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


1

HydroGeoLogic, Inc


1155 Herndon Parkway, Suite 900


Herndon, VA 20170


2

Allison 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 
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. 

ii 
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), multistressor, 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 

iii 
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 
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 

APPENDIX B 

APPENDIX C 

APPENDIX D 

APPENDIX E 

METAL PARTITION COEFFICIENTS USED IN SOME 

RECENT U.S. EPA RISK ASSESSMENTS 

SCATTER PLOTS FOR LINEAR REGRESSIONS USED TO 

ESTIMATE MEAN LOG K IN NATURAL MEDIA 

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL 

USED TO ESTIMATE METAL PARTITIONING IN 

SOIL/SOIL WATER SYSTEMS 

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL 

USED TO ESTIMATE METAL PARTITIONING TO DOC 

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL 

USED TO ESTIMATE METAL PARTITIONING IN 

WASTE MANAGEMENT SYSTEMS 

v 
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 
LIST OF FIGURES


page 

Figure B-1 Data used to develop regression equation to predict sediment Kd 

from soil Kd .................................................. B-1 

Figure B-2 Data used to develop regression equation to predict sediment Kd 

from suspended matter Kd ....................................... B-1 

Figure B-3 Data used to develop regression equation to predict soil Kd 

from suspended matter Kd (and vice versa) .......................... B-2 

Figure B-4 Data used to develop regression equation to predict waste Kd 

from soil Kd .................................................. B-2 

vii 
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 (Kd; 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. 

(1) 

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 Kd 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 Kd 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, Kd 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 Kd 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

(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 

2-1

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 (1987 - present) 

• Current Contents (1992 - present) 

• Dissertation Abstracts (1981 - present) 

• Ecology Abstracts (1982 - present) 

• EIS Digest of Environmental Impact Statements (1985 - present) 

• EI 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 

2-2

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: 

C 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) 

C 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) 

C 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 Kd is less likely to depend on metal concentration) 

C pH values in the natural range (4 to 10)


C No organic chelates in the extractant (e.g., EDTA)


C One partition coefficient per system studied


C 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. 

C 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 Kd 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 Kd values reported in previously 

published compilations of Kd values (Baes and Sharp, 1983; Baes et al., 1984; Coughtrey et al., 

2-3

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 Kd 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. 

2-4

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 Soil/Water 

Suspended 

Matter 

/Water 

Sediment/ 

Water DOC/Water 

Ag 

median 

2.6 4.9 3.6 

range 1.0 - 4.5 4.4 - 6.3 2.1 - 5.8 

N 21 15 

As 

median 

3.4 4.0 2.5 

range 0.3 - 4.3 2.0 - 6.0 1.6 - 4.3 

N 22 25 18 

Ba 

median 

4.0 

range 0.7 - 3.4 2.9 - 4.5 

N 14 

Be 

median 

3.1 4.1 

range 1.7 - 4.1 2.8 - 6.8 

N 2 17 

Cd 

median 

2.9 4.7 3.6 5.2 

range 0.1 - 5.0 2.8 - 6.3 0.5 - 7.3 3.4 - 5.5 

N 41 67 21 4 

Co 

median 

2.1 4.7 3.3 4.5 

range (-1.2) - 4.1 3.2 - 6.3 2.9 - 3.6 2.9 - 4.8 

N 11 29 3 2 

2-5

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 Soil/Water 

Suspended 

Matter 

/Water 

Sediment/ 

Water DOC/Water 

Cr(III) 

median 

3.9 5.1 4.5 

range 1.0 - 4.7 3.9 - 6.0 

N 43 25 

Cr(VI) 

median 

1.1 

range (-0.7) - 3.3 

N 24 

Cu 

median 

2.7 4.7 4.2 5.5 

range 0.1 - 3.6 3.1 - 6.1 0.7 - 6.2 2.5 - 7.0 

N 20 70 12 17 

Hg 

median 

3.8 5.3 4.9 5.3 

range 2.2 - 5.8 4.2 - 6.9 3.8 - 6.0 5.3 - 5.6 

N 17 35 2 3 

CH3Hg 

median 

2.8 5.4 3.6 

range 1.3 - 4.8 4.2 - 6.2 2.8 - 5.0 

N 11 2 4 

Mo 

median 

1.1 2.5 

range (-0.2) - 2.7 

N 8 

2-6

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 Soil/Water 

Suspended 

Matter 

/Water 

Sediment/ 

Water DOC/Water 

Ni 

median 

3.1 4.6 4.0 5.1 

range 1.0 - 3.8 3.5 - 5.7 4.7 - 5.4 

N 18 30 4 

Pb 

median 

4.2 5.6 5.1 5.0 

range 0.7 - 5.0 3.4 - 6.5 2.0 - 7.0 3.8 - 5.6 

N 33 48 24 9 

Sb 

median 

2.4 4.0 

range 0.1 - 2.7 2.5 - 4.8 2.7 - 4.3 

N 3 

Se

 median 1.0 3.6 

range -0.3 - 2.4 3.1 - 4.7 

N 23 

Sn 

median 

2.9 5.6 4.7 

range 2.1 - 4.0 4.9 - 6.3 

N 3 

Tl 

median 

3.2 

range 3.0 - 3.5 

N 6 

2-7

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 Soil/Water 

Suspended 

Matter 

/Water 

Sediment/ 

Water DOC/Water 

V 

median 

range 1.1 - 2.7 

N 

Zn 

median 

3.1 5.1 3.7 4.9 

range (-1.0) - 5.0 3.5 - 6.9 1.5 - 6.2 4.6 - 6.4 

N 21 75 18 9 

CN 

median 

3.0 

range 0.7 - 3.6 

N 3 

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 Kd values obtained from the literature as reported herein. 

