EPA/600/R-05/074
July 2005
PARTITION COEFFICIENTS FOR
METALS
IN SURFACE WATER, SOIL, AND WASTE
by
Jerry D. Allison,1>2
Terry L. Allison,.2
'HydroGeoLogic, Inc
1155 Herndon Parkway, Suite 900
Herndon,VA20170
2Allison Geoscience Consultants, Inc.
3920 Perry Lane
Flowery Branch, GA 30542
Work Assignment Manager: Robert B. Ambrose, Jr., P.E.
Contract No. 68-C6-0020 Ecosystems Research Division
National Exposure Research Laboratory
960 College Station Road
Athens, GA 30605
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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NOTICE
The information in this document has been funded wholly by the United States
Environmental Protection Agency under contract 68-C6-0020, Work Assignment 2-01 to
HydroGeoLogic, Inc. 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 of commercial
products does not constitute endorsement or recommendation for use.
n
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FOREWORD
The National Exposure Research Laboratory Ecosystems Research Division (ERD) in
Athens, Georgia, conducts process, modeling, and field research to assess the exposure risks of
humans and ecosystems to both chemical and non-chemical stressors. This research provides
data, modeling, tools, and technical support to EPA Program and Regional Offices, state and
local governments, and other customers, enabling achievement of Agency and ORD strategic
goals for the protection of human health and the environment.
ERD research includes studies of the behavior of contaminants, nutrients, and biota in
environmental systems, and the development of mathematical models to assess the response of
aquatic systems, watersheds, and landscapes to stresses from natural and anthropogenic sources.
ERD field and laboratory studies support process research, model development, testing and
validation, and the characterization of variability and prediction uncertainty.
Leading-edge computational technologies are developed to integrate core science
research results into multi-media (air, surface water, ground water, soil, sediment, biota), multi-
stressor, and multi-scale (organism, population, community, ecosystem; field site, watershed,
regional, national, global) modeling systems that provide predictive capabilities for complex
environmental exposure scenarios face by the Agency.
Exposure models are distributed and supported via the EPA Center for Exposure
Assessment Modeling (CEAM) (www.epa.gov/athens/ceampubl), the Watershed and Water
Quality Model Technical Support Center (www.epa.gov/athens/wwqtsc), and through access to
Internet tools (www.epa.gov/athens/onsite).
This research project is a component of the ERD hazardous waste research program,
which seeks to better understand the environmental cycling, exposure, and risk arising from the
release of organic and inorganic pollutants from treatment facilities. In this project, metal
partition coefficients were developed for the watershed, surface water, and source models used
in the Multimedia, Multi-pathway, Multi-receptor Exposure and Risk Assessment (3MRA)
technology. Knowledge and data gained in this evaluation will be used to improve exposure
and risk analysis capabilities for heavy metals evaluated by the 3MRA and other models used
by EPA in various regulatory programs.
Eric J. Weber, Ph.D., Acting Director Ecosystems
Research Division
Athens, Georgia
111
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ABSTRACT
This report presents metal partition coefficients for the surface water pathway and for
the source model used in the Multimedia, Multi-pathway, Multi-receptor Exposure and Risk
Assessment (3MRA) technology under development by the U.S. Environmental Protection
Agency. Partition coefficients values are presented for partitioning between soil and water;
partitioning between the suspended sediment load and the water in streams, rivers, and lakes;
partitioning between riverine or lacustrine sediment and its porewater; and partitioning between
dissolved organic carbon (DOC) and the inorganic solution species in the water of streams,
rivers, and lakes. Some partition coefficients are also presented to represent metal partitioning
between the solid phase of waste and its associated leachate. Partition coefficients are
presented for antimony (Sb), arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd),
chromium (Cr), cobalt (Co), copper (Cu), lead (Pb), molybdenum (Mo), mercury (Hg),
methylated mercury (CH3Hg), nickel (Ni), selenium (Se), silver (Ag), thallium (Tl), tin (Sn),
vanadium (V), and zinc (Zn).
A two-phase approach was used in developing the needed partition coefficients. In the
first-phase, a literature survey was performed to determine the range and statistical distribution
of values that have been observed in field studies. This included the collection of published
partition coefficients for any of the metals in any of the environmental media of interest, or our
estimation of partition coefficients from reported metal concentration data when feasible. In
the second-phase effort, statistical methods, geochemical speciation modeling, and expert
judgement were used to provide reasonable estimates of those partition coefficients not
obtained from our literature search and data processing. The report concludes with a discussion
of the many sources of uncertainty in the reported metal partition coefficients.
IV
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TABLE OF CONTENTS
page
1.0 INTRODUCTION AND BACKGROUND 1-1
2.0 LITERATURE SURVEY FOR METAL PARTITION COEFFICIENTS 2-1
2.1 SELECTION CRITERIA FOR PARTITION COEFFICIENTS 2-3
2.2 RESULTS OF THE LITERATURE SURVEY 2-3
3.0 ANALYSIS OF RETRIEVED DATA AND DEVELOPMENT OF
PARTITION COEFFICIENT VALUES 3-1
3.1 DEVELOPMENT OF PARTITION COEFFICIENTS FOR
NATURAL MEDIA 3-1
3.1.1 Estimation from Regression Equations Based on Literature Data ... 3-2
3.1.2 Estimation From Geochemical Speciation Modeling 3-3
3.1.3 Estimation from Expert Judgement 3-6
3.2 DEVELOPMENT OF PARTITION COEFFICIENTS
FOR WASTE SYSTEMS 3-16
3.2.1 Estimation from Analysis of Data Presented in the Literature 3-17
3.2.2 Estimation from Geochemical Speciation Modeling 3-19
4.0 DISCUSSION OF RESULTS AND SOURCES OF UNCERTAINTY 4-1
5.0 REFERENCES 5-1
APPENDICES
APPENDIX A
METAL PARTITION COEFFICIENTS USED IN SOME
RECENT U.S. EPA RISK ASSESSMENTS
APPENDIX B
SCATTER PLOTS FOR LINEAR REGRESSIONS USED TO
ESTIMATE MEAN LOG K IN NATURAL MEDIA
APPENDIX C
EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL
USED TO ESTIMATE METAL PARTITIONING IN
SOIL/SOIL WATER SYSTEMS
APPENDIX D
EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL
USED TO ESTIMATE METAL PARTITIONING TO DOC
APPENDIX E
EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL
USED TO ESTIMATE METAL PARTITIONING IN
WASTE MANAGEMENT SYSTEMS
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LIST OF TABLES
page
Table 1 Partition coefficients (log Kd in L/kg) from the literature search 2-5
Table 2 Linear regression equations used to estimate mean log Kd values
(L/kg) in natural media 3-3
Table 3 Metal partition coefficients (log Kd) in kg/L for soil/soil water 3-8
Table 4 Metal partition coefficients (log Kd) in kg/L for sediment/porewater 3-10
Table 5 Metal partition coefficients (log Kd) in kg/L for suspended matter/water . . 3-12
Table 6 Metal partition coefficients (log Kd) in kg/L for partitioning between
DOC and inorganic solution species 3-14
Table 7 Effective metal partition coefficients based on reported solid phase and solution
phase metals concentrations from leach tests reported in the literature .... 3-18
Table 8 Important parameters and constituent concentrations used in MINTEQA2
modeling of landfills in the acetogenic and methanogenic stages and
MSWI and CKD monofills 3-20
Table 9 Estimated range in log partition coefficients (L/kg) in waste for selected
metals determined from MINTEQA2 modeling 3-22
VI
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LIST OF FIGURES
page
Figure B-l Data used to develop regression equation to predict sediment K^
from soil K^ B-l
Figure B-2 Data used to develop regression equation to predict sediment K^
from suspended matter K^ B-l
Figure B-3 Data used to develop regression equation to predict soil Kd
from suspended matter K^ (and vice versa) B-2
Figure B-4 Data used to develop regression equation to predict waste Kd
from soil K B-2
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1.0 INTRODUCTION AND BACKGROUND
The purpose of this study was to develop metal partition coefficients for the surface water
pathway and for the source model used in the Multimedia, Multi-pathway, Multi-receptor
Exposure and Risk Assessment (3MRA) technology under development by the U.S.
Environmental Protection Agency. The 3MRA technology provides for screening-level human
and ecological risk assessments for chronic exposure to chemicals released from land-based
waste management units (WMUs) managed under the Hazardous Waste Identification Rule
(HWIR). The multimedia 3MRA model includes a surface water pathway model that requires
the partition coefficient for each metal to be modeled.
In natural media, metal contaminants undergo reactions with ligands in water and with surface
sites on the solid materials with which the water is in contact. Reactions in which the metal is
bound to the solid matrix are referred to as sorption reactions and metal that is bound to the
solid is said to be sorbed. The metal partition coefficient (K^; also known as the sorption
distribution coefficient) is the ratio of sorbed metal concentration (expressed in mg metal per
kg sorbing material) to the dissolved metal concentration (expressed in mg metal per L of
solution) at equilibrium.
sorted metal concentration {ms -: kp'\
K, = (1)
dissolved metal concentration (mg ' L)
During transport of metals in soils and surface water systems, metal sorption to the solid matrix
results in a reduction in the dissolved concentration of metal and this affects the overall rate of
metal transport. Thus, transport models such as those used in various pathways in the 3MRA
incorporate the metal K^ into the overall retardation factor (the ratio of the average linear
particle velocity to the velocity of that portion of the plume where the contaminant is at 50
percent dilution). The use of K^ in 3MRA transport modeling implies the assumption that local
equilibrium between the metal solutes and the sorbents is attained. This implies that the rate of
sorption reactions is fast relative to advective-dispersive transport of the metal.
For a particular metal, K^ values in soil are dependent upon various geochemical characteristics
of the soil and its porewater. Likewise for surface water systems- the Kd for a particular metal
depends on the nature of suspended solids or sediment and key geochemical parameters of the
water. Geochemical parameters that have the greatest influence on the magnitude of Kd include
the pH of the system and the nature and concentration of sorbents associated with the soil or
surface water. In the subsurface beneath a waste management facility, the concentration of
leachate constituents may also influence the metal K^ through competition for sorption sites.
The metals of interest in HWIR modeling are antimony (Sb), arsenic (As), barium (Ba),
beryllium (Be), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), lead (Pb),
molybdenum (Mo), mercury (Hg), nickel (Ni), selenium (Se), silver (Ag), thallium (Tl), tin
1-1
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(Sn), vanadium (V), and zinc (Zn). Methylated mercury (CH3Hg) and cyanide (CN) are also of
interest. In the surface water pathway, the 3MRA includes several transport processes that
require metal partition coefficients: (1) The overland transport of metal contaminants in runoff
water in the watershed and the consequent partitioning between soil and water; (2) partitioning
between the suspended sediment load and the water in streams, rivers, and lakes; (3)
partitioning between riverine or lacustrine sediment and its porewater; and (4) partitioning
between dissolved organic carbon (DOC) and the inorganic solution species in the water of
streams, rivers, and lakes.
The 3MRA modeling scenario also includes a source model for various types of waste
management units that also requires partition coefficients. For the source model, the partition
coefficients are used to represent the ratio of contaminant mass in the solid phase to that in the
leachate (water) phase. There are five types of waste management units for which the source
model requires partition coefficients: land application units, waste piles, landfills, treatment
lagoons (surface impoundments), and aerated tanks.
This report describes the two-phase approach used in developing the needed partition
coefficients. In the preferred (first-phase) method of obtaining the coefficients, a literature
survey was performed to determine the range and statistical distribution of values that have
been observed in field studies. This includes the collection of published partition coefficients
for any of the metals in any of the environmental media of interest, or our estimation of
partition coefficients from reported metal concentration data when feasible. The data retrieved
in the literature search were recorded in a spreadsheet along with associated geochemical
parameters (such as pH, sorbent concentration, etc.) when these were reported. We anticipated
that the literature search would not supply needed partition coefficients for all of the metals in
all of the environmental media of interest. Therefore, in the second-phase effort, statistical
methods, geochemical speciation modeling, and expert judgement were used to provide
reasonable estimates of those partition coefficients not obtained from our literature search and
data processing.
