Advances in Agronomy, Volume 58

A DVANCES IN Advisory Board Martin Alexander Eugene J. Kamprath Cornell University North Carolina State University...

Author: Donald L. Sparks

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A DVANCES IN

Advisory Board Martin Alexander

Eugene J. Kamprath

Cornell University

North Carolina State University

Kenneth J. Frey

Larry P. Wilding

Iowa State University

Texas A&M University

Prepared in cooperation with the American Society of Agronomy Monographs Committee P. S. Baenziger Jon Bartels Jerry M. Bigham M. B. Kirkham

William T. Frankenberger, Jr. Chairman David H. Kral Dennis E. Rolston Sarah E. Lingle Diane E. Stott Joseph W. Stucki Kenneth J. Moore Gary A. Peterson

D V A N C E S I N

onomy VOLUME 58 Edited by

Donald L. Sparks Department of Plant and Soil Sciences University of Delaware Newark, Delaware

ACADEMIC PRESS San Diego London Boston NewYork

Sydney Tokyo Toronto

This book is printed on acid-free paper.

@

Copyright 0 1997 by ACADEMIC PRESS All Rights Reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher.

Academic Press, Inc. 525 B Street, Suite 1900, San Diego, California 92101-4495, USA

http://www.apnet.com Academic Press Limited 24-28 Oval Road, London NW 1 7DX, UK http://www.hbuk.co.uk/ap/ International Standard Serial Number: 0065-21 13 International Standard Book Number: 0-12-000758-4

PRINTED IN THE UNITEDSTATES OF AMERICA 96 97 9 8 9 9 00 01 BB 9 8 7 6

5

4

3 2 1

Contents CONTRIBUI-ORS. ............................................ PREFACE. .................................................

ix xi

EFFECTOF SORPTIONON BIODEGRADATION OF SOILPOLLUTANTS Kate M. Scow and Carol R. Johnson I. Introduction. .............................................. 11. Overview of Sorption.. ..................................... 111. Biodegradation of Sorbed Chemicals. ......................... N. Analogies between Sorbed Chemical Degradation and Carbon Flow in Soil.. .................................. V. Factors Controlling Coupled Biodegradation and Sorption.. . . . . . VI. Bioremediation of Sorbed Chemicals. ......................... VII. Future Research Needs and Directions ........................ References ................................................

1 3 12 30 32 42 47 48

HERBICIDE RESISTANCE:IMPACT AND MANAGEMENT S. B. Powles, C. Preston, I. B. Bryan, and A. R. Jutsum I. Introduction: Major Crops and Herbicide Markets.. ............ 11. The Threat from Herbicide-Resistant Weeds. ..................

111. Managing Herbicide Resistance .............................. IV. Conclusions ............................................... References ................................................

57 64 71 83 84

PHYSICAL NONEQUILIBFUUM MODELING APPROACHES TO SOLUTETRANSPORT IN SOILS Liwang Ma and H. M. Selim I. Introduction. ..............................................

11. Mobile-Immobile Two-Region Models. ....................... 111. Two-Flow Domain Models. .................................

IV Capillary Bundle Models,. .................................. V. Multiple-Flow Domain Models

..............................

VI. Coupled Physical and Chemical Nonequilibrium Models . . . . . . . . V

95 101 116 122 124 126

CONTENTS

vi

w. Field Applications .......................................... VIII. Summary and Conclusion ................................... Appendix: Nomenclature .................................... References ................................................

I. I1. 111.

lv.

SILICON MANAGEMENT AND SUSTAINABLE RICEPRODUCTION N . K . Savant. G. H. Snyder. and L. E . Damoff Introduction ............................................... Silicon Nutrition in Rice .................................... Silicon in Soil and Water.................................... Silicon Management Agenda ................................. Potential Benefits of Silicon Management .....................

V. VI . Agronomic Essentiality of Silicon Management ................ VII . Determining Need for Silicon Fertilization .................... VIII. Suggestions for Research .................................... Ix. Summary ................................................. References ................................................

138 140 142 144

151 152 155 158 165 177 179 185 188 189

TISSUE CULTURE-INDUCED VARIATION AND CROP k R O V E M E N T

R . R. Duncan

I. Introduction............................................... I1. Causes and Range of Variation ............................... III. Methodological Basis for Variation ........................... n? Rate of Variation ........................................... V. In Vim Selection .......................................... VI. Conclusions ............................................... References ................................................

201 202 207 214 215 222 224

GEOSTATISTICAL ANALYSISOF A SOILSALINITYDATASET G. Bourgault. A. G. Journel. J . D . Rhoades. D . L . Corwin. and S. M. Lesch

I. I1. I11. TV. V.

Introduction ............................................... Exploratory Data Analysis ................................... Mapping the EC. Distribution ............................... Filtering Structures ......................................... Spatial Cluster Analysis .....................................

241 245 254 269 276

CONTENTS

VI . VII. VIII. IX.

Stochastic Imaging ......................................... Assessment of Spatial Uncertainty ............................ Ranking of Stochastic Images ................................ Conclusions ............................................... References ................................................

vii 280 287 291 291 292

FURTHER PROGRESS IN CROPWATERRELATIONS Neil C. Turner

I . Introduction ............................................... I1. Measurement of Water Deficits ..............................

111. “Sensing” Water Deficits .................................... n! Water Deficits and Yield .................................... V. Use of Reserves to Maintain Yields under Water Deficits ........ VI. Water Use Efficiency ....................................... VII. Drought Resistance ......................................... VIII . Concluding Remarks ....................................... References ................................................

293 294 296 302 305 307 314 324 325

INDEX.....................................................

339

This Page Intentionally Left Blank

Contributors Numbers in parentheses indicate the pages on which the authors' contributions begin.

G. BOURGAULT (241), Geological and Environmental Sciences Department, Stanford University, Stanford, California 94305 I. B. BRYAN (57), Zeneca Agrochemicals, Jealotts Hill Research Station, Bracknell, Berkshire, United Kingdom D. L. CORWIN (241), USDA-ARS, U. S. Salinity Laboratory, Riverside, California 92507 L. E. DATNOFF (1 5 I), Department of Plant Pathology, Everglades Research and Education Center, University of Florida, Belle Glade, Florida 33430 R. R. DUNCAN (201), Department of Crop and Soil Sciences, University of Georgia, Grzfin, Georgia 30223 CAROL R. JOHNSON (l), Department of Land, Air, and Water Resources, University of California, Davis, Davis, California 9561 6 A. G. JOURNEL (241), Geological and Environmental Sciences Department, Stanford University, Stanford, California 94305 A. R. JUTSUM (57), Zeneca Agrochemicals, Jealotts Hill Research Station, Bracknell, Berkshire, United Kingdom S. M. LESCH (241), USDA-ARS, U. S. Salinity Laboratory, Riverside, California 92507 LrWANG MA ( 9 9 , Department of Agronomy, Agricultural Center, Louisiana State University, Baton Rouge, Louisiana 70803 S. B. POWLES (57), C. R. C. for WeedManagement Systems, Waite Campus, University of Adelaide, Glen Osmond, Australia C. PRESTON (57), C. R. C. for Weed Management Systems, Waite Campus, University of Adelaide, Glen Osmond, Australia J. D. RHOADES (241), USDA-ARS, U. S. Salinity Laboratory, Riverside, California 92507 N. K. SAVANT (1 5 l), StaSav International, Florence, Alabama 35630 KATE M. SCOW (I), Department of Land, Air, and Water Resources, University of California, Davis, Davis, California 9561 6 H. M. SELIM (95),Department of Agronomy, Agricultural Center, Louisiana State University, Baton Rouge, Louisiana 70803 G. H. SNYDER (1 5 l), Departments of Soil- Water Science and Plant Pathology, Everglades Research and Education Center, University of Florida, Belle Glade, Florida 33430 NEIL C. TURNER (293), CSIRO Division of Plant Industry, Centrefor Mediterranean Agriczlltural Research, Wmblq, (Perth), W A. 6014, Australia ix


Preface Volume 58 consists of seven comprehensive and cutting-edge reviews on various aspects of plant and soil sciences. Chapter 1 is a contemporary review of the effect of sorption on biodegradation of soil pollutants. Sorption equilibrium and kinetics are covered, and extensive discussions on biodegradation of sorbed chemicals, factors controlling coupled biodegradation and sorption, and bioremediation of sorbed chemicals are presented. Chapter 2 deals with the impact and management of herbicide resistance, a major area of research in plant biotechnology. Chapter 3 provides a comprehensive discussion of physical nonequilibrium models that can be used to predict solute transport in soils. Various mobileimmobile, two and multiple flow, and coupled physical and chemical nonequilibrium models are described and applied to field settings. Chapter 4 thoroughly covers aspects of silicon in plants, soil, and water and its use and management in rice production. Chapter 5 provides a timely review on tissue-culture-induced variation and crop management. Topics that are discussed include causes and range of variation, the methodological basis for variation, the rate of variation, and in v i m selection. Chapter 6 is an extensive geostatistical analysis of a soil salinity data set. The final review, Chapter 7, discusses advances in crop water relations, including measurement and sensing of water deficits, deficit effects on crop yields, use of reserves to maintain yields under water deficits, water use efficiency, and drought resistance. Many thanks to the authors for their fine contributions.

