terrestrial biotic ligand model (tblm) for copper, and nickel toxicities to plants, invertebrates, and microbes in soils

TERRESTRIAL BIOTIC LIGAND MODEL (TBLM) FOR COPPER, AND
NICKEL TOXICITIES TO PLANTS, INVERTEBRATES,
AND MICROBES IN SOILS




by

Sagar Thakali






A dissertation submitted to the Faculty of the University of Delaware in
partial fulfillment of the requirements for the degree of Doctor of Philosophy in Civil
Engineering.




Spring 2006

Copyright 2006 Sagar Thakali
All Rights Reserved
UMI Number: 3221133
3221133
2007
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by ProQuest Information and Learning Company.





TERRESTRIAL BIOTIC LIGAND MODEL (TBLM) FOR COPPER, AND
NICKEL TOXICITIES TO PLANTS, INVERTEBRATES,
AND MICROBES IN SOILS



by

Sagar Thakali






Approved: __________________________________________________________
Michael J. Chajes, Ph.D.
Chair of the Department of Civil and Environmental Engineering



Approved: __________________________________________________________
Eric W. Kaler, Ph.D.
Dean of the College of Engineering



Approved: __________________________________________________________
Conrado M. Gempesaw II, Ph.D.
Vice Provost for Academic and International Programs
I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.


Signed: __________________________________________________________
Herbert E. Allen, Ph.D.
Professor in charge of dissertation

I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.


Signed: __________________________________________________________
William R. Berti, Ph.D.
Member of dissertation committee


I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.


Signed: __________________________________________________________
Ronald T. Checkai, Ph.D.
Member of dissertation committee


I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.


Signed: __________________________________________________________
Dominic M. Di Toro, Ph.D.
Member of dissertation committee

I certify that I have read this dissertation and that in my opinion it meets
the academic and professional standard required by the University as a
dissertation for the degree of Doctor of Philosophy.


Signed: __________________________________________________________
Donald L. Sparks, Ph.D.
Member of dissertation committee

v

ACKNOWLEDGMENTS
I express my profound gratefulness to my advisor and mentor Professor
Herbert E. Allen without whose extraordinary patience, guidance, and advice the
completion of this work was not possible. I am equally thankful to Professor Dominic
M. Di Toro who provided me with extensive assistance, guidance, and advice in the
course of this study. I am grateful to the Center for Study of Metals in the
Environment (CSME) at the Universidy of Delaware, the International Copper
Association (ICA), and the Nickel Producers Environmental Research Association
(NiPERA) for funding this study.
I am also grateful to Dr. William R. Berti, Dr. Ronald T. Checkai and
Professor Donald L. Sparks, for their service in my dissertation committee and for
providing me with comments and suggestions which have greatly improved the
quality of this study. I thank my collaborators, Dr. Alexander A. Ponizovsky, Ms.
Corrine P. Rooney, Dr. Fang-Jie Zhao, Dr. Steve P. McGrath, Ms. Peggy Criel, Ms.
Hilde Van Eckout, Professor Collin C. Janssen, Dr. Koen Oorts and Professor Erik M.
Smolders. They have been very gracious in providing me with the data necessary for
this study.

I want to express my sincere appreciation to Ms. Dana M. Crumety for
offering me help always, to Mr. Douglas J. Baker, Mr. Michael J. Davidson, Mr. Eric
C. Eckman and Ms. Lucille Z. Short for all their help, and to my colleagues, Dr.
Zhenqing Shi, Mr. David M. Metzler, Mr. Akash Sondhi and Mr. Sammy Lin for their
friendship and much more.

vi
I would like to thank my family for all the love and support that they gave
me. I am grateful to my grand parents, Mr. and Mrs. Gyan B. Thakali, and parents,
Mr. Gopal S. Thakali and Mrs. Kamala Thakali, for all the sacrifices they have made
so I can do what I do. I am grateful to my siblings, Mrs. Prabha S. Subba, Ms. Muna
Thakali and Mr. Deep S. Thakali, who have always been there to support in my
struggles and rejoice in my happiness. Finally, I would like to express the most sincere
gratefulness to my wife, Jasmeen Hirachan, for putting up with me and my ways and
loving me back even when I do not deserve. Without her all my effort will have no
meaning.