2.0 LITERATURE SURVEY FOR METAL PARTITION COEFFICIENTS
A literature survey was conducted to obtain partition coefficients to describe the partitioning of
metals between soil and soil-water, between suspended particulate matter (SPM) and surface
water, between sediment and sediment-porewater, and between DOC and the dissolved
inorganic phase in natural waters. In addition, partition coefficients were sought for
equilibrium partitioning of metals between waste matrix material and the associated aqueous
phase in land application units, waste piles, landfills, treatment lagoons, and aerated tanks. The
literature survey encompassed periodical scientific and engineering materials as well as some
non-periodicals, including books and technical reports published by the U.S. EPA and other
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government agencies. Electronic searches of the following databases were included as part of
the literature survey:
Academic Press Journals (1995 - present)
AGRICOLA (1970- present)
Analytical Abstracts (1980 - present)
Applied Science and Technology Abstracts
Aquatic Sciences and Fisheries Abstract Set (1981 - present)
CAB Abstracts (198 7 - present)
Current Contents (1992 - present)
Dissertation Abstracts (1981 - present)
Ecology Abstracts (1982 - present)
EIS Digest of Environmental Impact Statements (1985 - present)
El Tech Index (1987 - present)
Environmental Engineering Abstracts (1990 - present)
General Science Abstracts (1984 - present)
GEOBASE (1980 - present)
GEOREF (1785 - present)
National Technical Information Service
PapersFirst (1993 - present)
Periodical Abstracts (1986 - present)
Toxicology Abstracts (1982 - present)
Water Resources Abstracts (1987 - present)
Two search strings were used in the electronic searches: "distribution coefficient" and
"partition coefficient." Use of such general strings has the advantage of generating many
citations, decreasing the probability that relevant articles will be missed, but also carrying a
high labor burden because each citation returned must be examined for useful data. For metals
that are not as well represented in the published literature, even more general search strings
were used, sometimes with boolean operators (e.g., "barium" and "soil," "selenium" and
"partitioning"). The work of identifying articles containing useful data from among all those
retrieved was made easier by first reviewing the titles to eliminate those of obvious irrelevance,
then reviewing the abstracts, that were usually available on-line. Abstracts of citations that
showed promise for providing partition coefficients were printed and given a code consisting of
the first two letters of the lead author's last name and the last two digits of the year of
publication. The code, along with the first few words of the article title, was entered in a log
book for tracking. Logged articles were quickly reviewed at local university research libraries,
and those containing relevant data were copied for a more thorough review at our office. Most
of the articles were obtained from the University of Georgia Science Library or the Georgia
Institute of Technology Library. As each copied article or report was reviewed, a summary
page containing the assigned code was stapled to the front with notes indicating the type of data
found in the paper and the location (page number, table number, etc.) of useful data. Partition
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coefficients and other data from the articles were then entered into an EXCEL 97 spreadsheet
for compilation and analysis.
2.1 SELECTION CRITERIA FOR PARTITION COEFFICIENTS
The following criteria and guidelines were followed in the selection of partition coefficient
values from journal articles. Values were accepted from studies characterized by:
Use of "whole" natural media for determination of partition coefficients in natural
media systems (e.g., reject values from studies using pure mineral phases or treated
soils)
In soil systems, use of an extractant having low ionic strength (< 0.1 M); in surface
water systems, low salinity (freshwater preferred, salinity up to 10 parts per thousand
acceptable)
Use of low total metal concentrations (i.e., if coefficients were determined at multiple
total metal concentrations, choose the coefficient corresponding to the lowest
concentration where K^ is less likely to depend on metal concentration)
pH values in the natural range (4 to 10)
No organic chelates in the extractant (e.g., EDTA)
One partition coefficient per system studied
Where multiple partition coefficients are presented for a system due to experimental
variation of pH or other parameters, select the partition coefficient corresponding to the
conditions most closely approximating natural conditions.
Batch leaching tests (preferred over column tests if both are available for the same study
and soil, but column tests acceptable).
The geochemical parameters most likely to influence the partition coefficient were entered in
the spreadsheet along with reported or calculated coefficients if such were specified in the
source article or report. Examples of these parameters are pH, total concentrations of metal in
solution and sorbed, and concentrations of important metal complexing agents (including
DOC), and weight fraction of particulate organic matter and other sorbing materials. Physical
parameters necessary to convert sorbed concentration (mg/kg) over dissolved concentration
(mg/L) to partition coefficients in liters per kilogram (L/kg), i.e., porosity, water content, and
bulk density, were also recorded when reported in the articles. Equations and relationships
presented in journal articles that present K^ as a function of pH or other parameters were
recorded in a remark field in the spreadsheet.
2.2 RESULTS OF THE LITERATURE SURVEY
Approximately 245 articles and reports were copied and reviewed. A total of 1170 individual
Kd values were obtained from these sources, either directly or calculated from reported media
concentrations. This total does not include mean estimated K^ values reported in previously
published compilations of Kd values (Baes and Sharp, 1983; Baes et al., 1984; Coughtrey et al.,
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1985; Thibault et al., 1990). (The data from these previous compilations were recorded in the
spreadsheet and used in guiding the final estimates of appropriate central tendency values as
described in Section 3.1.3.) Approximately 80% of the 1170 values we obtained from the
literature pertained to the metals Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn. More Kd's were
recovered for Cd than any other metal, followed closely by Zn, Pb, and Cu. The most
frequently reported type of Kd was that for suspended matter in streams, rivers and lakes. (Data
pertaining to marine environments were generally rejected, but some data from estuaries were
included if reported as corresponding to low salinity.) The second most frequently reported Kd
values pertained to partitioning in soil. Suspended matter and soil Kd's together totaled 68% of
the reported data. Table 1 shows the median and range of K^ values retrieved in our literature
search for natural media. (Values shown are log Kd values). For some combinations of metal
and media type, too few partition coefficients were found in the literature to state a median or
even a reasonable range. In some of these cases, mean or median values were available from
previous compilations of partition coefficients. In Table 1, blank spaces in the table correspond
to no data found. Values in bold are from previous compilations.
No directly reported partition coefficients for the waste systems of interest were discovered in
the literature survey, and none are included in Table 1. There are many reasons for wishing to
understand the behavior of metals in natural systems. The rich literature of soil science, plant
nutrition, aquatic chemistry, geology, and toxicology are all examples of investigative areas of
longstanding where metal partition coefficients are frequently encountered. The impetus for
research with regard to waste systems is significantly different from that of natural systems.
Moreover, the behavior of metals in waste materials are typically studied and reported prior to
their disposal and consequent mixing with a host of other substances few studies have
focused on the behavior of metals within actual disposal units containing a (usually unknown)
mixture of materials. Most studies involving metal concentrations in waste are concerned with
predicting the metal concentration in leachate by means of a physical test (i.e., a leachate
extraction test). Section 3.2 presents further findings with regard to leach tests and appropriate
metal partition coefficients for waste systems.
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Table 1
Partition coefficients (log Kd in L/kg) from the literature search.
Median values listed in boldface are from a previous compilation.
Blank spaces represent instances for which no data was found or too
few values were found to provide meaningful statistics.
Metal
Ag
median
range
N
As
median
range
N
Ba
median
range
N
Be
median
range
N
Cd
median
range
N
Co
median
range
N
Soil/Water
2.6
1.0-4.5
21
3.4
0.3-4.3
22
0.7-3.4
3.1
1.7-4.1
2
2.9
0.1 -5.0
41
2.1
(-1.2) -4.1
11
Suspended
Matter
/Water
4.9
4.4 - 6.3
15
4.0
2.0-6.0
25
4.0
2.9-4.5
14
4.1
2.8-6.8
17
4.7
2.8-6.3
67
4.7
3.2-6.3
29
Sediment/
Water
3.6
2.1 -5.8
2.5
1.6-4.3
18
3.6
0.5-7.3
21
3.3
2.9-3.6
3
DOC/Water
5.2
3.4-5.5
4
4.5
2.9-4.8
2
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Table 1
Partition coefficients (log Kd in L/kg) from the literature search.
Median values listed in boldface are from a previous compilation.
Blank spaces represent instances for which no data was found or too
few values were found to provide meaningful statistics.
Metal
Cr(in)
median
range
N
Cr(VI)
median
range
N
Cu
median
range
N
Hg
median
range
N
CH3Hg
median
range
N
Mo
median
range
N
Soil/Water
3.9
1.0-4.7
43
1.1
(-0.7) - 3.3
24
2.7
0.1-3.6
20
3.8
2.2-5.8
17
2.8
1.3-4.8
11
1.1
(-0.2) - 2.7
8
Suspended
Matter
/Water
5.1
3.9-6.0
25
4.7
3.1-6.1
70
5.3
4.2 - 6.9
35
5.4
4.2 - 6.2
2
Sediment/
Water
4.5
4.2
0.7 - 6.2
12
4.9
3.8-6.0
2
3.6
2.8-5.0
4
2.5
DOC/Water
5.5
2.5-7.0
17
5.3
5.3-5.6
3
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Table 1
Partition coefficients (log Kd in L/kg) from the literature search.
Median values listed in boldface are from a previous compilation.
Blank spaces represent instances for which no data was found or too
few values were found to provide meaningful statistics.
Metal
Ni
median
range
N
Pb
median
range
N
Sb
median
range
N
Se
median
range
N
Sn
median
range
N
Tl
median
range
N
Soil/Water
3.1
1.0-3.8
18
4.2
0.7-5.0
33
2.4
0.1-2.7
1.0
-0.3 - 2.4
23
2.9
2.1-4.0
Suspended
Matter
/Water
4.6
3.5-5.7
30
5.6
3.4-6.5
48
3.1-4.7
5.6
4.9-6.3
3
Sediment/
Water
4.0
5.1
2.0-7.0
24
4.0
2.5-4.8
3
3.6
4.7
3.2
3.0-3.5
6
DOC/Water
5.1
4.7-5.4
4
5.0
3.8-5.6
9
2.7-4.3
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Table 1
Partition coefficients (log Kd in L/kg) from the literature search.
Median values listed in boldface are from a previous compilation.
Blank spaces represent instances for which no data was found or too
few values were found to provide meaningful statistics.
Metal
V
median
range
N
Zn
median
range
N
CN
median
range
N
Soil/Water
1.1-2.7
3.1
(-1.0) -5.0
21
3.0
0.7-3.6
3
Suspended
Matter
/Water
5.1
3.5-6.9
75
Sediment/
Water
3.7
1.5-6.2
18
DOC/Water
4.9
4.6 - 6.4
9
Partition coefficients used in several recent U.S. EPA risk assessments are presented in
Appendix A. Because the origin of these data is generally unknown, they were not included in
the collection of Kd values appearing elsewhere in our spreadsheet, nor were they included in
the statistical summary of K^ values obtained from the literature as reported herein.
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3.0 ANALYSIS OF RETRIEVED DATA AND DEVELOPMENT OF PARTITION
COEFFICIENT VALUES
The data gathered from published sources were insufficient to establish a reasonable range
and/or median value for the partition coefficient for all metals in all media-types. Therefore,
the second part of the effort was directed at augmenting the values obtained from the literature
so as to provide a reasonable range and central tendency of Kd for each metal in each media-
type. Statistical analysis of retrieved data, geochemical modeling, and expert judgement were
all used to develop these partition coefficient values. The nature of the available data for
natural media and waste systems was different to the extent that it seemed best to consider
these two categories separately.
3.1 DEVELOPMENT OF PARTITION COEFFICIENTS IN NATURAL MEDIA
In analyzing the partitioning data collected from the literature for soil and surface water
systems, we attempted to identify the shape of the probability distribution for each metal in
each medium. For a particular metal in a particular medium, the degree to which the literature
sample is truly representative of the population of metal partition coefficients is dependent on
the number of sample points, the actual variability of important medium properties that
influence partitioning (pH, concentration of sorbing phases, etc.), and how well this variability
is represented in the sample. In some cases, it was necessary to eliminate data points from the
literature sample to avoid obvious bias. For example, the sample of literature K^ values for
Cr(in) in soil included values obtained in a pH titration of three soils such that each of the three
was represented by eight different K^ values. Although they provide interesting data on the
dependence of K^ on pH in these soils, multiple measurements from the same soil and values
determined at other than the ambient soil pH introduce bias in the natural probability
distribution of Kd. Therefore, in cases where Kd associated with multiple pH values were
presented, the K^ associated with the pH value closest to the ambient soil pH was chosen. If
the ambient soil pH was not specified, then a single K^ value was picked randomly from among
those presented and the other K^ values for the soil were discarded. In this fashion, the sample
of literature data for each metal and media-type was edited before attempting to identify the
underlying probability distribution for K^.
Statistical tests were performed to determine the shape of the frequency distribution of Kd for
each metal and media-type. These tests employed widely recognized techniques available in
the statistical package Analyze-It (version 1.32), an add-on module for Microsoft EXCEL 97.
The Shapiro-Wilk test and the Kolmogorov-Smirnov test were used to test the samples for
normality. A positive test in Shapiro-Wilk does not ensure a normal distribution. Rather, it
provides a measure of confidence that the sample data are not inconsistent with a normal
distribution. The Shapiro-Wilk test is a general test for normality; it is not necessary to know
the population mean or standard deviation. The Kolmogorov-Smirnov test was used when
results from the Shapiro-Wilk test were negative. In only a few cases were the data sufficient
to identify the underlying distribution with any degree of certainty. Many of the sample sets
(including the most complete (largest) sample sets), gave a positive test for normality after
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transforming the available data to log space, suggesting that the frequency distribution of the
underlying population of Kd values for a particular metal in a particular medium is most likely
log-normal.
In some cases, there were too few representative data points in the sample to have confidence in
the descriptive statistics of the data. In these cases, three methods were used to augment the
available data in estimating the mean, standard deviation, and minimum and maximum K^
values. The three methods were: estimation from linear regression equations developed from
the literature samples, estimation from the results of geochemical speciation modeling, and
estimation by expert judgement. Each method is discussed below.
