&EPA
UmierJ States
Er.vu i,>nrr>r>r,tal Protection
Agency
Research ;ind Development
Environmental Research
Laboratory
Athens GA 30613
EPA/600/3-87/015
June 1987
Processes,
Coefficients, and
Models for Simulating
Toxic Organics and
Heavy Metals in
Surface Waters
-------
EPA/600/3-87/015
June 1987
PROCESSES, COEFFICIENTS, AND MODELS FOR
SIMULATING TOXIC ORGANICS AND HEAVY METALS
IN SURFACE WATERS
by
Jerald L. Schnoor
Chikashi Sato
Deborah McKechnie
Dipak Sahoo
Department of Civil and Environmental Engineering
The University of Iowa
Iowa City, Iowa 52242
Cooperative Agreement No. 811756
Project Officers
Robert B. Ambrose, Jr.
Thomas 0. Barnwell, Jr.
Assessment Branch
Environmental Research Laboratory
Athens, Georgia 30613
ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ATHENS, GEORGIA 30613
U S, Environmental Protection *§*"<*
Region 5. Library {PL-l2fl
77 West JacVsen Boulevard, utfi
Chicago, tl 60604-3590
-------
DISCLAIMER
The information in this document has been funded wholly or in part by
the United States Environmental Protection Agency under Cooperative
Agreement No. CR811756 with The University of Iowa. It has been subject to
the Agency's peer and administrative review, and it has been approved for
publication as an EPA document. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use by the
U.S. Environmental Protection Agency.
11
-------
FOREWORD
As environmental controls become more costly to implement and the
penalties of judgment errors become more severe, environmental quality
management requires more efficient analytical tools based on greater
knowledge of the environmental phenomena to be managed. As part of this
Laboratory's research on the occurrence, movement, transformation, impact,
and control of environmental contaminants, the Assessment Branch develops
management or engineering tools to help pollution control officials achieve
water quality goals.
In this work, rate constants and coefficients for toxic organic chemi-
cals and heavy metals used in pollutant fate modeling are compiled from a
review of literature through 1986. The compilation is intended to meet the
same data needs for organics and metals formulations as are provided for
"conventional" pollutants in the popular, Athens-developed handbook Rates,
Constants and Kinetics Formulations in Surface Water Quality Modeling. Also
inpluded in the handbook are evaluations of the EXAMS, TOXIWASP, HSPF, and
MINTEQ models for simulating transport and transformation of organics and
metals in the environment.
Rosemarie C. Russo, Ph.D.
Director
Environmental Research Laboratory
Athens, GA
111
-------
ABSTRACT
This is a reference manual for users of models that compute the fate
and transport of toxic organic chemicals and heavy metals in natural surface
waters. The primary purpose of this document is to assist potential users
in selecting proper models and to supply a literature review of rate
constants and coefficients, to insure the wise application of the models.
The manual describes basic concepts of fate and transport mechanisms,
providing kinetic formulations that are common to these models. Development
of generalized mathematical models and analytical solutions to the equations
are demonstrated. The manual includes a brief general description of four
models (EXAMS II, TOXIWASP, HSPF, and MINTEQ), example runs, and comparisons
of these models. Rates and coefficients provided in the manual were
collected through literature reviews through 1986.
This report was submitted in fulfillment of Cooperative Agreement No.
CR811756 by The University of Iowa under the sponsorship of the U.S.
Environmental Protection Agency. The report covers the period September 1,
1984, to December 31, 1986, and work was completed as of December 31, 1986.
IV
-------
CONTENTS
Foreword
Abstract iv
Figures viii
T abl es x
Acknowledgments xi i
1. Introduction 1
1.1 Objectives 2
1.2 Use of This Document 2
1.2.1 Exposure Analysis Modeling System 3
1.2.2 Water Quality Simulation Program 3
1.2.3 Hydrological Simulation Program •*• FORTRAN 4
1.2.4 Geochemical Equilibrium Program 4
1.3 Process Formulations and Data 5
1.3.1 Transport 5
1.3.2 Dispersion 5
1.3.3 Sorption 5
1.3.4 Volatilization 6
1.3.5 Hydrolysis 6
1.3.6 Oxidation 6
1.3.7 Photo-^transf ormation 6
1.3.8 Biological Transformation 6
1.3.9 Bioconcentration 7
1.4 Chemical Fate Modeling 7
1.5 References 10
2. Transport Phenomena 12
2.1 Introduction 12
2.1.1 Transport of Chemicals in Water 13
2.1.2 Advective^Dispersive Equation 16
2.2 Evaluating Coefficients 17
2.2.1 Longitudinal Dispersion Coefficient in Rivers 17
2.2.2 Lateral Dispersion Coefficient in Rivers 17
2.2.3 Vertical Dispersion Coefficient in Rivers 18
2.2.4 Vertical Eddy Diffusivity in Lakes 18
2.3 Compartmentalization 25
2.3.1 Choosing a Transport Model 25
2.3.2 Compartmentalization 26
2.4 Sediment Transport 31
2.4.1 Partitioning 31
2.4.2 Suspended Load 31
2.4.3 Bed Load 32
-------
2.4. 4 Sedimentation 32
2.4.5 Scour and Resuspension 33
2.4.6 Desorption/Dif fusion 34
2.5 Lake Dispersion Calculations 35
2.5.1 McEwen's Method 35
2.5.2 Second Derivative Method 40
2.5.3 Heat Budget Method 42
2.5.4 Steps in Calculation 44
2.6 References 45
3. Organic Reaction Kinetics and Rate Constants 49
3.1 Introduction 49
3.2 Biological Transformations 50
3.3 Chemical Hydrolysis 56
3.4 Chemical Oxidation 60
3.5 Photo-^Transf ormations 62
3.6 Volatilization 64
3.7 Sorption 69
3.8 Bioconcentration 73
3.9 References 78
4. Reactions of Heavy Metals 85
4.1 Introduction 85
4.2 Equilibrium and Kinetic Reactions for Heavy Metals 86
4.2.1 Cadmium 86
4.2.2 Arsenic 88
4.2.3 Mercury 96
4.2.4 Selenium 103
4. 2.5 Lead 109
4.2.6 Barium 114
4.2.7 Zinc 116
4.2.8 Copper 117
4.3 Fate and Transport Models 122
4.4 References 128
5. Analytical Solutions 140
5.1 Introduction 140
5.2 Completely Mixed Systems 141
5.3 Plug Flow Systems 147
5.4 Advective^Dispersive Systems
(Plug Flow with Dispersion) 150
5.5 Graphical Solutions , 154
6. Description of the Models 157
6.1 Introduction 157
6.2 TOXIWASP 157
6.3 EXAMS II 164
6.4 HSPF 167
6.5 MINTEQ 177
6.6 Summary 178
6.7 References 180
VI
-------
Examples and Test Cases
7.1 Introduction ............................................ 183
7.2 Alachlor in the Iowa River Using HSPF ................... 186
7.3 EXAM Simulations for Alachlor and DDT
in Coral ville Reservoir ................................. 192
7.4 Comparison of EXAMS and HSPF for Iowa River ............. 196
7.5 Heavy Metal Waste Load Allocation ....................... 209
7.6 References .............................................. 216
8. Summary [[[ 219
Appendix A. Literature Search for Rate Constants 224
A. I Biodegradation 224
A.2 Hydrolysis 257
A. 3 Oxidation • 248
A. 4 Photolysis 256
A.5 Solubility and Volatilisation 258
A. 6 Partitioning 270
A. 7 Biooonoentration Factors 282
Appendix B.
Physical, Chemical, and Biological Inputs 290
B. 1 TOXIWASP 290
B. 2 EXAMS II .- 291
-------
FIGURES
Number Page
1.01 Generalized approach for mass balance models 8
2.01 Schematic of transport processes 14
2.02 Schematics of velocity gradients created by shear stresses
at the air^-water, bed^water, and bank^water interfaces 15
2.03 Thermal stratification in a lake and the assumption of mixing
between two compartments 29
2.04 Suspended load and bed load 32
2.05 Lake Clara, Wisconsin, temperature profile -? Summer, 1982.... 36
2.06 Lake Clara average summer temperature profile •* 1982 37
2.07 Graphical method to find a and C^ (Lake Clara) 38
2.08 Graphical method to find a and C., (Lindley Pond) 39
3.01 Michaelis^Menton kinetics for microbial growth or substrate
utilization rate as a function of substrate concentration.... 52
3.02 Semi^log plot of a second^order biodegradation
reaction illustrating the increase in chemical
degradation (substrate utilization) as a function
of the bacteria biomass concentration, x 53
3.03 First-'order biodegradation plots 53
3.04 Effect of pH on hydrolysis rate constants 57
3.05 Two^film theory of gas^liquid interface 65
3.06 Dissolved and particulate toxicant as a function of time 72
3.07 Determination of partitioning rate constants 74
4.01 General schematic of NONEQUI 89
4.02 The Eh^pH diagram for As at 25°C and one atmosphere with
total arsenic TO'"5 M and total sulfur 10^3M 90
4.03 Cycle of arsenic in a stratified lake 97
4.04 Stability field for aqueous mercury species at various
Eh arid pH values 101
4 .05 Model of mercury dynami cs of NONEQUI 104
4.06 Schematic diagram of the cycling of mercury in
the environment 105
4.07 Cycling of lead in an aquatic ecosystem Ill
4.08 Speciation of copper(II) and carbonate as a function
of pH 123
5.01 Schematic of a completely mixed lake, and inputs and
effluent responses 141
5.02 Schematic of completely mixed lakes in series, and
inputs and effluent responses with reaction decay 145
5.03 A compartmentalized lake and effluent responses to
an inpulse input of tracer 148
Vlll
-------
FIGURES (Continued)
5.04 Schematic of plug-^flow system, and inputs and response
profiles , 149
5.05 Schematic of advective^dispersive system, and input and
steady^state profile of reactive chemicals
5.06 Chemical fraction removed versus particulate fraction
(KPM/1+KPM) versus td K(TDK) at Kstd = 4.' 153
6.01 TOXIWASP formulation 159
6.02 Transformation and reaction kinetics in TOXIWASP 163
6.03 TOXIWASP sediment buri al 165
6.04 TOXIWASP sediment erosion 166
6.05 lonization reactions in EXAMS II 168
6.06 SERATRA formulation 173
6.07 Transformation and reaction kinetics in HSPF 176
7.01 The Iowa River low^water (elevation above
sea level) profile 184
7.02 Flow and sediment loadings simulated by
HSPF for 1 977 188
7.03 Flow and sediment loadings simulated
by HSPF for 1978 189
7.04 Dissolved, suspended and sedimented alachlor concentrations
at Marengo, IA, simulated by HSPF for 1977 190
7.05 Dissolved, suspended and sedimented alachlor concentrations
at Marengo, IA, simulated by HSPF for 1978 191
7.06 Physical configurations of the completely mixed
compartments of the Iowa River for EXAMS 193
7.07 Iowa River/Coralville Environment (10 segments)
Model Pathways 195
7.08 Alachlor simulation results by EXAMS 197
7.09 Comparison of alachlor and DDT simulations by EXAMS II 199
7.10 The Iowa River (above Marengo) environment model pathways 205
7.11 Predicted total alachlor concentrations in the water
column and in the bed sediments of the Iowa River
(1 90 jniles above Marengo) 206
7.12 1977 "simulation at Rowan 207
7.13 1978 simulation at Rowan 208
7.14 Total copper concentration: Initial and with WLA 215
7.15 Total zinc concentration: Initial and with WLA 216
IX
-------
TABLES
Number
1.01 General Characteristics of Fate Models 10
2.01 Summary of Dispersion Measurements in Streams 20
2.02 Vertical Dispersion Coefficient for Lakes Across Thermocline.. 22
2.03 Whole Lake Average Vertical Dispersion Coefficient 24
2.04 Interstitial Sediment Pore Water Diffusion Coefficients 25
2.05 Outflow Concentration Divided by Inflow Concentration at
kt 30
2.06 Lake Clara Temperature Data 36
2.07 Linsley Pond Tabulations to Find c and b 37
2.08 Lake Clara Dispersion Coefficient 40
2.09 Linsley Pond Dispersion Coefficient 40
2.10 Lake Clara Second Derivative Method 41
2.11 Linsley Pond Second Derivative Method 42
3.01 Summary Table of Biotransformation Rate Constants 51
3.02 Summary Table of Hydrolysis Data 59
3.03 Summary Table of Oxidation Data 61
3.04 Summary Table of Photolysis Data 62
3.05 Kinetic Viscosity of Air 66
3.06 Summary Table of Volatilization Data 68
3.07 Summary Table of Partitioning Data 70
3.08 Summary Table of Bioconcentration Data 76
4.01 Extent of Complexation of Cadmium in Borehole Water
Settled Sewage, Sewage Effluents and River Water Samples 87
4.02 Equilibrium Constants for Cadmium 91
4.03 Constants for Cadmium Adsorption 93
4.04 Arsenic Species Commonly Found in Environmental Samples 95
4.05 Equilibrium Constants for Arsenic ; 98
4.06 Constants for Arsenic Adsorption 99
4.07 Equilibrium Constants for Mercury 106
4.08 Constants for Mercury Adsorption 107
4.09 Equilibrium Constants for Lead 112
4.10 Constants for Lead Adsorption 114
4.11 Equilibrium Constants for Barium 115
4.12 Equilibrium Constants for Zinc 118
4.13 Constants for Zinc Adsorption 120
4.14 Equilibrium Constants for Copper 124
4.15 Constants for Copper Adsorption 129
4.16 Summary of the Heavy Metals Models 130
6.01 Summary Comparison of the Models, TOXIWASP,
EXAMS II and HSPF 179
-------
TABLES (Continued)
6.02 Environmental Inputs for Computation of the
Transformation and Reaction Processes in
TOXIWASP, EXAMS II and HSPF 181
7.01 Properti es of Alachlor 185
7.02 HSPF Input Data used for Alachlor Simulation
in the Iowa River 187
7.03 Segmentation of the Iowa River Study Reach 192
7.0*1 Flow, Sediment and Alachlor Loads Used in EXAMS
Simulation in the 65 Miles of the Iowa River Reach,
Downstream of Marengo, IA 194
7.05 Chemical Input Data to EXAMS II for Alachlor and DDT 198
7.06 Comparison of Exposure, Fate and Persistence
between Alachlor and DDT (EXAMS II Outputs) 200
7.07 EXAMS Input Data for the 1977 Simulation of the
Iowa River Alachlor •?• Water Compartment 201
7.08 EXAMS Input Data for the 1978 Simulation of the Iowa River... 203
7.09 The EXAMS II Outputs for the 1977 Simulation of the
Iowa River for Alachlor 209
7.10 The EXAMS II Outputs for the 1978 Simulation of the
Iowa River for Alachlor 211
7.11 Chemical Species of Metals at Various Sites 217
8.01 Transport and Reaction Characteristics of
Selected Fate Models 220
8.02 Summary Table of Significant Reactions in the Literature 221
8.03 Summary Table of Significant Heavy Metal Reactions 223
XI
-------
ACKNOWLEDGEMENTS
This report could not have been completed without the helpful
discussions and abiding patience of Robert Ambrose, Jr., and Thomas 0.
Barnwell, Jr., Project Officers at the Athens Environmental Research
Laboratory. I would also like to thank my colleagues at Manhattan College,
Drs. Dominic Di Toro, Robert Thomann, John Connolly, and Richard Winfield
who taught me about water quality modeling, and especially to Dr.'Donald J,.
O'Connor, who first shared his insight into the importance of toxic
chemicals modeling with me as an NSF postdoctoral fellow in 1976. Without
them I would not have been born into this field.
Several scientists and engineers at Athens Environmental Research
Laboratory have contributed ideas and provided encouragement for our
modeling efforts, among them: Richard Zepp, Lee Wolfe, and Larry Burns.
Dr. Walter Sanders, Robert Swank and James Falco provided initial impetus
for toxics modeling efforts at The University of Iowa, and George Baughman
has made us a part of a US/USSR initiative in this area. Reviewers of the
draft report were Drs. Charles Delos, Richard Lee, and Betsy Southerland.
Dr. Southerland has been a principal proponent of using mathematical models
for toxic chemical waste load allocations. Her enthusiasm has created
demand for these models in EPA and State Agencies.
Finally, I would like to express my deepest gratitude to Ms. Jane
Frank, whose word processing abilities exceed even my abilities for entropy
production!
Jerry Schnoor
Iowa City, Iowa
April 17, 1987
xn
-------
SECTION 1
INTRODUCTION
There is an ever increasing need for water quality modeling in
protection of the nation's waters. For the first time, it is possible to
perform waste load allocations for some toxic organic and heavy metals
pollutants in the development of National Pollutant Discharge Elimination
System (NPDES) permits for water-quality limited stream segments, those
segments which are not expected to satisfy water quality standards even with
the implementation of best practicable control technology currently
available. There exist river and stream segments which exceed water quality
standards for some pesticides and heavy metals from nonpoint sources as
well. Water quality managers need to determine what constitutes best
management practices (BMP) in these cases and what improvements BMP's can
achieve.
New water quality criteria are currently being promulgated to account
for acute and chronic effects levels using frequency and duration
concepts. These criteria could eventually result in water quality standards
that are enforceable by law and would require the application of
mathematical models for waste load allocations, risk assessments, or
environmental impact assessments. On July 29, 1985, the U.S. Environmental
Protection Agency (EPA) published a notice of final ambient water quality
criteria documents in the Federal Register for nine toxicants: ammonia,
arsenic, cadmium, chlorine, chromium, copper, cyanide, lead, and mercury
(USEPA, 1985). The new criteria specify an acute threshold concentration
and a chronic-no-effect concentration for each toxicant as well as tolerable
durations and frequencies.
The new criteria state that aquatic organisms and their uses should not
be affected unacceptably if two conditions are met: (1) the U-day average
concentration of the toxicant does not exceed the recommended chronic
criterion more than once every three years on the average and (2) the 1-hour
average concentration does not exceed the recommended acute criterion more
than once every 3 years on the average. Criteria for other toxic pollutants
will be published in the near future specifying the same durations and
frequencies. The new criteria recognize that toxic effect is a function
both of the magnitude of a pollutant concentration and of the organism
exposure time to that concentration. A very brief exposure to a relatively
high concentration may be less harmful than a prolonged exposure to a lower
concentration.
-------
The EPA is considering application of site^specific water quality
criteria that also would require mathematical modeling to aid in determining
water quality standards. For example, Carlson et al. (1986) have shown that
copper exhibits much less toxicity and/or bioavailability in site-="specific
tests compared to singles-specie laboratory bioassay testing. It is likely
that aqueous copper forms strong complexes with ligands in^situ (organics
ligands in particular) that are not as toxic or bioavailable to aquatic
organisms. Because copper exceeds water quality criteria in some locations
naturally, this is an extremely important issue. Publicly owned wastewater
treatment plants often exceed water quality criteria for some heavy metals
under extreme, low flow conditions. The likelihood of in^situ toxicity,
either acute or chronic, becomes the point of primary concern. To provide
the proper perspective, we must have valid exposure assessments. These
assessments require the use of mathematical models. Hedtke and Arthur
(1985) have demonstrated the techniques for development of a site^-specific
water quality criterion for pentachlorophenol.
1.1 OBJECTIVES
This publication has three primary objectives:
1) to describe four existing mathematical models (EXAMS, TOXIWASP,
HSPF, and MINTEQ) that are supported by the Center for Water
Quality Modeling at EPA's Environmental Research Laboratory,
Athens, GA;
2) to aid the modeler in the proper choice of mathematical models,
rate constants, and kinetic formulations that are available in the
scientific literature; and
3) to present case studies that illustrate model capabilities,
differences, and limitations.
The publication extends the discussion of chemical fate and transport to
heavy metal reactions (as well as organic reactions) with the addition of
MINTEQ. Based on a literature review through 1985 and selected references
through 1986, it updates the chemical rate constant data of Callahan et al.
(1979) and Mabey et al. (1982).
1.2 USE OF THIS DOCUMENT
Sections 2 through 4 present a review of the literature and a summary
of available rate constants and equilibrium constants. Literature values
were screened for applicability to natural water conditions. A range and
summary of the constants are given in each Section; the Appendix includes
all the values from a computerized literature search, The Appendix updates
the work of Callahan et al. (1979) and Mabey et al. (1982) to 1985 for
organic priority pollutants.
Section 5 describes the techniques involved in mathematical modeling of
water quality including development of mass balance differential equations
and simplified analytic solutions. These solutions, in many cases, can be
used to understand the more detailed mathematical models and to check
results.
-------
Section 6 describes the EXAMS, TOXIWASP, HSPF, and MINTEQ models.
TOXIWASP and HSPF can be applied to toxic organics or heavy metals, EXAMS
was developed for organic pollutants only, and MINTEQ is for chemical
equilibrium speciation of heavy metals. MINTEQ does not include transport
or kinetics of these metals.
Section 7 gives an application and test case of EXAMS-II, HSPF, and
MINTEQ employed for a waste load allocation. It allows the modeler to
realize some of the data requirements for these models and compares the
output of EXAMS and HSPF for a stretch of the Iowa River.
Each of these models is supported by EPA's Center for Water Quality
Modeling at the Environmental Research Laboratory, Athens, GA. Support
involves the distribution of code and documentation, the correction and
updating of models through user experience, and the presentation of
workshops and training courses.
1.2.1 Exposure Analysis Modeling System
EXAMS-II (Burns, Cline, andLassiter, 1982; Burns and Cline, 1985), is
a steady-state and dynamic model designed for rapid evaluation of the
behavior of synthetic organic chemicals in lakes, rivers, and estuaries. An
interactive program, EXAMS-II allows the user to specify and store the
properties of chemicals and ecosystems, modify the characteristics of either
via simple English-like commands, and conduct rapid, efficient evaluations
of the probable fate of chemicals. EXAMS-II simulates the behavior of a
toxic chemical and its transformation products using second-order kinetics
for all significant organic chemical reactions. EXAMS-II does not simulate
the solids with which the chemical interacts. The concentration of solids
must be specified for each compartment; the model accounts for sorbed
chemical transport based on solids concentrations and specified transport
fields. Benthic exchange includes pore water advection, pore water
diffusion, and solids mixing. The latter describes a net steady-state
exchange associated with solids that is proportional to pore water
diffusion.
1.2.2 Water Quality Simulation Program
TOXIWASP is related to two other models ~ WASP3 and WASTOX. WASP3 (Di
Toro et al., 1982; Ambrose et al., 1986) is a generalized modeling framework
for contaminant fate and transport in lakes, rivers, and estuaries. Based
on the flexible compartment modeling approach, WASP3 can be applied in one,
two, or three dimensions given transport of fluxes between segments. WASP3
can read output files from the link-node hydrodynamic model DYNHYD3, which
predicts unsteady flow rates in unstratified rivers and estuaries given
variable tides, wind, and inflow. A variety of water quality problems can
be addressed with the selection of appropriate kinetic subroutines. Two
general toxic chemical modeling frameworks have been constructed from WASP -
TOXIWASP and WASTOX. These separate frameworks will be combined in WASPH.
TOXIWASP (Ambrose et al., 1983), a subset of WASP3, combines a kinetic
structure adapted from EXAMS with the WASP transport structure and simple
-------
sediment balance algorithms to predict sediment and chemical concentrations
in the bed and overlying waters. TOXIWASP predicts variable rate constants
using second^order kinetics for all significant organic chemical reactions
except ionization. Benthic exchange includes pore water advection, pore
water diffusion, an empirical biotur bat lorn-related dispersion, and
deposition/scour. Net sedimentation and burial are calculated.
WASTOX (Connolly and Winfield, 1984) simulates a toxic chemical and up
to three sediment size fractions in the bed and overlying waters. Second^
order kinetics are used for all significant organic chemical reactions
except ionization. Benthic exchange includes pore water advection, pore
water diffusion, and deposition/scour. Net sedimentation and burial rates
can be specified. An empirically based food chain model is linked to WASTOX
for calculating chemical concentrations in biota an'd fish resulting from
predicted aquatic concentrations (Connolly and Thomann, 1984).
1.2.3 Hydrologlcal Simulation Program •* FORTRAN
HSPF (Johanson et al., 1984) is a comprehensive package for simulation
of watershed hydrology and water quality for both conventional and toxic
organic pollutants. HSPF incorporates the watersheds-scale ARM and NPS
models into a basin^scale analysis framework that includes transport and
transformation in one^dimensional stream channels. The result of this
simulation is a time history of the runoff flow rate, sediment load, and
nutrient and pesticide concentrations, along with a time history of water
quantity and quality at any point in a watershed. HSPF simulates three
sediment types (sand, silt, and clay) in addition to a single organic
chemical and transformation products of that chemical. The transfer and
reaction processes included are hydrolysis, oxidation, photolysis,
biodegradation, volatilization, and sorption. Sorption is modeled as a
first'order kinetic process in which the user must specify a desorption rate
and an equilibrium partition coefficient for each of the three solids
types. Resuspension and settling of silts and clays (cohesive solids) are
defined in terms of shear stress at the sediment^water interface. For
sands, the capacity of the system to transport sand at a particular flow is
calculated and resuspension or settling is defined by the difference between
the sand in suspension and the transport capacity. Calibration of the model
requires data for each of the three solids types. Benthic exchange is
modeled as sorption/desorption and deposition/scour with surficial benthic
sediments. Underlying sediment and pore water are not modeled.
1.2.4 Geochemical Equilibrium Program
MINTEQ (Felmy et al., 1984a, Brown et al., 1987) is a geochemical model
that is capable of calculating equlibrium aqueous speciation, adsorption,
gas phase partitioning, solid phase saturation, and precipitation^
dissolution of 11 metals (arsenic, cadmium, chromium, copper, lead, mercury,
nickel, selenium, silver, thallium, and zinc). MINTEQ contains an extensive
thermodynamic data set and contains 6 different algorithms for calculating
adsorption. Proper application of MINTEQ requires some expertise because
kinetic limitations at particular sites may prevent the thermodynamically
possible reactions that are integral to the model.
-------
Nevertheless, thoughtful application of MINTEQ may describe the
predominant metals species at a site and thus give insight into potential
biological effects. For waste load allocation problems, MINTEQ must be run
in conjunction with one of the transport and transformation models described
above. It has been linked and tested with EXAMS (Felmy et al., 198^b;
Medine and Bicknell, 1986).
1.3 PROCESS FORMULATIONS AND DATA
To use mathematical models for environmental assessments, information
is required on chemical fate processes. Fate of chemicals in the
environment is determined by physical, chemical and biological processes
which include transport, dispersion, sorption, volatilization, hydrolysis,
oxidation, photo-transformations, biological transformations and
bioconcentration. In Chapter 2 through 4, the mathematical formulations of
these processes are presented with some theoretical background. Summary
data tables are also presented with complete data sets given in Appendix A.
1.3-1 Transport
The transport of a dissolved chemical in surface waters is influenced
by the velocity of the current or advective transport. Current velocities
must be measured directly or calculated from a knowledge of flowrate and
cross-sectional area through which it flows. Transport of an adsorbed
chemical requires knowledge of sediment movement within the surface water,
including sedimentation, resuspension/scour, and saltation along the bottom
as bed load. The concentration of suspended solids multiplied times the
flowrate of a river is a measure of the sediment transport or "wash load" of
the river. Mixing of both dissolved chemicals and, to some extent, adsorbed
chemicals occurs by dispersion in surface waters. Molecular diffusion of
chemicals in surface water is generally too slow to be of importance except
in pore waters of sediments. However, turbulent diffusion and dispersion
are important processes in predicting the environmental transport of a
chemical contaminant in surface waters.
1.3.2 Dispersion
Dispersion results from the mixing of surface waters under turbulent
conditions. It is enhanced when turbulence is coupled with temporal and
spatial variations in velocity within the water body. Dead zones (areas
with very still, quiescent waters) cause back-mixing of water and the
eventual "spread" of dissolved chemical pulses that is characteristic of
dispersion. Chemical concentrations could not be accurately simulated
without some knowledge of dispersion and mixing characteristics of the water
body.
1.3-3 Sorption
Chemicals that are dissolved in water can become sorbed to sediment and
suspended solids in the water body. Mechanisms of sorption include physical
adsorption (by attractive coulombic forces), chemisorption (chemical binding
to a specific site or ligand on the surface of the solids), and absorption
-------
(a solution phenomena of organic chemicals dissolving in a like phase or
organic matrix). The fate of a chemical in water is significantly affected
by its partitioning between solids and water. In general, for organic
chemicals, the more polar is the chemical, the more it tends to partition
into the aqueous phase. Conversely, the more nonpolar it is, the more it
tends to partition into the organic or solid phase. We estimate the
tendency for an organic chemical to sorb by use of its octanol/water
partition coefficient or KQW. The greater is KQW, the larger is its
potential to sorb to the solid phase (sediment and suspended solids) in the
water body.
1.3.4 Volatilization
Some chemicals evaporate (volatilize) from the water to the atmosphere
by gas transfer reactions. Volatile chemicals are characterized by a high
Henry's constant, H, that describes their tendency to partition into the gas
phase rather than the aqueous phase at chemical equilibrium. The rate that
chemicals volatilize is also dependent on their molecular properties in the
solvent (water) including molecular size, polarity and functional groups.
Chemical volatilization is often compared relative to that of dissolved
oxygen and reaeration rates in natural waters.
1.3.5 Hydrolysis
Hydrolysis reactions occur between chemicals and water molecules (H20,
OH~, or HgO ), resulting in the cleaving of a molecular bond and the
formation of a new bond with components of the water molecule. Tendency of
an organic chemical to undergo hydrolysis reactions depends on its
susceptibility to a nucleophilic attack. Organic esters, amides, amines,
carbamates, and alkyl halides are often hydrolyzed in natural waters.
1.3.6 Oxidation
Some chemicals can undergo a strict chemical oxidation in natural
waters due to their reducing potential. Oxidation may occur with dissolved
oxygen as a reactant or, more commonly, a free radical (such
as «OH, R00«) that is generated at low concentrations from other redox
reactions involving hydrogen peroxide, singlet oxygen, or ozone.
1.3.7 Photo-transformation
Photolysis is a light-induced degradation reaction that occurs when
photons strike organic molecules and excite them to a higher electron
state. Transformation of a chemical in natural waters may involve direct or
indirect photolysis depending upon whether the chemical of interest is
itself excited by the quantum of energy (photon) or whether it is
transformed by another light-energized molecule.
1.3.8 Biological Transformation
Perhaps the most universal and important reactions occurring in natural
waters are biological. Transformation reactions of organics and heavy
-------
metals are often caused or enhanced by microorganisms, especially bacteria,
fungi, and algae. Extra-cellular and intracellular enzymes catalyze a
variety of reactions including hydrolysis and chemical oxidation. For
example, many organo-phosphate ester pesticides are known to undergo
spontaneous strict chemical hydrolysis reactions, but in the presence of
microorganisms the reactions are greatly catalyzed. The reaction products
and reactants are the same, but the rate that the reaction proceeds is much
faster.
1.3.9 Bioconcentrati on
The capacity for a chemical to be taken-up from the aqueous phase by
biota in natural waters is termed, "bioconcentration". It correlates quite
well with the hydrophobia (lipophilic) nature of the chemical. Because some
chemicals that are hydrophobia accumulate in the fatty tissue of fish and
other organisms, bioconcentration is an important process that can
contaminate fisheries. Like sorption to suspended solids, bioconcentration
correlates with the tendency for chemicals to dissolve into octanol, as
opposed to water. One measures this tendency as the octanol/water partition
coefficient or KQW.
1.M CHEMICAL FATE MODELING
The fate of chemicals in the aquatic environment is determined by two
factors: their reactivity, and the rate of their physical transport through
the environment. All mathematical models of the fate of chemicals are
simply useful accounting procedures for the calculation of these processes
as they become quite detailed. To the extent that we can accurately predict
the chemical, biological, and physical reactions and transport of chemical
substances, we can "model" their fate and persistence and the inevitable
exposure to aquatic organisms.
Figure 1.01 is a schematic of a mass balance modeling approach to
chemicals in the environment. Key elements are:
a clearly defined control volume
a knowledge of inputs and outputs which cross the boundary of the
control volume
a knowledge of the transport characteristics within the control
volume and across its boundaries
a knowledge of the reaction kinetics and rate constants within the
control volume.
A control volume can be as small as an infinitesimally thin slice of
water in a swiftly flowing stream or as large as the entire body of oceans
on the planet earth. The important point is that the boundaries are clearly
defined with respect to their location so that the volume is known and mass
fluxes across the boundaries can be determined. Within the control volume,
the transport characteristics (degree of mixing) must be known either by
measurement or an estimate based on the hydrodynamics of the system.
Likewise, the transport in adjacent or surrounding control volumes may
-------
contribute mass to the control volume, so transport across the boundaries of
the control volume must be known or estimated.
A knowledge of the chemical, biological, and physical reactions that
the substance can undergo within the control volume is the subject of
Sections 3 and 4 in this manual. If there were no degradation reactions
taking place in aquatic ecosystems, every pollutant which was ever released
to the environment would still be present. Fortunately, there are natural
processes that serve to degrade some wastes and to ameliorate aquatic
impacts. One must understand these reactons from a quantitative viewpoint
in order to assess the potential damage to the environment from pollutant
discharges and to allocate allowable limits for these discharges.
A mass balance is simply the accounting of the mass inputs, outputs,
reactions and accumulation as described by the following equation.
Accumulation w/in
the control volume
Mass
Inputs
Mass
Outflows
± Reactions
(1)
MASS
INPUTS
transport
in
CONTROL
VOLUME
(WATER BODY)
physical, chemical, biological
reactions
transport
out
MASS
OUTFLOW
Figure 1.01 .
Generalized approach for mass balance models utilizing the
control-volume concept and transport across boundaries.
-------
If the substance is being formed or grown within the control volume such as
the combination of two reactants to form a product P (A + B •* P), then the
algebraic sign in front of the "Reactions" term is positive. If the
substance is being transformed or degraded within the control volume, then
the algebraic sign of the "Reactions" term is negative. If the substance is
conservative (i.e., non-reactive or inert), then the "Reactions" term is
zero.
To perform mathematical modeling of toxic chemicals, four ingredients
are necessary: 1) field data on chemical concentrations and mass discharge
information, 2) a mathematical model formulation, 3) rate constants and
coefficients for the mathematical model, and 4) some performance criteria
with which to judge the model.
One cannot stress enough the importance of field data. Depending on
the ultimate use of the model, the amount of field reconnaissance varies.
If the model is to be used in a waste load allocation for NPDES permits,
there should be enough field data to be confident of model results. Usually
this requires two sets of field measurements, one for model calibration and
one for verification under somewhat different circumstances.
Model calibration involves a comparison between simulation results and
field measurements. Model coefficients and rate constants should be chosen
initially from literature or laboratory studies. (In this manual, you may
use Appendix A if the chemical in which you are interested is listed.)
Discharge rates are also needed as input to drive the model. After you run
the model, a statistical comparison is made between model results for the
state variables (chemical concentrations) and field measurements. If errors
are within an acceptable tolerance level, the model is considered
calibrated. If errors are not acceptable, rate constants and coefficients
must be systematically varied (tuning the model) to obtain an acceptable
simulation. Thus the model is calibrated.
To verify the model, a statistical comparison between simulation
results and a second set of field data is required. Coefficients and rate
constants cannot be changed from the model calibration. This procedure
provides some confidence that the model is performing acceptably.
Performance criteria may be as simple as, "model results should be within
one order of magnitude of the field concentrations at all times," or as
stringent as, "the mean squared error of the residuals (difference between
field measurements and model results) should be a minimum prescribed or
optimal value". Performance criteria depend on the use of the model, but
criteria should be determined ji priori, in the advance of the modeling
exercise.
In this manual, Sections 3 and 4 and Appendix A can aid in the initial
selection of model rate constants and coefficients. Sections 5, 6, and 7
and Appendix B can aid in the understanding and use of four models
highlighted here and supported by Athens Environmental Research
Laboratory. Table 1.01 gives the general characteristics of the four models^
to aid in selection for your particular application.
-------
TABLE 1.01 GENERAL CHARACTERISTICS OF FATE MODELS
Model
EXAMS -I I
TOXIWASP,
WASTOX
HSPF
MINTEQ
Water
Body
L,R,E
L,R,E
R
L,R,E
Lake n
River,
Estuary
Time
Domain
S,D
D
S,D
D
S
Steady-State,
Dynamic
Chemical
0
0,M
0,M
M
Organic
Metal
Availability
A, PC
A, PC
A, PC
A, PC
Athens EPA,
Personal
Computer
Version
1.5 REFERENCES FOR SECTION 1
Ambrose, R.B., et al., 1983. User's Manual for the Chemical Transport and
Fate Model TOXIWASP Version 1. U.S. Environmental Protection Agency,
Athens, GA, EPA-600/3'83-005.
Ambrose, R.B., et al., 1986. WASP3, A Hydrodynamic and Water Quality Model-
Model Theory, User's Manual, and Programmer's Guide. U.S. Environmental
Protection Agency, Athens, GA, EPA^OO/S^SeHDS1!.
Brown, D.S., et al., 1987. MINTEQA1, An Equilibrium Metal Speciation
Model: User1s Manual. U.S. Environmental Protection Agency, Athens, GA.
Burns, L.A., et al., 1982. Exposure Analysis Modeling System (EXAMS):
Users Manual and System Documentation. U.S. Environmental Protection
Agency, Athens, GA, EPA'600/3"82"023.
Burns, L.A., and D.M. Cline, 1985. Exposure Analysis Modeling System,
Reference Manual for EXAMS II. U.S. Environmental Protection Agency,
Athens, GA, EPA-600/3-85K)38.
Callahan, M.A., et al, 1979. Waters-Related Fate of 129 Priority
Pollutants. U.S. Environmental Protection Agency, Washington, D.C.,
4*IOA-79"029b, 2 vol.
Carlson, A.R., D. Hammermeister and H. Nelson, 1986. Development and
Validation of Site^Specific Water Quality Criteria for Copper.
Environmental Toxicology and Chemistry. 5:997^1012.
10
-------
Connolly, J.P. and R.V. Thomann, 1984. WASTOX, A Framework for Modeling the
Fate of Toxic Chemicals in Aquatic Environments, Part 2: Food Chain. U.S.
Environmental Protection Agency, Gulf Breeze, FL.
Connolly, J.P. and R.P. Winfield, 1984. WASTOX, A Framework for Modeling
the Fate of Toxic Chemicals in Aquatic Environments, Part 1 : Exposure
Concentration. U.S. Environmental Protection Agency, Gulf Breeze, FL.
Di Toro, D.M., et al, 1982. Water Quality Analysis Simulation Program
(WASP) and Model Verification Program (MVP) ^ Documentation. Hydroscience,
Inc., Westwood, NJ for U.S. Environmental Protection Agency, Duluth, MN,
Contract No. 68^01'3872.
Felmy, A.R., et al., 1984a. MINTEQ » A Computer Program for Calculating
Aqueous Geochemical Equilibria, U.S. Environmental Protection Agency,
Athens, GA. EPA'600/3^84'032.
Felmy, A.R., et al., 1984b. MEXAMS » The Metals Exposure Analysis Modeling
System. U.S. Environmental Protection Agency, Athens, GA. EPA"600/3~84"
031.
Hedtke, S.F. and J.W. Arthur. 1985. Evaluation of Site-Specific Water
Quality Criterion for Pentachlorophenol Using Outdoor Experimental
Streams. Aquatic Toxicology and Hazard Assessment: Seventh Symposium, ASTM
STP 854, R.D. Cardwell, R. Purdy and R.C. Bahner, Eds., American Society for
Testing and Materials, Philadelphia, PA, pp. 551-564.
Johanson, R.C., et al., 1984. Hydrological Simulation Program-Fortran
(HSPF): Users Manual for Release 8.0. U.S. Environmental Protection
Agency, Athens, GA EPA-600/3-84-066.
Mabey, W.R., J.H. Smith, R.T. Podoll, H.L. Johnson, T. Mill, T.W. Chou, J.
Gates, I.W. Partridge, H. Jaber, and D. Vandenberg, 1982. Aquatic Fate
Process Data for Organic Priority Pollutants. SRI International. EPA
Report No. 440/4-81-014.
Medine, A.J., and Bicknell, B.R., 1986. Case Studies and Model Testing of
the Metals Exposure Analysis Modeling System (MEXAMS). U.S. Environmental
Protection Agency, Athens, GA EPA-600/3-86-045.,
USEPA, 1985. Notice of Final Ambient Water Quality Criteria Documents.
Federal Register, Vol. 50, No. 145, Monday, July 29.
11
-------
SECTION 2
TRANSPORT PHENOMENA
2.1 INTRODUCTION
The reactions that a chemical may undergo are an important aspect of a
chemical's fate in the environment, but an equally important process has to
do with the rate of a chemical's transport in the aquatic environment. In
this chapter, we shall discuss three processes of mass transport in aquatic
ecosystems: transport by the current of the water (advection), transport
due to mixing within the water body (dispersion), and transport of sediment
particles within the water column and between the water and the bed.
Toxic organic chemicals, at low concentrations in natural waters, exist
in a dissolved phase and a sorbed phase. Dissolved substances are
transported by water movement with little or no "slip" relative to the
water. They are entirely entrained in the current and move at the water
velocity. Likewise, organics that are sorbed to colloidal material or fine
suspended solids are essentially entrained in the current, but they may
undergo additional transport processes such as sedimentation and deposition
or scour and resuspension. These processes may serve to retard the movement
of the sorbed substances relative to the water movement. Thus in order to
determine the fate of toxic organic substances, we must know both the
water movement and sediment movement.
The importance of a good water budget cannot be understated. Physical
transport of water in a clearly defined control-volume is accounted for by a
water balance. Seldom are all of the terms in the water balance measured
accurately, so errors are generated in the water accounting procedure. A
complete water balance is presented below.
Accumulation Direct
= Inflows + precipitation ~ Outflows - Evaporation
of H20
+ Infiltration - Exfiltration + Overland Runoff
Water can be stored within lakes or rivers by a change in elevation or
stage. Inflows and outflows should be gaged or measured over the period of
investigation. Precipitation gages and evaporation pans can provide
sufficiently accurate data. In the best of situations, it is possible to
achieve an annual water balance within 5 percent (total inflows are within
12
-------
5% of total outflows). Confounding factors include infiltration,
exfiltration, and overland runoff.
2.1.1 Transport of Chemicals in Water
The transport of toxic chemicals in water principally depends on two
phenomena: advection and dispersion. Advection refers to movement of
dissolved or fine particulate material at the current velocity in any of
three directions (longitudinal, lateral or transverse, and vertical).
Dispersion refers to the process by which these substances are mixed within
the water column. Dispersion can also occur in three directions. A
schematic for advection, turbulent diffusion, and dispersion in a stream is
given in Figure 2.01. Three processes contribute to mixing (dispersion):
1. Molecular diffusion. Molecular diffusion is the mixing of
dissolved chemicals due to the random walk of molecules within the
fluid. It is caused by kinetic energies of molecular vibrational,
rotational, and translational motion. In essence, molecular
diffusion corresponds to an increase in entropy whereby dissolved
substances move from regions of high concentration to regions of
low concentration according to Pick's laws of diffusion. It is an
exceedingly slow phenomenon, such that it would take on the order
of 10 days for 1 mg/J, of dissolved substance to diffuse through a
10-cm water column from a concentration of 10 mg/Jl. It is
generally not an important process in the transport of dissolved
substances in natural waters except relating to transport through
thin and stagnant films at the air-water interface or transport
through sediment pore water.
2. Turbulent Diffusion. Turbulent or eddy diffusion refers to mixing
of dissolved and fine particulate substances caused by micro-scale
turbulence. It is an advective process at the microscale level
caused by eddy fluctuations in current velocity. Shear forces
within the body of water are sufficient to cause this form of
mixing. It is several orders of magnitude larger than molecular
diffusion and is a contributing factor to dispersion. Turbulent
diffusion can occur in all three directions, but is often
anisotropic (i.e., there exist preferential directions for
turbulent mixing due to the direction and magnitude of shear
stresses).
3. Dispersion. The interaction of turbulent diffusion with velocity
profiles in the water body causes a still greater degree of mixing
known as dispersion. Transport of toxic substances in streams and
rivers is predominantly by advection, but transport in lakes and
estuaries is often dispersion-controlled. Velocity gradients are
caused by shear forces at the boundaries of the water body, such as
vertical profiles due to wind shear at the air-water interface, and
vertical and lateral profiles due to shear stresses at the
sediment-water and bank-water interfaces (Figure 2.02). Also
velocity gradients can develop within the water body due to channel
morphology, sinuousity and meandering of streams, and thermal or
13
-------
e>
ADVECTION
w«^J5s/^\?Z!!:3Sc«»%W?^
EDDIES
TURBULENT
DIFFUSION
Z
VELOCITY
PROFILE
DISPERSION
Z
Figure 2.01.
Schematic of transport processes: 1) Advection, movement of
chemical entrained in current velocity; 2) Turbulent
diffusion, spread of chemical due to eddy fluctuations; 3)
Dispersion, spread of chemical due to eddy fluctuations in a
macroscopic velocity gradient field.
14
-------
WIND SHEAR
\\ ^ //// \\\ 7/ff \\\\
BED SHEAR
BANK SHEAR
SKEWED PROFILE
BANK SHEAR
NORMAL
PROFILE
DEAD SPOT
BACK-MIXING
Figure 2.02.
Schematics of velocity gradients created by shear stresses at
the air-water, bed-water, and bank-water interfaces.
15
-------
density stratification and instabilities in lakes and estuaries.
Morphological causes of dispersive mixing in rivers include dead
spots, side channels, and pools where back-mixing occurs. When
turbulent diffusion causes a parcel of fluid containing dissolved
substances to change position, that parcel of fluid becomes
entrained in the water body at a new velocity, either faster or
slower. This causes the parcel of fluid and the toxic substance to
mix forward or backward relative to its neighbors. The mixing
process is called dispersion and results in a mass flux of toxic
substances from areas of high concentration to areas of low
concentration. The process is analagous to molecular diffusion but
occurs at a much more rapid rate. The steady state mass flux rate
can be described by Pick's first law of diffusion:
where
J
K
A
dc
dx
- - K A (2.1)
= mass flux rate, M/T
= diffusion or dispersion coefficient, L /T
= cross sectional area through which diffusion occurs, \? t
and
concentration gradient, M/L -L.
The rate of movement of chemical is proportional to the cross sectional
area and the concentration gradient, which is the driving force for
diffusion.
2.1.2 Advective-Dispersive Equation
The basic equation describing advection and dispersion of dissolved
matter is based on the principle of conservation of mass and Pick's law.
For a conservative substance, the principle of conservation of mass can be
stated:
Rate of change
of mass in
control volume
Rate of change of
mass in control
volume due to
advection
Rate of change of
mass in control
volume due to
diffusion
Transformation
- Reaction Rates
(Degradation)
3C
at
where C
t
ui
IS
3x.
9C
Ui 3x. + 8x, ei'3xJ
concentration, M/L^
time, T
average velocity in the i'th direction, L/T
distance in the i'th direction, L. and
reaction transformation rate, M/L^-T.
R (2.2)
e
flow,
e. = e,
is the diffusion coefficient in the i'th direction. For laminar
. = EM, the coefficient of molecular diffusion. For turbulent flow,
+ (
where ET is the coefficient of turbulent diffusion.
In
'i T
Fickian diffusion theory, it is assumed that dispersion resulting from
16
-------
turbulent open-channel flow is exactly analogous to molecular diffusion.
The dispersion coefficients in the x, y, and z directions are assumed to be
constants, given by KX, K and KZ. The resulting equation, expressed in
Cartesian coordinates, is:
The solution of equation (2.3) depends on the values of KX, K and KZ.
Various authors have arrived at equations to approximate the values of the
dispersion coefficients (K) in the longitudinal (x), lateral (y), and
vertical (z) directions.
2.2 EVALUATING COEFFICIENTS
2.2.1 Longitudinal Dispersion Coefficient in Rivers
Liu (1977) used the work of Fischer (1967) to develop an expression for
the longitudinal dispersion coefficient in rivers and streams (Kx, which has
units of length squared per time):
u2 B3
Kx -"-^r-"6 ^ <2-1"
where Liu (1978) defined,
"*
B - 0.5 -
x
D = mean depth, L
B = mean width, L
UK = bed shear velocity, L/T
ux = mean stream velocity, L/T
A = cross sectional area, L , and
Qg = river discharge, L^/T.
B does not depend on stream morphometry but on the dimensionless bottom
roughness. Based on existing data for KX in streams, the value of KX can be
predicted to within a factor of six by equation (2.4). The bed shear
velocity is related empirically to the bed friction factor and mean stream
velocity:
/T /j; ^
(2.5)
2
in which T = bed shear stress, M/L-T
f = friction factor =0.02 for natural, fully turbulent flow
p = density of water, M/L
2.2.2 Lateral Dispersion Coefficient in Rivers
Elder (1959) proposed an equation for predicting the lateral dispersion
coefficient, Ky:
17
-------
K - $ D U, (2.6)
y *
where $ is equal to 0.23. The value of <{> = 0.23 was obtained by experiment
in long, wide laboratory flumes.
Many authors have since investigated the value of in both laboratory
flumes and natural streams. Sayre and Chang (1968) reported $ = 0.17 in a
straight laboratory flume. Yotsukura and Cobb (1972) report values of <|> for
natural streams and irrigation canals varying from 0.22 to 0.65, with most
values being near 0.3. Other reported values of $ range from 0.17 to
0.72. The higher values for $ are all for very fast rivers. The
conclusions drawn are: 1) that the form of equation (2.6) is correct in
predicting K , but 4> may vary, and 2) that application of Fickian theory to
lateral dispersion is correct as long as there are no appreciable lateral
currents in the stream.
Okoye (1970) refined the determination of 4> somewhat by use of the
aspect ratio, X = D/B, the ratio of the stream depth to stream width. He
found that $ decreased from 0.2H to 0.093 as X increased from 0.015 to
0.200.
The effect of bends in the channel on K is significant. Yotsukura and
Sayre (1976) reported that varies from 0.1 to 0.2 for straight channels,
ranging in size from laboratory flumes to medium size irrigation channels;
from 0.6 to 10 in the Missouri River; and from 0.5 to 2.5 in curved
laboratory flumes. Fischer (1968) reports that higher values of <|> are also
found near the banks of rivers.
2.2.3 Vertical Dispersion Coefficient in Rivers
Very little experimental work has been done on the vertical dispersion
coefficient, KZ. Jgbson and Sayre (1970) reported a value for marked fluid
particles of:
Kz = KUwz (1 - |) (2.7)
for a logarithmic vertical velocity distribution. K is the von Karman
coefficient, which is shown experimentally to be approximately = O.U
(Tennekes and Lumley, 1972). Equation (2.7) agrees with experimental data
fairly closely.
2.2.1* Vertical Eddy Diffuslvity in Lakes
Vertical mixing in lakes is not mechanistically the same as that in
rivers. The term "eddy diffusivity" is often used to describe the turbulent
diffusion coefficient for dissolved substances in lakes. Chemical and
thermal stratification serve to limit vertical mixing in lakes, and the eddy
diffusivity is usually observed to be a minimum at the thermocline.
Many authors have correlated the vertical eddy diffusivity in
stratified lakes to the mean depth, the hypolimnion depth, and the stability
18
-------
frequency. Mortimer (1941) first correlated the vertical diffusion
coefficient with the mean depth of the lake. He found the following
relationship.
1 ilQ
K = 0.0142 Z ^ (2.8)
z
p
in which K_ = vertical eddy diffusivity, m /day, and
Z
Z = mean depth, m.
Vertical eddy diffusivities can be calculated from temperature data by
solving the vertical heat balance or by the simplified estimations of
Edinger and Geyer (1965). Schnoor and Fruh (1979) have demonstrated that
the mineralization and release of dissolved substances from anaerobic
sediment can be used to calculate average hypolimnetic eddy diffusivities.
This approach avoids the problem of assuming that heat (temperature) and
mass (dissolved substances) will mix with the same rate constant, i.e., that
the eddy diffusivity must equal the eddy conductivity. A summary of
d.ispersion coefficients and their order of magnitude appears below.
Dispersion Coefficient, cm /sec
Molecular Diffusion 10~5
Compacted Sediment 10~^ - 10~^
Bioturbated Sediment 10~5 - 10
Lakes - Vertically 10~2 - 1Q1
Large Rivers - Lateral 10^ - 10^
Large River - Longitudinal 10J! - 10^'5
Estuaries - Longitudinal 10" - 10^
A literature summary of longitudinal dispersion coefficients for
streams and rivers is reported in Table 2.01. The wide range of values
reflects the site-specific nature of longitudinal dispersion coefficients
and the many hydrologic and morphologic properties which affect mixing
processes. An excellent reference for mixing processes in natural waters is
that of Fischer et al. (1979).
Vertical dispersion coefficients in lakes (eddy diffusivities) have
most commonly been determined by the heat budget method (Edinger and Geyer,
1965; Park and Schmidt, 1973; Schnoor and Fruh, 1979) or by McEwen's method
(1929). Radio-chemical methods also have been used with success (Quay et
al., 1980; Imboden et al., 1979; Torgersen et al., 1977). Table 2.02 gives
some literature values for the vertical dispersion coefficient at the
thermocline (minimum value), and Table 2.03 reports the mean vertical
dispersion coefficient for the entire water column. Vertical dispersion is
a function of the depth and morphometry of the lake, fetch-to-wind direction
relationship, solar insolation and light penetration, and other factors.
Example calculations for lake dispersion coefficients are presented at the
end of this chapter; data for these calculations were taken from actual
field measurements.
19
-------
TABLE 2.01. SUMMARY OF DISPERSION
MEASUREMENTS IN STREAMS
Reach
Missouri Ft., IA-HB
Chicago Ship Canal
Sacramento R.
River Derwent
Australia
S. Platte R., NB
Yuma Mesa Canal
Green-Duvamlsh
R., WA
Copper Creek, VA
Clinch R., TN
Powell R., TN
Clinch R., VA
Coachella Canal, CA
Monocacy R.,
MD
Antletam Cr.
MD
Missouri R.
NB-IA
Clinch R.
TN
Bayou Anacoco
LA
Nooksack R.
WA
Wind/Bighorn
Rivers WY
Depth Width
m ' m
8.07 18.8
1.00
.25
.16
3.15
1.10 20
.19 16
.85 18
.19 16
.10 19
.85 17
2.10 60
2.10 53
.85 31
.58 36
1.56 21
35.1
36.6
17.6
15.9
19.8
21.1
182.9
201 .2
196.6
17.3
53.1
59.5
19.8
25.9
36.6
61.0
86.0
67.1
68.6
Longitudinal
Velocity or Dispersion
U* Flow - m/sec Coefficient
cm/sec Slope (m'/sec) m /sec Reference
96.6 5.6x10" Sayre, 1973
1.91 3 Fischer, 1973
5.1 15 "
11 1.6 "
6.9 16.2 "
3.15 0.76 "
1.9 6.5-8.5 "
8 20 "
10 21 "
8 9.5 "
11.6 9.9 "
6.7 11
10.1 51 "
10.7 17 "
5.5 9.5
1.9 8.1 "
1.3 9.6 "
0.0006 0.11 (2.11) 1.6 McQulvey & Reefer, 1971
0.21 (5.21) 13.9 "
0.38 (18.11) 37.2
0.0001 0.20 (1 .98) 9.3 "
0.27 (1.36) 16.3
0.12 (8.92) 25.6
0.0002 0.91 (379.50) 161.7 McQulvey & Keefer, 1971
1.21 (911.92) 836.1 "
1.18 (93L58) 1,187.0 "
0.0006 0.21 (9.20) 13.9 "
0.11 (50.98) 16.5 "
0.65 (81.96) 55.8
0.0005 0.21 (2.11) 13.9
0.31 (8.21) 32.5 "
0.10 (13-15) 39.5 "
0.68 (32.57) 31.9
0.0098 1.3 (303-03) 153-3
0.0013 0.89 (59.33) H -8 "
1.56 (230.81) 162.6
20
-------
TABLE 2.01 (continued)
Width
Depth or Area U«
Longitudinal
Velocity or Dispersion
Flow - m/seo Coefficient
Reach
Elkhorn R., NB
John Day River
OR
Coral te R.
LA
Amlte R.
LA
Sablne R.. LA
Yadkln R., NC
Muddy Creek
NC
Sablne R.. TX
White R., IN
Chattahoochee R.
GA
Susquehanna R.
PA
Mlljacka R.,
Uvas Creek, CA
Copper Creek, VA
Clinch R., TN
Powell R., TN
Clinch R., VA
Coachella Canal, CA
m
0.285
0.3
0.29
0.295
0.49
0.85
0.49
0.10
0.85
2.13
2.10
0.85
0.58
1.55
m (m )
32.6
25.0
31.1
12.5
15.9
36.6
42.4
127.1
70.1
13.1
19.5
35.1
67.1
65.5
202.7
11.28
8.6
10.53
12.0
(0.3)
(0.15)
(0.30)
(0.12)
(0.72)
(0.82)
(2.08)
15.9
18.3
16.2
18.6
17.0
59.5
53.1
33.8
36.0
21.1
en/sec Slope
0.00073
0.00355
0.00135
0.00078
0.00061
0.00015
0.00011
0.00083
0.00018
0.00036
0.0052
0.00032
5.5
6.6
6.2
19
(m3/sec)
0.31 (1.25)
(11.16)
(69.10)
0.23 (0.99)
0.35 (2.11)
0.21 (8.61)
0.36 (11.16)
0.57 (118.95)
0.65 (389.11)
0.14 (70.80)
0.30 (3.96)
0.38 (10.62)
0.18 (7.36)
0.30 (12.71)
0.31 (0.03)
0.33 (0.10)
0.342/1.02
0.368/1.02
0.35/1.02
0.332/1.02
(0.0125)
(0.0125)
(0.0125)
(0.0125)
(0.0133)
(0.0136)
(0.0140)
(1 .53)
(8.50)
( i .36)
(13.68)
(9.15)
(81.96)
(50.98)
(3.96)
(6.80)
(26.90)
m2/sec Reference
9.3
13.9
65.1
7.0 "
13.9
23.2 "
30.2 "
316.0 "
669.1
213.8 "
13.9
32.5 "
39.5 MoQulvey & Keefer, 1971
30.2 "
32.5
92.9 "
2.22 Bajraktarevlc, 1982
5.66 "
0.07
0.12 Bencala & Walters, 1983
0.18 "
0.12 "
0.15 "
0.24
0.31
0.40 "
19.5 Fischer, 1968
21.4 "
9.5
9.9
13.9
53.9
46.5
9.5
8.1 "
9.6
21
-------
TABLE 2.02. VERTICAL DISPERSION COEFFICIENT
FOR LAKES ACROSS THERMOCLINE
Site
Lake Zurich, Switz.
Month
April
May
June
July
Aug
Sept
Oct
Vertical
Dispersion
era /sec
0.71
0.14
0.064
0.039
0.026
0.020
0.074
Thermocline
Depth
m
5
10
10
12.5
10
10-12.5
20
Data
from
temp
temp
temp
temp
temp
temp
temp
Reference
Li, 1973
ti
it
11
it
11
11
L. Greifensee, Switz,
L. Baldeggersee, Switz. May
(limno-corral)
June
July
Aug
Sept
June
Aug
Sept
Oct
L. Onondaga Lake, N.Y. May
June
July
Aug
Sept
Oct
L. Baikal, U.S.S.R.
L. Tahoe, Nevada
L. Ontario
L. Cayuga, N.Y.
L. Luzern, Switz.
L. Zurich, Switz.
L. Washington, WA
L. Tiberias, Israel
L. Sammamish, WA
L. ELA 305, Ontario
L. Mendota, WI
0.25
0.0021
0.08
0.003
0.08
0.0013
0.08
0.09
0.05
0.05
0.04
0.09
0.03
0.005
0.008
0.015
2.5-7.4
0.178
0.125,
0.178,
0.10
0.03
0.03
0.063
0.03
0.01
0.025
0.063
0.25
10 POjj Imboden &
Emerson, 1978
9 temp Imboden, et
al., 1979
8
7-9
7-9
9
7
6
9.5
9.5
ter
tei
tei
tei
Rn
Rn
Rn
Rn
11.5 temp Wodka, et al.,
1983
10.5 temp "
11.5 temp "
11.5 temp "
12.5 temp "
temp "
temp
temp
temp
temp
temp
temp
temp
temp
temp
temp
temp
Snodgrass &
O'Melia, 1975
it
it
n
n
22
-------
TABLE 2.02 (continued)
Linsley Pond, CT
ELA 210, Ontario
ELA 227, Ontario
Cayuga Lake, NY
Castle Lake, CA
July
ELA 227, Ontario
ELA 224, Ontario
Lake Valencia, Venezuela
Lake Erie
0.003
0.001
0.003
0.253
0.011
0.114
0.21
21
temp
temp
temp
temp
temp
July
July
July
July
July
August
August
August
August
August
August
Sept
0.0091
0.0069
0.0068
0.0068
0.0042
0.0004
0.0062
0.0041
0.0061
0.0036
0.0076
0.0077
0.0017
0.018
6
7
7
7
7
9
9
9
9
9
9
9
8
18
temp
temp
temp
temp
temp
temp
temp
temp
temp
temp
temp
temp
tritiui
tritiui
Powell &
Jassby, 1974
Jassby &
Powell, 1975
n
n
n
u
II
tl
II
It
II
II
It
It
1980
20 temp Lewis, Jr.,
1983
16 6 He Torgersen, et
al., 1977
23
-------
TABLE 2.03. WHOLE LAKE AVERAGE VERTICAL DISPERSION COEFFICIENT
Site
Lake Erie
Lake Huron
Lake Ontario
Vertical
Dispersion
Month cm /sec
0.58
1.16
3.47
Data
from
63 He
&* He
6J He
Reference
Torgersen, et
al., 1977
ii
it
Wellington Reservoir,
al.,
Australia
White Lake, MI
Lake LBJ, Texas
1.00
Lake Erie
al.,
Lake Huron
Lake Erie
Conolly,
Cayuga Lake
1977
L. Greifensee
Feb-April
May-June
July-Jan
0.18
0.12
0.01
15
(no stratification) 1.16
Unstratified 102
stratified 0.05-0.25
fall turnover
2.31
April
May-Aug
Sept-Nov
0.2
0.15
0.05
temp
Imberger, et
1978
Lung & Canal e,
1977
Park & Schmidt,
1973
Heinrich, et
1981
DiToro &
Matystik, 1979
DiToro &
1980
temp Bedford &
Babaj imopoulos„
temp
temp
temp
temp
temp
222
222
222
Rn Imboden, 1979
Rn "
Rn "
24
-------
Modeling hydrophobia chemical contaminants (e.g., DDT, PCB, kepone,
dioxin, dieldrin) that are strongly sorbed to sediments requires knowledge
of the diffusion and release rates from contaminated sediments into
overlying waters. Radio-tracers that occur naturally and from bomb-testing
have been used with success in analyzing sediment pore-water diffusion
rates. Table 2.04 reports some values found in the literature. Most pore
water diffusion coefficients are on the order of molecular diffusion
~5 ?
coefficients (-10 cm /sec) or smaller. Bioturbation by benthic fauna or
fish may significantly increase pore water transfer to overlying water.
TABLE 2.04. INTERSTITIAL SEDIMENT PORE WATER DIFFUSION COEFFICIENTS
Site
Vertical
Dispersion
cm /sec
Data
from
Reference
White Lake, MI
2x10
-6
Lung & Canale, 1977
Lake Erie
Lake Ontario
Green Bay, Lake Michigan
L. Greifensee
4 x 10~6
2 x 10~5
2 x 10~-j
2 x 10 6
2 x 10 5
1 .3x10~7
6.3x10 9
1°:;°
10 y
0.8 x 10 5
?°?J
1°f
210pb
210pb
230Tn
oooRa
222Rn
Lerman & Lietzke, 1975
ii
)f
Christensen, 1982
Imboden & Emerson, 1976
2.3 COMPARTMENTALIZATION
2.3-1 Choosing a Transport Model
It is possible to estimate the relative importance of advection
compared to dispersion with the Peclet number:
Pe = uL/K
in which Pe = Peclet number, dimensionless
u = mean velocity, L/T
(2.9)
25
-------
L = segment length, L, and
K = dispersion coefficient, L /T.
If the Peclet number is significantly greater than 1.0, advection
predominates; if it is much less than 1.0, dispersion predominates in the
transport of dissolved, conservative substances.
If there is a significant transformation rate, the reaction number can
be helpful:
Rxn No. = —-
kK
2
(2.10)
u
where k is the first order reaction rate constant, T . If the reaction
number is less than 0.1, then advection predominates and a model approaching
plug flow is appropriate. If the reaction number is greater than 10, then
dispersion controls the transport and the system is essentially completely
mixed. Otherwise a plug flow with dispersion model or a number of
compartments in series will best simulate the prototype water body.
2.3.2 Compartmentalizatlon
Compartmentalization refers to the segmentation of model ecosystems
into various "completely mixed" boxes of known volume and interchange.
Interchange between compartments is simulated via bulk dispersion or equal
counterflows between compartments. Compartmentalization is a popular
assumption in pollutant fate modeling because the assumption of complete
mixing reduces the set of partial differential equations (in time and space)
to one of ordinary differential equations (in time only). Nevertheless, Lt
is possible to recover some coarse spatial information by introducing a
number of interconnected compartments.
A completely mixed flow-through (CMF) compartment contains an ideal
mixing of fluid in which turbulence is so large that no concentration
gradients can exist within the compartment. This corresponds to the
assumption that K
Accumulation
of Mass w/in
Compartment j
dC.
x,y,z
Mass
Inflows +
to j
n
Equation (2.3) reduces to:
Dispersive Mass Dispersive Transformation
Inflows - Outflows - Outflows - Reactions
to j from j from j within j
n
n
n
(2.11)
in which V..
n
cQJ»k
?
<$
volume of j compartment, L
concentration within j compartment,
time, T
number of adjacent compartments to j
inflow from compartment k to compartment j, L^/
concentration in compartment k, M/L^
dispersive (interchange) flow from k to j, I?/I
outflow from j to k, L^/T
26
-------
Q' . = dispersive (interchange) flow from j to k,
k >J = pseudo-first order rate constant for transformation,
and
Q?.
,
,k
a symmetric matrix with zero diagonal.
Equation (2.11) can be rewritten in terms of bulk dispersion
coefficients:
dC
.
n
n
A -
where
K'
A
, k
'
J »K
bulk dispersion coefficient, L /T
interfacial area between compartments j and k, L
distance between midpoints of compartments, L.
and
There is one mass balance equation (e.g. equation 2.12) for each of j
compartments. This set of ordinary differential equations is solved
simultaneously by numerical computer methods.
Bulk dispersion coefficients between compartments are dependent on the
scale chosen for the compartments. They are not equivalent to measured
dispersion coefficients from dye studies, which are usually derived from the
continuous partial differential equations. The very nature of the
compartmentalized system introduces considerable mixing into the model.
Such mixing or numerical dispersion is in addition to the bulk dispersion
specified by the bulk dispersion coefficient.
Streams and swift-flowing rivers may approach a 1-D plug flow system
(i.e. the water is completely mixed in the lateral and vertical dimensions,
but there is no mixing in the longitudinal dimension). In an ideal plug
flow system, the longitudinal dispersion coefficient is equal to zero
because no forward or backward mixing occurs. For this case, an infinite
number of compartments (of inf initestimal length in the longitudinal
direction) would be required in order to produce zero longitudinal mixing.
Because it is impossible to specify an infinite number of compartments, one
chooses a finite number of compartments and accepts the artificial
dispersion that accompanies that choice. One method of estimating the
artificial or numerical dispersion of a compartmentalized model for an
ideal, plug flow system is given by equation (2.13).
where
EX =
u =
Ax =
At
artificial numerical dispersion coefficient, L/T
mean longitudinal velocity, L/T
longitudinal length of equally spaced compartments, L,
and
time step for numerical computation, T.
One approach would be to set the artificial dispersion coefficient
equal to the measured or estimated dispersion coefficient from equation
(2.4). With this approach, it is not necessary to use bulk dispersion
27
-------
coefficients; rather, one allows the artificial dispersion of the mo'del to
account for the actual dispersion of the prototype.
Another approach is to adjust the time step to minimize E while
preserving stability:
Ax
At = min (jj-i)
i i
where i refers to the physical compartments.
In general, most river simulations require many compartments due to
their nearly plug flow nature, as indicated by their large Peclet number
(equation 2.9). The greater the number of compartments, the greater the
tendency towards plug flow conditions. It is a poor practice to simulate a
riverine environment with one completely mixed compartment.
Lakes, reservoirs, and embayments may require a number of compartments
if one desires some spatial detail, such as concentration profiles. These
compartments should be chosen to relate to the physical and chemical
realities of the prototype. For example, a logical choice for a stratified
lake is to have two compartments: an epiliminion and a hypolimnion (Figure
2.03). Mixing between compartments can be accomplished by interchanging
flows:
J = QexCepi - QexChypo (2-11*)
or
^ex ^ epi hypo'
where J = net mass flux from epilimnion to hypolimnion due to
vertical mixing, M/T
Qex = exchange flow, L^/T, and
C = concentration of organic, M/L-^.
The magnitude of the interchange flow, Qex, can be determined from tracer
studies or from temperature profiles and simulations. Bulk dispersion
coefficients can then be calculated based on the interchange flow as K' =
Q % ... /A, where X, , ,. is the distance between centroids of two
ex epi/hypo epi/hypo
adjacent compartments.
Sometimes only coarse information is required for a given use of a
model. The literature offers many examples of modeling efforts based on
very simple transport models. The Great Lakes have often been simulated as
single compartment, completely mixed lakes in series (O'Connor and Mueller,,
1970; Chapra, 1977; Schnoor and O'Connor, 1980). Toxic chemical screening
methodologies are usually based on organic chemical properties that are
known only within an order of magnitude. In such cases it may not be
necessary to simulate transport with great accuracy. A distinct trade-off
exists between errors in transport formulations and errors in reaction rate
constants as shown in Table 2.05. If the sum of the pseudo-first order
reaction rate constant is accurately determined in the field or laboratory,
then an accurate model simulation will require a realistic transport
formulation. If the reaction rate constant and/or the detention time are
28
-------
VERTICAL EDDY .*
DIFFUSIVITY .'
PROFILE ..•"
EPILIMNION
THERMOCLINE
REGION
HYPOLIMNION
EPILIMNION
COMPARTMENT
HYPOLIMNION
COMPARTMENT
Figure 2.03. Thermal stratification in a lake and the assumption of mixing
between two compartments.
-------
low (kT = 0.01), the choice of the number of compartments is not very
critical. Errors in outflow concentration of greater than 10 percent will
occur, however, if the dimensionless number kt becomes greater than 1.0.
For example, consider a hypothetical lake whose steady-state outlet
concentration of a toxic chemical is determined to be 0.01 times the inflow
concentration. Suppose the hydraulic detention time, T, of the lake is 10
days, and the transformation reaction rate constant is determined to be
1.0/day (kt=10). The lake is behaving like three compartments in series
according to Table 2.05. The model calibration, however, would have
required a reaction rate constant of 10/day in order to obtain the observed
result of C/Co =0.01 if only the completely mixed compartment had been
assumed.
TABLE 2.05. OUTFLOW CONCENTRATION DIVIDED BY INFLOW CONCENTRATION
AT STEADY STATE AS A FUNCTION OF NUMBER OF COMPARTMENTS AND kT .
C/Co VALUES
Rate Constant x Detention Time
kt=0.01
lixed 0.99
k-r-0.1
0.91
kt=1
0.50
kt=10
0.09
k-r-100
0.01
CMF Completely Mixed
(1 -compartment)
3-compartment4' 0.99 0.91 0.42 0.01
10-compartment"1" 0.99 0.91 0.39 1 x 10
PFt Plug Flow 0.99 0.90 0.37 5x10
(» compartment)
-3
-5
2 x 10"5
4 x 10"11
4 x 10-^
C/Co = 1/(kT+1)
C/Co = 1/
C/Co = exp(-k-r)
where T = total hydraulic detention time
k = first order reaction rate constant
where n = number of compartments
If better than order-of-magnitude accuracy is required in the model,
one should estimate the dispersion coefficients from dye studies,
temperature simulations, or equations (2.4) through (2.8). This allows the
proper compartmental configuration to be selected, including consideration
of numerical dispersion based on equation (2.13).
30
-------
2.4 SEDIMENT TRANSPORT
2.4.1 Partitioning
A chemical is partitioned into a dissolved and partlculate adsorbed
phase based on its sediment-to-water partition coefficient, K (Karickhoff
et al . , 1979). The dimensionless ratio of the dissolved to the particulate
concentration is the product of the partition coefficient and the
concentration of suspended solids, assuming local equilibrium:
Cp/C = KpM (2.15)
where Cp = particulate chemical concentration, ug/Jl
C = dissolved chemical concentration, ug/8,
K = sediment/water partition coefficient, i/kg, and
M = suspended solids concentration, kg/X,.
The particulate and dissolved concentrations can be calculated from
knowledge of the total concentration, C^, as stated in equations (2.16) and
.(2.17).