vii

TABLE OF CONTENTS
LIST OF TABLES xi
LIST OF FIGURES xv
ABSTRACT xxii
Introduction 1
1.1 Background 1
1.2 The overall objective 4
1.3 The structure of the dissertation 4
1.4 References 6
Literature review 9
2.1 Bioavailability and ecotoxicity of metals in soils 9

2.2 Understanding metal speciation in soils 17
2.2.1 Total metal content of soils 18
2.2.2 Soil organic matter (SOM) 19
2.2.3 Oxides, carbonates and clays 20
2.2.4 Dissolved organic carbon (DOC) 21
2.2.5 pH 22
2.2.6 Dissolved cations 23
2.3 Modeling metal speciation 24
2.4 Summary and Conclusions 29
2.5 References 30
The terrestrial biotic ligand model 39
3.1 Introduction 39
3.2 The Windermere Humic Aqueous Model (WHAM VI) 41
3.2.1 Sub-model for humic substances 43
3.2.2 Sub-model for oxides 46
3.2.3 Sub-model for cation exchange 46
3.2.4 Limitations in modeling with WHAM VI 47
3.3 The toxicity model 48
3.4 The estimation of model parameters 51
3.5 References 52
Material and methods 55
4.1 Characterization of the soils 55
4.2 Experimental procedure for Cu and Ni partitioning and speciation 56
4.3 The bioassay experiments 60
4.3.1 Plant bioassays 60
4.3.1.1 Barley root elongation 61

viii
4.3.1.2 Tomato shoot growth 62
4.3.2 Invertebrate bioassays 62

4.3.2.1 Redworm cocoon production 62
4.3.2.2 Springtail juvenile production 63
4.3.3 Microbial bioassays 64
4.3.3.1 Potential nitrification rate 64
4.3.3.2 Glucose induced respiration 65
4.4 References 65
Modeling copper speciation 68
5.1 Introduction 68
5.2 Materials and Methods 69
5.3 Results and Discussions 70
5.3.1 The whole soil approach 70
5.3.1.1 Active organic matter 70
5.3.1.2 Phases besides SOM 74
5.3.1.3 Competitive binding of Fe
3+
and Al
3+
75
5.3.1.4 Sensitivity of calculated pCu to DOC 76
5.3.1.5 Sensitivity of calculated pCu to dissolved cations
and P
CO
2
77
5.3.1.6 Final calculations using the whole soil approach 80
5.3.2 The solution approach 80
5.3.3 Predicting the dissolved Cu concentration 84
5.4 Summary and Conclusions 86
5.5 References 87
Modeling nickel partitioning in soils 90

6.1 Introduction 90
6.2 Materials and Methods 92
6.3 Results and Discussions 93
6.3.1 Active soil organic matter 93
6.3.2 Competitive binding by Fe
3+
and Al
3+
94
6.3.3 Prediction of Ni
2+
activity 98
6.3.4 Sensitivity of the calculations to dissolved cations 99
6.3.5 Final Calculations using WHAM VI 99
6.3.6 Assessment of soils with OC content < 1% 101
6.4 Summary and Conclusions 105
6.5 References 105
Copper, and nickel toxicities to barley root elongation 109
7.1 Introduction 109
7.2 Experimental and Data Analysis Methods 111
7.2.1 Selection of soils 111
7.2.2 Bioassays 111
7.2.3 Whole soil metal speciation using WHAM VI 112

ix
7.2.4 Toxicity equations 113
7.2.5 Estimation of model parameters 113
7.3 Results and Discussion 114
7.3.1 WHAM VI speciation 114
7.3.2 Estimation of the TBLM parameters 117