3.1.1 Estimation from Regression Equations Based on Literature Data
Of the 13 metals for which literature data were retrieved characterizing Kd in soil, sediment,
and suspended matter, 12 of them exhibited a progression of decreasing affinity for sorption
material in the order suspended matter > sediment > soil. In other words, comparison of mean
Kd values for particular metals showed the result that K^ SPM > Kd Sediment > Kd Soil. In two other
cases where at least two of the K^ types could be characterized from the literature data, both
conformed to this same pattern. In addition, a somewhat consistent progression in K^
magnitude for metals within the three natural media was noted. For the best represented
metals, the following patterns of decreasing Kd were observed (based on ordering the mean K^
values from highest to lowest magnitude for each medium):
Soils: Pb > Cr111 > Hg > As > Zn = Ni > Cd > Cu > Ag > Co
Sediment: Pb > Hg > Cr111 > Cu > Ni > Zn > Cd > Ag > Co > As
SPM: Pb > Hg > Cr111 = Zn > Ag > Cu = Cd = Co > Ni> As
There was some shuffling about of the K^ magnitude ordering among these media-types, as
might be expected for a data set that is undoubtedly incomplete. The most obvious
inconsistency in the progression of K^ magnitude is for As. Nevertheless, the similarities are
worthy of note. Some aspects of the overall trend are in agreement with the hard-soft acid-base
(HSAB) concepts of Pearson (1963), however, Pb and Hg have greater affinities than HSAB
predicts. Certainly, there are multiple adsorption surfaces present in all of these materials. The
consistency of affinity relationships among these metals suggests that the distribution of K^ is
partly due to characteristics unique to the metals themselves and partly due to characteristics
associated with the sorbing surfaces. Regardless of the reason, it appears feasible to exploit
these trends to provide an estimate of K^ for a given metal in one medium if its value in another
medium is available. For example, the literature data provided a reasonable number of K^
values in soils and suspended matter for the nine metals Ag, Cd, Co, Cr(ni), Cu, Hg, Ni, Pb,
and Zn. For each of these metals, the mean value of K^ in soil was in the neighborhood of two
orders of magnitude less than the mean value in suspended matter. This trend was
characterized more exactly by developing a linear regression equation. The regression equation
was then used to estimate mean K^ values for metals for which the literature provided an
estimate of mean K^ in soil, but not in suspended matter. In a similar manner, linear regression
3-2
-------
equations were developed to estimate the mean K^ in sediment from the literature estimate of
mean Kd in soil or suspended matter, or the mean soil K^ from that in sediment or suspended
matter. The regression equations were developed from cases where the literature survey data
provided reasonable estimates of the mean K^ for at least two of the three media. The metals
used in developing the regression equations included cadmium, copper, zinc, and other metals
that were better represented in the literature. The distribution of Kd values for a particular
metal was assumed to be log-normal so that the regression equations were actually based on
mean log Kd and were used to predict mean log Kd. The standard deviation was estimated from
the mean and minimum values assuming the minimum value represents two standard
deviations from the mean. The standard deviation was also estimated using the mean and
maximum values rather than mean and minimum. The larger of the two estimates of standard
deviation was retained as the final estimate. The regression equations used are shown in Table
2 along with the number of observations upon which each equation is based, the correlation
coefficient (r2), and the 95% confidence interval for the slope and intercept. Scatter plots
showing the regressed data points and straight line regressions are shown in Appendix B.
Table 2
Linear regression equations used to estimate mean log K.J values (L/kg) in natural media.
Used to
Estimate
mean log Kd
sediment
mean log Kd
sediment
mean log Kd
suspended
matter
mean log Kd
soil
Independent
Variable
mean log Kd
soil
mean log Kd
suspended
matter
mean log Kd
soil
mean log Kd
suspended
matter
slope
( +/- 95% CI)
1.080
(1.035)
1.418
(1.923)
0.380
(0.444)
0.969
(1.136)
intercept
(+/- 95% CI)
0.796
(3.190)
-3.179
(9.868)
3.889
(1.338)
-1.903
(5.703)
r2
0.7
9
0.6
5
0.3
7
0.3
7
N
5
5
9
9
The regression equations were also used to estimate mean K^ values for suspended matter and
sediments from an estimate of the mean K^ in soil obtained from geochemical speciation
modeling as discussed in the next section.
3.1.2 Estimation From Geochemical Speciation Modeling
Geochemical speciation modeling was used to estimate soil/water partitioning if data-based
regression equations could not be used. The partitioning of metal cations between DOC and
3-3
-------
the inorganic portion of the solution phase was also estimated by speciation modeling. In both
cases, the U.S. EPA geochemical speciation model MINTEQA2, version 4.0 (Allison et al.,
1990), was used to estimate the Kd values. The input data for MINTEQA2 were developed
from various sources as presented in the following sections.
MODELING DETAILS AND INPUT DATA FOR SOIL PARTITION COEFFICIENTS
The concentrations of major ions used in geochemical speciation modeling for soils were the
average concentrations in river water as reported by Stumm and Morgan (1996). The soil-
water phosphate concentration was obtained from Bohn et al. (1979). The ionic strength was
held constant at 0.005 M after a sensitivity test in the range 0.01 to 0.001 M revealed that the
impact on results of doing so was significantly less than the effect of variability in other
important parameters. Model input values for several of the most significant "master"
variables affecting Kd were varied over reasonable ranges in order to capture the expected range
of Kd values. These master variables include pH, concentration of dissolved organic carbon
(DOC), concentration of particulate organic carbon (POC), and concentration of metal oxide
binding sites. The range for each of these master variables was characterized by low, medium,
and high assigned values, and the model was executed at all possible combinations of these
settings. The pH range corresponded to that reported from the STORET database (U.S. EPA,
1996a) with a slight downward adjustment (6.5 for the medium value instead of 6.8, and 4.5 for
the low value instead of 4.9) to account for the more acidic environment of surface watershed
soils. The concentrations used for DOC were 0.5, 5.0, and 50.0 mg/L, taken as a reasonable
range in soil-water. The assigned POC concentration values were obtained from analysis of
data in a database for shallow, silt-loam soils (Carsel et al., 1988 and R. Parrish, personal
communication). The low, medium, and high values corresponded to the 10th, 50th, and 90th
percentiles, respectively, for particulate organic matter concentration (0.41, 1.07, and 2.12
wt%).
The dominant metal oxide sorbing surface was assumed to be hydrous ferric oxide (HFO).
Because we had little reliable information as to the appropriate concentration range, and also in
consideration of the importance of this variable in determining Kd, the HFO concentration was
used as a calibrating variable. The low, medium, and high values were initially set to
correspond to the values used in U.S. EPA (1996a). Those values were based on a specialized
extraction of reactive Fe from a set of 12 samples from various aquifers and soils. The mean
Kd for Cd, Cu, Ni, Pb, and Zn were computed using these values in MINTEQA2. These
computed Kd values were compared with mean K^ values for these same metals in soil obtained
from our literature survey. The low, medium, and high HFO concentrations were scaled in
subsequent modeling such that the mean K^ value from MINTEQA2 was within the 95%
confidence interval of the mean literature Kd value for each of these metals. (Each MINTEQA2
execution resulted in 81 different K^ values due to utilizing all different combinations of low,
medium, and high assigned values for the four different master variables. The mean value from
MINTEQA2 was taken as the average of the three Kd values corresponding to the medium
setting of pH, DOC, and HFO and the low setting of POC; the medium settings of pH, DOC,
and HFO, and the medium setting of POC; and the medium settings of pH, DOC, and HFO,
3-4
-------
and the high setting of POC.) Appendix C shows a typical MINTEQA2 input file used in
estimating Kd for soil/water.
The minimum and maximum Kd values were established by combining the available literature
data and MINTEQA2 results. Again, the distribution was assumed to be log-normal. Once the
mean log Kd value for a metal was established for soil from the modeling exercise, the
previously described regression equations based on our literature analysis process were used to
estimate the mean K^ values for sediment and suspended matter if these were lacking from the
literature data. The standard deviation was estimated as described previously for the linear
regression estimates.
MODELING DETAILS AND INPUT DATA FOR DOC PARTITION COEFFICIENTS
The partitioning of metals between DOC and other inorganic forms in water is not usually
reported in terms of a partitioning coefficient. In fact, specialized algorithms within speciation
models are frequently employed to estimate the fraction of metal bound with DOC based on the
pH, major ion composition of the solution, and ionic strength. The development of such
specialized methods for estimating metal binding with DOC is an ongoing research area.
MINTEQA2 includes a specialized sub-model for estimating DOC interactionsthe Gaussian
distribution model (Dobbs et al., 1989; Allison and Perdue, 1994). This model represents DOC
as a mixture of many types of metal binding sites. The probability of occurrence of a binding
site with a particular log K is given by a normal probability function defined by a mean log K
and standard deviation in log K. A limitation of the DOC binding calculations in MINTEQA2
and similar models is that the metal-DOC reactions necessary to obtain results are known only
for a limited number of metal cations, and for none of the anionic metals. MINTEQA2
includes mean log K values for the metal cations Cd, Cu, Ba, Be, Cr(in), Ni, Pb, and Zn. For
other metal cations of interest (Ag, Co, Hg(II), Sn(n), and T1(I)), it was necessary to estimate
the mean log K for DOC binding for use with the Gaussian model. For Hg(n), the estimate of
the mean log K was determined from a regression of "known" mean log K values against the
binding constants for humic- and fulvic acid (HA and FA, respectively) reported by Tipping
(1994). The metals Cd, Cu, Ni, Pb, and Zn were also represented in the database of HA and FA
binding constants, so these data were used to develop the regression relationship shown in
Equation (2).
mean log %DOC,ffg = - O.C>10 log K^^ + 4 206 log KFAjSg - 3.645 (2)
As derived, Equation (2) has a correlation coefficient (r2) of 0.95, and produces an estimate of
9.0 for the mean log K for Hg2+ binding with DOC (mean log KDOC^g). (NOTE: This equation
gives the mean log KDOC, a formation constant for use in the MINTEQA2 speciation model for
the chemical reaction between DOC and Hg. It is not the same as the mean Kd for Hg binding
with DOC. The latter is always designated Kd; the objective of the MINTEQA2 modeling is to
estimate the K^ for those metals for which literature data is lacking.)
The mean log K values for DOC binding to the other cations (Ag+, Co2+, Sn2+, and Tl+) were
derived from a linear free energy relationship using the first hydrolysis constants (log K0ff) and
3-5
-------
the binding constant for acetate (log KAcet). The known values of log KOH and log KAcet for the
metals Cd, Cu, Fe, Ni, Pb, and Zn were used to derive the following relationship:
mean log KDOC = 0.3595log Kos + 0.6932 log K^ + 0,"9"4 (3)
The correlation coefficient (r1) for Equation (3) is 0.98. Equation (3) was used to estimate the
mean log KDOC values for Ag+, Co2+, Sn2+, and Tl+ for use in MINTEQA2 modeling. The mean
log KDOC values estimated for these metals were 2.0, 3.3, 6.6, and 1.0, respectively.
The estimation procedures outlined previously in this section cannot reliably be extended to
anions. However, anions are typically not as strongly bound to organic matter. Therefore, we
used MINTEQA2 to estimate K^ values for binding to DOC for cationic metals only, and
included conservative estimates of K^ values for the anions based on judgement alone.
The concentrations of major ions used in estimating metal-DOC binding with MINTEQA2
were the average concentrations in river water as reported by Stumm and Morgan (1996). The
concentration of DOC and the pH were treated as master variables, with each assigned three
levels corresponding to low, medium, and high. The assigned medium value was the mean of
the reported river and stream samples from the literature survey, and the low and high values
were selected to encompass the range observed in the literature survey data. Specifically, the
low, medium, and high concentrations of DOC were 0.89, 8.9, and 89 mg/L, respectively, and
the low, medium, and high pH were 4.9, 7.3, and 8.1, respectively. The binding of each of the
metal cations was computed in nine simulations that represented all possible combinations of
pH and DOC concentration level. The mean K^ value for each cation was specified as that
value computed by MINTEQA2 when the pH and DOC concentration were set to their reported
mean values in surface water. A typical MINTEQA2 input file used to estimate metal
partitioning to DOC is shown in Appendix D.
The results computed using MINTEQA2 for both soils and DOC were used to augment the
partitioning data collected in the literature survey. Although it was considered reasonable to
use MINTEQA2 to estimate mean partition coefficients, it was not possible to establish the
shape of the K^ frequency distribution curve from the MINTEQA2 results. However, there is
no compelling reason to suppose other than the log-normal distribution suggested by the
literature survey data.
3.1.3 Estimation from Expert Judgement
When neither the regression equations nor MINTEQA2 could reasonably be used to estimate a
needed mean log Kd, the mean value was estimated subjectively using expert judgement.
Factors considered in this process included any values obtained from our literature survey,
reported mean values or ranges from previous compilations, similarities of behavior among
metals, and qualitative statements from articles and reports. The minimum and maximum Kd
3-6
-------
values from the literature were also used if reasonable values were available. Otherwise, the
extremes in Kd were also estimated by expert judgement. In either case, the standard deviation
was estimated as described previously above for linear regression in Section 3.1.1.
Finally, a relative confidence level (CL) was subjectively assigned to each of the final values
presented. The CL values range from 1 to 4, with the highest confidence corresponding to a
value of 1 and the lowest to a value of 4. In general, estimates based on our literature survey
for a well-studied metal with a large literature sample was deemed to merit a CL of 1. Data for
a metal not represented in the literature for which the final values were purely estimates from
MINTEQA2 or other means with a notable degree of expert judgement involved were assigned
a CL of 4. Many values were determined in circumstances that warranted a CL between these
extremes (e.g., a range was given in the literature, a value was available from a previous
compilation, estimates from combinations of these latter circumstances could be combined with
estimates from modeling, etc.). In these cases, a CL of 2 or 3 was assigned as seemed
appropriate.
The final values we assigned to the metal partition coefficients for soil, sediment, suspended
matter, and DOC are presented in Tables 3, 4, 5, and 6, respectively. The method used to arrive
at each assigned value (use of all or a subset of the collected literature K^ values, use of
regression equations, modeling results, or expert judgement) is indicated for each metal and
media-type, as is the subjectively assigned confidence level.
