge schedule of all pumping/recharge wells. Gorelick (1983), Yeh (1992), Ahlfeld and Heidari (1994), Wagner (1995), and Ahlfeld and Mulligan (2000) have provided extensive reviews on coupling simulation models with optimization models. The mathematical formulation of a ground water management problem consists of an objective function that is related either to total remediation cost or to the total amount of pumped water, subject to a set of constraints that are based on hydraulic heads, ßows, or concentrations at selected locations. Depending on the kind of constraints, whether hydraulic heads or concentrations, the following two basic approaches appear to be employed: ground water management models involving hydraulic constraints and those involving concentration constraints. 3.3.1.1 Gradient Control Remediation Technology The primary goal of many ground water remediation systems is to contain impacted ground water by preventing ground water ßow beyond a speciÞed boundary (i.e., horizontally or vertically). This containment can be accomplished by controlling hydraulic gradients. Most pump-and-treat systems have been designed using



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numerical simulation models for ground water ßow, such as MODFLOW (Harbaugh and McDonald, 1996a, 1996b). Traditionally, the hydraulic simulation model is run repeatedly to simulate different pumping scenarios. Each scenario is typically evaluated with respect to the number of wells required and the total pumping rate necessary to achieve the required hydraulic containment while maintaining compliance with other design constraints (e.g., limits on water levels, drawdowns). These manually iterative simulations rely heavily on the experience and insight of the modeler, who must personally determine each successive trial. A limitation of this manually iterative approach is that there are an inÞnite number of well location and well rate combinations to consider, and only a small number of numerical simulations are practical. The linkage of mathematical optimization techniques with the ground water ßow simulator is an attractive alternative for gradient control problems. The most popular technique is the response matrix technique, which is described in detail in Gorelick et al. (1993) and Ahlfeld and Mulligan (2000). This approach capitalizes on the linear relationship between pumping rate and drawdown that applies to many ground water systems (i.e., the law of linear superposition) and easily extends to a linear relationship between pumping rates and hydraulic gradients. This linear relationship allows an optimization problem to be formulated as a linear (or mixedinteger linear) program, where the decision variables are the pumping locations and pumping rates. The optimization seeks to minimize an objective function (e.g., minimize total pumping rate) subject to a set of constraints that all must be satisÞed according the simulation model results, including limits on gradients that establish hydraulic control. Hydraulic optimization for gradient control problems is implemented easily because of the following: (1) most sites with ground water contamination have a site-speciÞc ground water ßow model, (2) the optimization approach is straightforward and easily understood, and (3) tools for performing the optimization are available as off-the-shelf technology. Applications of hydraulic optimization have appeared in the literature since the 1970s, and several codes for performing these evaluations are freely available such as MODMAN (Greenwald, 1998) and MODOFC (Ahlfeld and Rießer, 1999). A detailed discussion of formulation options associated with gradient control applications and demonstrations of these techniques for three sites is provided in Greenwald (1999). Gradient control techniques are limited by the predictive ability of the underlying simulation model, which is affected by uncertainty in parameter values, the conceptual hydrogeological model of the site, the experience of the modeler, input errors, and many other factors. Additional limitations of gradient control problems include the following: (1) contaminant concentrations cannot be included in the mathematical formulation; (2) cleanup time cannot be rigorously included in the mathematical formulation; and (3) for thin unconÞned aquifers (and several other circumstances), linear superposition (which allows the use of linear programming techniques) can be violated. For sites where cleanup is the main objective and predictions of contaminant concentrations or cleanup time are central to evaluating the objective function and key constraints, the limitations of hydraulic optimization can be prohibitive. Transport modeling and transport optimization can be applied


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in such cases. However, developing a transport simulation model and performing a transport-based optimization analysis can require signiÞcant effort and cost, and transport model predictions are subject to additional uncertainties (relative to ßow model predictions). 3.3.1.2 Concentration Constraints Remediation Technology As mentioned previously, ground water management problems combine a ground water numerical simulator and an optimization model. In the past few decades, several ground water numerical simulators have been presented and employed (e.g., MODFLOW, MT3DMS, SUTRA, FEMWATER [3-D], PTC) to represent ground water ßow and contaminant transport. The following optimization models can be categorized based on the theory that is used: • • • • • •

Nonlinear models (using nonlinear programming) Dynamic models (using dynamic programming) Genetic algorithm models Simulated annealing models ArtiÞcial neural network (ANN) models Cutting plane techniques models

The main characteristic of the ground water contaminant management problem is that the problem is nonconvex due to nonconvex behavior of the mass transport equation (constraints) and/or the objective function. Therefore, the majority of the above models have difÞculty determining a globally optimal solution. The main characteristics of and a historical review of each category of the above models are as follows: •

•

Nonlinear Models The majority of these models rely on gradient-based techniques. They require calculation of the derivative matrix for concentrations with respect to the decision variables, and a globally optimal solution is not guaranteed. In the past, a combination of ground water simulation with nonlinear programming techniques to solve ground water management problems has been presented by Gorelick et al. (1984), Willis and Yeh (1987), Ahlfeld et al. (1988), Charbi and Peralta (1994), McKinney and Lin (1995), Peralta et al. (1995), and Emech and Yeh (1998). Dynamic Models These models are based on dynamic programming theory where nonlinear and stochastic features of the ground water system can be translated into the formulation. SigniÞcant cost savings have been reported using these models (between 20 and 70%). In some cases, dynamic well strategies (i.e., the well locations are not Þxed between different management periods) have been incorporated. These models are mathematically complicated, numerical difÞculties have been reported, and a globally optimal solution is not guaranteed. Work on dynamic models



•

•

•

•

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has been presented by Jones et al. (1987), Chang et al. (1992), Culver and Shoemaker (1992, 1993, 1997), and Huang and Mayer (1997). In addition, some individuals have opted not to use the dynamic programming theory directly but rather the multiperiod approach. This work has been presented by Ahlfeld (1990), Rizzo and Dougherty (1996), and Karatzas et al. (1998). Genetic Algorithm Models One of the main characteristics of these models is that derivatives are not required. They are computationally intensive, but parallel computing can be applied. A globally optimal solution is not guaranteed. Their application to ground water problems began appearing in the early 1990s, and representative works have been presented by McKinney and Lin (1994), Rogers et al. (1995), Wang and Zheng (1997, 1998), and Aly and Peralta (1999a). Simulated Annealing Models These models are devised to solve large combinatorial optimization problems and do not require derivative computation. They have shown ßexibility in the selection of cost functions (convex or not convex) and, theoretically, they can Þnd global optima (but not in practice). They are computationally intensive, but parallel computing can be applied. Some studies suggest that they are competitive with other optimization techniques. Dougherty and Marryott (1991), Kuo et al. (1992), Marryott et al. (1993), Rizzo and Dougherty (1996), and Wang and Zheng (1998) have presented work on simulated annealing models. ANN Models These models are computationally intensive due to the neural network training. Following the training, they are consistent with most of the nonlinear models, with fewer calls to the simulator. Parallel computing can be also applied. Work on ANN models for ground water management has been presented by Rogers and Dowla (1994), Rogers et al. (1995), Rizzo and Dougherty (1996), and Aly and Peralta (1999b). Cutting Plane Technique Models In this category, the models are characterized as global optimization techniques due to the formulation of the objective function, which is required to be concave. When the objective function is concave, these optimization methods can be characterized as global optimization techniques. Only one function derivative (of the most violated constraint) is required at each iteration until the optimal solution is obtained. These models are based on cutting plane theory where the feasible region is enclosed into a polytope and where the most extreme point of the feasible region is determined by eliminating parts of the infeasible region (using cutting hyperplanes) (Karatzas and Pinder, 1993, 1996).

In the past few years, work has been presented where bioremediation simulators have been combined with optimization techniques such as Minsker and Shoemaker


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(1998), Yoon and Shoemaker (1999), and Smalley et al. (2000). In addition, models of multiple contaminants have been presented that are related to pulsed or continuous pumping for removing contaminants subject to rate-limited mass (Haggerty and Gorelick, 1994).

3.3.2 STOCHASTIC OPTIMIZATION DESIGN FAILURE

TO

ACCOMMODATE POTENTIAL

The uncertainties underlying ground water ßow and transport models (e.g., associated with characterizing subsurface heterogeneities, contaminant sources and plumes, reaction pathways and rates) have a profound effect on the reliability with which cleanup system performance can be predicted. Consequently, simulation model uncertainty is viewed as the most important source of errors in the simulation optimization design model. Research to date has focused on incorporating simulation model uncertainty into the optimization framework to assess the tradeoffs between reliability (or probability of failure) and cost effectiveness. SigniÞcant work has been presented in the past decade by Wagner and Gorelick (1987), Andricevic and Kitanidis (1990), Lee and Kitanidis (1991), Wagner et al. (1992), Whiffen and Shoemaker (1993), Morgan et al. (1993), Reichard (1995), Aly and Peralta (1999b), and Freeze and Gorelick (1999). Gorelick (1990, 1997), Wagner (1995), and Freeze and Gorelick (1999) provide a detailed review of stochastic ground water optimization models. The goal of ground water remedial design is to develop a remediation strategy that will lead ultimately to compliance with the ground water quality performance standards set forth by the controlling regulatory agencies. Therefore, failure of a remediation strategy is deÞned as any incident that violates the established performance criteria. As discussed previously, performance standards typically serve as constraints in the simulation optimization model. Therefore, the deÞnition of failure can be further extended to be the violation of performance constraints in the optimization model. Under ideal conditions, the design optimization model is a perfect representation of the remediation problem, and there is no possibility of failure. The objectives and constraints would reßect perfectly the goals and performance standards set forth by the regulatory agencies: the ground water simulation model(s) would reßect perfectly the geologic, hydrologic, and chemical conditions of the contamination site and would perfectly predict ßow and transport under alternative remediation strategies. Further, the optimization model would identify, with complete certainty, the design that best meets the problem’s objectives and constraints. Obviously, this does not occur in real-world applications. As a result, simulation optimization models will always be speciÞed incorrectly to some degree. The goal then is to develop remediation design optimization models that provide assurance, albeit risk qualiÞed, that remediation performance criteria will be met. Traditionally, engineering design has relied on the use of standardized design codes that deÞne deterministic safety factors to account for uncertainty in the design process. For ground water remediation, however, each problem is distinctly unique



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with little or no precedent on which standardized safety factors can be established. Consequently, the trend in ground water remediation design has moved away from the traditional “one size Þts all” safety factor approach and toward the use of more sophisticated stochastic analyses that account for site-speciÞc uncertainties. As discussed in later sections, many of these approaches do not completely abandon the safety factor approach; instead, they reÞne that approach to develop safety factors on a site-by-site basis. It is important to note that with uncertainty included in the optimization model, there is no longer a single best solution as in the deterministic case. Rather, there is a spectrum of stochastic optima, with each optimal solution associated with a speciÞed level of reliability (the complement of the probability of failure). The majority of research to date has focused on the following two stochastic optimization methods: chance-constrained optimization and multiple realization (sometimes referred to as stacking) approaches. Both methods assume that some simulation model parameters are unknown, and both approaches begin by estimating the unknown parameters and quantifying their uncertainties. They then introduce the effects of the model parameter and prediction uncertainties into the optimization model. It is at this point that the chance-constrained and multiple realization approaches diverge based largely on the manner in which the two approaches categorize simulation model uncertainty. The chance-constrained approach is linked with a conceptualization in which the simulation model parameters are viewed as uniform over large zones. In this case, model parameter and prediction uncertainties are included in the optimization model via Þrst-order uncertainty analysis. The multiple realization approach, on the other hand, is not limited to the small variance assumption with respect to the aquifer properties, and parameter uncertainty is included in the optimization model using Monte Carlo methods that are not constrained by the limitations of Þrst-order uncertainty analysis. 3.3.2.1 Chance-Constrained Ground Water Management Model The typical deterministic remediation design optimization model has a constraint set limiting the maximum permissible concentrations at selected compliance points: Ci < Ci*

(3.1)

where Ci is the simulated concentration at a space–time location i and Ci* is the maximum permissible concentration. When predicting system performance under simulation model uncertainty, there is a probability that a constraint will not be met (i.e., a probability of system failure), and it is necessary to replace the deterministic constraint with a stochastic one: Prob [Ci < C*] > R

(3.2)

where R is the accepted reliability level (or one minus the accepted probability of failure).


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As deÞned above, the probabilistic constraint cannot be solved in the optimization model; however, if it is assumed that the simulated concentrations as a function of model parameter uncertainty are (or are well approximated as) normally distributed, it can be reformulated as a deterministic equivalent known as a chance constraint (e.g., Tung, 1986; Wagner and Gorelick, 1987; and Freeze and Gorelick, 1999): E[Ci] + FN–1(R) S[Ci] < C*

(3.3)

where E[Ci] and S[Ci] are the expected value and standard deviation of Ci, respectively, and FN–1(R) is the value of the standard-normal cumulative distribution corresponding to reliability level R. An inspection of Equation 3.3 shows that the chance constraint has the following two components: an expected value component (Þrst term on left side) and a stochastic component (second term on left side). When the reliability is 0.5, FN–1(R) is zero, the stochastic component drops out, and the chance constraint reduces to the deterministic constraint (Equation 3.1). Thus, the deterministic optimization model that ignores uncertainty corresponds to the case where there is a 50% chance of failure. The stochastic component in Equation 3.3 is essentially a safety factor that controls the amount of overdesign needed to achieve the desired level of performance reliability. For a given level of model uncertainty, the stochastic component of Equation 3.3 increases with increasing reliability requirement. The effect of this can be best understood by moving the stochastic component to the right-hand side of Equation 3.3, which is equivalent to imposing a safety factor that redeÞnes (i.e., reduces) the maximum permissible concentration. However, unlike standardized safety factors used in many engineering disciplines, the magnitude of the stochastic component is a unique function of the simulation model uncertainty and the reliability level, and it is unknown prior to solving the chance-constrained optimization model (Tiedeman and Gorelick, 1993). One advantage of the chance-constrained approach is that the reliability level is explicitly considered in the optimization model, allowing the development of a remediation strategy designed to meet the decision maker’s reliability preference. It also allows the development of reliability–cost tradeoff curves (Wagner and Gorelick, 1987; Tiedeman and Gorelick, 1993; Freeze and Gorelick, 1999). Although it is natural to think that a decision maker will take a risk-averse stance and choose a design with a high degree of reliability, it is not realistic to assume that a reliable design will be implemented regardless of cost. The reliability–cost tradeoff curve allows the decision maker to evaluate the marginal cost associated with an increase or decrease in reliability and select a design that provides an acceptable balance between reliability and cost. Based on Þeld data to date, there have been two applications of the chanceconstrained ground water management model. One involves the application of a nonlinear chance-constrained optimization model to identify optimal pumping schemes for plume capture at a landÞll site near Ottawa, Canada (Gailey and Gorelick, 1993). The other applies nonlinear chance-constrained optimization to identify minimum pumping strategies for plume containment at a site in



129

southwest Michigan (Tiedeman and Gorelick, 1993). Both examples demonstrate the need for overdesign (pumping above that required in the deterministic case) to account for the performance uncertainties that arise from model parameter uncertainties. For the plume capture problem presented by Gailey and Gorelick, an overdesign of 27% was needed to achieve a reliability level of 0.90. For the plume containment problem presented by Tiedeman and Gorelick, a 40% overdesign was needed for a reliability level of 0.90. Tiedeman and Gorelick also provide an interesting analysis of the stochastic component of the chance constraints. Their analysis showed that not only could the safety factors not be deÞned a priori, they could vary signiÞcantly from one constraint to another within a given problem. 3.3.2.2 Multiple Realization Ground Water Management Model The chance-constrained optimization method is based on Þrst-order uncertainty analysis, which is known to have accuracy limitations that are frequently violated in real-world applications. The question then is: How is uncertainty originating from highly uncertain and heterogeneous subsurface properties incorporated into the ground water management model? The answer is to use a stacking approach in which multiple realizations of the uncertain parameters are included in the optimization model. Consider again the deterministic concentration constraint given in Equation 3.1. In the multiple realization model, this single constraint is replaced with the following series of constraints: Ci1 < Ci* for parameter realization 1

(3.4a)

Ci2 < Ci* for parameter realization 2

(3.4b)

Cin < Ci* for parameter realization n

(3.4c)

where Ci1, Ci2, and Cin are the simulated concentrations at space-time location (i) for parameter realizations 1, 2, and n. It is important to understand the structure of the multiple realization management model in order to understand how it identiÞes failure-averse designs. The multiple realization model solves the optimization problem simultaneously for all n parameter realizations. From the standpoint of failure, this means that the model provides a robust solution that is feasible (i.e., successful) for all parameter realizations included in the model. This simultaneous solution approach recognizes that, in general, no single realization can be used to identify a reliable remediation strategy. Rather, the unique design demands of each realization must be pooled in order to deÞne the optimal reliable solution. For example, consider the problem of minimizing pumping for plume containment. Each parameter realization can dictate pumping in a different part of the aquifer in order to meet the design constraints. By considering the inßuence of pumping across all realizations, the multiple realization method identiÞes the scenario requiring the


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least pumping that meets the demands of each realization. This pooled solution requires pumping in excess of that which would be required for any single realization. As in the chance-constrained model, this overdesign can be thought of as a safety factor, and, as in the chance-constrained model, this safety factor cannot be deÞned prior to solving the multiple realization model. It is important to note that the multiple realization management model is different from Monte Carlo optimization which solves a series of individual optimization problems, each with a different parameter realization. Monte Carlo optimization can provide information about the variability of the optimal solution from realization to realization, but, except for very limiting cases, it cannot identify reliability-based optimal designs. Wagner and Gorelick (1989) and Freeze and Gorelick (1999) provide a more detailed discussion of Monte Carlo optimization. Unlike the chance-constrained model, the multiple realization model does not explicitly contain reliability in its formulation. However, Chan (1993) presents theoretical analyses that deÞne the design reliability as a nonparametric function of the number of realizations included in the management model, R = n/(n+1). For example, if the model is formulated with 99 realizations, the estimated design reliability would be 0.99. For the case of optimal plume containment in the presence of a spatially varying and uncertain transmissivity, Chan (1993) evaluates the accuracy of the reliability estimator. Monte Carlo analyses show agreement between the reliability predicted by the nonparametric formula and the average reliability provided by the Monte Carlo results. (Analyses by Wagner and Gorelick [1989] similarly show agreement between the nonparametric reliability estimate and the design reliability obtained through Monte Carlo analysis.) Chan (1993) also presents a series of tests to gauge reliability prediction sensitivity to model parameter and structure changes. The results indicate that the nonparametric reliability estimate is robust with respect to a variety of changes (e.g., changes in the covariance and correlation structure of the transmissivity Þeld, changes in the location and magnitude of velocity constraints). The multiple realization approach described above has been modiÞed in a number of ways. Wagner et al. (1992), Morgan et al. (1993), and Chan (1994) present multiple realization methods that allow for constraint violations within the stack of constraint sets. All these works deal with designing reliable hydraulic containment strategies. Wagner et al. (1992) modify the objective function to include a penalty cost for constraint violations. Morgan et al. (1993) and Chan (1994) develop heuristic algorithms that generate solutions in which Rn of the constraint sets are satisÞed, where R is the design reliability level and n is the number of realizations. The assumption here is that if Rn of the constraint sets are satisÞed, the management strategy will satisfy R% of the constraint sets across the entire ensemble of realizations. Monte Carlo testing by Chan (1994) shows that the accuracy of this approach improves as the number of realizations increases. Ranjithan et al. (1993) used an ANN to reduce the number of realizations considered by the multiple realization model. The pattern recognition capabilities of the ANN were used to identify realizations that are likely to dictate the Þnal design. The multiple realization model was then applied to a small subset of these critical realizations. This approach was compared with that presented by Morgan et al. (1993) and was found to closely reproduce the cost–reliability trade-offs using fewer realizations and less computational time. Ritzel and Eheart (1994) use the



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multiple realization approach in a multiobjective model to evaluate the cost–reliability tradeoffs for optimal plume containment. Finally, Smalley et al. (2000) present a promising stacking model that is solved using a noisy genetic algorithm. They study the problem of risk-based design of in situ bioremediation where uncertainty stems from a heterogeneous hydraulic conductivity and unknown parameters of the exposure and risk model. For the test example, the noisy genetic algorithm was able to identify a reliable design from a relatively small number of parameter realizations. 3.3.2.3 Alternative Stochastic Optimization Methods The focus on chance-constrained and multiple realization methods in the above discussion mirrors the focus of stochastic ground water optimization methods research to date. A number of papers present alternatives or enhancements to these methods, such as those in the areas of coupled ground water management and monitoring. The above discussion highlights that the effect of failure-averse design is to introduce a cost of overdesign that increases with increasing model uncertainty or reliability. Additional data can potentially reduce model uncertainty and thereby reduce overdesign. The important issue in coupled ground water management and monitoring design is whether the reduction in management costs offsets data collection costs. Among those that address this problem are Andricevic and Kitanidis (1990), Tucciarelli and Pinder (1991), and Wagner (1999). This section would not be complete without a discussion of decision analysis, which has emerged as an alternative to stochastic optimization for reliable ground water management design. Like the stochastic optimization approach, the decision analysis approach seeks the least-cost, reliable design solution that accounts for ground water simulation model uncertainty. However, there are two important differences between the two decision-making frameworks. First, whereas stochastic optimization typically deals with minimizing costs, decision analysis involves a riskcost minimization. Second, stochastic optimization normally seeks to identify the least-cost solution for only one technological strategy, whereas decision analysis considers a suite of technological strategies from which one (not necessarily optimal) strategy is selected. A detailed comparison of the stochastic optimization and decision analysis frameworks can be found in Freeze and Gorelick (1999).

3.3.3 UNCERTAINTY Typically, the limitations of subsurface remediation technologies are thought of in a process engineering sense. In engineered systems, the efÞciency of a process can be improved through theoretical or experimental investigations. In the remediation of natural subsurface systems, however, there is far less control over the behavior of the process and much greater degrees of variability and uncertainty. Thus, the most signiÞcant technological limit in subsurface remediation is a soft limit: uncertainty. Engineers and others involved in designing and making decisions on subsurface remediation systems need to be familiar with the sources of uncertainty and their signiÞcance with regard to system performance. An informed decision maker is one


132


who identiÞes the most signiÞcant sources of uncertainty and either incorporates the uncertainty into the design or obtains the necessary information to reduce the uncertainty to a manageable level. Using mathematical optimization to design subsurface remediation systems has shown promise in providing designs that are more cost effective than designs based on trial and error or intuition (e.g., Yager and Greenwald, 1999). A signiÞcant effort has been dedicated toward developing advanced optimization tools for designing subsurface remediation system design. However, designs produced with even the most sophisticated optimization tools are doomed to failure if they are based on inaccurate or incomplete data or do not, in some way, take uncertainty into account. In this section, the sources of uncertainty that typically are encountered in the optimal design of remediation systems are presented. Three examples highlight a few of these sources of uncertainty. This section is not meant to be an exhaustive review of the approaches that have been developed for contending with uncertainty; rather, it gives an introduction to the role of uncertainty in optimal remediation design. 3.3.3.1 Sources There are many technical difÞculties associated with cleaning up contaminated ground water and soil. For example, the removal of nonaqueous phase liquids (NAPLs) or strongly sorbing organics and metals is inherently difÞcult to achieve at an acceptable cost. Not the least of the challenges is uncertainty about processes, parameters, and inputs. The biogeochemical processes are complex and inadequately understood. Geologic media are highly heterogeneous and their hydrogeologic and biogeochemical parameters are known imperfectly from a limited number of direct measurements or from inverse processes of observations of how the system responds to given stimuli. An additional source of uncertainty is the future inputs into the system, such as the intensity and quality of recharge or regional ßow. The subsections below are an attempt to categorize and describe the major sources of uncertainty in optimization as applied to decision making. 3.3.3.1.1 Hydrogeochemical The following four factors play a role in hydrogeochemical uncertainty: •

•

Aquifer Physical Characteristics Aquifer physical characteristics include the distribution of aquifer materials and their corresponding physical properties (e.g., hydraulic conductivity, porosity) as well as the transient nature of ßow boundary conditions. Contaminant Characteristics and Aquifer Contaminant Interactions The lack of detail regarding the chemical and biological composition of contaminants can contribute to hydrogeochemical uncertainty. The largely undeÞned distribution of contaminants also plays a key role. The distribution of aquifer material properties that relate to chemical interactions with contaminants (e.g., sorptive properties),



•

•

133

the distribution of aqueous chemistry (e.g., dissolved organic matter concentrations), and microbial distribution and corresponding biochemical conditions (e.g., electron acceptor, carbon source energy source concentrations) are all considerations when evaluating hydrogeochemical uncertainty. Plume Characteristics The following three factors should be considered when evaluating hydrogeochemical uncertainty: spatial extent of the plume, chemical composition and concentrations, and plume age. Source Characteristics Source characteristics such as composition and strength of the source, source location, and source history affect hydrogeochemical uncertainty.

3.3.3.1.2 Technology Technology uncertainty can be deÞned as the uncertainty in predicting the response of the contaminant(s) to a technology or combination of technologies (especially for innovative technologies). 3.3.3.1.3 Cleanup Goals Determining cleanup goals involves the consideration of many variables. First, the cleanup goal itself must be identiÞed based on different methods (e.g., point concentration based, mass based, risk based). Second, measuring the attainment of the cleanup goal has its own uncertainties (e.g., uncertainty in the relevance of a point measurement, sampling and analytical errors, uncertainty in the initial mass of contaminant). Third, there is uncertainty associated with the risk assessment (e.g., identiÞcation of exposed population, determination of exposure, estimation of health effects). Last, there is decision-making uncertainty. Remedial cleanups by their nature involve multiple stakeholders with conßicting views and preferences and varying degrees of risk aversion. 3.3.3.2 Examples 3.3.3.2.1 Aquifer Physical Characteristics Uncertainty In this example, the performance of an optimal remediation design was examined in the situation where the hydraulic conductivity distribution is uncertain. The optimal pumping schedule for a pump-and-treat system was obtained for the following two levels of uncertainty: (1) assuming that the aquifer is homogeneous in all three dimensions and (2) assuming that the aquifer is heterogeneous in the horizontal (aerial) plane but homogeneous in the vertical direction. The pumping schedules obtained under these conditions of uncertainty were applied to the real, three-dimensional (3-D), heterogeneous aquifer. The ability of the pumping scheme to achieve a given cleanup level was assessed. The aquifer contaminant system was modeled with ground water ßow and transport simulators (i.e., MODFLOW and MT3D). The aquifer was discretized into 31 ¥ 20 Þnite-difference nodes per layer, with a total of four layers. The size of the aquifer is 4101 ¥ 3000 ¥ 98 ft (Figure 3.1). No-ßow boundaries were imposed on


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No-Flow Boundary

Constant Head Boundary

Monitoring Wells Candidate Pumping Wells

Contaminant Source

Direction of Flow (without Pumping)

Constant Head Boundary

N

Scale

No-Flow Boundary

0

100 m

FIGURE 3.1 Aerial view of hypothetical ground water system used in examples.

the north, south, and bottom faces of the aquifer system. Constant head boundaries were applied on the east and west sides to produce a west to east ßow direction. The physical characteristics of the aquifer system are summarized in Table 3.2. A stochastic, conditional simulation technique was applied to generate 30 3D, spatially correlated, random Þelds of isotropic hydraulic conductivity. The mean and variance of the log-hydraulic conductivity are 5.31 ¥ 10–3 ft/s and 0.4, respectively. The correlation scales are 150 and 25 ft in the horizontal and vertical directions, respectively. The hydraulic conductivities in the domain range from 7.9 x 10–4 to 3.6 ¥10–3 ft/s, which is indicative of a mixture of clean sand and gravel materials. The initial plume was generated by introducing solute into the aquifer system at eight locations in the second and third layers. The plume was allowed to migrate until the concentrations at downgradient monitoring wells (Figure 3.1) reached a speciÞed standard. The conÞguration of the plume is shown in Figure 3.1. An average of the 3-D hydraulic conductivity Þeld was used in the plume simulation.

TABLE 3.2 Aquifer Physical Characteristics Parameter Longitudinal dispersivity (m) Transverse dispersivity (m) Diffusion coefÞcient (m2/s) Porosity Retardation coefÞcient Freundlich parameters (Kab, 1/n)

Value 3.3 0.3 10–6 0.35 1.0 3.79, 0.89



135

The homogeneous version of the aquifer was described with a single, hydraulic conductivity value equal to the mean hydraulic conductivity used to generate the random Þelds. A two-dimensional (2-D), horizontal hydraulic conductivity Þeld was generated by vertically averaging each of the 30 3-D Þelds and then averaging the resulting 2-D Þelds at each horizontal location. Optimal pumping schedules were determined for the homogeneous and vertically averaged 2-D versions of the aquifer. The objective was to determine an optimal remediation scheme to reduce the aqueous phase solute concentration in the aquifer to less than the drinking water standard (5 mg/l) in 5 years. The remediation planning horizon was divided into Þve 1-year management periods to allow for dynamic pumping rates. Up to 15 candidate pumping wells were utilized to remediate the plume. The objective function consists of the sum of a well installation cost term, a pumping lift cost term, and a ground water treatment cost term: N

N p (t )

Csi (t ) - Ct 0

Â Â a N (t) + a Q (t)[s (t) + d ] + a Q (t) K [C (t)] 0

t =1

i =1

p

1

i

i

i

2

1/ n

i

ab

(3.5)

si

where N is the total number of management periods in the entire planning horizon; Np(t) is the total number of extraction wells during the t-th management period; a0, a1, and a2 are cost coefÞcients for well installation, pumping lift, and treatment, respectively; Qi(t) is the ground water extraction rate of the i-th extraction well during t-th management period; si(t) is the drawdown in the i-th extraction well during management period t; di is the distance between the static water table and the ground surface at the extraction well i; Csi(t) is the ßow-weighted solute concentration in the i-th well during the t-th period; Ct0 is the treatment objective (5 mg/l); and Kab and 1/n are parameters related to the carbon adsorption treatment technology. The optimization formulation was subjected to remediation goal constraints, resource protection constraints, and pumping capacity constraints. The optimization formulation was solved by an application of the genetic algorithm. For more details, refer to Huang and Mayer (1996). Figure 3.2 shows the pumping schedules determined for the homogeneous and vertically averaged versions of the aquifer. These schedules were applied to each of the 30 3-D Þelds. Concentrations at the end of the 5-year remediation horizon were determined from the pumping and monitoring wells for each of the 3-D Þelds. The maximum concentration found in the wells was used as an indication of the success or failure of the remediation design. Figure 3.3 shows the resulting frequency distributions of the maximum concentration at the pumping wells for the 30 3-D Þelds. The results in Figure 3.3 indicate that, for the pumping schedule obtained with the homogeneous assumption, the remediation goal of 5 mg/l is exceeded for 80% of the 3-D Þelds. As expected, the pumping schedule obtained with the vertically averaged assumption performs better, where the remediation goal of 5 mg/l is exceeded for 64% of the 3-D Þelds. In addition, the extreme values of concentration (>100 mg/l) are avoided when horizontal heterogeneity is considered.


136


Total Pumping Rate (m 3/min)

1.00 Homogeneous Vertically Averaged

0.75

0.50

0.25

0.00 1

2

3

4

5

Year

FIGURE 3.2 Pumping schedules found for remediation systems based on homogenous and vertically averaged versions of aquifer. 60%

Homogeneous

Frequency

50%

Vertically Averaged

40% 30% 20% 10% 0%

1

5 10 50 100 500 Maximum Concentration (µg/l)

FIGURE 3.3 Maximum concentrations found for remediation systems based on homogenous and vertically averaged versions of aquifer.

3.3.3.2.2 Decision-Making Uncertainty In the previous example, the cleanup goal was Þxed, and the most cost-effective solution that achieved the cleanup goal was found. An alternative approach for optimizing subsurface remediation systems is to allow both the cleanup goal and the cost to vary. With this multiobjective approach, there are an inÞnite or at least very large number of possible designs. To make a decision on the appropriate remediation design, the preferences of the decision makers with regard to tradeoffs between cleanup goals and costs must be known. In other words, a decision maker must know how to weigh the relative importance of the remediation system cost and the cleanup performance to be achieved by the remediation system. Once these weights are known, the two objective functions (i.e., minimize cost and maximize cleanup performance) can be combined into a single objective



137

250,000

Total Cost ($)

200,000

150,000

100,000

50,000

0

1

10

100

Mass Remaining (%)

FIGURE 3.4 Tradeoff curve for multiobjective optimization of pump-and-treat design.

optimization problem. However, in most cases, a decision maker is not able to select these weights a priori. One approach for dealing with uncertainty in the decision-making process is to generate a tradeoff curve that allows the decision maker to see the full range of alternatives for a particular site. Figure 3.4 shows a tradeoff curve for pumpand-treat remediation of the hypothetical, contaminated aquifer described in the previous example, where up to 15 extraction wells are to be used. The total cost indicated on the y-axis consists of capital and operating costs for the remediation system over the remediation period. Here, the percent of contaminant mass remaining in the aquifer at the end of the remediation period is used as a measure of cleanup performance. Other measures of cleanup performance can be substituted easily (e.g., ground water concentrations at monitoring points, human health risk remaining after remediation). The design optimization problem is to Þnd all the best combinations of 15 pumping rates. In the case with 15 wells, there are on the order of 1018 potential combinations of pumping rates. The best designs are those that tend to minimize the cost and the mass remaining simultaneously. Each symbol on the graph in Figure 3.4 represents the best design found by the optimization algorithm for the corresponding position on the tradeoff curve. For example, the cheapest design able to achieve a cleanup goal of 10% mass remaining is estimated at about $125,000 (see dashed lines in Figure 3.4). The optimization algorithm is an advanced evolutionary method called the Niched Pareto Genetic Algorithm (NPGA). The NPGA sorts among the potential designs and attempts to improve the optimality of the designs as it iterates toward the best solutions. The NPGA is built around a ground water ßow and transport simulator, which provides information about the ground water pressures and


138


contaminant concentrations. This information is used to determine the cost and cleanup performance for each candidate design. For more details on the algorithm and the example shown here, refer to Erickson et al. (2001). 3.3.3.2.3 Risk Assessment Uncertainty Risk assessment is used to quantify the human health risks due to exposure to contaminated ground water and to develop cleanup goals. Risk assessment involves estimating the level of contamination at the point of exposure, the exposure of individuals to the contamination, and the resulting toxicological impact. Because the level of contamination at the exposure point depends on the properties of the aquifer and contaminant, risk assessment is subject to hydrogeochemical uncertainty. However, risk assessment is further complicated by uncertainty in exposure factors (e.g., uncertainty in the amount of contaminated ground water ingested) and toxicological factors (e.g., uncertainty in the sensitivity of the exposed population to the toxicological effect[s]). The impacts of these sources of uncertainty have been investigated by Pelmuder et al. (1996) and Maxwell et al. (1998), among others. The following example illustrates the risk assessment uncertainty. A linear form for estimating the risk of carcinogenesis due to ingestion of ground water contaminated with an organic chemical (McKone and Bogen, 1991) is used: Risk = CDI ¥ CSF = [C ¥ I ¥ ED ¥ EF/(BW ¥ AT)] ¥ CSF

(3.6)

where CDI is the chronic daily intake in milligrams per kilogram per day (mg/kg/day), CSF is the cancer slope factor (kg day/mg), C is the concentration at the exposure point (mg/l), I is the ingestion rate for contaminated water from the exposure point (l/day), ED is the exposure duration (years), EF is the exposure frequency (days/year), BW is body weight (kg), and AT is the averaging time (years). Each of the variables in Equation 3.6 could contain uncertainty either because their values are not perfectly known or because there is variability in the exposure or toxicological response among the affected individuals. In addition, the previous example regarding hydrogeochemical uncertainty showed that the concentration at the point of exposure can be uncertain. The frequency distribution given in Figure 3.3 to describe the uncertainty in the concentration at the exposure point is used. To describe the remaining, potential uncertainty in Equation 3.6, the variability in the cancer slope factor was divided by the body weight, CSF/BW. Variability in CSF/BW represents the situation where the sensitivity and size of the exposed individuals varies, as one would expect in a realistic population. Table 3.3 lists the values of the variables in Equation 3.6 used in the analysis, including the lognormal distribution used to describe the uncertainty in CSF/BW. The value of the geometric standard deviation used here results in a distribution that ranges over approximately two orders of magnitude. A Monte Carlo analysis was used to sample 300 values of the CSF/BW distribution. These CSF/BW values were then substituted into Equation 3.6 along with values from the concentration frequency distribution given in Figure 3.3.