7.3.3 Dose-response relationships 125
7.3.3.1 Nickel toxicity 125
7.3.3.2 Copper toxicity 131
7.3.4 Interaction of cations with the biotic ligand 131
7.3.5 Prediction of the EC50 total metal concentrations 135
7.4 Summary and Conclusions 137
7.5 References 138
TBLM for plants, invertebrates, and microbes 142
8.1 Introduction 142
8.2 Experimental and Modeling Methods 143
8.2.1 Bioassays 143
8.2.2 Estimating the EC50 values for individual soils 144
8.2.3 Whole soil metal speciation using WHAM VI 145
8.2.4 Estimation of model parameters 146
8.3 Results and Discussion 147
8.3.1 Estimation of model parameters 147
8.3.2 Dose-response relationships 153
8.3.3 Interaction of cations with the biotic ligand 178
8.3.4 The extent of the protective effect of the competing cations 180
8.3.5 Prediction of EC50 183
8.4 Summary and Conclusions 186
8.5 References 187
Copper, and nickel toxicities in calcareous soils 190
9.1 Introduction 190
9.2 Bioassay and soil solution composition data 193
9.3 Modeling approach 194
9.4 Results and Discussion 196
9.4.1 Dose response relationships for the BRE bioassays 196
9.4.2 Prediction of the EC50 values 201
9.5 Summary and Conclusions 204

9.6 References 204
Final conclusions and recommendations 207
Appendix A 211
Appendix B 223
Appendix C 289
Appendix D 293
Appendix E 306

x
Appendix F 309
Appendix G 311

xi

LIST OF TABLES
Table 2.1 The Coefficient of Determination for the regression between Zn
uptake rate of plants, Zn uptake by an isopod, and various
bioavailability estimations in field soils (extracted from Koster et
al. (2005)). 12
Table 2.2 Comparison of the models for cation binding by humic substances
(Extracted from Kinniburgh et al. (1996)) 28
Table 4.1 Properties of the soils used in the Cu study (Dr. E. Smolders and
Dr. K. Oorts, personal communication). 57
Table 4.2 Properties of the soils used in the Ni study (Dr. E. Smolders and
Dr. K. Oorts, personal communication). 58
Table 5.1 Effect of the input variables on the RMSE of the predicted pCu,
the linear regression, and Coefficient of Determination (R
2
)
between the predicted and measured pCu. 79

Table 5.2 A comparison between speciation based on the whole soil
approach and the soil solution approach. 83
Table 7.1 Model fit summary and the optimum parameters associated with
the three models 126
Table 8.1 The non-calcareous soils excluded for the analyses in this study
for each of the bioassays 145
Table 8.2 The dose-response parameters (EC50 and
β
) for the three models
considered: Total Cu Model (Total Cu), the Free Ion Activity
Model (FIAM) and the Terrestrial Biotic Ligand Model (TBLM).
The binding constants are associated with the TBLM. 151
Table 8.3 The dose-response parameters (EC50 and
β
) for the three models
considered: Total Ni Model (Total Ni), the Free Ion Activity
Model (FIAM) and the Terrestrial Biotic Ligand Model (TBLM).
The binding constants are associated with the TBLM. 152

xii
Table 9.1 The distribution of Cu and Ni in 5 calcareous soils (Moral et al.,
2005). Only the approximate ranges are listed 191
Table 9.2 Summary of the model fits to the barley root elongation data for
Cu and Ni 196
Table A-1 Data for copper partitioning and speciation study. (DOC and DIC
refer to dissolved organic and inorganic carbon, respectively) 212
Table A-2 Data for nickel partitioning and speciation study. (DOC and DIC
refers to dissolved organic and inorganic carbon, respectively) 217
Table B-1 Soil and soil solution properties and the bioassay data for Cu
toxicity to Barley Root Elongation (BRE) (With permission from

Dr. S. P. McGrath (Rothamsted Research, UK)). 224
Table B-2 Soil and soil solution properties and the bioassay data for Ni
toxicity to Barley Root Elongation (BRE) (With permission from
Dr. S. P. McGrath (Rothamsted Research, UK)). 230
Table B-3 Soil and soil solution properties and the bioassay data for Cu
toxicity to Tomato Shoot Yield (TSY) (With permission from Dr.
S. P. McGrath (Rothamsted Research, UK)) 235
Table B-4 Soil and soil solution properties and the bioassay data for Ni
toxicity to Tomato Shoot Yield (TSY) (With permission from Dr.
S. P. McGrath (Rothamsted Research, UK)) 241
Table B-5 Soil and soil solution properties and the bioassay data for Cu
toxicity to E. fetida Cocoon Production (ECP) (With permission
from Dr. C. Janssen (Ghent University, Belgium)) 246
Table B-6 Soil and soil solution properties and the bioassay data for Ni
toxicity to E. fetida Cocoon Production (ECP) (With permission
from Dr. C. Janssen (Ghent University, Belgium)) 251
Table B-7 Soil and soil solution properties and the bioassay data for Cu
toxicity to F. candida Juvenile Production (FJP) (With permission
from Dr. C. Janssen (Ghent University, Belgium)) 256
Table B-8 Soil and soil solution properties and the bioassay data for Ni
toxicity to F. candida Juvenile Production (FJP) (With permission
from Dr. C. Janssen (Ghent University, Belgium)) 262

