Advances in Agronomy, Volume 47

DVANCES I N

igronomy

V O L U M E 47

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

S. H. Anderson L. P. Bush R. N. Carrow

M. A. Tabatabai, Chairman G. L. Horst R. J. Lwmoore R. H. Miller

G. A. Peterson

c.w. Stuber S. R. Yates

D V A N C E S I N

onomy VOLUME 47 Edited by

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

ACADEMIC PRESS, INC. Harcourt Brace Jovanovich, Publishers San Diego New York Boston London Sydney Tokyo Toronto

This book is printed on acid-free paper. @

Copyright 0 1992 by ACADEMIC PRESS, INC. 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.

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United Kingdom Edition published !y

Academic Press Limited 24-28 Oval Road, London NWI 7DX Library of Congress Catalog Number: 50-5598 International Standard Book Number: 0-12-000747-9 PRINTED IN THE UNITED STATES OF AMERICA 9 2 9 3 9 4 9 5 9 6 9 7 BC 9 8 7 6 5 4 3 2

1

Contents CONTRIBUTORS ............................................... PREFACE......................................................

ix

xi

THE EFFECTS OF ACIDIC DEPOSITION ON FORESTED SOILS Wayne P. Robarge and Dale W.Johnson I . Introduction ............................................. 1 I1. Soil Acidification ......................................... I11. Forest Soils .............................................. IV. Case Studies of Soil Change ................................ V. FutureResearch .......................................... VI. Conclusions ............................................. References ..............................................

3 6 42 63 65 66

FINGERPRINTING CROP VARIETIES J . S. C. Smith and 0. S. Smith I . Introduction ............................................. I1. Characters Used to Fingerprint Cultivated Varieties . . . . . . . . . . . . I11. Discriminational Ability of Fingerprinting Techniques . . . . . . . . . . Iv. Usage of Fingerprints ..................................... V. NewTechniques ......................................... References ..............................................

85 86 108 119 125 127

TRANSPORT OF CHEMICALS THROUGH SOIL: MECHANISMS. MODELS. AND FIELD APPLICATIONS William A.Jury and Hannes Fluhler I . Introduction ............................................. I1. Transport and Transformation Processes ...................... I11. Analysis of Process Assumptions ............................. Iv. Field Studies of Solute Transport ............................ V. Concluding Remarks ...................................... References .............................................. V

142 143 169 175 191 195

vi

CONTENTS

EVOLUTION OF CORN Walton C. Galinat I. Introduction: Teosinte Is the Wild Corn ...................... I1. Transformation by Domestication and Isolation . . . . . . . . . . . . . . . .

I11. Time Required for Transformation under Domestication . . . . . . . . IV Multiple Domestications in the Origin of Corn . . . . . . . . . . . . . . . . V. Interpathway Heterosis .................................... VI. Husk Enclosure of the Ear ................................. VII . Current Direction of Corn Evolution and Where It Is Going . . . . . VIII . Summary and Conclusions ................................. References ..............................................

203 205 212 214 221 222 224 227 229

USEOF SURFACE COMPLEXATION MODELS IN SOIL CHEMICAL SYSTEMS Sabine Goldberg I. Introduction ............................................. I1. Description of Models .....................................

I11. Application of Models to Protonation-Dissociation Reactions on Oxides. Clay Minerals. and Soils .......................... Tv: Application of Models to Metal Ion Adsorption Reactions on Oxides. Clay Minerals. and Soils ............................ V. Application of Models to Inorganic Anion Adsorption Reactions on Oxides. Clay Minerals. and Soils .......................... VI. Application of Models to Organic Ligand Adsorption Reactions onoxides ............................................... VII . Application of Models to Competitive Adsorption Reactions on Oxides ............................................... VIII . Incorporation of Surface Complexation Models into Computer Codes ......................................... IX. Summary ............................................... References ..............................................

234 235 251 274 289 308 312 319 320 32 1

CONTENTS

vii

MODELING THE TRANSPORT AND RETENTION OF INORGANICS IN SOILS H . M . Selim Introduction ............................................. Transport Equations ...................................... Equilibrium Retention Models .............................. Kinetic Retention Models .................................. Multiple-Reaction Models ................................. Transport and Ion Exchange ................................ Transport in Layered Soil .................................. References ..............................................

331 333 337 342 350 367 377 381

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

385

I. I1. I11.

N. V.

VI. VII .

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

HANNES FLUHLER (141), Institute of Terrestrial Ecology, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland WALTON C. GALINAT (203), Eastern Agricultural Center, University of Massachusetts-Amherst, Waltham, Massachusetts 02 1 54 SABINE GOLDBERG (233), USDA-ARS, U.S. Salinity Laboratory, Riverside, California 92501 DALE W. JOHNSON (l), Desert Research Institute, Reno, Nevada 89J12 WILLIAM A. JURY (141), Department of Soil and Environmental Sciences, University of California, Riverside, Riverside, California 92J21 WAYNE P. ROBARGE (l), Department of Soil Science, North Carolina State University,Raleigh, North Carolina 2 769s H. M. SELIM (3 3 I), Agronomy Department, Louisiana State University, Baton Rouge, Louisiana 70803 J. S. C. SMITH (85), Plant Breeding Division, Pioneer Hi-Bred International, Inc., Johnston, Iowa 50131 0.S. SMITH (85), Plant Breeding Division, Pioneer Hi-Bred International, Inc., Johnston, Iowa 50131

ix

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Preface Environmental quality and biotechnology are currently two major research areas in the crop and soil sciences. Thus it is appropriate that a substantial portion of this volume of Advances in Agronomy is concerned with these topics. Three chapters deal with aspects of environmental quality. Chapter 1 discusses the effects of acidic deposition on forest soils, emphasizing physical and chemical processes in forest ecosystems that interact with, modify, and respond to acidic inputs; presenting case histories of soil changes in regions where acidic deposition may be causal and in regions where it is not; and giving recommendations concerning future research that is needed to more accurately characterize and develop models for the effects of acidic deposition on forest soils. Chapters 3 and 6 examine modeling of organic and inorganic chemical transport in soils. Chapter 3 provides a detailed discussion on the mechanisms, models, and field applications of chemical transport in soils, with particular emphasis placed on the importance of approaches that accurately describe chemical transport in heterogeneous field soils. Chapter 6 describes the features of models that govern retention of inorganic solutes in soils. Single, multiple, and multicomponent or competitive models that are based on equilibrium or kinetics are presented. Many equilibrium-based models have been promulgated in the literature to describe reactions at the solid-liquid interface. One group of these models is microscopically based and is referred to as surface complexation models. These models are the subject of Chapter 5. This chapter critically reviews five common surface complexation models of the mineral-solution interface and their use in describing soil chemical systems. Common characteristics and adjustable parameters are covered, the application of models to ion adsorption on soil constituents and soils is presented, and the incorporation of surface complexation models into computer codes is discussed. Chapters 2 and 4 deal with the crop sciences. Chapter 2 is concerned with fingerprinting crop varieties. Topics that are discussed include evolution in the scope and technology of fingerprinting, characters used in fingerprinting cultivated varieties, discriminational ability of fingerprinting techniques, use of fingerprints, and new techniques. Chapter 4 deals with the evolution of corn. It critically evaluates previous research in this area and assesses current thinking on this very important crop. I deeply appreciate the fine contributions from these authors.

DONALD L. SPARKS xi

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THE EFFECTS OF ACIDIC DEPOSITION ON FORESTED SOILS Wayne P. Robarge' and Dale W. Johnson2 'Department of Soil Science, North Carolina State University, Raleigh, North Carolina 27695 ?Desert Research Institute, Reno, Nevada 895 12

I. Introduction 11. Soil Acidification 111. Forest Soils A. Physical Factors B. Chemical Factors IV.Case Studies of Soil Change A. Categories of Soil Change and Methods for Measurement B. Intensity-Type Changes C. Capacity Changes Due to Leaching D. Capacity Changes Due to Uptake E. Seasonal Variations in Exchangeable Cations F. Interactions of Uptake and Leaching V. Future Research VI. Conclusions References

I. INTRODUCTION Acidic deposition is composed primarily of N and S acid-forming compounds that undergo gas-phase oxidation and aqueous-phase reactions in the atmosphere to form nitric and sulfuric acids (Tanner, 1989). Partial neutralization of these acids by NH3 in the atmosphere results in H + , NH;, NO;, and SO:- being the dominant ions in acidic rainwater and

1 Advances m Apmny, Volunr 47 Copyighr Q 1992 by Academic Press, Inc. All rights of reproduction in my form reserved.

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WAYNE P. ROBARGE AND DALE W. JOHNSON

cloudwater (Weathers et al., 1988; Reisinger and Imhoff, 1989; Saxena et al., 1989; Sigmon et al., 1989; Aneja et al., 1991). Acidification of soils near point sources of N and S acid-forming substances is well known, thus one of the earliest concerns over acidic deposition occurring over a regional basis was the potential for soil acidification (Ulrich, 1980). Since then, a number of reviews have been written and models proposed to explain how acidic deposition can increase soil acidity (Ulrich et al., 1980; Reuss, 1983; van Breemen et al., 1983; Kohlmaier et al. , 1983/1984; Bloom and Grigal, 1985; Cosby et al., 1985; Rechcigl and Sparks, 1985; Reuss and Johnson, 1985; 1986; Tabatabai, 1985; Krause et al., 1986; Ulrich, 1987; Binkley et al., 1989a; De Vries et al., 1989; Reuss and Walthall, 1989; Reuss et al., 1990; Walker et al., 1990). Such models have been used to develop sensitivity criteria to determine which soils on a regional basis will be most sensitive to acidification by acidic deposition (McFee, 1983; Binkley et al., 1989a). These model reactions, based on soil chemistry theory and combined with field observations of changes in soil chemical parameters and surface water chemistry of streams, have led to the general conclusion that acidic inputs into such ecosystems are capable of increasing soil acidity (Reuss et al., 1987). Objections to this general conclusion have largely been based on the argument that the effects of acidic deposition on forest soil systems can only be evaluated from the standpoint of how acidic inputs interact with the natural processes of soil acidification (Rosenquist, 1978; Krug and Frink, 1983a,b; Tabatabai, 1985). Central to this point of view is the fact that soil formation in humid temperature climates is an acidifying process, and that a correlation between areas of high acidic deposition and the presence of acidic soils and stream waters is not sufficient cause to conclude that acidic inputs have increased soil acidity in these ecosystems. Many of the temperate forests of northern Europe and eastern North America that currently receive acidic inputs have undergone substantial changes in land use policy during the past 200 years (see, e.g. ,Brand et al., 1986). As many of these forests are now aggrading, the natural soil acidification that accompanies such regrowth cannot be attributed to acidic deposition (Krug and Frink, 1983a). The discussion concerning the pros and cons of both hypotheses on the effects of acidic deposition on soil acidity still continues (Havas et al., 1984; N. M. Johnson et al., 1984; Krug and Isaacson, 1984; Norton et al., 1989), but it has served to illustrate the limitations in our current understanding about the processes of soil acidification in forest soils. Without such knowledge it will not be possible to understand fully the effects of acidic deposition on these soils, either now, at current levels of input, or in the future, as regulatory measures begin to reduce acidic inputs. It is becoming

ACID DEPOSITION ON FORESTED SOILS

3

clear that the rate at which forest soils are changing will require a rethinking of our approach to studying soil changes in these ecosystems (D. W. Johnson et al., 1991a). This review on the effect of acidic deposition on forest soils assumes that it is no longer necessary to argue whether acidic inputs are having an impact on soil systems. The levels of input are now well characterized and it is highly unlikely that any natural ecosystem would fail to respond in some way (at the very least by increased leaching) to such sustained inputs of potential energy. Emphasis instead is on what effects may be possible given the natural physical and chemical processes acting in forest ecosystems. Discussions of possible mechanisms will differ from earlier reviews in that more emphasis will be placed on the limitations of such mechanisms, both in terms of theory and the currently available body of knowledge. Reference to agricultural and other intensively managed soil systems is excluded because it is generally accepted that the influence from acidic deposition on such soils will be minimal (McFee, 1983; Tabatabai, 1985). Our specific approach is to first attempt to define the often used but poorly understood term “soil acidification,” followed by a brief synopsis of the dominant physical and chemical processes in forest ecosystems that interact with, modify, and respond to acidic inputs. This information is then used as background to discuss published case histories of soil change in regions where acid deposition may be causal, and in regions where it is not. Last, a set of recommendations is put forward concerning areas for future research that are needed to properly characterize and develop models for the effects of acidic deposition on forest soils.

11. SOIL ACIDIFICATION Soil acidification refers to a complex set of processes that result in the formation of an acid soil (pH < 7.0). Soil acidification, therefore, in the broadest sense, can be considered as the summation of natural and anthropogenic processes that lower measured soil pH (Krug and Frink, 1983a). In forest ecosystems, natural acidifying processes include base cation uptake (by plants or microbes); natural leaching by carbonic, organic, or nitric acid; and humus formation (Ulrich, 1980). Anthropogenic acidifying processes include biomass harvesting (which simulates increased uptake) (Binkley et al., 1989a), land use conversion (Berdbn et af., 1987; Billet el af., 1988, 1990b; D. W. Johnson et af., 1988b), fertilization (van Breeman el d., 1982), as well as atmospheric inputs of acidifying compounds (Reuss and Johnson, 1986). Barring inputs of lime from anthropogenic

4

WAYNE P. ROBARGE AND DALE W. JOHNSON

sources, or marine influences, forest ecosystems developed on noncalcareous-bearing parent materials in humid environments will have acid soils. Attempts to measure soil acidification as a result of acidic inputs often center on attempting to detect changes in soil pH (see, e.g., Tamm and Hallbacken, 1986). Though this approach seems intuitively obvious, practical considerations ranging from suitable analytical methodology, spatial variability, and determination of soil horizonation to changes in land use patterns often severely limit the usefulness of this rather simple approach. Soil acidification cannot be quantitatively described by a single index parameter, even though it is often assumed that soil pH is such a parameter (Matzner, 1989). Other changes in soils that may occur during soil acidification include loss of nutrients due to leaching; loss or reduction in the availability of certain plant nutrients (such as phosphorus and molybdenum, which are more strongly retained in acid soils); an increase in the solubility of toxic metals (primarily aluminum and manganese), which may influence root growth and nutrient and water uptake; and a change in microbial populations and activities (Binkley et al., 1989a). Such changes will often be accompanied by changes in overall soil pH, but the degree of change will be dependent on a combination of properties within a given soil system. A more quantitative measure of soil acidification can be obtained by defining it as a decrease in the acid-neutralizing capacity (ANC) of a soil (Berdtn et al., 1987; De Vries and Breeuwsma, 1987). This approach is similar to that for aqueous systems (Stumm and Morgan, 198l), wherein the ANC of a soil solution can be defined as the aqueous base equivalence minus the strong acid equivalence as determined by strong acid titration to a reference pH (typically pH 4.5) (van Breemen et al., 1984). The ANC of the inorganic fraction of a mineral soil can be defined as the sum of basic components minus the strongly acidic components (van Breemen et al., 1984) ANC = 6[A1203]+ 6[Fe203]+ 2[Fe0] + 4[Mn02]

+ 2[Mn0] + 2[Ca0] + 2[Mg0] + 2[Na20] + 2[K20] - 2[SO3] - 2[P205] - [HCl]

(1) where the brackets denote molar concentrations. Note that the metal oxides include the metal cations in the soil solids, as well as those on the exchange complex and in the soil solution. A decrease in the cationic components (such as CaO) or an increase in the acidic components (such as SO3) will result in an increase in soil acidification (a decrease in ANC)

ACID DEPOSITION ON FORESTED SOILS

5

(van Breemen et al., 1984). A decrease in the cationic components could occur through biomass uptake or leaching, whereas an increase in the acidic components could result from inputs of SOT2.This approach emphasizes the mass of acidic input, as equivalents of H+ or NO, and SO:-, rather than the intensity of input as measured by pH (Binkley and Richter, 1987). Thus, a detailed H+ budget for a forest ecosystem, which attempts to quantify all of the proton-producing and -consuming processes within a given ecosystem, offers a means of separating out soil acidification due to acidic deposition from natural acidification processes (van Breeman el al., 1983). Soil acidification could then be defined as the result of an irreversible flux of protons to the soil ecosystem. The limitations of this approach are that such budgets are difficult to construct, and the budget estimates for the various processes are often associated with a large degree of uncertainty both spatially and temporally (Binkley and Richter, 1987). The components of the soil that comprise the ANC can be divided into processes that are relatively fast (approach equilibrium rapidly), slow (processes that are rate limited but for which the kinetics are known), or very slow (may be essentially ignored as having an impact on the system) (Furrer ef al., 1990). Those processes considered to be fast have an immediate impact on the composition of the soil solution, and are also referred to as intensity factors (Reuss and Johnson, 1986). The soil components involved are predominately the soil solution and those soil surfaces that react rapidly to changes in the soil solution (e.g., cation and anion exchange capacity). Slow and very slow processes are referred to as capacity factors and essentially reflect an integration of changes in a soil system over time. These processes include cation and anion plant uptake, mineralization, oxidation and reduction, and primary and secondary mineral weathering (Furrer et al., 1990). Over the long term, the capacity tactors of a soil will control the range in intensity tactors that are ooserved. The ion pools that comprise the capacity factors greatly exceed those of the intensity factors and the inputs from acidic deposition (Keuss and Johnson, 1986; Reuss and Walthall, 1989). These relatively large pools of ions in already acid soils are the basis for the assumption that the impact from acidic deposition will be small compared to natural acidification processes (Krug and Frink, 1983a), and that substantial periods of time will be required before detectable changes in these bulk soil chemical properties will occur, if at all (Tabatabai, 1985). There is a growing consensus, however, that the primary effect of acidic deposition on forest soils is via the intensity factors and that substantial changes in the capacity factors by acidic deposition are not necessary to influence the composition of the soil solution (Reuss et al., 1987;

6

WAYNE P. ROBARGE AND DALE W. JOHNSON

D. W. Johnson et al., 1991a). This approach centers on the fact that the dependence of intensity factors on capacity factors in a soil is often nonlinear, and that small changes may result in relatively large changes in soil solution composition (Reuss and Johnson, 1986; Reuss and Walthall, 1989). It is also becoming apparent that the natural acidification processes within a given ecosystem predispose that system to the way it will respond to acidic inputs. It is argued that the change in the anion composition of the soil solution caused by the introduction of the NO; and SO:- can account for the observed changes in soil solution and surface water acidity without the necessity for involving further soil acidification. Such a scenario would mean that the effects of acidic deposition on forest ecosystems would occur fairly rapidly over a variety of soil types in a relatively short period of time once a critical loading of NO; and, in particular, SO:- is exceeded. It also follows that reduction in inputs below the critical input would have an immediate positive effect. Such responses have been observed both in the field (Wright et af., 1988a) and in the laboratory (Dahlgren et al., 1990). Soil acidification has long been cited as one of the effects of acidic deposition on forest ecosystems. However, it has often not been made clear that acidification of soils from acidic inputs must be viewed from the standpoint of being superimposed upon natural acidification processes. A quantitative measure of soil acidification can be obtained by using the concept of the ANC of a soil, but the term itself does not necessarily imply that there is a specific parameter (such as soil pH) or set of parameters that can be used to measure soil acidification. It is becoming apparent that there has probably been too much emphasis on change in soil pH and cation depletion as a necessary and expected effect of acidic deposition on soil systems (D. W. Johnson et al., 1991a). The influence of acidic deposition on forest soils might be better understood by focusing on the reactions of the mineral acid anions NO; and SO:- in soils-in particular, how these anions interact with soil acidity already present from natural acidification processes.

111. FOREST SOILS Model reactions of acidic deposition with soils often only emphasize chemical reactions within a theoretical soil horizon. Actual forest ecosystems are infinitely more complicated and offer a number of physical as well as chemical factors that must be considered in order to determine the effect of acidic deposition on forest soils and surrounding surface waters.

ACID DEPOSITION ON FORESTED SOILS

7

A. PHYSICAL FACTORS 1. Canopy Interactions

Acidic deposition reaches the forest ecosystem in the form of rainwater (Schaefer and Reiners, 1989), as cloud and fog droplet impact on the forest canopy (Lovett et al., 1982; Bruck et al., 1989; Reisinger and Irnhoff, 1989); Saxena et al., 1989; Sigmon et al., 1989; Saxena and Lin, 1990), and as dry deposition (Lindberg et al., 1986; Johnson and Lindberg, 1989; Murphy and Sigmon, 1989). Dry deposition is the accumulation of particulates and gases (such as HN03 and SO2) on the forest canopy in the absence of precipitation (Davidson and Wu, 1989). Interaction of these three forms of acidic deposition with the forest canopy changes their initial chemistry before they finally reach the forest floor and the underlying mineral soil as throughfall and stemflow. Throughfall is that fraction of wet deposition that comes in contact with the canopy before reaching the forest floor, whereas stemflow is that portion that drains down the branches and trunk (Parker, 1990). In most forest ecosystems with an intact canopy, it is throughfall and stemflow that are the major inputs of acidity and other ions directly into the soil system. The degree of interaction between acidic deposition and the forest canopy is illustrated by the data in Table I, which compares the relative chemical composition of throughfall to that of cloudwater and rainwater in a high-evaluation spruce-fir ecosystem in the Black Mountains of North Carolina (Bruck er al., 1989). The relative percentage of NO, and SO:- between cloudwater, rainwater, and throughfall are almost constant, with perhaps a slight decrease in NO, and a slight increase in SOT2 in the throughfall. The largest change observed is a shift in dominant cations, with H+ and NH,f replaced by K + , Ca2+, and Mg2+. These data are representative of throughfall measurements in other forest ecosystems that receive acidic deposition (Richter et al., 1983; Lindberg et al., 1986; Bredemeier, 1988, J o s h et al., 1988; Percy, 1989; Sigmon et al., 1989; Parker, 1990) and in studies using simulated acid rain treatments (Scherbatskoy and Klein, 1983; Kelly and Strickland, 1986; Kaupenjohann er al., 1988). More detailed information on throughfall chemistry under a variety of forest canopies can be found in Parker (1983, 1990) and Bredemeier (1988). A review of the processes that control throughfall chemistry can be found in Schaefer and Reiners (1989). The release of base cations from the canopy is largely in response to the fact that SO:- and, to a lesser extent, NO, are not adsorbed by the canopy along with H+ and NHT. It is now known, through the use of 35S(Garten

WAYNE P. ROBARGE AND DALE W. JOHNSON

8

Table I Total Ion Percent (pEq liter-') per Event for Cloudwater, Rainwater, and Throughfall Samples Collected in 1986"

Throughfall (%) Red spruce Cloudwaterb Ion

Fraser fir

Rainwater'

(%I

Site 1'

Site 2d

Site 1

Anions c1NO; s0:-

47.9 1.5 11.6 34.8

45.6 2.5 8.8 34.3

47.9 3.2 8.2 36.6

42.3 5.5 9.7 21.0

48.3 3.0 7.7 37.6

Cations

52.1 27.1 13.2 3.0 1.a 5.2 3.1 12.3

54.4 30.4 12.7 2.0 3.4 4.9 1.0 11.3

52.1 19.3 3.5 1.8 9.6 13.4 4.6 29.4

51.6 15.3 3.9 3.6 10.7 18.5 5.7 38.5

51.7 16.6 6.1 1.8 10.3 12.2 4.6 28.9

H+

Nb+ Na+

K+

Ca2+ Mg'+ Sum

"After Bruck et al. (1989); reprinted by permission of Kluwer Academic Publishers. bCollected at site 1. 'Mt. Gibbes (2006 rn); from June 29, 1986 to Sept. 21, 1986. d E a ~ face t of Commissary Ridge (1760 m); from June 29, 1986 to August 15, 1986.