2-8

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 mediatype. 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 Kd values for 

Cr(III) in soil included values obtained in a pH titration of three soils such that each of the three 

was represented by eight different Kd values. Although they provide interesting data on the 

dependence of Kd 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 Kd associated with the pH value closest to the ambient soil pH was chosen. If 

the ambient soil pH was not specified, then a single Kd value was picked randomly from among 

those presented and the other Kd 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 Kd. 

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 

3-1

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 Kd 

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 Kd, SPM > Kd,Sediment > Kd,Soil. In two other 

cases where at least two of the Kd types could be characterized from the literature data, both 

conformed to this same pattern. In addition, a somewhat consistent progression in Kd 

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 Kd 

values from highest to lowest magnitude for each medium): 

Soils: Pb > CrIII > Hg > As > Zn = Ni > Cd > Cu > Ag > Co 

Sediment: Pb > Hg > CrIII > Cu > Ni > Zn > Cd > Ag > Co > As 

SPM: Pb > Hg > CrIII = Zn > Ag > Cu = Cd = Co > Ni> As 

There was some shuffling about of the Kd 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 Kd 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 Kd 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 Kd 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 Kd 

values in soils and suspended matter for the nine metals Ag, Cd, Co, Cr(III), Cu, Hg, Ni, Pb, 

and Zn. For each of these metals, the mean value of Kd 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 Kd values for metals for which the literature provided an 

estimate of mean Kd in soil, but not in suspended matter. In a similar manner, linear regression 

3-2

equations were developed to estimate the mean Kd in sediment from the literature estimate of 

mean Kd in soil or suspended matter, or the mean soil Kd 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 Kd 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 (r

2

), 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 Kd values (L/kg) in natural media. 

Used to Independent slope intercept

Estimate Variable ( +/- 95% CI) (+/- 95% CI) r2 N 

mean log Kd mean log Kd 1.080 0.796 0.7 5 

sediment soil (1.035) (3.190) 9 

mean log Kd mean log Kd 1.418 -3.179 0.6 5 

sediment suspended (1.923) (9.868) 5 

matter 

mean log Kd mean log Kd 0.380 3.889 0.3 9 

suspended soil (0.444) (1.338) 7 

matter 

mean log Kd mean log Kd 0.969 -1.903 0.3 9 

soil suspended (1.136) (5.703) 7 

matter 

The regression equations were also used to estimate mean Kd values for suspended matter and 

sediments from an estimate of the mean Kd 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 soilwater 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 Kd 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 Kd 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 Kd 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 Kd 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 interactions—the 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(III), Ni, Pb, and Zn. For 

other metal cations of interest (Ag, Co, Hg(II), Sn(II), and Tl(I)), it was necessary to estimate 

the mean log K for DOC binding for use with the Gaussian model. For Hg(II), 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). 

(2) 

As derived, Equation (2) has a correlation coefficient (r

2

) of 0.95, and produces an estimate of 

9.0 for the mean log K for Hg2+ binding with DOC (mean log KDOC,Hg). (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 Kd 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 KOH) 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: 

(3) 

The correlation coefficient (r

2

) 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 Kd values for binding to DOC for cationic metals only, and 

included conservative estimates of Kd 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 Kd 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 Kd 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 Kd 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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Ag (I) 2.6 2.6 0.8 1.0 4.5 From literature data (raw, 

n=21); log-normal; CL=1 

Asa 3.4 3.2 0.7 0.3 4.3 From literature data (raw, 

n=21); (log-normal 

assumed); oxidation state 

usually not specified in 

literature; CL=2 

Ba(II) 2.0 0.7 0.7 3.4 Suspended matter Kd 

regression equation for 

mean; (log-normal 

assumed); CL=2 

Be(II) 2.2 1.0 1.7 4.1 Suspended matter Kd 

regression equation for 

mean; (log-normal 

assumed); CL=3 

Cd(II) 2.9 2.7 0.8 0.1 5.0 From literature data (edited, 

n=37); log-normal; CL=1 

Co(II) 2.1 2.1 1.2 -1.2 4.1 From literature data (raw, 

n=11); log-normal; CL=1 

Cr(III) 3.9 3.8 0.4 1.0 4.7 From literature data (raw, 

n=22); log-normal; CL=2 

Cr(VI) 1.1 0.8 0.8 -0.7 3.3 From literature data (raw, 

n=24); (log-normal 

assumed); CL=2 

Cu(II) 2.7 2.5 0.6 0.1 3.6 From literature data (raw, 

n=20); log-normal; CL=1 

3-8

---

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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Hg(II) 3.8 3.6 0.7 2.2 5.8 From literature data (raw, 

n=17); log-normal; CL=1 

MeHg 2.8 2.7 0.6 1.3 4.8 From literature data (raw, 

n=11); log-normal; CL=2 

Mo(VI) 1.1 1.3 0.6 -0.4 2.7 From literature data (raw, 

n=5); (log-normal 

assumed); oxidation state 

not always specified in 

literature data; CL=3 

Ni(II) 3.1 2.9 0.5 1.0 3.8 From literature data (raw, 

n=19); log-normal; CL=1 

Pb(II) 4.1 3.7 1.2 0.7 5.0 From literature data (edited, 

n=31); (log-normal 

assumed); CL=2 

Sbb 2.3 1.1 0.1 2.7 From literature data (mean 

is the average of several 

reported mean values, n=5); 