139

TABLE 3.3 Risk Assessment Variables Variable I (l/day) ED (year) EF (day/year) AT (year) CSF/BW (day/mg) geometric mean and standard deviation

Value 2 30 350 70 1.57 ¥ 10–3 7.28 ¥ 100

Figure 3.5 shows the frequency distribution of risks by using only the mean value of CSF/BW. This frequency distribution is simply a linear translation of the concentration frequency distribution given in Figure 3.3 and indicates that a 13% probability exists that the risk will exceed 10–4 but an 80% probability exists that the risk will exceed 10–5. When the uncertainty in CSF/BW is included, the resulting frequency distribution is extremely wide, as shown in Figure 3.6. Of course, the width of the distribution is directly related to the somewhat arbitrary choice of standard deviation in the CSF/BW distribution. Some of the very high risks (>10–2) indicated in Figure 3.6 are a result of using perhaps unrealistic, low values of BW or high values of SF. 3.3.3.2.4 Approaches for Addressing Uncertainty How mathematical models and optimization programs must be modiÞed to account for uncertainty transcends the use of mathematical models or optimization. Every strategy, no matter how derived, should be able to deal with the characteristics of various uncertainties discussed in previous sections. However, when a strategy is derived using mathematical simulation models and optimization techniques, the success of the methods should be judged on the basis of how well the schemes they generate represent these characteristics. Some approaches follow: 50%

Frequency

40% 30% 20% 10% 0%

2 x 10-6 1 x 10-5 2 x 10-5 1 x 10-4 2 x 10-4 1 x 10-6 Risk

FIGURE 3.5 Frequency distribution of risks based on geometric mean of CSF/BW.


140


40%

Frequency

30%

20%

10%

0%

1.1 x 10-7 1 x 10-6 1 x 10-5 1 x 10-4 1 x 10-3 1 x 10-2 1 x 10-2 Risk

FIGURE 3.6 Frequency distribution of risks incorporating uncertainty in CSF/BW.

•

•

•

Feedback A strategy should be adaptable to changing conditions and new information. This requirement appears easy to meet by using deterministic models that use best estimates as if they were the right values for the quantities they represent. When the estimates change, the models are run anew and the remediation scheme is adjusted. Hedging If there are many possible parameter or input values, the strategy should not be chosen to optimize one of them. Instead, it should be chosen to perform in a satisfactory way over the ensemble of possible values of the unknown quantities. It is of paramount importance to prevent high cost outcomes (i.e., avoid disastrous performance under a plausible scenario). Enforcing hedging in a rigorous fashion in an optimization scheme is computationally very challenging. Hence, applying optimization under uncertainty becomes an exercise in approximation. For example, Andricevic and Kitanidis (1990) and Lee and Kitanidis (1991, 1996) used a small perturbation approximation. Among more computationally intensive approaches, the multiple realization approach involves replacing possible scenarios with a Þnite number that is generated through Monte Carlo techniques (Gorelick et al., 1984; Wagner and Gorelick, 1989; Chan, 1993). Then, the optimal scheme is the one that has the smallest average cost and performs well over the largest percentage of realizations or a combination of the two. Sampling The signiÞcance of sampling when addressing uncertainty is two-fold. First is the anticipation that measurements will be collected in the future, thus requiring less hedging. For example, the multiple realization approach can be overly conservative or cautious by requiring a high pumping rate to ensure that 95% of plume realizations, based on initially available knowledge, are captured. In practice, however, the ensemble of



141

plausible plume realizations changes over time as new information is incorporated into the model reducing uncertainty. McGrath et al. (1996) use this approach in the context of an adaptive characterization method. Second is that sampling can become part of the optimization. For example, the pumping rate and collection of measurements can be manipulated to stimulate the system in a way that reduces uncertainty, thus reducing the total cost. This approach has been adopted by Andricevic and Kitanidis (1990) and Lee and Kitanidis (1991, 1996).

3.3.4 DESIGN-RISK COST TRADEOFF Risk has been deÞned in many different ways (Starr, 1969; Kaplan and Garrick, 1981), for example: Risk = Probability ¥ Consequence Risk =

Hazard Safeguards

(3.7) (3.8)

From these initial deÞnitions, risk can be linked with probability, uncertainty, frequency, and cost beneÞt. Risk in a cost–beneÞt framework is usually treated as a constraint or minimized. Freeze and Gorelick (1999) point out that there are often no economic beneÞts to environmental cleanup, just cost minimization. An alternative view, however, may be that the beneÞt takes the form of a reduction in human health risk. Schulze and Kneese (1981) and Morgan (2000) posit that the risk-costbeneÞt framework is an important decision-making tool and should be used to equitably mitigate risks to a populace. Risk, when used in the context of optimal subsurface contamination remediation, often includes the following two meanings: (1) that pertaining to remedial system reliability and (2) that signifying the potential for adverse impact on human health. Both are appropriate uses of the term, and either (or even both) can be used in a cost–beneÞt framework. In fact, it is common to see concentration goals used as a surrogate for health risk. From a reliability perspective, risk is deÞned in terms of the probability of failure of intended remedial design (or any engineering design). There is often a monetary or regulatory cost associated with failure. This stems from Equation 3.7, where the risk is deÞned as the probability of failure of the intended remedial design and the consequence of this failure deÞned as either regulatory or monetary penalties (e.g., Freeze and Gorelick, 1999). A predominant complicating factor in this scenario is the uncertainty due to hydraulic conductivity. One example of this reliability perspective is presented in Morgan et al. (1993). In this work, trade-off curves of reliability vs. pumping rate were developed (Figure 3.7). The authors simulated many different, equally likely realizations of hydraulic conductivity for varying degrees of heterogeneity. Reliability was quantiÞed as the number of realizations that did not violate the design constraints. The authors noted that a deterministic system has either zero or one reliability (i.e., either the design


142


Total Pumping Rate (10−1 m3/sec)

10

inferior 1

0.10 infeasible

0.01

1

11

21

Low Reliability

31

41

51

61

71

81

91

100

High Reliability

NUMBER OF REALIZATIONS NOT VIOLATED (100 Realizations)

FIGURE 3.7 Trade-off curve for reliability as a function of minimum pumping rate. (After Morgan et al., 1993.)

succeeds or fails) and demonstrated the change in reliability with the change in standard deviation of hydraulic conductivity (Figure 3.8). The standard framework for human health risk includes the processes of risk assessment and risk management (National Research Council [NRC], 1983, 1992). Risk assessment comprises the following four steps: hazard identiÞcation; exposure assessment; dose–response assessment; and risk characterization, including uncertainty and variability. In identifying hazards, one determines whether a physical insult can cause an increase in negative consequences for human health. Exposure is assessed by estimating the intensity, frequency, and duration of the insult as experienced by the at-risk population. The dose–response is the relationship between the exposure to the physical insult and the incidence of the human health consequences, and risk characterization is the synthesis of these to evaluate the health consequences of the physical insult. Risk management is how one chooses to manage the risks (i.e., mitigation strategies or neglect). This type of framework is often applied to complex systems (e.g., engineered, natural, human) that have relatively poor data. Several simplifying assumptions are often adopted to make assessments tractable, standardize the approach, and accommodate some of the limitations of the available data and models (e.g., USEPA, 1989). These simpliÞcations include linear no-threshold dose response functions for carcinogens; population-averaged values for dose–response, consumption patterns, and metabolic features; and an assessment of either a maximally exposed individual or a population-averaged exposure. It has



143

Total Pumping Rate (10−4 m3/sec)

10 σ = 0.7

σ = 0.8

σ = 0.6 1

σ = 0.5 σ = 0.1 σ = 0.3

0.10

σ = 0.0

0.01 1

11

21

31

41

51

61

Low Reliability

71

81

91

100

High Reliability

NUMBER OF REALIZATIONS NOT VIOLATED (100 Realizations)

FIGURE 3.8 Trade-off curve for reliability as a function of minimum pumping rate for varying degrees of subsurface heterogeneity as represented by the standard deviation of the hydraulic conductivity. (After Morgan et al., 1993.)

been shown that these simpliÞcations often lead to inaccurate estimates of risk, and their use results in poor management decisions (NRC, 1994). In this context, risk is often deÞned as the expected value of a Poisson process with low-dose linearity and saturation at higher doses as Risk = 1 – e[CPF¥ADD]

(3.9)

where CPF is the cancer potency factor (kg-d/mg), and ADD is the average daily dose. The ADD is a function of exposure and is often related not only to the environmental concentration of a contaminant but to individual consumers’ physiologies and behaviors. It should be noted that low-dose linearity is not always an appropriate relationship for chemical carcinogenesis (e.g., Bogen and Gold, 1997). Probabilistic or statistical approaches to account for uncertainty and variability in the areas of exposure assessment, dose–response assessment, and risk characterization have grown in sophistication, making it feasible to successively explore the impacts of these simpliÞcations and develop more robust tools that incorporate and preserve more of the available data. Following the concepts originally put forth in Bogen and Spear (1987), there is a growing body of literature that includes elements of uncertainty and variability into environmental risk analysis. Uncertain parameters are those that are not well known, while variable parameters can be well known but vary spatially, temporally, or between individuals. These studies have incorporated concepts of uncertainty and variability in risk assessment domains other than ground water (Burmaster


144


and Anderson, 1994; Hoffman and Hammonds, 1994; McKone, 1994; Burmaster and Wilson, 1996; Cohen et al., 1996; Frey and Rhodes, 1996; Rai et al., 1996) or have presented methods to simplify its implementation (Bogen, 1995). Katsumata and Kastenberg (1998) also have examined the relationship of the maximally exposed individual (MEI) to various conÞdence levels. Risk assessment methods have been used both to quantify the human health impacts of contaminated ground water and to aid in water resource management (Massman and Freeze, 1987a, 1987b; Reichard and Evans, 1989; Freeze et al., 1990; Massman et al., 1991; Andrecevic et al., 1994; Andrecevic and Cvetkovic, 1996; Maxwell and Pelmulder, 1996; Pelmulder et al., 1996; Maxwell et al., 1998, 1999; Maxwell and Kastenberg, 1999; Ozbek, 2000; Smalley et al., 2000; Daniels et al., 2000). These assessment methods generally involve parameters with elements of uncertainty and variability in ßow and transport models, exposure models, and human health models. Consideration of uncertainty and variability leads to a complex analysis involving many components that are coupled. A framework and series of case studies presented by Maxwell et al. (1998) and Maxwell and Kastenberg (1999) serve as examples of risk assessments for ground water. These works focused on the following two most prominent complicating factors in predicting ground water driven health risk: (1) the subsurface is a highly heterogeneous environment leading to spatial variability in the ground water concentration, and (2) individuals utilizing this water have physiological characteristics that vary across the exposed population. As depicted in Figure 3.9, the methodology applied in these case studies links together heterogeneous subsurface ßow and contaminant transport (A), subsurface receptor locations (B), a water distribution network (C), and a household exposure model (D). A parcel of contaminant is followed as it migrates through the subsurface. If it is captured by a downstream well, subsequent delivery is tracked to one of many households in a potentially exposed population where individuals might receive a dose of this contaminant (and, assuming the contaminant is a carcinogen, a corresponding

FIGURE 3.9 Illustration of the methodology presented in Maxwell et al. (1998) and Maxwell and Kastenberg (1999).



145

increase in the risk of cancer) through various household activities (e.g., washing, drinking, showering). In a study involving chemical contaminants migrating through an aquifer, spatial variability in hydraulic conductivity was found to exert a similar order of inßuence on the uncertainty of human health risk as exposure and cancer mechanisms, which were previously thought to be the dominant source of uncertainty (Maxwell et al., 1998; Maxwell and Kastenberg, 1999). Work by Smalley and coworkers (2000) looks at the reliability of an optimal, remedial design that is health-risk based. The authors used a noisy genetic algorithm using a framework that is similar to the one presented above but that does not distinguish between uncertain and variable parameters. When used to produce an optimal management strategy for the bioremediation of a hypothetical aquifer contaminated with benzene, they found that the optimal design was very reliable, even for a reduced number of Monte Carlo iterations per generation. Recent work by Ozbek (2000) takes a different approach. While using the same general framework shown in Figure 3.10, the risk equations and the two-stage Monte Carlo procedure were represented in a fuzzy logic approach. The author represented statements and preferences of practicing toxicologists as fuzzy rules. These rules were then used to relate a dose and pattern of exposure of benzene to its carcinogenic effects. Parameters typically represented by variability in other risk assessments were represented by rules describing individual susceptibility and were used to constrain the risk model in a way that preserves interindividual differences (Figure 3.10). The work went on to demonstrate how this type of framework can be used to determine the optimal, least-cost solution for a pump-and-treat remediation strat1 100

Risk

50 0.5 0 0 0 5

Severity of Exposure

5 10

10

0

(individual) Susceptibility

FIGURE 3.10 Example of the relationship between susceptibility of the exposed individual, severity of the exposure, and health risk as represented by the fuzzy model of Ozbek (2000).


146


egy for a hypothetical benzene contamination scenario. The work focused on complications in the risk model (e.g., uncertainty and individual susceptibility) over complications arising from uncertainty due to subsurface heterogeneity. A recent review of risk-based methods (NRC, 1999) concludes that “… monetary beneÞts to be gained from an uncertainty analysis can be substantial if such an analysis results in less overestimation of the risk than is typical when using conservative exposure parameters” and points to Maxwell et al. (1998) as an example methodology. Risk-based approaches, such as the ones presented here, provide a rational basis for formulating a risk–cost tradeoff curve to guide the optimization of site cleanup efforts.

3.3.5 LONG-TERM GROUND WATER MONITORING Ground water remediation management does not end with the installation of the cleanup system and the ßipping of a switch. Long-term monitoring of ground water systems is conducted to provide the data needed to ensure that the risks to human health and the environment are being properly managed in accordance with restoration plans. Long-term monitoring can be used for monitored natural attenuation, environmental restoration (i.e., cleanup), and plume containment (i.e., ßow management) and can have time horizons of up to 30 years or more. Because the collection and analysis of long-term monitoring data are so expensive, applying optimization methods to long-term monitoring can result in cost-effective monitoring designs that avoid or eliminate redundant samples without signiÞcantly changing risks. Figure 3.11 is a schematic indicating the tradeoffs between the quality of forecasts (e.g., state variables, errors, risk) and the incremental value added by incorporating new data. The horizontal axis represents the amount of data and information that might exist at a particular site. Sites that have very few data (perhaps nothing more than three wells in the ground) fall near the left-hand characterization side of the Þgure. Although it might be true that these sites qualify as long-term monitoring sites, the monitoring performed is for simple characterization purposes. Sites that High

High

Forecasting Quality

Value of Added Data

Low Characterization

Low Amount of Data and Information

Redundancy

FIGURE 3.11 Schematic indicating the tradeoffs between the quality of forecasts and incremental value added by incorporating new data.



147

are rich in data fall toward the redundancy end of this axis. For sites with few data, a new datum has much greater impact than the same datum for a site with many data. Conversely, forecasts at sites with few data are generally lower quality (in terms of predictive power) than when many data exist. 3.3.5.1 The Relationship between Remedy and Monitoring Remedies are site-speciÞc, and a selected remedy is a function of the amount of information available. As site knowledge increases, the proposed remedy changes, as would the corresponding performance monitoring scheme. In Figure 3.12, the amount of data axis (from Figure 3.11) has been modiÞed with boxes containing R and M symbols to suggest that the availability of fewer or more data leads to different remediation designs as well as plans to monitor the remedy performance. Although the regulatory and permitting processes usually require that a remedy be approved in a ROD Þrst and a monitoring plan be produced later, the remedy and monitoring are inextricably related. The performance of the remedy can be evaluated only by monitoring for parameters that indicate that particular type of performance. For example, it is common to discover that the life-cycle costs of a remedy/monitoring scenario that was selected for containment at some time in the past will lead to a nearly perpetual operation. This realization can result in a change in the remediation objectives, which in turn necessitates a new performance monitoring plan. Here, discussion is restricted to the case when remedy and monitoring have been selected consistently. In this case, the remedy is expected to behave in a certain way: ßow directions are supposed to change, concentrations at speciÞc locations should decrease, and the percentage of contaminant mass remaining in the subsurface is forecast to decline according to some plan or design. Because the forecast is based on incomplete information and imperfect models, each forecast will have errors. An observation is made in the presence of sample collection errors, handling errors, Amount of Data and Information Few

R1

M1 R2

M2 R3 M3 R4 M4

Many

Characterization Remedy

Monitoring

Expected Behavior

Observed Behavior

Consistent? No

Yes

FIGURE 3.12 Schematic indicating how monitoring is related to speciÞc site remedies.


148


analysis errors, and reporting errors. Quantitative comparison of observations with forecasts must account for these different sources of error. (Most practitioners censor observations based on quality assurance and quality control criteria, and then the reported observations are used as if certain.) The comparison between observed and expected behaviors then is used to modify the remedy or monitoring scheme. If the difference between observed and expected behaviors is small, the forecast has good predictive power and there is an opportunity to eliminate redundant observations. On the other hand, if the difference is large, inadequacies might be due to lack of predictive power in the forecasts, an insufÞcient monitoring scheme, or underperformance of the remedy. The Þrst two of these items lead to renewed characterization of the predictive model or augmentation of the monitoring scheme to improve characterization of the observed quantities. 3.3.5.2 Performance Monitoring Problems Taxonomy for the classiÞcation of long-term ground water monitoring problems makes it easier to discuss useful approaches and methods. One convenient approach to classiÞcation is to Þrst distinguish between source areas, in which NAPL or other residues can act as long-term sources of contamination even after source zone remediation, and plume areas that lie outside the source areas. When contaminants are subjected to biodegradation, redox, and other reactions, it can be convenient to subclassify the plume area according to transformation processes (e.g., oxic and anoxic regions). For convenience, plumes are not subdivided in this discussion. The long-term monitoring of source areas occurs after source zone remediation has occurred. If the source region has a stabilized distribution of contaminants that are decaying, the primary performance monitoring problems are as follows: • • •

Ensuring that the distribution is stable Characterizing the ßux of contaminant out of the source area through vadose and water saturated zones Estimating the contaminant distribution within the source zone

Optimizations of monitoring systems for the source area generally focus on the Þrst and third items above. If monitoring demonstrates that the source area is stable, then the monitoring network can be stable, and more complex analyses of timevarying conditions are not needed. In this scenario, ground water monitoring is similar to soil contamination monitoring and several methods are applicable, all of which can be expressed as taking samples to minimize the error (or the integral of error) over the region of interest. Numerous tools can be brought to bear in this situation based on either interpolation theory (e.g., polynomials and Delaunay triangulation) or statistical methods (e.g., trend analysis and linear regression). If the distribution of contaminants within the source area is changing in time, then the optimal set of M monitoring wells needed to estimate the concentration (or total mass) over the region of interest might also vary in time. The long-term monitoring of plume areas is deÞned differently by different entities — USEPA, DoD, DOE, and others use disparate deÞnitions — which can make for



149

confusing conversations. In some cases, it is monitoring that occurs after a probationary operating period after a ROD has been instituted, while in others it is monitoring after all active remediation has concluded. A convenient approach to dividing the plume monitoring problem is according to the objectives of the selected remedy and monitoring programs. The following three convenient classes are introduced for this discussion: •

•

•

Monitored Natural Attenuation Monitored natural attenuation is often instituted in the context of a stabilized plume, in which a balance exists among advection, dispersion, and reaction such that the shape and distribution of contaminants is not changing in time, while the magnitude of the contamination decreases. In such settings, long-term monitoring objectives are very similar to the source-zone monitoring problem — verifying that the plume is stabilized, looking at distributions of contaminant within the plume, and estimating the net decay rate for mass in the plume. When monitored natural attenuation is applied to a nonstationary plume, a spatially Þxed monitoring network is generally not optimal over the span of a monitoring operation and will need to be shifted downstream in response to plume movement and redistribution of contaminants. (Note that most site managers are well acquainted with a plume-chasing factor in the plume discovery and characterization phase of a project.) There are the questions of determining how many monitoring wells in a network (and which ones) are needed at a given time to interpret the plume shape and mass and how the set of monitoring wells change as time proceeds. Interestingly, the monitored natural attenuation problem is much more challenging than these two issues. Monitored natural attenuation is usually managed using a number of analytes, some of which are in stabilized distributions, while others used for performance monitoring are simultaneously in nonstationary plumes (e.g., dechlorination reduces solvent concentrations but acts as an ongoing source for a mobile chloride plume). Environmental Restoration Plume monitoring for cleanup situations requires that the amount of mass in the subsurface be characterized adequately, that the peak concentrations encountered decrease or reach speciÞed values, or that concentrations at points of compliance attain acceptable values. Because the plume is actively manipulated (either hydraulically or chemically), the remedy creates transient behavior of the plume, and monitoring must be planned with this time dependency in mind. Often the active cleanup period is followed by monitored natural attenuation, so monitoring objectives overlap between these two remedy phases of a project. Plume Containment Monitoring for containment (i.e., ßow management) is driven by two primary factors. First, the direction of ground water ßow in a region en-


150


compassing the plume is required to be inward across a compliance boundary. This requirement is typically ensured by measuring heads on either side of the compliance boundary. Second, plume containment is determined by requiring that the concentrations monitored near the compliance boundary do not trend upward. The rate of change of the plume is an important factor to consider along with the amount of data and information. If the rate of plume change is inÞnitesimally small, then steady state analysis methods can be brought to bear. In these circumstances, diagnostic methods of data analysis can be used. On the other hand, when the plume is changing more rapidly, the dynamics of the plume become important and a physically based approach is more acceptable than a purely spatial approach. In either case, the amount of data inßuences which analysis methods are applicable, not simply which ones can be applicable. In summary, performance monitoring objectives can vary widely. Typical questions that monitoring networks should address are as follows: • • • • •

Where is the plume? What is the distribution of mass within the plume? What is and what do is estimated to be the peak concentrations or selected points or regions of interest? What is the direction of ßow? Is the plume stable, shrinking, or expanding?

The answers to these questions provide useful estimates. Each estimate has an associated uncertainty, and the uncertainties in the error of estimation can be used as decision criteria. 3.3.5.3 Methods It is possible to apply optimization methods to long-term monitoring design that will result in cost-effective monitoring designs that avoid or eliminate redundant samples without signiÞcantly changing risks. The optimization of long-term monitoring can be accomplished using a variety of approaches. Selecting an appropriate method involves numerous criteria, the most important of which include the site-speciÞc longterm performance objectives and the amount and type of available data. Figure 3.13 broadly categorizes some of the methods used for long-term monitoring optimization by the quantity of available data. At sites with little data, monitoring design optimization focuses on characterization methods for determining the best location and number of samples. At sites with large amounts of data or during the operation phase of an existing monitoring network, optimization methods can be applied to improve the sampling frequencies and number of samples and thus reduce treatment costs. Several methods exist for optimizing existing long-term monitoring networks. Ad hoc, commonsense review of monitoring data trends is standard practice. A large number of guidance documents are available from American Society for Testing and Materials



High

High

Forecasting Quality

Value of Added Data

Low

Rule-Based Systems Decision trees KBES Screening tools

Low Amount of Data and Information x x x x x x x x

Statistical Kriging Parametric Nonparametric Physics Based Analytical 2-D Numerical 3-D Numerical Physical–Statistical Monte Carlo Kalman filter based

151

x x x x

x x

x

x x x x x x x

FIGURE 3.13 Methods characterized by amount of available data.

(ASTM), the military services, the USEPA, and experts. The AFCEE is preparing a long-term monitoring handbook as well as decision support software and algorithms that will aid in the optimization of monitoring and remedial costs (USEPA, 2000a). 3.3.5.3.1 Risk-Based Systems Decision-tree approaches formalize the commonsense approach and have been developed in several groups (e.g., Lawrence Livermore National Laboratory [LLNL] and Science Applications International Corporation [SAIC]). The AFCEE Monitoring and Remediation Optimization System (MAROS) software is a Microsoft Access database application developed to help perform ground water data trend analysis and long-term monitoring optimization at contaminated ground water sites. MAROS combines a modiÞed cost-effective sampling approach of Ridley and coworkers (1995) with a redundancy analysis that is based on comparing interpolation errors at monitoring locations if one (or more) monitoring wells were omitted. Additional qualitative lines of evidence can be combined via user-speciÞed weights to sift through monitoring options. Classical statistics and hot-spot statistics form the basis of a couple of well-known tools that are particularly useful for designing sampling strategies for contaminant surÞcial soils (e.g., Waste Policy Institute [WPI], ORNL). 3.3.5.3.2 Statistical Kriging is a widely used and known method of geostatistics that has recently been applied to long-term monitoring at a number of sites. Monitoring well applications were introduced about 20 years ago. Kriging produces an estimate mean and estimates the uncertainty associated with estimation errors. Only spatial statistics (i.e.,


152


a snapshot in time) are produced. Spatial correlation and measurement errors are included (de Marsily, 1986). Detrending, which removes statistical nonstationarity, is often needed with environmental data and is especially important when addressing plumes. Most of the reported long-term monitoring optimization applications do not address the nonstationarity issue. Kriging in time has recently been applied to long-term monitoring optimization and is known in electrical engineering circles as the Wiener Þlter. Kriging in time is a new approach for ground water, having been used only in the past few years. Like the more familiar spatial version of kriging, it provides estimates of the mean and of uncertainty. As the name suggests, this method involves temporal statistics only, providing a snapshot across space. Time correlation and measurement errors are included. The need to detrend and the concern for nonstationary situations exists, as for spatial kriging. Freeze et al. (1992) showed conceptually how to evaluate the worth of spatially correlated measurements of hydraulic conductivity in a heterogeneous aquifer when predicting contaminant travel time. James and Freeze (1993) developed a Bayesian data-worth framework for evaluating the worth of spatially correlated boreholes when searching for aquitard discontinuities in contamination at the SRS. Bogardi et al. (1985) and Rouhani (1985) designed optimal networks for sampling spatially correlated parameters. Measurement data are added based on which locations will have the greatest effect in decreasing the estimation of variance obtained from geostatistical interpolation. 3.3.5.3.3 Physics Based Monitoring designs that are optimal in the sense of minimizing costs while preserving the accuracy of the calculation of mass in the subsurface are being actively researched (Reed, 1999; Reed et al., 1999, 2001). This preservation is critical when monitoring to detect and diagnose natural attenuation and biodegradation effects. A simulation model is used to predict concentrations at some point in the future, resulting in an optimization scheme that Þnds the conÞguration of n wells that best estimates the contaminant mass for various n. This provides a tradeoff curve for monitoring cost vs. performance at individual points in time. 3.3.5.3.4 Physical–Statistical Rouhani and Hall (1988) modiÞed the objective of Rouhani (1985) to include the magnitude of contaminated concentrations. Wagner et al. (1992) and Tucciarrelli and Pinder (1991) designed optimal sampling networks in conjunction with ground water management problems. Christakos and Killam (1993) used simulated annealing to design sampling programs for locating contamination. Several researchers have examined problems of plume detection or parameter estimation while selecting the best sample location as the point of greatest uncertainty (de Marsily, 1986; Graham and McLaughlin, 1989; Loaiciga et al., 1992; McGrath et al., 1996; Herrera and Pinder, 1998). The FOCuS package developed by McGrath et al. (1996) brings sample locations, model parameters, concentration and hydraulic conductivity measurements, and uncertainty together in a Monte Carlo approach to determine plume delineation. This package serves as a good starting point for a number of monitoring and sampling problems, although it is computationally intensive.



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James and Gorelick (1994) provide an excellent literature review of Bayesian decision analysis, including the earliest applications in hydrology focused on stream and ground water ßow problems. Freeze et al. (1987) outline some of the limitations associated with Bayesian stochastic process theory. Despite the bias that can result from the subjectivity of the prior estimates (Merkhofer and Runchal, 1988), many researchers believe that engineering decision-making is best carried out in a Bayesian framework, that such data are included one way or another in all analyses, and that Bayesian analyses incorporate data in an open and objective way (e.g., Massmann and Freeze, 1987a, 1987b; Freeze et al., 1990). Incorporating spatial variability into Bayesian decision analysis is important when addressing monitoring data-worth problems in realistic contamination situations. There are several ways to evaluate the worth of data. Marin et al. (1989) outlined a Bayesian risk methodology for sampling contamination in spatially heterogeneous aquifers in which the worth of a measurement was quantiÞed by the degree that it increased the precision of an estimate of contaminant concentration. The Kalman Þlter and related extended Kalman Þlters (Kalman, 1960) have only recently been applied to the ground water monitoring problem (Herrera et al., 2000; Rizzo et al., 2000). This method (e.g., Eppstein and Dougherty, 1994, 1996, 1998a, 1998b) incorporates a time-varying sequence of measurements from different spatial locations. It directly uses measurements as well as spatial and temporal statistics. The Kalman Þlter produces an estimate of the mean state and error covariances. New to long-term monitoring applications, the method has some technical issues, including a need to robustly estimate covariances of so-called system noise (i.e., nonmeasurement noise), propagate the error covariances, and manage the rapidly growing size of the covariances (leading to substantial computing problems). Rizzo et al. (2000) present a time-varying Bayesian Þlter that overcomes some of the technical issues associated with the Kalman Þlter. Like the Kalman Þlter and kriging in time, it incorporates a historical record of data. The approach currently uses calibrated physical-based models and adapts predictions of error and uncertainties as new data are collected.

3.4 GAINING ACCEPTANCE This section focuses on the effort to gain acceptance of simulation and optimization modeling for remediation system design and long-term ground water monitoring. It is evident from experience that even the best technology is ineffective unless accepted by the practicing professional. In an effort to achieve this goal of acceptance, demonstrations and Þeld efforts are noted.

3.4.1 REMEDIATION SYSTEM DESIGN OPTIMIZATION DEMONSTRATIONS Using formal mathematical optimization in remediation system design remains uncommon. The trend of adopting optimization in remediation mimics the historical adoption of formal optimization in ground water supply planning. Researchers applied optimization methods to hypothetical water supply problems long before


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clients were willing to pay to have the methods applied to their real-world problems. But gradually it happened. Optimization applications in ground water supply management became more common after readily transferable simulation optimization software for addressing real-world problems appeared (Lefkoff and Gorelick, 1987) and became ever more powerful (Greenwald, 1998; Ahlfeld and Rießer, 1999; HydroGeoSystems Group [HGS] and Systems Simulation/Optimization Laboratory [SSOL], 2000). Codes for transport optimization include those by Zheng (1997) and HGS and SSOL (2001). However, applying optimization in ground water supply planning is still not the norm. Reasons for the time lag between model development and widespread application include the usually greater cost of developing a strategy that uses optimization rather than simulation alone, client uncertainty concerning the potential beneÞt and acceptability of optimization results to other stakeholders, insufÞcient computational robustness in initial (i.e., research) versions of the simulation optimization software (i.e., software inability to compute optimal strategies for complex, real-world problems), insufÞcient options in the initial software resulting in narrow applicability, and the requirement of some optimization techniques for data that are unavailable or cannot be collected within budget or time constraints. It can be challenging to formulate optimization problems that are environmentally, politically, socially, and economically acceptable. It is important to involve stakeholders in formulating the optimization problem(s) to be solved. Different stakeholders have different goals and require different evidence to convince them that their goals have been or probably will be satisÞed. To gain consensus before optimization, concepts must be explained in a persuasive manner. The same is true after an optimal strategy is developed. Gaining Þnal approval for construction requires the conÞdence of stakeholders, appropriate communication, and sometimes persuasion. Some of the demonstrations discussed in the subsections below involved considerable negotiation. Of the four reported simulation optimization model developed pump-and-treat strategies described in the subsections below, three are compared with strategies developed by other contractors that use simulation models alone. Simulation optimization modeling yielded better pump-and-treat system designs for all contrasted situations. Generally, the earlier that simulation optimization modeling is used in the pump-and-treat design process the better. BeneÞts of simulation optimization modeling vary depending on the optimization objective, the scenario, how much freedom the simulation optimization model is given for optimization, the capabilities of the software, and the skill of the practitioner. However, a 20% improvement in cost, pumping, or remediation is a reasonable minimum expectation. 3.4.1.1 Dissolved TCE Cleanup at Central Base Area, Norton Air Force Base, California This example illustrates simulation optimization model use to design a steady pumpand-treat strategy for cleaning up the source area of a TCE plume (Peralta and Aly, 1995b). The 6,000 ¥ 5,500-ft study area involved one aquifer layer. Transmissivity ranged from 15,000 to 35,000 ft2 day–1. MODFLOW (Harbaugh and McDonald, 1996a, 1996b) and MT3D (Zheng and Wang, 1998) were used to simulate ßow and transport. This area



155

contains the original source area for the plume. A ROD speciÞed that the source area be remediated. Two optimization efforts occurred at this site: linear programming to minimize the total steady pumping and gradient search nonlinear programming to maximize the total TCE mass removal. These two efforts are described below. 3.4.1.1.1 Linear Programming Linear programming was applied to minimize total steady pumping subject to the following: •

• • • •

Preventing pathlines originating within the 5-ppb plume contour from crossing an irregular site boundary and preventing plume migration downward from the uppermost model layer Placing all wells on-site and employing two existing extraction wells if possible Applying upper bounds on extraction or injection at respective existing and candidate wells Forcing total extraction to equal total injection Using the existing treatment facility if possible

Initially, over 20 candidate wells and 40 head-difference (i.e., gradient) constraints were assumed in the optimization problem. The wells were placed near the site boundary in a normal approach. With this approach, even the best solutions required more pumping than the existing treatment facility could handle. By placing candidate injection wells along streamlines that would otherwise escape from the site (Figure 3.14), it became possible to compute an optimal solution that achieved containment and employed an acceptable pumping rate.

FIGURE 3.14 Pathlines predicted to result from optimal strategy implementation. (After Peralta and Aly, 1995a.)


156


The computed optimal pumping system design and strategy involved three extraction wells, eight injection wells, and a total extraction of 2250 gal/min. Within the model, the pumping strategy mathematically satisÞed all the constraints. Pathline analysis predicted that the strategy would prevent contaminated water from crossing the site boundary (Figure 3.14). The system was installed per optimal design. Subsequent monitoring demonstrated that the pumping strategy achieved the design goals in the Þeld: it severed the plume at the base boundary, preventing further escape. AFCEE estimated that the optimal design saved about 25% ($2 million) in construction costs and would reduce O&M costs about 20% ($5.8 million in present value) during a 15-year project life when compared with the design without simulation optimization modeling (Table 3.4). Because the objective was containment of a plume that had a distant source, the optimal design was not intended to speed cleanup (i.e., reduce project life). 3.4.1.1.2 Nonlinear Programming Gradient search nonlinear programming was used to maximize total TCE mass removal subject to the following: • • • •

Applying upper bounds on total pumping based on available treatment facility sizes Applying upper bounds on pumping from individual wells Forcing total extraction to equal total injection to satisfy water right requirements Preventing the concentration of ßow entering the treatment facility from exceeding 150 ppb

Optimal steady pumping strategy A1 assumed there was no continuous source of TCE. This strategy pumped at a steady rate of 200 gal/min from two wells each. In postoptimization simulation (i.e., simulation using the pumping strategy developed by optimization), MT3D predicted that this strategy would remove 160% more mass during 3 years than the Þve-well strategy developed without a simulation

TABLE 3.4 Predicted Cost Savings Resulting from Simulation Optimization Modeling for 15 Years of Operating the Norton AFB Southwest Boundary Containment Pump-and-Treat System

Injection wells Extraction wells Auxiliary, construction (e.g., pipelines) O&M costs (per year) O&M costs (project life)

Original (3500 gal/min)

Optimized (2250 gal/min)

Reduction in Cost after Optimization

8 4 $8,000,000 $1,600,000 $24,000,000

7 3 $6,000,000 $1,250,000 $18,750,000

$100,000 $150,000 $2,000,000 $350,000 $5,250,000



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optimization model. Optimal time-varying pumping strategy B1 assumed there was a continuous source of TCE. Pumping strategy B1 required using three wells instead of the Þve proposed by the alternative pumping strategy. This example illustrates that applying simulation optimization modeling earlier in the design process can streamline well installation and avoid unnecessary well installation costs. To address the uncertainty of the existence of an ongoing contaminant source, the design must allow an additional well to be easily added. Regardless, using simulation optimization modeling early in the remediation planning process can signiÞcantly reduce system cost. 3.4.1.2 Model Calibration and TCE/PCE Plume Containment at March AFB, California This example illustrates simulation optimization model use for pump testing and design of a pump-and-treat system and strategy to achieve plume containment (Hegazy and Peralta, 1997). The PCE and TCE plumes extend for several miles in a four-layer, fractured ßow system. The SWIFT simulation model was used because of its ability to handle dual porosity fractured ßow systems. Because insufÞcient data were initially available to calibrate the simulation model adequately, optimization was used to guide pump testing and pumping strategy selection. Regulators wanted to prevent additional contamination from crossing the site boundary to the southeast. Because the simulation model was not adequately calibrated at the time of this project, the intent of the optimization was twofold: (1) to identify desirable locations for pump tests to improve site characterization (pump test wells were then used as extraction wells for the pump-and-treat system), and (2) to provide preliminary optimization strategies to guide pumping strategy selection after calibration. To the extent possible, proposed designs utilized existing wells along the site boundary. Different optimization scenarios addressed hydrologically normal, wet, or dry years by changing boundary conditions, background pumping, and constraints. Linear programming was applied to minimize total steady pumping extraction from wells subject to the following: •

• • • • •

Capturing all pathlines originating within 5 ppb and higher contamination contours found in any of the three contaminated layers (accomplished via 0.5-ft head difference constraints) Placing all new wells on-site and considering existing wells as candidates for optimization Preventing pumping at existing wells from exceeding their sustainable rates (as determined by Þeld tests) Permitting total extraction to exceed total injection Preventing ground water from rising too close to the bottom of a landÞll Using the existing treatment facility if possible

After treatment, extracted water could be reinjected to the aquifer, discharged to the surface via a storm drain, or a combination of these two options.