CT
(2.17)
1 + K M T
P
These concentrations can be calculated for the water column or the bed
sediment, by using the concentration of suspended solids in the water (M) or
in the bed (M^), where M^ = M/n, the bed sediment concentration in kg/8, of
pore water, and n = the porosity of the bed sediment.
2.4.2 Suspended Load
The suspended load of solids in a river or stream is defined as a flow
rate times the concentration of suspended solids, e.g., kg/day or tons/day;
the mean load is greatly affected by peak flows. Peak flows cause large
inputs of allochthonous material from erosion and runoff as well as
increases in scour and resuspension of bed and bank sediment.
The average suspended load is not equal to the average flow times the
average concentration, as stated in equation (2.18),
Q x C * QC (2.18)
but is calculated as stated in equation (2.19):
QC = (Q x C) + Q'C' (2.19)
The mean fluctuation of mass, Q'C', is usually greater than the first term
of equation (19) and contributes greatly to the average suspended load.
These equations hold true for the mass of suspended solids as well as the
mass of adsorbed chemical.
31
-------
2.4.3 Bed Load
Several formulae have been reported to calculate the rate of sediment
movement very near the bottom. These equations were developed for rivers
and noncohesive sediments, i.e., fine-to-coarse sands and gravel. It is
important to note that it is not sands, but rather silts and clays, to which
most chemicals sorb. Therefore, these equations are of limited predictive
value in environmental exposure assessments. Generally, bed load transport
is a small fraction of total sediment transport (suspended load plus bed
load). In estuaries, however, bed load transport of fine silts and clays
may be an important contributor to the fate of chemical contaminants.
Unfortunately, predictive equations have not been developed for bed load
transport in such applications. Bed load consists of those particles that
creep, flow, or saltate very near to the bottom (within a few particle
diamters). Figure 2.04 is a schematic of bed load and suspended load in a
stream or river.
SUSPENDED
LOAD
BED LOAD
VELOCITY
PROFILE
SUSPENDED SOLIDS
CONCENTRATION
PROFILE
MOVEABLE BED
SALTATION
"~7777 m-
FIXED BED
tin
Figure 2.04.
Suspended Load and Bed Load. Bed load is operationally
defined as whatever the bed load sampler can measure. Bed
load occurs within a few millimeta of the fixed bed.
2.4.4 Sedimentation
Suspended sediment particles and adsorbed chemicals are transported
downstream at nearly the mean current velocity. In addition, they are
transported vertically downward by their mean sedimentation velocity.
Generally, silt and clay-size particles settle according to Stoke1s Law, in
proportion to the square of the particle diameter and the difference between
sediment and water densities:
(2.20)
32
-------
in which
W = particle fall velocity, ft/sec
p = density of sediment particle, 2^2.7 g/cra^
3 o
p = density of water, 1 g/cm-*
W ?
g = gravitational constant, 981 cm/sec
d0 = sediment particle diameter, mm
o
y = absolute viscosity of water, 0.01 poise (g/cm^sec) @ 20°C
Generally, it is the washload (fine silt and claysize particles) that
carries most of the mass of adsorbed chemical. These materials have very
small fall velocities, on the order of 0.3^1.0 m/day for clays of 2^
4 ym nominal diameter and 3^30 m/day for silts of 10^20 ym nominal diameter.
Once a particle reaches the bed, a certain probability exists that it
can be scoured from the bed sediment and resuspended. The difference
between sedimentation and resuspension represents net sedimentation. Often
it is possible to utilize a net sedimentation rate constant in a pollutant
fate model to account for both processes. In many ecosystems where the bed
is aggrading, sedimentation is much larger than resuspension (Schnoor and
McAvoy, 1981). The net sedimentation rate constant can be calculated as
follows
ks-5'ku-Sl (2'22)
where ks = net sedimentation rate constant, 1/T
W = mean particle fall velocity, L/T
H = mean depth, L, and
ku =s scour/resuspension rate constant, 1/T.
2.4.5 Scour and Resuspension
Quantitative relationships to predict scour and resuspension of
cohesive sediments are difficult to develop due to the number of variables
involved. Sayre and Chang (1968) reported on the vertical scour and
dispersion of silt particles in flumes. Di Toro et al. (1982) recommended a
resuspension velocity (Wrg) of about 1 to 30 mm/yr based on model
calibration studies. The turbulent vertical eddy diffusivity for
sediment (e ) is also related to the scour coefficient and/or resuspension
velocity.
Under steady^state conditions, the sedimentation of suspended sediment
must equal the scour and resuspension of sediment.
33
-------
— 8C
wC + es ^ = 0 (2.23)
where w = sedimentation velocity, L/T
e = suspended sediment vertical eddy diffusiyity, L2/T, and
6 = concentration of suspended sediment, M/L^.
Under time- varying conditions, however, the boundary condition at the bed-
water interface is more complex. According to Onishi and Wise (1979), the
following equation applies, based on the work of Krone (1962) and
Partheniades (1965).
where p = probability that descending particle will "stick" to the bed
SD = ~- (1 -- — ) = rate of bed deposition, M/L2-T
TcD
SR = M, (— -- 1) = rate of bed scour, M/L2-T
J TcR
Mj = erodibility coefficient, M/L2-T
T R = critical bed shear required for resuspension, M/L-T
T°D = critical bed shear stress which prevents deposition, M/L-T2, and
h = ratio of depth of water to depth of active bed layer.
Equation (2.24) shows that the bed can either be aggrading or degrading at
any time or location depending on the relationship between SD and Sp.
2.4.6 Desorption/Dif fusion
In addition to sedimentation and scour /res us pens ion, an adsorbed
chemical can desorb from the bed sediment. Likewise dissolved chemical can
adsorb from the water to the bed. Both pathways can be presented by a
diffusion coefficient (K^) and a concentration gradient or difference
between pore water and overlying dissolved chemical concentrations.
Sediment mass balances must include terms for advection, sedimentation,
scour/resuspension, and possibly vertical or longitudinal dispersion. At-
the bottom, bed load movement may be included. Processes that affect the
fate of dissolved substances include desorption from the bed (or adsorption
from the water column), advection, dispersion, and transformation
reactions. Adsorbed particulate chemical is removed from the water column
by sedimentation and returned to the water column by scour. Models used to
evaluate transport and transformation should include these processes.
Often, it is possible to neglect the kinetics of adsorption and
desorption in favor of a local equilibrium assumption. Over the time scales
of interest, this may be a good assumption. Bed load movement is sometimes
small relative to wash load movement and can be neglected. Under steady-
state conditions, net sedimentation rates are often used to simplify the
34
-------
transport of sedimentation and scour. All of these assumptions have their
applications but should be carefully considered in each model application.
2.5 LAKE DISPERSION CALCULATIONS
The steps for calculating vertical dispersion across the thermocline in
a lake from temperature data are presented below for three different
methods. These methods were derived from the heat dispersivity equation,
assuming that E does not vary much with depth over the region of interest,
particularly the thermocline:
dx
where 9 is temperature, t is time, E is thermal dispersivity, and x is
distance. It is assumed that no heat has entered the lower part of the
water column under consideration by any mechanism other than vertical
turbulent transport, E. The assumption is made that mass transfer through
dispersion occurs at the same rate as heat transfer. The analogy is applied
by substituting concentration or mass (c) into the equation, thus
,2
— = E — (2 26)
dt b . 2 (d'db)
dx
For more information on the theory, the reader is referred to G. Evelyn
Hutchinson, (1957). Actual data are presented for Lake Clara, Wisconsin,
and Linsley Pond, Connecticut.
2.5.1 McEwen' s Method
This method of computing lake dispersion is based on fitting an
exponential curve to the mean temperature data in the thermocline and
hypolimnion. If the data are of a linear or otherwise nonexponential shape,
this method is inappropriate. The reader is referred to Hutchinson
(1941). Two examples are provided for Lake Clara, Wisconsin, in the summer
of 1982 and Linsley Pond, Connecticut.
Step 1 : Compile temperature data by date and depth (see Figure 2.05
and Table 2.06).
Step_2: Average temperature data at each depth for the period June-
August: 9 . 9 vs. depth is plotted in Figure 2.06.
z z
Step 3: Compute the change in temperature over the summer data period
at each depth: A9 /At.
Z
Step M: Compute C
a (graphical): C is the temperature that the data approach in the
hypolimnion (see Figure 2.06).
b (computational): Find C by linear regression using
35
-------
X5
Q.
^ ft
O 6
8
9
10
8
TEMPERATURE (°C)
10 12 14 16 18 20
22
v
I
6/1/82
8/1/82
Figure 2.05. Lake Clara, Wisconsin, temperature profile - Summer 1982.
TABLE 2.06 LAKE CLARA TEMPERATURE DATA
Depth
(m)
1
2
3
4
5
6
7
8
9
10
6/1/82
(°C)
16
16
16
12
10
8
8
7
7
7
7/1/82
(6C)
20
20
20
20
17
14
12
9
8
8
8/1/82
(°C)
20
20
20
20
20
16
13
10
10
10
6
<°C)
18.67
18.67
18.67
17.33
15.67
12.67
11.00
8.67
8.33
8.33
A6/At
(°C/morith)
2.00
2.00
2.00
4.00
5.00
4.00
2.50
1.50
1.50
1.50
36
-------
TEMPERATURE (°C)
8 10 12 14 16 18
20
E4
a.
UJ
o 6
7
8
9
10
T
T
T
5.5°C
Figure 2.06.Lake Clara average summer temperature profile - 1982.
TABLE 2.07 LINSLEY POND TABULATIONS TO FIND C AND b
z
(m)
4
5
6
7
8
9
e
(°C)
17.68
13.17
10.67
8.98
8.10
7.50
C = 6.82,
A6
(°C)
4.51
2.49
1.70
0.88
0.59
0.22
b = 2.42
37
-------
9 = C + bA9
z
where A9 = ? - ? (see Table 2.07)
Z \ 'Z*
Step 5: Compute a and C^
a (graphical): plot (9 - C) vs. z and A6/At vs. z on semi-log
paper (see Figures 2.07 and 2.03). In the thermocline region, the two
curves should be parallel. The line tangent to the (9* - C) curve at the
thermocline will have slope a and y-intercept C«. (Note: to find base e
slope on semi-log paper, find the change in z over one complete log cycle,,
i.e., z^ at y = 10 and 22 at y = 1).
• (0-C) C = 5.5
a A0/At
2.303
(11.1-5.3)
= 0.40
a = 2.303/(z2 - z.)
AREA FOR
CALCULATIONS
4 6
DEPTH (m)
Figure 2.07. Graphical method to find a and C1 (Lake Clara)
33
-------
100
10
O
•o
a
3 r
Q>
<
• (0-C) C=6.82
a A0/At
4 6
DEPTH (m)
8
Figure 2.08. Graphical method to find a and C1 (Lindley Pond).
b (computational):
a = -1n (1 - 1/b)
C1 -
Z (6 - C)
Z e
-az
in the thermocline region.
Parallelism tests two parameters: (i) whether E is constant, and (ii)
whether C^ and "a" reasonably describe the temperature curve. If these
curves are not parallel, one or both of the assumptions does not hold for
the data set, and McEwen's method should not be used.
Step 6: Compute dispersion coefficient E (see Tables 2.08 and 2.09).
39
-------
TABLE 2.08 LAKE CLARA DISPERSION COEFFICIENT
ZAfl/At"
5 5.00
6 4.00
7 2.50
8 1.50
2 -az
1 e
1.73
1.16
0.78
0.52
A0/At
„ 2 -az
C a e
2.89
3.45
3.21
2.88
C1 = 80°C E = 3.11 m2/month
a - 0.4/m = 0.0118 cm2/sec
TABLE 2.09 LINSLEY POND DISPERSION COEFFICIENT
z
4
5
6
7
8
9
A6/At
2.21
1.37
0.88
0.60
0.30
0.22
C,a2e az
i
3.08
1.82
1.06
0.63
0.37
0.22
A9/At
_ 2 -az
C.a e
0.72
0.76
0.83
0.95
0.81
1.02
C1 = 91.5°C E = 0.85 nT/month
a = 0.53/m = 0.0032 cm2/sec
2.5.2 Second Derivative Method
Make a table of the following format
z ? A^/Az A(A9/Az)/Az A9/At E
(1) (2) (3) (4) (5) (6)
(see Tables 2.10 and 2.11) where column (2) is the_average summer
temperature at each depth, column (3) is equal to 6 _, - e (as z is
measured from the water surface down^, and column (5)_is similar to (3); one
calculates the difference between (A6/Az) and (A0/Az) . Column (5) is
Z"" I Z
40
-------
the change in temperature over the summer period at each depth. The method
assumes that heat is transferred by vertical eddy conductivity (dispersion)
and that there are no sources or sinks of heat within the vertical distance
(z), only dispersive transport.
TABLE 2.10 LAKE CLARA SECOND DERIVATIVE METHOD
z
(m)
1
2
3
4
5
6
7
8
9
10
6
18.67
18.67
18.67
17.33
15.67
12.67
11 .00
8.67
8.33
8.33
A6/Az
0.00
0.00
1.34
1.66
3.00
1.67
2.33
0.34
0.00
A(A0/Az)/Az
(°C/m2)
-0.32
-1.34
1.33
-0.66
2.00
A9/At
(°C/month)
2.00
2.00
2.00
4.00
5.00
4.0
2.50
1.50
1.50
1.50
E
(m2/month)
-12.50
-3.73
3.01
-3.79
0.75
E = -3.25 m2/month
0.0122 cm2/sec
41
-------
TABLE 2.11 LINSLEY POND SECOND DERIVATIVE METHOD
z
(m)
e
A6/Az A(A9/Az)/Az
(°C/m) (°C/m2)
A9/At
(°C/month)
E
(m /month)
4
5
6
7
8
9
17.68
13.17
10.67
8.98
8.10
7.40
2.51
1.69
0.88
0.60
2.00
0.82
0.81
0.28
2.21
1.37
0.88
0.60
0.30
0.22
0.68
1.08
0.73
1.06
E = 0.88 m2/month
= 0.0034 cm2/sec
The dispersion coefficient is calculated from
E =
A9/At
A(A?/Az)
Az
(2.27)
One notes that the dispersion coefficient for Lake Clara is negative,
which is meaningless and indicates that this method should be discarded and
McEwen's method used for this particular lake. Hutchinson (1941) points out
that "any errors in the original data are apt to produce inflection points
in the temperature curve. Such inflection points cause ... changes of sign
in the second [derivative]." There was good agreement in the calculated
dispersion coefficient between both methods for Linsley Pond.
2.5.3 Heat Budget Method
The vertical thermal dispersivity also can be estimated from the total
heat entering and leaving the lake. A number of field measurements are
necessary (pyroheliometer data, air temperature) as well as temperature
profiles throughout the lake. A heat budget method results in vertical
dispersion coefficients that are both a function of depth and time, E =
f(z,t). The reader is referred to G.G. Park and P.S. Schmidt, 1973, "Heat
dissipation in a power plant cooling bay," ASME Winter Annual Meeting, New
York, New York for further details on the heat budget method.
The basic equation is based on heat transfer and is formulated similar
to a mass balance:
42
-------
d(v.e )
-ir = < Vu - Q0jV + (Qvjej-i -
- (E a /Az) (9. - 9, .,) + (E aj + 1/Az) (2.28)
' V + Vjhnet/(')cAz
where V, is the volume of the jth slice (m3), 9. is the mean
the jth slice (°C), Qj and QQ are inflows and outflows to sli
temperature in
slice j,
respectively, as 9 and 9. are the temperatures associated with those flows
(Q in m^/sec and 9°in °C)^ Qy, is the vertical flow rate at the bottom of
the jth element, where upward flow is positive (m-Vsec), Ej is the
dispersion across the bottom of slice j (cm /sec), 3j is the bottom surface
area of slice j (m ), t is time (sec), hne^ is the net heat flux(cal/sec-
cm^), (p) is density (g/cm^), c is specific heat (cal/g-°C), and Az is the
thickness of a slice, which must be the same for all slices (m).
The heat flux at the surface, h', equals h ./Az, and
ns u
W = &hs + ha - hb - he + jc (2-29)
where 3 is the fraction of short-wave radiation absorbed at the surface, hg
is short-wave radiative flux, ha is long-wave or atmospheric radiative flux,
hfe is back radiation, he is evaporative energyflux, and hc is convective
energyflux.
The net heat flux to the jth slice is
"net = Y Az (2'30)
Below the surface, only short-wave radiation is absorbed. In deeper
slices, h.= ' is an exponential function of depth. The quantity of solar
radiation absorbed by the jth element, is expressed as
where
j (aj+a.+1)/2Az
*(z) = (1 - B) ha exp [-(h) (zn - z)]
s n
and 6 and hs are defined above. The short-wave radiative flux can be
estimated from pyroheliometer data (where the field data are given in
cal/cm2—sec), and 8 is O.H. (h) is the exponential decay constant for the
absorption of solar raidation with depth, zn is the elevation of the bottom
of the surface element, and z is the elevation of interest.
The long-wave radiative flux is
h_ - 1.17 x 10~18 (8n + 273)6C. (2.32)
^* cl Li
where 9 is the air temperature 2m above the water surface (°C), C, is 1 +
0.17 (fraction cloudy)2.
43,
-------
The back radiation is
hb = 1.192 x 10~111 (0g + 273)4 (2.33)
where 0 is the surface water temperature (°C).
s
The evaporative heat flux, he> at the surface is
he = 2.23 x 10~5 w (eg - efl) (2.34)
where w is the wind speed (kph), es is the saturation vapor pressure at the
water surface (mm Hg), and ea is the water vapor pressure (mm Hg).
The convective heat flux, hc, at the surface is
hp - 1.89 x KT4 hp (6 - 9 ) P /(e - e ) (2.35)
*•• c a s a s a
where he, 9 , 9 , es and ea are defined above, and Pa is teh atmospheric
pressure (mm Hg).
2.5.1 Steps in Calculation
1. Calculate Bh , ha, hK, h0 and hn.
3 GI U " *-*
2. Find hnet at the surface and each subsurface slice.
3. Compile the available temperature data by date and slice.
1. Calculate A9./At at each depth for temperature data taken in the
lake over time.
5. Find the outflow and inflow of heat to the lake, Q .9 . and Q..0..,
respectively. OJ OJ 1J 1J
6. Find the vertical flow, Qy^, for each slice.
7. Find the horizontal area of the bottom of each slice, a.=.
8. Write the basic heat transfer equation for each slice. The E* term
drops out for the top slice (at the air-water interface) and a term
drops out for the bottom slice (at the sediment interface). The
EJ + I term for the bottom slice can either be set equal to zero,
i.e., there is no heat exchange at the bottom, or the sediment
temperature can be set equal to a fixed temperature (which assumes
an infinite source/sink of heat) which eliminates 9.+1 as a
variable for the bottom slice. J
One now has n equations (one for each slice) and n unknowns (E^). The
equations can be set up as a set of simultaneous equations and solved using
standard matrix techniques.
44
-------
2.6 REFERENCES FOR SECTION 2
Bajraktarevic-Dobran, H. 1982. Dispersion in Mountainous Natural Streams.
Journal of the Environmental Engineering Division, ASCE. 108(3) =502-51 4.
Bedford, K.W. and C. Babajimopoulos. 1977. Vertical Diff usivities in
Areally Averaged Models. Journal of the Environmental Engineering Division,
ASCE.
Bencala, K.E. and R.A. Walters. 1983. Simulation of Solute Transport in a
Mountain Pool-and-Riffle Stream: a Transient Storage Model. Water
Resources Research, 19(3) =71 8-724.
Chapra, S.C. 1977. Total Phosphorus Model for the Great Lakes. Journal of
the Environmental Engineering Division, ASCE. 103:147-161.
Christensen, E.R. 1982, A Model for Radionuclides in Sediments Influenced
by Mixing and Compaction. Journal of Geophysical Research. 87(C1 ) :566-572.
Di Toro, D.M. and W.F. Matystik. 1979. Mathematical Models of Water Quality
in Large Lakes, Part 1 : Lake Huron and Saginaw Bay. EPA-600/3~80-056, U.S.
Environmental Protection Agency, Duluth, MN, 165 pp.
Di Toro, D.M. and J.P. Connolly. 1980. Mathematical Models of Water Quality
in Large Lakes, Part 2: Lake Erie. EPA-600/3-80-065, U.S. Environmental
Protection Agency, Duluth, MN, 231 pp.
Di Toro, D.M., et al . , (1982). Simplified Model of the Fate of Partitioning
Chemicals in Lakes and Streams. In: Modeling the Fate of Chemicals in the
Aquatic Environment, K.L. Dickson, A.W. Maki , J. Cairns, Jr., Eds., Ann
Arbor Science Publishers, Inc., 165-190.
Edinger, J.E. and J.C. Geyer. 1965. Heat Exchange in the Environment.
Cooling Water Studies for the Edison Electric Institute. John Hopkins
University, Baltimore, MD.
Elder, J.W. 1959. The Dispersion of Marked Fluid in Turbulent Shear Flow.
Journal of Fluid Mechanics, 5(4) :544-560.
Fischer, H.B. 1967. The Mechanics of Dispersion in Natural Streams.
Journal of the Hydraulics Division, ASCE, 93(6) :187~21 6.
i, .
Fischer, H.B. 1968. Dispersion predictions in natural streams. Journal of
the Sanitary Engineering Division, ASCE. 94(5) :927~943.
Fischer, H.B. 1968. Methods for Predicting Dispersion Coefficients in
Natural Streams, with Applications to Lower Reaches of the Green and
Duwamish Rivers Washington. U.S. Geological Survey Professional Paper 582-
A.
Fischer, H.B. 1973. Longitudinal Dispersion and Turbulent Mixing in Open-
channel Flow. Annual Review of Fluid Mechanics. 5:59-78.
45
-------
Fischer, H.B. 1979. Mixing in Inland and Coastal Waters. Academic Press.
New York.
Heinrich, J., W. Lick and J. Paul. 1981. Temperatures and Currents in a
Stratified Lake: a Two-dimensional Analysis. Journal of Great Lakes
Research. 7(3):261-275.
Hutchinson, G.E. 1941. Limnological Studies in Connecticut, IV, The
Mechanism of Intermediary Metabolism in Stratified Lakes. Ecological
Monographs. 11:21-60.
Hutchinson, G.E. 1957. A Treatise on Limnology, Vol. 1, John Wiley & Sons,
New York, pp. 466-68.
Imberger, J. et al. 1978. Dynamics of Reservoir of Medium Size. Journal of
the Hydraulics Division, ASCE. 104(5):725~743.
Imboden, D.M. and S. Emerson. 1976. Study of Transport through the
Sediment-water interface in Lakes Using Natural Radon-222. In: Interaction
Between Sediments and Water, H.L. Gotterman, Ed.
Imboden, D.M. and S. Emerson. 1978. Natural Radon and Phosphorus as
Limnologic Tracers: Horizontal and Vertical Eddy Diffusion in Greifensee.
Limnology and Oceanography. 23(1):77~90.
Imboden, D.M., et al. 1979. MELIMEX, an Experimental Heavy Metal Pollution
Study: Vertical Mixing in a Large Limno-corral. Schweizerische Zeitschrift
fur Hydrologie. 41(2):177-189.
Imboden, D.M. 1979. Natural Radon as a Limnological Tracer for the Study of
Vertical and Horizontal Eddy Diffusion. Isotopes in Lake Studies,
International Atomic Energy Agency, Vienna, 213~218.
Jassby, A. and T. Powell. 1975. Vertical Patterns of Eddy Diffusivity
During Stratification in Castle Lake, California. Limnology and
Oceanography. 20(4):530-543.
Jobson, H.E. and W.W. Sayre. 1970. Vertical Transfer in Open Channel
Flow. Journal of the Hydraulics Division, ASCE. 96(3)-.703-724.
Karickhoff, S.W., D.S. Brown, and T.A. Scott. 1979. Sorption of Hydrophobia
Pollutants on Natural Sediments. Water Research 13:421-428.
Krone, R.B. 1962. Flume Studies of the Transport of Sediment in Estuarial
Shoaling Processes. Hydr. Eng. Lab., Univ. Calif., Berkeley.
Lerman, A. and T.A. Lietzke. 1975. Uptake and Migration of Tracers in Lake
Sediments. Limnology and Oceanography. 20(4):497-510.
Lewis, W.M., Jr. 1983. Temperature, Heat, and Mixing in Lake Valencia,
Venezuela. Limnology and Oceanography, 28(2):273~286.
46
-------
Li, Y.H. 1973. Vertical Eddy Diffusion Coefficient in Lake Zurich.
Schweizerische Zeitschrift fur Hydrologie. 35:1-7.
Liu, H. 1977. Predicting Dispersion Coefficient of Streams. Journal of the
Environmental Division, ASCE. 103(1):59~69.
Liu, H. 1978. Discussion of, "Predicting Dispersion Coefficient of
Streams." Journal of the Environmental Division, ASCE. 101(4):825-828.
Lung, W.S. and R.P. Canale. 1977. Projections of Phosphorus Levels in White
Lake. Journal of the Environmental Engineering Division, ASCE. 103(^)5663-
676.
McEwen, G.F. 1929. A Mathematical Theory of the Vertical Distribution of
Temperature and Salinity in Water Under the Action of Radiation, Conduction,
Evaporation and Mixing due to the Resulting Convection. Bulletin of the
Scripps Institution of Oceanography, Technical Series 2:197~306.
McQuivey, R.S. and T.N. Keefer. 1974. Simple Method for Predicting
Dispersion in Streams. Journal of the Environmental Engineering Division,
ASCE. 100(H):997-1011.
Mortimer, C.H. 1941. The Exchange of Dissolved Substances between Mud and
Water in Lakes. Journal of Ecology. 29:280-329.
O'Connor, D.J. and J.A. Mueller. 1970. A Water Quality Model of Chlorides
in Great Lakes, Journal of the Sanitary Engineering Division, ASCE.
96(M):955-975.
Okoye, J.D. 1970. Characteristics of Transverse Mixing in Open-Channnel
Flows. Report No. KH-R-23. W.M. Kech Laboratory of Hydraulics and Water
Resources, California Institute of Technology, Pasadena, California.
Onishi, Y., and S.W. Wise. 1982. Mathematical Model, SERATRA, for Sediment
and Pesticide Transport in Rivers and Its Application to Pesticide Transport
in Four Mile and Wolf Creeks in Iowa. EPA-600/3-82-045, U.S. Environmental
Protection Agency, Athens, Georgia.
Park, G.G. and P.S. Schmidt. 1973. Numerical Modeling of Thermal
Stratification in a Reservoir with Large Discharge-to-volume Ratio. Water
Resources Bulletin. 9(5):932-94l .
Park, R.A., et al. 1981. Modeling Transport and Behavior of Pesticides in
Aquatic Environments. Report No. 7, Center for Ecological Modeling,
Rensselaer Polytechnic Institute, Troy, New York, 163 PP.
Partheniades, E. 1965. Erosion and Deposition of Cohesive Soils. Proc. Am.
Soc. Engrs., 91.
47
-------
Powell, T. and A. Jassby. 1974. The Estimation of Vertical Eddy
Diffusivities Below the Termocline. Water Resources Research, 10(2):191-
198.
Quay, P.D., et al. 1980. Vertical Diffusion Rates Determined by Tritium
Tracer Experiments in the Thermocline and Hypolimnion of Two Lakes.
Limnology and Oceanography. 25(2):201-218.
Rubey, W.W. 1933. Equilibrium-Conditions in Debris-Laden Streams. Trans.
Am. Geophys. Union.
Sayre, W.W. and P.M. Chang. 1968. A Laboratory Investigation of Open-
Channel Dispersion Processes for Dissolved, Suspended and Floating
Dispersants. U.S. Geological Survey Professional Paper 433-E.
Sayre, W.W. 1973. Natural Mixing Processes in Rivers. In: Environmental
Impact on Rivers, H.W. Shen, Ed., H.W. Shen, Fort Collins, CO.
Schnoor, J.L. and E.G. Fruh. 1979. Dissolved Oxygen Model of a Short
Detention Time Reservoir with Anaerobic Hypolimunion. Water Resources
Bulletin. 15(2):506-518.
Schnoor, J.L. and D.J. O'Connor. 1980. A Steady State Eutrophication Model
for Lakes. Water Research. 14:1651-1665.
Schnoor, J.L. and D.C. McAvoy. 1981. A Pesticide Transport and
Bioconcentration Model. Journal of the Environmental Engineering Division,
ASCE. 107(6):1229-1246.
Snodgrass, W.J. and C.R. O'Melia. 1975. Predictive Model for Phosphorus in
Lakes. Environmental Science and Technology, 9(10):937~944.
Tennekes, H. and J.L. Lumley. 1972. A First Course in Turbulence. The MIT
Press, Cambridge, Massachusetts, and London, England.
Torgersen, T., et al. 1977. A New Method for Physical Limnology-Tritium-
Helium-3 ages - Results for Lakes Erie, Huron and Ontario. Limnology and
Oceanography. 22(2):181-193.
Wodka, M.C., et al. 1983. Diffusivity-based Flux of Phosphorus in Onondaga
Lake. Journal of the Environmental Engineering Division, ASCE. 109(6) :1403-
1115.
Yotsukura, N. and E.D. Cobb. 1972. Transverse Diffusion of Solutes in
Natural Streams. U.S. Geological Survey Professional Paper 582-C.
Yotsukura, N. and W.W. Sayre. 1976. Transverse Mixing in Natural
Channels. Water Resources Research. 12:695-704.
48
-------
SECTION 3
ORGANIC REACTION KINETICS AND RATE CONSTANTS
3.1 INTRODUCTION
Reaction rates for fate processes are presented for the organic
priority pollutants. These organic chemicals fall into nine groups and a
few chemicals were selected from each group to build summary tables for each
fate process. The individual chemicals are intended only for comparisons.
Pesticides
PCBs
Halogenated aliphatic
hydrocarbons
Halogenated ethers
Monocyclic aromatics
Phthalate esters
Polycyclic Aromatic
hydrocarbons
Nitrosamines & Miscellaneous
Chemical
Carbofuran (carbamate)
DDT (chlorinated)
Parathion (organo-phosphate)
Aroclor 12*18
Chloroform.
2-Chloroethyl vinyl ether
2,4-Dimethylphenol
Pentachlorophenol
Bis(2-ethylhexyl)phthalate
Anthracene
Benzo[a]pyrene
Benzidine
Dimethyl nitrosamine
Specific information on 221 chemicals is presented in tables in the
appendix and is indexed by chemical name at the end of this chapter for the
reader's convenience. These data were compiled from references found by a
computer literature search. The following data bases were used:
AQUALINE
CA Search
ENVIROLINE
Environmental Bibliography
49
-------
Pollution Abstracts
Water Resources Abstracts
The period of literature reviewed, generally, is 1979-1985 to update the
data presented in Callahan (1979).
The fate processes addressed include the major kinetics observed in
surface fresh waters. These processes are: biotransformation, hydrolysis,
oxidation, photolysis, volatilization, partitioning, and bioconcentration.
Discussions include a brief overview of the kinetics development, a summary
of types of experiments used to generate kinetics data, and synopses of the
journal articles from which these data were gathered.
3.2 BIOLOGICAL TRANSFORMATIONS
Biological transformations refer to the microbially mediated
transformation of organic chemicals, often the predominant decay pathway In
natural waters. It may occur under aerobic or anaerobic conditions, by
bacteria, algae or fungi, and by an array of mechanisms (dealkylation, ring
cleavage, dehalogenation, etc.). It can be an intr a- cellular or extra-
cellular enzyme transformation.
The term "biodegradation" is used synonomously with
"biotransformation," but some researchers reserve "biodegradation" only for
oxidation reactions that eventually lead to (X^ and 1^0 as products.
Reactions that go all the way to C^ and 1^0 are referred to as
""mineralization." In the broadest sense, biotransformation refers to any
microbially mediated reaction that changes the organic chemical. It does
not have to be an oxidation reaction, nor does it have to yield'carbon or
energy for microbial growth or maintenance. The term "secondary substrate
utilization" refers to the utilization of organic chemicals at low
concentrations (less than the concentration required for growth) in the
presence of one or more primary substrates that are used as carbon and
energy sources. "Co-metabolism" refers to the transformation of a substraite
that cannot be used as a sole carbon or energy source but can be degraded in
the presence of other substrates, e.g., DDT.
The biodegradation tables in the Appendix contain half-life and
kinetics data, along with specific characteristics of the experiments.
Table 3.01 contains a summary of biodegradation rate constants.
Biological reactions generally follow Michaelis-Menton kinetics, where
dc/dt - - kb c (3.1)
and kb is defined as
k = (?
Kb Y (Ku + c) U<<
* n
In equation (3.2), y is the maximum growth rate of the culture, X is the
biomass concentration, Y is the yield coefficient (cells produced/ toxicant
50
-------
TABLE 3.01 SUMMARY TABLE OF BIOTRANSFORMATION RATE CONSTANTS
* Zero-order rate constant, pM/day
Rate Constant Range
(day"1)
Pesticides
Carbofuran 0.03
DDT 0:0 - 0.10
Parathion 0.0 - 0.12
PCBs
Aroclor 1248 0.0 - 0.007
Halogenated aliphatic hydrocarbons
Chloroform 0.09 - 0.10
Halogenated ethers
2-Chloroethyl vinyl ether 0.0 - 0.20
Monocyclic aromatics
2,4-Dimethylphenol 0.24 - 0.66
Pentachlorophenol 0.00 - 33.6
Phthalate esters
Bis(2-ethylhexyl)phthalate 0.00 - 0.14
Polycyclic aromatic hydrocarbons
Anthracene 0.007 - 14.69
Benzo[a]pyrene 0.0 - 0.075
" 0.48 - 3.12*
Nitrosamines & Miscellaneous
Benzidine 0.0
Dimethyl nitrosamine 0.0
removed), and KM is the half-saturation constant (the value of c at which kb
= 1/2 p). Figure 3.01 shows Michaelis-Menton kinetics in graphical form.
When the toxicant concentration, c, is < KM, equation (3.2) reduces to
kb = FIT = kb2 X (3'3)
M
51
-------
(_>
•o
LJ
cr
x
o
cr
UJ
fl/2 -
SUBSTRATE CONC., C
Figure 3.01. Miohaelis-Menton kinetics for microbial growth or substrate
utilization rate as a function of substrate concentration.
which converts equation (3.1) to
dc/dt = - kb2 X c
so that equation (3.1) becomes first-order in c and X, and is second-order
overall (see Fig. 3.02). The second-order rate constant k^, has units of
1/(cell concentration-time). For constant values of X, the rate may be
expressed as a pseudo-first-order reaction rate (1/time), where the
investigator would observe an exponential decay of toxicant in the presence
of a fixed population (see Fig. 3.03).
If the toxicant concentration is
equation (3.2) reduces to
y X
Y c
X
kb0 c
which converts equation (3.1) to
dc/dt
- kb0 X
(3.5)
52
-------
o
o
o
o
c
VARIABLE POPN. DENSITY
X1
-------
which is zero-order in c and first-order in X. The zero-order rate
constant, k^*, has units of toxicant concentration/cell concentration-time.
Biotransformation experiments are conducted by batch and chemostat
experimental methods. Other fate pathways (photolysis, hydrolysis,
volatilization) must be accounted for in order to correctly evaluate the
effects of biodegradation.
There are several basic types of biodegradation experiments. Natural
water samples from lakes or rivers can have organic toxicant added to them
in batch experiments. Disappearance of toxicant is monitored. Toxicant can
be added to a water-sediment sample to simulate in-situ conditions, or a
contaminated sediment sample alone may be used without a spiked addition.
Primary sewage, activated sludge, or digester sludge may purposefully be
contaminated to test degradability and measure toxicant disappearance.
Degradation by periphytic and epilithic organisms can also be examined.
Radio-labeled organic chemicals can be used to estimate metabolic
degradation (mineralization) by measuring CC^ off-gas, and anabolic
incorporation into biomass. These experiments are called heterotrophic
uptake experiments. The organic chemical may be added in minute
concentrations to simulate exposure in natural conditions, or it may be the
sole carbon source to the culture.
Biodegradation is affected by numerous factors that influence
biological growth:
1. Temperature. Temperature effects on biodegradation of toxics
are similar to those on biochemical oxygen demand (BOD) or
ammonia removal.
2. Nutrients. Nutrients are necessary for growth.
3. Acclimation. Adaptation is necessary for expressing repressed
(induced) enzymes or fostering those organisms that can
degrade the toxicant through gradual exposure to the toxicant
over time. A shock load of toxicant may kill a culture that
would otherwise adapt if properly exposed.
4. Population Density or Biomass Concentration. Organisms must.
be present in large enough numbers to significantly degrade
the toxicant (a lag often occurs if the organisms are too
few).
Some recent results of biotransformation experiments are discussed in
the next few pages. Ward and Matsumura (1978) found that evaporation was
the major fate process for dioxin in lake water and sediment and that
biodegradation was only a minor fate process. Saeger (1979) studied the
fate processes of 11 trialkyl, alkyl aryl and triaryl phosphate esters.
Solubility in distilled water and octanol-water partition coefficients (KQW)
were measured. Biodegradation studies using Mississippi River water and
activated sludge showed that phosphate esters are rapidly degraded
biologically.
54
-------
Boyle (1980) tested the degradation of pentachlorophenol in a lentic
microcosm containing filamentous algae. The aquaria were operated at a
combination of conditions — aerobic or anaerobic, with or without sediment,
and dark or lighted. Boyle found persistance was aided by the absence of
light and sediment, low dissolved oxygen concentrations, and pH < 4.8.
Cartwright (1980) found that zero-order kinetics best described the
biodegradation of alachlor. Gledhill (1980) studied butyl benzyl phthalate
in order to assess its environmental safety. Photolysis was measured in
natural sunlight over a period of 28 days. Solubility in distilled water,
the octanol-water partition coefficient, biodegradation using lake water,
bioconcentration, and aquatic toxicity were measured as part of the
experiment. The authors found that biodegradation was the most important
removal mechanism.
Monnig (1980) investigated the biological treatability of carbaryl,
toluene, and a-naphthol using municipal wastewater. A 90% or greater
reduction in the toxicants was noted without a decrease in performance of
COD removal, but ammonia increased through the treatment units causing the
effluent to be more toxic than the influent. Nesbitt and Watson (1980)
studied the Avon River (Australia) for degradation of 2,4-D over one
winter. Laboratory experiments of field-collected samples with 2,4-D added
measured biodegradation half-lives. Sharom (1980) measured the persistence
of 12 pesticides in sterile and natural water. DDT, parathion, and lindane
degraded only in unsterilized water. Dieldrin, endrin, ethion, and
leptophos were the most stable in natural water. Parathion, p,p'-DDT,
carbaryl and carbofuran were most easily degraded in natural water.
Fochtman (1981) measured biodegradation of eight organic pollutants
after a 7~day period. The study focused on activated carbon adsorption and
biodegradation as a treatment scheme for water and wastewater. Liu (1981 a)
tested fenitrothion and 2,4-D for biodegradability under sole carbon source
and cometabolism (mixed substrate) conditions and under aerobic and
anaerobic conditions. Liu (I98lb) measured the biodegradability of
pentachlorophenol by bacterial cultures under aerobic and anaerobic
conditions, as sole carbon source and co-metabolism with monochlorophenol.
Degradation was enhanced in aerobic conditions. Paris (1981) observed
second-order kinetics in the biodegradation of malathion, 2,4-D butoxyethyl
ester, and chlorpropham in natural water samples. Sharom and Miles (1981)
investigated the degradation of parathion and DDT in the presence of
ethanol, glucose and acetone. The maximum degradation rates were observed
for DDT and ethanol, and parathion and glucose. Tabak (1981) collected data
on the biodegradability of 96 organic compounds. Cultures were kept in the
dark, at 25°C, for 7 days, and analyzed for the test compound. Primary
sewage was used as the innoculum, and the solution was recultured for a
total of 28 days to allow acclimation.
Furukawa (1982) measured the biodegradability of 31 mono- and
polychlorinated biphenyls (pure isomers) by cultures of Alcaligenes sp. and
Acinetobacter sp. after 1 to 2 hours of incubation. Kilbane (1982) used
Pseudomonas cepacia to degrade 2,4,5-T as a sole carbon source, in which 97%
disappeared after 6 days. Muir and Yarechewski (1982) studied the
degradation of terbutryn under varying redox conditions. Terbutryn degraded
55
-------
slowly under aerobic conditions in natural water samples and sediments.
Papanastasiou (1982) used Monod kinetics to describe a 2,4-D acclimated
activated sludge culture that utilized 2,4-D and glucose. A 20-day lag was
observed. Scow (1982) developed biodegradation summary data for aquatic
systems of 40 organic compounds.
Bailey (1983) used Tittabawassee River (Michigan) water to measure the
biodegradation of radio-labeled biphenyl and three chlorinated biphenyls.
Biphenyl and the monochlorinated biphenyls degraded in less than 3 days,
but the tetrachlorinated biphenyl did not degrade in 98 days. Hallas and
Alexander (1983) measured the degradation of nine nitroaromatics in sewage
effluent under aerobic and anaerobic conditions. Knowlton and Huckins
(1983) conducted a littoral microcosm study using radio-labeled
pentachlorophenol. Mineralization and incorporation into macrophyton
biomass was observed. Petrasek (1983) reported KQW and Henry's constant as
part of a study of toxicants in an activated sludge plant. The influent was
spiked with 22 organics, and process flows and sludges were monitored.
Pignatello (1983) monitored the photolytic and biological degradation of
pentachlorophenol in artificial freshwater stream ecosystems using
Mississippi River water. They found: "(1) photolysis of PCP in the near
surface waters initially was the primary mechanism of PCP removal, (2) after
a period of weeks the aquatic microflora became adapted to PCP
mineralization and supplanted photolysis as the major PCP removal process,
(3) attached microorganisms were primarily responsible for PCP
biodegradation, and (4) total bacterial numbers were not significantly
affected by PCP concentrations of micrograms per liter."
Guthrie (1984) examined the fate of pentachlorophenol on anaerobic
digestion of sewage sludge. The digesters were acclimated to the chemical,
and the digesters were run at three different sludge ages. Methanogenesis
was inhibited in unacclimated cultures at concentrations exceeding
200 ug/fc. Soluble pentachlorophenol was removed to levels below 5 yg/fc.
Johnson (1984) investigated the biodegradation of four phthalate esters in
freshwater lake sediments. Experiments were conducted under aerobic and
anaerobic conditions, and at low, medium, and high chemical
concentrations. Johnson found that phthalate esters with complex alkyl
groups degraded only very slowly, and degradation was favored by nutrient-
rich systems with temperatures above 22°C. Walker (1984) developed half-
lives for nine pesticides and dibutylphthalate in sterile and natural water
systems. Bravo , Hoelon , methyl parathion, and Bolero degraded more
quickly in natural than sterile samples, whereas endosulfan and dimilin
degraded most quickly in sterile conditions.
3.3 CHEMICAL HYDROLYSIS
Chemical hydrolysis is that fate pathway by which a toxicant reacts
with water. Particularly, a nucleophile (hydroxyl, water or hydronium
ions), N, displaces a leaving group, X, as shown (Neely, 1985):
R-X + N -»• RN + X (3-6)
Hydrolysis does not include acid-base, hydration, addition or elimination
56
-------
reactions. The hydrolysis reaction consists of the cleaving of a molecular
bond and the formation of a new bond with components of the water molecule
(H+, OH~). It is often a strong function of pH (see Fig. 3.0*0.
101
Q
I
UJ
o
UJ
o
o
o
<
UJ
oc.
10
-2
10
-3
o:
UJ
o
a:
o
- 10-
10
-5
• -o*
i i i i
j i
T MALATHION
D DIAZOXON 20°
• DIAZINON 30°
A DIAZINON 20°
• METHOXYCHLOR
A PARATHION
o DDT
i i i
1
2 3 4 5 6 7 8 9 10 11 12 13 14
pH
Figure 3.04 Effect of pH on hydrolysis rate constants.
Two examples of a hydrolysis reaction are presented below (Harris,
1982b):
+H2°
CH3CH2CH2CHCH3 ----+ CH.CHgCHgCH-CH + Br + H
Br
alkyl halide
0
0
OH
alcohol
(3.7)
carbamate
alcohol amine
57
-------
The types of compounds that are generally susceptible to hydrolysis are
(Harris, 19825):
Alkyl halides
Amides
Amines
Carbamates
Carboxylic acid esters
Epoxides
Nitriles
Phosphonic acid esters
Phosphoric acid esters
Sulfonic acid esters
Sulfuric acid esters
The kinetic expression for hydrolysis is
dc/dt = - k^H^c - kNc - kb[OH~]c (3.8)
where c is the concentration of toxicant, ka, k^, and k^ are the acid-,
neutral- and base-catalyzed hydrolysis reaction rate constants,
respectively, and [H+] and [OH ] are the molar hydrogen and hydroxyl ion
concentrations, respectively.
Hydrolysis data available are presented in tables in the appendix. A
summary of these data is presented in Table 3.02.
Hydrolysis experiments usually involve fixing the pH at some target
value, eliminating other fate processes, and measuring toxicant
disappearance over time. A sterile sample in a glass tube, filled to avoid
a gas space, and kept in the dark eliminates the other fate pathways. In
order to evaluate k_ and k^, several non-neutral pH experiments must be
conducted.
Wolfe (1977a) measured hydrolysis and photolysis of malathion and found
alkaline hydrolysis to be a significant fate process. Wolfe (I977b) also
measured hydrolysis of methoxychlor and DDT. At common aquatic environment
pH values, methoxychlor was pH-independent and DDT was pH-dependent.
Khan (1978) observed first-order hydrolysis kinetics for atrazine in
aqueous fulvic acid solutions. Acid conditions favored the hydrolysis of
atrazine. Wolfe (1978) measured hydrolysis of carbaryl, propham, and
chlorpropham. At pH 7, the half-lives of photolysis and alkaline hydrolysis
for carbaryl varied by a factor of two. .The alkaline hydrolysis half-lives
for propham and chlorpropham exceeded 10 days; biolysis was the most
significant degradation process.
Harris (1982b) compiled base, neutral and acid hydrolysis rate
constants for 15 pesticides.
Lemley and Zhong (1983) investigated hydrolysis of aldicarb, aldicarb
sulfoxide, and aldicarb sulfone. Base hydrolysis was first-order with
58
-------
TABLE 3.02 SUMMARY TABLE OF HYDROLYSIS DATA
Hydrolysis Range
of Values
Pesticides
Carbofuran
DDT
Parathion
PCBs
Aroclor 12H8
Halogenated aliphatic hydrocarbons
Chloroform
Halogenated ethers
2-Chloroethyl vinyl ether
Monocyclic aromatics
2,H-Dimethylphenol
Pentachlorophenol
Phthalate esters
Bis(2-ethylhexyl)phthalate
Polycyclic aromatic hydrocarbons
Anthracene
Benzo[a]pyrene
Nitrosamines & Miscellaneous
Benzidine
Dimethyl nitrosamine
N/A
35.6/M-hr (alk)
6.8M E-6/M-hr (acid)
82.8/M-hr (alk)
0.000162/hr (neut)
0/M-hr (alk)
0/M-hr (acid)
0/M-hr (neut)
0.216/M-hr (alk)
2.5E-9/M-hr (neut)
4E-6/M-hr (neut)
0/M-hr (alk)
0/M-hr (acid)
0/M-hr (neut)
0/M-hr (alk)
0/M-hr (acid)
0/M-hr (neut)
O.VM-hr (alk)
4.0E-5/M-hr (acid)
0/M-hr (neut)
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
0/M-hr
(alk)
(acid)
(neut)
(alk)
(acid)
(neut)
(alk)
(acid)
(neut)
(alk)
0/M-hr (acid)
0/M-hr (neut)
59
-------
respect to OH~, and acid hydrolysis was first-order. Temperatures were
varied from 5 to 35°C for base hydrolysis. Wolfe (1982) measured solubility
in distilled water, KQW, vapor pressure, and'Henry's constant of
hexachlorocyclopentadiene as part of an investigation into the fate
processes of this chemical. Hydrolysis in water was measured at pH 2.88,
5.08, 6.70, 7.0 and 9.76 at temperatures of 30 to 50°C. Photolysis in
natural sunlight was also measured. Wolfe found that photolysis was the most
important degradation process, with hydrolysis next in importance.
3.4 CHEMICAL OXIDATION
Chemical oxidation reactions take place in natural waters when oxidants
(often formed photochemically) are present in sufficient concentrations to
favor the reaction. Chlorine and ozone are commonly reported oxidants. The
basic equation for oxidation is
^•f = -K Ox C (3.9)
where K is the second-order rate constant, Ox is the concentration of
oxidant and C is the concentration of toxicant.
In natural waters, the oxidant is generally a free radical at low
concentrations. If the free radical formation rate is relatively constant
(as expected in natural waters), then the free radical oxidation of the
toxicant can be computed as a first-order reaction:
42- -K' C (3.10)
where K' is the pseudo-first-order rate constant.
Oxidation by ozone is a strong function of pH. At high pH, OH~
radicals catalyze the decomposition of ozone, which is then further
decomposed by its own decomposition products (Stumm and Morgan, 1981).
The oxidation rates and oxidants are presented in the appendix; a
summary of these data is given in Table 3.03.
Dennis et al. (1979) oxidized diazinon with Clorox, and reported the
oxidation half-lives as a function of pH. LC^Q values also were determined
for Lepomis macrochirus (bluegills), Pimephales promelas (fathead minnows),
arid Daphnia magna.
Koshitani et al. (1982) studied the oxidation of anthracene by oxygen,
copper(II) acetate, and sodium chloride. The rate of anthracene degradation
was found to be first-order in regard to anthracene and sodium chloride, and
1/2-order in regard to copper(II) acetate.
Kuo and Soong (198*0 studied the oxidation of benzene by ozone. The
degradation of benzene was found to be zero-order with respect to benzene
and first-order with respect to ozone at neutral pH.
60
-------
TABLE 3.03 SUMMARY TABLE OF OXIDATION DATA
Oxidation Range
of Values
Pesticides
Carbofuran
DDT
Parathion
N/A
< 3600/M-hr (Dp)
3600/M-hr (R02)
N/A
PCBs
Aroclor 1248
Halogenated aliphatic hydrocarbons
Chloroform
Halogenated ethers
2-Chloroethyl vinyl ether
Monocyclic aromatics
2,4-Dimethylphenol
Pentachlorophenol
Phthalate esters
Bis(2-ethylhexyl)phthalate
Polycyclic aromatic hydrocarbons
Anthracene
Benzo[a]pyrene
Nitrosamines & Miscellaneous
Benzidine
Dimethyl nitrosamine
<360/M-hr (02)
<1/M-hr (R02)
<360/M-hr (02)
0.7/M-hr (R02)
1E10/M-hr (02)
34/M-hr (R02)
< HE6/M-hr (Op)
1.1E8/M-hr (ROp)
< 7E3/M-hr (Op)
1E5/M-hr (R02)
«360/M-hr (Op)
7.2/M-hr (R02)
5E8/M-hr (02)
2.2E5/M-hr (R02)
5E8/M-hr (02)
2EM/M-hr (R02)
r (02)
1,1E8/M-hr (R02)
no reaction
61
-------
3.5 PHOTO-TRANSFORMATIONS
Photolysis, the light-initiated degradation reaction, is a function of
the incident energy on the molecule and the quantum yield of the chemical.
Data for photolysis reactions are compiled in the photolysis table in the
appendix and are summarized in Table 3.01. Surface photolysis rates, half-
lives, quantum yields and wavelength data are presented.
TABLE 3.04 SUMMARY TABLE OF PHOTOLYSIS DATA
Photolysis Reaction Rates
Range of Values
Pesticides
Carbofuran
DDT
Parathion
PCBs
Aroclor 1248
Halogenated aliphatic hydrocarbons
Chloroform
Halogenated ethers
2-Chloroethyl vinyl ether
Monocyclic aromatics
2,4-Dimethylphenol
Pentachlorophenol
Phthalate esters
Bis(2-ethylhexyl)phthalate
Polycyclic aromatic hydrocarbons
Anthracene
Benzo[a]pyrene
Nitrosamines & Miscellaneous
Benzidine
Dimethyl nitrosamine
N/A
< 5E-7/hr
0.0024-0.003/hr
N/A
N/A
N/A
N/A
0.2295-1.224/hr
N/A
0.924-1.188/hr
0.348-1.386/hr
11.09/hr
N/A
When light strikes the pollutant molecule, the energy content of the
molecule is increased and the molecule reaches an excited electron state.
This excited state is unstable and the molecule reaches a normal (lower)
62
-------
energy level by one of two paths: (1) it loses its "extra" energy through
energy emission, i.e., fluorescence or phosphorescence, or (2) it is
converted to a different molecule through the new electron distribution that
existed in the excited state.
Photolysis may be direct or indirect. Indirect photolysis occurs when
an intermediary molecule becomes energized which then energizes the chemical
of interest. The basic equation for direct photolysis is of the form:
dc/dt = - k 4 c (3.11)
Si
where c is the concentration of toxicant, kg is the rate constant for
adsorption of light by the toxicant, and is the quantum yield of the
reaction. The quantum yield is defined by
Number of moles of toxicant reacted
Number of einsteins absorbed
,,
An einstein is the unit of light on a molar basis (a quantum or photon is
the unit of light on a molecular basis). The quantum yield may be thought
of as the efficiency of photo-reaction. Incoming radiation is measured in
units of energy per unit area per time (e^g., cal/cm -sec). The incident
light in units of einsteins/cm /sec /nm can be converted to
watts/ cm"2/ run"1 by multiplying by the wavelength (nm) and 3.03 x 10™.
The intensity of light varies over the depth of the water column and
may be related by
Iz = I0 e~Kez (3.13)
where Iz is the intensity at depth z, IQ is the intensity at the surface,
and Ke is an extinction coefficient for light disappearance. Light
disappearance is caused by the scattering of light by reflection off
particulate matter, and absorption by any molecule. Absorbed energy can be
converted to heat or cause photolysis. Light disappearance is a function of
wavelength and water quality (e.g., color, suspended solids, dissolved
organic carbon) .
The rate constant ka is the product of I (at any depth, or an average
over the depth) and the absorption of light by the chemical.
Indirect photolysis occurs when a nontarget molecule is transformed
directly by light, which in turn, transmits its energy to the pollutant
molecule. Changes in the pollutant molecule then occur as a result of the
increased energy content. The kinetic equation for indirect photolysis is
dc/dt = -k2 c x = - kp c (3.14)
where y.^ is the indirect photolysis rate constant, x is the concentration of
the nontarget intermediary, and kp is the overall pseudo-first order rate
constant. Recently the important role of inducing agents (e.g., algae
exudates and nitrate) have been demonstrated by Zepp et al . (1984) and Zepp
et al. (1987).
63
-------
Lu (1977) used radio-labeled vinyl chloride, benzidine, and
benzo[a]pyrene for fate analysis in a microcosm ecosystem. Photolytic
degradation of benzidine and benzo[a]pyrene was measured, and K w was
reported. Bioconcentration in algae (Oedogonium cardiacurn), fish (Gambusia
affinis), daphnia (Daphnla magna), mosquito larvae (Culex pipi ens
quinquefasciatus) and snails (Physa sp.) was measured. Vinyl chloride did
not bioaccumulate, whereas benzo[a]pyrene and benzidine bioaccumulation were
closely related to their KQW. Zepp (1977) measured the photolysis of DDE
and DMDE, which are the photodegradation products of DDT and methoxychlor,
respectively.
Hautala (1978) tested the effects of surfactants on the photolysis of
2,1-D, carbaryl and parathion. Quantum yield and half-lives were measured
for irradiation by monochromatic light.
Que Hee and Sutherland (1979) measured the photolysis of 2,4-D butyl
ester by irradiation of 300 nm light. The half-life was 13 days.
Gledhill (1980) studied butyl benzyl phthalate in order to assess its
environmental safety. Photolysis was measured in natural sunlight over a
period of 28 days. Solubility in distilled water, KQW, biodegradation using
lake water, bioconcentration and aquatic toxicity were measured as part of
the experiment. The authors found that biodegradation was the most
important removal mechanism.
Harris (1982a) presented quantum yield, half-life and wavelength data
for 53 organic compounds (including pesticides and polycyclic aromatics).
Wolfe (1982) measured the solubility in distilled water, KQW, vapor
pressure, and Henry's constant for hexachlorocyclopentadiene as part of an
investigation into the fate processes affecting the chemical. Photolysis in
natural sunlight was measured, as was hydrolysis at various pH levels.
Wolfe found that photolysis was the most important degradation process, with
hydrolysis next in importance.
Pignatello (1983) monitored the photolytic and biological degradation
of pentachlorophenol in artificial freshwater stream ecosystems using
Mississippi River water. Photolysis of PCP in the near surface waters
initially was the primary mechanism of PCP removal.
3.6 VOLATILIZATION
The transfer of pollutants from water to air or from air to water is an
important fate process to consider when modeling organic chemicals.
Volatilization is a transfer process; it does not result in the breakdown of
a substance, only its movement from the liquid to gas phase, or vice
versa. Gas transfer of pollutants is analogous to the reaeration of oxygen
in surface waters, and will be related to known oxygen transfer rates. The
rate of volatilization is related to the size of the molecule (as measured
by the molecular weight). The molecular weight is given in the Index to
Chemicals at the end of this chapter.
64
-------
Gas transfer models are often based on the two-film theory (Figure
3.05). Mass transfer is governed by molecular diffusion through a stagnant
liquid and gas film. Mass moves from areas of high concentration to areas
of low concentration. Transfer can be limited at the gas film or the liquid
film. Oxygen, for example, is controlled by the liquid-film resistance.
Nitrogen gas, although approximately four times more abundant in the
atmosphere than oxygen, has a greater liquid-film resistance than oxygen.
Volatilization, as described by two-film theory, is a function of
Henry's constant, the gas-film resistance and the liquid-film resistance.
The film resistance depends on diffusion and mixing. Henry's constant, H,
Ca, BULK GAS PHASE
y CONCENTRATION
GAS FILM
LIQUID
FILM
-sg
HCSJ(
Cjf, BULK LIQUID PHASE
CONCENTRATION
Figure 3.05. Two-film theory of gas-liquid interface.
is a ratio of a chemical's vapor pressure to its solubility. It is a
thermodynamic ratio of the fugacity of the chemical (escaping tendency from
air and water).
H = p/c
(3.15)
where p is the partial pressure of the chemical of interest, and c is its
solubility. Henry's constant can be dimensionless Cmg/J, (in air)/mg/X, (in
water)] or can have concentration units, e.g., mm mm Hg/mg/JL, atm-m'/M.
The value of H can be used to develop simplifying assumptions for
modeling volatilization. If either the liquid-film or the gas-film
controls, i.e., one resistance is much greater than the other, the lesser
resistance can be neglected. The threshold of Henry's constant for gas or
liquid film control is approximately 0.1 for dimensionless H, or 2.2 x 10~
65
-------
n-VM. Above this threshold value, the chemical is liquid-film
controlled, and below it, it is gas-film controlled.
The diffusion coefficients in water and air have been related to
molecular weight (O'Connor, 1980):
Dfc = 22 x 10~5 cm2/sec Mw~2/3 (3.16)
where D is the diffusivity of the chemical in water and MW is the molecular
weight, and
D - 1.9 cm2/sec Mw"2/3 (3-17)
where D is the diffusivity of the chemical in air. The diffusion can then
be related to the oxygen reaeration rate, Ka, by a ratio of the diffusivity
of the chemical to that of oxygen:
Kn/Ka - (VD02)1/2 (3
_c p
where DQO is 2.4 x 10 -' cm /sec at 20°C. The reaeration rate, Kg, can be
calculated from any of the formulae available (e.g., Tsivoglou, O'Connor-
Dobbins, Owens, etc.).
The gas film transfer rate may be calculated from
where v is the kinematic viscosity of air (a function of temperature) as
presented in Table 3.05, h is the water depth, and W is the wind speed in
m/sec. K has units of 1/time.
o
The overall mass transfer rate is K, , as given by
11.1 ,„ ,
IS If TJ -If
i\_ A._ . n i\ .
1. li gi
TABLE 3.05 KINEMATIC VISCOSITY
\3. ^u;
OF AIR
Temperature (°F) v (cm /sec)
0
20
MO
60
80
100
120
Rouse, Hunter (19M6). Elementary Mechanics of Flui
0.117
0.126
0.136
0.147
0.156
0.167
0.176
ds. Dover Publications,
Inc., New York, New York. p. 363.
66
-------
where H must be dimensionless, and K^ is in units of length/time. The KL
found from this expression can be incorporated into a mass flux expression
such as:
KL
-f- (C0 - C) = dc/dt (3.21)
d S
where Co is the saturation value or solubility of the toxicant, d is the
mean depth of the water body, and C is the dissolved concentration of
toxicant. The overall mass transfer coefficient is sometimes referred to as
the "piston velocity", i.e., the velocity that the chemical penetrates the
stagnant film.
Solubility, vapor pressure and Henry's constant data are present in the
Solubility and Volatilization table at the end of this chapter. (Henry's
constant can be converted from atm-m^/M to a dimensionless number by
multiplying by 44.64 = 1000 8,-M/22.4S,-m .) A summary of these data is
presented in Table 3.06.
Yalkowsky (1979) measured the solubility of 26 halogenated benzenes at
25°C and developed the following relationship
log Sw = -0.01 MP - 0.88 log PC - 0.012 (3.22)
where Sw is solubility (M/8,), MP is the melting point (°C) and PC is the
calculated partition coefficient.
Gossett and Lincoff (1981) studied the effects of temperature and ionic
strength on Henry's constant for six chlorinated organic compounds. Matter-
Muller (1981) reported values of Henry's constant for six organic chemicals
as part of a study to evaluate the stripping efficiency of several water and
wastewater processes. Jaffe and Ferrara (1983) reported partial pressure,
solubility, Henry's constant and KQW for ten organic compounds as part of a
comparison between a kinetics approach and an equilibrium approach in toxics
modeling. Lyman (1982b) compiled solubility data on 78 organic compounds
and presented estimation methods based on KQW for different classes of
compounds. He also included a method based on the molecular structure.
Mackay (1982) measured Henry's constant for 22 organic chemicals as part of
a study of volatilization characteristics. Transfer coefficients for the
gas and liquid phases were correlated for environmental conditions as:
KL = 34.1 x 10~6 (6.1 + 0.6U10)°'5U10 ScL~°'5
KG = 46.2 x 10~5 (6.1 + 0.63U10)°'5U10 ScG~°'67
where U10 is the 10-m wind velocity (m/s), ScL and ScG are the dimensionless
liquid and gas Schmidt numbers. Thomas (1982) compiled solubility, vapor
pressure and Henry's constant data for 43 organic compounds. Wolfe (1982)
measured solubility in distilled water, KQW, vapor pressure, and Henry's
constant of hexachlorocyclopentadiene as part of an investigation into the
fate processes of this chemical. Wolfe found that photolysis was the most
important degradation process, with hydrolysis next in importance.
McCall (1983) reported solubility, vapor pressure, Henry's constant and
bioconcentration factors for seven organic chemicals as part of a fate model
67
-------
?i
j<
o
z
o
hH
H
N
,-J
H
. "1
O
Cb
0
2
Jr^
PN
•<
H
^^
r*
2
S
CO
B
vO
O
•
CO
W
«
H
4J
C
cd
4J
2 _
O t, O
cj -i CM
fO X
n
» o< ot
>» ^ \
C. 00 to
C E E
CD • — •
X
*
CD
3
co **•
8 to
x
a- e
t, -S
a
CO
>
^j
vH *-*•
rH —t
2 ~ei
3 E
CO
t_
rH | "*
3 .c
o 00
0) — i
rH O)
O 3
a- in
i .
o o
— i
i —
m O
i .
0 0
—
t— .=r
1 l
Cd Cd
< ON < Oi
X^ • >v •
Z — Z a-
CJ
O a-
• in
o
O 1 O
SO — l*-
1 O —
in o o
JT O CJ O
in in
=T 01
in 01
m cj
CO
c ex
CO C —
0) %-! C O
•O O *» rH
— i jo co O
4J cj o a. to «:
8 o
a. a.
in
o
VO
a*
i
O
in
o
o
vO
i
o
o
o
oo
.q-
cyv
r>
•"
s
.?
L
fl)
O
0
^
>.
J=
o
CO
0.
To E
T^fe
0) «-,
Ib
«> ^
O CJ
iH
10
X
CNJ
0
o
OO
VO
CJ
§
O
in
\o
^o
o
—
4)
x:
4^
4)
,_^
>,
C
—4
c_
a> ^>
T3 O
0) !_
JJ 0
IB rH
a> o
O CJ
rH
CO
X
CO
*_ ^
§§
O O
O O
*
o
o
o
1
—
c\i o
vO O
o o
0 0
o
O» JT
in —
C\J ZT
CJ VO
CM vO
— CJ
8||
211
c_ £ t_
CO *> O
O> rH
o e 2
-i .3 o
rH O CO
>,3- C
o « at
o CM a.
c
o
£
^f
1
O
o 7
—in
• i
o o
—
1 0\
1 c- Cd
Cd
CM — m
CO
en
o
— o
in •
o o
• 1
o t-
in o
=t o
=T O O
O O 0
CM
— CD CM
o\ t— in
m — cj
§
CD S
4-> (-
CO co
rH O
ra o
s: t.
*J -0
J= ><
a j=
x j-> 01
CO fl> CO C
S rH 8 t,
JJ X !_,
0) 4-> CD I—I
a> o o co
JJ CM rH C, O
rH CO >. J-> C
S— o c «
CO >>< PO
i= -3
a. a.
1 T-
Cd .
in oo
CD
0 rH
O O
in -H
1 0
II
CM —
st a-
CD t—
*~
CO
3
8
c
CO CD
rH C
1 I
CO CO
i: i-
"c
SO)
C rH
C — >,
g — 1 4->
CO C 8
O 0) -H
k 00 O
Z
CO
CD
O
C
CO
I
3
CO
>•.
*-4
rH
n
3
O
CO
J
0
rH
[_
O
n_
2
0
H
X
I
,_(
o
co
^
CL
>
—
"
X
68
-------
for fish in an aquatic system with sediments. Spencer and Cliath (1983)
measured the vapor pressure of six pesticides at various tempertures by the
gas saturation and Knudsen methods. Smith (1983) measured Henry's constant
for five organic chemicals by three different methods. Swann (1983)
measured solubilities, KOC, and KQW of 14 organic chemicals using reverse
phase high-performance liquid chromatography. Wasik (1983) measured vapor
pressure, solubility and KQW of 15 organic chemicals using a generator
column and high performance liquid chromatography. Yalkowsky (1983)
reported solubility, KQW, and melting points for 162'aromatic compounds, and
developed the following relationship
log Sm = -0.994 log KQW - 0.01 MP + 0.323 (3.24)
where Sm is the solubility (M/l), and MP is the melting point (°C).
Miller et al. (1985) presented an equation relating KQW and solubility
log KQW = [(Y1 - Y2)/Y1]log Cs + (X2 - X^/Y.,) (3.25)
where X1, Xp, Y. and Y2 are functions of molar volume. Miller measured
solubilities and K^,, for 100 chemicals.
3.7 SORPTION
The sorption of toxicants to suspended particulates and bed sediments
is a significant transfer mechanism. Partitioning of a chemical between
particulate matter and the dissolved phase is not a transformation pathway;
it only relates the concentration of dissolved and sorbed states of the
chemical. The partitioning table in the appendix contains octanol-water
partion coefficients (KQW) and sediment-water partition coefficient
values. The summary of KQW values is given in Table 3.07.
The octanol/water partition coefficient, KQW, is a measure of the
solubility of a chemical in water. The less soluble a chemical is in water,
the more likely it is to sorb to the surfaces of sediments or
microorganisms.
The laboratory procedure for measuring KQW is (Lyman, I982a):
1. Chemical is added to a mixture of pure octanol (a nonpolar solvent)
and pure water (a polar solvent). The volume ratio of octanol and
water is set at the estimated KQW.
2. Mixture is agitated until equilibrium is reached.
3. Mixture is centrifuged to separate the two phases. The phases are
analyzed for the chemical.
4. KQW is the ratio of the chemical concentration in the octanol phase
to chemical concentration in the water phase, and has no units.
The logarithm of KQW has been measured from -3 to +7.
69
-------
The Langmuir isotherm derives from the kinetic equation for sorption-
desorption:
TABLE 3.07 SUMMARY TABLE OF PARTITIONING DATA
log KQW
Pesticides
Carbofuran 1. 60
DDT 4.89-6.9
Parathion 3.81
PCBs
Aroclor 1248 5.75-6.11
Halogenated aliphatic hydrocarbons
Chloroform 1.90-1.97
Halogenated ethers
2-Chloroethyl vinyl ether 1.28
Monocyclic aromatics
2,4-Dimethylphenol 2.42-2.50
Pentachlorophenol 5.01
Phthalate esters
Bis(2-ethylhexyl)phthalate 8.73
Polycyclic aromatic hydrocarbons
Anthracene 4.34-4.63
Benzo[a]pyrene 4.05-6.04
Nitrosamines & Miscellaneous
Benzidine 1.36-1.81
Dimethyl nitrosamine 0.06
dc/dt - -K.,0 Ccpc - cp] + K2cp (3.26)
where c is the concentration of dissolved toxicant, c is the concentration
of particulate toxicant, c is the maximum adsorptive concentration of the
solids, and K1 and Kp are the adsorption and desorption rate constants,
respectively. A substitution can be made for c and c . If m is the
concentration of solids, r is the ratio of adsorbed toxicant to solids by
mass, and r is the maximum adsorptive capacity of the solids, then
\s
70
-------
dc/dt = -K^m [rc - r] + K2rm (3.27)
At steady-state, equation (3.27) reduces to the famous Langmuir Isotherm In
which the amount adsorbed is linear at low dissolved toxicant concentrations
but gradually becomes saturated at the maximum value (rc) at high dissolved
concentrati ons .
(3-28)
Generally, the adsorption capacity of sedimentals inversely related to
particle size: clays > silts > sands. Sorption of organic chemicals is
also a function of the organic content of the sediment, as measured by KQC,
and silts are most likely to have the highest organic content.
mass of toxicant sorbed/mass of organic carbon in sediment
oc ~ toxicant concentration in dissolved phase
At low concentrations, equation (3.15) reduces to
r = Kp c (3.29)
where K = K r /Kp. The partition coefficient is K for small
concentrations of dissolved toxicant, where the units are £/kg.
Sometimes a Freundlich isotherm is inferred from empirical data. The
function is of the form
r = K c1/n (3.30)
where n is usually greater than 1. In dilute solutions, when n approaches
1, the Freundlich coefficient, K, is the partition coefficient, K
(O'Connor, 1980).
The partition coefficient is derived from
do (3.3D
dt~ k1 C " k2 cp
where k., is the adsorption rate constant and kg is the desorption rate
constant.
The total concentration of toxicant, CT, is
CT = c + Cp = fdcT + fpcT (3.32)
where fd and f are the dissolved and particulate fractions, respectively:
fd - C./CT ' 1/(1 + Kpm) (3*33)
ft _ / n if _. f f * . if _^ \ / *1
fp = °p» T = V ° P } (3
71
-------
and the ratio of the reaction rates is related by
k. c Km
1 p» p
where the » subscripts indicate steady-state.
From kinetics experiments where dissolved and particulate
concentrations are monitored over time, the ratio of steady-state
concentrations can be read from the graph (Figure 3.06).
(3.35)
CT
CP
TIME (hr)
Figure 3.06. Dissolved and particulate toxicant as a function of time.
Sorption reactions usually reach chemical equilibrium quickly, and the
kinetic relationships can often be assumed to be at steady-state. This is
sometimes referred to as the "local equilibrium" assumption, when the
kinetics of adsorption and desorption are rapid relative to other kinetic
and transport processes in the system.
Lu (1977) used radio-labeled vinyl chloride, benzidine, and
benzo[a]pyrene for fate analysis in a microcosm ecosystem. Photolytic
72
-------
degradation of benzidine and benzo[a]pyrene was measured, and KQW was
reported.
Chiou et al. (1979) reported partition coefficients for 15 compounds,
and related them to solubility (uM) by
log Kp - 4.01 - 0.557 log S (3.36)
Karickoff (1979) investigated the sorption of ten organic chemicals that
have varying solubilities. KQW was measured for all chemicals, and
partition coefficients were measured for pyrene and methoxychlor.
Schwarzenbach and Westall (1981) correlated KQW with the partition
coefficient but the slopes and intercepts varied from one sediment sample to
another. Karickhoff et al. (1979) were the first investigators to report
the dependence of the sediment-water partition coefficient on the fraction
of organic carbon in the solid phase, indicating a solubility or solution
phenomenon rather than true adsorption. O'Connor and Connolly (1980)
investigated the effects of solids concentration on the partition
coefficient. Chemicals included were kepone, heptachlor, DDT, dieldrin,
PCB, and lindane. Partitioning varied inversely with solids concentration
for sediment data.
DiToro et al. (1982) observed that PCB adsorption was a function of
sediment solids concentration in Saginaw Bay, Lake Huron, Michigan.
