# PARTITION COEFFICIENTS FOR METALS

IN SURFACE WATER, SOIL, AND WASTE



# DRAFT

Work Assignment Manager Robert B. Ambrose, Jr., P.E. and Technical Direction: U.S. Environmental Protection Agency Office of Research and Development Athens, GA 30605

Prepared by: HydroGeoLogic, Inc 1155 Herndon Parkway, Suite 900 Herndon, VA 20170
and Allison Geoscience Consultants, Inc. 3920 Perry Lane Flowery Branch, GA 30542
Under Contract No. 68-C6-0020



# U.S. Environmental Protection Agency Office of Solid Waste Washington, DC 20460
June 22, 1999

### TABLE OF CONTENTS
page

1.0 INTRODUCTION AND BACKGROUND

........................ 1-1
2.0 LITERATURE SURVEY FOR METAL PARTITION COEFFICIENTS

..... 2-1
3.0 ANALYSIS OF RETRIEVED DATA AND DEVELOPMENT OF PARTITION COEFFICIENT VALUES

.......................... 3-1 3.1 DEVELOPMENT OF PARTITIONING COEFFICIENTS IN 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 PARTITIONING COEFFICIENTS FOR WASTE SYSTEMS

............................... 3-15 3.2.1 Estimation from Analysis of Data Presented in the Literature

... 3-16 3.2.2 Estimation from Geochemical Speciation Modeling

.......... 3-18
4.0 REFERENCES

......................................... 4-1
APPENDICES
APPENDIX A METAL PARTITION COEFFICIENTS USED IN SOME PREVIOUS

U.S. EPA RISK ASSESSMENTS
APPENDIX B EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO

ESTIMATE PARTITIONING IN SOIL
APPENDIX C EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO

ESTIMATE PARTITIONING WITH DOC
APPENDIX D EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO

ESTIMATE PARTITIONING WITH WASTE SYSTEMS

### LIST OF TABLES
page

Table 1 Median, range, and number of samples (N) for partition coefficients

(log K d 

### in L/kg) from the literature search

.................... 2-3 Table 2 Linear Regression Equations used to Estimate Mean log K d 

### Values (L/kg) in Natural media

................................ 3-3 Table 3 Metal partition coefficients (log K d 

### ) in kg/L for soil/soil water

....... 3-7 Table 4 Metal partition coefficients (log K d 

### ) in kg/L for sediment/porewater

.... 3-9 Table 5 Metal partition coefficients (log K d 

### ) in kg/L for suspended matter/water

. 3-11 Table 6 Metal partition coefficients (log K d 

### ) in kg/L for partitioning between DOC and inorganic solution species

........................ 3-13 Table 7 Effective partition coefficients based on reported solid phase and

solution phase metals concentrations from leach tests reported in the literature

....................................... 3-17 Table 8 Important parameters and constituent concentrations used in MINTEQA2

modeling of landfills in the acetogenic and methanogenic stages and MSWI and CKD monofills

.............................. 3-19 Table 9 Estimated range in log partition coefficients (L/kg) for selected metals

determined from MINTEQA2 modeling

...................... 3-20

### 1-1



## 1.0 INTRODUCTION AND BACKGROUND The purpose of this study was to develop contaminant partition coefficients for the surface water pathway and for the source model used in the multimedia approach for the Hazardous Waste Identification Rule (HWIR). Partition coefficients for certain metal contaminants in environmental media are needed to perform multimedia exposure and risk assessment modeling for HWIR. The multimedia model includes a surface water pathway model that requires partition coefficients to account for removal of contaminant from the solution phase and retardation of contaminant movement. The contaminants of interest are the metals: 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 (Sn), vanadium (V), and zinc (Zn). Methylated mercury (CH 3 

### Hg + 

### ) and cyanide (CN) are also of interest. In the surface water pathway, the HWIR modeling scenario 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 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 HWIR modeling scenario 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 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 scenarios. This includes the collection of published partition coefficients for any of the metals in any of the environmental media of interest, or the 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 content, etc.) when these were reported. It was anticipated that the literature search would not supply partition coefficients for all of the metals in all of the environmental media of interest. In the second-phase effort, statistical methods, geochemical speciation modeling, and expert judgement were used to provide reasonable estimates of partition coefficients not available from the literature.

### 2-1



## 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 and some non-periodicals including books and technical reports published by the U.S. EPA and other 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, which were usually available on-line. Abstracts of citations that showed promise for providing partition coefficients were printed and given a

### 2-2

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 the 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 coefficients and other data from the articles were then entered into an EXCEL 97 spreadsheet for compilation and analysis. 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 are pH, total concentrations of metal and important metal complexing agents including DOC, and weight fraction of particulate organic matter and other sorbing materials. Physical parameters necessary to convert concentration ratios to partition coefficients in L/kg, including porosity, water content, and bulk density were also recorded when reported in the articles. Approximately 245 articles and reports were copied and reviewed. A total of 1170 individual K d 

### values were obtained from these sources directly or calculated from reported media concentrations. This total does not include mean estimated K d 

### values reported in previously published compilations of K d 

### values (Baes and Sharp, 1983; Baes et al., 1984; Coughtrey et al., 1985; Thibault et al., 1990). (The data from 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 obtained from the literature pertained to the metals Cd, Co, Cr, Cu, Hg, Ni, Pb, and Zn. More K d 

### ’s were recovered for Cd than any other metal, followed closely by Zn, Pb, and Cu. The most frequently reported type of K d 

### was that for suspended matter in streams, rivers and lakes. (Data pertaining to marine environments were generally avoided, but some data from estuaries were included if reported as corresponding to low salinity.) The second most frequently reported values pertained to partitioning in soil. Suspended matter and soil K d 

### ’s together totaled 68% of the reported data. Table 1 below shows the median and range of literature K d



### values for natural media for each metal and K d 

### type. (Shown as log K d 

### values.) 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 metals 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 disposal units containing a (usually unknown) mixture of materials. Most studies involving metal concentrations in waste are

### 2-3

concerned with predicting the metal concentration in leachate by means of a physical test (a leach test). Section 3.2 presents further findings with regard to leach tests and appropriate metal partition coefficients for waste systems.

### 2-4 Table 1 Median, range, and number of samples (N) for partition coefficients (log K d 

### in L/kg) from the literature search. Range or median specified without N are from a previous compilation. Open spaces represent combinations of media-type and metal for which either no data was found, or too little data was 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 2 0 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 4.5 range 1.7 - 4.1 2.8 - 6.9 3.0 - 5.3 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 4.6 range (-1.2) - 4.2 3.2 - 6.3 2.9 - 6.3 2.7 - 4.8 N 11 29 3 2 Cr(III) median 3.9 5.1 4.5 range 1.0 - 4.8 3.9 - 6.0 N 43 25 Cr(VI) median 1.1 range (-0.7) - 3.3 N 24 1 Cu median 2.7 4.7 4.2 5.5

### Table 1 (continued)

Median, range, and number of samples (N) for partition coefficients (log K d 

### in L/kg) from the literature search. Range or median specified without N are from a previous compilation. Open spaces represent combinations of media-type and metal for which either no data was found, or too little data was found to provide meaningful statistics.

Metal Soil/Water Suspended Matter /Water Sediment/

Water DOC/Water



### 2-5 range 0.2 - 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 5.4 range 2.2 - 5.8 4.2 - 6.9 3.8 - 6.0 5.3 - 5.6 N 17 35 2 3 CH 3 Hg median 2.8 5.4 3.9 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 Ni median 3.1 4.6 4.0 5.1 range 1.0 - 3.8 3.5 - 5.7 0.4 - 4.7 - 5.4 N 18 30 5 4 Pb median 4.2 5.6 5.1 5.1 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 6 3 Se median 2.1 4.2 3.6 range -0.3 - 2.4 3.2 - 4.7 N 23 2 Sn median 2.8 5.6 4.7 range 2.1 - 4.0 4.9 - 6.3 N 3

### Table 1 (continued)

Median, range, and number of samples (N) for partition coefficients (log K d 

### in L/kg) from the literature search. Range or median specified without N are from a previous compilation. Open spaces represent combinations of media-type and metal for which either no data was found, or too little data was found to provide meaningful statistics.

Metal Soil/Water Suspended Matter /Water Sediment/

Water DOC/Water



### 2-6 Tl median 3.2 range 3.0 - 3.5 N 6

### Table 1 (continued)

Median, range, and number of samples (N) for partition coefficients (log K d 

### in L/kg) from the literature search. Range or median specified without N are from a previous compilation. Open spaces represent combinations of media-type and metal for which either no data was found, or too little data was found to provide meaningful statistics.

Metal Soil/Water Suspended Matter /Water Sediment/

Water DOC/Water



### 2-7 V median range 0.6 - 2.7 N 1 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 K d



### values appearing elsewhere in the spreadsheet, nor were they included in the statistical summary of K d 

### values obtained from the literature.

### 3-1



## 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 for the partition coefficient for all metals in all media-types. 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 for each metal in each media-type. Statistical analysis of retrieved data, geochemical modeling, and expert judgement were all used in developing the partition coefficients. The nature of the available data for natural media systems and waste systems was different to the extent that it seemed best to consider these separately.