3-7
-------
Table 3
Metal partition coefficients (log Kd) in L/kg for soil/soil water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4=lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Ag(I)
Asa
Ba(II)
Be(II)
Cd(II)
Co(II)
Cr(III)
Cr(VI)
Cu(II)
Median
2.6
3.4
___
2.9
2.1
3.9
1.1
2.7
Mean
2.6
3.2
2.0
2.2
2.7
2.1
3.8
0.8
2.5
Std.
Dev.
0.8
0.7
0.7
1.0
0.8
1.2
0.4
0.8
0.6
Min
1.0
0.3
0.7
1.7
0.1
-1.2
1.0
-0.7
0.1
Max
4.5
4.3
3.4
4.1
5.0
4.1
4.7
3.3
3.6
Comments
From literature data (raw,
n=21); log-normal; CL=\
From literature data (raw,
n=21); (log-normal
assumed); oxidation state
usually not specified in
literature; CL=2
Suspended matter Kd
regression equation for
mean; (log-normal
assumed); CL=2
Suspended matter Kd
regression equation for
mean; (log-normal
assumed); CL=3
From literature data (edited,
n=37); log-normal; CL=\
From literature data (raw,
n=ll); log-normal; CL=\
From literature data (raw,
n=22); log-normal; CL=2
From literature data (raw,
n=24); (log-normal
assumed); CL=2
From literature data (raw,
n=20); log-normal; CL=l
-------
Table 3
Metal partition coefficients (log Kd) in L/kg for soil/soil water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4=lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Hg(H)
MeHg
Mo(VI)
Ni(II)
Pb(II)
Sbb
Se(IV)c
Median
3.8
2.8
1.1
3.1
4.1
1.4
Mean
3.6
2.7
1.3
2.9
3.7
2.3
1.3
Std.
Dev.
0.7
0.6
0.6
0.5
1.2
1.1
0.4
Min
2.2
1.3
-0.4
1.0
0.7
0.1
-0.3
Max
5.8
4.8
2.7
3.8
5.0
2.7
2.4
Comments
From literature data (raw,
n=17); log-normal; CL=\
From literature data (raw,
n=ll); log-normal; CL=2
From literature data (raw,
n=5); (log-normal
assumed); oxidation state
not always specified in
literature data; CL=3
From literature data (raw,
n=19); log-normal; CL=\
From literature data (edited,
n=31); (log-normal
assumed); CL=2
From literature data (mean
is the average of several
reported mean values, n=5);
(log-normal assumed);
CL=4
From literature data (edited,
n=ll); (log-normal
assumed); CL=2
3-9
-------
Table 3
Metal partition coefficients (log Kd) in L/kg for soil/soil water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4=lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Se(VI)
Sn(II)
T1(I)
V(V)
Zn(II)
CN-
Median
___
3.1
Mean
-0.2
2.7
0.5
1.7
2.7
0.7
Std.
Dev.
1.1
0.7
0.9
1.5
1.0
1.6
Min
-2.0
2.1
-1.2
0.5
-1.0
-2.4
Max
2.0
4.0
1.5
2.5
5.0
1.3
Comments
Mean estimated from
MINTEQA2 result; (log-
normal assumed); min, max
from expert judgement;
CL=4
From literature data; (log-
normal assumed); CL=3
Estimated from
MINTEQA2 result; (log-
normal assumed); CL=4
Mean, min, max from
suspended matter Kd
regression equation; (log-
normal assumed); CL=4
From literature data (raw,
n=21); (log-normal
assumed); CL=\
Estimated from
MINTEQA2 result; (log-
normal assumed); CL=4
Published partitioning data for As does not allow differentiation of As(ni) and As(V).
It is probable that published values represent results involving both oxidation states.
Published partitioning data for Sb is rare and does not allow differentiation of Sb(III)
and Sb(V).
Positive result in Shapiro-Wilk test for normality of data not log-transformed. But
sample size is small and data may not be very representative.
3-10
-------
Table 4
Metal partition coefficients (log Kd) in L/kg for sediment/porewater. Values in italics
were estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4= lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Ag(I)
Asa
Ba(II)
Be(II)
Cd(II)
Co(II)
Cr(III)
Median
2.2
3.7
Mean
3.6
2.4
2.5
2.8
3.3
3.1
4.9
Std.
Dev.
1.1
0.7
0.8
1.9
1.8
1.0
1.5
Min
2.1
1.6
0.9
0.8
0.5
2.9
1.9
Max
5.8
4.3
3.2
6.5
7.3
3.6
5.9
Comments
Mean from soil K^
regression equation; (log-
normal assumed); min, max
from literature data; CL=3
From literature data; log-
normal; oxidation state not
specified in literature data;
CL=2
Mean, min, max from
suspended matter K^
regression equation; (log-
normal assumed); CL=3
Mean, min, max from
suspended matter Kd
regression equation; (log-
normal assumed); CL=3
From literature data (n=14,
edited); log-normal; CL=\
Mean from soil K^
regression equation; (log-
normal assumed); min, max
from literature data; CL=3
Mean, min, max from soil
Kd regression equation;
(log-normal assumed);
CL=4
3-11
-------
Table 4
Metal partition coefficients (log Kd) in L/kg for sediment/porewater. Values in italics
were estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4= lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Cr(VI)
Cu(II)
Hg(H)
MeHg
Mo(VI)
Ni(II)
Pb(II)
Median
4.1
___
___
5.1
Mean
1.7
3.5
4.9
3.9
2.5
3.9
4.6
Std.
Dev.
1.4
1.7
0.6
0.5
0.8
1.8
1.9
Min
0.0
0.7
3.8
2.8
0.4
0.3
2.0
Max
4.4
6.2
6.0
5.0
3.7
4.0
7.0
Comments
Mean, min, max from soil
Kd regression equation;
(log-normal assumed);
CL=4
From literature data (raw, n
= 12); log-normal; CL=l
From literature data (raw,
n=2); (log-normal
assumed); CL=2
From literature data (edited,
n=2); (log-normal
assumed); CL=2
Mean from literature data
(reported mean value with
oxidation state not
specified); (log-normal
assumed); min, max from
soil Kd regression equation;
CL=4
Mean from soil K^
regression equation; (log-
normal assumed); min, max
from literature data; CL=3
From literature data (edited,
n=14); log-normal; CL=l
3-12
-------
Table 4
Metal partition coefficients (log Kd) in L/kg for sediment/porewater. Values in italics
were estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro- Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4= lowest). An entry of " " for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
Sbb
Se(IV)
Se(VI)
Sn(II)
T1(I)
V(V)
Zn(II)
Median
___
4.8
Mean
3.6
3.6
0.6
3.7
1.3
2.1
4.1
Std.
Dev.
1.8
1.2
1.2
0.7
1.1
0.9
1.6
Min
0.6
1.0
-1.4
3.1
-0.5
0.4
1.5
Max
4.8
4.0
3.0
5.1
3.5
3.2
6.2
Comments
From literature data
(reported mean value); (log-
normal assumed); CL=4
Mean from literature data
(reported mean value); (log-
normal assumed); min, max
from expert judgement;
CL=4
Mean, min, max from soil
Kd regression equation;
(log-normal assumed);
CL=4
Mean, min, max from soil
Kd regression equation;
(log-normal assumed);
CL=3
Mean, min from soil K^
regression equation; (log-
normal assumed); max
from literature data; CL=4
Mean, min, max from
suspended matter Kd
regression equation; (log-
normal assumed); CL=4
From literature data (edited,
n=13); (log-normal
assumed); CL=\
3-13
-------
Table 4
Metal partition coefficients (log Kd) in L/kg for sediment/porewater. Values in italics
were estimated by regression or from MINTEQA2 results. An entry of "log-normal"
indicates that the sample data gave a positive result in the Shapiro-Wilk test for
normality of the log-transformed data. An entry of "log-normal assumed" in
parentheses means that data were not sufficient to establish the distribution, but log-
normal has been assumed. Relative confidence in the data is indicated by the CL value
of 1 to 4 (l=highest, 4= lowest). An entry of "" for the median occurs where
regression equations were used to estimate the mean, minimum, and maximum values
and no estimate was made for the median.
Metal
CN-
Median
Mean
1.6
Std.
Dev.
1.7
Min
-1.8
Max
2.2
Comments
Mean, min, max from soil
Kd regression equation;
(log-normal assumed);
CL=4
a Published metal partitioning data does not allow differentiation of As(III) and As(V). It is
probable that the data presented include results for both oxidation states.
b Published partitioning data for Sb is rare and does not allow differentiation of Sb(III) and
Sb(V).
3-14
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Table 5
Metal partition coefficients (log Kd) in L/kg for suspended matter/water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal" indicates that
the sample data gave a positive result in the Shapiro- Wilk test for normality of the log-
transformed data. An entry of "log-normal" in parentheses means that data were not sufficient
to establish the distribution, but log-normal has been assumed. Relative confidence in the data
is indicated by the CL value of 1 to 4 (l=highest, 4= lowest). An entry of " " for the median
occurs where regression equations were used to estimate the mean, minimum, and maximum
values and no estimate was made for the median.
Metal
Ag(I)
Asa
Ba(II)
Be(II)
Cd(II)
Co(II)
Cr(III)
Cr(VI)
Cu(II)
Hg(II)b
Median
5.2
4.0
4.0
4.1
5.0
4.7
5.1
4.7
5.3
Mean
5.2
3.9
4.0
4.2
4.9
4.8
5.1
4.2
4.7
5.3
Std.
Dev.
0.6
0.5
0.4
0.7
0.6
0.8
0.4
0.5
0.4
0.4
Min
4.4
2.0
2.9
2.8
2.8
3.2
3.9
3.6
3.1
4.2
Max
6.3
6.0
4.5
6.8
6.3
6.3
6.0
5.1
6.1
6.9
Comments
From literature data (edited, n=9);
log-normal; CL = 2
From literature data (raw, n=25);
(log-normal assumed); oxidation
state not specified in the literature
data; CL=2
From literature data (raw, n=14);
log-normal; CL=2
From literature data (raw, n=17);
log-normal; CL=2
From literature data (edited,
n=38); log-normal; CL=\
From literature data (edited,
n=20); log-normal; CL=\
From literature data (raw, n=25);
log-normal; assumes unspecified
oxidation state is (III); CL=2
Mean, min, max from soil Kd
regression equation; (log-normal
assumed); CL=4
From literature data (edited,
n=42); log-normal; CL=\
From literature data (edited,
n=26); log-normal; CL=\
3-15
-------
Table 5
Metal partition coefficients (log Kd) in L/kg for suspended matter/water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal" indicates that
the sample data gave a positive result in the Shapiro- Wilk test for normality of the log-
transformed data. An entry of "log-normal" in parentheses means that data were not sufficient
to establish the distribution, but log-normal has been assumed. Relative confidence in the data
is indicated by the CL value of 1 to 4 (l=highest, 4= lowest). An entry of " " for the median
occurs where regression equations were used to estimate the mean, minimum, and maximum
values and no estimate was made for the median.
Metal
MeHg
Ni(II)b
Mo(VI)
Pb(II)c
Sbd
Se(IV)
Se(VI)
Sn(II)
Median
4.3
___
5.7
___
Mean
4.9
4.4
4.4
5.7
4.8
4.4
3.8
4.9
Std.
Dev.
0.7
0.4
1.0
0.4
0.5
0.4
1.0
0.8
Min
4.2
3.5
3.7
3.4
3.9
3.8
3.1
4.7
Max
6.2
5.7
4.9
6.5
4.9
4.8
4.6
6.3
Comments
Mean from soil K^ regression
equation; (log-normal assumed);
min, max from literature data;
CL=3
From literature data (edited,
n=25); log -normal; CL=l
Mean, min, max from soil K^
regression equation; (log-normal
assumed); CL=4
From literature data (edited,
n=38); (log-normal assumed);
CL=\
Mean, min, max from soil K^
regression equation; (log-normal
assumed); CL=4
Mean, min, max from soil K^
regression equation; (log-normal
assumed); CL=4
Mean, min, max from soil K^
regression equation; (log-normal
assumed); CL=4
Mean, min from soil K^ regression
equation; (log-normal assumed);
max from literature data; CL=4
3-16
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Table 5
Metal partition coefficients (log Kd) in L/kg for suspended matter/water. Values in italics were
estimated by regression or from MINTEQA2 results. An entry of "log-normal" indicates that
the sample data gave a positive result in the Shapiro-Wilk test for normality of the log-
transformed data. An entry of "log-normal" in parentheses means that data were not sufficient
to establish the distribution, but log-normal has been assumed. Relative confidence in the data
is indicated by the CL value of 1 to 4 (l=highest, 4= lowest). An entry of "" for the median
occurs where regression equations were used to estimate the mean, minimum, and maximum
values and no estimate was made for the median.
Metal
Median
Mean
Std.
Dev.
Min
Max
Comments
4.1
1.0
3.0
4.5
Mean from soil K^ regression
equation; (log-normal assumed);
other parameters from expert
judgement; CL=4
V(V)
3.7
0.6
2.5
4.5
Mean from literature data (raw,
n=5); (log-normal assumed); min,
max from expert judgement;
oxidation state not always
specified in literature; CL=3
Zn(II)
5.1
5.0
0.5
3.5
6.9
From literature data (edited,
n=47); log-normal; CL=\
CN-
4.2
0.6
3.0
4.4
Mean, min, max from soil Kd
regression equation; (log-normal
assumed); CL=4
a Positive result for Shapiro-Wilk test for normality of data not log-transformed. Published
metal partitioning data does not allow differentiation of As(III) and As(V). It is probable
that the data represented include results for both oxidation states.
b Failed Shapiro-Wilk test for normality of log-transformed data, but passed the
Kolmogorov-Smirnov test and histogram exhibits log-normal character.
c Failed Shapiro-Wilk and the Kolmogorov-Smirnov test for normality of log-transformed
data, but histogram exhibits log-normal character
d Published partitioning data for Sb is rare and does not allow differentiation of Sb(III) and
Sb(V).