et af., 1988; Garten, 1990), that SO:- in particular is conserved within the canopy, and that an increase in sulfate loading, either as H2S04 or NH,HSOa in cloudwater or rainwater ( J o s h et af., 1988) or as SO2 in dry deposition, will increase the concentration of base cations in throughfall and stemflow (Parker, 1990). As an example, Johnson and Lindberg (1989) estimates that between 40 and 60% of the base cations in throughfall collected at the Walker Branch Watershed in East Tennessee during 19811983 was due to canopy exchange with deposited airborne acids. This increase in base cation loss must come at the expense of the nutrient pool within the canopy, which in turn may mean an increase in base cation uptake. For the time period cited, this extra base cation uptake due to "neutralization" of acidic input via the canopy equals a total H + input of between 0.9 and 1.1 kmol( +) ha-' yr-' of internal acidification potential within the rooting zone (Johnson and Lindberg, 1989). Calculations for the Solling Forest in West Germany (Matzner, 1989) and for forests in the Netherlands (van Breeman et af., 1986) yield similar results. Neutralization of acidic inputs via the canopy, therefore, represents an indirect means of

ACID DEPOSITION ON FORESTED SOILS

9

increasing the acid load on a forest soil (Matzner, 1989; Ulrich, 1989). In certain ecosystems, almost half of the H+ loading from acidic deposition can be transferred to the soil system before the water transporting the acidic anions enters the soil (Johnson and Lindberg, 1989; Matzner, 1989). The acidity not neutralized by the canopy enters the soil via throughfall and stemflow essentially as a salt solution dominated by Ca2+ and K+ salts of SO:-, with a relatively minor contribution from the remaining mineral acids (Johnson and Lindberg, 1989). The composition of this mixture is not constant but varies considerably depending on a variety of factors, such as season of the year (Parker, 1990), seasonal changes in deposition loading (Johnson and Lindberg, 1989), the overall nutrient status of the ecosystem (Leininger and Winner, 1988; Reynolds et al., 1989; Huettl et al., 1990; Klumpp and Guderian, 1990), and stand age (Stevens, 1987). Throughfall and stemflow composition will also vary depending on the relative health of a stand (Alenas and Skarby, 1988). On a shorter time scale, throughfall and stemflow composition will vary due to length of time between rainfall events [i.e., amount of dry deposition loading varies (Velthorst and van Breemen, 198911, and even during storm events. The overall ionic strength of throughfall and stemflow generally decreases significantly during the course of an individual event (Kelly and Strickland, 1986; Lovett et al., 1989) due to washoff of particulates from the leaf surfaces (Schaefer and Reiners, 1989) and loss in ability of the canopy to buffer the reactions with rainwater during the course of a storm (Parker, 1990). Spatial variability in throughfall composition usually exceeds 25% when expressed as the coefficient of variation due to the flow of water through the canopy (Duijsings et al., 1986). The forest canopy is both a modifier and conduit of acidic deposition into the forest ecosystem. As illustrated above, the interaction between acidic inputs and the canopy can have profound influences on both the pathways and the chemistry of acidic substances that actually enter the forest floor and the underlying mineral soil. These changes, together with varying residence times within the different soil horizons, will influence the nature of soil reactions that are likely to occur. 2. Soil Horizons Acidic inputs entering a forest soil as throughfall and stemflow encounter a gradation in organic matter content extending from the forest floor, which is essentially 100% organic matter, to the underlying parent material of the mineral soil, which usually contains no innate source of organic carbon. Depending on the interactions of the soil-forming factors (Buol et al., 1980), the gradation in organic matter content may occur as distinct

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WAYNE P. ROBARGE AND DALE W. JOHNSON

boundaries between soil horizons (e.g., Fig. 1) or as a gradual decrease in organic matter content with depth. The mixing of organic matter and mineral soil gives a range of reactive surfaces that will respond differently to changes in the percolating soil solution, depending on the initial composition and rate of input of throughfall and stemflow, and on how the chemical composition of this initial solution is changed as it passes through each succeeding soil horizon. The nature of the chemical reactions that may occur within each soil horizon will be discussed elsewhere in this review, but Fig. 1 does serve to illustrate the point that generalizations about a “soil’s” response to acidic inputs need to be properly defined in terms of the scale of observation (Fernandez, 1989). System level studies dealing with nutrient cycling avoid the issue of differences among soil horizons by integrating observations across the entire soil pedon (Adriano and Havas, 1989). In such studies, actual mechanisms within the plantsoil system are of secondary importance, especially in terms of element cycling between the canopy and soil and between soil horizons within the soil pedon due to natural processes. Studies addressing the specific mechanisms of the effects of acidic deposition on tree growth cannot ignore differences between soil horizons, as the rooting zones of trees are seldom confined to a given soil horizon (Fernandez and Struchtemeyer, 1985; Coutts, 1989; Fernandez, 1989). The gradation of soil organic matter throughout the forest soil pedon also means that attempts at measuring differences in soil properties over time, even within the same morphological horizon, must be approached

Figure 1. Exchangeable cations (1 M NH4CI extractable) and pH (0.01 M CaCI,) from a sampling (n = 23) of undisturbed well-drained and moderately well-drained pedons under forest cover in Maine. After Fernandez (1989).

ACID DEPOSITION ON FORESTED SOILS

11

with due caution (Fernandez, 1989). A soil sample from a particular depth within a given soil pedon can be expressed as the summation of its organic matter component and its mineral soil component: soil = organic matter

+ mineral soil

(2) Because the ability of soil organic matter to retain metal ions greatly exceeds that of the mineral soil fraction on a mass basis (Sposito, 1989), relatively small changes in soil organic matter content can dominate the overall physical and chemical properties of a given soil sample. Comparison of different soil samples from the same horizon and location over time, therefore, requires attention not only to location on the landscape, but also to the proportion of soil organic matter and mineral soil within the sample itself. This is especially true if changes due to acidic inputs are restricted to very narrow spatial scales within a soil (Fernandez, 1987,1989; Haun et af., 1988). The extent of spatial variability in soil physical and chemical properties in forest ecosystems is well characterized and typically exceeds 25% coefficient of variation (Mader, 1963; McFee and Stone, 1965; Ike and Cutter, 1967; Ball and Williams, 1968; Troedsson and Tamm, 1969; Beckett and Webster, 1971; Quesnel and Lavkulich, 1980; Federer, 1982; Neilsen and Hoyt, 1982; Arp, 1984; Arp and Krause, 1984; Riha et al., 1986a,b; Wolfe et al., 1987; Bringmark, 1989; Pallant and Riha, 1990). Attempts to measure differences in soil properties over time need to account for the inherent variability in most soil systems (McBratney and Webster, 1983; Webster and Burgess, 1984; Kratochvil et a f . , C. E. Johnson et af., 1990).

3. Forest Hydrology The forest canopy together with the various soil horizons, parent material, and underlying bedrock make up the forest watershed. As discussed in the previous two sections, the forest watershed can be considered as a series of chemical reservoirs that interact with atmospheric inputs that are transported with the drainage water (Schecher and Driscoll, 1989). The degree of chemical interaction and the residence time of the drainage water in each reaction zone determine the flux of ions through the watershed and eventually into the stream-lake environment (Chen ef al., 1984). Residence time within a given soil horizon will depend on its position on the landscape (Veneman and Bodine, 1982; Veneman et al., 1984; Roberge and Plamondon, 1987) and the number and direction of water flow paths present in a given volume of soil (Whipkey and Kirkby, 1978; Beven and Germann, 1982). It should not be assumed that in most forest ecosystems water movement will be confined to the vertical direction (Schecher and

WAYNE P. ROBARGE AND DALE W. JOHNSON

12

Driscoll, 1989). Lateral movement is common in forest ecosystems on hillslopes (Jones, 1987) and can account for a substantial portion of flow, especially on an event basis (Fig. 2) (Roberge and Plamondon, 1987; Gaskin et al., 1989; Hopper et al., 1990). Rapid movement through a given soil horizon will favor control of the soil solution by intensity factors and a decrease in the ANC of the drainage water (Chen et af., 1984). Longer retention within a soil horizon will increase the ANC of the soil solution, primarily through soil mineral weathering (Peters and Driscoll, 1987). Prolonged contact between inputs in drainage water and the soil along preferred flow paths will result in changes in the nearby soil that are not evident from analysis of the bulk soil

a] AVERAGE ANION FLUX

3 Y

P

TF

SF

FF

BA,

BAl

Btv

Btl

BCv

BCI

SAMPLING LEVEL

Figure 2. Average nutrient flux for all storms at each sampling level; P, precipitation; TF, throughfall; SF, stemflow; FF, forest floor leachate; BA,, BA soil solution, vertical component; BA, , BA horizon soil solution, lateral component; Bt, , horizon soil solution, vertical component; Bt,, Bt horizon soil solution, lateral component; BC,, BC horizon soil solution, vertical component; BC, , BC horizon soil solution, lateral component. After Gaskin et al. (1989).

ACID DEPOSITION O N FORESTED SOILS

13

(Hildebrand, 1986a,b, 1987, 1990; Jardine et al., 1990). This chemical disequilibria because of soil structure (Hildebrand, 1987) can result in an underestimation of acidification and cation leaching due to acidic inputs, and an overestimation of sulfate retention capacity (Caspary, 1990). Preferred flow paths will also influence the chemical composition of soil solutions as collected by zero-tension and tension lysimeters (David and Driscoll, 1984; Turner et al., 1985; Cozzarelli et al., 1987). Soil water collected by zero-tension lysimeters travels by preferred flow paths and will have a chemical composition more closely associated with the overlying surface horizons. Soil water collected by tension lysimeters is usually different in chemical composition and more representative of the surrounding bulk soil (Cozzarelli et al., 1987). The number and type of water flow paths in a soil pedon depend on a variety of soil properties, including particle size distribution (Kirkby, 1988), changes in saturated hydraulic conductivity with depth (Gaskin et al., 1989), depth to bedrock or other impermeable layers (Driscoll et al., 1985), the presence of root channels and animal burrows (Whipkey and Kirkby, 1978), and soil moisture conditions prior to an individual storm event or snow melt (Gaskin et al., 1989; Schecher and Driscoll, 1989; Swistock et al., 1989; Hopper et al., 1990; Vogt et al., 1990). All of these factors interact within a given watershed such that the contribution of quickflow (water that moves through the upper soil horizons) (Schecher and Driscoll, 1989) and of groundwater to the stream-lake environment will vary between seasons of the year and even between storms. During dry periods, the water table height decreases in most forest watersheds. Substantial inputs of rainfall, therefore, will first be retained in the bulk soil and will increase the height of the water table. Groundwater inputs to the stream-lake environment will dominate the surface water chemistry during this period of time (DeWalle et al., 1988; Gaskin et al., 1989; Swistock et al., 1989; Hopper et al., 1990). As the height of the water table increases, the zone of saturated soil will move upslope. Additional inputs of water into the soil pedon will encounter this zone of saturated soil, which effectively acts as an impermeable layer. The presence of this saturated zone, together with a change in saturated hydraulic conductivity with depth (Gaskin et al., 1989), results in more lateral flow, especially at the interface of the forest floor and underlying mineral soils (Gaskin et al., 1989; Swistock et al., 1989; Bishop et al., 1990; Hopper et al., 1990; Rosenqvist, 1990; Vogt et al., 1990). The particular upper soil horizon that contributes to quickflow depends on the antecedent soil moisture conditions (Gaskin ef al., 1989). As the chemical composition of soil water differs markedly between soil horizons (Fig. 2), the effect of quickflow on stream water chemistry will vary between storm events. The chemical

14

WAYNE P. ROBARGE AND DALE W. JOHNSON

composition of quickflow will also change as the effective drainage area of the watershed continues to increase with the increase in the height of the water table (Jones, 1987; Gaskin et al., 1989; Hopper ef al., 1990). In the northeastern watersheds of the United States, it is now postulated that, during these periods of high water tables within a forest watershed, a significant contribution of quickflow to stream water chemistry occurs in the headwaters of the watershed (where the soils are typically shallow, coarse textured, and highly acidic, with (114) Values of these constants are provided in Table X. Adsorption reactions of actinide elements were investigated for uranium adsorption on goethite and amorphous iron oxide (Hsi and Langmuir, 1985), plutonium adsorption on goethite (Sanchez et al., 1985), thorium adsorption on goethite (LaFlamme and Murray, 1987; Hunter et al., 1988) and manganese oxide (Hunter et al., 1988), and neptunium adsorption on amorphous iron oxide (Girvin et al., 1991). In most of these applications of the triple-layer model a large number of surface complexation constants were fit to the adsorption data. Plutonium adsorption on goethite was described using four surface complexes: SO--PuOH3+, SO--Pu(OH):+, SO--Pu(OH)l , and SO---PU(OH)~ (Sanchez et al., 1985). Thorium adsorption was described using five complexes, SO--Th4+, SO--ThOH3+, SO--Th(OH)z+, SO--Th(OH):, and SO--Th(OH)4, for goethite (LaFlamme and Murray, 1987; Hunter et al., 1988) and three surface complexes, SO--Th(OH):+, SO--Th(OH):, and SO--Th(OH)4 for manganese oxide (Hunter et al., 1988). Uranium adsorption on iron oxides as the uranyl species was accurately described using two surface complexes: SO--U020H+ and SO--(U02),(OH)f (Hsi and Langmuir, 1985). Fit of the triple-layer model to plutonium adsorption on amorphous iron oxide as the plutonyl species was excellent using one surface complex: SOH-Np02( OH) (Girvin et al., 1991). Hsi and Langmuir (1985) observed that excellent fits to their data could also be obtained by adding additional uranyl surface complexes or by varying the combination of surface constants. This observation was also made by Catts and Langmuir (1986), who added surface complexes for SO---M(OH);? and SO--MNO: (in addition to SO--M2+ and SO--MOH+) to describe copper and zinc adsorption on manganese oxide. As has been observed previously in surface complexation modeling, good fits can be obtained for various combinations of surface complexes. For this reason it is necessary to limit the surface complexation reactions to a small number of the simplest and most chemically reasonable surface complexes. As the number of adjustable parameters is increased, the quality of the model fit improves. This does not

2 82

SABINE GOLDBERG

necessarily indicate any increased chemical insight and may compromise the representation of chemical reality. The modified triple-layer model was applied by Hayes and Leckie (1987) to describe ionic strength effects on cadmium and lead adsorption on goethite. These authors found that only by using an inner-sphere surface complex could the small ionic strength dependence of the adsorption reactions of these cations be accurately described. These authors assert that the modified triple-layer model can be used to distinguish between inner-sphere and outer-sphere surface complexes. Hayes (1987) used the pressure-jump relaxation technique to investigate lead adsorption/desorption kinetics on goethite The author was able to describe both his adsorption and his kinetic data using the modified triplelayer model. Based on the kinetic results, an inner-sphere surface complex between a lead ion and an adsorbed nitrate ion, SOHPb2+-N0;, was postulated in addition to the inner-sphere surface complex, SOPb+, obtained from equilibrium results. The magnitude of the log Kkb(int) value obtained from kinetics was identical to that obtained from equilibrium data. This result is expected because surface complexation model parameter values from equilibrium experiments are necessary to analyze the kinetic data. Therefore the kinetic approach is not independent. The first application of the triple-layer model to alkaline earth metal adsorption on heterogeneous systems was the study of Charlet (1986; Charlet and Sposito, 1989) on a Brazilian Oxisol soil. These authors found good fits of the triple-layer model to calcium and magnesium adsorption on the Oxisol using inner-sphere surface complexes. However, Charlet and Sposito (1989) suggested that these cations may also form outer-sphere surface complexes.

C. STERNVSC-VSP MODEL The Stern VSC-VSP model has been used to describe adsorption of the metals copper, lead, and zinc on the iron oxide (goethite) surface (Barrow et al., 1981). Application of the model to other metal ions or other oxide surfaces is not available. In the Stern VSC-VSP model values of surface site density, maximum adsorption, equilibrium constants, and capacitances were optimized to fit charge and adsorption data. Table XI presents values of N s , NT, log&, and Ciobtained by computer optimization for metal adsorption on goethite. For copper and zinc the adsorption of the species MOH+ and MC1+ is postulated; for lead the adsorption of Pb2+ and PbC1' is postulated based on goodness-of-fit criteria (Barrow et al., 1981). The ability of the Stern VSC-VSP model to describe zinc adsorption on goethite is indicated in Fig. 22. The model describes the data very well.

283

SURFACE COMPLEXATION MODELS Table XI

Values of Maximum Surface Charge Density, Maximum Adsorption Density, Binding Constants, and Capacitances Obtained with the Stern VSC-VSP Model by Computer Optimization for Metal Adsorption on Goethite"

Parameter

Copperb

Leadb

Maximum surface charge density (pmol m-') Maximum metal adsorption density (pmol m-') Capacitances c,, (F m-'1 Cop (Fm-') c p d (F m-') Binding constants 1% K H log KOH log KNa 1% KCl log KUOHt log KM1+ log K M C P

10.0 6.0

10.0

6.0

6.30 1.82 0.97

5.54

8.0 6.69 -0.7 -0.36 8.61

8.0 6.69 -0.7 -0.36

-

6.60

1.82 0.97

-

7.89 5.60

Zinc'

10.4 7.28 4.8 0.99 0.97 8.02 6.03 -0.96 -0.92 6.45

-

6.01

"From Barrow ef a&.(1981). bBased on experimental data of Forbes er al. (1976). 'Based on experimental data of Bolland er al. (1977).

- -4

6

8

lo

PH Figure 22. Fit of the Stern VSC-VSP model to zinc adsorption on goethite. Model results are represented by solid tines. Model parameter values are provided in Table XI. From Barrow et al. (1981), based on experimental data of Bolland et al. (1977).

2 84

SABINE GOLDBERG

The Stern VSC-VSP model was extended to describe ion adsorption by soil materials (Barrow, 1983) and then further extended to describe the rate of adsorption (Barrow, 1986a). This model was applied to a range of anions but generally was limited to one soil sample (details will be provided in Section V,C). The extended Stern VSC-VSP model has been called a mechanistic model and has been applied to describe zinc adsorption on several soils (Barrow, 1986~).The mechanistic Stern VSC-VSP model contains the following assumptions: (1) individual sites react with adsorbing ions as with sites on variable-charge oxides, (2) a range of sites exists whose summed adsorption behavior can be modeled by a distribution of parameters of the variable-charge model, and (3) the initial adsorption reaction induces a diffusion gradient into the particle interior and begins a solid-state diffusion process. The equations for the mechanistic Stern VSC-VSP model describe the following conditions (Barrow, 1986a): (A) Heterogeneity of the surface:

4 = l/(u/,/A277)

exp[-0.5(qaoj - qa0/a)2]

(115)

where is the probability that a particle has initial potential qaoi, qd is the average of Taoj, and u is the standard deviation of qaOj. (B) Adsorption on each component of the surface: (1) at equilibrium: 6. =

K p y c exp( -2,.Fqaj/RT) 1 + Kiayc exp(-ZiFqaj/RT)

(116)

where 6, is the proportion of the jth component occupied by the ith ion, Ki and Ziare the binding constant and valence for the ith adsorbing ion, qajis the potential of the jth component, (Y is the fraction of adsorbate present as the ith ion, y is the activity coefficient, and c is the total concentration of adsorbate. (2) rate of adsorption: 6.Jt =

KTc(1- 6,) - kz6, (1 - exp[-t(krc kfc + kz

+ kz)]}

(117)

where O, is the increment in 6, over time interval t , and kf = klayexp(&Fqaj/RT)

(118)

(119) kz = k2ayexp(-iiF'.Iaj/RT) where kl and k2 are rate coefficients and & and G are transfer coefficients. (C) Diffusive penetration:

SURFACE COMPLEXATION MODELS

285

where Mi is the amount of material transferred to the interior of the jth component on an area basis, Coj is the surface concentration of the adsorbed ion at time r, c k j is the value of Coj at time t k , b is the coefficient related to the diffusion coefficient via the thickness of the adsorbed layer, and f is the thermodynamic factor. (D) Feedback effects on potential: (1) for a single period of measurement: .-m10, a) a01 (121)

*.=*

where 'Paj is the potential of the jth component after reaction and ml is a parameter. (2) for measurement through time: a] a01. - m 1ej-m~MjINrnj (122)

*.=*

where Nmj is the maximum adsorption on component j and m2 is a parameter. (E) Effects of temperature :

b =Aexp(-E/RT)

(123)

where E is an activation energy and A is a parameter. These equations were incorporated into a computer program. The continuous distribution of Eq. (115) was divided into 30 discrete elements. The 30 sets of equations were solved by an iterative procedure using a computer program and the criterion of goodness of fit to ion sorption (Barrow, 1983). Because of the very large number of adjustable parameters, the use of the mechanistic Stern VSC-VSP model should be regarded as a curve-fitting procedure, although of course fit to the data is usually excellent. The mechanistic Stern VSC-VSP model has been used to describe the effects of time and temperature on zinc sorption on an Australian soil (Barrow, 1986b), the effect of pH on zinc sorption on several soils (Barrow, 1986c), and the point of zero salt effect for zinc sorption on an Australian soil (Barrow and Ellis, 1986b). The model was able to describe the data well in all cases using the assumption that the species ZnOH+ adsorbs on the surface. Serious difficulties in the use of the model result because calcium carbonate was added to raise the pH and calcium nitrate solutions were used as the background electrolyte. Unless calcium can be shown to act solely as an inert background electrolyte, the chemical significance of the parameters obtained in these modeling procedures is expected to be compromised by specific adsorption of calcium. The mechanistic Stern VSC-VSP model has also been used to describe zinc, nickel, and cadmium adsorption by a goethite slightly contaminated

2 86

SABINE GOLDBERG

with silicon (Barrow et al., 1989). As was the case for soil, excellent fits t f J the adsorption data were obtained. Although in these experiments pH w a i adjusted with additions of sodium hydroxide, the background electrolyte was still calcium nitrate.

D. GENERALIZED TWO-LAYER MODEL Application of the generalized two-layer model to metal adsorption has been restricted to the hydrous ferric oxide surface (Dzombak and Morel, 1990). As discuksed in Section II,D, adsorption of metal ions is postulated to occur on two types of sites of high or low affinity. Intrinsic conditional equilibrium constants for the generalized two-layer model for metal adsorption were obtained with the computer program FITEQL (Westall, 1982). Individual values of log K',(int) and best estimates of log Kh(int) were obtained as described for protonation-dissociation constants in Section II1,D. Metal adsorption is defined by reaction Eqs. (51) and (52) for silver, cobalt, nickel, cadmium, zinc, copper, lead, and mercury. For adsorption of alkaline earth cations, the reaction on the strong sites, Eq. (51), is replaced by: SSOH+ M2+

S'OHMZ+

+ Sr2+ + H 2 0

S"0SrOH

(124) For strontium adsorption an additional reaction on the weak sites is defined:

+ 2H+

(125) Adsorption of trivalent chromium metal occurs only on the strong sites by one reaction: S"OH

SsOH + Cr' t H 2 0

* S"OCrOH++ 2H+

(126) Table XI1 presents values of best estimates of logKh(int) obtained by FITEQL computer optimization. The ability of the generalized two-layer model to describe copper adsorption on hydrous ferric oxide is indicated in Fig. 23. The model describes the data very well using both individual data set values and the best estimates of log K',(int). The surface precipitation model for metal ions (see Section IV,A) has been incorporated into the generalized two-layer model (Dzombak and Morel, 1990). Adsorption of metal ions is assumed to occur on both the strong and the weak sites via the reactions

+ = MOH; + H+ + M2++ H 2 0 z= SW(OH),,,, + = MOHZ + H+

r S S O H+ M2+ + HzO ES"OH

S'(OH),,,,

(127) (128)

287

SURFACE COMPLEXATION MODELS Table XI1

Values of Intrinsic Metal Surface Complexation and Surface Precipitation Constants Obtained with the Generalized Two-Layer Model Using FITEQL Computer Optimization for Hydrous Ferric Oxide"

Metal

logKL(int)b

Ca2+ Sr2+ Ba2+

4.97 ? 0.10 5.01 f 0.03 5.46 f 0.12 -1.72 f 0.20 -0.46 f 0.12 0.37 f 0.58 0.47 f 0.03 0.99 f 0.02 2.89 f 0.07 4.65 It 0.14 7.76 f 0.02 2.06 f 0.15

&+

co2+ Ni2+ Cd2+ Zn2+ cu2+ Pb2+ Hg2+ Cr3+

log Kh(int)'

log Kh(int)d

Surface complexationf -5.85 f 0.19 -6.58 & 0.23 -17.60 f 3.06

Number of data sets' 9 12 6 6 13 2 24 21 10 4 12 4

Surface precipitationg

zn2+ Hg2+

log Kg(int)

log Ky(int)

1% KspM

3.49 10.26

0.51 8.95

11.7 3.88

1 11

"From Dzombak and Morel (1990). blogKh(int) is defined for reaction Eq. (124) for alkaline earth metals, for reaction Eq. (126) for chromium, and for reaction Eq. (51) for all other metals. "logK$(int) is defined for reaction Eq. (52). log KL(int) is defined for reaction Eq. (125). 'Experimental data sources provided in Chapter 6 of Dzombak and Morel (1990). r95% confidence intervals. glogKspFe= 2.5, logK$(int)= log Kh(int) + logKspFe, log Kl(int) = logKh(int) + 1% K s p k '

The intrinsic equilibrium constants for the reactions as written are Ki(int) for Eq. (127) and KT(int) for Eq. (128). The reactions for precipitation of M2+ and Fe3+ are as written in Eqs. (111) and (112). The surface precipitation model generally describes the metal sorption data well.