(log-normal assumed); 

CL=4 

Se(IV)c 1.4 1.3 0.4 -0.3 2.4 From literature data (edited, 

n=11); (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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Se(VI) -0.2 1.1 -2.0 2.0 Mean estimated from 

MINTEQA2 result; (lognormal assumed); min, max 

from expert judgement; 

CL=4 

Sn(II) 2.7 0.7 2.1 4.0 From literature data; (lognormal assumed); CL=3 

Tl(I) 0.5 0.9 -1.2 1.5 Estimated from 

MINTEQA2 result; (lognormal assumed); CL=4 

V(V) 1.7 1.5 0.5 2.5 Mean, min, max from 

suspended matter Kd 

regression equation; (lognormal assumed); CL=4 

Zn(II) 3.1 2.7 1.0 -1.0 5.0 From literature data (raw, 

n=21); (log-normal 

assumed); CL=1 

CN- 0.7 1.6 -2.4 1.3 Estimated from 

MINTEQA2 result; (lognormal assumed); CL=4 

a Published partitioning data for As does not allow differentiation of As(III) and As(V). 

It is probable that published values represent results involving both oxidation states. 

b 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


c 
---

---

---

---

---

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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Ag(I) 3.6 1.1 2.1 5.8 Mean from soil Kd 

regression equation; (lognormal assumed); min, max 

from literature data; CL=3 

Asa 2.2 2.4 0.7 1.6 4.3 From literature data; lognormal; oxidation state not 

specified in literature data; 

CL=2 

Ba(II) 2.5 0.8 0.9 3.2 Mean, min, max from 

suspended matter Kd 

regression equation; (lognormal assumed); CL=3 

Be(II) 2.8 1.9 0.8 6.5 Mean, min, max from 

suspended matter Kd 

regression equation; (lognormal assumed); CL=3 

Cd(II) 3.7 3.3 1.8 0.5 7.3 From literature data (n=14, 

edited); log-normal; CL=1 

Co(II) 3.1 1.0 2.9 3.6 Mean from soil Kd 

regression equation; (lognormal assumed); min, max 

from literature data; CL=3 

Cr(III) 4.9 1.5 1.9 5.9 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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Cr(VI) 1.7 1.4 0.0 4.4 Mean, min, max from soil 

Kd regression equation; 

(log-normal assumed); 

CL=4 

Cu(II) 4.1 3.5 1.7 0.7 6.2 From literature data (raw, n 

= 12); log-normal; CL=1 

Hg(II) 4.9 0.6 3.8 6.0 From literature data (raw, 

n=2); (log-normal 

assumed); CL=2 

MeHg 3.9 0.5 2.8 5.0 From literature data (edited, 

n=2); (log-normal 

assumed); CL=2 

Mo(VI) 2.5 0.8 0.4 3.7 Mean from literature data 

(reported mean value with 

oxidation state not 

specified); (log-normal 

assumed); min, max from 

soil Kd regression equation; 

CL=4 

Ni(II) 3.9 1.8 0.3 4.0 Mean from soil Kd 

regression equation; (lognormal assumed); min, max 

from literature data; CL=3 

Pb(II) 5.1 4.6 1.9 2.0 7.0 From literature data (edited, 

n=14); log-normal; CL=1 

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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

Sbb 3.6 1.8 0.6 4.8 From literature data 

(reported mean value); (lognormal assumed); CL=4 

Se(IV) 3.6 1.2 1.0 4.0 Mean from literature data 

(reported mean value); (lognormal assumed); min, max 

from expert judgement; 

CL=4 

Se(VI) 0.6 1.2 -1.4 3.0 Mean, min, max from soil 

Kd regression equation; 

(log-normal assumed); 

CL=4 

Sn(II) 3.7 0.7 3.1 5.1 Mean, min, max from soil 

Kd regression equation; 

(log-normal assumed); 

CL=3 

Tl(I) 1.3 1.1 -0.5 3.5 Mean, min from soil Kd 

regression equation; (lognormal assumed); max 

from literature data; CL=4 

V(V) 2.1 0.9 0.4 3.2 Mean, min, max from 

suspended matter Kd 

regression equation; (lognormal assumed); CL=4 

Zn(II) 4.8 4.1 1.6 1.5 6.2 From literature data (edited, 

n=13); (log-normal 

assumed); CL=1 

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 lognormal has been assumed. Relative confidence in the data is indicated by the CL value 

of 1 to 4 (1=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 

CN- 1.6 1.7 -1.8 2.2 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

---

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 logtransformed 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 (1=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 

Ag(I) 5.2 5.2 0.6 4.4 6.3 From literature data (edited, n=9); 

log-normal; CL = 2 

Asa 4.0 3.9 0.5 2.0 6.0 From literature data (raw, n=25); 