158


During optimization, 74 candidate extraction wells were used, of which 9 were existing extraction wells. Five candidate injection wells were used, most of which were to be screened in multiple aquifer layers. For hydraulic control, 384 head control locations and 192 head difference constraints were used. The computed optimal strategy involved extracting and treating 237 gal/min, reinjecting 6 gal/min via wells, and discharging 231 gal/min to a storm drain. Recharge at the storm drain helped reverse the hydraulic gradient, preventing ßow off-site. Within the model, the optimal strategy caused head differences of at least 0.5 ft at control locations. Simulated pathline analysis predicted plume capture but identiÞed locations where contamination might escape under some conditions. Because aquifer data were lacking in those areas, additional pump tests were recommended. Pump test wells were subsequently used as part of the pump-and-treat system, and the computed optimal strategy guided pump-andtreat operation. To complete containment along the site’s eastern boundary, two extraction wells were added to the north of the previous target area, and these wells were integrated into the pump-and-treat system. The resulting 17 wells extract about 185 gal/min. Annually, an average of 15 gal/min is reinjected through 5 injection wells, 50 gal/min is discharged to the storm drain, and the rest is used for irrigation. Note that although the optimal strategy required 237 gal/min, the current strategy requires only 185 gal/min to contain a larger plume. This decrease illustrates the conservatism in ensuring containment and the beneÞt of carefully monitoring and managing system operation after installation. By evaluating the containment effect of the system, the on-site contractor has been able to reduce total pumping and costs. Furthermore, the site has obtained a public relations beneÞt as irrigation water users receive an increased supply. 3.4.1.3 Containment and Cleanup of TCE and DCE Plumes, Wurtsmith AFB, Michigan This example demonstrates simulation optimization model use for designing a steady pump-and-treat strategy to achieve cleanup and containment of TCE and DCE plumes (Aly and Peralta, 1997). The 22,000 ¥ 19,000-ft study area contained one sandy aquifer formation that was modeled using three layers. Hydraulic conductivity ranged from 75 to 70 ft/d in the area of primary concern, and saturated thickness was about 50 ft. MODFLOW and MT3D were used to simulate ßow and transport. Although pump-and-treat systems were already operating in the area, this project was aimed at providing additional pump-and-treat system design to contain and clean up a speciÞed portion of the plumes. The new pump-and-treat strategy was to be used in lieu of the existing pump-and-treat system. Contaminated ground water in the target area eventually ßows to surface water bodies. Negotiation with regulators resulted in 94-ppb TCE and 230-ppb DCE as the cleanup and containment goals. TCE was found at concentrations exceeding these limits in all three of the modeled layers, with DCE detected only in the second layer. The lateral extent of the TCE and DCE plumes and the location of the hot spots differ between contaminants.



159

The Þrst phase of the effort involved selecting the objective function to be used to drive the clean-up optimization. Among those considered were the following: • • • • •

Present worth of installation, pumping, and treatment costs Mass removal of TCE plus DCE Mass removal of TCE Sum of the greatest residual concentrations of TCE and DCE Greatest residual TCE concentration

A total of 24 candidate well locations within the plumes were selected. A total of 500,180-gal/min pumping strategies were randomly generated, and the results of implementing those strategies for 6 years were simulated. The strategy results were evaluated with respect to whether mandatory cleanup goals were achieved and with respect to the above Þve candidate objectives for subsequent optimization. The least-cost formulation was discarded because treatment costs were much greater than well installation and pumping costs combined. Lack of transport model calibration was weighed against using a concentration constraint to determine when pumping could cease. Furthermore, the penalty costs of not achieving cleanup within 6 years were unknown. The evaluation revealed that the strategy that removed most TCE also had acceptably low Þnal concentrations. DCE cleanup goals were achieved automatically when achieving TCE cleanup. As a result of the evaluation, the second phase of strategy development used the objective function of maximizing TCE removal subject to the following: • • • • •

Causing TCE and DCE concentrations within the target area to drop below 94 and 230 ppb, respectively, within 6 years Applying an upper limit on total pumping well below the treatment plant capacity Applying a 400-ppb upper limit on the blended concentrations of TCE and DCE Not permitting the injection of water via wells Preventing pumping in each well from exceeding an assumed maximum sustainable rate that would not completely dewater a modeled aquifer layer

The second phase used a simple genetic algorithm and ANN for transport optimization and yielded a pumping strategy that achieved cleanup goals. The third phase involved hydraulic optimization and yielded a pumping strategy that was predicted to achieve cleanup and containment goals. Pathline analysis indicated that containment would probably occur. In the fourth phase, pumping rates were manually increased to the treatment plant capacity to assess potential economic beneÞts in accelerating cleanup. Table 3.5 shows the resulting six strategies. All have ßow rates that can be treated to below 5 ppb. The time needed to achieve cleanup is estimated by the transport model. The present worth includes only the electrical costs of pumping and the treatment costs of the speciÞed rates. It does not include monitoring or other costs


160


TABLE 3.5 Comparison of Manually Edited Versions of Optimal Pumping Strategy for Wurtsmith AFB

Strategy

Total Extraction (gal/min)

Time Needed for TCE and DCE Cleanup (years)

Estimated Largest Treated Water Concentration (ppb)

Estimated Present Worth of O&M Costs ($)

A B C D E F

265 310 330 350 370 400

4.1 3.6 3.3 3.1 3.0 2.7

1.4 2.2 3.0 3.7 4.4 5.0

803,600 827,900 817,300 848,200 847,400 850,000

associated with ongoing operation or construction costs because all strategies were assumed to employ the same physical system. 3.4.1.4 Dissolved TCE Cleanup at Massachusetts Military Reservation This example illustrates a simulation optimization model used to design a pumpand-treat system and strategy for plume containment and cleanup. The three-lobed TCE plume emanating from a chemical spill on-site is about 3 miles long and 1 mile wide (Figure 3.15). The plume lies in the Mashpee Pitted Plain, a broad glacial outwash. The plain consists primarily of coarse- to Þne-grained sands with discontinuous deeper silty and clayey layers. The hydraulic conductivity generally ranges from 150 to 290 ft/day. The plume extends to 200 ft below ground surface. Flow and transport were simulated in the area, using MODFLOW and MT3DMS in a 21layer, 118-row, and 114-column Þnite difference grid. An interim ROD indicated the capture of the leading edge of the plume and cleanup while protecting previously clean aquifer and surface water resources and the ecosystem. Stakeholders were involved to determine how these concerns should be implemented in constraining an optimal pumping strategy. This information was used to design a system to contain and remediate the plume to the extent practicable during a 30-year period (HGS and SSOL, 2000). A total of 13 extraction wells, 6 injection wells, and 2 injection trenches were installed or were identiÞed as needing to be installed with identiÞed locations. A secondary contractor was to determine where to place several other extraction wells, if appropriate, to remove as much TCE mass as possible within 30 years while considering cost. A strategy was developed for expanding the existing 5-well in-plume remediation design involving adding 3 extraction wells to prevent the western plume lobe from smearing to the east and contaminating the clean aquifer, reduce the time wells north of Sandwich Road will be pumping, and signiÞcantly reduce total cleanup-period duration. An additional well was tentatively proposed to aid cleanup and prevent plume spread.



161

FIGURE 3.15 Plume, model grid, constraints, and optimal pumping locations. (After HGS and SSOL, 2000.)

A set of candidate wells were identiÞed that would, if pumping with the 5 existing wells, extract the most TCE mass during 30 years of steady pumping and satisfy desired constraints. Head and head-difference constraints were created that could be used to control heads and gradients near a USGS research site at the south end of the plume (Figure 3.15) and near another plume in the northwest. The Þrst optimal strategy maximized 30-year TCE extraction from 8 in-plume wells plus Sandwich Road wells subject to the following: •

• • •

Preventing unacceptable head change in Edmunds and Osborne Ponds (i.e., preventing more than 0.5 ft of head change in those ponds within the regional model) Preventing concentrations exceeding 5 ppb from crossing the site boundary and moving from the western lobe to the southwestern lobe Preventing total extraction at in-plume wells from exceeding 2700 gal/min Forcing total extraction at eight Sandwich Road wells to equal that of the original strategy


162


• • • •

Forcing total recharge at the west and east trenches to equal total extraction from the eight in-plume wells Forcing total injection at the six Sandwich Road injection wells to equal total extraction at the eight Sandwich Road extraction wells Preventing pumping of active individual in-plume wells from exceeding upper limits based on well, pump, or line sizes Using loose constraints on head or head difference near the USGS research site and the head of the plume in the northwest

No compliance monitoring requirements were used as constraints or in the objective function. Mass removal from both in-plume and Sandwich Road wells was maximized because extraction at an in-plume well upgradient of Sandwich Road affects mass extraction at Sandwich Road. Therefore, optimizing the management of the total system requires coordinated optimization of in-plume and Sandwich Road wells. Several pumping strategies for different sets of assumptions were developed. The Þnal recommendation was to implement an optimal pumping strategy of eight in-plume wells. Subsequent simulation predicted that 30 years of such pumping extracts about 6% more mass than the original strategy, although it requires 50 gal/min less total extraction. Regional model simulation predicted the strategy would satisfy all imposed constraints. With minor adjustments, the new wells have been constructed. Thus, optimization aided well placement and will enhance mass removal and likely ultimately will result in a shortened cleanup period. If simulation optimization modeling had been implemented sooner, it probably would have yielded greater beneÞts. This project was not an ideal demonstration of the power of optimization because not all the constraints could be included within the optimization model. Strategy effects and desirability were evaluated by using a regional model and other models and tools that could not be included within the optimization model. (The plume model itself required 30 min to run, and the other models took longer.) Furthermore, the scope was limited to maximizing mass removal rather than performing economic optimization because it was physically impossible in the model to develop strategies that can achieve cleanup within the 30-year pumping period. The physical impossibility results from a limitation on permitted total annual pumping (cost) and because it is inadvisable to screen wells in the deep contaminated silt layers. Without screening in those layers, cleanup cannot be predicted within 30 years.

3.4.2 LONG-TERM MONITORING FIELD STUDIES Table 3.6 summarizes some recently reported long-term monitoring optimization results that clearly show that software-based analyses can provide signiÞcant beneÞts. Of the Þve sites where the monitoring costs before and after the optimization analysis are available, the amount of cost savings resulting from the optimization studies ranges from 10 to 55%. For example, Hansen (1999) showed how a total of 220 monitoring wells was reduced to 170 at the Badger Army Ammunition Plant. The signiÞcant reduction in the total number of wells, along with the reduced number of analyses and sampling frequency, led to a total cost savings of $400,000 per year from the annual monitoring



163

TABLE 3.6 Some Reports of Long-Term Monitoring Optimization Study Results and Savings Author(s)

Application

Key Features

Reed (1999)

Uses Hill AFB data. BTEX and natural attenuation. Study of monitoring network design for total mass estimates.

Klawitter and Barry (1999)

Naval Air Station, Brunswick, ME. Mostly solvents and pump and treat with UV/oxidation. Long-term monitoring.

Cronce et al. (1999)

80-acre Naval Industrial Reserve Ordnance Plant, Fridley, MN. TCE and six-well pump and treat with air stripping. Long-term monitoring. Badger Army Ammunition Plant. Long-term monitoring.

12 monitoring well design obtained using inverse distance. Reduced to 8 monitoring wells using kriging. Also obtained 8 monitoring wells using hybrid method. Initially 36 monitoring wells sampled quarterly at $550,000/year. Geostatistics and DQO led to 22 monitoring wells at reduced frequency, data gap monitoring wells, and savings of about $300,000/year. Cost of assessment was $40,000. Savings of about 10% using SmartSite approach.

Hansen (1999)

Naber et al. (1999)

Formerly used defense site. BTEX and MTBE with air sparging and natural attenuation.

Hassig et al. (1999)

Pantex (DOE) site. Extensive list of contaminants. Using DQO process to develop basis for decisions.

Singh and Harvey (1999) Hunter (1999), Cameron (1999)

McClellan AFB. Soil vapor extraction.

TuckÞeld and Ridley (1999)

Wurtsmith AFB. TCE and trans-1,2DCE with pump and treat and air stripping. LLNL.

Initially 220 wells. Reduced to 170 wells. Reduced number of analytes and sampling frequency. Savings of $400,000/year from $1,400,000/year in long-term monitoring Methods involve data visualization and common-sense procedures. Initially 54 monitoring wells. Reduced to 49 monitoring wells. 10% savings in sampling and lab costs using redundancy analysis. Initially 70 monitoring wells on quarterly or semiannual schedule. Claim reduction of $400,000/year using unspeciÞed decision logic. Saved $30,000/year in analytical costs using decision tree method. Reduced frequency 75% using time series analysis and LLNL decision tree method. $200,000/year (25 to 40%) savings in monitoring. Cost of analysis $85,000 using decision tree (costeffective sampling) method. (continued)


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TABLE 3.6 (CONTINUED) Some Reports of Long-Term Monitoring Optimization Study Results and Savings Author(s)

Application

Key Features

Rizzo et al. (2000)

Active defense site. TCE and pump and treat. Preliminary study of long-term monitoring optimization.

Johnson et al. (1996)

SRS (DOE), Area F. Nine contaminants.

Ridley et al. (1995); Ridley and Johnson (1996)

LLNL Main Site and Site 300. TCE with pump and treat and others.

Reduced from 15 to 9 monitoring wells to monitor containment on part of property boundary with same uncertainty. Used geostatistics for spatial and temporal redundancy, incorporating site simulation model. Initially 89 monitoring wells on quarterly schedule. Reduced to 35 monitoring wells quarterly, 51 semiannually, 3 annually for estimated $177,000/year lab savings. Initially (1992 data) for Main Site/Site 300: 212/297 wells sampled quarterly. Sampled 77/0 monitoring wells semiannually, and 7/26 annually. Decision tree (costeffective sampling) led to sampling 81/180 monitoring wells quarterly, 65/117 semiannually, and 150/134 annually. Savings of $230,000/year at Main Site and $160,000/year at Site 300.

Note: DQO = data quality objective.

budget of $1,400,000. In another example, Hunter (1999) and Cameron (1999) showed that the sampling frequency could be reduced effectively by 75% at the Wurtsmith Air Force Base, MI. Finally, Klawitter and Barry (1999) showed that only 22 monitoring wells would be needed of the initial 36 at the Naval Air site in Brunswick, ME. Coupled with reduced sampling frequency, the monitoring network optimization led to an annual cost savings of $300,000 from a total budget of $550,000.

3.4.3 COMMUNICATION IMPROVEMENTS Clearly there is a need to improve the awareness, understanding, and the ease of use of mathematical optimization techniques to encourage more widespread use within the remediation and monitoring community. There are three groups of users that require different levels of information: the problem holder, the consulting engineer, and the regulator. Because the problem holder (e.g., industry, government) will likely pay for the analysis, it is crucial to communicate the potential beneÞts to be achieved



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using optimization. In particular, mathematical optimization can not only provide solutions for improved remediation or monitoring system designs but it can also substantially reduce the construction, O&M, and monitoring costs of a remediation system. Problem holders are particularly interested in the cost-savings aspect of these approaches. Furthermore, mathematical optimization can provide an unbiased evaluation of different remedial designs and can determine if a feasible solution for a given set of constraints is possible. This application of mathematical optimization can bring regulators and problem holders together to identify cost-effective solutions that are protective to human health and the environment. Examples of such applications, either through documented case studies or other cost and performance documents, should be shared with problem holders. Presently there is a lack of availability of these documents in the public domain, and many problem holders are unaware of the beneÞts of these approaches. Federal and state governments should take the lead in distributing cost and performance information on these technologies where they have been applied to government-owned sites. Typically, consulting engineering Þrms are integrally involved in managing cleanup projects. Problem holders can rely on suggestions from consultants for site characterization, remedial design, and regulatory negotiations. Currently, there are very few consultants aware of mathematical optimization techniques and even fewer consulting Þrms with in-house expertise. Most expertise with mathematical optimization remains in the academic community. If consultants are unaware of these techniques, do not have in-house expertise, and are not aware of readily accessible vendors, they are unlikely to suggest the use of mathematical optimization to their clients. More cost and performance case studies and training on mathematical optimization approaches need to be made available to consultants. Also, there is a need to package optimization algorithms into user-friendly software programs and distribute the software in the public domain. Regulators need to be aware of these approaches to avoid becoming a roadblock to their use. Typically regulators are involved in a review role and do not require the use of mathematical optimization codes at the sites they manage. However, at some Superfund sites where there is no Þnancially viable responsible party (fund-led site), the USEPA manages and funds the cleanup. In a new initiative in 2000, the Superfund Program plans to evaluate fund-led pump-and-treat sites for optimization potential (USEPA, 2000a). In this initiative up to two sites with pump-and-treat systems in each USEPA region will be evaluated for optimization potential, and a list of recommendations and a plan to implement these recommendations will be developed. The goals of this initiative are to begin employing optimization approaches at USEPA’s fund-led pump-and-treat sites and to increase the national awareness of the need and beneÞt of optimization. The initial work will not involve the use of mathematical optimization algorithms; however, sites that can beneÞt from mathematical optimization will be identiÞed and considered for future applications. This initiative will be an opportunity for the USEPA to showcase various optimization approaches and share the cost, performance, and lessons learned with the remediation community. In summary, there is a need to provide more real-world examples of the applications of mathematical optimization, publish cost and performance case study reports, and provide training and packaged software programs to improve the under-


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standing of these techniques. Federal and state governments need to take the lead in sharing information on applications of mathematical optimization for the design, redesign, and monitoring of contaminated waste sites.

3.5 CHALLENGES AND EMERGING ISSUES 3.5.1 OPTIMIZATION ALGORITHMIC CHALLENGES IDENTIFIED THROUGH APPLICATION NEEDS In an optimal remediation or monitoring strategy the ultimate goal is, of course, to Þnd an effective and efÞcient solution that works in the real world. But methods are based on idealized and approximate models, both because knowledge of the realworld system is limited and because the computational requirements of optimization algorithms can be formidable. Because the models used to derive remediation and monitoring designs are imperfect, these designs are likely to be suboptimal. That is, the performance obtained from the design is likely to be poorer than the best possible performance that could be achieved. Unfortunately, there is no general way to determine how much performance decreases with a suboptimal solution (because the optimal design is not known). Therefore, how to evaluate the performance penalties incurred by particular approximations and simpliÞcations is usually not known. Consequently there are few, if any, guidelines to help analysts select reasonable approximations and simpliÞcations. The process remains an art that relies heavily on personal experience and judgment. To many analysts, an ideal model is one that accounts for all the natural processes (i.e., physical, chemical, and biological) and all the logistic or policy constraints that could have a signiÞcant inßuence on real-world performance. Because this standard is rarely achieved, there is nearly always room for improvement in a modeling or remediation investigation. The desire to improve model realism exerts pressure to increase model complexity. Many of the improvements that make models more complex seem reasonable enough on the surface. For example, in ground water remediation applications one can make a good case for considering most, or even all, of the following factors: • • •

Natural variability over space and time Multiple constituents Multiple phases

These factors are discussed in the following subsections. 3.5.1.1 Natural Variability Over Space and Time Natural variability ßuctuations in hydrogeologic and geochemical properties cover a wide range of space scales (from centimeters to kilometers), as well as temporal variations in quantities such as ground water recharge rates, water table elevations, and discharge and pumping rates. It is well known that such variability can have an important effect on contaminant mixing (or macrodispersion), but it is difÞcult to



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account for this effect in practical applications where information on variability is usually quite limited. 3.5.1.2 Multiple Constituents In many practical applications (e.g., industrial or oil spills) the contaminants of interest are mixtures of a possibly unknown number of chemicals with different properties. Because the constituents can interact, it can be important to model the mixture explicitly. 3.5.1.3 Multiple Phases Many of the most persistent and difÞcult to remediate contaminants can coexist in several distinct phases. These can include aqueous and NAPLs, vapor-phase contaminants, and contaminants sorbed onto soil particles. Accurate descriptions of such contaminants must include the physical transport of each phase as well as the movement of constituents between phases. The pressure to include some or all of the factors mentioned in this list imposes substantial computational burdens for contaminant modeling and optimization. To give some feeling for these burdens, the effect of accounting for natural variability can be considered while including multiple phases and chemical constituents. To properly incorporate natural variability into a modeling study, important variations in subsurface properties must be resolved or an upscaling procedure must be determined that can predict the aggregate effects of such variations. SigniÞcant variability can occur at spatial scales of O-(1 ft) in the horizontal and O-(0.1 ft) in the vertical. This implies O-(106) degrees of freedom in a spatially discretized 3-D model of a relatively small site of size O-(102 ft) horizontal by O-(10 ft) vertical. The computational effort required to run such a model generally increases as the number of degrees of freedom raised to a power between 1 and 2. This implies that the effort needed to run a 106 degree of freedom model is orders of magnitude greater than the effort required to run a more typical model of 103 degrees of freedom. The computational challenge is clearly compounded when many different constituents, phases, and chemical processes are added. Effort increases because the number of degrees of freedom increases (e.g., from 106 to 107 if Þve constituents are followed in each of two phases) and because the nonlinear relationships frequently used to describe phase transitions, microbiological processes, and chemical reactions require iterative solvers that can easily increase computational effort by two orders of magnitude over a purely linear problem. Models that account for natural variability as well as reasonably realistic chemistry and microbiology over regions of practical interest are essentially beyond the current state-of-the-art practice. But computational effort is only part of the challenge presented by natural variability. An even more fundamental problem is the lack of information needed to characterize subsurface variability in either a deterministic or a statistical sense. This problem is not likely to be resolved in the foreseeable future, except for certain variables that are amenable to remote sensing (e.g., topography). Hence, upscaling


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continues to be an important research topic in the hope that these methods can sufÞciently account for the macroscopic effects of geological variability that exists but cannot be described. Such methods typically rely on assumptions about the nature of natural variability, which is difÞcult to verify. Upscaling theories are often highly mathematical and have not been widely applied. Moreover, these theories cannot address anomalies that extend over distances that are signiÞcant fractions of the problem scale. For all these reasons, proper treatment of natural variability remains an open question. As models become more complex, computational and data requirements can easily get out of hand, rendering optimization infeasible. Clearly, intelligent models that focus only on the essential features of a problem are needed rather than exhaustive models that attempt to include all potentially relevant processes. It is possible to derive smaller, more focused optimization models from larger, more comprehensive simulation models using formal techniques such as response matrix analysis and model reduction. Efforts to derive intelligent optimization models undoubtedly will be more successful if these models incorporate Þeld data as well as information obtained from detailed simulation models. This is possible only if the optimization models are kept relatively simple so that they can be used with measurement conditioning and adaptive estimation procedures. In the end, the best way to address the computational and data challenges outlined above is to change the view of the models used for optimization studies. Instead of treating these models as simpliÞed approximations of reality, they should be thought of as data-processing algorithms that can help organize and interpret information, especially Þeld data, but cannot be expected to make good predictions without constant support and intervention. They must be able to identify and adapt to changing conditions rather than anticipate every possible behavior. As a result, optimal (really suboptimal) designs must incorporate feedback whenever possible so that the underlying optimization model can learn from failures as well as successes. Interestingly, this is reßected in the current trend towards intrinsic remediation and the movement away from elaborate, expensive, and inßexible treatment facilities. An emphasis on models that are small, intelligent, and adaptive does not imply replacing good physical, chemical, and biological understanding with a set of ad hoc rules. Rather, physical understanding must guide model development in new and innovative ways. The models needed for optimization cannot be obtained simply by discretizing and iteratively solving sets of coupled nonlinear partial differential equations over very large grids. More parsimonious ways are needed to incorporate physics, chemistry, and biology into the optimization process. This is the primary conceptual and practical challenge to be addressed in the coming decade.

3.6 SUMMARY The topic of optimization and modeling for remediation and monitoring has been discussed in this chapter, including the perspectives on the methodology, the current state of knowledge, and the challenges in gaining acceptance for the methods and emerging issues. It was found that, although there is consensus within the engineering design community that these methods lead naturally to more cost-effective designs



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for ground water contamination identiÞcation, remediation, and monitoring, their use has been limited. Several reasons for this are evident, but possibly the most important is a lack of knowledge regarding their potential, utilization, and effectiveness. This chapter has provided numerous examples of the use of optimization and ground water modeling technologies. Demonstrably cost-effective and appropriate for a number of different ground water contamination situations, these optimal design tools are gradually being adopted not only by industry but also by government. Future challenges to their broad acceptance are both technological and sociological. Exposure of the professional ground water community to this methodology will facilitate its utilization in practical application.

ACKNOWLEDGMENTS The material presented in this chapter reßects the discussions and deliberations of the Optimization and Modeling for Remediation and Monitoring Panel. In addition to the authors, Christine Shoemaker of Cornell University participated in the panel discussions, and David E. Dougherty and Donna M. Rizzo provided invited sections of this chapter. Calvin C. Chien of DuPont and Chunmiao Zheng of the University of Alabama made signiÞcant contributions to the initial editing of this chapter. The panel wishes to recognize the contribution of Bob Genau, panel liaison with DuPont; Calvin Chien who conceived of, organized, and managed the workshop and the publication of this book; and Kathleen O. Adams, DuPont contract technical writer, who went above and beyond the call of duty in bringing this manuscript together.

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4

Modeling Fate and Transport of Chlorinated Organic Compounds in the Subsurface prepared by Brent E. Sleep with contributions by Neal D. Durant, Charles R. Faust, Joseph G. Guarnaccia, Mark R. Harkness, Jack C. Parker, Lily Sehayek

CONTENTS 4.1 4.2

4.3

Introduction and Overview ..........................................................................181 Basic Concepts and Equations.....................................................................182 4.2.1 Multiphase Fluid Flow.....................................................................183 4.2.1.1 Darcy’s Law for Multiphase Flow ...................................183 4.2.1.2 Capillary Pressure and Relative Permeability Relations.... 184 4.2.2 Multicomponent Mass Transport .....................................................188 4.2.2.1 Mass Balance and Transport Flux Equations...................188 4.2.2.2 Interphase Mass Transfer..................................................189 4.2.3 Modeling Biotic and Abiotic Transformations................................193 4.2.3.1 Review of Chlorinated Solvent ............................................. Transformation Mechanisms193 4.2.3.2 Petroleum Hydrocarbon Biodegradation Models.............197 4.2.3.3 Chlorinated Solvent Biodegradation Models ...................199 4.2.4 Computational Issues .......................................................................202 4.2.4.1 Spatial Discretization........................................................202 4.2.4.2 Temporal Discretization....................................................203 4.2.4.3 Linearization of Nonlinear Equations of Multiphase Flow and Transport...........................................................203 4.2.4.4 Solution of Linear Equations ...........................................204 Modeling DNAPLs — State of Practice .....................................................205 4.3.1 Roles of Modeling ...........................................................................205 4.3.1.1 Research and Education ...................................................205

0-56670-667-X/04/$0.00+$1.50 © 2004 by CRC Press LLC

179


180

4.4

4.5

4.6

4.7


4.3.1.2 Policy Development..........................................................207 4.3.1.3 Site Assessment and Remedial Design ............................208 4.3.2 Overview of Existing Models..........................................................209 4.3.2.1 Three-Phase Models .........................................................209 4.3.2.2 Two-Phase Models............................................................210 4.3.2.3 Single-Phase Models ........................................................211 4.3.2.4 Chlorinated Solvent Biodegradation Models ...................212 4.3.3 Model Selection and Limitations.....................................................213 Site Applications ..........................................................................................215 4.4.1 Gulf Coast EDC DNAPL Release...................................................215 4.4.2 DNAPL Source Area Characterization Coastal Plain of New Jersey .. 216 Research Needs ............................................................................................221 4.5.1 Constitutive Relationships for Multiphase Flow, Transport, and Interphase Mass Transfer ..........................................................222 4.5.1.1 k–S–P Relationships .........................................................222 4.5.1.2 Mass Transfer Relationships ............................................224 4.5.1.3 Summary ...........................................................................224 4.5.2 DNAPLs In Fractured Media...........................................................225 4.5.2.1 Unsaturated Water Flow ...................................................225 4.5.2.2 NAPLs...............................................................................225 4.5.2.3 Summary ...........................................................................226 4.5.3 Impact of Biodegradation on DNAPL Dissolution .........................227 4.5.3.1 Model Development .........................................................227 4.5.3.2 Reductive Dechlorination Rates at High Concentrations of Dissolved PCE ....................................229 4.5.3.3 Reductive Dechlorination in the Presence of DNAPL ....230 4.5.3.4 Summary ...........................................................................230 4.5.4 Plume Attenuation............................................................................231 4.5.4.1 Biotransformation Kinetics...............................................231 4.5.4.2 Halorespiration..................................................................232 4.5.4.3 Spatial Variability in Redox Conditions...........................233 4.5.4.4 Complex Mixtures ............................................................233 4.5.4.5 Bioavailability and Mass Transfer from Sorbed Phase ...234 4.5.4.6 Summary ...........................................................................234 Technology Transfer ....................................................................................235 4.6.1 Approach ..........................................................................................236 4.6.1.1 QA Standards....................................................................237 4.6.1.2 Expert Decision Support System .....................................238 4.6.1.3 Model Application Archive and Database Support..........239 4.6.1.4 Training Support ...............................................................240 4.6.2 Implementation Recommendations..................................................240 Summary, Conclusions, and Recommendations..........................................242 4.7.1 Multiphase Flow and Transport .......................................................244 4.7.2 Chlorinated Hydrocarbon Biodegradation.......................................244 4.7.3 Technology Transfer ........................................................................245


Chlorinated Organic Compounds in the Subsurface

181

Acknowledgments..................................................................................................245 References..............................................................................................................245

4.1 INTRODUCTION AND OVERVIEW Halogenated organic compounds such as chlorinated aliphatic and aromatic compounds have been used widely as solvents since the early 1940s. As a result of widespread production, transportation, use, and disposal, these compounds are common ground water contaminants. Due to their limited but environmentally signiÞcant aqueous phase solubilities, spills of these compounds typically result in the formation and migration of a separate organic phase that is denser than water. This dense nonaqueous phase liquid (DNAPL) can move signiÞcant distances in the subsurface, contaminating large volumes of the subsurface environment. The residual DNAPL left in the wake of DNAPL ßow can persist as a source of contamination for decades, slowly dissolving into the water phase and volatilizing into the soil–gas phase in the vadose zone. Chlorinated organic compounds are among the most serious ground water contaminants because of their mobility and persistence in the subsurface, their widespread use, and their health effects. Consequently, billions of dollars are being spent in efforts to remediate ground water contamination from chlorinated organic compounds. Developing and applying reliable, accurate, and readily available fate and transport models is greatly needed to assess the risks posed by spills of these compounds to the subsurface and to aid in evaluating and designing remediation programs to address these spills. A variety of sophisticated research level models to predict chlorinated organic compound fate and transport in the subsurface have been developed. The use of these models is limited, however, due to mathematical complexity, signiÞcant data demands, and the inability to validate the many assumptions inherent in these models. Research on the physics, chemistry, and biology of these compounds and the prediction of their fate and transport in a complex, heterogeneous subsurface is ongoing, with many questions yet to be answered. The panel discussed issues associated with simulating chlorinated organic compound behavior in the subsurface. Presentations by panel members focused on modeling chlorinated organic compound fate and transport under natural conditions rather than under enhanced remediation conditions. The uncertainty associated with constitutive relationships appropriate for use in Þeld-scale modeling was identiÞed as an area for continued research. Aspects of modeling DNAPL source dissolution and volatilization were covered, and practical techniques for modeling source-term behavior at the Þeld scale were presented. The problems of dealing with subsurface heterogeneities in simulating Þeld-scale DNAPL behavior were identiÞed, and the need for upscaling methods and robust inverse modeling techniques were emphasized. Several presentations addressed the current practices for modeling the natural attenuation of chlorinated compounds. Chlorinated compound biodegradation models of varying levels of complexity were reviewed. The need for a framework for choosing the appropriate level of simpliÞcation in modeling was discussed, and the need for technology transfer to end users of models was emphasized.


182


The objective of this chapter is to review the state of the art with respect to the simulation of chlorinated organic compounds in the subsurface and to present the conclusions and recommendations of the panel with respect to basic research needs and issues of technology transfer. Basic concepts and equations underlying models for chlorinated organic compound behavior in the subsurface are reviewed in Section 4.2. The current state of practice with respect to modeling chlorinated organic compound fate and transport is described in Section 4.3. Section 4.4 contains Þeld applications presented by the panel. Research needs identiÞed by the panel are discussed in Section 4.5, while Section 4.6 examines aspects of technology transfer required for the effective promulgation of simulation models for chlorinated organic compounds.

4.2 BASIC CONCEPTS AND EQUATIONS Halogenated organic compounds exhibit a broad range of physical and chemical properties. Those of particular concern as ground water contaminants include industrial solvents, such as tetrachloroethylene (also known as perchloroethylene or PCE), trichloroethylene (TCE), and carbon tetrachloride (CT), and a variety of polychlorinated biphenyl (PCB) oils used in various industrial applications (Pankow and Cherry, 1996). These compounds are liquids at normal subsurface temperatures and have limited solubility in water and speciÞc gravities greater than water. They thus fall under the deÞnition of DNAPLs (Schwille, 1988). The speciÞc gravity of chlorinated aliphatic hydrocarbons can be as high as 1.6 (Mercer and Waddell, 1993). Low solubility and relatively high density are key properties that lead to the complexity of their distribution in the subsurface. In this section, these and other important factors are discussed. The transport and fate of halogenated organic compounds in the subsurface are controlled by complex phenomena in a wide variety of hydrologic settings. Halogenated organic compounds can occur as a nonaqueous phase liquid (NAPL) and as species in the aqueous, vapor, and soil phases (Figure 4.1). The phenomena that govern the behavior of halogenated organic compounds in the subsurface can be classed into two broad categories that control the following: •

•

Fluid-phase distributions and bulk ßow of NAPL, water, and vapor phases in the subsurface as affected by gravity, capillary and buoyancy forces, pore geometry, and larger-scale heterogeneity Interphase mass transfer, transport, and attenuation of halogenated compounds and their by-products through dissolution, volatilization, sorption/desorption, colloidal transport, diffusion–dispersion, and chemical and biological reactions

In general, modeling halogenated organic compounds involves simulating subsurface systems composed of more than one ßuid phase and requires a conceptual understanding of the relevant chemical, physical, and biological processes that control the distributions, interactions, and reactions within all phases.



183

DNAPL Release

DNAPL Vapor

Dissolved DNAPL Plume Residual DNAPL Ground Water Flow

Low-permeability layer

DNAPL Pool

DNAPL

FIGURE 4.1 Fate and transport of chlorinated organic compound releases in the subsurface.

Mathematical models for multiphase migration of organic contaminants in soils and aquifers were presented by numerous authors beginning in the 1980s (e.g., Abriola and Pinder, 1985; Faust, 1985; Osborne and Sykes, 1986; Kuppusamy et al., 1987; Faust et al., 1989; Sleep and Sykes, 1989, 1993) based on earlier models developed for petroleum reservoir engineering (Aziz and Settari, 1979).