E. ONE-&MODEL Application of the one-pK model to metal adsorption has so far been restricted to cadmium adsorption on the iron oxides, hematite and amorphous iron oxide (van Riemsdijk et al., 1987). Adsorption of cadmium was

SABINE GOLDBERG

288 100

TOTFe = 1.WE -3 M TOTCu = 1.WE -7 M 0 TOTCU 2.WE -7 M A TOTCU= 5.00E -7M

-

a

u 60

a

-

3

c 40

Q,

2

a"

20 0 -

2.5

3.5

4.5

5.5

6.5

PH Figure 23. Fit of the generalized two-layer model to copper adsorption on hydrous ferric oxide. Model results are represented by a solid line for individual logK&,(int) and a dashed line for logK&(int); logK&,(int) = 2.91, logK$,(int) was not necessary; logK&,(int) is provided in Table XII. From Dzornbak and Morel (1990),based on experimental data of Benjamin (1978) and Leckie et al. (1980), reproduced with permission from John Wiley and Sons.

modeled as occurring in the Stern plane. Good model fits to data were obtained by considering only the formation of the hydrolysis surface complex as given in Eq. (66). The adsorption density of the metal surface complex formed by reaction Eq. (65) was negligible (van Riemsdijk et al., 1987). The equilibrium constant expression for the hydrolysis surface complex is provided by Eq. (72). To describe the adsorption of cadmium on iron oxides, values for log K H, log K c + , log KA- , and C were obtained from potentiometric titration data and have already been provided in Table VIII. To describe cadmium adsorption on hematite, no surface complexation constants for the background electrolyte are considered. The ability of the simplified one-pK model to describe cadmium adsorption on hematite is indicated in Fig. 24. The model describes the data quite well. To describe cadmium adsorption on amorphous iron oxide, values of log Kc+ and log KA- were included. The fit was similar in quality to that obtained on hematite (van Riemsdijk et al., 1987). The value of the equilibrium constant, logK& = -6.97, for amorphous iron oxide is very similar to that for hematite, log K d: = -6.41. The one-pK model was expanded to include surface heterogeneity (van Riemsdijk et al., 1987). As for surface charging behavior, the sensitivity of metal adsorption toward the degree of heterogeneity was low. The fit of the model could not be improved significantly by including surface heterogeneity, thus allowing a homogeneous model to be used.

SURFACE COMPLEXATION MODELS

log [ Cd

2 89

2c]

Figure 24. Fit of the simplified one-pK model to cadmium adsorption on amorphous iron oxide. Model results are represented by solid lines; log K&, = -6.41. From van Riemsdijk eral. (1987).

V. APPLICATION OF MODELS T O INORGANIC ANION ADSORPTION REACTIONS ON OMDES, CLAY MINERALS, AND SOILS A. CONSTANT CAPACWANCE MODEL Characterization of anion adsorption behavior as a function of solution pH results in curves termed adsorption envelopes. The constant capacitance model has been used to describe inorganic anion adsorption envelopes on iron oxides (Sigg and Stumm, 1981; Goldberg and Sposito, 1984a; Goldberg, 1985, 1986a,b; Goldberg and Glaubig, 1985), aluminum oxides (Hohl et af., 1980; Goldberg and Sposito, 1984a; Goldberg and Glaubig, 1985, 1988a; Goldberg, 1986a,b; Bleam et af., 1991), clay minerals (Goldberg, 1986b; Goldberg and Glaubig, 1986b, 1988b,c; Motta and Miranda, 1989), and soils (Goldberg and Sposito, 1984b; Goldberg, 1986b; Goldberg and Glaubig, 1986a, 1988b,c; Sposito ef al., 1988). In the application of the constant capacitance model to inorganic anion adsorption, the surface complexation reactions are usually written in terms of undissociated acids (Sigg and Stumm, 1981). The reactions Eqs. (8) and (9) are replaced by the expressions

+ HzO + (i - 1)H+ = SzH(x-j)L(2-i)+ 2Hz0 + ( j - 2)H"

SOH + H,L = SH(x-i)L('-i) 2SOH + H,L

(129) (130)

2 90

SABINE GOLDBERG

where x is the number of protons present in the undissociated form of the acid, 1 5i 5 n , and 2 sj I n , where n is the number of anion surface complexes and is equal to the number of dissociations undergone by the acid. The intrinsic conditional equilibrium constants describing these reactions are ( l - i ) H+ (i-1) KL(int) = [SH(x-i)L I[ exp[(l - i ) F T / R T ] (131) [SOHI[HxLl

Sigg and Stumm (1981) postulated bidentate reactions [Eq. (130)] for phosphate and sulfate adsorption on goethite; Hohl et al. (1980) postulated bidentate species for sulfate adsorption on aluminum oxide. However, Goldberg and Sposito (1984a) obtained good fits to the phosphate adsorption data of Sigg (1979) by considering only monodentate species. All other applications of the constant capacitance model have been restricted to monodentate anion adsorption reactions [Eq. (129)l. The fit of the constant capacitance model to sulfate adsorption was not good (Sigg, 1979; Sigg and Stumm, 1981). Sigg (1979) postulated that this poor fit could have been caused by the presence of a NaSO, ion pair that had not been included in model calculations. An alternative explanation is that sulfate adsorbs via an outer-sphere mechanism and that therefore use of the constant capacitance model is not appropriate. Intrinsic conditional equilibrium constants for anions in the constant capacitance model are obtained using the computer program MICROQL (Westall, 1979) or by computer optimization using the program FITEQL (Westall, 1982). Figure 25 presents the ability of the constant capacitance model to describe silicate adsorption on goethite. The ability of the model to describe the adsorption data is very good. Table XI11 provides values for intrinsic inorganic anion surface complexation constants obtained with the constant capacitance model for various materials. In the work of Goldberg and co-workers (Goldberg and Sposito, 1984a; Goldberg, 1985, 1986a,b; Goldberg and Glaubig, 1985, 1988a) values of log K,(int) were averages obtained from a literature compilation of experimental log K,(int) values. Values of the protonation-dissociation constants, the phosphate surface complexation constants (Goldberg and Sposito, 1984a), and the boron surface complexation constants (Goldberg and Glaubig, 1985) obtained in this fashion were not significantly different statistically for aluminum and iron oxide minerals. Applications of the constant capacitance model to anion adsorption edges on clay minerals have been carried out for boron (Goldberg and

SURFACE COMPLEXATION MODELS

..“

.-

0

A

0 3

-

U

I

4

5

6

7

0

9

1

0

291

1

PH Figure 25. Fit of the constant capacitance model to silicate adsorption on goethite. Model results are represented by solid lines. Model parameters are provided in Table XIII. From Sigg and Stumm (1981).

Glaubig, 1986b), selenium (Goldberg and Glaubig, 1988b), arsenic (Goldberg and Glaubig, 1988c), and molybdenum adsorption (Motta and Miranda, 1989). To describe boron, arsenic, and selenium adsorption on kaolinite and selenium adsorption on montmorillonite, log K,(int) values were based on averages for a literature compilation of aluminum oxides. The assumption was made that adsorption occurs via ligand exchange with aluminol groups on the clay mineral edges (Goldberg and Glaubig, 1986b). To describe boron and arsenic adsorption on montmorillonite and boron adsorption on illite, log K,(int) were optimized with the anion surface complexation constants (Goldberg and Glaubig, 1986b, 1988~).Although the fit to anion adsorption was generally good (see Fig. 26), in some cases the optimized value of log K+(int) was larger than the optimized absolute value for log K-(int) or the optimized value for log K-(int) was insignificantly small. These are chemically unrealistic situations that would potentially reduce the application of the model to a curve-fitting procedure. Additional research is needed. Alternatively, in the application of the constant capacitance model to molybdate adsorption on clays, log K,(int) values were obtained from potentiometric titration data (Motta and Miranda, 1989). Fit of the model to molybdate adsorption data was good, although the zero point of charge values for illite were surprisingly high. The first application of the constant capacitance model to adsorption on heterogeneous soil systems was the study of Goldberg and Sposito (1984b) of phosphate adsorption on 44 soils. These authors used log K,(int) values that were averages obtained from a literature compilation of log K,(int) values for aluminum and iron oxide minerals. The authors calculated a

Table XIII

Values of Intrinsic Inorganic Anion Surface Complexation Constants Obtained with the Constant Capacitance Model Using Computer Optimization" Solid y-AI2O3 Y-A1203b Y-A1203

Y-&o3 h,

UJ

N

6-A1203 &A1203 6-A1203 6-A1203 a-Numinab Hydrous aluminab Activated aluminab a-N(OH), a-AI(OH),' a-AI(OH), a-AI(OH), a-Al(OH), 7-AIOOH Pseudoboehmite Al(OH),(a& Al(oH),(a~n)~ AI(OH),(am)

Ionic medium 0.01 M NaCIO, 0.1 M NaCl 0.1 M NaCl 0.1 M NaCIO, 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.01 M NaCl 0.01 M KCI I=O 0.1 M NaCl NaCl KCI 0.1 M NaCl 0.1 M NaCl 0.001 M KCI 0.1 M NaCl 0.01 M NaCIO, 0.01 M NaCIO, 0.1 M NaCl

log K:(int) 10.34 8.50 9.78 -3.6' 4.14 5.13 5.56 2.87 8.69 7.79 5.09 9.46 11.11 9.01 9.72 9.74 7.28' 5.09 9.89 11.06 5.92

log Kt(int)

-

4.30 2.21 9.4' 3.17 2.89 2.19 5.26 3.98 2.56 3.41 3.64

-

3.32 3.55

-

log K:(int)

Reference

-2.81

Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Hob1 et al. (1980) Goldberg and Glaubig (1988a) Goldberg and Glaubig (1985) Goldberg and Glaubig (1985) Goldberg and Glaubig (1988a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg (1986b) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg (1986a) Goldberg (1986b) Bleam ef al. (1991) Goldberg and Glaubig (1985) Goldberg (1986a) Goldberg (1986b) Goldberg and Glaubig (1985)

-3.61 -

-4.78 0.62 -3.75 -3.58

-

-1.05' -4.52

-3.19

$

a-FeOOH a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOHb a-FeOOH a-FeOOH a-FeOOHb a-FeOOHb a-FeOOH a-FeOOH a-Fe203b a-Fe,03 Fe(OH)3(am)b Fe(OH)3(am)b Fe(OH),( am)b Fe(OH)3(am)b Fe(OH)3(am)b Fe(OH)3(am) Kaolinitesg Kaolinite Kaolinite Kaolinite

0.1 M NaCIO, 0.1 M NaCIO, 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCIO, 0.1 M NaClO, 0.1 M NaCIO, 0.1 M NaCIO, 0.1 M NaC10, 0.1 M NaCl I=0 0.1 M NaCl 0.01 M NaC10, 0.125 M NaC10, 1 M NaC104 0.1 M NaClO, 0.01 M NaCIO4 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.1 M NaCl 0.01 M NaCl

9.5' 10.54 10.43 10.49 11.22 10.10 10.87 10.02 11.10 -5.8' 4.1 3.82 4.48

-4.8' 5.25 7.43 4.88 11.84 10.78 11.75 10.72 -

5.63 5.28 f 0.2 11.04 11.07 4.95

5.1' 7.25 6.25 6.27 7.06 5.80 6.52 5.36 5.80 - 13.5' -3.3 -4.27 -3.43

-

2.06 5.60 3.86 4.22 4.63 3.39 8.24 3.34 0.95

-1.5' 2.94 0.17 0.17 0.99 -0.63 0.29

-

-4.23

-

-0.65 -3.67 -0.27 -1.63 -4.57 -

-3.21 -

-

Sigg and Stumm (1981) Goldberg and Sposito (1984a) Goldberg (1985) Goldberg (1985) Goldberg (1985) Goldberg (1986a) Goldberg (1986a) Goldberg (1985) Goldberg (1985) Sigg and Stumm (1981) Sigg and Stumm (1981) Goldberg (1985) Goldberg (1985) Sigg and Stumm (1981) Goldberg and Glaubig (1985) Goldberg and Sposito (1984a) Goldberg and Glaubig (1985) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Sposito (1984a) Goldberg and Glaubig (1985) Goldberg and Glaubig (1986b) Goldberg and Glaubig (1988~) Goldberg and Glaubig (1988b) Motta and Miranda (1989) (continues)

Table XIII (Continued) Solid Montmorillonitesh Montmorillonite Montmorillonite Illites‘ Illite Soils’ Soils* Panoche soil Imperial soil

Ionic medium

log Kt(int)

0.1 M NaCl 0.1 M NaCl 0.01 M NaCl 0.1 M NaCl 0.01 M NaCl

6.37 f 1.3 10.92 5.23 5.39 ? 0.8

0.01 M NaCl 0.05 M NaCl 0.1 M NaCl

8.71 f0.6 5.48 f0.4 7.35‘ 9.94

log KE(int)

Reference

-

Goldberg and Glaubig (1986b) Goldberg and Glaubig (1988b) Motta and Miranda (1989) Goldberg and Glaubig (1986b) Motta and Miranda (1989) Goldberg and Sposito (1984b) Goldberg and Glaubig (1986a) Sposito et al. (1988) Goldberg and Glaubig (1988~)

3.40 2.75 2.41 f 2.3 0.85‘ 3.71

-5.14 f 1.7 -4.78

“The work of Goldberg and co-workers was based on logK+(int) = 7.38, log K-(int) = -9.09 for aluminum oxides and kaolinites, and logK+(int) = 7.31, logK-(int) = -8.80 for iron oxides. All soils work was based on logK+(int) = 7.35, logK-(int) = -8.95 unless indicated otherwise. bExperimental data source provided in the reference. ‘logK;(int) and l o g K $ n t ) are defined for reactions Eqs. (8) and (9), respectively. dThe aqueous solution species AlH,PO:+ and AIHPO: are also included. T h e bidentate constants for the formation of S2HP04and S,PO;, reaction Eq. (9), were also optimized; logK$(int) = 8.5 and logK;(int) =4.5. ’logK;(int) is defined for reaction Eq. (8). gAverage for four kaolinites. log K+(int) = 10.62 f 1.6 and log K-(int) = -10.46 f 1.3 were also optimized. Average for three montmonllonites. ‘log K-(int) = 9.30 f 1.3 and log K-(int) = -10.43 f 0.5 were also optimized. Average for three illites. ’Average for 44 soils. ‘logK+(int) = 9.34 f 0.8 and logK-(int) = -10.64 f 0.9 were also optimized. Average for 14 soils. ‘log K;,(int) = 20.05 defined for reaction Eq. (9) was also optimized.

295

SURFACE COMPLEXATION MODELS

-I

0

Y

-

z E

1.5-

z

I.0-

v

Q)

0

0.5-

a

-

20 -

-7

n

I

1

8

9

I

6

II

10

12

I

I3

14

PH Figure 33. Fit of the generalized two-layer model to vanadate adsorption on hydrous ferric oxide. Model results are represented by a solid line for individual logK$(int) and a dashed line for logK$(int); logK$(int) = 13.34; logK$(int) is provided in Table XVI. From Dzombak and Morel (1990), based on experimental data of Leckie et ul. (1984), reproduced with permission from John Wiley and Sons.

307

SURFACE COMPLEXATION MODELS

model to describe vanadate adsorption on hydrous ferric oxide. The model describes the data well using both the individual data set value and the best estimate of log K4,(int). The incorporation of the surface precipitation model into the generalized two-layer model to describe anion sorption has been carried out and is described by Dzombak and Morel (1990). However, no applications to actual anion sorption data are available.

E. Om-& MODEL Application of the one-pK model to anion adsorption has so far been restricted to phosphate adsorption on the iron oxide, goethite (van Riemsdijk and van der Zee, 1991). Adsorption of phosphate is modeled as occurring at the d-plane and described by reaction Eq. (67) and equilibrium constant Eq. (73). The adsorption of potassium in the d-plane is also considered: reaction Eq. (68) and equilibrium constant Eq. (74). The equilibrium constant for potassium adsorption is optimized both with the phosphate adsorption equilibrium constant and for the charging data in the absence of phosphate. The ability of the one-pK model to describe phosphate adsorption on goethite is indicated in Fig. 34. The fit of the model is excellent over the entire, wide pH range investigated.

8 9 I0

0' 0

1

I

0.2

I

I

0.4

I

0.6

Solution P (rnrnol liter-')

Figure 34. Fit of the one-pK model to phosphate adsorption on goethite. Model results are represented by solid lines; log KHpo4= 6.65, log KK = -0.5. From van Riemsdijk and van der Zee (1991), based on experimental data of Bowden er af. (1980), reproduced with permission from Kluwer Academic Publishers.

308

SABINE GOLDBERG

M.APPLICATION OF MODELS TO ORGANIC LIGAND ADSORPTION REACTIONS ON OXIDES A. CONSTANT CAPACJTANCE MODEL The constant capacitance model has been used to describe organic ligand adsorption envelopes on aluminum oxide (Kummert and Stumm, 1980), silicon oxide, titanium oxide (Gisler, 1980), iron oxide (goethite) (Sigg and Stumm, 1981), and natural organic matter (Baccini et a / . , 1982). In the application of the constant capacitance model to organic anion adsorption, the surface complexation reactions are written in terms of undissociated acids as for inorganic anions (Kummert and Stumm, 1980; Sigg and Stumm, 1981). However, only monodentate surface complexes are considered as described by reaction Eq. (129) and intrinsic conditional equilibrium constant Eq. (131), where i = 1 or 2. In the application of the model to adsorption of amino acids on oxides, the following reactions leading to bidentate surface complexes are defined (Gisler, 1980): SOH + SOH,'

+ HL' e (SOH)HL(SOH;)

+ SOH: + HL' SO- + SOH + HL'

SO-

e (SO-)HL(SOH:)

(144) (145)

(SO-)HL(SOH)

(146) where HL' represents an amino acid, H,N+-CHR-COO-. The intrinsic conditional equilibrium constants are equal to the conditional equilibrium constants because the surface charge remains unchanged and are as follows: S

[(SOH)HL(SOH;)] K' = [SOH][SOH,+][HL']

(147)

[(SO-)HL(SOH:)] - [SO-][SOH:][HL']

K2 -

[(SO-)HL(SOH)] K' = [SO-][SOH][HL']

(149)

For adsorption on titanium oxide, all three of the above reactions are considered. For adsorption on silicon oxide, only reaction Eq. (146) is considered because no positive SOH: surface groups are found in the experimental pH range (Gisler, 1980). Figure 35 presents the ability of the constant capacitance model to describe phthalate adsorption on aluminum oxide. The model describes the data well at various total organic acid

SURFACE COMPLEXATION MODELS

3 09

Figure 35. Fit of the constant capacitance model to phthalate adsorption on aluminum oxide. Model results are represented by solid lines. Model parameters are provided in Table XVII.After Kummert and Stumm (1980).

concentrations. Table XVII provides values for intrinsic organic ligand surface complexation constants obtained with the constant capacitance model for oxide minerals.

B. TRIPLE-LAYER MODEL The triple-layer model has been used to describe organic ligand adsorption envelopes on the iron oxide, goethite (Balistrieri and Murray, 1987) and on amorphous iron oxide (Davis and Leckie, 1979). In the application of the triple-layer model to organic anion adsorption on goethite, the reactions Eqs. (24) and (25) and the equilibrium constants Eqs. (30) and (31) are considered. For adsorption of glutamate on amorphous iron oxide, reaction Eq. (24) is replaced by the formation of a neutral surface complex, SOH-H2L (Davis and Leckie, 1979). Figure 36 presents the ability of the triple-layer model to describe glutamate adsorption on amorphous iron oxide. The model describes the data well for three different total organic ligand concentrations. Unfortunately, values for the intrinsic surface complexation constants were not provided by the authors. Table XVIII provides values for intrinsic organic ligand surface complexation constants obtained with the triple-layer model for iron oxides.

Table X V n

Values of Intrinsic Organic Ligand Surface Complexation Constants Obtained with the Constant Capacitance Model

Solid

Amino acids Si02(am) Si02(am) Si02(am) Si02(am) TiOz, rutile TiOz, rutile

Ligand

Ionic medium

Benzoate Catechol Phthalate Salicylate Acetate

0.1 M NaC10, 0.1 M NaClO, 0.1 M NaClO, 0.1 M NaClO, 0.1 M NaClO,

Glycine a-Alanine P- Alanine y-Aminobutyric acid Glycine Glycine

1.0 M NaClO, 1.O M NaClO, 1.0 M NaClO, 1.0 M NaCIO, 1.0 M NaC10, 1.0 M NaClO,

log K:(int) 3.1

3.7 1.3

6.0 2.9

3.0 3.3

log Kt(int)

log K:(int)

90% equilibrium; the adsorption and desorption times for curves at 200 hr were approximately the same. The time-dependent adsorption and the nonsingularity or hysteretic behavior of the adsorption-desorption processes are illustrated in Fig. 9, when nonlinear retention [see Eq. (21)] was considered. It is apparent that the hysteretic property is for linear or nonlinear kinetic reactions. Hysteretic behavior has been observed by several scientists (Munns and Fox,

MODELS OF INORGANICS IN SOILS

80

.

347

1 NONLINEAR 4

1-

h

5

-0

200

400

600

800

1000

0

- ADSORB

0

200

SOLUTION

400

GOO

800

1000

CONCENTRATION (w/crn3)

Figure 9. Simulated adsorption-desorption isotherms using nonlinear kinetic retention. Desorption was initiated after 10 and 50 hr for each successive sorption step. From Selim et al. (1976), with permission.

1976; Fluhler et al., 1982). Examples of hysteretic behavior for fluoride adsorption-desorption isotherms for two calcareous Swiss soils are given in Fig. 10. The nonlinear behavior of fluoride sorption isotherms is clearly shown from this data set. First-order kinetic reactions have been utilized to quantify the irreversible retention processes in soils. Specifically, the irreversible term Q in the convection-dispersion Eq. (6) or (7) is commonly used to account for various (sink/source) reactions, including precipitation/dissolutions, mineralization, immobilization, biological transformations, volatilization, and radioactive decay, among others. Models that account for Q as first-order kinetic (sink/source) and sequential first-order (irreversible) decay reactions include those of Cho (1971), Selim and Iskandar (1981), Rasmuson and Neretienks (1981), and Amacher et al. (1988). The

H. M. SELLM

348

zoo00

15 WO

?i fl

SCHITTERWALD 0-30 cm

10 OOO

A- - -A m. ........