Reversibility of adsorption and desorption was investigated, and a
resistant-reversible model was developed. DiToro and Horzempa (1982) tested
the resistant-reversible model developed for PCB with atrazine, picloram,
and 2,4,5-T. The model "provides additional insight into the influence of
kinetics, sediment type, and aqueous-phase modifications since it is
possible to observe the effects on each of the components individually."
Jaffe and Ferrara (1983) reported partial pressure, solubility, Henry's
constant, and KQW for ten organic compounds as part of a comparison between
a kinetics approach and an equilibrium approach in toxics modeling. Lyman
(1982a) presented KQW data on 92 organic chemicals and gave estimation
methods based on the fragment method, solubility, and activity coefficients
(Figure 3.07).
3.8 BIOCONCENTRATION
Bioconcentration of toxicants is defined as the direct uptake of
aqueous toxicant through the gills and epithelial tissues of aquatic
organisms. This fate process is of interest because it helps to predict
human exposure to the toxicant in food items, particularly fish.
Bioconcentration is part of the greater picture of bioaccumulation and
biomagnification that includes food chain effects. Bioaccumulation refers
to uptake of the toxicant by the fish from a number of different sources
including bioconcentration from the water and biouptake from various food
items (prey) or sediment ingestion. Biomagnification refers to the process
whereby bioaccumulation increases with each step on the trophic level.
Bioconcentration experiments measure the net bioconcentration effect
after x days, having reached equilibrium conditions, by measuring the
73
-------
h-
z
o
UJ °*
o w
Q
LANGMUIR
ISOTHERM
DISSOLVED CONC.
KP as a
f(foc>
^-JKOC
WT. FRACTION ORGANIC-C
ON SOLID PHASE (foc)
ss
Koc as a
f(Kow)
0 LOG KQW
OCTANOL-H20
PART. COEFF.
Figure 3.07. Determination of partitioning rate constants,
toxicant concentration in the test organism. The BCF (bioconcentration
factor) is the ratio of the concentration in the organism to the
concentration in the water.
74
-------
The BCF derives from a kinetic expression relating the water toxicant
concentration and organism mass:
dF/dt = e^C/B - k2F (3.37)
in which
e = efficiency of toxic adsorption at the gill
k1 = (£ filtered/kg organism-day) (kg organism/!!,)
kp = depuration rate constant including excretion and clearance of
metabolites, day"
C = dissolved toxicant, yg/Jl
B = organism biomass, kg/&, and
F = organism toxicant residue (whole body), ug/kg.
The steady-state solution is
F = ek.,C/k2B = (BCF)(C) (3.38)
where BCF has units of (ug/kg)/(yg/il). Bioconcentration is analagous to
sorption, which was discussed previously. Organic chemicals tend to
partition into the fatty tissue of fish and other aquatic organisms, and BCF
is analagous to the sediment-water partition coefficient, K .
Bioconcentration also can be measured in algae and higher plants, where
uptake occurs by adsorption to the cell surfaces or sorption into the
tissues. Several studies have improved and provided more detail on the
simple bioconcentration model using pharmacokinetic or bioenergetic
approaches (Norstrom et al. 1976; Blau et al. 1975; Jensen et al. 1982;
Spacie and Hamelink, 1983; Mackay, 1982; Mackay and Hughes, 198M; Hawker and
Connell, 1985; and Suarez et al. 1987).
Many of the chemicals of interest are hydrophobia (lipophilic), which
makes them more prone to bioconcentration. Several investigators (Kenaga
and Goring, 1980; Veith, 1980; Bysshe, 1982; Oliver and Niimi, 1983)
observed strong correlations between the octanol/water partition coefficient
and the BCF. (The octanol/water partitioning coefficient data, K , are
compiled in the Partitioning table in the appendix.) Concentration of
lipophilic toxicants in biological tissues is expected because the lipid
concentration in cells is much higher than that in the water column.
Because the chemical is more easily dissolved in a nonpolar solvent (e.g.,
lipids), it will seek out biological tissues because it does not dissolve
well in polar solvents (e.g., water).
The information presented in the bioconcentration table in the appendix
includes only direct uptake of the toxicant from the dissolved phase; a
summary of bioconcentration factors is presented in Table 3.08. The
tabulation does not include food chain bioaccumulation, where the prey is
contaminated and another route of exposure to the test organism is through
its food. Biomagnification also includes bioconcentration:
biomagnification is that phenomenon in which the toxicant body burden
increases as one moves up the food chain from primary producers to top
predator.
75
-------
Bioconcentration experiments, per se, do not measure the metabolism or
detoxification of the chemical. Chemicals can be metabolized to more or
less toxic products that may have different depuration characteristics. The
TABLE 3.08 SUMMARY TABLE OF BIOCONCENTRATION DATA
Bioconcentration Factor ( ..—)
Range of Values yg
Pesticides
Carbofuran 0
DDT 5900-85,000
Parathion 335
PCBs
Aroclor 1248 72,950
Halogenated aliphatic hydrocarbons
Chloroform 6
Halogenated ethers
2-Chloroethyl vinyl ether N/A
Monocyclic aromatics
2,H-Dimethylphenol 150
Pentachlorophenol 16-900
Phthalate esters
Bis(2-ethylhexyl)phthalate 20-13,600
Polycyclic aromatic hydrocarbons
Anthracene 917
Benzo[a]pyrene 920-13^,248
Nitrosamines & Miscellaneous
Benzidine 55-2617
Dimethyl nitrosamine N/A
bioconcentration experiment only measures the final body burden at
quilibrium (although interim data that were used to determine when
equilibrium was reached may be available). The fact that a chemical
bioaccumulates at all is an indication that it resists biodegradation and is
somewhat "biologically hard" or "non-labile."
76
-------
Lu (1977) used radio-labeled vinyl chloride, benzidine, and
benzo[a]pyrene for fate analysis in a microcosm ecosystem. Photolytic
degradation of benzidine and benzo[a]pyrene was measured, and KQW was
reported. Bioconcentration in algae (Oedogonium cardiacutn) , fish (Gambusia
affinis), daphnia (Daphnia magna), mosquito larvae (Culex pipiens
quinquefasclatus) and snails (Physa sp.) was measured. Vinyl chloride did
not bioaccumulate; benzo[a]pyrene and benzidine bioaccumulation were closely
related to their KQW.
Glooschenko (1979) measured bioconcentration of chlordane in the alga
Scenedesmus quadricauda. Chlordane stimulated respiration and reproduction
at 1 to 100 yg/J, during the 12-day experiment. Kenaga and Goring (1980)
compiled solubility, KQC, KQW and bioconcentration data for 170 organic
compounds. They found that bioconcentration data for daphnia were generally
within an order of magnitude of bioconcentration data for fish. Bivariate
equations were developed relating all the variables. Veith (1980) measured
solubility in distilled water and the KQW of 28 organic chemicals as part of
a study to estimate the bioconcentration in fish from these physical
parameters. Bioconcentration experiments were run using bluegill sunfish
(Lepomis macrochirus) for an exposure period of 28 days. The test water was
at pH 7.1 with a hardness of 35 mg/J, CaCOo. The bioconcentration experiment
was followed by a 7 day depuration period. Correlation between the KQW and
bioconcentration was observed as:
log BCF = 0.76 log KQW - 0.23 (3.39)
which has an r of 0.907 for 8H data.
Bysshe (1982) gave many bioconcentration data for organic compounds,
and included Veith's equation for estimating bioconcentration from KQW, and
Kenaga and Goring's equation for estimating bioconcentration from
solubility. Schnoor (1982) calculated bioconcentration factors from field
data for six pesticides and PCB from field data for fish. The
bioconcentration factors were normalized based on fish oil (or lipid
content) and correlated with KQW. Virtanen and Hattula (1982) tested the
environmental fate of 2,4,6-trichlorophenol in a microcosm ecosystem.
Bioconcentration was measured in waterweed (Elodea sp.), algae (Oedogonium
sp.), guppy (Poecllia reticulatus), sowbug (Asellus aquaticus), snail
(Lymnea stagnalis), and emergent macrophyte (Echlnodorus sp.) after 36 days
of exposure.
Ghisalba (1983) compiled bioconcentration data for many organic
compounds as part of a project to evaluate the biodegradability of these
compounds. McCall (1983) reported solubility, vapor pressure, Henry's
constant, and bioconcentration factors for seven organic chemicals as part
of a fate model for fish in an aquatic system with sediments. Oliver and
Niimi (1983) studied bioconcentration in rainbow trout (Salmo gairdneri)
with ten chlorobenzenes. The bioconcentration experiments lasted 119 days
and were maintained at 15°C. For equilibrium conditions, the authors
developed equations relating bioconcentration and KQW:
log BCF = -0.632 + 1.022 log K,
ow
77
-------
for high exposures, and (3.40)
log BCF = -0.869 + 0.997 log KQW
for low exposures. Hexachlorobenzene did not reach equilibrium in the
experiments and was not included in the regression equations.
Banerjee, Sugatt and O'Grady (1984) developed a simple method for
determining the bioconcentration of stable lipophilic compounds.
Bioconcentration for those chemicals tested (pentachlorobenzene, 1,2,3,4-
tetrachlorobenzene, 1,2,3f5-tetrachlorobenzene, 1,4-diiodobenzene), and
predicted BCF based on KQW were in agreement with the test results. The
test organisms were Salmo gairdneri (rainbow trout), Lepomis macrochirus
(bluegill sunfish) and Poecilla reticulata (guppy). Call et al. (1984) used
^ C to study bioconcentration of five pesticides in Pimephales promelas
(fathead minnow). Their study also included LC^Q testing of 23 compounds
(including pesticides and heavy metals] on 8 organisms. Thomann and
Connolly (1984) reported bioconcentration values for PCB in phytoplankton,
Mysis relicta, Alosa pseudoharenqus (alewife), and Salvelinus namycush (lake
trout) calculated from KQW data as part of a food chain model for Lake
Michigan. They determined that uptake of PCB from prey items was a more
important source of contaminant to the top carnivore (lake trout) than
bioconcentration from the dissolved phase through the gill membrane.
3.9 REFERENCES FOR SECTION 3
Bailey, R.E., S.J. Gonsior, and W.L. Rhinehart. 1983. Biodegradation of the
Monochlorobiphenyls and Biphenyl'in River Water. Environmental Science and
Technology, 17(10):617-621.
Banerjee, S., R.H. Sugatt and D.P. O'Grady. 1984. A Simple Method for
Determining Bioconcentration Parameters of Hydrophobia Compounds.
Environmental Science and Technology, 18(2):79-81.
Boyle, T.P., et al. 1980. Degradation of Dentachlorophenol [sic] in
Simulated Lentic Environment. Bulletin of Environmental Contamination and
Toxicology. 24:177-184.
Bysshe, S.E. 1982. Bioconcentration Factor in Aquatic Organisms. In:
Handbook of Chemical Property Estimation Methods, Warren J. Lyman, et al.,
Eds., McGraw-Hill, Inc., New York, 5-1 - 5-30.
Call, D.J., et al. 1984. Toxicological Studies with Herbicides, Selected
EPA Priority Pollutants and Related Chemicals in Aquatic Organisms. EPA-
600/3-83-097, U.S. Environmental Protection Agency, Duluth, MN.
Callahan, M.A., et al. 1979. Water-Related Fate of 129 Priority
Pollutants. EPA-440/4-79-029b, 2 vol., U.S. Environmental Protection
Agency, Washington, D.C.
78
-------
Cartwright, K.J. 1980. Microbial Degradation of Alachlor Using River Die-
Away Studies. University of Iowa , M.S. Thesis, 130 pp.
Chiou, C.T., L.J. Peters, and V.H. Freed. 1979. A Physical Concept of Soil-
Water Equilibria for Nonionic Organic Compounds. Science. 206:831-832.
Dennis, W.H., et al. 1979. Degradation of Diazinon by Sodium Hyochlorite -
Chemistry and Aquatic Toxicity. Environmental Science and Technology,
13(5):594-598.
Di Toro, D.M., et al. 1982. Reversible and Resistant Components of PCB
Adsorption-Desorption: Adsorbent Concentration Effects. Journal of Great
Lakes Research. 8(2):336~349.
Di Toro, D.M. and L.M. Horzempa. 1982. Reversible and Resistant Components
of PCB Adsorption-Desorption: Isotherms. Environmental Science and
Technology, 16(9):594-602.
Fochtman, E.G. 1981. Biodegradation and Carbon Adsorption of Carcinogenic
and Hazardous Organic Compounds. EPA-600/2-81-032, U.S. Environmental
Protection Agency, Cincinnati, OH.
Furukawa, K. 1982. Microbial Degradation of Polychlorinated Biphenyls
(PCBs). Biodegradation and Detoxification of Environmental Pollutants, A.M.
Chakrabarty, ed., CRC Press, pp. 3**-57.
Ghisalba, 0. 1983. Microbial Degradation of Chemical Waste, an Alternative
to Physical Methods of Waste Disposal. Experientia. 39:1247-1257.
Gledhill, W.E., et al. 1980. An Environmental Safety Assessment of Butyl
Benzyl Phthalate. Environmental Science and Technology, 14(3):301~305.
Glooschenko, V., et al. 1979. Bioconcentration of Chlordane by the Green
Alga Scenedesmus quadricauda. Bulletin of Environmental Contamination and
Toxicology. 21:515-520.
Gossett, J.M. and A.H. Lincoff. 1981. Solute-Gas Equilibria in Multi-
Organic Aqueous Systems. Final report, Air Force Office of Scientific
Research, AFOSR-81-0074, 33 pp.
Guthrie, M.A., et al. 1984. Pentachlorophenol Biodegradation — II.
Anaerobic. Water Research. 18(4):451-461.
Hallas, L.E. and M. Alexander. 1983. Microbial Transformation of
Nitroaromatic Compounds in Sewage Effluent. Applied and Environmental
Microbiology. 45(4):1234-1241.
Harris, J.C. 1982a. Rate of Aqueous Photolysis. In: Handbook of Chemical
Property Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-Hill,
Inc., New York, 8-1 - 8-43.
79
-------
Harris, J.C. 19825. Rate of Hydrolysis. In: Handbook of Chemical Property
Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-Hill, Inc., New
York, 7-1 - 7-^8.
Hautala, R.R. 1978. Surfactant Effects on Pesticide Photochemistry in Water
and Soil. EPA-600/3-78-060, U.S. Environmental Protection Agency, Athens,
GA, 80 pp.
Jaffe, P.R. and R.A. Ferrara. 1983. Desorption Kinetics in Modeling of
Toxic Chemicals. Journal of the Environmental Engineering Division, ASCE.
109(4):859-867.
Johnson, B.T. and M.A. Heitkamp. 1984. Environmental and Chemical Factors
Influencing the Biodegradation of Phthalic Acid Esters in Freshwater
Sediments, Environmental Pollution. (Series B), 8:101-118.
Karickhoff, S.W., D.S. Brown and T.A. Scott. 1979. Sorption of Hydrophobia
Pollutants on Natural Sediments. Water Research. 13(3):24l-248.
Kenaga, E.E. and C.A.I. Goring. 1980. Relationship Between Water
Solubility, Soil Sorption, Octanol-Water Partitioning, and Concentration of
Chemicals in Biota. Aquatic Toxicology. (ASTM STP 707):78-115.
Khan, S.U. 1978. Kinetics of Hydrolysis of Atrazine in Aqueous Fulvic Acid
Solution. Pesticide Science. 9:39-43.
Kilbane, J.J., et al. 1982. Biodegradation of 2,4,5-Trichlorophenoxyacetic
Acid by a Pure Culture of Pseudomonas cepacia. Applied and Environmental
Microbiology. 44(1):72-78.
Knowlton, M.F. and J.N. Huckins. 1983. Fate of Radiolabeled Sodium
Pentachlorophenate in Littoral Microcosms. Bulletin of Environmental
Contamination and Toxicology. 30:206-213.
Koshitani, J., et al. 1982. Kinetics of Oxidation of Anthracene by Use of
Copper(II) Chloro Complexes in a Mixture of Acetic Acid and Water. Journal
of Organic Chemistry. 47:2879-2882.
Kuo, C.H. and H.S. Soong. 1984. Oxidation of Benzene by Ozone in Aqueous
Solutions. The Chemical Engineering Journal. 28:163-171.
Lemley, A.T. and W.Z. Zhong. 1983. Kinetics of Aqueous Base and Acid
Hydrolysis of Aldicarb, Aldicarb Sulfoxide and Aldicarb Sulfone. Journal of
Environmental Science and Health. B18(2):189-206.
Liu, D., et al. 198la. Determination of the Biodegradability of Organic
Compounds. Environmental Science and Technology, 15(7):788-793.
Liu, D., K. Thomson and W.M.J. Strachan. I98lb. Biodegradation of
Pentachlorophenol in a Simulated Aquatic Environment. Bulletin of
Environmental Contamination and Toxicology. 26:85-90.
80
-------
Lu, P.Y., et al. 1977. The Environmental Fate of Three Carcinogens:
Benzo[a]pyrene, Benzidine, and Vinyl Chloride Evaluated in Laboratory Model
Ecosystems. Archives of Environmental Contamination and Toxicology. 6:129-
142.
Lyman, W.J. 1982a. Octanol/Water Partition Coefficient, In: Handbook of
Chemical Property Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-
Hill, Inc., New York, 1-1 - 1-54.
Lyman, W.J. 1982b. Solubility in Water, In: Handbook of Chemical Property
Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-Hill, Inc., New
York, 2-1 - 2-52.
Mabey, W.R., J.H. Smith, R.T. Podoll, H.L. Johnson, T. Mill, T.W. Chou, J.
Gates, I.W. Partridge, H. Jaber, and D. Vandenberg, 1982. Aquatic Fate
Process Data for Organic Priority Pollutants. SRI International. EPA
Report No. 440/4-81-014.
Mackay, D., et al. 1982. Volatilization of Organic Pollutants from Water,
EPA-600/3-82-019, U.S. Environmental Protection Agency, Athens, GA, 202 pp.
Matter-Muller, C., W. Guj er and W. Giger. 1981. Transfer of Volatile
Substances from Water to the Atmosphere. Water Research. 15:1271-1279.
McCall, P.J., et al. 1983. Estimation of Environmental Partitioning or
Organic Chemicals in Model Ecosystems. Residue Reviews. 85:231-244.
Mill, T., et al. 1983. Laboratory Protocols for Evaluating the Fate of
Organic Chemicals in Air and Water. EPA-600/3-82-022, U.S. Environmental
Protection Agency, Athens, GA, 329 pp.
Miller, M.M., et al. 1985. Relationships between Octanol-Water Partition
Coefficient and Aqueous Solubility. Environmental Science and Technology,
19(6):522-529.
Monnig, E., et al. 1980. Treatability Studies of Pesticide Manufacturing
Wastewaters: Carbaryl. EPA-600/2-80-077a, U.S. Environmental Protection
Agency, Research Triangle Park, NC, 36 pp.
Muir, D.C.G. and A.L. Yarechewski. 1982. Degradation of Terbutryn in
Sediments and Water Under Various Redox Conditions. Journal of
Environmental Science and Health. B17(4):363-380.
Neely, W.B. 1985. Hydrolysis, In: Environmental Exposure from Chemicals,
W. Brock Neely and Gary E. Blau, Eds., CRC Press, Inc., Boca Raton, FL, pp.
157-173.
Nesbitt, H.J. and J.R. Watson. 1980. Degradation of the Herbicide 2,4-D in
River Water — I. Description of Study Area and Survey of Rate Determining
Factors. Water Research. 14:1683-1688.
81
-------
Nesbitt, H.J. and J.R. Watson. 1980. Degradation of the Herbicide 2,4-D in
River Water — II. The Role of Suspended Sediment, Nutrients and Water
Temperature. Water Research. 14:1689-1694.
Norstrom, R.J. et al. 1976. A Bioenergetics-Based Model for Pollutant
Accumulation by Fish. Simulation ofPCB and Methylmercury Residue Levels in
Ottawa River Yellow Perch. J. Fish. Res. Board Can. 33:248-267.
O'Connor, D.J. 1980. Physical Transfer Processes, In: Modeling of Toxic
Substances in Natural Water Systems, Manhattan College, New York, NY.
O'Connor, D.J., and J.P. Connolly. 1980. The Effect of Concentration of
Adsorbing Solids on the Partition Coefficient. Water Research. 14:1517-
1523.
Oliver, B.C., and A.J. Niimi. 1983. Bioconcentration of Chlorobenzenes from
Water by Rainbow Trout: Correlations with Partition Coefficients and
Environmental Residues. Environmental Science and Technology, 17(5):287~
291.
Papanastasiou, A.C. and W.J. Maier. 1982. Kinetics of Biodegradation of
2,4-Dichlorophenoxyacetate in the Presence of Glucose. Biotechnology and
Bl©engineering. 24:2001-2011.
Paris, D.F., et al. 1981. Second-Order Model to Predict Microbial
Degradation of Organic Compounds Innatural Waters. Applied and
Environmental Microbiology. 41(3):603~609.
Petrasek, A.C., et al. 1983. Fate of Toxic Organic Compounds in Wastewater
Treatment Plants. Journal of the Water Pollution Control Federation.
55(10):1286-1296.
Pignatello, J.J., et al. 1983. Biodegradation and Photolysis of
Pentachlorophenol in Artificial Freshwater Streams. Applied and
Environmental Microbiology. 46(5):1024-1031.
Que Hee, S.S. and R.G. Sutherland. 1979. Vapor and Liquid Phase Photolysis
of the N-Butyl Ester of 2,4-Dichlorophenoxyacetic Acid. Archives of
Environmental Contamination and Toxicology. 8:247-254.
Saeger, V.W., et al. 1979. Environmental Fate of Selected Phosphate
Esters. Environmental Science and Technology, 13(7):840-844.
Schnoor, J.L. 1982. Field Validation of Water Quality Criteria for
Hydrophobia Pollutants. ASTM Special Technical Publications. 766:302-315.
Schwarzenbach, R.P. and J. Westall. 1981. Transport of Nonpolar Organic
Compounds from Surface Water to Groundwater. Laboratory Sorption Studies.
Environmental Science and Technology, 15(11):1360-1367.
82
-------
Scow, K.M. 1982. Rate of Bi©degradation, In: Handbook of Chemical Property
Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-Hill, Inc., New
York, 9-1 - 9-85.
Sharom, M.S., et al. 1980. Persistence of 12 Insecticides in Water. Water
Research. 14:1089-1093.
Sharom, M.S. and J.R.W. Miles. 1981. The Degradation of Parathion and DDT
in Aqueous Systems Containing Organic Additives. Journal of Environmental
Science and Health. B16(6):703~711.
Smith, J.H., D. Mackay and C.W.K. Ng. 1983. Volatilization of Pesticides
from Water. Residue Reviews. 85:73~88.
Spacie, A. and J.L. Hamelink. 1983. Alternative Models for Describing the
Bioconcentration of Organics in Fish. Environmental Toxicology and
Chemistry. 1:309-320.
Spencer, W.F. and M.M. Cliath. 1983. Measurement of Pesticide Vapor
Pressures. Residue Reviews. 85:57-71.
Stumm, W. and J.J. Morgan. 1981. Aquatic Chemistry: An Introduction
Emphasizing Chemical Equilibria in Natural Waters, John Wiley & Sons, New
York. 473 pp.
Suarez, L.A., et al. 1987. Gets, A Simulation Model for Dynamic
Bioaccumulation of Nonpolar Organics by Gill Exchange: A User's Guide.
EPA/600-S3-86-057. U.S. Environmental Protection Agency, Athens, GA.
Swann, R.L., et al. 1983. A Rapid Method for the Estimation of the
Environmental Parameters Octanol/Water Partition Coefficient, Soil Sorption
Constant, Water to Air Ratio, and Water Solubility. Residue Reviews. 85:17~
28.
Tabak, H.H., et al. 1981. Biodegradability Studies with Organic Priority
Pollutant Compounds. Journal of the Water Pollution Control Federation.
53(10):1503-1518.
Thomann, R.V. and J.P. Connolly. 1984. Model of PCB in the Lake Michigan
Lake Trout Food Chain. Environmental Science and Technology, 18(2):65~71.
Thomas, R.G. 1982. Volatilization from Water, In: Handbook of Chemical
Property Estimation Methods, Warren J. Lyman, et al., Ed., McGraw-Hill,
Inc., New York, 15-1 - 15-34.
Veith, G.D., et al. 1980. An Evaluation of Using Partition Coefficients and
Water Solubility to Estimate Bioconcentration Factors for Organic Chemicals
in Fish. Aquatic Toxicology. (ASTM STP 707):116-129.
Virtanen, M.T. and M.L. Hattula. 1982. The Fate of 2,4 6-Trichlorophenol in
an Aquatic Continuous-Flow System. Chemosphere. 11(7):641~649.
83
-------
Walker, W.W. 1984. Development of a Fate/Toxicity Screening Test. EPA-
600/4 -84-07*1, U.S. Environmental Protection Agency, Gulf Breeze, FL, 39 pp.
Ward, C.T. and F. Matsumura. 1978. Fate of 2, 3,7,8-Tetrachlorodibenzo-p-
dioxin (TCDD) in a Model Aquatic Environment. Archives of Environmental
Contamination and Toxicology. 7:349-357.
Wasik, S.P., et al. 1983. Determination of the Vapor Pressure, Aqueous
Solubility, and Octanol/Water Partition Coefficient of Hydrophobia
Substances by Coupled Generator Column/Liquid Chromatographic Methods.
Residue Reviews. 85:29-42.
Wolfe, N.L., et al . 1977a. Kinetics of Chemical Degradation of Malathion in
Water. Environmental Science and Technology, 1 1(1 1 ) :1077~1081 .
Wolfe, N.L., et al . 1977b. Methoxychlor and DDT Degradation in Water:
Rates and Products. Environmental Science and Technology, 1 1 (1 1 ) : 1077-1 081 .
Wolfe, N.L., R.G. Zepp and D.F. Paris. 1978. Carbaryl , Propham and
Chlorpropham: a Comparison of the Rates of Hydrolysis and Photolysis with
the Rate of Biolysis. Water Research. 12:565-571.
Wolfe, N.L., et al. 1982. Transformation Pathways of
Hexachlorocyclopentadiene in the Aquatic Environment. Chemosphere.
Yalkowsky, S.H., R.J. Orr and S.C. Valvani. 1979. Solubility and
Partitioning, 3. The Solubility of Halobenzenes in Water. I & EC
Fundamentals. 18:351-353.
Yalkowsky, S.H., S.C. Valvani and D. Mackay. 1983. Estimation of the
Aqueous Solubility of Some Aromatic Compounds. Residue Reviews. 85:43-55.
Zepp, R.G., et al . 1977. Photochemical Transformation of the DDT and
Methoxychlor Degradation Products, DDE and DMDE, by Sunlight. Archives of
Environmental Contamination and Toxicology. 6:305-31^-
Zepp, R.G. et al. 1984. Dynamics of Pollutant Photoreactions in the
Hydrosphere. Fresenius Z. Anal. Chem. 319:119-125.
Zepp, R.G. et al. 1987. Nitrate-Induced Photooxidation of Trace Organic
Chemicals in Water. Environmental Science and Technology (in press).
84
-------
SECTION 4
REACTIONS OF HEAVY METALS
4.1 INTRODUCTION
"Heavy metals" usually refer to those metals between atomic number 21
(scandium) and atomic number 84 (polonium), which occur either naturally or
from anthropogenic sources in natural waters. These metals are sometimes
toxic to aquatic organisms depending on the concentration and chemical
speciation. The lighter metal aluminum (atomic number 13) and the non-
metals arsenic and selenium (atomic numbers 33 and 34) also are included in
this broad class of pollutants.
Heavy metals differ from toxic organic pollutants in that they
frequently have natural background sources from dissolution of geologic
strata or volcanic activity. In addition, the total metal concentration is
conservative in the environment, so that while the pollutant may change its
chemical speciation, the total remains constant. It cannot be "mineralized"
to innocuous end-products as is often the case with toxic organic
chemicals. Heavy metals are a pollution problem in terms of violations of
water quality standards. Thus, waste load allocations are needed to
determine the permissable discharges of heavy metals by industries and
municipalities.
Heavy metals frequently adsorb or "bind" to solid surfaces. The
mechanism of sorption or attachment is different than for organic
pollutants. This mechanism has been described as primarily a solution
phenomenon of "likes-dissolving-likes", that is, organic pollutants sorbing
into the organic matrix of sediments or suspended solids. For heavy metals,
the phenomenon is via: 1) physical adsorption to solid surfaces, 2)
chemical sorption or binding by ligands at the solid-water interface, or 3)
ion exchange with an ion at the solid-water interface. In addition, if the
heavy metal is complexed in solution by an organic ligand, it could sorb
into the organic solid phase much like an organic pollutant. The
mathematical formulation for describing the partitioning of the heavy metal
between the solid phase and the aqueous phase is usually called the
"distribution coefficient" for heavy metals, although it may be referred to
as the partition coefficient or the binding constant in some cases.
KD=cR (4-1)
where KD = the distribution coefficient, fc/kg
C = the concentration of the metal in the sorbed phase,
85
-------
C = the concentration of the metal in the dissolved phase, ug/&
M = the concentration of solids, kg/8,
To calculate the ratio of the concentration of the adsorped particulate
phase to the dissolved phase, one needs the solids concentration M to
estimate the important dimensionless number K^M.
KDM = Cp/C (l|.2)
The calculation of the fraction of the total (whole water) concentration in
either the dissolved or the particulate phase is identical to that of
organic chemicals, only the distribution coefficient KD replaces the
partition coefficient K .
In addition to the distribution between the solid and aqueous phase,
one frequently requires knowledge of chemical speciation. Sometimes one
chemical species is known to be much more toxic than another for a given
heavy metal. This is especially important because some States and EPA have
been moving towards "site-specific water quality standards," in which the
chemical speciation will be considered on a site-by-site basis. For
example, a site that is known to have a great deal of naturally occurring;
dissolved organics may not require as stringent of a water quality standard
because the dissolved organic material may complex the heavy metal and
render it non-toxic to biota.
In this Section, we will examine the equilibrium and kinetic reactions
that are characteristic of an important subset of heavy metals (Cd, As, Hg,
Se, Pb, Ba, Zn, Cu). These elements are frequently cited in the literature
as being of concern due to their aquatic toxicity or human
carcinogen!city. In addition, they frequently occur in wastewater
discharges (Cd, Zn, Cu), from coal and fossil fuel combustion (Hg, Pb, Cd,
Zn) and the inter-media transport of atmosphere to water, from leaching of
mine-tailings or agricultural return waters (As, Se, Ba), and from natural
background sources (As, Se, Ba, Cu).
4.2 EQUILIBRIUM AND KINETIC REACTIONS FOR HEAVY METALS
^.2.1 Cadmium
2+
Divalent cadmium ion, Cd , is the predominant species of cadmium found
in surface waters, although organic complexes account for a variable
(frequently significant) percentage of the dissolved concentration. Other
species (CdSO^, CdCOo, CdOH+ and CdCl"1") are present in lesser
?+
concentrations. Cd is usually the dominant (60 - 90$) species in natural
water even in the presence of high concentrations of cadmium-complexing
ligands. This is significant because free cadmium (Cd ) is widely accepted
as playing an important role in aquatic toxicity (Shepard et al., 1980).
Gardiner (197^3) calculated the levels of various species of cadmium in
different samples (Table ^.01).
86
-------
TABLE 1.01 EXTENT OF COMPLEXATION OF CADMIUM IN BOREHOLE WATER,
SETTLED SEWAGE, SEWAGE EFFLUENTS, AND RIVER WATER SAMPLES
Calculated Proportion (/£) as
Sample
No.
1 BH
2 SS
3 SE
4 SE
5 RW
6- RW
7 RW
8 RW
9 RW
10 RW
11 RW
CdOH"1"
1.4
1.8
3.2
3.6
2.8
6.5
4.9
5.7
4.8
3.6
2.6
CdC03
3.9
9.0
15
12
6.1
21
16
18
12
9.7
3.9
CdCl*
1.8
5.3
5.2
6.2
4.6
2.6
6.0
3.8
10
9.2
3.5
CdSOjj
0.6
3.1
2.5
3.0
7.2
2.6
4.3
4.1
5.1
7.7
7.2
Cd
Humic
Complex
0
39
38
37
24
9.3
24
16
12
20
24
Cd2+
92
41
35
38
55
59
44
52
56
51
58
EQ-E
(mV)
1.5
12.3
14.4
12.9
6.0
7.8
13.8
8.6
5.5
8.1
7.9
Observed
Proportion
as Cd2"1"
(*)
89
38
32
29
63
54
35
51
6.5
53
54
Total added cadmium concentration was 1.0 rag 1~1 except for Samples 2-4
(10.3 mg 1 1) and Sample 5 (2 mg l"1)
BH: Borehole water
SS: Filtered Settled Sewage
SE: Filtered Settled Effluent
RW: Filtered River Water
Among the mechanisms by which cadmium is removed from the water column
are precipitation and adsorption or chemisorption on the surface of
solids. Concentrations of cadmium in freshwater are usually lower than the
maximum permitted by the solubility product of the carbonate, which is
probably the least soluble salt in most natural waters. Adsorption,
therefore, would be the most important factor influencing the partitioning
of cadmium between aqueous and solid phases and its transport in a water
course.
Adsorption with river mud samples usually occurs fast and a great
percentage of the equilibrium concentration of the solid phase is achieved
within 2 minutes (Gardiner, 1974a). T.H. Christenson (1984) observed that
equilibrium with regard to sorption of Cd to soil samples was achieved in
approximately 1 hour. The concentration factor (distribution coefficient)
87
-------
for river mud samples varied with types of solid, its state of subdivision,
time of contact, and the concentration of complexing agent (Gardiner,
1974a). Organic materials such as humic acids were the main component of
the mud samples responsible for adsorption of cadmium.
Suzuki et al., (1979) have shown that, in the case of sediments from
the Tama River, adsorption capacity of the organic matter was 95 times the
adsorption capacity of inorganic matter. Suspended solids, especially
organic matter (20mg/l), had seven times more capacity than the aqueous
phase for transport of Cd in the flowing water. Binding or complexing
agents such as alginic and humic acids increased the uptake of cadmium on
kaolinite, whereas EDTA diminished the uptake (Hass and Horowitz, 1986; and
Laxen, 1981). The results suggest that the enhancement of uptake is due to
the formation of an adsorbed organic layer on the clay serving as a solid
phase ligand.
Fristoe and Nelson (1983) applied a chemical speciation model to Cd in
activated sludge. They observed that adsorption of cadmium by bacterial
solids and cadmium complexation by dissolved organics were both pH
dependent. Soluble cadmium speciation was dominated by the free Cd ion at
pH below 6, by cadmium-organic complex at pH 6 and pH 7 and by inorganic
species at pH 8 and pH 9. Cadmium that was adsorbed to bacterial cells
increased greatly with pH, from nearly 30% at pH 4 to 90% at pH 9.
Cadmium is extremely toxic to fish even at low concentrations of 5 to
10 ug/fc. Sunda et al. (1978), in their study on the effect of speciation on
toxicity of cadmium to Grass shrimp (Palaemontes pugia), found that
complexation by NTA and chloride greatly reduced cadmium toxicity. An LC,-Q
of 4xTO~^ M of Cd2+ were reported. The dynamics of Cd and uptake into
different organs of Aondonta cygnea L^ was studied by Balogh and Solanki
(1984). The rate and amount of bioaccumulation of Cd in the kidney was
higher than in other organs. Fayed and Abd-EI-Shafy (1985) found that the
concentration factor for Cd in plants of the Nile River (Eichhornia
crassipes) was approximately 300. The distribution coefficient in sediments
was much higher.
The fate of Cd can be described by the generalized schematic given by
Fontaine (1984) in Figure 4.01. Fontaine's (1984) model includes a number
of ligands or substrates for complexation or binding in both the water
column and the active sediment compartment. It also includes transport by
advection or groundwater inputs or export.
4.2.2 Arsenic
Arsenic can occur in four stable oxidation states in the environment
(+5, +3» 0, -3). It therefore has an unusually complex chemistry. Because
extremely low redox potential conditions are required for -3 states, its
occurence is rare. A list of arsenic species commonly found in
environmental samples is given in Table 4.04.
88
-------
O
y
CO
g
-------
Sergeyeva and Khodakovsky (1969) have used a thermodynamic approach to
calculate the stabilities of the arsenic in different oxidation states in an
aquatic system and plotted an Eh-pH diagram that illustrates clearly the
predominant soluble and solid species. They overlooked the significance of
sulfur and its reaction with arsenic in nature, however. Ferguson and Gavis
(1972) presented a more detailed Eh-pH diagram that takes into account the
influence of sulfur (Figure 4.02). Tables 4.02 and 4.03 show the major
equilibrium constants and sorption (binding) constants for cadmium at
various pH values and sorbents.
0.75 -
0.50 -
0.25 r
§ o
LU
-O.Z5
-0.50 -
-0.75 -
Most Surface
Waters
Most Grouitfd-
Waters
Figure 4.02 The Eh-pH diagram for As at 25°C and one_atmosphere with total
~ ~
arsenic
~1
_
mol L~ and total sulfur 10 mol L
_
3
'
Solid
species are enclosed in parentheses in cross-hatched area,
which indicates solubility less than 10
-5.:
mol £
90
-------
TABLE 4.02 EQUILIBRIUM CONSTANTS FOR CADMIUM
Ligand Log K
OH~ 5.0
10.6
10.0
10.0
Cl" 2.0
2.7
2.1
HCO~ 2.1
3
C02~ 4.1
5
F~ 1 .1
1.5
Br~ 2.17
2.9
I~ 2.15
3.6
2-
SOj 2.3
3.5
S~ -27
Equation and Comments
2+ - •* +
Cd + OH <- CdOH
P + — •>
Cd + 2)H «• Cd(OH)
Cd + 30H~ ^ Cd(OH)~
3
?+ — -> p_
Cd + 40H <- Cd(OH)f
H
Cd2+ + Cl~ + CdCl+
2+ - •»•
Cd + Cl * CdCl
2+ - ->
Cd + 3C1 *- CdCl.
j
Cd2+ + HCO~ + CdHCO^
3 3
2+ 2- ->
Cd + CO^ •*- CdCO.
3 3
Cd + F~ * CdF+
2+ - ->
Cd + 2F <- CdF
2+ - ->• +
Cd + Br •«- CdBr
Cd + 2Bf~ * CdBr2
Cd2+ + 10" * Cdl+
2+ - -*•
Cd + 21 •«- CdI2
2+ 2- ->
|l |l
Cd2+ + 2S02~ * CdS02~
2+ 2-
Cd -»• S * CdS
Reference
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Zirino and
Yamamoto (1972)
Gardiner (1974)a
Felmy, Grivin,
and Jenne (1985)
Felmy, Grivin,
and Jenne (1985)
Felmy, Grivin,
and Jenne (1985)
Felmy, Grivin,
and Jenne (1985)
Felmy, Grivin,
and Jenne (1985)
Felmy, Grivin,
and Jenne (1985)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
Sillen and
Martell (1964)
91
-------
TABLE H.02 (continued)
Ligand
Log K
Equation and Comments
Reference
NTA
EDTA
Glycine
FA
HA
10.00
16.4
4.74
3.9
4.7
6.9
K. Aerogenes 5.16
Polymer
Cd2+ + NTA3 + CdNTA~
2+ 4- •> 2-
Cd + EDTA <- Cd-EDTA
Cd2+ + Gly + Cd-Gly+
Cd2+ + 2G1 Cd(Gly).
Cd2+ + FA £ CdFA(pH=6.5)
Cd2+ + 2HA £ Cd(HA) (1=0)
Cd2+ + L -»• Cd-L (pH=6.8)
Sill en and
Martell (1964)
Sillen arid
Martell (1964)
Sillen arid
Martell (1964)
Sillen arid
Martell (1964)
Sterritt and
Lester (1984)
Stevenson
(1976)
Rudd et al.,
(1984)b
92
-------
TABLE 4.03. CONSTANTS FOR CADIUM ADSORPTION
Adsorbent
Langmulr
Comments
Reference
Ul K ' 'HIU mol'
Humic Acid
(HA)
Kaolin
Illlte
Montmorlllonlte
Bentonite
Fe(OH),
Mn02
Humlc
Sandy Loam
Kaolin
Montmorlllonite
Adsorbent
Sepiolite
Bentonite
330
310
690
575
17
H9
315
0.81)
13
3.5
0.17
D7.7
31.1
5.9
2.2
3.8
1.1
2.0
5.0
8.0
13.0
10.0
11.5
0.6
17.6
10.87
7.85
pH-H.H (Sample A)
pH-i).l) (Sample B)
pH-l.i) (Sample C)
pH-H.H (Sample D)
25 °C, pH-5
Na* form clay
Temp-25eC
pH-8
pH-6
Freundlich
K n
95
2.25
0.65
0.6")
x(ppb), C(ppb)
X(mg/g), C(mg/ml)
Beverldge
and Pickering
(1980)
Farrah et al . ,
(1980)
Oakley et al.,
(1981)
Christenson (1981))
Mlragaya
(1986)
CommentsRef erence
Rybica
Guy et al., (1975)
Adsorbent
Bentonite
Fe(OH),
Mn02
Humlc
Montmorillonite
Sandy Loam
Partition
Coeff, Kd(L/Kg)
1100
1)1100
1500
200
500
50
10
20
HO
200
H50
21 HO
Comments
pH-8
pH-8
pH-8
pH-8
I-0.01M pH-5
I-0.1M pH-5
I-1M pH-5
pH-H
pH-5
pH-6
pH-7
pH-7.7
Reference
Oakley (1981)
Oakley (1981)
Oakley (1981)
Oakley (1981)
Egozy
Christenson (198M)
Christenson (198H)
Christenson (198U)
Christenson (198M)
Christenson (1981))
93
-------
TABLE 4.03 (continued)
Adsorbent Partition Comments
Coeff, Kd(L/Kg)
Loamy Sand 20 pH-1
60 pH-5
225 pH-6
8100 pH-7
3910 pH-7.7
Silica 1000
Kaolin 380
Humlc Acid 20,000
Fish Faecal Matter 200-1000
Plant Material 1000
Langmulr Isotherm
C 1 C
q " Kb b
Reference
Chrlstenson (1981)
Chrlatenson (1981)
Chrlstenson (1981)
Chrlstenson (1981)
Chrlstenson (1981)
J. Gardiner (1971b)
J. Gardiner (1971b)
J. Gardiner (1971b)
J. Gardiner (1971b)
J. Gardiner (1971b)
C • concentration
q • moles solute/unit mass adsorbent
K - bonding constant
b • sorption capacity
Freundllch Isotherm
X - K Cn
K, n • constants
X - mass solute sorbed/unit mass adsorbent
C « Concentration
HA - Humlc Acid
FA - Fulvlc Acid
94
-------
TABLE M.OM ARSENIC SPECIES COMMONLY FOUND IN ENVIRONMENTAL SAMPLES
Species Names Oxidation State
Aso;3
AsO"3
CH3AsO(CH)2
(CH3)2AsOOH
Arsenate
Arsenite
Methanearsonic
Monomethyl Arson! c Acid
Hydroxydimethyl Arsine Oxide
+5
+3
+3
Dimethyl Arsinic Acid
Cacodylic Acid
io Arsine -3
(CHo)2AsH Dimethyl Arsine -3
'""^ •- Trimethyl Arsine -3
Ferric arsenate (pK = 20.2) is stable only at pH < 2.3 and at an Eh
of +0.71* V, and therefore is not normally significant. At high Eh values
encountered in oxygenated waters, arsenic acid species (
2_ o_
H2As01(, HAsO. and AsOj; ) are stable. At very low Eh values arsine (AsH^)
may be formed and is very toxic. The organic arsenicals are stable at
extremely low Eh values. These compounds are unstable with respect to the
organic part of the molecule.
Except for a few oxidation-reduction reactions very little information
exists about kinetic rates of arsenic reactions in solution. Specific rate
constants are unknown. The rate of oxidation of arsenite with 02, for
example, is reported to be very slow at neutral pH, but proceeds measurably
in several days in strong alkaline or acidic solution.
Wagemann (1978) has presented a study in which barium arsenate was
added to water as a solid phase in addition to oxides and sulfldes. Barium
ion effectively limited the dissolved arsenate concentration by the common
ion effect in the pH range 6-9, and soluble arsenate concentrations were
less than 5 ug/X,. Cupric ion and ferric ion activity were controlled by
ferric hydroxide and cupric oxide in oxygenated, surface waters. Tenorite,
a cupric oxide, at pH 6 allowed 3.8 mg/S, of Cu to be soluble which complexed
arsenate to 1.8 mg/Jl. Copper-arsenate complexes can be important in some
natural waters.
At 5 mg/S, of TOC, approximately UO to 50% of total dissolved metals
were present as metal-fulvic acid complex (Reuter and Perdue, 1977).
Equilibrium calculations indicated that the formation of metal-organic
complexes occurred largely at the expense of inorganic metal complexes, and
95
-------
the free metal ion concentration changed little unless the concentration was
very high. The amount of dissolved organic matter in most freshwater is
sufficiently low that metal-organic complexes are not important. Because
arsenic exists only as an anion, its complexation with humic and fulvic acid
could be formed only via a metal-organic acid complex.
Braman and Foreback (1973) found methylarsenic and dimethylarsenic acid
in a wide range of natural waters. In this study, lakes and ponds had a
higher metnlyarsenic acid content than rivers. A large fraction of arsenic
in bird egg shell and sea-shells was found to be methylarsenic acid or
dimethylarsenic acid. The reported toxicity of As (III) is approximately 25
times greater than that of dimethylarsenic acid. Dimethylarsenic is the
more prevalent form of organo-arsenic compounds in natural water.
Methylation may be serving as a means of detoxification in organisms.
Arsenic is removed from solution by adsorption onto clay or
coprecipitation into metal ion precipitates. Anderson et al. (1976) studied
arsenate adsorption on amorphous aluminum hydroxide as a model system for
aqueous anion adsorption on oxide surfaces. The adsorption of arsenite and
arsenate on iron hydroxide obeyed a Langmuir isotherm at low concentrations
and low ionic strength (Pierce and Moore, 1982). The adsorption on oxide
including aluminum and iron was pH dependent. Arsenite adsorbed on
manganese oxide was oxidized to arsenate. Arsenate forms an insoluble salt
with Mn , Ni2+ or other alkaline cations. Takamatsu et al. (1985) state
that the high concentration of arsenic in a manganese concretion from Lake
Biwa is evidence for the accumulation of As into Mn - rich sediments.
A cycle for arsenic in a stratified lake (Ferguson and Gavis, 1972) is
illustrated in Figure *».03. Tables ^4.05 and 4.06 show the equilibrium
constants for arsenate and Langmuir sorption constants on iron and aluminum
hydroxides.
4.2.3 Mercury
Mercury occurs in nature in three oxidation states (0, +1, +2). The
presence of the predominant species is dependent on the redox potential and
pH of the environment, the existence of anions and other ligands that might
form complexes with mercury.
Mercury is released into the air by outgassing of soil, by
transpiration and decay of vegetation, and by volatilization and combustion
processes. Most mercury is adsorbed onto atmospheric particulate matter.
This is removed from air by dry fallout and rainout. Humic material forms
complexes that are adsorbed onto alluvium, and only a small soluble fraction
is taken up by biota. Small clay particles and rainout particles are
distributed throughout the oceans because of slow settling velocities.
Pelagic organisms agglomerate the mercury bearing clay particles, thus
promoting sedimentation and affecting the fate of mercury in mid-oceanic
chain. Another fate process is the uptake of dissolved mercury by
phytoplankton and algae.
96
-------
z
o
liJ
z
T3
01
•H
4J
CO
U
•H
C
(U
CO
l-i
03
o
-------
TABLE 4.05. EQUILIBRIUM CONSTANTS FOR ARSENIC
Llgand
H*
Solids In
Equilibrium
A1A.O,
Ba3(Ag01|)2
Ca3(g01()2)
Cd3(A3Oy)2
C03(AgOy)2
Cu3(Ag014)2
Cr AgOij
Fe AgO,,
Mg3(AgOi,)2
Nl3(AgOl4)2
Pb(AgO,,)2
Sr3(Ag01))2
Zn(Ag01))2
Mn,(A o,).
Log K
20.5
18.5
11.0
31.6
25.1
13.1
Solubility
Product
Log Ksp
-15.8
-50.11
-18.16
-32.6
-28.1
-35.1
-20.1
-20.2
-19.6
-25.5
-31.1
-18.1
-27.1
-28.7
Equation and Comments Reference
As°.ij~ * 3H+ + HjAsO,, Servgeyera & Khodakovsky (1969)
AsOjj" * 2H* * H2AsOJ| Servgeyera & Khodakovsky (1969)
3— + *• ?—
AsO^ + H + H AsOj. Servgeyera & Khodakovsky (1969)
AsO^~ * 3H* * H3As03 Servgeyera & Khodakovsky (1969)
AsO^" * 2H* * HgAsO. Servgeyera & Khodakovsky (1969)
Aso|" * H* + H As03" Felmy et al (1985)
Comment Reference
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
Frankenthal (1963)
98
-------
TABLE 4.06. CONSTANTS FOR ARSENATE ADSORPTION
Adsorbent
Al (OH) 3
Suspension
Fe(OH),
Fe(OH)3
Langmuir
im mot\ „(•
bl Kg J KU
1600
1178
1179
838
680
501
1530
1100
850
151
311
226
136
182
157
163
190
503
513
188
117
117
O5 L 1
0 molj
1.23
1.19
1.78
1.31
0.72
0.11
11.6
25.2
32.3
11.1
11.9
11.0
11.5
6.6
Arsenlte -
9.7
15.2
18.3
22
23.2
20.2
15.1
5.5
Comments
pH-5
pH-6
pH-7
pH-8
pH-8.5
pH-9
pH-1
pH»5
pH-6
pH»7
pH-7.5
pH-8
pH-9
pH-9.9
Adsorption
pH-1
pH-5
pH-5.7
pH-6.1
pH-7.0
pH-8.0
pH-8.8
pH-9.0
Reference
Anderson et al . ,
Anderson et al .
Anderson et al .
Anderson et al .
Anderson et al .
Anderson et al.
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce i Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce & Moore
Pierce 4 Moore
Pierce & Moore
(1976)
(1976)
(1976)
(1976)
(1976)
(1976)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
(1982)
99
-------
A typical Eh-pH diagram for the predominance of mercury species is
presented in the paper by Gavis and Ferguson (1972) in which only the
inorganic system is considered. In natural water systems, where pH is
likely to fall between 6 and 9 and the measured electrode potential (Eh)
values seldom are higher than 0.5v, metallic mercury Hg° and HgS are the
species most likely to enter into equilibrium with mercury species in
solution. The Eh-pH diagram for the soluble species in equilibrium with the
solids phase shows that Hg(OH)2 and HgCl2 are the predominant species in
most surface waters. At low redox potentials observed in reducing
sediments, mercury is effectively immobilized by sulfide ion. At extremely
low redox potential and?gH greater than 9, the solubility increases markedly
by the formation of HgS ions. The stability field for aqueous mercury
constructed by Stolzenburg et al. (1986) is shown in Figure 4.04. Bartlett
and Craig (1981) have summarized mercury chemistry over a wide range of
redox conditions within the sediment. Fagerstrom and Jernelov (1972) and
others have reported that the rate or extent of mercury methylation is
increased when sediments are exposed to air, e.g., on dredging or during ebb
tide.
Methylmercury is produced in sediments by bacteria through the
methylation of inorganic mercury (Hg2+) (Spangler et al., 1973). Two types
of methylation are possible: microbial (enzymatic) and chemical (non-
enzymatic by methylcobalamine). They have noted the presence of bacteria
capable of degrading methylmercury to methane and Hg which volatilizes and
escapes into the atmosphere. The rate of methylation increases with
temperature. The rate is higher with suspended material and in the
surficial sediment rather than deep sediment (Jernelov, 1970). The rate is
also higher at low oxygen concentrations. Formation of dimethylmercury is
not favored in acidic environments (Gavis and Ferguson, 1972), and the
amount of dimethylmercury formed is usually several orders of magnitude less
than that of monomethylmercury ion, CHoHg"1". Fagerstrom and Jernelov (1972)
reported the formation of both species in organic sediments at various pH,
with a maximum of dimethylmercury production at pH 9 and a maximum of
methylmercury at pH 6.
Lee et al. (1985) studied the catalytic effect of various metal ions on
the methylation of mercury in the presence of humic acids (HA).
Methylmercury production (in dark reactions during 2-4 day incubations at
30°C) increased with the concentration of Hg ions and fulvic acid as well as
with the addition of metal ions. Metal ions competitively reduced the Hg
bonding with HA, thus freeing it for methylation. The observed catalytic
activity of metal ions followed the order: Fe^+ (Fe ) > Cu + Mn + >
A1^+. The production of methylmercury had a pH optimum of 4 to 4.5.
Bartlett and Craig (1981), from their study of the Mersey Estaury, drew
correlations between total mercury, methylmercury, silt and organic carbon
contents of the sediments. The computer-generated, least-square fitted
lines were:
Total Hg (ng/g) - -148 + 217 methyl Hg (ng/g)
Methyl Hg (ng/g) = - 10.24 + 5.29 organic C ($)
Total Hg (ng/g) = -749 + 623 organic C ($)
100
-------
Total Hg (ng/g)
Methyl Hg (ng/g)
1.20
1.00 -
0.80 -
-388 + H6 silt (%)
- JJ.34 * 0.33 silt
Most surface
water within
this field.
Most ground
water within
this field.
0.60 -
0.80
Figure 4.04 Stability fields for aqueous mercury species at various Eh and
pH values (chloride and sulfur concentrations of 1 mM each were used in
the calculation; common Eh-pH ranges for groundwater are also shown).
The correlation coefficient of the above relations were 0.76, 0.55, 0.77,
0.94 and 0.76, respectively. The greater was the organic or silt content of
the sediment, the higher was the mercury concentration in nanogram per gram
of dry sediment.
101
-------
The proportion of methylmercury to the total amount of mercury in
waters is significant at approximately 30%. The concentration of Hg2+
was -50/K and the remaining 20% were other species (Kudo, 1982). Modeling of
mercury dynamics indicated that mercury in well water is highly unlikely to
be methylated to the toxic methylmercury form (Stolzenburg et al . , 1986).
The stability constants for Hg-fulvic acid complexes (Hg-FA) at pH 3
and pH 5 were reported as log % FA of 4.9 and 5.1, respectively (Cheam and
Gamble, 197*0. A strong complexation between Hg and FA is well documented
(log K = 10-19.7) at pH = 8 (Montoura et al . , 1978).
Hg+2 + FA'2 J Hg-FA(aq) KHg_FA
_ -}
Inoue and Munemori (1979) examined the coprecipitation of Hg (II) with
Iron (III) hydroxide. Mercury is coprecipitated over the whole pH range of
*1 to 12. Flouride does not affect the coprecipitation whereas Cl~ and Br~~
suppress it at low pH depending on the stability of Hg (Il)-halogen
complexes and the halogen ligand concentrations. The Hg(II) species that
coprecipitated was inferred to be Hg(OH)p based on chemical equilibrium
considerations.
Adsorption of both Aretan (2-methoxymethylmercury) and HgClg correlated
well with the distribution of organic carbon and with the cation exchange
capacity (CEC) of soils (Semu et al . , 1986). The lack of such correlation
in the other soils studied suggests other reactions like precipitation may
also be involved in Hg retention by soils in addition to purely adsorptive
process. The affinity of mercury for the sulfhydryl group can bind it to
suspended organic matter, both living (e.g., plankton) and non-living (e.g,,,
peat and humus). In aquatic environments, as organic and inorganic
suspended matter settles, mercury is delivered to the sediment.
The bioconcentration factor (BCF) in fish ranges from 1 CH to 1CP (D.
Gardiner, 1978).
BCp = residue in fish tissue _ mg/kg =
dissolved cone, in water mg/X,
A greater methylation rate would result in higher mercury levels in fish.
Nishimura and Kumagai (1983) reported, based on their survey of Hg pollution
in and around Minamata Bay, that there was a good correlation between Hg
levels of croaker and that of the sediments. A better correlation was
observed between Hg content of croaker and that of zooplankton. A very
close relationship was found between Hg content of zooplankton and suspended
parti culate matter. A pathway of Hg from sediment to fish via suspended
matter ingestion was suggested. Wren et al . (1983) examined the
concentration of 20 elements in .sediments, clams, fish, birds, and
mammals. Mercury was the only element to exhibit biomagnifi cation in both
aquatic and terrestrial food chains. Mercury was the only metal that
accumulated in the muscle tissue with increased age and size of all fish
tested. Wren and MacCrim'mon (1983) in their study at Tadenca Bay and
Tadenca Lake have found clear evidence of bioaccumulation of mercury.
102
-------
Mercury levels in biota in adjacent waters can differ due to different
sediment mercury levels and ambient water quality characteristics.
By using the REDEQL II chemical equlibrium program, Vuceta and Morgan
(1978) studied a hypothetical toxic freshwater system (pE = 12, PCQ =
10~3'5 atm), which contained four major cations (Ca, Mg, K, Na), ni3e trace
metals (Pb, Cu, Ni, Zn, Cd, Co, Hg, Mn, and Fe), eight inorganic
ligands (C0^~, S0^~, Cl", F~, Br~, NH3> P0jj~ and OH~), and a solid surface
with adsorption characteristics of Si02(s). They found that Hg(II) was
present mainly either as chloro-complexes (pH < 7.1) or as hydroxo-complexes
(pH > 7.1). They also modeled the conditions at pH of 7 with the presence
of organic ligands (EDTA, citrate, histine, aspartic acid, and cystine).
From the viewpoint of quantitative ecology of mercury, Fagerstrom and
Jernelov (1972) presented a detailed description about the conversion among
the mercury compounds that included: HgS, Hg°, Hg2+-organic material, Hg2 -
inorganic material of silica type, Hg -inorganic material of ferro-magnetic
type, CH3HgX and (CH3)2Hg.
Huckabee and Goldstein (1976) developed a linear, eight-compartment
model to describe the dynamic redistribution of methylmercury in a pond
ecosystem following a pulse input. The eight compartments were water,
sediment, seston, benthic invertebrates, mosquitofish, bluegill, largemouth
bass, and carp. Using radioactive ^Hg-tagged CH^Hg as tracers, they
found that seston, especially plankton-organic detritus, is the major
reservoir of CHgHg"1" in the system.
Fontaine (1984) has proposed a mercury submodel (Figure 4.05),
incorporating the special features of mercury, along with the generalized
model NONEQUI. Elemental mercury can volatalize under commonly encountered
environmental conditions. The submodel includes as state variables the
species Hg (ionic form), CHoHg"1" (monomethylmercury), and Hg (elemental
mercury). The Hg"1" formed by the disproportion of Hg° and Hg2+ was not
included. Dimethylmercury also was not included in the model as a state
variable because its formation was not favored in acidic conditions and the
amount formed was usually very small. For the purpose of this model, these
reactions were set at equilibrium until better information becomes available
on their kinetics.
A schematic diagram on the cycling of Hg in the environment by
Stolzenburg et al. (1986) is given by Figure M.06. Equilibrium constants
for mercury are summarized in Table 4.07 with sediment/water partition
coefficients (distribution coefficients) in Table 4.08.
4.2.U Selenium
Selenium is one of the most widely distributed minerals in the earth's
crust, usually associated with sulfide minerals. Selenium enters aquatic
environments by natural weathering processes; combustion of fossil fuels;
metal mining, melting and refining; and other industrial activities. Nriagu
and Wong (1983) reported that the Se concentrations within a 30 km radius of
103
-------
(B
u
•H
8
3
U
^
I
V
•o
in
O
0
•i-i
104
-------
0)
I
V
(-1
3
u
1-1
i
00
c
0
f*.
u
(U
8
(-1
60
(I)
CO
§
.C
o
CO
VO
o
t-l
60
105
-------
TABLE 4.07. EQUILIBRIUM CONSTANTS FOR MERCURY
Llgand
OH"
Cl"
F"
Br"
I"
CO2"
S2"
SO2"
Log K
10.1
21.8
21.3
-3.7
6.7
13.2
11.2
15.2
1.01
1.03
1.05
9.1
17.1
19.8
21.1
12.9
23.9
27.7
29.9
16.05
-53
1.34
Equation and Comments
Hg2+ *
Hg2* *
Hg2* *
HgO(s)
Hg2* +
Hg2* +
Hg2+ +
Hg2* *
Hg2* +
Hg2+ *
Hg2+ *
Hg2+ +
Hg2* +
Hg2+ +
Hg2+ *
Hg2* *
OH" + Hg(OH)* (1-0)
20H" * Hg(OH)2 (1-0)
30H" * Hg(OH)"
» H20 » Hg(OH)2
Cl" * HgCl*
2C1" + HgCl2
3C1" + HgCl"
1C1" * HgCl2"
— * -f
2F" + HgF2
3F~ + HgF"
Br" * HgBr*
2Br" + HgBr2
3Br~ * HgBr
1Br~ * HgBi-jj
I" $ Hgl*
Hg2* +21" * HgI2
Hg2* *
Hg2+ +
Hg2t *
Hg2+ +
Hg2+ +
31" $ Hgl'
1)1" $ Hgl2"
CO2" + HgCOj
S2" + HgS
2- *•
SO^ + HgSOi,
Reference
Sillen &
Martell (1971)
Slllen &
Martell (1971)
Rubin (1971)
Rubin (197D
Slllen 4 Martell (1971)
Sillen & Martell (1971)
Slllen 4 Martell (1971)
Slllen & Martell (1971)
Sillen & Martell (1971)
Sillen 4 Martell (1971)
Slllen 4 Martell (1971)
Slllen 4 Martell (1971)
Sillen 4 Martell (1971)
Slllen 4 Martell (1971)
Slllen 4 Martell (1971)
Sillen 4 Martell (1971)
Sillen 4 Martell (1971)
Sillen 4 Martell (1971)
Slllen 4 Martell (1971)
Slllen 4 Martell (1971)
Sillen 4 Martell (1971)
Sillen 4 Martell (1971)
106
-------
TABLE 4.07 (continued)
Ligand Log
2
CN~ 17
15
3
2
NTA 14
EDTA 21
Glyclne 10
8
TABLE 4.
K Equation and Comments
.4 Hg2* + 2S02~ + Hg(SOM)2~
.00 Kg2* + CN~ + HgCN*
.75 Hg2+ * 2CN" J Hg(CN)2
.56 Hg2+ + 3CN" * Hg(CN)~
.66 Hg2+ + 4CN~ * Hg(CN)2"
.6 Hg2+ + NTA * Hg-NTA
.8 Hg2+ + EDTA*1" * Hg-EDTA2~
.3 Hg2* * Gly" * Hg-Gly+
.9 Hg2* + 2Gly" * Hg-Gly
Reference
Slllen & Martell (1971)
Slllen & Martell (1971)
Slllen !. Martell (1971)
Slllen & Martell (1971)
Sillen & Martell (1971)
Slllen & Martell (1971)
Sillen & Martell (1971)
Sillen & Martell (1971)
Sillen & Martell (1971)
08. CONSTANTS FOR MERCURY ADSORPTION
Adsorbent Partition Coeff . Comments
^
Bentonlte
Bentonlte
108 pH-6.7 0.01M Ca(N03)2
21 1 pH«7.3 0.01M Ca(N03)2
179 pH-7.9 0.01M Ca(N03>2
119 pH-8.9 0.01M Ca(N03)2
110 pH-10.2 0.01M Ca(N03)2
156 pH-10.7 0.01M Ca(N03)2
164 pH-11.0 0.01M Ca(N03)2
30.0 pH-6.6 0.01M CaCl2
29 pH-6.ll 0.01M CaCl2
29.2 pH-7.2 0.01M CaCl2
58 pH-8.1 0.01M CaCl2
141 pH-8.9 0.01M CaCl2
164 pH-00.5 0.01M CaCl2
224 pH-10.9 0.01M CaCl2
Reference
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al., (1976)
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al . , (1976)
Newton et al., (1976)
Newton et al . , (1976)
Newton et al., (1976)
Newton et al., (1976)
Newton et al., (1976)
107
-------
Sudbury (Ontario), where Cu-Ni ore is mined and smelted, ranged from 0.1 to
0.4 ug/X,. The selenium content of the suspended particles ranged from 2 to
6 ug/a. The Se profiles in the lake paralleled the history of Cu-Ni
production in the Sudbury district. The present day selenium accumulation
rate in the sediment is 0.3 ~ 12 mg/m -yr.
Typical river concentrations average 0.2 ug/&, although in some surface
waters concentrations exceeding 200 ug/X, have been reported. The selenium
content of sea water has an average value of 0.1 ug/X,. Selenium exists in
two common oxidation states as either Se(VI) or Se(IV).
Very few data are available for selenium speciation in natural
waters. Seawater has a significant concentration of selenium(IV)
concentrations, but it is less than 8% of the total selenium in river water
(Florence and Batley, 1980). Measure and Burton (1978) suggested that the
remainder was most probably Se(VI) as the selenate ion, but the presence of
organically associated species, or more importantly, selenium-collodial
matter could contribute significantly. Frost (1983) has presented a
probable selenium cycle in the environment.
According to a study by van Dorst and Peterson (1984), the
concentrations of both selenite and selenate were much greater at pH 7 than
at pH 4.5. The occurrence of selenoglutathione also was noted.
2-
Selenite (SeO_ ) was stable in alkaline to mildly acidic conditions and
should be present in nature. The presence of- low levels of selenite
measured by Shamberger (1981) in soil indicated that most of the active
selenite had sorbed on mineral surfaces.
Adsorption of Se from seawater and river water onto colloidal ferric
hydroxide and a range of clays has been demonstrated by Kharkar et al.
(1968). The presence of sediments in the water or on the bottom of
enclosures reduced selenium bioaccumulation (Rudd and Turner, 1983). This
suggests the binding of Se by fine sediment particles.
The water quality criterion for Se by U.S.EPA is 10 ug/£ for domestic
water supplies. Klaverkamp et al. (1983) determined that the selenium
concentration required to produce 50% mortality in fish was approximately
11 mg/X. for northern Pike (Esox Lucius), 29 mg/X, for white sucker
(Catostomes commersoni), and 5 mg/X, for yellow perch (Perca flovescens)
after 75, 96, and 240 hours of exposure, respectively. Turner and Rudd
(1983) summarized the literature on toxicity of Se to aquatic biota.
Wehr et al. (1985) demonstrated an absolute physiological requirement
of the planktonic alga Chrysochromulina breviturrita for Se in axenic
culture. This alga is capable of utilizing dimethylselenide (DMSe) and this
is believed to be the first demonstration of the utilization of DMSe as a Se
source by any organism.
Elevated selenium appeared to retard the rate of heavy metal
bioaccumulation by fish, Crayfish, and haptobenthos (Rudd et al., 1980).
Turner and Swick (1983) observed that waterborne selenium did not alter the
108
-------
amount of Hg accumulated from water or its subsequent partitioning among
pike tissues sampled. When elevated in food, however, selenium decreased
both the body burden of Hg in pike and the proportion in muscle.
Certain plants, fungi, bacteria, and rats have the ability to
synthesize volatile Se compounds. The volatile compounds were predominantly
dimethylselenide, although dimethyldiselenide (DMDSe) and dimethyselenone
are also volatile. Zeive and Peterson (1981) in a laboratory study showed
that volatilization was dependent upon microbial activity, temperature,
moisture, time, concentration of water-soluble Se, and season of the year.
Jiang et al. (1983) identified DMSe and DMDSe compounds in air in
concentrations up to 2.4 ng m~3 near different aquatic system. Using model
simulations, Medinsky et al. (1985) predicted that most of the selenium in
human tissue is likely to come from diet; selenium in urban atmosphere
contributed a very small fraction of the total body selenium.
Frost (1983) reported that bioavailability of Se is on the decline, and
this could trigger a Se-responsive disease. Selenium is thought to be an
anti-cancer agent at low concentrations. The use of sulfated fertilizers
favors the uptake of S over Se by plants. The modeling of selenium poses
considerable difficulty because of a lack of knowledge of its partitioning
and chemical speciation.
4.2.5 Lead
Lead in soil may be derived from natural or anthropogenic sources. The
natural sources include weathering of rocks and ore deposits, volcanoes
(mantle degassing), fires, and wind-blown dust. Anthropogenic contributions
of lead in soils is a relatively recent event (100 years or so), but it has
increased to such an extent that the build-up of lead concentrations in many
soils has significant biological effects.
The movement of lead over long distances invariably involves transport
in particulate or sorbed form. Close to ore deposits, lead may be mobilized
at a relatively high rate due to the production of acidic components as a
consequence of the weathering of sulfides:
PbS + 4H2• Pb2+ + SOJj" + 8H+ + 8e~
?+ 0 ?- + - (4.5)
2PbS + 4H20 <---»• 2Pb + S + SO^ + 8H + 8e
The lead ion so derived may be sorbed onto other soil components or
converted into secondary materials such as anglesite (PbSOjj), currusite
(PbCOg), hydrocerrusite (Pb(OH)2 ' 2PbCC>3), pyromorphite, etc. Because of
the low solubilities of those secondary minerals and the strong binding
capacities of the soil components for lead, the metal has a low geochemical
mobility (as measured by distance of penetration from the ore bed into the
host rock).
Zirino and Yamamoto (1972) constructed a pH dependent model for the
chemical speciation of lead in seawater. Between pH 7 and pH 9, lead in
seawater is mainly complexed with carbonate ions and to some extent chloride
109
-------
ions. At pH values near 7, PbCCs and PbCX, are present in nearly equal
amounts and there is an appreciable amount of PbCJIO . As the pH increases,
however, PbCO becomes the predominant species. Lead exists in aqueous
solution almost entirely as Pb(II) species. The equilibrium: reaction Pb
?+
+ 2e «-—-> Pb , has a pE value of over +21 , and thus Pb(IV) species exist
only under extremely oxidizing conditions. Pb(II).forms a number of
hydroxide complexes. These include Pb(OH), Pb(OH) , and Pb(OH) . Lead is
predominantly Pb(OH)+ at pH > 6.3 and lead activities less than^O.001 |4.
Pb(OH) dominates above pH 10.9', and polynuclear species dominate when total
Pb(Iir> 0.001 M.
Lead forms organic complexes with various ligands: amino acids,
proteins, polysaccharides and fulvic and humic acids. The stability
constants of Pb-FA were determined by Schnitzer and Skinner (1967) to be:
log K of 3.09 and 6.13 at pH 3.5 and 5.0, respectively. Stevenson (1976)
reported a value of log K of 8 for Pb-humic acid complex. Schecher and
Driscoll (1985) observed that the presence of sulfate enhance the adsorption
removal of lead by cells of Nostoe muscorum.
Tetra-alkyl lead compounds apparently can be formed in natural aquatic
sediments. This can have serious implication for man-made pollution of
waterways, because tetralkyl lead is considerably more toxic than inorganic
lead. Craig and Rapsomanikis (1985) demonstrated the production of methyl
lead derivatives from the reaction of Pb(II) ions with CH donor agents.
They also suggested some reaction mechanisms. Two static bioassays (on
rainbow trout) in hard water resulted in a 96 hr LCj-Q (lethal concentration
with 50? survival) of 1.32 and 1.47 mg/X, dissolved lead with a total lead
LC50 of 542 and 471 mg/&, respectively (Davis et al., 1976). The experiment
demonstrated that the dissolved fraction is directly toxic to fish in
aquatic environments.
Chau and Lum-Shue-Chan (1974) found that in 16 out of 17 Canadian lakes
studied, lead was readily adsorbed on inorganic adsorbents. Gonzalves et
al., (1985) measured the particulate and dissolved Pb in the presence of
hydrous Mn02 and silica using voltammetric methods. This was accompanied
without separation or filtration of the solid phase.
In addition to precipitation and complexation, adsorption represents
another important process in the environmental cycling of lead. Based on
statistical thermodynamic considerations, the exchange equilibrium constant
(log K ) for the reaction was calculated to be 1.4 for Utah bentonite and
Wyoming montmorillonite clays.
Pb^+ , + Ca-clay <---•> Ca2+ + Pb-clay K = 101'4 (4.6)
Q aq) ex
p+ +
Regarding ion exchange, it has been found that the Kgx for the Pb -K
exchange was about an order of magnitude greater than the value for Pb
Ca exchange. They also observed that vermiculite was an excellent
exchanger for lead ions; the vermiculite virtually removed all the lead in
the solution.
110
-------
Lead is transported from source areas either in ionic solution and/or
as more stable organo-metallic complexes. Reservoirs or lakes interrupt
transportation in a fluvial system, and because of the long water residence
times, the metal ions are adsorbed onto clay minerals and are deposited.
The sediment therefore acts as a sink for this heavy metal (Pita, 1975).