4.2.1 MULTIPHASE FLUID FLOW 4.2.1.1 Darcy’s Law for Multiphase Flow When an organic ßuid enters the subsurface, it ßows downward due to gravity and capillary forces and moves laterally due to capillary forces. In the vadose zone, the organic displaces the air and water as it moves through the soil pores. DNAPLs can move below the water table, whereas LNAPLs pool on the water table. Below the water table, a DNAPL displaces the water phase as it moves downward. The movement of the organic phase through the soil pores is affected by the organic ßuid density, viscosity, interfacial tension with water and air, contact angle of the phase interface with the aquifer solids (i.e., wettability), and by the soil porosity, permeability, and pore-size distribution. The inßuence of these factors is manifested in the following generalized form of Darcy’s law that can be used to describe continuum level multiphase ßow in porous media: qb = -

krb k mb

(—P + r g—z) b

b

(4.1)


184


In this equation, qb is the Darcy velocity of phase b (b = g for gas, w for water, and n for NAPL), krb is the relative permeability of phase b, k is the intrinsic permeability tensor, m b is the viscosity of phase b, Pb is the phase pressure, rb is the phase mass density, g is the gravitational acceleration constant, and z is the elevation. Intrinsic permeability is so called because it is generally assumed to be an inherent characteristic of the porous medium. This assumption is usually valid in coarse-grained media but can be a poor assumption in certain cases in which interactions between ßuids and soil grains result in temporal changes in the pore structure or if the porosity or pore structure are affected by changes in pore pressure (e.g., consolidation, shrink-swell, hydraulic fracturing). Although in most applications permeability is assumed to be constant over time for a given porous medium, in certain cases consideration of possible relationships to present or historical phase pressures or species concentrations may be necessary for accurate predictions. The tensorial nature of permeability reßects anisotropy that may develop due to a nonrandom spatial orientation of the pore structure or ßuid distributions or of larger scale heterogeneities (e.g., fractures, layering). Fluid densities vary as a function of respective ßuid pressures. For liquids subjected to small pressure variations, ßuid compressibility can often be safely disregarded. Gas-phase compressibility is signiÞcantly greater than that of liquids, and, if gas ßow is modeled, compressibility should be considered. Gas compressibility can be easily modeled based on ideal gas theory. Density effects may be signiÞcant in the vapor transport of dense volatile organics in permeable porous media (Sleep and Sykes, 1989). The mobility of NAPLs is inßuenced by viscosity. Less viscous NAPLs tend to migrate farther and more rapidly than others (Cohen and Mercer, 1993). Capillary pressures (i.e., the pressure differences between phases) are related to interfacial tension between the phases, wettability, and pore geometry. Decreases in interfacial tension or increases in contact angle decrease capillary pressures between phases for a given pore geometry. Decreases in interfacial tension or increases in contact angle thus decrease entry pressures for nonwetting phases into Þne-grained media, increasing the mobility of the nonwetting phase. In general, ßuid density, viscosity, and interfacial tensions vary as functions of temperature and phase composition (Ma and Sleep, 1997). In isothermal, noncompositional models, the dependence of ßuid properties on temperature and ßuid composition and their temporal and spatial variations are disregarded. However, where these effects are expected to be signiÞcant, they should be taken into account. Temperature effects on these properties are well understood, and mixture theories exist to compute compositional effects. In cases where the effects of surfactants may be under consideration, speciÞc experimental studies are required to characterize the behavior. 4.2.1.2 Capillary Pressure and Relative Permeability Relations In multiphase systems, pressure differences (i.e., capillary pressures) exist between phases as a result of interfacial tension between phases and curvature of the phase interfaces. The capillary pressure across a curved interface with principal radii r1 and r2 is given by the following Laplace–Young Equation (Hunter, 1991):



È1 1 ù pc = p ¢¢ - p ¢ = g Í + ú Î r1 r2 û

185

(4.2)

where p≤ and p¢ are pressures on opposite sides of the interface. The principal radii for an interface in a soil pore are related to pore shape, the location of the phase interface in the pore, and the contact angle between the phase interface and the aquifer solids. The contact angle, measured through the denser ßuid, is determined by the chemical nature of the ßuids and the solid. Wetting ßuids have contact angles less than 90°, while nonwetting ßuids have contact angles greater than 90°. In the vadose zone, liquids (i.e., NAPLs or water) are usually wetting ßuids compared with air. In the saturated zone, most natural porous media are strongly water-wet (Anderson, 1986); the exception may be when signiÞcant quantities of natural organic matter, graphite, silicates, and many sulÞdes are present in the porous medium. When determining the wettability of multiphase systems containing NAPLs, several factors should be considered, including water chemistry, NAPL chemical composition, presence of natural organic matter, presence of other agents (e.g., surfactants), aquifer saturation history, and mineral composition of the porous medium. For a three-phase (i.e., gas, water, NAPL) system, water is usually the most wetting phase, gas the least wetting, and NAPL the intermediate (Parker et al., 1987). The physically relevant capillary pressures are thus: pgn = pg – pn

(4.3a)

pnw = pn – pw

(4.3b)

where subscripts g, w, and n designate gas, water, and NAPL, respectively. For monotonically changing ßuid saturations, the gas–NAPL interface curvature, and, thus, the gas–NAPL capillary pressure, is expected to be a function of gas-phase saturation. The gas-phase saturation controls gas-relative permeability. Similarly, NAPL–water capillary pressure is expected to be a function of water saturation, which controls water-relative permeability. Various mathematical functions have been proposed to describe ßuid saturation–capillary pressure (k–S–P) relationships for two-phase and three-phase ßuid systems (Aziz and Settari, 1979; Corey, 1986; Parker et al., 1987). For example, for a two-phase NAPL–water system, the Brooks–Corey relationship for NAPL–water capillary pressure is as follows: l

Sew

Êp ˆ = Á dow ˜ for pcow > pdow Ë pcow ¯

Sew = 1

for pcow = pdow

(4.4a)

(4.4b)


186


where Sew is the effective water saturation calculated from the water saturation, Sw; the maximum water saturation, Sm; and the irreducible water saturation, Swr: Sew =

Sw - Swr Sm - Swr

(4.5)

where pdow is the entry or bubbling pressure for the nonwetting (i.e., NAPL) phase, and l is an empirical constant termed the pore size index. In contrast, Lenhard and Parker (1987) used relationships based on the following van Genuchten (1980) formulation:

[

Sew = 1 + (apcow )

]

n -m

(4.6)

where a, m, and n are empirical parameters. It should be noted that the Brooks–Corey model incorporates a distinct entry pressure for the onset of nonwetting-phase displacement of the wetting phase, whereas the van Genuchten–based model does not. As ßuid saturations change in multiphase systems, the relative permeability of the phase changes. Stone (1973) proposed a method for computing three-phase relative permeabilities from measured two-phase air–NAPL and NAPL–water relative permeabilities assuming the water relative permeability to be a function of water saturation only, air relative permeability to be a function of air saturation only, and NAPL relative permeability to be a function of air and water saturations. Relative permeability expressions based on ßuid saturations and the parameters from the capillary pressure saturation relationships also have been developed for the Brooks–Corey relationship by using the Burdine (1953) pore size distribution model by integrating the capillary pressure saturation relationships over the range of allowable saturations. The Mualem (1976) model was used with the van Genuchten model to develop corresponding relative permeability expressions. For given organic-phase saturations, the Burdine relative permeability is always smaller than the Mualem relative permeability (Oostrom and Lenhard, 1998). Oostrom and Lenhard (1998) compared results from one-dimensional column LNAPL inÞltration experiments with predictions of a simulator by using the Brooks–Corey and the van Genuchten models. They found that the Brooks–Corey model gave a better match to experimental data than did the van Genuchten model. This match was attributed to the Þnite entry pressure of the Brooks–Corey model as well as to differences in the relative permeability expressions associated with the two models. Direct measurement of three-phase k–S–P relations is difÞcult and tedious, and three-phase relations are generally predicted theoretically from two-phase data. Relative permeabilities are seldom measured but are predicted using the parameters determined from capillary pressure saturation measurements. Leverett (1941) was the Þrst to propose that three-phase air–NAPL–water k–S–P functions could be predicted from two-phase relationships (i.e., air–NAPL and NAPL–water functions)



187

assuming ßuid wettability in the order of water > NAPL > air such that NAPL occurs as Þlms between water and gas phases. Similar arguments supported by experimental data suggest that water saturation may be regarded as a function of NAPL–water capillary pressure only and air saturation may be regarded as a function of air–NAPL capillary pressure. Furthermore, two-phase S–P functions for different ßuids should be scalable by the respective ßuid–ßuid interfacial tensions. Parker et al. (1987) presented a method for estimating three-phase relations from two-phase air–water capillary pressure–saturation data using a scaling approach air–water and NAPL–water interfacial tension data. The Leverett (1941) scaling approach used by Parker et al. (1987) assumes that the spreading coefÞcients for the systems are zero. The spreading coefÞcient, S, is calculated from the interfacial tensions between the phases (Oren and Pinczewski, 1995): S = saw – san – snw

(4.7)

where saw, san, and snw are the air–water, air–NAPL, and NAPL–water interfacial tensions, respectively. Some NAPLs have positive spreading coefÞcients, whereas others, including many chlorinated solvents, have negative spreading coefÞcients. For those with positive spreading coefÞcients, the organic phase forms a Þlm between the air and the water phases in a three-phase system, resulting in a system that effectively has a spreading coefÞcient of zero (Oren and Pincewski, 1995). The existence of this Þlm has important implications for residual NAPL saturations in three-phase systems. For NAPLs with negative spreading coefÞcients, discontinuities in the NAPL phase may be more pronounced in three-phase systems, leading to higher residual NAPL saturations in the vadose zone (Zhou and Blunt, 1997). Hofstee et al. (1997) found that Leverett scaling could be applied only to a small portion of water–PCE–air retention curves. Below a critical PCE saturation, the total liquid content appeared to become a function of the capillary pressure across the air–water interface rather than a function of capillary pressure across the air–PCE interface as implied by Leverett scaling. Below the critical saturation, PCE breakup into microlenses was observed. These microlenses constituted a signiÞcant fraction of the PCE saturation, resulting in higher residual PCE saturations than would be expected for spreading NAPLs (Hofstee et al., 1997). When subjected to nonmonotonic saturation histories, relative permeability vs. saturation and capillary pressure vs. saturation functions exhibit signiÞcant hysteresis. This is especially signiÞcant for NAPL due in part to the occurrence of residual NAPL that has been hydraulically immobilized as occluded blobs or disconnected Þlms induced by incomplete NAPL displacement by water or air. Hysteresis in two-phase systems has been studied extensively, and reasonably reliable models have been developed to predict two-phase hysteric functions from primary drainage and imbibition path measurements (e.g., Mualem, 1974; Gillham et al., 1976; Scott et al., 1983; Kool and Parker, 1987). However, whereas only 2 directions of saturation change are possible in two-phase systems (i.e., wetting-phase drainage or imbibition), 12 path directions are possible in three-phase systems (i.e., IID, IDI, IDD, DII, DID,


188


DDI, CDI, CID, ICD, DCI, IDC, and DIC), where I, D, and C designate imbibition, drainage, and constant saturation, respectively, of water, NAPL, and air (Saraf et al., 1982). This complexity effectively precludes characterizing three-phase hysteretic k–S–P functions based solely on direct experimental observations and requires predictive models to be quite complicated to handle all circumstances properly. Mathematical models for hysteretic three-phase k–S–P relations have been presented for petroleum reservoir applications by Evrenos and Comer (1969) and Killough (1976). Parker et al. (1987), Lenhard and Parker (1987), Lenhard et al. (1989), and Guarnaccia et al. (1997) presented relationships of environmental interest based on the van Genuchten model. These models involve Þve parameters to describe the main air–water drainage and imbibition S–P functions (including maximum residual water and air saturations), two ßuid-dependent scaling factors, and two additional residual saturations. The Parker–Lenhard model considers the maximum residual NAPL saturation for displacement by water and the maximum residual air saturation for displacement by NAPL. Residual NAPL caused by displacement by air is not considered. The Guarnaccia model considers residual NAPL saturations caused by displacement by water or air. Air entrapment is assumed to be the same for displacement by water or NAPL. Hysteresis in k–S–P relations introduces a signiÞcant degree of complexity and uncertainty to the characterization of three-phase ßow. Incorporating hysteresis into multiphase ßow models increases programming complexity. The need to change k–S–P curves with reversals between drainage and imbibition creates instabilities in nonlinear iterations of these models, often requiring small time steps to be resolved.

4.2.2 MULTICOMPONENT MASS TRANSPORT 4.2.2.1 Mass Balance and Transport Flux Equations The general macroscopic mass balance equation for species a within phase b (=g for gas, w for water, n for NAPL) may be written as follows:

(

)

(

)

(

)

∂ fSbrb Xab + — ∑ rb Xab qb + — ∑ fb Sb Jab + rab + Gab = 0 ∂t

(4.8)

where f is porosity, Sb is phase saturation, rb is phase molar density, Xab is the mole fraction of species a in phase b, qb is the Darcy velocity for phase b described by Equation 4.1, Jab is the diffusive–dispersive mass ßux for species a in phase b, rab is the net mass transfer of species a to (positive) or from (negative) phase b , and Gab represents the net sources (or sinks if negative) for species a representing internal reactions and external sources/sinks. A mass balance for the adsorbed phase, assuming no transport in the adsorbed phase, may be written as follows: ∂ (r (1 - f) Xas ) + ras + Gas = 0 ∂t s

(4.9)



189

where Xas is the adsorbed mass per mass of solids, rs is the density of soil solids, and ras and Gas are mass transfer rates and source/sink terms for the adsorbed phase. Equations 4.8 and 4.9 are subject to the following physical constraints:

ÂS

b= g ,w ,n

b

=1

Â

rab = 0

ÂX

ab

b= s ,g ,w ,n

a

(4.10a)

(4.10b)

=1

(4.10c)

where the summations are over the indicated phases or chemical species. It is often assumed that the porous medium is incompressible and, hence, that porosity is temporally invariant. However, in certain situations, it may be warranted to regard porosity as a function of ßuid pressure and/or temperature (e.g., case relevant compressibility or thermal expansion coefÞcients would be required). The dispersive–diffusive ßux is described by the following: Jab = -rb Dab ∑ —Xab

(4.11)

where Dab is the diffusion–dispersion tensor with components commonly deÞned by the following: ij Dab = a T qb d ij + (a L - a T )

qbi qbj qb

o + d ij t b Dab

(4.12)

where aL and aT are the longitudinal and transverse dispersivities, respectively; qbi o and qbj are components of the Darcy velocity; dij is the Kronecker delta; Dab is the molecular diffusion coefÞcient; and tb is a tortuosity factor. 4.2.2.2 Interphase Mass Transfer The interphase transfer of organic mass occurs by several processes (Figure 4.1), including NAPL dissolution, NAPL volatilization, and partitioning between water and adsorbed phases and water and gas phases. The simplest approach to phase partitioning is to assume local equilibrium between phases. In this case, the concentration in a given phase can be written as a function of the concentration in another phase multiplied by a partitioning coefÞcient. Typically, for sparingly soluble organic compounds such as the chlorinated organics, Henry’s law is used for equilibrium partitioning between the water and the gas phases, and Raoult’s law is used for


190


partitioning from the organic phase to the water and gas phases. In a three-phase system, the mole fractions of an organic species a in the water, gas, and organic phases at equilibrium would be governed by the following (Sleep and Sykes, 1993): HXaw = Xag Pg = Xao Pa0

(4.13)

where H is the Henry’s law coefÞcient (Pa), Pg is the gas phase pressure (Pa), and Pa0 is the pure component vapor pressure (Pa) of the organic species. From this equation, it can be seen that the mole fraction of the organic in the water phase (and therefore the effective solubility of the organic in the water phase) in equilibrium with a multicomponent organic phase is proportional to the mole fraction of the organic in the organic phase. Molar concentrations of each NAPL components are often unknown. In such cases, the mass fraction or volume fraction of the component of interest can be used as a surrogate for its mole fraction (Mackay et al., 1991). More soluble components preferentially dissolve from NAPL mixtures over time, resulting in an asymptotic reduction in their mole fraction and hence effective solubility (Mackay et al., 1991). It has been argued that removal of more soluble species from a NAPL mixture can lead to a stabilized source for which natural attenuation processes can contain the dissolution of the remaining sparingly soluble components (Adeel et al., 1997). Raoult’s law and Henry’s law are based on ideal phase behavior. The activity coefÞcient quantiÞes the degree of nonideality experienced by a component due to intermolecular interactions in a NAPL or aqueous solution. For ground water with typical contaminant concentrations, the activity coefÞcients are governed primarily by water–solute interactions. For NAPL mixtures, the degree of nonideality can be associated with the similarity of components (Mackay et al., 1991). For example, if all the components in a NAPL mixture are alkanes, then the activity coefÞcient can be assumed to be unity (i.e., the NAPL behaves as an ideal mixture and follows Raoult’s law). The use of nonideal phase partitioning relationships becomes particularly necessary when dealing with surfactants and cosolvents. In most practical situations, the activity coefÞcients of individual components in complex, aged liquid mixtures cannot be accurately measured. Several methods are available in published literature for estimating activity coefÞcients (e.g., Fredenslund et al., 1977; van Ness and Abbot, 1982). In addition to deviations from ideality due to chemical interactions, deviations from equilibrium between phases are expected when ßuid ßow rates are high such as during pump-and-treat or soil vapor extraction operations. Sleep and Sykes (1989) showed that rate limitations on mass transfer between phases could have a signiÞcant impact on the fate and transport of volatile organics in variably saturated porous media. In particular, the rates of removal of organics from the subsurface by pumpand-treat or soil vapor extraction are lower than predicted when using an equilibrium partitioning model. As contact areas between the organic phase and the water and gas phases are expected to decrease with mass removed, the rates of organic removal are expected to decline with time, resulting in very long times required to achieve cleanup goals.



191

The model of Sleep and Sykes (1989) assumed that kinetic interphase mass transfer could be simulated as a Þrst-order process, governed by the following: raij = g ij r j fS j ( Xaeqj - Xaj )

(4.14)

where raij is the mass transfer rate of species a from phase i to phase j per porous media volume (or from phase j to phase i if raij < 0), gij is the mass transfer coefÞcient between phase i and phase j, rj is the density of phase j, f is porosity, Sj is the saturation of phase j, Xa is the actual mass fraction of species a in phase j, and Xaeqj is the mass fraction in phase j that would occur if equilibrium existed with phase i. In recent years, several studies have been published that demonstrate the importance of rate-limited organic-phase dissolution and propose a variety of correlations to predict Þrst-order mass transfer coefÞcients for NAPL dissolution from residual saturations of organics. Mass transfer coefÞcients were calculated from Sherwood numbers. The Sherwood number is a dimensionless mass transfer number deÞned in terms of the Þrst-order mass transfer coefÞcient; the molecular diffusion coefÞcient, D; and a length scale, l, of the porous medium (typically d50): Sh =

g l2 D

(4.15)

The Sherwood number in turn is related to ßuid velocities, diffusion coefÞcients, and organic ßuid saturations. For example, Miller et al. (1990) found that Sherwood numbers describing NAPL dissolution in centimeter-scale sand columns could be Þt by the following: Sh = 12(f - q n ) Re 0w.75 q n Scw0.5

(4.16)

where f is porosity, qn is the nonwetting-phase volume fraction, Rew is the waterphase Reynolds number, and Scw is the water-phase Schmidt number (i.e., ratio of water-phase velocity to water-phase diffusion coefÞcient of organic species). Miller et al. (1998) summarized correlations found by others such as Powers et al. (1992, 1994). It is likely that the empirical coefÞcients and exponents of the mass transfer correlations are sensitive to organic-phase emplacement techniques and soil pore size distributions. It must also be stressed that these models were developed for millimeter- or centimeter-scale experiments. Testing these models at the Þeld scale has not been attempted, and it is certain that an upscaling procedure would be required before applications in models discretized at the Þeld scale. Investigations of mass transfer between the water and gas phases indicate that this process is rapid compared with the partitioning between organic and water phases. Miller et al. (1998) summarize correlations published for rate constants for gas–water and gas–organic partitioning. The partitioning of chlorinated organic compounds to the soil phase (adsorption) can also have a very signiÞcant impact on fate and transport in the subsurface


192


(Schwarzenbach et al., 1993). In recent years, sorption mechanisms have been the focus of a numerous investigations. Brusseau and Rao (1989) and Weber et al. (1991) summarize current concepts of adsorption in subsurface systems. In many systems (e.g., those involving nonionic hydrophobic solute sorbing from the water phase to natural soils with signiÞcant organic content), linear sorption models may be appropriate (Miller et al., 1998). When sorption behavior deviates from linearity, models such as the Freundlich model or the Langmuir model are often used (Weber et al., 1991). Recent investigations have found that sorption and desorption processes can take from months to years to reach equilibrium. Cornelissen et al. (1997) examined the temperature dependence of slow adsorption and desorption in batch experiments with chlorobenzenes, PCBs, polycyclic aromatic hydrocarbons (PAHs), and laboratory- and Þeld-contaminated sediments. The laboratory-contaminated sediments were maintained in contact with the chemicals of interest for 34 d. Cornelissen et al. (1997) identiÞed three stages of desorption: a rapid stage corresponding to the Þrst few hours of desorption, an intermediate stage corresponding to a few weeks, and a slow stage corresponding to several months of desorption time. Rate limitations of sorption and desorption are usually attributed to slow diffusion within soil grains, or within soil aggregates (Brusseau and Rao, 1989). Some models assume that this process can be described by a Þrst-order model, whereas others use a diffusion-based model incorporating assumptions about soil grain or aggregate geometry. Both Þrst-order models and diffusion-based models cannot reproduce the rapid initial sorption rates observed experimentally. As a result, a number of two-site models have been proposed that include both equilibrium and kinetic sorption sites (e.g., Nkedi-Kizza et al., 1984; van Genuchten and Wagenet, 1989). The Þndings of Cornelissen et al. (1997) that a range of sorption coefÞcients were required to model kinetic sorption has been addressed by several other researchers. Connaughton et al. (1993); Pedit and Miller (1994); Chen and Wagenet (1995); and Culver et al. (1997) presented models that employed distributions of Þrst-order rate constants to characterize sorption kinetics. Culver et al. (1997) used both lognormal and gamma distributions for sorption rate constants. Cunningham et al. (1997) developed a model that attributed sorption to intergranular diffusion. Diffusion was characterized by a gamma distribution of diffusion coefÞcients rather than by a distribution of rate constants. The model was Þt to TCE sorption experiments conducted at 15, 30, and 60°C with silica gel and natural sediments. Considerable research has been performed on vapor sorption to low moisture content soils and the effect of humidity on sorption. In the case of vapor sorption to low moisture content soils, nonlinear sorption isotherms are required that incorporate the effects of soil humidity on sorption (Chiou and Shoup, 1985; Unger et al., 1996a). Comprehensive modeling of organic vapor transport in low moisture content soils thus requires nonlinear sorption models as well as predictions of water vapor transport. Research both at the laboratory and the Þeld scales indicates that sorption rates often occur at rates slow enough to warrant the use of a kinetic model that accounts for at least two sorption regimes. Methods for determining the appropriate effective Þeld-scale parameters for these models has not been developed, and scaling issues have not yet been adequately addressed (Miller et al., 1998). The multisite models also introduce additional variables that must be solved, increasing the computational cost of modeling.



4.2.3 MODELING BIOTIC

AND

193

ABIOTIC TRANSFORMATIONS

4.2.3.1 Review of Chlorinated Solvent Transformation Mechanisms 4.2.3.1.1 Biodegradation Mechanisms Once dissolved into ground water, CAHs may be subject to a variety of biotransformation mechanisms depending on the redox chemistry and availability of other carbon sources (e.g., natural organic matter or petroleum cocontaminants). The three general biodegradation mechanisms are reductive dehalogenation, oxidation, and aerobic cometabolism. Of these three mechanisms, reductive dehalogenation is believed to be the most effective and perhaps the most important mechanism contributing to the natural destruction of CAHs in aquifers. The state-of-the-science regarding the potential for CAH biodegradation under various conditions is summarized in Table 4.1.

TABLE 4.1 Conditions for Biotic and Abiotic Transformations of Chlorinated Solvents Anaerobic Biodegradation Potential Compound PCE TCE 1,1-DCE t-DCE c-DCE VC PCA 1,1,2-TCA 1,1,1-TCA 1,1-DCA 1,2-DCA CA CT CF DCM CM

Aerobic Biodegradation Potential

Primary Substrate

Cometabolic

Primary Substrate

Cometabolic

XXX XXX Perhaps Perhaps XX XX

XXX XXX XX XX XX XX XXX XXX XXXX XX X

0 0 Perhaps Perhaps XX XXX 0 0 0

0 XX X XXX XXX XXXX

Perhaps Yes

XXXX XX

XX XX 0 Yes

Perhaps X X XX 0 X XXX

Abiotic Degradation Potential XX XX X X X XXX XXX XXXX XX XXX XXXX XX

Notes: 0 = Very small (if any) potential; X = Some potential; XX = Fair potential; XXX = Good potential; XXXX = Excellent potential; 1,1-DCE = 1,1-Dichlorethene; t-DCE = trans-1,2-Dichloroethene; c-DCE = cis-1,2-Dichloroethene; VC = Vinyl chloride; PCA = Tetrachloroethane; 1,1,2-TCA = 1,1,2-Trichloroethane; 1,1,1-TCA = 1,1,1-Trichloroethane; 1,1-DCA = 1,1-Dichloroethane; 1,2-DCA = 1,2-Dichloroethane; CA = Chloroethane; CF = Chloroform; DCM = Dichloromethane; CM = Chloromethane. Source: After McCarty and Semprini, 1994; McCarty, 1997; Butler and Hayes, 1999; Lorah and Olsen, 1999; Bradley and Chapelle, 2000; Maymo-Gatell et al., 2001.


194


Names of speciÞc CAHs are abbreviated in the discussion below. Table 4.1 provides an index of name abbreviations for each of the CAHs considered here. 4.2.3.1.2 Reductive Dehalogenation Under certain anaerobic conditions, a variety of bacteria can use chlorinated solvents for respiration, transforming them in a process known as halorespiration (McCarty, 1997). In this process, halogenated compounds such as PCE and TCE are biodegraded by reductive dehalogenation. Reductive dehalogenation is a reaction in which one or more chlorine atoms are replaced with hydrogen atoms, reducing the carbon atom on the CAH molecule. Certain bacteria that mediate this reaction can gain energy and grow through mediation of reductive dehalogenation (Maymo-Gatell et al., 1997, 2001). Reducing equivalents for this process are supplied by hydrogen produced by the biodegradation of naturally occurring or anthropogenic carbon compounds. The biodegradation of the organic matter is thought to be carried out by fermentative organisms, not by the dechlorinating organisms. Most microbial dechlorinating consortia also contain methanogens that compete with dechlorinators for hydrogen (Fennel and Gossett, 1998). Figure 4.2 illustrates the common pathways for reductive dehalogenation of chlorinated compounds. For PCE, reductive dehalogenation follows the order of PCE Æ TCE Æ c-DCE Æ VC Æ ethene. The rate of reductive dehalogenation tends to decrease as the number of chlorine substituents on the CAH molecule decreases (Vogel, 1994). In addition, the rate and extent of dehalogenation depends on the

Notes: Pathways shown are for anaerobic biological reductive dehalogenation unless noted. Oxidation = both aerobic and anaerobic biooxidation. 2E = biologically mediated dichloroelimination. Abiotic-E = abiotic elimination. Abiotic-H = abiotic hydrolysis.

FIGURE 4.2 Natural degradation pathways for CAHs.



195

redox conditions, with DCE and VC dehalogenation occurring more rapidly under more reducing (i.e., sulfate-reducing or methanogenic) conditions. Complete reductive dehalogenation is most commonly reported at those sites where signiÞcant concentrations of codisposed petroleum contaminants or leachate are also present (Semprini et al., 1995). Conversely, PCE, TCE, and DCE tend to persist at sites where organic electron donors are absent and redox potentials are relatively high (i.e., microaerobic or nitrate-reducing conditions) (Sturchio et al., 1998). At some sites, intermediates such as DCE or CF can accumulate as evidence of past reductive dehalogenation that occurred prior to exhaustion of the electron donor supply. Accordingly, one important step in constructing chlorinated solvent biodegradation models is to consider the amount of electron donor available to sustain reductive dehalogenation (National Research Council [NRC], 2000), as well as inhibition effects from transformation products such as CF. DCE, CF, and VC can also persist due to an absence of halorespiring bacteria. Halorespirers are not ubiquitous in nature, and not all subsurface bacteria are able to mediate complete halogenation (e.g., PCE Æ ethene). In most laboratory studies where complete dehalogenation of PCE and TCE has been observed, a microbial consortium has mediated the CAH transformation sequence. To date, Dehalococcoides ethenogenes is the only microorganism known to be capable of completely dechlorinating PCE (Magnuson et al., 2000). In addition to occurring via halorespiration, reductive dehalogenation of CAHs can also occur cometabolically. During cometabolic reductive dehalogenation, transformation of a given CAH occurs fortuitously during microorganism growth on another substrate. Many methanogenic and sulfate-reducing bacteria are able to mediate cometabolic reductive dehalogenation, which effectively can result in the dehalogenation of PCE and TCE but not DCE (Bagley and Gossett, 1990; Gantzer and Wackett, 1991). In this process, dehalogenation of the CAH molecule occurs due to reactions with bacterial cofactors (e.g., porphyrins containing redox-sensitive metals), but the cell does not derive energy for growth. The relatively slow reductive dehalogenation that results from this cometabolic reaction may be the cause of the incomplete dehalogenation that is observed at many sites. Recently, evidence has been presented that the degradation of VC by D. ethenogenes is cometabolic (Maymo-Gatell et al., 2001). 4.2.3.1.3 Oxidation Because chlorine atoms are electronegative by nature, the susceptibility of CAHs to degradation via direct oxidation decreases as the number of chlorine substituents increases (Figure 4.2). Consequently, polychlorinated ethenes such as hexachlorobenzene (HCB), PCE, TCE, CT, and CF are largely recalcitrant to direct oxidation. However, it has been shown that lightly chlorinated ethenes such as VC and DCE can be biodegraded oxidatively under aerobic, iron-reducing, and methanogenic conditions (Hartmans and de Bont, 1992; Bradley and Chapelle, 1996, 1997, 1999; Bradley et al., 1998). When used as the sole electron donor, VC can serve as a growth substrate (Verce et al., 2000), and c-DCE can support cell metabolism (Bradley and Chapelle, 2000). Consequently, the rate and extent of natural subsurface CAH destruction does not depend on reductive dehalogenation alone. DCE and VC


196


that are produced in reducing zones of an aquifer may be oxidized when the plume enters zones where sulphate, nitrate, iron, or oxygen can act as electron acceptors. 4.2.3.1.4 Aerobic Cometabolism During aerobic cometabolism, the destruction of TCE, DCE, and/or VC is coupled to the biooxidation of a more easily degraded primary substrate such as methane or ammonia. Of the three mechanisms for CAH biodegradation, aerobic cometabolism is likely the least signiÞcant under natural subsurface conditions (NRC, 2000). Aerobic cometabolism is most often characteristic of engineered bioremediation systems, where oxygen and a primary substrate such as methane, toluene, phenol, butane, or ammonia are introduced to promote chlorinated ethene biodegradation. However, aerobic cometabolism can occur intrinsically where CAHs come into contact with methane in slightly aerated zones of an aquifer, as has been suggested by Bradley and Chapelle (1998) and Lorah and Olsen (1999). 4.2.3.1.5 Abiotic Transformations In general, biodegradation is the most important natural process governing the destruction of CAHs in the subsurface. For those CAHs that have been observed to degrade abiotically under natural conditions, abiotic degradation rates are typically much slower than biodegradation rates. However, these reactions can still be significant within the time scales commonly associated with ground water movement. A variety of investigations have shown chloroethenes to be resistant to abiotic degradation. A growing body of evidence suggests, however, that PCE; TCE; PCA; 1,1,1-TCA; and CT can be degraded abiotically in the presence of ferrous sulÞdes that are common in reduced aquifers and wetlands (Kriegman-King and Reinhard, 1992; Curtis and Reinhard, 1994; Devlin and Muller, 1999; Butler and Hayes, 1999, 2000). CT can also be reduced abiotically in the presence of dissolved phase Fe2+, HS_, and pyrite. Natural organic matter that coats aquifer material can accelerate this process signiÞcantly by acting as an electron shuttle. The effect of iron sulÞde and organic matter could play an important role in CT reduction for plumes that intercept bogs or wetlands. In addition, green rust, a naturally occurring iron hydroxide, can also promote the degradation of CT (Heron et al., 1994; Erbs et al., 1999). The chemical pathway(s) for the natural abiotic degradation of PCE and TCE can differ from the biodegradation pathway in that acetylene (C2H2) is a major abiotic transformation intermediate for both PCE and TCE. Therefore, detecting C2H2 in chlorinated solvent plumes can serve as evidence that abiotic degradation is occurring. Relative to chloroethenes, chloroethanes are more susceptible to abiotic degradation. 1,1,1-TCA and 1,2-dichloroethane (1,1-DCA) are both susceptible to degradation via hydrolysis. 1,1,1-TCA can also be degraded via an abiotic elimination (alkane => alkene) reaction to form 1,1-DCE (Figure 4.2). This abiotic degradation of 1,1,1-TCA likely is the primary source of 1,1-DCE in contaminated aquifers. PCA degrades to TCE by the same type of abiotic reaction (Lorah and Olsen, 1999). The CA that forms as a result of TCA biotransformation also can be transformed abiotically.



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4.2.3.2 Petroleum Hydrocarbon Biodegradation Models Early fate and transport models that described contaminant biodegradation were primarily developed for simulating the aerobic biodegradation of petroleum hydrocarbons (Sykes et al., 1982; Borden and Bedient, 1986; Molz et al., 1986). Subsequently, a variety of biodegradation models have been published in the literature, including models for both aerobic and anaerobic biodegradation processes, as well as saturated and unsaturated zone transport (Srinivasan and Mercer, 1988; Widdowson et al., 1988; MacQuarrie et al., 1990; Chen et al., 1992; McClure and Sleep, 1996; Rathfelder et al., 2000). In recent years, emphasis on anaerobic biodegradation has grown, and models have been developed that simulate biodegradation under multiple electron-accepting conditions including nitrate-reducing, iron-reducing, sulfate-reducing, and methanogenic conditions (Lu et al., 1999; Waddill and Widdowson, 2000). Most of these models incorporate substrate-limited biodegradation by using Monod kinetics. In addition, electron acceptor limitations are incorporated by adding extra Monod terms based on electron acceptor concentrations and electron acceptor half-saturation constants (dual Monod kinetics). For example, Borden and Bedient (1986) modeled hydrocarbon biodegradation coupled to biomass growth and decay using versions of the following two expressions: È CD ù È A ù dCD = - Xt k Í úÍ ú dt Î K D + CD û Î K A + A û

(4.17)

È CD ù È A ù dXt = Xt Yk Í úÍ ú - bXt dt Î K D + CD û Î K A + A û

(4.18)

where CD is the aqueous-phase concentration of hydrocarbon (or electron donor), Xt represents the total active biomass concentration, k is the maximum hydrocarbon utilization rate, KD is the hydrocarbon half-saturation constant, A is the electron acceptor (oxygen) concentration, KA is the electron acceptor half-saturation constant, Y is the microbial yield coefÞcient (cell mass created per unit substrate consumed), and b is the microbial decay and endogenous respiration coefÞcient. Widdowson et al. (1988) incorporated additional Monod terms to include the impact of nutrient limitation on hydrocarbon biodegradation. Complete mineralization of hydrocarbon to carbon dioxide and water is usually assumed in hydrocarbon biodegradation modeling, although Malone et al. (1993) included the formation of intermediate products in the biodegradation of aromatic hydrocarbons. Most of the hydrocarbon biodegradation models simulate only aqueous-phase transport with the exception of the McClure and Sleep (1996) model, which includes full three-phase ßow and transport with equilibrium interphase partitioning. Rathfelder et al. (2000) modeled aqueous and gaseous-phase transport to allow bioventing simulation. Malone et al. (1993) and Rathfelder et al. (2000) included kinetic NAPL dissolution from residual NAPL sources. Recent laboratory experiments have shown


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that PCE biodegradation can occur in the presence of the organic phase and that this can enhance the rate of PCE DNAPL dissolution (Yang and McCarty, 2000; Cope and Hughes, 2001). These empirical Þndings conÞrm the predictions of Seagren et al. (1994), who were among the Þrst to provide a quantitative description of enhanced NAPL dissolution resulting from biodegradation. Mass transfer limitations from both the NAPL and sorbed phases can also limit the biodegradation rate in the aqueous phase (Ghoshal et al., 1996; Bosma et al., 1997). Because substrates are typically hydrolyzed prior to biodegradation, nonaqueous and sorbed-phase hydrophobic organic contaminants generally are thought to be unavailable for biodegradation (Zhang et al., 1998). Consequently, the impact of biodegradation on NAPL and plume attenuation is often limited by the rate of NAPL dissolution and/or desorption from aquifer solids. Most hydrocarbon biodegradation models are based on the recognition that biomass predominantly is present as a bioÞlm in a porous medium; however, there is some variation in the representation of the bioÞlm and the substrate uptake processes of the bioÞlm. Baveye and Valocchi (1989) reviewed the three most common representations of bioÞlm processes in porous media. The Þrst bioÞlm model (Rittmann et al., 1980; Bouwer and McCarty, 1984) describes the bioÞlm as having uniform thickness, covering all solid surfaces. The transfer of solutes into the bioÞlm occurs across a stagnant boundary layer into a thin bioÞlm that is conceptualized to be fully penetrated so that mass transfer limitations are all associated with the stagnant boundary layer. Therefore, this model requires solution of algebraic equations to determine bioÞlm concentrations and also requires assumptions about the stagnant boundary thickness and bioÞlm surface area. The second class of bioÞlm models (Molz et al., 1986; Widdowson et al., 1988; Chen et al., 1992) assumes that biomass grows in microcolonies that are constant in size but increase in number as substrate consumption occurs. A stagnant boundary layer governing mass transfer to the microcolonies is hypothesized as with the Þrst class, thereby requiring solution for microcolony substrate concentrations and assumptions about boundary-layer thickness and microcolony geometry. The third class of bioÞlm models (Sykes et al., 1982; Borden and Bedient, 1986; Kindred and Celia, 1989; MacQuarrie et al., 1990; Wood et al., 1994; McClure and Sleep, 1996) makes no assumption about bioÞlm conÞguration and assumes that diffusion limitations in stagnant boundary layers and in the bioÞlm may be neglected and that biodegradation kinetics are based directly on bulk water phase concentrations. Although this is a simpliÞcation of the pore-scale processes, at present there is no sufÞcient experimental evidence to discount the practical value of this simpliÞed model (Baveye and Valocchi, 1989). The models of Molz et al. (1986); Widdowson et al. (1988); and Chen et al. (1992) assume a stationary biomass phase with no bacterial transport. In contrast, the models of Borden and Bedient (1986), MacQuarrie et al. (1990), and McClure and Sleep (1996) assume advective–dispersive transport of bacteria with attachment of biomass to aquifer solids modeled as a linear sorption process. Sorption coefÞcients are typically chosen so that 95% of the biomass is attached to the soil (Borden and Bedient, 1986).