3.1 DEVELOPMENT OF PARTITIONING COEFFICIENTS IN NATURAL MEDIA In analyzing the partitioning data collected from the literature for soil and surface water systems, we attempted to identify the shape of the probability distribution for each metal in each medium. For a particular metal in a particular medium, the degree to which the literature sample is truly representative of the population of metal partition coefficients is dependent on the number of sample points, the actual variability of important medium properties that influence partitioning (pH, concentration of sorbing phases, etc.), and how well this variability is represented in the sample. In some cases, it was necessary to eliminate data points from the literature sample to avoid obvious bias. For example, the sample of literature K d



### 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 8 different K d 

### values. Although they provide interesting data on the dependence of K d 

### 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 K d 

### . Therefore, for each of these soils, one of the eight K d



### values was picked randomly and the other seven were discarded in deriving the probability distribution. In similar fashion, the sample of literature data for each metal and media-type was edited before attempting to identify the underlying distribution. Statistical tests were performed to determine the shape of the frequency distribution of K d 

### for each metal and media-type. The tests employed widely recognized techniques available in the statistical package Analyze-It (version 1.32), an module add-on for Microsoft EXCEL 97. In only a few cases were the data sufficient to identify the underlying distribution with any degree of certainty. Many of the samples, including the most complete samples (largest sample size), gave a positive test for normality after transforming the available data to log space, suggesting that the frequency distribution of the underlying population of K d 

### values for a particular metal in a particular medium is most likely log-normal. The Shapiro-Wilk test and the Kolmogorov-Smirnov test were used to test the log transformed 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

### 3-2

In some cases, there were too few representative data points in the sample to have confidence in the descriptive statistics of the data. In these cases, three methods were used to augment the available data in estimating the mean, standard deviation, and minimum and maximum K d 

### 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 of these 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 K d 

### in soil, sediment, and suspended matter, 12 of them exhibited the progression K d, SPM 

### > K d,sediment



### > K d,soil 

### (determined by comparison of mean values). In the two other cases where at least two of the K d 

### types could be characterized from the literature data, both conformed to this pattern. In addition, consistency was noted in the magnitude of K d 

### for metals within a specific media. For the best represented metals, the following K d 

### (affinity) patterns were observed (based on mean K d 

### ):
Soils: Pb > Cr III 

### > Hg > As > Zn = Ni > Cd > Cu > Ag > Co Sediment: Pb > Hg > Cr III 

### > Cu > Ni > Zn > Cd > Ag > Co > As SPM: Pb > Hg > Cr III 

### = Zn > Ag > Cu = Cd = Co > Ni> As There is some shuffling about of the affinity order among these media-types, as might be expected for a data set that is doubtlessly incomplete. It may be that the As affinity for soils in our literature sample is too high. 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). Pb and Hg have higher affinity than HSAB predicts. Certainly, there are multiple adsorbing surfaces present in all of these materials. The consistency of affinity relationships among these metals suggests that the distribution of K d 

### is partly due to characteristics unique to the metals themselves and partly due to characteristics associated with the sorbing surfaces. Regardless of the cause, it appears feasible to exploit these relationships to provide an estimate of K d 

### for a metal in one media if its value in another could be ascertained. For example, the literature data provided a reasonable number of samples of K d



### 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 values of K d 

### 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 that was exploited to estimate mean K d 

### values for metals for which the literature provided an estimate of mean K d



### in soil, but not in suspended matter (or the opposite). In a similar manner, linear regression equations were developed to estimate the mean K d 

### in sediment from the literature estimate of mean K d



### in soil or suspended matter, or the mean soil K d 

### from that in sediment or suspended matter. The regression equations were developed from cases where the literature survey data provided reasonable estimates of the mean K d 

### 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 was assumed to be log-normal so that the regression equations were actually based on mean log K d 

### and were

### 3-3

used to predict mean log K d 

### . The standard deviation was estimated from the mean and minimum values assuming the minimum value represents two standard deviations from the mean. It was also estimated in like manner using the mean and maximum values. 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 on which each equation is based, the correlation coefficient ( r 2 

### ), and the 95% confidence interval for the slope and intercept.
Table 2 Linear Regression Equations used to Estimate Mean log K d 

### Values (L/kg) in Natural media.

Used to Estimate Dependent Variable slope

( +/- 95% CI) intercept

(+/- 95% CI) r 2 

### N mean log K d sediment mean log K d

soil 1.080 (1.035) 0.796

(3.190) 0.79 5
mean log K d sediment mean log K d

suspended matter 1.418 (1.923) -3.179

(9.868) 0.65 5
mean log K d suspended matter mean log K d

soil 0.380 (0.444) 3.889

(1.338) 0.37 9
mean log K d suspended matter mean log K d

sediment 0.457 (0.619) 3.257

(2.555) 0.65 5
mean log K d soil mean log K d

sediment 0.728 (0.697) 0.071

(2.878) 0.79 5
mean log K d soil mean log K d

suspended matter 0.969 (1.136) -1.903

(5.703) 0.37 9



### The regressions equations were also used to estimate mean K d 

### values for suspended matter and sediments from an estimate of the mean K d 

### in soil obtained from geochemical speciation modeling.

3.1.2 Estimation From Geochemical Speciation Modeling Geochemical speciation modeling was used to estimate soil/water partitioning if regression equations could not be used. The partitioning of metal cations between DOC and 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 K d 

### values. The input data for MINTEQA2 was developed from various sources. Modeling details for soil partition coefficients

### 3-4

The concentrations of major ions were the average concentrations in river water as reported by Stumm and Morgan (1996). The soil water phosphate concentration was obtained from Bohn et al. (1979). The ionic strength was held constant at 0.005 M after a sensitivity test in the range 0.01 to 0.001 M revealed that the impact was significantly less than other important variables. Model values for several of the most significant variables affecting K d 

### were varied over reasonable ranges in order to capture the expected range in K d 

### . 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 was characterized by low, medium, and high 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 POC concentrations were obtained from analysis of shallow silt-loam soils from a soils database (Carsel et al., 1988 and R. Parrish, personal communication). The low, medium, and high values were as corresponds to the 10 th 

### , 50 th 

### , and 90

th 

### percentiles, respectively, for particulate organic matter (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 K d 

### , the HFO concentration was used as a calibrating variable. The low, medium, and high values were first 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 aquifers and soils. The mean K d 

### for Cd, Cu, Ni, Pb, and Zn were computed using these values in MINTEQA2. These were compared with mean K d 

### values for these metals in soil from the literature survey. The low, medium, and high HFO concentrations were scaled in subsequent modeling such that the mean K d



### value from MINTEQA2 was within the 95% confidence interval of the mean literature K d



### value for each of these metals. (Each MINTEQA2 execution resulted in 81 different K d



### values due to utilizing all different combinations of low, medium, and high for the four different master variables. The mean value from MINTEQA2 was taken as the average of the three K d



### values corresponding to the medium setting of pH, DOC, and HFO and the low POC; the medium settings of pH, DOC, and HFO, and the medium POC; and the medium settings of pH, DOC, and HFO, and the high setting of POC.) Appendix B shows a typical MINTEQA2 input file used in estimating K d 

### for soil/water.
The minimum and maximum K d 

### values were established from the available literature data and the MINTEQA2 results. The distribution was assumed to be log-normal. Once the mean log K d 

### value for a metal was established for soil from the modeling exercise, the regression equations were used to estimate mean values for sediment and suspended matter if these were lacking in the literature data. The standard deviation was estimated as described above for linear regression estimates. Modeling details for DOC partition coefficients

### 3-5 (1)

(2) 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 available 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 with the binding constants for humic- and fulvic acid (HA and FA, respectively) as given by Tipping (1994). The metals Cd, Cu, Ni, Pb, and Zn were represented in the database of HA and FA binding constants, so these were used to develop the regression relationship:

This relationship was derived with a correlation coefficient ( r 2 

### ) of 0.95. It was used to estimate a mean log K DOC
, Hg 

### of 9.0 for Hg+2 binding with DOC. The mean log K for the other cations (Ag + 

### , Co

2+ 

### , Sn

2+ 

### , and Tl + 

### ) were derived from a linear free energy relationship using the first hydrolysis constants (log K OH 

### ) and the binding constant for acetate (log K Acet 

### ). The log K OH



### and log K Acet



### for the metals Cd, Cu, Fe, Ni, Pb, and Zn were used to derive the following relationship:

The correlation coefficient ( r 2 

### ) for this relationship is 0.98. It was used to estimate the mean log K DOC 

### for Ag

+ 

### , Co

2+ 

### , Sn

2+ 

### , and Tl + 

### for use in MINTEQA2 modeling. The mean log K DOC 

### values estimated for these metals were 2.0, 3.3, 6.6, and 1.0, respectively. The estimation procedures outlined above cannot reliably be extended to anions. However, anions are typically not as strongly bound to organic matter. We therefore have used

### 3-6

MINTEQA2 to estimate K d 

### values with DOC for cationic metals only and have included conservative estimates for the anions based on judgement alone. The concentrations of major ions used in estimating metal-DOC binding using 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 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 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 value K d 

### value for the cations was represented by the value computed by MINTEQA2 when the pH and DOC concentration were set to their mean values in surface water. A typical MINTEQA2 input file used to estimate metal partitioning with DOC is shown in Appendix C. 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 is not possible to establish the shape of the distribution 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 the mean log K d



### , the mean value was estimated subjectively using expert judgement. Factors considered included any values obtained from the literature survey, reported mean values or ranges from precious compilations, similarities of behavior among metals, and qualitative statements from articles and reports. The minimum and maximum K d 

### values from the literature were used if reasonable values were available. Otherwise, these were also estimated by expert judgement. In either case, the standard deviation was estimated as described above for linear regression. 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 the 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 were assigned a CL

of 4. Many data were determined in circumstances that warrant 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 circumstances could be combined

### 3-7

with estimates from modeling, etc.). In these cases, a CL of 2 or 3 was assigned as seemed best. The metal partition coefficients in soil, sediment, suspended matter, and DOC are presented in Tables 3, 4, 5, and 6, respectively. The method used to arrive at each estimate is indicated for each metal and media-type, as is the subjectively assigned confidence level.