3-17
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Table 6
Metal partition coefficients (log Kd) in L/kg for partitioning between DOC and
inorganic solution species. Values in italics were estimated by regression or from
MINTEQA2 results. Log-normal distributions are assumed. Relative confidence in the
data is indicated by the CL value of 1 to 4 (l=highest, 4=lowest).
Metal
Ag(I)
As
Ba(II)
Be(II)
Cd(II)
Co(II)
Cr(III)
Cr(VI)
Cu(II)
Hg(H)
Mean
2.5
2.0
3.6
2.1
3.8
3.8
1.1
2.0
5.4
5.4
Std.
Dev.
1.0
1.0
1.0
1.0
0.9
0.9
1.6
1.0
1.1
1.2
Min
1.5
0.0
2.5
1.1
2.0
2.0
-0.6
0.0
2.5
3.0
Max
4.5
3.0
4.0
3.8
5.5
5.5
4.3
3.0
7.0
6.0
Comment
Mean estimated from MINTEQA2
results; other parameters from expert
judgement; (log-normal assumed);
CL=3
No data, values from expert judgement
(conservative); (log-normal assumed);
(log-normal assumed); CL=4
Mean estimated from MINTEQA2
results, values for other parameters from
expert judgement; (log-normal
assumed); CL=3
All parameters estimated from
MINTEQA2 results; CL=3
Mean estimated from MINTEQA2
results; min, max from expert
judgement; CL=3
Mean estimated from MINTEQA2
results; min, max from expert
judgement; CL=3
Mean estimated from MINTEQA2
results; min, max from expert
judgement; CL=4
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
From literature data (raw, n=17); (log-
normal assumed); CL=2
Mean from literature data (raw, n=3);
(log-normal assumed); min, max from
expert judgement; CL=4
3-18
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Table 6
Metal partition coefficients (log Kd) in L/kg for partitioning between DOC and
inorganic solution species. Values in italics were estimated by regression or from
MINTEQA2 results. Log-normal distributions are assumed. Relative confidence in the
data is indicated by the CL value of 1 to 4 (l=highest, 4=lowest).
Metal
MeHg
Ni(II)
Mo(VI)
Pb(II)
Sb
Se(IV)
Se(VI)
Sn(II)
T1(I)
Mean
5.0
3.7
2.0
4.9
2.0
2.0
2.0
2.0
1.6
Std.
Dev.
1.1
0.9
1.0
0.5
1.0
1.0
1.0
1.0
1.0
Min
2.8
1.9
0.0
3.8
0.0
0.0
0.0
0.0
0.0
Max
5.5
5.4
3.0
5.6
3.0
3.0
3.0
3.0
3.0
Comment
Mean, min, max estimated based on
relative Kd's of Hg(H) and MeHg for
suspended matter and Hg(n) Kd with
DOC; (log-normal assumed); CL=4
Mean estimated from MINTEQA2
results; min, max from expert
judgement; (log-normal assumed);
CL=3
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
From literature data (raw, n=9); (log-
normal assumed); CL=2
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
Mean estimated from MINTEQA2,
values for other parameters from expert
judgement; (log-normal assumed);
CL=4
3-19
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Table 6
Metal partition coefficients (log Kd) in L/kg for partitioning between DOC and
inorganic solution species. Values in italics were estimated by regression or from
MINTEQA2 results. Log-normal distributions are assumed. Relative confidence in the
data is indicated by the CL value of 1 to 4 (l=highest, 4=lowest).
Metal
V(V)
Zn(II)
CN-
Mean
2.0
5.1
2.0
Std.
Dev.
1.0
0.7
1.0
Min
0.0
4.6
0.0
Max
3.0
6.4
3.0
Comment
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
From literature data (raw, n=9); (log-
normal assumed); CL=3
No data, values from expert judgement
(conservative); (log-normal assumed);
CL=4
3-20
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3.2 DEVELOPMENT OF PARTITIONING COEFFICIENTS FOR WASTE SYSTEMS
The multimedia, multi-pathway risk assessment for 3MRA utilizes a source model that assumes
equilibrium partitioning in land application units (LAUs), waste piles, landfills, treatment lagoons
(surface impoundments), and aerated tanks. The available data for characterizing the partitioning of
metals in waste consists almost exclusively of leachate extraction test results for specific wastes.
Our literature search did not produce any study that specifically provides measured partitioning
coefficients for metals in the mixed materials present in waste management units.
Several studies have addressed the issue of the applicability of leachate extraction test data to
predict the leachate composition exiting landfills (U.S. EPA, 1991). The U.S. EPA Toxicity
Characteristic Leaching Procedure (TCLP) was specifically designed to characterize leachate
compositions produced by specific wastes co-disposed with municipal solid waste. Recent papers
suggest that the concentration observed in any leach test depends a great deal on leaching time and
the cumulative solid-liquid ratio (van der Sloot et al., 1996). Three "regimes" are recognized in the
leaching process (de Groot and van der Sloot, 1992). In the first regime, the leachate composition is
controlled by initial wash-off of loosely adhered contaminant; in the second, the leachate
composition is controlled by dissolution of primary materials and perhaps re-precipitation of more
stable phases; and in the third, the leachate composition is controlled by the diffusion of waste
constituents from the interior of waste particles to the particle surface. The time of onset and
duration of these regimes are highly variable, and depend on the life-cycle of the specific waste
system (acetogenesis, methanogenesis, etc.). The overall chemical composition (major ion
concentration and concentration of metal-complexing organic ligands) is also important in
determining the metal leachate concentration that will be observed in any particular case. In
general, it would seem that the highest metal leachate concentrations would be expected during the
initial wash-off period, with concentrations declining thereafter. An immediately obvious question
is: What period is of concern in the modeling for the 3MRA as used for HWIR rulemaking? Since
the 3MRA model does not allow a time-variable partition coefficient, it would seem that an
aggregate partition coefficient that represents an average over an appropriate exposure or waste
management unit lifetime would be desired. Unfortunately, there is currently no way to know
whether the "partitioning" observed in a TCLP test corresponds to such an average value. Most
authors seem to regard the TCLP as an aggressive test that may overestimate metal leachate
concentrations. However, there is no consensus on this point.
In view of the lack of data describing partitioning of metals in different types of waste units, the
following simplifications are proposed:
1) For land application units, the partition coefficients for soils presented in Table 3 should be
used. This simplification assumes that the partitioning behavior of metals in an LAU is likely to
be dominated by the sorptive characteristics of the soil underlying the unit.
2) For surface impoundments and aerated tanks, the partition coefficients for suspended matter
presented in Table 5 should be used. This seems a reasonable step in that partitioning in such
systems must involve sorption to suspended particles and sediments. The composition and
3-21
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quantity of suspended and sedimented sorbing particles must be quite variable, but there is no
source of data on which to base more specific modeling or other estimating techniques.
3) Waste piles and landfills should be treated the same as regards metal partitioning.
Adopting these simplifications, it is only necessary to derive separate estimates of metal partition
coefficients specifically for waste piles and landfills. The following sections detail how these latter
two sets of waste management unit coefficients have been estimated from available TCLP and
similar leachate extraction tests that characterize both the solid phase and the corresponding
leachate metal concentrations. We have also used statistical methods and geochemical speciation
modeling to extend results to metals not represented in reported TCLP or other leach test results,
and to examine the similarity between expected waste partitioning and partitioning in natural media.
3.2.1 Estimation from Analysis of Data Presented in the Literature
There are numerous papers and journal articles describing results from a TCLP or similar leach test
for a particular waste. These published studies often focus on waste constituent leachability before
and after a waste stabilization or treatment process. There are many published studies of the
leachability of metals from incinerator ash, with the aim of investigating the suitability of the ash
materials for disposal or for use in construction. Unfortunately, leachate extraction test results
(metal leachate concentrations) often are reported without the corresponding concentration in the
solid phase. This omission makes those data useless in estimating expected metals partitioning.
Our literature survey produced 203 leach test results for which both leachate and solid phase data
were presented. Table 7 shows the range and mean values of effective partition coefficients
calculated for each metal for which sufficient data was found. We refer to these as effective
partition coefficients because they are simply the ratio of metal concentration in the solid phase to
that in the solution phase as represented in the leach test results. These coefficients may or may not
represent equilibrium partitioning.
Several authors discussed the similarities in metal leachability over a range of different materials. A
study by van der Sloot et al. (1996) examined the leaching behavior of Cd and Zn from various ash
materials, shredded municipal solid waste, sewage sludge-amended soil, and soil. Similar
characteristics were noted in pH dependent leaching of both Cd and Zn from the nine different
materials studied. Differences among the different materials were attributed to waste-specific
chemical parameters that caused a different chemical speciation. For example, the authors cite
possible Cd complexation with chloride that they investigated using MINTEQA2. They found that
an increased leachability of Cd in some of the ash materials was correlated with increased chloride
concentration in the waste.
Flyhammar (1997) concluded that there are similarities in the metal binding properties of municipal
solid waste (MSW) and sediments. He found that the fractionation of metals among various
available and reactive forms (as determined by sequential chemical extractions) was similar between
fresh MSW and an oxic sediment. Similarities were also found in the fractionation patterns of aged
MSW and anoxic sediments.
3-22
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Table 7
Effective metals partition coefficients based on reported solid phase and solution phase metals
concentrations from leach tests reported in the literature. N is number of samples; mean and
range are expressed in log units (L/kg).
Metal
As
Ba
Be
Cd
Co
Cr(III)
Cr(VI)
Cu
Hg
Ni
Pb
Sb
V
Zn
N
11
7
2
31
6
27
6
16
8
12
31
4
4
23
Mean
2.8
3.0
2.8
1.3
2.8
3.0
4.1
3.3
3.1
2.3
2.7
2.7
2.9
2.6
Range
1.0-5.1
1.8-3.7
2.7-6.8
0-3.9
1.6-3.8
0.6-6.2
2.2-6.2
2.0-5.1
1.7-4.4
1.3-4.7
0.0-4.9
1.7-3.2
2.7-3.1
1.2-4.7
The consistency in the metals partitioning affinity relationships noted in Section 3.1.1 and the
similarities noted by these latter authors in the fractionation and behavior of metals in waste versus
that in soils and sediments leads to the supposition that the partitioning behavior of metals in mixed
waste systems might not be altogether different from that in a natural medium. It would perhaps be
surprising if the relative affinities for different metals in waste were markedly different from their
relative affinities in natural materials. There may certainly be some deviations due to the presence
of one or more complexing agents in waste systems that have a preference for combining with
certain of the metals; however, in the absence of data to quantify this effect, and also in
consideration of the paucity of actual partitioning data for waste systems, we have developed a
regression equation that predicts waste K^ from soil K^ for use with metals for which little or no
data was found. We chose to use soil K^ as the predictor because a comparison of Kd values for
soils, sediments and suspended matter suggested that the solid to liquid concentration ratio is
important in determining the magnitude of K^. (This apparent dependence of K^ on solid to liquid
ratio has been noted in other studies and is sometimes referred to as the "particle concentration
3-23
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effect.") This solid to liquid concentration ratio for landfills and waste piles is probably more
similar to that of soils than to any other medium. Also, we note that landfilled waste is typically
covered with soil to form soil/waste layers within a landfill cell. In developing our regression
relationship, we used the effective partition coefficients for the metals for which we had the most
complete (largest) sample. The regression equation thus determined is:
lo$^.«,* = 0710.?*^,+ 0-3 (4)
This relationship has a rather low correlation coefficient (r2) of 0.4 implying that only 40% of the
variation in log Kdwaste from the leach test data is accounted for. The implication is that log Kd
values predicted by means of this equation must be regarded as highly uncertain. Appendix B
shows the scatter plot of data from which this relationship was developed.
3.2.2 Estimation from Geochemical Speciation Modeling
The MINTEQA2 geochemical speciation model was used to investigate the possible range of metal
partition coefficients for landfills. The input requirements of the model for estimating metal
partitioning include the concentrations of major solute ions, the pH, the concentrations of sorbing
phases, and the DOC concentration. Four landfill modeling scenarios were developed,
distinguished primarily by the concentrations of major solute ions, the DOC concentration, the POC
concentration, and the pH. These scenarios included landfills containing municipal solid waste in
the acetogenic stage and in the methanogenic stage, a mono fill containing ash from incineration of
municipal solid waste (MSWI ash), and a mono fill containing cement kiln dust (CKD).
For each of the MINTEQA2 modeling scenarios, a hydrous ferric oxide sorbing phase was assumed.
A particulate organic carbon sorbent was also assumed for the acetogenic and methanogenic MSW
landfills. Particulate organic carbon was assumed to have been consumed in the incineration process
for the MSWI and CKD scenarios. The concentration of the sorbent is crucial in determining the
number of sites available for metal sorption. Unfortunately, the concentration of sorbent
appropriate in the various waste management systems is subject to a very high degree of uncertainty.