- 5000

7

P

m-

.-..-.."

G L.

o

5

t Y

Dewrption

*-.-.-t

i! .-s

- ---m

Adsorption

I

500

I

I

1500

lo00

20000

I

2ooo

2! 0

SCHITTERWALD 3 0 - 5 O ~ n

LL

18

2 15000 10 OOO

-

Adwrption

.---A

..........

5000

.-----m

Desorption

0%

500

loo0

1500

2000

2500

Dissolvedconccntrrtion C,[pgF *rnl-']

Figure 10. Fluoride adsorption-desorption hysteresis isotherms tor a Swiss calcareous soil. From Fliihler et al. (1982), with permission.

MODELS OF INORGANICS IN SOILS

349

first-order irreversible retention form is

Q = k&C

(23) where kirris the irreversible rate of reaction (hr-I). On the other hand, description of precipitation reactions that involve secondary nucleation is not an easy task and it is often difficult to distinguish between precipitation and adsorption. In fact, Sposito (1984) stated that the problem of differentiating adsorption from precipitation is made more severe by the facts that new solid phases can precipitate homogeneously onto the surface of existing solid phases and that weathering solids may provide host surfaces for the more stable phases into which they transform chemically. First-order kinetic reactions have also been used as an approximate method for describing the retention reaction due to ion exchange on pure clays. Jardine and Sparks (1984) studied the kinetics of potassium retention on kaolinite, montmorillonite, and vermiculite and found that the adsorption process is kinetic in nature, as shown in Fig. 11. They also found that a single first-order decay-type reaction described the data adequately for kaolinite and montmorillonite, whereas two first-order reactions were

0

20

40

60

80

100

Time (min) 120 140

160

180

200

220 240

260 -l

-.2

-.4 -.6

Y

=m -1.0 -1.2 -1.4

-1.6 -1.8

Figure 11. First-order plots of potassium adsorption on clay minerals, where K,is quantity adsorbed at time t and K, is quantity adsorbed at equilibrium. From Jardine and Sparks (1984),with permission.

350

H. M. SELZM

necessary to describe potassium retention on vermiculite. Jardine and Sparks (1984) suggested that deviations of experimental data from firstorder kinetics at larger times (when equilibrium is approached) are likely due to the fact that potassium retention is not an irreversible but rather a reversible mechanism. At large times (or large amounts of potassium adsorbed) the contribution of the reverse or backward retention process becomes significant and thus should not be ignored. Other cations and anions that exhibited kinetic ion exchange behavior include Al, NH4, and several heavy metals. An extensive list of studies that illustrate kinetic behavior has been recently compiled by Sparks (1989). According to Ogwada and Sparks (1986), observed kinetic ion exchange behavior in soils is probably due to mass transfer (or diffusion) and chemical kinetic processes. They stated that for chemical sorption to occur, ions must be transported to active (fixed) sites of the soil particles. The film of water adhering to and surrounding the particles, and water within the interlayer spaces of the particles, are both zones of low concentrations due to depletion by adsorption of ions onto the exchange sites. The decrease in concentration in these two interface zones may be compensated by diffusion of ions from the bulk solution. Other kinetic models that are used to describe the rate of reversible and irreversible reactions, such as the Elovich model and the diffusioncontrolled model, are given in Table I. A variety of other kinetic reactions are given by Travis and Etnier (1981) and Murali and Aylmore (1983) and a detailed discussion of the characteristics of several reactions is available (Sparks, 1989).

V. MULTIPLE-REACTION MODELS The problem of identifying the fate of solutes in soils must include accounting for retention reactions and transport of the various species in the soil environment (Theis, 1988; Barrow, 1989). In fact, Barrow (1989) stated that the use of single-reaction models, such as those described above, is not adequate because such models describe the fate of only one species with no consideration to the simultaneous reactions of others in the soil system. This is supported by the work of Amacher et al. (1986), who showed that sorption-desorption of Cd, Cr, and Hg from batch studies on several soils were not described by use of single-reaction equations of the equilibrium Langmuir or Freundrich type. They also found that a firstorder kinetic reaction was not capable of describing Cd, Cr, and Hg con-

MODELS OF INORGANICS IN SOILS

351

centrations in the soil solution with contact time. Aringhieri et al. (1985) showed that retention of Cd and Cu on an organic soil was highly kinetic. A description of Cd and Cu reactions using a second-order approach gave adequate predictions of their behavior provided that the reaction rate coefficients were time dependent. Multisite or multireaction models deal with the multiple interactions of one species in the soil environment. Such models are empirical in nature and are based on the assumption that a fraction of the total sites are highly kinetic whereas the remaining fraction of sites interact slowly or instantaneously with those in the soil solution (Selim er al., 1976; Jardine et al., 1985). Nonlinear equilibrium (Freundlich) and first- or nth-order kinetic reactions were the associated processes. Such a two-site approach proved successful in describing observed extensive tailing of breakthrough results. Another two-site approach was proposed by Theis et al. (1988) for Cd mobility and adsorption on goethite. They assumed that the nature of reactions for both sites was governed by second-order kinetic reactions. The reactions were assumed to be consecutive, with the second reaction being irreversible in nature. Amacher et al. (1988) developed a multireaction model that includes concurrent and concurrent-consecutive processes behavior of Cd and Cr(V1) with time for several soils. In addition, the model predicted that a fraction of these heavy metals were irreversibly retained by the soil. Recently, Amacher et al. (1990) concluded that the multireaction model was also successful in describing adsorption of mercury for several soils.

A. T w o - S m M o ~ ~ u One of the earliest multireaction models is the two-site model proposed by Selirn et al. (1976). This model was developed in order to describe observed batch results that showed rapid initial retention reactions followed by slower types of reactions. The model was also developed to describe the excessive tailing of breakthrough results obtained from pulse inputs in miscible displacement experiments. The two-site model is based on several simplifying assumptions. First, it is assumed that a fraction of the total sites (referred to as type I sites) react rapidly with the solute in soil solution. In contrast, we assume that type I1 sites are highly kinetic in nature and react slowly with the soil solution. The retention reactions for both types of sites were based on the nonlinear (or nth-order) reversible kinetic approach as discussed previously. Therefore, the convectivedispersive transport equation with the two-site retention mechanisms may

352

H. M. SELIM

be expressed as

ST = s 1 + s,

(27)

where S1and Sz are the amount retained by sites I and sites 11, respectively, S, is the total amount of solute retained by the soil matrix (pg/g soil), and kl ,k 2 , k 3 , and k4 are the associated rate coefficients (hr-'). The nonlinear parameters n and m were considered less than unity and n f m . For the case m = n = 1, the retention reactions are of the first-order type and the problem becomes a linear one. This two-site approach was also considered for the case when type 1sites were assumed to be in equilibrium with the soil solution, whereas type 2 sites were considered of the kinetic type. Such conditions may be attained when the values for the forward and backward (or kl and k 2 )rate coefficients are extremely large in comparison to the water flow velocity (4).Under these conditions, the solute convective-dispersive transport equation for a combination of equilibrium and kinetic retention is (Selim et al., 1976)

as, - k3-o C" -at

P

- k&

Here Kd is the Freundlich distribution coefficient associated with type 1 sites for nonlinear equilibrium reaction (S, = KdC"). The term R is the retardation factor, which for this nonlinear case is a function of C . Jardine et al. (1985) found that the use of the equilibrium and kinetic two-site models provided good predictions of breakthrough curves (BTCs) for A1 from kaolinite at different pH values (see Fig. 12). Selim et al. (1976) found that the two-site model yields improved predictions of the excessive tailing of the desorption or leaching side and the sharp rise of the sorption side of the BTCs in comparison to predictions using single-reaction equilib-

MODELS OF INORGANICS IN SOILS

353

Figure 12. Breakthrough curve for A1 in a kaolinite column. Solid curve is calculated using the kinetic-equilibrium two-site model. From Jardine et at. (1985), with permission.

rium or kinetic models. The two-site model has been used by several scientists, including De Camargo et al. (1979), Nkedi-Kizza et al. (1984), Jardine ef al. (1985), and Parker and Jardine (1986), among others. The model proved successful in describing the retention and transport of several dissolved chemicals, including aluminum, 2,4-D, atrazine, phosphorus, potassium, cadmium, chromium, and methyl bromide. However, there are several inherent disadvantages of the two-site model. First, the reaction mechanisms are restricted to those that are fully reversible. Moreover, the model does not account for possible consecutive-type solute interactions in the soil system.

B. MULTIREACTION MODELS A schematic representation of the multireaction model is shown in Fig. 13. In this model we consider the solute to be present in the soil solution phase (C) and in four phases representing solute retained by the soil matrix as S, , S1,SZ, S 3 , and Sin. We further assume that S, , S1,and S2 are in direct contact with the solution phase and are governed by concurrent-type reactions. Here we assume S, as the amount of solute that is sorbed reversibly and is in equilibrium with C a t all times. The governing equilibrium retention/release mechanism was that of the nonlinear Freundlich type, as discussed previously.

3 54

H. M. SELIM

Figure 13. A schematic representation of the multireaction model. From Selim et al. (1990a), with permission.

The retention/release reactions associated with S1and S2 were considered to be in direct contact with C and reversible processes of the (nonlinear) kinetic type govern their reactions; S, = KdCb

(31)

where kl to k4 are the associated rate coefficients (hr-I). These two phases ( S , and S2) may be regarded as the amounts sorbed on surfaces of soil particles and chemically bound to A1 and Fe oxide surfaces or other types of surfaces, although it is not necessary to have a priori knowledge of the exact retention mechanisms for these reactions to be applicable. Moreover, these phases may be characterized by their kinetic sorption and release behavior to the soil solution and thus are susceptible to leaching in the soil. In addition, the primary difference between these two phases not only lies in the difference in their kinetic behavior but also on the degree of nonlinearity as indicated by the parameters n and m. The multireaction model also considers irreversible solute removal via a retention sink term Q in order to account for irreversible reactions such as precipitation/ dissolution, mineralization, and immobilization, among others. We expressed the sink term as a first-order kinetic process

where kirris the associated rate coefficient (hr-I).

MODELS OF INORGANICS IN SOILS

355

The multireaction model also includes an additional retention phase (S3) that is governed by a consecutive reaction with S 2 . This phase represents the amount of solute strongly retained by the soil that reacts slowly and reversibly with S2 and may be a result of further rearrangements of the solute retained on matrix surfaces. Thus, inclusion of S, in the model allows the description of the frequent observed very slow release of solute from the soil (Selim, 1981). The reaction between S2 and S3 was considered to be of the kinetic first-order type, i.e., d&dt = k5S2 - k&

(35) where k5 and k6 (hr-I) are the reaction rate coefficients. If a consecutive reaction is included in the model, then Eq. (33) must be modified to incorporate the reversible reaction between S2 and S,. As a result, Eq. (36)

must be used in place of Eq.(33). The above reactions are nonlinear in nature and represent initial-value problems that were solved numerically using finite-difference approximations (explicit-implicit). The initial conditions were that of a given initial solute concentration and assumed no solute retained at time zero, as is the case for kinetic batch experiments (see Amacher et al., 1988). Details of the numerical scheme and computer model code are documented in the multireaction model (MRM) program and are in Selim et al. (1990a). In addition, the above retention mechanisms were incorporated, in a separate model, into the classical convection-dispersion equation in order to predict solute retention as governed by the multireaction model during transport in soils (Selim et al., 1989). The initial and boundary conditions used were those described earlier [Eqs. (11)-(13)], where it is assumed that a solute solution of a concentration C, is applied to a soil column having a length L for a given duration T and is thereafter followed by a solute-free solution. The numerical solution for the multireaction and transport model (MRTM) is documented in program MRTM and is given in Selim et al. (1990a). To test how well the multireaction model is capable of predicting the kinetics of solute retention patterns, several MRM variations were examined (Amacher etal., 1988). This ranged from models wherein all solute reactions (see Fig. 13) were included to model variations wherein only three phases (C, S1,and Sir,)were considered. All model variations were tested using Cr(V1) retention data for a Windsor soil at an initial Cr(V1) concentration in solution of 1.0 pg/ml. Amacher et al. (1988) showed that the MRM version that accounted for two concurrent, nonlinear reversible reactions and one concurrent, first-order irreversible reaction provided the

H. M.SELIM

356

best overall prediction (lowest root mean square) of this data set. However, they concluded that a number of model variations can produce simulations of the data that are indistinguishable. A similar conclusion was made by Skopp (1986). For example, it was not possible to determine whether the irreversible reaction is concurrent or consecutive, because both variations provided similar overall fit of the batch data. Consequently, due to its simplicity, the concurrent reaction version was utilized for futher predictions of other data sets, as shown by the solid curves of Fig. 14. Amacher et al. (1988) found that the magnitude of the rate coefficients that provided the predictions shown were highly dependent on the initial (input) concentration Co. This was an indication that although the model is successful in describing kinetic data for a given Co, the same rate coefficients cannot be used to describe data for substantially different initial concentrations. Thus, the model may be considered as an oversimplification and does not provide a complete description of the actual processes that occur during sorption/desorption of solutes in soils. MRM is best considered as a representation of an apparent rate law, rather than a mechanistic rate law. The predictive capability of the multireaction and transport model was tested for Cr(V1) and P for several soils. Breakthrough curves for Cr(V1) CR I

-

WINDSOR

0

0 . I'

4b

0's

164

1b2

240

I

200

I

336

TIME, HR Figure 14. Experimental data of Cr(VI) retention with time for a Windsor soil for several initial concentrations (C& Predictions using the multireaction model are shown by the solid lines. From Amacher et al. (1988), with permission.

MODELS OF INORGANICS IN SOILS

357

from miscible displacement experiments are shown in Fig. 15 for Windsor soil (Selim et al., 1989). The solid and dashed curves are MRTM calculations using model parameters ( k , , k 2 , k 3 , k4, and kirr) derived from the batch studies of Amacher et al. (1988). Different MRTM model predictions were obtained because a unique set of values for the rate coefficients were not realized, rather a strong dependence on input concentration was observed. It is obvious that underestimation of Cr(V1) retention, and thus overestimation of potential mobility of Cr(V1) in Windsor soil, was consistently observed for all data sets of rate coefficients used. Because an excellent fit of a data set does not in itself constitute a proof of any specific retentionfrelease mechanism, no efforts were made to examine the capabilities of different model variations based on curve fitting alone. Instead, MRTM was utilized along with the optimization (curvefitting) scheme to test its capability for describing the transport data without reliance on parameter estimates from the batch experiments. The hypothesis was that a model gives an inaccurate representation of the reaction mechanism and should thus be discarded if it is incapable of describing the experimental data. As indicative by the curve shown in Fig. 14, adequate model predictions were obtained for the Cr(V1) data and the goodness of fit as measured by r2 exceeded 0.90. I

I

I

I

I

I

I

CR - Windsor I .o

0.a

e V

0.6

0.4

0.2

0

---_ 1

4

8

12

v/ v, Figure 15. Measured (closed circles) and predicted breakthrough curves for Cr(V1) in Windsor soil. Curves A, B , and C are MRTM model predictions using batch rate coefficients for C, = 2, 5, and 100 mg/liter, respectively. Curve D is a BTC using parameters obtained using least-squares best fit. From Selim ef al. (1989), with permission.

358

H. M. SELIM

For the above Cr(V1) simulations, model predictions were performed such that the consecutive reactions between S2 and S3 were ignored. The presence of a consecutive S3phase may occur as a result of further surface rearrangement of the adsorbed phase (see Fig. 13). In order to illustrate the versatility of the multireaction approach, we show phosphorus (P) results for which predictions were obtained when the consecutive sorbed phase was considered in the model (Fig. 16). Such predictions were obtained after several attempts were made for a Norwood soil using different model variations (Selim et al., 1990b). These simulations suggest that incorporation of the consecutive reaction improved the MRTM prediction capability of P in this soil.

C. SECOND-ORDER MODELS In this section, we present an analysis of a second-order kinetic approach for the description of multiple-solute retention reactions during transport in soils (Selim and Amacher, 1988). The basis for this approach is that it accounts for the sites on the soil matrix that are accessible for retention of

l.o

1 V

= 0.460

cm/hr

Q

0.60

0.4--

0.2--

0

I

0

4

a

I

I

I

12

16

20

,

24

1

I

2a

32

36

VlV,

Figure 16. Measured (closed circles) and predicted BTCs for P in Norwood soil. Curves

A and B are MRTM model predictions using nonlinear least-squares with and without S3,

respectively. From Selim et al. (1990b),with permission.

MODELS OF INORGANICS IN SOILS

3 59

the reactive solutes in solution. We incorporated this approach into two transport models; namely, the chemical nonequilibrium model and the diffusion-controlled mobile-immobile (two-region) model. Model evaluation was carried out using batch and miscible displacement data sets from different soils. 1. Second-Order Two-Site

In this model one assumes that there exist two types of retention sites on soil matrix surfaces.. The major difference between the two types of sites is based on the rate of the proposed kinetic retention reactions. We also assume that the retention mechanisms are site specific, e.g., the sorbed phase on type I sites may be characteristically different (in their energy of reaction and/or the identity of the solute-site complex) from that on type 2 sites. An additional assumption is that the second-order reactions associated with sites 1 and 2 may be considered as kinetically controlled, heterogeneous chemical retention reactions (Rubin, 1983). One can assume that these processes are predominantly controlled by surface reactions of adsorption and exchange. In this sense, the previousiy described model is along the same lines as the earlier two-site model of Selim etal. (1976). We denote F as the fraction of type 1 sites to the total amount of sites ST. We also denote 4 as the amount of unfilled or vacant sites in the soil, ( b l = ST1

- S1= FST - S1

= ST, - Sz= FST- Sz

(37)

(38) where 41and +z are amounts of vacant sites, S1and Sz are the amounts of solute retained on type 1 sites and type 2 sites, respectively. As the sites become occupied by the retained solute, the amount of vacant sites approaches zero [(41+ &) --f 01 and the amount of solute retained by the soil approaches that of the total capacity of sites [(Sl + Sz) + ST]. Therefore, we propose here that the retention mechanisms follow a second-order kinetic-type reaction whereby the forward process is controlled by the product of concentration (C) and the amount of unoccupied sites (4), by the reversible processes, 42

k

c+41+& and

3 60

H. M. SELIM

where kl to k4 are the associated rate coefficients. A result, the rate of retention may be expressed as p

as, = k1091C - k2pS1 as2

p-

dt

= k3O&C

- k4pS2

for type 1 sites

(39)

for type 2 sites

(40)

It is convenient to regard type 1 sites as those wherein equilibrium is rapidly reached. In contrast, type 2 sites are highly kinetic and may require several days or months for apparent local equilibrium to be achieved. As t + 00, i.e., when both sites achieve local equilibrium, the rates of retention become klO&C - k2pSl = 0

or

--

(41)

here w1 and w2 represent equilibrium constants for the retention reactions associated with type 1 and 2 sites, respectively. The formulations of Eqs. (41) and (42) are analogous to expressions for homovalent ion exchange equilibrium reactions. In this sense, the equilibrium constants o1 and w2 resemble the selectivity coefficients for exchange reactions and S, resembles the exchange capacity (CEC) of soil matrix surfaces (see Sposito, 1981, Chapter 5). However, a major difference between ion exchange and the proposed second-order approach is that no consideration for other competing ions in solution or matrix surfaces is incorporated in the rate of reactions. In a strict thermodynamic sense, the above equations should be expressed in terms of activities rather than concentrations. However, an implicit assumption is that solution-phase ion activity coefficients are constant in a constant ionic strength medium. Moreover, the solid-phase ion activity coefficients are assumed to be incorporated in the selectivity coefficients (wl and w z ) as in ion exchange formulations (Sposito, 1981). For the case wherein only one type of active site is dominant in the soil, the reaction can be described by the reversible kinetic Langmuir equation ,

Here k f and kb are the forward and backward retention rate coefficients and S is the total amount of solute retained by the soil matrix surfaces.

MODELS OF INORGANICS IN SOILS

361

Reaction (43) at equilibrium obeys the widely recognized Langmuir isotherm equation,

where K = Okf/pkb and K is equivalent to w of Eq. (16). [For a discussion of the formulation of the kinetic Langmuir equation, see Rubin (1983) and Jennings and Kirkner (1984) .] The above second-order two-site (SOTS) model along with a sink term of the first-order kinetic type such as that of Eq. (34) were incorporated into the classical convection-dispersion equation in a manner similar to that for the multireaction model (for details, see Selim and Amacher, 1988). The appropriate equations were solved numerically using finitedifference approximations (explicit-implicit). The resultant numerical scheme is documented in program SOTS. 2. Second-Order Mobile-Immobile

In this model, we consider the processes of retention to be controlled by two types of reactions-namely, a chemically controlled heterogeneous reaction and a physically controlled reaction (Rubin, 1983). We assume that the chemically controlled heterogenous reaction is governed according to the second-order approach. In the meantime, the physically controlled reaction is chosen to be described by diffusion or mass transfer of the mobile-immobile concept (Coats and Smith, 1964; Van Genuchten and Wierenga, 1976). To utilize this concept, we assume 0" as the mobile water content that is present inside large (interaggregate) pores, where solute transport occurs by convection and dispersion. The immobile water (Oim) is located inside aggregate pores (intraaggregate), where the solute transfer occurs by diffusion only. Moreover, C" and C'" are solute concentrations in the mobile and immobile phases, respectively. In addition, the soil matrix is assumed to be divided into two regions (or sites), namely, a dynamic or easily accessible region and a less accesible region. The dynamic region is located close to the mobile water phase whereas the stagnant region is in contact with the immobile phase. Analogies between the mobile-immobile concept and that of the two-site approach can be made. One may regard the dynamic and stagnant regions for solute retention analogous to sites 1 and 2 of the two-site concept. Nkedi-Kizza et al. (1984) presented a detailed discussion on the equivalence of the mobileimmobile and the equilibrium-kinetic two-site models. The form of the transport equation for the mobile-immobile water concept can be written

H. M. SELIM

362 as

acm dS " 0" dt +pf"-+@i"- d t

dC'" dt

dS'"

+ P ( W r n ) 7

where Dm is the hydrodynamic dispersion coefficient in the mobile water region and S" and Simare the sorbed amounts in the dynamic and stagnant regions, respectively. The associated transfer equation governing the interaction between solute in the mobile and immobile phases is dC'" dS'" 0'"+ p (1 - f") dt = (y (C" - Cim)- Q " dt

where a is the mass transfer coefficient between the mobile and immobile phases. The parameterf" represents the fraction of dynamic or active sites to the total sites S, and is analogous to F of the two-site model described above. Again, the terms Q" and Qimrepresent irreversible retention for the mobile and immobile regions, respectively, and were considered in a similar manner as discussed in the two-site model,

The retention mechanisms associated with the mobile and immobile phases were governed by our second-order kinetic approach. Specifically, the rate of reaction for S" and Simwere considered as

Here Cp" and represent the vacant or unfilled sites within the dynamic and the stagnant regions, respectively. In addition, the terms 4" and #m can be expressed as

#" = s,m- S"

=f"S,

- S"

= s p - s i m = (1 - f m ) s ,

(51)

- Sim

(52)

where ST,SF,and SF are the total amounts of the sites in the soil matrix, total sites in the dynamic region, and the total in the less accessible region,

MODELS OF INORGANICS IN SOILS

363

respectively. These terms are related by ST = SF + sirn =f"sT + (1 - f")sT

(53) The unfilled sites +rn and 4" are analogous to and & of the two-site concept [Eqs. (37) and (38)]. Again we assume that ST,which represents the total amount of filled and unfilled sites, remains time invariant. An important feature of the second-order retention approach [Eqs. (49) and (50)] is that similar reaction rate coefficients ( k , and k2) associated with the dynamic and stagnant regions were chosen. Specifically, it is assumed that the retention mechanism is equally valid for the two regions of the porous media. A similar assumption was made by van Genuchten and Wierenga (1976) for equilibrium linear reactions and by Selim et al. (1987) for selectivity coefficients for homovalent ion exchange reactions. The differential equations of the second-order mobile-immobile model (SOMIM) described above were solved using explicit-implicit finitedifference approximations subject to the appropriate initial and boundary conditions. The numerical scheme is documented in program SOMIM.