### Table 3

Metal partition coefficients (log K d 

### ) in kg/L for soil/soil water. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the Shapiro-Wilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4=lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments Ag (I) Log Normal 2.6 2.6 0.8 1.0 4.5 From literature data (raw, n=21); CL =1
As a (log-normal) 3.4 3.2 0.7 0.3 4.3 From literature data (raw, n=21); oxidation state usually not specified in literature; CL =2 Ba(II) (log-normal)

--- 2.0 0.7 0.7 3.4 Suspended matter K d regression equation for mean; CL =2 Be(II) (log-normal)

--- 2.2 1.0 1.7 4.1 Suspended matter K d regression equation for mean; CL =3 Cd(II) Log Normal 2.9 2.7 0.8 0.1 5.0 From literature data (edited, n=37); CL =1 Co(II) Log Normal 2.1 2.1 1.2 -1.2 4.1 From literature data (raw, n=11); CL =1 Cr(III) Log Normal 3.9 3.8 0.4 1.0 4.7 From literature data (raw, n=22); CL =2 Cr(VI) (log-normal) 1.1 0.8 0.8 -0.7 3.3 From literature data (raw, n=24); CL =2 Cu(II) Log Normal 2.7 2.5 0.6 0.1 3.6 From literature data (raw, n=20); CL =1 Hg(II) Log Normal 3.8 3.6 0.7 2.2 5.8 From literature data (raw, n=17); CL =1 MeHg Log Normal 2.8 2.7 0.6 1.3 4.8 From literature data (raw, n=11); CL =2 Mo(VI) (log-normal) 1.1 1.3 0.6 -0.4 2.7 From literature data (raw, n=5); oxidation state not always specified in literature data; CL =3 Ni(II) Log Normal 3.1 2.9 0.5 1.0 3.8 From literature data (raw, n=19); CL =1

### Table 3 (continued)

Metal partition coefficients (log K d 

### ) i n kg/L for soil/soil water. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the ShapiroWilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4=lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments Pb(II) (log-normal) 4.1 3.7 1.2 0.7 5.0 From literature data (edited, n=31); CL =2
Sb b (log-normal)

--- 2.3 1.1 0.1 2.7 From literature data (mean is the average of several reported mean values, n=5); CL =4 Se(IV) c (log-normal) 1.4 1.3 0.4 -0.3 2.4 From literature data (edited, n=11); CL =2 Se(VI) (log-normal)

--- -0.2 1.1 -2.0 2.0 Mean estimated from MINTEQA2 result; min, max are guesses; CL

=4 Sn(II) (log-normal)

--- 2.7 0.7 2.1 4.0 From literature data, CL =3 Tl(I) (log-normal)

--- 0.5 0.9 -1.2 1.5 Estimated from MINTEQA2 result. CL =4 V(V) (log-normal)

--- 1.7 1.5 0.5 2.5 Mean, min, max from suspended matter K d regression equation; CL

=4 Zn(II) (log-normal) 3.1 2.7 1.0 -1.0 5.0 From literature data (raw, n=21); CL =1
CN- (log-normal)

--- 0.7 1.6 -2.4 1.3 Estimated from MINTEQA2 result. 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).

c 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.

### Table 4

Metal partition coefficients (log K d 

### ) in kg/L for sediment/porewater. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the Shapiro-Wilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4= lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments Ag(I) (log-normal)

--- 3.6 1.1 2.1 5.8 Mean from soil K d regression equation; min, max from literature data; CL

=3
As a Log-normal 2.2 2.4 0.7 1.6 4.3 From literature data; Oxidation state not specified in literature data; CL

=2 Ba(II) (log-normal)

--- 2.5 0.8 0.9 3.2 Mean, min, max from suspended matter K d regression equation; CL

=3 Be(II) (log-normal)

--- 2.8 1.9 0.8 6.5 Mean, min, max from suspended matter K d regression equation; CL

=3 Cd(II) Log-normal 3.7 3.3 1.8 0.5 7.3 From literature data (n=14, edited); CL =1 Co(II) (log-normal)

--- 3.1 1.0 2.9 3.6 Mean from soil K d regression equation; min, max from literature data; CL

=3 Cr(III) (log-normal)

--- 4.9 1.5 1.9 5.9 Mean, min, max from soil K d regression equation; CL =4 Cr(VI) (log-normal)

--- 1.7 1.4 0.0 4.4 Mean, min, max from soil K d regression equation; CL =4 Cu(II) Log-normal 4.1 3.5 1.7 0.7 6.2 From literature data (raw, n = 12); CL =1 Hg(II) (log-normal)

--- 4.9 0.6 3.8 6.0 From literature data (raw, n=2); CL =2 MeHg (log-normal)

--- 3.9 0.5 2.8 5.0 From literature data (edited, n=2), CL =2 Mo(VI) (log-normal)

--- 2.5 0.8 0.4 3.7 Mean from literature data (reported mean value with oxidation state not specified); min, max from soil K d regression equation; CL

=4

### Table 4 (continued)

Metal partition coefficients (log K d 

### ) in kg/L for sediment/porewater. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the ShapiroWilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4= lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments Ni(II) (log-normal)

--- 3.9 1.8 0.3 4.0 Mean from soil K d regression equation; min, max from literature data; CL

=3 Pb(II) Log-normal 5.1 4.6 1.9 2.0 7.0 From literature data (edited, n=14); CL =1
Sb b (log-normal)

--- 3.6 1.8 0.6 4.8 From literature data (reported mean value); CL =4 Se(IV) (log-normal)

--- 3.6 1.2 1.0 4.0 Mean from literature data (reported mean value); min, max are guesses; CL =4 Se(VI) (log-normal)

--- 0.6 1.2 -1.4 3.0 Mean, min, max from soil K d regression equation; CL =4 Sn(II) (log-normal)

--- 3.7 0.7 3.1 5.1 Mean, min, max from soil K d regression equation; CL =3 Tl(I) (log-normal)

--- 1.3 1.1 -0.5 3.5 Mean, min from soil K d regression equation; max from literature data; CL

=4 V(V) (log-normal)

--- 2.1 0.9 0.4 3.2 Mean, min, max from suspended matter K d regression equation; CL

=4 Zn(II) (log-normal) 4.8 4.1 1.6 1.5 6.2 From literature data (edited, n=13); CL =1
CN- (log-normal)

--- 1.6 1.7 -1.8 2.2 Mean, min, max from soil K d regression equation; 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).

### Table 5

Metal partition coefficients (log K d 

### ) in kg/L for suspended matter/water. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the Shapiro-Wilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4= lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments Ag(I) Log-normal 5.2 5.2 0.6 4.4 6.3 From literature data (edited, n=9); CL = 2
As a (log-normal) 4.0 3.9 0.5 2.0 6.0 From literature data (raw, n=25); oxidation state not specified in the literature data; CL =2 Ba(II) Log-normal 4.0 4.0 0.4 2.9 4.5 From literature data (raw, n=14); CL =2 Be(II) Log-normal 4.1 4.2 0.7 2.8 6.8 From literature data (raw, n=17); CL =2 Cd(II) Log-normal 5.0 4.9 0.6 2.8 6.3 From literature data (edited, n=38); CL =1 Co(II) Log-normal 4.7 4.8 0.8 3.2 6.3 From literature data (edited, n=20); CL =1 Cr(III) Log-normal 5.1 5.1 0.4 3.9 6.0 From literature data (raw, n=25); assumes unspecified oxidation state is (III); CL =2 Cr(VI) (log-normal)

--- 4.2 0.5 3.6 5.1 Mean, min, max from soil K d regression equation; CL =4 Cu(II) Log-normal 4.7 4.7 0.4 3.1 6.1 From literature data (edited, n=42); CL =1 Hg(II) Log-normal b 5.3 5.3 0.4 4.2 6.9 From literature data (edited, n=26); CL =1 MeHg (log-normal)

--- 4.9 0.7 4.2 6.2 Mean from soil K d regression equation; min, max from literature data; CL

=3 Ni(II) Log-normal b 4.3 4.4 0.4 3.5 5.7 From literature data (edited, n=25); CL =1 Mo(VI) (log-normal)

--- 4.4 1.0 3.7 4.9 Mean, min, max from soil K d regression equation; CL =4 Pb(II) (log-normal) c 5.7 5.7 0.4 3.4 6.5 From literature data (edited, n=38); CL =1

### Table 5 (continued)

Metal partition coefficients (log K d 

### ) in kg/L for suspended matter/water. Values in italics were estimated by regression or from MINTEQA2 results. An entry of “log-normal” in bold indicates that the sample data gave a positive result in the Shapiro-Wilk test for normality of the log-transformed data. An entry of “log-normal” in parentheses means that data were not sufficient to establish the distribution, but log-normal has been assumed. Relative confidence in the data is indicated by the CL value of 1 to 4 (1=highest, 4= lowest).

Metal Distribution

(assumed) Median Mean Std. Dev. Min Max Comments
Sb d (log-normal)

--- 4.8 0.5 3.9 4.9 Mean, min, max from soil K d regression equation; CL =4 Se(IV) (log-normal)

--- 4.4 0.4 3.8 4.8 Mean, min, max from soil K d regression equation; CL =4 Se(VI) (log-normal)

--- 3.8 1.0 3.1 4.6 Mean, min, max from soil K d regression equation; CL =4 Sn(II) (log-normal)

--- 4.9 0.8 4.7 6.3 Mean, min from soil K d regression equation; max from literature data; CL

=4 Tl(I) (log-normal)

--- 4.1 1.0 3.0 4.5 Mean from soil K d regression equation; other parameters are guesses; CL =4 V(V) (log-normal)

--- 3.7 0.6 2.5 4.5 Mean from literature data (raw, n=5); min, max are guesses; oxidation state not always specified in literature; CL

=3 Zn(II) Log-normal 5.1 5.0 0.5 3.5 6.9 From literature data (edited, n=47); CL =1
CN- (log-normal)

--- 4.2 0.6 3.0 4.4 Mean, min, max from soil K d regression equation; CL =4

a Positive result for Shapiro-Wilk test for normality of data not log-transformed. But 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 lognormal character

c Failed Shapiro-Wilk and the Kolmogorov-Smirnov test for normality of log-transformed data, but histogram exhibits log-normal character d Published partitioning data for Sb is rare and does not allow differentiation of Sb(III) and Sb(V).