Seasonal variations of lead concentrations in an oligotrophic lake
(Crystal Lake, Wisconsin) were studied by Talbot and Andren (1984). They
observed that during the transient periods of high biological productivity,
a large net flux of radio-labeled lead nuclides was deposited to the
sediment. It was during these short periods that most of the annual net
removal of lead occurred. Pb sources to the water column appeared to occur
mainly from atmospheric inputs. A conceptual model is depicted
qualitatively in Figure 4.07. Although considerable information would be
required to model the system, the Figure serves to illustrate the
interdependence between the chemical and biological processes. Tables 4.09
and 4.10 give the equilibrium constants for lead complexation and sorption.
DEAD/OYINO EXCREMENT
UPTAKE
-LEAD
ACCUMULATION
AT INTERFACE
WATER
SPHERE
SEDIMENT
SPHERE
Figure 4.07 Cycling of lead in an aquatic ecosystem.
Ill
-------
TABLE 4.09. EQUILIBRIUM CONSTANTS FOR LEAD
Llgand Log K
OH" 6.2
7.0
11.5
10.9
13.9
16.3
7.61
Cl" 1.6
1.73
1.78
1.68
1.38
F" 1.25
2.56
3.12
3.10
Br" 1 .77
1.11
I" 1.91
3.19
HCO" 2.9
CO2" 6.3
9.8
10.61
S2" 27.5
SOJ" 2.7
3.17
HS" 15.27
16.57
Equation and Comments
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
Pb2*
+ OH" *• PbOH*
+ OH" * PbOH*
«• 20H" * Pb(OH)2
«• 20H" + Pb(OH)2
»• 30H" + Pb(OH)"
+ KGB)' 5 Pb(OH)J"
+ (OH)" + Pb2(OH)3*
* Cl" * PbCl*
* Cl" + PbCl*
+ 2C1" * PbCl2
+ 3C1" + PbCl"
* 1C1" + PbCl jj~
+ F" * PbF*
+ 2F~ * PbF2
f 3F" + PbF"
+ IF" + PbF2"
+ Br" + PbBr*
+ 2Br" * PbBr
* I" + PbI2
«• 21" + PbI2
«• HCO" * PbHCO*
+ CO2" * PbC03
* 2C02" * Pb(C03)2"
2- «• 2-
«• 2CO, -» Pb(CO-,)t
3 3 2
+ S2~ * PbS
*• so2" £ pbso,,
+ 2SO^" * PbfSOi,)2,"
* 2HS" 5 Pb(HS)2(aq)
* 3HS" + Pb(HS)"
Reference
Sillen & Martell (1961)
Blllinski et al., (1976)
Billlnaki et al., (1976)
Sil-len & Martell (1961)
Sillen & Martell (1961)
Sillen & Martell (1961)
Felmy et al., (1985)
Hegelson (1969)
Sillen & Martell (1971 )
Hegelson (1969)
Hegelson (1969)
Hegelson (1969)
Felmy et al . , (1985)
Felmy et al., (1985)
Felmy et al . , (1985)
Felmy et al . , (1985)
Felmy et al . , (1985)
Felmy et al . , (1985)
Felmy et al . . (1985)
Felmy et al., (1985)
Zirlno & Yamamoto (1972)
Billnski et al . , (1976)
Blllnski et al . , (1976)
Ernst et al . , (1975)
Sillen & Martell (1961)
Sillen & Martell (1961)
Sillen & Martell (1961)
Felray et al. , (1985)
Felmy et al., (1985)
112
-------
TABLE 4.09 (continued)
Llgand
NO'
NTA
EOT A
Glycine
FA
HA
Sludge Solids
Colloids
Ceothlte
Mn02
Silica
Log K
1.17
11.47
17.9
17.7
5.t7
6.3
3.1
6.1U
3.1
6.13
8.7
6.3
6.17
-1.9
1 .2
-1.1)
Equation and Comments
Pb2* + NO" *> PbNO*
Pb2* + NTA * Pb-NTA
Pb2* * EDTA11" + PbEDTA2"
Pb2* * EDTA14" 5 Pb-EDTA2"
Pb2* + Cly * PbGly
Pb2* * 2Cly + Pb(Gly)2
Pb2* » FA *> PbFA
(ISE method) pH - 6.5
Pb2* + FA + PbFA
pH - 3.5
Pb2* + FA * PbFA
pH - 5.0
Pb2* + FA + PbFA
pH - 3.5)
Pb2* + FA *• PbFA
pH - 5)
Pb2* + 2HA + Pb(HA),
(I - 0)
(ISE method)
Pb2* + L * PbL
(Filtration method)
Pb2* * L + PbL
M-OH + Pb2* * MOPb + H*
M-OH + Pb2* * MOPb * H*
2(MOH) + Pb2* * (M-0)2Pb+
Reference
Felmy et al.. (1985)
SUlen & Martell (1976)
Slllen & Martell (1976)
J. Gardiner (1976)
Slllen & Martell (1976)
Sillen & Martell (1976)
Sterritt & Lester (1981)
Schnltzer & Skinner (1967)
Schnltzer & Skinner (1967)
Schnltzer (1969)
Schnltzer (1969)
Stevenson (1976)
Sterritt & Lester (1985)
Sterritt & Lester (1985)
Gonzalves (1985)
Gonzalves (1985)
2H* Gonzalves (1985)
[M • particle surface]
113
-------
TABLE 4.10 CONSTANTS FOR LEAD ADSORPTION
Adsorbent
HA
Sample A
B
C
D
Kaolin
Illite
Montmorillonite
Langmuir
rm molf v(-\ry> *•*
K mol
g
990 1'.0
740 1.2
1235 6.8
810 12.2
19 2.6
68 3.8
347 9.7
Comments
Beveridge &
Beveridge &
Beveridge &
Beveridge &
Reference
Pickering
Pickering
Pickering
Pickering
25°C pH=5 Farrah et al . ,
(Na+ form Clay)
(1980)
(1980)
(1980)
(1980)
(1980)
4.2.6 Barium
Barium occurs in nature chiefly as barite, BaSO^, and witherite, BaCOo,
both of which are highly insoluble salts. Many of the salts of Ba are
soluble in both water and acid, and soluble barium salts are reported to be
poisonous. They can affect the muscular and nervous system. Insoluble
barium salts are not toxic. Brennimann (1979) recently suggested that there
was a positive correlation between the occurence of cardiovascular diseases
and barium concentration in drinking water. The U.S. EPA recommends
a 1 mg/X, limit for barium in domestic water supplies.
In a study of the behaviour of barium in soil, Lagas et al. (1984)
observed that 18 to 39$ of barium added leached-out, probably as Ba-
complexes. Part of the Ba was adsorbed or precipitated in the sand; part of
the Ba remained in the original state as BaCOo. Complexation of Ba-ions is
comparable to those for Ca (Smith and Martell, 1976). Barium complexes with
fatty acids. In its complexed form, Ba is much more mobile in soil water.
Adsorption of Ba ions will be stronger than that of Ca or Mg ions
because the radius of the hydrated Ba-ions is smaller than the radii of the
hydrated Ca and Mg ions. Ba-ion forms surface complexes with sand and
exchanges with H+ and Al-^"1" (Lagas et al., 1984). Despite adsorption by sand
and waste material, Ba always reaches the ground water to some extent.
BaSOjj precipitation is another important path for Ba removal from natural
waters.
114
-------
Sebesta et al. (1981) studied uranium mine waste waters and observed
that the main factors regulating the concentrations and the forms of radium
and barium in adjacent surface waters were the dilution of waste water with
river water and the sedimentation of particulate forms in the river.
The distribution of both elements between the water phase and suspended
solids obeyed the homogenous distribution law for isomorphous
coprecipitation of radium and barium sulfate. Consequently the predominant
particulate form of radium and barium in such waters was BaRaSO^. If
certain limiting conditions are fulfilled, the distribution of radium and
barium between solutions and barium sulfate crystals can be described by:
(CRa/CBa)diss
(CBa/CRa)particles
(4.7)
Here KgaRaSO is the so-called homogenous distribution coefficient or
separation factor, and CRa and CBa are concentration of radium and barium.
The values of the separation factors were 1.8, 1.8, 2.2, 2.9, 1.9, and 3.5
at pH 4, 5, 6, 7, 8 and 9, respectively (Sebesta et al., 1981).
The main particulate form of radium and barium in a uranium mine waste
water system was BaRaSO^ (Benes et al., 1983). River water upstream of the
mine water discharge contained Ba mainly as BaSO^ or detritus.
Knowledge of the physicochemical forms of barium and its interaction
with various ligands and particulates in natural waters is scarce. Further
information on adsorption, ion exchange, and complexation behavior of Ba
would be required to effectively model its fate in the aquatic
environment. Because Ba is in many respects similar to Ca, however, we may
examine the possibility of using the exchange, complexation, and adsorption
behavior of Ca to approximately model Ba. Table 4.11 gives the few
equilibrium constants available in the literature for barium.
TABLE 4.11 EQUILIBRIUM CONSTANTS FOR BARIUM
Ligand
Log K Equation and Comments
Reference
OH
0.85
Ba
2+ + OH > Ba(OH)+
Sillen & Martell (1964)
SO2"
EDTA
NTA
Glycine
8.0
4.85
0.77
Ba
2+
EDTA
il- •*-
^
BaEDTA2~ Sillen & Martell (1964)
NTA -> Ba-NTA
Ba2""" + Gly~ + BaGly"1"
Sillen & Martell (1964)
Sillen & Martell (1964)
115
-------
4.2.7 Zinc
Recent studies have indicated that the toxicity of zinc is due to the
presence of the free zinc ion and thus may not be directly related to the
total metal concentration. Shephard et al. (1980) measured the
concentration and distribution of Zn in Palestine Lake, Indiana. The
average dissolved Zn concentrations in the lake were as high as 293 ug/8,,
but the concentration associated with suspended solids was less than
293 ug/X,. Average levels of Zn in the dissolved fraction exceeded those in
the suspended fraction. Under anaerobic conditions occurring in lake
hypolimnia, a marked decrease in the dissolved fraction and concommitant
increase in the suspended fraction was noted.
Mouvet and Bourg (1983) used the computer model ADSORB to calculate the
speciation of Zn in Meuse River (France). Adsorption sites were treated as
conventional ligands and the adsorbed organic-solids interactionswere also
considered. The complex formation of zinc with OH", HCO, and CO, have been
previously determined at high and low pH. Billinski et al. (1976)
determined the complexation between Zn(II) and hydroxide and carbonate ions
under conditions that approximate those in natural waters, i.e., [Zn]^ <
10 M. The carbonate complexes of Zn(II) are less stable; hence, the metal
is present in natural waters, depending on solution variables, as aquo-,
hydroxo-, or chloro- (sea water) complexes. Chemical speciation of Zn and
other trace metals in mixed freshwater, seawater, and brine solution have
been modeled by Long and Angino (1977). A large fraction of Zn was
calculated to be as free ions. In fresh water, bicarbonate and sulfate
complexes were predominant below pH 6.5. At. pH greater than 6.5, the
carbonate and Zn(OH)2 species predominated. Zinc complexes strongly with
chloride ion when a small amount of sea water is present.
The sorption of zinc species by clay minerals such as kaolinite,
illite, and montmorillonite have been investigated by Farrah and Pickering
(1976). The uptake of Zn by clay increased significantly as the pH was
increased from 3.5 to 6.5. The stability of bound zinc hydroxide was great
at high pH. Sorption is the dominant fate process affecting Zn, and it
results in the enrichment of suspended sediments relative to the water
column (Nienke and Lee, 1982). Variables affecting the mobility of Zn
include the concentration and composition of suspended and bed sediments,
the dissolved and particulate iron and manganese concentrations, the pH, the
salinity, the concentration of complexing ligands, and the concentration of
Zn. Rybicka (1985) presented an isotherm for Zn on sepiolite. Sepiolite
has a large adsorption capacity for zinc, which is almost identical to that
of montmonillonite and illite. Miragaya et al. (1986) studied Cd and Zn
sorption by kaolinite and montmorillonite from low concentration
solutions. The metal sorption affinity decreased markedly with increasing
concentration for both layer silicates. There is a greater affinity
(distribution coefficient) for both Zn and Cd by kaolinite and
montmorillonite at low concentrations.
The complexation of Zn with humic acid was quantified by Randhawa and
Broadbent (1965): Log K-zn-EA. °^ ^.8 *>or a num*c acid-Zn complex at pH
7.0. Ardhakani and Stevenson (1971) determined log KZn_HA = 3.1 - 5.1 at pH
116
-------
6.5 for a soil humic acid - Zn(II) complex. Metal ion formation constants
were determined for several sedimentary humic acids from fresh water and
coastal marine environments, and conditional log KZn_HA values between 4.5
and 5.5 at pH 8.0 (I = 0.01 M) were found (Sohn and Hughes, 1981). With so
few data available, no conclusion should be made regarding the difference in
values of Zn-humic acid stability constants.
Peterson (1982) observed the influence of Zn (and Cu) on the growth of
a freshwater alga, Scenedesmus quadricauda. The results suggest that the
free metal ion is the chemical specie that is toxic to algae. Harding and
Witton (1978) found that submerged plants in the Derwent Reservoir
accumulated large amounts of Zn. The Zn concentration in Nitella flexilis
ranged from 500 ug/g to 1500 ug/g. The Zn concentration in water was 0.216
ug/L. These plants could increase or decrease the rate of deposition of
metals depending upon the rapidity with which the plants get buried and
decompose.
Table 4.12 represents the equilibrium contants for zinc in natural
waters, and the adsorption constants are presented in Table 4.13.
4.2.8 Copper
In aquatic environments, metals can exist in three phases —
particulate, colloidal, and soluble. Particulate matter includes oxide,
sulfide, and malachite (C^tOH^ COO precipitates, as well as insoluble
inorganic complexes and copper adsorbed on clays and on other mineral
solids. Soluble matter includes free cupric ion and soluble complexes;
colloidal matter includes polypeptide material and some fine
clays (£ 2pm) and metallic hydroxide precipitates. In unpolluted fresh
waters, two types of processes are possible, namely precipitation and
complex formation (Stiff, 1971).
Two precipitation reactions are thermodynamically possible — (a)
precipitation of cupric hydroxide followed by conversion of hydroxide to
oxide (b) precipitation of malachite.
2+ , ou r\ _.». n,,frtu^ j. ou"1" x r>,in j. ou+ j. u n
Cu2+ + 2H~0 --> Cu(OH)5 + 2H+ -•> CuO/Q^ + 2H
<— <— \ O /
Cu2+ + 2HP0 + HCO~ —»• Cu? (OH)? CO, + 3H+ (4.8)
c J ^(s)
Both reactions are dependent on the pH values and on the bicarbonate
(alkalinity) concentration of the solution. Based on equilibrium
calculations, malachite will be the only precipitated specie in the pH range
of most fresh waters. In the presence of copper precipitates, the free
cupric ion can further complex with bicarbonate ions to form soluble CuCO?
species. This explains why the concentration of free cupric ion only
represents a small fraction of the total soluble copper (Stiff, 1971). A
speciation of Cu(II) diagram is presented by Sylva (1976) as shown in Figure
4.08.
117
-------
TABLE 4.12. EQUILIBRIUM CONSTANTS FOR ZINC
Llgand Log K
OH" i).H
12.89
11.0
15.0
Cl" 0.1)3
0.61
0.53
0.2
CO*" 5.3
9.63
HCO" 2.1
SOJj" 2.3
HS" 14.9
16.1
F" 1.15
Br" -9.58
-0.98
I" -2.91
-1.69
CN~ 17.5
16
20.2
Equation and Comments
Zn2+ + OH" :
Zn2+ + 20H"
Zn2* + 30H"
Zn2* + 40H"
Zn2+ + cr
Zn2+ «• 2C1"
Zn2+ + 3C1"
Zn2* + 4C1~
2+ 2-
Zn + Co,
» Zn(OH)+
* Zn(OH)2
* Zn(OH)~
* Zn(OH)2"
+ ZnCl*
* ZnCl2
+ ZnCl"
5 ZnCl2'
* ZnCO"
Zn2+ + 2C02" * ZnCO2"
Zn2* + HCO"
Zn2* * SO2"
Zn2* + 2HS"
Zn2+ + 3HS"
Zn2* * F" 5
Zn2+ + Br"
Zn2+ + Br"
Zn2+ * I" 5
Zn2* + 21"
Zn2* + 3CN"
Zn2* + 4CN"
Zn2+ + 5CN~
+ ZnHCO*
+ ZnSOjj
+ Zn(HS)2
+ Zn(HS)^
ZnF*
* ZnBR*
* ZnBR2
Znl*
* ZnI2
* Zn(CN)"
* Zn(CN)2"
* Zn(CN)^"
Reference
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen i Martell (1964)
Slllen & Martell (1964)
Slllen i Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
Felmy, et al . , (1985)
Felmy, et al . , (1985)
Felmy, et al., (1985)
Felmy, et al . , (1985)
Felmy, et al . , (1985)
Slllen & Martell (1964)
Slllen & Martell (1964)
Slllen & Martell (1964)
118
-------
TABLE 4.12 (continued)
Ligand
Log K
Equation and Comments
Reference
EOT A
NTA
Glyclne
FA
HA
21.7
1.61
10.5
5.23
1.3
6.76
1.76
1.73
2.31
1.12
6.18
6.80
m5l|
Zn2* + S2" * ZnS
Zn2* * EDTA * Zji-EDTA
Zn2* * NTA + Zn-NTA
Zn2+ * Gly * Zn-Gly
Zn2* * 2Gly » ZnGly
Zn2* + 2FA + Zn-(FA)2
Zn2* *• .51FA + Zn(FA)
Zn2* + FA + Zn-FA
(pH - 3.5)
Zn2* + FA + Zn-FA
(pH - 5.0)
Zn2* * 1.25HA + Zn(HA). 25
(pH - 3.6)
Zn2* + 1.59HA + Zn(HA), 5g
(pH - 5.6)
Zn2* + 1.70HA + Zn(HA), ,0
(pH - 7.0)
Slllen & Martell (1976)
Slllen & Martell (1976)
Slllen & Martell (1976)
Sillen & Martell (1976)
Slllen & Martell (1976)
Tan et al., (1971)
Tan et al., (1971)
Sohnltzer (1969)
Sohnltzer (1969)
Randhawa & Broadwent
(1965)
Randhawa & Broadwent
(1965)
Randhawa & Broadwent
(1965)
119
-------
TABLE 4.13. CONSTANTS FOR ZINC ADSORPTION
Adsorbent
1
Huralc Acids A
B
C
D
Kaolin
Montmorlllonlte
Fe hydrous
oxide
Al hydrous
oxide
Clays
(A Horizon)
Decatur cl
Cecil si
Norfolk Is
Leefleld Is
(B 2t Horizon)
Decatur cl
Cecil si
Norfolk
Leefleld Is
Adsorbent
Bentonlte
Seplollte
Langmuir
360
290
565
M20
57.1
363
117
13.2
392
37
63
55
20
55
55
27
28
71
Freundllch
K
O5^)
2.M
1.8
6.1
1.7
6.73
1.53
5.1
0.2
5.9
.08
0.53
0.31
O.MO
0.31
0.2M
0.3M
O.MM
O.ll
n
1.98 0.6
105 0
.65
Comments Reference
pH - M.I) Beverldge & Pickering (1980)
pH - M.M Beverldge & Pickering (1980)
pH - M.M Beverldge & Pickering (1980)
pH - M.M Beverldge & Pickering (I960)
Mlragaya (1986)
Mlragaya (1986)
fresh Shuman (1977)
aged Shuman (1977)
fresh Shuraan (1977)
aged Shuraan (1977)
Shuman (1976)
Shuman (1976)
Shuman (1976)
Shunan (1976)
Shuman (1976)
Shuman (1976)
Shuman (1976)
Shuman (1976)
Comments Reference
Guy et al., (1975)
Ryblcka (1985)
120
-------
Mouvet and Bourg (1983) have used the computer model ADSORB to
calculate the speciation of copper in the Meuse River (France). Adsorption
sites were treated as inorganic ligands. The conditional stability
constants of copper-fulvic acid were: log K of 4.0, 4.9, and 6.0, at pH 4,
5, and 6, respectively. This indicates a strongly bound complex (Buffle et
al., 1977). The conditional stability constants of Cu-fulvic acid and Cu-
humic acid were also determined by Shuman & Cromer (1979).
Nriagu et al. (1981) determined that, in general, 50 to 80? of the
copper in Lake Ontario was bound to suspended particles. Adsorption of Cu
onto hydrous ferric oxide was significantly modified in the presence of
humic substances (Laxen, 1981). Copper uptake was either enhanced or
reduced depending, respectively, on whether the metal-ligand complex formed
was strongly bound by oxide surfaces or was a non-adsorbing complex in
solution. Blutstein and Shaw (1981) suggested that naturally occurring
organic compounds in Albert Park lake effectively reduced the adsorption of
copper(II) onto particulate matter by the formation of non-adsorbing
complexes. Elliot and Huang (1980) investigated the adsorption of Cu(II) by
aluminosilicates with varying Si/Al ratios. The presence of complex-forming
organic ligands (NTAm, Glycine) modified Cu adsorption characteristics that
can influence its fate. The adsorption of Cu(II) on wollastonite was
studied by Pandey et al. (1986). There was a higher adsorption at increased
pH, which is explained by the adsorption of hydrolyzed Cu(II) species at the
solid surface interface. Fayed et al. (1985) found that the concentration
factor of metals including Cu was lower in plants as compared to the
sediment.
The most important species of copper causing toxicity (studied for
culthroat trout) was Cu2+, Cu(OH)+ and Cu(OH) (Chakoumakos et al., 1979).
The concentrations of each of these species varies with pH and alkalinity.
Lower alkalinity concentrations favor all these species; CuHCO_, CuCO
and Cu(CO_)? were not toxic. The lethal toxicity of copper to rainbow
trout was found to be related to the total concentration of Cu and CuCOo (in
the absence of organic complexes) rather than to the concentration of either
of these forms alone (Shaw 1974). Sato et al. (1986a), in their study of
the effects of copper on the growth of Nitrosomonas europaea, observed that
copper inhibition caused a decrease in the growth rate. In another study,
Sato et al. (1986b) determined that the decrease in specific growth rate of
N. europaea was linearly correlated with the logarithmic activities of
Cu(II)-amine species, regardless of the total Copper(II) activity in the
medium.
Geesey et al. (1984) studied the effect of flow rate on the
distribution of Cu species and other metals in rivers. Increased flow
resulted in a loss of soluble reactive copper from sediments of Still Creek
and Fraser River (British Columbia). Decreased flow was accompanied by an
increase in the levels of reactive copper. Chemical speciation of copper
can be estimated from the MINTEQ model if pH, alkalinity, and complexing
ligand concentrations are specified. The fate of copper in natural waters
can be modeled by the non-equilibrium model NONEQUI as developed by Fontaine
(1984). Information on rate constants is scarce, however. A schematic of
121
-------
the model is presented in Figs. 4.01 and 4.08. Tables 4.14 and 4.15 are a
summary of the many equilibrium constants available for copper.
4.3 TRANSPORT AND TRANSFORMATION MODELS
A number of computer models exist to calculate transport and
transformation of toxic organics in the aquatic environment, but few models
are currently available for heavy metals. The model developed by Woodard et
al. (1981) takes into account dynamic processes of rivers, and relies
heavily on the relationship between transport of suspended matter and that
of heavy metals. Christophensen and Seip (1982) developed a model for
stream water discharge and chemical composition, incorporating a two-
reservoir hydrology model (Lundquist, 1976 and 1977) with sulfate and cation
submodels. The cation submodel includes H+, Al^+, Ca , and Mg , and is
based on the mobil anion concept. Chemical processes include cation
exchange, weathering, dissolution/precipitation of gibbsite, and
adsorption/desorption and mineralization of sulfate. The model was
developed for application to acidification of streams by acid deposition.
A model developed by Chapman (1982) includes the effects due to
processes such as precipitation, sedimentation, and adsorption, and it
adopts the program MINEQL as a basis for the chemical equilibrium model.
The Metal Exposure Analysis Modeling System (MEXAMS), developed by Felmy et
al. (1984), has a capability for assessing the impact of priority pollutant.
metals on aquatic systems. The program allows the user to consider the
complex chemistry affecting the behavior of metals in conjunction with the
transport processes that affect their migration and fate. The modeling
system is accomplished by linking a geochemical model, MINTEQ, with an
aquatic exposure assessment model, EXAMS. The NONEQUI model, developed by
Fontaine (1984a and 1984b), can simulate sorption and ion exchange kinetics
among a variety of heavy metals and organic ligands interactions (Figure
4.01). MINTEQ, developed by Felmy et al. (1983) is the most recent computer
program to calculate speciation at chemical equilibrium. The program
computes aqueous speciation, adsorption, and precipitation/dissolution of
solids. It requires thermodynamic data and water quality data as input.
A summary of the heavy metal models is given in Table 4.16. The first
three models are stream models, and the distribution of heavy metals is
controlled by advective-dispersive flow and metal-adsorbed sediment
transport (settling and scouring) processes. NONEQUI and MEXAMS can be
applied for both a stream and a lake where physical, chemical and biological
reactions (e.g., volatilization, speciation, and biouptake) are more
important. MEXAMS calculates equilibrium concentrations of chemical species
using MINTEQ. NONEQUI uses a non-equilibrium approach considering the
kinetics of sediment-water exchange, metal-organic complexation reactions,
cation exchange, methylation/demethylation, and humic acid mediated
reduction reactions.
122
-------
100
Figure H.08. Speciation of copper(II) (total concentration 2 ppm) and
carbonate as a function of pH. (A) Cu2, (b) Cu?(OH). (C)
CuOH
(D) CuC0, (E) HC0, (F)
O \ ^" / ry
(G) ptt at which Cu(OH)2
will precipitate, (HO pH at which Cuo(OH)2(C03)2 will
precipate, (I) pH at which Cu2(OH)2C03 will precipitate.
123
-------
TABLE 4.14. EQUILIBRIUM CONSTANT FOR COPPER
Ligand
OH"
Cl"
F"
<
s2-
__
so^
HS"
Log K
6.37
6.21
11.7
11.3
15.0
16.0
17.7
0.02
2.05
-0.71
-2.3
-1.6
1.26
5.97
6.0
6.31
6.8
6.2
10.3
9.83
10
2.25
26
Equation and Comments
Cu2* + OH" *• CuOH*
Cu + OH" * CuOH* (pH-8.
9 + - •»
Cu + 20H «• Cu(OH)2 (pH
Cu2* + 20H" *• Cu(OH)
Cu + 30" *• Cu(OH)"
J
Cu2* + 10H~ *• Cu(OH)2"
2Cu + 20H~ *• Cu2(OH)2*
Cu2* + Cl" *• CuCl*
Cu2* + Cl" * CuCl*
9 + - +
Cu + 2C1 * CuCl
Cu2* + 3Cl" * CuCl"
2+ - * 2-
Cu + 1C1 * CuClf
Cu2* + F~ -» CuF*
Cu2* + CO2" * CuCO (aq)
Cu + CO, «• CuCO, (aq)
Cu * + CO, *• CuCO, (aq)
9+ 9- ->
Cu + CO^ «- CuCO (aq)
9+ 9- •>
Cu + CO^ * CuCO (aq)
(pH-8.1)
Cu2* + 2C02" * Cu(CO,)2"
J J £
Cu2* + 2C02" J CU(C03)2"
Cu2* + S2" •» CuS
9+ 2- ->
Cu » SO^ * CuSO, (aq)
*4 M
Cu2* + 3HS" i Cu(HS)"
3
Reference
Slllen and
Martell (1971)
1) Borgman and
Ralph (1983)
•8.1) Borgman and
Ralph (1983)
Slllen and
Martell (1971)
Slllen and
Martell (1971)
Slllen and
Martell (1971)
Felmy et al . (1985)
Hegelson (1969)
Slllen and
Martell (1971)
Hegelson (1969)
Hegelson (1969)
Slllen and
Martell (1971)
Felmy et al (1985)
Ernst et al . , (1975)
Billnsk et al., (1976)
Scaife (1957)
Stiff (1971)
Borgman and
Ralph (1983)
Borgman and
Ralph (1983)
Mesmer and Baes (1975)
Slllen & Martell (1971)
Slllen and
Martell (1971)
Slllen and
Martell (1971)
124
-------
TABLE 4.14 (continued)
Ltgand
CM"
NTA
EOT A
HA"
FA
Log K
28.6
30.3
25
13.05
18.8
8.9
6.23
6.5
6.55
6.56
6.0
6.1
6.6
5.7
6.1
6.0
5.9
5.9
5.6
6.3
6.t
5.1
5.8
5.9
Equation and Comments
Cu+ + 3CN~ * Cu(CN)^"
Cu+ + 1CN~ * Cu(CN)jj~
Cu2+ + 14CM" * Cu(CN)2"
Reference
Sillen and
Martell (1971)
Sillen and
Martell (1971)
SI 11 en and
Martell (1971)
Sillen & Martell (1971)
Cu2* + EDTA * Cu-EDTA Sillen and
Martell (1971)
Cu2* + 2HA~ * Cu-(HA)
Cu + HA * Cu-HA
Site: GL
(freshwater sediment)
pH-8, I-0.01M
Cu2+ + HA *• Cu-HA
Site: SH, pH-8, 1-0.
Cu2* + HA *• Cu-HA
Site: BV, pH-8. 1-0.
Cu2* + HA * Cu-HA
Site: SR, pH-8. 0-0.
Cu2+ + FA * Cu-FA (pH-6.
(Shawaheen River)
(Ogeechee River)
(Ohio River)
(Missouri River)
(South Platte River)
Cu2* «• FA * Cu-FA
(Bear River) (pH-6.2
(Como River) (pH-6.2)
Deer Creek
Hawaian River
Black Lake
Island Lake
Bralnard Lake
Merrll Lake
Suvannee Lake
Stevenson (1976)
Sohn and Huges
(1981)
Sohn and Huges
01M (1981)
Sohn and Huges
01M (1981)
Sohn and Huges
01M (1981)
0) McKnight et al . ,
(1983)
McKnight et al . , (1983)
McKnight et al . ,
(1983)
McKnight et al . ,
(1983)
McKnight et al.,
(1983)
McKnight et al.,
(1983)
McKnight et al.,
(1983)
McKnight et al . ,
(1983)
McKnight et al . ,
(1983)
McKnight et al . ,
(1983)
McKnight et al . ,
(1983)
McKnight et al.,
(1983)
McKnight et al . ,
(1983)
MoKnlght et al.,
(1983)
125
-------
TABLE 4.14 (continued)
Llgand Log K
6.0
6.0
5.7
5.6
6.1
5.7
5.14 -
6.6
3-3
1.0
9.1
FA 6.5
Ami no Acids
Glyclne 8.51
15.5
Alanlne 8.27
15.1
Vallne 8.1
11.7
Leucine 8.1
11.6
Phenylalamlne 7.87
11.77
B-Alanlne 7.1
13
Glyclne 9.3
15.1
Equation and Comments
Hawaln Marsh
Yum a Canal
Yuma Canal (chlorinated)
Cu2* + FA •» Cu(FA)
(pH-6.9)
Cu + FA + Cu(FA)
p(pH-7.0)
Cu + FA •» Cu(FA)
.JpH-6.2)
Cu + FA + Cu(FA)
2ipH-3)
Cu + FA + Cu(FA)
2{pH-5
Cu + FA + Cu(FA)
(pH-8)
Cu2+ + FA * CuFA (pH-6.5)
2+ •»
Cu + L «• Cu-L
2+ •»
Cu + 2L * Cu-L
2+ ->
Cu + L <• Cu-L
?+ •*
Cu + 2L * Cu-L2
2* •*
Cu + L «- Cu-L
?+ •*
Cu + 2L «• Cu-L2
Cu + L * Cu-L
Cu2+ + 2L * Cu-L2
Cu2* + L J Cu-L
Cu2+ + 2L + Cu-L2
Cu2* + L * Cu-L (pH-8.t)
?+ •*
Cu + 2L * Cu-L (pH-8.1)
C
Cu2* + L i Cu-L (pH-8.M)
Cu2* + 2L J Cu-L (pH-8.1)
&
Reference
McKnlght et al . ,
(1983)
McKnlght et al . ,
(1983)
McKnlght et al . ,
(1983)
Breshnan et al . ,
(1978)
Shuman and
Crocner (1979)
McKnlght et al . ,
(1983)
Y.H. Lee
(19814)
Y.H. Lee
(19814)
Mantoura et al . ,
(1978)
Sterrltt and
Lester (1981)
Sillen and
Martell (1971)
Sillen and
Martell (1971)
SUlen and
Martell (1971)
Sillen and
Martell (1971)
Sillen and
Martell (1971)
SUlen and
Martell (1971)
Sillen and
Martell (1971)
Sillen and
Martell (1971)
Sillen and
Martell (1971)
Sillen and
Martell (1971)
Borgman and
Ralph (1983)
Borgman and
Ralph (1983)
Borgman and
Ralph (1983)
Borgman and
Ralph (1983)
126
-------
TABLE 4.14 (continued)
Llgand
Glutamate
K. A erogenous
Polymer
Sludge Solids
Sludge Extract
Polymer
Soil FA
Water-FA
FA
HA
FA
Llgand in Natural
Gloucester
Lake Huron
White Water Lake
Onaplng Lake
Windy Lake
Lake Ontario
Log K
9.71
15. 4
7.69
6.75
5.93
7.3
5.9
5.6
6.0
6.3
5.17
6.0
5.95
6.1
5.67
5.96
5.78
8.69
Waters
9.3
9.2
8.6
8.6
7.2
9.5
8.6
Equation and Comments
Cu2* + L + Cu-L (pH=»8.H)
Cu2+ + 2L * Cu-L, (pH-8.4)
d
. 2+ . •* Cu-L (pH-6.8)
Cu •*• L *•
ISE-method
Filtration-method
Extracted Extracellular
Polymer
pH'1
pH-5
pH=6
pH-4
PH=1.7
pH-5.0
pH-6.0
Correction for Kinetics
Correction for Kinetics
pH-3.5
pH-5
Cu2*+L •» Cu-L (pH-8.1)
(pH-8.3)
(pH-8.6)
(pH-7.8)
(pH-6.6)
(pH-8.1)
(pH-7.4)
Reference
Borgman and
Ralph (1983)
Borgman and
Ralph (1983)
Rudd et al., (1986)
Sterrit and
Lester (1985)
Sterrit and
Lester (1985)
Rudd et al., (1981)
Rudd et al . .
(1984)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Bresnahan et al . ,
(1978)
Shuman and
Cromer (1979)
Shuman and
Cromer (1979)
M. Schnltzer (1969)
M. Schnitzer (1969)
van de Berg (1979)
van de Berg (1979)
van de Berg (1979)
van de Berg (1979)
van de Berg (1979)
van de Berg (1979)
van de Berg (1979)
127
-------
TABLE 4.14 (continued)
Llgand
Dickie No. 5
Dickie No. 6
Fulvlc Acid
Fresh Water Organic
Swains Mill
Chapel Mill
L. Waccaman
Black Lake
Log K
8.5
7.75
7.8
7.8
Uganda
5.7
1.87
5.0
5.15
5.2
1.5
1.8
Equation
pH-6.5
pH-5.7
pH-6.0
pH-6.5
pH-7.0
pH-6.5
pH-6.5
and Comments Reference
(pH-8.1) van de Berg (1979)
(pH-7.6) van de Berg (1979)
(pH-7.6) van de Berg (1979)
(pH-7.6) van de Berg (1979)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
Shuman and Woodard (1977)
4.4 REFERENCES FOR SECTION 4
Ahsanullah, M. and D.H. Palmer, 1980. Acute Toxicity of Selenium to Three
Species of Marine Invertibrates, with Notes on Continuous Flow Test
System. Aust. J. Mar. Freshwater Res. 31:795-802.
Anderson, M.A., J.F. Ferguson and J. Gavis, 1976. Arsenate Adsorption on
Amorphous Aluminum Hydroxide. J. Colloid and Interface Science. 54:391-399.
Ardhakani, M., and F.J. Stevenson, 1972. A Modified Ion Exchange Technique
for the Determination of Stability Constants of Metal-soil Organic Matter
Complexes. Soil Sci. Soc. Am. Proc. 36:884-890.
Balthrop, J.E. and S.A. Braddon, 1985. Effects of Selenium and
Methylmercury upon Glutathione and Glutathione-S-Transferase in Mice. Arch.
Environ. Contam. Toxicol. 14:197-202.
Benes, P., F. Sebesta, J. Sedlacek, M. Obdrzalek and R. Sandrik, 1983.
Particulate forms of Radium and Barium in Uranium Mine Waste Waters and
Receiving River Waters. Water Research. 17:619-624.
Balogh, K.V. and J. Salanki, 1984. The Dynamics of Mercury and Cadmium
Uptake into Different Organs of Anodonta Cygnea. Water Research. 18:2482-
2487.
Bartlett, P.O. and P.J. Craig, 1981. Total Mercury and Methyl Mercury
Levels in British Estuarine Sediments - 11. Water Research. 15:37-47.
128
-------
TABLE 4.15. CONSTANTS FOR COPPER ADSORPTION
Adsorbent
t
Langmulr
J(~T~) K^10 moT
g
Comments
)
Reference
Ottawa River Sediments
Sample 75-15
75-35
75-6
75-16
75-22
Huralc Acid
Kaolin
llllte
Montmorlllonlte
Bentonlte Clay
Fe (OH)3
Mn02
Humic
Wollastonlte
Adsorbent
Bentonlte
Adsorbent
Bentonlte
Fe(OH)3
Mn02
Hunlc
0.009
0.107 28.5
0.055 4.8
0.01 1.4
0.01 0.8
605 3.6
450 8.5
970 5.1
250 4.0
22 0.1
40 2.2
367 6.1
83 5.1
830 2.16
11000 6.67
192 7.4
12.4 0.12
14.7 0.15
17.3 0.227
Freundlioh
K n
2.96 0.65
Partition Coeff .
43000
205000
7340000
366000
% Org. matter • 0.
J Org. matter « 3.
t Org. matter • 35
J Org. matter - 0.
> Org. Matter - 2.
Sample A pH-4.4
Sample B pH-4.4
Sample C pH-4.4
Sample D pH-4.4
6 Ramamoorthy (1978)
2 Ramamoorthy (1978)
.7 Ramamoorthy (1978)
6 Ramamoorthy (1978)
4 Ramamoorthy (1978)
Beverldge & Pickering (1980)
Beveridge & Pickering (1980)
Beverldge & Pickering (1980)
Beveridge & Pickering (1980)
pH - 5, Temp. 25°C Farah et al . , (1980)
(Na+ form clays)
pH - 8
pH - 8
pH - 8
pH - 8
Temp. - 20°C
Temp. - 30°C
Temp. • 40»C
Farah et al., (1980)
Farah et al., (1980)
Oakley (198D
Oakley (1981)
Oakley (1981)
Oakley (1981)
Panday et al., (1986)
Panday et al . , (1986)
Panday et al., (1986)
CommentaRef erence
C - rag/ml, X - mg/g Guy et al . , (1975)
Comments
Reference
Oakley et al . , (1981)
Oakley et al., (1981)
Oakley et al . , (1981)
Oakley et al., (1981)
129
-------
co
rJ
U
o
o
rC
co
«^J
H
U
^^
W
33
U
E
pB^
O
Pi
5
s
5
\fj
i
vO
i— (
,
.
a
H
M
Fontaine, II
(1984 a,b)
NONEQUI
re
J»> '"^ CO
|"cg
4)
[t.
C
re r-»
8, oo
22?
CJ ••»
Q.
0)
to
•a
^^
to r\i
t- (C
0) <7>
£ r~
0.^
O
n
£
O
rH
0) —
re •-
T3 ^-*
1
0*3 >, 8 u
41 N -O -< 01 - - C
x -i re j-> to- c V C C OO O •
e n) a> jj c .. re-o _< _ c 0.0-0 oi
jj o m ,j CM .c c •>« i jj c o i ~i a> :*
>> c c c j* o o o JJ T3 - re re >H c c. .0 re
rHOireo c x — i ra — i c N to jj o4> rn
01 B rH c eon n u jj rH o -o o -. t_ ra c £ wj
jj jj ra o -H o o nj>, i. a - wto-i -HCrHjr.jJjj-HiO) » n t« »-> g
i-ire (HOI jj 3 jj jj o >• jj ^ re o-urHrH jj o t^ to re
a ex n w jj o.«- a> a> -! x: a> -< -i 3rerea c g o TJ 41
EenTjre t.«->c c *j jj B B o T3 rH jj B — i JJ c ra t.
85 re o -u o -i -< -H ra D S 9 a> aiooig o<3o jj
tiBBn m v x xozofB t, > e O o,^ L. >H n
c
C 0
O -"
r-T n re
- --0 41 t. rH -H
rH-ooi-ooireo jj
4)4)NOO>4J4) C
•ox>HBnoia •-•
O -H rH -1 B «> O
6 E (0 HI O! T3 0. 41
jj u jj v. e i j<
4>>>ccn-o3 c re
n ^ a) re JJ c -i o rH
rea>BrHnoc t. a c
(OrHre^ojJrHz jj re
£o,Q.na4>o,>4rH c 41
§<3®r*L33E *r4 L.
B n < n to 3 o- n
8 §
C O -rJ -rt
0 -" 0 JJ
re o co » o
•H a. jj a> o a
jJrH coot o. i_ in g
01 -H B « <1) •- 3 rH
-•on > -< re
OO «-. S O •• 1- JJ
i e - o 3 n jj 01
•- C -H CO *> "H B
O> O C t. _J t. C rH
-rH -1 O J3 CT I) 4) 3
e a jj — i — < u f c cr n B
nre o jj rHz«4Q4i'o jj re
OI^H OlO-'r-j.rHCOQ.-H C 01
t-t- >t. SZCE'OrH -rH t.
4-> n) TJ O o*^ o o c O O a
co> *H
N4>o> 3 eunot.'O re
rHore n c o c c + re
ra e •>-> « o -H uj -o
jj+ o
•H>> xcre-jjntM jj re
BCtt-D C 4>^iJJ4irec.bO C
jJOfl O t.- in n + i cvoi
g-6 c u j^reoijoctM c t. «-
>>o o revc^^HCrjore o *>%H
CJ I C CO U3O.COOUO C CO 01
.
•H CO
re t.
*J rH 4J 1 01 O 41
S41 OI-OlTJCrH U>
•o B c a) t. -i ra ra it.
EO41 O N CO JJ wre
— 1 E JJ .C
Tire Oj^corepjoB !3^
CO t. CO C JJ 41 .rt rH ? -1
o ocrHg**-re v
§a>> -i 5 o S c -o jj e •"
COTJ jj e -i n o cj o • jj ffl t_ o
even a -H jj o »-i jj o c ai 41 a>
t. ra o 3 a.
re .cunt. o a.
co a. a. u a. co -*
130
-------
Beveridge, A. and W.F. Pickering, 1980. Influence of Humate-Solute
Interactions on Aqueous Heavy Metal Ion Levels. Water, Air and Soil
Pollution. 14:171-185.
Bilinski, H., R. Huston and W. Stumm, 1976. Determination of the Stability
Constants of Some Hydroxo and Carbonate Complexes of Pb(11), Cu(11), Cd(11)
and Zn(11) in Dilute Solutions by Anodic Stripping Voltametry and
Differential Pulse Polarography. Analytica Chimica Acta. 84:157-164.
Blutstein, H. and Roman F. Shaw, 1981. Characterization of Copper Binding
Capacity in Lake Water. Environmental Science and Technology. 15:1100-
1102.
Borgmann, U. and K.M. Ralph, 1983. Complexation and Toxicity of Copper and
the Free Metal Bioassay Technique. Water Research. 17:1697-1703.
Braman, R.S. and C.C. Foreback, 1973. Methylated Forms of Arsenic in the
Environment. Science. 182:1247-1249.
Brennimann, G.R., 1979. Environmental Research. 20:318-
Bresnahan, W.T., C.L. Grant and J.H. Weber, 1978. Stability Constants for
the Complexation of Copper(11) Ions with Water and Soil Fulvic Acids
Measured by an Ion Selective Electrode. Analytical Chemistry, 50:1675-1679.
Brosset, C., 1981. The Mercury Cycle. Water, Air and Soil Pollution.
16:253-255.
Buffle, J., 1980. A Critical Comparison of Studies of Complex Formation
Between Copper (II) and Fulvic Substances of Natural Waters. Analytica
Chim, Acta. 118:29-49.
Buffle, J., F.L. Greter and W. Haerdi, 1977. Measurement of Complexation
Properties of Humic and Fulvic Acids in Natural Waters with Lead and Copper
Ion-selective Electrades. Analytical Chemistry. 49:216-222.
Chakoumakos, C., R.C. Russo and R.V. Thurston, 1979. Toxicity of Copper to
Cutthroat Trout (Salmo clarki) Under Different Conditions of Alkalinity, pH,
and Hardness. Environmental Science and Technology. 13:213-219.
Chau, Y.K. and K. Lum-Shue-Chan, 1974. Determination of Labile and Strongly
Bound Metals in Lake Water. Water Research. 8:383-388.
Cheam, V. and D.S. Gamble, 1974. Metal-fulvic Acid Chelation Equilibrium in
Aqueous NaNOo Solution. Canadian J. Soil Sci. 54:413-417.
Chow, T.J., J.L. Earl, J.H. Reeds, N. Hansen and V. Orphan, 1978. Barium
Content of Marine Sediments Near Drilling Sites: A Potential Pollutant
Indicator. Marine Pollution Bulletin. 9:97~99.
Christenson, T.H., 1984. Cadmium Soil Sorption at Low Concentrations: 1.
Effect of Time, Cadmium Load, pH, and Calcium. Water, Air and Soil
Pollution. 21:105-114.
131
-------
Craig, P.J. and S. Rapsomanikis, 1985. Methylation of Tin and Lead in the
Environment: Oxidative Methyl Transfer as a Model for Environmental
Reactions. Environmental Science and Technology. 19:726-730.
Davis, P.H., J.P. Goettle, Jr., J.R. Singley and N.F. Smith, 1976. Acute
and Chronic Toxicity of Lead to Rainbow Trout Salmo gairdneri, in Hard and
Soft Water. Water Research. 10:199-206.
Elliot, H.A. and Chin-pao Huang, 1980. Adsorption of Some Cu-1,1-Aminoacid
Complexes at the Solid-solution Interface: An Effect of Ligand and Surface
Hydrophobicity. Environmental Science and Technology.. 14:87-92.
Egozy, Y., 1980. Adsorption of Cd and Co on Monmorillonite as a Function of
Solution Composition. Clays and Clay Minerals. 28:311-318.
Ernst, R., H.E. Allen and K.H. Mancy, 1975. Characterization of Trace Metal
Species and Measurement of Trace Metal Stability Constants by
Electrochemical Techniques. Water Research. 9:969-979.
Fagerstrom, T. and A. Jernelov, 1972. Some Aspects of the Quantitative
Ecology of Mercury. Water Research. 6:1193-1202.
Farrah, H. and W.F. Pickering, 1976. The Sorption of Zinc Species by Clay
Minerals. Australian Journal of Chemistry. 29:1649-1656.
Farrah, H., D. Hatton and W.F. Pickering, 1980. The Affinity of Metal Ions
for Clay Surfaces. Chemical Geology. 28:55-68.
Fayed, S.E. and H.I. Abd-El-Shafy, 1985. Accumulation of Cu, Zn, Cd, and Pb
by Aquatic Macrophytes. Environment International. 11:77-87.
Felmy, A.R., D.C. Grivin and E.A. Jenne, 1985. MINTEQ - A Computer Program
for the Calculating Aqueas Geochemical Equilibria. EPA-600/3-84-032, U.S.,
Environmental Protection Agency, Athens, GA.
Ferguson, J.F. and J. Gavis, 1972. A Review of the Arsenic Cycle in Natural
Eaters. Water Research. 6:1259-1274.
Fontaine, T.D., 1984. A Non-equilibrium Approach to Modeling Metal
Speciation in Acid Aquatic Systems: Theory and Process Equations.
Ecological Modeling. 21:287-313.
Florence, T.M. and G.E. Batley, 1980. Chemical Speciation in Natural
Waters. CRC Critical Reviews in Analytical Chemistry. 9:219-296.
Frankenthal, R.P., 1963. Equilibrium Constants. In: Handbook of
Analytical Chemistry, 1st edition (edited by Meites, L.). pp 1-13 to 1-
19. McGrawHill, Toronto.
Frost, D.V., 1983. What Do Losses in Selenium and Arsenic Bioavailability
Signify for Health? The Science of the Total Environment. 28:455-466.
13:
-------
Fristoe, B.R. and P.O. Nelson, 1983. Equilibrium Chemical Modeling of Heavy
Metals in Activated Sludge. Water Research. 17:771-778.
Gardiner, D., 1978. Mercury in Fish and Waters of the Irish Sea and Other
United Kingdom Fishing Grounds. Nature. 272:49-51.
Gardiner, J., 1974a. The Chemistry of Cadmium in Natural Water - 1. A
Study of Cadmium-Calcium Formation Using the Cadmium Specific Ion
Electrode. Water Research. 8:23-31.
Gardiner, J., 1974b. The Chemistry of Cadmium in Natural Water - 2. The
Adsorption of Cadmium on River Muds and Naturally Occuring Solids. Water
Research. ' 8:157-164.
Gardiner, L.R., 1974c. Organics vs. Inorganic Trace Metal Complexes in
Sulfide Waters - Some Speculative Calculations Based on Available Stability
Constants, Geochim Cosmochim Acta. 38:1297-1302.
Gardiner, J., 1976. Complexation of Trace Metals by EDTA in Natural
Waters. Water Research. 10:507-514.
Gavis, J. and J.F. Ferguson, 1972. The Cycling of Mercury Through the
Environment. Water Research. 6:989-1008.
Geesey, G.G., L. Borstad and P.M. Chapman, 1984. Influence of Flow-related
Events on Concentration and Phase Distribution of.Metals in the Lower Fraser
River and a Small Tributary Stream in British Columbia, Canada. Water
Research. 18:233-238.
Gonzalves, M.L.S., L. Sigg and W. Stumm, 1985. Voltametric Methods of
Distinguishing Between Dissolved and Particulate Metal Ion Concentration in
the Presence of Hydrous Oxides: A Case Study of Lead(11). Environmental
Science and Technology. 19:141-146.
Guy, R.D., C.L. Chakrabarti and L.L. Schramm, 1975. The Application of a
Simple Chemical Model of Natural Waters to Metal Fixation in Particulate
Matter. Canadian Journal of Chemistry. 53:661-6169.
Guy, R.D., C.L. Chakravarty and D.C. McBain, 1978. An Evaluation of
Extraction Technique for the Fractionation of Copper and Lead in Model
Sedimentation Systems. Water Research. 12:21-24.
Haas, C.N. and N.D. Horowitz, 1986. Adsorption of Cadmium to KaolLnite in
the Presence of Organic Material. Water, Air and Soil Pollution. 27:131-
140.
Harding, J.P.C. and B.A. Witton, 1978. Zinc, Cadmum and Lead in Water,
Sediments and Submerged Plants of the Derwent Reservoir, Northern England.
Water Research. 12:307~316.
Hegelson, H.C., 1969. Thermodynamics of Hydrothermal Systems at Elevated
Temperatures and Pressures. Am. J. Sci. 266:729-804.
133
-------
Huckabee, J.W. and R.A. Goldstein, 1976. Dynamic Redistribution of
Methylmercury in a Pond Ecosystem. Electric Power Research Institute, Palo
Alto, CA.
Inoue, Y. and M. Munemori, (1979). Coprecipitation of Mercury(ll) with
Iron(111) Hydroxide. Environmental Science and Technology. 13:443-445.
Jernelov.A., (1970). Release of Methyl Mercury from Sediments with Layer
Containing Inorganic Mercury at Different Depths. Limnol. Oceanogr.
15:958-960.
Jiang, S., H. Robberecht and F. Adams, 1983. Identification and
Determination of Alkylselenide Compounds in Environmental Air. Atmospheric
Environment. 17:111-114.
Kharkar, D.P., K.K. Turekian and K.K. Bertine, (1968). Stream Supply of
Dissolved Silver, Molybdenum, Antimony, Selenium, Chromium, Cobalt, Rubidium
and Cesium to the Oceans. Geochimica et. Cosmochimia Acta. 32:285-298.
Klaverkamp, J.F., D.A. Hodgins and A. Lutz, 1983. Selenite Toxicity and
Mercury-Selenium Interactions in Juvenile Fish. Arch. Environ. Contam.
Toxicol. 12:405-413.
Kothny, E.L., 1971. The Three Phase Equilibrium in Nature. Trace Elements
in the Environment, E.L. Kothny (Ed.), published by ACS.
Kudo, A., H. Nagase and Y. Ose, 1982. Proportion of Methylmercury to the
Total Amount of Mercury in River Waters in Canada and Japan. Water
Research. 16:1011-1015.
Lagas, P..J.P.G. Loch, C.M. Bob and L.J.A. Gerringa, 1984. The Behavior of
Barious ijj a Landfill and the Underlying Soil. Water, Air and Soil
Pollution. 22:121-129.
Laxen, D.P.H. and Roy M. Harrison, 1981. The Physiochemical Speciation of
Cadmium, Lead, Copper, Iron and Manganese in the Final Effluent of Sewage
Treatment Works and its Impact on Speciation in the Receiving Water. Water
Research. 15:1053-1066.
Lee, Ying-Hua, H. Hultberg and I. Andersson, 1985. Catalytic Effect of
Various MM Ions on the Methylation of Mercury in the Presence of Humic
Substances. Water, Air and Soil Pollution. 25:391-400.
Long, D.T. and E.E. Angino, 1977. Chemical Speciation of Cd, Cu, Pb, and Zn
in Mixed Freshwater, Seawater and Brine Solutions. Geochima. Cosmochima.
Acta. 41:1183-1191.
Mantoura, R.F.C., A. 'Dickson and J.P. Riley, 1978. The Complexation of
Metals with Humic Materials in Natural Waters. Estuarine and Coastal Marine
Science. 6:387-408.
134
-------
McKnight, D.M., G.L. Feeler, E.M. Thruman, R.L. Werdshaw, and J.C. Westall,
1983. Coraplexation of Copper by Humic Substances from Different
Environments. The Sci. of the Total Environment. 28:65-76.
Measure, C.L. and J.D. Burton, (1978). Behavior and Speciation of Dissolved
Selenium in Estuarine Waters. Nature. 273=293-295.
Mendinsky, M.A., R.G. Cuddihy, W.C. Griffith, S.H. Weissman and R.O.
McClellan, 1985. Projected Uptake and Toxicity of Selenium Compounds from
the Environment. Environmental Research. 36:181-192.
Mesmer, R.E. and C.F. Baes Jr., 1975. The Hydrolysis of Cations. ORNL-NSF-
EATC-3. Part III. Oak Ridge National Laboratory.
Miller, M.H. and A.J. Ohlrogge, 1958. Water Soluble Chelating Agents in
Organic Materials. Soil Sci. Soc. Am. Proc. 22:225-228.
Miragaya, J.G. and M. Davalos, 1986. Sorption and Desorption of. Zn on Ca-
Kaolinite. Water, Air and Soil Pollution. 27:217-224.
Mouvet, C. and A.C. M. Bourg, 1983. Speciation (Including Adsorbed Species)
of Copper, Lead, Nickel and Zinc in the Meuse River. Water Research.
17:641-619.
Newton, D.W., R. Ellis, Jr., and G.M. Paulson, 1976. Effect of pH and
Complex Formation on Mercury(II) Adsorption by Bentonite. J. for
Environmental Quality. 5:251-254.
Nienke, G.E. and G. Fred Lee, 1982. Sorptin of Zinc by Lake Michigan
Sediments. Water Research. 16:1373-1378.
Nishimura, H. and M. Kumagai, 1983. Mercury Pollution of Fishes in Minimata
Bay and Surrounding Water: Analysis of Pathway of Mercury. Water, Air and
Soil Pollution. 20;401-411.
Nriagu, J.O. and H.K. Wong, 1983. Selenium Pollution of Lakes Near the
Smelters at Sudbury, Ontario. Nature. 301:55~57.
Nriagu, J.O., H.K.T. Wong and R.D. Coker, 1981. Particulate and Dissolved
Trace Metals in Lake Ontario. Water Research. 15:91-96.
Oakley, S.M., P.O. Nelson and K.J. Williamson, 1981. Model of Trace MM
Partitioning in Marine Sediment. Environmental Science and Technology.
15:474-480.
Panday, K.K., G. Prasad and V.N. Singh, 1986. Use of Wollastonite for the
Treatment of Cu(II) Rich Effluents. Water, Air and Soil Pollution. 27:287-
296.
Peterson, R., 1982. Influence of Copper and Zinc on the Growth of a
Freshwater Algae, Senedesmus Quadricauda: The Significance of Chemical
Speciation. Environmental Science and Technology. 16:443-447.
135
-------
Pierce, M.L., and C.B. Moore, 1982. Adsorption of Arsenite and Aresate on
Amorphous Iron Hydroxide. Water Research. 16:1247-1253.
Pita, F.W. and N.J. Hyne, 1975. The Depositional Environment of Zinc, Lead
and Cadmium in Reservoir Sediments. Water Research. 9:701-706.
Ramamoorthy, S. and B.R. Rust, 1978. Heavy Metal Exchange Processes in
Sediment-Water Systems. Environmental Geology. 2:165-172.
Randhawa, N.S. and F.E. Broadbent, 1965. Soil Organic Matter-Metal
Complexes: 6. Stability Constants of Zinc-Humic Acid Complexes at
Different pH Values. Soil Science. 99:362-366.
Reuter, J.H. and E.M. Perdue, 1977. Importance of Heavy Metal-Organic
Matter Interactions in Natural Waters. Geochim. Cosochim. Acta. 41:325-
334.
Rubin, A.J., 1974. Ed., 'Aqueas Environmental Chemistry of Metal', p. 59,
Ann Arbor Science Publishers, Ann Arbor, MI.
Rudd, T., R.M. Sterritt and J.N. Lester, 1984a. Complexation of Heavy MMs
by Extracellular Polymers in the Activated Sludge Process. WPCF. 56:1260-
1268.
Rudd, T., R.N. Sterritt and J.N. Lester, 1984b. Formation and Conditional
Stability Constants of Complexes Formed Between Heavy MMs and Bacterial
Extracellular Polymers. Water Research. 18:379-384.
Rudd, J.W.M., and M.A. Turner, 1983. The English-Wabigoon River System:
II. Suppression of Mercury and Selenium Bioaccumulation by Suspended and
Bottom Sediments. Can. J. Fish. Aquat. Sci. 40:2218-2227.
Rudd, J.W.M., M.A. Turner, B.E. Townsend, A. Swick and A. Furutani, 1980.
Dynamics of Selenium in Mercury Contaminated Experimental Freshwater
Ecosystem. Can. J. Fish. Aquat. Sci. 37:848-857.
Rybicka, E.H., 1985. Sorption of Ni, Zn and Cd on Sepiolite. Clay
Materials. 20:525-527.
Sato, C., J.L. Schnoor and D.B. McDonald, 1986a. Characterization of
Effects of Copper, Cadmium and Nickel on the Growth of Nitrosomonas
europaea. Environmental Toxicology and Chemistry. 5:403-416.
Sato, C.,J.L. Schnoor and D.B. McDonald, 1986b. Effects of Copper and
Nickel on the Growth of Nitrosomonas europaea. Toxicity Assessment: An
International Quarterly, 1:357-376.
Scaife, J.F., 1957. Can. J. Chem. 35:1132-
Schecher, W.D. and C.T. Drisscol, 1985. Interaction of Copper and Lead with
Nostoc Muscorum. Water, Air and Soil Pollution. 24:85-101.
136
-------
Schnitzer, M. and S.I. M. Skinner, 1967. Organo-Metallic Interactions in
Soils: 7. Stability Constants of Pb++, Ni++, Mn++, Co++, Ca++ and Mg++ -
Fulvic Acid Complexes. Soil Science. 103-247-252.
Schnitzer, M., 1969. Reaction Between Fulvic Acid, a Soil Humic Compound and
Inorganic Soil Constituents. Soil Sic. Soc. Am. J. 33=75-81.
Sebesta, F., P. 3enes, J. Sedlacek, J. John and R. Sandrik, 1981. Behavior
of Radium and Barium in a System Including Uranium Mine Waste Waters and
Adjacent Surface Waters. Environmental Science and Technology. 15:71-75.
Semu, E., B.R. Singh and A.R. Selmer-Olsen, 1986. Adsorption of Mercury
Compounds by Tropical Soils. Water, Air and Soil Pollution. 27:19-27.
Sergeyeva, E.I. and I.L. Khodakovsky, 1969. Physicochemical Conditions of
Formation of Native Arsenic in Hydrothermal Deposits. Geochimistry
International (Geokhimiya). 7:846-849.
Shamberger, R.J., 1981. Selennium in the Environment. The Science of the
Total Environment. 17:59~74.
Shaw, T.L. and U.M. Brown, 1974. The Toxicity of Some Forms of Copper to
Rainbow Trout. Water Research. 8:377~382.
Shephard.B.K., A.W. Mclntosh, G.J. Atchison and D.W. Nelson, 1980. Aspects
of Aquatic Chemistry of Cadmium and Zinc in a Heavy Metal Contaminated
Lake. Water Research. 14:1061-1066.
Shuman, L.M., 1977. Adsorption of Zn by Al Hydrous Oxide as Influenced by
Aging and pH. -Soil Sci. Soc. Am. J. 41:703~706.
Shuman, L.M., 1976. Zn Adsorption Isotherms for Soil Clays with or without
Ironoxide Removal. Soil Sci. Soc. Am. J. . 40:349-352.
Shuman, M.S. and G.P. Woodward, Jr., 1977. Stability Constants of Copper-
Organic Chelates in Aquatic Samples. Environmental Science and
Technology. 11:809-813.
Shuman, M.S. and J.L. Cromer, 1979. Copper Association with Aquatic Fulvic
and Humic Acids. Estimation of Conditional Formation Constants with a
Titrimetric Anodic Stripping Voltametry Procedure. Environmental Science
and Technology. 13:543-545.
Sillen, L.G. and A.E. Martell, 1964. Stability Constants of Metal-Ion
Complexes. Special publication NO. 17. The Chemical Society, London.
Sillen, L.G. and A.E. Martell, 1971. Supplement No.'l to Stability
Constants of Metal-Ion Complexes. Special publication No. 25. The Chemical
Society, London.
Sohn, M.L. and M.C. Hughes, 1981. Metal Ion Complex Formation Constants of
Some Sedimentary Humic Acids with Zn(II), Cu(II), and Cd(II). Geochima
cosmochima Acta. 45:2393-2399.
137
-------
Smith, R.M. and A.E. Martell, 1976. Critical Stability Constants. Vol. 4,
Inorganic Complexes, Plenum Press, New York, and London.
Spangler, W.J., J.L. Spigarelli, J.M. Rose and H.M. Miller, 1973.
Methylmercury: Bacterial Degradation in Lake Sediments. Science. 180:192-
193.
Sterritt, R.M. and J.N. Lester, 1985. Aspects of the Determination of
Complexation Parameters for the Metal-Particulate Complexes in Activated
Sludge. Water Research. 19:3155-321.
Sterritt, R.M. and J.N. Lester, 1984. Comparison of Methods for
Determination of Complexation Parameters for MM Particulate Complexes in
Activated Sludge. Water Research. 18:1149-1153.
Stevenson, F.J., 1976. Stability Constants of Cu++, Pb++ and Cd++ Complexes
with Humic Acid. Soil Sci. Soc. Am. J. 40:665-672.
Stiff, M.J., 1971. Copper/Bicarbonate Equilibria in Solutions of
Bicarbonate Ion at Concentrations Similar to Those Found in Natural Water.
Water Research. 5:171-176.
Stolzenburg, T.R., R.R. Stanforth and David G. Nichols, 1986. Potential
Health Effects of Mercury in Water Supply Wells. Journal AWWA. 78:45-48.
Sunda, W.G., D.W. Engel and R.M. Thuotte, (1978). Effect of Chemical
Speciation on Toxicity of Cadmium to Grass Shrimp, Palaemonetes pugio:
Importance of Free Cadmium Ion. Environmental Science and Technology.
12:409-413.
Suzuki, M., T. Yamada and T. Miyazoki, 1979. Sorption and Accumulation of
Cadmium in the Sediment of the Tama River. Water Research. 13:57-63.
Sylva, R.N., 1976. The Environmental Chemistry of Copper(II) in Aquatic
Systems. Water Research. 10:789-792.
Talbot, R.F. and A.W. Andren, 1984. Seasonal Variations of 210 Pb and 210
Po Concentrations in an Oligotrophic Lake. Geochemica et Cosmochimica
Acta. 48:2053-2063.
Takamatsu, T., M. Kawashima and M. Koyama, 1985. The Role of Mn2"1" - Rich
Hydrous Manganese Oxide in the Accumulation of Arsenic in Lake Sediments.
Water Research. 19:1029-1032.
Tan, K.H., L.D. King, H.D. Morris, 1971. Complex Reactions of Zinc with
Organic Matter Extracted from Sewer Sludge. Soil Sci. Soc. Am. Proc.
35:745-751.
Turner, M.A. and A.L. Swick, 1983. The English - Wabigoon River System:
IV. Interaction Between Mercury and Selenium Accumulated from Waterborne
and Dietary Sources by Northern Pike (Esox lucius). Can. J. Fish. Aquat.
Sci. 40:2241-2250.
138
-------
Turner, M.A. and J.W.M. Rudd, 1983.. The English - Wabigoon River System:
III. Selenium in Lake Enclosures: its Geochemisry, Bioaccumulation and
Ability to Reduce Mercury Bioaccumulation. Can. J. Fish. Aquat. Sci.
40;2228-2240.
van den Berg, C.M. G. and J.R. Kramer, 1979. Determination of Complexing
Capacities of Ligands in Natural Waters and Conditional Stability Constants
of Complexes by Means of Manganese Dioxide. Analytica Chemica Acta.
106:113-120.
van Dorst, S.H. and P.J. Peterson, 1984. Selenium Speciation in the Soil
Solution and its Relevance to Plant Uptake. J. Sci. Food Agri. 35:601-605.
Vuseta, J. and J.J. Morgan, 1978. Chemical Modeling of Trace Metals in
Fresh Waters: Role of Complexation and Adsorption. Environmental Science
and Technology. 12:1302-1309.
Wagemann, R., 1978. Some Theoretical Aspects of Stability and Solubility of
Inorganic Arsenic in Freshwater Environment. Water Research. 12:139-145.
Wehr, J.D. and L.M. Brown, 1985. Selenium Requirement of a Bloom Forming
Planktonic Alga from Softwater and Acidified Lakes. Can. J. Fish. Aquat.
Sci. 42:1783-1788.
Wren, C.D., H.R. Maccrimmon and B.R. Loescher, 1983. Examination of
Bioaccumulation and Biomagnification of Metals in a Precambrian Shield
Lake. Water, Air and Soil Pollution. 19:277-291.
Zieve, R. and P.J. Peterson, 1981. Factors Influencing the Volatilization
of Selenium from Soil. The Science of the Total Environment. 19:277-284.
Zirino, A. and S. Yamamoto, 1972. A pH-Dependent Model from the Chemical
Speciation of Cu, Zn, Cd and Pb in Seawater. Limnol. Oceanography. 17:66.1-
671.
139
-------
SECTION 5
ANALYTICAL SOLUTIONS
5.1 INTRODUCTION
Modeling the transport and transformation of toxic chemicals is
performed by development of a mass balance around a clearly defined control
volume or ecosystem. If toxic substances are discharged into receiving
waters, they will be transported by advection and dispersion, and they may
be subject to chemical, physical, and biological reactions and phase-
transfer.
For reservoirs and lakes, far enough away from discharge sites, one can
generally assume that the substances are well mixed and uniformly dispersed,
mainly by turbulence and differential advection caused by wind and bed shear
over the time of scale of interest (days to years). A completely mixed
model (CSTR, continuously stirred tank reactor) -is appropriate for those
lakes where dispersive transport is predominant. For streams and rivers,
one can assume that advection is the primary transport mechanism and that
there is negligible mixing due to diffusion or dispersion in the direction
of flow. The plug-flow (PF) model may be applicable for those rivers and
streams where diffusive transport is minimal and advection by the current.
velocity is predominant. A third type of model, the advective-dispersive
(plug-flow with dispersion) model, is appropriate for estuaries and
reservoirs where toxic chemicals advect and diffuse simultaneously as they
move through the system.
As was discussed in Section 2, a key feature in determining the
appropriate model is the magnitude of the mixing, which approaches infinity
in the completly mixed system and approaches zero in the plug-flow system.
The plug-flow-with-dispersion (PFD) model lies in between the idealization
of completely mixing and of plug-flow. For many applications in water
quality analysis, the completely mixed model or plug-flow model are
appropriate when a first approximation of water quality is required.
Once the mixing characteristics of the surface water are determined,
then an effort should be made to express the dynamics in mathematical form
as a mass balance equation around a control volume (bay, lake, or
compartment). The model formulation may incorporate transport and reactions
based on the principle of conservation of mass. A mass balance around a
control volume may be expressed as
140
-------
Accumulation = Inputs - Outputs ± Reactions
of mass
In this Section, analytical solution techniques are described for
idealized systems. It should be recognized, however, that the models
described here represent only the simplest, most ideal mixing conditions.
In spite of this fact, they may be quite useful in checking more complicated
numerical results and in gaining insight to the dynamics of toxic chemical
movement in the environment.
5.2 COMPLETELY MIXED SYSTEMS
An ideal completely mixed system is illustrated, using a lake as an
example, in Figure 5.01. The major assumptions involved in this model are
in
Gout
(al)INPULSE INPUT
c
6
0 TIME,!
(bl) CONTINUOUS INPUT
c
o
TIME.t
O
(a2)EFFLUENT RESPONSES TO
AN INPULSE INPUT
K>0
0 TIME, t
(b2)EFFLUENT RESPONSES TO
A CONTINUOUS INPUT
O
K>0
TIME.t
Figure 5.01
Schematic of a completely mixed lake, with inputs and effluent
responses.
that the concentration of chemicals in the lake is uniform (completely-
mixed) and the lake outlet has a concentration, C, and the concentration is
the same everywhere within the lake. The mass balance yields
Change in
Mass in
the lake
Mass in
Inflow
Mass in
Outflow
Mass reacting
in the lake
141
-------
This can be expressed mathematically as
A(VC)
-AT" = Qin Cin - Qout C * rV
where Cin = chemical concentration in inflow (ML~3),
C = chemical concentration in the lake and in outflow
Qin = volumetric inflow rate (L^T"1),
Qout = volumetric outflow rate (L^T~1),
V = volume of the lake (L^),
r = reaction rate (ML~^T~ ); positive and negative signs indicate
formation and decay reactions, respectively,
and
t = time (T).
The limit as At approaches zero gives the ordinary differential equation
below.
"$* • "in'ln -
-------
Dividing by V yields
d£ _ - Q C - - £- (54)
dt ' V C " t . (^ }
d
where td = V/Q = mean hydraulic detention time (T). With an initial
condition of C = CQ at t = 0, equation (5.4) can be integrated as
C t
J ~ dC = - ~- Jdt (5.5)
C d 0
o
Integrating equation (5.5) for the time-interval zero to t yields
C = CQ exp (-t/td) (5.6)
Equation (5.6) is the analytical solution to an impulse input for a
conservative tracer.
In the event that a reactive chemical was spilled to the lake, equation
(5.2) may be reduced to
V ~| = -QC - KCV (5.7)
which can be solved similarly:
C = CQ exp -(K + 1/td)t (5.8)
Equation (5.8) is the analytical solution to an impulse input for reactive
substances. A graphical sketch of the responses to an impulse input for
reactive (K>0) and non-reactive (K=0) chemicals is shown in Figure 5.01.
Response to a continuous load, such as a waste discharge from a
municipality or an industry to a lake, is also represented by equation
(5.2), which can be rewritten as:
§ * £- + K) C = ^ (5.9)
dt fcd d
Equation (5.9) has the form of a first-order, nonhomogeneous linear
differential equation. If only the steady-state concentration is desired,
then solution of equaiton (5.9) can be obtained noting that the change in
concentration is zero (dC/dt = 0). The steady-state solution of equation
(5.9) is given as:
Css ' Q-T% ' 1 +1Ktd (5'10)
where Csg = steady-state concentration (ML~3). Note that there is no change
in concentration with respect time, t.
If one desires to see the change in concentration with time, the non-
steady-state solution can be obtained for equation (5.9), a first-order
linear differential equation that has a general form:
143
-------
y' + p(x)y = q(x) (5.11)
with the general solution
y = yQ exp(-P(t)) + exp (-P(t)) exp(P(t)) q(t)dt (5.12)
where P(t) = J p(t) dt
This solution technique is known as the integrating factor method. The
solution of equation (5.9) can be obtained as the integral equation:
-(K + ~)t -(K + ^)t (K + l-)t c
C = Cne d +e d H e d ^ dt (5.13)
u o td
Integrating this equation over the interval 0 to t yields
-(K + l-)t c -(K + l-)t
d
Note that the solution is composed of two concentration changes; the first
term on the right-hand side of the equal sign represents "die-away" of the
initial concentration, and the second term represents "build-up" of
concentration due to continuous input. When t -approaches infinity, equation
(5.1*0 reduces to equation (5.10), the steady-state equation.