(log-normal assumed); oxidation 

state not specified in the literature 

data; CL=2 

Ba(II) 4.0 4.0 0.4 2.9 4.5 From literature data (raw, n=14); 

log-normal; CL=2 

Be(II) 4.1 4.2 0.7 2.8 6.8 From literature data (raw, n=17); 

log-normal; CL=2 

Cd(II) 5.0 4.9 0.6 2.8 6.3 From literature data (edited, 

n=38); log-normal; CL=1 

Co(II) 4.7 4.8 0.8 3.2 6.3 From literature data (edited, 

n=20); log-normal; CL=1 

Cr(III) 5.1 5.1 0.4 3.9 6.0 From literature data (raw, n=25); 

log-normal; assumes unspecified 

oxidation state is (III); CL=2 

Cr(VI) 4.2 0.5 3.6 5.1 Mean, min, max from soil Kd 

regression equation; (log-normal 

assumed); CL=4 

Cu(II) 4.7 4.7 0.4 3.1 6.1 From literature data (edited, 

n=42); log-normal; CL=1 

Hg(II)b 5.3 5.3 0.4 4.2 6.9 From literature data (edited, 

n=26); log-normal; CL=1 

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 logtransformed 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 (1=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 

MeHg 4.9 0.7 4.2 6.2 Mean from soil Kd regression 

equation; (log-normal assumed); 

min, max from literature data; 

CL=3 

Ni(II)b 4.3 4.4 0.4 3.5 5.7 From literature data (edited, 

n=25); log-normal; CL=1 

Mo(VI) 4.4 1.0 3.7 4.9 Mean, min, max from soil Kd 

regression equation; (log-normal 

assumed); CL=4 

Pb(II)c 5.7 5.7 0.4 3.4 6.5 From literature data (edited, 

n=38); (log-normal assumed); 

CL=1 

Sbd 4.8 0.5 3.9 4.9 Mean, min, max from soil Kd 

regression equation; (log-normal 

assumed); CL=4 

Se(IV) 4.4 0.4 3.8 4.8 Mean, min, max from soil Kd 

regression equation; (log-normal 

assumed); CL=4 

Se(VI) 3.8 1.0 3.1 4.6 Mean, min, max from soil Kd 

regression equation; (log-normal 

assumed); CL=4 

Sn(II) 4.9 0.8 4.7 6.3 Mean, min from soil Kd regression 

equation; (log-normal assumed); 

max from literature data; CL=4 

3-16

---

---

---

c 

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 logtransformed 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 (1=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 

Tl(I) 4.1 1.0 3.0 4.5 Mean from soil Kd 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=1 

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. 

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

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 (1=highest, 4=lowest). 

Metal Mean 

Std. 

Dev. Min Max Comment 

Ag(I) 2.5 1.0 1.5 4.5 Mean estimated from MINTEQA2 

results; other parameters from expert 

judgement; (log-normal assumed); 

CL=3 

As 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

(log-normal assumed); CL=4 

Ba(II) 3.6 1.0 2.5 4.0 Mean estimated from MINTEQA2 

results, values for other parameters from 

expert judgement; (log-normal 

assumed); CL=3 

Be(II) 2.1 1.0 1.1 3.8 All parameters estimated from 

MINTEQA2 results; CL=3 

Cd(II) 3.8 0.9 2.0 5.5 Mean estimated from MINTEQA2 

results; min, max from expert 

judgement; CL=3 

Co(II) 3.8 0.9 2.0 5.5 Mean estimated from MINTEQA2 

results; min, max from expert 

judgement; CL=3 

Cr(III) 1.1 1.6 -0.6 4.3 Mean estimated from MINTEQA2 

results; min, max from expert 

judgement; CL=4 

Cr(VI) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Cu(II) 5.4 1.1 2.5 7.0 From literature data (raw, n=17); (lognormal assumed); CL=2 

Hg(II) 5.4 1.2 3.0 6.0 Mean from literature data (raw, n=3); 

(log-normal assumed); min, max from 

expert judgement; CL=4 

3-18

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 (1=highest, 4=lowest). 

Metal Mean 

Std. 

Dev. Min Max Comment 

MeHg 5.0 1.1 2.8 5.5 Mean, min, max estimated based on 

relative Kd’s of Hg(II) and MeHg for 

suspended matter and Hg(II) Kd with 

DOC; (log-normal assumed); CL=4 

Ni(II) 3.7 0.9 1.9 5.4 Mean estimated from MINTEQA2 

results; min, max from expert 

judgement; (log-normal assumed); 

CL=3 

Mo(VI) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Pb(II) 4.9 0.5 3.8 5.6 From literature data (raw, n=9); (lognormal assumed); CL=2 

Sb 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Se(IV) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Se(VI) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Sn(II) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Tl(I) 1.6 1.0 0.0 3.0 Mean estimated from MINTEQA2, 

values for other parameters from expert 

judgement; (log-normal assumed); 

CL=4 

3-19

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 (1=highest, 4=lowest). 

Metal Mean 

Std. 