3. Evaluation In order to examine the capability of the SOTS and SOMIM models, Selim and Amacher (1988) utilized Cr(V1) transport data for three soils from miscible displacement experiments. The necessary parameters needed for model validation were either independently measured or estimated by indirect means. Specifically, they relied on parameter estimate for ST, F, kl , k 2 , k 3 , k 4 , and kirr,from kinetic batch studies. Estimates of STand F were based on the assumption that Cr(V1) in solution and the two types of sites attained quasi-equilibrium in 336 hr even though small amounts of Cr(V1) were still being retained by the soil. Moreover, if the magnitude of the irreversible term is small, as was the case here, then reliable estimates of S, and F can be made, although the actual S , is somewhat smaller. The statistical results from the parameter optimization scheme indicated a close approximation of the two-site Langmuir Eq. (17) to the experimental sorption isotherms (Fig. 6). Estimated ST and F were subsequently used with the SOTS model to describe the time-dependent retention of Cr(V1) by Cecil, Olivier, and Windsor soils. Estimates for kl , k 2 , k 3 , k4, and kirr, which provided best fits of the batch data, were obtained. Selim and Amacher concluded that, based on the high r2 values and low parameter standard errors, good predictions of the kinetic behavior of Cr(V1) using the SOTS model were obtained for all three soils. Moreover, SOTS model predictions of the batch data were indistinguishable from those obtained using the multireaction model described earlier.

364

H. M. SELIM

In fact, predictions using SOTS were similar to those shown in Fig. 14 for Windsor soil with MRM predictions. However, it should be emphasized that close approximation of the data by either model does not in itself constitute proof that the two types of sites actually exist nor that the retention reactions postulated in both models are the actual mechanisms. The predicted BTCs shown in Figs. 17 and 18 were obtained using different sets of parameter values for the rate coefficients ( k , , k2,k 3 , k4, and kirJ for the SOTS model. This is because a unique set of values for these rate coefficientswas not obtained from the batch data, rather a strong dependence of rate coefficients on input concentration was observed (Selim and Amacher, 1988). The use of batch rate coefficients at Co = 100 pg/ml, which is the concentration of Cr pulse inputs, grossly underestimated Cr retention by the predicted BTCs for the Windsor and Olivier soils. Reasons for this failure are not fully understood, with a most likely explanation being that the model is an apparent rather than a mechanistic rate law, which may not completely account for all reactions and reaction components. For both soils, closest predictions were realized using batch rate coefficients from Co = 10 to 25 pg/ml. The capability of the second-order mobile-immobile model to describe Cr miscible displacement results was also examined and predictions for OLlVlER

-

SOTS

1.0--

0.8,-

0

1

2

3

4

v I v,

5

6

7

8

Figure 17. Effluent concentration distributions for Cr(V1) in Olivier soil. Curves A , B, C, D, and E are predictions using the second-order two-site (SOTS)model with batch rate coefficients for C, of 100,25, 10, 5, and 1 pg/ml, respectively. From Selim and Amacher (1988),with permission.

365

MODELS OF INORGANICS IN SOILS

WINDSOR - S O T S

I

1.c

0.a 0

0.6 0 0.4

0.2

L

0

2

4

6

8

10

12

14

16

v / v, Figure 18. Effluent concentration distributions for Cr(V1) in Windsor soil. Curves A , B, C, D, and E are predictions using the second-order two-site (SOTS)model with batch rate coefficients for C, of 25, 10, 5, 2, and 1 pg/ml, respectively. From Selim and Amacher (1988), with permission.

Windsor and Olivier soils are shown in Figs. 19 and 20. To obtain the predicted BTCs shown, several assumptions were necessary for the estimation of model parameters. Values for S,, kl, k 2 , k 3 , k 4 , and kirrwere those used with the SOTS model as obtained from the kinetic batch studies. Estimates for the ratios of the immobile water fraction (@"/a) were obtained based on soil moisture retention relations and the fraction of sites f" was assumed to be equal to am/@ because independent measurement off" is not available. Estimates for a and D" were also obtained and were based on the assumption of uniform size aggregates (for details, see Selim and Amacher, 1988). Predicted BTCs were obtained using different sets of batch rate coefficients due to their strong dependence on input concentrations (Co values). Closest predictions to experimental Cr measurements were obtained from batch rate coefficients at low Co values (Co5 10 pg/ml). Moreover, the use of rate coefficients at higher Co values resulted in decreased tailing and reduced retardation of the BTCs. These observation are consistent with previous predictions using the SOTS model (Fig. 10). However, overall predictions of measured Cr using the SOMIM are considered less than adequate in comparison to SOTS model predictions. Reasons for the less than adequate predictions of BTCs for the three soils using SOMIM are not fully understood. It is conceivable that a

H. M. SELIM

3 66

OL l VIE R-SO M IM

0

1

2

3

5

4

6

7

8

v I v, Figure 19. Eftluent concentration distributions for Cr(V1) in Olivier soil. Curves A, B, C, and D are predictions using the SOTS model with batch rate coefficientsfor C, of 25, 10, 5 , and 1 pg/ml, respectively. From Selim and Amacher (1988), with permission.

0

2

4

6

8

VI

10

12

14

16

v,

Figure 20. Effluent concentration distributions for Cr(V1) in Windsor soil. Curves A, B, C, and D are predictions using the SOTS model with batch rate coefficients for C, of 25, 5 , 2, and 1 pg/ml, respectively. From Selim and Amacher (1988), with permission.

MODELS OF INORGANICS IN SOILS

3 67

set of applicable rate coefficients over the concentration range for the Cr transport experiments cannot be obtained simply by using the batch procedure described in this study. In addition, several parameters used in model calculations were estimated and not measured, for example, Om, a, and D m . Other possible factors responsible for these predictions may be due in part to lack of nonequilibrium conditions between the mobile and immobile fractions for the SOMIM model (Valocchi, 1985).

VI. TRANSPORT AND ION EXCHANGE The models described above were focused on the description of the transport and retention of one solute species only. Such an assumption implies that all other interactions that occur in the soil do not inflluence the behavior of the solute species under consideration. Although research on multicomponent interactions and transport in ion exchange columns has been an important subject in ion chromatography literature for several decades (see, e.g., Helfferich, 1962; Helfferich and Klein, 1970), this research area was addressed in the soil science literature only during the last two decades. One of the early works dealing with the transport and interaction of two species is that of Lai and Jurinak (1972). In their approach an equilibrium reaction was assumed to govern the exchange of two ions between the solution and the soil surfaces; this reaction was incorporated into the classical convective-dispersive equation. The work of Rubin and James (1973) may be considered one of the earliest classical studies describing competitive ion exchange during the transport of multiple cation species in the soil profile. They developed a transport model (convective-dispersive) that dealt with ion exchange for multiple cations present in the soil solution. Ion exchange reactions were assumed to be instantaneous, which implies local equilibrium conditions. Valocchi et al. (1981) extended this work to include multiple ion transport under conditions of varying ionic strength of the soil solution. Their model was used to simulate the transport of Na, Ca, and Mg ions in a shallow underground aquifer. The use of ion concentrations rather than activities was shown not to restrict the predictive capability of the model. Models that consider several processes, for example, ion exchange, complexation, and competitive adsorption, include FIESTA (Jennings er al., 1982), CHEMTRAN (Miller and Benson, 1983), and TRANQL (Cederberg et al., 1985), among others. A major disadvantage of such multicomponent models lies in the basic assumption of local equilibrium of

3 68

H. M. SELIM

the governing reactions. In addition, due to their complexity, several of these models have not been fully validated. CHEMTRAN and TRANQL models were used successfully to describe the Ca and Mg concentration data presented earlier by Valocchi et al. (1981). Kirkner et al. (1985) utilized the FIESTA model to describe Ni and Cd breakthrough results on a sandy soil. Model predictions provided higher retardation of Cd and lower retardation for Ni. However, improved predictions were obtained when a kinetic approach was used with parameters obtained from batch experiments. An overview of ion exchanged and transport models is presented below. The intent is to emphasize the importance of ion exchange as the retention mechanism during water flow in soil. Models for which nonequilibrium ion exchange behavior is described using physical (two-region) or chemical kinetic approaches using specific sorption are discussed.

A. CLASSICAL APPROACH We consider ion exchange as the governing mechanism for the retention of cations on soil matrix surfaces. In a standard mass-action formulation, the exchange reaction for two competing ions i and j may be written as (Sposito, 1981).

where TKii denotes the thermodynamic equilibrium constant and a and a* (omitting the subscripts) are the ion activity in soil solution and on the exchanger surfaces, respectively. Based on Eq. (54), one can denote the parameter vKi, as

where “K is the Vanselow selectivity coefficient and 6 is the activity coefficient on the soil surfaces. For binary homovalent exchange, i.e., vi= vj = v, and assuming similar ion activities in the solution phase, rearranging Eq. (55) yields

where Kij is a generic selectivity coefficient of ions i over j (Rubin and James, 1973) or a separation factor for the affinity of ions on exchange

MODELS OF INORGANICS IN SOILS

3 69

sites (Helfferich, 1962). In addition, ciand cj are the relative ion concentrations (dimensionless) such that ci= Ci/CT and c, = Ci/CT, where Ci, Cj, and CT [mmol( +)/ml] are the concentrations in the soil solution of ions i and j the total concentration. Also, si and sj are amounts retained on the solid matrix surfaces (dimensionless) and are expressed as equivalent fractions where si= SJR and sj = S,/R. Here, Si and S, are the amounts adsorbed [mmol(+)/g soil] and R is the cation exchange (or adsorption) capacity of the soil [mmol( +)/g soil]. Therefore, for homovalent exchange we have (Valocchi ef al., 1981)

rearrangement of Eq. (57) yields the following isotherm relation (for ion 1)

Because C, and R are assumed constant, the respective values for ion 2 (i.e., c2 and s2) can be easily obtained. This isotherm relation Eq. (58) indicates that for K12# 1 we have a nonlinear-type sorption isotherm. Upon incorporation of the above ion exchange sorption isotherm into the convective-dispersive transport Eq. (7), we have (for ion 1)

where the term R may be referred to as the retardation factor, expressed as

and R is a function of relative concentration when K12# 1. Breakthrough results for Ca and Mg leaching from Abist soil columns, packed uniformly with two aggregate sizes, are shown in Fig. 21. These results are from Selim et al. (1987), wherein a Mg pulse was introduced into Ca-saturated soil columns and the total concentration (CT) was maintained constant. The solid and dashed lines shown are model calculations of Ca and Mg results based on the classical convective-dispersive transport equation with ion exchange as the retention mechanism. In general, adequate model predictions for both ions were achieved for the two aggregate sizes for early times or pore volumes. Less than adequate predictions were obtained, however, for large times, i.e., during leaching of the Mg pulse by Ca. Selim et al. (1987) suggested that such model deviations may be due to lack of complete local equilibrium between the ions in solution and those

H. M. SELIM

370

-

1-2 rnm (aggregates)

e E 2 +

3

-

Y

IEz

-

Y

z

0 -

F

0

a

6

c

5

W

4

U

i

0

f 0 0

5

10

20

15

25

4-5 rnm

-

5 '

AA

3 2

0

0

5

1 Ca

10

15

20

V N ,

1

/ - -

1

-

30

25

30

Figure 21. Calcium and Mg breakthrough results from soil columns for two aggregate sizes of Abist soil. Predictions obtained using the classical ion exchange model are shown by the smooth curves. From Selim et al. (1987),with permission.

on the ion exchange surfaces. Similar findings were reported by Gaston and Selim (1990) for binary (Ca and Mg) and ternary (Ca, Mg, and Na) systems in a well-aggregated Sharkey clay soil.

B. M O B I L E - ~ O BAPPROACH ILE In an attempt to describe the transport and exchange reactions of cations in aggregated porous media, the physical nonequilibrium approach of the mobile-immobile (or two-region) concept was utilized. Van Eijkeren and Loch (1984) and Schulin et al. (1986) considered the exchange of cations to be governed by a general form of the exchange equation, which was then incorporated into the mobile-immobile, convective-dispersive transport equation. The capability of this approach was examined by Selim et al. (1987) for describing the mobility of Ca and Mg ions in soil columns,

MODELS OF INORGANICS IN SOILS

371

packed with 1- to 2-mm and 2- to 4-mm aggregates, under conditions of constant and variable ionic strength of the soil solution. Mansell et al. (1988) examined this mobile-immobile approach for the transport and exchange reactions for ternary systems (Na-Ca-Mg) in soil. In their analysis, Mansell et al. (1988) allowed cation exchange selectivities to vary with fractional coverage of the exchange sites, which provided improved predictions of cation breakthrough results. To describe ion retention in the dynamic and less accessible regions of the mobile-immobile concept, the sorption process is governed by the equilibrium ion exchange relationship of Eq. (57). We also assume that such a relationship is valid for the exchange sites of both regions, i.e., ion affinity for the dynamic region is the same as that for the less accessible sites, (61) Such an assumption was used by van Eijkeren and Loch (1984), Selim et al. (1987), and Gaston and Selim (1990). To test the capability of the mobile-immobile concept of describing the transport of cations, the breakthrough results shown previously (see Fig. 21) were utilized. Model predictions of Ca and Mg concentrations in the leachate for the two aggregate sizes are given by the solid and dashed lines shown in Fig. 22. Obvious improvements in model predictions, in comparison to the classical approach (Fig 21), were achieved and may be considered as evidence of lack of local equilibrium between the ions in the soil solution and those on the exchange surfaces. Improved predictions using this approach were achieved by Schulin et al. (1989) for Ca and Mg transport under conditions of variable ionic strength for two wellaggregated forest soils and by Mansell et al. (1988) for a ternary (Na-CaMg) system in a Yo10 loam soil. (K12Irn= (K12)'" = K12

C. VARIABLESELEOTWTES In the preceding classical and mobile-immobile concepts, the distribution of each pair of cations, i and j , can be described by a constant selectivity coefficient K i j . Such assumptions make it possible to arrive at recursion formulas for multiple ions, as were introduced by Rubin and James (1973). Generalized isotherms for multiple ions were based on binary exchange coefficients for all combinations of ions present in the soil system. Such generalized isotherms were used by Valocchi et al. (1981) and Mansell et al. (1988). However, such an assumption of constant exchange selectivity is often unfounded as evidenced by several isotherm data in the

H. M. SELIM

3 72 "

I-

U

6

I-

z

5

u

z

4

0

3

[I

w

0

I -2 mm (aggregates)

0

5

10

0

5

10

Z

0 -

I

15

20

25

:1 30

2 1

0

15

20

25

30

VIV, Figure 22. Calcium and Mg breakthrough results from soil columns for two aggregate sizes of Abist soil. Predictions obtained using the two-region ion exchange model are shown by the smooth curves. From Selim ef nl. (1987),with permission.

literature (Sposito, 1981; Jardine and Sparks, 1984; Parker and Jardine, 1986; Mansell et al., 1988). As a result, Kij coefficients are no longer constant but vary with the relative fraction of cations on the exchange surfaces. Mansell et al. (1988) utilized the sorption isotherm results of Lai et al. (1978) for Mg-Ca and Na-Ca in order to calculate binary selectivity coefficients Kij versus relative ion concentration (C/C,), shown in Fig. 23. The dashed curves represent least-squares best fit of the data to the empirical relation log(Kij) = a + bC. The results clearly show a strong Kij dependency over the concentration range with high affinity of Mg over Ca for C/CT less than 0.4. As expected, adsorption of Ca or Mg was preferred to adsorption of Na. However, the results show an increased Kij for Na --* Ca at low (C/CT < 0.4) and high (C/CT > 0.85) relative concentrations. The influence of variable selectivity coefficients on the predictions of cation transport was investigated by Mansell et al. (1988). The transport data sets used were those from miscible displacement experiments of Lai et af. (1978). Mansell et al. (1988) incorporated additional terms into the

373

MODELS OF INORGANICS IN SOILS

0

Mg --+ Ca

L

-

2

5 !2 w *

-*

0.3 -

.--

Na

I

3-

3

- - --

Ca

.-;---0

- \

0.1

-\

0

0.2I

*

I

.

I

-

,

I

,

classical (convective-dispersive) equation in order to account for variable Kji values for the ternary systems (Na-Ca-Mg) of Lai et al. (1978). They also utilized the mobile-immobile approach in conjunction with variable Kji values in order to describe the same data set. Mansell et al. (1988) found that good breakthrough predictions were obtained for the relatively noncompetitive Na when either constant or variable Kii values were used with the classical model (see Fig. 24). However, the use of constant Kij values underestimated the tailing of the Mg breakthrough data. Description of Mg tailing was improved when variable Kii values were used, but the extent of Mg retardation (peak location) was somewhat overestimated. The combined use of the mobile-immobile approach and variable K j j values provided the best overall description of Na and Mg breakthrough results.

D. SPECIFIC SORPTION AND KINETICIONEXCHANGE In this approach, two mechanisms were considered as the dominant retention processes in the soil, namely, ion exchange and specific sorption. We consider ion exchange as a nonspecific sorption/desorption process. Ion exchange is a fully reversible mechanism and is assumed here to be

H. M. SELIM

3 74 0.5

CONSTANT SELECTIVITIES

0 VARIABLE SELECTIVITIES

0

1

2

3

4

5

6

VIV, Figure 24. Sodium and Mg breakthrough results from soil columns of Yolo soil (Lai etal., 1978). Solid and dashed curves are predictions using the classical and two-region models, respectively. Exchange selectivity coefficients used in model predictions were considered constant (top figure) or variable (bottom figure). From Mansell et al. (1988), with permission.

either rapid (i.e., instantaneous) or may be considered as a kinetic process. Specific sorption is considered as a kinetic process wherein ions have high affinity for specific sites on matrix surfaces. Furthermore, retention of ions via specific sorption is regarded as an irreversible or weakly reversible process. The kinetic ion exchange reaction was analogous to mass transfer or between the solid and solution phase such that (for ion i) ds,/dt = a(si*- S i )

(62) where si is the amount sorbed (at time t ) on matrix surfaces, sT is the equilibrium sorbed amount, and a is an apparent rate coefficient (hr-'). Here s* (for ion 1) was calculated using the equilibrium isotherm Eq. (58). Obviously, as t + and/or large values of a,s* and s become equal and equilibrium conditions prevail, Expressions similar to the above equation have been used to describe mass transfer between mobile and immobile water and chemical kinetics (Parker and Jardine, 1986; Selim and Amacher, 1988). Studies that illustrate kinetic ion exchange behavior include those of Jardine and Sparks (1984) and Ogwada and Sparks (1986).

MODELS OF INORGANICS IN SOILS

375

It was postulated that in 2 :1 types of minerals, intraparticle diffusion as a possible rate-controlling mechanism governs the kinetics of adsorption of cations (Sparks, 1989). The specific sorption process was considered as a kinetic reaction whereby the rate of sorption is governed by a second-order mechanism such that

+

is where k f and kb are the forward and backward rate coefficients (hr-'), the amount of vacant specific sites, and I)is the amount specifically sorbed [mmol( +) g-'1, respectively. Vacant specific sites are not strictly vacant. They are assumed occupied by hydrogen, by hydroxyl, or by other specifically sorbed species. The role of specific sorption and its influence on the behavior of metal ions has been recognized by several investigators. Sorption/desorption studies showed that highly specific sorption mechanisms are responsible for metal ion retention for low concentrations (Tiller et al., 1979, 1984). The general view was that metal ions have a high affinity for sorption sites of oxide minerals surfaces in soils. In addition, these specific sites react slowly with heavy metals and are weakly reversible. In the absence of competing metal ions for specific sites (e.g., Ni, Co, and Cu), as is the case in this study, it is reasonable to consider specific sorption as an irreversible process. Therefore, the above second-order reaction was modified to describe irreversible or weakly reversible retention by setting the backward rate coefficient k b as zero,

p(a+/at) = k@+C (64) For several metal ions (e.g., Cd, Ni, Co, and Zn), specific sorption was shown to be dependent on time of reaction. Therefore, the use of kinetic rather than an equilibrium sorption mechanism is recommended. Although our model formulation is based on direct reaction between metal ions in soil solution and specific sorption sites, others have considered a consecutive-type approach for Cd sorption. According to Thesis et al. (1988), a set of two second-order reactions was considered; one fully reversible step was followed by an irreversible reaction. Ion exchange, as discussed above, was not considered. Theis et al. (1988) argued that the amount adsorbed on geothite surfaces was susceptible to migration (via surface diffusion) from primary to secondary surface sites. Other possible mechanisms may include formation of surface complexes, hydrolysis of sorbate at the surface, and surface complexation. Tiller el al. (1979) quantified specific sorption as the amount of sites that retain metal ions following several washings of the soil with high concentrations of a nonspecifically sorbed cation (0.01 M Ca2NO3). As a result, metal ions on specific

H. M. SELIM

3 76

sites are not easily replaceable by Ca ions, but can be replaced (exchanged) by competing (specifically sorbed) ions such as Ni, Cd, Co, and Zn. Figure 25 shows miscible displacement results of Cd in a Windsor sandy loam soil. A solution of 0.005 M Ca(N03)2 was applied to a packed soil column at a Darcy flow velocity of 0.271 m/day. A pulse of Cd(N03)2 dissolved in 0.005 M Ca(NO& solution was applied to the soil column and the effluent solution was collected and analyzed for Cd. The concentration of Cd in the input pulse was 100 mg/liter, therefore a small change in the total cationic concentration occurred from 10 mmol,/liter for the background solution to 11.786mmol,/liter for the Cd pulse. The use of the competitive transport model, assuming equilibrium conditions and ignoring specific sorption (kf= 0), resulted in an overestimation of the peak concentration and the BTC was more retarded than that experimentally measured (curve A, Fig. 25). For this model prediction, a selectivity coefficient (KCdCa)of 2, indicating a slight preference of Cd over Ca to the exchange surfaces, was used. This value was based on experimental measurements for a Cd-Ca sorption isotherm. All other parameters were experimentally measured or estimated using other experimental measurements, e.g., the dispersion coefficient was estimated using 3H20 BTCs. The use of the kinetic ion exchange mechanism along with specific sorption appears to provide an improved prediction of miscible displacements for Cd as shown by curves B-D in Fig. 25. Although the use of smaller values for a provided improved prediction of the observed tailing, BTCs were less retarded in comparison to the measured BTC. It is obvious that additional evaluation of this approach is needed for the prediction of the retention of other ions during transport in the soil profile.

h

u"

Cd Breakthrough Curve, 100 mglliter - Windsor Soil

0.8-

.-..-

5

PORE VOLUME (V/V,) Figure 25. Measured (closed circles) and predicted BTCs for Cd in Windsor soil. Prediction using equilibrium ion exchange model is shown by curve A. Curves B, C, and D are calculations using the kinetic ion exchange model with a of 2 day-' and specific sorption (kf) values of 0, 0.5, and 1.0 day-', respectively.

MODELS OF INORGANICS IN SOILS

377

VII. TRANSPORT IN LAYERED SOIL None of the mathematical models presented thus far deals with the problem of the fate of reactive or noreactive solutes in nonhomogeneous or layered soils. Because soil profiles are seldom uniform, it is essential to consider the fate of dissolved chemicals in stratified soil systems. The study of Shamir and Harleman (1967) is one of the earliest papers dealing with nonreactive solute transport through layered porous media having great depths ( z --* w). Others, including Rubin and James (1973), Selim et al. (1977), Selim (1978), and Barry and Parker (1987), considered the fate of reactive solutes in layered soils under various flow and boundary conditions. A schematic diagram of a soil of length L with three distinct layers I, 11, and I11 is shown in Fig. 26. Each layer has specific but not necessarily the same 0,p, and solute retention characteristics. First, we consider the case of solute transport in layered soils wherein fully saturated conditions under constant Darcy’s flux (steady flow) prevail. This is followed by cases for water-unsaturated layered soils under steady and transient water flow conditions.