xiii
Table B-9 Soil and soil solution properties and the bioassay data for Cu
toxicity to Potential Nitrification Rate (PNR) (With permission
from Dr. E. Smolders (Katholieke Universiteit Leuven, Belgium)). 267
Table B-10 Soil and soil solution properties and the bioassay data for Ni
toxicity to Potential Nitrification Rate (PNR) (With permission
from Dr. E. Smolders (Katholieke Universiteit Leuven, Belgium)). 273

Table B-11 Soil and soil solution properties and the bioassay data for Cu
toxicity to Glucose Induced Respiration (GIR) (With permission
from Dr. E. Smolders (Katholieke Universiteit Leuven, Belgium)). 278
Table B-12 Soil and soil solution properties and the bioassay data for Ni
toxicity to Glucose Induced Respiration (GIR) (With permission
from Dr. E. Smolders (Katholieke Universiteit Leuven, Belgium)). 284
Table C-1 An example of WHAM VI input file for the computation of cation
activities for Ni toxicity to BRE. In addition to the following the
temperature = 293K and the partial pressure of CO
2
= 10
-3.5
atm 290
Table D-1 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to Barley Root
Elongation (BRE). 294
Table D-2 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to Barley Root
Elongation (BRE). 295
Table D-3 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to Tomato Shoot
Yield (TSY). 296
Table D-4 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to Tomato Shoot
Yield (TSY). 297
Table D-5 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to E. fetida Cocoon
Production (ECP) 298
Table D-6 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to E. fetida Cocoon

Production (ECP) 299

xiv
Table D-7 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to F. candida
Juvenile Production (FJP). 300
Table D-8 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to F. candida
Juvenile Production (FJP). 301
Table D-9 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to Potential
Nitrification Rate (PNR) 302
Table D-10 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to Potential
Nitrification Rate (PNR) 303
Table D-11 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Cu toxicity to Glucose Induced
Respiration (GIR). 304
Table D-12 EC50 total metal concentrations estimated by TRAP and the
interpolated solution properties for Ni toxicity to Glucose Induced
Respiration (GIR). 305
Table E-1 Concentrations of dissolved major cations in soil solutions of
different soils. 307
Table F-1 WHAM VI input file for speciation of Cu at EC50 for Barley Root
Elongation (BRE) bioassays for calcareous soils 310
Table G-1 An example file for the response, the total Ni content and the
WHAM VI model output data for speciation computation for Ni
toxicity to barley root elongation bioassay in non-calcareous soils
with OC content > 1.0%. 312
Table G-2 An example spreadsheet for the calculations involving the three

models for Ni toxicity to barley root elongation in non-calcareous
soils with OC content > 1.0% 314


xv

LIST OF FIGURES
Figure 3.1 A schematic of the interactions considered in a TBLM for the
ecotoxicity of metals in soil systems (Modified from Di Toro et
al. (2005)). 42
Figure 5.1 The predicted pCu values using WHAM VI based on the whole
soil approach and the measured pCu values. The solid line
represents the 1:1 ratio and the dashed lines are a unit above and
below the 1:1 line. 81
Figure 5.2 The predicted pCu values using WHAM VI based on soil solution
composition and the measured values of pCu. The solid line
represents the 1:1 ratio and the dashed lines are a unit above and
below the 1:1 line. 82
Figure 5.3 Predicted vs. measured dissolved Cu concentration. The solid line
represents 1:1 ratio, and the dotted lines represent a half log unit
above and below the 1:1 ratio. 85
Figure 6.1 (a) Comparison of predicted log [Ni]
dis
values using the whole
soil approach and the measured values for all non-calcareous
soils; The solid line represents a 1:1 ratio and the dotted lines are
a log unit above and below the 1:1 line. (b) The residual error
(measured-predicted) as related to the total Ni content in the soil
samples. The residual error for the spiked samples of Montpellier
and Aluminusa soils are shown in a box 97