1 h ~ ~ i . L. 1 ) SPARKS

Xi


EFFECTOF SORPTIONON BIODEGRADATION OF SOIL POLLUTANTS Kate M. Scow and Carol R.Johnson Department of Land, Air and Water Resources, University of California, Davis, California 95616

I. Introduction 11. Overview of Sorption A. Sorption Thermodynamics B. Sorption Kinetics 111. Biodegradation of Sorbed Chemicals A. Coupled Process Models under Batch Conditions B. Coupling Advection, Sorption, and Biodegradation- Column Studies C. Extrapolation of Microbial Rate Parameters across Experimental Systems IV. Analogies between Sorbed Chemical Degradation and Carbon Flow in Soil V. Factors Controlling Coupled Biodegradation and Sorption A. Sorption Partition Coefficient B. Diffusion C. Rate of Metabolism D. Spatial Distribution at the Pore and Aggregate Scale E. Effect of Soil Moisture F. Temporal Changes in Bioavailability VI. Bioremediation of Sorbed Chemicals A. Inoculation B. Decreasing Aggregate Size by Mixing and/or Crushing C. Addition of Surfactants D. Evaluation of Rate-Limiting Steps in Bioremediation E. Regulatory Implications of Reduced Bioavailability VII. Future Research Needs and Directions References

I. INTRODUCTION Without the existence in soil of natural processes that act to preserve organic compounds from microbial activity, there would be little organic matter, and thus 1 Adurnires m A p n m n y . Volume 58 Copyright 0 1997 hy Academic Press. Inc. All rights nf reproduction in any form reserved.

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K. M. SCOW AND C. R. JOHNSON

life, left in soil. The major, relatively labile reservoirs of organic carbon and energy in ecosystems, such as cellulose, hemicellulose, and lignin, are highly insoluble and over time become increasingly sequestered through associations with mineral components. Consequently, these compounds and their products (e.g., soil humus) cannot be readily degraded by microorganisms. Many organic pollutants also become associated with mineral and organic surfaces or enmeshed in the three-dimensional organic-mineral complex of soil organic matter. These physical processes also reduce the biodegradation rates of pollutants. The term bioavailability designates the state of that fraction of a chemical that is available for uptake and/or transformation by living organisms. Although associated primarily with ecotoxicology, and usually in reference to organic and metallic pollutants, the term bioavailability is also relevant to native organic material. Thus, the “problem” of bioavailability has existed for microorganisms far longer than has the presence of xenobiotic chemicals in the environment. Sorption, insolubility, and related processes are largely responsible for controlling the bioavailability of many pollutants to microorganisms in soils and sediments. The turnover rates of forms of a chemical that are not bioavailable are usually slower than those for the same chemical form in solution (Scow, 1993; Rijnaarts et al., 1991; Ogram et al., 1985). In addition, for many chemicals with limited bioavailabilities, the rate-limiting step in their biodegradation is mass transfer from an unavailable to available form. Consequently, because multiple processes are involved, prediction of these chemicals’ biodegradation rates is far more complex than is the case for readily available chemicals. The practical issues associated with the reduced bioavailability of pollutants are many. How strongly a pollutant is sorbed, as indicated by its sorption partition coefficient, and how rapidly it biodegrades are criteria used for screening its potential to leach to groundwater (Gustafson, 1989; Jury et al., 1987). However, pollutants that should be readily biodegradable (and are not strongly sorbed) have been found to persist for decades and eventually find their way to groundwater, such as has been observed for ethylene dibromide (Pignatello et al., 1987). Soils polluted with organic contaminants become increasingly difficult to remediate, by any method, the longer the pollutant has been in contact with the soil. Recently, the issue of what is an acceptable treatment endpoint in contaminated soil and groundwater has been discussed with respect to the issue of bioavailability (Alexander, 1995; Beck et al., 1995). Some researchers challenge whether a strongly sorbed pollutant poses a significant environmental problem if the pollutant is not available for uptake by sensitive populations, even if its limited bioavailability also means it cannot be taken up by microorganisms that can potentially degrade it. Much of the debate over this issue is fueled by uncertainty in our understanding of coupled sorption/desorptionand biodegradation processes and of their impact on bioavailability. The objectives of this chapter are to demonstrate the importance of physical

EFFECT OF SORPTION ON BIODEGRADATION

3

mass transfer processes-including nonequilibrium sorption, diffusion and other pollutant-soil interactions-in governing rates of biodegradation of organic pollutants in soil. To understand the coupling of sorption/diffusion and biodegradation kinetics, one must answer four questions: (i) of the total pollutant entering the soil, which fraction is available, in time and space, to microorganisms?; (ii) what are the mechanisms of exchange between the available and unavailable pools?; (iii) what controls the kinetics of metabolism of the available portion of the pollutant?; and (iv) what interactions between mass transfer and metabolism must be considered in describing the biodegradation of sorbed pollutants? For each question, one must identify the forces and processes involved, develop mathematical descriptions of key processes, and conduct well-designed experiments to test and modify hypotheses about the phenomena. We discuss several approaches, of increasing complexity, for describing coupled mass transferbiodegradation reactions in soil and present data to illustrate important concepts. We also discuss the implications of this knowledge for the bioremediation of sorbed chemicals. This chapter differs from previous reviews discussing impacts of sorption on biodegradation. Sims et al. (1991) reviewed the effect of sorption on pesticide biodegradation and implications for pollutant transport. Scow ( 1993) provided a broad overview of sorption and biodegradation of organic pollutants in soil, including discussion of indirect effects of pollutant-sorbent interactions on microorganisms and methodological limitations. Mihelcic et al. (1993) reviewed the impact of phase partitioning, with respect to both sorption and solubilization, on the biodegradation of hydrophobic pollutants. Brusseau et al. (1992b) provided an overview of coupled chemical-biological process models for solute transport. Beck et al. (1995) reviewed the implications of mass-transfer limitations on biodegradation in the context of soil quality limits.

11. OVERVIEW OF SORPTION

A. SORPTIONTHERMODYNAMICS Sorption refers to the association of a chemical with soil solids and includes a wide variety of mechanisms (Hamaker and Thompson, 1972; Koskinen and Harper, 1990). Which mechanism(s) dominate the interaction of a particular compound with soil mineral and/or organic matter depends on the chemical’s properties and the soil composition. Ionic compounds can bond via coulombic forces to charged soil sites (ion-exchange reactions). Some functional groups such as benzylic amines are capable of forming covalent bonds with soil organic matter. Basic compounds such as amines with available lone pair electrons can

4

K M. SCOW AND C. R. JOHNSON

complex with positive metal centers displacing the water or inorganic hydroxyl previously attached to the metal (ligand exchange). The mechanisms listed previously result in strong bonds to soil solids. Weaker bonds may be formed by cation bridging in which an anionic or polar functional group on the organic compound forms an inner sphere complex (one with no intervening molecules) with an exchangeable cation on the soil surface. If the organic compound cannot displace the waters hydrating the exchangeable cation, an outer sphere complex may form (water bridging). Other weak sorption mechanisms include charge transfer (partial election orbital overlap and exchange of electron density between electron-rich and electron-poor compounds) and hydrogen bonding. Finally, the weakest forces, but ones that apply to all organic compounds, are Van der Waals forces resulting from attractions between fixed and/or fluctuating molecular dipoles. This chapter will focus primarily on hydrophobic compounds; that is, compounds that are nonionic, contain no polar functional groups, and are relatively insoluble in water. Many environmental pollutants are hydrophobic compounds. Chemicals such as alkanes and chlorinated alkanes, alkylated and chlorinated benzenes, and polyaromatic hydrocarbons belong in this class. In general, the lower a compound’s water solubility, the more it sorbs to soil solids (Chiou et al., 1983). Hydrophobic compounds interact with soil solids primarily through nonspecific low-energy Van der Waals forces. Biodegradation studies involving ionic and polar organic compounds will be discussed in this chapter only to illustrate important general principles. Organic matter is the primary soil sorbent in surface soils for nonpolar organic chemicals. The extent of soil sorption for a particular chemical may be expressed as an equilibrium partition coefficient, Kp, for the chemical between soil solids and water.