If a number of lakes are present in series, these water bodies can be
analyzed collectively. Figure 5.02 shows a series of lakes that consists of
n equal-volume, completely mixed lakes. As was done above for a single
lake, the approach is based on a mass balance around each lake of series.
Before deriving time variable solutions, the steady-state solution will be
developed.
The mass balance for the 1st lake is given as
dC
V dT - ^in - QC1 - KC1V
and solved for
For the 2nd lake,
dC
V dT - S - QC2 - KC2V
and solved for
144
-------
o
UJ
T3
C
CM
UJ
UJ
I
1
CM
0
t
cJ
>
i
c
o
'$
IK
wo
UJ
i
H
tc —
_itn
u.z
u-o
ID O
CM
UJ
en
a.
2
?H
2 2
CO
cu
CO
o
a
CO
CU
t-l
0)
s
•a
c
to
CO
4-1
•a
c
CO
ca
0)
•H
(-1
a)
CO
CO
cu
TJ
ID
0)
4J •
cu f^
1-1 co
a o
B v
O T>
O
C
<*-i o
o -H
4J
o o
•H CO
4J 01
CO l-i
CD &
,e -w
O ••-(
CO |3
eg
O
m
cu
60
•1-1
145
-------
Substitution of equation (5.15) into equation (5.16) yields
Q
c_ -- : — i-^— (5.17)
where t.j is the detention time of each individual lake, not the overall
detention time.
The mass balance for the nth lake is given as
dC
V -rr^ - QC „ - QC - KG V
dt n-1 n n
and solved for
where n is the number of lakes in question and n-1 designates the upstream
lake. Therefore, the analytical solution for the nth lake is given as
C
C - - i2 - - (5.19)
n (1 + Ktd)n
The time variable solution can be obtained for an impulse input of
conservative tracer. The mass balance for the first lake may be given as
dC
V^i=-QC1 (5.20)
Integrating equation (5.20) for the time-interval zero to t with an initial
condition of C1 = C.J/Q\ at t = 0, yields
C1 = C1(Q) exp (- t/dt) (5.21)
The mass balance for the second lake gives
ac c c
dt^-tf
Substituting equation (5.21) into equation (5.22) and rearranging yields
dC C C . . exp (-t/dt)
d d
Equation (5.23) can be solved using the integrating factor method for
C2 = 1[0) exp (~t/td) (5.24)
For the third lake, the mass balance yields
jj/"1 f* f*
do_ i/_ u_
dt t, t,.
d d
146
-------
Substituting equation (5.24) into equation (5.25) and solving using the
integrating factor yields
C t2
C = 1(° exp (-t/td) (5.26)
2td
Thus, the general formula for n lakes in series that receive an impulse
input of conservative tracer is given as
cn ' TTTT t> «o <-*"„> (5-27)
where td is the detention time of an individual lake, V/Q.
In the case that a lake or reactor vessel is segmented into n
compartments as shown in Figure 5.03, the effluent response to an impulse
input of non-reactive chemical may be given by
C nn n-1 ,
Cn - -^q-yy (-V) exp (-nt/td) (5.28)
where t . represents the detention time of the entire vessel (^total^1^ anc*
CQ is the initial concentration if the impulse input were delivered to the
entire vessel (M/vVotal). Tne effluent responses with respect to the number
of compartment are illustrated in Figure 5.03. As seen, the greater the
number of compartment, the greater the tendency towards plug flow
conditions .
Equation 5.28 is particularly powerful because it provides effluent
responses that are intermediate between the ideal plug-flow model and the
ideal completely mixed model (n = » and n = 1 in Figure 5.03). For lakes
and reservoirs that have, in reality, plug flow and dispersion
characteristics, equation (5.28) can be used with a hypothetical number of
compartments (n) to obtain the best fit to an impulse injection of tracer,
and thus obtain the mixing characteristics of the system for modeling of
other pollutants.
5.3 PLUG-FLOW SYSTEMS
An ideal plug-flow system is illustrated, using a river as an example,
in Figure 5.04. The major assumptions involved in this model are that the
bulk of water flows downstream with no longitudinal mixing and that
instantaneous mixing occurs in the lateral and vertical directions. It is a
one- dimensional model. The mass balance is developed around an incremental
volume V and is given as
= QC - Q(C -•- AC) - KCV (5.29)
147
-------
M at t * 0
Q
Cn
ui
Figure 5.03.
Jt.
o
z
UJ
_J
CC
UJ
o
CC
H
U.
O
d
z
0
o
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
n=l
.nsco
3.0
TIME CONSTANTS
A compartmentalized lake and effluent responses to an impulse
input of conservative tracer.
where V = AAX (L2L),
A = cross-sectional area (L2),
Ax = an increment of finite thickness of the stream (L), and
At = a time interval (T), and
K = a first-order decay rate (T~^).
Dividing equation (5.29) by V and simplifying yields
AC
At
QAC
AAx
The limit of equation (5.29) as At ->• 0 is:
r. , = ~ T" ~r\ ~ KC = ~ U t\ ~~ KG
9t A 8x 9x
(5.30)
(5.3D
148
-------
.xn
i
1 QC »
1
s
^1
^^
8
£&
AX
--"!
r^QtC+AO
• _ _
7*
XX1
*
/
lal) IMPULSE INPUT (a2) MOVEMENT OF A PLUG OF
CONSERVATIVE TRACER DOWNSTREAM
'in
0 TIME, t
TIME, t
(bl) CONTINUOUS (b2) STEADY-STATE PROFILE OF
INPUT REACTIVE CHEMICALS
C|n
0 TIME.t
DISTANCE,
Figure 5.04. Schematic of plug-flow system, with inputs and response
profiles.
where u = Q/A = mean velocity. This is the general equation for a plug-flow
system. Note that the concentration C is a function of both time, t, and
distance, x.
At steady state (3C/3t = 0), equation (5.31) reduces
With a boundary condition of C = CQ at x = 0, equation (5.32) can be
integrated by separation of variables to yield
C = C0 exp (- Kx/u)
(5-32)
(5.33)
149
-------
This is the steady-state solution to the plug flow equation. Effluent
responses to an impulse and constant inputs are illustrated in Figure 5.4.
5.4 ADVECTIVE-DISPERSIVE SYSTEMS (PLUG FLOW WITH DISPERSION)
An ideal plug-flow system is illustrated, using an estuary as an
example, in Figure 5.05. As was done in the plug-flow model, the mass
balance is written around an incremental control volume of small but finite
volume.
Accumu- Advective Dispersive
lation = transport + transport
inputs inputs
Advective Dispersive
transport - transport ± Reactions
outputs outputs
A mass balance over an infinitesimal time interval can be written for the
differential volume, AAx, as
- (Q(C + AC) + (-EA (|| + A —))) - KCV (5. 34)
where V = AAx (L2L)
E = dispersion coefficient (L2T 1)
K = first-order reaction coefficient (T~1)
Simplifying equation (5.34) yields
Dividing by V = AAx
H- E^-|- uf|- KC (5.36)
3t 9x2 3x
Equation (5.36) is the time-variable equation for advective-dispersive
systems with constant coefficients, Q, A, E, and K.
The steady-state equation for an estuary system may be obtained by
setting the left-hand side of equation (5.36) equal to zero, 9C/8t = 0.
0 = E^-| - u|| - KC (5.37)
dx
Equation (5.37) is a second-order, ordinary, homogeneous, linear
differential equation, which has the general form:
150
-------
(a) UPSTREAM
+ 00
QC
Q(C + AC)
(al) CONSTANT INPUT
W
TIME, t -00
(a2) STEADY STATE
+ 00
Figure 5.05. Schematic of advective-dispersive system, and input and
steady-state profile of reactive chemicals.
0 = ay'' + by' + cy
where y = f(x), and the general form of the solution is given as
y = B exp(gx) + D exp(jx)
where
g • J
-b ± /b - 4ac
2a
The roots of the quadratic equation of coefficients gives g and
J (g * j)» and B and D are integration constants obtained from boundary
conditions. Note that g and j refer to positive and negative roots,
respectively. Accordingly, the solution of equation (5.37) is obtained as
151
-------
C = B exp(gx) + D exp(jx) (5.38)
where
, u ± At" + 4EK u .. A .
g • J = 2E = 2E (1 ±m)
m = /1 +
UEK
2
u
In order to solve equation (5.38), boundary conditions must be used. To
establish boundary conditions, the estuary system of question may be divided
into upstream and downstream segments at the point of chemical discharge
(Figure 5.05).
In the upstream segment, we can set two boundary conditions (BC 1 and
BC 2) as follows.
BC 1: At the upstream segment, far from the discharge point, the
concentration approaches zero, that is, C = 0 at x = -». Under this
condition, we obtain
D = 0 and C = B exp(gx) (a1)
BC 2: The concentration at the point of discharge is CQ, that is, C =
CQ at x = 0. Under this condition, we obtain
B = C0
Therefore, the concentration in the upstream segment is given as
Ca = C0 exp(gx) = CQ exp (|| (1 + m)) (a2)
In the downstream segment, two additional boundary conditions can be set.
BC 1 : The concentration approaches zero downstream, far from the
discharge point, that is, C = 0 at x = +». Under this condition, we obtain
B = 0 and C = D exp(jx) (b1)
BC 2: At the point of discharge, the concentration is CQ, that is C =
CQ. Under this condition, we obtain
D- C0
Therefore, the concentration in the downstream segment is given as
Cb = C0 exp(jx) = C0 exp (~ (1 - m)) (b2)
The boundary concentration (CQ) at the discharge point can be obtained
by making a mass balance at x = 0 (Figure 5.06).
152
-------
e-o
vo
NOIWVR1.1
a
o
•r-l
4-1
O
tfl
(-1
i-l CO
3 i
o >-,
•r^ -O
4-1 CO
I-l V
CO 4-1
a. ca
CO 4-)
S CO
en
> II
T3 -
0) 4-1
>
0) 4-1
l-i CO
O M
•H O
W H
U ^x
CD ^
cd
o ca
•ri fl
B to
a) t-i
x: 01
o >
VO
o
in
0)
60
153
-------
dC
QCo - EA dF
Mass in = Mass out
dCl
x=0
+ W - QCQ - EA —
(01)
x=0
The reaction is negligible because Ax is inf initesiraally small.
equation (a1 ) ,
dC,
"dx~ .
x=0
and from equation (b1),
From
dx
x=0
Substituting equations (a1)' and (a2) ' into (c1) yields
- EAgB + W = -EAjD
Since B = D = CQ, we obtain
0 EA(g - j)
Substituting g.j of equation (5.38) into equation (c2) and simplifying
yi el ds
C -3-
0 mQ
The final solution is summarized as follows:
C = CQ exp(gx) at x $ 0
C = CQ exp(jx) at x £ 0
where Cn = — -r
0 mQ
g • J - (1 ± m)
(c2)
(o3)
m = /1 +
HKE
The response to a constant input under steady state conditions is presented
in Figure 5.05.
5.5 GRAPHICAL SOLUTIONS
Removal efficiencies for toxic chemicals in lakes or reservoirs may be
estimated using the graph shown in Figure 5.06. The 3-D graph shows the
removal efficiency of a steady-state lake as a function of three
154
-------
K M
dimensionless terms: t IK, K t and +p^ . The constant IK refers to
P
the sum of all reaction rates of the dissolved chemicals, and Kg refers to
the settling rate constant of suspended solids. K and M are the partition
coefficient and the suspended solids concentration, respectively. It is
indicated that the fraction of chemicals removed increases as the
dimensionless number K M/(1 + KM) increases and as the dimensionless
number t .IK increases.
d
This relationship can be obtained in a mathematical form applying the
same principle used in the modeling a completely mixed system. Mass balance
on chemicals around a completely mixed impoundment may be expressed as
dC
QC - QC - KCV (5'39)
dT T(in) T T
Here, the chemical flux is described as QCx(in)' which equals the rate of
mass input or the loading rate, W. The washout of chemicals is given as
and the mass reacted is expressed as KC^V. The constant K refers to the
rate of total sinks of chemicals, which is assumed to be first order with
respect to the total chemical concentration. As was discussed in Section 2,
toxic chemicals released into receiving waters are associated, to a lesser
or greater extent, with suspended and sedimented particles via sorption
processes. Assuming that the reactions such as photolysis, volatilization,
oxidation, and biodegradation occur primarily in the dissolved phase, and
that the adsosrbed chemicals in water are removed predominantly by settling,
one can develop the following reaction term.
KCTV = (IKC + K C )V (5.40)
1 S p
where IK refers to the sum of all reaction rates of the dissolved chemicals,
and Kg refers to the settling rate constant of adsorbed chemicals. (Note:
For sediments, the reactions are likely to occur in the adsorped phase as
well as the dissolved phase.) Incorporating these assumptions, equation
(5.39) can be rewritten as
dC
V -rr1 = W - CTQ - (IKC - K C )V (5.^1)
QL- 1 S p
Under the condition of sorptive equilibrium, C and C may be replaced by
their equivalents in terms of CT. That is,
IK C
c = T-nrM
P
and
KKM C
r =
p 1 + K M
Describing C and Cp as a function of CT, and dividing by V yields
*L U S £KCT KsKPM C
dt ~ V t. 1+KM 1+KM
d p p
155
-------
Solving for Cm under steady^state condition yields
W
C = -. - : - - - (5.44)
rt + rry, <« * V V
Multiplying by td and rearranging for dimensionless term, ^j^, the overall
chemical removal efficiency may be expressed as
°T 1
c
p P
where e = overall chemical fraction removal.
The sum of the decay rate constants (EK) depends on the solubility,
volatility, and chemical structure^reactivity. Some heavy metals and some
low vapor pressure/high solubility, persistant chemicals would, therefore,
not be susceptible to transfer by these mechanisms and equation (5.45)
reduces to
KM
Chemicals that are strongly adsorbed are analogously trapped or removed
in reservoirs, but in a lesser amount than suspended solids. The greater is
the degree of adsorption, the more equal are the removal efficiencies
between the chemical and the suspended solids. This degree of adsorption is
described by KM, the dimensionless product of the partition coefficient
times the suspended solids concentration. KM is equal to the ratio of
adsorbed particulate chemical to dissolved chemical, Cp/C. If KM were
equal to zero, all of the chemical would be in the dissolved phase, and, in
the absence of other reaction decay processes, the fraction removed would be
zero. Because steady-state conditions are rarely observed in lakes and
reservoirs, the above analysis should be considered as a first approximation
or order ^of ^magnitude solution to the problem of estimating the fate of
toxic chemicals in impoundments. Figure 5.06 is plotted based on equation
(5.45) for the conditions KstQ = 4.
156
-------
SECTION 6
DESCRIPTION OF MODELS
6.1 INTRODUCTION
The chemical fate models (TOXIWASP, EXAMS II, HSPF) and one chemical
equilibrium model (MINTEQ) are described in this chapter. They are
supported by the Center for Water Quality Modeling of the Environmental
Research Laboratory, U.S. Environmental Protection Agency, Athens, GA. The
TOXIWASP, EXAMS II, HSPF models represent tools to predict short-term or
long-term effects of toxic chemicals on various aquatic environments. They
provide a basis for quantifying the interactions between toxic chemicals and
receiving water systems. Each model is uniquely structured to account for
relevant transport, transfer and reaction processes, using different spatial
and temporal discretization and numerical solution techniques. The MINTEQ
model, on the other hand, is designed to compute geochemical equilibria of
various inorganics and heavy metals. It calculates aqueous speciation,
equilibrium adsorption/desorption, and precipitation/dissolution of solid
phases, but it does not simulate chemical kinetics or transfer and transport
processes.
6.2 TOXIWASP
TOXIWASP is a dynamic, compartmentalized model that examines the
transport and transformation of toxic chemicals in various receiving water
bodies. It was developed by combining the transport framework of WASP (Di
Toro et al., 1982), with the kinetic structure of EXAMS (Burns et al.,
1982), and including a mass balance for solids and sediment. TOXIWASP can
be applied to streams, lakes, reservoirs, estuaries, and coastal waters, but
it is directed to toxic chemicals, both organics and heavy metals.
The mathematical formulation of TOXIWASP is based on the principle of
conservation of mass, as given in Figure 6.01. Equations (6.1) and (6.2)
can be used to calculate sediment and chemical concentrations in the water
compartment. Transport in TOXIWASP is an advective-dispersive process
represented by a flow and a mixing process defined by a dispersion or
exchange rate. Equations (6.3) and (6.4) calculate the concentrations of
dissolved and sorbed chemicals in the sediment bed. Equation (6.3) accounts
for diffusion-dispersion and pore water transport of chemical between the
bed and the overlying water. Sediment-water exchange is described as a
diffusion or dispersion process. Equation (6.4) accounts for sediment-bound
chemical transport by scour from the bed and deposition to the bed.
Sediment is assumed to be a conservative constituent which is advected and
dispersed among water segments and which can be suspended or fixed in the
157
-------
sediment bed. As in all the models, the reaction and transformation rates
are based on an addition of pseudo^first order rate constants for
hydrolysis, oxidation, biodegradation, volatilization, and photolysis of a
toxic chemical dissolved in water or sorbed to sediments. Most chemical
transformation and reaction rates vary in time and space, depending on
chemical characteristics and environmental conditions.
Figure 6.02 presents the phase transfer and reaction kinetics used in
TOXIWASP. The overall rate of hydrolysis reaction is given by the sum of
three competing reactions: acid^catalyzed, neutral, and base^catalyzed
TOXIWASP FORMULATION
(1) For chemical and suspended sediment concentration in water
compartments.
3C. 3C. . 3C. W.
—1 = ^ u_l + 1_ (E—1) + _1 , K + s (6>1)
ot oX oX oX V 1
3C0 3C. 3C_ W,
£ _ ., u £ + ° rE £j + _£ ., ^ + g (62)
where C^ = concentration of chemical (ML'3)
C2 = concentration of suspended sediment (ML"^)
u = flow velocity of water (LT"1)
W. = mass loading of chemical (MT )
W2 = mass loading of sediment (MT )
_^o ^1
S.| = net exchange of chemical with bed '*" •r>'r
S2 = net exchange of sediment with bed
x = longitudinal distance (L)
t = time (T)
E = longitudinal dispersion (L2T^1)
V = segment volume (L^
K = kinetic degradation or transformation rate
W S. W .S
s b d w
2" h, ' K,
where Ws = scour (erosion) velocity of bed sediment (LT )
W^ = deposition velocity of suspended sediment (LT )
L^y = depth of active bed sediment layer (L)
LW = depth of water layer (L)
158
-------
(2) For the dissolved and particulate chemical concentration in the
sediment bed.
3C , K3C 3(U C )
it* ' fe (»V) - -£*- ' "s 'X
8C 3C WC WC
where C = concentration of dissolved chemical in bed (pore water)
C^ = concentration of sorbed chemical in bed (ML^^)
D = vertical dispersion coefficient for dissolved chemical (L T )
D = vertical dispersion coefficient for sorbed chemical (L T )
U = velocity of net pore water movement into or out of the bed (LT )
Wd = deposition velocity of sediment between bed and water column (LT )
W = scour velocity of sediment between bed and water column (LT )
LW = depth of water compartment (L)
Lg = depth of active bed layer (L)
y = vertical distance (L)
RS = net rate of chemical transfer between dissolved and sorbed state
(ML'V1)
K = kinetic degradation or transformation rate
R— Q (v r * -r \f r\r • ^
g - •-> vKg uw •* KUbg ;
where kg = rate constant for sorption (L-^M T"1)
kd = rate constant for desorption (T )
C
C ' = —
w $
C
V = s5
w
where Cw = concentration of dissolved chemical in water
= porosity or volume water per volume segment
C = concentration of sorbed chemical in water
O
•^
Sw = concentration of sediment in water
S = concentration of sediment
Figure 6.01. TOXIWASP formulation.
159
-------
TRANSFORMATION AND REACTION KINETICS IN TOXIWASP
The overall rate of transformation and reactions:
^- ? KC
dt - J, KJC
where K^ = pseudo-="first order rate constant for the jth processes (i.e.,
hydrolysis, biodegradation, and oxidation, photolysis, and
volatilization) which can vary in space and time (T )
n = number of processes operating on the chemical
C = concentration of chemical of interest (ML^)
(1) Hydrolysis
khyd - katH+] = kn + kb ^OH^
where k- ^ = pseudo^first order rate constant for hydrolysis (T'")
ka = second order rate constant for acid^catalyzed hydrolysis
aW1)
kn = first order rate constant for neutral hydrolysis (T'1 )
k^ = second order rate constant for base^catalyzed hydrolysis
aW1)
[H+] = hydrogen ion concentration
[OH^] = hydroxide ion concentration
(2) Biodegradation
Kbio = kbioB
where Kbio = pseudo^-first order rate constant for the biodegradation
second order rate constant for biodegradation '^"
B = bacteria concentration (ML
(3) Oxidation
K
oxi =
where KQxl = pseudo^first order rate constant for oxidation (T'1 )
k j = second order rate constant for oxidation (L^n^'T'1)
[RO-] = molar concentration of free radical oxygen (oxidant) (nL'3)
(4) Photolysis
3
Kpho - kpho[L] ^ *i«i
160
-------
where K ho = pseudo-^ first order rate constant for photolysis (T )
k . = average first order photolysis rate constant for water surface
during cloudless conditions in summer (day )
average reaction quantum yield for compound ii
dissolved, sediment^sorbed, biosorbed) (moles per einstein)
<(>. = average reaction quantum yield for compound in form "i" (i.e.,
a. = attenuation coefficient (L )
[L] = fraction of cloudless summer surface light intensity in segment
(unit less)
1 - exp("K zD )
[L] = { = ne } {1 - 0.056(CLOUD)} (FL) (LIGHT)
Ke Z Df
where Ke = segment light extinction coefficient (per meter)
z = depth of water (L)
Df =» ratio of optical path length to vertical depth (unit less)
CLOUD = average cloud cover (tenths)
FL = latitude correction factor
LIGHT = normalized time function for light, representing diurnal or
seasonal changes (0^1.0)
(5) Volatilization
Kvol = kvol
where K •, = pseudo^first order rate constant for volatilization (T )
k _, = first order rate constant for volatilization; mass transfer
rate coefficient (ky) divided by the average depth of the water
body (L)
1 -.1 x
kv = D 5"~ = overall mass transfer coefficient (LT )
Rg+Rl
where R_ = vapor^phase transport resistance (TL )
T»I
R^ = liquid^phase transport resistance (TL'1)
8.206 x 01"5T
n =
K H/18/MW
W V
where T = Kelvin temperature (degrees Kelvin)
K..., = water vapor exchange constant = 0.1857 + 11.36 (W,,)
w v v
H = Henry's Law constant (atm^nP per mole)
MW = molecular weight of water,
Wy = wind velocity at 10 cm above the water surface (meters per hour)
161
-------
KQ2/32/MW
R1
where Kg2 = reaeration velocity in segment or oxygen exchange constant
(meters per hour)
KQ2 = (0.01 K2Q) 1.024 exp (Tg ' 20.0)
where TS = segment temperature (°C)
In a stream:
When z > 2 feet, K2Q =27.6 u°-67/z°-85,
When z < 2 feet, K2Q = 14.8 U0.969z0.673f
or K2Q = 16.4 u°-5/z°'5,
where K2Q = reaeration velocity at 20 degree C (cm per hour)
u = average segment velocity (feet per sec)
z = average segment depth (feet)
In a lake (wind^induced reaeration):
K2Q = ^0.46 W(t) + 0.136 W*t)
where W/^\ = time^varying wind velocity (meters per sec)
(6) Sorption
P — C (n -f n + rt \
123
1
a =
where C^. = total chemical concentration in segment (mg/L)
C = dissolved chemical concentration in the segment (mg/L)
162
-------
K p = partition coefficient of the chemical on the sediment (L/kg)
K = partition coefficient of the chemical with biota (L/kg)
S = concentration of sediment (mg/kg)
B = concentration of biomass (mg/kg)
<(> = porosity of segment = water volume/total volume
1
fraction of chemical dissolved in water phase of segment
a_ = fraction of chemical sorbed onto sediment phase of segment
a_ = fraction of chemical sorbed onto biological phase of segment.
Figure 6.02. Transformation and reaction kinetics in TOXIWASP.
reactions. The rate of biological degradation is expressed using a
simplified Monod relationship at low organic substrate concentrations, i.e.,
a second order reaction proportional to both bacteria and chemical
concentrations. The rate of oxidation is also expressed by a second order
equation assuming the reaction is proportional to both oxidant and chemical
present in the system. The photolysis rate is influenced by both the
intensity of solar radiation and structure of the compound through its
quantum yield, the efficiency by which a quantum of energy (photon) creates
a reaction at the molecular level. Environmental inputs to estimate the
photolysis rate include water depth, cloudiness, latitude, and time of the
year.
The volatilization kinetics were formulated based on a Lewis-Whitman's
two-film resistance model with a uniform layer assumption. Environmental
variables for the computation of the volatilization rate include water
temperature and local and time-varying wnd speed. TOXIWASP calculates the
overall mass transfer coefficient as a function of the longitudinal
advective velocity and depth. (EXAMS II requires that the mass transfer
coefficient for volatilization be specified by the user.) Adsorption of
chemicals to solids (sediment and biomass) is computed assuming local
equilibrium using a chemical-specific partition coefficient and the
spatially varying environmental organic carbon fractions. The
concentrations of total chemical and solids are calculated by finite-
difference approximations of their mass balance equations.
The physical, chemical, and biological inputs required for TOXIWASP are
listed in Appendix B. The model considers three sorptlon possibilities
(i.e., dissolved, sediment sorbed, and bio-sorbed) for an unionized form of
the chemical. (lonization of chemical is not considered in TOXIWASP.) The
model calculates total sediment and chemical concentrations explicitly every
time step for every segment. The segments can be arranged in a one-', two-,
or three-dimensional configuration. TOXIWASP can handle both point and
nonpoint source loads, and can estimate time-varying chemical exposure
resulting from pulse chemical loads.
163
-------
WASTOX is a similar model to TOXIWASP. The largest difference between
the two models is in how bioaccumulation is treated. Both TOXIWASP and
WASTOX have the unique feature of sediment burial or erosion, based on a
mass balance for solids. This feature is required to assess long term
behavior of persistent, hydrophobia chemicals such as PCB's, DDT, dioxin,
etc. Figures 6.03 and 6.0*1 illustrate the segments where sediment
deposition exceeds scour and where scour exceeds sediment deposition,
respectively. The review documents are those by Ambrose et al. (1983),
Ambrose et al. (1986), and Connolly and Winfield (1984).
6.3 EXAMS II
The Exposure Analysis Modeling System (EXAMS) (II) is a steady^state or
time variable, compartmentalized model that yields exposure, fate and
persistence information about organic chemicals in aquatic systems. EXAMS
was developed for screening of new chemicals, but it also can be used as a
first approximation in site specific cases. It is an interactive program
that allows the user to enter and store information on a specific chemical,
environment, and loading scheme. It evaluates the chemical behavior and
conducts sensitivity analyses on the probable fate in the aquatic
environment. EXAMS II also contains a few chemicals and canonical
environments that are useful as test cases and are stored on floppy disk
(IBM^compatible) with the source code.
As in all the models, the EXAMS II model is formulated based on the
principle of conservation of mass. The compartments in the EXAMS II model
contain water sediments, biota, dissolved chemicals, and sorbed chemicals
under the completely mixed condition. Loadings and exports are represented
as mass fluxes across the compartments. Like TOXIWASP, sediment^water
exchange is described as a dispersion (Fickian diffusion) process.
EXAMS II sums the overall pseudo^first order reaction rate constants
with respect to the chemical concentration. Its kinetic structures are
similar to those described for TOXIWASP. A simplified two^resistance model
is used to define the process of volatilization. Wind speed, temperature,
and compartment dimensions are necessary environmental data. Photochemiceil
transformation is defined with respect to he chemical absorption spectrum
and quantum efficiency of the chemical. The solar spectrum is subdivided
into 39 wavelength intervals, and the total rate constant is computed as the
sum of contributions from each spectral interval.
The environmental inputs for photolysis include concentrations of
chlorophyll'like pigments, dissolved organic carbon, and sediments. Water
depth is also specified as input. The hydrolysis rate is defined with three
competing reactions: acid^catalyzed, neutral and base^catalyzed reactions,
as a function of pH. The second order rate of biodegradation is described
as a function of chemical concentration and viable degrading microbial
population. Environmental inputs are bacterial population density and the
proportion of total bacterial population that actively degrades the
chemical. The rate of oxidation is also expressed by a second order rate
164
-------
+ At
1
1
*
J.
Sequent
1
a
a
4
Time - t0
Depth Density Cone
d» 1.0 Ct(0)
d, to 0.0
d, />, 0.0
d, ft C,(0)
Time = ta
Depth Dendlf Cone
dt-d,(2) 1.0 Ct(3)
«t(0)-l-d,g ft Crfa)
d, ft 0.0
*. ft C«(0)
Time = ta + At
Depth Deaettj Cone
di-d, 1.0 Ct(a)
«• ft C«(2)
«• ft c»(a)g
d, ft 0.0
Figure 6.03.
t Compaction: SVOL Compacted = Vo!
Pore water volume SVOL squeezed
into water column.
TOXIWASP sediment burial.
During the time = tQ to t1, as sediment and sorbed chemical
settle from the water column, the top bed segment (2)
increases in volume, depth, chemical mass, and sediment
mass. During the time = t2 to t2 + At, at the time when the
top bed segment depth and volume (2) exceed the initial top
two bed segments depth and volume, the top bed segment (2) is
compressed into two segment, and the previous segment (4) at
time t2 is buried.
equation as a function of the concentrations of oxidant and chemical to be
oxidized. Molar concentration of oxidants is an environmental input. The
chemical properties and environmental characteristics that should be
provided by the user are listed in Appendix B.
165
-------
+ At
1
1
Segment
1
8
3
4
Time = IQ
Depth Den*Ujr Cone
dt 1.0 0.0
d. ft C,(0)
da ft c^o)
d, ft C4(0)
Time = \,2
Depth Density Cone
dt+d.(0) 1.0 Cl(Z)
0 ft C,(0)
da ft Ca(0)
d, ft C4(0)
Time = t2 + At
Depth Density Cone
di+dt<0) 1.0 Ci(2)
d, ft C,(2)
d, ft c«(a)
d, ft 0.0
Figure 6.04.
TOXIWASP sediment erosion.
During the time = tg to t^, as sediment and sorbed chemical
erode from the bed, the top bed segment (2) decreases in
volume, depth, chemical mass, and segment mass. During the
time = ^2 to t2 + At, at the time when the segment mass in the
top bed layer equals zero, then the segments are renumbered,
and a new segment (4) is included.
Output from EXAMS II includes up to 20 tables containing the following
information: (1) a transport profile of the natural water system, (2) a
kinetic profile of the chemical, (3) a canonical profile of the system, (4)
the toxicant loading for each system segment, (5) the distribution of the
chemical at steady state, (6) the average, maximum, and minimum
concentrations at steady state in both water and sediment compartments, (7)
an analysis of steady state of the chemical, (8) a simulation of the system
response after load ceases, and (9) exposure (fate and persistence) analysis
summary. The EXAMS II program can be run in three modes: 1) a constant
166
-------
"annual average" loading or input that results in a steady state solution,
2) impulse inputs that simulate spills and result in a dynamic solution, and
3) a monthly average input with or without pulses for a period of 12 months.
The EXAMS II code can handle up to 20 compartments (called segments)
that can be arranged in an arbitrary fashion of littoral, epilimnetic,
hypolimnetic, or benthic compartments. Therefore, rivers, lakes, streams,
and ponds can all be simulated including sediment compartments that can be
layered vertically such that one can simulate an active, exchanging bed
layer and a fixed, deeper sediment compartment. The EXAMS II model,
however, does not contain a solids balance in the situation where the bed is
aggrading or degrading.
Novel features of the EXAMS II model include the introduction of
"canonical" environments, i.e., typical environmental systems and variables
that provide the necessary input variables to solve the seconds-order
reaction equations. In addition, 12 chemicals that were studied by Smith et
al., (1977) are made available to the user such that rate constants are
internally specified and not require as user input. Several canonical
environments are available (a eutrophic lake, an oligotrophic lake, a pond,
and a river) as well as Lake Zurich. Templates are available for the user
to specify a new chemical or new environment. Transformation products are
computed for each spatial and temporal segment defined by the user. Unlike
other models, EXAMS II accounts for ionization of organic chemicals (Figure
6.05). It allows for molecules of +3, +2, +1, 0^1, "2, and ^3 charge, and
each charged state can be either dissolved, adsorbed, or biosorbed for a
total 21 different distribution coefficients. Sorbed and biosorbed
fractions are available for photolysis, hydrolysis, oxidation, and
biological transformations as a user option.
EXAMS II is an extended version of EXAMS suitable for the IBM PC XT or
AT. EXAMS II can handle spatial and temporal changes of the transport and
transformation processes of products that result from transformation
reactions. It provides greater flexibility in specifying the timing and
duration of chemical loadings entering a receiving water body. EXAMS II
expanded the treatment of ionic speciation and sorption to include trivalent
ions and complexation with dissolved organic matter. The inputs to EXAMS II
are also expanded to include the effects of seasonal variation by adding
monthly environmental data. EXAMS II estimates some quantities which EXAMS
requires as input data. For example, EXAMS II generates solar light field
from meteorological data. The review documents'for EXAMS are those by
Lassiter et al. (1978), Burns et al. (1982), and for EXAMS II, the one by
Burns and Cline (1985).
6.4 HSPF
The Hydrologic Simulation Program "• FORTRAN (HSPF) is a comprehensive
package program designed for continuous simulation of watershed hydrology
and receiving water quality. HSPF was developed from the Hydrocomp
Simulation Program (HSP) which includes the Agriculture Runoff Management
(ARM) model (Donigian and Davis, 1978) and the Nonpoint Source (NFS) model
167
-------
IONIZATION REACTIONS IN EXAMS II
(1) Basic Reactions:
SH0 + H00 «*• SH,, + OH
2+ ,
SH, - H.O «• SH^ + OH K
425
2+ 3+ -
SH_ + H_0 '** SH.- •*• OH
(2) Acidic Reactions:
•^ +
OU .1. LJ r\ -•v OU a. U A
Oflo T tip1-' O •?
120 «• SH2"
?^ 1^ +
out- j_ tj r\ *± o-' j. u r\ v
on + tioU ** o T n_u "^o ~
c. 3 d j
where SHo = unionized or neutral parent molecule,
SH,, SH , SH^ = singly, doubly, triply charged cation, respectively,
SHp, SH ", S = singly, doubly, triply charged anion, respectively,
^b1' ^b2' ^b'R = eQuili':)rium constants for the basic reactions, and
Ka1 » ^a2» Ka3 = eQuilit>rium constants for the acidic reactions.
Figure 6.05. lonization reactions in EXAMS II.
(Donigian and Crawford, 1976) for runoff simulation, and incorporates the
SERATRA model (Onishi and Wise, 1982) for sediment transport, pesticide
decay, sediments-contaminant partitioning, and risk assessment. The model is
fully dynamic and can simulate chemical behavior over an extended period of
time, using a constant time step selected by the user.
168
-------
HSPF includes time series'-based simulation modules (PERLIND, IMPLND and
RCHRES), and utility modules (COPY, PLTGEN, DISPLAY, DURANL, and GENER).
The simulation (application) modules include mathematics for the behavior of
processes that occur in a study watershed. The watershed is divided into
three segments •*•*• pervious land, impervious land, and a receiving water
system (i.e., a single reach of an open channel or a completely mixed
impoundment). The module PERLND simulates the pervious land segment with
homogeneous hydrologic and climatic characteristics, including snow
accumulation and melt, water movement (overland flow, interflow and
groundwater flow), sediment erosion and scouring, and water quality
(pesticides, nutrients). The IMPLND module simulates the impervious land
segment where little or not infiltration occurs. The IMPLND processes
include snow and water movements, solids, and water quality constituents.
The module RCHRES simulates the segment of receiving water body, including
hydrologic behavior, conservative and nonconservative constituents,
temperature, sediments, BOD and DO, nitrogen, phosphorus, carbon, and pH.
The utility modules perform "housekeeping" operations, designed to provide
the user flexibility in managing simulation inputs and outputs. For
example, the COPY module manipulates time series.
The HSPF model includes a simplification of the code of SERATRA, which
simulates the fate of chemical in the receiving water systems. Mathematical
formulation of SERATRA is presented in Figure 6.06. Transport in SERATRA is
by advective processes, represented by the horizontal and vertical
convections, and vertical diffusion. Equation (6.5) gives the mass balance
equation for sediment, which accounts for sediment erosion and deposition.
It considers two types of sediments: cohesive sediment (i.e., silt and
clay) and noncohesive sediment (i.e., sand) for calculation of scour and
deposition. HSPF solves time series from upstream to downstream for 1^D
branching networks.
Deposition occurs when shear stress at the bed^-water interface is less
than the critical shear stress for deposition. When shear stress is greater
than the critical shear stress for scour, scouring of cohesive bed sediment
occurs. The critical shear stresses for deposition and scour are specified
by the user. Noncohesive sediment is scoured from the bed when the amount
of sand being transported is less than the capacity of flow to carry the
sediment, and deposition occurs when the noncohesive sediment (sand)
transport rate exceeds the sediment^carrying capacity of the river.
Equation (6.6) gives the mass balance for dissolved chemical, which
accounts for chemical and biological reactions as well as phase transfer
(volatilization and sorption) processes. The mass balance equation for
adsorbed chemical is given by Equation (6.7), which accounts for processes
of sorption, erosion and deposition. A linear sorption between dissolved
chemical in the overlying water and organic sediment in the bed is assumed.
Kinetics of the transformation and reaction decay processes used in
HSPF are presented in Figure 6.07. The formulations for these processes are
similar to the previous two models except that volatilization is related to
the molecular diameter of oxygen and the contaminant, and sorption has a
kinetic formulation with a Fruendich isotherm at equilibrium. To compute
169
-------
the biodegradation rate, biomass data are supplied by a constant, monthly,
or a time series input. HSPF also allows the user to specify a unique set
of biomass data for each chemical (i.e., parent and daughter) compounds.
For computation of the photolysis rate constant, the solar spectrum is
subdivided into 18 wavelength intervals. (EXAMS II divides it into 39.)
The total rate constant is calculated as the sum of contributions from each
spectral interval. Environmental inputs include water^surface shading,
light intensity, cloud cover, concentrations of suspended sediment and
phytoplankton, and water depth.
Adsorption and desorption processes are specified by the user by one of
three methods; (1) first order kinetics, which assume that the chemical
adsorbs and desorbs at a rate based on the adsorbed concentration in soil
solution and on the suspended particle; (2) the single^value Freundlich
isotherm, which makes use of a single adsorption/desorption curve for
determining the concentration on the soil and in solution; and (3) the
multiple curves method, which is based on a variable Freundlich
SERATRA FORMULATION
(1) Mass conservation of sediment
f^mjBl) + (uom.B , u^.B) * ^ {m.(W
vertical convection
rate of accum. horizontal convection (not in HSPF)
3m.
vertical diffusion sediment erosion
(not in HSPF) or deposition
where the vertical fall velocity, W ,, is:
sj
''
Wgj = K . m.
For cohesive sediments, the sediment erosion and deposition rates are
defined:
SRj - Hj (J- - ,)
SDj = WsjCj (1
For noncohesive sediments, sediment erosion and deposition rates are
defined:
S ******
SRJ - A
170
-------
s =
SDj A
(2) Mass conservation of dissolved chemical
IT- (CBl) + (u CB - U.C.B) + fr (BCB4)
ot O 11 oi/
vertical advection
rate of accum. horizontal advection (not in HSPF)
' k [Ez 1BJI) ' XCB* ' ^ Kmi CM
vertical diffusion radionuclide chemical/biological decay
(not in HSPF) decay and volatilization
adsorption to suspended sediments desorption from suspended sediments
- Yj(1'3)DjKbj (V - C^ (6-6)
adsorption to bed sediments desorption from bed sediments
f ,/M' f
Kaj
CPJ • V or °PJ
(3) Mass conservation of adsorbed chemical
(uoCpjB , UiCpijB) + _|(W,Wsj)CpjBJl}
vertical convection
rate of accum. horizontal convection (not in HSPF)
vertical diffusion radionuclide (6.7)
(not in HSPF) decay adsorption
contaminated sediment
desorption erosion and deposition
171
-------
where
mj = concentration of sediment of jth size fraction (ML"3)
m^* = concentration of sediment of horizontal inflow for jth size
fraction (ML"3)
SDj = sediment deposition rate per unit area forxjth sediment size
fraction (ML'3)
Spj = sediment erosion rate per unit area for jth sediment size fraction
(ML"3)
B = river width (L)
h = water depth (L)
5, = longitudinal distance (L)
t = time (T)
U = horizontal inflow velocity
UQ = horizontal outflow velocity (LT"1)
W = vertical flow velocity (LT"1)
Wsj = fall velocity of sediment particle of jth size fraction (LT"1)
EZ = vertical diffusion coefficient (L2T"1)
z = vertical direction
N = number of sediment size fractions considered (i.e., sand, silt and
clay, N-3)
Q.J. = sediment transport capacity of flow (ML)
Qj.a = actual amount of sand being transported in a river water
*}
A = river bed surface area (L )
M., = erodability coefficient for sediment of jth size fraction (ML"3)
T. = bed shear stress (ML )
b
Tpn. = critical shear stress for sediment deposition for jth sediment size
^J ~
fraction (ML"^)
= critical shear stress for sediment erosion for jth sediment size
fraction (ML"2)
K. = an empirical constant depending on the sediment type
D. = diameter of jth sediment (L)
C gj = participate chemical concentration (ML"3) per unit weight of
sediment in jth sediment size fraction in river bed
Y. = specific weight of jth sediment (ML"3)
C = dissolved chemical concentration
172
-------
, = dissolved chemical concentration in horizontal inflow
C • = participate chemical concentration (ML") per unit weight of jth
PJ
sediment
K . = first order reaction rate of contaminant degradation due to
hydrolysis, oxidation, photolysis, volatilization and biological
activities (T'1)
K,. = transfer rate of chemical with jth non^moving sediment in bed (T"1)
bJ i
Kd- , K = rate of adsorption and desorption between dissolved contaminant
and sediment (suspended and bed load sediments) of jth size
fraction, respectively (T )
t
K., K. = transfer rate of contaminants for adsorption and desorption,
Jo -
respectively, with jth sediment in motion (T'1)
X = decay rate constant of radioactive material (T )
e = porosity of bed sediment
Kdj» Kd" = distrlbution coefficient (LMH )
f_. = fraction of contaminant sorbed by jth sediment
^J
f,T = fraction of contaminant left in solution
i
M. = weight of jth sediment (M)
o
V,. = volume of water (L-5)
W
C j. = particulate concentration per unit volume of water associated with
the jth sediment size fraction in horizontal inflow
Figure 6.06. SERATRA formulation.
TRANSFORMATION AND REACTION KINETICS IN HSPF
(1) Hydrolysis
Khyd - ka£H+] + kn + kb^OH^
where K^ d = pseudo^first order rate constant for hydrolysis (T )
ka = second order rate constant for acid catalyzed hydrolysis
(LW1)
kn = first order rate constant for neutral hydrolysis (T )
173
-------
second order rate constant for base' catalyzed hydrolysis
[H+] = hydrogen ion concentration
[OH^] = hydroxide ion concentration
(2) Biodegradation
Kbio = kbi2B or Kbio * kbi1
where K^Q = pseudo^first order rate constant for the biodegradation
kbi2 = second order rate constant for biodegradation (L^M"1T"1)
B = concentration of active biomass (ML"^)
kbi1 = generalized firsts-order decay rate (T )
(3) Oxidation
Koxi - koxi £R02]
where KQ ^ = pseudo^first order rate constant for oxidation (T"1 )
koxi = second order rate constant for oxidation (L^rT1!"1)
[R0«] = molar concentration of free radical oxygen (oxidant)
(U) Photolysis
) * 1I i()[l'° ' exp ('2'76 ))e
Kx - ax + Yx ' m + 6xBPhy
10-° - CcKeff
x oo
where Cf = factor accounting for surface shading,
D60/24. = conversion from day to hour intervals,
= reaction quantum yield for photolysis of chemical
I. = seasonal day^average, 24 hour light intensity (einstein per
A ?
cm ^day)
C = fraction of total light intensity of wavelength X which is not
A
absorbed or scattered by clouds
a. = base absorbance term for the light of wavelength X for the system
(Cm'1)
174
-------
m
absorbance term for the light absorbed by suspended sediment
"1 Hx
(8,-Cm mg )
total suspended sediment (ML'3)
6. = absorbance term for the light absorbed by suspended phytoplankton
11
^ '
B no = phytoplankton concentration
e = absorbance term for light of wavelength X absorbed by chemical
A
(L/mol^cm)
z = water depth (cm)
C_ = cloud cover (tenths)
O
°f cloud cover in intercepting light of wavelength X.
(5) Volatilization
Kvol = (k(Pw rvo
where K -^ = pseudo^f irst order rate constant for volatilization (T
(kQ)w = oxygen reaeration rate through waters-air interface (T"1
r = ratio of volatilization rate to oxygen reaeration rate
(6) Sorption
(a) First order kinetics:
F
ads =
Fdes = CmadKdes Thdes(T'35.)
where Fa(js» ^des = current adsorption and desorption fluxes of chemical,
respectively (ML ) per interval)
/^
C d = storage of adsorbed chemical (ML' )
Cmgu = storage of chemical in solution (ML )
Kads = first order adsorption rate parameter (per interval)
Kdes = first order desorption rate parameter (per interval)
Thadg = temperature correction parameter for adsorption (unitless)
Th^gg = temperature correction parameter for desorption (unitless)
T = soil layer temperature (°C)
(b) Single^value Freundlich Parameter:
X = Kf1 C exp(1./N1) + Xfix
175
-------
where X = chemical adsorbed on soil (ppm of soil)
Kfi = single value Freundlich coefficient (unitless)
C = equilibrium chemical concentration in solution (ppm of solution)
N1 = single value Freundlich exponent (unitless)
Xfix = chemical which is permanently fixed in soil (ppm of soil)
(c) Non^single value Freundlich parameter:
X = Kf2 C exp(1./N2) + Xfix
Xf1
Kf2 = X ^X exp(N1/N2) (X - X )
ld jet Xfix Jct I1X
where K^ = non^single value Freundlich coefficient (unitless)
N2 = non^single value Freundlich exponent parameter (unitless)
X.jct = adsorbed concentration where desorption started (ppm of soil)
Figure 6.07. Transformation and reaction kinetics in HSFP.
coefficient. The HSPF model considers the generation of transformation
products, each of which is subject to reaction.and transformation
processes. "Parent^daughter" relationships allowed in HSPF are that a
"daughter chemical^2" may be produced by decay of a "Parent chemical^l," and
that a "daughter chemical^S" may be produced by decay of a "chemical^"!"
and/or "chemical^2."
In order to simulate the hydrologic and receiving water systems, HSPF
requires a considerable amount of information. The user must prepare two
types of data: time series data and user^controlled inputs. All hydrologic
simulations of runoff require time series precipitation and
evapotranspiration data. If the user wants to simulate snowmelt for
hydrologic studies or to simulate water temperature for water quality
studies, then additional time series data of air temperature, wind speed,
solar radiation, and dewpoint temperature are needed. The user's control
inputs include characteristics of the land surface (e.g., land use patterns,
soil types) and agricultural practices. For model applications in which
channel processes are important, additional data on stream flow, channel
geometry, and instream chemical concentrations are necessary. The chemical
and environmental information required in the user's control inputs are
listed in Appendix B. Input data must represent the spatial and temporal
variations in flow and/or chemical loadings resulting from the combined
meteorologic, hydrologic, chemical, and biologic processes of the entire
study area.
The results of an HSPF simulation are time histories of the quantity
and quality of the runoff (flow rate, suspended and bed sediment load, and
176
-------
nutrient and pesticide concentrations). The model then takes these results
and characteristics of the receiving water and simulates the processes that
occur in the aquatic environment. This part of the simulation produces a
time history of water quality and quantity at any point in the watershed.
The review documents are those by Donigian et al. (1984) and Johanson et al.
(19811).
6.5 MINTEQ
MINTEQ is a thermodynamic equilibrium model that computes aqueous
speciation, equilibrium adsorption/desorption, and the mass of metal
transferred into or out of solution as a result of the dissolution or
precipitation of solid phases. It was developed by Felmy et al. (1983) by
combining MINEQL (Westall et al., 1976) and WATEQ3 (Ball et al., 1981), for
incorporation into MEXAMS (Felmy et al., 1984) to assess the fate of
selected priority pollutant metals in aqueous systems. MINTEQ alone,
however, does not have the capability of computing kinetic, transfer or
transport processes.
The program requires two types of data: (1) thermodynamic data and (2)
water quality data. The user is only required to provide the water quality
data; thermodynamic data are contained in a MINTEQ data base. The
thermodynamic data are equilibrium constants, enthalpies of reaction, and
other basic information required to predict the formation of each species or
solid phase. The supplemental data include charge, gram formula weight,
carbonate alkalinity factor, extended Debye^-Huckel parameters, and name and
ID number of each species. Although the MINTEQ data base is probably the
most thoroughly documented and evaluated thermodynamic data base used in any
currently available geochemical model, it is suggested that it should be
updated when new and more reliable information is published, or when data
for reactions not presently included in the data base become available.
There are Several limitations for efficient use of the model. First,
the data base contains equilibrium constants for a limited number of heavy
metals and organic ligands (fulvate, fumate). Equilibrium constants of
heavy metals included in the data base are those of arsenic, cadmium
chromium, copper, lead, mercury, nickel, selenium, thallium, and zinc. The
other metals' constants can be inserted into the data base by the user as
they become available. A number of organics can form complexes with heavy
metals in natural waters, but equilibrium constants for these complexes are
widely varying in the literature. Second, the program treats every reaction
as if it were at chemical equilibrium. In fact, chemical reactions of
precipitation/dissolution and oxidation/reduction are often not at chemical
equilibrium. The kinetics of these reactions are slow. Third, the program
has not been verified for reactions that would occur in natural waters.
Equilibrium constants are based on thermodynamic relationships, assuming a
certain set of environmental conditions. If conditions vary, as they do
from site to site, conditional stability constants are needed to account for
the special chemistry of the site, which is not considered explicitly.
There are analytical chemistry problems with verifying the speciation model
also. Current analytical techniques do not provide high enough precision to
measure separately the activity of individual metal species and complexes.
177
-------
Pertinent review documents are those by Felray et al. (1983) and Felray et al.
(1984) for the MINTEQ model, and a recent update by Brown et al. (198?) for
MINTEQA1.
6.6 SUMMARY
A summary of the three transport models, TOXIWASP, EXAMS II, and HSPF
is given in Table 6.01. All the models are constructed as systems of
differential equations organized around mass balances, considering various
physical, chemical, and biological processes. HSPF uses a finite^difference
numerical solution to the advective equation, whereas TOXIWASP and EXAMS II
are completely mixed compartmentalized models with f inite^diff erence
solutions to the set of time^ variable, ordinary differential equations.
TOXIWASP and EXAMS II can provide either the steady^-state or time^variable
simulation, and can handle both point and nonpoint source loads. The HSPF
model is fully dynamic and can be used for evaluation of both short-" and
long-term migration and fate of a chemical in rivers. In long term
simulations where the contaminant source is in^situ (contaminated sediment),
it would be necessary to use a model with an explicit solids balance so that
sediment burial and scour could be accounted. Both TOXIWASP and HSPF
include a mass balance and conserve solids for this purpose.
HSPF computes a time^varying runoff load to the receiving water.
TOXIWASP can be used for cases requiring more dynamic transport loading
capabilities than EXAMS II, but less detailed and mechanistic sediment
predictions that HSPF. Only HSPF can provide quantitative estimates of the
non^point source chemical load " it is the only model that includes a
field^to^stream sub^model that can be used to estimate the effects of best
management practices (BMPs) in agriculture. For the TOXIWASP and EXAMS II
models, the chemical loadings must be specified based on monitoring data in
the field or predictions from hydrologic simulation.
EXAMS II is readily generalizable to a wide variety of environmental
systems, but it was developed to be used as a screening tool for evaluation
of long-term chemical loadings. EXAMS II is modularly programmed,
relatively easy to use, and well documented. TOXIWASP is sufficiently
general to be applied to all types of natural water systems. HSPF is
comprehensive and general enough to applicable to nontidal rivers, streams
and narrow impoundments. In general, HSPF is more applicable to upland
streams and one^dimensional reservoirs, whereas TOXIWASP is more suited to
stratified lakes and reservoirs, large rivers, estuaries, and coastal
waters. Both TOXIWASP and EXAMS II can be used to simulate one, two, or
three-dimensional segments of aquatic systems by the arbitrary configuration
of completely-mixed compartments which is available for user specification.
The EXAMS II model includes a sophisticated kinetic structure that
allows a full treatment of ionizable compounds (seven different ionic forms)
and ion^specific chemical reactivities (e.g., volatilization, sorption).
Similar kinetics are incorporated into TOXIWASP, but no ionization of
chemical is considered. TOXIWASP has the unique feature of sediment burial,
erosion and scour, based on a mass balance for solids. HSPF and EXAMS II
include a process of generation of transformation products.
178
-------
TABLE 6.01 SUMMARY COMPARISON OF THE MODELS, TOXIWASP, EXAMS II AND HSPF
TOXIWASP EXAMS II HSPF
Types
Steady state model x
Dynamic model x x x
Completely mixed compartments x x
Advective'dispersive model x x
Sediment^water exchange x x x
Applications (r=river, l=lake, r,l,e i"»l»e r
e=estuary)
Numerical Method
Gaussian elimination x
Finite difference x x x
Order of transformation and reaction
Ion reactions x
Daughter product reactions x x
Hydrolysis x x x
(acid and base catalyzed, neutral)
Biolysis (second-border) x x x
Oxidation (second^order) x x x
Photolysis (direct, first order) x x x
Volatilization x x x
(Lewis^-Whitman two^film)
Sorption, equilibrium isotherm L L L & NL
Sorption, kinetic (nonequilibrium) x
Sediment transport
Mass balance on solids
Res us pension/scour
Sedimentation
Deep^-sedimentation
Bed load
Cohesive and Noncohesive sediment
x
X
X
X
fractions
x
X X
X
X
X
L = linear isotherm
NL = nonlinear isotherm (Fruendlich)
179
-------
Physical, chemical, and biological characteristics of chemical and the
receiving environment are essential inputs to all the models. Most rate
constants for the transformations and reactions are treated as variables
that depend on chemical properties and environmental conditions. Table 6.02
lists the environmental inputs for the kinetic constants. EXAMS II has the
user advantage of being interactive, which allows convenient data
manipulation. TOXIWASP and HSPF must be operated in a batch mode.
Environmental data to EXAMS II are contained in a file composed of concise
descriptions of the aquatic systems. TOXIWASP and EXAMS II require much
less effort for data management than HSPF. Effective use of HSPF requires a
considerable amount of data, which may limit wide use of this model. As
stated by Grenney et al. (1978), the selection of a model-for a particular
situation requires a tradeoff between the practicability and economy of the
model application and the amount and refinement of information to be
provided by the model.
MINTEQ is the only model discussed in this chapter that is applicable
expressly for heavy metals. MINTEQ is a geochemical equilibrium model that
is capable of calculating heavy metals speciation, adsorption/desorption,
and precipitation/dissolution reactions. It was developed to link with
EXAMS (for incorporation into MEXAMS) to assess fate and transport of toxic
heavy metals in aquatic systems. MINTEQ alone, however, does not have the
capacity of simulation of kinetic, transfer and transport processes.
6.7 REFERENCES FOR SECTION 6
Ambrose, Jr., R.B., S.I. Hill and L.A. Mulkey, 1983. User's Manual for the
Chemical Transport and Fate Model TOXIWASP, Version 1. EPA^OO/S^'SS-'OOS,
U.S. Environmental Protection Agency, Athens, GA.
Ambrose, Jr., R.B., S.B. Vandergrift and T.A. Wool, 1986. A Hydrodynamic
and Water Quality Model ^ Model Theory, User's Manual, and Programmer's
Guide. EPA^600/3^86^03^, U.S. Environmental Protection Agency, Athens, GA.
Ball, J.W., E.A. Jenne and M.W. Cantrell, 1981. WATEQ3: A Geochemical
Model with Uranium Added. U.S. Geological Survey, Open File Report 81H183.
Brown, D.S. et al., 1987. MINTEQA1. U.S. Environmental Protection Agency,
Athens, GA.
Burns, L.A. and D.M. Cline, 1985. Exposure Analysis Modeling System:
Reference Manual for EXAMS II. EPA-600/3^85"038, U.S. Environmental
Protection AGency, Athens, GA.
Burns, L.A., D.M. Cline, R.R. Lassiter, 1982. Exposure Analysis Modeling
System (EXAMS II): User Manual and System Documentation, EPA^600/3^82^023,,
U.S. Environmental Protection Agency, Athens, GA.
Connolly, J.P. and R.P. Winfield, 1984. A User's Guide for WASTOX, A
Framework or Modeling the Fate of Toxic, Chemicals in Aquatic Environments,
Part I: Exposure concentration, EPA^600/3^8^077, U.S. Environmental
Protection Agency, Gulf Breeze, FL.
180
-------
TABLE 6.02 ENVIRONMENTAL INPUTS FOR COMPUTATION OF THE
TRANSFORMATION AND REACTION PROCESSES IN TOXIWASP, EXAMS II AND HSPF
PROCESS
Bi ©degradation
Hydrolysis
Photolysis
Oxidation
Volatilization
Sediment Sorption
ENVIRONMENTAL INPUT
Active degrading population
Total bacteria population
Temperature
PH
Temperature
Depth
Chlorophyll, phytoplankton
Latitude
Cloudiness
Dissolved organic carbon
Suspended Sediment
Spectral high intensity
a surface
Temperature
Time of day, year
Temperature
Oxidant, free radical
oxygen concentration
Temperature
Compartment dimensions,
area, depth and volume
Mixing
Wind
Slope
Water velocity
Organic carbon content
% water of benthic sediment
Bulk density benthic sediment
Suspended sediment
Biomass
Compartment dimensions
volume, area, depth
Particle size
Temperature
WATER
TOXI
WASP
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
QUALITY MODELS
EXAMS
II
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
HSPF
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
181
-------
Di Toro, D.M., J.J. Fitzpatrick, and R.V. Thomann, 1982. Water Quality
Analysis Simulation Program (WASP) and Model Verification Program (MVP) -
Documentation. Hydroscience, Inc., Westwood, N.J. Prepared for U.S.
Environmental Protection Agency, Duluth, MN, Contract No. 68"01^3872.
Donigian, A.S., Jr., and N.H. Crawford, 1976. Modeling Nonpoint Pollution
from the Land Surface, EPA^600/3"76^083, U.S. Environmental Protection
Agency, Athens, GA.
Donigian, A.S., Jr., and H.H. Davis, Jr., 1978. User's Manual for
Agricultural Runoff Management (ARM) Model, EPA^600/3^78K)80. U.S.
Environmental Protection Agency, Athens GA.
Donigian, A.S., Jr., J.C. Imhoff, B.R. Bicknell, and J.L. Kittle, Jr.,
1984. Application Guide for Hydrological Simulation Program ^ FORTRAN
(HSPF), EPA^600/3^84^065, U.S. Environmental Protection Agency, Athens, GA.
Felmy, A.R., D.C. Girvin and E.A. Jenn, 1984. MINTEQ ^ A Computer Program
for Calculating Aqueous Geochemical Equilibria. EPA^600/3"84'-032, U.S.
Environmental Protection Agency, Athens, GA.
Felmy, A.R., S.M. Brown, Y. Onishi, S.B. Yabusaki, R.S. Argo, D.C. Girvin,
and E.A. Jenn, 1984. Modeling the Transport, Speciation, and Fate of Heavy
Metals in Aquatic Systems. EPA^600/S3'84-'033, U.S. Environmental Protection
Agency, Athens, GA.
Johanson, R.C., J.C. Imhoff, J.L. Kittle, Jr., and A.S. Donigian, Jr.,
1984. Hydrological Simulation Program ^ FORTRAN (HSPF): User's Manual for
Release 8.0, EPA^600/3^84^066, U.S. Environmental Protection Agency, Athen,
GA.
Lassiter, R., G. Baughman, L. Burns, 1978. Fate of Toxic Organic Substances
in the Aquatic Environment, pp. 219^246. In: State^of^the^Art in
Ecologioal Modeling, Jorgensen, S.E. (ed.), vol. 7, Int. Soc. Ecol. Mod.,
Copenhagen.
Onishi, Y. and S.E. Wise, 1982. Mathematical Model, SERATRA, for Sediment^
Contaminant Transport in Rivers and its Application to Pesticide Transport
in Four Mile and Wolf Creeks in Iowa. EPA^600/3^82'045, U.S. Environmental
Protection Agency, Athens, GA.
Smith, J.H., W.R. Mabey, N. Bononos, B.R. Holt, S.S. Lee, T.W. Chou, D.C.
Bomberger, and T. Mill, 1977. Environmental Pathways of Selected Chemicals
in Freshwater Systems, EPA^-600/7^77'113, U.S. Environmental Protection
Agency, Athens, GA.
Westall, J.C., J.L. Zachary and F.M.M. Morel, 1976. MINEQL, A Computer
Program for the Calculation of Chemical Equilibrium Composition of Aqueous
Systems. Tech. Note 18, Dept. Civil Eng., Massachusetts Institute of
Technology, Cambridge, MA.
182
-------
SECTION 7
EXAMPLES AND TEST CASES
7.1 INTRODUCTION
Purposes of this chapter are (1) to show example runs of the EXAMS and
HSPF models, and (2) to compare the simulation results of the two models.
As test cases, alachlor and DDT dynamics are simulated for the Iowa River
and Coralville Reservoir. The Iowa River, located in central Iowa, runs
through prime Iowa farm land from northwest to southeast before flowing into
the Mississippi River. The low-water profile (elevation above sea level) of
the Iowa River is shown in Figure 7.01. Alachlor is a herbicide widely used
to control weeds in corn and soybeans in Iowa. DDT is an insecticide that
was previously used to control corn rootworm and cutworm in Iowa. DDT,
banned in 1970, is no longer used. Properties of alachlor and DDT are
summarized in Table 7.01
Alachlor, 2^chloro"2f,6'^diethyl^N^(methoxymethyl) acetanilide, is one
of the most widely used herbicides in the United States and its use in Iowa
has been steadily increasing. It is a preemergence herbicide used for
controlling certain broadleaf weeds and yellow nutsedge. Application rate
in an emulsified form is 1 to 4 pounds active ingredient per acre (Weed
Science Society of America, 197*0. Alachlor is much less likely to adsorb
to a sediment particle than to remain in solution because of a relatively
high solubility (242 mg/O and low partition coefficient (50^100 Jl/kg).
Literature reviewed by Cartwright (1980) shows that chemical hydrolysis is
not significant at normal aquatic pH levels. Photolysis of alachlor may be
negligible because alachlor does not absorb radiation above 2800 Angstroms
(solar radiation is greater than 2900 Angstrom wavelength). Volatilization
is very small because a low Henry's constant (1.3 x 10 ) reflects the
tendency for alachlor to remain in the aqueous phase. Noll (1980) found
•that bio'uptake of alachlor was non^detectable in sunfish, clams and algae
in a microcosm experiment.
Of these processes, the main route by which alachlor is degraded in
soil and water is biological transformation by microorganisms. First order
kinetics with respect to alachlor concentration have been used by Beestman
and Deming (1971) to describe biodegradation of alachlor in soil.
Cartwright (1980) reported a first order biological transformation rate
constant of 0.05 day' .
DDT, 1,1,1^trichloro^2,2 bis(p^chlorophenyl)ethane, is a chlorinated
hydrocarbon (organochlorine) insecticide. It was used to control insect
183
-------
3DN3mjNOO
2 NVMOU
u Nvsais sosn
X33UD
HVO sinvj VMOI
S11VJ VMOI
ONV »«OJ HinOS '
N33M139 INIOdOIW - U>9Z
*
ONOW139
SMOQ
N3Q1V
smvj VMOI-
XOOU 1V09NV31S
30N3mjNOO XUOJ HJLnOS-
{$•9*2
ll
3DN3mjNOO
A3NOH-
10'CCZ
Ul
E
ro
CM
NMOmVHSdVW
30VO WV3U1S SOSO-
NOINO
NMOniVHSUVW
yj-zzz
30N3mjNoo X33MO dvons—\ s'ioz
3DN3mjNOO X33MD U33Q \0'69l
33N3mjNOO »33«0 aNVHHOia \B'Zil
30N3mJNOD xaaHDnw-Xe-M^ 3nnag
VHV1
30N3mjNOO X33M3 A3NOH-
* 30V9 NV3UIS SOSn-
iZ'OSt
VNWWV - |f9U VNWWV
30QIU8 BIZ IStKH
300I«9 33JJVHVN IS'89
wva
tree
3XVINI A.1IO VMOI - 3'W
AID
VMOI
OCC
OZC
QIC
00£
06Z
oez
o/z
09Z
osz
OfrZ
ocz
ozz
OIZ
oo z
061
081
0/1
091
ost
Obi
oct
OZl
Oil
001
06
08
01
8
N
§ 8
— o
8
o
o
o
o
o
o
o
o
H3A3T V3S 3A08V 'i33d Nl 'NOI1VA313
C
CO
•H
60
•H
C
o
o
I-.
a.
0)
cfl
ai
CO
cu
>
o
JD
CO
C
o
eo
01
1-4
Ol
O)
4-1
CO
(J 00
> rH
•H
es •
I-H
CO CO
O 4J
M 0)
O
r~-
00
•l-l
184
-------
TABLE 7.01 PROPERTIES OF ALACHLOR AND DDT
Alachlor
DDT
A. Nomenclature
,6'^diethy
1 ^N-=" (ra et ho xyra ethyl)
acetanilide
1,1,1,^tricnloro"2,2
bis(p"Chlorophenyl)
ethane
Molecular formula
Molecular Weight
(gram/mole)
269.8
ethylidene)bis[4-
chlorobenzene]
C14H9C15
354.5
B. Physical and Chemical Properties
Melting Point
Vapor pressure
Water Solubility
Sediment patition
coefficient
(L/Kg dry wt.)
Bioconcentration
(L/Kg wet wt.)
100 °C at 0.02 mmHg,
135 °C at 0.3 mm Hg
2.2 x 10"5 mmHg
at 25 °C
242 ppm at 25 °C
50
75
108.5 " 109 °C
1 x 10 ' mmHg
at 20 °C
1.2 x 10"3 mg/L
1 x 10-
1.3 x 10-
C. Water Quality Criteria and Toxicological Properties
Criteria
0.001 ug/L for
freshwater and marine
aquatic life
LD50(96 hrs)
2.3 ppm for Rainbow Trout
13.4 ppm for Bluegill
19.5 ppm for crayfish
6.6 ppm for catfish
LC,
50(96 hrs)
0.24 ug/L for crayfish
2 ug/L largemouth bass
27 ug/L for goldfish
185
-------
pests in Iowa from the late 1940's until it was banned in 1970. DDT was
applied at the rate of 1 to 2 pounds active ingredient per acre. Although
no longer used, DDT and its metabolites (DDE, ODD) still exist in sediments
and fish of the Iowa River. Freitag (1978) reported DDT concentrations of
145, 90, 60, and 40 ppb in carp, buffalo, catfish, and carp sucker,
respectively, in the Iowa River. DDT has significant adsorbing affinity to
sediment indicated by its low solubility (1.2 ug/A) in water and high
partition coefficient (100,000 fc/kg dry wt.). The DDT adsorbing capacity of
the sediment is affected by pH, ion exchange capacity, and sediment
compositions. The actual volatilization rate is dependent on environmental
conditions although potential volatility of DDT is related to its vapor
pressure. DDT degrades photochemically in aquatic environments. Because
DDT is chemically stable and lipid^soluble, it accumulates in sediments and
biota. Bioaccumulation ranges from 10^ to 10 . Factors affecting rates and
the extent of biomagnification include the water composition and
temperature; the exposure route; and the age, sex, size of the organism.
Biodegradation is one of the most important processes for self purification
of DDT^contaminated streams.
Three examples are given in this section. First, hydraulic flow and
fate of alachlor in a 190^mile reach (300 kilometers) of the Iowa River
upstream of Marengo, Iowa, were simulated by HSPF. Second, Coralville
Reservoir (a 65^mile reach of the Iowa River below Marengo) was simulated
for alachlor and DDT using EXAMS. The hydrologic information produced by
the above HSPF simulation was used as the EXAMS inputs. A simple comparison
was made between the two chemicals. Third, the 190^mile stretch of the Iowa
River (upstream of Marengo) was simulated for alachlor by EXAMS, and EXAMS
results were compared with the previous HSPF prediction.
7.2 ALACHLOR IN THE IOWA RIVER USING HSPF
The 190'mile reach of the Iowa River upstream of Marengo was divided
into 13 segments as shown in Figure 7.01. Segmentation methodology was
described in detail by Donigian, Jr. et al. (1984). The simulation was
performed using the HSPF program written for the PRIME 750 computer in The
University of Iowa, Iowa City, Iowa. Both the time series and the user
control input data for the Iowa River reach were provided by U.S. EPA,
Washington, D.C. (NCC Data Processing Support MD-24 RTP, NC 27710). The
user control input data for alachlor are summarized in Table 7.02. The
single^value Freundlich isotherm method was selected for
adsorption/desorption processes. Three constant values (XF1, K1, N1) are
provided for surface soil, upper soil, lower soil layers, and groundwater.
In all the land segments, an alachlor degradation rate (summation) of 0.12
day"1 is given for 0.25 inch of surface soil layer, 0.045 day"1 for upper
soil layer, and 0.04 day" for lower soil layer and ground water. In the
receiving water segments, the degradation rate of 0.04 day" for water and
0.045 day" for suspended solids and bed sediments was used.
The simulated flow and associated suspended sediment concentration at
Marengo for years 1977 and 1978 are shown in Figures 7.02 and 7.03,
respectively. The dissolved, suspended, and sedimented alachlor
concentrations at Marengo are presented in Figures 7.04 and 7.05,
186
-------
TABLE 7.02 HSPF INPUT DATA USED FOR
ALACHLOR SIMULATION IN THE IOWA RIVER
Land Segment
Pesticide Parameters for Surface Soil Upper Soil Lower Soil Groundwater
Single Value Freundlich
Method ^
XFLX (ppm)
K1
N1
Pesticide Degradation
Rate (day"1)
Initial Pesticide Storage
Crystal
Adsorbed
Solution
Solid Layer Depth (inch)
Bulk Density (lb/ft3)
Receiving Water Segment
Suspended
Sand
Partition Coeffi^ 3.2
cient (L/Kg)
Adsorption rate 36
(day"') Temp 1 .0
correction coeff.
Initial concen^ 0.0
tration on sediments
Pesticide degradation rate
in receiving water
Temp, corerction factor
Layer
0.0
4.0
1.4
0.120
(Ib/acre)
0.0
0.0
0.0
0.25
62.4
Suspended
Silt
9.5
36
1.0
0.0
(day"1)
Layer Layer
0.0 0.0
4.0 2.0
1.4 1.4
0.045 0.04
0.0 0.0
0.0 0.0
0.0 0.0
5.71 41.30
79.2 81.7
Suspended Bed Sedi^ Bed Sedi-=-
Clay ment Sand ment Silt
19 3.2 9.5
36 0.00001 0.00001
1 .0 1.0 1 .0
0.0 0.0 0.0
Water Suspended
Solids
0.004 0.045
1.07 1.07
0.0
0.2
1.4
0.04
0.0
0.0
0.0
60.0
85.5
Bed Sedi^
ment Clay
19.0
0.00001
1.0
0.0
Bed
Sediment
0.045
1.07
a) X = K1 * C ** (1/N1)
XFLX
187
-------
MM*
MM*
MM*-
I
!
MM*
I**M
1977 Simulation
100
150 200 250
JUUAN DAY
300 350
MOOO
§c«o
J
§ MOO
0
trt 4ote.
Q
a
1000
u
50 100 150 200 250 300
JUUAN DAY
350
Figure 7.02. Flow and sediment loadings simulated by HSPF for year 1977
188
-------
4«ttt-
*••!••
Mtt*-
1978 Simulation
SO 100
ISO 200 2SO
JUUAN DAY
300 5SO
10000-
.000
d
g 0000
4000-
sooo
31
a
a
a
II
SO 100 ISO 200 250 300 350
JUUAN DAY
Figure 7.03. Flow and sediment loadings simulated by HSPF for year 1978.
1G9
-------
0.0-1
0.10-
O£
3
i
•t
0.05-
0.00
1977 Simulation
Legend
DISSOLVED AUCHLOR
BED SEP. AL^CHLflP
50 100 ISO 200 250
JULIAN DAY
300
550
O.OtO-i
0.008-
y J
o o.oo«-
I
0.004-
a
m
0.000
BO 100 ISO 200 250
JULIAN DAY
300
350
'l3 J.
y
8
a:
i 3
ig
s
Figure 7.04. Dissolved, suspended and sediraented alachlor concentrations at
Marengo, IA, simulated by HSPF for year 1977.