Figure 6.2 Predicted vs. measured log [Ni]
dis
for non-calcareous soils with
OC content > 1%. The solid line represents a 1:1 ratio and the
dotted lines are a half log unit above and below the 1:1 line 100
Figure 6.3 Relationships between the average residual error (measured –
predicted) of the predicted dissolved Ni concentrations and (a) the
OC content, and (b) the clay content of the soils. The two soils
(Montpellier and Aluminusa) are identified in the figures. The
dotted lines represent the no residual error. (Note that there
appears only 9 soils in (b) because the point (21% Clay and
Residual error of -0.10) overlaps for two soils) 102

xvi
Figure 6.4 The predicted vs. measured dissolved Ni concentrations based on
the inclusion of clay. The solid line represents the 1:1 line and the
dashed lines are a half log unit above and below the 1:1 ratio. The
legends “No Clay”, “Default”, “50%” and “30%” represent the
calculations without any clay, with all the clay fraction assumed
to be the default clay in WHAM VI, with 50% of the clay to be
montmorillonite, and with 30% of the clay to be montmorillonite,
respectively 103
Figure 7.1 Comparisons between dissolved metal concentrations predicted
by using the whole soil approach, (a) dissolved Ni concentration
and (b) dissolved Cu concentration. The solid line represents the
1:1 ratio and the dotted lines represent a half log unit above and
below the 1:1 line. 116
Figure 7.2 The RMSE of the predicted % root elongation as a function of log
K
NiBL

(y-axis) and various TBLM parameters (x-axis): (a) f
50
, (b)
log K
CaBL
. In the calculations, the value of
β
is fixed at 2.60 in all
cases. None of the competitive cations were included in (a) and
only Ca was included in (b) 119
Figure 7.2 The RMSE of the predicted % root elongation as a function of log
K
NiBL
(y-axis) and various TBLM parameters (x-axis): (c) log
K
HBL
, and (d) log K
HBL
. In the calculations, the value of
β
is fixed
at 2.60 in all cases. Only H
+
was included in (c) and both Ca
2+
and
H
+
were included in (d) with log K
CaBL

fixed at 1.5 120
Figure 7.3 The root mean square error (RMSE) of the predicted % root
elongation as a function of log K
CuBL
(y-axis) and various TBLM
parameters (x-axis): (a) log f
50
with no competitive cations and
β

= 1 and (b) log K
HBL
with only protons as the competing cation
and
β
= 1 123
Figure 7.3 The root mean square error (RMSE) of the predicted % root
elongation as a function of log K
CuBL
(y-axis) and various TBLM
parameters (x-axis): (c) log K
CaBL
with only Ca
2+
as the competing
cation and
β
= 1 and (d) log K
MgBL
with only Mg

2+
as the
competing cation and
β
= 1. 124
Figure 7.4a Dose-response relationships for Ni toxicity to barley root
elongation (BRE): (i) the TMM, (ii) the FIAM, (iii) the TBLM.
The lines represent the models 127

xvii
Figure 7.4b Predicted vs. observed barley root elongation (BRE) for Ni
toxicity models: (i) the TMM, (ii) the FIAM, (iii) the TBLM. The
dotted lines represent the 1:1 ratio. The equations and the R
2

values for the linear regression between the predicted and the
observed % BRE (solid lines) are also shown. 128
Figure 7.5a Dose-response relationships for Cu toxicity to barley root
elongation (BRE): (i) the TMM, (ii) the FIAM, (iii) the TBLM;
The lines represent the models 129
Figure 7.5b Predicted vs. observed barley root elongation (BRE) for Cu
toxicity models: (i) the TMM, (ii) the FIAM, (iii) the TBLM. The
dotted lines represent the 1:1 ratio. The equations and the R
2

values for the linear regression between the predicted and the
observed % BRE (solid lines) are also shown. 130
Figure 7.6 The relationship between the Cu
2+
and H