Dev. Min Max Comment 

V(V) 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

Zn(II) 5.1 0.7 4.6 6.4 From literature data (raw, n=9); (lognormal assumed); CL=3 

CN­ 2.0 1.0 0.0 3.0 No data, values from expert judgement 

(conservative); (log-normal assumed); 

CL=4 

3-20

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

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

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 N Mean Range 

As 11 2.8 1.0 - 5.1 

Ba 7 3.0 1.8 - 3.7 

Be 2 2.8 2.7 - 6.8 

Cd 31 1.3 0 - 3.9 

Co 6 2.8 1.6 - 3.8 

Cr(III) 27 3.0 0.6 - 6.2 

Cr(VI) 6 4.1 2.2 - 6.2 

Cu 16 3.3 2.0 - 5.1 

Hg 8 3.1 1.7 - 4.4 

Ni 12 2.3 1.3 - 4.7 

Pb 31 2.7 0.0 - 4.9 

Sb 4 2.7 1.7 - 3.2 

V 4 2.9 2.7 - 3.1 

Zn 23 2.6 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 Kd from soil Kd for use with metals for which little or no 

data was found. We chose to use soil Kd 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 Kd. (This apparent dependence of Kd on solid to liquid 

ratio has been noted in other studies and is sometimes referred to as the “particle concentration 

3-23

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: 

(4) 

This relationship has a rather low correlation coefficient (r2) of 0.4 implying that only 40% of the 

variation in log Kd,waste 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 monofill containing ash from incineration of 

municipal solid waste (MSWI ash), and a monofill 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 1.35x10-4 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

value of 0.7 g/L, and using 7 g/L as a reasonable upper-range value. In both cases, a site density 

1.35x10-4 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 

Scenario 

MSW 

Acetogenic 

MSW 

Methanogenic 

MSWI 

Ash 

Monofill 

CKD 

Monofill 

pH 4.5, 6.1, 7.5a 7.5, 8.0, 9.0a 8.0, 9.0, 

10.0b 

9.0, 10.0, 

11.0c 

Ionic Strength 

(M) 

0.1c 0.1c 0.1c 0.1c 

Ca (mg/L) 6000d 975d 1,700b 2850f 

Mg 

(mg/L) 

625d 500d 10b 10f 

3-25

c 

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 

Scenario 

MSW 

Acetogenic 

MSW 

Methanogenic 

MSWI 

Ash 

Monofill 

CKD 

Monofill 

Na (mg/L) 1350e 1350e 300b 300f 

K (mg/L) 1100e 1100e 380b 400f 

CO3 (mg/L) 500c 250c 50c 50f 

Cl (mg/L) 2100e 2100e 1,200b 380f 

Fe (mg/L) 780e 0 0 0 

SO4 (mg/L) 500d 80d 1,400b 630f 

DOC (mg/L) 100c 50c 15c 15c 

POC (mg/L) 100,000c 50,000c 0 0 

a Minimum, average, and maximum values reported in Ehrig (1992). 

b Obtained from analysis of MSWI data obtained in our literature survey. 

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 Kd 

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

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 

Estimated log Kd (L/kg) 

MSW 

Acetogenesis 

MSW 

Methanogenesis 

MSWI 

Ash 

Monofill 

CKD 

Monofill 

Be 0.8 - 3.9 3.3 - 4.4 (-0.4) - 4.0 (-2.7) - 2.4 

Cd (-0.3) - 0.0 0.6 - 1.7 (-1.0) - 1.1 (-0.4) - 1.2 

Co 0.2 - 0.3 0.9 - 1.8 (-0.9) - 0.4 (-2.0) - 0.2 

Cr(III) 1.1 - 3.5 3.8 - 4.8 (-0.2) - 3.2 (-2.5) - 2.3 

Cu 1.1 - 1.9 2.0 - 2.5 0.0 - 2.9 (-2.0) - 2.1 

Ni 0.2 - 0.4 1.1 - 1.9 (-0.04) -

1.1 

(- 1.5) - 0.9 

Pb 1.7 - 2.7 3.3 - 4.2 2.4 - 3.6 0.7 - 3.4 

Zn 0.4 - 0.7 1.5 - 2.1 (-0.6) - 1.3 (-2.7) - 1.1 

We compared the partition coefficients estimated for wastes using MINTEQA2 with values 

predicted by the previously discussed regression equation (log Kd,waste = 0.7 log Kd,soil + 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 Kd 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(III), 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

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

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 Kd 

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 Kd values obtained from the literature are almost certainly not 

representative of the true national frequency distribution of Kd 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 Kd for a particular metal in different media types, and in the ranked magnitude of Kd 

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 Kd values 

obtained from the literature. Sources of uncertainty in individual literature Kd values include: 

C Detection limits in measuring metal concentrations can result in limiting the observed maximum 

Kd value. 

C 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

Equilibrium may or may not have been attained, and unaccounted-for non-equilibrium processes 

may have occurred. 

C Some variability in collected Kd 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). 

C Some variability in collected Kd 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 Kd 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. 

C 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 Kd 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. 

C There is uncertainty in the Kd values due to neglecting the impact of total system metal 

concentration on the magnitude of Kd. Numerous studies have documented the dependence of 

Kd on total metal concentration—Kd 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 Kd on total metal concentration. It is assumed that the Kd 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 Kd 

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 Kd 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 (Bäverman 

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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5-19

APPENDIX A 

METAL PARTITION COEFFICIENTS USED IN 

SOME RECENT U.S. EPA RISK ASSESSMENTS 
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Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single Range 

Value 

Ag US EPA Region soil/water 0.1 - 110 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 0.1 - 110 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment/wa 0.1 - 110 

6 Combustion ter 

[USEPA96b] 

S oil-Screening soil/water 0.1 - 110 

Guida nc e 

[USEPA96c] 

Sewage Sludge soil/water 290 

Rule, Round 2 

[USEPA96d] 

As US EPA Region soil/water 25 - 31 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 25 - 31 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment/wa 25 - 31 

6 Combustion ter 

[USEPA96b] 

Sewage Sludge waste/leach 20 

Rule [USEPA92] ate 

Sewage Sludge suspended 63,700 

Rule [USEPA92] matter 

/water 

Soi l-Scr eening soil/water 25 - 31 

Guid an c e 

[USEPA96c] 

C h l o r i n a t e d soil/water 29 

Aliphatics Listing 

[USEPA?] 