190
-------
0.15-1
. 0.10-
O
Z
o
o
o:
3
3 0.05-
0.00
1978 Simulation
Legend
DISSOLVED ALACHLOR
BED SEP. ALACHLOR
SOS. SJP.-,ALACHLOR.
SO 100 150 200 250
JUUAN DAY
300
550
0.010-
'3
K*
*NJ 0.008-
E
0
o o.ooe-
o
fy»
3
X
9 0.004-
£
a '
CD
o rtnn-
f
k
I
.
\ \ \ \
\ \§. \
' f*i « \
« i V\ V
\ 1 v \ ^- — „»
-2
1
-1.5 E
0
z
8
a:
-i g
|
•
-0.50
^
to
-a
SO 100 150 200 250
JUUAN DAY
soo
S50
Figure 7.05. Dissolved, suspended and sedimented alachlor concentrations at
Marengo, IA, simulated by HSPF for year 1978.
respectively, for years 1977 and 1978. The summer stream flow at Marengo
was computed to be very low, which can be explained by a summer.
191
-------
Consequently, concentrations of alachlor (dissolved, suspended, and bed
sediment) in the receiving water are predicted to be extremely low in
1977. The 1978 simulation showed high flow in March, April, June, and
August. High suspended solid concentrations are indicated in June and
August. Elevated concentrations of alachlor (dissolved, suspended, and bed
sediment) are predicted to occur in April and June, but not in August. A
peak concentration is calculated to occur at 6.85 ug/A (dissolved) in June
21, 1977. (An average concentration in 1977 was simulated to be
0.266 ug/X). The highest level of alachlor in 1978 was calculated to be
0.128 mg/fc, which occurred on May 13. (Average concentration in 1978 is
1.61 ug/J,).
The simulation results indicate that high runoff of alachlor occurs
directly after the alachlor application followed by a rainfall event.
Alachlor dissipated quickly by July. Maximum concentrations measured in
1975 and 1976 were approximately 1.0 ug/S, and 1.7 ug/£, respectively (Ruiz
1979). Both concentrations occurred in May. Little alachlor appeared in
the runoff after the crop season. Runoff of the alachlor was not
necessarily a function of the turbidity or suspended solids because of its
high solubility in water. Alachlor runoff is a function of rainfall events
shortly after application (generally from April 15 to May 15).
7.3 EXAMS SIMULATIONS FOR ALACHLOR AND DDT IN CORALVILLE RESERVOIR
A 65^mile reach of the Iowa River downstream of Marengo was divided
into 5 segments based on river morphology (Table 7.03). The width of the
stream channel varies from 35 to 586 meters and the depth from 1.2 to 1.7
meter. Compartment 4 (Table 7.03) represents Coralville Reservoir, which is
a mainstream impoundment of the Iowa River and receives extensive
agricultural runoff via inflow from upstream. Figure 7.06 shows the
physical configurations of the completely mixed compartments defined for the
EXAMS application. The IBM.PC.XT version of EXAMS II program was provided
TABLE 7.03 SEGMENTATION OF THE IOWA RIVER STUDY REACH
Compartment Identified Points Segment Length Drainage Area
m (mi) sq. km (sq. mi)
1 Marengo to Amana 39,816 (21.0)
2 Amana to Route 218 Bridge 33,370 (17.6)
3 R 218 B to Mahaffee Bridge 22,752 (12.0)
4 M.B. to Coralville Dam 9,860 (5.2)
5 C.D. to Iowa City intake 17,255 (9.1)
311 (120)
262 (101)
179 (69)
80 (3D
404 (156)
192
-------
3XV1NI
A.1IO VMOI T
E
in
in
WVQ
3-niAVHOO
39<]IH8 _
33JJVHVW
390188
3ino«
09N3«VW — •*
VNVWV —4
d Z
Q. LJ
Ut CD
UJ CD
O
CO
4J
i
o
T3
U
X
•i-(
6
>>
1-1
a>
4-1
a>
i-H
cx
o
Q)
CO
C
o
M X
0 W
t>0
•rl tJ
IM O
g1"
O l-i
0)
^-1 >
ca -H
o PC;
•H
co CO
>. >
^ o
Oi M
o
•
l~-
0)
(-!
3,
193
-------
by U.S. EPA, Athens, Georgia. Major inputs to EXAMS II are presented in
Table 7.04. Physical dimensional and advective/dispersive parameters are
shown in Figures 7.06 and 7.07, respectively.
TABLE 7.04 FLOW, SEDIMENT AND ALACHLOR LOADS USED IN EXAMS
SIMULATION IN THE 65 MILES OF THE IOWA RIVER
REACh, DOWNSTREAM OF MARENGO, IA.
Descriptive Parameter
(Unit) EXAMS Parameter
1977
1978
ENVIRONMENT INPUT THROUGH
Stream Flow
Stream born sediment load
Suspended Sed. Cone.
ENVIRONMENT INPUT THROUGH
Nonpoint sediment load
Nonpoint flow
Suspended sediment Cone.
ALACHLOR LOADING INPUT
STREAM FLOW
(m3/hr) STFLO (1,13)
(kg/hr) STSED (1,13)
(mg/L) SUSED (1,13)
RUNOFF (NONPOINT)
(m3/nr) NPSED (1,13)
(kg/hr) NPSFL (1,13)
(mg/L) NPSED (1,13)
NPSED (3,13)
NPSFL (3,13)
NPSED (3,13)
NPSED (5,13)
NPSFL (5,13)
NPSED (5,13)
NPSED (7,13)
NPSFL (7,13)
NPSED (7,13)
NPSED (9,13)
NPSFL (9,13)
NPSED (9,13)
Loading through stream flow (kg/hr) STRLD (1,1,13)
Loading through runoff
(kg/hr) NPSLD (1,1,13)
NPSLD (3,1,13)
NPSLD (5,1,13)
NPSLD (7,1,13)
NPSLD (9,1,13)
215047
50
376
2871
3
376
2416
2.5
376
1650
1.8
60
740
0.8
60
3732
3.9
190
5.742 x 10"2
2.0 x 10'5
1.7 x 10"5
1.1 x 107?
5.0 x 10"6
2.6 x 10"5
336944
50
507
2871
3
507
2416
2.5
507
1650
1.8
60
740
0.8
60
3732
3.9
190
6.530 x 10^
2.0 x 10'5
1.7 x 10*-!
1.1 x 10 ;?
5.0 x 10"6
2.6 x 10"5
194
-------
(a)
1.0
1.0
1.0
1.0
1.0
LP-J4
i.o
8
(b)
4.33XKT5 4.33X10"5 1.08X10"5 1.08X10"5
4.33X1
1
0-5
3
4.33XKT5
i 1
2
5
1.08X10'5
t i
4
7
1.08X10'5
* 1
6
9
4.33X10'5
8
10
Figure 7.07.
Iowa River/Coralville Environment (10 segments) Model
Pathways.
(a) Advective Transport Pathways (15 pathways)
Proportion of flow advected (dimensionless)
(b) Dispersive Transport Pathways (9 pathways)
Dispersion Coefficient (m2/hr)
Values of bulk density of sediments, percent water in sediment, stream
flow, stream^born sediment load, suspended sediment concentration, non^point
source sediment load, runoff, and bacterial population for 1977 were time^
averaged for the entire year. The previous HSPF simulation results at
Marengo were used for estimating the alachlor loadings to the new segment
below Marengo. Alachlor loading data were entered in the model in the form
of a stream^borne load to the littoral segment at the compartment 1, and
195
-------
nom-point source loads entered either the littoral or epilimnetic segments
in all five compartments. Stream^borne and runoff values for the alachlor
loadings were assumed because no useful information was available. A pseudo
first'order biolysis rate of 0.05 day" was estimated. The other decay
reactions are assumed insignificant.
Simulation results (steady^state concentrations) of EXAMS II are shown
in Figure 7.08. The droughts-like precipitation levels for the summer of
1977 resulted in extremely low alachlor concentrations downstream, although
the average flow for the year of 2100 ft^/sec is considered normal. The
average alachlor concentrations in 1977 are 0.266 ug/fc at Marengo (HSPF
result) and 0.25 ug/8, at the Iowa City intake, showing 6% reduction in the
65 miles of the Iowa River segment. In 1978, average" alachlor
concentrations at Marengo were 1.6^4 ug/i and 1.56 ug/S, at Iowa City intake,
indicating 5% reduction. Several mechanisms are responsible for removal of
alachlor. Of these, the major means of removal is by microbial
degradation. Photolysis and volatilization rates of alachlor were assumed
negligible.
The fate of alachlor and DDT in Coralville Reservoir are compared using
EXAMS II. All input data, except for chemical data, are the same as the
above Coralville Reservoir simulation data of 1977. The chemical input data
are summarized in Table 7.05. The calculated alachlor and DDT
concentrations are shown in Figure 7.09. DDT shows higher concentration
than alachlor in all the water compartments. The DDT concentration
decreases slowly. In the bed sediments, DDT concentration is significantly
greater than the alachlor concentration, which can be expected due to the
DDT's low water solubility and very high partition coefficient. Increased
concentrations in the bed segments 6 and 8 occurred due to the high sediment
loadings into these segments. DDT concentration in the sediments is
influenced dramatically by sediment loadings, but not apparent in the water
column. The exposure, fate and persistence of alachlor and DDT estimated by
EXAMS are summarized in Table 7.06.
7.H COMPARISON OF EXAMS AND HSPF FOR IOWA RIVER
In selecting a model, it is important to understand the nature of the
results. In making a comparison between the HSPF estimates and the EXAMS
predictions, the 190'mile stretch of the Iowa River upstream of Marengo was
simulated by EXAMS. The study reach was segmented into 20 compartments
(i.e., 10 water compartments and 10 bed sediment compartments), which is the
maximum number of compartments allowed by the PC version of EXAMS. In HSPF,
the reach was simulated as a continuum and not compartmentalized. The
environment and loading data for 1977 and 1978 are presented in Tables 7.07
and 7.08, respectively. Information on advective and dispersive transports
are shown in Figure 7.10. It should be noted that the suspended sediment
concentration, stream flow, nonpoint flow and loadings (load via stream flow
and nonpoint source) were either generated by HSPF simulation or estimated
from its output. Chemical data were previously given in Table 7.05.
The computed total alachlor concentrations in the water column and in
the bed sediments are shown in Figure 7.11. As discussed above, alachlor
196
-------
TOTAL ALACHLOR CONC. (mg/L) x
o
— ro >
OJ
(a) IN THE WATER COLUMN
1
1
' 19f8
1 1 1977
1 1 1 1
1357
WATER SEGMENT
0.2 • •
en
O
Z
O
O
or
o
o
_i
<
_j
<
(b) IN THE BENTHIC SEDIMENTS
•1978
•1977
-f-
2 4 6 8 10
BENTHIC SEDIMENT SEGMENT
Figure 7.08. Alachlor simulation results by EXAMS.
197
-------
TABLE 7.05 CHEMICAL INPUT DATA TO EXAMS II FOR
ALACHLOR AND DDT
Alachlor
DDT
Gram molecular weight
(g/mole)
Vapor pressure (mmHg)
Solubility (mg.L)
K for bioraass
K for sediment
Acid hydrolysis rate
(2nd order)
Base hydrolysis rate
(2nd order)
Biolysis rate (2nd order)
Q10 value for planktonlysis
Biolysis by sediment
bacteria (2nd order)
Q10 value for benthic
bacterial biolysis
Mean decadic molar
light extinction
coefficient in 48
wavelength interval
over 280^825 nm
(/cm/(mol/L))
MWT(1)
VAPRO)
SOL(1,1)
KPB(1,1)
KPS(1,1)
KAH(1,1,1)
KBH(1,1,1)
KBACW(1,1,1)
QTBAS(1,1,1)
ABS( 1,1,1)
ABS(2,1,1)
ABS(3,1.D
2.700E+02
2.200E'05
2.400E+02
1.000E+02
3.540E+02
1.900E-07
1.2
1.000E+OH
2.380E+05
9.900E-03
2.0
2.080EK)7
2.0
1.600E'02
7.000E^03
3.000E"03
ABS(5,1,1)
198
-------
xlO
-4
E^
z
o
cr
h-
z
o
2.0
1.0
(a) IN THE WATER COLUMN
DDT
I
I ,
!
ALACHLOR
P 3.0
<
cr
5 2.0
o
o
_l
.< 1.0
1357
WATER SEGMENT
(b) IN THE BENTHIC SEDIMENTS
DDT
ALACHLOR
2 ' 4 ' 6 ' 8 ' 10
BENTHIC SEDIMENT SEGMENT
Figure 7.09 Comparison of alachlor and DDT simulations by EXAMS II.
199
-------
TABLE 7.06 COMPARISON OF EXPOSURE, FATE AND PERSISTENCE
BETWEEN ALACHLOR AND DDT (EXAMS II OUTPUTS)
Alachlor
DDT
EXPOSURE (maximum steady^state concentration)
Water column: dissolved (mg/L) 2.502EHD4
total (mg/L) 2.596EK14
Benthic layer: dissolved (mg/L) 2.502E^04
total (mg/Kg dry) 2.522E^>02
FATE
Total steadystate accumulation 138
In the water column (/£) 6.08
In the sediments (%) 93.92
Total chemical load 1.4/Kg/day
Dispositions:
biotransformed (%) 29.83
other passway (%) 70.17
PERSISTENCE
95% Cleanup time (years) 10
1.672E"05
2.634EKI4
1.672E"05
3.98
1.647E+04
0.06
99.94
504 Kg/Year
1.67
98.33
110
concentrations in 1977 were predicted to be much smaller than those in
1978. In the 1978 simulation, increased loading rates in compartments 11,
13i 15» 17» and 19 resulted in elevated alachlor concentrations in water and
bed sediments. The comparison between EXAMS and HSPF was made using the
dissolved and benthic sediments alachlor concentrations at Rowan
(compartments 1 and 2). The simulations for 1977 and 1978 are shown in
Figures 7.12 and 7.13, respectively. The alachlor concentrations computed
by HSPF are time variable (e.g., daily) values, whereas EXAMS concentrations
are steady state (e.g., yearly averaged) values. EXAMS^II has the
capability to simulate monthly^average loadings over one year periods, but
the steady state mode of EXAMS was used in this comparison. Because runoff
of alachlor occurs in slugs, especially concentrated in May and June
runoffs, the steady state concentrations of alachlor provide less
information for alachlor management purposes. Unfortunately, no measurement
data are available for these 2 years to examine the accuracy of the models
predictions.
200
-------
.
(A
W
>
M
OS
^
S
o
^^
H
33
H
ptj
0
55
o
g ^
c^
^
p
M
CO
•^
CTv
rH
H
£
^^
O
PM
^^
H
^c
Q
H
PL,
M
co
^
bd
•
O
r-.
w
m
H
.
H
ss
H
£
£*•!
<
1
**
as
B
<
B
3
c.
jj
g
1
jj
§
5
t,
S
o
t.
*
«
3
i
t.
s
9
O^
^_
(-
in
f*^
•-
^
r"
Gh
t-
in
m
*•
o>
V
05 *^
s i
M c
n)
flu
Descriptive
Parameter
o
00
en
^-
0
en
en
O
O
VO
(M
O
Ov
fvj
O
O
en
eg
O
f-
VO
O
O>
vo
O
in
o
eg
en
00
eg
§
eg
in
in
o
t—
?
i
! S
ENVIRONhENT IN
Stream length
t—
.•
*-
•
a
in
en
o
in
o
Ov
in
in
in
in
0
00
vO
if
a-
en
CO
en
CO
in
en
ar
in
en
5
~
Stream width (
•y
at
t
(M
in
vO
*
«—
OO
m
^
<\J
,
CM
U^
^D
^
8
<^
(^
CM
JX
o
X
9
^
o
X
co
r™
o
X
£
^»
o
in
vo
vO
en
w
0
X
in
en
oo
•o
"M
t-
vO
rn
ar t^-
O 0
X X
xr 0 O
"• o .
o en en »-
_3 Q Q J
C _j u o-
co C co u
flu H H ^
Z tO CO 03
I ^
r-l 1 -l «-« O
5 ^* &~
«- *:- "s 5^
oB SJB 8^ Ss^
Q.~x •>>. 0) JJ 4J V,
cm t-en t- e o o
OS *» H *> «> M —
z — co — co B pa
en
i
o
X
en
vo
•
en
en
l
o
X
VO
in
en
i
o
X
CO
en
en
i
o
X
o
•
in
m
i
o
^
en
vo
en
en
o
X
in
a-
en
l
O
X
.
t~
en
o
X
vO
en
en
i
0
X
o
VO
CM
1
O
X
00
^
o o
K CO
I-i Du
CO Z
»
.1 0
fc J> fa^
H' *• ^^ J ^^
flu *^ L O L
O ™ J< C J<
S T3 * an
SB 0 C 10
O rH O O
J J «-, Z r-l
201
-------
^
•o
0)
3
C
4-1
C
o
o
^"^
r-
O
w
«
H
|
c.
n
c
3
•6
c
x 3
(3
cu
4>
4J 4)
•*•<
Z E M 0)
g xj~ c
5 01 j->
M ^ m «
> rH C 4J ^-,
£ 34> nj »»
U CD "O CZ *-^
CQ
CO
,
ca
CQ
ca
ca
ca
m
ca
ca
u
cu
X
cu
a
jj
JJ
g
E
4J
t
a
§
u
c
o
•~H
to
1
u.
a.
co
I
g
t_
T3
0)
C
nj
^>
O
to
41
3
iH
Oj
>
,.
^~*
- —
202
-------
^.
H
>
M
«
^
O
M
W
SB
H
PM
O
_.
0
M
H
3
55
M
W
00
ON
I-H
W
H
03
O
PK
^,
H
^*J
SD
pL^
e
M
CO
^S
U
00
o
!**••
S
s
H
8
0)
Jj
s
B
•o
*»
c
0
B
I
g
O
fe
IB
i
c.
n
3
2^
t-
m
m
~
o>
c-
in
m
^_
m
V
si
0.
>
Sfe
Q.4J
~H 0)
O 0.
S
m
e-
o
on
vo
m
O
t-
S
CM
O
O
CM
O
O
co
a-
CM
0
(w
vO
1
in
o
CM
co
§
CM
in
o
a*
f
z
(O
J
S 9
a. ^--
z
M JC
IE e?
S tH
1 I
U CO
in
^T
*-
in
t^
in
a
co
in
oo
in
0
in
in
in
t-
co
O
a-
O
a?
,_
OO
ro
-;
00
PO
X
M
^
S
*^
f.
%
*
i
tt
CO
c>
^^
•
CM
*o
o
CM
*
»-
OO
f>
CM
CM
CM
CM
in
*~
in
•-
•-.
^^
^
*
*~
CM
CM
r-
O.
CO
^^
e
e.
•S
S
t.
*>
CO
J
J
J
_J
J
J
_]
J
J
fcj
1
w
a
X
H
•g
|
k
8
o
CM
O
in
O^
0
OO
a
JOT
I
*
*-
a
a
CM
^
5t
-
m
o-.
m
v-*
CM
9
m
CM
a
CM
CM
^>
•"
N
eo
vO
^^
O
U
CO
3
1 >.
-i 00
» 5
0
»
!§
i!
ii
m
•
00
CM
in
00
CM
o
^-
o
?
0
Q\
^
(M
fv
eo
O
•
£
o
oo
o
•"•
OO
o
o
flu
•r
£
I ^^
•S S
n
c S
8.^
I:
*o
X
co
t-
in
•
CM
ar
O
X
o
o
m
in
^
o
X
o
CM
O
r-
X
o
»o
CM
a
O
X
o
£
•
CM
a-
£1
in
•
CM
9
O
X
oo
o
at
—
0
X
01
o
CM
O
X
OO
o
** ^
o ^-
«- o
S X
§
in o
o *-* m •-
-J Q O _3
E J CO OL,
CO 6* co cj
Z CO CO CD
43 •
C iH
B e x-> 2.
4J «H « M fl) 0(
C ^% ^> Jt -H B
S.-, « •> « -o *> *-.
Cm um u « o o
OH *> B 4J 6 «>-
z ^ co — co M co
o
*—
X
CM
a-
•
co
•~
o
T—
X
CM
CO
*-
CM
1
O
"x
ro
CO
00
i
0
X
CO
"
r
CM
1
O
CM
a-
•
co
ro
o
X
o
co
•
CM
T*
O
X
«0
a-
ro
i
o
X
vO
CM
CM
CO
1
o
X
CM
«
a-
^_
^
•
o
i a
M Z
SO)
0
S t. "* 3 ^%
a. *J c. o t.
z en £ n jc
O -i JC C X
Z > ^^ -I ^
IH O
O TJ O O- T3
g 8A §8
J J*i Z rH
203
-------
^
tl)
3
C
4J
C
o
o
*^^
co
o
•
i--
w
-J
PQ
H
So
0)
t.
s
3
O
•o
.p
§
s
CO
§
°
1 \
c
E
•o
0>
in
•o
cu
CO
§
0)
S-
g
3
D
CO
vO
^_
-
~
o
*"
CO
VO
i»
OJ
EXAMS
arameter
0.
CD
°"55
fe g
St.
CO
O 0.
CM O
CO
.- ^- CQ
CM O
> 03
.- »- CQ
CM O
CO
r- •- CQ
CM O
> CO
.- T- CO
>
1-1 J-! O -H iJ
SO T3
^ 4> 4J
Z E M CO C
(jj -^ *-^ > -H J->
CO JJ t-
ce -H t. co
IH *t ex
> ,-, C *J ^ g
£ 3 a> « »* o
U CQ "O ^ v^ o
0
CO
e
*-<
CO
III
a.
a
Cw
•o
0>
c
CO
^J
a
o
3
1-1
CO
..
^
204
-------
(a)Advective Tranport pathways (30 pathways)
proportion of flow advected (dimensionless)
_r
(ffr
11
o_
13
17
19
o ©o ©
o o
o 13
o e
0
0 E8
10
14
. < <"\«h> ^A. ^^-
^Ti W^ ^Ti V^
16
18
20
-ye*
U8) (21)
(b) Dispersive Transport pathways (19 pathways)
Dispersion coefficient (m2/hr)--- 4.33 x 10'5
11
15
17
-»
B)
19
10
12
14
16
18
20
Figure 7.10.
The Iowa River (above Marengo) environment model pathways,
(a) Advective transport pathways (30 pathways)
Proportion of flow advected (dimensionless)
(b) Dispersive Transport pathways (19 pathways)
Dispersion coefficient (m /hr) ^^ +.33 x 10"^
Tables 7.09 and 7.10 give some outputs tables of EXAMS for 1977 and
1978, respectively. As described in the previous section, EXAMS produces
not only chemical concentrations, but also summary tables of the results.
EXAMS computed, for 1977 data, the maximum exposure concentrations of
0.35 ug/J, in water column and 0.28 ug/kg in bed sediment at steady state
conditions. About 18$ of alachlor was biotransformed; the remaining 82$ was
transported out of the system. The model also estimated it would take 5
months for 95$ recovery after the cessation of inputs. For 1978, the total
alachlor concentrations were estimated to be 2.5 ug/J, and 2 ug/kg in water
column and bed sediments, respectively. Approximately 92$ of alachlor is
distributed in the water column, and 8$ in the benthic sediments. About 12$
205
-------
is biodegraded and 88$ is transported out of the system.
time is estimated to be 6 months.
7.5 HEAVY METAL WASTE LOAD ALLOCATION
A 95$ recovery
With increased industrialization and consequent discharge of toxic
metals into the environment, some surface or ground waters could be rendered
unusable by contamination. To prevent this situation, permits issued under
the National Pollutant Discharge Elimination System should include a waste
load allocation based on toxioologioal data and water quality standards.
X10
-3
(a) IN THE WATER COLUMN
1978
1977
5 7 9 11 13 15 17 19
WATER SEGMENT
xlO
<•-*
o>
-3
(b) IN THE BENTHIC SEDIMENTS
Figure 7.11.
1978
1977
2 4 6 8 '10 12 14 16 18 20
BENTHIC SEDIMENT SEGMENT
Predicted total, alachlor concentrations in the water column
and in the bed sediments of the Iowa River (190 miles above
Marengo).
206
-------
0.04-
0> 0.03-
£
(J
o
8
or 0.02-
3
X
0
3
•
gj 0.01-
0
0.00-
c
«"
'oJ
l«-
d
z
0 3.0-
u
o:
3
y 2.0-
a
U)
1.0-
a
n n_
\
L
ill
» 50 100 150
1977 Simulation at Rowan
Legend
HSPF
EXAMS
k.,
V Vt^TB. ,,. t ii^, , ,
200 250 300 350 400
JUUAN DAY
\ Legend
1 1
I HSPF
EXAMS
1
\
\
\
\
\
.
, ^T*»J... . . . ~*
0 50 100
Figure 7.12. 1977 Simulation at Rowan.
150 200 250
JUUAN DAY
300
350
400
207
-------
0.15-1
O)
^ 0.10
O
O
O
o
0.03-
0.00
1978 Simulation at Rowan
Legend
Hspr
EXAMS
SO 100 150 200 250 300 550 400
JUUAN DAY
0.015-1
O)
0 0.010-
z
o
o
cc
3
X
< 0.005-
a
a
n nnn.
! Legend
n HSPF
\ EXAMS
\
1 \
\
r\ K! \
50
100
ISO 200 2SO
JUUAN DAY
Figure 7.13. 1978 Simulation at Rowan.
300
350
400
208
-------
TABLE 7.09. THE EXAMS II OUTPUTS FOR THE 1977 SIMULATION
OF THE IOWA RIVER FOR ALACHLOR
Table 15.01. Distribution of chemical at steady state.
Seg Resident Mass ********
II Total
Kilos % rag/*
Chemical Concentrations ********
Dissolved Sediments Diota
mg/L ** mg/kg ug/g
In the Water Column:
1 0.
3 0.
5 0.
7 0.
9 0.
11 0.
13 0.
15 0.
17 0.
19 0.
1C
SO
24
63
34
43
85
43
65
60
3
10
4
13
7
B
17
8
13
12
.36
.34
.95
.03
.12
.85
.60
.92
.41
.41
3
2
2
2
2
2
2
2
1
1
.358E-04
.300E-04
.243E-04
.090E-04
.089E-04
.067E-04
.003E-04
.075E-04
.992E-04
.960E-04
3
2
2
2
2
2
2
2
1
1
. 358E-04
. 380E-04
.243E-04
.090E-04
.089E-04
.067E-04
. 083E-04
.075E-04
.992E-04
.968E-04
6.
4.
4.
3.
3.
3.
3.
3.
3.
3.
380E-06
522E-06
261E-06
970E-06
969E-06
926E-06
957E-06
942E-06
704E-06
739E-06
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
OOOE-01
OOOE-01
OOOE-01
OOOE-OX
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
4.8
90.25
and in the Denthic Sediments:
2 2.
4 7.
6 3.
0 7.
10 4.
12 4.
14 5.
16 4.
18 6.
20 4 .
91E-02
70E-02
90E-02
74E-02
61E-02
24E-02
991i-02
46E-02
42E-02
02E-02
5
14
7
14
8
8
11
a
12
7
.60
.79
.65
.86
.85
.14
.51
.56
.33
.71
2
1
1
1
1
1
1
1
1
1
.750E-04
.949C-04
.03VE-04
.711E-04
.711F.-04
.693E-04
.706E-04
.699E-04
.631E-04
.612E-04
3
2
2
2
2
2
2
2
1
1
.358E-04
. 3DOE-04
.243E-04
.090E-04
.089E-04
.OG7E-04
.083E-04
.075E-04
.992E-04
.968E-04
6.
4.
4.
3.
3.
3.
3.
3.
3.
3.
300E-06
522E-06
261E-06
970E-06
969E-06
926E-06
957E-06
942E-06
784E-06
739E-06
0
0
0
0
0
0
0
0
0
0
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
0.52 9.75
Total Mass (kilograms) -
5.342
* Units: mg/L in Water Column; mg/kg in Benthos.
** Includes complexes with "dissolved" organics.
Table 17.Ol. Steady-state concentration means and oxtrama.
tlumbor in parens (Seg) indicates segment where value was found.
Total
Seg mg/*
Dissolved
Seg mg/L **
Sediments
Seg mg/kg
Biota
Seg ug/gram
Water Column:
Moan 2.234E-04
2.234E-04
4.245E-06
0.OOOE-01
MAX (1) 3.358E-04 (1) 3.350E-04 (1) 6.380E-06 (1) 0.OOOE-01
Min (19) 1.960E-04 (19) 1.9G8E-04 (19) 3.739E-06 (1) 0.OOOE-01
Bonthic Sediments:
Mean 1.B30E-04 2.234E-04 4.245E-06 0.OOOE-01
Max (2) 2.750E-04 (2) 3.350E-04 (2) 6.3B01S-06 (2) 0.OOOE-01
Hin (20) 1.612E-04 (20) 1.96BE-04 (20) 3.739E-06 (2) 0.OOOE-01
* Units: mg/L in Water Column; mg/kg in Benthos.
*» Includes complexes with "dissolved" organics.
209
-------
TABLE 7.09 (continued)
Table 18.01. Analysis of steady-state fate of organic chemical.
Steady-state Values Mass Flux % of Load Half-Life*
by Process Kg/ hour hours
Hydrolysis
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
Bacterioplankton 1.0027E-02 18.12 369.2
Benthic Bacteria
Surface Water-borne Export 4.5323E-02 81.80 81.69
Seepage export
Volatilization
Chemical Mass Balance:
Sum of fluxes - 5.5350E-02
Sum of loadings - 5.S350E-02
Allochthonous load:
Autochthonous load:
Residual Accumulation - 4.66E-09
100.0
0.0
0.0
* Pseudo-first-order estimates based on flux/resident mass.
Table 19. Summary time-trace of dissipation of steady-state
chemical mass, following termination of allochthonous loadings.
Time Average Chemical Concentrations Total Chemical Mass
Hours Water Column Dcnthic Sediments Water Col Bonthic
Free-mg/L Sorb-mg/kg Poro-mg/L Sod-mg/kg Total kg Total kg
0
11
22
33
44
55
66
77
88
99
110
121
132
2
1
1
1
1
9
7
6
5
4
3
2
2
.23E-04
. 84E-04
.54E-04
.29E-04
. 09E-04
.15E-05
. 64E-05
.34E-05
.22E-05
. 26E-05
.45E-05
.77E-05
. 20E-05
4
3
2
2
2
1
1
1
9
8
6
5
4
.25E-06
.49E-06
.92E-06
.46E-06
.07E-06
.74E-OG
.45E-OG
. 20E-06
.91E-07
.09E-07
.55E-07
.26E-07
.19E-07
2
2
2
2
2
2
2
2
2
2
2
2
2
.23E-04
.23E-04
.23E-04
.23E-04 '
.23E-04
.23E-04
.22E-04
.22E-04
.22E-04
.21E-04 '
.21E-04
.21E-04
. 20E-04
1.25E-OG
J.24E-06
I.24E-06
I.24E-06
.23E-06
.23E-06
.22E-06
.22E-06
.21E-06
I.20E-06
1.20E-06
J.19E-06
1.18E-06
4
4
3
3
2
2
2
1
1
1
0.
0.
0.
.8
.2
.7
.2
.8
.4
.0
.7
.4
.2
96
77
62
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
52
52
52
52
52
52
52
52
52
52
52
51
51
Table 20.01. Exposure analysis summary.
Exposure (maximum stefady-state concentrations):
Water column: 3.350E-01 mg/L dissolved; total - 3.358E-04 mg/L
Denthic sediments: 3.358E-04 mg/L dissolved in pore water;
maximum total concentration - 2.750E-04 mg/kg (dry weight).
Diota (ug/g dry weight): Plankton: Benthos:
Fate:
Total steady-state accumulation: 5.34 kg, with 90.251
in the water column and 9.75% in the benthic sediments.
Total chemical load: 5.54E-02 kg/ hour. Disposition: 0.00%
chemically tranoformed, 18.121 biotransformed, 0.00%
volatilized, and 81.881 exported via other pathways.
Persistence:
After 112. hours of recovery time, the water column had
lost 87.11% of its initial chemical burden; the benthic zone
had lost 1.32%; system-wide total loss of chemical - 78.7%.
Five half-lives (>95% cleanup) thus require ca. 5. months.
210
-------
TABLE 7.10.
THE EXAMS II OUTPUTS FOR THE 1978 SIMULATION
OF THE IOWA RIVER FOR ALACHLOR
Tibia 15.01
Seg
1
Distribution of chemical at (toady state.
Resident Mass
Kilos *
»*
****** chemical Concentrations
Total Dissolved Sediments
nig/* mg/L ** mg/kg
*********
Biota
ug/g
In tha Water Column:
1
3
5
7
9
11
13
15
17
19
and
2
4
6
8
10
12
14
16
18
20
1.7
3.6
1.5
2.8
1.5
2.9
5.6
4.1
7.0
6.6
37.
in the
0.23
0.47
0.21
0.30
0.16
0.21
0.39
0.34
0.55
0.36
3.2
Total Mass
4
9
4
7
4
7
15
10
IS
17
92
.66
.68
.01
.53
.07
.78
.01
.88
.69
.68
.03
Danthic
7
14
6
9
5
6
11
10
17
11
7
.23
.42
.38
.44
.06
.61
.94
.56
.17
.19
.97
2.
1.
1.
7.
6.
9.
1.
1.
1.
1.
500E-03
352E-03
098E-03
980E-04
U13E-04
742E-04
322E-03
495E-03
61DE-03
704E-03
2
1
1
7
6
9
1
1
1
1
. 500E-03
.352E-03
. 09BE-03
. 980E-04
.813E-04
.742E-04
.322E-03
.495E-OJ
.618E-03
.704E-03
4
2
2
1
1
1
2
2
3
3
.750E-05
. 569E-05
.087E-05
.516E-05
.294E-05
.B51E-05
.511E-05
.841E-05
.075E-05
.237E-05
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
Sediments :
2.
1.
8.
6.
5.
7.
1.
1.
1.
1.
(kilograms)
04VE-03
107E-03
997E-04
536E-04
579E-04
978E-04
083E-03
225E-03
325E-03
395E-03
40
2
1
1
7
6
9
1
1
1
1
.500E-03
. 352E-03
. 098E-03
.980E-04
.B13E-04
.742E-04
.322E-03
. 495E-03
.618E-03
.704E-03
4
2
2
1
1
1
2
2
3
3
.750E-05
. 569E-05
.OB7E-05
. 516E-05
.294E-05
.851E-05
.511E-05
.B41E-05
.075E-05
.237E-05
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
OOOE-01
.53
* Units: mg/L in Mater Column; mg/kg in Benthos.
** Includes complexes with "dissolved" organics.
Table 17.01. Steady-state concentration means and extrema.
Number in parens (Sag) indicates segment where value was found.
Seg
Total
mg/*
Water Column:
Mean 1.354E-03
Max (1) 2.500E-03
Min (9) 6.813E-04
Denthic Sediments:
Mean 1.109E-03
Max (2) 2.047E-03
Min (10) 5.579E-04
Dissolved
Seg mg/L **
(1)
(9)
(2)
(10)
1.354E-03
2.500E-03
6.813E-04
1.354E-03
2.500E-03
6.813E-04
Sediments
Seg mg/kg
(1)
(9)
(2)
(10)
2
4
1
2
4
1
.573E-05
.750E-05
.2a4E-05
.573E-05
.750E-05
.294E-05
Biota
Seg ug/gran
(1)
(1)
(2)
(2)
0
0
0
0
0
0
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
.OOOE-01
* Units: mg/L in Hater Column; mg/kg in Benthos.
** Includes complexes with "dissolved" organics.
211
-------
TABLE 7.10 (conLiiiueci)
Table 10.01. Analysis of steady-state fata of organic chemical.
————__——_____——__———___—___-_____—____.._____..___—______ — _ — __
Steady-state Values Mass Flux * of Load Half-Life*
by Process Kg/ hour hours
Hydrolysis
Reduction
Radical oxidation
Direct photolysis
Singlet oxygen oxidation
Dacterioplankton 7.7D09E-02 11.91 362.1
Benthic Bacteria
Surface Water-borne Export 0.5741 00.09 48.94
Seepage export
Volatilization
Chemical Maso Balance:
Sun of fluxes - 0.6517
Sum of loadings - 0.6S17
Allochthonous load:
Autochthonous load:
Residual Accumulation - 7.45E-09
100.0
0.0
0.0
* Pseudo-first-order estimates baaed on flux/resident mass.
Table 19. Summary time-trace of dissipation of steady-state
chemical mass, following termination of allochthonous loadings.
Time Average Chemical Concentrations Total Chemical Mass
Hours Water Column Dcnthic Sediments Water Col Benthic
Free-rag/L. Sorb-mg/kg Pore-mg/L Sed-mg/kg Total kg Total kg
0
7
14
21
28
35
42
49
56
63
70
77
84
1
1
9
a
7
6
5
4
4
3
3
2
2
.35E-03
.15E-03
.05E-04
.52E-04
.41E-04
.47E-04
. 66E-04
.97E-04
.37E-04
.B5E-04
.39E-O4
.99E-04
..63E-04
2.
2.
1.
1.
1.
1.
1.
9.
8.
7.
6.
5.
5.
57E-05
1BE-05
87E-05
62E-05
41E-05
23E-05
088-05
45E-06
31E-06
32E-06
45E-06
68E-06
OOE-06
1
1
1
1
1
1
1
1
1
1
1
1
1
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
.35E-03
2
2
2
2
2
2
2
2
2
2
2
2
2
.57E-05
.57E-05
.57E-05
.57E-05
.57E-05
.57E-05
.57E-05
.57E-05
.56E-05
.56E-05
. 56E-05
.56E-05
.56E-05
37.
33.
29.
26.
23.
20.
IB.
16.
14.
12.
11.
9.7
8.6
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
Table 20.01. Exposure analysis summary.
Exposure (maximum steady-state concentrations):
Water column: 2.500E-03 mg/L dissolved; total - 2.500E-03 mg/L
Benthic sediments: 2.500E-03 mg/L dissolved in pore water;
maximum total concentration - 2.047E-03 mg/kg (dry weight).
Biota (ug/g dry weight): Plankton: Benthos:
Fate:
Total steady-state accumulation: 40.5 kg, with 92.03%
in the water column and 7.97% in the benthic sediments.
Total chemical load: O.C5 kg/ hour. Disposition: 0.00%
chemically transformed, 11.91% biotransformed, 0.00%
volatilized, and 00.09% exported via other pathways.
Persistence:
After 04.0 hours of recovery time, the water column had
lost 76.84% of its initial chemical burden; the benthic zone
had lost 0.57%; system-wide total loss of chemical - 70.8%.
Five half-lives (>95% cleanup) thus require ca. 6. months.
212
-------
Since speciation of a metal in the aquatic environment is an important
determinant of its toxic characteristics, however, discharge criteria based
on total concentration of the compound may not be adequate.
Fate modeling of heavy metal species after discharge is an important
step in establishing a waste load allocation. Model predictions are then
coupled with the promulgated standards to estimate allowable discharge
limits. Water qualiby based toxic control can be achieved by establishing a
concentration standard for each metal species in the receiving waters. It
is necessary, therefore, to be able to predict the concentration of a metal
in a water body downstream from the discharge point of a given amount of the
pollutant. The model used in this study for heavy metals waste load
allocation is:
._ k M,k b C k M.r
dC s 1 _,_ s 1 ,„ ,,.
dx" " ' ujl + bTT "IT" (7'1)
1000
where C = dissolved metal concentration (ug/fc)
x = distance (mile)
ks = settling coefficient (1/day)
u = mean velocity in the reach (mile/day)
b = binding constant for adsorption (L/mg)
r = sediment metal concentration (ug/Kg),
k = maximum adsorption capacity (m/kg),
MI = suspended solids concentration (kg/X,)
The equation may be solved numerically. The solution giv-es the dissolved
concentration of heavy metal in the water column at any location along a
stream. The model can be used to predict the level of discharge allowable
in order to keep the pollution down to the recommended water quality
standard. If the standard for the individual metal species were available,
a waste load allocation based on the individual species could be used. This
would be a more accurate methodology as each of these chemical species
affect toxicity quite differently for the same metal. The species
concentration can be calculated using the existing.MINTEQ model and imposing
the total metal concentration already available or calculated. The data
used to calibrate this model were obtained from the Deep River study
conducted by the North Carolina Department of Natural Resources and
Community Development in cooperation with the EPA (1985). The computer
model, in conjunction with the waste load allocation methodology, was
applied for Cu and Zn.
The Deep River originates in eastern Forsyth County and flows through
Piedmont, North Carolina to its confluence with the Haw River at the Catham"
Lee County line. The upper Deep River from High Point Lake to the town of
Randleman was the primary focus of this study. The study was conducted
during August and September, 1983. At the time of the study, the Deep River
was used extensively as a receiving stream for waste discharges. From its
source to the Worthville Dam, the river receives 41 NPDES^permitted point
source discharges. The majority of the facilities are small domestic
discharges. There are several cooling water discharges to the river.
213
-------
To evaluate the compliance with the standards, concentrations outside
of the mixing zone were used. Usually the length of the mixing zone is
specified on a case to case basis. We used the following equation
y
where y^ = flow distance, required to achieve complete mixing
m = a parameter that varies from 0.4 to 0.5 (95% mixing)
D = lateral dispersion coefficient
w = width of the stream
u = flow velocity
Dy = 0.6 d u* ± 50%
d = water depth
u* = shear velocity = (gdS) -*
g = acceleration due to gravity
S = slope of the channel
Because this was a slow flowing river, a slope of 1:1000 was assumed. An
average width of 40 ft was used for the initial stretch of the stream where
excessive pollution was observed. The mean water depth was 0.2 m and the
velocity was 0.021 m/sec. The mixing length was calculated to be 12.1 m,
i.e., about 0.01 miles. For the purpose of this waste load allocation,
however, the concentrations after 0.25 miles from the discharge point were
to be checked for compliance. The total initial concentration, the
concentration after implementation of the waste load allocation, and the
standard are plotted in Figures 7.14 and 7.15. It can be seen that copper
clearly exceeds the standards between the 3 mile and 7 mile reaches. Zinc
concentrations are rather high" and range from 0.1 mg/X, to 0.4 mg/X, for 5
miles of the reach. A large amount of the water from the river is used for
drinking water. There is not much evidence that zinc is deleterious to
humans in these concentrations. It is seen that by reducing the
concentration in the Jamestown discharge to 0.048 mg/X, from 0.250 mg/X,, the
total copper concentration is brought down to the required standard of
0.02 mg/X,. The zinc concentration in the Jamestown discharge needs to be
reduced from 0.465 mg/X, to 0.093 mg/X, to bring it below 0.05 mg/Jl in the
river. For the rest of the river, none of the metals exceeded the water
quality standards.
If water quality criteria and standards were given for each chemical
species rather than total heavy metal concentration, the waste load
allocation would need to include chemical speciation. The speciation of
copper and zinc were calculated at various points along the river using
MINTEQ. The speciation of the dissolved metal was calculated first in the
absence of organic ligands (e.g., fulvic acid) and then with a hypothetical
organic ligand concentration of 10 ^ _M, which is a relatively large
concentration. The results are listed in Table 7.11. In Table 7.11, with
an absence of organic ligands, zinc was predominantly in the free Zn form,
whereas copper (II) was present mostly as the neutral hydroxy complex
the Deep
_ copper
associated strongly with fulvic acid and upwards of 50% of the metal was
Cu(OH)2(aq) at pH around 7.2 and alkalinity averaging 100 mg/X, in
River. When 10 M organic acid is assumed to be present, coppe
214
-------
complexed as Cupful vate.
around 12?.
Zinc was associated in Zn^fulvate to the extent of
A waste load allocation for zinc would not be much affected by organic^
Zn complexation, but an allocatin for copper would be significantly
affected. If the complexed form of copper is not toxic, then the allowable
discharge could be almost twice as large when organics are present at the
10 '^ M concentration (probably a brown water system).
200
O
150-
Z
O
< 100
fr-
ill
O
Z
O 50-
o
Legend
D TOTALCONCTN
B WITH.WLA.
• STANDARD
10 15
DISTANCE (MILES)
20
Figure 7.14. Total copper concentration: Initial and with WLA.
215
-------
300
250
O
3 200
Z
O
< 150
IU
Z
O
O
100
50
Legend
O TOTAL CONCTN
V WITH WLA
• STANDARD
777 IT*
0 5 10 15 20
DISTANCE (MILES)
Figure 7.15. Total zinc concentration: Initial and wiwth WLA.
7.6 REFERENCES FOR SECTION 7
Beestman, G.B. and J.M. Deming, 1974. Dissipation of Acetanilide Herbicides
from Soils. Agronomy Journal. 66:308^311.
Cartwright, K.J., 1980. Microbial Degradation of Alachlor Using River Die-
Away Studies. M.S. Thesis, The University of Iowa, Iowa City, IA 522*12.
Donigian, Jr., A.S., J.C. Imhoff, B.R. Bicknell, and J.L. Kittle, Jr.,
1984. Application Guide for Hydrological Simulation Program - FORTRAN
(HSPF). EPA-600/3^84-065. U.S. Environmental Protection Agency, Athens,
GA.
216
-------
CO
H
n
Vj
w
o
M
OS
>
H
co
w
§3
pt<
o
CO
w
H
w
a,
co
^
u
M
22
PE]
PC
u
B
i-t
i— i
r-.
0)
f— 1
CO
fc~*
iJ
U)
z a
2 3
i O
O t/1
c— ( V)
»-*
it a
a J
O f-4
<: o
,, •"*
—• u*
1°
b§
Ul
a*
•J
^
f-«
Ul
o
*^U1
U J
taH O
Z CO
< CO
£S
o
t •*
0 0
p *"
1-1 y
^ »-
u.
o
[_
Ul
CJ
OS
a.
o
—
Ot
o
a
§
<< Q
p
co
en
a
a
8
u
a
a
to
tO
o
CM
a
tH
CM
a
Ot
a
o
a
I
H 3
<
H
CO
in
a
^*
Q
en
a
•-t
a
n
u
u
•i
a
CO
r» ^ CO to ~*
. . • • «-4 •
r» I en -« -« -CM
^ ~+ CM CM
in "•" to CM -^
. to
•-i i in CM to • ••*
l/l ^» ^H CM
••4
^ f-4 r* ct CM •?
• • • • • CO *
•H ^4 in CO CO • •"•
in -• CM
** to co to et
. • en
en i en r* CM -CM
«^ »H CM et
r* •* O -* en
^ i -< to CM • r»
rn r* en r*
co r** ot en co in
to •-< •» r> en -CM
.» CM CM
"• 1
^-N /-^ 4J CM
tr tr • CM
t5 * ^. .2 ^n
+ ^ O en a o
+ X 0 U 0 U. 0
CM o co s: o i *•*
e c c c c e e
N CM N N CM CM M
CM ** r* in
crt i m o •»
e^ ^4
n CM r» n
o' i in in co i
r» «
•» to .» CM p»
to « to et tn i
m CM
ot en r-. o «
et i tn en o CM
to • -• -H i
CM ^4 ct in to •#
• . • • • •
•* •-< en en •» CM
m -H CM
et co •* m en
co i in •» co -H
in «« •-<
o en to CM e» to
Ct t-l tO O O *4
m •-• CM
m o to ct CM
r^ i «^ ct •* en
•a- — i IM
m m en r« ot
f^ i •-* r*» *y r*«
en -H en
CM et to en ••< et
en ** m co f- CM
m CM
i
o" or CM
a + n f*
+ •* O en o
•f X 0 U 0 0
CM O CO SS 0 ^
c c c c c c
ttl /M N IM CM to
1
I
(
1
C
•f
CM
t
O
• co CM o -t en
o* «
^-t a u
er *-* «
c} CM -(• >
~^n s o s
+ OO O U,
3 3 'B' 3s
U O U CJ U
f** en tf>
CM ^r et • • •
iC *+ en M ** "^
«M ^r -^
o eg crt •« M
r» CM CM m i •<
*^ m «^ *^
— rt o
en in co CM i i
en m
to co co r~ ~*
en CM o O -< i
^^ m CM ^*
CM O «» — '
•» f ei ^ ii
m en
in CM en en
r» in r»
* m o e-* ii
to CM
n CM IH f»
•M en co
-------
Noll, R.M., 1980. Pesticides and Heavy Metals: Fate and Effects in a
Laboratory Microcosm. M.S. Thesis, The University of Iowa, Iowa City, IA
52242.
North Carolina Department of Natural Resources and Community Development,
1985. Water Quality Evaluation Upper Deep River Cape Fear River Basin
1983. Division of Environmental Management Water Quality Section.
Ruiz Calzada, C.E., 1979. Pesticide Interactions in Iowa Surface Waters.
Ph.D. Thesis, The University of Iowa, Iowa City, IA 52242.
Schnoor, J.L., 1982. Field Validation of Water Quality Criteria for
Hydrophobia Pollutants. Aquatic toxicology and hazard assessment: fifth
conference ASTM STP 766. Pearson, J.G., Foster, R.B., and Bishop, W.E.
(eds.), American Society for Testing and Materials, Philadelphia, PA, pp.
302-315.
U.S. Geological Survey, Water Resources Data, Iowa. Water Year 1983
U.S.G.S. Water-Data Report IA-83-1, U.S. Department of the Interior,
Geological Stirvey, Iowa City, IA.
Weed Science Society of America, 1974.
Society of America, Champaign, IL.
Herbicide Handbook of Weed Science
218
-------
SECTION 8
SUMMARY
Each model has its proper application and limitation. As shown in
Table 6.06, the EXAMS'II model is ideally suited for screening studies of
toxic organics. It is modularly programmed and easy to use as a steady^
state or time^variable model. TOXIWASP is best applied to toxic organic or
heavy metal problems that are time^variable and may involve a contaminated
sediment regime. It is particularly useful in the assessment of in^place
pollutants, contamination, and bioaccumulation. HSPF is by far the most
detailed and data intensive of the four models. It can be used for toxic
organics or heavy metals, and it is the only model that tracks the fate of
pollutants all the way from field to stream. Thus, it can best be used for
nonpoint source problems that are highly dynamic. MINTEQ is the only model
discussed for heavy metals speciation under chemical equilibrium
conditions. It does not include transport or kinetics, but a test case
showed how it is easily coupled with a simple transport model. It is well
suited for site^specific water quality management of heavy metal pollutants.
Table 8.01 is a summary of the reaction and transport characteristics
of the four models. As a chemical equilibrium model, MINTEQ has neither
advective nor dispersive transport. EXAMS ^11 and TOXIWASP are
compartmentalized models in which the user can specify an arbitrary
arrangement for the compartments. In EXAMS^II, it is necessary to specify
concentrations of suspended solids in each compartment, but in HSPF and
TOXIWASP, the sediment concentrations are calculated as state variables from
input parameters and initial conditions. Table 2.01 provides a summary of
literature values for longitudinal velocity and dispersion characteristics
in streams, and Table 2.02 provides vertical dispersivities for lakes.
Benthie sediment compartments can be layered in EXAMS"!I and TOXIWASP
because of the user^specified arrangement of compartments. HSPF has only
one surficial, active sediment layer without the possibility of sediment
burial. EXAMS^II is limited to a total of 20 compartments.
The chemical kinetics of EXAMS^II are second^order or pseudo^first
order reactions, and it is possible to follow transformation products (e.g.,
metabolites or daughter products). In TOXIWASP and HSPF, it is possible to
specify either first or second order kinetics for transformation
reactions. MINTEQ has no kinetics, only chemical equilibrium. EXAMS-^II
allows for ionizations and acid^base reactions for up to a tri^-protic
system. MINTEQ includes all ionization and complexation reactions to be
considered for heavy metal pollutants. All of the models assume a local
219
-------
TABLE 8.01 TRANSPORT AND REACTION CHARACTERISTICS OF SELECTED FATE MODELS
Model
Advective Sediment Benthic
Transport Balance Sediment Kinetics lonization Sorption
EXAMS -I I I
TOXIWASP, I,S
I
S
L
L
S,T
F,S
E E
E
WASTOX
HSPF
MINTEQ
• s
Input,
Simulated
S Su F,S,T
E
Surficial, First-order
Layered (empirical),
K
E E
Equilibrium,
Kinetic
Second-order,
Transformation
product
equilibrium for sorption with suspended solids and bed sediment (sorption
reactions are rapid relative to other transport and chemical reactions), but
HSPF also has a kinetic option. The rate constants for the forward reaction
(sorption) and backward reaction (desorption) must be known or calibrated.
Table 8.02 provides a summary of the reactions data provided in this
manual from literature sources through 1986. For most carbamates and
organo-P pesticides, like the pesticides carbofuran and parathion, chemical
hydrolysis and biologically-mediated hydrolysis are the predominant
reactions.
The fate of hydrophobia and persistent chemicals, such as DDT and PCBs
and pentachlorophenol, is largely determined by sorption reactions over
short time periods (days to a few years). These chemicals are quite
resistant to reaction, have a large octanol/water partition coefficient, and
are long-lived. Over time periods of years to decades, slow but significant
processes become important, such as volatilization and biotransformation.
The major challenge of predicting the fate and exposure concentrations for
these chemicals is to properly quantify the slow transformation reactions
that occur in the sediment over long periods of time, as well as gas
transfer or volatilization with the atmosphere. There remains considerable
uncertainty in estimating gas transfer/volatilization rates in large lakes
for isomeric mixtures like PCBs and pesticides (e.g., chlordane) because the
driving force for the reaction is often a small difference between two
220
-------
c •
O 0
£1
u o
W ffi
W I
r* ^
O £ c
H £ o
B
2 o t.
f\ £ 0
" O, «-.
to
O rH O
10 -H
M o *J
H —< re
0 12
•"S £ X
W
H 2
55 rH (0
•< 10 >>
0 " 0
M e °
fa v -a
M £ >>
SB "*
M m
w sl
fa fO 4->
O s- re
-OB
r i ° «-
W -i O
rj CQ 4-1
FO
H
*W
ON
^^
s
5
C/3
.
fsj
O
CO
w
hJ
H
X X
X XX
X
XXX X
XXX X X
s
&
i.
re
0
o
i.
•o
j?
0
10
Q.
tr4
OO rH
C (MB
tO C •- T3 t-
tn C o re Q ra cot. o£
IOOOO. CQ< rH O
t, m
i. w re a
a) re o o
£ rH O 4>
a! £ TJ ra
4J >. rH 01
rH £ £ rH C
>> « rH O. tl) "H
COOrH ^-0 °§
to-i-ico rH-H tore
t->4->a>c >,4J -Htn
tu re£Q> nx to a>s: o
£rH BO.£ l.tl) g C t-
4-> >, OrHQ. 0>£ O
> O .UrH i. i. -H
jj reft. to>> retu>> to c
•OQJ 4JO tU£ CO. Ott)
IDO OOJrH iJ OtUr-1 CCrH
4JI. ^HE£ (DID -HOre -<-<>,
reo rH'rio -^i rnrei — i s'o.c
CrH OOre tOt\J UJ_O £ >> 1 J_) rH — >>£N M N 01
too o ir c ren O.PC OCR
01 o-tu £^H >>ca> t-tu-i
rnrj CPJO, jj(n rH
-------
relatively large numbers (the polluted atmospheric gas phase concentration
and the aqueous phase lake concentration).
For many organic chemicals with intermediate octanol/water partition
coefficients, the most important reactions are biotransformation reactions
(Table 8.02). This is true for halogenated aliphatic hydrocarbons,
aromatics and phthalate esters. Some of the chemicals undergo a variety of
reactions including hydrolysis, phototransformation and volatilization, but
biological transformations are often the most important. Because biological
transformations are so importartt, there is no substitute for laboratory arid
field studies on biotransformation rates. Theory on prediction of
biotransformation rates from structure activity relationships is not so
advanced to give much guidance.
In summary, to determine the fate of an organic chemical in a given
transport regime, there are three important parameters: the octanol/water
partition coefficient (KQW), the volatilization rate constant which is
dependent on Henry's constant (H), and the sum of the pseudo^first order
rate constants for all other reactions (Ik). The octanol/water partition
coefficient provides an estimate of sorption and bioconcentration. KQW
values are presented in Table 3.07 and Appendix A2. Henry's constants (and
the solubility and vapor pressure data needed to estimate Henry's constants)
are given in Table 3.06 and Appendix A5. Other rate constants in the
literature, including biotransformation, are provided in Appendix A.
Section 7 shows the considerable difference in fate of a hydrophobia,'
persistent chemical (DDT) and a pesticide (alachlor) that undergoes
biological transformation. The dynamic nature of pesticide runoff events
could only be captured by HSPF.
Table 8.03 is a summary of the most important reactions for the eight
heavy metals discussed in this report. Two of the metals, arsenic and
selenium, often occur as anions in aerobic environments, arsenate and
selenate. All of the metals are known to undergo ion exchange or sorption
with iron oxide and aluminum oxide coating in sediments, but cadmium, lead,
zinc and copper are the most reported in the literature. All of the metals
that exist as cations (Cd, Hg, Pb, Ba, Zn, Cu) take on hydroxyl-"groups and
inorganic ligands such as chloride, sulfate, and carbonate. They are
hydrated in water and normally have a coordination number of four (two times
their valence) in complexation reactions. Organic complexation is a
particularly important reaction, and difficult to quantify, for copper and
(to a lesser extent) for cadmium, mercury, lead and zinc. Surrogate
organics (salicylate, oxalate, humic acids) can be used to investigate the
strength of organic^metal complexes, but knowledge of the conditional
stability of complex formation in the surface water being modeled is
strongly advised.
Perhaps the heavy metal reactions that are most difficult to quantify
are the methylation reactions (for Hg and As) and other redox reactions (for
As, Se, and Pb). These reactions are slow compared to the other acidebase
and complexation reactions. It is not appropriate to use a chemical
equilibrium thermodynamic model for these reactions, so the kinetic rate
222
-------
constants must be known or calibrated from field measurements and model
simulations.
Because water quality standards and maximum contaminant levels (MCLs)
for drinking water have been adopted for most heavy metals, future modeling
for waste load allocations is imminent. MINTEQ can be combined with another
fate model such as EXAMS^II, TOXIWASP, or a simple analytic model (Section 5
and 7.5) to estimate the chemical concentrations and speciation. A
recursive scheme could be developed in which the fate model would be used to
estimate the total metal concentration and MINTEQ would be used to partition
the metal and determine the concentration of each species.
This report should aid the modeler in understanding and choosing
appropriate models, in determining rate constants for input to the models,
and in interpreting the results.
TABLE 8.03 SUMMARY TABLE OF SIGNIFICANT HEAVY METAL REACTIONS
Anion Sorption Acid^Base Complexation Complexation Methylation
Exchange Potential Hydrolysis w/Inorganic w/Organic or Redox
Ligands Ligands Rxns.
Cadmium x x x x
Arsenic x
Mercury x x x x
Selenium x
Lead xxx x
Barium x x
Zinc xxx x
Copper xxx x
x
x
x
x
223
-------
2
<
g
§
M
CD
Q C
o o o
•r^ >^ >r4
g.0 JJ
O -i
.C t. T3
o c
<< o
o
t_
^
0)
o
c
o
o
ation
s/ml
o.
o
o.
0) 0)
T3 4J
i. ra
I
tn (0
r O
-I 0)
b. O
VO \O
1 1
v£> ^O V
t- t-
oooooocococooococooococo
^^»-^^-^^^^^^^ c c"
COCOCOoOOOcOGOCOCOCOCOCOCOCDCOCDCOCOftJ
C»-^^-r-^^-f->-f-f-l-^?^?r-?^(T<
co (0 *<0 (OcOfljcOcocOfOfdfljcOflJcOfliJcOrtflJcocOfljflJcocOcocococococorotflCj
f-HHt-e-E-t-t-f-f-f-'f-ooof-.f-'Ht-f-'He-'t-e-f-HHe-f-f-t-e-f-w
>>>>>>>>>>><>>x>.x><>>
>%>>>^>>5».>,>>>,>>>>>>>>>.S«>>>>>>>>>
II
!g8gSI|88i
0 0
° ° o
°. ° §
oo
0
g
g
C.LL.C.t.C.L.L.
rH ^
TD *
*> "m
o a) a;
C C O> 0) C C
n) nl O O n) 0!
d) a) D 0) O) 0)
CCCC
.l->.C.Ct_l.l_!_.C.C:C.C.C.l->.l->t-!_l.[-I_l.t.(_t.t->i>-,L.t.t.E-a.
OO<1)(DOOOOOOOOOOOCCOOOOOOOOOOCCOOOOO
-H H- e- •"> -> IH
t. t. i i t. s. x:
I 4) V -H -rt -I ^ -<
- I I I I I I I
I I I I I I I I t
224
-------
•o
I
*-4
4->
C
o
o
0)
ec
Chemical
Concentration Aerobic
ug/i Conditio
c
O rH
-" e
J-> -v
a
o o
a.
t,
0) 0)
•a 4j
t. nj
o 0=
i
n re
t. o
~H 0)
b. O
Ch
i.
nj
£
o>
c
C
n)
.c
o
n)
O
OO
c
o
co •—
Os 1
C
to
£
o
«
t-
OOOO
CC CO
•8 — co co coco"~oooooococo
n)
COCOGOCOCOCOCO4
COCOOOCO
a
t- H
tnto
0)0;
~H C
SCO
a
O f-H
. . 4J .
t- t-
S. a-
s. -~>
(- !-
.-. X>
i, n
- a.
3
•>-rt)CCr-^^^
(0 (Q
.......ne-c----
ff]nnninujen)j^^^j£jC^'^tujjnijc^£jc^
H-t'rHHH^H^Hj^COCAdlfllCOtQCOCIIrHJHP-ltOtOCOCO
I. 1. t- L LH-Oj->j3^i£iJ3fiJ3nnH o ^H U n X5 £)
0. 0. o. O. a. .(^a.f-.HHt-'HHHXti.OHI-HH
t^ t-
Os OO
t. -
to a
o o
CO H
ntntotntoncotncotncn
— O
§§
_ — .-.
§§§
-
OO
" ^"
OO
o§§o
i
88
..
§§
O
8
o) n
>> ><
ka
0|o go
g.o°°
o"oo
§o
O.ogo.o
w
3«reron)ni
nj
0)
"n nmoimmnmn
nlnj
re
•.-i
o
reram
o o o X o X
3oag00ogo°o"-o
^.O -^oo ' „ -cTO>->.>.>«X>.X>s
S
<«
a>
n
S. >< >. Ss >.
*~
-? 0.00.0.^,0.0.0.0.0.
•O
0)
>
i.
a>a>3^i~i^
3i-l>.>>
>.
c
a>
£r-l
a. o
-iC
-O 4>
--
!_>>XOOOOOOO>4O.£C(l)a>>,O
jS4)«)~l^-<-<-i-i^^OOOOOi:-H
oeeccccccc>,!_t.v.Lc-^ic
^H^-t'^<^H^-*^-(^HTH'rH^H4-> O O O O O >%^4
OQOaCiaaOaOaOaCiaaOaCiaaO'
I I I I I I I I I I I I I I I I I I I I I I
0) H ^ rH rH r-1
O 2 J3 S £ 2
I I I I I I I
225
-------
a
I
U]
_J
00
Reference
o o
N-t ^4
X) *J
O -i
bl
•'cS
c
o
^-(
^( i>
CO R)
O I- ^
§C DO
4) a
x: o
o c
ation
s/ml
12
g-g
tx.
t-
D 4>
•O 4J
t. re
00=
*> >,
2 S
_i 4>
b. O
Ch
00
ON
,»
oo
ON
CO OO
• ON ON
C T- *_
1
NO
CO
O.
ON
oo oo
-ON ON
C i- •-
.=r
OO
NO
•
o.
ON n
[-co
CTx CT*
OO
. -ON
o
CO
ON
00 *>
ON x:
CO
i
(\i
o.
ON on
t- CO
ON ON
oo oo oo co
* ON ON ON • ON
1
in
ON
o.
ON
t—
ON
00 CO
-CO ON
OJ
T
10
m
ex
ON
f-
ON
CO 00
ON • ON
CNJ
1
VO
ro
0.
ON
t-
CO CO
- ON ON
CNJ
1
(N-l
a
ON c*"t
t- OO
ON ON
CO OO OO OO
» ON ON « ON ON
CJ
V
CO
O.
ON
&
*
n) ra
. s ...H -.fin «-t.£ ...j) -x: --.C -£ --jc .-n -
x^xxax^nmx^xaxxxas^a -xxmxaxxax^a^xxa
2JCnimrH(anli-it.a)n)^n)cfl<$t.fflrH3alnif-inif-(n)nirHni<0(.n)ni>H
UAX)>HX>X3i-I.PXNjO t,r-)^3X>XI4JX3r-l OOO^O^OO^ao^aOi-t
(UOn)njm(iiidn]>
ra n
n n
ra n
4) 4)
8
n a
4) 4>
n n ra
4) 4) 4)
n
4)
8n
4>
n
4)
ra ra
4> 4)
ra n
4) 4)
8o) ra
4) 4)
r-i
§
o
O O,-.
8 8§
<>
8S8
4>
bo
n
^
€
-^
k
4>4>
boto
rt rt
* v
ran
t. t-
Q n)
B B
kk
4) 4)
S4> 00 DO 4) 4)
00 TT -O 00 DO
(4 (8 3 3 . >, CB 4> .. ,.
L t» ^ *•* t« t_
e e > > s e
4-4 ^-t MH ^4 ^H v4
I. «. JJ JJ t, t
o. o. o o o. a
ra rt
4)4)
noeo
4)
)tO(l)
O'oao
4)
)D0
O'o
4)4)
)b04)4)Q0
o-oaaaa-O
bO'
ran na
a Snna nSf^na n ran ran
•* ^3-*v' ^^ *O T?
S ^SfiE^S^^nSB^S^SB^SBS*'
f-4 ,vH^-4w^^^4-t^24Hv4»H^4v^»H»-4w4wHw4H-l
t. L-I_t.I_.ULt-2.I-t_4Jt-4->!_l..Ot-t_.U
a a.aaoa Jaaoaoaaocxao
o.o.o
^
»s.=rco^(N->pn
-
eo m -^
ro »— ON
^T *r m ...__.
OO O t-O«-«-OO O
oo o ^-ooooo o
• -oo «oo oo •
oo o oooooo o*
rvi
co
o
in-
csj
O.-<\IOOO>
^i rH c
>> >N 4)
c c x:
l -< — 1 Q.
C ^4 H >N
4) >>>>>> C r-l
x:ccct>>-if-i>>
jj_i_,^,n4)Dax:x:x:
S £ £ § £
cooot.t-t.t.x;jCx:o ooooot.i.£-i.fc.t-t.i.t.t.t.t.£*t.
•rtCesooooQ.a.o.HCccrtnjnjnjrooooooooooooooo
-1 1 1 1 1
226
-------
•o
«
3
C
U
O
(V
o
c
a)
5_
0)
V-,
IV
ce
n
c
o o
•^1 *^l
O 4J
O "H
bT3
C
•< o
o
c
o
V-*
rl ±>
a re
o i. o>
^H 4J —
e c M
5 0) 3
£ O
o c
o
o
Population
cells/ml
rder
ate
II
o
OO
ON
,-_,_,—
COCOOO
ON ON
CO
^-J3- -3" ^ £T CO C\J
CO CO CO OO CO *- •—
ON ON ON ON ON 1 1
«- i- ,- •- *-sfJ3-
ON ON
o. • •
E a. o-
03
.* * »
jj ON ON
cx p. CL Q.
BE e e
rt TO
.M: _*
nj
CO -iH ..H -H-H •,-! f- I—
00)0) 0)0) 0> ON ON
f\J
<—
1
m o ON
CO CO f-
ON ON ON ON ON 01 EEE » * * 05 O O * O O * O .C ,C »*eQ-«H.CcOGS •
.O.O O -^ -° O-P O O O O OjOJOjQ.ij.C.C.O.C.C.O.CrHi-HJO.OjJ iH C tfl (0 .O
f-f^COHHCOQ-COCOCi,t*-3OOE--«Ha.Oo:ECOCOH
v> to
bOt10'0
5 §=
S S n
oo
L
a
1
corn
»— o
cvjro^t— in
—--oooo
oooooo
oo -f w -^ c\j ro m zf r~ in o o o o o *~ in o o
ooooo ooo •- o oo oo ooooo
ooooo ooo oo oo oo ooooo
oo oo>*«o*>* . . o • • o *
ooooo ooo ooooo oo oooooooooooo
L i.
C£X:j->i->4J4->4->4J4-'jJiJ4J
£ fl) fl)iHr-tfH»HrHi—*f-*rHi—(*H
0) B
g
>t>«^>JJ-UX»4J4J4J^>4->^>
>, o o 0.0.0.0.0.0.0.0.0.0.
O CXO.iHiHiHiH»Hi—(rHiHrHi—I
ccs
o o
(\i rj
"~ *~ «
O O -i
OOcfl
t. i. **
o
«
c c
N M
v J->*J4J C (L C C CrHfH C JJ
I i i ii i i i i i it-.t*.o.a.ot.t.t-,>,>> n ^5
oojrviPjrjpjcjPjMtMrjMfNaty o Or-'rHr-' <9 « O O
ra<««
227
-------
c
o
o
3
CD
C
a>
i.
a>
v,
01
K
Aerobic
Conditio
Chemical
Concentration
Mg/i
a) m
rH r-<
3 r-l
o. crs rH oo>o>o>a>o>o>o>9\4>
OOoOOOOOoOCiJrvlOOCOOOOOCO COCTxoOOOOOOOfMZOOoOO *~ GO
0)
n
•— — cr> «B •- •-
fflcflramraSJnjnJrtrarat-njboraranJnJ^t-ranj
& o n & a o oonooo^ovtooooo man
n)njroron!Oora<8(T)rai8a)fflrtnl'Bn)n!t)x:n!4->AJ4->^)^> ® *^
onnrtr»r»r»^'*-»Q0
ao.o.0,
C. t. L t. I. 4J I- t-t-L
I- L
a a
co to o) o5 co n
f\jooco
CvJ ro -.
c
ai
rHrHrHrHlHrtJflJflJITJflJ
Q.ao.ao.jsjcx;jcx:
a o. o. a. o.
ajnJ^3jOjOZ3T3'CI«-,«-,CCC
CT3T3OOOOOOOOO)0)0)r
ot_i_i-t.L.t.t.t.i-i-(ntoco
X)OOOOOOOOOOt->.>.(
OO3333O
OOOOUOOOOOOOOOOOQQOQQQQQQQQQOQQaQO
228
-------
•o
0)
3
C
Ul
_>
m
o>
OS
Aerobic
Conditio
c
o
tH
.H •>->
03 ra
O t- e»
~l JJ -v
gC M
o> a
.c o
o c
o
o
Population
cells/ml
I.
0) 0)
T3 40
o cc
ro ro
co i*— co COCOEEC^N- co co ro
0*1^00^0^ OO^O^^9o^•3" OO^ O> CO
T-^F- CO*— t--»— »—'-'— CO*— •— tO C0<— CO<— •— »- ^- i— ^-«-CO*-<— f— ,— ,— ,— *— ON
CO CM O** O"* CO CO CO CO O*^ 4-> *^ O^ CO CO CO CO CO CO CO O^ CO CO CO CO CO
.*«- CCT»*~ C^r-r-f-^^^^XSE c'"'-'-'-'-'— r- *- »-^^^-*-»-i,f^
a,HOWCOUOOQ) Q)Q)a>4>Q>Q>Q? 0)a)Q>a)QJQ>vU
;>•>,>>>, >, >. c c >, >N><>.XX>I>. >,><>,>i>>x>,>»
s§
0
§
iiii
Jf
0°
T3 M
0,0)
to
•
ucx
nj
a)Q)(ua> bobo
to CO 0(5 60 , T3 -O
i
V 4)0)
0)0)0)0)0)0)0) o)tioa)0)Q)a)bobo
t«cOcObOcOtObO,bO-OcOtiOtCtO-OT3
''!<«'')<«<'J«J<«m'«3«ln>««)33
S**?**?^''-1'*?*-'^1
-
nnnn
'
.
niifl
g-L
ra
•^H *r-<
L t,
a a
~* -*4
(. i.
a ex
«-3
o
ra
-*H
4J
O
m
^g1-
o
w( v<
t, t.
a a
x-1 f-( *H
t. t. t.
a. a. a.
t-4 ~4
i. i.
o. a
t.