### Table 6

Metal partition coefficients (log K d 

### ) in kg/L 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 Distribution

(assumed) Mean Std. Dev. Min Max Comment A g(I) (log-normal) 2.5 1.0 1.5 4.5 Mean estimated from MINTEQA2 results; other parameters are guesses; CL = 3
As (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Ba(II) (log-normal) 3.6 1.0 2.5 4.0 Mean estimated from MINTEQA2 results, values for other parameters are guesses; CL =3 Be(II) (log-normal) 2.1 1.0 1.1 3.8 All parameters estimated from MINTEQA2 results; CL =3 Cd(II) (log-normal) 3.8 0.9 2.0 5.5 Mean estimated from MINTEQA2 results; min, max are guesses; CL

=3 Co(II) (log-normal) 3.8 0.9 2.0 5.5 Mean estimated from MINTEQA2 results; min, max are guesses; CL

=3 Cr(III) (log-normal) 1.1 1.6 -0.6 4.3 Mean estimated from MINTEQA2 results; min, max are guesses; CL

=4 Cr(VI) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Cu(II) (log-normal) 5.4 1.1 2.5 7.0 From literature data (raw, n=17); CL =2 Hg(II) (log-normal) 5.4 1.2 3.0 6.0 Mean from literature data (raw, n=3); min, max are guesses; CL =4 MeHg (log-normal) 5.0 1.1 2.8 5.5 Mean, min, max estimated based on relative K d ’s of Hg(II) and MeHg for suspended matter and Hg(II) K d with DOC. Ni(II) (log-normal) 3.7 0.9 1.9 5.4 Mean estimated from MINTEQA2 results; min, max are guesses; CL

=3 Mo(VI) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Pb(II) (log-normal) 4.9 0.5 3.8 5.6 From literature data (raw, n=9); CL =2

### Table 6 (continued)

Metal partition coefficients (log K d 

### ) in kg/L 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 Distribution

(assumed) Mean Std. Dev. Min Max Comment
Sb (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Se(IV) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Se(VI) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Sn(II) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Tl(I) (log-normal) 1.6 1.0 0.0 3.0 Mean estimated from MINTEQA2, values for other parameters are guesses; CL =4 V(V) (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4 Zn(II) (log-normal) 5.1 0.7 4.6 6.4 From literature data (raw, n=9); CL =3
CN- (log-normal) 2.0 1.0 0.0 3.0 No data, values are conservative guesses; CL =4

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### 3.2 DEVELOPMENT OF PARTITIONING COEFFICIENTS FOR WASTE SYSTEMS The multimedia, multi-pathway risk assessment for HWIR 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 leach test results for specific wastes. The 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 leach test data in predicting the leachate concentration from landfills (U.S. EPA, 1991). The U.S. EPA Toxicity Characteristic Leaching Procedure (TCLP) was specifically designed to predict leachate concentrations for wastes co-disposed with municipal solid waste. Recent papers present the idea 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): 1) leaching concentration controlled by initial wash-off of loosely adhered contaminant, 2) leaching concentration controlled by dissolution of primary materials and perhaps re-precipitation of more stable phases, and 3) leaching concentration controlled by the diffusion of waste constituent from the interior of waste particles to the particle surface. The time of onset and duration of these regimes is highly variable and is interrelated with the life-cycle of the waste system (acetogenesis, methanogenesis, etc.). The chemical composition (major ion concentration and concentration of metal-complexing organic ligands) is also important in determining the leaching concentration that will be observed in any particular case. In general, it would seem that the highest concentrations are 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 HWIR rulemaking? Since, the model does not allow a time-variable partition coefficient, it would seem that an aggregate partition coefficient that represents an average over time 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 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 quantity of suspended and sedimented sorbing particles must be quite variable, but there is no source of data on which to base modeling or other estimating techniques.

### 3-17 HydroGeoLogic, Inc 11/16/99



### 3) Waste piles and landfills should be treated the same as regards metal partitioning. Adopting these simplifications, it is necessary to derive estimates of metal partition coefficients for waste piles and landfills only. The sections below detail how these have been estimated from available TCLP and similar leach tests that present both solid phase and corresponding leachate concentrations. We have 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 leachability of metals from incinerator ash with the aim of investigating the suitability of the ash materials for disposal or for use in construction. Frequently, leach test results (leachate concentrations) are reported without the corresponding concentration in the solid phase. This omission makes those data useless in estimating expected partitioning. The literature survey produced 203 leach test results for which both leachate and solid phase data are presented. Table 7 shows the range and mean values of effective partition coefficients 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 in the solid and solution phases as represented in the leach test results. They 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. The authors gave an example of Cd complexation with chloride which they investigated using MINTEQA2. The increased leachability of Cd in some of the ash materials was correlated with 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.

### 3-18 HydroGeoLogic, Inc 11/16/99 

### Table 7 Effective 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 affinity relationships of metals noted in Section 3.1.1 and the similarities noted by these 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 of 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 complexing certain of the metals. However, in the absence of data to quantify this effect, and also in consideration of the paucity of actual partitioning data for waste systems, we have developed a regression equation that predicts waste K d 

### from soil K d



### . We chose to use soil K d



### as the predictor because a comparison of K d 

### values for soils, sediments and suspended matter suggests that the solid to liquid concentration ratio is important in determining the magnitude of K d 

### . This ratio for waste is probably more similar to that of soils than 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 this relationship, we used the effective partition coefficients for the metals for which we had the

### 3-19 HydroGeoLogic, Inc 11/16/99



### most complete (largest) sample. The regression equation thus determined is log K d,waste 

### = 0.7 log K d,soil 

### + 0.3. The relationship has a correlation coefficient of (r 2 

### ) of 0.4 implying that 40% of the variation in log K d,waste 

### from the leach test data is predicted.

3.2.2 Estimation from Geochemical Speciation Modeling The MINTEQA2 geochemical speciation model was used to investigate the range of metal partitioning coefficients for landfills. The input requirements of the model for estimating metal partitioning include the concentrations of major 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 ions, the DOC concentration, the POC concentration, and the pH. The 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 waste 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 not unlikely that 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 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 the modeling was conducted both with their 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 the modeling for the four 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

### 3-20 HydroGeoLogic, Inc 11/16/99



### 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 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 D. 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.

### 3-21 HydroGeoLogic, Inc 11/16/99 

### 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, 7.5 a 7.5, 8.0, 9.0 a 8.0, 9.0, 10.0 b 9.0, 10.0, 11.0 c
Ca 6000 d 975 d 1,700 b 2850 f Mg 625 d 500 d 10 b 10 f
Na 1350 e 1350 e 300 b 300 f
K 1100 e 1100 e 380 b 400 f
CO 3 500 c 250 c 50 c 50 f
Cl 2100 e 2100 e 1,200 b 380 f
Fe 780 e

--- --- --- SO 4 500 d 80 d 1,400 b 630 f
Ionic Strength (M) 0.1 c 0.1 c 0.1 c 0.1 c

DOC 100 c 50 c 15 c 15 c
POC 100,000 c 50,000 c

--- ---

a 

### Minimum, average, and maximum values reported in Ehrig (1992) b 

### Obtained from analysis of data MSWI obtained in literature survey

c 

### Reasonable guesses d 

### Computed from typical dissolved values reported in Ehrig (1992) and 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 estimated from the MINTEQA2 modeling exercise for several metals are shown in Table 9. The values presented were converted to units of 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 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

### 3-22 HydroGeoLogic, Inc 11/16/99



### 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 concentration value for each of the major 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 appear to be related to complexation with chloride, which is entered at high concentrations in all scenarios. This effect is in keeping with observations by others (van der Sloot et al., 1996). Another major ion whose concentration level may influence metal partitioning is calcium. At the high concentrations of these ions 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 metals whose partitioning is significantly influenced by the concentration level of a major ion, it is expected that this fact 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) for selected metals determined from MINTEQA2 modeling.

Metal Estimated log K d 

### (L/kg)

MSW Acetogenesis MSW

Methanogenesis MSWI

Ash Monofill CKD

Monofill
Be 0.8 - 3.9 3.3 - 4.4 (-0.4) - 4.0

--- 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

--- 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 --- 

### In comparing the partition coefficients estimated using MINTEQA2 with those for soils, there is greater agreement with values predicted by the derived regression equation (log K d,waste 

### = 0.7 log K d,soil



### + 0.3; see Section 3.2.1) for some metals than for others. (The measure of “agreement” for a metal is whether the value predicted by the equation using the mean soil K d



### value of Table 3 falls within the range of MINTEQA2 estimates for that metal. Using this rather lax requirement for agreement, the modeled K d 

### 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.) Like the literature-reported K d 

### values for natural media, Pb and Cr(III) tend to have high K d

### 3-23 HydroGeoLogic, Inc 11/16/99



### estimates from MINTEQA2 . In general, the results for the acetogenic and methanogenic landfill scenarios agree more closely with the regression relationship with soil K d 

### values than do the more alkaline cases for ash and CKD. It is probable that the lower K d 

### values in the latter are due to the combination of high major ion concentrations that compete for sorbing sites and solubilize the metals by complexation, and the absence of particulate organic carbon in the model systems. In view of the uncertainties inherent in the model results, a possible alternative for representing metal partitioning in waste piles and landfills is to use the regression equation presented in Section 3.2.1. This has the advantage of preserving the relative affinities among metals that has been noted as common to the natural media. However, the model results do suggest that the K d



### values in alkaline systems may be significantly lower than in municipal landfills. This might be accounted for by treating the slope and intercept coefficients in the regression as variables subject to uncertainty that can be represented in the monte carlo iterations. In the overall modeling strategy of HWIR, if the frequency of occurrence of a highly alkaline waste system can be established and used in the monte carlo realizations, the coefficients could be adjusted to give lower K d 

### 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 leachate concentrations of metals, 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, 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 processes in waste systems.