A-1

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single 

Value 

Range 

Ba US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 11 - 52 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

11 - 52 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment/wa 

ter 

11 - 52 

S oil-Screening 

Guida nc e 

[USEPA96c] 

soil/water 11 - 52 

Sewage Sludge 

Rule, Round 2 

[USEPA96d] 

soil/water 6 

C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 530 

Be US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 23 -

100,000 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

23 -

100,000 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment/wa 

ter 

23 -

100,000 

Soil-Screening 

Gui da nc e 

[USEPA96c] 

soil/water 23 -

100,000 

Sewage Sludge 

Rule, Round 2 

[USEPA96d] 

soil/water 43 

A-2

---

---

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single 

Value 

Range 

Cd US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 15 - 4300 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

15 - 4300 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment 

/water 

15 - 4300 

Sewage Sludge 

Rule [USEPA92] 

waste/leach 

ate 

431 

Sewage Sludge 

Rule [USEPA92] 

suspended 

matter 

/water 

174,000 

Soil -Screening 

Guida nc e 

[USEPA96c] 

soil/water 15 - 4300 

C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 162 

Co C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 45 

Cr US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 1200 -

4.3E06 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

1200 -

4.3E06 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment 

/water 

1200 -

4.3E06 

Sewage Sludge 

Rule [USEPA92] 

waste/leach 

ate 

59 

Sewage Sludge 

Rule [USEPA92] 

suspended 

matter 

/water 

255,000 

A-3

---

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single 

Value 

Range 

Soil-Screening 

G ui danc e 

[USEPA96c] 

soil/water 1200 -

4.3E06 

Cr(VI) US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 14 - 31 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

14 - 31 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment 

/water 

14 - 31 

Soil-Screening 

Gu idanc e 

[USEPA96c] 

soil/water 14 - 31 

C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 18 

Cu Sewage Sludge 

Rule [USEPA92] 

waste/leach 

ate 

98 

Sewage Sludge 

Rule [USEPA92] 

suspended 

matter 

/water 

132,000 

C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 22 

Hg US EPA Region 

6 Combustion 

[USEPA96b] 

soil/water 1000 

US EPA Region 

6 Combustion 

[USEPA96b] 

suspended 

matter 

/water 

1000 

US EPA Region 

6 Combustion 

[USEPA96b] 

sediment 

/water 

3000 

A-4

---

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single Range 

Value 

Sewage Sludge waste/leach 330 

Rule [USEPA92] ate 

Sewage Sludge suspended 125,000 

Rule [USEPA92] matter 

/water 

Soil -Screenin g soil/water 0.04 - 200 

G ui danc e 

[USEPA96c] 

Mo C h l o r i n a t e d soil/water 20 

Aliphatics Listing 

[USEPA?] 

Ni US EPA Region soil/water 16 - 1900 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 16 - 1900 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment 16 - 1900 

6 Combustion /water 

[USEPA96b] 

Sewage Sludge waste/leach 63 

Rule [USEPA92] ate 

S ewage Sludge suspended 100,000 

Rule [USEPA92] matter 

/water 

Soil -Screening soil/water 16 - 1900 

Gu id anc e 

[USEPA96c] 

C h l o r i n a t e d soil/water 82 

Aliphatics Listing 

[USEPA?] 

Pb US EPA Region soil/water 900 

6 Combustion 

[USEPA96b] 

A-5

---

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single Range 

Value 

US EPA Region suspended 900 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment 900 

6 Combustion /water 

[USEPA96b] 

Sewage Sludge waste/leach 621 

Rule [USEPA92] ate 

Sewage Sludge suspended 185,000 

Rule [USEPA92] matter 

/water 

C h l o r i n a t e d soil/water 280,000 

Aliphatics Listing 

[USEPA?] 

Sb US EPA Region soil/water 45 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 45 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment 45 

6 Combustion /water 

[USEPA96b] 

Soil-Screeni ng soil/water 45 

Guida nc e 

[USEPA96c] 

Sewage Sludge soil/water 6 

Rule, Round 2 

[USEPA96d] 

Se US EPA Region soil/water 2.2 - 18 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 2.2 -18 

6 Combustion matter 

[USEPA96b] /water 

A-6

---

---

---

---

---

---

---

---

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single Range 

Value 

US EPA Region sediment 2.2 - 18 

6 Combustion /water 

[USEPA96b] 

Soil-Screening soil/water 2.2 - 18 

Gu idanc e 

[USEPA96c] 

Tl US EPA Region soil/water 44 - 96 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 44 - 96 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment 44 - 96 

6 Combustion /water 

[USEPA96b] 

Soil-Screening soil/water 44 - 96 

G uidan c e 

[USEPA96c] 

V So il-Screenin g soil/water 1000 

G ui danc e 

[USEPA96c] 

Sewage Sludge soil/water 39 

Rule, Round 2 

[USEPA96d] 

C h l o r i n a t e d soil/water 50 

Aliphatics Listing 

[USEPA?] 