kp =

(ng chemical sorbed/g soil solid) (ng chemical dissolved/ml soil solution)

K p values may vary by several orders of magnitude for the same compound on different soils. These differences are reduced when the Kp value is divided by the fractional organic carbon content of these soils to produce the organic carbon partition coefficient, K, (Lyman, 1982). KO, values for the same chemical on different soils have been estimated to vary by a factor of three to five for nonpolar organic chemicals (Rutherford et al., 1992; Curtis et al., 1986). Mineral surfaces, especially dry surfaces, also sorb organic compounds. In moist soils, however, water generally displaces organic compounds from mineral sorption sites, and the small remaining contribution of mineral sorption is overshadowed by organic matter sorption (Chiou, 1990). Contributions of mineral components must be considered in subsurface soils in which organic matter


5

contents are very low or in soils with large ratios of high surface area clays (such as montmorillonite) to organic carbon (Karickhoff, 1984). Interactions between the organic and mineral components of a soil may influence sorption. Murphy et al. (1990) found the K , values of several hydrophobic pollutants to vary with the type of clay mineral present in humic-mineral complexes to which the pollutants were sorbed. Nonpolar organic compounds, containing no strongly charged sites or reactive functional groups, interact with organic matter only through weak bonding mechanisms. Water’s free energy gain upon expulsion of the organic compound drives the sorption reaction. Chiou et al. (1983) described sorption of hydrophobic organic compounds as a partitioning process between water and soil organic matter that is depicted as an amorphous polymeric substance. Evidence supporting the partitioning model includes (i) the dependence of sorption extent on soil organic carbon content, (ii) sorption isotherm linearity to relatively high sorbate concentrations, and (iii) low sorption enthalpies for nonpolar organic chemicals from water to soil. The linear sorption isotherms predicted by partitioning theory have been widely observed. For linear isotherms, all sorption behavior is contained in a single concentration independent parameter, Kp or K,, which can be estimated from chemical properties. Due to their mathematical simplicity, Kp and KO, values have been widely used to predict sorption equilibrium in pollutant fate models. Not all experimental data support a linear partitioning model, however. Nonlinear isotherms have been reported for a variety of compounds: tetrachloroethylene, 1,4-dichlorobenzene, 1,2,4-trichlorobenzene (McGinley et al., 1993), phenanthrene (Young and Weber, 1995), phenylurea herbicides (Spurlock and Biggar, 1994b), and pesticides and other compounds (Hamaker and Thompson, 1972; Mingelgrin and Gerstl, 1983). Spurlock and Biggar (1994a,b) have proposed that for nonionic compounds that contain polar functional groups, such as phenylurea herbicides, specific organic matter-sorbate interactions lead to changes in sorption energy with the amount of chemical sorbed resulting in nonlinear isotherms. This theory does not explain, however, the nonlinear isotherms observed for molecules with no polar functional groups such as phenanthrene. Organic matter between soils or within the same soil has been reported to differ in polarity, elemental composition, aromaticity, condensation, and degree of diagenetic evolution from a loose polymer to condensed coal like structures (Grathwohl, 1990; Rutherford et al., 1992; Garbarini and Lion, 1986; Gauthier et al., 1987; Weber et al., 1992a; McGinley et al., 1993; Young and Weber, 1995; Preston and Newman, 1992). A second theory, the distributed reactivity model, views soil as an assemblage of discrete components each of which has a distinct sorption capacity and linearity (Weber et al., 1992a; McGinley et al., 1993; Young and Weber, 1995). Observed sorption behavior is the sum of the

6


behaviors of each of the individual sorbing units. The degree of diagenetic alteration of the organic matter associated with each soil component is considered the main determinant of that component's sorptive behavior. Older, more condensed organic matter, as found in kerogens, may display sorptive characteristics closer to those of a surface adsorptive model (nonlinear isotherms, competition among sorbates for specific sorption sites). Sorptive processes in younger, more amorphous organic matter will be well described by a partitioning model (linear isotherms and no competition effects). In general, nonlinear soil isotherms can be described by the Freundlich equation: If considered over a narrow concentration range, all sorption isotherms appear linear and, as stated previously, many authors have reported that observed soil isotherms fit a linear model. Most environmental fate models do not consider the effect of sorption nonlinearity on pollutant transport and degradation. Simulations and experimental results of soil column studies show that isotherm nonlinearity can affect the transport of solutes in soil and obfuscate the interpretation of soil column studies (Spurlock et af., 1995; Brusseau and Rao, 1989b; Weber et af., 1992). The effect of equilibrium nonlinearity on coupled transport and first-order transformation kinetics has been evaluated by Brusseau (1995). He concluded that for Freundlich exponents less than 0.95, nonlinear sorption equilibrium can alter transport kinetics and retard biodegradation rates.

B. SORPTION KINETICS 1. Processes

Partitioning of a chemical between soil solids and soil water can require hours or months to reach equilibrium. For a given soil, sorption and desorption kinetics slow as the sorbate K p value increases (Karickhoff and Morris, 1985; Brusseau and Rao, 1989a). For nonpolar organic compounds in soil, sorption (and desorption) kinetics generally exhibit two stages. Roughly 1-50% of the chemical will be sorbed in a relatively short initial period (minutes to hours) with the remaining chemical sorbed slowly (days to months) (Karickhoff and Moms, 1985; Coates and Elzerman, 1986; Ball and Roberts, 1991). With increased residence time in the soil, the fraction of the sorbate exhibiting slow desorption kinetics increases (Steinberg er al., 1987; Pignatello, 1990a,b; Pavlostathis and Mathavan, 1992). The exact mechanism underlying sorption/desorption kinetics in soil is unknown. The reader is referred to Pignatello and Xing (1996) for an in-depth review of the subject. Slow sorption/desorption kinetics can be caused by the


7

activation energy of chemical bonds or physical transport processes. For hydrophobic compounds, which primarily form weak bonds with soil solids, ratelimited chemical reactions are usually not considered as important as physical rate-limiting processes (Brusseau and Rao, 1989a). Two transport process have been proposed to explain sorption/desorption rates: (i) diffusion through intraaggregate pores, and (ii) diffusion through the soil organic matter matrix itself (Pignatello, 1989; Brusseau and Rao. 1989a,b; Pignatello and Xing, 1996). In soil, however, a variety of mechanisms may occur simultaneously. For example, phosphate sorption, which does involve slow chemical reaction rates, has been found to be ultimately diffusion controlled (Aharoni et al., 1991). For hydrophobic chemicals in soil, it may be that both proposed diffusion mechanisms operate or that diffusion processes are combined with the slow release of a fraction of the sorbate bound to sites with high desorption activation energies (physical transport and chemical bonds) (Pignatello and Xing, 1996).

2. Mathematical Models for SorptiodDesorption Kinetics a. First-Order Mass-Transfer Equations The simplest first-order model postulates that the sorption/desorption rate is proportional to the concentration of dissolved/sorbed chemical. This model, however, cannot emulate the biphasic sorption rates often observed in the laboratory (Connaughton et al., 1993). A more useful model divides sorption sites into equilibrium and kinetic sites (also called labile and resistant sites or rapid and slow sites). Chemicals in equilibrium sorption sites maintain equilibrium partitioning between these sites and the aqueous phase at all times. Kinetic sorbed sites exchange chemical with the dissolved phase according to first-order kinetics. This formulation is mathematically simple and does not require detailed information about sorption mechanisms or soil microstructure. Two-site models (also called two-compartment models) can represent a variety of physical systems. Both chemical rate and diffusion-dependent explanations for sorption kinetics can be approximated by this approach. The two sorbed compartments may represent two classes of sorbing sites, a chemical reaction in series, interior and exterior sites in the soil aggregate, or interior and exterior sites on the organic matter itself (Wu and Gschwend, 1986; Brusseau, 1995). The parameters used in the two site model cannot be calculated a priori from soil and solute properties. Karickhoff (1980) successfully used a two-site sorption model (shown in Fig. 1.) to describe sorption/desorption kinetics for polyaromatic hydrocarbons in soil slumes Studies of solute transport in soil columns have used first-order sorption kinetics in a variety of models to describe coupled sorption-advection kinetics (see references in Brusseau and Rao, 1989b). The two-site and two-region models are examples of this approach (Fig. 2). The two-site model is very similar to Ka-