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It is recognized that the process of attached microbial growth, bacterial deposition in porous media, and bioÞlm shearing is much more complex than can be simulated with a linear sorption model. Complex models incorporating biomass attachment and detachment as a function of bioÞlm aqueous-phase concentration, bioÞlm thickness, and shear forces related to ground water velocity have been developed (Taylor and Jaffe, 1990; Cunningham et al., 1991). Most of these models assume an idealized geometry of soil particles and of uniform bioÞlm growth. These models incorporate changes in soil permeability, porosity, and dispersivity as a function of bioÞlm thickness and have been applied to laboratory-scale experiments (Taylor and Jaffe, 1990), but no Þeld-scale veriÞcation has been performed. Including the effects of bioÞlm growth on permeability, porosity, and dispersivity in a biodegradation model may be necessary in engineered remediation systems where biomass stimulation through nutrient addition and high carbon loading produce high rates of biological growth. Such models can also be used to optimize nutrient and substrate addition to minimize formation plugging (Taylor and Jaffe, 1991). 4.2.3.3 Chlorinated Solvent Biodegradation Models For the reasons discussed in Section 4.2.3.1, modeling chlorinated solvent biodegradation can be substantially more complex than modeling petroleum hydrocarbon biodegradation. Model selection depends on a variety of factors, including the type of dechlorinating microorganisms present, the type and amount of electron donor present, and the redox conditions in the aquifer. In general, the practice of modeling chlorinated solvent biodegradation is less well established than that for modeling petroleum hydrocarbon biodegradation. Nevertheless, the following three basic types of chlorinated solvent biodegradation models have emerged: • • •

Monod models that simulate halorespiration Monod models that simulate cometabolic biodegradation Sequential transformation models that assume a Þrst-order kinetics for each sequential transformation

Fennel and Gossett (1998) and Bagley (1998) presented some of the Þrst models for simulating halorespiration. In the batch model of Bagley (1998), dual Monod kinetics based on chlorinated ethene concentration and hydrogen concentration was used to simulate each of the transformation steps from PCE to ethene. For example, the biodegradation of PCE was represented by the following: ù È ù È c H2 cPCE rPCE = - kX PCE Í ú úÍ Î K PCE + cPCE û ÍÎ K H2 + c H2 úû

(4.19)

where XPCE is the biomass concentration of PCE degraders, cPCE and cH2 are the water-phase concentrations of PCE and hydrogen, respectively, and KPCE and KH2 are the half-saturation constants for PCE and hydrogen, respectively. The production of hydrogen from the fermentation of ethanol to acetic acid and propionic acid and


200


from the fermentation of propionic acid was included, as was the use of acetic acid and hydrogen by methanogens. In addition to the simulation of 10 chemical species, the model includes the growth of 2 dechlorinating species (PCE to c-DCE and c-DCE to ETH), ethanol and propionic acid fermenters, and aceticlastic and hydrogenotrophic methanogens. Inhibition terms were also included in the Monod terms to account for VC degradation inhibition by the other chlorinated ethenes and thermodynamic limitations on fermentation reactions. The batch model of Bagley (1998) has been incorporated into the compositional model of McClure and Sleep (1996) as described in Aschwanden (2001). The resulting model involves solution for concentrations of 10 chemical species and 5 microbial species and is therefore not readily applicable to multidimensional Þeldscale modeling of anaerobic PCE biodegradation. In addition to metabolic dehalogenation, many chlorinated organics can be biodegraded cometabolically. Because the organisms do not derive any beneÞt from this cometabolic biodegradation but grow on other substrates, modeling cometabolic biodegradation involves simulating the chlorinated contaminant and growth substrate. Many chlorinated compounds create intermediates that are toxic to microorganisms. For example, the aerobic biodegradation of TCE and VC produces epoxides that inactivate microorganisms (Wackett et al., 1989). Chang and Alvarez-Cohen (1995), building on the work of Criddle (1993), developed a model that included biodegradation of a growth substrate and a chlorinated organic by oxygenaseexpressing cultures. Cell growth, reducing energy limitation, product toxicity, competition between growth substrate, and cometabolic substrate for oxygenase enzymes were all included in the model. The resulting equations for degradation of growth substrate and chlorinated contaminant are as follows: ˆ Sg Ê R ˆÊ = - kg X Á Á ˜ ˜ dt Ë K R + R ¯ Ë K Sg (1 + Sc / K Sc ) + Sg ¯

(4.20a)

ˆ Ê R ˆÊ dSc Sc = - kc X Á Á ˜ ˜ dt Ë K R + R ¯ Ë K Sc (1 + Sg / K Sg ) + Sc ¯

(4.20b)

dSg

where X is the biomass concentration, R is the reducing energy electron equivalent concentration, KR is the half-saturation constant for reducing energy, Sg is the concentration of growth substrate, Sc is the concentration of chlorinated organic, KSg is the half-saturation constant for growth substrate, and KSc is the half-saturation constant for chlorinated organic. The terms Sc/KSc and Sg/KSg account for competitive inhibition between growth substrate and chlorinated organic. Growth is modeled by the following: dSg 1 dSc dX = -Y + -b dt dt Tc dt

(4.21)



201

where Tc is the transformation capacity of the cometabolic substrate, representing the amount of cometabolic substrate degraded divided by the amount of cells inactivated due to product toxicity. An additional equation is also involved to predict changes in reducing equivalents due to consumption from chlorinated organic degradation and regeneration from external sources. The model of Chang and Alvarez-Cohen (1995) was veriÞed with laboratory batch experiments. As with the model for chlorinated ethene biodegradation, this model involves many parameters, including rate constants, yields, half-saturation constants, transformation capacity, stoichiometric constants for reducing energy production and consumption, initial reducing energy electron equivalent concentrations, and initial microbial population. Although incorporating this model into a multiphase ßow and transport model is straightforward algorithmically and mathematically, determining the appropriate parameters and initial conditions for reducing energy and biomass is difÞcult in the Þeld. For cometabolic biodegradation models, perhaps the most important point of consideration is that these models have limited utility for modeling chlorinated solvent fate in nonengineered systems. Although these models have been useful for analyzing cases where a primary substrate has been introduced to enhance biodegradation (Semprini and McCarty, 1992; Travis and Rosenberg, 1997), there are few natural conditions for which these models would be appropriate (NRC, 2000). Methane generation at landÞlls provides a primary substrate source to support methanotrophic chloroethene degradation, but halorespiration is expected to be a more dominant process when landÞll leachate is present. When biomass levels, microbial community composition, and electron donor or acceptor concentrations are relatively constant at a particular site, the multiple Monod expressions of Equations 4.19, 4.20a, and 4.20b reduce to single Monod kinetics expressions. Under conditions of high-chlorinated organic concentrations relative to the substrate half-saturation constant, Monod kinetics reduces to zeroorder kinetics. At low-chlorinated organic concentrations, the Monod model reduces to Þrst-order kinetics. Biokinetic studies by Haston and McCarty (1999) found that a Þrst-order kinetic approximation could be appropriate for reductive dehalogenation when chloroethene concentrations are in the microgram per liter (µg/l) range. Those researchers also concluded, however, that Monod kinetics offer the best approach for modeling chloroethene biodegradation. First-order biokinetic models are appropriate for certain cases, particularly when chloroethene concentrations are sufÞciently low to warrant this approach. Clement et al. (1998) developed the code RT3D, a three-dimensional (3-D) numerical fate and transport model that simulates the sequential transformation of PCE to VC using a series of Þrst-order transformation expressions. The kinetics of the biological reactions are represented in RT3D as follows: d[ PCE ] = - k PCE [ PCE ] dt

(4.22)

d[TCE ] = YTCE / PCE k PCE [ PCE ] - kTCE [TCE ] dt

(4.23)


202


d[ DCE ] = YDCE / TCE kTCE [TCE ] - k DCE [ DCE ] dt

(4.24)

d[VC] = YVC / DCE k DCE [ DCE ] - kVC [VC] dt

(4.25)

where YTCE/PCE is a yield coefÞcient whose value is determined stoichiometrically based on the parent–daughter decay of PCE to TCE, and kPCE represents the Þrstorder decay coefÞcient. A variety of methods are available for deriving a site-speciÞc estimate of sequential transformation decay coefÞcients (U.S. Environmental Protection Agency [USEPA], 1998). Use of the Þrst-order approximation requires careful consideration of the potential errors implicit in the approach. Further discussion is provided in Section 4.5.4.

4.2.4 COMPUTATIONAL ISSUES 4.2.4.1 Spatial Discretization Finite element (Kuppusamy et al., 1987), Þnite difference (Sleep and Sykes, 1993), and control volume Þnite element methods (Forsyth, 1991) have been used for spatial discretization of the multiphase ßow equations. Finite difference and control volume Þnite element methods have the advantage that they are mass conservative, whereas most Þnite element methods used for multiphase ßow simulation may incur large mass balance errors. The Þnite element and control volume Þnite element methods have the advantage of allowing irregular mesh shapes. However, as a result of limited grid generation packages, especially in three dimensions, most multiphase ßow modeling has been performed with rectangular grids. The equations of multiphase ßow are classiÞed as parabolic partial differential equations. As the capillary pressure gradients are decreased, the problem becomes more hyperbolic in nature, giving way to the propagation of sharp fronts (e.g., the Buckley–Leverett solution). Solving multiphase ßow equations, whether with Þnite element or Þnite difference methods, usually necessitates using a method that has sufÞcient numerical dissipation to guarantee convergence to the correct method. The most common method is one point upstream weighting of relative permeabilities, which guarantees unconditional convergence to the correct solution as the grid is reÞned (Aziz and Settari, 1979). This guaranteed convergence comes at the price of numerical dispersion that smears sharp fronts (Aziz and Settari, 1979). Higher-order methods of relative permeability weighting have also been applied, such as twopoint and third-order upstream weighting (Sleep and Sykes, 1993) and ßux limiters (Unger et al., 1996b). The major difÞculty with the higher-order spatial discretization techniques is that they introduce a higher-order connection pattern, decreasing the sparsity of the Jacobian matrix formulated with Newton linearization. The solution of the compositional equations for both ßuid ßow and transport can be accomplished with similar techniques as those used for simulating multiphase ßow. Additional discretizations are needed for solute advection and dispersion within the ßuid phases. Although it is common to use upstream weighted methods for



203

advection terms, this technique introduces numerical dispersion that can be much more signiÞcant than that introduced in multiphase ßow simulation. The same higher-order methods used for relative permeability weighting can be used for discretizing solute advection terms (Sleep and Sykes, 1993; Unger et al., 1996b). 4.2.4.2 Temporal Discretization The most robust method for temporal discretization is the fully implicit method (Aziz and Settari, 1979). The fully implicit method is unconditionally stable, although there is still a time-step limitation associated with converging the nonlinear iterations in multiphase ßow and transport. The disadvantage of the fully implicit method is that the mass balance equations for ßow and transport are fully coupled, and, consequently, a large set of coupled equations must be solved simultaneously. It has been commonplace in the petroleum industry (Aziz and Settari, 1979) to use an approach termed “implicit in pressure, explicit in saturations” (IMPES), which allows governing equations to uncouple, forming a set of equations that can be solved implicitly for pressures, followed by an explicit solution of a second set of equations for ßuid saturations. This method is signiÞcantly less computationally expensive than the fully implicit method for a given time step. However, the IMPES method is only conditionally stable. The allowable time-step size decreases as the importance of capillarity increases in the problem, making it particularly unsuitable for simulating three-phase ßow and transport problems. Adaptive time-stepping methods developed for petroleum reservoir simulation (Thomas and Thurmau, 1983) have also been applied to simulations of multiphase ßow and transport in ground water systems (Sleep and Sykes, 1993). In these methods, cells are adaptively switched from IMPES to fully implicit when changes in saturations or concentrations or ßow rates are too large. This method allows the use of larger time steps than allowable in a fully IMPES simulation but at lower computational cost than a fully implicit method. In addition to reducing computational times, the use of adaptive implicit methods allows the use of higherorder weighting schemes at IMPES nodes without changing the sparsity of the system matrix. The major drawback of using adaptive implicit methods is the absence of easily calculated criteria for switching cells from IMPES to implicit (Fung et al., 1989). 4.2.4.3 Linearization of Nonlinear Equations of Multiphase Flow and Transport The equations of multiphase ßow are highly nonlinear due to the nonlinear dependence of relative permeability and capillary pressure on ßuid saturations. Solving multiphase ßow equations requires a linearization technique. The simplest approach is successive iteration or Picard iteration, where the discretized equations are solved using relative permeabilities and capillary pressures calculated from current values of saturations. The iteration process is started with an initial guess of saturations, usually equal to the initial saturations or the saturations at the end of the previous time step. This method of successive iteration is not very robust because it is sensitive to initial guesses of satura-


204


tions and does not perform well for systems with highly nonlinear relative permeability and capillary pressure relationships (typical of most soils) or systems with rapidly changing saturations due to high ßow rates. In these cases, the lack of robustness of this method leads to a requirement of extremely small time steps or to complete failure to converge. A more robust method for solving the nonlinear multiphase ßow and transport equations is the use of Newton linearization (i.e., the Newton Raphson method), which requires computing the Jacobian of the coefÞcient matrix of the mass balance equations. This computation makes code development more difÞcult because mass balance equation derivatives with respect to the solution variables are required. However, numerical differentiation methods can be used to eliminate the need for analytical derivative determination. Solving equations of multiphase ßow and transport in compositional simulators is complicated by equations changing depending on the number and types of phases present in a discretized element. This necessitates a change in the solution variables when there is a change in the ßuids present. Sleep and Sykes (1993) discuss the primary variable substitution for a three-phase compositional simulator. In their model for a system composed of water, air, and one organic species, there are three degrees of freedom for each discrete element (Þnite difference block in this case). In systems with all three phases present, the solution variables are water head, water saturation, and total liquid saturation (i.e., water plus organic-phase saturations). In contrast, regions containing only water and total liquid saturations are the same. However, the water phase may contain dissolved organic, so it is necessary to solve for the concentration of organic in the water phase. In addition, the air or organic phase can enter the region at some time, which necessitates the calculation of water saturations to detect the condition of water desaturation. Because water velocities are also required, it is necessary to compute water heads. Thus, when air and organic phases enter a water-saturated region, the solution variables are changed from water head, water saturation, and organic concentration in the water phase to water head, water saturation, and total liquid saturation. Choices of solution variables for other phase conditions and criteria for switching solution variables are given by Sleep and Sykes (1993). It has been demonstrated that the variable substitution methods with the appropriate choice of solution variables can impact solution times for unsaturated ßow by an order of magnitude (Forsyth et al., 1995). For multiphase ßow and transport problems, improvements in performance of factors of three to Þve were obtained with optimal variable substitution (Forsyth et al., 1998). 4.2.4.4 Solution of Linear Equations SigniÞcant advances in algorithms for solving large sets of sparse linear equations generated by applying Þnite differences and Þnite elements were made in the 1980s and early 1990s. Iterative methods are the most powerful of these algorithms and rely on incomplete factorization methods coupled with acceleration methods. Incomplete lower upper factorization using Gaussian elimination has been shown to be an effective method and has been combined with orthomin acceleration (Behie and Vinsome, 1982) and stabilized biconjugate gradients (van der Vorst, 1992) to produce robust solvers applicable to large systems.



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4.3 MODELING DNAPLS — STATE OF PRACTICE The speed and memory capacity of computational resources has grown phenomenally over the past 50 years and is likely to continue growing at a similar pace. As a result, computational tasks that were onerous 10 years ago can be performed with little effort today; tasks that were virtually impossible 20 years ago are within easy grasp today; and problems that are impractical today will become increasingly tractable, if not trivial, as the years pass. Thus, the state of practice in modeling is a very transient one, with great promise for advances in the years ahead. Of course, powerful computers do not guarantee efÞcient and accurate models. Advances in the fundamental understanding and ability to quantify physical, chemical, and biological processes and their interactions in the subsurface have been made in recent years, although many issues remain poorly understood and difÞcult to quantify at the present time. In this section, the role that models play in research, policy, and engineering is reviewed. The types of models and speciÞc computer codes available at this time are discussed, as well as some of the limitations of present models.

4.3.1 ROLES

OF

MODELING

4.3.1.1 Research and Education Soils and aquifers are very complex systems that involve interactions of numerous physical, chemical, and biological processes. Computer models are an important means of cataloging our current understanding and expanding the limits of our understanding. As the number of variables and processes that are considered and the sophistication of their mathematical description increase, the ability to make “intuitive” a priori projections of system behavior, even in the most qualitative manner, becomes tenuous. Models become the necessary roadmaps that allow calculation through the tortuous paths of interacting phenomena. Computer models are powerful research tools. Beginning from a foundation of well-tested theories and methods, a tier is built one at a time, checking the soundness of the structure during building and tearing down and rebuilding as necessary. This approach follows the classical mode of scientiÞc inquiry: •

•

•

Model Develop a mathematical model of the phenomena of interest, and implement the model to make predictions for deÞned conditions. Measure Conduct experiments to measure the phenomena under a range of relevant conditions. Modify Evaluate the accuracy of predictions. Accept or reject the model, and modify or reÞne it as appropriate.

The foregoing process is incremental and iterative. Often, apparent success is achieved during initial model testing under a relatively limited range of


206


conditions, only to discover that the model deviates from observations under more complex conditions. For example, measurements of ßuid saturation–pressure relations can be described accurately and consistently by monotonic functions for experiments in which an NAPL displaces water from core samples. However, the same functions fail to accurately describe NAPL displacement by water. Laboratory measurements on a wide variety of porous media have shown that mass transfer coefÞcients can be estimated from correlations with grain size and pore velocity. However, Þeldscale observations generally indicate that these correlations fail miserably. When such failures occur, researchers may pine for a world controlled by linear laws with constant coefÞcients. Nevertheless, such Þndings are valuable because they represent the Þrst step toward developing an improved understanding of the processes that occur below the ground surface. Some of the most pressing issues in subsurface modeling involve uncertainty in the scaling up of constitutive relations from the laboratory scale, which is relatively well understood, to the Þeld scale, which is less well quantiÞed. Another research paradigm that has broad application for investigating this and other issues is as follows: •

•

•

Model at Low Resolution Develop a course-scale model of the phenomena of interest and implement it to predict large-scale behavior for deÞned conditions. Model at High Resolution Perform “numerical experiments” with a Þne-scale distributed parameter model to predict large-scale averaged phenomena under a range of conditions. Modify Low-Resolution Model Evaluate the accuracy of the large-scale model predictions. Accept or reject the model, and modify or reÞne it as appropriate.

This approach is predicated on an assumption that processes in the Þne-scale model and parameter distribution can be accurately represented and that there are no additional processes at the large scale. Validation of the Þne-scale model must be predicated on the Model–Measure–Modify paradigm. Advantages of the “Low Model–High Model–Modify” approach are as follows: • • •

A much wider range of conditions can be studied in a shorter period because the approach does not rely on nature. Much greater precision is possible than in Þeld studies where measurement error and uncertainty in site characterization can obscure results. It is much less costly.

Model sensitivity analyses are a valuable means of gaining insight into phenomena. Numerical experiments allow researchers to study the effects of individual variables that may be difÞcult or impossible to isolate with conventional experiments and that would certainly be more time-consuming and costly without complications



207

attributed to measurement error. Such studies often point to issues that warrant more detailed study numerically or experimentally. In addition to their utility as research tools, models serve as a valuable teaching tool by providing a hands-on, boots-off means of “observing” how complex systems respond to various external factors and system properties. Especially when deployed with interactive graphical pre- and postprocessors, the virtual reality laboratory becomes a powerful learning environment. 4.3.1.2 Policy Development Regulatory policy development is another broad area in which subsurface models play a key role. Models provide a means of assessing the reasonableness of policies, developing design standards, evaluating emerging concepts and technologies, and establishing cleanup criteria. Protocols for developing risk-based cleanup levels (RBCLs) rely heavily on models. RBCLs represent the maximum contaminant concentration at a compliance point in a given media (e.g., soil, ground water) at a given location that will result in a human health risk or ecological risk less than a speciÞed probability (e.g., fewer than one additional death per 1 million people). To make such an assessment, it is necessary to Þrst identify all potential exposure pathways (e.g., ingestion or contact with contaminated ground water, breathing of air contaminated by vapor migration through soil into buildings). Next, the magnitude of the contaminant concentrations at the potential exposure points (e.g., ground water concentration at a water supply well, vapor concentration in indoor air) are estimated over a speciÞed potential exposure period (e.g., the average time individuals stay in a residence), which is then used to compute the risk based on toxicological studies. For each potential exposure pathway, an estimate is made of the average attenuation over the exposure period between the compliance point and the exposure point. Models are critical for determining attenuation factors, which can increase or decrease with time. Various approaches have been adopted for determining RBCLs. At one end of the spectrum, site-speciÞc values can be determined by using relatively sophisticated models in conjunction with detailed site characterization data. Regulatory guidance may or may not exist for speciÞc modeling approaches or models. At the opposite extreme, predetermined RBCLs may be speciÞed for certain ranges in site conditions (e.g., depth to ground water, distance to water supply wells, soil or aquifer type) based on results from generic model scenarios using conservative parameter values. Intermediate methodologies for determining site-speciÞc RBCLs involve the use of simpliÞed models with generic (i.e., conservative) parameters based on limited site data. Regulations for land disposal are strongly linked to modeling for systems ranging from common landÞlls, which are designed on the basis of hydrologic control models, to high-level radioactive waste disposal, which must consider the potential for transport over millennia. Models also play an important role in new technology evaluation and guideline development for assessing and implementing monitored natural attenuation.


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4.3.1.3 Site Assessment and Remedial Design Models serve a critically important role in site assessment and remedial design activities. During site assessment investigations, models facilitate meaningful interpretation of site characterization data to identify migration pathways and rates, estimate potential exposure, determine appropriate cleanup levels, and set project priorities. Sensitivity analyses with preliminary site models at the early investigation stages allow data gaps to be identiÞed and characterization efforts to be optimized to meet project needs with minimum cost. Remedial technology selection and design generally involve numerous steps. First, remedial technologies are screened for feasibility and appropriateness considering site conditions (e.g., types and amounts of contamination and media, soil/aquifer properties). Following a preliminary screening, a more detailed assessment of the effectiveness and cost of feasible alternatives is performed. SimpliÞed models can be used at this stage to estimate system requirements and operating times for comparison. If a Þnal remedy selection cannot be clearly identiÞed at this point, sensitivity analyses can help identify critical data requirements to narrow the decision and design pilot tests or establish other site characterization needs. Following model reÞnement using the additional data, the models can be used to extrapolate results from the pilot test to the full-scale system and select a Þnal remedy selection. Final system design (and, in some cases, Þnal remedy selection) involves establishing optimum values for a number of design parameters (e.g., number and locations of wells, ßow rates). Models are uniquely suited for design optimization. Extrapolating pilot test results to full-scale operation is a nonlinear and frequently nonintuitive problem. Furthermore, only one full-scale system can be implemented; therefore, models provide the only possible means to evaluate the cost effectiveness of multiple full-scale options in a short period of time and inexpensively. As an example, Table 4.2 presents a study of remedial options for a fuel spill (Parker et al., 1997). For each of the four remedial strategies, designs were developed based on a simple direct interpretation of pilot test data or by optimizing design parameters based on model analyses results. Estimated total costs for the various methods (i.e., capital plus discounted operating cost) illustrate the beneÞt of using models for remedy selection and design.

TABLE 4.2 Estimated Remediation Costs for Various Options and Design Protocols Total Cost (Dollars in Thousands) Remedial Strategy

Nonoptimized Design

Optimized Design

A B C D

92 93 78 70

84 86 73 59



4.3.2 OVERVIEW

OF

209

EXISTING MODELS

4.3.2.1 Three-Phase Models Models capable of simulating transient ßow of three ßuid-phase soil and ground water systems made their Þrst appearance in the mid-1980s (e.g., Faust, 1985). Facilitated by advances in basic understanding of multiphase ßow and transport and numerical techniques (e.g., Abriola and Pinder, 1985; Parker, 1989; Mercer and Cohen, 1990) and by advances in computer hardware, numerous multiphase numerical models were introduced in the 1990s (Table 4.3). Multiphase ßow models are sophisticated numerical codes that incorporate highly nonlinear three-phase relative permeability–ßuid saturation–capillary pressure (k–S–P) relations to simulate the transient ßow of water, air, and/or NAPL. Various formulations are employed to describe the k–S–P relations, with more rigorous models considering complexities due to nonwetting ßuid entrapment and hysteresis (Lenhard et al., 1989; Guarnaccia et al., 1997). In some models, air mobility is assumed to be sufÞciently great so that air pressure remains at atmospheric, allowing the air ßow equation to be eliminated (Richards’ assumption). In addition to bulk ßuid ßow, many multiphase models simulate the transport of one or more chemical species partitioning among vapor, aqueous, NAPL, and solid (i.e., adsorbed) phases. In the simplest models, local equilibrium phase parti-

TABLE 4.3 Overview of Available Three-Phase Flow and Transport Models Program FEHM MAGNUS MOFAT MUFTE NAPL

NUFT STOMP COMPFLOW COMPSIM

UTCHEM

Description 3-D three-phase ßow; multispecies transport; heat transport; dual porosity 3-D three-phase ßow; single species transport 2-D three-phase ßow; multispecies transport 3-D three-phase ßow; single species transport 3-D three-phase ßow; single species transport; nonequilibrium interphase mass transfer coefÞcients as functions of NAPL saturation 3-D three-phase ßow; multispecies transport; heat transport; dual porosity; steam injection 3-D three-phase ßow; multispecies transport; dual porosity; heat transport 3-D three-phase ßow; multispecies transport; dual porosity; discrete fractures 3-D three-phase ßow; multispecies transport; dual porosity; biodegradation; heat transport 3-D three-phase ßow; multispecies transport; nonequilibrium interphase mass transfer; various reaction models; surfactant effects

Ref. Zyvoloski et al., 1995; Dash et al., 1997 Huyakorn et al., 1994 Katyal et al., 1991 Helmig et al., 1994 Guarnaccia et al., 1997

Nitao, 1996 Lenhard et al., 1995; White and Oostrom, 1996 Unger et al., 1995 Sleep and Sykes, 1993; Sehayek et al., 1999; Sleep et al., 2000 Pope et al., 1999


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tioning is assumed. More sophisticated models consider nonequilibrium exchange described by Þrst-order mass transfer functions. Mass transfer coefÞcients in such models are commonly user-speciÞed, but in some cases can be computed internally as functions of ßuid saturations and velocities and other factors. These functions are not well understood at the Þeld scale at the present time. Some models consider nonequilibrium mass transfer between “mobile” pore regions (e.g., fractures or other macropores) and “immobile” pore regions, which is usually described by a Þrstorder mass transfer function. SpeciÞcation of three-phase k–S–P relations that are accurate and consistent with multiregion transport models is a problematic issue at this time. Reported models range from those that simulate only one species subject to linear partitioning and Þrst-order decay to those that model multiple species subject to a variety of equilibrium or kinetically controlled reactions. Of particular signiÞcance for chlorinated solvents are models that consider the production and decay of a series of daughter products of the original contaminant (i.e., chain decay). A few models account for relationships between phase concentrations of chemicals (individually or in total) and bulk ßuid properties (e.g., density, viscosity, surface and interfacial tensions), other chemical properties (e.g., cosolvent effects on solubility), and/or k–S–P relations (e.g., residual NAPL saturations). The resulting coupling of the ßow model to the transport model increases the nonlinearity of the numerical problem but can be critical to predict certain behavior, such as the effects of surfactant injection on remediation. Several models have been developed and simulate heat transport to enable the assessment of thermal-assisted remediation technologies (e.g., steam injection) or radioactive waste burial. In addition to solving a heat transport equation and ßow and chemical transport equations, further nonlinearity is induced due to the temperature-dependence of equilibrium-phase partitioning relations and bulk ßuid properties. 4.3.2.2 Two-Phase Models For many problems of practical importance, NAPLs exist in the vadose zone at a residual saturation that is essentially immobile under ambient Þeld conditions. In such cases, two-phase air–water models may be appropriate tools to assess volatile chemical emissions or evaluate remediation involving vapor extraction, soil heating, or steam injection (Table 4.4). The simplest models of this class simulate unsaturated water ßow based on Richard’s assumption (i.e., gas phase at constant pressure), which eliminates the need to solve an airßow equation. Depending on the numerical formulation used to solve the water-ßow equation, the solution can be limited to simulations of vadose zone problems in which the domain remains unsaturated (i.e., unsaturated ßow models), or the solution can allow simulation of fully saturated media as a special case (i.e., variably saturated models). In contrast to the Richard’s unsaturated ßow formulation, true two-phase ßow models simulate gas-phase ßow explicitly. For example, a dynamic gas phase must clearly be considered in certain circumstances to model soil vapor extraction or steam injection or to assess the effects of natural air pressure ßuctuations on vapor transport.



211

TABLE 4.4 Overview of Unsaturated Flow and True Two-Phase Flow and Transport Models Program CHAIN_2D HBGC123D + FEMWATER 3DMURF +3DMURT R-UNSAT

SUTRA TOUGH2 +T2VOC VLEACH VS2DI

Description 2-D unsaturated ßow and multispecies transport; chain decay 3-D variably saturated ßow; multispecies transport; heat transport; bio- and geochemical reactions; dual porosity 3-D unsaturated ßow and single species transport; dual porosity 1-D vertical or 2-D radial unsaturated ßow; multispecies dissolved transport with vapor diffusion; NAPL source model; chain decay 2-D variably saturated ßow, single species transport 3-D two-phase ßow; multispecies transport; chain decay; heat transport; dual porosity; steam injection 1-D unsaturated ßow; single species transport with vapor diffusion 2-D variably saturated ßow, single species transport or heat transport

Ref. Simunek and van Genuchten, 1994 Yeh et al., 1998 Gwo et al., 1994, 1995 Lahvis and Baehr, 1997 Voss, 1984 Pruess, 1991; Pruess et al., 1999 Turin, 1990 Lappala et al., 1987; Healy and Ronan, 1996

Transport analyses in air–water models range from single species formulations with simple linear adsorption and Þrst-order decay to multispecies formulations with complex kinetically controlled biological and geochemical species transformations. Some models consider nonequilibrium, interphase mass transfer; diffusion-limited, matrix-fracture mass transfer; or heat transport. By deÞnition, unsaturated ßow models cannot consider gas-phase advection, although they can consider vapor-phase diffusion without adding signiÞcantly to the computational burden. True two-phase ßow models must be used to predict advective transport in both vapor and aqueous phases. Some air–water models consider an immobile NAPL phase with phase partitioning that can be modeled as equilibrium controlled or as a Þrst-order mass transfer process. However, because the quantity and distribution of NAPL is rarely known with any degree of certainty, NAPL is often not explicitly modeled. In the latter case, NAPL dissolution and volatilization are handled as boundary conditions by either specifying species concentrations in dissolved and/or vapor phases or rates of dissolution or volatilization within a source area (i.e., region with residual NAPL). 4.3.2.3 Single-Phase Models The most common use of models to date for assessing chlorinated solvent transport has focused on analyzing ground water transport under natural and engineered conditions. Because DNAPL in aquifers is expected to occur primarily at residual saturations that are immobile under ambient conditions and because the quantity and distribution of DNAPL is usually unknown, most ground water solvent transport models do not consider DNAPL explicitly. As with unsaturated zone models, most


212


TABLE 4.5 Overview of Single-Phase Flow and Transport Models Program AIR3D BIOCHLOR

HST3D MOCDENSE + MODFLOW MT3D + MODFLOW RT3D + MODFLOW

Description 3-D transient air ßow model (could be coupled with MT3D or RT3D to model vapor transport) 1-D steady-state uniform ground water ßow with 3-D multispecies transport; chain decay with option for two zones of decay coefÞcients 3-D ground water ßow, single-species transport; heat transport 2-D ground water ßow; transport for two species; ßuid properties as linear function of Þrst species 3-D ground water ßow; single-species transport 3-D ground water ßow; multispecies transport; chain decay; rate-limited sorption; Monod kinetics; electron receptor limited decay; userdeÞnable reaction models

Ref. Joss and Baehr, 1995 Aziz et al., 2000

Kipp, 1986, 1997 Sanford and Konikow, 1985 Zheng, 1990 Clement, 1997

ground water transport models treat NAPL dissolution implicitly by specifying the dissolved species concentration or dissolution rate at the source area. Available models that have been utilized for simulating dissolved-phase chlorinated hydrocarbon transport in ground water are summarized in Table 4.5. It should be noted that models designated as variably saturated ßow codes in Table 4.4 can be used to model saturated ßow with computational efÞciency comparable with single-phase ground water models. Furthermore, some two- and three-phase ßow models allow special cases in which the solution of speciÞed phases is eliminated to reduce the computational burden of the rigorous solution. Single-phase gas ßow models have also been developed that are appropriate for modeling forced air remediation in which temporal variations in water saturations can be reasonably disregarded. Numerically, single-phase air ßow models are very similar (or in some cases identical) to single-phase ground water ßow models (e.g., the air ßow model AIR3D is actually a modiÞcation of the ground water ßow model MODFLOW). Similar variations in single-phase transport model formulations (e.g., number of species modeled, types of reactions, equilibrium- vs. nonequilibrium-phase transfer) occur as those found in two- and three-phase models. SimpliÞed solutions have also been developed for use as preliminary assessment tools (e.g., the BIOCHLOR model) for screening-level analyses. 4.3.2.4 Chlorinated Solvent Biodegradation Models Most chlorinated solvents are biodegraded under a variety of terminal electron accepting processes and can serve as electron donors under anaerobic conditions.



213

In addition, chlorinated solvent biodegradation often produces transformation products that are of as much environmental concern as the parent compounds. At present, BIOCHLOR and RT3D are the only two publicly available models that are commonly used for modeling chlorinated solvent biodegradation (Table 4.5). Both BIOCHLOR and RT3D model the chain decay of contaminants, but RT3D allows the use of multiple electron acceptors and Monod kinetics. Microbial growth is not modeled in either model, so kinetic rate constants used are lumped parameters that implicitly include microbial population. Thus, the models are limited to sites at which the microbial population is relatively stable. In addition, because microbial activity and population can vary signiÞcantly from site to site, kinetic constants must be determined from site-speciÞc contaminant concentration data. The DECHLOR model of Shoemaker et al. (2001) includes microbial growth and the production and consumption of hydrogen, with thermodynamic limits on hydrogen production. This model is more comprehensive than RT3D and BIOCHLOR but has had limited Þeld application.