The uncertainty arises from the variable composition of wastes that are disposed in landfills and the
possible changes in composition over time as leachate percolates through the materials. It is likely
that solid surfaces exposed to landfill gas and leachate undergo changes with respect to their
sorptive character over time. Possible changes include dissolution or precipitation of oxide or
organic surface coatings. These processes have not been studied in actual landfill samples in
sufficient detail to allow quantitative representation. Kersten et al. (1997) cited evidence of sorption
control of Pb leaching in MSWI leach tests. They attempted to model the observed Pb
concentrations by utilizing a speciation model with surface complexation sorption reactions
parameterized for the constant capacitance model assuming hydrous ferric oxide (HFO) as the
sorbent. They obtained reasonable results assuming 0.7 g/L for the HFO concentration and using a
site density of 1.35xlO"4 mol sites/g HFO. The MINTEQA2 modeling presented here utilized a
similar surface complexation model (the diffuse-layer model). Kersten et al. (1997) had noted that
their sorbent concentration was perhaps too low, so our modeling was conducted both with their
3-24
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value of 0.7 g/L, and using 7 g/L as a reasonable upper-range value. In both cases, a site density
1.35xlO~4 mol sites/g HFO was used.
The values of other parameters and constituent concentrations used in our modeling for the four
landfill scenarios are shown in Table 8. After concentration of sorbing sites, the most critical model
parameter is pH, so the modeling was conducted at three different pH values for each scenario. The
three pH values used for the acetogenic and methanogenic scenarios (4.5, 6.1, 7.5 and 7.5, 8.0, 9.0,
respectively) were in keeping with the minimum, maximum and mean pH cited for these landfill
stages in a study of 15 landfills by Ehrig (1992). The major ion concentrations for the acetogenic
and methanogenic scenarios were also as specified in Ehrig (1992). The three pH values for the
MSWI scenario (8.0, 9.0, 10.0) were selected to define a reasonable range and central tendency
value for this scenario. These values were based on data collected in the literature review portion of
this study, as were the major ion concentrations for the MSWI scenario. The pH values associated
with the CKD scenario (9.0, 10.0, 11.0) were selected with due consideration to the highly alkaline
conditions associated with this material, but they lack statistical significance. An example
MINTEQA2 input file for each of the scenarios is presented in Appendix E.
It should be noted that the confidence level associated with all of the modeling parameters for waste
systems is low. There is not an extensive database of observations from which to extract reasonable
model values for most of these parameters, especially the concentration of sorbents and sorbing
sites. Without reliable information for characterizing the sorbents, it is not possible to accurately
establish the total system concentrations of competing ions (Ca, Mg, etc.) that should be used in the
model. The results must be interpreted in light of this shortcoming.
Table 8
Important parameters and constituent concentrations used in MINTEQA2
modeling of landfills in the acetogenic and methanogenic stages and MSWI and
CKD monofills.
Model
Parameter
pH
Ionic Strength
(M)
Ca (mg/L)
Mg
(mg/L)
Scenario
MSW
Acetogenic
4.5, 6.1,7.5a
O.lc
6000d
625d
MSW
Methanogenic
7.5, 8.0, 9.0a
O.lc
975d
500d
MSWI
Ash
Monofill
8.0, 9.0,
10.0b
0.1°
l,700b
10b
CKD
Monofill
9.0, 10.0,
11. Oc
0.1°
2850f
10f
3-25
-------
Table 8
Important parameters and constituent concentrations used in MINTEQA2
modeling of landfills in the acetogenic and methanogenic stages and MSWI and
CKD monofills.
Model
Parameter
Na (mg/L)
K (mg/L)
C03 (mg/L)
Cl (mg/L)
Fe (mg/L)
S04 (mg/L)
DOC (mg/L)
POC (mg/L)
Scenario
MSW
Acetogenic
1350e
1100e
500C
2100e
780e
500d
100C
100,000C
MSW
Methanogenic
1350e
1100e
250C
2100e
0
80d
50C
50,000°
MSWI
Ash
Monofil!
300b
380b
50C
l,200b
0
l,400b
15C
0
CKD
Monofil!
300f
400f
50f
380f
0
630f
15C
0
a Minimum, average, and maximum values reported in Ehrig (1992).
b Obtained from analysis of MSWI data obtained in our literature survey.
c Reasonable guesses.
d Computed from typical dissolved values reported in Ehrig (1992), assuming equilibrium
with the model sorbents at the median pH for acetogenic and methanogenic cases.
e Reported as typical values in Ehrig (1992).
f Generated from simulation of TCLP on CKD using MINTEQA2 (U.S. EPA, 1998b).
The partitioning coefficients for selected wastes estimated from the MINTEQA2 modeling exercise
for several metals are shown in Table 9. The partition coefficients were calculated as the ratio of
the simulated sorbed and dissolved concentrations as expressed in Equation (1). The units of K^
were converted to L/kg by assuming that one liter of leachate solution is associated with 5 kg of
waste material. The range in estimated partition coefficients is shown for each landfill modeling
scenario. In interpreting these results, it must be remembered that no statistical significance can be
assigned because none can be associated with most of the model input parameters. At best, these
results should be regarded as indicating a possible range of central tendency values, and even this
must be qualified because the results are so sensitive to several poorly characterized parameters,
most notably, the concentration of sorbents. The results also reflect only a single set of
concentration values for the major ambient ions variability in these concentrations will influence
metal partitioning. Some ions exert greater influence on the partitioning of particular metals. For
example, the low partition coefficients associated with Cd in Table 9 appear to be related to
complexation with chloride that is entered at relatively high ambient concentration in all scenarios.
3-26
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This effect is in keeping with observations by others (van der Sloot et al., 1996). Another major
ambient ion whose concentration level can influence metal partitioning is calcium. At the high
concentrations of calcium cations found in waste systems, especially MSWI ash and CKD, the
competition for binding sites can become very important with regard to trace metal binding. For
those trace metals whose partitioning is significantly influenced by the concentration level of a
major ambient ion such as chloride or calcium, it is expected that this fact alone would contribute to
a broader range of observed partition coefficients in real systems than that calculated in this
modeling exercise.
Table 9
Estimated range in log partition coefficients (L/kg) in waste for selected metals determined
from MINTEQA2 modeling.
Metal
Be
Cd
Co
Cr(III)
Cu
Ni
Pb
Zn
Estimated log Kd (L/kg)
MSW
Acetogenesis
0.8-3.9
(-0.3) - 0.0
0.2 - 0.3
1.1 -3.5
1.1 - 1.9
0.2 - 0.4
1.7-2.7
0.4 - 0.7
MSW
Methanogenesis
3.3-4.4
0.6- 1.7
0.9-1.8
3.8-4.8
2.0-2.5
1.1 - 1.9
3.3-4.2
1.5-2.1
MSWI
Ash
Monofill
(.0.4) - 4.0
(-1.0) -1.1
(.0.9) - 0.4
(-0.2) - 3.2
0.0-2.9
(-0.04) -
1.1
2.4-3.6
(-0.6) -1.3
CKD
Monofill
(-2.7) - 2.4
(-0.4)- 1.2
(-2.0) - 0.2
(-2.5) - 2.3
(-2.0) -2.1
(-1.5) -0.9
0.7-3.4
(-2.7) -1.1
We compared the partition coefficients estimated for wastes using MINTEQA2 with values
predicted by the previously discussed regression equation (log Kdwaste = 0.7 log Kdsoil + 0.3; see
Section 3.2.1). The degree of agreement varied among metals. (We defined the measure of
"agreement" for a metal to be whether the value predicted by the regression equation using the mean
soil IQ value of Table 3 falls within the range of MINTEQA2 estimates for that metal. Using this
rather lax requirement for "agreement", the MINTEQA2-modeled Kd values for Be, Cr(ni), Cu, and
Pb "agree", those of Cd and Ni "do not agree," and those of Co and Zn are "marginal") In
agreement with the literature-reported Kd values for natural media, Pb and Cr(III) tend to have high
Kd estimates from the MINTEQA2 waste simulations. In general, the MINTEQA2 results for the
acetogenic and methanogenic landfill scenarios agreed more closely with values estimated by the
regression relationship based on soil Kd values than for the more alkaline ash and CKD landfill
3-27
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scenarios. It is probable that the lower Kd values in the latter scenarios are due to the combination
of higher major ambient ion concentrations that compete with the trace metals for sorbing sites and
solubilize the metals by complexation, plus the assumed absence of particulate organic carbon in the
model landfill systems.
3-28
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4.0 DISCUSSION OF RESULTS AND SOURCES OF UNCERTAINTY
Partition coefficients obtained from literature data are subject to numerous sources of uncertainty.
Many previous studies have demonstrated that in a variety of soils and for a variety of metals,
partition coefficients vary with pH and with the concentration of sorbing phases in the soil matrix
(e.g., weight percent organic matter content and weight percent hydrous ferric oxides and
corresponding oxides of aluminum and manganese) (Janssen et al., 1997; Hassan and Garrison,
1996; Bangash and Hanif, 1992; Anderson and Christensen, 1988). It is well known that dissolved
ligands present in soil porewater (e.g., dissolved organic matter, anthropogenic organic acids) can
complex with metals, reducing their propensity for sorption in proportion to the concentration of the
ligands (Christensen et al., 1996). In multi-metal systems, competition among metals for sorption
sites and the attendant reduction in the partition coefficient in comparison with single-metal systems
has also been reported (Jin et al., 1996). Within the population of soils, the natural variability in
soil composition and composition of associated soil porewater are such as to result in variation in K^
over orders of magnitude, even for a single metal. For this reason, any comprehensive compilation
of Kd values selected from the literature should be expected to present values that define a
distribution. In fact, for any particular metal, Kd depends on these and other characteristics of the
natural media system (soil, sediment, surface water), and in a nationwide risk assessment it is
desirable to sample the national population of such natural media systems to obtain a frequency
distribution of Kd.
Unfortunately, the collection of natural media systems chosen for study by various researchers and
reported in the literature is almost certainly not representative of the national population of such
systems, and collections of K^ values obtained from the literature are almost certainly not
representative of the true national frequency distribution of K^ for any particular metal of interest.
Furthermore, the degree to which the natural systems reported in the literature are adequately
representative of the population of such systems varies greatly among the different metals for which
Kd values have been obtained. The manner in which the Kd values obtained in this study were used
to develop estimates of the frequency distributions of Kd for the metals of concern were presented in
Section 3.0. Statistical tests suggested that the data collected for the most studied metals were
consistent with a log normal distribution. In addition, certain consistencies were observed in the
magnitude of K^ for a particular metal in different media types, and in the ranked magnitude of K^
for different metals in a particular medium. These facts were used to advantage in developing the
Kd frequency distributions for what must be considered a sparse dataset. The use of such patterns to
discern the underlying frequency distributions of Kd is subjective, and implies a significant degree
of uncertainty in the derived distributions.
Apart from uncertainties in representing the expected variation in Kd that arise from variation in
soil/aquifer properties, there are significant uncertainties associated with individual IQ values
obtained from the literature. Sources of uncertainty in individual literature K^ values include:
Detection limits in measuring metal concentrations can result in limiting the observed maximum
Kd value.
Equilibrium conditions may not have prevailed in the experiment when measuring the media
concentrations. Most batch experiments are carried-out over a time span of one or two days.
4-1
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Equilibrium may or may not have been attained, and unaccounted-for non-equilibrium processes
may have occurred.
Some variability in collected K^ values may reflect variability in the different methods of
measurement (e.g., batch experiments, measurements from natural soil and associated
porewater, calculation from tracer/retardation studies).
Some variability in collected K^ values may reflect variability in the extractants used in batch
tests. Some researchers used soil porewater or groundwater as the extractant. Others used
distilled water or a solution of electrolyte. The modeling in which these K^ values are to be used
may implicitly prescribe an extractant that is dissimilar to any used in the literature studies that
produced the Kd values. For example, landfill leachate may contain high concentrations of
organic acids, Ca, Na, Cl, SO4, and other ions. The presence of these constituents can result in
lower Kd values relative to the values reported for more "pristine" systems. Lower Kd values
can also result from increased competition for sorption sites or from complexation of the metal
with dissolved ligands.
Some uncertainty in the reported Kd values is associated with uncontrolled or unknown redox
conditions during the course of experimental measurements, especially for redox-sensitive
metals (e.g., Cr, As, Se). Separate K^ values for different oxidation states of the same metal
were obtained whenever reported, but authors frequently did not report the oxidation state.
Even when reported, the oxidation state must be regarded as somewhat uncertain sorption
reactions can be intimately associated with oxidation-reduction.
There is uncertainty in the Kd values due to neglecting the impact of total system metal
concentration on the magnitude of K^. Numerous studies have documented the dependence of
Kd on total metal concentrationKd tends to decrease as the total metal concentration increases.
No attempt has been made in this compilation of literature values to investigate or represent the
dependence of K^ on total metal concentration. It is assumed that the K^ values compiled here
are likely to be more representative of those in systems with low metal concentration than
systems with high metal concentration.
Finally, the magnitude of the uncertainty in Kd values presented in this database of literature values
should be regarded as having a significant metal-dependent component. As noted already, several
metals have been more widely studied (e.g., Cd, As, Pb). For some of the metals of interest in this
study, most notably Tl and Sb, there is very little partitioning data available for soil and
groundwater systems. In addition, some sources of uncertainty listed above are associated with
metal-specific phenomena (e.g., detection limits, redox transformations, propensity for dependence
of Kd on metal concentration).