K. M. SCOW AND C . R.JOHNSON

8

Resistant Phase

Sorbed Phase

Phase

Figure 1 Two-site sorption model. Double-headed arrow links compartments that reach equilibrium instantaneously. Single-headed arrows link Compartments that transfer solute according to firstorder kinetics (from Karickhoff, 1980).

rickhoff’s model in that a fraction of the sorption sites is considered to be in equilibrium with dissolved chemical in the mobile phase (the solution phase that moves down the soil column), whereas the remaining sorption sites exchange sorbate with the mobile phase according to first-order kinetics. The two-region model divides water in the soil column into mobile and immobile regions. Firstorder kinetics describe solute transfer between these two regions. Sorption sites are divided between mobile and immobile regions, but sorbed solute is always at equilibrium with respect to dissolved chemical in its region. The nature of the immobile solution is not specified. It could include dead-end pores, intraaggregate pores, or pores formed by matrix packing. A combination of the two-site and two-region models, a multiple nonequilibrium process model, was proposed by Brusseau er af. (1992a) to simulate situations in which both sorption kinetics (e.g., intraorganic matter diffusion) and diffusion into immobile water regions (e.g., intraaggregate pores) affect sorption/diffusion kinetics in the column (Fig. 2). Although extremely useful, two compartment models can only be an approximation to the soil’s true complexity. More accurately, an infinite number of compartments exist in soil representing sites with slightly different diffusion pathlengths or sorption energies. Connaughton er al. (1993) modeled desorption kinetics using a continuous distribution of first-order rate constants. The shape of the rate constant distribution is described by the gamma function, a two-parameter probability distribution whose mathematical properties are well defined. The shape of the rate constant distribution function determines what fraction of the sorbed chemical experiences fast or slow desorption kinetics and thus controls observed sorption/desorption rates. Connaughton er al. (1993) successfully fit the model to data describing naphthalene desorption kinetics from soils in which the chemical had aged for different periods of time, ranging from 3 days to 30 years. b. Diffusion Equations A second approach to modeling sorption/desorption kinetics assumes that diffusion within the soil matrix controls chemical release to and removal from the


r Multiprocess

9

Nonequillbrlum Model

Mobile Aqueous Phase

Labile Sorbed Phase

Phase

7-F Immobile Aqueous Phase

Kpim>

Labile Sorbed Phase

“iml

4

Resist ant Sorbed Phase

aim2

Two lhgion Model


Two Site Model

Labile Sorbed Phase


Resist ant Sorbed Phase

Ffgure 2 Examples of coupled sorption/advection models used in soil column studies (adapted from van Genuchten and Wagenet, 1989; Brusseau et al., 1992a).

10

K. M. SCOW AND C. R.JOHNSON

soil solution. Fick’s law is used to describe diffusion rates. Models of this type generally postulate that solute moves through intraaggregate pores. Sorption to pore walls is instantaneous, reversible, and retards the diffusive movement of the compound. The diffusion equation approach requires a more exact hypothesis concerning the underlying processes controlling sorption equilibrium and kinetics as well as a picture of the soil microgeometry. For diffusion models, it is theoretically possible to measure all model parameters a priori because each parameter represents a measurable physical quantity. Given the complexity of soil, such information is difficult to obtain except for simplified well-defined systems. When working in real systems, one or more model parameters are often estimated from fitting the model equations to experimental data. Wu and Gschwend (1986) used a diffusion model to describe sorption kinetics of four chlorobenzene congeners in soil slurries. Soil and silt particles were modeled as porous spheres and only one model parameter, the effective intraaggregate diffusion coefficient, was unknown and thus obtained from fitting the model to experimental data (Fig. 3). Rao et al. (1980a) used a radial diffusion model to predict the transport of nonsorbing solutes in soil columns containing sand and artificial porous aggregates. All model parameters were measured in independent experiments. The intraaggregate diffusion model accurately predicted observed solute transport kinetics. In many cases, both the first-order model and radial diffusion model describe experimental data equally well and it is important to determine when the simpler

Figure 3 Radial diffusion model (from Schwarzenbach er al., 1993, with permission of the publisher).


11

model may be used. Goltz and Roberts (1986), working with transport data from a sandy aquifer, and Miller and Weber (1986), in column studies, compared transport models that utilized sorption kinetics described either by radial diffusion or first-order rate models. Both models described the data equally well. The first-order rate model can be used to approximate the diffusion model under some conditions. Rao et al. (1980b) and Wu and Gschwend (1988) obtained analytical expressions for the first-order rate constant of a two compartment model in terms of diffusion model parameters. The expressions obtained depended not only on the system’s physical characteristics but also on the duration of the sorptiondesorption experiment. The first-order rate constant must vary with time to account for the time-dependent concentration gradients within the diffusioncontrolled regime. In soil column experiments, the contact time between solute and sorbent depends on mobile phase velocity. Rao et a1 (1980b) predicted that first-order rate constants used to describe sorption in soil column studies should vary with mobile phase velocity and such results have been observed. The simplicity of first-order kinetics, the uncertainties found in experimental data, the applicability of the two-compartment model to a variety of mechanisms, and the inherent difficulty in defining soil microgeometry often make the first order model an acceptable alternative to describe sorption kinetics.

3. Irreversible Sorption: Aging and Bound Residue Formation The longer nonpolar organic compounds remain sorbed to soil, the more time and/or energy is required to fully remove them (Steinberg et al., 1987; Pignatello, 1990a,b; Pavlostathis and Mathavan, 1992). It has been suggested that this phenomenon, termed aging, may result from very slow desorption kinetics as discussed previously, bound residue formation, or physical trapping of nonpolar organic compounds in soil (Pignatello and Xing, 1996; Calderbank, 1989). Although different theories of sorption kinetics predict that the time for desorption will increase the longer a pollutant remains in the soil, ultimately all of the pollutant is assumed to desorb. Pollutants that are physically trapped or fixed by strong chemical bonds to soil particles will never, for all practical purposes, desorb without some change in soil structure or a chemical reaction. Most existing models describing sorption and desorption do not include an irreversibly sorbed pool. The best understood category of irreversible sorption is formation of strong ionic or covalent bonds, also known as bound residue formation. This topic has been reviewed by Calderbank (1989) and Khan (1982). Bound residues may be formed through ionic bonds between clay and cationic pesticides such as diquat and paraquat. Bound residues are formed through covalent bonds between organic matter and compounds such as amines and phenols, certain urea and anilide

12


pesticides that are readily converted into aromatic amines, and phenolic and quinone residues that are created by transformation of phenoxy pesticides. Many commonly occurring extracellular oxidases and certain minerals in soil can catalyze these reactions (Scheunert and Mansour, 1992). The phenomenon of physical trapping is not well understood. Examples of chemicals subject to physical trapping include aliphatic halocarbons such as ethylene dibromide, trichloroethylene, and others (Pignatello, 1990a,b). Portions of these chemicals can be released and made bioavailable by pulverization of soil aggregates. Reduced bioavailability has also been observed for other pesticides and pollutants, such as simazine, phenanthrene, and styrene, that have had long-term contact with soil (Scribner et al., 1992; Hatzinger and Alexander, 1995; Scow et al., 1994). Many hypotheses for aging have been suggested (Pignatello and Xing, 1996);however, more research on the topic is needed. Aging, its link to natural carbon cycling, and its effect on pollutant biodegradation will be discussed in more detail in a later section. Although a theoretical distinction may be made between chemicals that are irreversibly sorbed to soil and those that have extremely slow release kinetics, experimentally it is difficult to distinguish between them. Generally, the issue is not addressed in the literature. A chemical is considered “bound” or “aged” if it is not removed from soil by a specified solvent extraction method or if a bioassay indicates that it is not bioavailable. Why the fraction remains in soil is usually not known. A second consideration is the practical relevance of the distinction. If the process of interest occurs on a time scale much shorter than a chemical’s desorption kinetics, it matters little if the chemical is irreversibly or reversibly bound.