### 4-1 HydroGeoLogic, Inc 11/16/99



## 4.0 REFERENCES The reference list includes the complete bibliography of papers, articles, and reports that were copied and reviewed in the literature search. Those articles which provided data for spreadsheet entry are identified by a code in square brackets at the end of the citation. The code can be cross-referenced to spreadsheet entries. Further explanation of spreadsheet entries is provided in the spreadsheet itself. Abdel-Moati, M. A. R., 1998. Speciation of selenium in a Nile Delta lagoon and SE Mediterranean Sea mixing zone. Estuarine, Coastal and Shelf Science , 46:621-628. Albino, V., R. Cioffi, L. Santoro, and G. L. Valenti, 1996. Stabilization of residue containing heavy metals by means of matrices generating calcium trisulphoaluminate and silicate hydrates. Waste Management & Research, 14:29-41. [Al96] Allen, H. E., Y. Chen, Y. Li, C. P. Huang, and P. F. Sanders, 1995. Soil partition coefficients by column desorption and comparison to batch adsorption measurements. Environmental Science & Technology , 29(8):1887-1891. [Al95]
Allison, J.D., D.S. Brown, and K.J. Novo-Gradac, 1990. MINTEQA2/PRODEFA2, A Geochemical Assessment Model for Environmental Systems: Version 3.0 User's Manual . U.S. Environmental Protection Agency, Athens, GA. EPA/600/3-91/021. Allison, J.D. and E.M. Perdue, 1994. Modeling metal-humic interactions with MINTEQA2, in Humic Substances in the Global Environment and Implications on Human Health
, edited by N. Senesi and T.M. Miano, Elsevier Science B.V. Amdurer, M., 1983. Chemical Speciation and Cycling of Trace Elements in Estuaries: Radiotracer Studies in Marine Microcosms . Ph.D. Dissertation, Columbia University, 475 p. [Am83] Anderson, M. A., R. K. Ham, R. Stegman, and R. Stanforth, 1979. Test factors affecting the release of materials from industrial wastes in leaching tests. Toxic and Hazardous Waste Disposal, Volume 2: Options for Stabilization/Solidification , R. B. Pojasek, ed., Ann Arbor Science, Ann Arbor, Michigan, p. 145-168. [An79] Anderson, P. R. and T. H. Christensen, 1988. Distribution coefficients of Cd, Co, Ni, and Zn in soils. Journal of Soil Science , 39:15-22. [An88] Andres, A. and J. A. Irabien, 1994. Solidification/stabilization process for steel foundry dust using cement based binders: influence of processing variables. Waste Management & Research , 12:405-415. [An94]

### 4-2 HydroGeoLogic, Inc 11/16/99



### Angelidis, M. and R. J. Gibbs, 1989. Chemistry of metals in anaerobically treated sludges. Water Research

, 23(1):29-33. Avezzù, F., G. Billolotti, C. Collivignarelli, and A. V. Ghirardini, 1995. Behaviour of heavy metals in activated sludge biological treatment of landfill leachate. Waste Management & Research , 13:103-121. [Av95] Baes, III, C. F. and R. D. Sharp, 1983. A proposal for estimation of soil leaching and leaching constants for use in assessment models. Journal of Environmental Quality
, 12(1):17-28. [Ba83] Baes, III, C. F., R. D. Sharp, A. L. Sjoreen, and R. W. Shor, 1984. A Review and Analysis of Parameters for Assessing Transport of Environmentally Released Radionuclides Through Agriculture

. U. S. Department of Energy, Oak Ridge National Laboratory, ONRL-5786, 150 p. Balls, P. W., 1989. The partition of trace metals between dissolved and particulate phases in European coastal waters: a compilation of field data and comparison with laboratory studies. Netherlands Journal of Sea Research , 23(1):7-14. [Ba89a] Bangash, M. A. and J. Hanif, 1992. Sorption behavior of cobalt on illitic soil. Waste Management

, 12:29-38. [Ba92] Barkay, T., M. Gillman, and R. R. Turner, 1997. Effects of dissolved organic carbon and salinity on bioavailability of mercury. Applied and Environmental Microbiology
, 63:4267-4271. Barna, R., P.Moszkowicz, J. Veron, and M. Tirnoveanu, 1994. Solubility model for the pore solution of leached concrete containing solidified waste. Journal of Hazardous Materials

, 37:33-39. [Ba94] Baskaran, M., M. Ravichandran, and T. S. Bianchi, 1997. Cycling of 7 

### Be and 210



### Pb in a high DOC, shallow, turbid estuary of southeast Texas. Estuarine, Coastal and Shelf Science , 45:165-176. [Ba97] Bäverman, C., A. Sapiej, L. Moreno, and I. Neretnieks, 1997. Serial batch tests performed on municipal solid waste incineration bottom ash and electric arc furnace slag, in combination with computer modelling. Waste Management & Research , 15:55-71. [Ba97b] Beaublen, S., J. Nrlagu, D. Blowes, and G. Lawson, 1994. Chromium speciation in the great lakes. Environmental Science & Technology , 28:730-738. [Be94b]

### 4-3 HydroGeoLogic, Inc 11/16/99



### Becker, U. and S. Peiffer, 1997. Heavy-metal ion complexation by particulate matter in the leachate of solid waste: a multi-method approach. Journal of Contaminant Hydrology
, 24:313-344. [Be97] Behel, Jr., D., D. W. Nelson, and L. E. Sommers, 1983. Assessment of heavy metal equilibria in sewage sludge-treated soil. Journal of Environmental Quality
, 12(2):181-186. Belevi, H., D. and P. Baccini, 1989. Long-term behaviour of municipal solid waste landfills. Waste Management & Research , 7:43-56. [Be89] Belevi, H., D. M. Stampfli, and P.Baccini, 1992. Chemical behaviour of municipal solid waste incinerator bottom ash in monofills. Waste Management & Research , 10:153- 167. [Be92b] Benedetti, M. F., W. H. van Riemsdijk, L. K. Koopal, D. G. Kinniburgh, D. C. Gooddy, and C. J. Milne, 1996. Metal ion binding by natural organic matter: From the model to the field. Geochimica et Cosmochimica Acta , 60(14):2503-2513. Benoit, G., 1995. Evidence of the particle concentration effect for lead and other metals in fresh waters based on ultraclean technique analyses. Geochimica et Cosmochimica Acta , 59(13):2677-2687. [Be95] Benoit, G., S. D. Oktay-Marshall, A. Cantu, II, E. M. Hood, C. H. Coleman, M. O. Corapcioglu, and P. H. Santschi, 1994. Partitioning of Cu, Pb, Ag, Zn, Fe, Al, and Mn between filter-retained particles, colloids, and solution in six Texas estuaries. Marine Chemistry

, 45:307-336. [Be94] Bhat, P. N. and K. C. Pillai, 1997. Leachability and immobilisation of beryllium from solid waste (red-mud) generated in processing beryl. Water, Air, and Soil Pollution
, 94:297-306. [Bh97] Bishop, P. L., 1988. Leaching of inorganic hazardous constituents from stabilized/solidified hazardous wastes. Hazardous Waste & Hazardous Materials , 5(2):129-143. [Bi88] Breault, R. F., J. A. Colman, G. R. Aiken, and D. McKnight, 1996. Copper speciation and binding by organic matter in copper-contaminated streamwater. Environmental Science & Technology

, 30(12):3477-3486. [Br96]
Bohn, H. L., B. L. McNeal, and G. A. O’Connor, 1979. Soil Chemistry, WileyInterscience, John Wiley and Sons, New York. Brannon, J. M. and W. H. Patrick, Jr., 1987. Fixation, transformation, and mobilization of arsenic in sediments. Environmental Science & Technology , 21(5):450-459. [Br87]

### 4-4 HydroGeoLogic, Inc 11/16/99



### Bunde, R. L., J. J. Rosentreter, and M. J. Liszewski, 1998. Rate of strontium sorption and the effects of variable aqueous concentrations of sodium and potassium on strontium distribution coefficients of a surficial sediment at the Idaho National Engineering Laboratory, Idaho. Environmental Geology , 34(2/3):135-142.
Carsel, R.F., R. S. Parrish, R. L. Jones, J. L. Hansen, and R. L. Lamb, 1988. Characterizing the uncertainty of pesticide leaching in agricultural soils. Journal of Contaminant Hydrology

, 2: 111-124.

Cabanis, S. E. and M. S. Shuman, 1988. Copper binding by dissolved organic matter: I. Suwannee River fulvic acid equilibria. Geochimica et Cosmochimica , 52:185-193. Cabanis, S. E. and M. S. Shuman, 1988. Copper binding by dissolved organic matter: II. Variation in type and source of organic matter. Geochimica et Cosmochimica
, 52:195-200. Campbell, D. J. and P. H. T. Beckett, 1988. The soil solution in a soil treated with digested sewage sludge. Journal of Soil Science , 39:283-298. [Ca88c] Cernuschi, S., M. Giugliano, and I. de Paoli, 1990. Leaching of residues from MSW incineration. Waste Management & Research , 8:419-427. [Ce90] Chang, C., 1993. Toxicity of Sediment-Bound Thallium to Marine Organisms . Ph.D. Dissertation, University of Washington, 163 p. [Ch93b] Cheeseman, C. R., E. J. Butcher, C. J. Sollars, and R. Perry, 1993. Heavy metal leaching from hydroxide, sulphide, and silicate stabilised/solidified wastes. Waste Management

, 13(8):545-552. [Ch93c] Cheng, K. Y. and P. L. Bishop, 1992. Sorption, important in stabilized/solidified waste forms. Hazardous Waste & Hazardous Materials , 9(3):289-296. [Ch92c] Cheng, K. Y. and P. Bishop, 1992. Metals distribution in solidified/stabilized waste forms after leaching. Hazardous Waste & Hazardous Materials , 9(2):163-171. Chichester, D. L. and S. Landsberger, 1996. Determination of the leaching dynamics of metals from municipal solid waste incinerator fly ash using a column test. Journal of the Air & Waste Management Association , 46:643-649. [Ch96c] Chiffoleau, J., D. Cossa, D. Aufer, and I. Truquet, 1994. Trace metal distribution, partition and fluxes in the Seine estuary (France) in low discharge regime. Marine Chemistry

, 47:145-158.