There are great uncertainties inherent in the use of equilibrium speciation modeling to estimate
metal partition coefficients in waste systems. Much uncertainty in the model result is due to not
having sufficient data to characterize the range of waste compositions, especially the character and
concentration of sorption sites. In view of the uncertainty in speciation model estimated values, a
possible alternative for representing metal partitioning in waste piles and landfills is to use the
regression equation (i.e., Equation (4)) relating Kd in waste and soil (presented in Section 3.2.1).
The latter has the advantage of preserving the relative affinities among metals that has been noted to
be common to the natural media. However, the speciation model results do suggest that the K^
values in alkaline systems may be significantly lower than in municipal landfills. This might be
4-2
-------
accounted-for by treating the slope and intercept coefficients in the regression equation (Equation
(4)) as variables subject to uncertainty that can be represented in the 3MRA monte carlo iterations.
In the overall modeling strategy of 3MRA, if the frequency of occurrence of a highly alkaline waste
system can be established and used in the monte carlo realizations, the regression equation
coefficients could be adjusted to give lower K^ values for the appropriate fraction of realizations to
reflect alkaline systems. This topic needs further study, as does the entire issue of equilibrium
partitioning in waste. It should be noted that of the several studies reviewed whose authors
suggested mechanisms controlling the concentrations of metals in leachate from waste management
systems, most advocated a mineral solubility control rather than equilibrium partitioning (Baverman
et al, 1997; Kersten et al, 1997; Johnson et al, 1996; Eighmy et al, 1995; Yan and Neretnieks,
1995; Fruchter et al., 1990; Moretti et al., 1988; Gould et al., 1988). However, the difficulty in
distinguishing solubility controls from effects of sorption is also noted. It is possible that metals are
initially mobilized by dissolution of solid phases, especially in ash and CKD wastepile/landfill
scenarios, but that surface coatings that form upon aging eventually control solution phase metal
concentrations via sorption (van der Sloot et al., 1996). More research is need to quantify these and
other processes in waste management systems.
4-3
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5.0 REFERENCES
This reference list includes the complete bibliography of papers, articles, and reports that were
copied and reviewed in our literature search. Those articles that provided data for spreadsheet entry
are identified by a code in square brackets at the end of the citation. The code can be cross-
referenced to spreadsheet entries. Further explanation of spreadsheet entries is provided in the
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U.S. EPA, 1996a. EPA 's composite model for leachate migration with transformation products
(EPACMTP): Background document for metals. United States Environmental Protection
Agency unpublished report, Office of Solid Waste, Washington, BC, 77p.
5-17
-------
U.S. EPA, 1996c. Soil Screening Guidance: Technical Background Document, U. S.
Environmental Protection Agency, Office of Solid Waste and Emergency Response,
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U.S. EPA, 1996d. Technical Support document for the Round Two Sewage Sludge Pollutants, U. S.
Environmental Protection Agency, Office of Water, Washington, D. C., EPA-822-R-96-003.
[USEPA96d]
U.S. EPA, 1998. National Sediment Bioaccumulation Conference. U.S. Environmental Protection
Agency, Office of Water, Washington, D.C., EPA 823-R-98-002, 360 p.
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-------
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5-19
-------
APPENDIX A
METAL PARTITION COEFFICIENTS USED IN
SOME RECENT U.S. EPA RISK ASSESSMENTS
-------
Metal
Ag
As
Risk
Assessment
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
Type of Kd
soil/water
suspended
matter
/water
sediment/wa
ter
soil/water
soil/water
soil/water
suspended
matter
/water
sediment/wa
ter
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
Kd(L/kg)
Single
Value
290
___
20
63,700
...
29
Range
0.1-110
0.1 - 110
0.1-110
0.1 - 110
25-31
25-31
25-31
___
25-31
___
A-l
-------
Metal
Ba
Be
Risk
Assessment
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
Type of Kd
soil/water
suspended
matter
/water
sediment/wa
ter
soil/water
soil/water
soil/water
soil/water
suspended
matter
/water
sediment/wa
ter
soil/water
soil/water
Kd(L/kg)
Single
Value
6
530
___
43
Range
11-52
11-52
11-52
11-52
___
23-
100,000
23-
100,000
23-
100,000
23-
100,000
A-2
-------
Metal
Cd
Co
Cr
Risk
Assessment
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Type of Kd
soil/water
suspended
matter
/water
sediment
/water
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
waste/leach
ate
suspended
matter
/water
Kd(L/kg)
Single
Value
431
174,000
162
45
___
...
___
59
255,000
Range
15-4300
15-4300
15-4300
15-4300
1200-
4.3E06
1200-
4.3E06
1200-
4.3E06
A-3
-------
Metal
Cr(VI)
Cu
Hg
Risk
Assessment
Soil-Screening
Guidance
[USEPA96c]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Type of Kd
soil/water
soil/water
suspended
matter
/water
sediment
/water
soil/water
soil/water
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
Kd(L/kg)
Single
Value
18
98
132,000
22
1000
1000
3000
Range
1200-
4.3E06
14-31
14-31
14-31
14-31
___
A-4
-------
Metal
Mo
Ni
Pb
Risk
Assessment
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
Type of Kd
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
soil/water
Kd(L/kg)
Single
Value
330
125,000
20
___
63
100,000
___
82
900
Range
0.04 - 200
16-1900
16- 1900
16-1900
16- 1900
___
...
A-5
-------
Metal
Sb
Se
Risk
Assessment
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Sewage Sludge
Rule [USEPA92]
Sewage Sludge
Rule [USEPA92]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Type of Kd
suspended
matter
/water
sediment
/water
waste/leach
ate
suspended
matter
/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
soil/water
soil/water
soil/water
suspended
matter
/water
Kd(L/kg)
Single
Value
900
900
621
185,000
280,000
45
45
45
45
6
___
~
Range
___
___
___
2.2- 18
2.2-18
A-6
-------
Metal
Tl
V
Zn
Risk
Assessment
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Soil-Screening
Guidance
[USEPA96c]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
Chlorinated
Aliphatics Listing
[USEPA?]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
US EPA Region
6 Combustion
[USEPA96b]
Type of Kd
sediment
/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
soil/water
soil/water
soil/water
soil/water
soil/water
suspended
matter
/water
sediment
/water
Kd(L/kg)
Single
Value
1000
39
50
62
62
62
Range
2.2-18
2.2- 18
44-96
44-96
44-96
44-96
___
___
...
...
A-7
-------
Metal
CN
Risk
Assessment
Soil-Screening
Guidance
[USEPA96c]
Chlorinated
Aliphatics Listing
[USEPA?]
Soil-Screening
Guidance
[USEPA96c]
Sewage Sludge
Rule, Round 2
[USEPA96d]
Type of Kd
soil/water
soil/water
soil/water
soil/water
Kd(L/kg)
Single
Value
40
9.9
0.0014
Range
16-530
A-8
-------
APPENDIX B
SCATTER PLOTS FOR
LINEAR REGRESSIONS USED TO
ESTIMATE MEAN LOG K VALUES
-------
Fi
s
*K
1 3
234!
Soil Mean log Kd 41/kgi
Figure B-l. Data used to develop regression equation to
predict sediment K^ from soil IQ.
~ 5 -
_o
i 4
0
ifeE
4»
1 3 -
34567
Suspended Matter Mean log Kd {L:'k0
Figure B-2. Data used to develop regression equation to
predict sediment IQ from suspended matter K^.
B-l
-------
3 -|
*
"S
= 2 -
o
CO
* *
456
Suspended Mattel Mean log K<1 iL'kgl
Figure B-3. Data used to develop regression equation to
predict soil K^ from suspended matter Kd (and vice versa).
*
«s
*
Soil Me tin log Kd (L/k
-------
APPENDIX C
EXAMPLE INPUT FILE FOR THE MINTEQA2
MODEL USED TO ESTIMATE METAL PARTITIONING IN SOIL/SOIL WATER
SYSTEMS
-------
Estimate Kd in soil/soil water
MMMM: Md Dissolved OM , Md FeO Sorbent, Md Particulate OM
17.00 MG/L 0.010 3.56000E+00
0110231010012
Co_soil.prn 200
5.00 19100. 145.00 0.000 0.00
4 1 7
5.940E-01 600.000.0000.00081
330 O.OOOE+00 -4.90 y /H+l
200 l.OOOE-03 -12.32 y /Co+2
150 1.320E+01 -2.92 y /Ca+2
460 3.600E+00 -3.24y /Mg+2
410 1.200E+00 -4.13y /K+l
500 5.300E+00 -3.02 y /Na+1
140 5.200E+01 -2.51 y /CO3-2
180 5.700E+00 -3.37 y /Cl-1
580 5.000E-01 -6.02 y /PO4-3
732 6.600E+00 -3.58 y /SO4-2
90 6.200E-02 -4.00 y /H3BO3
144 O.OOOE+00 -6.00 y /DOM1
145 O.OOOE+00 -6.00 y /DOM1
811 3.337E-05 -4.45 y /ADS1TYP1
812 1.335E-03 -2.84 y /ADS1TYP2
/ADSlPSIo
,MdpH
813 O.OOOE+00
-6.00 y
-4.45 y
-2.84 y
0.00 y
3 1
330 6.5000 0.0000
6 1
813 0.0000 0.0000
/H+l
/ADSlPSIo
2 74
8113302 =FeOH2+
0.003 1.000811 1,
0.000 0 0.000 0
0 0.000 0 0.000 0
8113301 =FeO-
0.003 1.000811 -1
0.000 0 0.000 0
0 0.000 0 0.000 0
8123302 =FeOH2+
0.003 1.000812 1,
0.000 0 0.000 0
0 0.000 0 0.000 0
8123301 =FeO-
0.003 1.000812 -1
0.000 0 0.000 0
0 0.000 0 0.000 0
0.0000 7.2900
000330 1.000813
0.000 0 0.000 0
0.000 0
0.0000 -8.9300 0.
.000330 -1.000813
0.000 0 0.000 0
0.000 0
0.0000 7.2900
000330 1.000813
0.000 0 0.000 0
0.000 0
0.0000 -8.9300 0.