A. SATURATED SOILSUNDER CONSTANT FLUX Simulations were carried out using equilibrium linear and nonlinear (Freundlich) [see Eq. (15)] as well as first-order kinetic (reversible and

.!

T

Ll

I

Figure 26. Schematic diagram of a three-layered soil.

378

H. M. SELIM

irreversible) retention [see Eqs. (19) and (23)] to describe solute behavior in each layer of a multilayered soil profile. Different simulations were conducted to evaluate the importance of soil layer stratification and adsorption characteristics on the shape and position of effluent concentration distributions layered _ _ from _ ~ ~ soil profiles. For the linear case, a retardation factor R [ R = 1 + p K d / O ;see Eq. (29)] represents the magnitude of retention for each layer and is represented by R 1 , R 2 , etc. (Selim et al., 1977). Solid lines in Fig. 27 are simulated BTC results from columns in which the solute passed first through L1 and then L 2 ; open circles are calculated results of solute flow in the opposite direction. The dashed lines are for the homogeneous cases wherein L = L1 or L = L2 and these cases have the appropriate retardation factors, R1 or R 2 , respectively. As expected, the BTCs for the two-layered cases lie between the homogeneous cases R1 and R2 (dashed lines). The retardation factor R1 equals one and represents a nonreactive solute whereas R2 equals 10. Increasing L2 (or decreasing L,) causes the breakthrough curves to move to the right toward the homogeneous R2 case. The most striking result in Fig. 27 is the failure of the order of soil layers to influence the shape or position of the effluent concentration distribution. Based on these results, a layered soil profile could be regarded as homogeneous with an average retardation factor used to calculate emuent concentration distributions. An average retardation factor R for N-layered soil can simply be obtained from

0

2

4

6

8

v

/

10

12

14

16

v.

Figure 27. Simulated effluent concentration distribution for two-layered soils with retardation factors R, and RZ (linear sorption) and varying lengths L , and Lz. Solid lines are simulations wherein layer 1 with R, is first encountered, whereas open circles are for flow in the reverse direction. Dashed curves are for homogeneous soils. From Selim et al. (1977), with permission.

MODELS OF INORGANICS IN SOILS

3 79

BTCs identical to those in Fig. 27 were obtained using the solution to the convention-dispersion Eq. (7) presented by Lindstrom et al. (1967) and an average retardation factor. This averaging procedure [Eq. (65)] can also be used to describe the BTCs from a soil profile composed of three or more layers (see Selim et al., 1977). However, if solute distribution within the profile is desired, the use of an average retardation factor is no longer valid and the problem must be treated as a multilayered case. The nonlinear (Freundlich) equilibrium and first-order kinetic retention were also considered for a two-layered soil column with L1= L2 = 0.5L (Selim ef al., 1977). The BTCs were identical regardless of the sequence of soil layers (not shown in Fig. 27). This is a significant conclusion and can be used to simplify field problems involving solute movement through nonhomogeneous field soils under steady water-saturated flow conditions. Unlike the linear adsorption cases, an average retardation factor R for nonlinear adsorption cannot be obtained, because of the dependency of R on the solute concentration C.

B. WATER-UNSATURATED SOILS Selim et al. (1977) simulated solute transport through water-unsaturated multilayered soil profiles, in which a steady, vertically downward water flow (q = constant) was considered. A soil profile was assumed to consist of two distinct layers, sand and clay, each having equal lengths, and was underlain by a water table at a depth L = 100 cm. The case for which the water table was at great depth (z 4 m) was also considered. When a constant flux was assumed, the steady state 0 and water suction (h) distributions for a sand-clay and a clay-sand soil profile were calculated (see Fig. 28 for the clay-sand case). Solute concentration versus pore volume of effluent (collected at a 100-cm depth) for a nonreactive and reactive solute having linear (equilibrium) retention is shown in Fig. 29. As expected, similar BTC results for the nonreactive solute for sand-clay or clay-sand soil profiles were obtained. In contrast, BTCs for the reactive solute show a distinct separation, with lower retardation factors for the soil profiles having a water table at z = 100 cm than at z + m. This observation is consistent for the sand-clay as well as clay-sand profiles. Due to the higher water contents in the soil profiles, wherein the water table was at x = 100, the retardation factor R is less in comparison to the case for which the water table was at great depth ( z + m). If the water content distributions were considered uniform, with an average water content within each individual layer (see Fig. 28), the problem of solute transport and retention through unsaturated multilayered soil profiles can be significantly simplified, as discussed in the

H. M. SELIM

3 80

Water Suction h, cm 40 60 80 100

A

6

20

Water Suction h, cm

0

5 2o 5 n

, I

,

/

/

/

__

100

40

#.-

60

53

80 100

0 0.1 0.2 0.3 0.4 0.5

I

0

.

0.1 0.2 0.3 0.4 0.5 Water Content e,cm3/cm3

Water Content 8,cm 31cm 3 Figure 28. Simulated water content 0 and water suction h versus depth in a clay-sand profile having a water table at (A) 100-cm depth and (B) great depth. From Selim et al. (1977), with permission.

previous section. The open circles in Fig. 29 are calculated results of concentration distributions for the reactive and nonreactive solutes when an average water content within each layer was used. These results show that, for all unsaturated profiles considered, the use of average water contents (open circles) provided concentration distributions identical to those obtained when the actual water content distributions were used (dashed and solid lines). Thus, when a steady water flux is maintained

1.0

0.8

0" 0.6

.

u

water table at x = 1 0 0 c m

0.4

0.2

0 0

1

2

3

4

5

6

7

8

v I v, Figure 29. Simulated effluent concentration distribution for reactive and nonreactive solute in an unsaturated clay-sand profile. Open circles are simulations based on average water content 0 for each soil layer. From Selim et al. (1977), with permission.

MODELS OF INORGANICS IN SOILS

381

through the profile, BTCs of reactive and nonreactive solutes at a given location in the soil profile can be predicted with average water contents within unsaturated soil layers. Based on the above results we can conclude that average microhydrologic characteristics for a soil layer can be used to describe the movement of solutes leaving a multilayered soil profile. This conclusion supports the assumption made earlier that uniform soil water content can be used to represent each soil layer in order to simplify the solute transport problem. However, such a simplifying approach was not applicable for the general case of transient water flow conditions of unsaturated multilayered soils. As illustrated by Selim (1978), the transport of reactive, as well as nonreactive, solutes through multilayered soils, for transient water flow, was significantly influenced by the order in which the soil layers were stratified.

REFERENCES Amacher, M. C., Kotuby-Amacher, J., Selim, H. M., and Iskandar, I. K. (1986). Retention and release of metals by soils-evaluation of several models. G e o d e m a 38, 131-154. Amacher, M. C., Selim, H. M . , and Iskandar, I. K. (1988). Kinetics of chromium (VI) and cadmium retention in soils: A nonlinear multireaction model. Soil Sci. SOC. Am. J . 52, 398-408. Amacher, M. C., Selim, H. M., and Iskandar, I. K. (1990). Kinetics of mercuric chloride retention in soils. J . Environ. Qual. 19, 382-388. Aringhieri, R., Carrai, P., and Petruzzelli, P. (1985). Kinetics of Cu and Cd adsorption by an Italian soil. Soil Sci. 139, 197-209. Barrow, N. J. (1989). Suitability of sorption-desorption methods to simulate partitioning and movement of ions in soils. Ecol. Stud. 74, 3-17. Barry, D. A., and Parker, J. C. (1987). Approximation of solute transport through porous media transverse to layering. Tramp. Porous Media 2, 65-82. Brenner, H. (1962). The diffusion model of longitudinal mixing in beds of finite length. Numerical values. Chem. Eng. Sci. 17, 220-243. Buchter, B., Davidoff, B., Amacher, M. C., Him, C., Iskandar, I. K., and Selim, H. M. (1989). Correlation of Freundlich Kd and n retention parameters with soils and elements. Soil Sci. 148, 370-379. Cederberg, G. A,, Street, R. L., and Leckie, 0. J. (1985). A groundwater mass transport and equilibrium chemistry model for multicomponent systems. Water Resour. Res. 21, 1095-1104. Cho, C. M. (1971). Convective transport of ammonium with nitrification in soil. Can. J . Soil Sci. 51, 339-350. Coats, K. H., and Smith, B. D. (1964). Dead-end pore volume and dispersion in porous media. SOC.Pet. Eng. 4, 73-84. Crank, J. (1956). “Mathematics of Diffusion.” Oxford Univ. Press. London. Danckwerts, P. V. (1953). Continuous flow systems. Distribution of residence times. Chem. Eng. Sci. 2, 1-13. De Camargo, 0. A., Biggar, J. W., and Nielsen, D. R. (1979). Transport of inorganic phosphorus is an Alfisol. Soil Sci. SOC.Am. J . 43, 884-890.

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Fiskell, J. G. A,, Mansell, R. S . , Selim, H. M., and Martin, G. G. (1979). Kinetic behavior of phosphate sorption by acid sandy soil. J . Environ. Qual. 8, 579-584. Fliihler, H., Polomski, J. and Blaser, P. (1982). Retention and movement of fluoride in soils. J . Environ. Qual. 11, 461-468. Fried, J. J., and Combarnous, M. A. (1971). Dispersion in porous media. A d v . Hydrosci. 7, 169-282. Gaston, L. A., and Selim, H. M. (1990). Transport of exchangeable cations in an aggregated clay soil. Soil Sci. SOC. Am. J. 54, 31-38. Helfferich, F. (1962). “Ion Exchange.” McGraw-Hill, New York. Helfferich, F., and Klein, G. (1970). “Multicomponent Chromatography,” pp. 1-147. Dekker, New York. Holford, I. C. R., and Mattingly, G. E. G. (1975). The high- and low-energy phosphate adsorption surfaces in calcareous soils. J . Soil Sci. 26,407-417. Holford, I. C. R., Wedderburn, R. W. M., and Mattingly, G. E. G. (1974). A Langmuir two-surface equation as a model of phosphate adsorption by soils. 1. Soil Sci. 25, 242-254. Jardine, P. M., and Sparks, D. L. (1984). Potassium-calcium exchange in a multireactive soil system. I. Kinetics. Soil Sci. SOC. Am. J . 48, 39-45. Jardine, P. M., Parker, J. C., and Zelazny, L. W. (1985). Kinetics and mechanisms of aluminum adsorption on kaolinite using a two-site nonequilibrium transport model. Soil Sci. SOC.Am. J . 49, 867-873. Jennings, A. A. (1987). Critical chemical reaction rates for multicomponent groundwater contamination models. Water Resour. Res. 23, 1775-1784. Jennings, A. A., and Kirkner, D. J. (1984). Instantaneous equilibrium approximation analysis. 1. Hydraul. Div., A m . SOC.Civ. Eng. 110, 1700-1717. Jennings, A. A., Kirkner, D. J., and Theis, T. L. (1982). Multicomponent equilibrium chemistry in groundwater quality models. Water Resour. Res. 18, 1089-1096. Kirkner, D. J., Jennings, A. A . , and Theis, T. L. (1985). Multisolute mass transport with chemical interaction kinetics. J . Hydrol. 76, 107-117. Kreft, A. and Zuber, A. (1978). On the physical meaning of the dispersion equation and its solutions for different initial and boundary conditions. Chem. Eng. Sci. 33, 1471-1480. Lai, S.-H., and Jurinak, J. J. (1972). Cation adsorption in one dimensional flow through soils. A numerical solution. Water Resour. Res. 8, 99-107. Lai, S.-H., Jurinak, J. J., and Wagenet, R. J. (1978). Multicomponent cation adsorption during convection-dispersive flow through soils. Experimental study. Soil Sci. SOC.Am. J . 42, 240-243. Langmuir, I. (1918). The adsorption of gases on plane surfaces of glass, mica and platinum, J . A m . Chem. SOC.40, 1361-1402. Lapidus, L., and Amundson, N. R. (1952). Mathematics of adsorption in beds. M.The effect of longitudinal diffusion in ion exchange and chromatographic column. J . Phys. Chem. 56, 984-988. Lindstrom, F. T., Hague, R. Freed, V. H., and Boersma, L. (1967). Theory on movement of some herbicides in soils. Linear diffusion and convection of chemicals in soils. Environ. Sci. Technol. 1, 561-565. Mansell, R. S., Selim, H. M., Kanchanasut, P., Davidson, J. M., and Fiskell, J. G. A. (1977). Experimental and simulated transport of phosphorus through sandy soils. Water Resour. Res. 13, 189-194. Mansell, R. S . , Bloom, S . A., Selim, H. M., and Rhue, R. D. (1988). Simulated transport of multiple cations in soil using variable selectivity coefficients. Soil Sci. SOC.A m . J. 52, 1533- 1540. Miller, C. W., and Benson, L. V. (1983). Simulation of solute transport in a chemically

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reactive heterogeneous system. Model development and application. Water Resour. Res. 19, 381-391. Munns, D. N., and Fox, R. L. (1976). The slow reactions which continues after phosphate adsorption; kinetics and equilibrium in some tropical soils. Soil Sci. SOC. Am. J . 40, 46-51. Murali, V., and Aylmore, L. A. G. (1983). Competitive adsorption during solute transport in soils. 1. Mathematical models. Soil Sci. 135, 143-150. Nielsen, D. R., van Genuchten, M. Th., and Biggar, J. W. (1986). Water flow and solute transport processes in the unsaturated zone. Water Resour. Res. 22, 89s-108s. Nkedi-Kizza, P., Biggar, J. M., Selim, H. M., van Genuchten, M. Th., Wierenga, P. J., Davidson, J. M., and Nielsen, D. R. (1984). On the equivalence of two conceptual models for describing ion exchange during transport through an aggregated soil. Water Resour. Res. 20, 1123-1130. Ogwada, R. A., and Sparks, D. L. (1986). Kinetics of ion exchange on clay minerals and soil. I. Evaluation of methods. Soil Sci. SOC.Am. J. 50, 1158-1162. Ozisik, M. N. (1968). “Boundary value Problems of Heat Conduction.” International Textbooks, Scranton, Pennsylvania. Parker, J. C., and Jardine, P. M. (1986). Effect of heterogeneous adsorption behavior on ion transport. Water Resour. Res. 22, 1334-1340. Rasmuson, A., and Neretienks, I. (1981). Migration of radionuclides in fissured rock. The influence of micropore diffusion and longitudinal dispersion. J . Geophys. Res. 86,37493758. Rubin, J. (1983). Transport of reactive solutes in porous media. Relation between mathematical nature of problem formulation and chemical nature of reactions. Water Resour. Res. 19, 1231-1252. Rubin, J., and James, R. V. (1973). Dispersion-affected transport of reacting solution in saturated porous media. Galerkin method applied to equilibrium-controlled exchange in unidirectional steady water flow. Water Resour. Res. 9, 1332-1356. Schmidt, H. W., and Sticher, H. (1986). Long-term trend analysis of heavy metal content and translocation in soils. Geoderma 38, 195-207. Schulin, R., Fliihler, H., Mansell, R. S . , and Selim, H. M. (1986). Miscible displacement of ions in aggregated soils. Geoderma 38, 311-322. Schulin, R., Papritz, A., Fluhler, H., and Selim, H . M. (1989). Calcium and magnesium transport in aggregated soils at variable ionic strength. Geoderma 44, 129-141. Selim, H. M. (1978). Transport of reactive solutes during transient unsaturated water flow in multilayered soils. Soil Sci. 126, 127-135. Selim, H. M. (1981). Modeling kinetic behavior of cadmium interaction in soils. I n “Summer Computer Simulation Conference, Washington, D. C., 1981,”pp. 385-390. Simulation Councils Inc., La Jolla, California. Selim, H. M. (1989). Prediction of contaminant retention and transport in soils using multireaction models. Environ. Health Perspect. 39, 69-75. Selim, H. M., and Amacher, M. C . (1988). A second order kinetic approach for modeling solute retention transport in soils. Water Resour. Res. 24, 2061-2075. Selim, H. M., and Iskandar, I. K. (1981). Modeling nitrogen transport and transformations in soils. 1. Theoretical considerations. Soil Sci. 131, 233-241. Selim. H. M., and Mansell, R . S. (1976). Analytical solution of the equation of reactive solutes through soils. Water Resour. Res. 12, 528-532. Selim, H. M., Davidson, J. M., and Mansell, R. S . (1976). Evaluation of a 2-site adsorptiondesorption model for describing solute transport in soils. I n “Summer Computer Simulation Conference, Washington, D. C., 1976” pp. 444-448. Simulation Councils Inc., La Jolla, California.

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Selim, H. M., Davidson, J. M., and Rao, P. S. C. (1977). Transport of reactive solutes through multilayered soils. Soil Sci. SOC.Am. J . 41, 3-10. Selim, H. M., Schulin, R., and Fliihler, H. (1987). Transport and ion exchange of calcium and magnesium in an aggregated soil. Soil Sci. SOC.Am. J . 51,876-884. Selim, H. M., Amacher, M. C., and Iskandar, I. K. (1989). Modeling the transport of chromium (VI)in soil columns. Soil Sci. SOC. Am. J . 53, 996-1004. Selim, H. M., Amacher, M. C., and Iskandar, I. K. (1990a). “Modeling the Transport of Heavy Metals in Soils,” CRREL-Monogr. 90-2. U. S. Army Corps of Engineers, Washington. D. C. Selim, H. M., Amacher, M. C., Persaud, N., and Mansell, R. S. (1990b). Retention and transport of phosphorus in soils; a multireaction approach. I n “Summer Computer Simulation Conference, Calgary, Alberta, 1990,” pp. 596-603. Simulation Councils Inc., La Jolla, California. Shamir, U. Y., and Harleman, D. R. F. (1967). Dispersion in layered porous media. J. Hydraul. Div., Am. SOC. Civ. Eng. 93, 237-260. Skopp, J. (1986). Analysis of time-dependent chemical processes in soils. J. Environ. Qunl. 15, 205-213. Sparks, D. L. (1989). “Kinetics of Soil Chemical Processes.” Academic Press, San Diego, California. Sposito, G. (1980). Derivation of the Freundlich equation for ion exchange reactions in soils. Soil Sci. SOC. Am. J . 44,652-654. Sposito, G. (1981). “The Thermodynamics of Soil Solutions.” Oxford Univ. Press, New York. Sposito, G. (1984). “The Surface Chemistry of Soils.” Oxford Univ. Press, New York. Theis, T. L. (1988). Reactions and transport of trace metals in groundwater. In “Metal Speciation: Theory, Analysis and Application” (J. R. Kramer and H. E. Allen, eds.), pp. 81-89. Lewis, Chelsea, Michigan. Theis, T. L., Iyer, R., and Kaul, L. W. (1988). Kinetic studies of cadmium and ferricyanide adsorption on goethite. Environ, Sci. Technol. 22, 1032-1017. Tiller, K. G., Nayyar, V. K., and Clayton, P. M. (1979). Specific and nonspecific sorption of cadmium by soil clays as influenced by zinc and calcium. Aust. J. Soil Res. 17, 17-28. Tiller, K. G., Gerth, J., and Briimmer, G. (1984). The relative affinitiesof Cd, Ni, and Zn for different soils clay fractions. Procedures and partitioning of bound forms and their interpretations. Geoderrna 34, 1-16. Travis, C. C., and Etnier, E. L. (1981). A survey of sorption relationships for reactive solutes in soil. J . Environ. Qual. 10, 8-17. Valocchi, A. J. (1985). Validity of the local equilibrium assumption for modeling sorbing solute transport through homogeneous soils. Water Resour. Res. 21, 808-820. Valocchi, A. J., Street, R. L., and Roberts, P. V. (1981). Transport of ion-exchange solutes in groundwater. Chromatographic theory and field simulations. Water Resour. Res. 17, 1517-1527. Van Eijkeren, J. C. M., and Loch, J. P. G. (1984). Transport of cation solutes in sorbing porous media. Water Resour. Res. 20, 714-718. van Genuchten, M. Th., and Alves, W. J. (1982). Analytical solutions of the onedimensional convective-dispersive solute transport equation. U.s., Depr. Agric., Bull. 1661, 1-151. van Genuchten, M. Th., and Wierenga, P. J. (1976). Mass transfer studies sorbing porous media I. Analytical solutions. Soil Sci. SOC. Am. J. 40,473-480.