•r* f4
i. -U
a. o
ra
*r^ *r4 *r^
kkk
fH *^4 <*H
S. 4J 4J
a. o o
ra n
.
voincMvOincovo
oo
oo
OO
incocNj
ooo
OOO
OOOOOOO
o o
CO"-
ONON
o^-
OO
lTlt-
isr
uo
COO
OOOO
t-
*— O vo ^x IT*
vO •- ON fNj (\1
PO O VO O VO
O O O »- O
O O O O O
OOOOOOO • • * O O • •
OOO O O
o
O
OO
O*
0
1
0)
4J
0)
Jj
tfl
.C
O. 0) 0)
03 C C
OflJnj
££:X:
o.jjjj
0)0)
rHEE
>>OO
CBE
tt) O 0
-0 C
oo
C i— I
oo
CN
"-ic
MO)
n)i3
Il
c
O O C C c >
>OOl.t.l.>.£:CC
*Jr-jf-t'OT3io.e.p~*-rt
3jrx:iH-Hi-(4ja>xx
£>OOO)0)0)0)EOO
c c c c c c c
raajraranjnjnj
•O TJ
c c
W 0) ff) OJ W v-4
T3 T3 TJ T3 T3 T3
c e c c c c
0)0>0)
CCC
JZ & £
4Jx>U
cec
n)nJn)
t.i.i.
•6 -O £
C C 4J ,
ce
0)1U
i.i.
oo
33
c S
0) O
t- fl)
S4J
a
-I 0)
229
-------
•a
i
-H
a
C
s
CD
0)
o o
a.
t.
t- a
o cc
i
C 8
_< 0)
It. O
Ch
m rn
oo co
os ON
ro
co
os
ro
CO
OS OS OS
.s.
m
CO
OS
cooooooocoooeooocoaocoooosos oo oo ON oo oo co oo co
OSOSOSOSCT»OSOSOSOSOSOSOS*— •— *OSOS*— OSOSOSO> *OS
-oo
0) .-
•o
I 8
0) .-I O
r-l -J 00
«e z os
^
. . o>
t. t. n
o> o>
.* — .8 rf) •«
ro
CO
o>
o «-
CM CO
OS ,- .*
^•9981 .
.* oo
O OS
3 »-
X
•B O*OO 00
r-l O\ OS
c <-* *- *-
o «
OOOOODOOOOOO n & o a a ±> a
f-HHI-if-f-HHt-HE-HOT'Wa.HHM(-t-(-iHa.E-.
X X CO CO
o >»*>
w ffl a.
o
* O. H (-
nnnnnnnnnnnn nnn ncnmnnnn »i nnnnnn
4)a)Q)Q>tf>tt>4>^a)a?a)Q} O>Q)Q) a)A>a>o>a)4>a;O(D o)a)0>a)(UQ}
>,>>>,>>>,>.>,><>,>>>,>< >,>,>, >,>,!>,>1>,><>.C>-, >,>,>>>,>>>,
oo
oo
g
00
gg
iDUjcucuiDiuaiaJDDaja)
nnnnnnnnnnnn
a.o.aaaaQ.o.Q.aao.
!>3?3trH3
a)
aiaSintonn n
I
w w w
a a.
a a o. a o o.
. ™ ii 45 t t
* O i> 0.0.
•o " •*
c •"*
a
AJOO
srco
-
' — in»—
Ja-rut^-
<\jco>-
OOO
OO
coco
voco
o—
OO
OO
o o
o o _ _ _ _
OOOO-'OOOOOO»'-OO'-O
OO OOO OO OOOOOOOOOOOOOOO
^
co — i— >-pjcrico
m<\icor>Jm^a-
Ot-Or-OOO
OOOOOOO
oin
-
Ot-Or-OOOOOO
OOOOOOOO
«-.-.~
O.-OO
O O O O
•o -o
Q.
8
IB rt re IB 0) £
a. a. 4J *> r-i ^-i re
rt'iB^xi'Or) o.1?,
™ C
rerenirerereo.0.
4>a>o>a>a>a>.-i'o
c c
~ —
E e
ro ro CD CD
-.,
XXO)0)OOOOOOC--(
aQNNr^rHrHiHfHr-iai><
accoooooo^c
a>a>a>o>»>.>-,>,>>>>>>Q.O)
J3-OOOOOOO-I.C
t-t.i.t.oooooooo'oa
OOOOLI_I_t-!l.t.Ut- >-l
r-lrjr-li-jOOOOOOOOrH!>i
o. a
o o
Kfe.
t t
-H -< a a.
i§
sift
c c
0) 0)
i i
OO
(0
c s. £ s
a> a a a a
0)CCC OOOO
c « ttt-t-
KMNNCCCOOOO
i-lCCCOOO'-|rHr-|i-l
o o
c c
a> a>
s&
o o
jJOj->4JjJn'n(jjj->iJiJjj
Q.Q.D.Q.XXXXXXXXOOCCCCCCCCO.4JiJJJt.t.t.CCCCCC
a)a>a)4)a>a)V(i)0>a)0>a)nn^^-^-ni i i i
-------
o o
X3 4J
O -i
b-o
c
«< o
o
r
Soj
t- 0>
n)
t.
jj
$ a>
£ o
cj c
o
o
•o
C
o
o
u
J
CD
ation
s/ml
o^S
a.
t-
a> n
(_ re
i
4J X
in re
i. O
._< D
Ch
•- co
- o •»
O CO
.H ON C
F-l r- O
ID n
oo
ON
c
nj
x:
o
ro nj
CO L
ON *J
^- eo
« oB
2c
rH O
a> in
ra m rn
a- co oo oo
OON.-I-.-'- r- r- r- t~ r- *-
OOO-- OOOOCNJOOOO (NJOJCMONOOOO
OO ON • . ON ON OO ON ON . CO OO OO *~ ON ON
oa
ON
^t~"- — — '-t— COt— •-.-
ON OO OO CO OO ON ON ON CO OO
. ^- ON ON ON ON *~ *~ *^~ ON ON
•CQmOO-a.HHWHHO-:CZZWHHtt.U,>,>»>» c
CO CO CO CO
c c c >>>*>>>»
co en CD Q>
>> t>> >.>»>>>>
CO CO CO
>>»>,>*
s.§§°
o ,xT ,~ o
o
o
n 4)
X X
So
°o
x q>aJjJCCCCCUO
333i re<«
.r- •-
nxxo
o £ £ 3
cjJUr-i
Q. 11) 0) «-,
att ooo
CC<-ll.l.L.
a>a>xooo
a> n
t. n
S2
a>
ID rH
£ O
^8
O t-
£ o
CCCCCCCC4)4>ll)(D(l)l.t,t.t,t.t.t.jJ*JX><
a>a>(ui>ii>iV4)ii)£;x:x:££Xxxa>4)a>a>a)4)oot.t.c.t.i.c.(.
a.a.o-cua-a.CLa.a,a.oua.o,ixouCv.HHHt-HHl-f-HHHHHf-(-H
in
•o
0)
i>
c
0)
a
re
jj
5
e
«.,
•o
0)
4J
n)
rH
3
O
r-*
01
o
231
-------
•o
§
•H
*J
8
«
m
s
a.
al
ation Aerobic
Conditions Referenc
0 t- 0»
>H 4J -V
crobial Che
pul ation Conce
ella/ml \j
— 1 O O
r o.
L ID
01 4J
•o «
•0 0
8t5
0) >>i>.
coco oo eocooipomcocooooo0.0-0-
^S^^ooooiTjeoSa-Sa-oooornrornoocoooocjQQQ
1 ^ O O O
OO OO OOOO OOOtt-t.
OOOOOOOOOOOOOOOOOOOOOO V 1> 0>
OOOOOOOOOOOOOOOOOOOOOO4J4J*J
O - -OO • -OO O - -0 -OO -O -0 .(BBJ(8
-OO • -OO - -O -OO -O • -O -O -O323
oooooooooooooooooooooo
OOOOOOOOOOOOOOOOOOOOOOi-ti-HrH
o - -oo - -oo -o - -o -oo -o -o -raranj
-OO - -OO • -O -OO -O - -O -O -Ot-t-t.
OOOOOOOOOOOOOOOOOOOOOO 3 3 3
jy . -.za-jq- - -J3-J3- -iT - -JT -CTJT -^" -JT -(Qnln]
tytM rvjrvj (\i <\jco c\j oo CM CMCCC
<1)(1)<1)OQ>QJQ)0>
(1) *3 C V
C 1> 0) C
Ifl O N 0) Q) **H Q) fl) V
ococxro-ococ mm
t.O!L.4)^3L.>t OO
onsofvcLC. &a.acoca mmmm a. a.
r-lt.i-lOOO'HOOOOO OOOO II
oi— tiHr-f- - o.o.comci.cL.mm- » - — oo - -mcomm
HCMHOOOCXOOO • -inino-a-vovoa-a. - - - -i i . -0.0.0.0.
232
-------
1
c
o
o
m
IS
o
g
£_
0)
0>
ion Aerobic
Conditions K
^ as
IB £. oj
0 i) -v
-H C 00
§4> 3.
O
£ C
0 O
c
—lOii
f8 -H E
-1 •"-> V.
.O IB 01
° •— ' lH
O Q= Ct>
z a.
M
C. 4>
T3 IB
t- (E
? C
C -i
? **
Zero-Order Se<
Chemical Reaction Rates Rea<
CMCOCOCM CO COCMCM
OOCOCOOO OO OOOOCO
COCOCMCOCOCMCOCM COCOCMCO
. > - .oooococococoaoco -aoncoco » - -
COCOOOCOCOOOOOCOCOCOCOCOCOCOCOCBlBIBlti IB IBIBIB
mmn3fln3n3fi3nJajnjn3n3OOon3a](i3n3d3D3333dn33333(T3mn3
nnnnninnnninion
oooooooooooo cocoooeococo !C I?
o" o o o" o" o" o o" o" o" o" o ^ ^ CM w CM w ?3 w * 2 a>cua>a>a>aia>cua)a>coa>IDa> oooooooo oooo
IBlBIB'lBIB'lBIBIBCdlB'lBIBnncr) 0-0-0-0- 0-0-
«nm oooooooo oooo
333333333333jJiJiJ OOOOOOOO OOOO
CCCCCCCCCCCCCTCTCT CO CM CO CO CM CM
IB IB IB
iii^JiUm T¥Tfe ~s ~sTk~k
QJ^HrHrHrHrHCOrHiHlHlHfH ^Ll^JLl^ ^ rHrH-ll
xooocjo-^ooooo 'aJ'cu'co'ai "3 "a "a "»
2 0 0 0 0 0 ° 0 0 00 0 »«=»«--» -» o« =» o.
tdiiii7uiii7i 2222! ~ l?™™
.lot-a-inro^ina-oocrxo utdtLiu dj utdu
IfN 1 ^ •— ^^ ^^
. vo *
CO •
^ t.
£
r-l O>
o
>-< C i-t >, e
occi-t & ccai
ccucuo a. a> — i .-i
4)3£C 0 3>IB
£ ^H a CD t. .-H jc
O4Jrncxocj rHUO u>>x:
\ !_ o >-, o a. a. n a. a. o £ a.
o t_ j= t_ i imcamcacamoi iaacai.ua)
r-llJUU- - CJOOOOO-r-l- - OCJUVC
x: — .
-------
•o
4>
3
C
C
J
ff
Microbial Chemical
Population Concentration Aerobic
i.
•o
o
1
•o
c
4)
CO
Zero-Order
4.
c
(D
cells/ml Vig/Z, Conditions Refer
ra
c
o
'.H
fl
4)
(C
8
4->
re
a:
C
0
.u
CJ
a
«
1
iH
CJ
in
i
u
1 -PCB 0 nM/ml-hr
hlorobenzidine
.3- o
m m
CNJ
oo
ON
H,
4,
D C
1
V
CNJ
1
U
tro-o-cresol
•*•<
VO
CNJ CNJ
00 CO
ON ON
. C
n o.
ex
C 4)
8fc
Q. O
m o
i i
(NJ
03
ON
J
4)
J <
1
O
0
1
u
henol
u.
f
CJ OJ (\J CNJ
CO CO GO OO
CT* CT^ O^ O^
^•S-3^
4) 4, (D 4,
D 0 4) 4)
1 1 1 1
cj o o o
1 1 1 1
U Cd U U
ro m ro PO
SO)
,_,
C rH -I
4) X t-
£ K 4J
J-> 4J C .-J
4> 4> t. t-
O O CJ CJ
1 1
4> |||||||4>4)4> «4)4)4)4)
J^iocjcjooooiii 11111
o m m m in m m m ooo oocjoo
'"oil 1 1 1 1 1 lototo? eterfoioiot
Cd CJ U U Cd CJ tx3
V 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
r. Id [iJUCx] [dCd[£]&Ob]
~1(N->f\)CNjCNjtM(\)CNj<\iT-'-'- cN-in-itv-imm
1 1 1 1 1 1 1
f.N r.i f.-; f.^ f.-| f.N r,i
co m ro ro ro m pa
t- rn CNJ
NO *~ O
o
o>
c
1
c c ^
CJ C C rH .C
410CNJCNJCNJCNJCNJCNJ CXXXrH^-ll—lO
nJ'OjJOOOOOOOCCCCCCCCC^O
<-4>4)4>4>4)4>0)4)^
ro
CO
W
4,
4)
1
»-)
O
c4
1
U
ro
s:
c
o
c.
"n
(M
oo
w
4)
^
1
rH
»H
CJ
o>
1
U
t.
4)
a
o
o
o
c
m
•r-i
234
-------
•o
4)
f4
*J>
c
o
o
•*
J
CQ
0)
o
c
4)
CM
0)
ra
•o or;
!_
?!
b«
N re
4)
cc
rH
re
o
v4
§
£
O
CNJCNJCNJCNJCNJCNJCNJfNJ
OOoOOOCOOOOOCOCO
CM re rererere re re 3
CO
34)4)4)4)4)4)4)4)
orerererererere re
cozzjEjesezsei:
n
1
n
x
«
o
re
§•
g>
4J 4J
re re
rH f-f
re re 4>
5 5 4) re
£ £ C rH
ex ct re re
>, >> 4> r
x x e a
£ £ t. rH C
'S.'XrH N N
£ £ £ C 4) C
4J 4J O 4) C 4) 4)
ai a> -i £> re £> c
1 I T3 TJ O 4)
™ rj o rH t. t. n
— ^ g s, o o x
<001O*JrHrHl.QUl
**H *r4 £• 3 £ £ £ Q Q
cammmoooaa
cy cj cacacycjojcjCNj c\jojc\j<\JCJ<\joj<\i\£)OJCjf>jc\joj
OO CO GO CO OO CO OO OO OO OO CO CO CO OO CO CO GO O> CO CO CO CO CO
O\ C^ O** O^ C^ O^ ON ^K O^ ON O\ O^ ON ON O^ ON ON ON ON ON ON O^
a
^w^(Nj73^«««««oj(a«'S «««««?«««««
CO CO CO ON -
Q)£Q)!VO)3KQ>
rt o (0 O (0 J§ J9 fO(Q(Q(QO(Omi4'Ort'QiQ'Q'0'Q(0(OfQ(0
9C CO £ CO » >> o
n a a E
o o o TJ
re re re n
§• §• §•
(fl • cd (Q
£
,1,
1 "* gjllllll rH 1 1 1 1 1 1 1 Illll
O Jj "'OOOOOO ioOOOOOO O O O 0 0
10 » (Jjinirvpoinoin WPnf\if\jrorri<\irNj rorommro
i . •" i i i i i i • i i i I i i i I i t i i
(i£ • * [iatdCtlUbdCd ITVUtdUUUUU CdCiJUtOCs]
f""1 ON rocnT-m^m ^-mfn»-»-mm ^ p-, ^ ^ ,-
|>Ci> «
• X ^ 2
c *-< i
^ rH — 00
Til 4) rH B
7% O rH 1
**«--• w «-
•g B 3 *
J < rH Z
CO ° ^ ^
111 jjj f fn
2 oo [t] u
° o in CNJ
<"> CO CNJ*
4> « 4) 4)
e 4) c c c
••H c (Q re re
*J .U *J .U (9 O C C C 4)4)4)
retomrenrerB •r44)4)*4 4) £££
rH r-j rH rH O I, £ 0) B JJ U B 4J OOO
£ £ £ £ t> 4J 4) re Q rH rH n %H 4) OOO
4J 4J 4J tJ -. C B rH 6 re rt O rH T) X >, >,
£ £ £ £ C re O «(.££(. 3 X OOO
ex a a a I—IL. £^>x)*J4Jccn £4)4)4) ooo
rH £ o jj -H £ £ -i re re 4>ccc (.1.1.
rHiHrHrHX^rH £COiOiC4-i4HC T)4)4)4) OOO
XXXXCLIO£ O. rH rH « rHN££ rH rH rH
4J4J4J4JOI— IOC rH rH rH rH 3 3 t. reCJJ-U4)£££
33oo(.oo~nrH aicccooo
Z>AOOCXNH(,X£££COO3CCX2rei«4)«)ren
1 1 1 1 1 COT3£*J*J*J4>'O'On-'— irHt.t-t.XXX
CCCCC4)t-rH4JNOpO JD a> a> E B B Q.U td'O'O-a£353xxx
QQQQOQQOQQCIQQQ SalUUtdWU.U.ti. OO3
-------
T3
0)
3
C
*H
4J
C
o
o
5
u
CO
s
o
*H
o
o
Si
<
c
o
*H
iJ
ra
rH t.
(8 4J
0 C
-< <8
0 ,-j
&il
-H O
r o.
t.
0)
•o
^
1
0)
co
L
D
•o
!
&
N
0)
y
C
0)
b
«-i
0)
1C
c?
o
-*4
*->
•o
c
o
o
«>»
^
60
3
•>
•^
co
i-H
^
o>
o
8
1o
tt*
c
o
w
(8
0)
ce
8
4J
(8
ce
0
wH
4->
O
(8
0)
CC
rH
S
•^
1
o
c\j CM c\j CM
GO GO CO CO
CD 0) CD CD
0) 0) 0) 0)
(8 (0 (8 (0
2£ 3E Z 3£
CU CD CD 01 C
L L L L
1 1 1 1
o o o o
Min^^
i i i i
V
e
S
O> O)
£ CD C
O TJ 0> 01
,-< -« C "H
O X 0) TJ
X O N (8
U Q, C **
O 4) 4) 3
S. £) X5
-I O t- !_
ft) *> (8 (0
X Q. X X
I 4) 0) 0>
00 CM
CO CO
VJ'J
01 0)
0) 0)
18 (8
£ £
U 0) C
t t
1 1
O 0
ro m
i i
0)
c
CD
*-t
(8
C
Q. CD
O C
"o 5
>> JJ
O 0)
(_ t-
0 0
X X
0) 0)
CM CM
CO OO
**g
0) CD
0) 0) 'H
(8 (8 (0
£ Z O-
K A
.^ .^ ^H
cfl nj nj
a. OL a.
(0 01 OD
XXX
o o o
*— »— r—
•> « 4b
0 O O
o o o
N— ' **^ SM^
0> fe 0)
355
rH rH fH
Hj 03 (fl
31 3 ^
(0 (8 (0
C C C
£ ££
1 1 1
CD CD 0)
0 O O
V ^ ^
1 1 1
— oo a-
c c e
555
flt fu (t)
CO »
CT^ O^
*" *"
0 O
CO CO
n n
tn co
0 0
2 2
3 3
cr o-
(8 (8
0) 0>
0 0
i-t i-H
B e
— *^
cr. cr>
U [L
OO CO
u^ o
CM CM
C C
5 5
'nj "ifl
CO CO
O^ ON
3" 3"
8 °
CO CO
to en
en co
o o
I! 3
3 3
o- cr
(8 (C
b
H
M ^
f »
en "^
^~" *-O
o )
. °o
o ^
,_
c c
5 £
To «
1 1
in tTv CM c\j
CT» CT*i CO CO
a a
» •.
a (0 a? o?
(0 (8 (8 (8
-H ^H 3C £
lo ra
u o
J_
1
t.
>
L
0) 0) C
C, L
1 1
ow
1 1
u
~v I_
a I
in —
ro bo
— a
O *-O
c^- ^
»— o
^
.
o
0)
0) 01 0) C
c c c o>
0> 0) CD N
5 5 5 I
a. ex a. *j
(0 (0 (0 fH
CMCMCMCMCMCMCMOJCMCMCMCvJ
cococococococococoeococo
CO
0)0)0)0)0)0)0)Q)OJfl)CUO)3P
(8(0(8(8(8(8(8(0(8(8(8(00
s:z:3:i:zr:s:zs:zz:s:co
CO
n
o
4J
(0
3
(fl
--^
u^--Jo)o)0)0)0>0)a>o) c ^H
o en) o
C 0) £ CD CD CO
CD x: 4J c c (0 0) I_
a. CD 0) @ jr £ o
O C ' O O j_> 4J 1
L, 0) t« L, CD CD E
O t. O O CD E O 1
fH£ r-l^-l C O (. O iH
jC t ' r^ ff 0) 0) E O t, O
C CO 0) t.O*J+Jr-« X'r-t^HOO
a>££>,oo)a)oot_t.i i
CD
3
r-t
(8
>
T3
CD
J->
(0
E
m
[d
CD
236
-------
to
>-<
o
cc
Q
X
X
CJ
M
z:
x
u
cd
J
en
o>
cc
n c
•<-• (TJ
n *J
>-. m
rH C
o o
t. o
•o
>. o>
X 4J
a)
cc
Chemical
Concentration
yg/J,
«
o
e
o>
.c
o
1
CO
1
CO
O*i O^ ON O^ O^ ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON
i ON
ON
• t—
1 ON
4J4J4J4->4-> C4-»4-94J4->4J4->4->4-14->4J4^ C4J4^4^4J4J4->4J4J4^4^4^4^4J4J4-> 43
>>><>>>>Xn]><>.^i>,>N><>i>>>>>i>>><>>><><><>N>N>>>N>i>>>i>>
333
C C C C C 0) 0)
0)0)0?
CCC
0) 0) 0)
c c c
Doj^Dgjg)
jcx:jcccc
4^4^4^0)4)0)
4->4->4J4J4Jt.L.{.i.i.ouo)o>a>4J4J4J>N>>>a>o)i.i.
a>o>0)o>ooooonin)x>x>£>a)0)0)x:£:£O.cxuooooojOjO4Oo.o.
OOOOOOOOOOO)4)OOOOOOCCCOO I
rHfHf-Ht—IrH'^—H'^-rH.^f-it~«*Hl—*t—(rHcHfH 0) 0) 0) *—( r-H 0)
£££x:£:c.i.t.c.(.i i£cx:£;x:x:cjcx:££:c
i i i i i
i i i i i
i i i i i i i i i i i i
..
OOOOO
.
OO
t- t. LJTJSCCJ;
i i i ^ _<__,._,
- - - i i i i i
237
-------
0)
o
c
0)
0)
t-
i ON
4J4->4JJ->CiJiJ4-)JJ4Ji>4J4J4J*JjJJJ4->J->JJ4J4J4J4->JJ4JJJ4->4->J->4J4J4->JJ
JC
>,>,*,*,«) :>,XX>NX>.>>>>>.><>,X>,S.,>,>>>,>.*,>,>,>,>,>>>>>>>,S-,>.
•o
o>
4->
c
CO
_1
CQ
s
.1
Hydrolysis
Rate Constant
-
** -r4 3 *^ £ «*^
-
3 1-1 ~3 •••< 3 ^^ 3 t-t
00
Chemical
Concentration
jjg/4
ca
Ch
0>OO>>
CCC
o>o>o)
N N N
C C C
0)0)0)
>>>>
c:c
0)0)0)
CCC
0)0)0)
i-lrHiH
CC
0>0)
£J=
. i -
O O O
r-lr-lrH CCC
r-IOOO 0)tt>0>0)0)4)a)0)0)
occc^i-ir-iccecjrxrccc
CO>0>0>OOOO>0)0)CXQ.CXa)0>0>
0)££jeCCC333OOO333
jCCXCXa.O)0)0)i-l-lr-lt.t.lL.r-4fHf-I
OOOOO
i--r-.
.CjC-COOOO
O.a.O.4J4J^>rH
c c
0) 0>
X) A
o o
JC JC JC
o o o
0) -irH
.C.C.C.C.C
oaaa.c.c.ca>-i-!-<-.-<-.t-t.s_-.-i-ioo
t.OOOOOOEeBCCCCCCf-.(-HCCC-i-i
*|.(.(.>4->4-Ho4-H-H«4-<4>4-^-rt<-ll I I -4 -rt ^ Q O
4J4J4Joooaoaoooaoavovovooao i i
i i i i i i i i i i i i ...iii..
2iJ
, V
I I I
238
-------
o
c
4)
0)
I*
0>
13
4)
C
O
m
S
.u
CO C
-« IB
OT .u
>• to
rH C
O O
t. o
•O
S-, 0>
X <->
(B
£ O
0 §
0
IB
O
s:
o
cocooocooococooocooocOcooocoooenoocooOGOcooocooooo*—GOCOCOOOOOQOCOGO
CO
S. rH rH ,
4J4J4->4->iJ4-)*JjJ4J4JJJ4->i->4-)i->i->JJ4J4J4-J4J4-)4->4->iJ^)4J4->4->J->4->*-)*J4J
>,>,>,>^>tX>t><>>XX>>^r^>t~S>>X>^rn>^r*l>l>>>>4>>>>l>l>>:->>.>.>.
• 5:
O O vO
vo
in
t-
m
t. t. t.
i. i. t, 0> 0> 4)
o> a> a> £ s: .c
0)
.,4 c C C 4) O) 4>
N 4> o) o> £ s: jc
o. o. ex
rH rH rH
1-HrHr-l >»>>XrHrH
o o o
03 0) W
4) 4) 4)
bob
OCCC4)0)4)C
rH 0|0|0|
c c o o o c c
4) 4) «)
CCC
0) 4) O)
rH rH rH
t- i. I.
4J4->J->QOQCOra>>.>,;
I I I ---OOOOOOOOUOOO.
4) 4) 0) O O
C C C — r-
O O O t. i.
i. 1. t, l-l rH
rHrHCCCt.{.
239
-------
o
c
JJJJ^)JJJJ4JjJ4JiJ^
t- t—
4J4J4->4J4->4->4^ONON4J-U4^4J4->4J^>4J4->4->4->4^4->
>1>>>>>t>>>>>>>i>t>iX>>>>>>>N>t><>>>> > ->>>>>,>>>>><>>>>>>>«><>>>>
OOOOOOOOOOOOODODOOO It nOOOOOOOOOOOOO
<0
•*4
a
c
o
o
(M
u
J
0
<
fN]
Hydrolysis
Rate Constant
Chemical
Concentration
ug/i
in in
in in
ca
Ch
O<\IC\l(NJ(NJ<\irgCNJ V H rH r-4 i-(
a>VO>fl)«)KJt-,t-it-<«-<
rH^-lr-lrHiHiHrHrHrHi-trH^lf-lrHrHrHrtiHrtNNflJfljaj-^-^-^OOOOOOO
OOOOOOOOOOOOOiyOOOOOnjmNNNNNNNNNNMMN
OOOOOOOOOOOOOOOOOOOt-t-CCCCCCCCCCCCC
240
-------
(V
o
c
c.
0)
3
C
tH
4J
C
o
o
bl
J
03
Hydrolysis
Rate Constant
Chemical
Concentration
ug/Z,
IS
O
e
o>
o
ON CT»
ON
• t-
ON
• t- t- -
• ON ON •
. t- . . .00 O
• ON • • • ON (\l
U i> i> 4-> 4J 4-> 4J
n)fl)n)fl)fl] n)n} n!(Q(0 njnl nlnjco ON
• « •• <» • •> •> ^
C4->J->4->4->4->C4-)a-=C4->4J4->CC4JJ->C4->JJiJ—l
««) ^Si: >,'C>>>,>)S5
CO
r-
ON PO vo
•- I I
— co
° £ T:
- °
-j O - «- O
ON fl) Q>
4J 4-> ^> 4-> C -C
CTJ (fl (0 (fl 03 -»J
MrHrHr-H£(l>
UJ 0)
0)
c c
o>
s: x: c c c
*— a>jj££i£j:'-~a.4jcccc
X^H^^^^^^—^ x t.^-~£x:£i:
o o o o
N N N N
o o
N N
O O OJ t\J
N N -^ -^
OC\J(\l t-4 iH rH <8
! >> >. X >• £>
SB
(l)
-------
Reference
1
o
C\JCMCJt\IC\JCJOJf\J4J-U4->-t>J->4->-P4J^>4^ C
>,>,S>,>>>>X>.X>>XX«J-i-rt
• •• t- £> O • - • • " t^ I—
• • C^ CVJ Ovl • • • • • O\ G\
•-liH.-COCO'-lrHi-lr-l-H"-.-,
OJiJJJJJjJCCjJjJ^C^
• t- ^ .O •
• CT\ ON M O jO •
1-1 «- •- oo e~ t^ t-i
«- ^- 4J
..-.- ..-.....-.., - . >»>>>»>»>>fllj
a>0)0)0)0>0)a>o>a>0)a>-^t.i.0)0)0)DOoaooaGat*~4 i. (.AAXIAAI-I
a) >< >> >> Q
PH a> a) a> >H
> c c
x: c n • •
>>>,>,njrt—ia>a>><
a>va>iHt-it.t-i<-ia>
•a
§
i
<
3
to
Hydrolysis
Rate Constant
0 0
Chemical
Concentration
ug/1
in in
•=T J*
in in
m m
§8
ca
Ch
0)(ua)
cccojaiojaid)
o>
-------
fe
4>
0=
nt
U
J
CD
s
Hydrolysis
Rate Constant
Chemical
Concentration
yg/t
ca
Ch
o
cvi
I
<\i 4-> 40 4J 4-> .U
JD JO .O .«....(—...«.(-
CMCMPJ O> O>
OOOOOOfHr-t«-Hf-lfH>H<— *HrHf-1«HiH<— i-4iHrHi-t
i-H ,—t »-H tH iH rH «— *H rH i-4 «H iH •— i-*iHrHr-(i-(^H»-»-*rH
JJ Jj 4J 4J 4J 4J C 4-> 4-> 4-> 4-> 4-> C4Jj->4JjJUU C 4J 4->
£ n n tn £ £ £
>> >, nj >,>,>-,>,>,>.-H^~I ><>>>i>t>>>>xnt SIXXXXXID xx
3
d)
c
3
a
c
T3
~*
o
«s
• —
.1 o» x
^"t— ru
OO
oo
^^ <-< — ^ in i
x
in
mx
»(\i
O O 0
m PO m
0> 0) 0)
CCC
t>4)0)
ccc
4) 4) >><>>>|><>.O.O.O,
SmffliloSliiSS
*-* f^ r^ t. t. i. l.i-lf-lr-l££
• • »rH^Hr-4*Hw^*^»^4)4)
0) 1)
4-> jj
nra
ss
CC
^H*H
4) 0) E B
jj jj S S
ntnan
£ £ C C
0.0,
I.I 1 1 1 1 1 t
-^^^CCC
t. t_ t.
iHrH^X)T5T3T3jrj=
'@oooo'«'«'»'v««
OQOQQOOOQOOQQOOClOQClQQQClOOOClQClO
.
CCC C| C C C C C N N M 0) 0) 0) . _ . .
iii iiii i i itiaf>f>ofiof>
£X:^>4J
«>4> t) 0>
va>ee
oaaa
243
-------
I I
t— t-
OJ OJ
in
i
00
OJ
m
I
m
OJ OJ OJ OJ • OJ OJ OJ
CO CO CO CO CX CO CO CO
• • oj c\j oj oj oj oj PU • <\j og oj oj oj oj
O.Q.COCOCOCOCOCOCO Q. CO GO CO CO CO CO
• OJOJOJOJOJOJOJOJOJ
O.OOOOOOCOCOCOCOOOOO
c
0)
£_
0)
• t-- t~
> cr> cr>
• t-
' O>
C C J-> 4-> J-> iJ
55
> (fl (Tj
JJ C 4-> 4-> 4-) *J .M JJ CJ->4Jj->4->4J4-)4->4Jj-3
o>2 « (i>
C
o
o
es
<
u
j
CO
(-
4J
n c
~H nj
n 4J
X co
-4 C
O O
£°
>, 0)
Z1^
ce
o
~H
n) m
o i- o»
e c M
0) 0) 3
£ O
° g
O
a
o
o>
c
0) o) a>
c c c
OrtrtnjnJOOO
~«££££-<-t-<
3 3
c c c c to n
A« J>« j« •** I/ ur u* u/ u/ u/
o>a>o>t-cecc:cc
oa>a)o>cccccc
4->j->4-)j->iJiDa)a>er)cow'O-on-Dn«.^-r-i-<^i—c—i—irHrH^H^i_Li-
0)0)0)0)0)£££OOOCCCCOOt,t.I,t.t.t.t,>,!><>»>,OOO
E E B 6 6 O.Q.D.'O'OT3Uti:UU'a'Ot3pOT3t)T3'OT3££££ 3 3 3
_.^._,_|»H_^»JCCCI I I I CCCCCCCCC4J^>.P-Ur-<<-lrHrHlH--l
O O O O C3 O O O U W U O O cfl OQ u U U U U U U'U U U U U U F" b. Cw r*i CL. [z.
L t. C-
§§§
244
-------
c
0)
0)
os
nue
a
CO
m c
—< ID
n 4J
o o
L. o
c
o
e c
0) 0)
£ O
°§
O
a- .
I
CO
I
I I
ON O
f\j co co co t/^
• •<\J<\J(\J4JJ->*JiJJJ^>CC4->iJ4->-U- „ - _
•i«*"x**"!l^xll"^>,4>'D^ -*'"'1'
ON ON
r_ .........t-jQA
ON ON<\J<\JHJ«!
•— CNJ»-tfHfHrH'-4rH*H»—(•—!•— CO GO t^ l*-
» ON « ^ ^- ON ON
C»— 4^4->4;>+>^>4J-l->*J4JG •— •—
0 £ n n - -
T3 T3 .
' *«4 -^
O O
ro ro
ro co
Q) 4)
C C
Q) 0)
C C
ccexxxxxccce
—i C C C-H-^-r4£A£££ a.a.a.CX4> 4) 4>
xxxxN >,
o. a. a.
T3 T3 T3
o o o
I I I
__________OOOOOOOOOOOOOfNlONl c e c
(.1-t.t.t.OOOOO
c c c c
o o o o
245
-------
4>
V
c
4>
i
4>
0=
•o
c
CM
CO
J5
« c
»< id
n t>
SS
o o
S°
X 0)
*«
oe
al
Concentration
yg/t
Ch
a*
m
•<\jr\)CMf\j<\ic\j<\ic\j<\i
Acooooooocooooooooo
jo £) en
COOOl^!<^t^»— (HrMrHr-tiHr-l
4J iJ JJ
--^3X5 .........
• • CM (M
r-lfHCOOO ^HrHrHf--l>-(f-lfHrHiH
^- ^- OO
JJ *J 4J J-> 4J 4J O\J->^J4JiJ4J4J4->4->4->
4J JJ JJ ^> 4-> *3 A>
— -4 4) 4)
t- C- «i »-
I. t, l-l r-(
4) ID
4)4)4)4>4)4)4)4>4)4)4)4)
t,^tOf>OOOOOOOOOOOOOO
8—" «> M ™
«. , *•* I *^>
'
i -. i i i i
0 O 0
0)
"O
t-
O
OOO
nmtn
.
t- j: a) o> a)4)
t. t-
oo
..-
a> 41 4>.c.c.c.c.e.c >,>>>,oocj 4>
04 oj o|ou a.a.a.a-a.Q-Q-a.o-a-0-a.o-OL.^-Hfr-t-
246
-------
c
0)
01
0>
3
C
•rH
±J
C
o
o
in
in in
in
i
in
m
a- oo
i i
oo in
in in
OOCO • -COOOOOOO 'OOCOOOOOOOOJCOCOOO • •000000000000
CO CO O. D. CO CO CO CO D-COOOCOCOCOOOCOCOCO O.D.COCOCOCOCOCO
. . f- t~
(7v <^
• t- t-
' ON CT>
r-1r-)»— •— r-(r-*^rH»-i-tfH^H»-ti-(iH.-»iHi-(t— *-H*Hr-1pHrHr^
4J^> C C4J4J-U^ C4J^>-P-P4J-UJ->-^>-U C C4J-U4J4J-U4J
a)4)a)(i)®«>4)<1)
><>»« a >>>,>,>,« >.>«x>>>i!x>i>>Siio « xxxxt>sx
3
1)
C
.2-1
iiooi
sr • -z
-. 3 t.fS'-'
)'
r;
j.ii
?zs:
X
in
x
in
247
-------
S
M
e-
g
x
o
J
ca
« c
o iv
•^ 0)
•o u>
as <-
ce a.
5 ^
jJ C
rt o
—c 4J
X O
0 g
cc
o
I i
a- a-
o o
&£
l^ t- 00
^CVW^MWWWW™MC^WW(\IWfVMWWCVWM~^fVWWWM(VMCl>i<V
cp op Qpcpopoc>cpcpcOCOjDopqpcpGpopcpcpcpQp opopopopoocooocoopop
•• ON ON *> O^ Oi ON ON ON ON
ft
OS
» CF\ C CT* O"% ONtT^^ChONON^ON * * <^ ON ^ ^ <
(Q (0 (u
N 9N ON ON
CM
co
ON
:-a 73
C 4-> iO C4JJ->*Jj->J->J->4->4-> C4J4->U^^>4JH^>434J C C4JjJ4J4J*JjJ4J4J4JiJ
««>
4J
0)
.
'Oooce.
ss
nj (0
Q> 4) (U
C 0) C C
.
oO'o
NNQ.CLNNC CO)
ccooceai&.c
I I I I I I
I I I I I . . I I I I I I I I I I I I I I I I I I I II
' »^(NJ (NJ
-------
•a
1
J
CQ
•O 4)
cc a.
c ra
o a:
x o
0 $
(C
Ch
c\iroc\ic\)C\ic\ioocNjCNj<\j^>^>4J4J4J^>^4J^4->AJ^>4^-^^-U^>^4J^>4J
^>JJ4JjJ4-)*J4->4J4J4-> 4J 4J
S,^,>,>,>, >,>,>,>,>,>,^,>,>,>,XS-,>.>.>.>1>.>,>.X>.^>.>>>.>.>.>>>.>1>>
j3jD£l£)r>£lX3£in£)^3jO£IJ3£>JDiD£>£)£>j3J3nfljO^3£l£1^3r>J3a£iaX3n
CNICNICNJOJCNJCJCNJCNJCNJ
O CNJO CNJO CNJO CNJO POO POO CNJO POO
^
] CM <\1 PO CO f\l CN| OJ
POO PJO CNJO CNJO CNJO CNJO COO CNI
oo
o
in
o
o o o o
xoom xom o _
»—«—•— j* t-- •— ir»- m t—
xxs/ir x v a- x *r v v
"o
s
vo«-_xoxoxooom'
N§
• m
in t- oo
o o o o
- X O vo O X O CdX
'— \/»-v»-,— o-iN/vv
II
Jj ^
~-( ,-H 4) 4)
O O
^H C C rH r-l 4) 4)
O t)4)4>4)4)4>>i>«CC
X X
' C\J V/ OO
xu
in*—
X XXXVN/VV
•CNjva-minvvvv
L. t.
C. C. 4> 4>
4) 4) Si H
s e *j *>
*> *> 4) 4)
0) 4) 4) 4>
C C r-l rH
•SSooxxcc
•^i*r*iinQlCC4)4)
X> A I I i-l f-H 4) 4)
I !>>>iCCOO4)4>4)4)
OOCCQ)"— ^
«. t. 4) « r
-^
4)4>
^,H
— i _i
t. t.
jC<.C.L.t.UC)l.l.4J^>ll)(aj:£:4)4>r:ji-lt.t.4>aiJCjS4)4)4l4)'S'»l
D.-H—i-H«S-t.—I—IOOOOOOCXO.OO—I—1O.Q.OOO.Q.4JJJ4JJJCC—I—.
OOOOOOLC-OQ—i-rfBe°°'-t-o-o-Q'P-41'l'OO
ooiHrHJJwiamnini^i^^rH^
t_i.22-i~,>.i:
CBencjOZZ 4) 4) 4) 4) t t. t. t-TJ
i i i i t i iT ..>.i i i i i loooobooo
249
-------
•o
4)
3
e
MH
s
o
o
5
3
o
<
H
0)
O
§
t.
a;
«.,
(D
CC
fH 4J
(0 C
0 4)
38
2£
4>
c3
o oe
~*
w c
ro O
•O -4
-< *>
M O
o 4>a,4.a.4)4.a.4)a.
SSn*S^S"5Sso*55*"5
(0(t!OnIfl)(3fO'T)(ti(Qn}(TJ{0fl}(T)(0(TJ(tf
zo^sxirirzirrzrzEZzsrEz:
4)
(0
1
^
^H
t-l
tVI CV»w- (NJ (NJ (NJ 00 00 CNJ CNJ
O O 3 C\O OOO CNJO tNJO CNJO POO POO P>
ccaroOecoccotEOccotcoccotco
i.
\
CO L
^- £ in
ox oo o o o o o o
ro o •— •— PJ
inoomPOvvvvvvv/vvNyvvv
a> 4> at
OOCJO£~&.C.t-f..L.t.t«t.£.&.l.t.
•Oj->4J*->4JOOOOOOOOOOOOO
rHCCCCt. t-I_t-!_Lt.t.tt.t.t.L
m
i in vo >o in in in
CM III III
o in t— t- in in in
*— ON ON ON ON ON ON
a o. a o. a. o. a.
. ... ...
ON ON ON ON ON ON ON
ON & S; 0^ &i ON ON
PO C\l C C C 4^
St *« X>>(T3>»>»njnjnJ>tXflJ S t, C PO
ft . N xt- CO C. £ X t. O t.
~x4)CO COOO^XrO AO'C
— o to.or">.-t-^<^ 4) 4)
O O O O O
(0<0(0(0(00>4)4)4)
•- 4)4>4)(0(0(0(0(0a.o.o.a.
r— 1 4) 4) 4) "^ <^4 *H OOOOOOOOO
ON NNNNNNNNNNNNNN
OC CCCCCCCCCCCCCC
t.4) m^}^(pQ)0)4)4)4)4)Q)4)4)4)
250
-------
•o
§
o
con
r-|
0
ff
Ref
Radical
Present
Oxidation
Reaction Ra
Ch
i
oo
CT»
r\i
i
=t
VO
Q.
S;
oj^<\j<\jojcjrjejcjc\j<\iojcjrjc\jojcgc^e^
co co CD oooooooocooocooocooo GOoococooocooooooooooocooooocooooocoeoooeo
•3
4J
4-> .l> J-> C-U4->^>4->4J4^-O4J-P4-> Cl4J4J4J.I->.*J.P*J.lJ.fc>+J*J*>*J*J.U*>4J±J4J*J*l
>>>»»". nj >,>,>>>,>,>.;>,>,>,>.« >>>%>>>,>.>.x>»>.xx>%>.>.i>.>.x>»x>>>,
CM oj cvi rj eg CM (Ml rvirvicvirviNCMrviruruCM
O do f-t fjo ojo rjo coo cvio x <\o (MO cvio (MO (MO (MO cvjo d> 0)
£ £ iJ 4J
4J iJ nj a
££CCCXX^7
tkk:
5
gtt_t.t.c.a)— i— i
og.va>
-------
•o
0
3
C
g
o
<*N
o
S
8
§
fe
%
cc
rj 4J
cooocooo
ON ON ON ON ON ON ON ON ON ON ON •— ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON C ON ON ON ON
> CO
ri r, * • • •
0
n E
OOOOdOOOOOO COflOOOOOOOOOflOOOO O O O O O
rzs:s:i:a:3:3:s:i:z:ozs:zzzrz:zz:3:z:zs:2:zzu.z:s:zj:
i-4
in a- o a-
oo o o o oot- oo o o o ooo ifloooo
OOinO >O vO OO COCO ^NO VO O VO OOvO 0QOO--
• VOV'Ny* »— X • \/ • \X« OV» \/«O •
00 00
C C 00 c C
•rH *>H C C tB flj
00006S 0000£JC 00 00
4J 4^ ^) ^3 (Q (Q O O C CM 4^ 4-^ C* C 0 C C
(OfOnJfOCQCQ 03fO(d(Q00 *^-l-vH00C*^>r-<
>H>-4>Hr-lOO $3 t "^ •*- 6 6 CU0BH4->-P— IBB
(Or^cOALC^ X.C4J4JOO 4J4Jn(uTIcog(0(0
4j ij 4-> jj -. -, ccsgoocc iHrHOon'iJaoo
x: £ jr jr c c to«t5O33toto tofOt_i..c.cot_f_
a D. Q. a r-ir-i l_ t. r-< rH £ C iCjJWjJjJLiJxJCC
•Hr- 1 £iCOO4-i4-<4J4J 4J4Jv^*H,C£4->>*Hv4(0(0
rHiHtHM^t^i • »r-((— 4*^**^00 fj»CC(XCX**>4CCd%-i
&N >-,>,>> Q. a. tflto£jc-o-ose aa c rj ^
1 1 1 1 1 l-^CcS5i-li-jt-)i-j€T9££4J4J*>4J4J00r3iO
Q UJ M H (-1 1 1 1 1 1 1 (0£>r>jO^3OOOO0a>00eEBEEO.D.[iltiJ
QOQQQOQQQOdClCiaQOQDClOQOClQClQOClClQQ 0 8
T
(M
•
a
(NKM?
co oo
ON ON -
10
•
0 05
Z TE O
J (M
(MO
O cc
^
*^
3- J»
§0 i
t— Cd
v"
t
c c1^
(a a) I
*3 *^
T3 ^ 03
ceo
W CiJ "O
1 9 C
1 r ••
jftfl QQ m
252
-------
in
i
t-
ON
t-
ON
COaOCOOOGOOOCOGOGOGOGOGOGDGOOOGOGOCOOOCOGOODCOGOCOCOGOGOGOGbab'
w'-<-'-t~T'-1->->-*- — •- — •-•-•"•--••-'"'"'"•" *~ •- •"
4J
if\jc\ico
OOGOCO
aocOGO
ON ON ON
•o
1
0)
o
Refer
C4->JJ4JiJj->a-'iJ4->4J4->JJ4J4-)4J4J4->4JiJjJ^->l-'^4->JOiJ*J4->JJ4->4J4->JJ4JJ->iJ
nj 0) 0) 0)0)0) 4) 0) 4) 0) 0) 0)0)4)0)4)0) <"^x> o o ooo •- o
a vO VO vO O m VO VO VO O O O NO
om m mo ommm«->-om
• o o .-NXCNJ xxm
^TvcNjvCNjvmvt^N/VN/mmCNjv
O
o
U3
rn
O
o
VD
m
O
o
VO
m
O
o
vo
m
O
vom
m»-O
CNjvvOvNON/vON/NONyNxvvoN/i— ooir*
GO .
O O
N- - 5«
t-
O
o
CCCCCCCC
(^n}«
CC
D i>
c. t_
x>.
o. o-
(TI %^ ^*<
0) 3 3
C C C
So)0)4)
o.oooooooocc4JjJooj?f:i i
s_ooooooooooooOooocNJ"r\io>
Mr-(r-(iHr-tr-lr-lt.t.t.t.t.t.L.t, .-C
1_ t- t. t. O
0000
07 0) OT t-t -^ f*
T3 T3 T3 T3 T3 T3
t.t.t.t.c8cOc8nJXXX
xxxooooooooc
C£ 3
-
O.D.Q.O.a;xXXXXXX X X X X X X X XTS'O O
a>a>T i i i i iii4>4>4>a>a>4>4>4>cen
XXSX O amcn-OKJJ-^IXZZXZXXl-lt-IM
253
-------
T>
I
i
8
3
o
<~! -U
« C
o S
S 8
ce a.
«
jj
c n)
o cc
4-> C
fO O
•O -i
X O
o «
JC
o
T?
in t-
o\ o^
o. d
TT
in t—
o\
s
OO.DOOOOCOCOOOOO
^ O^ ON *
D. Q.
?:§:
n
'to
«!
CO CO Op CO
ON O\ ON • •
oooooocococooooooooooooococooooooooocooo
ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON ON
m
4J *>
(fl ig
j-> j->
4_) C JJ 4J JJ JJ 4J 4J C C W 4-> J-> 4J C C4J4J4JjJjJjJ4JJJjJ4->4J4->i->4->4->iJi>JJj->4J
>1>i:»,:»i>>>>>l>>>>>>><
jj C *J 4J *J .U 4J *J C c: 4J 4J .!-> 4-» C C*-»4-»*-»4-».4J*J4-».*-»4J4-»
E £ t: £ s
>>4J>>>>>>>>>iX«i(i)>>>>>i>>>>>>>>t>»x>«>i>i>i
(NJ CM CM CM CM CM CM CM CMCMCMCMCMCMCMCVJCMCM
O m CMO OUO <\IO O O CMO CMO fH CMO CMO CMO CMO CMO CMO CNJO CMO CMO CMO
ceootcOEEOiEeicoccOccocjOceOKOicOceOceOKOKOceOsOce
co >. (,
m ^- ON in t. JS JT
O O Ol O £-x OO O
o NO •—tn o u o •—t*- \ uxo •— o o
UNO r<1>-XOO<-NONOXOt^aOOXNO —
S "~v\/ ><_•• 7<-^'^<« ^" v
o o
ro «- m
o
NO
o
m
s
. Oovo
a> a>
oo
c c
0) 0)
"? ® c 6
x:
s§
iJ 4J
« a>
ge « a>
o c c
a> a> a. o.
0) D O) C C O O
c o 4> §
aitt)t_t.
o
_
NNo
ill
4) 0)
o o
§g
DDOID
4J4J4J4J--lrH4>a)
t. t. t. i_ a)««a>^t.eB
o o o o OOIBBOOOOOO
,Hr-li-IH CCOO!LC_t_l.l-t_
254
-------
T5
1
-4
«
O
O
e->
<
O V
5 8
« t-
0= Ou
a e
a o
•o -i
** jj
x o
o 4*
o> D
0 0)
x> o
rt n)
(\J
MO
O tC
in
o
•-t—
x o
t- —
X
v •-
o o
s s
b b
255
-------
K
s
^
CD
S
X
0-
ca
*
§
jj
c
3
or
0)
n n)
•3*
>> c
•-4 O
O -i
J-> JJ
o o
£ IT)
D- 0)
CC
C
o
— *
•H JJ
(0 n)
O C
— 1 JJ
§c
0)
£ 0
0 C
0
O
4)
0
C
4>
5
a:
g
'""^
•D
*H
4)
J^
O>
O
a
i.
2
n
SM*
•H
"So
3
3
0
f-t
i
£
O
t—
OS
a- •-
1
in -
OO T3
.3
a M
t.
> 01
Os £
C* 2 00
- c
n) Q o>
r-l £ 41
JJ ^
X 0 CT
4J
£ in
MOO
wH OO
i-4 1
c in
3 OS
0] CSJ
SO
o
o
t
ill
O CSJ CSJ
ooo
ooo
ooo
OO
OO
0)
JJ
(0
0)
•Ssfc
JC 4->
a) a>
X i-l
o ^*
3^
— '
Q O O
1 1 1
CSJ csj CSJ
CSJ
OO
OS
. . 0)
X X S
SO
o
o
5^
I fi
O so
o *-
0 0
0 O
f, s. v
4) 4) C
4-> *J 0)
(0 « 3
0
>. >> o
£ £ t.
lie
???
CSJ CSJ CSJ
^r
ro —
1 I
CSJ os
CSJ • •
co a D.
OS
Os os
- ^ f- IB
r-l r- ^ OO
(v ffN
4> C C
4) rt (0 •
S 0 0 X
-
ro
ro
o
o
d
1 ^
I co a-
CJ 00 CSJ
r- --OS
Csj «- O
0)
c
•o
N
C
o>
o
o
7! ® *
O C d) Q)
o 4) R) a
CSJ
oo
OS
ra -
OO r-4
^h flj
jj t1^
» 4) t~-
X S J
JT
in
CSI
PO
o
o
C5
^
-- o
o —
r —
0) 4)
SC 4)
93 C
«J (0 T3
J2 £" "N
C C 0)
in
^_
i
Os
a
OS
n) t- nj
00 «- CO
OS OS
"~ C *~
. n) .
O £ 05
fcS fc
X 0 X
ro
no
O
0
o
t_
IS
CSJ CSJ
0 O
4) O> 0)
c c c
4) 4) 4)
OOO
t, t, !_
C C C
nj (TJ (fl
i it — » i i
§!§
m m m
r_
CSJ
1
CO
OS
co a. co
Os os
Os O
— t~- rt nj — oo (TJ
i-H r- CO OO r-l •- OO
nj os c\i os nj os
- r- OO t- - —
J-> C Os t- JJ ^
0>(B .r-t- .4)rH -
£ CO OS CO «- 1 0}
sroxzjxzox
4J
(30
»H
•a
in 3
csj m
OS
00
o
o
o
o
£^»vXv, £csjso
Osoosooa- oooo
roovooono inoo
•- •- o •- o ooo
V
OO
in t-
CSJ Os
4)
4> a
§r-l
(4
0 £
^L.i.i.?.i.L.S
C ^> ^» ^» ^* ^» ^» ^»
nt a a o. D. o. Q. N
rtjfljajsj* « « « >.
Ililills1!
oamcammomcau
OO
Os
A
n
L
(B
a.
£> rt) «
OO O. CO
os a. os
r- 0) r-
n - to
X S X
4-> 4->
£ JC
bO bO
f-4 rH
§ §
n n
in
in
0 --
o o
0 0
|
CO
OO
.=r
O
o
o
o
CSJ
i. (- i.
Ill
ooo
CO
1
ro
CJ
• CSJ
D.CO
OS
* p-
Os
oo nj n) i— -
OS CO OO •- rH
-- OS OS
t-
t* 0 §
it m m
£>:>>>,
flj £ j5 Q O
ro
1
CSJ
D.
Os
t—
2^
^_
C OS
-H D.
0 N
.sa-
in
CSJ
VO
CSJ 00
O 0
1
SO
o
CSJ
o
o
^
U Cd
O Q
256
-------
•o
1
4J
|
_3
00
0
C
a:
Wavelength
(nm)
Quantum
Yield
4>
n flJ
~> K "»
n a)
!>, C 0
1-4 O ID
*J *J C.
O O 3
C as m
a. 4> ^
c
o
•fl
a) ccj
o t. .H
g""1 C- ^S>
O> 3
£ O
0 C
o
o
Chemical
CM cj vo m co
ill 1 1
st ro in vo o*
C\J CM CM CM CM
O. O. D.OO O. O.
I— t— t— • t— ca c8 t--
— •-.-.-l.-COCO«-CMt—
C C C 4J c"~ "~ C 2? ?
Snj cti 4) td * * rt
££ £no)£«»
njmn)>,n— i— iflDO)
»— in in in
P^» CM CM CM
CM
o in
O vo vo O CM
m «- •- o o
OOO O O
^ a- x t co
u o m x st
in o o CTI o
v o o m o
ON
a-
I
•o
n
c
CL
O
o
a> « >.
c c o
o> o> t. o
C ** *^ rH O O
•** C C £ f-j <*^
QQQoSu.tXXZ
CM CO
1 1
PO GO
O. O.
PO
.00
flJCvlfMPvlONCVJPVI CMCMt^ON CMPVICVJPvlfM
C~- OO OO OO — CO OO OOGOON*- -OO OO CO OO CO
2? c o> - c" .H
^felfe^fefe^fe felsl'fe ^111
vo
O VO
CO CO
m
0 1
co o PO PO m co
CM ON •- •- •- .-
/\ CM PO CO CO PO
in «- o CM CM
«- o o •- o o
PO O O O OOO
o o do ood
V
tx t-t, t,i Jfe 5.1, v.
C'Cvo ££ £x X13££ C
t»- VO O CM ON CM CO CO ^ CJ* CM O
O-a-O OO OO OCMCMCO CM
OOO OO OO OCMOJO O
OOO OO OO O.-OO «-
V
o in o
o in o
o • o
» a- -
VO — O
rvl •-
0)
T3 ^ i^
-i O O
,3 S|
c- i. S1 a a a) «>
OOO4>41 O O C C C
cg_>gg ccoo™8' ~*
rzzrrzza.a.a.D.a.a.a.a.tx'ix'H
257
-------
§
I
g
g
s
CO
o
g
fc
^
OS
4J
c
fl!
4J
Sz
o -^
Tf
xS
t, nt
4)
Q n
is
^t
3 e_
CO
&
CM .-
I oo
o
•B
C
CO
CM CM
I I
mo _ _ _
a- in c. oo c— oo iH
L
4)
60
3
C.
oo a. co cxoooooo ex o.
I OO «J
m ON >
O* CO 4> CO
• •— S» CM -^ • . >•
o. oo 3 ex «-i
i i
in in
- _ O - - _
-p .-.,-.,-,-,-., no - to m »- " a
ON O ON ON ON ON * C ON C C" OO . ON O
t1* C . t>- • [•— . » . t^ fr>— f_ «^ f- "-f £• ON . t« fr-- C
CM • OJ • «M • (M
OO O. OO O. OO Q. OO
1* OO ON ON ON
>-i ta n
t. to £ ra 3
ca)(>on)tioo
rH rH 00 *~ «J CO »-* »— CO »H *- fH ' .
t ^ 4-> I C "~J^J^4JC WC4JC1"
OO
»- m
o' d
a m
a i i
i o o
o «- —
^« ' CM VO ON
c«i i-Ia d
cu
in
m
m
I
o oo
•- in in
X O O
o
o
I
o
O a-
.". °
•- O ON O
m
1--0
o ^
drj
bObObO'-*eo bo ^^<^^tObO
XXXOX X^OOXX
e *-^ o o o
fBginguBo oingg
!l(\JBo BONCMBB
"~" - - ~ i ^ o f-
o
o
fiO
X
1TV
(M
bO bO 00
XXX
. X O X
CJ O O O CJ
o o o o o
in in o o o
Oi 7< U
ON in in
' o
o
ro m m m CM
C5
m
o m o
e O o
o .- o
oin omintMooxofMo
O»- T- »— OvoOvo^OOt*-
o - - • o -
a- •- oo oo co vo CM
fMm
oo o
CM •-
X
4) 4)
C C
CO CO
4>4>4>4>.U.U4>4>
CCCC4)4)CC
4>
s
C 4> 4) 4) 4) 4) 4)
4) C C C C C C
O).0<»a>4>
CONNNNNM
rHH<-ll.t.
MOOOOOOtfrH
•H *> B
f* 41 O
a n
c c
10 (0
t. f_
oooooooooooooo
£jCC£jJ4J££t.<_(.l.C.t.4J£;££:x:£4IO<_I_I.(_l.I.t..t.L.l.t.t.(_t.
CJOOO4>4)OOOOOOOO4>OOOe)OBBOOOOOOOOOOOOOO
III! ••!! iH •'H -. t ••-* -. I ««^ • I I I I ^ J «^ i ._ i . ^.^ .^ j ^^ [ ^_j _ [ ^^ ._ j -_ i ._ i ^.^
•-•-•-•-CMCMCMCMOQQQQQa-aaaaaaQQQQQQQOOOQQQOQ
....... .111111 .......ililllllilllil)
•~«->-«-^^--^^>-»-t-'-f-
-------
TABLE A5 (continued)
§
J->
S2
o >>
""I
« i
*S
(. a
X
V
q ct
>.
*>
••H -^
r-4 •»
S "5*
3 B
"o ^
M
•H
s
•vH
i
£
CO
ON
ON ON
ON' J&
• fH •
•-1 O -H
(VI C (VI (VJ C
in) i i o» to
•=r>ino ino^m o
r~>-(inco t~ oo c. rH co eo
ID Ox Ox V n] ON ON
(VJ • ^ (VJ • *~ (\J • *— 'fj ^> (VJ r— ^
CO O. CO O. CO D. 3 CO
ON«BCTi-ON >O>t)ON- •
•— » m«— »bo »— «bo m»— nJmmmmropommmmmmbpmromco
ONC.CO ON C OxC • t. CO tjCOcOCOcOCOcOCOCOCOcoeOcOCoOOOCOCO
rHCO^ ^^OCOiH(VJr-OrH i-t i. O m
03 kn) ID i co>>n)tiOH>>cn)booo>>a>ooooooooooooeoooooc
o
o
UN
(VI
^ mm
ON II CO ON
1 0 UN O J» 1 (VJ
om r- (vja-
co •-•-'"mc-c-t— t-.=r •**••. oooooooooo»-oooooo
^ r-(xj(VI(VICO l~^ OOOOOOOOOOOOOOOOO
(VI
m
o
X
t-
(VI
CD
o
o. oa oa
1 O O
4> XO O. O.
S a> .11
TJDCCCCCCCCIUCCCCCC c UN in OQ i o mmm
(dCO^O^VOJVVViDCiUVO^VOJQ) 0).»OOQ.OQ O, OOO
C.IDNNNNO.O.a.CXVNNNNNN f-lininO.OvOC> 1 O.O.O.
T3o.ccccoooo3ccccec^-ini..io.-a. oxo 111 cam
>>oa>a>a>a)i_i.i.t.i-ia>4>a>a>4>a>ox:.-. i- ' o -co ... ou
>.otLtLt.t.Lt.oi.t.i.t.t.t,«o,a-a-a-a-inuNo,. -i o.inin"voa.. -
JC t.!c£££!c£££v^£!cjc!c££ t. ocomcommo'Nm^'^'^in^r^'^r^uNin
i^i^i i i i i i i iT2- ................
259
-------
eference
43
at
+>
Sr
O V
""a
n i
+>
c
o
o
a
03
s
>ct
ty
2^,
a B
«v
CO
hemi
CM
I
CM CM
i i
o in »-
OO OO O*
OU
m
CM CO
i i o\
o •- •-
CT* CO
CM
i
t—
• CM i-
O.CO
oocococococn
• «- to
n e
• CM'CM'-
~~ ~ cr\ co
>> • »t^ »t~c^ «*
»- •««•- «t-m .,-
c?^
01 • •- • o t_ •
. . •- Jf tH •- i-l •- •-! CM O »H
«n) o ro n- IB co o
x:
>-, x >* x
^ozrxttZoisozoxocozEoiCoxrocoxozxos:-]*:
VD
o
X
o
Me
Z in
CM
e-^
BCM
ON
1
u
£
vO
1
O
X
CO
T
o
^
X
c-
1
o
^
^
in
a-
t-o VO
OM 1
.-00
o x IK
o in o
o
^
x
o
in
• irtn >o
T-I I
i .- o o
O O <- •-
^ox x
XOCOVD
a-ooin
in
o^-
r
o
&
to to
35 MO * O
ixoiS>
§ CM CM
I O\ CM CM
O .- O O O
tO O O O O
0
00 60
O
i-m
O
rM
O
CM
a o
r^ O
•- oo in in t—
X r- t- t- r-
~ o . • o
• • >o >o •
in o CM CM o
o M
O X
CM
?- 1
O CM
O CM
U
0
O
-^ o
(VI
«3
5 O O O
O o' O O
in
CM
o c o ooo in
oino CMOOo CMO
'CMONOOOOOCMOOOO CMCMOtn r- <~- O
» v^ O O O CM O *—' O *~ t*- '^^ *«* O CM & CO 31 in
IO OO*in*-'CMO^O'— CM OO »*— t^- »t^ -
o *n • ON -co t~ co uvr • CM -co
co ^T o ^ in in o CM ^ ^ o ^o ^ vo CM
11
o
f\i
CMCO in
rgrorMfM
o o
o en o
o en
>-• O CM ,-
o r-cn o o o o (3
^ . O • . . . CT>
XCM*-O JTOOCM
in x
OO «-
CM
CM
S8S
•?-^t
> in -
in -=r
in a- *t
o o
ss
^t
o o
So o
r-l r-1
a. a £ £
1000
*. f-11 2
in in (-1 H o
rra-vOvoQQQQ
• » i i i i
4J
€,€. ao,£
t. L ">> >• O
8 O £ C C.
£ e V 4>
O O B B
OOOOrHr-(4->4J
ooooO
i.i.(.i.t.
ii i i> b W
££
4J
CCcccc
?????????
_ o
ob-
32
(8 (0
o o
^b
33
4J O O
a S §
<0 C £
c ct ex
ooo
bb-0-
332
a o.a-
o o -
i. i. ^
f f
N
C
.
tHa
O N CO «H
-. c o c
a fl) a. -H
J.? J ?
O
n
t>
t,
o
I
0
i
o
(.
260
-------
C
U
32
5m;
•o
i
•vH
s
in
<
a
03
i-l o>
•^ "V
£> M
3 B
i-H ^-*
O
CO
=r
i
O
•-
x
o
too
x o
exj
o
^-
X
ox
en
'
o
in
i
o
.-O.-XX
xoxinvo
in o •- a- vo
00
x
oo
§•-' g
Eco &n
in
cM"—
~ too
xo
in o
o in
O T^
t- o g a- B 171
CM T- I ,- I
O t- CM CM O CO
O CM CM CM O CM
oxox
CXJO
in o o o o o
• . • CM CM O
*- O O CM CM •-
m
l
O
—
x
oo
cj
o
oo
•
CM
O
o
?f
o o
o •-
O X
O XO
• •
o —
bO 60
x —^ x
o
E too g
B X»nB
CM
t-
1
U
t-
co
in
l
O
•-
x
vo
x
rn
w
«-
m
i
u
inu
CM MO
o B=r
.-i
O
'-
x
60?
E~
Em
m o
I .- Ox
W X •
CM vo vO •-
• I *- t— t—
• ax • '
in
CM
oo
oo
in o in
CMOCM
ooxomm
ooo»->
in in o o o
CMCMOCMOOOCM
<-' ^-xO•-^oo^na•
CMmm-tninofMCM
\o o
X
p *- m o=r
x t-coi^
CM X
in
CM
§m in m
t- co a- t-
« CM O CM «- O O
t-
o o o o o o
m
c\i
m
in
CM
»- in o> in —'in —*
in a- sr a- (M CM oo o
•ooooa-a-o •
ino
OOOOOOOr-9
*J 4->
0) 0)
iH i-l
>> X
X >>
C C
SC 0 4)
CD U Si
42 C. iH O. O.
a a. >.
4) c»
C C
CUCUIDQ)
CCrHnH
t)Ci>x>>
0) 0) 0)
§^H
X
XXfiCCOOCU
SCCXVCUCCC
d)-^ic£a>
£j=x>a.o.x:£££;j:£:jB£: 4-> 4-> t>
O.D.OOOCl.CX4Ji)jjjJjJ4JCC-H"HvHt.
C,(.C,OO£:JS£j:££-^-rHCCCO
ooo<.(_o.Q.o.o.aaii>ci)OoOiH
'drd'7i^->JC<'CdldCdldldiHr-liHi-l>H£
222-.-icccdccooxx>,o
OOOZza>*ll)c5(D5t.t.Lt.t.ro
1 1 1 1 1 OOOOOOUu8cjc3r-l
g
CXJ
a
CM
.-
vO\O>O>-f-
^.^.r.CMCM
4)4)a>a>a>«ia>ooocMCM
ccccccc^-^-f-^r-
' 1> ffi
OOOOOOOt-tt-t-t.
cccccdcoidcdidid{dOoooo
>->H-r<»H^<,.C.C,C,,t,C_I.fH>-li-lr-liH
ii.t.t.t.£££j:5£x!oc3ooo
T3-e'O'54JJJ4JjJ4J*JiJOOOOO
r-<^rHrHCCCCCCCt.i:(.(.t.
261
-------
CO
ON
I
o
£
fc
%
OS
4J
s
4J
S2
5m;
•:A
t. eg
a
rH 0»
3 ^
eo
cal
vO
m
l
vO
m
VO m O
rococo
*O O
moo
I
VO m
moo
CXCO CXCOO.
c\j
CO
bo ^
C
»
CT> 'O>t\J • O« (. 1.
•-r-t—COtH'-C.O
(d ONOJ vo
.
^-li-t.
^to O>
.
COrtO
-&U
C
CM
1
t—
d
o>
CM ,-
1
r-
•
1
t. CM
O CO
O ON
(-
0)
DO
O
W3
t.
•?
O
fe
rH
3
oo m
ON 00
cvj
O
t—
&\
m
i
OO
c\
co
CO
-oj
D. CO
m
I
o
in
I
o
xmxm
moooo
•-OOO
m
i
ox
OU3
o •-
ox
O>£>
t~ N-
ooa-
OOt—
»-o«-
bo" boGtObO
xinxGOexK
CMrem
g^gCMBS
Bm B g -^ S §
1 |m
x CM
^^
Bin B 8 Bin
Bl Bl
oinino
in •- i i in •-
IXUUIX
Ut-inincdin
t~^oo»-o
ooo
OOO
ooo
00
x
vO
t-
in
ON
in
CM
m >-» o sr
a- t— CM CM
. o • •
r- a- O o
in
CM
in
CM
ir
minmi-inincM
CMOO
ino
CJ'o
^^t—t-in
cvi>-t^
-------
Referenc
ro
i
eo
o\
co
I
<\j(\j
oo GO
ON ON
CNJ
oo
>CM
ex oo
ON
CM
i
o
r—
*CM
0,00
ON
CM
i
in
e\j
i
>CNJ
ex oo
ON
-CNJ
ex co
ON
in
i
a-
ON
.(NJ
ex GO
ON
>CM
ex GO
ON
o
oo
ON
• - t- ON
• • ON O r-
M r-l »- CM
(0 (0 ao •
CO
ON -
r— •• «— •• i— • i-~ •* »—
«
e?
1-t
t- .0
si
oo
ON
ex
ON
--
Qa>
x:
•o
|
•H
S
o
o
3
00
y's Constant
tm-nP/M)
Vapor
Pressure
in
t^ i
1 O
o--
^x
XCM
ONCVI
inco
oo o
ot-
^-1
xo
t-
i
O
irv
>
O
x
CNJ
t—
1
o
«-
X
tn
1
o
o
>-
X
o
«-
X
CM
O
O
o
o bo
in x
CNJ *-*
>-'BCJ
oio
7
• X
inin
0^-
r-it~B
XUI
c«"io>o—
voin«— •
.-XO
bOobO
*^*
BCMB
*N
bOo
O
CM
XO
t—
^^^IfM
.«-«-ininoo6fNj
fc»«— '—'
. O X O O
O
o
O
(Mv
^-^
0
O t-
o o
O
bOCM
X
O(\j
X
(N-l
60
X
voin
l"
u o
>-<-i-'- oo
X
\o
O
ON
Solubility
(mg/l)
ofNivovoino
o — CNJ (M CM in
in^ooooj
CM»-OOO^^
"-^ o o o o=r
ro .... i
IOOOOO
o *-
— X
x >o
GO .
. CM
ro
-
ooom
inooo--oo
cMoinoxot-
^'OCJ
Jc
(0
.2
V CD
0) 4)
** 4J (TJ (0
t. «> 0) .H ^H
C CC-~»-^4>a>CXCXJS£*J4J
a>a>4>a>a>a>a>><>t'^'->ooa.cxQ>a>
CC
0)4)
0(TJ>>>>>>>>(On)4JJJ4->4J(n«XX£i: BB
St.t_i.i_i_i.t-— i— i-"-> oo
O4>0>4)4)OOOOOOOO£:x:4)4)4)a>a)(.l.
t.3aaexa33t..t.t.«_t.t.rHrHggcccoo
>»r-tl— 'I— 11— II— lrHrHOOOOOO>>>>OO4)4)4>i-lr-l
re m nj
's's's's's's's'siiiieici
4)4)4)t>4)4>4)4)-r4-r4^-^^*H
i i iSSnjDc-oSweeee-0-0-0-0
CMCMCMOOOOOOOOOOOOrHrHi-lr-1
a>
T3
4J 4J JJ 4J f
c s c c o
4)4>4)4>aO.Q.eX (d
ccccrH^i-iM t.
(0(0<0(0>>X>>>>CCC4J
fci «-, i-l C
ESES
t.t-t.t-t.t.t.t.t.t-333
263
-------
s
41
in
i
Vapor
Pressure
iH •!
2^
a a
3~
CO
heai
O
co
cvjroo
(Mooco
O*
to
C
^H
i-(M(\j
OCOOO
OO>os
o
eo
* ^^
r-j
(v
>
r\J Csl
oo oo
Os os
c\j i-
1 oo
o Os **">
9 •- oo
os
c\j
1
co
m
m
l
t-
csj
1
co o
. CM • •- CM
a.coa.oo
os •> Os
* co •— •.tp*-
ONOOOsC
n"t o
co oo
Os os
8
oo o.*-. a
O* %-t ••
•— • o m *
OsOLos
*»«^CCd h-
• • -os-Ht.t-4t-Jt.CO<-aO<-ICO>- rHr-OrH
(i)co(d «os osaios >n) OKI
••»(«««-•»- »-»>, -
4-> i> *j c c -U c ^ w c «e w
CM
co
os
LC
(Q^4
i. i.
fcO
0 (-
fsl pr>
O O
O O
O O
bOBO
x x
m
i
o
rsi
i
o
.- — (N
ooa-a-
lAr->-
ooo
OOO
o
O
oj-a-o
OOO
rOOOO
OOOOOO
^ O O
o » e
einto o
'-^OO>UU •- Os»-
**^
0
4n
<\j
^
so
oooooooin
2°.x,n
t-XvO'
'Ovoo
t> O '^
coo
in o o
|f
inovOCd
x
in
O
O 60
ox
in
(MB
2i
XCVI
m •
-o
co
l
O
»-
x
(VI
CM
(\i
O
o
o
O
bOtO
xw
CJ UI
inin
in
CM c\j
OS CM t~ CM CM in O OOOOX CM CM (\J in £T SO
OsOO«^v^ \t\ oom •-'CM vooo«»(MO om o o o <- o o
cp oro •>
X •-
9 X
in (M
osoot--t-vo«-cooo
60
Cd
in
o
(M in
v- m
o o
o o o d o o
O O O
«Koi
c. t. t,
4) « 0
« 0 « «l «) 0)
ccecccoo)
o«««)«)a)ee
0)001
c c c m
S m 3 o
OOOOUOUOOOOOOOOOOOOOCJOOCJOUOQOQQQQClOCl
264
-------
•a
I
i*
t
o
o
in
<
u
j
CO
s
4J
c
m
jj
gg
O X
om
-"B
X.P
t= «
o a
Oi 0)
> t.