Zn US EPA Region soil/water 62 

6 Combustion 

[USEPA96b] 

US EPA Region suspended 62 

6 Combustion matter 

[USEPA96b] /water 

US EPA Region sediment 62 

6 Combustion /water 

[USEPA96b] 

A-7

---

---

---

---

Metal Risk 

Assessment 

Type of Kd Kd(L/kg) 

Single 

Value 

Range 

Soil-Screening 

G ui danc e 

[USEPA96c] 

soil/water 16 - 530 

C h l o r i n a t e d 

Aliphatics Listing 

[USEPA?] 

soil/water 40 

CN Soi l-Screenin g 

Gu id anc e 

[USEPA96c] 

soil/water 9.9 

Sewage Sludge 

Rule, Round 2 

[USEPA96d] 

soil/water 0.0014 

A-8

APPENDIX B 

SCATTER PLOTS FOR 

LINEAR REGRESSIONS USED TO 

ESTIMATE MEAN LOG K VALUES 
Figure B-1. Data used to develop regression equation to 

predict sediment Kd from soil Kd. 

Figure B-2. Data used to develop regression equation to 

predict sediment Kd from suspended matter Kd. 

B-1

Figure B-3. Data used to develop regression equation to 

predict soil Kd from suspended matter Kd (and vice versa). 

Figure B-4. Data used to develop regression equation to 

predict waste Kd from soil Kd. 

B-2

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 , Md pH 

17.00 MG/L 0.010 3.56000E+00 

0 1 1 0 2 3 1 0 1 0 0 1 2 

Co_soil.prn 200

 5.00 19100. 145.00 0.000 0.00 

4 1 7 

5.940E-01 600.00 0.000 0.000 81

 330 0.000E+00 -4.90 y /H+1 

200 1.000E-03 -12.32 y /Co+2 

150 1.320E+01 -2.92 y /Ca+2 

460 3.600E+00 -3.24 y /Mg+2 

410 1.200E+00 -4.13 y /K+1 

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 0.000E+00 -6.00 y /DOM1 


145 0.000E+00 -6.00 y /DOM1 


811 3.337E-05 -4.45 y /ADS1TYP1 


812 1.335E-03 -2.84 y /ADS1TYP2 


813 0.000E+00 0.00 y /ADS1PSIo 


3 1


 330 6.5000 0.0000 /H+1 


6 1


 813 0.0000 0.0000 /ADS1PSIo 


2 74 

8113302 =FeOH2+ 0.0000 7.2900 0.000 0.000 1.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 330 1.000 813 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 

8113301 =FeO- 0.0000 -8.9300 0.000 0.000-1.00 0.00 0.00 0.0000

 0.00 3 1.000 811 -1.000 330 -1.000 813 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 

8123302 =FeOH2+ 0.0000 7.2900 0.000 0.000 1.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 330 1.000 813 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 

8123301 =FeO- 0.0000 -8.9300 0.000 0.000-1.00 0.00 0.00 0.0000

 0.00 3 1.000 812 -1.000 330 -1.000 813 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-1

8111000 =FeOHBa+2 0.0000 5.4600 0.000 0.000 2.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 100 2.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 100 -1.000 330 1.000 813 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 0.000 2.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 150 2.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 150 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 460 -1.000 330 1.000 813 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 870 -1.000 330 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 870 -1.000 330 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 20 -1.000 330 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 20 -1.000 330 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 540 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 540 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 200 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 200 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 160 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 160 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 950 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 950 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 231 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 231 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 600 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 600 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 110 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 110 -1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 361 -2.000 2 1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 361 -2.000 2 1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 790 -2.000 2 1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 790 -2.000 2 1.000 330 1.000 813 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 0.000 1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 211 -1.000 2 1.000 813 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 811 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 812 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 811 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 812 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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 580 3.000 330 -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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 580 3.000 330 -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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 580 2.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000 

C-4

 0.00 5 1.000 812 1.000 580 2.000 330 -1.000 2 -1.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 580 1.000 330 -1.000 2 -2.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 580 1.000 330 -1.000 2 -2.000 813 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 811 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 0.000 0.00 0.00 0.00 0.0000

 0.00 3 1.000 812 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 61 -1.000 2 -1.000 330 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 61 -1.000 2 -1.000 330 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 903 -2.000 330 1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 903 -2.000 330 1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 732 1.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 732 1.000 330 -1.000 2 -1.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 732 -2.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 732 -2.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 761 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 761 -1.000 2 -1.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 761 -1.000 330 -2.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 761 -1.000 330 -2.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 762 1.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 762 1.000 330 -1.000 2 -1.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 762 -2.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 762 -2.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 212 1.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 212 1.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 811 1.000 480 1.000 330 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 5 1.000 812 1.000 480 1.000 330 -1.000 2 -1.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 480 -2.000 813 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 0.000-2.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 480 -2.000 813 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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 741 1.000 330 -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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 741 1.000 330 -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 0.000-1.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 741 -1.000 2 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 741 -1.000 2 -1.000 813 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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 811 1.000 143 1.000 330 -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 0.000 0.00 0.00 0.00 0.0000

 0.00 4 1.000 812 1.000 143 1.000 330 -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 0.000-1.00 0.00 0.00 0.0000

 0.00 3 1.000 811 1.000 143 -1.000 813 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 0.000-1.00 0.00 0.00 0.0000

 0.00 3 1.000 812 1.000 143 -1.000 813 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 , Md pH 