.000330 -1.000813
0.000 0 0.000 0
0.000 0
0.000 0.0001.000.000.00 0.0000
0.000 0 0.000 0 0.000 0
0.000 0 0.000 0
000 0.000-1.000.000.00 0.0000
0.000 0 0.000 0 0.000 0
0.000 0 0.000 0
0.000 0.0001.000.000.00 0.0000
0.000 0 0.000 0 0.000 0
0.000 0 0.000 0
000 0.000-1.000.000.00 0.0000
0.000 0 0.000 0 0.000 0
0.000 0 0.000 0
C-l
-------
8111000 =FeOHBa+2 0.0000 5.4600 0.000 0.0002.000.000.00 0.0000
0.003 1.000811 1.000100 2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121000=FeOBa+ 0.0000 -7.2000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000100-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8111500 =FeOHCa+2 0.0000 4.9700 0.000 0.0002.000.000.00 0.0000
0.003 1.000811 1.000150 2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121500=FeOCa+ 0.0000 -5.8500 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000150-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8124600 =FeOMg+ 0.0000 -4.6000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000460-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8118700 =FeOTl 0.0000 -3.5000 0.000 0.0000.000.000.00 0.0000
0.003 1.000811 1.000870-1.000330 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8128700 =FeOTl 0.0000 -6.9000 0.000 0.0000.000.000.00 0.0000
0.003 1.000812 1.000870-1.000330 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8110200 =FeOAg 0.0000 -1.7200 0.000 0.0000.000.000.00 0.0000
0.003 1.000811 1.00020-1.000330 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8120200 =FeOAg 0.0000 -5.3000 0.000 0.0000.000.000.00 0.0000
0.003 1.000812 1.00020-1.000330 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8115400 =FeONi+ 0.0000 0.3700 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000540-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8125400 =FeONi+ 0.0000 -2.5000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000540-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8112000 =FeOCo+ 0.0000 -0.4600 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000200-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
C-2
-------
0 0.000 0 0.000 0 0.000 0
8122000 =FeOCo+ 0.0000 -3.0100 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000200-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8111600 =FeOCd+ 0.0000 0.4700 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000160-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121600=FeOCd+ 0.0000 -2.9000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000160-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8119500 =FeOZn+ 0.0000 0.9900 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000950-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8129500 =FeOZn+ 0.0000 -1.9900 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000950-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8112310=FeOCu+ 0.0000 2.8900 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000231 -1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8123100=FeOCu+ 0.0000 0.6000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000231 -1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8116000 =FeOPb+ 0.0000 4.6500 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000600-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8126000 =FeOPb+ 0.0000 0.3000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000600-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8111100=FeOBe+ 0.0000 5.7000 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000110-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121100=FeOBe+ 0.0000 3.3000 0.000 0.0001.000.000.00 0.0000
0.004 1.000812 1.000110-1.000330 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8113610 =FeOHg+ 0.0000 13.9500 0.000 0.0001.000.000.00 0.0000
0.005 1.000811 1.000361 -2.000 2 1.000330 1.000813 0.000 0
C-3
-------
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8123610 =FeOHg+ 0.0000 12.6400 0.000 0.0001.000.000.00 0.0000
0.005 1.000812 1.000361 -2.000 2 1.000330 1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117900 =FeOSn+ 0.0000 15.1000 0.000 0.0001.000.000.00 0.0000
0.005 1.000811 1.000790 -2.000 2 1.000330 1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127900 =FeOSn+ 0.0000 13.0000 0.000 0.0001.000.000.00 0.0000
0.005 1.000812 1.000790 -2.000 2 1.000330 1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8112110=FeOCrOH+ 0.0000 11.6300 0.000 0.0001.000.000.00 0.0000
0.004 1.000811 1.000211 -1.000 2 1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8110600 =FeH2AsO3 0.0000 5.4100 0.000 0.0000.000.000.00 0.0000
0.003 1.000811 1.000 60 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8120600 =FeH2AsO3 0.0000 5.4100 0.000 0.0000.000.000.00 0.0000
0.003 1.000812 1.000 60 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8110900 =FeH2BO3 0.0000 0.6200 0.000 0.0000.000.000.00 0.0000
0.003 1.000811 1.000 90 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8120900 =FeH2BO3 0.0000 0.6200 0.000 0.0000.000.000.00 0.0000
0.003 1.000812 1.000 90 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8115800 =FeH2PO4 0.0000 31.2900 0.000 0.0000.000.000.00 0.0000
0.004 1.000811 1.000580 3.000330 -1.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8125800 =FeH2PO4 0.0000 31.2900 0.000 0.0000.000.000.00 0.0000
0.004 1.000812 1.000580 3.000330 -1.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8115801 =FeHPO4- 0.0000 25.3900 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000580 2.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8125801 =FeHPO4- 0.0000 25.3900 0.000 0.000-1.000.000.00 0.0000
C-4
-------
0.005 1.000812 1.000580 2.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8115802 =FePO4-2 0.0000 17.7200 0.000 0.000-2.000.000.00 0.0000
0.005 1.000811 1.000580 1.000330 -1.000 2 -2.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8125802 =FePO4-2 0.0000 17.7200 0.000 0.000-2.000.000.00 0.0000
0.005 1.000812 1.000580 1.000330 -1.000 2 -2.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8110610 =FeH2AsO4 0.0000 8.6100 0.000 0.0000.000.000.00 0.0000
0.003 1.000811 1.000 61 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8120610=FeH2AsO4 0.0000 8.6100 0.000 0.0000.000.000.00 0.0000
0.003 1.000812 1.000 61 -1.000 2 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8110611=FeHAsO4- 0.0000 2.8100 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.00061-1.000 2-1.000330-1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8120611 =FeHAsO4- 0.0000 2.8100 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.00061-1.000 2-1.000330-1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8119032 =FeHVO4- 0.0000 -3.7000 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000903 -2.000330 1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8129032 =FeHVO4- 0.0000 -3.7000 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.000903 -2.000330 1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117320 =FeSO4- 0.0000 7.7800 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000732 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127320 =FeSO4- 0.0000 7.7800 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.000732 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117321 =FeOHSO4-2 0.0000 0.7900 0.000 0.000-2.000.000.00 0.0000
0.003 1.000811 1.000732 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
C-5
-------
8127321 =FeOHSO4-2 0.0000 0.7900 0.000 0.000-2.000.000.00 0.0000
0.003 1.000812 1.000732 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117610 =FeSeO3- 0.0000 4.2900 0.000 0.000-1.000.000.00 0.0000
0.004 1.000811 1.000761 -1.000 2-1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127610 =FeSeO3- 0.0000 4.2900 0.000 0.000-1.000.000.00 0.0000
0.004 1.000812 1.000761 -1.000 2-1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117611 =FeOHSeO3-2 0.0000 -3.2300 0.000 0.000-2.000.000.00 0.0000
0.004 1.000811 1.000761 -1.000330-2.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127611 =FeOHSeO3-2 0.0000 -3.2300 0.000 0.000-2.000.000.00 0.0000
0.004 1.000812 1.000761 -1.000330-2.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117620 =FeSeO4- 0.0000 7.7300 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000762 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127620 =FeSeO4- 0.0000 7.7300 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.000762 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117621 =FeOHSeO4-2 0.0000 0.8000 0.000 0.000-2.000.000.00 0.0000
0.003 1.000811 1.000762 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127621 =FeOHSeO4-2 0.0000 0.8000 0.000 0.000-2.000.000.00 0.0000
0.003 1.000812 1.000762 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8112120=FeCrO4- 0.0000 10.8500 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000212 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8122120=FeCrO4- 0.0000 10.8500 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.000212 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8114800 =FeMoO4- 0.0000 9.5000 0.000 0.000-1.000.000.00 0.0000
0.005 1.000811 1.000480 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
C-6
-------
0 0.000 0 0.000 0 0.000 0
8124800 =FeMoO4- 0.0000 9.5000 0.000 0.000-1.000.000.00 0.0000
0.005 1.000812 1.000480 1.000330 -1.000 2 -1.000813 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8114801 =FeOHMoO4-2 0.0000 2.4000 0.000 0.000-2.000.000.00 0.0000
0.003 1.000811 1.000480 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8124801 =FeOHMoO4-2 0.0000 2.4000 0.000 0.000-2.000.000.00 0.0000
0.003 1.000812 1.000480 -2.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117410 =FeSbO(OH)4 0.0000 8.4000 0.000 0.0000.000.000.00 0.0000
0.004 1.000811 1.000741 1.000330 -2.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127410=FeSbO(OH)4 0.0000 8.4000 0.000 0.0000.000.000.00 0.0000
0.004 1.000812 1.000741 1.000330 -2.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8117411 =FeOHSbO(OH4 0.0000 1.3000 0.000 0.000-1.000.000.00 0.0000
0.004 1.000811 1.000741 -1.000 2-1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8127411 =FeOHSbO(OH4 0.0000 1.3000 0.000 0.000-1.000.000.00 0.0000
0.004 1.000812 1.000741 -1.000 2-1.000813 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8111430 =FeCN 0.0000 13.0000 0.000 0.0000.000.000.00 0.0000
0.004 1.000811 1.000143 1.000330-1.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121430 =FeCN 0.0000 13.0000 0.000 0.0000.000.000.00 0.0000
0.004 1.000812 1.000143 1.000330-1.000 2 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8111431=FeOHCN- 0.0000 5.7000 0.000 0.000-1.000.000.00 0.0000
0.003 1.000811 1.000143-1.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
8121431 =FeOHCN- 0.0000 5.7000 0.000 0.000-1.000.000.00 0.0000
0.003 1.000812 1.000143-1.000813 0.000 0 0.000 0 0.000 0
0.000 0 0.000 0 0.000 0 0.000 0 0.000 0 0.000 0
0 0.000 0 0.000 0 0.000 0
C-7
-------
APPENDIX D
EXAMPLE INPUT FILE FOR THE
MINTEQA2 MODEL USED TO ESTIMATE METAL PARTITIONING TO DOC
-------
Compute Kd-DOC in riverwater
MXXM: Md Dissolved OM , No FeO Sorbent, No Particulate OM
16.00 MG/L 0.000 8.90000E-06
0110230010012
Co_doc.prn 200
0.00 8.90 144.00 0.000 0.00
000
330 O.OOOE+00 -4.90 y
200 l.OOOE-03 -12.32 y
150 1.320E+01 -2.92 y
460 3.600E+00
410 1.200E+00
500 5.300E+00
,MdpH
-3.24y
-4.13 y
-3.02y
492 6.010E+00 -3.00y
140 5.200E+01 -2.51 y
180 5.700E+00 -3.37 y
732 6.600E+00 -3.58 y
144 O.OOOE+00 -6.00 y
3 1
330 7.3000
0.0000
/H+l
/Co+2
/Ca+2
/Mg+2
/K+l
/Na+1
/NO3-
/CO3-2
/Cl-1
/SO4-2
/DOM1
/H+l
D-l
-------
APPENDIX E
EXAMPLE INPUT FILE FOR THE MINTEQA2
MODEL USED TO ESTIMATE METAL PARTITIONING IN WASTE MANAGEMENT
SYSTEMS
-------
Estimate partitioning in acetogenic landfill. Assumes 0.7gFeOOH/L
DLM; uses 3x site densities used in Ke97; Mean major ions.
17.00 MG/L 0.100 5.00000E+00
0110231010212
3H+1 ACTIVITY mol/L
1 330 1.000
4.50 6.10 7.50
Co_waste.prn 200
100.00 99999. 145.00 0.000 0.00
4 1 7
7.000E-01 600.000.0000.00081
330 O.OOOE+00 -4.90 y
200 l.OOOE-03 -12.32 y
150 6.000E+03 -2.92 y
460 6.250E+02 -3.24y
140 5.000E+02 -2.51 y
500 1.350E+03 -3.37 y
410 1.100E+03 -3.37 y
180 2.100E+03 -3.37 y
280 7.800E+02 -9.00 y
732 5.000E+02 -3.58 y
144 O.OOOE+00 -6.00 y
145 O.OOOE+00 -6.00 y
811 2.363E-06 -4.45 y
812 9.212e-05 -2.84 y
813 O.OOOE+00 0.00 y
3 1
330 4.0000 0.0000
6 1
813 0.0000 0.0000
/H+l
/Co+2
/Ca+2
/Mg+2
/CO3-2
/Na+
/K+
/Cl-1
/Fe+2
/SO4-2
/DOM1
/DOM1
/ADS1TYP1
/ADS1TYP2
/ADSlPSIo
/H+l
/ADSlPSIo
2 74
(same HFO reactions as for soil/water partitioning; see Appendix B)
E-l
-------
Estimate partitioning in methanogenic landfill. Assumes 0.7gFeOOH/L
DLM; uses 3x site densities used in Ke97; Mean major ions.
17.00 MG/L 0.100 5.00000E+00
0110231010212
3H+1 ACTIVITY mol/L
1 330 1.000
7.50 8.00 9.00
Co_waste.prn 200
50.00 50000. 145.00 0.000 0.00
4 1 7
7.000E-01 600.000.0000.00081
330 O.OOOE+00 -4.90 y
200 l.OOOE-03 -12.32 y
150 9.750E+02 -2.92 y
460 5.000E+02 -3.24 y
140 2.500E+02 -2.51 y
500 1.350E+03 -3.37 y
410 1.100E+03 -3.37 y
180 2.100E+03 -3.37 y
732 8.000E+01 -3.58 y
144 O.OOOE+00 -6.00 y
145 O.OOOE+00 -6.00 y
811 2.363E-06 -4.45 y
812 9.212e-05 -2.84 y
813 O.OOOE+00 0.00 y
3 1
330 7.0000 0.0000
6 1
813 0.0000 0.0000
/H+l
/Co+2
/Ca+2
/Mg+2
/CO3-2
/Na+
/K+
/Cl-1
/SO4-2
/DOM1
/DOM1
/ADS1TYP1
/ADS1TYP2
/ADSlPSIo
/H+l
/ADSlPSIo
2 74
(same HFO reactions as for soil/water partitioning; see Appendix B)
E-2
-------
Estimate partitioning in MSWI ash monofill. Assumes 0.7gFeOOH/L
DLM; uses 3x site densities used in Ke97; Mean major ions.
17.00 MG/L 0.100 5.00000E+00
0110231010212
3H+1 ACTIVITY mol/L
1 330 1.000
8.00 9.00 10.00
Co_waste.prn 200
15.00 0. 0.00 0.000 0.00
4 1 7
7.000E-01 600.000.0000.00081
330 O.OOOE+00 -4.90 y /H+l
200 l.OOOE-03 -12.32 y /Co+2
150 1.700E+03 -2.92 y /Ca+2
460 l.OOOE+01 -3.24y /Mg+2
140 5.000E+01 -2.51 y /CO3-2
500 3.000E+02 -3.37 y /Na+
410 3.800E+02 -3.37y /K+
180 1.200E+03 -3.37y /Cl-1
732 1.400E+03 -3.58y /SO4-2
144 O.OOOE+00 -6.00 y /DOM1
811 2.363E-06 -4.45 y /ADS1TYP1
812 9.212e-05 -2.84 y /ADS1TYP2
813 O.OOOE+00 0.00 y /ADSlPSIo
3 1
330 4.0000 0.0000
6 1
813 0.0000 0.0000
/H+l
/ADSlPSIo
2 74
(same HFO reactions as for soil/water partitioning; see Appendix B)
E-3
-------
Estimate partitioning in CKD mono fill. Assumes 7.0gFeOOH/L
DLM; uses 3x site densities used in Ke97; Mean major ions.
17.00 MG/L 0.100 5.00000E+00
0110231010212
3H+1 ACTIVITY mol/L
1 330 1.000
9.00 10.00 11.00
Co_waste.prn 200
15.00 0. 0.00 0.000 0.00
4 1 7
7.000E-00 600.000.0000.00081
330 O.OOOE+00 -4.90 y
200 l.OOOE-03 -12.32 y
150 2.850E+03 -2.92 y
460 l.OOOE+01
140 5.000E+01
500 3.000E+02
410 4.000E+02
180 3.800E+02
732 6.300E+02
144 O.OOOE+00
811 2.363E-05
812 9.212e-04
813 O.OOOE+00
-3.24y
-2.51 y
-3.37 y
-3.37 y
-3.37 y
-3.58 y
-6.00 y
-4.45 y
-2.84 y
0.00 y
/H+l
/Co+2
/Ca+2
/Mg+2
/CO3-2
/Na+
/K+
/Cl-1
/SO4-2
/DOM1
/ADS1TYP1
/ADS1TYP2
/ADSlPSIo
3 1
330 4.0000 0.0000 /H+l
6 1
813 0.0000 0.0000 /ADSlPSIo
2 74
(same HFO reactions as for soil/water partitioning; see Appendix B)
E-4
------- |