4.3.3 MODEL SELECTION

AND

LIMITATIONS

Selecting the most suitable model for a given problem is an exercise in compromise that requires careful consideration of the objectives of the analysis and the costs, beneÞts, and drawbacks of various modeling approaches. On initial consideration, it may appear self-evident that the most rigorous model will always be the “best” model. However, all models are, by deÞnition, simpliÞcations of reality that can be peeled away to Þnd yet another layer of complexity beneath. From a practical standpoint, the best model is the one that can answer the question posed to it with acceptable accuracy for the least cost. Because accuracy and cost are often inversely related, model selection imposes tradeoffs. In principle, more rigorous models are capable of making predictions with greater accuracy, i.e., answering the questions posed with less uncertainty. The problem when selecting a model is to determine to which processes and parameters the answers are sensitive and to which they are not. In some cases, this is selfevident (e.g., no need to model gas advection to evaluate a ground water pumpand-treat system), while in other cases it is not intuitively obvious (e.g., the need to model seasonal temperature ßuctuations to design a soil vapor extraction system in Michigan). These nonintuitive issues can be resolved by performing sensitivity analyses with relatively simple models. In some cases, the only way to answer the question is to make comparisons between the simpler and more complex models for prototype problems to assess sensitivity vis à vis the problem under consideration. For example, if the problem is to optimize a remediation system design, the concern is the sensitivity of the design decision to model results, which may not relate to primary model output (e.g., ßuxes, concentrations, cleanup goal time) in a linear manner. Although more complex and rigorous models theoretically are capable of greater accuracy, in practice, complex nonlinear models become increasingly subject to numerical errors in their solutions. These errors can be reduced by the Þnesse of improved numerical solution techniques, or, up to a point, by brute strength of


214


computer power through Þner spatial and temporal discretization. Numerical error can be difÞcult to detect in complex models that can (correctly) respond in ways that are intuitive. Avoiding or at least minimizing numerical error in complex models generally requires signiÞcant skill and experience by the user. In addition to requiring more experienced personnel to operate, complex models require more effort to set up and run, more computer resources to execute, and more supporting effort to obtain required input data. This all translates to additional cost, which must be balanced against the potential beneÞts of more complex models. Improvements in the robustness and efÞciency of numerical methods and computer performance will gradually reduce the effort and cost of using more complex models. Developing increasingly sophisticated pre- and postprocessing software will also make complex models easier and faster to operate by personnel with less experience. In addition to the foregoing computational issues, signiÞcant knowledge gaps remain in the basic understanding of Þeld-scale subsurface processes. The knowledge of transport processes at the laboratory scale is generally good. However, substantial uncertainty exists regarding the mathematical representation of processes at the Þeld scale. With currently available computer resources and Þeld characterization technology, it is impractical to model Þeld heterogeneity at a Þne resolution. However, attempts to scale up by averaging small-scale parameters or functions often lead to apparent phenomena at the larger scale. A well-known example of an apparent process due to scaleup is hydrodynamic dispersion. Another well-known problem (especially in petroleum reservoir modeling) is the scaleup of k–S–P relations. This scaleup is a particularly difÞcult problem when dealing with DNAPLs in ground water due to complications associated with unstable ßow in heterogeneous media. Whether this issue can be resolved sufÞciently to enable quantitative prediction of DNAPL movement for practical cases is not clear at this time. Another important scaleup issue involves the quantiÞcation of Þeld-scale mass transfer kinetics. This is a basic problem that spans the range in models from simple single-phase dissolved models to the most complex three-phase models. A considerable volume of research exists regarding the quantiÞcation of mass transport at the laboratory scale for a wide range of porous and other systems (e.g., Cussler, 1984; Miller et al., 1990). However, it is likely that heterogeneity in porous media properties and phase distributions (i.e., spatial distributions of NAPL saturations and their interfacial areas with other phases) will induce apparent processes at the Þeld scale that are not observed in the laboratory (e.g., Guarnaccia et al., 1997). As the potential level of detail that can be simulated with available computer technology increases, limitations become increasingly apparent in our understanding of chemical and biological interactions among species and with the porous media. Many complex biogeochemical reactions are not well understood at the laboratory scale, much less at the Þeld scale. Using the last quarter of the 20th century as a gauge, great advances in the capability of subsurface computer models are expected to solve important environmental problems in the 21st century.



215

4.4 SITE APPLICATIONS The practical research objective of organic simulation in the subsurface is to provide tools for solving environmental problems at the Þeld scale. To illustrate how models and theory have been used in practice, two example applications are summarized below. The two approaches were radically different: one used a compositional simulator and the other a ground water ßow model and empirical relationships. Both approaches led to evaluations that permitted the companies involved and the regulating agencies to make informed decisions regarding remediation at the two sites. Likewise, each of these applications shows that complex mathematical models are limited by the availability of data at the Þeld scale.

4.4.1 GULF COAST EDC DNAPL RELEASE In 1994, approximately 15,000 gal of DNAPL were released from a pipeline to a nearby drainage ditch on a site located in the U.S. Gulf Coast. The DNAPL was composed of 1,2-dichloroethane (EDC). Emergency actions were taken to remediate the ditch area and impacted sediments; however, EDC DNAPL remained in shallow water-bearing units in the vicinity of the ditch. To address long-term concern related to the DNAPL and dissolved-phase EDC, detailed site investigations and modeling were performed to achieve the following: • • • • • •

ConÞrm conceptualization of the hydrologic system. Simulate ßow in water-bearing zones. Provide input for conceptual containment system design. Evaluate the potential for EDC DNAPL to reach the regional aquifer. Provide worst-case scenario input for modeling dissolved-phase EDC transport in the regional aquifer. Estimate exposure concentrations.

A combination of models was applied to focus on different issues and scales of interest. A 3-D ground water ßow model was developed to simulate ßow over a wide areal extent and in multiple hydrostratigraphic units. The ßow model provided the framework for a two-dimensional (2-D) (i.e., cross section) compositional model of the EDC DNAPL transport. Results of the DNAPL simulations were used as a basis for source conditions to assess dissolved EDC transport in the regional aquifer. The modeling approach used information and concepts from Þeld investigations and observations conducted concurrently with and after emergency actions. These investigations characterized the hydrogeologic conditions as well as the nature, fate, and extent of EDC in the subsurface. From the ground surface down, the stratigraphic units at the site consisted of a 40-ft sand; upper interbedded clays, sands, organic silts, peat; interbedded sands, silts, and clays; a gumbo clay; sandy clay; and the Upper Chicot (a sandy aquifer). Early Þndings of the Þeld investigations concluded the following: • •

Immobile DNAPL was present in the organic/silt peat and inorganic clay. Mobile DNAPL was present in the 40-ft sand only.


216


• • • •

Aquitards contained microfractures. EDC DNAPL did not extend beyond the interbedded clay. Dredging had a very limited affect on total mass removal of EDC. Evidence of natural attenuation was observed in all aquifers.

Laboratory studies were conducted to provide the additional data necessary to evaluate DNAPL migration and dissolved EDC attenuation. Saturation–capillary relationships for the representative soil samples and EDC saturation in the organic silt and peat were determined. Microcosm studies for EDC were conducted to assess intrinsic bioremediation potential. The vertical cross-sectional model for EDC DNAPL was critical to the evaluation of potential risks and remedial alternatives; however, its application was limited by typical challenges posed by DNAPL model applications at the Þeld scale. Among these challenges were the use of local-scale capillary pressure–saturation relationships in a heterogeneous system at Þeld scale, the general lack of DNAPL saturation data necessary for calibration and validation, and the longer simulation time and convergence issues required for solving the complex governing equations. Given these limitations, sensitivity analyses and worst-case scenarios were used to bound the problem and provide conceptual insight. Sensitivity to fracture and matrix properties, the impact of dissolution assumptions, and the threshold entry pressures for DNAPL in fractures were important variables assessed. For example, Figure 4.3A and Figure 4.3B show a comparison of the results for EDC saturations in the fractures for two assumed sets of values of the matrix entry pressures. Even with the unrealistically low entry pressures used to generate Figure 4.3B, predicted vertical DNAPL movement is limited to the top of the Gumbo clay due to greater DNAPL attenuation in the matrix predicted with the low entry pressures. On the basis of the DNAPL modeling analysis, Þeld observations and concepts were supported, primarily that no DNAPL would penetrate signiÞcantly into the clay units that conÞne the Upper Chicot aquifer.

4.4.2 DNAPL SOURCE AREA CHARACTERIZATION COASTAL PLAIN OF NEW JERSEY This example illustrates the application of a simpliÞed mass ßux model to predict current and future trends in mass loading from source areas to an unconÞned shallow aquifer. The site is a 1500-acre Superfund site in southern New Jersey with multiple sources of several halogenated compounds. As a result of the existence of multiple source areas, multiple chemicals of concern, heterogeneous geological conditions, and incompletely characterized source areas, use of sophisticated models for remedial design and exposure prediction was not feasible. Temporal trends in ground water quality downgradient of a source area were used as the basis for evaluating the source mass, composition, and future impact on ground water. The site overlays the Cohansey formation, which is generally composed of coarse sand, but also includes clay and silt lenses and layers that can extend over larger areas. Ground water is the primary exposure pathway for this site.



217

FIGURE 4.3 (A) EDC saturations in matrix and fractures for base case; (B) EDC saturations in matrix and fractures for air–water entry pressures of 0.05 m for all units.

The empirical approach used to model source zone mass ßux consisted of the following four general steps: 1. Characterize the structure of the ground water plume downgradient of the source areas to help identify the cross-sectional area of the source. 2. Determine source area attributes (i.e., hydrogeology and contaminant mass) to provide information as to the heterogeneity of water ßow and mass distribution.


218


3. Compile a ground water database of source zone efßuent conditions (i.e., estimate the magnitude and temporal trend in dissolved mass ßux from the source zone) over a period of several years. 4. Use a simpliÞed mass ßux model that approximates nonequilibrium mass transfer to predict the dissolved mass loading rate over time. The model used in the Þnal step incorporates the information and data from the preceding steps. First, source areas are divided into blocks for which a mass loading rate can be determined by the following: Mú (t ) = Qavg (t ) ¥ Cavg (t )

(4.26)

where Qavg(t) is the time-dependent ßow through the block and Cavg(t) is the timedependent area averaged concentration leaving the block (see Figure 4.4). The mass loading rate can also be determined based on source zone attributes from the following: Mú (t ) ~ Qeff (t ) ¥ Ceq (t )

(4.27)

where Qeff is the effective ßow or ßushing rate and Ceq is the equilibrium concentration of that constituent in water in contact with a NAPL of known composition. The equilibrium concentration for NAPL mixtures can be determined theoretically (e.g., using Rault’s law), whereas the effective ßushing rate is determined empirically (e.g., a power law relationship to NAPL mass content). Calibration involves determining the leaching efÞciency, Qeff /Qavg(t), for each source block that yields the best match between the mass loading rate computed by Equation 4.27 and that determined from Þeld data using Equation 4.26. For this Superfund site application, potential source areas were identiÞed over the course of investigations in which numerous soil borings were installed to

area

q avg (t)

C avg (t)

M(t) = Q avg (t) x Cavg(t) ~ Q eff (t) x C eq (t)

FIGURE 4.4 SimpliÞed mass ßux model used for DNAPL source area characterization at site in Coastal Plains, NJ.



219

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FIGURE 4.5 Ground water elevations and ßow patterns showing impact of pumping wells at New Jersey site in Coastal Plain.

delineate the extent and the amount of DNAPL in the subsurface. Several pumping wells were installed to capture impacted ground water (Figure 4.5). Mass removal rates from the pumping wells, compiled over 10 years, were used to estimate source zone mass ßux, calibrate the model (Equation 4.27), and assess the reliability of the methodology. Results of the modeling effort are shown in Figure 4.6, Figure 4.7, and Figure 4.8 for total chemicals of concern (COCs), chlorobenzene, and PCE, respectively. The comparisons of cumulative loadings are generally good for total COCs, but the model tends to overpredict the removal of the more soluble compounds (e.g., chlorobenzene) and underpredict the less soluble compounds (e.g., PCE). The methodology developed for this site provided the predictive tool for design of a long-term remedial plan. The empirical model incorporated important aspects of the DNAPL dissolution, ground water system, and plume dynamics. The advantage of this approach compared with a multidimensional, compositional DNAPL model or a solute transport model was the ability to calibrate the model (i.e., few Þtting parameters). The disadvantage is the loss of some physical insight into the controlling physical chemical processes. Limitations of this methodology include the application of Raoult’s law at the scale of the source blocks and for complex mixtures, the selection of appropriate size for source block discretization, and the need for a substantial time record of mass loading data.


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FIGURE 4.7 Observed and modeled cumulative extraction of mass for chlorobenzene at New Jersey site in Coastal Plain.



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time

FIGURE 4.8 Observed and modeled cumulative extraction of mass for PCE at New Jersey site in Coastal Plain.

4.5 RESEARCH NEEDS A variety of models have been developed that incorporate multiphase ßow and transport of multicomponent NAPLs with biotic and abiotic transformations. Sophisticated equilibrium- and nonequilibrium-phase partitioning relationships have been incorporated into these models. However, despite this level of sophistication, many of the constitutive equations used in these models for k–S–P relationships, mass transfer rates, and biological transformation processes have not been validated in the laboratory or Þeld. The data requirements for such validations are tremendous, and, in some cases, sampling and monitoring technologies required for such validation are in the early stages of development. In addition, most constitutive relationships have been developed from centimeter-scale laboratory experiments or from theoretical considerations. The parameters employed in these constitutive relationships are highly variable in the Þeld (e.g., the k–S–P parameters) but, for reasons of cost or technical difÞculty, are usually assessed from a few small-scale samples and treated as deterministic parameters in subsequent simulations. Methodologies for upscaling the constitutive relationships and associated parameters remain in the early stages of development, as discussed in the following section. The applicability of these models to DNAPL movement in fractured rock, the impact of Þngering on DNAPL movement, the biodegradation of DNAPL source zones, and the modeling of biodegradation of complex mixtures of chlorinated solvents by complex microbial communities are all issues where improved


222


understanding of the basic processes involved is required to allow model development that will reßect actual processes. These emerging issues are the subject of this section.

4.5.1 CONSTITUTIVE RELATIONSHIPS FOR MULTIPHASE FLOW, TRANSPORT, AND INTERPHASE MASS TRANSFER 4.5.1.1 k–S–P Relationships Simulation models for multiphase ßow typically utilize empirical equations for k–S–P relationships (e.g., Brooks and Corey, 1966; Lenhard and Parker, 1987). The parameters in the k–S–P relationships are typically determined under pseudostatic equilibrium conditions in small pressure cells. Alternatively, the parameters can be determined under dynamic conditions using procedures such as the one-step outßow experiment (Kool et al., 1985). It is unclear at this time how important dynamic effects are and whether dynamic procedures are necessary to determine k–S–P relationships (Miller et al., 1998). Two-phase k–S–P relationships have been widely tested at the laboratory scale. Direct experimental measurements of three-phase k–S–P conditions with varying saturation paths, required for validation of three-phase k–S–P relationships, have not been reported to date. The assumption of water-wet porous media that underlies three-phase k–S–P models is not universally valid. Geologic media exist that exhibit partial hydrophobicity, which will cause some parts of the porous media to be waterwet while other parts may be oil-wet (Hui and Blunt, 2000). No general models are available to describe three-phase k–S–P relations for such systems. The use of Leverett (1941) scaling to produce three-phase k–S–P relationships from two-phase measurements has been shown to have limitations for organic compounds with negative spreading coefÞcients, such as chlorinated organic compounds (Hofstee et al., 1997; Zhou and Blunt, 1997), particularly as residual saturations are approached. This deÞciency in current models to predict NAPL behavior near residual saturations is particularly signiÞcant. Accurate modeling of organicphase mobility and the amount of organic remaining as a long-term ground water contamination source are necessary to estimate risks and cleanup times reliably. One area of promise is the work with micromodels and pore network models to relate k–S–P constitutive equations to pore structure. Of particular interest is the work on three-phase ßow and gravity drainage that relates to residual organic-phase saturations and organic-phase relative permeabilities in the vadose zone (e.g., Soll et al., 1993; Oren and Pincziewski, 1995). Very limited data on k–S–P relations typically are available at NAPL-contaminated Þeld sites. In many cases, k–S–P relations must be estimated indirectly from correlations with permeability measurements or grain-size distribution data (e.g., Mishra et al., 1989) or by calibration to Þeld observations. However, even at very well-characterized sites, the volume of samples tested represents a tiny fraction of the total system of interest. Furthermore, the modeler must contend with information from disparate scales. For example, data may be available from small core or soil samples and from Þeld measurements (e.g., piezometer pressures, ßuid levels in



223

wells, pumping or extraction rates, soil vapor measurements, logs from borings, penetrometer probe or geophysical tests). Given the sparsity of measurements and the variability of properties, important questions arise concerning the selection of parameters to use in a model and the relation they have with measurements. Because numerical model discretization is generally at a much larger scale than laboratory data and many Þeld measurements, there is a need for techniques to upscale smallscale measurements to the scale used for modeling Þeld sites. Most of the research on upscaling and determining large-scale effective k–S–P parameters has been conducted in the area of unsaturated water ßow. Those studied have indicated that the appropriate upscaled constitutive relationships may be more complex than the corresponding parameters for small-scale or homogeneous systems. For example, Yeh et al. (1985), Mantoglou and Gelhar (1987), Polman (1990), and McCord et al. (1991) found that Þeld-scale effective properties exhibited complex hysteretic and anisotropic behavior dependent not only on mean saturation and saturation path but on rates of ßuid pressure change and pressure gradients. In a numerical study of unsaturated ßow in statistically homogeneous media, El-Kadi and Brutsaert (1985) found that geometric mean permeabilities approximated the effective conductivity at distances far from the wetting front, but in general they observed apparent time dependence of effective permeability. In another numerical study of unsaturated ßow, Zhu et al. (1989) found that deviations between the geometric mean and effective permeability could be quantiÞed as a function of the permeability variance and the local pressure gradient, effectively resulting in time dependence of the effective properties. These studies indicate the need for averaging approaches to incorporate the disparate scales of subsurface heterogeneity into parameterization of Þeld-scale modeling of chlorinated hydrocarbon fate and transport. Vertical equilibrium models are one example of the use of an averaging approach that has been widely employed in petroleum reservoir engineering (Martin, 1968; Coats et al., 1971) and the environmental Þeld (Parker and Lenhard, 1989; Kaluarachchi et al., 1990; Parker et al., 1994). The approach is predicated on the assumption that hydraulic gradients occur primarily in the horizontal direction and that vertical pressure gradients remain close to hydrostatic due to good vertical communication within the porous media. This assumption allows the 3-D ßow equations to be integrated analytically in the vertical direction because the vertical pressure distribution is known a priori by assumption. As a result, the 3D problem is reduced numerically to a 2-D problem with vertically integrated k–S–P functions that are much less nonlinear than the local functions. To minimize errors due to deviations from vertical equilibrium, Lenhard and Parker (1990) recommended integrating pseudo-equilibrium k–S–P functions rather than true equilibrium functions, the former being adjusted to account for limited vertical drainage at the time- and space-scales of the Þeld problem. Waddill and Parker (1997a, 1997b) suggested using time-dependent vertical drainage rates to further mitigate nonequilibrium effects. Jacks et al. (1973) developed procedures for determining “dynamic” vertically integrated k–S–P functions to accommodate severe nonequilibirum conditions by averaging results from 2-D vertical cross-section simulations for the range of expected Þeld conditions.


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Inverse modeling methods are particularly useful for determining effective Þeld-scale parameters because they allow diverse data measured in the Þeld (e.g., ßuid saturations and pressures, ßuid levels in wells, ßow rates) and laboratory to be used to determine effective parameters to match observed Þeld behavior statistically. The validity of the effective models determined in this manner is limited by the intrinsic accuracy of the assumed parametric model to describe Þeld-scale processes, as well as by the ability of the data to deÞne a unique solution to the inverse problem. A fundamental difÞculty in any parameter estimation analysis is the ambiguity in comparing model-predicted attributes (e.g., ßuid saturations, pressures, and velocities) with Þeld observations at disparate and generally smaller measurement scales. To some extent, measurement-scale disparities are accommodated statistically by best-Þtting parameters in an average sense. However, the issue of weighting given to various measurements with differing support volumes and reliability is a difÞcult one. In addition, the nonlinearities and computational complexity of multiphase ßow models makes inverse modeling extremely difÞcult. 4.5.1.2 Mass Transfer Relationships As outlined in Section 4.2.2, many researchers have demonstrated the kinetic nature of mass transfer among phases in the subsurface. A variety of semiempirical correlations have been developed to predict dissolution rates as a function of ßuid velocities, porosities, and ßuid saturations. However, these correlations were developed for laboratory-scale 1-D column experiments. The determination of appropriate parameters for these models at Þeld sites is problematic because little may be known about the conditions of DNAPL emplacement, extent of the DNAPL zone, and state of the immobilized DNAPL with respect to saturation and DNAPL blob size and shape. In Þeld-scale simulations of DNAPL fate and transport, data collection and model discretization is at the meter scale. It is unlikely, even in relatively homogeneous soils, that the centimeter-scale correlations can be applied directly to Þeld-scale simulation. There is a need for detailed multiscale measurements of Þeld-scale interphase mass transfer and for the development of upscaling procedures to allow the derivation of constitutive relationships for Þeld-scale simulation that are based on the statistical characteristics of soil properties and NAPL distributions (Miller et al., 1998). Issues of the scale of measurement must be addressed and methods for measuring the Þeld-scale statistical characteristics cost effectively must be developed. 4.5.1.3 Summary Based on the foregoing overview, the following pressing research needs are identiÞed: •

More accurate three-phase k–S–P models should be developed, especially with regard to hysteresis, residual ßuid entrapment, and the effects of spreading coefÞcients and wettability. Limits to the validity of Leverett



•

•

225

scaling for predicting three-phase k–S–P models from two-phase data need to be established. Field-scale k–S–P and mass transfer models should be developed that incorporate dominant effects introduced by the process of averaging up to practical modeling scales. Methods should be established for determining the limitations for proposed Þeld-scale formulations as well as for conventional mass transfer k–S–P models. EfÞcient Þeld protocols and numerical tools should be developed that employ inverse modeling methods to determine effective Þeld-scale k–S–P model parameters. Methods are required for reconciling disparities in scales of various measurements and model-predicted variables.

4.5.2 DNAPLS IN FRACTURED MEDIA Many contaminated sites are underlain by fractured clay or fractured rock. The current understanding of the processes of multiphase ßow in fractured rock is derived mostly from small-scale, laboratory-scale experiments with artiÞcially fractured rock and fracture replicas. Interactions between fracture and matrix and variations in fracture apertures have important consequences for multiphase ßow in fractured systems. 4.5.2.1 Unsaturated Water Flow Wang and Narasimham (1985) described the mechanisms operative in the movement of water in partially saturated fractured porous media and derived drainage curves and relative permeability curves from fracture aperture distributions. Pruess and Tsang (1990) presented a method for generating k–S–P relationships for two-phase ßow in rough-walled rock fractures based on the assumption that rough-walled rock fractures could be treated as heterogeneous porous media. For a given aperture distribution and assumptions of local parallel plate behavior, ßuid occupancy of fracture regions was determined from entry pressure based on aperture. Simulation of 2-D wetting and nonwetting ßow through the fracture regions for the corresponding ßuid was performed to produce the relative permeability curves. Persoff and Pruess (1995) presented two-phase ßow visualization and relative permeability measurements in natural rough-walled fractures that supported the earlier modeling work. Tokunaga and Wan (1997) demonstrated that water-Þlm ßow was an important phenomenon in unsaturated fractures in tuff rock and that unsaturated ßow in fractures could not be predicted using only the aperture distribution. This was further supported by Pruess (1999). The persistence of water ßow in fractures in contact with unsaturated matrix blocks related to ßow channeling was also an important process in unsaturated fractures (Pruess, 1999). 4.5.2.2 NAPLs Murphy and Thomson (1993) presented one of the Þrst dynamic models of 2-D, two-phase NAPL–water ßow in a variable aperture fracture. Results of the modeling, which assumed incompressible parallel plate ßow in fracture subregions, indicated


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that ßuid exclusion effects, pinch-off of nonwetting ßuid, and migration of isolated ßuid blobs were important processes in the model. Reitsma and Kueper (1994), following on the work of Pruess and Tsang (1990) with air–water systems, measured PCE–water capillary pressure curves for an artiÞcially induced fracture in a limestone block. Curves with a shape well matched by the Brooks–Corey equation were obtained. Longino and Keuper (1999) measured nonwetting-phase retention in similar limestone fractures. Field-scale modeling of multiphase ßow is common in the petroleum industry. Models for fractured petroleum reservoirs are commonly based on the dual porosity models of Barenblatt et al. (1960), Warren and Root (1963), and Kazemi (1969). In dual porosity models, the fracture and matrix systems are each assumed to be continuous domains in which porous medium mechanics applies. The exchange of ßuids between fractures and matrix is assumed to depend on ßuid pressures, matrix block geometry, and ßuid saturations, with variations in the models used for this exchange process. This dual porosity concept has also been applied to modeling unsaturated water ßow in fractured media by Gerke and van Genuchten (1993). Sehayek et al. (1999) used the dual porosity concept to model movement of a DNAPL through fractured aquitards. Their study demonstrated the importance of matrix entry pressures on the attenuation of DNAPL ßow in fractures. The importance of dissolution of DNAPL and diffusion into the matrix, previously identiÞed by Parker et al. (1994), was also demonstrated. Slough et al. (1999a) presented a model for multiphase ßow in fractured rock in which fractures were represented as discrete elements rather than as an equivalent porous medium. Darcy’s law was assumed to apply to fracture ßow, with the Brooks–Corey model used to represent k–S–P relationships for both fracture and matrix. Modeling results demonstrated that DNAPL mobility was much greater in systems with high matrix entry pressures than in systems with low matrix entry pressures in which DNAPL movement into the matrix caused signiÞcant DNAPL attenuation. Slough et al. (1999b) demonstrated that very Þne fracture discretizations were required near fracture junctions when modeling fractures as discrete elements in 2-D Þeld-scale simulations. The 3-D simulation of multiphase ßow and transport in networks of discrete fractures is a computational problem, even for the fastest computers available today. The modeling of DNAPL fate and transport in fractured media remains a challenge. Conceptual models embedded in current multiphase fractured media simulators are not well validated even at the laboratory scale. Issues of channeling, Þlm ßow, DNAPL blob movement, and scaleup issues must be resolved. Even with improved understanding of mechanisms and with scaling algorithms, characterizing fractured systems to allow representative modeling is challenging and costly. In this context, models may be useful to provide cost-effective sensitivity analyses to aid in collecting the most important data and constraining risk assessments of DNAPLs in fractured systems. 4.5.2.3 Summary Based on the foregoing overview, the following pressing research needs are identiÞed:



•

•

•

227

Issues of channeling, Þlm ßow, DNAPL blob movement, fracture–matrix interaction, and the validity of multiphase ßow equations and k–S–P constitutive relations developed for porous media must be addressed through careful laboratory and Þeld experimentation and theoretical modeling. The applicability of simpliÞed modeling methods such as equivalent porous medium assumptions must be determined. As fracture networks are likely poorly characterized at most Þeld sites, methods for determining the impact of uncertainty in fracture network characteristics on uncertainty in DNAPL movement are required. Upscaling techniques to determine appropriate large-scale effective parameters for models of DNAPL ßow in fractured systems must also be developed. Further development of numerical methods for modeling chlorinated organic compound ßow and transport in fractured systems is required because computational demands of current models limit practical application of these models.

4.5.3 IMPACT

OF

BIODEGRADATION

ON

DNAPL DISSOLUTION

Until recently, it was widely assumed that the high aqueous-phase concentrations of chlorinated organic compounds in the vicinity of DNAPLs were inhibitory to microbial activity and that biodegradation in or near source zones would not likely occur. However, recent studies indicate that dehalorespiring bacteria can tolerate concentrations of chlorinated compounds (Isalou et al., 1998; Nielsen and Keasling, 1999; Carr et al., 2000; Yang and McCarty, 2000), which, for some compounds such as PCE, approach the compound’s aqueous-phase solubility. If biodegradation can occur in or near source zones, then there is potential for biodegradation to enhance the rate of dissolution of the DNAPL source, thereby reducing the source restoration time. This section seeks to provide a theoretical basis for dissolution enhancement of DNAPL due to biodegradation and to show that dechlorinating bacteria can biodegrade chlorinated organic compounds at rates and aqueous-phase concentrations of practical interest. Experimental results are presented, and the relevance of this process to monitored natural attenuation and enhanced biodegradation is brießy discussed. 4.5.3.1 Model Development Johnson and Pankow (1992) and Seagren et al. (1993, 1994) modeled the dissolution of chlorinated organic compounds from DNAPL into ground water. Both used an idealized formulation whereby clean ground water passed over a ßat pool of DNAPL of Þnite length in a porous medium. The model assumes that water–phase mass transfer controls dissolution and that interface solute concentrations are at equilibrium. In other words, interphase mass transfer in and out of the NAPL phase is fast relative to water side processes, and the aqueous phase is saturated with the COC (e.g., C = Csat) at the NAPL–water interface for a pure-phase NAPL. Based on these assumptions, the following simple advective–dispersive–reactive equation describes steady ßow in a homogeneous medium with a reaction term:


228


Ê ∂C 2 ˆ Ê ∂C ˆ v x Á ˜ = Dz Á 2 ˜ - R Ë ∂x ¯ Ë ∂z ¯

(4.28)

where vx is the ground water velocity (LT–1), C is the aqueous-phase COC concentration (ML–1), Dz is the vertical dispersion coefÞcient (L2T–1), R equals –KBC or the biological reaction rate (ML–4), and KB is a Þrst-order kinetic constant (T–1). The dissolution ßux (J) can be described as follows: Ê ∂C ˆ J = - nDz Á ˜ Ë ∂z ¯

(4.29)

where n is the porosity of the medium. Johnson and Pankow (1992) addressed the case of dissolution without biodegradation (e.g., R = 0) and showed that at typical ground water velocities of 0.3 to 1 ft/day, the dissolution mass ßux increases as the square root of the ground water velocity. This suggests that a 25-fold increase in ground water velocity would be required to increase the mass ßux by a factor of Þve. Therefore, pump-and-treat is unlikely to have a substantial impact on DNAPL dissolution. Seagren et al. (1994) addressed the case where biodegradation was present. They deÞned an enhancement factor (E), which represented the ratio of ßux under biotic conditions to that under abiotic conditions, as E = J (biotic)/J(abiotic). They were able to express E as a function of the Damkohler number, Da2 = KBLx/vx, which is the ratio of biodegradation rate to the advection rate, and showed that E ~ 1 (no enhancement) when Da2 < 0.1 and E ~ 0.5*(Da2p)1/2 when Da2 is large. Therefore, if the length of the DNAPL pool and ground water velocity are constant, then E increases as the square root of KB when Da2 is greater than 0.1. For example, if the pool length is 30 ft and the ground water velocity is 0.3 ft/day, then the relationship between biodegradation rate and E is as shown in Table 4.6. In this example, dissolution enhancement is initiated when biodegradation half-lives are on the order of 1 month and becomes signiÞcant when half-lives are less than 1 week. If biodegradation rates are fast enough, the impact on DNAPL dissolution is substantial.

TABLE 4.6 Relationship between Biodegradation Rate and Dissolution Enhancement (E) KB (days–1) 0.002 0.023 0.1 0.7 2.8

Half-life (days) 365 30 7 1 0.25

Da2

E

0.19 2.3 9.9 69.3 277

1.0 1.3 2.8 7.4 14.7



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TABLE 4.7 Selected Laboratory Results for High-Rate PCE and TCE Biodegradation Study DiStefano et al., 1991 Isalou et al., 1998 Nielsen and Keasling, 1999 Yang and McCarty, 2000 Carr et al., 2000 Harkness et al., 1999 Yang and McCarty, 2000

Parent Compound PCE PCE PCE PCE PCE TCE TCE

Maximum Concentration (% aqueous saturation) 91 mg/l (60) 100 mg/l (66) DNAPL DNAPL DNAPL 170 mg/l (15) 300 mg/l (27)

Half-life (d) 0.2 0.05 2–3 5–10 0.16 0.15 3–5

4.5.3.2 Reductive Dechlorination Rates at High Concentrations of Dissolved PCE In order for biodegradation to signiÞcantly affect DNAPL dissolution, reductive dechlorination must occur rapidly enough to promote dissolution enhancement and at concentrations high enough to approximate those likely to be encountered near DNAPL surfaces. Both of these conditions have been observed in laboratory experiments performed with microbial populations adapted to elevated chlorinated hydrocarbon concentrations. The concentration ranges and biodegradation rates observed are summarized in the discussion that follows and in Table 4.7. DiStefano et al. (1991) demonstrated complete reductive dechlorination of PCE to ethene in methanol-fed microcosms at PCE concentrations of 91 mg/l, equivalent to 60% of aqueous saturation for PCE. The PCE half-life in these experiments was about 0.2 d. High PCE concentrations did not inhibit reductive dechlorination but did inhibit methanogenesis, which is a microbial process that competes with the dechlorinating bacteria for hydrogen. This resulted in a highly efÞcient use of electron donor, whereby 33% of the electrons produced were used to support dechlorination. Isalou et al. (1998) demonstrated PCE dechlorination in methanol-fed soil columns at PCE concentrations of greater than 100 mg/l, equivalent to 66% of water saturation. The PCE half-life in this case was 0.05 d, and ethene and VC were the terminal daughter products produced. Subsequently, Nielsen and Keasling (1999) demonstrated complete dechlorination of PCE and TCE to ethene in glucose-fed microcosms under saturated conditions where the DNAPL phase was present in the bottles. TCE and PCE half-lives were on the order of 2 to 3 d in these studies. Little or no VC accumulated under these conditions. Harkness et al. (1999) showed complete dechlorination of TCE to ethene in lactate-fed soil columns at TCE concentrations of 170 mg/l, equivalent to 15% of water saturation. The TCE half-life in this study was 0.15 d. The results of Tables 4.6 and 4.7 indicate that the laboratory-measured biodegradation rates of high concentrations of PCE were rapid enough to provide enhancements in dissolution by factors of three or more. At present, not enough Þeld data are available on the biodegradation rates of high-chlorinated compound concentrations to determine if these enhancements of dissolution would be achieved in the Þeld.


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4.5.3.3 Reductive Dechlorination in the Presence of DNAPL Recent research suggests that biodegradation can accelerate DNAPL dissolution consistent with the model of Seagren et al. (1994). For example, Yang and McCarty (2000) conducted hydrogen-fed microcosm studies in which PCE was reductively dechlorinated under saturated conditions with PCE DNAPL present in the bottles. In the same experiments, TCE was dechlorinated at TCE concentrations of 300 mg/l. PCE and TCE half-lives ranged from 3 to 10 d in these studies. Parallel studies conducted by Yang and McCarty (2000) indicated that a Þvefold increase in PCE dissolution was achieved due to biodegradation of PCE DNAPL in a continuous ßow soil column. Carr et al. (2000) demonstrated that PCE could be reductively dechlorinated to c-DCE in the presence of DNAPL (12% PCE in tridecane) in formate-fed reactors, resulting in a 14-fold increase in PCE dissolution from the DNAPL phase compared with abiotic controls. Calculated half-lives for the PCE and TCE dechlorination were 0.16 and 0.11 d, respectively. The authors also noted that overall source longevity was sensitive to daughter products repartitioning back into the DNAPL phase. Subsequent experiments by the same laboratory found that biodegradation by halorespiring microorganisms enhanced chloroethene dissolution from DNAPL by a factor of 5 to 6.5 times faster than dissolution alone (Cope and Hughes, 2001). Those researchers hypothesized that relative to dilute plume conditions, halorespiring organisms may thrive proximal to DNAPL source zones because halorespirers have a competitive advantage over other microorganisms in the presence of high chloroethene concentrations. Evidence of robust dehalogenation activity in the presence of DNAPL is not limited to laboratory studies alone. Major et al. (1995) observed signiÞcant dechlorination activity in a shallow bedrock aquifer beneath some former solvent evaporation pits. TCE and c-DCE concentrations in the ground water at that site were 860 and 430 mg/l, respectively. At the St. Joseph’s Michigan site, natural reductive dechlorination activity was high despite TCE concentrations of 133 mg/l and c-DCE concentrations of 128 mg/l. These data provide further evidence that reductive dechlorination is possible in the presence of chloroethene DNAPL and that chloroethene concentrations at the 100-ppm level are not toxic to dechlorinating microorganisms. The suitability of simpliÞed models such as that of Seagren et al. (1994) for Þeld-scale modeling of biodegradation in DNAPL source zones must be determined through the collection of Þeld data and use and comparison of models of varying levels of sophistication. 4.5.3.4 Summary Based on the foregoing overview, the following pressing research needs are identiÞed: •

Basic research is needed to understand the toxicity effects of high concentrations of various chlorinated solvents typical of DNAPL source zones on the activity of dechlorinating microbial consortia. Models for microbial



•

•

231

growth and inhibition in the presence of high concentrations of chlorinated solvents are required. Research on the impact of biodegradation in DNAPL source zones on DNAPL dissolution is required to allow the development of appropriate mass transfer models in the presence of biological activity. Determination of the appropriate level of modeling sophistication for various Þeld scenarios is necessary to aid practitioners in model selection and usage for assessing DNAPL source zone bioremediation.