### 4-5 HydroGeoLogic, Inc 11/16/99



### Christensen, T. H., 1985. Cadmium sorption at low concentrations. IV. Effect of waste leachates on distribution coefficients. Water, Air, and Soil Pollution , 26:265-274. [Ch85] Christensen, T. H., F. Bertelsen, and G. Gissel-Nielsen, 1989. Selenite fixation by soil particle-size separates. Journal of Soil Science , 40:641-647. [Ch89] Coker, E. G. and P. J. Matthews, 1983. Metals in sewage sludge and their potential effects in agriculture. Water Science Technology , 15:209-225. Comber, S. D. W., A. M. Gunn, and C. Whalley, 1995. Comparison of the partitioning of trace metals in the Humber and Mersey estuaries. Marine Pollution Bulletin , pp.851- 860. [Co95] Cornett, J., L. Chant, and B. Risto, 1992. Arsenic transport between water and sediments. Hydrobiologia , 235/236:533-544. [Co92a] Cote, P. L. and D. Isabel, 1984. Application of a dynamic leaching test to solidified hazardous wastes. Hazardous and Industrial Waste Management and Testing: Third Symposium, ASTM STP 851 , L. P. Jackson, A. R. Rohlik, and R. A. Conway, Eds., American Society for Testing and Materials, Philadelphia, pp. 193-212. [Co84] Coughtrey, P. J., D. Jackson, and M. C. Thorne, 1985. Radionuclide Distribution and Transport in Terrestrial and Aquatic Ecosystems: A Compendium of Data; Volume Six

. A. A. Balkema, Rotterdam and Boston. [Co85] Cutter, G. A., 1989. The estuarine behavior of selenium in San Francisco Bay. Estuarine, Coastal and Shelf Science , 28:13-34. [Cu89] Davis, A. and J. N. Galloway, 1993. Distribution of Pb between sediments and pore water in Woods Lake, Adirondack State Park, New York, U.S.A. Applied Geochemistry
, 8:51-65. [Da93] de Groot, G. J. and H. A. van der Sloot, 1992. Determination of leaching characteristics of waste materials leading to environmental certification. Stabilization and Solidification of Hazardous, Radioactive, and Mixed Wastes, 2nd Volume, ASTM STP 1123, T. M. Gilliam and C. C. Wiles, Eds., American Society for Testing and Materials, Philadelphia, 1992, pp.149-170. [Gr92] Del Castilho, P. and W. J. Chardon, 1995. Uptake of soil cadmium by three soil crops and its prediction by a pH-dependent Freundlich sorption model. Plant and Soil , 171:263- 266.

### 4-6 HydroGeoLogic, Inc 11/16/99



### Deverel, S. J. and S. P. Millard, 1988. Distribution and mobility of selenium and other trace elements in shallow groundwater of the western San Joaquin Valley, California. Environmental Science & Technology , 22(6):697-702. Dobbs, J. C., W. Susetyo, L. A. Carreira, and L. V. Azarraga, 1989. Competitive binding of protons and metals ions in humic substances by lanthanide ion probe spectroscopy. Analytical Chemistry

, 61:1519-1524. Dusing, D. C., P. L. Bishop, and T. C. Keener, 1992. Effect of redox potential on leaching from stabilized/solidified waste materials. Journal of the Air & Waste Management Association

, 42:56-62. [Du92] Dzombak, D.A. and F.M.M. Morel, 1990. Surface Complexation Modeling: Hydrous Ferric Oxide , John Wiley and Sons, New York. Earle, C. D. A., 1997. The Fate of Mercury in Municipal Solid Waste Landfills and Its Potential for Volatilization . Ph.D. Dissertation, University of Florida, 261 p. Eckert, Jr., J. O. and Q. Guo, 1998. Heavy metals in cement and cement kiln dust from kilns co-fired with hazardous waste-derived fuel: application of EPA leaching and acid-digestion procedures. Journal of Hazardous Materials , 59:55-93. [Ec98] Egemen, E. and C. Yuteri, 1996. Regulatory leaching tests for fly ash: a case study. Waste Management & Research , 14:43-50. [Eg96b] Ehrig, H., 1992. Leachate quality. In: Landfilling of Waste: Leachate, T. H. Christensen, R. Cossu, and R. Stegmann, eds., Elsevier Applied Science, London and New York, 520 p. [Eh92] Eighmy, T. T., J. D. Eusden, Jr., J. E. Krzanowski, D. S. Domingo, D. Stämpfli, J. R. Martin, and P. M. Erickson, 1995. Comprehensive approach toward understanding element speciation and leaching behavior in municipal solid waste incineration electrostatic precipitator ash. Environmental Science & Technology , 29(3):629-646. [Ei95] Elbaz-Poulichet, F., J. Garnier, D. M. Guan, J. Martin, and A. J. Thomas, 1996. The conservative behavior of trace metals (Cd, Cu, Ni and Pb) and As in the surface plume of stratified estuaries: example of the Rhone River (France). Estuarine, Coastal and Shelf Science , 42:289-310. [El96] Faust, B. C., 1992. The octanol/water distribution coefficients of methylmercuric species: the role of aqueous-phase chemical speciation. Environmental Toxicology and Chemistry

, 11:1373-1376.

### 4-7 HydroGeoLogic, Inc 11/16/99



### Faye, M. S. and M. L. Diamond, 1996. The role of phytoplankton in the removal of arsenic by sedimentation from surface waters. Hydrobiologia , 324:117-123. [Fa96]Fernandez-Sempere, J., M. L. Barrueso-Martinez, R. Font-Montesinos, and M. C. SabaterLillo, 1997. Characterization of tannery waste — Comparison of three leachability tests. Journal of Hazardous Materials , 54:31-45. [Fe97] Fio, J. L., R. Fujii, and S.J. Deverel, 1991. Selenium mobility and distribution in irrigated and non-irrigated alluvial soils. Soil Science Society of America Journal , 55:1313- 1320. [Fi91] Fisher, S., 1979. The problems of developing consensus on methodology for predicting the leaching characteristics of deposited waste. Toxic and Hazardous Waste Disposal, Volume 2: Options for Stabilization/Solidification , R. B. Pojasek, ed., Ann Arbor Science, Ann Arbor, Michigan, p. 169-187. [Fi79] Fletcher, P. and P. H. T. Beckett, 1987. The chemistry of heavy metals in digested sewage sludge — I. Copper(II) complexation with soluble organic matter. Water Research
, 21(10):1153-1161. [Fl87] Flyhammar, P., 1997. Estimation of heavy metal transformations in municipal solid waste. The Science of the Total Environment , 198:123-133. Flyhammar, P., F. Tamaddon, and L. Bengtsson, 1998. Heavy metals in a municipal solid waste disposal cell. Waste Management and Research , 16(5):403-410. [Fl98] Fotovat, A. and R. Naidu, 1997. Ion exchange resin and MINTEQA2 speciation of Zn and Cu in alkaline sodic and acidic soil extracts. Australian Journal of Soil Research
, 35:711-726. Fruchter, J. S., D. Rai, and J. M. Zachara, 1990. Identification of solubility controlling solid phases in a large fly ash field lysimeter. Environmental Science & Technology
, 24:1173-1179. Gagnon, C. and N. S. Fisher, 1997. Bioavailability of sediment-bound methyl and inorganic mercury to a marine bivalve. Environmental Science & Technology , 31(4):993-998.Gambrell, R. P. and W. H. Patrick, Jr., 1989. Cu, Zn, and Cd availability in a sludgeamended soil under controlled pH and redox potential conditions. In: Ecological Studies, Volume 74

, B. Bar-Yosef, J. Goldschmid, and N. J. Barrow, eds., SpringerVerlag, New York, 200 p. [Ga89] Gao, S., W. J. Walker, R. A. Dahlgren, and J. Bold, 1997. Simultaneous sorption of Cd, Cu, Ni, Zn, Pb, and Cr on soils treated with sewage sluge supernatant. Water, Air, and Soil Pollution

, 93: 331-345.[Ga97b]

### 4-8 HydroGeoLogic, Inc 11/16/99



### Garnier, J. -M., J. -M. Martin, J. -M. Mouchel, and K. Sioud, 1996. Partitioning of trace metals between the dissolved and particulate phases and particulate surface reactivity in the Lena River estuary and the Laptev Sea (Russia). Marine Chemistry , 53:269- 283. [Ga96] Garnier, J. -M., M. K. Pham, P. Ciffroy, and J. -M. Martin, 1997. Kinetics of trace element complexation with suspended matter and with filterable ligands in freshwater. Environmental Science & Technology , 31(6):1597-1606. [Ga97a] Gavasci, R., F. Lombardi, A. Polettini, and P. Sirini, 1998. Leaching tests on solidified products. Journal of Solid Waste Technology and Management , 25(1):14-20. [Ga98] Gerritse, R. G., R. Vriesema, J. W. Dalenberg, and H. P. De Roos, 1982. Effect of sewage sludge on trace element mobility in soils. Journal of Environmental Quality , 11:359- 364. [Ge82] Gooddy, D. C., P. Shand, D. G. Kinniburgh, and W. H. van Riemsdijk, 1995. Field-based partition coefficients for trace elements in soil solutions. European Journal of Soil Science , 46:265-285. [Go95b] Gould, J. P., F. G. Pohland, and W. H. Cross, 1988. Mobilization and retention of mercury and lead from particulates co-disposed with municipal solid wastes. Particulate Science and Technology

, 6:381-392. [Go88]

Gounaris, V., P. R. Anderson, and T. M. Holsen, 1993. Characteristics and environmental significance of colloids in landfill leachate. Environmental Science & Technology
, 27(7):1381-1387. Goyette, M. L. and B. -A. G. Lewis, 1995. K d

### in screening-level ground-water contaminanttransport model. Journal of Environmental Engineering , 121(7):537-541. Graham, E. R., 1973. Selective distribution and labile pools of micronutrient elements as factors affecting plant uptake. Soil Science of America Proceedings , 37:70-74. Grimanis, A. P., M. Vassilaki-Grimani, and N. Kalogeropoulos, 1994. Pollution studies of silver and antimony in Saronikos Gulf, Greece by INAA. Journal of Radioanalytical and Nuclear Chemistry, Articles , 179(2):231-241. Guo, R., C. L. Chakrabarti, K. S. Subramanian, X. Ma, Y. Lu, J. Cheng, and W. F. Pickering, 1993. Sorption of low levels of cyanide by granular activated carbon. Water Environment Research , 65:640-644.