Index A Abscission, corn, 209-210, 227 Absorption matrix, fingerprinting crop varieties, 89 Acid exchange, deposition on forested soils, 40 Acid-neutralizing capacity (ANC), deposition on forested soils, 4-6 forest soils, 12, 16, 18.24, 36,39 Acidic deposition on forested soils, 1-3. 65-66 chemical factors cation exchange, 19-29 hydrogen budgets, 38-40 mineral weathering, 35-38 nitrate retention, 29-32 soil organisms, 40-42 soil pH, 15-19 sulfate retention, 29, 32-35 future research, 63-65 physical factors canopy interactions, 7-9 hydrology, 11-15 soil horizons, 9-11 soil acidification, 3-6 soil change studies intensity-type changes, 44-48 leaching, 48-56,61-63 measurement, 42-43 seasonal variations, 59-61 uptake, 56-59,61-63 Acidification, soil, see Acidic deposition on forested soils Activity coefficient inorganics in soils, 368 surface complexation models, 234-235, 238 Adaptation, corn, 206-207,214,222 Adjustable parameters, surface complexation models, 236-237 Adsorption acidic deposition on forested soils, 33, 54-56,64

chemical transport through soil processes, 161-165.169, 171-175 solute transport, field studies of, 182-185, 188 inorganics in soils, 332, 340 ion exchange, 369,372-376 kinetic retention models, 345-347, 349-350 layered soil, 378-379 multiple-reaction models, 351, 358-359 surface complexation models competitive adsorption reactions, 312-318 description, 235,235-238.241. 243-248,320 inorganic anion adsorption, 289-307 metal ion adsorption, 274-289 organic ligand adsorption, 308-312 protonation-dissociation reactions, 5 1, 256-257,261,266,274 Adsorption density, surface complexation models, 283 Adsorption envelopes, surface complexation models, 289,301,308-309 Adsorption isotherm models, chemical transport through soil, 161 Adsorptive additivity, surface complexation models, 278 Air pollution, acidic deposition on forested soils, 63 Alleles corn, 208,211-212,227 fingerprinting crop varieties, 98, 105 Alumino hydroxy sulfate, acidic deposition on forested soils, 33-34 Aluminum acidic deposition on forested soils chemical factors, 16, 19-29.31.34-35 future research, 63 hydrogen budgets, 40 mineral weathering, 37-38 physical factors, 14-15 soil change studies, 43,45-46.58-59, 61,63

385

Aluminum (conrinued) soil organisms, 42 inorganics in soils, 352-353 surface complexation models, 276,278, 300-302,315 toxicity, acidic deposition on forested soils, 65 Aluminum oxide acidic deposition on forested soils, 32-34 inorganics in soils, 351 surface complexation models, 237 inorganic anion adsorption, 289-291, 297.300-301,305 metal ion adsorption, 275, 279 organic ligand adsorption, 308 protonation-dissociation reactions, 252-254,261,265 Aluminum trihydroxide, acidic deposition on forested soils, 22, 26 Amino acids fingerprinting crop varieties, 89 surface complexation models, 308,310 Amplification, fingerprinting crop varieties, 100,126 Anatase, surface complexation models, 252, 261 Anion exchange, acidic deposition on forested soils, 34 Anions acidic deposition on forested soils, 55-56, 61,64 inorganics in soils, 350 surface complexation models, 242,244, 249,266,320 competitive adsorption reactions, 314-316,318 inorganic anion adsorption, 289-307 Antibodies, fingerprinting crop varieties, 89 Antigens, fingerprinting crop varieties, 89 Apparent dispersivity, chemical transport through soil, 181-182 Apples, fingerprinting crop varieties, 94, 97 Arsenate, surface complexation models, 314-316,318 Arsenic, surface complexation models, 291, 295 Arsenite, surface complexation models, 305, 307,318 Artificial selection, corn, 222 Asymptotic dispersion models, chemical transport through soil, 151-154

Atrazine, chemical transport through soil, 184, 187-188 Autoradiograms, fingerprinting crop varieties, 103-104

B Back-crossing, fingerprinting crop varieties, 108, 112,121-122 Barley, fingerprinting crop varieties, 94, 113-114, 123 Base cations, acidic deposition on forested soils chemical factors, 28, 31.36-37 soil change studies, 54,57, 61 Base saturation (BS), acidic deposition on forested soils, 19-20.26 Bicarbonate salts, acidic deposition on forested soils, 20 Bidentate metal complex, surface complexation models, 241, 247,281, 290,295, 308 Biological transformation, chemical transport through soil, 168-169 Block inheritance, corn, 211 Boehmite, surface complexation models, 250, 253-254.256 Borate, surface complexation models, 304-305 Boron, surface complexation models, 243. 290-291,300,303 Boundary conditions, inorganics in soils, 335-336 Branching habit, corn, 219,221,226 Brassicas, fingerprinting crop varieties, 123, 125 Breakthrough curves, inorganics in soils ion exchange, 376,378-379 layered soil, 381 multiple-reaction models, 352, 356, 364-365 Bromide, chemical transport through soil, 176, 178,183-185,187 Buffer capacity, acidic deposition on forested soils, 18-19 Buffering mechanisms, acidic deposition on forested soils, 16, 35 Buffering ranges, acidic deposition on forested soils, 20, 26, 31,40, 66 Bulk density, inorganics in soils, 332, 334

INDEX C Cadmium inorganics in soils ion exchange, 368, 375-376 kinetic retention models, 342 multiple-reaction models, 350-351, 353 transport equations, 339-340 surface complexation models competitive adsorption reactions, 312-313,318 computer codes, 319 metal ion adsorption, 278,282,285, 287-288 Calcium acidic deposition on forested soils chemical factors, 17.20-21,24,26,28 mineral weathering, 36-37 physical factors, 7 , 9 soil change studies, 45, 52,54,56-62 deficiency, acidic deposition on forested soils, 29 inorganics in soils, 367-373, 376 surface complexation models, 282,285, 305,312-313,318 Calcium carbonate, surface complexation models, 285, 305 Calcium nitrate, surface complexation models, 285-286 Canopy, acidic deposition on forested soils, 7-9,28,31-32,41 Capacitance density, surface complexation models, 236,238 Capacity changes, acidic deposition on forested soils leaching, 48-56 uptake, 56-59 Capacity factors, acidic deposition on forested soils, 5-6, 66 Carbon acidic deposition on forested soils, 9, 30, 4 1-42 surface complexation models, 315 Carbon dioxide, acidic deposition on forested soils, 18 Carbonate, surface complexation models, 315, 317 Carbonic acid, deposition on forested soils, 3, 56,66 forest soils, 16, 18-19, 27-28

387

Cation exchange acidic deposition on forested soils, 66 chemical factors, 19-29, 31,38 soil change studies, 42,45,55.57, 59-63 inorganics in soils, 369-372 Cation exchange capacity, acidic deposition on forested soils, 16, 37 Cations inorganics in soils, 350,367-368, 375 surface complexation models, 242, 244, 249,254 competitive adsorption reactions, 312, 318 metal ion adsorption, 282,286 Celery, fingerprinting crop varieties, 124-125 Charge balance, surface complexation models, 236,245-246 Charge-potential relations, surface complexation models, 240,245, 250 Chemical mass flux laws, chemical transport through soil, 145-154 Chemical phase concentrations, chemical transport through soil, 144-145 Chemical speciation models, 319 Chemical transport through soil, 142-143, 191-192 field regime dispersion, 193-194 preferential Row, 192 rate processes, 193 history, 194-195 processes, 143-144 chemical mass flux laws, 145-154 chemical phase concentrations, 144-145 concentrations, 160-161 convection-dispersion, 154-156 dispersion transport, 169-171 interphase mass transfer laws, 161-167 mass conservation law, 144 react ions, 167- I69 solute adsorption, 171-175 transfer function models, 156-160 solute transport, field studies of, 175-176 indices of solute disperion, 189-191 mean solute velocity, 176-179 preferential flow, 185-188 scale of heterogeneity, 189 solute adsorption, 182-185 solute dispersion, 179-182 CHEMTRAN, inorganics in soils, 367-368

388

INDEX

Chloride chemical transport through soil, 176,181, 183-184, 186 surface complexation models, 266 Chromate, surface complexation models, 300-303,315,318 Chromatography fingerprinting crop varieties characters, 89,95-96,98 discrimination, 110 usage, 123,125 inorganics in soils, 367 Chromium, inorganics in soils, 340, 350-351,353,364,367 Chromosomes corn, 207-209,214,219,227 chromosome 4,210-212.227 fingerprinting crop varieties, 102, 124, 126 Citrate, surface complexation models, 304, 312 Clay minerals inorganics in soils, 332 surface complexation models, 320 inorganic anion adsorption, 289-291, 295 metal ion adsorption, 275,277 protonation-dissociation reactions, 254-256,267-268 Climate, acidic deposition on forested soils, 63 Clones, fingerprinting crop varieties, 100-101,103-104 Cloudwater, acidic deposition on forested soils, 7-8 Cobalt inorganics in soils, 339, 375-376 surface complexation models, 278, 286, 318 Coleoptile isozymes, fingerprinting crop varieties, 112-113, 115 Competitive adsorption reactions, surface complexation models, 312-318 Competitive transport models, inorganics in soils, 376 Complementary DNA, fingerprinting crop varieties, 99-100 Computer codes, surface complexation models, 319-320 Computer programs, surface complexation models, 279,290,297,319-320, see also FITEQL Concentration, inorganics in soils, 369, 380

Concentration outflow, chemical transport through soil, 172 Concentration-depth curves, chemical transport through soil, 169 Concentration-time curves, chemical transport through soil, 169 Condensation, corn, 209,228 Conditional equilibrium constants, surface complexation models, 236,238-240, 242-244,248 competitive adsorption reactions, 316-3 17 inorganic anion adsorption, 290,297,300, 305 metal ion adsorption, 275, 279 organic ligand adsorption, 308 protonation-dissociation reactions, 25 1, 257 Constant capacitance model, 234,320 competitive adsorption reactions, 312, 314-317 computer codes, 319 description, 238-240,242-249 inorganic anion adsorption, 289-297 metal ion adsorption, 274-279 organic ligand adsorption, 308-310 protonation-dissociation reactions, 251-256,267 Convection chemical transport through soil, 193-195 processes, 147-151,156,159-160, 175 solute transport, field studies of, 178, 187 inorganics in soils, 334 Convection-dispersion model, chemical transport through soil, 152-156,166, 176, 189 Convective-dispersive equation, inorganics in soils equilibrium retention models, 337 ion exchange, 367,369-370, 373 kinetic retention models, 347 layered soil, 379 multiple-reaction models, 351-352,355, 361 transport equations, 335-336 Convective-dispersive flux, chemical transport through soil, 154-156, 158, 161, 195 Convective-lognormal transfer function, chemical transport through soil, 159-160

INDEX Copper inorganics in soils, 339-340, 342, 351 surface complexation models, 281,283, 286,312-313 Corn, evolution of, 203-205, 224,227-228 husk enclosure of ear, 222-224 increased femaleness, 225 interpathway heterosis, 221-222 multiple domestications, 21 4-215 cob morphology, 215-219 plant habit, 219-221 partitioning of photosynthate, 225-226 transformation, 205-207 domestication, 212-214 isolation, 211 key traits, 207-211 tunicate locus, 212 Cross-overs, corn, 211-212.227 Crossing fields, corn, 226 CSTT theory, corn, 213-215, 224 Cucumbers, corn, 224 Cupules. corn, 209-210,217,219,222, 227 Cycling, nutrient, acidic deposition on forested soils, 10, 31-32, 34, 63 Cytoplasm, fingerprinting crop varieties, 100

D Deacidification, deposition on forested soils, 32,34 Degradation, chemical transport through soil, 168 Desorption acidic deposition on forested soils, 56 chemical transport through soil, 164, 172 inorganics in soils, 332 ion exchange, 373,375 kinetic retention models, 345-347 multiple-reaction models, 350, 356 surface complexation models, 266, 282 Diffuse double-layer theory, surface complexation models, 236, 245, 250 Diffuse layer, surface complexation models, 260, 267,319 Diffuse layer charge, surface complexation models, 246-247,260 Diffusion chemical transport through soil, 193 processes, 147-149, 151-152,155, 166 processes, analysis of, 173-174 inorganics in soils, 334-335,350,359, 361 surface complexation models, 284

389

Diffusion-controlled model, inorganics in soils, 350 Diffusion-dispersion coefficient, chemical transport through soil, 156 Diffusive mass transfer, chemical transport through soil, 174 Digital image analysis, fingerprinting crop varieties, 87-88, 103 Discrimination, fingerprinting crop varieties ability, 108-110 new techniques, 125, 127 usage, 123 Dispersion, see also Convection-dispersion model; Convective-dispersive equation chemical transport through soil field regime, 193-195 processes, 152, 154-156, 158, 161, 167 solute transport, field studies of, 176, 179-182, 187,189-191 inorganics in soils, 334-336, 361-362 Dispersion coefficient, chemical transport through soil, 181 Dispersion models, asymptotic, chemical transport through soil, 151-154 Displacement, inorganics in soils, 357, 359, 364,372,376 Dissociation acidic deposition on forested soils. 17 surface complexation models, 234, 320 inorganic anion adsorption, 290, 301-302 metal ion adsorption, 286 protonation-dissociation reactions, 25 1-274 Dissolution chemical transport through soil, 165-166, 171,193 inorganics in soils, 332, 347, 354 Distance measures. fingerprinting crop varieties, 104-105 Distinctness, fingerprinting crop varieties, 119-120 Distribution coefficient chemical transport through soil, 163 inorganics in soils, 337 DNA, fingerprinting crop varieties, 86 characters, 88-89, 98-108 discrimination, 110, 112-118 new techniques, 125-127 usage, 119-121,124-125 Domestication of corn, 226,228 interpathway heterosis, 221-222

390

INDEX

Domestication of corn (continued) origin, 214-215 cob morphology, 215-219 plant habit, 219-221 tqansformation,205-2 12 time required, 212-214 Drainage water, acidic deposition on forested soils, 65 chemical factors, 39-40 physical factors, 11-12, 14 Dry deposition, acidic deposition on forested soils, 7, 35,40,48 Dual velocity models, chemical transport through soil, 167

E Effective dispersion coefficient, chemical transport through soil, 155 Electric double-layer theory, surface complexation models, 247 Electrical neutrality, acidic deposition on forested soils, 30 Electrolytes, surface complexation models, 235 description, 235,238,240,242,244 inorganic anion adsorption, 300 metal ion adsorption, 277,286,288 protonation-dissociation reactions, 254-255,267,272-273 triple-layer models, 256-258, 260-261, 263-264,266 Electrophoresis, fingerprintingcrop varieties characters, 89,95-99, 101 discrimination, 110-115 new techniques, 125-126 usage, 119-121,123,125 Electrostatic potential terms, surface complexation models, 236,246,320 Elongation, corn, 221,223,226-227 Endonucleases, restriction, fingerprinting crop varieties, 98, 100-101, 126 Environment, fingerprinting crop varieties, 87-88,95-97,120 Enzymes, see also Restriction enzymes fingerprintingcrop varieties, 89,98, 102, 104,109, 113 Equilibrium acidic deposition on forested soils, 45 inorganics in soils, 333,379 equilibrium retention models, 337-340

ionexchange, 367-369,371,374,376 kinetic retention models, 343,345-346 multiple-reaction models, 350, 352-353,360,363 Equilibrium adsorption, chemical transport through soil, 162, 165,172,174,185, 187 Equilibrium concentrations, chemical transport through soil, 168 Equilibrium constants, surface complexation models, 234-235 competitive adsorption reactions, 316-317 computer codes, 319 description, 236-237,239-240,242-245. 248,250 inorganic anion adsorption, 290,297,300, 305,307 metal ion adsorption, 275,277-279,282, 287-288 organic ligand adsorption, 308-309 protonation-dissociation reactions, 25 1, 254-255,257,267,273 Equilibrium partitioning, chemical transport through soil, 171 Equilibrium pressure, chemical transport through soil, 146 Equilibrium sorption, chemical transport through soil, 183 External-internal proton ratio (EIPR), acidic deposition on forested soils, 39-40

F Faraday constant, surface complexation models, 236, 246 FASTCHEM, surface complexation models, 320 Feedback corn, 223,228 surface complexation models, 285 Femaleness, corn, 221-222 Feminization, corn, 222, 225 Ferric oxide, hydrous, surface complexation models, 270-271,286-287.305-307 Fertilizer, inorganics in soils, 332-333 Field regime, chemical transport through soil, 192-195 FIESTA, inorganics in soils, 367-368 Fingerprinting crop varieties, 85-86 characters DNA data, 98-108

391 morphology, 86-88 protein data, 88-98 discrimination, 108-1 18 new techniques, 125-127 usage, 119 genetic diversity, 122-123 genetic purity, 123 germplasm improvement, 123-124 minimum distance, 121-122 misappropriation, 124-125 plant variety protection, 119-121 FITEQL, surface complexation models computer codes, 319 inorganic anion adsorption, 290,297, 305-306 metal ion adsorption, 277,279,286-287 protonation-dissociation reactions, 254-255,265-266,270-271 Fluoride inorganics in soils, 347 surface complexation models, 237,303, 3 15 Flux concentration, chemical transport through soil, 157, 160-161 Flux of dissolved chemical in soil, 147-151 Forested soils, acidic deposition on, see Acidic deposition on forested soils Freundlich adsorption, chemical transport through soil, 171 Freundlich isotherm models, chemical transport through soil, 162-163, 171-172 Freundlich models, inorganics in soils, 333, 345 equilibrium retention models, 337-340 multiple-reaction models, 350, 352

G Gametophyte genes, corn, 210-211, 227 Gene expression, corn, 227-228 Generalized two-layer model, 234,320 description, 246-248,270-271 inorganic anion adsorption, 305-307 metal ion adsorption, 278, 286-287 Genes, corn, 227-228 husk enclosure of ear, 223-224 multiple domestications, 215,217,219,221 transformation. 206-210, 212 Genetic control, fingerprinting crop varieties, 96-97,105

Genetic distance, fingerprinting crop varieties, 87-88,102,104-105, 108 Genetic diversity corn, 207 fingerprinting crop varieties, 86 characters, 87, 96, 100-102 discrimination, 109-110 new techniques, 126-127 usage, 122-123 Genetic markers, fingerprinting crop varieties, 107, 125, 127 Genetic purity, fingerprinting crop varieties, 123 Genomic DNA, fingerprinting crop varieties, 101-102 Genotype, fingerprinting crop varieties characters, 87-88,96,101-102, 104-106, 108 discrimination, 109-1 15 usage, 119,121-124 GEOCHEM model, chemical transport through soil, 168 Geographic diversity, corn, 200,215 Geothite, inorganics in soils, 375 Germplasm corn, 207,211,219,225 fingerprinting crop varieties, 108-109, 123-124,126 Gibbsite acidic deposition on forested soils, 22, 25 surface complexation models, 314-315 Gliadin, fingerprinting crop varieties, 95, 111, 119-120, 124 Glitelins, fingerprinting crop varieties, 95, 111, 114-115 Globulin, fingerprinting crop varieties, 95-97, 114 Glume, corn, 210-212,227 Glutamate, surface complexation models, 308 Goethite, surface complexation models, 237 competitive adsorption reactions, 312, 314-315.318 inorganic anion adsorption, 290,297,300, 302-304,307 metal ion adsorption, 276, 279, 282-283, 285 organic ligand adsorption, 309,312 protonation-dissociation reactions, 251-254,256,258.266-268.274 Groundwater acidic deposition on forested soils, 13, 35

INDEX

392

Groundwater (conrinued) chemical transport through soil, 142, 154, 174, 187, 189 inorganics in soils, 331

H Harvesting acidic deposition on forested soils, 3, 31, 34,63 corn, 209,214 Hematite, surface complexation models, 288 Herbicides, chemical transport through soil, 182-183 Hermaphroditism, corn, 205,223-224 Heterogeneity chemical transport through soil, 143, 189, 191 inorganics in soils, 359,361 surface complexation models, 320 competitive adsorption reactions, 314 inorganic anion adsorption, 291 metal ion adsorption, 277,284,288 protonation-dissociation reactions, 255, 266-267,273-274 Heterosis fingerprinting crop varieties, 105, 122, 124 interpathway, corn, 221-222 High-performance liquid chromatography, reversed-phase, fingerprinting crop varieties, 95-96, 111-115 Hordeins, fingerprinting crop varieties, 113-114 Hormonelike control, corn, 223-224 Hormones, corn, 223,228 Humidity, acidic deposition on forested soils, 2, 16,18,63 Humus, acidic deposition on forested soils, 3, 64 Husk enclosure of corn ear, 222-224 Hybridization corn, 219,221-223.225-226 fingerprinting crop varieties characters, 97.99-101, 104-105 discrimination, 108-109, 113-114 new techniques, 126 usage, 122-124 HYDRAQL, surface complexation models, 297,319 Hydraulic conductivity, chemical transport through soil. 146, 154-155,158,191, 194

Hydrodynamic dispersion chemical transport through soil, 149 inorganics in soils, 334-335, 362 Hydrogen acidic deposition on forested soils, 1, 5 , 64-65 budgets, 38-40 chemical factors, 15-19,21-24,30, 33-34 mineral weathering, 36-37 physical factors, 7-9 soil change studies, 44,48,55 soil organisms, 40-42 inorganics in soils, 375 HYDROGEOCHEM, surface complexation models, 320 Hydrology acidic deposition on forested soils, 11-16, 27 chemical transport through soil, 149,191, 194 Hydrolysis acidic deposition on forested soils, 21, 24 chemical transport through soil, 168 inorganics in soils, 375 surface complexation models, 281,288 Hydrophobicity, fingerprinting crop varieties, 98 Hydrous ferric oxide, surface complexation models, 270-271,286-287.305-307 Hydroxyl groups, surface complexation models, 235-237,245,251,254,256 Hydroxyl ions, surface complexation models, 240,245,249,251,256 Hysteresis chemical transport through soil, 164, 172 inorganics in soils, 346-347

I Identical by descent, fingerprinting crop varieties, 105 Identity in state, fingerprinting crop varieties, 105 Illite, surface complexation models, 256, 267 Image analysis, fingerprinting crop varieties, 87-88,103 Immobile regions chemical transport through soil, 166, 173-174 inorganics in soils, 332 layered soil, 370-371, 373-374

INDEX multiple-reaction models, 359, 361-363, 365,367 Immobile water content, chemical transport through soil, 166,171 Immobilization, inorganics in soils, 347, 354 Infinite-time model of solute dispersion, chemical transport through soil, 152 Inflorescence, corn, 205,208,219,223-226 Inhibitors acidic deposition on forested soils, 45, 61 corn, 209-210,223-224 Inner-sphere surface complexes, surface complexation models, 238, 240,243, 245-246 competitive adsorption reactions, 313 inorganic anion adsorption, 300-301 metal ion adsorption, 282 Inorganics in soils, modeling of, 331-333 equilibrium retention models, 337 Freundlich, 337-340 Langmuir, 340 Langmuir two-site, 340-342 ion exchange, 367-368 classical approach, 368-370 mobile-immobile approach, 370-371 specific adsorption, 373-376 variable selectivities, 371-373 kinetic retention models, 342-350 layered soil, 377 constant flux, 377-379 water-unsaturated soils, 379-38 1 multiple-reaction models, 350-35 1 multireaction models, 353-358 second-order models, 358-367 two-site models, 351-353 transport equations, 333-336 Intensity acidic deposition on forested soils, 5-6, 44-48,66 Interpathway heterosis, corn, 221-222 Interphase transfer laws, chemical transport through soil, 161-167 Interspace trait, corn, 206-207 Introgression, corn, 210,214 Ion exchange, inorganics in soils, 332-333, 367-370 adsorption, 373-376 kinetic retention models, 349-350 mobile-immobile models, 370-371 multiple-reaction models, 360, 363 selectivities, 371-373

393

Ionic strength inorganics in soils, 360, 371 surface complexation models, 255,257. 260,265,270,300 Ions chemical transport through soil, 167, 184 inorganics in soils, 350 surface complexation models, 235, 320 competitive adsorption reactions, 312-313, 318 description, 235, 237-238, 240-242, 245-247 metal ion adsorption, 274-289 protonation-dissociation reactions, 25 1, 254,256,274 Iron acidic deposition on forested soils, 36 surface complexation models, 278, 287, 301-302 Iron oxide acidic deposition on forested soils, 33-34 inorganics in soils, 354 surface complexation models, 243 competitive adsorption reactions, 312-313, 315,317-318 inorganic anion adsorption, 289-291, 296-298,300,303,307 metal ion adsorption, 275-276, 278-279,281-282.287-288 organic ligand adsorption, 308-309 protonation-dissociation reactions, 251-253,267-268,270 triple-layer model, 256, 258-259, 265 Irrigation chemical transport through soil, 176, 178-179, 182-183, 186 corn, 205,207 Isoelectric focusing, fingerprinting crop varieties, 112, 114-115 Isoenzymes, fingerprinting crop varieties, 89-90,96-97 Isolation, corn, 205-207, 211 ISOQUAD, surface complexation models, 319 Isotherms chemical transport through soil, 161-165, 171-172 inorganics in soils equilibrium retention models, 339-341 ion exchange, 369,371-372,374,376 kinetic retention models, 343-345, 357 multiple-reaction models, 361, 363

INDEX

394 Isozymes corn, 215 fingerprinting crop varieties characters, 97-98, 103 discrimination, 109. 112-113, 115 usage, 120,122, 124-125

K Kaolinite acidic deposition on forested soils, 36 inorganics in soils, 349, 352 surface complexation models inorganic anion adsorption, 291,297, 301 metal ion adsorption, 275,277 protonation-dissociation reactions, 254-256 Kernels, corn, 207,212,225-227 exposure, 217-219 Key trait genes, corn, 207-211,227-228 Kinetic mass transfer, chemical transport through soil, 165 Kinetics inorganics in soils, 333,337-338,377 ion exchange, 368,373-376 kinetic retention models, 342-350 multiple-reaction models, 351-352, 354-456,358-364 surface complexation models, 266,282, 300-301.320

L Langmuir isotherms, chemical transport through soil, 162, 164 Langmuir models, inorganics in soils, 333, 337,340,350,360-361 Langmuir two-site model, inorganics in soils, 340-342.363 Layered soil, inorganics in soils, 377-381 Leaching acidic deposition on forested soils, 3-5 chemical factors, 18, 30, 32 future research, 64 physical factors, 13 soil change studies, 43,423-57,59, 61-63 chemical transport through soil, 167,176, 178,182-184 inorganics in soils, 331, 354,369