111. BIODEGRADATION OF SORBED CHEMICALS The biodegradation of a sorbed chemical is actually a coupled process that includes a biological component, i.e., the metabolism of the chemical, and a physical/chemical component, i.e., the distribution and movement of the chemical in the physical environment in relation to the microbial population able to degrade it. The relative importance of these processes depends on how strongly sorbed and how rapidly degraded is the particular compound in a given soil. The biological component of biodegradation of sorbed chemicals is usually described very simply; however, this is only because of the limited number of kinetic expressions that have been developed to model metabolism or substrate disappearance. The most commonly used forms are the Monod (for growthlinked metabolism) and Michaelis-Menten (for non-growth-linked metabolism) equations, and the simple expressions of first- or zero-order kinetics that can be derived from both equations (Table I) (Simkins and Alexander, 1984; Alexander


13

Table I Monod-Derived Biodegradation Kinetics Model

Equation

Monod

Michaelis-Menten

dCldt =

v,,,,xc ~

(K,,, + C )

k,C

First order

dCldt

Zero order

dCldt = k,,

=

Note. C . substrate concentration (mglliter); 1. time (days); u,,,,,,maximum specific growth rate ( I /day); B. biomass concentration (mglliter biomass); Y. yield coefficient (mglliter biomass produced per mg/liter substrate degraded): K,, half saturation constant for growth (mg/liter); V,,,,,. maximum reaction velocity (mglliterl day); K,,,.Michaelis-Menten constant (mglliter); k , , first-order biodegradation rate constant ( I /day); k,,, zero-order biodegradation rate constant (mg/liter/day). Units of mglliter refer to substrate concentration unless otherwise specified.

and Scow, 1989). Whether these are the best expressions for describing metabolism has been questioned (Baveye and Valocchi, 1989); however, surprisingly few alternatives have been developed and these will not be discussed further in this chapter. It is important to recognize, however, that equations describing metabolism disregard a large number of the biological interactions that actually occur within the soil. Thus, the phenomena described are usually limited to the uptake of the pollutant (Alexander and Scow, 1989), uptake of other limiting nutrients (Celia ef al., 1989), and may include a decay rate or maintenance requirements of the microbial population (Celia et af.,1989). Not considered is the biodegradation of the same chemical by multiple microbial species, predation of biodegrading populations, competition between biodegrading and other organisms for the same resources, concentration dependency of growth and metabolism, and other issues (Schmidt, 1992). In this section, we explore different approaches for considering the effect of sorption on biodegradation rates. Effects of pollutant sorption on microbial ecology and metabolism will not be discussed except in terms of pollutant bioavailability. Models will be grouped according to how they represent sorption kinetics. This discussion will thus mirror the description of sorption kinetics


14

Table I1 Coupled Process Models under Batch Conditions Physical model

Equilibrium sorption coupled to biodegradation Equilibrium sorption

Compartment models First-order mass-transfer rates Continuous distribution of firstorder rates Radial diffusion models Radial diffusion coupled with linear sorption

Biological model

Examples

First order or Michaelis-Menten

Mihelcic and Luthy (1991), Stem et al. (1980), Miller and Alexander (1991), Guerin and Boyd (1992), Ogram et al. (1985)

First order

Hamaker and Goring (1976), Scow et al. (1986) Gustafson and Holden (1990)

First order

First order or Michaelis-Menten

Scow and Hutson (1992). Chung et al. (1993), Mihelcic and Luthy (1991). Rijnaarts et al. (1991)

presented previously. Table I1 contains a summary of the different approaches used to model coupled sorption/desorption and biodegradation processes.

A. COUPLED PROCESS MODELSUNDER BATCHCONDITIONS 1. Equilibrium Sorption

If a constant equilibrium is maintained between a dissolved substrate available to microorganisms and its sorbed, unavailable form, sorption should affect biodegradation kinetics only by lowering the amount of substrate metabolized in each time step. This rule assumes, however, no concentration-dependent effects on enzyme induction or biodegradation rate constants. The same expressions utilized to describe biodegradation in well-mixed liquid culture (first-order, Michaelis-Menten, and Monod equations) apply in this case except that the dissolved concentration is reduced according to the applicable sorption isotherm expression. Kinetics resembling liquid culture biodegradation rates will also occur when sorption/desorption kinetics are very fast compared to microbial kinetics. Systems involving soluble compounds that exhibit relatively fast sorptioddesorption rates, sorbents with short diffusive path lengths, and low microbial metabolism rates could be expected to behave in this manner. Mihelcic and


1.5

Luthy ( I 99 I ) found that naphthalene biodegradation in anaerobic soil slurries was equally well described by a Michaelis-Menten equation, in which the solute phase was reduced according to a linear sorption isotherm, and by the more complex model coupling radial diffusion and biodegradation. Chemicals associated with colloidal organic matter or sorbed to mineral surfaces have a short diffusion pathway to reach the aqueous phase and, in such cases, their biodegradation can be well described if the solution concentration is corrected for equilibrium sorption (Steen er al., 1980; Miller and Alexander, 1991). It should be noted that chemicals bound to mineral surfaces can exhibit desorption-controlled biodegradation kinetics if chemical forces involved are sufficiently strong (Smith er al., 1992) An equilibrium sorption-biodegradation model for evaluating biodegradation kinetics in soil suspensions was developed by Guerin and Boyd (1992, 1993). Initial degradation rates were compared with predicted biodegradation rates based on the equilibrium aqueous naphthalene concentration in soil slurries. If sorbed naphthalene was unavailable to microbes and desorption was slow, initial degradation rates should equal those in soil-free cultures with the same aqueous naphthalene concentration. Rates higher than the predicted response were assumed to indicate the ability of organisms to use a portion of the sorbed pool of chemical. Another modeling approach that assumes equilibrium sorption of the pollutant is that of Ogram er al. (1985). They developed a family of simple models coupling linear sorption partition coefficients and first-order biodegradation kinetics to predict the biodegradation of a sorbed chemical under different conditions. With regard to the chemical, either the dissolved phase only or both the dissolved and sorbed phase was assumed to be metabolized. With respect to microbial population distribution, organisms were assumed to be either attached or not attached. The model that best described the biodegradation of 2,4-dichlorophenoxyacetic acid ( 2 , 4 - ~ by ) a pure culture of bacterium in soil suspensions assumed that only the dissolved phase was used and that both attached and suspended organisms were responsible for degradation.

2. First-Order Approximation to Sorption Kinetics and Two-Compartment Models As discussed previously, first-order mass-transfer expressions may be used to model sorption kinetics. In such models, the soil is divided conceptually into compartments that are considered homogeneous and well mixed. The model specifies which compartments may exchange chemicals and lists the equations controlling these exchanges. Models such as that described previously are well known in the fields of pharmacokinetics and environmental modeling and are referred to as compartment models (Godfrey, 1983). In the models considered in

16


this chapter, transfer between compartments is either controlled by first-order rate expressions or is instantaneous if the compartments are assumed to maintain an equilibrium distribution. First-order compartment models usually describe the movement of solute between compartments by a set of differential equations (Fig. 4) for which standard methods exist to find solutions (Simmons, 1972). The resulting expression for solute concentration in any compartment as a function of time has the form of a sum of exponential terms. The parameters in the model solutions are actually complicated functions of the rate constants used in the model and the initial substrate concentrations in the various compartments. It is not always possible to solve for the model rate constants from the parameters obtained by fitting the model solution to experimental data. Figure 4 illustrates the equations and solution for the rate of product formation using the soil biodegradation model proposed by Hamaker and Goring (1976). Compartment sorption models coupled to first-order biodegradation kinetics have also been used to explain biodegradation kinetics in soil. Figure 5 presents several examples of compartment models that have been proposed to describe biodegradation kinetics in batch systems (Scow et al., 1986; Hamaker and Goring, 1976; C. R. Johnson and K. M. Scow, unpublished data). The solution for the rate of product formation has the same form for each of these models; thus, they are indistinguishable from observations of biodegradation kinetics alone. Model I (Fig. 5) couples the sorption kinetics model proposed by Karickhoff (1980) to first-order metabolism kinetics for solute in the dissolved phase. Hamaker and Goring (1976) used model I1 to describe the biodegradation rates of Triclopyr (2,3,5-trichloro-2-pyridyloxyacetic acid) in two soils. In Hamaker’s model, the dissolved and equilibrium sorbed phases of Karickhoff’s model are lumped into one labile compartment that exchanges solute with a resistant compartment according to first-order kinetics. According to Hamaker, rapid exchange between dissolved and equilibrium sorbed chemical makes all material in the labile pool available to microbes. “The rate of degradation for the labile pool is, therefore, considered as a single reaction, even though it is internally complex” (Hamaker and Goring, 1976). Hamaker and Goring chose first-order kinetics to describe transfer to and from the labile and resistant pools partially due to the mathematical simplicity of the form and partially because first-order kinetics describes systems that contain small amounts of substrate relative to large initial population densities of microorganisms. Figure 6 shows the effect on biodegradation kinetics for varying ratios of k , and k2 in the Hamaker model. If the initial pollutant concentrations in the labile and sorbed phases are known, all model rate constants may be determined by fitting the model to experimental data (Hamaker and Goring, 1976). Scow et al. (1986) used model III (Fig. 5) to describe mineralization kinetics for phenol, aniline, and nitriloacetic acid in soil. This model is similar to Hamaker’s except that biodegradation is possible from