+
activities at EC50 for
barley root elongation. The observed data are based on WHAM
VI speciation and the model predictions are based on the FIAM
(dotted line) and the TBLM (solid line) 133
Figure 7.7 The fraction (f) of the total biotic ligand sites on the barley roots
bound by various cations (Ni
2+
, H
+
, Ca
2+
and Mg
2+
) at EC50 for
Ni toxicity in 8 soils. 135
Figure 7.8 The predicted and the observed EC50 values for Ni and Cu
toxicity to barley root elongation. The solid line represents the 1:1
ratio and the dotted lines are a factor of 2 above and below 137
Figure 8.1a Dose response relationships for tomato shoot yield (TSY) based
on i. total Cu as the dose, ii. free Cu
2+
activity as the dose, and iii.
fraction (f) of total biotic ligand bound by Cu
2+
. The lines
represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 154
Figure 8.1b The observed vs. predicted tomato shoot yield (TSY); i. the
TMM, ii. the FIAM, and iii. the TBLM. The solid lines represent

the linear regression for which the equations and the R
2
values are
given, and the dotted lines represent the 1:1 line. 155

xviii
Figure 8.2a Dose response relationships for F. candida juvenile production
(FJP) based on i. total Cu as the dose, ii. free Cu
2+
activity as the
dose, and iii. fraction (f) of total biotic ligand bound by Cu
2+
. The
lines represent the TMM, the FIAM and the TBLM, respectively
for i, ii and iii 156
Figure 8.2b The observed vs. predicted F. candida juvenile production (FJP);
i. the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 157
Figure 8.3a Dose response relationships for E. fetida cocoon production
(ECP) based on i. total Cu as the dose, ii. free Cu
2+
activity as the
dose, and iii. fraction (f) of total biotic ligand bound by Cu
2+
. The
lines represent the TMM, the FIAM and the TBLM, respectively
for i, ii and iii 158

Figure 8.3b The observed vs. predicted E. fetida cocoon production (ECP); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 159
Figure 8.4a Dose response relationships for potential nitrification rate (PNR)
based on i. total Cu as the dose, ii. free Cu
2+
activity as the dose,
and iii. fraction (f) of total biotic ligand bound by Cu
2+
. The lines
represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 160
Figure 8.4b The observed vs. predicted potential nitrification rate (PNR); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 161
Figure 8.5a Dose response relationships for glucose induced respiration (GIR)
based on i. total Cu as the dose, ii. free Cu
2+
activity as the dose,
and iii. fraction (f) of total biotic ligand bound by Cu
2+
. The lines
represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 162

Figure 8.5b The observed vs. predicted glucose induced respiration (GIR); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 163

xix
Figure 8.6a Dose response relationships for tomato shoot yield (TSY) based
on i. total Ni as the dose, ii. free Ni
2+
activity as the dose, and iii.
fraction (f) of total biotic ligand bound by Ni
2+
. The lines
represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 164
Figure 8.6b The observed vs. predicted tomato shoot yield (TSY); i. the
TMM, ii. the FIAM, and iii. the TBLM. The solid lines represent
the linear regression for which the equations and the R
2
values are
given, and the dotted lines represent the 1:1 line. 165
Figure 8.7a Dose response relationships for F. candida juvenile production
(FJP) based on i. total Ni as the dose, ii. free Ni
2+
activity as the
dose, and iii. fraction (f) of total biotic ligand bound by Ni
2+
. The

lines represent the TMM, the FIAM and the TBLM, respectively
for i, ii and iii 166
Figure 8.7b The observed vs. predicted F. candida juvenile production (FJP);
i. the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 167
Figure 8.8a Dose response relationships for E. fetida cocoon production
(ECP) based on i. total Ni as the dose, ii. free Ni
2+
activity as the
dose, and iii. fraction (f) of total biotic ligand bound by Ni
2+
. The
lines represent the TMM, the FIAM and the TBLM, respectively
for i, ii and iii 168
Figure 8.8b The observed vs. predicted E. fetida cocoon production (ECP); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 169
Figure 8.9a Dose response relationships for glucose induced respiration (GIR)
based on i. total Ni as the dose, ii. free Ni
2+
activity as the dose,
and iii. fraction (f) of total biotic ligand bound by Ni
2+
. The lines

represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 170
Figure 8.9b The observed vs. predicted glucose induced respiration (GIR); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 171