4.5.4 PLUME ATTENUATION The use of natural attenuation as a remedial alternative at sites contaminated with CAHs requires extensive data collection to demonstrate that a contaminant plume is stable or shrinking, usually due to intrinsic bioremediation. Modeling is often a key component of efforts to establish that natural attenuation is an acceptable remedial alternative. However, there are a variety of models of varying levels of sophistication available to predict intrinsic bioremediation. Generally, it is desirable to employ the simplest model that accurately captures the important phenomena controlling natural attenuation at a site. This requires a careful evaluation of the attenuation processes operative at a site, particularly the kinetics of biotransformation. 4.5.4.1 Biotransformation Kinetics While Monod and double-Monod models have the potential to more accurately describe biodegradation in the Þeld, their practical use is limited because accurate determination of the biokinetic parameters is a costly process. Because the biokinetic parameters k, KD, and KA used in Equations 4.17 and 4.18 are speciÞc to both the contaminant (i.e., substrate) and the bacteria in a given plume, appropriate use of the Monod model requires determination of these parameters. Typically, these parameters are measured under carefully controlled laboratory conditions. However, some investigators treat k and KA as model Þtting parameters without independently analyzing the error intrinsic in each estimate. Although convenient, this approach can lead to signiÞcant errors in biodegradation predictions (Robinson, 1985; Saez and Rittman, 1992). Moreover, phylogenetic techniques for enumeration of unculturable dehalogenating organisms are still in the development stage; therefore, determining initial microbial species populations is sometimes not feasible even in the laboratory. The Monod model involves additional limitations for practitioners. The Monod equation was originally formulated to describe the use of a single rate-limiting substrate by a pure microbial culture suspended in liquid at constant temperature. The subsurface is far more complex than the system described by the Monod model. The presence of inhibitory or competing substrates, for example, may lead to biodegradation kinetics that depart signiÞcantly from the traditional single-substrate Monod model (Chang et al., 1993). Despite this limitation, the model offers the potential to provide a representation of in situ CAH biotransformation kinetics that is more accurate than Þrst- or zero-order approximations (if the biokinetic parameters can be determined with reasonable accuracy).


232


At sites where aqueous chloroethene concentrations are well below the halfsaturation constant (KA), it may be appropriate to model reductive dechlorination as a Þrst-order process. Haston and McCarty (1999) reported half-saturation constants for chloroethenes on the order of 2 mg/l. Above this concentration, it may be appropriate to use a zero-order approximation. If a Þrst-order approximation is used at sites where aqueous chloroethene concentrations are high (greater than 2 mg/l), subsequent predictions will have a tendency to underestimate actual cleanup times signiÞcantly (Bekins et al., 1998). One principal advantage of using Þrst- and zeroorder rate coefÞcient approximations is that they avoid the need for estimating Monod biokinetic parameters. Another obvious beneÞt in simulating biodegradation as a Þrst-order process lies in the simplicity and convenience of the approach. The governing equations are linear, allowing the use of analytical models and linear numerical models. As long as conditions are relatively constant at a site, the rate coefÞcients determined remain representative of site conditions. A database of Þrst-order CAH biodegradation rates is available in the literature, including rates for individual CAH compounds under speciÞc redox conditions (Suarez and Rifai, 1999). Caution must be exercised when using literature values to verify that the chosen literature value is representative of the site-speciÞc conditions (e.g., comparable redox conditions). In addition, laboratory-estimated rate coefÞcients should be avoided because they typically are 10-fold higher than rate coefÞcients estimated from the Þeld (Sturman et al., 1995). A variety of methods can be applied for estimating a site-speciÞc Þrst-order biodegradation rate coefÞcient (USEPA, 1998). Appropriate use of these methods requires consideration of plume concentrations and the concentration range for which a Þrst-order approximation is suitable. Depending on the method that is used, estimating sitespeciÞc rates can offer the ßexibility of averaging rate coefÞcients over spatially variable terrain or developing discrete rate coefÞcients for distinct redox zones. 4.5.4.2 Halorespiration The microbiology of dechlorinating consortia and syntrophy and competition between members of these consortia (i.e., dechlorinators, fermenters, methanogens) is complex and poorly understood at present. Chlorinated compound biodegradation rates in the subsurface depend on microbial community composition as well as electron donor and acceptor concentration. The effects of high chlorinated compound concentrations can have a signiÞcant impact on microbial community and, therefore, on dechlorination rates. For example, Yang and McCarty (2000) noted that methanogenesis was suppressed in the presence of high PCE concentrations, thereby reducing competition for hydrogen to support dechlorination. Batch models for reductive dehalogenation of chlorinated ethenes developed by Bagley (1998) and Fennell and Gossett (1998) included the growth of several different microbial species and demonstrated the impact of consortium composition on dechlorination rates and electron donor usage efÞciency. Incorporating these batch models into ground water models (e.g., the DECHLOR model of Shoemaker et al., 2001) is straightforward conceptually, but the resulting models are very computationally demanding. Even more problematic is that the models



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have numerous unknown biokinetic parameters and initial microbial species concentrations to be speciÞed. Quantitative characterization of microbial community composition is extremely difÞcult, particularly for anaerobic microorganisms that are resistant to culturing. Advances are being made in the area of microbial community characterization by using phylogenetic characterization methods that can identify some of the species present in dechlorinating consortia. However, at present, most of these techniques are not quantitative in nature and cannot be used to determine initial community composition for modeling purposes. Thus, at present, using models that incorporate complex microbial community growth dynamics is not practical for Þeld-scale modeling. Additional research on understanding the microbiology and growth dynamics of dechlorinating consortia is needed. Protocols must be developed to aid practitioners in selecting the appropriate level of modeling sophistication for modeling chlorinated organic compound biodegradation. Protocol development requires a combination of Þeld studies and model application to these studies to determine the important parameters and the acceptability of various simplifying assumptions. 4.5.4.3 Spatial Variability in Redox Conditions Depending on the type of CAH plume, site-speciÞc microbiology, and redox conditions at a given site, a variety of transformations are possible, including reductive, oxidative, and hydrolytic transformations. For example, proximal to a comingled fuel hydrocarbon and DNAPL chloroethene source area, PCE and TCE can be transformed reductively by halorespiration. Downgradient of the source, however, c-DCE and VC might degrade oxidatively in the presence of ferric oxides or at the plume fringes where pristine aerobic ground water mixes with an otherwise anaerobic plume. This type of spatial variability in predominant transformation mechanisms is common in most CAH plumes, but there are no models available capable of explicitly describing both reductive and oxidative CAH transformations. Since the late 1980s, microbiologists have known that VC can be biodegraded aerobically. More recently, the feasibility of aerobic biooxidation of c-DCE has been demonstrated. In addition, recent research has provided evidence of the anaerobic biooxidation of VC and c-DCE under methanogenic and ferrogenic conditions. As the occurrence and importance of these oxidative transformations become better understood, it may become appropriate to develop CAH biodegradation models that describe both reductive and oxidative transformations. 4.5.4.4 Complex Mixtures Models for chlorinated solvent biodegradation published to date are not applicable to complex mixtures of chlorinated solvents and hydrocarbons that exist at many sites. The biodegradation of organic compound mixtures can be subject to signiÞcant competitive inhibition that strongly affects the biodegradation rates of individual compounds. In addition, many chlorinated solvents are inhibitory. For example, Hughes and Parkin (1996a, 1996b) and Kaseros et al. (2000) documented the inhibitory effects present in mixtures of chlorinated ethanes, ethenes, and methanes. In


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the study of Kaseros et al. (2000), CT and CF inhibited PCE biodegradation, but the degree of inhibition was transient as a result of acclimation of the PCE-degrading community to CT and CF. Because the mechanisms of this inhibition are not well understood and are likely signiÞcantly inßuenced by microbial community composition and the physiological states of the members of the microbial community, developing practical mechanistic models to biodegrade complex mixtures of chlorinated solvents remains a challenge. 4.5.4.5 Bioavailability and Mass Transfer from Sorbed Phase Moisture is a critical prerequisite for biodegradation because water provides the medium by which bacteria uptake the nutrients, electron donors, and electron acceptors necessary for cell growth and maintenance. Water is also the primary building block of the bacterial cell, and most biodegradation reactions occur within aqueous medium inside the cell or at the cell wall. Consequently, organic compounds must be dissolved in water to become bioavailable, that is, available for bacterial uptake and biodegradation. Sorption to aquifer solids can signiÞcantly reduce the rate at which CAHs and other halogenated organic compounds biodegrade in the subsurface (Pignatello, 1989; Rijnaarts et al., 1990; Pavlostathis and Mathavan, 1992; Adriaens et al., 1995). The extent of sorption is controlled in part by the organic carbon content of the aquifer material. Organic compounds sorbed to aquifer solids (including sand grains) can reside in microporous regions or impermeable zones to which bacterial access is obstructed (Bosma et al., 1997; Zhang et al., 1998). Sorption limitations on biodegradation are more pronounced at older sites where aging of the contaminant plume has allowed contaminants to sorb strongly (Hatzinger and Alexander, 1995; Tang et al., 1998). Biodegradation of the sorbed component typically occurs only when the aqueous-phase concentrations drop relative to the sorbed-phase concentration and desorption occurs. The desorption process can be slow, particularly in aged plumes. For natural attenuation evaluations at some sites, it may be appropriate for modeling predictions to recognize bioavailability constraints and the role of rate-limited mass transfer on the duration required for aquifer restoration by natural attenuation. This issue is particularly relevant for dilute plumes at locations distant from the source. 4.5.4.6 Summary Based on the foregoing overview, the following pressing research needs are identiÞed: •

•

Continued measurement and reporting of site-speciÞc biodegradation rate coefÞcients is needed to expand the database available to the scientiÞc community. The database should be supplemented to provide average rate coefÞcients for various types of sites (e.g., landÞlls, hydrocarbon comingled plumes, CAH-only plumes) and various types of redox conditions. Protocols are needed to provide guidance on sustainability and select appropriate biodegradation rate coefÞcients. Further developments in the quantitative characterization of dechlorinating consortia are required to allow Þeld-scale application and validation



•

•

•

•

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of biodegradation models that incorporate the dynamics of microbial community composition. Further development of models that include the effects of electron donor limitations is necessary. Development of a database of biokinetic constants for electron donor utilization by dechlorinating consortia is required. For plumes containing both polychlorinated (e.g., PCE, TCE) and lightly chlorinated compounds (e.g., c-DCE, VC), it may be appropriate to develop models that can simultaneously describe reductive and oxidative transformations in spatially distinct portions of the model domain. Improved understanding of the biodegradation of mixtures of chlorinated compounds, particularly with respect to toxicity effects, is needed to allow the development of biodegradation models for chlorinated mixtures. For model applications focused on predicting cleanup times, rate-limited desorption should be considered. Bioavailability models that have been developed to simulate laboratory bioreactors should be incorporated — where appropriate — into Þeld-scale fate and transport models.

4.6 TECHNOLOGY TRANSFER Modeling the fate of chlorinated organic compounds is an extremely challenging task. The theoretical underpinnings (i.e., conceptual models) associated with these problems are multidisciplinary, involving physics, chemistry, and biology. In addition, the current understanding of the physical, chemical, and biological processes that control the system and measurement scales (i.e., time and space) necessary to deÞne model parameters continue to evolve. The mathematical description of the problem involves systems of coupled nonlinear equations, and, depending on what simplifying assumptions are employed, it may be difÞcult to solve the problem computationally. Choosing the appropriate model for a given physical setting and purpose is complicated by the lack of a systematic way to evaluate all potentially useful models. Improvements in computer speed and memory availability allow the simulation of more complex problems, and, commensurately, researchers are developing models that include more of the physics, chemistry, and biology of chlorinated solvent subsurface fate and transport. However, without evaluation and validation protocols in place and appropriate training, and as problem complexity increases, the potential for model misuse increases. Given that these challenges exist, models must nevertheless be used for problems associated with NAPL migration and fate in the context of risk assessment and environmental remediation. Industry needs models to assess risks at contaminated sites and to aid in the choice and design of appropriate and cost-effective remediation technologies. Regulators need models to aid in interpreting data, to verify site conceptual models, and to assess the sensitivity of those parameters that are involved in risk assessment and decision making. Researchers use computer models to verify their conceptual understanding of the theoretical underpinnings of the physical, chemical, and biological systems.


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It is the role of technology transfer to bridge the gap between model development and model use as a decision-making tool. In particular, technology transfer should provide the following: • • • •

SufÞcient training and reference resources so that models are used correctly A framework for model QA by setting rules for model development, documentation, validation, maintenance, and support Feedback for research interests of model application success/failure, thereby accelerating development of better modeling tools Feedback for the development of data-gathering technologies and remediation technologies, thereby coordinating the interdependence between these technologies and modeling

Regarding the last bulleted point, the development of truly effective remedial technology applications for NAPL sites can be attained only when the interdependence between modeling, data, and remediation technology is realized. In particular, technology transfer facilitates the development of better modeling tools through information transfer. Because models require data for parameter estimation and prediction veriÞcation, data-gathering technology must evolve to Þll the needs of new conceptual models. Finally, because a major role of model application is to facilitate the choice and design of appropriate remediation technologies, as modeling tools improve, the ability to apply new, innovative, remediation technologies improves. The remainder of this section provides an approach that accomplishes the goals of technology transfer and concludes with recommendations for implementing such an approach.

4.6.1 APPROACH The premise of this approach is that technology transfer can not only facilitate the appropriate use of the proper model for a given problem, but, through efÞcient information transfer, can also facilitate the advancement of modeling capabilities for problems involving NAPLs. To realize this premise, the approach for technology transfer for models must include the following components: •

•

•

•

QA Standards for Model Development and Documentation Provide standards for developmental procedures and documentation content. Expert System Decision Support Given a sufÞcient QA standard, develop a decision tree model selection tool based on one or more model classiÞcation systems. Model Application Archive and Database Support Maintain an archive where model use is documented and site-speciÞc databases are compiled. Training Support In addition to model documentation based on QA standards, provide an opportunity for users to gain experience from others.



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Each of these four components is discussed below. 4.6.1.1 QA Standards The goal for applying QA standards to individual models is to encourage consistency and completeness in their development. By using a rigorous documentation standard, model correctness, applicability, and performance characteristics can be established efÞciently. The result is to facilitate the correct use of a given model as a decisionmaking tool (e.g., American Society for Testing and Materials [ASTM], 1992, 1997a). Therefore, in this context, model QA standards are an important component for technology transfer. Model correctness is established through a rigorous validation process that ensures that the conceptual model represents the reference system (consistent with purpose), the mathematical formulation represents the conceptual model (given a known set of assumptions), the computational methods represent the mathematics, and the output represents the reference system through model testing (e.g., comparison with data) (ASTM, 1997a). The last feature establishes the relationship between model parameter estimation and data requirements (i.e., establish appropriate timeand space-scales for data). Model applicability is established through functionality analysis (e.g., ASTM, 1997b), which involves identifying and describing the code functions (i.e., simulated processes, boundary conditions, and operational capacities) and subsequently evaluating each code function or group of functions for conceptual correctness and errorfree operation (van der Heijde and Elnawawy, 1993). Performance characteristics are established through performance evaluation (e.g., ASTM, 1992, 1997a; van der Heijde and Elnawawy, 1993), which establishes the operational characteristics of the code, including the following: • • • • • • • •

Capabilities and limitations (e.g., range of applicable parameter values) Simulation accuracy (e.g., screening vs. predictive) Computational accuracy (e.g., discretization error analysis) Computational efÞciency (e.g., memory requirements, execution time, convergence of iterative solution methods) Data needs for parameter estimation and model calibration (e.g., appropriate time and space scales) Parameter sensitivity (e.g., identify the parameters upon which the solution is most sensitive) Code reliability over the range of parameter values for which the code is intended (e.g., robustness) Ease of use (e.g., code input setup requirements, the use of a graphical user interface)

The information compiled from the functionality analysis and performance evaluation is sufÞcient to provide a basis for code classiÞcation (e.g., ASTM 1997a, 1997b), which facilitates the selection and proper use of the most appropriate code for a particular application. Conversely, it can be used to determine whether a code of choice (e.g., based on familiarity) is appropriate.


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Finally, completeness in model development can be attained only if there is a mechanism in place that allows for model maintenance and update. With this mechanism, the code can be modiÞed as additional requirements or more appropriate methods are identiÞed. The information discussed above is disseminated to users and developers of models through a rigorous documentation standard (e.g., ASTM, 1997b). In addition to this information, the role of documentation is to facilitate code use (e.g., a user’s manual which includes a step-by-step guide for model setup, execution, and visualization of results). It should be clear that these basic attributes form a basis not only for facilitating acceptance of a particular model but also for choosing the best model for a particular situation through intermodel comparison. 4.6.1.2 Expert Decision Support System The primary purpose for the expert decision support system is to aid users and reviewers of models in choosing the correct model for a given application and determine the appropriateness of model choices. In addition, the system provides an archive of model information, including model functionality and performance attributes. Based on its utility as a reference guide, the system provides an educational tool for users (i.e., describes what models are available and their attributes) and an important reference tool for researchers (i.e., what works, what does not, and why). In order for models to be compared and contrasted, the decision support system must rely on model classiÞcation to meet different objectives for model use. In this regard, it is necessary for model developers to follow a QA standard of the type discussed in the previous section. SpeciÞcally, model documentation must follow a standard that allows for intermodel comparison of important use objectives. Different types of classiÞcation systems are useful in determining the appropriateness of models for a given task. For example, models can be classiÞed in terms of their functionality and performance (ASTM, 1996, 1997c): • • •

Model functions or processes (e.g., ßuid ßow, mass transfer, mass degradation) Model Þdelity or degree of realism (e.g., screening vs. prediction) Model formulation or mathematical basis (e.g., deterministic vs. statistical, boundary conditions, dimensionality, solution method)

van der Heijde and Elnawawy (1993) proposed a classiÞcation system based on the following model design objectives: • • •

Management objectives (e.g., problem types, resolution requirements, and representation of speciÞc remediation strategies) Functional use objectives (e.g., evaluate new theory as an education tool vs. as an engineering design tool) Computational output objectives (e.g., screening, prediction, and parameter estimation)



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In addition, models can be classiÞed based on the nature of the physical system being modeled. For example, Santillan et al. (1997) developed a chart to facilitate the selection of a code that meets the minimum requirements to simulate a given situation at a site. Model selection is based on such site-speciÞc attributes as saturated zone, unsaturated zone, site complexity (i.e., geology, hydrology, geochemistry), single and multiple ßuid, single and multiple phases, as well as model purpose. In summary, classiÞcation should be ßexible and, as such, utility is adaptable to need. For example, where model users may have interest in functionality and performance, regulators may have interest in applicability, and researchers may have interest in formulation and process description (i.e., as a tool for literature search). With regard to the needs of researchers, the reality is that the science of modeling NAPL-related problems is still evolving, and there are many more models developed for research purposes than are developed as Þeld tools. While these models are not intended for widespread use, their utility for advancing the science should be recognized and, as such, these models should be included in the support system. 4.6.1.3 Model Application Archive and Database Support The model application archive and database support utility houses reference information on the models supported in the expert decision system. In addition, it provides a database for Þeld data generated at NAPL sites. The content and purpose for developing and maintaining such a utility is as follows: •

•

•

•

Model Functionality and Validity Documentation In some cases, promising models are released for use before sufÞcient functionality and validity testing has been performed. In addition, because of process complexity, site characteristic diversity, and database quality diversity, model functionality assessment, by nature, becomes an ongoing task. Test Case Development Developing test cases for a variety of problems (e.g., benchmarking) will facilitate peer review and educational requirements for model use. Problem and Data Set Creation The creation of problems and data sets for researchers will allow the testing of novel theoretical developments. Model Documentation Clearinghouse The clearinghouse should include references to model documentation (e.g., user’s guide), conference proceedings, technical journals and academic theses, and public-domain publications.

This utility is consistent with the need to document model use as discussed in Section 4.6.1.1 (also see ASTM, 1996). In addition, it facilitates code maintenance by providing feedback to developers regarding model utility for different problems and data sets. This attribute will aid in the advancement of the science behind models.


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4.6.1.4 Training Support The complexity of NAPL problems, diversity of model applications, and support network required to ensure effective model use leads to the requirement that training support be made available. Training must occur at different levels as described below. •

•

•

•

•

Theoretical Underpinnings of NAPL Modeling Gain an understanding of the basic processes that govern the migration and fate of NAPLs in the environment. This goal can be achieved through courses (i.e., physical or web based) and textbooks. Use of Expert System Determine whether the user is looking for an appropriate model or whether a particular model is right for the job. This goal can be achieved through web-based tutorials. Use of Particular Models Understand functionality and performance as discussed under model development QA. This goal can be achieved through model documentation, short courses, and real-time support. Because of the complexity of the problem and mathematics, model implementation is (in part) experience driven. Therefore, model use training is best achieved through the implementation of well-deÞned test problems and, if necessary, interaction with individuals well-versed on model use. Use of QA Standards Model developers must provide consistent information so that models can be compared and contrasted. This goal can be achieved by developing guidance documentation. Model Archive and Database Support The developer and user must assist in developing model archive and database support. This goal can be achieved by participating in the guidance documentation process.

The last two bullets are highly important in determining whether the technology transfer model as described herein will work. SpeciÞcally, everyone involved with the development and use of models must participate in the system.

4.6.2 IMPLEMENTATION RECOMMENDATIONS Models are an integral part of the identiÞcation and engineering design of appropriate remediation technologies for sites contaminated with NAPLs. In general, modeling the processes associated with NAPL migration and fate is a difÞcult task involving a steep learning curve for users. Currently, no systematic method exists to identify appropriate models or to ensure that the appropriate model is used correctly for a given application. In addition, it is important to collect the appropriate Þeld data for model calibration and application. Finally, the situation is complicated because the science associated with modeling NAPL-related processes continues to evolve.



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To attempt to resolve these issues, a holistic approach for model technology transfer was introduced. It consists of the following four interrelated components: 1. Develop QA standards for model development and documentation so that model functionality and performance can be assessed and an expert decision support system can be developed. 2. Develop an expert decision support system to classify models in terms of functionality, performance, and objective; provide links to model application archive and database utility for information on individual models. 3. Develop a model application archive and database utility to document model utility and provide data for research and development. 4. Develop a training program on the proper use of models, and incorporate developers and users into the technology transfer system. Implementing such a holistic approach can be achieved only through a collaborative effort on the part of federal and state agencies, industry, and academic interests. These consortia already exist for remediation technology transfer. For example, consider the Remediation Technology Development Forum (RTDF) and the Interstate Technology Regulatory Cooperation (ITRC), which are governmentled public and private partnerships used to conduct remediation technology transfer, including research, development, demonstration, evaluation, and training. In the case of creating a framework for technology transfer for NAPL-related models, the partnership would bring together the developers and users of these models to form the infrastructure and culture necessary to implement the four components listed above. Consider the following process for implementation. The Þrst step is to develop QA standards for model development and documentation. To a large extent, this is already occurring. For example the ASTM (ASTM 1992, 1996, 1997a, 1997b, 1997c), and the USEPA (van der Heijde and Elnawawy, 1993; van der Heijde and Kanzer, 1997) have developed rigorous QA protocols for generic models. These standards can be edited to achieve the goals stated herein, and implementation can be achieved through guidance documentation. A major challenge for the group will be how to include research-level models (as opposed to production-level models). SpeciÞcally, academia is not in the business of developing production-level codes because the many tasks associated with cleaning up the code and properly documenting it are not consistent with degree requirements or funding mechanisms. With regard to funding, oftentimes the funding period ends before the code is fully validated. In addition, there are few mechanisms to support and maintain codes that originate from the university system. Private software developers support only a small subset of these codes and convert them into production codes. As such, many good ideas are lost. There must be a basic set of standards required for research-level codes so that these codes can be included in the overall system. Commensurate with developing a QA framework, an expert decision support system must be developed based on different classiÞcation systems so that a variety of modeling objectives can be accommodated (e.g., research, review, management). An example of one such a system is provided by the Air Force Center for Environ-


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mental Excellence (AFCEE) (Santillan et al., 1997). Because of the interconnectedness, expert system development must be coordinated with QA protocols. Ideally, the system would be Web based with online help, guidance documentation, and tutorials describing system use. In addition, for each model supported, the system would provide links to model information (i.e., the model archive utility). An important component of the technology transfer system, which facilitates code validity assessment, maintenance, and development, is the model archive and database support utility. An excellent model upon which to base such a utility is the Netlib repository at the University of Tennessee and the Oak Ridge National Laboratory. Netlib is a collection of mathematical software, papers, and databases. Implementing this concept for NAPL-related models would require a consistent effort on the part of all those who use models, which can be facilitated by enforcing QA protocols for the submission of site-related reports and databases. For example, the New Jersey Department of Environmental Protection requires data to be submitted in geographical information system (GIS)-compatible format. In addition, for the purpose of model validation, research, and user education, a series of benchmark problems and databases must be developed. The problems should address issues of process description, parameter estimation, and scale. The model archive should also provide links to model documentation and support (e.g., government and vendor locations) and references on model development and use (e.g., conference proceedings, technical journals, academic theses, and public-domain remedial investigation/feasibility study documents). Finally, training is necessary to implement the system. Training must occur at different levels (i.e., process theory, model use, and system participation). With regard to system participation, a culture must be developed where users and developers contribute a component of the modeling effort to the technology transfer system. Training can be conducted by using tools such as guidance documents, Webbased courses, and discussion forums. In summary, to ensure proper use of NAPL-related models and facilitate the development of new modeling tools, a dedicated technology transfer system must be developed. If properly maintained, this system would complement those already developed for remediation and data-gathering technologies (e.g., RTDF, ITRC).

4.7 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS Halogenated hydrocarbons have been recognized as a widespread threat to the environment since the early 1980s. Over the past two decades, investigation, remediation, and research have resulted in the development of an extensive knowledge base on halogenated hydrocarbon occurrence and behavior in the subsurface, yet much work remains. Not only must knowledge gaps be Þlled (research needs), but developed knowledge must be transferred to practice. In a general sense, the transport of halogenated hydrocarbons can be considered in the following three stages: migration as a separate immiscible phase, stabilization as an immobile free phase that acts as a source to the dissolved phase, and migration as a dissolved phase in ground water. While early research focused on the Þrst stage



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(multiphase ßow), early investigation and remediation focused on the larger and more immediate threat of the dissolved plume. In many ways, the emphasis has shifted in the past few years. DNAPL source remediation, once considered infeasible or impracticable, is currently being reconsidered as aggressive remedies have proved somewhat successful in pilot studies. Existing simulation models have become quite comprehensive, including processes such as multiphase ßow and nonequilibrium interphase mass transfer, heat transport, and biodegradation processes. Some of these models have been validated at the laboratory scale, but rigorous Þeld-scale validations of existing models for multiphase ßow in porous or fractured media, interphase mass transfer (i.e., NAPL dissolution, volatilization), DNAPL source zone biodegradation, or dissolved chlorinated solvent plume biodegradation have not been performed. Validation efforts are hampered by the complexities and costs of collecting data on pressures and saturations in multiphase systems, characterizing subsurface heterogeneities at Þeld sites, and measuring microbiological variables in the subsurface. The lack of efÞcient, readily deployed sampling devices for these systems and theoretical methods for upscaling local-scale measurements to the scale of model discretization also hinders the validation effort. In addition, most data come from sites where spill conditions and historical site conditions are usually not well known. Legitimate concerns of introducing additional harmful chemicals into the environment restrict the possibilities of conducting well-characterized and controlled Þeld-scale experiments with chlorinated hydrocarbons. Much of the complexity of modeling multiphase ßow originates in interrelationships between relative permeabilities, ßuid saturations, and capillary pressures (i.e., k–S–P relationships). Current constitutive models still do not fully address issues of ßuid scaling between two- and three-phase systems, hysteresis, and predicting residual saturations in three-phase systems. Developing Þeld-scale k–S–P relationships and methods for determining them from local-scale measurements remain hurdles. Similarly, developing Þeld-scale relationships for interphase mass transfer is lacking. The applicability of multiphase ßow and transport models to fractured media remains in the early stages of development with many fundamental issues unresolved. Currently, tremendous interest exists in the intrinsic bioremediation of chlorinated hydrocarbons in the subsurface. Initial laboratory studies indicate that the bioremediation of DNAPL source zones might be possible for chlorinated compounds with limited toxicity. This source zone bioremediation would be expected to enhance DNAPL dissolution, but many fundamental issues related to the microbiology of dechlorinating microbial consortia and interphase mass transfer in the presence of biodegradation must be resolved. A variety of biodegradation models for dissolved chlorinated hydrocarbon plumes have been developed, ranging from Þrst-order contaminant decay models to complex models that incorporate multiple Monod kinetics for biodegradation and growth of multiple microbial species. Determining parameters for the more complex models is extremely difÞcult with currently available sampling methods. Limits of application of the simpler models have not been well established. The widespread application and use of models for chlorinated hydrocarbons in the subsurface is limited due to the sophisticated nature, signiÞcant computational requirements, and extensive data needs of these models. Because many of the models


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remain predominantly in the research domain, technology transfer is an important issue. Field-scale validation of models is required to provide users with conÞdence in models. Training programs are necessary to provide potential end users with the tools to choose the appropriate models, use these models correctly, and make effective use of the model capabilities.

4.7.1 MULTIPHASE FLOW

AND

TRANSPORT

In reviewing the current state of the art with respect to models for chlorinated hydrocarbons in the subsurface, the panel made the following recommendations pertaining to research and technology transfer needs: •

•

•

•

Validate theoretical relationships for k–S–P models and address issues of scaling from two- to three-phase systems, hysteresis modeling, and residual saturation predictions. Develop upscaling or averaging methods to allow determination of appropriate k–S–P and interphase mass transfer relationships for Þeld-scale modeling from small-scale measurements. Develop methods suitable for estimating Þeld-scale parameters for multiphase ßow models. Validate Þeld-scale multiphase ßow models, and improve methods and devices for measuring saturation, pressures, and individual phase concentrations in multiphase ßow systems in the Þeld. Continue both laboratory- and Þeld-scale studies of multiphase ßow and transport in fractured media to establish the validity and limits of applicability of porous media equations. Establish the range of applicability of equivalent porous medium concepts.

4.7.2 CHLORINATED HYDROCARBON BIODEGRADATION In reviewing the current state of the art with respect to models for chlorinated hydrocarbons in the subsurface, the panel made the following recommendations pertaining to research and technology transfer needs: •

• •

•

•

Develop improved understanding and predictive models of toxicity and inhibition effects of high concentrations typical of DNAPL source zones and chlorinated compound mixtures. Develop and test models for DNAPL dissolution enhancement using DNAPL source zone biodegradation. Leverage advances in microbial characterization techniques to improve the understanding of dechlorinating microbial consortia community dynamics and allow biodegradation model development and testing that incorporates microbial community dynamics. Develop numerical models that can simultaneously describe reductive and oxidative transformations that often occur in spatially distinct portions of Þeld sites. Continue developing a database of Þeld-scale site-speciÞc data on biodegradation parameters.



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4.7.3 TECHNOLOGY TRANSFER In reviewing the current state of the art with respect to models for chlorinated hydrocarbons in the subsurface, the panel made the following recommendations pertaining to research and technology transfer needs: • •

• •

Establish QA standards for model development and documentation so that model functionality and performance can be assessed. Develop expert decision support systems to classify models in terms of functionality, performance, and objective; provide links to model application archive and database utility for information on individual models. Establish model application archive and database utilities to document model utility and provide data for research and development. Develop and institute training programs on the proper use of models, and incorporate developers and users into the technology transfer system.

ACKNOWLEDGMENTS This chapter originated from the Simulation of Halogenated Hydrocarbons in the Subsurface Panel of the Modeling and Management of Emerging Environmental Issues Expert Workshop 2000. The structure of this chapter was designed and initiated by Charles Faust, who chaired the Panel. Craig Bartlett (DuPont Corporate Remediation Group), panel assistant leader, provided valuable support to the panel discussion, including taking copious notes which were helpful in preparing this chapter. Section 4.2 was written by Brent Sleep, with contributions from Charles Faust and Neal Durant (panel assistant leader). Section 4.3 was written by Jack Parker. Section 4.4.1 was prepared by Lily Sehayek and Joseph Guarnaccia. Section 4.4.2 was prepared by Joseph Guarnaccia. Section 4.5 was written by Brent Sleep and Neal Durant, with contributions from Mark Harkness. Sections 4.1 and 4.7 and overall editing were performed by Brent Sleep. The assistance of Kathleen Adams in the Þnal editing is gratefully acknowledged. The contributors would like to express their gratitude to the DuPont Company for organizing the workshop that made this book possible. In particular, the vision, help, and encouragement of Calvin Chien are greatly appreciated.

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Appendix A: Workshop Panels PANEL 1 MIXING ZONE: DISCHARGE OF CONTAMINATED GROUND WATER INTO SURFACE WATER BODIES EXPERT PANEL LEADER Miguel A. Medina, Jr., Duke University

ASSISTANT EXPERT PANEL LEADER Nancy R. Grosso, DuPont Company

EXPERT PANEL MEMBERS Robert L. Doneker, Oregon Graduate Institute of Science and Technology Henk Haitjema, Indiana University D. Michael Johns, Windward Environmental LLC Wu-Seng Lung, University of Virginia Steven C. McCutcheon, USEPA National Exposure Research Laboratory Farrukh Mohsen, Gannett Fleming, Inc. Aaron I. Packman, Northwestern University Philip J. Roberts, Georgia Institute of Technology J. Bart Ruiter, DuPont Company

PANEL 2 CONTAMINATED SEDIMENT: ITS FATE AND TRANSPORT EXPERT PANEL LEADER Danny D. Reible, Louisiana State University

ASSISTANT EXPERT PANEL LEADER Richard H. Jensen, DuPont Company

0-56670-667-X/04/$0.00+$1.50 © 2004 by CRC Press LLC

259


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EXPERT PANEL MEMBERS Sam Bentley, Louisiana State University Mimi B. Dannel, USEPA Headquarters Joseph V. DePinto, Limno-Tech, Inc. James A. Dyer, DuPont Company Kevin J. Farley, Manhattan College Marcelo H. Garcia, University of Illinois David Glaser, Quantitative Environmental Analysis John M. Hamrick, Tetra Tech, Inc. Wilbert J. Lick, University of California at Santa Barbara Robert A. Pastorok, Exponent Environmental Group Richard F. Schwer, DuPont Company C. Kirk Ziegler, Quantitative Environmental Analysis

PANEL 3 OPTIMIZATION MODELING FOR REMEDIATION AND MONITORING EXPERT PANEL LEADER George F. Pinder, University of Vermont

ASSISTANT EXPERT PANEL LEADER Robert B. Genau, DuPont Company

EXPERT PANEL MEMBERS Robert M. Greenwald, GeoTrans, Inc. Hugo A. Loaiciga, University of California at Santa Barbara George P. Karatzas, Technical University of Crete Peter K. Kitanidis, Stanford University Reed M. Maxwell, Lawrence Livermore National Laboratory Alexander S. Mayer, Michigan Technological University Dennis B. McLaughlin, Massachusetts Institute of Technology Richard C. Peralta, U.S. Air Force Reserve and Utah State University Christine A. Shoemaker, Cornell University Brian J. Wagner, U.S. Geological Survey Kathleen M. Yager, USEPA Technology Innovation OfÞce William W.-G. Yeh, University of California at Los Angeles


Appendix A: Workshop Panels

PANEL 4 SIMULATION OF HALOGENATED HYDROCARBONS IN THE SUBSURFACE EXPERT PANEL LEADER Charles R. Faust, GeoTrans, Inc.