17

Definina Eauat ions

a = klC2 - Cl(k2 + kb) dt = kzC1

-

klC2

dt Solution for m t e of Product Evolution

he = Aexp(mlt) + f3exp(m2t) dt

If at time t = 0 , P = 0 , C2 = 0 , and C1 = C, then: A = kbG(m1 + k l ) (m, - m 2 )

B = kb$(m, (m2

+ kt) - mi)

FFgure 4 Hamaker’s two-compartment biodegradation model (adapted from Hamaker and Goring, 1976). Concentrations expressed per total soil mass.

both Compartments. The compartments are not identified as labile or nonlabile prior to fitting the experimental data. As discussed previously, many different two-compartment models lead to biodegradation rates that have the form of a sum of two exponential terms. Biodegradation rates best described by this form have been reported by Hyzak and Zimdahl (1974) for three triazine herbicides at 35°C and one triazine herbicide at 20°C and by Zimdahl and Gwynn (1977) for three dinitroaniline pesti-

18

Ei-H.-.l-oduct K. M. SCOW AND C . R.JOHNSON

Aqueous

kb

Model 1, Johnson & Scow, unpublished data

Resist ant

Model II, Hamaker & Goring, 1976

-

Phase One

a1

Phase

Two

a,

Model 111, Scow et a/., 1986

Figure 5 Coupled sorption/biodegradationcompartment models (adapted from Hamaker and Goring, 1976; Scow et al., 1986, C . R. Johnson and K. M.Scow, unpublished data).

cides at 30°C. Interestingly, these pesticides exhibited first-order biodegradation kinetics at lower temperatures. The shift from first-order to two-compartment kinetics at higher temperatures (and higher degradation rates) is consistent with data generated in our lab that show a shift from first-order to two-compartment kinetics as phenanthrene degradation rates increase (C. R. Johnson and K. M. Scow, unpublished data). Smith et al. (1992) observed biphasic biodegradation kinetics for a bacterium using quinoline in a montmorillonite clay suspension. Mineralization kinetics could be described by dividing the mineralization curve into two sections each with its own first-order rate constant. Quinoline binds to clay surfaces via interactions of the molecule’s lone pair nitrogen electrons and TI elections in its


19

aromatic rings. Smith postulated that in this case chemical reaction rates, rather than diffusion kinetics, controlled desorption rates and limited biodegradation kinetics during the second section of the mineralization curve. Guerin and Boyd, (1992,1993)compared naphthalene degradation kinetics for two bacterial stains, Afcafigenes NP-ALK and Pseudomonas purida (ATCC 17484). As discussed previously, a coupled equilibrium partitioning-first-order biodegradation model was used to evaluate differences in the strains’ ability to uti-

100

k (decamp.) = 0.0152 (tl,2decomp. = 4 5 . 6 d a y s )

70

50

40

30

20

10

I

50

100

\ I

150

I

200

\I

250

I

300

J

350

Rgure 6 Effect of varying k , and k, on Hamaker model biodegradation kinetics. R = k, (binding) / k , (unbinding). k - and k , in figure equal k , and k, in text (from Hamaker and Goring, 1976, with permission of the publisher).

,

20


lize sorbed naphthalene. Strain 17484 could utilize sorbed naphthalene, whereas strain NP-ALK could not and differences were observed in the shape of the biodegradation curves for the two species. Naphthalene mineralization in soil slurries followed first-order kinetics for strain NP-ALK, whereas a biphasic pattern was observed for strain 17484. Guerin and Boyd interpreted these results in terms of Karickhoff’s two-compartment sorption model. Strain 17484 was hypothesized to have access to naphthalene in the labile sorbed compartment, whereas strain NP-ALK did not. Utilization of the labile sorbed naphthalene stimulated desorption of resistant sorbed naphthalene that could then be utilized by strain 17484. Initial biodegradation rates for strain 17484 were also measured as a function of naphthalene-soil contact time prior to inoculation with the bacterium and these results were also consistent with a two-site sorption model. Initial rates decreased as naphthalene incubation time prior to inoculation increased; however, eventually most of the aged naphthalene was degraded. During incubation prior to inoculation, chemical was transfered from the labile to resistant sorbed phase after which, being unavailable to stain 17484, it lowered the initial mineralization rate. A consequence of the utilization of the remaining labile sorbed naphthalene by strain 17484 was the transfer of naphthalene from the resistant sorbed to the labile compartment. Guerin and Boyd (1993) also pointed out that the behavior of strains NP-ALK and 17484 is consistent with the intraparticle and intraorganic matter diffusion theories proposed to explain observed sorption kinetics in soil. In a treatment very similar to that of Connaughton et al. (1993),Gustafson and Holden ( 1990) represented pesticide biodegradation rates by assuming soil contained a continuum of spatially segregated compartments each exhibiting firstorder dissipation kinetics. A probability function, the gamma function, was used to describe the distribution of compartments having the same first-order dissipation rate constant. Specifying the two parameters controlling the shape of the gamma function determined the distribution of fast and slow degrading compartments and thus the observed dissipation kinetics. Analysis of 45 data sets of pesticide dissipation, in lab and field studies, indicated that the gamma function model usually fit better than a first-order decay model. The model reduces to a first-order decay equation when the rate constants approach uniformity. The relative variability in the rate constant was similar for data describing pesticide disappearance at the scale of both the flask and the field. Thus, the authors speculated that the length scale of the variability was at the pore scale and perhaps due to sorption and/or diffusion processes affecting the availability of the pesticide for degradation. The homogeneous well-mixed boxes portrayed in compartment models may seem to have little relationship to the heterogeneous soil environment. To relate the model to reality, one needs some method to experimentally quantitate the


21

amount of chemical in labile and resistant compartments. The measurement method itself then serves as an operational definition of these pools. Cheng ( 1990) has suggested developing chemical fractionation techniques to characterize soil organic matter and pollutant residues into pools of differing bioreactivity or bioavailability. Burford et al. (1993) suggested that differences, with regard to extraction kinetics and efficiencies using supercritical fluid extraction, between recently added and aged PAHs might be useful in exploring the locations and interactions of sorbed chemicals within the soil matrix. Several studies have attempted to relate different pools of a pollutant in soil, as defined by chemical extraction methods, to their biodegradability. For styrene in soil, pools defined included a readily desorbed, not readily desorbed but extractable, and not desorbed nor readily extractable fraction (Fu er al., 1994). Robinson et al. (1990) found that greater than 90% of toluene in soil slumes was readily extractable by water and the remaining 10% was not extractable by water and partially extractable by an organic solvent. These two pools appeared to correspond to the biodegradation kinetics with removal of an initially rapidly degraded, large fraction of the toluene, followed by removal of a slowly degradable, smaller fraction of toluene. Weissenfels er al. (1992) used solvent extraction to remove the nonbiodegradable fraction of PAHs from a high organic carbon soil. They were then able to show that the fraction was actually bioavailable, in the absence of the mass-transfer limitation imposed by the soil, as evidenced by its metabolism by a mixed culture of microorganisms in solution culture. The use of chemical fractionation techniques to measure the amount of pollutant in different soil compartments is flawed because these methods often alter organic matter structure and thus the results are difficult to interpret. Relating conceptual compartment models to measurable quantities in soil is also a challenge faced by scientists studying natural carbon and nutrient flows in soil systems. Similar methods, curve fitting and chemical fractionation, as well as density and size fractionation have been used to define natural soil organic carbon pools. Results of these studies are discussed under Section 1V.

3. Coupling Radial Diffusion with Sorption and Biodegradation Several investigations have combined radial diffusion models describing the kinetics of sorption and desorption, such as that of Wu and Gschwend (1986), with biodegradation kinetics. This approach has been used to describe the metabolism of chemicals limited by mass transfer in artificial aggregates (Scow and Hutson, 1992) and soil (Mihelcic and Luthy, 1991; Rijnaarts et al., 1991). All studies made some attempt to compare predictions using the radial diffusion and less complex models, such as those described previously, to identify conditions when the more complex approach was necessary.