### 4-9 HydroGeoLogic, Inc 11/16/99



### Hall, W. S., K. L. Dickson, F. Y. Saleh, J. H. Rodgers, Jr., D. Wilcox, and A. Entazami, 1986. Effects of suspended solids on the acute toxicity of zinc due to daphnia magna and pimephales promelas

. Water Resources Bulletin , 22(6):913-920. Hassan, S. M. and A. W. Garrison, 1996. Distribution of chromium species between soil and porewater. Chemical Speciation and Bioavailability , 8(3/4):85-103. [Ha96b] Hassan, S. M., A. W. Garrison, H. E. Allen, D. M. Di Toro, and G. T. Ankley, 1996. Estimation of partition coefficients for five trace metals in sandy sediments and application to sediment quality criteria. Environmental Toxicology and Chemistry
, 15(12):2198-2208. [Ha96a] Hawley, N., J. A. Robbins, and B. J. Eadie, 1986. The partitioning of beryllium in fresh water. Geochimica et Cosmochimica Acta , 50:1127-1131. [Ha86b] Henke, K. R., B. J. Zobeck, C. J. Moretti, and M. D. Mann, 1987. Leaching studies of solid wastes generated from fluidized bed combustion of low-rank western coals. Proceedings of the 1987 International Conference on Fluidized Bed Combustion
, pp.954-959. [He87] Hering, J. G. and F. M. M. Morel, 1988. Humic acid complexation of calcium and copper. Environmental Science & Technology , 22(10):1234-1237. Hemming, C. H., R. L. Bunde, M. J. Liszewski, J. J. Rosentreter, and J. Whelan, 1997. Effect of experimental technique on the determination of strontium distribution coefficients of a surficial sediment from the Idaho National Engineering Laboratory, Idaho. Water Research

, 31(7):1629-1636.

Herrera, Jr., E., M. Tittlebaum, F. Cartledge, and H. Eaton, 1992. Evaluation of the leaching properties of solidified heavy metal wastes. Journal of Environmental Science and Health, Part A , 27(4):983-998.
Herut, B. and Kress, N., 1997. Particulate metals contamination in the Kishon River estuary, Israel. Marine Pollution Bulletin , 34(9):706-711. Hiraide, M., S. Hiramatsu, and H. Kawaguchi, 1994. Evaluation of humic complexes oftrace metals in river water by adsorption on indium-treated XAD-2 resin and DEAESephadex A-25 anion exchanger. Fresenius Journal of Analytical Chemistry , 348:758- 761. [Hi94] Hooper, K., M. Iskander, G. Sivia, F. Hussein, J. Hsu, M. Deguzman, Z. Odion, Z. Ilejay, F. Sy, M. Petreas, and B. Simmons, 1998. Toxicity characteristic leaching procedure fails to extract oxoanion-forming elements that are extracted by municipal solid waste leachates. Environmental Science & Technology , 32:3825-3830. [Ho98]

### 4-10 HydroGeoLogic, Inc 11/16/99



### Hue, N. V. and S. A. Ranjith, 1994. Sewage sludges in Hawaii: Chemical composition and reactions with soils and plants. Water, Air, and Soil Pollution , 72:265-283. Hurley, J. P., M. M. Schafer, S. E. Cowell, J. T. Overdier, P. E. Hughes, and D. E.Armstrong, 1996. Trace metal assessment of Lake Michigan tributaries using lowlevel techniques. Environmental Science & Technology , 30(6):2093-2098. [Hu96]
Ishak, C. F., 1993. Characterization and Trace Contaminant Chemistry of Coal Combustion By-Products

. Ph.D. Dissertation, University of Georgia, Athens, Georgia. [Is93] Jackson, D. R., B. C. Garret, and T. A. Bishop, 1984. Comparison of batch and column methods for assessing leachability of hazardous waste. Environmental Science & Technology

, 18(9):668-673. [Ja84] Janssen, R. P. T., W. J. G. M. Peijnenburg, L. Posthuma, and M. A. G. T. van Den Hoop, 1997. Equilibrium partitioning of heavy metals in Dutch field soils. I. Relationship between metal partition coefficients and soil characteristics. Environmental Toxicology and Chemistry , 16(12):2470-2478. [Ja97] Johnson, C. A., L. Sigg, and U. Lindauer, 1992. The chromium cycle in a seasonally anoxic lake. Limnology and Oceanography , 37(2):315-321. Johnson, C. A., M. Kersten, F. Ziegler, and H. C. Moor, 1996. Leaching behaviour and solubility— controlling solid phases of heavy metals in municipal solid waste incinerator ash. Waste Management , 16:129-134. [Jo96] Johnson, W. H., S. M. Serkiz, and S. B. Clark, 1994. Determination of site specific distribution coefficients of mixed waste contaminants using an in-situ approach. Proceedings of the 27 th 

### Mid-Year Meeting of the Health Physics Society: Managing Mixed and Radioactive Waste , Albany, New York, February 13-17, 1994. [Jo94b] Jones, K. C., B. E. Davies, and P. J. Peterson, 1986. Silver in Welsh soils: Physical and chemical distribution studies. Geoderma , 37:157-174. [Jo86] Jones, L. W., R. M. Bricka, and M. J. Cullinane, Jr., 1992. Effects of selected waste constituents on solidified/stabilized waste leachability. Stabilization and Solidification of Hazardous, Radioactive, and Mixed Wastes, 2nd Volume, ASTM STP 1123, T. M. Gilliam and C. C. Wiles, Eds., American Society for Testing and Materials, Philadelphia, 1992, pp. 193-203. [Jo92b] Jopony, M. and S. D. Young, 1994. The solid— solution equilibria of lead and cadmium in poluted soils. European Journal of Soil Science , 45:59-70.

### 4-11 HydroGeoLogic, Inc 11/16/99



### Joshi, S. R. and B. S. Shukla, 1991. The role of the water/soil distribution coefficient in the watershed transport of environmental radionuclides. Earth and Planetary Science Letters , 105:314-318. [Jo91] Jin, X., G. W. Bailey, Y. S. Yu, and A. T. Lynch, 1996. Kinetics of single and multiple metal ion sorption processes on humic substances. Soil Science , 161(8):509-520. [Ba96] Kaminski, M. D., 1998. Assessment of Gross Accumulation and Leaching Characteristics of Heavy Metals in a Contaminated Urban Soil . Ph.D. Dissertation, University of Illinois at Urbana-Champaign, 216 p. Karuppiah, M. and G. Gupta, 1997. Toxicity of and metals in coal combustion ash leachate. Journal of Hazardous Materials , 56:53-58. Kersten, M., H. C. Moor, and C. A. Johnson, 1997. Speciation of trace metals in leachate from a MSWI bottom ash landfill. Applied Geochemistry , 12:675-683. Kida, A., Y. Noma, and T. Imada, 1996. Chemical speciation and leaching properties of elements in municipal incinerator ashes. Waste Management , 16(5/6):527-536. [Ki96b] Kiratli, N. and M. Ergin, 1996. Partitioning of heavy metals in surface Black Sea sediments, 1996. Applied Geochemistry , 11:775-788. Koelmans, A. A., F. Gillissen, and L. Lijklema, 1996. Influence of salinity and mineralization on trace metal sorption to cyanobacteria in natural waters. Water Research , 30(4):853-864. Koelmans, A. A. and L. Luklema, 1992. Sorption of 1,2,3,4-tetrachlorobenzene and cadmium to sediments and suspended solids in Lake Volkerak/Zoom. Water Research , 26(3):327-337. [Ko92] Korzun, E. A. and H. H. Heck, 1990. Sources and fates of lead and cadmium in municipal solid waste. Journal of the Air Waste Management Association , 40(9):1220-1226. Kuo, S. and E. J. Jellum, 1991. Affinity and behavior of Cd sorption in some acid soils. Water, Air, and Soil Pollution , 57-58:369-376. [Ku91] Lagas, P., C. M. Bom, M. J. t’ Hart, H. A. M. de Kruijf, and J. P. G. Loch, 1981. Model experiments on the behaviour of cyanide and barium in a landfill and in the soil. Studies in Environmental Science , W. van Duijvenbooden, P. Glasbergen, and H. van Lelyveld, Eds. 17:539-543.

### 4-12 HydroGeoLogic, Inc 11/16/99



### Lake, D. L., P. W. W. Kirk, and J. N. Lester, 1984. Fractionation, characterization, and speciation of heavy metals in sewage sludge and sludge-amended soils: A review. Journal of Environmental Quality , 13(2):175-183. Lamy, I., S. Bourgeois, and A. Bermond, 1993. Soil cadmium mobility as a consequence of sewage sludge disposal. Journal of Environmental Quality , 22:731-737. Lecuyer, I., S. Bicocchi, P. Ausset, and R. Lefevre, 1996. Physico-chemical characterization and leaching of desulphurization coal fly ash. Waste Management and Research

, 14:15-28. [Le96b] Lee, C. -H., H. -C. Wang, C. -M. Lin, and G. C. C. Yang, 1994. A long-term leachability study of solidified wastes by the multiple toxicity characteristic leaching procedure. Journal of Hazardous Materials , 38:65-74. [Le94b] Lee, P. -L., P. Baillif, and J. -C. Touray, 1997. Geochemical behaviour and relative mobility of metals (Mn, Cd, Zn, and Pb) in recent sediments of a retention pond along the A-71 motorway in Sologne, France. Environmental Geology , 32(2):142-152. [Le97b]
Lee, S. -Z., 1993. Chemical Interactions Between Heavy Metals and Soils . Ph.D. Dissertation, University of Delaware, 286 p. Lee, S. -Z., H. E. Allen, C. P. Huang, D. L. Sparks, P. F. Sanders, and W. J. G. M. Piejnenburg, 1996. Predicting soil-water partition coefficients for cadmium. Environmental Science & Technology , 30:3418-3424. [Le96] Li, Y. -H., L. Burkhardt, and H. Teraoka, 1984. Desorption and coagulation of trace elements during estuarine mixing. Geochimica et Cosmochimica Acta , 48:1879-1884. [Li84]
Lin, T. -S., 1997. Thallium Speciation and Distribution in the Great Lakes . Ph.D. Dissertation, University of Michigan, 137 p. Lyon, B. F., R. Ambrose, G. Rice, and C. J. Maxwell, 1997. Calculation of soil-water and benthic sediment partition coefficients for mercury. Chemosphere , 35(4):791-808. [Ly97] Madruga, M. J. and M. C. V. Carreiro, 1992. Experimental study of 60 

### Co behavior in Tejo river sediments. Hydrobiologia , 235-236:661-668. [Ma92] Mahony, J. D., D. M. Di Toro, A. M. Gonzalez, M. Curto, M. Dilg, L. De Rosa, and L. Sparrow, 1996. Partitioning of metals to sediment organic carbon. Environmental Toxicology and Chemistry , 15(12):2187-2197.