ASSISTANT EXPERT PANEL LEADERS Neal D. Durant, GeoTrans, Inc. Craig L. Bartlett, DuPont Company

EXPERT PANEL MEMBERS Robert C. Borden, North Carolina State University Ronald J. Buchanan, Jr., DuPont Company Randall Charbeneau, University of Texas Eva L. Davis, USEPA Kerr Laboratory Joseph G. Guarnaccia, CIBA-Geigy Specialty Chemicals Mark R. Harkness, General Electric Corporation Jack C. Parker, Oak Ridge National Laboratory Hanadi Rafai, University of Houston Lily Sehayek, Penn State Great Valley Brent E. Sleep, University of Toronto Jon F. Sykes, University of Waterloo Albert J. Valocchi, University of Illinois

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Index Note: Italicized page numbers refer to illustrations and tables

NUMBERS 1Q10 low ßow condition, 46 3DLIF (three-dimensional laser-induced ßuorescence), 41–42 3DMURF + 3DMURT model, 211

A Aberdeen Proving Ground, Maryland, 37 Abiotic transformations, 196 Acetic acid, 199 Acid mine drainages, 28 Acid volatile sulÞdes (AVS), 70–71, 92 ACLs (alternate concentration limits), 12 Acoustic doppler current proÞlers (ADCPs), 44 Across trophic level system simulation (ATLSS) multimodel, 87 Acute toxicity, 11 ADCPs (acoustic doppler current proÞlers), 44 ADD (average daily dose), 143 Advective transport, 80–81 equation, 32 hyperheic zone, 94–95 Aerobic biodegradation, 193 Aerobic cometabolism, 196 AFBCA (Air Force Base Conversion Agency), 118 AFCEE (Air Force Center of Environmental Excellence), 117 AIR3D model, 212 Air Force Base Conversion Agency (AFBCA), 118 Air Force Center of Environmental Excellence (AFCEE), 117 Air stripping, 122 Aldrin, 71 Allocated impact zone, 11 Alternate concentration limits (ACLs), 12 Ambient density, 18 Ambient temperature, 18 Ambient turbulence, 37–38 Ammonia, in riverine system, 47–48

Anaerobic biodegradation, 193 Animal burrows, 81 Animals, in contaminant transference, 83–84 ANN (artiÞcial neural network) models, 125 ANN models, 130 Antimony, 70 Aquatox model, 85 Aquifers, 30–31 contamination, 30 effective porosity, 31 physical characteristics, 132, 134 physical characteristics uncertainty, 133–136 physical properties of, 30 thickness of, 31 time scales in, 27 Aquitards, 216 Aroclors, 72 Arsenic, 22 biomagniÞcation, 70 oxidation-reduction reactions, 92 plumes, 36 ArtiÞcial neural network (ANN) models, 125 Assessment and Remediation of Contaminated Sediments, 97 ATLASS (across trophic level system simulation) multimodel, 87 Atomic absorption spectrometry, 113 Atomic emission spectrometry, 113 Average daily dose (ADD), 143 AVS (acid volatile sulÞdes), 70–71, 92

B Badger Ammunition Plant, 162–164 Bank storage zone, 6 Baseline risk assessment, 65–66 Base Realignment and Closure Act (BRAC), 116–117 BASINS (Better Assessment Science Integrating Point and Nonpoint Resources) system, 68 Bathymetry, 17, 76 Bayesian analysis, 89, 153 BCCs (bioaccumulative chemicals of concern), 7, 14 Bedforms, 19–20 Bedform scale, 21

263


264


Bed load transport, 73 Bed sediment grain scale, 21 Bed sediment transport, 19–20 Bed shear stress, 92 Benchmarking, 48–49, 51 Benthic communities, 16 Benthic organisms, 82 assimilative capacity, 98–100 contaminant bioaccumulation in, 98–100 Beryllium, 70 Bessel function, 47 Better Assessment Science Integrating Point and Nonpoint Resources (BASINS) system, 68 Bioaccumulation, 84–85, 98–100 Bioaccumulative chemicals of concern (BCCs), 7, 14 Bioavailability, 234 BIOCHLOR model, 212 Biodegradation, 193–194 of chlorinated organic compounds, 244 of chlorinated solvents, 199–202, 212–213 impact on DNAPL dissolution, 227–231 of petroleum hydrocarbon, 197–199 Biodeposition, 81 Biodiffusion, 97 BioÞlms, 97, 198 Biokinetics, 201–202 Biological contaminants, degradation of, 71 Biologically active zone, 6, 66 Biological process modeling, 101 BiomagniÞcation, 70, 72 Biomass, 197, 200 Bioremediation, 22, 121 Biota and sediment accumulation factor (BSAF), 84, 99 Biotransformation kinetics, 231–232 Bioturbation, 81–83 depth-dependent intensity, 96 in sediment and contaminant transport, 95–98 Borehole geophysics, 112 Bottom shear stress, 74–75 Boundary conditions, 37 Bounding estimates, 89–90 BRAC (Base Realignment and Closure Act), 116–117 Brooks-Corey model, 185–186 Brownian diffusion, 79 BSAF (biota and sediment accumulation factor), 84, 99 Buckley-Leverett solution, 202 Buoyancy, 17 Burrowing, 81

C Cadmium, 70, 92 Calibration, 88–90 Cancer potency factor (CPF), 143 Cancer slope factor (CSF), 138–139 Capillary pressure, 184–188 Carbon, 24 Carcinogenesis, risks of, 138–139 CCC (criterion continuous concentration), 9, 11 CCD (charge-coupled device) camera, 40–42 CE-EQUAL-ICTOX model, 91 Cesium, 111 CFD (computational ßuid dynamics) model, 40 CHAIN_2D model, 211 Chance-constrained optimization, 127–129 Channel scale, 21 Charge-coupled device (CCD) camera, 40–42 Chemical diffusivity, 78 Chemical Manufacturers Association, 8 Chemical probes, 113 chemical sensors, 44–45 Chlordane, 71 Chlorinated organic compounds, 182–183; see also Dense nonaqueous phase liquid (DNAPL); Nonaqueous phase liquid (NAPL) bioavailability and mass transfer from sorbed phase, 234 biodegradation models, 199–202, 244 biotic and abiotic transformations, 193–202 biotransformation kinetics, 231–232 complex mixtures, 233–234 fate and transport of, 183 halorespiration, 232–233 multicomponent mass transport, 188–192 multiphase ßuid ßow, 183–188, 244 petroleum hydrocarbon biodegradation models, 197–199 spatial variability in redox conditions, 233 transformation mechanisms, 193–196 Chlorinated solvents, 193–196 abiotic transformations, 196 aerobic cometabolism, 196 biodegradation, 193–194, 212–213 oxidation, 195–196 reductive dehalogenation, 194–195 Chlorine, 10 Chlorobenzene, 220 Chloroethane, 193 Chloroethenes, 196 Chloroform, 193 Chloromethane, 193 Chromium, sorption of, 21 Chrysene, 72


Index

Cladocerans, 15 Cleanup goals, 133 Clean Water Act, 8, 12 CMC (criterion maximum concentration), 9–11 Coal liquids, 73 Coarsening, 80 Coastal waters, 17 point source discharges, 8 sediment processes in, 67 tides in, 29 COCs (contaminants of concerns), 69, 220 Cohansey formation, 216 Cohesive sediments, 75, 91–92 Coliform bacteria, 24, 45 Colloidal organic carbon, 79–80 Colloids, 28 Cometabolic substrate, 200–201 COMPFLOW model, 209 Complexation, 92 Compliance points, 207 COMPSIM model, 209 Computational ßuid dynamics (CFD) model, 40 Computer models, 205 Concentration constraints remediation, 124–126 Conceptual models, 65 Consulting engineers, 165 Contaminant concentration, 26 in biota and sediment accumulation factor (BSAF), 84 initial distribution of, 33 level of, 34 pattern, 47 Contaminants, 69–73 bioaccumulation of, 98 biological, 71 characteristics of, 132–133 controlled vs. uncontrolled discharges, 21–23 mass ßows, 69–70 models, 101 potential availability, 98–100 release and availability, 92–94 and sediments, 69–73 subsurface reservoir of, 22 Contaminants of concerns (COCs), 69, 220 Contaminant transport, 52 bioturbation, 95–98 models, 24–27 via food webs, 83–85 via gas movement through sediments, 83 Contaminated sediments, 62; see also Contaminants; Sediments advective processes in hyperheic zone, 94–95 baseline risk assessment, 65–66 biological process modeling, 101 bioturbation, 95–98

265

contaminant bioaccumulation, 98–100 contaminant process modeling, 101 contaminant release and availability, 92–94, 102 contaminants in, 70–73 erosion and deposition processes, 73–75, 91–92 in food webs, 83–85 human and ecological risks, 85–87 management of, 63–65 metal release and availability, 101–102 modeling applications, 65–69 potential effects of, 86 remedial management plans, 68–69 removal of, 64–65 site model development and testing, 65 sites, 64 stable, 77–83 total maximum daily load (TMDL), 66–68 transport model, 75–76, 100–101 unstable, 76–77 Contaminated soil sites, 64 Controlled contaminant discharges, 21–23 Conventional pollutants, 10 Copepods, 15 Copper, 70 CORMIX models, 23, 40 CPF (cancer potency factor), 143 Crank-Nicolson algorithm, 33 Criterion continuous concentration (CCC), 9, 11 Criterion maximum concentration (CMC), 9–11 Cross borehole tomography, 112 Crustaceans, 15 CSF (cancer slope factor), 138–139 Cutting plane technique models, 125

D Darcy’s law, 94, 183–184 Darcy velocity, 80, 184, 188 DDT (dichlorophenyltrichloroethane), 7, 71–72 Dechlorinators, 232 DECHLOR model, 232 Decision-making uncertainty, 136–138 Decision support systems (DSS), 114 Deep sediments, 66 Defecation, 81 Defense Environmental Restoration Program (DERP), 116 Dehalococcoides ethenogenes, 195 Delaunay triangulation, 148 Dense nonaqueous phase liquid (DNAPL), 181–182; see also Chlorinated


266

organic compounds; Nonaqueous phase liquid (NAPL) in fractured media, 225–227 impact of biodegradation on, 227–231 mass transfer relationships, 224 models, 205–214 multiphase ßow, 183–184 reductive dechlorination rate, 229–230 release of, 215–216 single-phase models, 211–212 source area characterization, 216–221 unsaturated water ßow, 225 Density logging, 112 Department of Defense (DoD), 116–117 Department of Energy (DOE), 110–111 model development programs, 119 monitoring programs, 119–120, 119–120 optimization programs, 117–119, 117–119 technology demonstration programs, 115–116, 115–116 Deposition, 73–75 DERP (Defense Environmental Restoration Program), 116 Desorption, 93, 192 Detector tubes, 113 Dichloroethanes, 71–72, 193, 215–216 Dichloroethenes, 158–160, 193 Dichloromethane, 193 Dichlorophenyltrichloroethane (DDT), 7, 71–72 Dieldrin, 71 Diesel, 73 Diffusion-dispersion tensor, 189 Dioxins, 7 Direct sonic drilling, 112 Dispersion coefÞcient, 32 Dispersive-diffusive ßux, 189 Dispersivity, 25, 32 Dissolution ßux, 228 Dissolved organic carbon, 79 Dissolved oxygen, 24 Divalent metals, 92 DNAPL (dense nonaqueous phase liquid), 181–182; see also Chlorinated organic compounds; Nonaqueous phase liquid (NAPL) in fractured media, 225–227 impact of biodegradation on, 227–231 mass transfer relationships, 224 models, 205–214 multiphase ßow, 183–184 reductive dechlorination rate, 229–230 release of, 215–216 single-phase models, 211–212 source area characterization, 216–221 unsaturated water ßow, 225


DoD (Department of Defense), 116–117 DOE, see Department of Energy (DOE) Dose-response relationships, 86, 142 Drag, 19 Dredging, 216 Drilling, 112 DSS (decision support systems), 114 Dual porosity models, 226 Dunes, 94 Dynamic models, 124–125

E Ecological risk assessment, 85–87 Eel Shelf, 96 Effective porosity, 31 Efßuents channels, 48–49 hydrodynamics, 17 velocity, 46 Electrical conductivity sensor, 113 Electrical resistivity, 112 Electromagnetic conductivity, 112 electron acceptor, 197 electron donor, 197 Electronic leak detection system, 112 Endosulfan, 71 Endrin, 71 Entrainment models, 39–40 Environmental Protection Agency (EPA) contaminated sediment management goals, 63 sediment regulation, 2 Water Quality Standards (WQS) regulation, 7 Environmental restoration, 149 long-term ground water monitoring in, 146 and regulatory process, 115–116 Environmental test kits, 113 Environmental toxicology, 86 EPA (Environmental Protection Agency), see Environmental Protection Agency (EPA) Equation errors, 88 Erosion, 73–75 of cohesive sediments, 91–92 rate of, 74 Estuarine sediment sampling stations, 62 Estuarine system, 17 concentration pattern in, 47 point source discharges, 8 sediment processes in, 67 tides in, 29 Ethanol, 199 Exchange dynamics, 52 Exit concentration, 33–37


Index

Expert systems, 114 Explosive sensor, 113 Exposure, 142

F Far-Þeld, 18, 37–39 FAV (Þnal acute value), 11 Fecal coliform bacteria, 10 Federal facility compliance agreements (FFCAs), 116 Federal regulations, 115–116 Feedback, 140 Feeding behaviors, 84 FEHM model, 209 Fermentation, 199–200 Fermenters, 232 FFCAs (federal facility compliance agreements), 116 Fiber-optics sensor, 113 Fick’s law, 79 Field sampling method, 45 Final acute value (FAV), 11 Fish, 16 in contaminant transference, 83–84 spawning areas, 8 Flocs, 91 Fluid densities, 184 Fluoranthene, 72 Fluorescence spectrometry, 113 Fluorescent dyes, 44 Flushing rate, 218 Fluxes, 17, 77 Food webs, 83–85, 100 Fractured petroleum reservoirs, 226 Free-product sensor, 113 Frequency-domain capacity probes, 112 Freshwater sediment sampling stations, 62 Freundlich parameter, 134 Friction velocity, 74

G Gas chromatography, 113 Gas phase pressure, 190 Gaussian elimination, 204 Geiger counter, 113 Generic algorithm models, 125 Generic ground water-surface water interface (GSI), 14 Geoprobe penetrometer, 112 Geoprobe soil corer, 115 GMS (Ground Water Modeling System), 119

267

Gradient control remediation, 122–124 Grease, 71 Great Lakes, 7, 72 Green rust, 196 Ground-penetrating radar, 112 Ground water, 6–7 aerial view, 134 contaminants, 22 ßow velocities, 81 interaction with surface water, see ground water-surface water interaction long-term monitoring, 146–147 remediation design, 126 remediation techniques, 121–122 risk assessments for, 144–146 sampling, 112 Ground water management chance-constrained optimization model, 127–129 multiple realization stochastic model, 129–131 Ground Water Modeling System (GMS), 119 Ground water monitoring, 146–147 design optimization, 122 methods, 150–153 performance monitoring problems, 148–150 and remedies, 147–148 Ground water-surface water interactions, 2–3; see also Mixing zones acute toxicity of pollutant discharges, 11 boundary conditions, 32 contaminant discharges, 21–23 contaminant-transport models for, 52 ecological and health risks, 14–17 environment boundaries and scope, 17–18 exit concentration, 33–37 federal guidelines on pollutant discharges, 11 interfaces in, 18 models, 3, 49–50 nonpoint sources, 11–13 overview of issues in, 3–6 regulatory mixing zones, 11 stream-subsurface exchange processes, 18–21 technical background, 6–7 technical issues in, 49–50 tidal exchanges and oscillations, 29 toxic dilution zone (TDZ), 9–10 two-stage mixing, 10–11 zone of initial dilution (ZID), 8–9 Growth substrate, 200 GSI (generic groundwater-surface water interface), 14 Gulf Coast, dichloroethane DNAPL release at, 215–216


268


H Halogenated organic compounds, 182–182, 242 Halorespiration, 232–233 Hanford Reservation, 110 HBGC123D + FEMWATER model, 211 Heat-conduction equation, 30 Heavy fuel oils, 73 Heavy metals, 70–71 Hedging, 140 HEM-3D (hydrodynamic-eutrophication) model, 24 Henry’s law, 189–190 Heptachlor, 71 High-gradient streams, 19 High-performance liquid chromatography, 113 High-resolution model, 206 Horizontal drilling, 112 HSTCM model, 91 Human risk assessment, 87 Hydraulic conductivity, 25, 31 hydraulic fracturing, 184 Hydraulic optimization, 123 hydrodynamic-eutrophication (HEM-3D) model, 24 hydrodynamic mixing zones, 7 Hydrodynamic mixing zones, 38 Hydrogen sulÞde, 83 Hydrogeochemical uncertainty, 132–133 Hydrophobic contaminants partition coefÞcient of, 83 release and availability, 102 Hydrophobicity, 70 Hydrophobic organic compounds partition coefÞcient, 70, 79 release and availability, 102 sorption of, 83 Hyperheic zone, advective process in, 94–95 Hyporheic communities, 15–16 Hyporheic exchange, 6 Hyporheic zone, 14–15 biological communities, 15–16 deÞnition of, 6 inßuence on ground water contaminant discharges, 22 position during river ßows, 15 Hysteresis, 93

I Idaho National Environmental Engineering Laboratory (INEEL), 110 immunoassay test kits, 115 IMPES method, 203

INEEL (Idaho National Environmental Engineering Laboratory), 110 Infrared spectrometry, 113 Ingestion, 81 Interfacial tension, 184 International Association of Hydrological Sciences, 24 International Hydrological Programme, 24 Interphase mass transfer, 189–192 Interpolation theory, 148 Interstate Technology Regulatory Cooperation (ITRC), 241 Intrinsic permeability, 184 Inverse modeling, 223 Ion mobility spectrometry, 113 Iron, 92 Irreducible water saturation, 186 Isopleths, 48 ITRC (Interstate Technology Regulatory Cooperation), 241

J James River, 47

K Kalman Þlters, 153 Kinematic viscosity, 78 Kriging, 151–152 Kronecker delta, 189 k-S-P functions, 186–188, 222–224

L Lacustrine system, sediment processes in, 67 Lagrangian model, 39 Lakes, 8, 17 LandÞlls, 207 Laplace-Young equation, 184–185 Laser-induced breakdown spectrometry, 113 Laser-induced ßuorescence (LIF), 40–42 Latin hypercube sampling, 25 LC50, 11 Lead, 70, 92 LIF (laser-induced ßuorescence), 40–42 Ligands, 92 Limnodrilus hoffmeisteri, 97 Lindane, 71 Linear equations, 204 Linear programming, 155–157 Linear regression, 148 Lipids, 84


Index

Longitudinal dispersion coefÞcient, 47 Longitudinal dispersivity, 32, 134 Low-gradient streams, 19 Low-resolution model, 206 Lysimeter, 112

M Magnetometer surveys, 112 MAGNUS model, 209 Mamala Bay, Hawaii, 44 Manganese, 92 March Air Force Base (California), TCE/PCE plume containment at, 157–158 MAROS (Monitoring and Remediation Optimization System), 151 Massachusetts Military Reservation, dissolved TCE cleanup at, 160–162 Mass balance equation, 188–189 Mass discharge rate, 47 Mass ßux, 217–218 Mass spectrometry, 113 Mass transfer, 224 coefÞcients, 78, 97 interphase, 189–192 from sorbed phase, 234 Mass transport, multicomponent, 188–192 Mathematical models, 39–40 Mathematical optimizations, 109–110 cost of, 110 users of, 164–166 Maximum water saturation, 186 McClellan Air Force Base, 163 MDEQ (Michigan Department of Environmental Quality), 7–8, 13–14 Measurement error, 26 Meiofaunal communities, 16 Membrane-based testing devices, 113 Mercury, 7, 70 Metals, 70–71 as pollutants, 10 release and availability, 101–102 speciation, 92–93, 102 Methane, 83 Methanogens, 200, 232 Methyl mercury, 84–85 Michigan Department of Environmental Quality (MDEQ), 7–8, 13–14 Microbial communities, 16 Mine contamination, 28 Mine-derived contaminants, 22 Mixing, 37–38 mathematical models, 39–40 two-stage, 10–11

269

Mixing zones, 6; see also Ground water-surface water interactions analysis of, 24 boundaries, 14 far-Þeld, 37–39 Þeld techniques for, 44–45 mathematical models, 39–40 modeling applications for, 46–47 models, 49–50 momentum-induced dilution, 46 near-Þeld, 37–39 numerical dimension of, 53 regulatory deÞnition of, 7, 13–14 rules for, 8 technological limitations of, 27 toxic dilution zone (TDZ), 9–10 zone of initial dilution (ZID), 8–9 MOCDENSE + MODFLOW model, 212 Model-measure-modify paradigm, 206 Models and modeling, 210–211; see also Optimization biological process, 101 calibration, 88–89 conceptual, 65 contaminant process, 101 developing, 50 errors, 26 measures of acceptability, 90 in policy development, 207 in research and education, 205–207 roles of, 205–208 selections and limitations, 213–214 single-phase, 211–212 site applications, 215–221 in site assessment and remedial design, 208 technical issues in, 4 three-phase, 209–210 uncertainty, 25–27, 88–89 MODFLOW model, 27, 51, 123 ModiÞed Bessel function, 47 MODMAN model, 123 MODOFC model, 123 MOFAT model, 209 Molecular diffusion, 79–80, 189 Mollusks, 96 Momentum ßux, 46 Momentum-induced dilution, 46–47 Monitored natural attenuation, 149 Monitoring, 147–148, 162–164 Monitoring and Remediation Optimization System (MAROS), 151 Monod kinetics, 197, 199, 231–232 Monte Carlo analysis, 26, 89, 130 MT3D + MODFLOW model, 212 Multicomponent mass transport, 188–192


270


interphase mass transfer, 189–192 mass balance equation, 188–189 transport ßux equation, 188–189 Multiphase ßuid ßow, 183–188 capillary pressure, 184–188 Darcy’s law, 183–184 linearization of nonlinear equations of, 203–204 models, 209–210 relative permeability, 184–188 Multiple realization optimization, 129–131

N Naphthalene, 72 NAPL, see Nonaqueous phase liquid (NAPL) NAPL model, 209 National Oceanic and Atmospheric Administration (NOAA), 16 National Pollutant Discharge Elimination System (NPDES), 10 National Research Council, 24 National Sediment Quality Survey, 62 natural parameter variability, 26 Naval Facilities Engineering Service Center (NAVFAC), 117 Naval Industrial Reserve Ordnance Plant, 163 NAVFAC (Naval Facilities Engineering Service Center), 117 Navier-Stokes equation, 40, 51 Near-Þeld, 17, 37–39 Negative buoyancy, 17 Net sediment transport, 75 Neutral buoyancy, 9 New Bedford Harbor, 97 New Jersey, DNAPL source area characterization, 216–221 New River, Virginia, 47 Newton linearization, 204 Newton Raphson method, 204 Niched Pareto Genetic Algorithm (NPGA), 137 Nickel, 70 Nitrogen, 24 NOAA (National Oceanic and Atmospheric Administration), 16 Nonaqueous phase liquid (NAPL), 182; see also Chlorinated organic compounds; Dense nonaqueous phase liquid (DNAPL) capillary pressure and relative permeability, 184–188 interphase mass transfer, 189–192 k-S-P relationships, 222–224 multiphase ßow, 183–184, 225–226

three-phase models, 209–210 two-phase models, 210–211 Noncohesive sediments, 75 Nonconventional contaminants, 10 Nonlinear models, 124 Nonlinear programming, 156–157 Nonpoint sources, 11–13 Norton Air Force Base (California), dissolved TCE cleanup at, 154–157 NPDES (National Pollutant Discharge Elimination System), 10 NPGA (Niched Pareto Genetic Algorithm), 137 Nuclear logging, 112 Nuclear magnetic resonance, 113 NUFT model, 209 Numerical implementation errors, 88 Numerical models, 114–115 Nutrient recycling, 16

O Oak Ridge National Laboratory (ORNL), 110 Oceanographic instruments, 44 Oil, 71 Oligochaetes, 15 Open-loop optimization, 115 Optimization, 108–109; see also Models and modeling by Department of Defense (DoD), 116–121 by Department of Energy (DOE), 110–116 by Environmental Protection (EPA), 109–110 and industry, 121 models, 114–115 multiple constituents, 167 multiple phases, 167–168 natural variability over space and time, 166–167 open-loop, 115 simulation, 122–126 stochastic, 126–127 uncertainty, 131–141 Organic carbon-based partition coefÞcient, 70, 79–80 Organic chemicals, 93 Organochlorines, 71–72 ORNL (Oak Ridge National Laboratory), 110 Oscillating velocity, 31 Ostracods, 15 OTIS model, 27, 51 Oxidation, 195–196 Oxidation-reduction reactions, 92–93 Oxygenase enzymes, 200–201 Oxygen-demanding contaminants, 71


Index

P PAHs (polycyclic aromatic hydrocarbons), 70–3, 94 Parameterization errors, 89 Partial error propagation, 51 Partition coefÞcient, 83 organic carbon-based, 70, 79–80 as predictor of BASF, 99 Passive diffusion bag (PDB) samplers, 119–120 Pathogens, 10 PCBs, see Polychlorinated biphenyls (PCBs) PCE, see Perchloroethylene (PCE) PDB (passive diffusion bag) samplers, 119–120 Peclet number, 80 Perchloroethylene (PCE), 182; see also Contaminants abiotic transformations, 196 biodegradation of, 229 cumulative extraction of mass for, 221 plume attenuation, 157–158, 158–160 reductive dechlorination of, 229–230 Performance monitoring problems, 148–150 Pesticides, 71–72 Petroleum hydrocarbon biodegradation models, 197–199 Petroleum reservoir engineering, 223, 226 pH, 92–93 Phosphorus, 24 Photoionization detector, 113 Physical-statistical monitoring, 152–153 Physics-based monitoring, 152 Phytoplanktons, 85 Phytoremediation, 122 Piezoelectric sensors, 113 Planar laser-induced ßuorescence (PLIF), 40–41 Plan-form exchange ßows, 28 PLIF (planar laser-induced ßuorescence), 40–41 Plume attenuation, 231–235 bioavailability and mass transfer from sorbed phase, 234 biotransformation kinetics, 231–232 complex mixtures, 233–234 halorespiration, 232–233 spatial variability in redox conditions, 233 Plumes, 36 characteristics of, 133 containment, 146, 149–150 density, 9 trajectories, 43 Pneumatic Well Logging (PneuLog), 120 Point sources, 17 discharge regulations, 7–8 ßux characteristics of, 17 Polar organic compounds, 69

271

Polychaetes, 96 Polychlorinated biphenyls (PCBs), 72 as ground water contaminant, 182 immunoassay test kits for, 115 in regulatory mixing zones, 7 Polycyclic aromatic hydrocarbons (PAHs), 70–3, 94 Polynomials, 148 Porosity, 134 Positive buoyancy, 17 Potential exposure points, 207 Predictive modeling, 67–68 Problem holders, 164–165 Propionic acid, 199–200 Public funding, 49 Pump-and-treat systems, 109–110 annual costs, 117 evaluation of, 117–118 in ground water remediation, 121 optimal pumping schedules for, 133, 136 tradeoff curve for optimization of, 137 Pumping, 19 Pyrene, 70, 72, 83

Q Quality assurance standards, 237–238

R Radiation detectors, 113 Radioisotopes, 44 Radionuclides, 97, 111 Raman spectroscopy, 113 Raoult’s law, 189–190 RAP (remedial action plan), 14 RBCLs (risk-based cleanup levels), 207 RBDM (risk-based decision making), 12 RCRA (Resource Conservation and Recovery Act), 12 Reach scale, 21 Records of Decisions (RODs), 110 Recovery model, 91, 95 Redox potential, 92 Reductive dechlorination, 229–230 Reductive dehalogenation, 194–195 Regulators, 165–166 Regulatory agencies, 5–6 Regulatory mixing zones, 7; see also Mixing zones deÞnition of, 13–14 dimensions of, 11 vs. hydrodynamic mixing zone, 38


272

Relative permeability, 184–188 Remedial action plan (RAP), 14 Remedial management plans, 68–69 Remedial technologies, 208 Remediation, 109 concentration constraints, 124–126 design optimization, 153–154 design-risk cost tradeoff, 141–146 estimated costs, 208 gradient control, 122–124 March Air Force Base, 157–158 maximum concentrations for, 136 and monitoring, 147–148 Norton Air Force Base, 154–157 pumping schedules, 136 site assessment and design, 208 Wurtsmith Air Force Base, 158–161 Remediation Process Optimization (RPO), 118–119 Remediation Technology Development Forum (RTDF), 241 Remedies, 147–148 Remote sensing, 112 Reservoirs, 17 Resistivity surveys, 112 Resource Conservation and Recovery Act (RCRA), 12 Response matrix technique, 123 Retardation coefÞcient, 134 Reynolds equation, 40, 51, 191 Risk, 141 Risk assessment, 142 for ground water, 144–146 methods, 144 uncertainty, 138–139 Risk-based cleanup levels (RBCLs), 207 Risk-based decision making (RBDM), 12 Risk-based systems, 151 Risk management, 142 Risk reduction, 64 Riverine system, 17 ammonia toxicity in, 47 concentration pattern in, 47 sediment processes in, 67 RODs (Records of Decisions), 110 Root systems, 81 Rotifers, 15 Rotosonic drilling, 112 RPO (Remediation Process Optimization), 118–119 RT3D + MODFLOW model, 212 RTDF (Remediation Technology Development Forum), 241 R-UNSAT model, 211


S Salinity, 76 Sampling, 112, 140–141 Sampling bailers, 112 Savannah River site, 110 Schizocardium, 97, 98 Schmidt number, 78, 191 Sculpins, 72 Sea ßoor, 19 SED2D model, 91 Sedßume, 76, 91 Sediment oxygen demand, 71 Sediment processes, 67 and animals, 81 Sediments, 2 and common contaminants, 69–73 density, 91 erosion and deposition processes, 73–75 unstable, 75–77 Sediment transport bioturbation, 95–98 and hydrodynamics, 19 Sediment transport model, 100–101 minimum requirements, 75–76 sediment erosion and deposition process in, 73–76 Sediment-water interface, 77 Sediment-water partition coefÞcient, 69 Seepage meters, 81 Seismic methods, 112 Self-induced turbulence, 37–38 SEM (simultaneously extractable metal), 71 SESOIL model, 114 Shear stress, 74–75, 92 Sherwood number, 191 Silica cycles, 24 Siltation, 80 Silver, 70 Simulated annealing models, 125 Simulation models, 23–24 Simulation optimization, 122 concentration constraints remediation, 124–126 gradient control remediation, 122–124 Simultaneously extractable metal (SEM), 71 Single-phase models, 211–212 Site assessment, 208 Site characterization and monitoring technologies, 111–114 Soil-gas monitoring, 112 Soil porosity, 183–184 Soil vapor extraction (SVE) wells, 120 Soluble meals, 69 Sorption, 93, 192


Index

Source zones, 217–218 Spatial discretization, 202–203 Species diversity, 84 Spills, 21 Spreading coefÞcients, 187 Stacking, 127 State regulations, 115–116 Statistical monitoring, 151–152 Stochastic optimization, 126–127 alternative methods, 131 chance-constrained, 127–129 multiple realization, 129–131 STOMP model, 209 Storage coefÞcient, 30 StratiÞed jets, 44 Streambed geometry, 75 Stream ecology, 28 Streams, 17 Stream-subsurface exchange, 18–21, 27–29, 51 Striped bass, 85 Strontium, 111 Submerged plants, 16 Super-critical ßuid chromatography, 113 Superfund, 12 sites, 16 use of pump-and-treat systems, 110 Surface geophysics, 112 Surface runoff, 14 Surface shear stress, 74 Surface water, 6–7 column, 6 interaction with ground water, see ground water-surface water interactions models, 50 Surfactants, 28 Suspended algae, 24 Suspended load, 73 Suspended sediment concentration, 75 SUTRA model, 211 SVE (soil vapor extraction) wells, 120 SWIFT simulation model, 157 Synthetic organics, 10

T Tailing, 21 TCE, see Trichloroethene (TCE) TDZ (toxic dilution zone), 9–10, 13 Technology demonstration programs, 115–116 Technology transfer, 235–236, 245 expert decision support system, 238–239 implementation recommendations, 240–242 model application archive and database support, 239

273

quality assurance standards, 237–238 training support, 240 Technology uncertainty, 133 Temperature, 10 at end of mixing zones, 14 in sediment transport model, 76 Temporal discretization, 203 Tensiometers, 112 Tetrachloroethane, 193 Thallassinid shrimp, 96 Thallium, 70 Thermistors, 44 Thermocouple psychrometers, 112 Thief sampler, 112 Thin-layer chromatography, 113 Thorium, 96 Three-dimensional laser-induced ßuorescence (3DLIF), 41–42 Three-phase models, 209–210 Tidal exchanges, 29 Tidal oscillations, 29 Tidal rivers, efßuent channels, 48–49 Tidal system, concentration pattern in, 47 Time-domain reßectometry, 112 Time scales, 27 TMDL (total maximum daily load), 5, 66–68 Tortuosity, 79, 189 Total maximum daily load (TMDL), 5, 66–68 Total petroleum hydrocarbon (TPH), 71 Total velocity Þeld, 31 TOUGH2 + T2VOC model, 211 Toxic dilution zone (TDZ), 9–10, 13 Toxic substances, 9–11 criterion continuous concentration (CCC), 9 criterion maximum concentration (CMC), 9–10 federal guidelines, 11 Toxic unit (TU), 11 TOXIWASP model, 27 TPH (total petroleum hydrocarbon), 71 Tracer detection apparatus, 45 Transient storage, 21 Transistor-to-transistor logic (TTL), 42 Transition zones, 3, 20–21 Transmissivity, 30 Transport ßux equation, 188–189 Transverse dispersivity, 134 Trend analysis, 148 Trichloroethanes, 193 Trichloroethene (TCE), 182; see also Contaminants abiotic transformations, 196 biodegradation of, 229 cleanup of, 118, 154–162 plume containment, 157–160


274


reductive dechlorination of, 229–230 TTL (transistor-to-transistor logic), 42 Tube building, 81 TubiciÞd worms, 97 Tubifex tubifex, 97 Turbulence, 37–38, 92 TU (toxic unit), 11 Two-phase models, 210–211 Two-stage mixing, 10–11

Viruses, 10 Visual PLUMES, 39 VLEACH model, 211 VOCs (volatile organic compounds), 115 Void ratio, 91–92 Volatile organic compounds (VOCs), 115 von Karman constant, 78 VPI (Virginia Polytechnic Institute), 46 VS2DI model, 211

U

W

UM3 model, 39 Uncertainty, 25–27 approaches for addressing, 139–141 aquifer physical characteristics, 133–136 decision-making, 136–138 evaluating, 89–90 hydrogeochemical, 131–132 risk assessment, 138–139 sources of, 88–90, 132–133 Uncontrolled contaminant discharges, 21–23 Unsaturated water ßow, 225 Unstable sediments, 75–77 UnstratiÞed jets, 44 U.S. Environmental Protection Agency (USEPA), 109–110 U.S. Geological Survey (USGS), 28 UTCHEM model, 209 UV-visible spectrometry, 113

WASP5 model, 91 Wastewater ßow rate, 46 Wastewater treatment plant, 47 Water, kinematic viscosity of, 78 Water quality parameters, 24 Water Quality Standards (WQS) regulation, 7 Water saturation, 186 Wetting ßuids, 185 WET (whole efßuent toxicity), 10, 47 White perch, 85 Whole efßuent toxicity (WET), 10, 47 Wiener Þlter, 152 Wind, 76 Wind pumping, 19 Worm tubes, 82 WQS (Water Quality Standards) regulation, 7 Wurtsmith Air Force Base (Michigan), TCE/DCE plume containment at, 158–160

V Vadose zone, 183 multiphase ßuid ßow in, 183 water and gas monitoring, 112 Van Genuchten model, 186 Vapor extraction, 122 Vapor pressure, 190 Velocity, 32 Vertical equilibrium models, 223 Vertical settling velocity, 75 Vinyl chloride, 193 Virginia Polytechnic Institute (VPI), 46

X X-ray ßuorescence, 113

Z ZID (zone of initial dilution), 8–9 Zinc, 70 Zone of initial dilution (ZID), 8–9 Zone of transition, 6 Zooplanktons, 85

L1667_bookTOC.fm Page xiv Tuesday, October 21, 2003 3:51 PM

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