22

Coupled radial diffusion-biodegradation models (Scow and Hutson, 1992; Mihelcic and Luthy, 1991) describe biodegradation kinetics in a saturated slurry containing spherical porous aggregates. Figure 7 depicts the processes and their spatial locations within the aggregate-solution system (Mihelcic and Luthy, 1991). The assumptions about diffusion and sorption have been described previously. Microorganisms, and thus biodegradation, are assumed to occur only outside of aggregates in a well-mixed outer solution; evidence for this assumption is discussed below. Biodegradation is described by the Michaelis-Menten or Monod equations, which can reduce to first-order kinetics. Simulations using coupled radial diffusion-biodegradation models have successfully described experimental data in several cases (Scow and Alexander, 1992). The biodegradation of phenol by a pure culture of Pseudomonas sp. in the absence and presence of two sizes of clay aggregates was measured in an experimental system in which all input parameters could be determined by measure-

metabolism of aqueous-phase

associated solute;

solute sorbed along micropore,

solute in micropore

solute in mocropore

I Rgure 7 Multiple processes occurring during the biodegradation of a chemical distributed between soil solution and an aggregate. Microorganisms are excluded from the pores inside the aggregate and a local equilibrium exists between the solute in the aggregate and that sorbed onto micropore surfaces (from Mihelcic and Luthy, 1991, with permission of the publisher).


23

ment. Using measurements of the mass-transfer coefficient, first-order rate constant, and the physical system, model simulations compared well to experimental data for both sizes of clay aggregates and at two initial population densities. Model simulations, again using independently measured input parameters, also compared reasonably well to data describing phenol biodegradation by a pure culture of Pseudomonas sp. in the presence of different amounts of polyacrylamide gel exclusion beads that sequestered part of the solution from bacteria. Model simulations were performed to identify conditions under which Scow er a l . 3 (1986) two-compartment model or a first-order model could adequately describe biodegradation data generated by the more complex coupled radial diffusion-biodegradation model (Scow and Hutson, 1992). With a small aggregate radius (0.05 cm) and low sorption partition coefficient ( 5 1 0 dm3 kg-I), first-order kinetics provided a reasonable fit to the biodegradation data in the presence of aggregates. In the presence of larger aggregates (0.25 cm), even at a low sorption partition coefficient, biodegradation curves were clearly biphasic and better fit by the two-compartment rather than the first-order model. Biodegradation was overestimated when long-term predictions of the chemical’s persistence in 0.25-cm aggregates were calculated based on a first-order half-life fit to the first part of the biodegradation curve. Half-lives in the presence of aggregates were substantially greater than equivalent half-lives in the absence of aggregates when the sorption partition coefficient was 2 3 . 2 and the aggregate radius was 20.32 cm. The radial diffusion-biodegradation model developed by Scow and Hutson (1992) was adapted to include nonlinear conditions for adsorption and a masstransfer term across the surface of the aggregate (Chung et al., 1993). Also, for use in describing the behavior of the model under different conditions, four dimensionless groups were derived from model equations and are presented in Table 111. Sensitivity analyses were performed using the various dimensionless groups to determine the dependence of a chemical’s biodegradation kinetics on the chemical, physical, and biological parameters comprising the model (Chung et al., 1993). The overall rate of biodegradation of a chemical is a function of (i) its rate of mass transfer across the surface of the aggregate, as determined by the number of aggregates, their surface area, their volume, and the mass-transfer coefficient; (ii) its diffusion within the aggregate, as determined by its sorption partition coefficient, diffusion coefficient, the aggregate’s radius and internal porosity, and the tortuosity term; and (iii) its intrinsic rate of biodegradation. Different shapes of biodegradation time-course curves were observed depending on whether the reaction was controlled by mass transfer or the reaction rate as well as on whether the chemical started out sorbed (inside the aggregate) or not sorbed (in the outside solution) (Fig. 8). As seen from Table 111, an increase in $ can result from an increase in sorption


24

Table 111 Dimensionless Parameters Describing Coupled Radial Diffusion/Biodegradation Model(’ Dimensionless group

Definition J[E

+ K(lD;

Rk,

Dc U

kRZ

Diffusive resistance Biological rxn Transfer velocity into aggregate Biological rxn

11

Bi

E)]

Comment

Film diffusion lntraaggregate diffusion Initial chemical distribution within system

Note. E , aggregate porosity; K. adsorption partition coefficient (cm3/cm3 solid); R . aggregate radius (cm); k. biodegradation first-order rate constant; D,. effective diffusion coefficient in aggregate pores (cm2/min); n . number of aggregates per volume liquid (cm-3); v p , aggregate volume (cm2);a,,, surface area per volume aggregate (cm- 1); k,., mass-transfer coefficient from aggregate to external fluid (cmlhr); C,,, initial chemical concentration inside aggregate ( g i c d ) ; C,,,, initial chemical concentration outside aggregate (g/cm3). “From Chung e r a / . (1993).

partition coefficient or aggregate radius, or from a decrease in the diffusion coefficient (Fig. 8). If biodegradation is rapid relative to the mass-transfer rate (q = O.l), there is little impact of sorption on the rate of degradation of a chemical starting out in the solution phase (u = a);however, sorption is more important if the chemical is initially completely sorbed (u = 0). For chemicals starting out in solution and with slower degradation rates relative to the mass-transfer rate (q = l.O), initially there is a brief and rapid rate of biodegradation of the chemical available in solution. Once most of the solution concentration has been depleted by biodegradation or diffusion into the aggregate, there is a much slower rate of biodegradation controlled by mass transfer of the chemical back out of the aggregate. These kinetics are similar to the biphasic mineralization curves described by two-compartment kinetics, as discussed previously. Scow and Hutson (1992) found that curves of this shape could be fit by a two-compartment model. When the chemical starts out entirely in the sorbed phase, an increase in the sorption partition coefficient changes the kinetics of biodegradation from first order to zero order, at least within the time period shown on the graph.

1 .o

0.8 0.6 0.4

0.2

0.0

0 , dimensionless time Figure 8 Dimensionless mineralization (@) vs dimensionless time (8)for different values of dimensionless parameters Q.q, and u. Bi value fixed. Coupled radial diffusion/biodegradationmodel with an additional diffusive resistance at the aggregateiouter solution boundary. @, fraction substrate mineralized; 8 = kr; k, first-order biodegradation rate constant (l/day). See Table I1 for dimensionless parameter definitions (from Chung et al.. 1993, with permission of the publisher).

26


Mihelcic and Luthy (1991) used a coupled radial diffusion-biodegradation model to describe the biodegradation of naphthalene under denitrification conditions in soil-water suspensions under well-defined conditions. As mentioned previously, predictions of the more complex model were compared to a simple model coupling equilibrium desorption with Michaelis-Menten kinetics. Both models could describe the experimental data and this indicated that intraparticle diffusion of naphthalene was rapid compared to the rate of microbial degradation. Rijnaarts et al. (1991) used a radial diffusion model coupled to first-order biodegradation kinetics to evaluate data describing the effect of desorption and intraparticle mass transfer in a well-mixed slurry containing soil from a waste site that had been contaminated with a-hexachlorocyclohexane 20 years previously. Desorption and biodegradation were measured in a stirred (well-mixed) bioreactor and end-over-end (less well-mixed) bioreactor. Because the starting conditions for this study were unknown, unllke the more controlled studies described previously, it was not possible to directly measure many of the model input parameters. Therefore, some values were obtained by curve fitting or calculated from published values. Desorption kinetics in the stirred reactor could be described equally well by the radial diffusion model or a simple mass-transfer equation; however, desorption in the less well-mixed system could only be described by the radial diffusion model. The coupled radial diffusion-biodegradation model described biodegradation data in both systems; however, suprisingly, a first-order decay equation fit the biodegradation data equally well. Penetration of bacteria into the aggregates may have occurred that would decrease the diffusion path length, lessening the importance of mass-transfer limitations, and possibly resulting in first-order kinetics being sufficient to describe the phenomenon. To better understand interrelationships between soil structure and microbial processes in soil, Priesack (1991) derived a radial diffusion model simulating solute diffusion and biodegradation in which microorganisms are present inside but not outside of the aggregates. The model equations could be solved analytically for the case of linear adsorption and logarithmic growth of microorganisms. In model simulations, he found that the diffusing chemical did not reach the centers of aggregates because it was depleted in the outer layer by biodegradation. Also, the sizes of the unaffected centers increased as the microbial growth rate increased, as the diffusion coefficient decreased, or as the sorption partition coefficient was decreased. Priesack and Kisser-Priesack (1993) compared model simulations to experimental data describing glucose mineralization in synthetic soil aggregates of 1.8-cm diameter. Patterns of chemical disappearance as well as biomass distributions supported the idea that the inner zone of the aggregate was an area of low activity and population density, which the model explained as being due to depletion of glucose before it reached the center.