0-
>t
4->
••H ^^
iH o»
S^
3 B
iH *~*
O
CO
O
oo
in
i
a-
ON
OOoo
i— co o*
I
O
to
i
PJ
oj
i
vOO
CVJGO
I
*" ° i
O CO ON CO ON
co aco a co aco aco aco a coco a
ON ON ON ON » ON ON ON ON *
r- «J . .- . ,- *,-.- -M
„ t^ . t- » f- * -H t- * fr— "t*- * •[*-^H
• O> " O> • ON • t. O*» • ON • OS • • CT» t- (
t\j (Q nj 03 O (Q (0 OJ fl3 O f
ON
ON
ON
in
i
.=r
ON
(NJ •
CO O.
ON
ON
• t—
i • ON a
c^
ia
ITV
i
CVJ
l
ON
ON
m
I
£
CM -Oil «CJ • CJ CM CJ •
co aco a co a co oo co a
ON ON ON ON ON ON
ON
• t—
ON
ON
• t—
ON
ON
• t—
' ON
ON
• t-
i ON
oo col t~
4-moi
oo—o
o
o
M)
x
Id
ON
o co
oir»
x
flO
irt
i
o
r-
x
t-
eo
i
o
m
I
o
•-
x
co m
ONO
t--i
p o
T »-
U1X
o t—
oiri
vo I in
i 01
o^o
^x^
x in x
CM «— iv>
if>
-=r in '
110
oo»-
^»-X
xx>-
vo o ON
in in in
(M CO 00(M
t^ xin a-
'o e'o i'o
X X PJ ^<
"~ C3
too
Xa- oa-
,- i
X O
in •-
oo
moo
o mo
^- cjcj
x
B x
oo in
t- r- o
i . •
o
o
in
OJ
oo
oo
^^iniri
O
-oooocot~ooin<- B
8 8
o
o o
o o
<-» in in
O CM CJ
§in o o
CM moo vo
^•" in CM cj
• o • • •
0:3-000
~i — 4
oo
t- t_
- •-
c. c c c -^ ~->
D.O.O.O.CC
a a>
C C
DC nj nj
c a) a; D £ c
0> O C C j-> iJ
o « g oj m oj
4> 0)
wo>cc
f
cd
: jc
_
i_ .c *J 4->
jr 4_> 0) 01
. t. n
4-> c e e o o c
c ro 0 o 3 3 to
n r-i v. i. rH >-< je
O O «_ Li JJ
tJ 4J jj ra ra
ra m ra ^ r-i
o
s_cc
s-, >, >,
o. a.
£ £ £ Q. O. ^ — 1 — 1 t. ^H C
a. a. a cc c^jrHta
>>>>>>.c.e.e.ccc
'
nj3OOO.D.CNNgeOOOtIl!
f- f- H t- H H i i i i i i (D.o.o.a.ooocja'ai'a'aiuottBeB&oabibi'o
aaaQaaaaaaaaoaaaaoQaaaaaaaae>cinciacN.ocau3
265
-------
«
o
o
S
0
a
O X
°"B
a i
xS
feJS
4)
X
o a
is
> t.
0.
>.
4->
-»* *"*
i-l •«
^* V.
O M
3 B
•H ^-*
O
OT
hemi
ON
f-
ON
f>
I
CM
I
coo
CM co
ON
CM
I
eo
• CM • •- CM CM •
0.00 a coco o.
ON • ON ON
• •— » bo •— *- •
CM
il
CM »o
«• ON
CM*'
000.0.
ON '
l
in
ON
CM'
000.
ON
ON 'ONI. • 'ONCM
«-rH»-O>Hi-l»-flO
<« oai«) ONooa
• » •«— ON
C 4J C W) 4-> JJ C r-
a> i>
£
CM CM CM
i i i
ON o o t— m o
CM eo o-> i— co oo
ON ON o*
«-CM'CM'--T-
0.00 Q.OO a.
ON • ON ON * •
for- »aO'— ••— *(floo
aooNCcrtcnt.c
CM
00
te
tiO
^ • O\ a •- «O»t. -ON
iH^CMfH r-jt-OrH^
- n) OOPO-IO on)
>* -ONONX
^4-lC<-<-^4J
to
«H
i.co
t-ON
O •-
eo
ON
UC4JC
4>nia>a)
nn
3 3
CM
i
>o
in
CM • CM CM
0,0000
ON ON
— •*-»—
cn
«t^»«
'ON • '
rH^^Cj-)4J
a>aia>a)
cvi
i
CM
i
• CM •
O.OO O.
ON
ON -ON
»->H'-
rd
" •
C4JC
ffia)g)
c 2
t-t
<8
in
i
o
•-
X
vo
u
ebO
in x
CM
r-
i ON
01
o x
o
O
ro
i
o
f>
vO
vo
i
o
t-
X
in
in
i
o
i i in
oovo
•-•— o
XXO
ONONO
X
oo
i
voo
CMX
o o
X
in
O
*-^o
o in
oCM
in---
CMt-
^ltO
r~os
oo
X
T
o t- ox
•- I V- O
X b) X •
»- CM CM CM
0 CJ
tno
CMin
(\i
O B S
• B
8S,n
£2i
O
in
CM
rO
^— \
O
o
in
CM
x
o
in
CM
OO
bo
KM
X
o
o
m
CMin
O OO ON
— O O
X • •
O r~. f^
O " Mo9 M
CM O X O X
-* 00'— CM CM
in x o -^ g ^ g
i o m Be- B
o B 01 i
»- 1 CM o in o t-
ON in in x u x b)
O •- •- in in oo oo
T- O O CM CM CM CM
r-CMincMinv^ininoo^-a-vo^vooooot-t-ONoomooinin
tNjCMONONi-CMvO"-O^-OOOO
— CM CM O CM •- «- t- CM t- CM
o in - • • •
o o o o o in T- m o o o
voinoo
omo
OOO
O
ino
oin
OCM
on ^r
VO fMCM
«4>
4JW
a as
1
o
X
VO
c a
a jj
^ 4)
«) a> a> n a> A
0) 4) C C j; X £ X
-o'oa>a>a>a>a>a>a>ot>ot>
-.-i—icccccc— C-H,-I.C,_<.C
xxa>ai5a)$D'cinoooo
_
T3NNNNO
80^— i^-<
ot.i.t.c.
-
.,
OOOOOOOOOOO
0> 1 0)
266
-------
con
SS
a
03
Reference
C
m
4->
S£
o ^
oco
n i
x3
x
O OJ
CX 0)
52
CL.
Solubility
(mg/l)
oj
i
t—
IT*
OJ • OJ
CO O.OO
OS Os
_..._
Os
« t^ .
•OS -
,H.-,-l
nl n)
oo
-os
i
coon
ZT CO
OS
OJ • •-
GO O.
Os«
r-.IO
OS I.
» t-. cd
-OSI.J3
Jr-|T-t.<\J
co 4>oO
oo
i
oo
OS
OJ • OJ
GO O.OO
OsOs
.-.^
os
«t^.
-OS -
.-l.-r-l
(0 (0
oj
i
oo
OO
• OJ
O.CO
Os
-.-
Os
t--..
OS -
oo
i oo
o oj o oo
CO CO GO OS
Os OS •- OS
o
GO
oj «—
oo i GO
o oo os os
GO OS CO T—
OS •-
«- '•
- CL 60
C OS C <0 C
.^t— ~4 ~» VH -^w^t^c t~>» os-^*
t.OSl.COrH-t •
O'-OCOOOOO<\jCMOO'-JOO>-Ji:OOcr> OrH
o oos OSOGOCOO os OosoooO'O
- !-•»•- osos i
.8c.B m^^rf c cr ^^wjw
« - t. - i. to 4J - to - n w
nn)fl)a)On)ianl
i \o
01
T-O
x
O
OJ
^ t-vo t- x
XIM>-«-Os
COOOO^
OO
co oo vo
i i i
ooo
CO »-»-«-
x x
t-
in
in
t-
O
£
vO
i—
o
O
GO
t-
oj
CO
o o
o o
• •
o o
t— in
I V-
U •r-
t~ O
Os O
r^ O
CO
CO
V
ID
z:
in
os
XO
vOO
o o
a DOa
OEO^^
og ry t>o o
--ie — XobOb0
in B=r inxX
llgoj
oinol^gg
— 1.- r-BB
X W x .- co
i— t— so co oa'«
O
o
O
O
OJ
<>o>- bo^
xOXO
l
O
PO
a-t-ococo
_ _ ,-. o
O
o
in
oo
u
o
m
OJ
ooo
O
in
OJ
ow«-oo
bo
X
I
u
oj
to bo
X box
1^5
in so
1 VD |
oj co a-
• • •
CO O Os
bO
box
XbObO
^S3131
g B
B
oj
to
x
OJ . .=!
t— OJ O Os O
• v£> O -=r •
•— co a oo o
^
ou
^
r-
co
oo
o
OJ
ojoj
• oo oo oo oo .^^
,— *-•••• O t— O
coojt-'-r-T-irieooj.in
O
o sr
moo
OJO«-
»->ox
iT-oj
IOJ* •••
o ^ •- co t— o o
x
00
ojinin
in»-»-
CO
o
coinino
o
O
oj
in
oj
PO«->-^-O
oo co oo co t— t— a-
-
•- o in •-
CO£T
«-co
ooco
iH W « 0)
at n o> 4) w *>
S^-^ C *^ <*H "^
4) fl TJ T3 T3
X C X <0 to (fl
C C
VO)
XC >>,
OQ)OO>0)0> O.O,
r-i£>-ickcxci.a>a>a)a)i— lrHrHO
I 01 I 0) Q) t) 0) 0) 0)
0)0»0>
^OVTI
c c -i — i -<
oott-t.
vlfHOOO
--— oo
«> c c n « o)
to to 4) 4>
tBnj(on)
-------
Refer
•o
0
3
c
con
S
m
g
tant
n i
V -u
!:£
0)
x
Vapor
Pressure
ty
S^
3 B
i-* ^-^
O
CO
ON m
t-- CO
ON
- ID
ON t.
os t. a
— t. cj
4) OO
- U, ON
i-l
CM
oo
ON
o
CO
ON
o
OO
OS
CM
1
CO
•
o.
1
co
IA
r\j •
co o,
60 -60 .-- .60*0
C ON C C7NONC
COr-|
o\n>
£1 L. j3
(MO<\J
coooo
m oo <\j ro
1 ON II ON
vo o — <*•> o t~ o o
ON CO CO CO ON CO CO
ON • ON ON ON
• — 60 r\j • — ry • — CM o
M (M •- -e^t. • ON
^OOrO i-l •- O i-l •-
*> «e — *e — c m
.%* S*r
x
— — S *» c
w id
t_ —
o
o -
•a jj c •» j«
u) n a
to
o
CO .8
ON O
— CO t.
OS 0
J~3
8g°.
n)njoo\
U 4->
0) 0
J O rH
U >H
•a 3
•e x
-< 4J ra
m in
<\i cjl
o S°
8 8^
o o m
o o —
01
l
orn
u ^ oo
OXO
3-000
6060
xx
xo
o
S O
B Cj
UN in c~- UN
r *- M —
OO
60
X
O
o
o
U
Mo
SCO
C\J
60
60
X
ga- bog
1 X i
a- O B a-
I<-BI
bixsmu
4- >- .00
. . =r IT .
,-.-,-.- IO
i o
fa o
t- o
•=r o
CJ O
60
X
bi
—
.
(M
o
x
cy
•
CM
U"*
f\J
tO
3d
x
l
xo
in —
60
X
e '— 60
UX
a- e
IOB
UOJB
o\ ^-
• a- o
JT — 00
OOOOO
Ooooocj
OsONC-Ov^
UN —
— (TV
— — — CUOCVJCMOO'
0
e\j
in o
(\j o O
>-'O
-OO
in
<\w
Oinr>JU1O~-I
o\ oooO(Njropororoo
CJO — O--
CJSCOOOOOOCVI
X
ro
ON
O
I C- -ii-ir-iiHva>
O0OOOOCC
lU££
««»« AAaa
CCCC OOOOOOJJ4J
4>4>4>4>
ida>
§H >H
£«>.>>><
Q. £ S £ C
—I 4J 4J J-> 4J
OOOOO
OOOOO
.HCCCCCCC^Hi-I^HWOttO ^OOCJOO
l>£j:£££££x:££ >.^>>>>>S fc « «u a! tu oJ
CC
a)a)
268
-------
O
£
&
•s
1C
c
nj
O V.
ocoa
n i
T3
4)
a
c
§
a
CO
0)
X
4)
C.
I. 3
o n
CL CO
fl) 4)
> t.
Q_
n no
3 5
o
eo
o
i
o
oo
OJ •
CO Q.
1.
4>
QO
43
I
in o
moo
i co i
f\J OS OO CO
in •- oo vo
eo
co o. co co cxoo t-
O. CO CO
t- . «-I t-
oj c\j i-
I I OO
if
CL
• (\1 -
a. co 4-
*> c £
4) ffl . I
£ >. i.
4>n) 4>4)ia4)jJc4fn>4>4> m - <« 4> 4->
,C(t £ 4-> .c 05 nj •> .c eg .c n
to
i— vO VO
00
o
o
00 I
ooo
ooo
bo
X
CO
00
o
X
o
1- !<
I vO
O CO t--
X O O
•- o o
osoo
m
I
o
X
O ^- OO
in o CM
o to
o
cvi
in
oo
oo ^--,
bo--- to x o o
X O X co
-^ o bo e O O
fl-^aoxlS ojog
o o ^ e ^ XT irt
I o en o> B • • •
oo oj • • • t- t- O
vo in
vO t-
o
x
bo bo
XX —•
o
§B o bO
E OX
00
O O ^^ B
vo oo in E
s^ in o
c\ic\)
o
OJ 00
in rr :
oo ro
o •-»
in c o
-- oj o o
o •-' o oj
• mm *- ~--
• • o -co
x •-
t- X
O r-
ro i-
o o
o o
o o
OO OJ
ov
o
oj
x x
•- OJ
o •
• •- OO O
in
oo
4) 4) 41
C C C
*> *> *J
0) 4) 4> 4) 4)
4>4>4>4)4)OOO
-
4>
g
O O
8m
4)
22 b b
t-333333<0(Bnj
^>rHrHl-lrH>Hi-l X XX
t C C O O O
> > o. O. Q.
269
-------
CD
o
c
IV
t.
0)
V-,
D
02
O
z
o
<
D.
SO
<
u
_I
m
t-
Partition Coef
(ug/kg)/(ug/l)
360
OO
Chem
ON
t-
CO
ON •
•— >»
co
l
t-t\) • C. IM • (M
u.co cxbaoaeo
O> CT^ ON
ri)<- -.«.--—
1 l>H>-li-lf-
-a- inc. «_ooi-,«-,cot~-^-i.8
«no>cno)ain)
• (M (M • « 4>*-a>a>—.S»-<(\J
O.OOOOQ.X 2 73 a. coo
PS CT» « ••CtflCTN
. ?- ^ . rf rt) 60^) •« M - >—
C
CO
O1
.-ON
t~
• ON
>> ON •—
. -^t.S»C\J
a cu, co
(d^dON
• > rt) r-
rH t.
- 1
T3a-
ai a-
t- •
h. O.
t- eo- t— CD • t- « t--»(— c ^:-H^:X:— it— t-«- t-- M n t. >
ON t.- ON t. • a> • ON.-ONO ot.oot.ONO>'
ON t. • ON t. • ON
"~ co •" («oo"~aJ CB" rteo"~ n) iB^ocoJioB^cs
» ON fl) ON • Q) » ^^ • • C ON c C C
nj a) n « N N NM
\-<£>3jO~lZ4O£>3xl- C. O • t. ON
• •noo oo *4J *>alco
..
tfl m CO 1) n) CO CO 0) n
x:3» - ^ - 3 - 3 --£
rHlHX3 COr-i^H^H^^l—(A-FH-^
U1
»•
m ^-
a>'^'<^'~trHl~<'Ht4l-*>^'l-lf-4>^>H>r-l>H
LL(-t.l I I |t.c,t.£J3££lVt.l I L t. t. t_
rH^HOOOOOOOOOO
oooooooooooo
I I I I
I I ^ _< ^
- I - - I I I I I I
I I I I I I I I I
270
-------
s
g
&
«-,
•o
1-
~*
J->
c
vO
u
_]
00
<
H
rtition Coef
g/kg)/(pg/i)
*oe
00
X -1
Ch
in in 0) —
00 Q. CO CL tu CO CXcO
,- . .- . OS r- ' '-
CM
i
t-
Q.
o
(fl
oo
•- o>
t-
• ^
>, --
«
o -T
oo
cr>
oo
r- 0>
i^
- o>
>t ^-
CO CO CO CO CO CO CO
CO CO CO CO CO OO CO
i O>
I"
rt flj
o s
ca.
n)
£-
a
ra a>
f-i r>
o s:
CO
- o>
c
5
r-4 £-
> O
oo - -
& -H -H
>, X >> >. >, >> >,
.O^H O O O O O O O
^iOOt--«b'rH oO«0*0*^«t)«(9W5«0
»'"O.C>OD ""'S'c'e'e'c'cc
•BbO-> *- <«>•»!.>>>>
oo in
oo *~ aoo
*~ ^ o^"~
• &. CO ON • O t» 9^ ^ O * Oj t-
r-IOOO^-rHOrOfflO^ rHOUt-
n)CJO> n) co co JJ O -««ooo)
1-- O\ Ui
m
.C •
^- ^---
nna> -na> N a :c x >-
Q CU^A-^ C0£ Ci-HrHrHX3 ^«-ifHr-li-I^HpHi-liH
Z«SOZ>*Wb«!O>-<>-lXj>-5>-">-">-'X>H>-l>-i
cooocTNOO
•
ss
ID (0
t. t.
a) a> a>
3 m 4>
o o o
o o o
^£3
a; a> D
ccc
000
o o o
2
Q) Q)
C C
— i— .
c
a>a>
cc
NNNCXO.O.
NI4MNMNNM
o a a
i i i
a o a
i i i
o a o
i i i
o o
o o
ss
o o
o-o
S3
O 0
o o
o o
23
O 0
Q
I
o o
I I
??•
0) - .
c in in ca vo
x:>«>in>»
a «
oe---»--
t. ororocomroro
4Jt ......
•H O CO CO CO CO CO CO
f s. •.-.-.•.•.•
271
-------
TABLE A6 (continued)
R
g
fc
«H
)W Partition Coef
)g (ug/kg)/(ug/t) F
x •-*
.— i
Q
O
g
XT
m co on ro PO m ro ro ro ro no ro ro no
CO CO CO CO CO OO CO CO CO CO CO CO CO CO
ON ON ON ON ON ON ON ON ON ON ON ON ON ON
-
X X X X X X ON ON X X X XONXXXX
O O O O O O • v- O O O O--OOOO
r r s! s: a§ zSin-zzs s-szrr7 ? 71 71 7 7
ON CO V >>>S»t>LOt.>>> >t->>>>ON •'cmoN " ON »ON »ON »ON •
O O(t)fflQ)n]fo
O (QCQCOCQCQ 0)0) iHrH^HrHO 0)0)0)0)
0. OOOOOOO £JC OOOOCrHrHCCCC
i oa.a.a.a.0- o. a ccccoiooo>o)o>o)
to \O Oullltl CO CO OO 0)0>0)0>£CC3333
8a.-ucairN.iAiriuiirtvOGa
-------
oo
ON
ON
erence
cj
1
t—
.
O
(0
2:
CM
i
CO
>
n)
.*
O C\l
cj ID i cjcj
i JE (w it
co o o m 01
CO •& v- CO ON v£>
o
CO
cj
1
ON
CO
>>
(0
.*
O
nJ
2:
1
in
D.CO C O. CO D.OO Q.OO C CLOO Q.OO Q.OO D.OO CX CO C D-OO O.OO
.. — > .. ,- . ,- • .- > - ,- M> - .- • •- bo - »- • «- > -•- -•-
O^ »—f O^ c^ O^ r-1 ON C ON CT* C CT* CT^ rH ON CT^
t~ «(0t— - t-- -t^ -(Ot— «-^it^- »C-- »^Ht^ »t^ »(0t— »t^ -
t\i
i
in
O
*—
OJ •CJ
OO O.OO
ON CTN
o o
OO CO
ON ON
— OJ *-
CO
g-c5
(floo
nJ
(0»
(OO
(0
(O
I O ^H O CJ
I O (0 O CO
-
a; ra
£
-
i> «
x:
•o
I
x>
c
o
o
m
s
Partition Coef
(ug/kg)/(yg/l)
3tO
oo
o--o — i-«-t-(— vo
• f"> O O t— O —
I I
i.
03 i.
.co)
jj x:
H) Q>
t_ S-
»>i
cc
£ £ r-t
o. a. >>
c c
a. o.
a> a>
cc
-1 1 laO
i i i i i i i i i i
.Hrj.u.uaiainirarerH.HrH.c-.-ct.L
c c
I I I I I I I OUUUOOOOrH
273
-------
•o
w
CQ
01
O
C
0>
b
<$
0=
q , ,-~»
V o»
0 bO
o ^
JJ bo
i. bO
ro a
3 DO
O O
ro
o
1
0
ro
CO
O*» O> (Ti
t- «- f-
O^ i— • CT"i
«- • CO •-
- C? ^ *
*J -^ 4-J
4J O » 4J
IOZ:i III II 1 CO<-HO
vO CO vO O vO VO *O PO O vO vD O vO CO O** O tO CO
ON 08 ro ooro c^t ro OOCOPO rooo coco *-ootj
D.CCCOQ.QO D.OOCXCOD,CO cxcoa. cocx co <— * «— tfi *>•— •»«— » »— 05 tt) *»*— - to <— » ctf *— E tt> *** O
ONt-rH a* co^ a% t-Ccr» CTNC cr«t. a>c i_
t-aanj *i^. ».^t^. *t- »t— »nJ-^Ht- * t— -^ «t^-{fl -N^.CCO
o> > «ON «t-cr« • & -os «t,t.o> 'Ot. «^L »t,s-o
,— » ^Hf— r-tO*— r-t«— rH*— rHt.O'— »H <— O.-I*— t. iH O O (ti -
•> tj '^H * • * * [Xj * • » fat C 'k.-i
C O -^ 4-J C t^ *W C ^* C JJ C 4-* "^J C J-* C *B ^j c J-* *4) *8 QJ O
(U x: eo M ^r
"-2S £"5 &«5 a? * &^n S*«u «5 a?5 rt«?^5^ wo « S -2
1« iam'S'fd'S £rt«'3'Srt'S^o Sm'Srt S'S'S^S'SS So «
c^
0 § ON
*" t*- O
*~ QQ
ina-roinoooocooN a-ooo-in — -vopooa-— «• in oo — —
C\J
tNJ
—
^vOvoV^wcvKMtMcoeocoeo^fl-^000
<1) jjjjooooooooooooooooooot-t.cc:
ro
CO
ON
ON r~-
^.
ON -
"~ as
-HO DO — p -
I- iH C 0 ON
i- ro -i - c t-
o m 1-1 j —
>i-4 — H ON •{} -
J* ^ °« — j-3 t- c
3 3 ro * ON JJ £
S° ro°| «)"" n*
««§»"« 3* §"«
rommoorovovo —
c c c
0)0)0)0)0)— I— i— I
cceceTj-o-a
0) Q) 0) 0) (D — H «»H (
NNNNHNNN
cccccccc
274
-------
Reference
•o
0)
3
C
1-4
4J
C
o
o
vC
<
fa
j
m
«*
Partition Co
(iig/kg)/(ug/
360
00
s
KO CT» *O CO
ON
• OJ • C\J • f\J • CM •-
D.OO O.OO D.CO O. OO
O"> CT^ CK c^ •
(— _t-_. •- co
• roajt--ra
XT 0>£
(Uoo
oiococo
CT* CT*
(Qco
'--'-—r--
raa)n30) njoma)ifl i-ira
£ £ -£ £ £ £ n)-.£ S. C. .-.£
-
.O~H Vr-t
t— voir»ro»—
ro'»- co co
I
cocoooo^iot— o
aj a> a> o)
c c
55
0) 4>
C C
4>4>.e.c.Cr-i,H.c.C4>4>
>, >>^ ^ ^ o o a. a. a) ai
X X rH |H r-H L t, ^~* ^^ ^^ '"^
0) 0)
C C
S|
4) 4)
C
4) <>iooa>4)a)r-0)0>~^.HO4>CO
i i i i i i.c.c.a.o.n'O-otHgE
i"^ ^^ i"^ r^ r-1 JU. A-
£ C R £ £ 4J *>
I I I
a a
rH rH
>, >»
N N
? g
r>
s
N N N
4) 0) 41
N N
4) 4)
N •
§
Lt-t-t-!_t.t_!-
3 3
m m
275
-------
o
c
o CNJ
CM (\J
i i
ro o co
co ir m
m
co
^ CM W > 1-4 i- . C\I<\IC\J ^ • .(\J
coat c o. co co co CXQ.CO
C. t-t-tCCTN C-O^CT*
ra . c, in ra IH t^ ... ra t~~ t^ •
ro
00
>,
ffl
o
ro ra
i z:
t- o
o
o
o
m*-(vjcjojoJc\iinrj
ra
f-4
rt
£
Q.
0)
•o
bfefe
o o o o
fO flj to rt
!_ t- f, t.
0} Ci) Q) CP d) CO QJ
gcccccccuo
CVCDCDCUCUCUCC
0)
•o
t_
o
4->
d>
e
0) 0)
ra ra o
CDUUUUUUOCJOUUUUUUOUUUUOUUUUOOOOOOQOO
276
-------
Ref
•a
CD
3
C
con
•£>
<
a
j
CQ
*t
E-
Partition Coef
(iig/kg)/(ug/i)
3 CO
00
3
e
0)
CO
ON ON
O -O
CO <\j
CO
ON
'tvi
O.OO
ON
'f\j
O.CO
'f\i
O.OO
ON
-- J
- «B
ON
t^
ON
ON
t—
ON
ON
ON
O ON
-CO • I— -
r-lt-^HO — l-(O«-.-|t-l-l»-Ol
- O*N» ON- - -ON
- ----
a>nua)
x: 4-> r: x:
ro
- r:
-
a> to
c
O C
OO}
o
o
0
0
o
a-O<\ir— t-CNivDN£>eot~iN-)roooo
ir>arCNia-vOt-inovoin— «— t— i— m
«) c in ra
-H-HCcii)ii) .u.u.i->*~4*'-t~4~4
fH r-l >» >1 JC .C "TJ*"1
O O >—"i—11—i O O
c .c x: o. o. o.-.
a. a. a. :
0)
u
c^ C
3 «-i
4J 4J *> JJ
3 3 O O
H (-• I-1 (- H I I I I I I O O O O £> O O O 4> e» Q>
-------
Re
•o
0
*H
JJ
c
o
o
5
a
03
z
Partition Coef
(ug/kg)/(ug/l)
3 tO
00
u: —i
Chem
oco
cot\i
O.OO
60 - ^
ro m
CO OO
CT* CT>
m .ro
o o
i i r T1 ac
0.00 a c aoo c
.2^ . >. . 2? >
C^> O^ »~( O^ rH
^ « [^ ro t— « ro
CT\ • ON > ON • >
ro
OO
CT> CT»
,- CTl f-
t-- CTv
• CT> •-
rt
^
O
CMRJ
i s:
t^
i~«»
'-H
O.C
JJ>
OCJ
o i
(0
is nj co ro
3
Q.CO CO CO
CT^ CT> O** ., — ^
. r- .-.-«>«-> •- O -
t-- -co • - -^ ro (— n i_ • • cr, ' • t. t. cr> > o • o>
co C o. co co co co co 0.0.0.0.
ro -co ro ro a o i>
c ^H r> n n.-i.o_4.Hr-f.ai-i e ^ c cj x> ^ c iv_
(urorotDrorororororororo ^> ro ro * ro ro o to
«)«•« ronjrororo
1-H.O (LrH^>J3OX3X3^HMrHfH
§
ro rom»—
V
T3
—
0)
-------
c
JJ
c
o
t)
vO
r«i
J
m
H
0)
o
g
t.
01
0)
0=
«-. *->
o ^
O 00
C.3
O -v
••H S-*
JJ bO
*•> *x
I- bO
10 3
CL ^-<
3bO
O O
f—l
<0
0
1
o
»""
|
CO CO
.=r oo
CO CX CO
*— » C0 T—
. £ $5
CO (0 r-l vO
0) Q)
o . >,
o. a
r-1 I — i
•O T3
CM f\l 0)
^ _^ rt
^ ^ y
'g *o o
rn
00
t— — t-
O^ O"> ^
^ - ^ 00
t- - ra - v-
r- JJ 0 JJ
CM CM OCMnJOPOi-f v-
ro c\j n o co o CT* co u"* o ra ro o^
00 ^aJS oeS^ "^.a^S^ S^
cxco cx u- c cx c c a. 3 co Q.CO
*^- .o»M ObO* >O"bO««Jr- 03*^*
S-Sco ^«5 m-S^ 'fom^S.c* co^*
r-rH»-*— CO*— Q)Ot-- * O «— m »t— OCO«-HCOt-'— rHCO
nj CT* jJOt^-^O co*ft-« o^nJON K »— •> Q) O"\ Q~» » O^i ^^ %-< • C »— f't * *—
C *^ I- t- C O< «0 «~~ O «4) C ^~ -^ O C ^J <1J 4J C *J
S^CCMJC -ra -SreS -SSS«"a'rH"85o
1—4 o) o o ra rH o re ^-« *^ ra r*n -^ j£ *^-* rH ra 3 o) ra %-« rH co o
iHJOOCJCJM'^ CrH t- CiH 0]M t-M CCiJO«-.iHJ3H
O O O —
o o m o CM "y
O O t^ r- ' .
»— 1— *—
OMO ^-CM o>ooo*-tr*u~>ij"»>-ot----vocTvLr»irtir»t'-
t— CM --t— ooa-oc^CMmrommmmCMt^oooooo
.— CM corn CMmu^*— .-mmrnmmmmcM*-.— *-
0)
•o
O t.
ooraoo>a)a>a>a)o>a> ccc
0)0) nH-jt. CCCCCCCCO>0)0)
ooo)ccccSxx^n^iSf:x:£S£:£o.x3£)£i
^__
00
•r-
.
M
o ra
CO t-i
" *
00 •»
C
o ra
O -"
c
•tl 0}
<0 t-
(0 3
C Jg
CO OO
m m
c c
O O
279
-------
•o
0)
3
C
o
o
u
J
m
0) e4
O -v
O 60
a.
c ^-
o >.
w-t "-^
jj bo
l_ bo
a a
a. ^
•o
o
00
o t-
OO CO
1 O3T
vooco
I
m
co
*j
5 o
. o
S i
m T)
in 0)
t. i— r_
n
t.
c ceo 0.00 »-i c i c a. co co
• rt -*H
»H CJ J »
IB GO d
4J 2 O
i. 0 >
CM
m CT\ t.
CO
a\
(Do
c
r: -4 .
o t. •
I ^H
O
o
o
o
C\J
m «) jj £ c s:
ooooooccccc
t,t.t_£_t_Lfl)
OOOOOOt.t,t.£,t.
QJ fl) flJ
C C C 0)
o o o o o o o
oooooooo
!c2!c!3!c332 a> .t>.u.u.iJ ^:Si:Si:2jc2a>a>ii)a)
XJ OOOOOOCClCCC »H f-H Q>Q>Q}Q)(1IQ)O(P OOOOOOOOCCCC
t-CCcCCCO)ll)«)(l)(Ut.t,Ut.t.t.t.t.ClxJ_)JJiJ4J4JJJ4J^l^-(^l^l
280
-------
H
<$ co TJ (fl nj
tl4 -
*J C 4-> C 4-> -P
O I
CO •
• CM
, D.CO
I
S
c
4J
C
o
o
Q) Q) d) O O O C
C C C .-I S- t. to
£33*3
ra
-------
CO
Os
CD
Os
j;
"-<"H 3
2ZCO
a)
i_ i. o>
> > Jj
•-f —t a;
oo>oo
1*.
CO
._-*•*-( C-rH-rHOSOS -H ~H -H
E-HSB~-iEEt-f- e e E
•^•1 t_ «H v( t. "»H -^ O^ O^ *** t-< «--H
-ZOKKO-ZrZ OOKZaOZ-ZCOZZ
» " (7^ O^ O^
O
co
60
C
e e -.
OOO
OS
O
mrneo
co eo o-s
OS OS •—
. - 60
•r-l -< C
5St
•^ -^t O
2 2 O
£
>nJ>>>rHrHiJt«->JJr»>4J>>
••-4 C*^*H CvH*rHrHrH-rH-r-lv~l>pHvH'pH.lH-rHw4
rHfl>r-Hr-*Q,r-('-Hfl3fl,->
C -M
s> > ra
-I -> C
w
g
«:
b.
g
g
g
8
Organism
ra
c.
w t.
c o
0) J->
o o
C («
O b.
33
C.t.
3
l.
!?
JC -i
LLCCLCCCt-LCC
OO'D'OCJ'a'O'aOO'O-O
e E
E E
ooo
oo
t-t.
4)a>
C C
T3T3
t-t.
COM
oo
n
3
i.
co
3
i.
CC
'D'O
o T! u
"d>0>
c
t.1.
Q>a;
CC
t-CCLCCI-
OT3T3OT3T3OT3T3
~H -rH g ~4 -rH g ^4 -rH E .rH .rH
njnjrHto rara om-=rcsjmr-T- cvim ^-oo c~-^r (sj mt^o
CO
-3
m
c
o
•rH
1$ «
O t. OJ
-H 4J ^
ecu
|g »
°g
o
m
o
CMOOCOCSJO
43-vOOOOJOO
of--
OO
csj
O
^-o
OJO
m
ir\o
mosoooooo
(V 0)
•rH -rH
T) T3
n) a
4-> ^J
3 3
a o
a.mm
c--
oo
a> a> o osoa- JTO oso
OOCTs.— COOOSt— OO vc>O
f- • •
oofsjmt^oomooooo
Os —
NIIDNNNNN
.
ccc
HMN
>-|r>>OOOOONNOOONMNNHa)(i.o
.
r-(lHr-lrHOOOO
£jCJ=jCt.t.£.t.
oooooooo
t. 5- L t, L
I I I I I
r
r-|r-IOOOOOOOO
C£tt.£.t.t.t.t---
uooooooooo
.cs-s,
o o E* t*
.c.c.c
o o o o o o
.e x: -3-
o o .
282
-------
•o
3
C
H-t
4J
C
o
o
*-**
r*
<
a
CD
f-«
Q)
C
0)
L
V
E
CO
'c
CO
bo
1.
O
C
0
4->
CO
c_
JJ t.
c o
Q) jJ
O 0
C CO
gfc.
o
M-t
m
c
o
.H
m co
O i. o»
•H J-> ~v
§c to
c c c c c co IH
j-j j-J 4-j *-> *-> nJ o]
h ^ iii g1^
CO
3
t.
o
T3
O CO
C >,,-<
C O 00 O. CO CO
O *•- 1 rH 3 C •"•*
O \O (M O CM
^ rr - 0 rn
h- t-- t- f- I—
o o o o o
0 O O O O
C C C C C
Q) CD tl) d) ^> 4) 0) Q) >-i Q) 3 3
3 3 3
t, t. rH
•H *H O CD
O O § 60 CO
OO-Ut. rH>X COCO
bb§tt ra«S §&
COCO t,CQ CrH3 -^CO
e a 40 a5 cu o. Q. e
rH 0) O -1
co to 3 nj nJ £_ •*-* (0 Q, (0
B'B So. "c i 'a ^i e « x'c
Q.o.cnoj.-itnencoo.b03cocoflO.(ncoincoLmrHQ.
jjt«CM t,cwtx«_twO ffijo*-, e eoo
o i- "~
OO ON
OO
O ON CN-^ CNO
t-
,
c
rH CB > •
^> (1) CX Q) ^— »— -y ^r\ u*\
C £ ~< x: UOOCJCMCM
aiocnoO!_!_n HN-H»H
1 2- 22«JO-it.t.l.t_Ll.t,SoOOOO(ONN
CNr<\i2r2rrr<<<<«<<<«<<<,
0
CO
-H T3
C t.
t-, 0 0)
^1 rH
3 • O.
nj *H CX
••H C CO CO
no —i
3 S) CO C
ra CD £ co
I/N f— m o
m ^~ ^ o
vO^vO ^
-------
TJ
I
JJ
O
O
CO
J
CQ
e
n
'c
s
t-
o
on
CO
i.
4-> C.
c o
83
C CO
p u.
on
l-f 4J
CO CO
o t-
f-l I N
§c
CD
J= O
°§
o
o
oo
ON
ON
t—
ON
PO PO m m OOOOOOO
i i i T i T i
31 3- 31 3- 3" 3" 3"
oooooooo
O*N ON ON ON
OO
OOaO
ON ON
t*-t--t— tn in IT* in in
ill i i i i i
o ojfNjcMocNjrucNjcAicNj
co CNjtMCNjcot-t— i— t— i—
ON ON
D. a. o. a
o. a. o. D. o. a. o.
0.0.0.
0.0.0.0.0.
ONONONON
^- [^ t— r-
ON ON ON ON
M M
no
• •>»bO»~*«*
ONCJNON CONONONONON
ONONONONONONON OCC C ONONONCONONONf
l~t~t— t— t^t~t— CO»H-H _-, (\j f- ^- t-—it— t— t—l— t—
ON ON ON ONONCJNON ONW.L (.CO ON ON ON t. ON ON ON ON ON
O«- — •-•-»-'-t-O«- O OO O ON •- r- *- O<-^-«-^-t-o
CO COOC3COO-- O CO
*> TJ CO CO 4J CO C
-I ^^wHCOCl?CO—*COr-(*HbCa'
J^4-rlLT3»-,QcOt.Einj
(\j
co
— •-(— t-
CD
SCCCCCCCBC4)«)(UIU4>a>CUa>a>a>c])cua>o.e.c.C£:.cjc.co.o.
(.LC.l.C.C.l.l.t.L.t.r-l'-irH'-'rHr-irHi-liH
fecS
o o
t- t.
ccc
O O jJ c C C _. ..
N.X *-f *^r Q> CD 0) CD .:>.J3X»^3OOOOOOOOOOOO
4J4J*JJ->4JJ-)CCt,
i i i i i i n r> 4_
CD «
-------
0
3
3
C
^^
4->
0
O
*-^
t~
<
3
OQ
*~
fl)
O
C
0)
£
•S
02
e
n
c
10
t.
4J l_
c o
o o
C ft
o u.
o
0
CD
c
o
rH 4J
« n)
o t. «rf
— i u >.
g j^ M
(B ^O>OOaO
Q. O. O. Q. D. D. O. Q. D. O. D. O. O.CLO.Q.O.Q.Q.
m t- t. ^CMO>^o^^^CNCTNt. t-oj
CMOOOO — CTV O> O O* C^ OOCTi
ri)>8— CC-CB C C C *S K C C C C C C C C C C C «B rf)
* nJ(flt.{3cd**(3nj(0» (0*>(Q(flfd(Q*k(Q{0(00](y(d(0 *
r~4 (ft (Q • jj jC O ii- £ 1) QJ J— JC iC rH (0 i£Z 1) £ *C £ f 4) £ £ J- >
z^^CQoocooococoooor^ueQouoooQoejoooou^^eD
to
i s
(T)
CO C "3 CO X
3 E *J - C V •»-< t- 0)
4_j 3 S HJ O
,— t O O JS£ OO 6 OJ — H (TJ C
d) ^ ^j j ^ gj g (Q Q) jj 4-> O (0 Z3 C t O Q}
^ t „_( ^^ ^^ ^^ (^J ,^| H^ ^^ ^_( £J «rH t, 0) 3 £ t |
££'£!SS?.ciroif3'rH^3'a. ££*3rt£0'Si'r^!^E£6«>£rt£:£'3
tf)0)VU)3cOCOO}34)Ct)OO COO.VbOC003n
« 1o IB Ifl 1o 1o
f-H *H *— t »— t f-H I— 1
||55||
n a. a. a a. a. a.
o
— i X >>>>>> X ><
L 4J4J4JiJ*JJJCCC
O. A^A.OAAfil^'t^
i- t i i i i i .* n T3
rHOCilUUUUUUUUUIblUHHHE-tt-'HE-'t-i-'i-il 1 1 1 1 1 « 1> 0>
\O *O
\o \o
(^ <\J
Q. 0.
ON O>
C^ ON
c c
ffl (0
•C £
•-1 rH
'3 ij
0 U
«J £
M CO
n S
0 0
o o
ITi i-
t-VO
c c
i! *£
T3 T5
4) «
O O
285
-------
VO
I
NO
CM
O.
ON
CM t-
00 ON
a a- a- a a a
i i i i i i
a- a a- a- a- a
m m m ro m on
NO NO NO m
i i i i
OOOOOOOOOOCTNONONONO
coc\icMCM(McocMCMCM*J|-l^.-(r-lr-(^H
ti CO ,
* g'
.25.3.2
fl
s
-H C
rH £
3.3
S3
•o
I
•»4
*J
O
O
CO
>H
C
fl
OB
£
c
o
••4
JJ
fl
c,
4J C.
c o
S.U
o
C IB
O U.
0) co
33
(. I.
v4 .^
£ £
o o
oo
t. t,
o o
rout
EE
toco
CO
^^
CO
«H
-V
e c no
£S a
°§
o
O CM CO
O ON CO
CM
ON
o
o
(MONO'-ONOCNJO
Ch
*J <0
£ a
a.
c c ^
I! t! "xr:
TJ T3 £ *> .
c
X
§.
t,L.
OO
Lt-
OO
X
O
D.
CD
t-t-
OO
XX
OO
Q.Q.
CDCD
•
t-t.
OO
CD
'OCD
.-. C
XCD
ON
O.C
CDCD
O
l.O
Ot_
cccccccc
CDCDCPCDCDCDCDCD
NNNNNMNN
CCCCCCCC
CDCUCDCUCDCDCUCD
OOOOOOOO
OOOOOOOO
c.t.(.C.C.C.t..{.
nicocBntcaflcaiBflflOuououooo
CCCCCCDCDCDCDCD
CDCDCDCPCDCDCDCDCUCDCDCUCD
286
-------
•o
4>
3
C
~*
4-J
C
bl
_:
m
«:
H
-
0)
(C
on
W
t.
j-> t.
C O
8«
c ra
O b.
O
O
iH
CD
C
o
TH
I-l 4J
n a
u c. »i
«-< 4J -X
B C M
Is3
°§
O
Ch
I I I I I I I I I I I I I I I I I I I I I 1 I I
c--t--£—i--e— t— t-*-c—i-— c—t— t— t— t— t— t--c— t—t~t—r-t~- oo CNJ cjo
i— i— t— t"-t--c—-t^fr—c— t~-c— t—PO ro t— c— t— t-~t— u^u*iu^u"*u"i roro coco f*t ro GO
CO CO GO CO O> O> O*>
.•••.•.•.•.•CTvO^*"****'"'* O^O^ «— v— • ••—
CLO.O.CXa.O.Q.Q.O.O.O.O.'-'-O.CXO.Q.O.O.Q.CLO.CX T-^- O. D.
t—c-t— t— t-t-t— t— t—t^c-t-eet— t— c-t— t— t-t-t—
>
. . MM
.-i .rf C C
BE - .
. 60
I— t- —<
' o -< !rt o o ocor-co.- o
• o*
av
rH^^^H^^rHrH^H^HrH^ > >r-HrHrH>-lr-lr-lr-l—IrHr-IU > > 4-> ID R) 0) o a>
S S £ S
287
-------
-
a)
a:
•o
I
•H
4J
C
o
o
s
CO
c
o
—(
4->
a
t.
4J i.
C O
CD j->
O O
C ID
O [f
C
o
•-< 4J
ft (0
O C. erf
-H 4J V,
e c eo
| g a
Ȥ
o
Ch
o
OO
ON
bo
c
3- a- JT
OO OO OO
ON ON ON
O .— •- •-
00
ON - > >
»- >,>,>,
cf gg§
=T
CO
ON
«- O
CO
- ON
>l —
m pn
CO CO
ON ON
CO OO OO CO GO CO CO
C^ O"* CT* O1* &* ON CT*
S, i. i. i. i. i. i. I
OOOOOOOOt-
.......COCO
OOOOOOOON.
o
CO
ON
s
^O \O VO *O v^
I I I I I
o in in in in m
co co en m m ro
ON
,- ,- •«•»•»•« .8 «B .6
4J 4J .U ±J
JJ 4-> 4-' 4J
4J i> BO -
JJ 4-> C ON
cf cf
0.0.0.0.0.
C o> ON ON ON ON
O CO CO
rf) "~ *~
00 .C ,H
rt co (0
O o O O
•t) •() «9
•e
c c c
5c c c
nj « ra
o o o
O CO
•t) ON
•8 i-
C
Ifl CO J^
e ra jj
•rt -< O
Z Z CO
ON
.«•«--
a) fe £
> > j->
3333333O.-COcOeO
COWCOtOWCOWO O>ONON
-««.-..mj^"~"~'~
Q)(D<1)Q><1}(D4) tO ai^o
t,t_t_t_t-Lt_(O^H«nm
o co
o o
ON
•» r- •»
l~4 fl) • (0 £
>rH M JE co a
(0 rt *^ rt r-H
c c c c c
(D nj rt nj
« ™ n) « o
t, >>
c rt c f
o « o c
X ~-> 3 m
C rH co c
^n t. a. -H
a r-i
o n ra a>
o a)
i. C
a L.
e"S
oo
3
t.
-i S
t, O
«) O
C i.
(9
4J
n)
IT:
-H (1)
«i
t t.
(d
co o
o ra
a. co
n) ra
co co
•""! W
nJ -i
CO t-
(8 as
O O
o> a>
II
rt to
c c
n) ra
o o
n
3
s.
o
b
n
C
m
> 4J
t-3
rao
o.
n o
c o jc n) to .c
t, j I- rt co «-,
SJD
crc
n)
L
OOOOOOOiTOO i-
o o o o in m ro o in
(\)
t—
ZT ON in f\j o> o
a-" co o
VDVD
t\i
in ir> in in
oooo
OOOO
•- CO
o ON
inooro
»-OOC^
oooo
inooe-
in
^O vo CO CO
. . . . v£>
O t-- (M O O
ro t^ •-
o
o
c
O
ra
i.
oooooooooooooo c
1-t.^i.i.i.i.t-t.i.i.t.i.t. a>
oooooooooooooo t-
-----
rH 0) H)
x c c
c a>
Vr-irH
£><>>
a^rx:
-, jJ j->
D
-------
^
§
e
jj
8
^x
r-
ui
J
m
s
0)
o
c
0)
b
^3
a:
e
n
~c
5
L
0
c
o
4J
re
t_
*> t.
c o
0) 4-)
0 t)
C (fl
O b.
O
O
ca
c
o
rH J->
re re
O L o>
§C 60
a) a
s: o
0 C
o
o
f-l
re
o
'i
it
£
O
to
1
in o
^S
ex
*•?
^b
u
C «8
n)
£ a
re »
1-1 re
.-H C
re o>
o *:
^r
re n
c ~*
n v-c
o o
o t-
VD in
C^ *T
c
•r-l
-------
TABLE B1. TOXIWASP INPUTS
(1). Exchange coefficients, segment volume and flow.
•=• Exchange coefficient between segments.
" Dispersion coefficient for the interface between segments.
" Interfacial cross^sec'tional area between segments.
' Length of segments.
" Volumes of segments.
T Flow between segments.
(2). Boundary conditions, forcing functions.
" Boundary conditions (concentrations) of segments.
" Sources (loads) or sinks of the toxic chemical.
^ Segment depth.
(3). Environmental characteristics.
" Average temperature for segment.
Depth of segment.
^ Average veloity of water in segment.
*• Average wind velocity 10 cm above the water surface.
" Bacterial population density in segment.
Proportion of bacterial population that actively degrades the
chemical.
Total actively sorbing biomass in segment.
•=> Biotemperature in segment.
" Molar concentration of environmental oxidants in segment.
" Organic carbon content of sediments as fraction of dry weight.
" Percent water in benthic sediments, expressed as fresh/dry
weight.
Fraction of sediment volume that mixes.
Hydrogen ion activity in segment.
" Single^valued zenith light extinction coefficients.
^ Total first order decay rates calculated externally.
CO. Chemical characteristics (constants).
" Arrhenius activation energy of specific^base-catalyzed
hydrolysis of the chemical.
•> Arrhenius activation energy of neutral hydrolysis of the
chemical.
" Arrhenius activation energy of' specific'-acid^catalyzed
hydrolysis of the chemical.
*• Second order rate constants for specific^base^-catalyzed
hydrolysis of the chemical.
•" Second order rate constants for specific^acid^catalyzed
hydrolysis of chemical.
290
-------
First order rate constants for neutral hydrolysis of the
chemical.
Arrhenius activation energy of oxidative transformation of the
chemical.
Second order rate constants for water column bacterial biolysis
of the chemical.
Q"10 values for bacterial transformation rate in the water
column.
Second order rate constants for benthic sediment bacterial
biolysis of the chemical.
QHO values for bacterial transformation of organic chemical in
benthic sediments.
Organic carbon partition coefficient.
Octanol water partition coefficient.
Organic carbon content of the compartment biomass as a fraction
of dry weight.
The molecular weight of the chemical.
Henry's Law constant of the chemical.
Vapor pressure of compound.
Measured experimental value for volatilization (liquid^phase
transport resistance, expressed as a ratio to the reaeration
rate.
Aqueous solubility of toxicant chemical species.
Exponential term for describing solubility of the toxicant as a
function of temperature.
Molar heat of vaporization for vapor pressure described as a
function of temperature.
Constant used to compute the Henry's Law constants for
volatilization as a function of environmental temperature.
A near^surface photolytic rate constant for the chemical.
Reference latitude for corresponding direct photolysis rate
constant.
Average cloudness in tenths of full sky cover.
Geographic latitude of ecosystem.
Distribution function (ratio of optical path length to vertical
depth).
Reaction quantum yield in photolytic transformation of chemical.
Trigger concentration that define a peak event.
TABLE B2. EXAMS II INPUTS
(1) Chemical data and rate constants
Gram molecular weight of the toxic chemical.
" Aqueous solubility of toxicant chemical species.
Enthalpy term for describing solubility of the toxicant as a
function of temperature.
291
-------
Refrence latitude for corresponding direct photolysis rate
constant.
Measured experimental value for (volatilization) liquid^phase
transport resistance, expressed as a ratio to the reaeration
rate.
Henry's Law constant of the toxic chemical.
Vapor pressure of toxic chemical.
Molar heat of vaporization for vapor pressure described as
function of temperature.
Partition coefficients for computing sorption of toxicant on
compartment sediments.
Partition coefficient for computing sorption of toxicant with
compartment biomass (BIOMS).
Multiplication of KOC (partition coefficient corrected for
organic carbon) by the fractional organic carbon content of each
system sediment yields the partition coefficient for sorption of
unionized compound to the sediment.
Octanol-'water partition coefficient of toxicant.
Near^surface photolysis rate constant for the chemical speci€;s
of the toxicant.
Reaction quantum yield in photolytic transformation of toxic
chemical.
Second^order rate constants for specific^acid^catalyzed
hydrolysis of toxicant.
Second^order rate constants for specific^base^catalyzed
hydrolysis of toxicant.
Arrhenius activation energy of specific^acid^catalyzed
hydrolysis of he toxicant.
Arrhenius activation energy of specific^base^-catalyzed
hydrolysis of the toxicant.
Rate constant for neutral hydrolysis of organic toxicant.
Second-border rate constants for oxidation transformation of
toxicant.
Arrhenius activation energy of neutral hydrolysis of the
toxicant.
Arrhenius activation energy of oxidative transformation of the
toxicant.
Second^order rate constants for water column bacterial biolysis
of the organic toxicant.
QHO values for bacterial transformation of toxicant in the
water column of the system.
Second'Order rate constants for benthic sediment bacterial
biolysis of the organic toxicant.
QHO values for bacterial transformation of organic toxicant in
benthic sediments.
Absorption spectrum (molar extinction coefficients) for each
chemical species of the toxicant.
(2) Global parameters
Average rainfall in geographic of the system.
Average cloudiness in tenths of full sky cover.
292
-------
Geographic latitude of the ecosystem
(3) Biological parameters
Total actively sorting biomass in each ecosystem compartment.
Fraction of total biomass in each compartment that is
planktonic, i.e., subject to passive transport via entrainment
in advective or turbulent motions.
Biotemperature in each ecosystem compartment, i.e., temperature
to be used in conjunction with CM 0 expressions for biolysis
rate constants.
" Bacterial population density in each compartment.
Proportion of total bacterial population that actively degrades
toxicant.
Concentration of chlorophyll and chlorophyll^like pigments in
water column compartments.
(4) Depth and inflows
•* Average depth of each compartment.
" Stream flow entering ecosystem compartments.
" Stream^borne sediment load entering ecosystem compartments.
Nom-point'source water flow entering ecosystem compartments.
^ Nom-point^source sediment loading entering ecosystem
compartments.
•=> Interflow (subsurface water flow, flow seepage) entering each
compartment.
(5) Sediment characteristics
" Percent water in bottom"sediments as fraction of dry weight.
" Organic carbon content of compartments as fraction of dry
weight.
^ Cation exchange capacity of sediments in each compartment.
-* Dissolved organic carbon concentration in water column
compartments.
(6) Aeration, light and others
' Reaeration parameter at 20 degrees C in each ecosystem
compartment.
" Average wind velocity at a reference height of 10 cm above the
water surface.
^ Single^valued zenith light extinction coefficient for water
columns, dummy variable for benthic compartments.
" Distribution function (ratio of optical path length to vertical
depth) for each compartment.
" Evaporative water losses from ecosystem compartments.
" Area of ecosystem elements (compartments).
293
-------
TABLE B3. HSPF INPUTS
INPUTS TO PERLND
(1) Inputs to correct air temperature for elevation difference.
" Difference in elevation between the temperature gage and the
pervious land segment.
•»• Air temperature over the pervious land segment.
(2) Inputs to simulate accumulation and melting of snow and ice.
•*• Latitude of the pervious land segment.
Mean elevation of the pervious land segment.
^ Fraction of the pervious land segment which is shaded from solar
radiation by, for example, trees.
" Maximum pack (water equivalent) at which the entire pervious
land segment will be covered with snow.
^ Density of cold, new snow relative to water.
^ Air temperature below which precipitation will be snow, under
saturated conditions.
" A parameter which adapts the snow evaporation equation to field
conditions.
' A parameter which adapts the snow condensation/convection melt
equation to field conditions.
' Maximum water content of the snow pack, in depth water per depth
water equivalent.
Maximum rate of snowmelt by ground heat, in depth of water
equivalent per day.
^ Quantities of snow, ice and liquid water in the pack (water
equivalent).
^ Density of the frozen contents (snow + ice) of pack, relative to
water.
*• Mean temperature of the frozen contents of the pack.
^ Current pack (water equivalent) required to obtain complete
areal coverage of the pervious land segment.
^ Current remaining possible increment to ice storage in the pack.
" Fraction of sky which is assumed to be clear at the present
time.
(3) Inputs to simulate water budget for pervious land segment.
^ Fraction of the pervious land segment which is covered by forest
which will continue to transpire in winter.
^ Lower zone nominal storage
" Length and slope of the assumed overland flow plane
^ Basic groundwater recession rate.
Air temperature below which evapotranspiration will arbitrarily
be reduced below the value obtained from the input time series.
294
-------
Temperature below which evapotranspiration will be zero
regardless of the value in the input time series.
^ Exponent in the infiltration equation.
" Ratio between the max and mean infiltration capacities over the
pervious land segment.
Fraction of groundwater inflow which will enter deep (inactive)
groundwater and, thus, be lost from the system.
" Fraction of remaining potential evapotranspiration which can be
satisfied from baseflow (groundwater outflow), if enough is
available. »
" Fraction of remaining potential evapotranspiration which can be
satisfied from active groundwater storage if enough is
available.
•^ Interception storage capacity.
^ Upper zone nominal storage.
Manning's n for the assumed overland flow plane.
Interflow inflow and recession parameters.
" Lower zone evapotranspiration parameter.
•" Monthly interception storage capacity.
" Monthly upper zone storage.
" Monthly Manning's n values.
^ Monthly interflow parameters.
Monthly interflow recession constants.
" Monthly lower zone evapotranspiratino parameter.
Interception storage.
^ Surface (overland flow) storage.
' Storages of upper, lower and interflow zones.
Active groundwater storage.
^ Surface storage (upper zone and interflow).
CO Inputs to produce and remove sediment.
Supporting management practice factor. It is used to simulate
the reduction in erosion achieved by use of erosion control
practices.
^ Coefficient in the soil detachment equation.
Exponent in the soil detachment equation.
•* Fraction by which detached sediment storage decreases eaach day,
as a result of soil compaction.
Fraction of land surface which is shielded from erosion by
rainfall.
" Rate at which sediment enters detached storage from the
atmosphere.
" Coefficient and exponent in the detached sediment washoff
equation.
^ Coefficient and exponent in the matrix soil scour equation.
" Monthly erosion related cover values.
^ Monthly net vertical sediment input.
Initial storage of detached sediment.
295
-------
(5) Inputs to estimate soil temperature.
" Surface layer temperature, when the air temperature is 32
degrees F (ASLT).
' Slope of the surface layer temperature regression equation
(BSLT).
' Smoothing factor in upper layer temperature calculation (ULTP1).
^ Mean difference between upper layer soil temperature and air
temperature (ULTP2).
^ Smoothing factor for calculating lower layer/groundwater soil
temperature (UGTP1).
' Mean departure from air temperature for calculating lower
layer/groundwater soil temperature (UGTP2).
" Intercept in the upper layer soil temperature regression
equation.
" Slope in the upper layer soil temperature regression equation.
Monthly values for ASLT, BSLT, ULTP1, ULTP2, LGTP1, and LGTP2.
' Initial air temperature.
" Initial surface layer soil temperature.
' Initial upper layer soil temperature.
" Initial layer/groundwater layer soil temperature.
(6) Inputs to estimate water temperature and dissolved gas concentrations.
•=• Elevation of the pervious land segment above seal level.
. ' Concentration of dissolved oxygen nd C02 in interflow outflow,
and in active groundwater flow.
" Monthly interflow DO and C02 concentrations.
•* Monthly groundwater DO and C02 concentrations.
' Initial surface and interflow outflow temperature.
Initial active groundwater outflow temperature.
Initial DO and C02 concentrations in surface outflow, interflow
outflow, and active groundwater outflow.
(7) Inputs to simulate quality constituents using simple relationships with
sediment and water yield.
" Washoff potency factor.
" Scour potency factor.
Note: A potency factor is the ratio of constituent yield to
sediment (washoff or scour) outflow.
^ Initial storage of constituent on the surface of the pervious
land segment.
Rate of accumulation of constituent.
" Maximum storage of constituent.
" Rate of surface runoff which will remove 90 percent of stored
constituent per hour.
^ Concentration of the constituent in interflow outflow.
•" Concentration of the constituent in active groundwater outflow.
^ Monthly washoff and scour potency factors.
' Monthly accumulation rates of constituent.
^ Monthly limiting storage of constituent.
296
-------
^ Monthy concentrations of constituent in interflow and
groundwater.
(8) Inputs to estimate the moisture and fractions of solutes being
transported in the soil layers.
Nominal upper and lower zones storage.
^ Initial surface detention storage.
" Initial surface detention storage on each block of the pervious
land segment.
Initial moisture content in the surface storage, in the upper
principal storage, and in the upper transitory (interflow)
storage.
" Initial moisture storages in the lower layer, and in the active
groundwater layer.
(9) Inputs to simulate pesticide behavior in detail.
^ Chemical first^order reaction temperature correction parameters
which is used to adjust the desorption and adsorption rates.
Desorption and adsorption rates (first^order) at 35°C.
^ Maximum solubility of the pesticide in water.
^ Maximum concentration (on the soil) of pesticide which is
permanently fixed to the soil.
" Coefficient and exponent parameters for the Freundlich
adsorptiom-desorption equation.
•* Pesticides degradation rates in the surface, upper, and active
groundwater layers.
^ Initial storage of pesticide in crystalline adsorbed and
solution forms in surface, upper, lower or groundwater layer.
Initial storage of pesticide in the upper layer transitory
(interflow) storage.
(10) Inputs to simulate nitrogen behavior in detail.
Plant nitrogen uptake reaction rate parameters for the surface
layer, upper layer, lower layer, and active groundwater layer.
•=» Monthly plant uptake parameters for nitrogen, for the surface,
upper, lower or groundwater layer.
^ Parameters intended to designate which fraction of nitrogen
uptake comes from nitrite and ammonium.
•* Temperature coefficients for plant uptake, ammonium desorption,
ammonium adsorption, nitrate immobilization, organic N
ammonification, N03 denitrification, Nitrification, and ammonium
immobilization.
^ Maximum solubility of ammonium in water.
^ Initial storage of N in organic N, adsorbed ammonium, nitrate,
and plants.
" Initial storages of ammonium and nitrate in the upper layer
transitory (interflow) storage.
297
-------
(11) Inputs to simulate phosphorus behavior in detail.
" Plant phosphorus uptake reaction rate parameters for the surface
layer, upper layer, lower layer, and active groundwater layer.
" Monthly plant uptake parameters for phosphorus, for the surface,
upper, lower or groundwater layer.
Temperature correction parameters for phosphorus plant uptake,
phosphate desorption, phosphate immobilization, and organic P
mineralization.
^ First^order reaction rates for phosphate desorption, phosphate
adsorption, phosphate immobilization, and organic P
mineralization.
Maximum solubility of phosphorus in water.
" Initial phosphorus storage (in organic P, adsorbed P, solution
P, and P stored in plants) in the surface, upper, lower or
groundwater layer.
" Initial storage of phosphate in upper layer transitory
(interflow) storage.
(12) Inputs to simulate the movement of a tracer (conservative).
^ Initial storage of tracer (conservative) in the surface storage,
upper principal storage, upper transitory storage, lower
groundwater layer, and active groundwater layers.
INPUTS TO IMPLND
(1) Inputs to correct air temperature for elevation difference.
^ See temperature inputs in the PERLAND section.
(2) Inputs to simulate the accumulation and melting of snow and ice.
' See snow inputs in the PERLND section.
(3) Inputs to simulate water budget for impervious land segment.
" Length and slope of the assumed overland flow plane.
^ Manning's n for the overland flow plane.
^ Retention (interception) storage capacity of the surface.
^ Air temperature below which evapotranspiration will arbitrarily
be reduced below the value obtained from the input time series.
" Temperature below which evapotranspiration will be zero
regardless of the value in the input time series.
" Monthly retention storage capacity.
" Monthly Manning's n values.
' Initial retention storage.
^ Initial surface (overland flow) storage.
Inputs to estimatg accumulation and removal of solids.
' Coefficient in the solids washoff equation.
298
-------
•* Exponent in the solids washoff equation.
" Rate at which solids are placed on the land surface.
Fraction of solids storaage which is removed each day; when
there is no runoff, for example, because of street sweeping.
^ Monthly solids accumulation rates.
Monthly solids unit removal rates.
Initial storage of solids.
(5) Inputs to estimate water temperature and dissolved gas concentrations.
' Elevation of the impervious land segment above sea level.
" Surface water temperature, when the air temperature is 32°F
(AWTF).
•=• Slope of the surface water temperature regression equation
(BWTF).
Monthly values for AWTF and BWTF.
Initial values for the temperature, DO and C02.
(6) Inputs to simulate quality constituents using simple relationships with
solids and/or water yield.
" Washoff potency factor.
" Initial storage of constituent on the surface of the impervious
land segment.
" Rate of accumulation of constituent.
•" Maximum storage of constituent.
" Rate of surface runoff which will remove 90 percent of stored
constituent per hour.
INPUT TO RCHRES
(1) Inputs to simulate hydraulic behavior.
^ Length of the receiving water body (RCHRES).
^ Drop in water elevation from the upstream to the downstream
extremities of the RCHRES.
Correction to the RCHRES depth to calculate stage.
•" Weighting factor for hydraulic routing.
Median diameter of the bed sediment (assumed constant throughout
the run).
Initial volume of water in the RCHRES.
(2) Inputs to prepare to simulate advection of entrained constituents.
•=- Ration of maximum velocity to mean velocity in the RCHRES cross
section under typical flow conditions.
"• Volume of water in the RCHRES at the start of the simulation.
(3) Inputs to simulate behavior of conservative constituents.
Initial concentration of the conservative.
299
-------
Inputs to simulate heat exchange and water temperature.
" Mean RCHRES elevation.
" Difference in elevation between the RCHRES and the air
temperature gage.
" Correction factor for solar radiation.
' Longwave radiation coefficient.
' Conduction^convection heat transport coefficient.
^ Evaporation coefficient.
Water temperature at the RCHRES.
Air temperature at the RCHRES.
(5) Inputs to simulate behavior of inorganic sediment.
" Width of the cross^section over which HSPF will assume bed
sediment is deposited regardless of stage, top^width, etc.
*• Bed depth.
•* Porosity of the bed (volume voids/total volume).
" Effective diameter of the transported sand, silt and clay
particles.
' Fall velocity of the sand, silt and clay particles in still
water.
^ Density of the sand, silt and clay particles.
^ Critical bed shear stresses for deposition and scour.
" Erodibility coefficient of the sediment.
*• Initial concentrations (in suspension) of sand, silt, and clay.
" Initial total depth (thickness) of the bed.
f Initial fractions (by weight) of sand, silt and clay in the bed
material.
(6) Inputs to simulate behavior of a generalized quality constituent.
Latitude of the RCHRES.
" Initial concentration of constituent.
' Second order acid and base rate constants for hydrolysis.
^ First order rate constant of neutral reaction with water.
" Temperature correction coefficient for hydrolysis.
" Second order rate constant for oxidation by free radical oxygen.
*• Temperature correction coefficient for oxidation by free radical
oxygen.
"• Molar absorption coefficients for constituent for 18 wavelength
ranges of light.
' Quantum yield for the constituent in air^saturated pure water.
» Temperature correction coefficient for photolysis.
" Ratio of volatilization rate to oxygen reaeration rate.
" Second order rate constant for biomass concentration causing
biodegradatino of constituent.
^ Temperature correction coefficient for biodegradation of
constituent.
" Concentration of biomass causing biodegradation of constituent.
» Monthly concentration of biomass causing biodegradation of
constituent.
300
-------
•? First order decay rate for constituent.
^ Temperature correction coefficient for first order decay of
constituent.
^ Decay rate for constituent adsorbed to suspended sediment.
^ Temperature correction coefficient for decay of constituent on
suspended sediment.
" Decay rate for constituent adsorbed to bed sediment.
•* Temperature correction coefficient for decay of constituent on
bed sediment.
"• Partition coefficient ^ distribution coefficients for
constituent with: suspended sand, suspended silt, suspended
clay, bed sand, bed silt, bed clay.
^ Transfer rate between adsorbed and desorbed states for
constituent with: suspended sand, suspended silt, suspended
clay, bed sand, bed silt, bed clay.
" Temperature correction coefficients for adsorbtion^desorbtion
on: suspended sand, suspended silt, suspended clay, bed sand,
bed silt, bed clay.
-* Initial concentration of constituent on: suspended sand,
suspended silt, suspended clay, bed sand, bed silt, bed clay.
" Initial values for water temperature, pH, free radical oxygen
concnetration, cloud cover, and total suspended sediment
concentration.
" Phytoplankton concentration (as biomass).
•*• Monthly values of water temperature, pH, and free radical
oxygen.
" Base adsorption coefficients for 18 wavelengths of light passing
through clear water.
•*• Increments to base absorbance coefficient for light passing
through sediment^laden water.
" Increments to the base absorption coefficient for light passing
through plankton^laden water.
•=• Light extenction efficiency of cloud cover for each of 18
wavelengths.
^ Monthly values of average cloud cover.
" Monthly average suspended sediment concentration values.
•* Monthly values of phytoplankton concentration.
(7) Inputs to simulate behavior of constituents involved in biochemical
transf ormat i ons.
^ Velocity above which effects of scouring on benthal release
rates is considered.
(a) Inputs to simulate primary DO, BOD balances.
Unit BOD decay at 20 °C.
. ^ Temperature correction coefficient for BOD decay.
Rate of BOD settling.
•» Allowable dissolved oxygen supersaturation.
^ RCHRES elevation above sea level.
" Benthal oxygen demand at 20°C.
301
-------
' Temperature correction coefficient for benthal oxygen
demand.
^ Benthal release of BOD at high oxygen concentration.
" Increment to benthal release of BOD under anaerobic
conditions.
" A correction factor in the lake reaeration equation to
account for good or poor circulation characteristics.
•*• Empirical constant in Tsivoglou's equation for reaeration.
" Temperature coefficient for surface gas invasion.
' Length of the RCHRES.
" Energy drop over its length.
•=• Temperature correction coefficient for surface gas
invasion.
" Empirical constnat for equation used to calculate
reaeration coefficient.
•=* Exponent to depth used in calculation of reaeration
coefficient.
^ Exponent to velocity used in calculation of reaeration
coefficient.
*• Dissolved oxygen.
» Biochemical oxygen demand.
' Dissolved oxygen saturation concentration.
(b) Inputs to determine primary inorganic nitrogen and phosphorous
balances.
*• Benthal release of inorganic nitrogen, and orthophosphate.
» Concentration of dissolved oxygen below which anerobic
conditions exist.
» Unit oxidation rate of ammonia and nitrite at 20°C.
" Initial concentration of nitrate (as N), ammonia (as N),
and nitrite (as N).
" Concentration of ortho^phosphorus (as phosphorus).
" Concentration of denitrifying bacteria.
(c) Inputs to simulate behavior of plankton populations and
associated reactions.
" Ratio of chlorophyll "A" content of biomass to phosphorus
content.
" Nonrefractory fraction of algae and zooplankton biomass.
-* Fraction of nitrogen requirements for phytoplankton growth
satisfied by nitrate.
' Base extinction coefficient for light.
" Maximal unit algal growth rate.
^ Michaelis^Menten constant for light limited growth.
" Nitrate Michaelis^Menten constant for nigrogen limited
growth.
^ Nitrate Michaelis^Menten constant for phosphorus limited
growth.
' Phosphate Michaelis^Menten constant for phosphorus limited
growth.
302
-------
' Temperatures above and below which algal growth ceases.
^ Temperature below which algal growth is retarded.
^ Algal unit respiration rate at 20°C.
^ High algal unit death rate.
" Low algal unit death rate.
^ Inorganic nitrogen concentration below which high algal
death rate occurs (as phosphorus).
^ Minimum concentration of plankton not subject to advection
(SEED).
^ Concentration of plankton not subject to advection at very
low flow (MISTAY).
" Outflow at which concentration of plankton not subject to
advection is midway between SEED and MXSTAY.
^ Chlorophyll "A" concentration above which high algal death
rate occurs.
^ Rate of phytoplankton settling.
" Rate of settling for dead refractory organics.
" Maximum zooplankton filtering rate at 20°C.
Zooplankton filtering rate at 20°C (MZOEAT).
^ Natural zooplankton unit death rate.
•* Increment to unit zooplankton death rate due to anaerobic
conditions.
^ Temperature correction coefficient for filtering.
^ Temperature correction coefficient for respiration.
" The fraction of nonrefractory zooplankton excretion which
is immediately decomposed when ingestion rate is greater
than MZOEAT.
' Average weight of a zooplankton organism,
" Maximum benthic algae density (as biomass).
" Ratio of benthic algal to phytoplankton respiration rate.
" Ratio of benthic algal to phytoplankton growth rate.
" Initial conditions for phystoplankton (as iomass),
zooplankton algae (as biomass), benthic algae (as biomass),
dead refractory organic nitrogen, dead refractory organic
phosphorus, and dead refractory organic carbon.
(d) Inputs to simulate pH and carbon species.
Ratio of carbon dioxide invasion rate to oxygen reaeration
rate.
^ Benthal release of C02 (as C) for aerobic and anaerobic
conditions.
" Initial total inorganic carbon for pH simulation.
" Initial carbon dioxide (as C) for pH simulation.
Initial pH.
«U. S.GOVERNMENT PRINTING Off ICE : 1987- 748-1 21 (40729 303
------- |