xx
Figure 8.10a Dose response relationships for potential nitrification rate (PNR)
based on i. total Ni as the dose, ii. free Ni
2+
activity as the dose,
and iii. fraction (f) of total biotic ligand bound by Ni
2+
. The lines
represent the TMM, the FIAM and the TBLM, respectively for i,
ii and iii 172
Figure 8.10b The observed vs. predicted potential nitrification rate (PNR); i.
the TMM, ii. the FIAM, and iii. the TBLM. The solid lines
represent the linear regression for which the equations and the R
2

values are given, and the dotted lines represent the 1:1 line 173
Figure 8.11 A comparison of the predicted vs. observed EC50 total metal
concentrations for Cu toxicity to barley root elongation based on
a) the TMM, b) the FIAM, and c) the TBLM. The solid lines
represent the 1:1 ratio and the dotted lines represent a factor of 2
variation above and below the 1:1 lines. The linear regression

relationships between the log of predicted and observed EC50
values are also given. 176
Figure 8.12 A comparison of the binding constants for (a) Cu toxicity and (b)
Ni toxicity 179
Figure 8.13 The protective effects of the competitive cations in (a) Cu toxicity
and in (b) Ni toxicity. The legend “None” represents the
theoretical EC50 when none of the competitive cations are
present. The reduction in Cu toxicity is computed at pH 6.5, 5.5,
4.5 and at 4.5 with Ca (= 2.87 mM) and Mg (= 0.73 mM)
indicated by the legend “pH 4.5*” in (a). The reduction in Ni
toxicity (b) is computed at pH 5.5 (“Only H”), with only Ca (=
2.87 mM) (“Only Ca”), with only Mg (= 0.73 mM) (“Only Mg”)
and with Ca (= 2.87 mM), Mg (= 0.73 mM) at pH 5.5 (“All”) 182
Figure 8.14 The relationship between the predicted and the observed EC50
values for (a) Cu toxicity and (b) Ni toxicity to various endpoints.
The solid lines represent the 1:1 ratio and the dotted lines are a
factor of 2 above and below the 1:1 line. 184
Figure 9.1a The dose response relationship for Cu toxicity to barley root
elongation (BRE) in the calcareous soils. The models represented
are (i) the TMM, (ii) the FIAM and (iii) the TBLM. The lines
represent the best fit models developed for the non-calcareous
soils in Chapter 7 197

xxi
Figure 9.1b The relationship between the predicted vs. measured barley root
elongation (BRE) for Cu toxicity based on (i) the TMM, (ii) the
FIAM and (iii) the TBLM. The linear regression lines (solid
lines), the associated equations, R
2
values and the perfect-fit line

(dotted lines) are shown. 198
Figure 9.2a The dose response relationship for Ni toxicity to barley root
elongation (BRE) in the calcareous soils. The models represented
are (i) the TMM, (ii) the FIAM and (iii) the TBLM. The lines
represent the best fit models developed for the non-calcareous
soils in Chapter 7 199
Figure 9.2b The relationship between the predicted vs. measured barley root
elongation (BRE) for Ni toxicity based on (i) the TMM, (ii) the
FIAM and (iii) the TBLM. The linear regression lines (solid
lines), the associated equations, R
2
values and the perfect-fit line
(dotted lines) are shown. 200
Figure 9.3 Predicted vs. observed EC50 cation activities of (a) Cu
2+
and (b)
Ni
2+
for the various endpoints in the solutions of the of calcareous
soils. The solid line represents the 1:1 ratio and the dotted lines
are a factor of 5 above and below the 1:1 line. 203


xxii

ABSTRACT
Contamination of soils with metals is a world-wide problem that could
threaten the sustainability of essential soil functions. Therefore, risk assessments
utilizing appropriate and prudent soil quality criteria are needed. Although it is widely
recognized that the total metal concentration in soils does not represent its