27

Dhawan et a f . (1991, 1993) developed a radial diffusion model coupling diffusion, sorption, and biodegradation in soil for use in site characterization and screening of bioremediation treatment strategies. The model assumed that microorganisms are present inside of the aggregates; however, there was no evidence provided to support this assumption. Biodegradation was described by Monod kinetics in which the reaction was dependent on chemical, biomass, and oxygen concentrations. Model simulations for the case in which the chemical was initially distributed inside the aggregate indicated that most of the chemical was degraded before it could diffuse outside of the aggregate. The region within the aggregate sustaining substantial microbial activity coincided with regions containing sufficient concentrations of oxygen and pollutant substrate. This active region moved toward the center of the aggregate over the course of biodegradation.

4. Dissolution from Solids and NAPLS Similar to the approaches used to model sorption-limited and other types of mass-transfer-limited biodegradation, coupled process models have also been formulated to describe biodegradation of pollutants in solid form or dissolved in nonaqueous phases such as organic solvents. The transfer of phenanthrene from crystalline solid form, or from silicone oil or heptamethylnonane, to a solution in which it could be degraded by Pseudomonas sp. was measured and compared to predictions using a coupled model (Bouchez et a f . , 1995). Growth was measured by oxygen consumption that was found to be representative of phenanthrene disappearance, biomass increase, and carbon dioxide evolution. There was an initial phase of exponential growth with the same specific growth rate for the bacteria in all three systems. The second phase of growth was system dependent and virtually identical in rate to the masstransfer rate determined under abiotic conditions for each system (Bouchez et al., 1995). Volkering et al. (1992) developed a model describing biodegradation of PAHs in solid form or in a sorbed phase. They described growth by a Monod expression and dissolution by a first-order mass-transfer equation, as described previously. The model predicted that with a low initial cell density, growth would first be logarithmic until PAH availability became limited by mass transfer from the solid to solution phase. When the cell density was high (e.g., after growth), the model predicted linear growth that could not exceed the mass-transfer rate. Experiments with a mixed bacterial culture in batch systems growing on crystalline naphthalene found that bacterial growth was initially exponential and then linear, as the model predicted. Unfortunately, mass transfer of the chemical from the solid to solution phase was not measured to test whether the linear rate was proportional to the mass-transfer rate. The rate of the second phase of growth

28


was found to be inversely proportional to the diameter of naphthalene particle and thus appeared to be related to surface area; however, this was not tested using model calculations. For all three PAHs, the maximum specific growth rate was proportional to the saturation concentration (in mineral salts medium).

B. COUPLING ADVECTION, SORPTION, AND BIODEGRADATION COLUMNSTUDIES The studies described previously concern biodegradation and sorption occurring in batch systems in which it is often difficult to measure biodegradation and abiotic processes independently. In column studies, it is possible to determine the mass-transfer kinetics independently of biodegradation by measuring breakthrough curves in sterile soil columns and by observing the behavior of nonsorbing tracer chemicals (typically 3H,O and C1-) in the system. An in-depth discussion of coupled advection/sorption/degradationmodels is beyond the scope of this review. In the same manner that first-order biodegradation kinetics is coupled to abiotic sorption models, most models used to predict biodegradation in soil column studies couple first-order biodegradation kinetics to existing two-site and two-region models described previously. Table IV presents a list of some studies that have compared experimental results to coupled

Table IV Column Studies Utilizing Coupled Advection/Sorption/BiodegradationModels Chemical used 2.4.5-T"

Sorption model Two region

Biodegradation model First order

2,4,5-T"

Multiprocess First order nonequilibrium Alkylbenzenes Two site First order

Quinoline

Nonequilibrium"

First order

2.4-0'

Two site

First order or Monod

Comment

Reference

Biodegradation rate constant fitted No parameters fitted

Gamerdinger er a/. (1990) Brusseau er a / . ( 1992) Biodegradation parame- Angely er a / . ters from mass( 1992) balance approach Biodegradation parame- McBride er a / . ters from mass bal(1992) ance approach Saturated and unsatuEstrella er a / . rated systems ( 1992)

tartarate > acetate and chloride. From this they concluded that the dissolution of Si in soil is increased by chelation, which helps to lower the effective concentration of silicate fixers such as A1 and Fe in the soil. Recognizing the potential role of A1 and Fe compounds in the dissolution kinetics of Si in soil, Kawaguchi et af. (1958) and Kawaguchi and Matsuo (1958) probably have proposed concurrent examination of the Si:Fe and Si:AI ratios and the amounts of Si extracted by the acetate buffer or 0.5 N HCl for evaluating Si-supplying power of the soil. Khalid et u1. (1978) used both waterextractable Si and Si extractable by 0.1 M acetic acid (pH 3.5) containing 50 mg P/liter as Ca(H,PO,), to study residual Si in soils after application of calcium silicate slag. The assumption was that water-extractable Si is a measure of the solution concentration at near equilibrium with the soil system (an intensity factor), whereas the acidified phosphate-extractable Si is an index of the amount of Si that remains in the soil in an adsorbed form (a capacity factor). Thus, both methods of extraction may provide different but useful information. Imaizumi and Yoshida (1958) put forward a criterion for Si fertilizer application that states that if the extractable Si (sodium acetate buffer, pH 4.0) content in the soil is less than 4.9 mg Si /lo0 g soil, beneficial effects of a silicate fertilizer application may be observed. The acetate buffer method has been used in Japan and Taiwan and, with some modifications in the extraction ratio and time and temperature of shaking, for estimating availability of Si in soils for rice plants in Korea (Kim et al., 1971; Park, 1976; Lian, 1976). The acetate buffer is made by diluting 49.2 ml acetic acid and 14.8 g anhydrous sodium acetate to 1 liter and adjusting to pH 4.0 with acetic acid or sodium acetate (K. Nonaka, personal communication). Unfortunately, it appears that no papers have been published in English that specifically describe the development and use of this buffer. The sodium acetate buffer-extractable Si content of calcareous soils of China ranged from 7.1 to 18.1 mg Si/100 g of soil and still rice yields responded to the application of silicate fertilizer to these soils (Liang et al., 1994). Therefore, it seems that the buffer probably overestimates available Si in calcareous soils under field conditions, and there is a need to develop a suitable soil test (probably extractant) for calcareous paddy soils. In general, criteria for using acetate buffer-extractable Si to determine the need for Si application to tropical rice soils have not been adequately investigated.

181

SILICON MANAGEMENT

In India, Nayer et af. (1977) compared N sodium acetate buffer (pH 4.0) with three other chemical extractants (distilled water, 0.2 N HCl, and 0.025 M citric acid) and reported the extracting power of the reagents for Si as: 0.2 N HCl > 0.025 M citric acid > N acetate buffer > water with some exceptions in some soils. In a greenhouse experiment, they found that the 0.025 M citric acidextractable Si in soils showed better correlation with the Si uptake by the rice plants (var. Jaya). For tropical rice soils of Malaysia and Thailand, Kawaguchi (1966) used 3.3 mg Si/IOO g soil and for those of Sri Lanka, Takijima et al. (1970) used 3.8 mg Si/ lOOg soil as tentative criteria of acetate buffer-extractable Si for describing Si deficiency. Dependable criteria for describing Si deficiency in tropical and subtropical soils are not seen in the literature and need investigation (see Section VIII). IRRI workers observed a positive relationship between the Si content of flooded soil percolates and the Si content of rice plants (IRRI,1965). According to Lian (1976), therefore, a percolation method may be more realistic because it will directly measure the Si content in the submerged soil, whereas the acetate buffer method estimates the available Si indirectly in air-dried soil samples. A Si soil test based on extraction with 0.5 M acetic acid has been developed by Snyder (1991) for identifying the need for Si fertilization of Histosols planted to rice in the Everglades Agricultural Area in Florida. This soil test has not, however, been adequately evaluated for soils previously amended with calcium silicate. For improving the dependability and scope of the chemical soil tests for available Si to rice plants, two points relative to soil samples to be used merit consideration: (i) Decreased redox potential (Eh) of flooded lowland rice soil increases water-soluble Si, and therefore air-dried soil samples with high redox potential may not truly represent the soil environment in which rice roots have to absorb Si; (ii) most of these extractants are likely to underestimate the need for Si fertilization of soils that have been previously amended with slags. This is because Si extracted by the acetate buffer evidently increases in soil treated with slag (Nonaka and Takahashi, 1990). It seems that the “too strong acetate buffer dissolves some nonavailable Si from the slag previously added to soil. In order to address these issues, Nonaka and Takahashi (1988, 1990) developed a method for measuring water-soluble Si in rice soil that involves flooded soil incubation. With this method, a 10-g dry soil sample (air-dried and 6.1% Si in straw in Japan and Korea, and 5.1% Si (for both the first and second rice crops) in Taiwan. For >5% additional yield, the critical percentage of Si in straw is

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