16.00 MG/L 0.000 8.90000E-06 

0 1 1 0 2 3 0 0 1 0 0 1 2 

Co_doc.prn 200

 0.00 8.90 144.00 0.000 

0 	 0 0

 330 0.000E+00 -4.90 y 

200 1.000E-03 -12.32 y 

150 1.320E+01 -2.92 y 

460 3.600E+00 -3.24 y 

410 1.200E+00 -4.13 y 

500 5.300E+00 -3.02 y 

492 6.010E+00 -3.00 y 

140 5.200E+01 -2.51 y 

180 5.700E+00 -3.37 y 

732 6.600E+00 -3.58 y 

144 0.000E+00 -6.00 y 

0.00 

/H+1 

/Co+2 

/Ca+2 

/Mg+2 

/K+1 

/Na+1 

/NO3-

/CO3-2 

/Cl-1 

/SO4-2 

/DOM1 

3 1


 330 7.3000 0.0000 /H+1 


D-1

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 

0 1 1 0 2 3 1 0 1 0 2 1 2

 3 H+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-01 600.00 0.000 0.000 81

 330 0.000E+00 -4.90 y /H+1 

200 1.000E-03 -12.32 y /Co+2 

150 6.000E+03 -2.92 y /Ca+2 

460 6.250E+02 -3.24 y /Mg+2 

140 5.000E+02 -2.51 y /CO3-2 

500 1.350E+03 -3.37 y /Na+ 

410 1.100E+03 -3.37 y /K+

 180 2.100E+03 -3.37 y /Cl-1 

280 7.800E+02 -9.00 y /Fe+2

 732 5.000E+02 -3.58 y /SO4-2 

144 0.000E+00 -6.00 y /DOM1 

145 0.000E+00 -6.00 y /DOM1 

811 2.363E-06 -4.45 y /ADS1TYP1 

812 9.212e-05 -2.84 y /ADS1TYP2 

813 0.000E+00 0.00 y /ADS1PSIo 

3 1

 330 4.0000 0.0000 /H+1 

6 1

 813 0.0000 0.0000 /ADS1PSIo 

2 74 

(same HFO reactions as for soil/water partitioning; see Appendix B) 

E-1 
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 

0 1 1 0 2 3 1 0 1 0 2 1 2

 3 H+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-01 600.00 0.000 0.000 81

 330 0.000E+00 -4.90 y /H+1 


200 1.000E-03 -12.32 y /Co+2 


150 9.750E+02 -2.92 y /Ca+2 


460 5.000E+02 -3.24 y /Mg+2 


140 2.500E+02 -2.51 y /CO3-2 


500 1.350E+03 -3.37 y /Na+ 


410 1.100E+03 -3.37 y /K+


 180 2.100E+03 -3.37 y /Cl-1 


732 8.000E+01 -3.58 y /SO4-2 


144 0.000E+00 -6.00 y /DOM1 


145 0.000E+00 -6.00 y /DOM1 


811 2.363E-06 -4.45 y /ADS1TYP1 


812 9.212e-05 -2.84 y /ADS1TYP2 


813 0.000E+00 0.00 y /ADS1PSIo 


3 1


 330 7.0000 0.0000 /H+1 


6 1


 813 0.0000 0.0000 /ADS1PSIo 


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 

0 1 1 0 2 3 1 0 1 0 2 1 2

 3 H+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-01 600.00 0.000 0.000 81

 330 0.000E+00 -4.90 y /H+1 


200 1.000E-03 -12.32 y /Co+2 


150 1.700E+03 -2.92 y /Ca+2 


460 1.000E+01 -3.24 y /Mg+2 


140 5.000E+01 -2.51 y /CO3-2 


500 3.000E+02 -3.37 y /Na+ 


410 3.800E+02 -3.37 y /K+


 180 1.200E+03 -3.37 y /Cl-1 


732 1.400E+03 -3.58 y /SO4-2 


144 0.000E+00 -6.00 y /DOM1 


811 2.363E-06 -4.45 y /ADS1TYP1 


812 9.212e-05 -2.84 y /ADS1TYP2 


813 0.000E+00 0.00 y /ADS1PSIo 


3 1


 330 4.0000 0.0000 /H+1 


6 1


 813 0.0000 0.0000 /ADS1PSIo 


2 74 

(same HFO reactions as for soil/water partitioning; see Appendix B) 

E-3

Estimate partitioning in CKD monofill. Assumes 7.0gFeOOH/L 

DLM; uses 3x site densities used in Ke97; Mean major ions. 

17.00 MG/L 0.100 5.00000E+00 

0 1 1 0 2 3 1 0 1 0 2 1 2

 3 H+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-00 600.00 0.000 0.000 81

 330 0.000E+00 -4.90 y /H+1 


200 1.000E-03 -12.32 y /Co+2 


150 2.850E+03 -2.92 y /Ca+2 


460 1.000E+01 -3.24 y /Mg+2 


140 5.000E+01 -2.51 y /CO3-2 


500 3.000E+02 -3.37 y /Na+ 


410 4.000E+02 -3.37 y /K+


 180 3.800E+02 -3.37 y /Cl-1 


732 6.300E+02 -3.58 y /SO4-2 


144 0.000E+00 -6.00 y /DOM1 


811 2.363E-05 -4.45 y /ADS1TYP1 


812 9.212e-04 -2.84 y /ADS1TYP2 


813 0.000E+00 0.00 y /ADS1PSIo 


3 1


 330 4.0000 0.0000 /H+1 


6 1


 813 0.0000 0.0000 /ADS1PSIo 


2 74 

(same HFO reactions as for soil/water partitioning; see Appendix B) 

E-4