bioavailability, soil quality criteria and risk assessment of metals continue to be
predominantly based on the total metal concentrations. This study provides a
theoretical model, the Terrestrial Biotic Ligand Model (TBLM), based on which
ecotoxicities of metals in soil systems can be assessed. The TBLM achieves a
mathematical integration of the soil-solution-organism interaction by way of chemical
equilibrium models. The active sites on the organism are treated as a ligand, the Biotic
Ligand (BL), to which metal ions bind and may cause a toxic response. The level of
toxicity is correlated only to the fraction of the total BL sites bound by the metal.
Additionally, other cations, principally protons, Ca
2+
, and Mg
2+
, also compete to bind
on the BL sites and, consequently, may alleviate metal toxicities.
For the development of the TBLM, a speciation study was conducted in
up to nineteen soils spiked with different levels of Cu, and Ni in the laboratory under
field moisture conditions. The soils cover nine countries in the European Union and
represent seven major soil groupings. A whole soil approach with Windermere Humic
Aqueous Model (WHAM VI) using the total metal concentration, the organic carbon
(OC) content, the solution pH, and dissolved concentrations of major cations, as inputs
was developed to predict speciation of Cu, and Ni in soil solutions of the non-

xxiii
calcareous soils (pH < 7 and CaCO
3
content ≅ 0%). Dissolved Cu concentrations (log
transformed) were predicted with a root mean squared error (RMSE) of 0.39 and the
free copper ion (Cu
2+
) activities (log transformed) were predicted with a higher RMSE

of 0.77 most likely due to errors introduced during sample preparation, handling, and
measurements. Similarly, the dissolved Ni concentrations in the non-calcareous soils
were predicted with a RMSE of 0.26 for non-calcareous soils with OC content > 1%.
For the remaining two non-calcareous soils with OC content < 1%, the importance of
clay fraction in the partitioning was indicated.
The whole soil approach using WHAM VI was then applied to speciate
Cu, and Ni in the soil solutions associated with the bioassays. The bioassays
represented Cu, and Ni toxicities to plants (barley (Hordeum vulgare cv. Regina) root
elongation and tomato (Lycopersicon esculentum cv. Moneymaker) shoot yield),
invertebrates (reproduction of redworms (Eisenia fetida) and springtails (Folsomia
candida)) and microbes (potential nitrification rate and glucose induced respiration) in
the same soils that were used for the corresponding speciation studies, and under
similar experimental conditions.
The analyses of the bioassay results using the TBLM framework showed
that protons compete with Cu
2+
ions in their interaction with the BL sites associated
with the six endpoints. In addition to the protons, the major cations Ca
2+
and/or Mg
2+

also compete with Ni
2+
ions in these endpoints. These competing cations (protons,
Ca
2+
, and Mg
2+
) at their average ambient soil solution concentrations reduce the EC50

free metal ion activities of both Cu and Ni by more than two orders of magnitude from
the values in the absence of their competition. The significance of this reduction in

xxiv
metal toxicities by these major cations is currently not incorporated in models for
ecotoxicities of metals in soils.
A comparison of the TBLM predictions to the predictions based on
models employing as the dose the total metal concentration or solution free metal ion
activity in the solution shows that the TBLM is consistently able to achieve a better
normalization of the wide variation in toxicological results in the non-calcareous soils.
The TBLM predictions of the EC50 total metal concentrations were generally within a
factor of two of the observed values with RMSE of the predicted values (log
transformed) of 0.26 for Cu toxicity and 0.17 for Ni toxicity. For Cu, 48 out of the 61
observed EC50 total metal concentrations associated with the six endpoints were
predicted with within a factor of two. For Ni, almost all of the predicted EC50 total
metal concentrations (43 out of 45) were within a factor of two of the observed values.
Additionally, 90% of the predicted EC50 total Cu concentrations and 100% of the
predicted EC50 total Ni concentrations were within a factor of three of the observed
values. The application of the TBLM to the calcareous soils based on solution
speciation using solution composition predicted the EC50 Ni
2+
activities generally
within a factor of five. However, for Cu in these soils, the predictions of EC50 Cu
2+

activities were poor most likely due to the inaccurate speciation using solution
composition and the possible interaction of CuOH
+
and CuHCO
3

+
with the associated
biotic ligand sites.
This study, which incorporates Cu, and Ni toxicities to six different
endpoints associated with higher plants, invertebrates, and microbes for up to eleven
non-calcareous soils of disparate properties is, to my knowledge, the first one to use a
single theoretical framework for modeling metals’ toxicities in terrestrial systems. Its