Lead inorganics in soils, 339-340 surface complexation models competitive adsorption reactions, 312-313,318 metal ion adsorption, 278,282-283, 286 Ligands, surface complexation models, 238, 240-241,243,248,320 competitive adsorption reactions, 314-318 inorganic anion adsorption, 291,300 organic ligand adsorption, 308-312 protonation-dissociation reactions, 25 1, 257 Lime, acidic deposition on forested soils, 55 Linear equilibrium adsorption, chemical transport through soil, 187 Linear equilibrium hypothesis, chemical transport through soil, 162 Linear isotherms, chemical transport through soil, 162-163, 172 Linear solute transport models, chemical transport through soil, 162 Linkage corn, 210-211,227 fingerprinting crop varieties, 103 Loam chemical transport through soil, 184, 187-188, 190 inorganics in soils, 371, 376 Loamy sand, chemical transport through soil, 186-188,190 Local dispersion, chemical transport through soil, 191 Lognormal frequency distribution, chemical transport through soil, 159 Longitudinal dispersion coefficient, inorganics in soils, 334-335 Lysimeters, acidic deposition on forested soils, 13

M Macropores, chemical transport through soil, 183, 187 Magnesium acidic deposition on forested soils chemical factors, 20-21, 36-37 physical factors, 7 soil change studies, 45,52,54,57-59, 61-63

395 inorganics in soils, 368-373 surface complexation models, 279,282, 312-313.318 Magnetites, surface complexation models, 256,258,297,300 Maize evolution of, see Corn, evolution of fingerprinting crop varieties, 94-97, 102-103 discrimination, 109- 113 new techniques, 125 usage, 120,122-123.125 Maleness, corn, 221,225-226 Manganese, surface complexation models, 279 Manganese oxide, surface complexation models, 259, 268,297 competitive adsorption reactions, 312-3 13, 315 inorganic anion adsorption, 300,303 metal ion adsorption, 279, 281 Mapping, fingerprinting crop varieties, 103-104, 126 Markers corn, 211 fingerprinting crop varieties, 103,125, 127 Masculinization, corn, 223,225-226 Mass balance chemical transport through soil, 160-161, 166, 181 surface complexation models, 238, 244 Mass balance equations, surface complexation models, 236, 242,250,258,320 Mass conservation law, chemical transport through soil, 144, 147,156 Mass flow, inorganics in soils, 334 Mass transfer chemical transport through soil, 187, 193 processes, 160-167 processes, analysis of, 172-175 inorganics in soils, 350,361-362, 374 Mass velocity, chemical transport through soil, 178 Mean convection rate, chemical transport through soil, 178, 187 Mean solute velocity, chemical transport through soil, 176-179 Mercury, surface complexation models, 286 Metal-ligand interactions, surface complexation models, 316-318

Metal-metal competition, surface complexation models, 312-313 Microbial population density, chemical transport through soil, 168-169 MICROQL, surface complexation models, 290,297, 319 MICTOQL, surface complexation models, 279 Migration, chemical transport through soil, 189, 193 MINEQL, surface complexation models, 279, 297,319 Mineral weathering, acidic deposition on forested soils, 35-40 Mineralization acidic deposition on forested soils, 5, 30-31,41,64 inorganics in soils, 347,354 Minerals clay, see Clay minerals inorganics in soils, 375 surface complexation models, 235,237, 309,316,320 protonation-dissociation reactions, 25 1, 254-256,267,270 MINETEQ, surface complexation models, 319-320 Minimum distance, fingerprinting crop varieties, 121-122, 126-127 Mobile regions chemical transport through soil, 173 inorganics in soils, 332,367,374 Mobile water content, chemical transport through soil, 166-167,174 Mobile-immobile models, inorganics in soils, 332 ion exchange, 370-371 multiple-reaction models, 359, 361-363, 367 Mobile-immobile water model, chemical transport through soil, 166,174 Modeling of inorganics in soils, see Inorganics in soils, modeling of Modified Roger's distance, fingerprinting crop varieties, 105 Molybdate, surface complexation models, 291, 300-301, 303-304, 315-316 Molybdenum, surface complexation models, 291,303 Montmorillonite inorganics in soils, 349

396

INDEX

Montmorillonite (continued) surface complexation models, 256,267, 270,29I Morphology corn, 215-219,221,225 fingerprinting crop varieties, 86-88, 108, 119-122 Multiple-reaction models, inorganics in soils, 350-351 multireaction models, 353-358 second order models, 358-367 two-site models, 351-353 Multireaction and transport model (MRTM), inorganics in soils, 355-358 Multireaction model (MRM), inorganics in soils, 355-356, 364 Multivariate analysis, fingerprinting crop varieties, 104-108 MUSIC program, surface complexation models, 274 Mutation corn, 205,213,224 multiple domestications, 215,221 transformation, 206,210 fingerprinting crop varieties, 98 Mycorrhizae, acidic deposition on forested soils, 64-65

N Napropamide, chemical transport through soil, 183,187-188 Neutralization, see also Acid-neutralizing capacity (ANC) acidic deposition on forested soils, 1 chemical factors, 24, 36, 38,40 future research, 64 physical factors, 8 surface complexation models, 235 Nickel inorganics in soils, 368, 375-376 surface complexation models, 285-286 Nitrates acidic deposition on forested soils, 1-3, 5-6.66 chemical factors, 17, 19,27 physical factors, 7, 14-15 retention, 29-32 soil change studies, 43.45-46,48,56 chemical transport through soil, 142, 167 surface complexation models, 282

Nitrification, acidic deposition on forested soils, 30,32,40,48,64 Nitrogen acidic deposition on forested soils, 65 chemical factors, 26,41 retention, 29-32 soil change studies, 45,48,52,56 fingerprinting crop varieties, 95 Nonaqueous phase liquid (NAF'L), chemical transport through soil, 145-147, 165 Nonlinear adsorption models, chemical transport through soil, 171-172 Novelty, fingerprinting crop varieties, 121 Nucleotides, fingerprinting crop varieties, 98 Nutrient cycling, acidic deposition on forested soils, 10,31-32,34,63 Nutrient uptake, acidic deposition on forested soils, 4,28,43,45,65

0 One-pKmodel, 234,249-251.307, 320 metal ion adsorption, 287-289 protonation-dissociation reactions, 212-214 Organic acids, acidic deposition on forested soils, 3, 28 Organic carbon, chemical transport through soil, 163, 168 Outer-sphere surface complexes, surface complexation models, 240-243 competitive adsorption reactions, 313 inorganic anion adsorption, 300-301 metal ion adsorption, 282 protonation-dissociation reactions, 255 Oxidation, acidic deposition on forested soils, 1,5, 30 Oxides inorganics in soils, 375 surface complexation models, 235-238, 240,243 competitive adsorption reactions, 312-318 inorganic anion adsorption, 296, 301 metal ion adsorption, 277-278.282, 284 organic ligand adsorption, 308-312 protonation-dissociation reactions, 25 1, 254,256,270 Oxisol, surface complexation models, 266, 282,301

397 Oxygen, surface complexation models, 249-250 Ozone, acidic deposition on forested soils, 41

P Pedigree, fingerprinting crop varieties, 105-106, 111, 122, 124-125 Permeability, chemical transport through soil, 175, 186, 189, 192 Pesticides, chemical transport through soil processes, 163-164, 171-172 solute transport, field studies of, 182-184, 186-187 PH acidic deposition on forested soils, 3-4,6, 65-66 chemical factors, 15-20.23-28.30-31 mineral weathering, 38 soil changes, 45,49.51,56-60.62 sulfate retention, 33-35 chemical transport through soil, 164, 168 fingerprinting crop varieties, 89 inorganics in soils, 332, 352 surface complexation models, 235,308, 313,319 inorganic anion adsorption, 300,303, 305 metal ion adsorption, 274,285-286 protonation-dissociation reactions, 25 1, 261,265 Phase concentration models, chemical transport through soil, 161 Phenotype corn, 215 fingerprinting crop varieties, 88, 105 Phosphate inorganics in soils, 337 surface complexation models competitive adsorption reactions, 314-315.318 inorganic anion adsorption, 290-291, 295-297,300-301.303-305,307 Phosphorus, inorganics in soils, 333, 340, 342,353,356,358 Photodecomposition, chemical transport through soil, 168 Photosynthate partitioning, corn, 225-226 Phylogenetic information, fingerprinting crop varieties, 98, 124

Pinus, acidic deposition on forested soils, 57-58 Piston flow velocity, chemical transport through soil, 176,178-179,184,187 Pith abscission, corn, 209, 211 Plant breeding corn, 203, 213,228 fingerprinting crop varieties, 119, 127 Plant habit, corn, 219-221.223 Plutonium, surface complexation models, 281, 317 Pollen, corn, 221-222,226-228 multiple domestications, 219,221 transformation, 207,212 Polymerase chain reaction, fingerprinting crop varieties, 100, 126 Polymers, acidic deposition on forested soils, 16.23-24 Polymorphism, see also Restriction fragment length polymorphisms fingerprinting crop varieties, 99, 101-102, 104, 110, 126 Pore water velocity, chemical transport through soil, 156 Porosity, chemical transport through soil, 146, 148, 175 Potassium acidic deposition on forested soils chemical factors, 21, 36 physical factors, 7, 9 soil change studies, 57-61 inorganics in soils, 349-350, 353 surface complexation models, 307 Potato chemical transport through soil, 186 fingerprinting crop varieties, 101, 115, 120 Potentiometric titration, surface complexation models, 237,291 metal ion adsorption, 277,288 protonation-dissociation reactions, 254-256,265,267,270-271.273 Precipitation acidic deposition on forested soils. 27, 33, 35,37-38,40 chemical transport through soil, 167 inorganics in soils, 332,347,349,354 surface complexation models, 278,287, 296,305, 307,319 Preferential flow of solute, chemical transport through soil field regime, 192-193

INDEX

398

Preferential flow of solute, chemical transport through soil (continued) field studies, 179, 181, 185-188 processes, 152,174-175 Probability density function (pdf), chemical transport through soil, 157-160, 162, 169-170,194 Probes, fingerprinting crop varieties, 100-104 Productivity, corn, 206,208,225-226,228 interpathway heterosis, 221 multiple domestications, 219 Prolamins, fingerprinting crop varieties, 114-115 Protandry, corn, 222-223 Protein, fingerprinting crop varieties, 86, 123-125 characters, 88-98, 104 discrimination, 109-110,112,114-118 Protogyny, corn, 222-223 Proton charge, surface complexation models, 235,238,243 Protonation, surface complexation models, 234,241-242,245,248,320 inorganic anion adsorption, 290, 301-302 protonation-dissociation reactions, 251-274 Protons, surface complexation models, 240, 245,249

Q Quantitative trait loci, fingerprinting crop varieties, 124 Quickflow, acidic deposition on forested soils, 13-15.35

R Rachilla, corn, 210,222 elongation, 212,217,227 multiple domestications, 217-219 Radial diffusion, chemical transport through soil, 166, 173 Radiolabeling, fingerprinting crop varieties, 89, 126 Rainfall acidic deposition on forested soils, 9, 13,27 chemical transport through soil, 167, 176, 181,184,187, 190 Rainwater, acidic deposition on forested soils, 7-9

Random dispersion, chemical transport through soil, 187 Rate coefficients chemical transport through soil, 173-174 inorganics in soils ion exchange, 375 kinetic retention models, 344 multiple-reaction models, 352, 354-357,360,363-365.367 surface complexation models, 284 Rate-limited adsorption, chemical transport through soil, 172-174 Rate-limited mass transfer model, chemical transport through soil, 166 Rate-limited sorption, chemical transport through soil, 164-165 Rate processes, chemical transport through soil, 193 Recombination corn, 227 fingerprinting crop varieties, 121 Reduction, acidic deposition on forested soils, 5 Relative permeability, chemical transport through soil, 146-147 Relative saturation, chemical transport through soil, 146 Resident concentration pulse, chemical transport through soil, 178 Resident concentrations, chemical transport through soil, 160-161 Restriction digests, fingerprinting crop varieties, 101-102 Restriction endonucleases, fingerprinting crop varieties, 98, 100-101, 126 Restriction enzymes, fingerprinting crop varieties, 102, 104, 126 Restriction fragment length polymorphisms ( R E P S ) , fingerprinting crop varieties characters, 98-100,103-108 discrimination, 110, 112-115 new techniques, 125 usage, 120, 122-125 Retardation factor chemical transport through soil processes, 162-164,173 solute transport, field studies of, 183-184, 187-199 inorganics in soils, 352, 365, 369, 378-379 Retention, inorganics in soils, 332-333, 336 computer codes, 368-369,371,373

INDEX equilibrium retention models, 337-342 kinetic retention models, 342-350 layered soil, 377-379 multiple-reaction models, 350-353,355, 357-365 Reversed-phase high-performance liquid chromatography, fingerprinting crop varieties, 95-96, 111-115 Reversing Acidification in Norway (RAIN), acidic deposition on forested soils, 27-28.37-38,66 Rhizosphere, acidic deposition on forested soils, 42, 64 Rhodamine, chemical transport through soil, 184,186-188 Rind abscission, corn, 209-210 RNA, fingerprinting crop varieties, 100 Root zone, inorganics in soils, 331 Rooting zones, acidic deposition on forested soils, 8, 10, 64 Roots, acidic deposition on forested soils, 4, 31, 65 Runoff, acidic deposition on forested soils, 27, 35.38

S Saturated hydraulic conductivity, chemical transport through soil, 146,154, 191 Saturation acidic deposition on forested soils, 13, 15, 30-33,35,49 chemical transport through soil, 154, 165, 189 inorganics in soils, 333, 377-379 Seasonal changes, acidic deposition on forested soils, 9, 30-31, 59-61 Second-order mobile-immobile model (SOMIM), inorganics in soils, 361-365, 367 Second-order models, inorganics in soils, 358-367.375 Second-order two-site model (SOTS), inorganics in soils, 359-361,363-365 Secondary sex traits, corn, 205,212, 222-224 Seed corn, 228 fingerprinting crop varieties, 8 9 , 9 5 , 9 7 , 110, 119 Segregation, corn, 208-209, 211,227

399

Selection, corn, 226 Selectivity coefficients, inorganics in soils, 360,363,368,371-372 Selenate, surface complexation models, 300-301,303,305,318 Selenite, surface complexation models competitive adsorption reactions, 314-315, 318 inorganic anion adsorption, 296,300-301, 303-305 Selenium, surface complexation models, 237, 291, 295 Sequences, fingerprinting crop varieties, 98-99, 101, 126 Serology, fingerprinting crop varieties, 89 Shattering cob, corn, 209 Sigmoidicity. inorganics in soils, 341-342 Silica, surface complexation models, 274, 278,286 Silicate acidic deposition on forested soils, 16, 36-38,40 surface complexation models, 290,296, 3 14-3 15 Silicon dioxide, surface complexation models, 308, 316 Silver, surface complexation models, 278-279.317 Sodium acidic deposition on forested soils, 21, 36 inorganics in soils, 367, 370-373 Soil chemical transport through, see Chemical transport through soil fingerprinting crop varieties, 96 forested, acidic deposition on, see Acidic deposition on forested soils inorganics in, see Inorganics in soils, modeling of Soil acidification, see Acidic deposition on forested soils Soil chemical systems, surface complexation models in, see Surface complexation models Soil coring, chemical transport through soil, 178-179,188 Soil horizons, acidic deposition on forested soils cation exchange, 19-20.24 future research, 64 physical factors, 9 , 17, 30-31, 33,40

Soil matrix chemical transport through soil, 148,152, 157,181,184,193 inorganics in soils, 332, 335, 337 ion exchange, 368 multiple-reaction models, 352-353, 358-362 Soil moisture, acidic deposition on forested soils, 13, 16,30 Soil organisms, acidic deposition on forested soils, 40-42 Soil pH, acidic deposition on forested soils, 15-19 Soil solution, acidic deposition on forested soils, 43, 52,54 SOILCHEM, surface complexation models, 319 Solute adsorption, chemical transport through soil, 161-165, 171-175, 182-185 Solute dispersion, chemical transport through soil, 151-153, 179-182,189-191, 194 Solute mass, chemical transport through soil, 187 Solute retention, inorganics in soils, 339, 342, 345,358-360 Solute transport chemical transport through soil, 154, 192-193,194 field studies, 175-191 inorganics in soils, 332-336 ion exchange, 367 layered soil, 377-378,381 multiple-reaction models, 350, 353, 355-356, 359,362 Solute velocity, chemical transport through soil, 176-179,189-191 Sorghum, fingerprinting crop varieties, 101, 115 Sorption chemical transport through soil, 164-165, 174,185 inorganics in soils, 332-333,368-369, 311-372.374-376 equilibrium retention models, 337, 340-341 kinetic retention models, 342-343, 345 multiple-reaction models, 350, 352-354,356,358-359,362-363 surface complexation models computer codes, 319

inorganic anion adsorption, 296,305, 307

metal ion adsorption, 278,285,287 Soybean, fingerprinting crop varieties, 101, 113, 125

Spikelets, corn, 205,207-211.225.227-228 Sprinklers, chemical transport through soil, 183,186-187,190-191

Squash, 223 Stability, fingerprinting crop varieties, 119-120

Stagnant water phase models, chemical transport through soil, 166-167, 173-174

STEADYQL, surface complexation models, 319

Stemflow, acidic deposition on forested soils, 7-10,31

Stem plane, surface complexation models, 249,288

Stem VSC-VSP model, 244-246,319-320 inorganic anion adsorption, 303-305 metal ion adsorption, 282-286 protonation-dissociation reactions, 267-270

Stochastic continuum model, chemical transport through soil, 153-154 Stochastic local solute transport model, chemical transport through soil, 154 Stochastic-convective flux, chemical transport through soil, 156,161, 189,194 Stochastic-convective transfer function, chemical transport through soil, 159 String cob trait, corn, 207,219,228 Sulfates acidic deposition on forested soils, 1-2, 5-6,66

chemical factors, 17, 19, 27 future research, 64 mineral weathering, 37 physical factors, 7-9, 13-15 retention, 29, 32-35 soil change studies, 45-46,52,54-56 surface complexation models, 290,301, 305, 315,318

Sulfur acidic deposition on forested soils, 65 chemical factors, 26, 29, 32-35 physical factors, 7 soil change studies, 52,54-56

INDEX fingerprinting crop varieties, 95,97 surface complexation models, 235 Surface charge, surface complexation models, 247,308,320 Surface charge density, surface complexation models, 246,267,283,300,320,341 Surface complexation models, 234-235, 320 competitive adsorption reactions anion-anion, 314-316 metal-ligand, 316-318 metal-metal, 312-313 computer codes, 319-320 description characteristics, 235-237 constant capacitance model, 238-240 generalized two-layer model, 246-248 one-pK model, 249-25 1 Stem VSC-VSP model, 244-246 triple-layer model, 240-244 inorganic anion adsorption constant capacitance model, 289-297 generalized two-layer model, 305-307 one-pK model, 307 Stern VSC-VSP model, 303-305 triple-layer model, 297-303 metal ion adsorption constant capacitance model, 274-279 generalized two-layer model, 286-287 one-pK model, 287-289 Stem VSC-VSP model, 282-286 triple-layer model, 279-282 organic ligand adsorption constant capacitance model, 308-310 Stern VSC-VSPmodel, 311-312 triple-layer model, 309, 311 protonation-dissociation reactions constant capacitance model, 251-256 generalized two-layer model, 270-27 1 one-pK model, 272-274 Stem VSC-VSP model, 267-270 triple-layer model, 256-266 Surface hydroxyl groups, surface complexation models, 235-237, 245, 251,254,256 Surface precipitation model, surface complexation models, 278, 287, 296-297,307, 312 Surface site density, surface complexation models, 237,267,282, 312

40 1

Surface-solution interface, surface complexation models, 238,240,247, 249

T Tassels, corn, 208,212,219,224-226,228 Taxonomy, fingerprinting crop varieties, 119-120 Tehuacan corn, 206-207,212-213,223 Temperature chemical transport through soil, 165-166, 168 surface complexation models, 236,265, 285,303 Teosinte, see Corn, evolution of Thermodynamics inorganics in soils, 360, 368 surface complexation models, 234,243, 285 Through fall, acidic deposition on forested soils, 7-10, 31 Tight linkage, corn, 210-211,227 Titanium oxide, surface complexation models, 237,308,316 metal ion adsorption, 275, 279 protonation-dissociation reactions, 252-253,256,258-259,265,271 Titration, see also Potentiometric titration acidic deposition on forested soils, 19 surface complexation models, 237,291 metal ion adsorption, 277, 288 protonation-dissociation reactions, 252-256,258,265-267, 270-274 Tortuosity, chemical transport through soil, 146, 148 Tracers, chemical transport through soil, 184, 191 TRANQL inorganics in soils, 367-368 surface complexation models, 319 Transcription, fingerprinting crop varieties, 89 Transfer coefficient models, chemical transport through soil, 165 Transfer coefficients, surface complexation models, 284 Transfer function, chemical transport through soil, 156-160,169,194

402 Transformation chemical transport through soil, 143-145 concentrations, 160- 161 convection-dispersion , 154- 156 interphase mass transfer laws, 161-167 mass flux laws, 145-154 reactions, 167-169 transfer function models, 156-160 corn, 205-214 inorganics in soils, 333,347 Transport equations, inorganics in soils, 333-336 Transport models inorganics in soils, 359,368, 370-372, 376 surface complexation models, 319-320 Transport through soil, chemical, see Chemical transport through soil Transport volume, chemical transport through soil, 158, 160 Travel time, chemical transport through soil processes, 156-157,159-160.162, 169-170 solute transport, field studies of, 190-191 Tricklers, chemical transport through soil, 178,190-191 Triple-layer model, surface complexation models, 234,240-247,320 competitive adsorption reactions, 312-313, 315-318 computer codes, 319 inorganic anion adsorption, 297-303 metal ion adsorption, 279-282 organic ligand adsorption, 309,311 protonation-dissociation reactions, 256-267 lhnicate locus, corn, 212 Two-site models, inorganics in soils, 351-353,361-362

U Uniformity, fingerprintingcrop varieties, 119-120

V Vapor-dissoved phase partitioning, chemical transport through soil, 165-166 Vapor flux, chemical transport through soil, 145-146

Variable charge model, metal ion adsorption, 284 Variable selectivities, inorganics in soils, 371-372 Variable surface charge model, see Stern VSC-VSP model Variable surface potential model, see Stern VSC-VSP model Velocity chemical transport through soil, 193 processes, 149-153, 167, 173,175 solute transport, field studies of, 176-179,181,189-191 inorganics in soils, 334-335, 352 Vermiculite, inorganics in soils, 349-350 Viscosity, chemical transport through soil, 174,192 Volume-averaged local solute transport model, chemical transport through soil, 153

W WATEQ3, surface complexation models, 319 Water flow chemical transport through soil chemical mass flux, 151,153 interphase mass transfer, 161-162, 166-167 processes, 155-156,158, 175 solute transport, field studies of, 183, 185-186 inorganics in soils, 333-335,368,377, 379,381 Water flow velocity, inorganics in soils, 332, 334,365 Water flux chemical transport through soil processes, 147-148.153-154, 156,175 solute transport, field studies of, 176, 179 inorganics in soils, 332, 380 Water-phase division model, chemical transport through soil, 166 Water-unsaturated soils, inorganics in soils, 379-381 Water velocity chemical transport through soil processes, 149,151-152, 156-157, 174 solute transport, field studies of, 176, 179

INDEX inorganics in soils, 335 Watershed, acidic deposition on forested soils chemical factors, 31, 34-35,40 physical factors, 11, 13-14 soil change studies, 54 Weathering, acidic deposition on forested soils, 35-40, 5 5 , 57 Wheat, fingerprinting crop varieties, 95, 125 discrimination, 110-111 usage, 123-125

Z Zea mays,evolution of, see Corn,evolution of Zeins, fingerprinting crop varieties, 112-113

403

Zero point of charge, surface complexation models, 251,253,257,261,272 Zero surface charge, surface complexation models, 243,256,261 Zero-time dispersion model, chemical transport through soil, 159 Zero-time model of solute dispersion, chemical transport through soil, 151-152 Zinc inorganics in soils, 340,375-376 surface complexation models competitive adsorption reactions, 312-313,318 metal ion adsorption, 279,281-286

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