### 4-13 HydroGeoLogic, Inc 11/16/99



### Major, M. A. and D. H. Rosenblatt, 1991. The octanol/water partition coefficient of methylmercuric chloride and methylmercuric hydroxide in pure water and salt solutions. Environmental Toxicology and Chemistry , 10:5-8. Mason, R. P., J. R. Reinfelder, and F. M. M. Morel, 1996. Uptake, toxicity, and trophic transfer of mercury in a coastal diatom. Environmental Science & Technology
, 30(6):1835-1845. Mason, R. P. and K. A. Sullivan, 1997. Mercury in Lake Michigan. Environmental Science & Technology

, 31(3): 942-947. McBride, M. B., 1995. Toxic metal accumulation from agricultural use of sludge: Are USEPA regulations protective? Journal of Environmental Quality , 24:5-18. McDonald, P. and K. Johnson, 1997. The distribution coefficient of 60 

### Co in sediments from the Solway Firth, UK. Journal of Radioanalytical and Nuclear Chemistry , 220(1):9- 13. [Mc97] McIlroy, L. M., J. V. DePinto, T. C. Young, and S. C. Martin, 1986. Partitioning of heavy metals to suspended solids of the Flint River, Michigan. Environmental Toxicology and Chemistry

, 5:609-623. [Mc86] Meeussen, J. C. L., M. G. Keizer, W. H. van Riemsdijk, and F. A. M. de Haan, 1994. Solubility of cyanide in contaminated soils. Journal of Environmental Quality
, 23:785-792. [Me94b] Meeussen, J. C. L., W. H. van Riemsdijk, and S. E. A. T. M. van der Zee, 1995. Transport of complexed cyanide in soil. Geoderma , 67:73-85. Merrington, G. and B. J. Alloway, 1994. The flux of Cd, Cu, Pb, and Zn in mining polluted soils. Water, Air, and Soil Pollution , 73:333-344. [Me94] Merrington, G., L. Winder, and I. Green, 1997. The bioavailability of Cd and Zn from soils amended with sewage sludge to winter wheat and subsequently to the grain aphid Sitobion avenae

. The Science of the Total Environment , 205: 245-254. [Me97] Miskimmin, B. M. 1991. Effect of natural levels of dissolved organic carbon (DOC) on methyl mercury formation and sediment-water partitioning. Bulletin of Environmental Contamination and Toxicology , 47:743-750. [Mi91] Mizutani, S., T. Yoshida, S. Sakai, and H. Takatsuki, 1996. Release of metals from MSW I fly ash and availability in alkali condition. Waste Management , 16(5/6):537-544. [Mi96]

### 4-14 HydroGeoLogic, Inc 11/16/99



### Mok, W. and C. M. Wal, 1990. Distribution and mobilization of arsenic and antimony species in the Coeur D'Alene River, Idaho. Environmental Science & Technology
, 24(1):102-108. [Mo90]
Moretti, C. J., K. R. Henke, and C.A. Wentz, 1988. Immobilization of arsenic in coal combustion fly ash leachates. Particulate Science and Technology , 6:393-404. [Mo88] Neal, C., A. J. Robson, H. A. Jeffery, M. L. Harrow, M. Neal, C. J. Smith, and H. P. Jarvie, 1997. Trace element inter-relationships for the Humber rivers: Inferences for hydrological and chemical controls. The Science of the Total Environment
, 194/195:321-343. [Ne97] Neal, R. H. and G. Sposito, 1986. Effects of soluble organic matter and sewage sludge amendments on cadmium sorption by soils at low cadmium concentrations. Soil Science , 142(3):164-172. Neal, R. H. and G. Sposito, 1989. Selenate adsorption on alluvial soils. Soil Science Society of America Journal , 53:70-74. [Ne89] Neal, R. H., G. Sposito, K.M. Holtzclaw, and S.J. Traina, 1987. Selenite adsorption on alluvial soils: I. Soil composition and pH effects. Soil Science Society of America Journal , 51:1161-1165. [Ne87] Ng, B., A. Turner, A. O. Tyler, R. A. Falconer, and G. E. Millward, 1996. Modelling contaminant geochemistry in estuaries. Water Research , 30(1):63-74. [Ng96] Nordqvist, K. R., M. A. Benjamin, and J. F. Ferguson, 1988. Effects of cyanide and polyphosphates on adsorption of metals from simulated and real mixed-metal plating wastes. Water Research , 22(7):837-846. O’connor, D. J., 1988. Models of sorptive toxic substances in freshwater systems. II: Lakes and reservoirs. Journal of Environmental Engineering , 114(3):533-551.
Parsa, J., S. H. Munson-McGee, and R. Steiner, 1996. Stabilization/solidification of hazardous waste using fly ash. Journal of Environmental Engineering , 122(10):935- 940. [Pa96] Paucot, H. and R. Wollast, 1997. Transport and transformation of trace metals in the Scheldt estuary. Marine Chemistry , 58:229-244. [Pa97] Paulson, A. J., H. C. Curl, Jr., and J. F. Gendron, 1994. Partitioning of Cu in estuarine waters, I. Partitioning in a poisoned system. Marine Chemistry , 45:67-80.

### 4-15 HydroGeoLogic, Inc 11/16/99



### Pavelka, C., R. C. Loehr, and B. Haikola, 1994. Hazardous waste landfill leachate characteristics. Waste Management , 13(8):573-580.
Pearson, R.G., 1963. Hard acids and soft bases. Journal of the American Chemical Society
, 85:3533-3539. Pilarski, J., P. Waller, and W. Pickering, 1995. Sorption of antimony species by humic acid. Water, Air, and Soil Pollution , 84:51-59. [Pi95b] Pinochet, H., I. De Gregori, D. Delgado, N. Gras, L. Munoz, C. Bruhn, and G. Navarrete, 1995. Cadmium and copper in bivalves, mussels, and associated bottom sediments and waters from Corral Bay-Chile. Environmental Technology , 16:539-548. Prakash, A., 1996. Desorption of soil contaminants due to rainwater infiltration. Journal of Hydraulic Engineering

, 122(9):523-525. Puls, R. W., R. M. Powell, D. Clark, and C. J. Eldred, 1991. Effects of pH, solid/solution ratio, ionic strength, and organic acids on Pb and Cd sorption on kaolinite. Water, Air, and Soil Pollution

, 57-58:423-430. Quemerais, B. and K. R. Lum, 1997. Distribution and temporal variation of cadmium in the St. Lawrence River. Aquatic Sciences , 59:243-259. [Qu97] Quemerais, B., K. R. Lum, and C. Lemieux, 1996. Concentrations and transport of trace metals in the St. Lawrence River. Aquatic Sciences , 58(1):52-68. Raouf, M. W. A., K. Farah, M. Nofal, and A. Alian, 1997. Studies on sorption of antimony and europium from liquid organic and aqueous radioactive wastes on different sorbents. Journal of Radioanalytical and Nuclear Chemistry , 221(1-2):153-159. [Ra97] Rawat, J. P., S. M. U. Iraqi, and R. P. Singh, 1996. Sorption of equilibria of cobalt(II) on two types of Indian soils — the natural ion exchangers. Colloids and Surfaces A: Physicochemical and Engineering Aspects , 117:183-188. [Ra96] Rivera-Duarte, I. and A. R. Flegal, 1997. Pore-water silver concentration gradients and benthic fluxes from contaminated sediments of San Francisco Bay, California, U.S.A. Marine Chemistry

, 56:15-26. [Ri97] Richard, F. C. and A. C. M. Bourg, 1991. Aqueous geochemistry of chromium: A review. Water Research

, 25(7):807-816. Rodgers, Jr., J. H., E. Deaver, and P. L. Rogers, 1997. Partitioning and effects of silver in amended freshwater sediments. Ecotoxicology and Environmental Safety , 37:1-9.

### 4-16 HydroGeoLogic, Inc 11/16/99



### Robbins, J. A., G. Linder, W. Pfeiffer, J. Kleiner, H. H. Stabel, and P. Frenzel, 1992. Epilimnetic scavenging of Chernobyl radionuclides in Lake Constance. Geochimica et Cosmochimica Acta

, 56:2339-2361. Saeki, K. and S. Matsumoto, 1994. Influence of organic matter on selenite sorption by andosols. Communications in Soil Science and Plant Analysis , 25:3379-3391. [Sa94]

Sanudo-Wilhelmy, S. A., I. Rivera-Duarte, and A. R. Flegal, 1996. Distribution of colloidal trace metals in the San Francisco Bay estuary. Geochimica et Cosmochimica Acta , 60(24):4933-4944. [Sa96] Sarmani, S. B., 1989. The determination of heavy metals in water, suspended materials and sediments in the Langat River, Malaysia. Hydrobiologia , 176/177:233-238. [Sa89] Schimmack, W., K. Bunzl, and H. Bachhuber, 1987. Variability of the sorption of Cs, Zn, Sr, Co, Cd, Ru, Tc, and I at trace concentrations by a forest soil along a transect. Environment International , 13:427-436. [Sc88]Schulter, K., 1997. Sorption of inorganic mercury and monomethyl mercury in an ironhumus podozol soil of southern Norway studied by batch experiments. Environmental Geology , 30(3/4): 266-279. [Sc97] Senesi, N., G. Sposito, K. M. Holtzclaw, and G. R. Bradford, 1989. Chemical properties of metal-humic acid fractions of a sewage sludge-amended aridisol. Journal of Environmental Quality

, 18:186-194. Servos, M. R. and D. C. G. Muir, 1989. Effect of suspended sediment concentration on the sediment to water partition coefficient for 1,3,6,8-tetrachlorodibenzo- p -dixon. Environmental Science & Technology , 23(10):1302-1306. Shafer, M. M., J. T. Overdier, J. P. Hurley, D. Armstrong, and D. Web, 1997. The influence of dissolved organic carbon, suspended particulates, and hydrology on the concentration, partitioning and variability of trace metals in two contrasting Wisconsin watersheds (U.S.A.). Chemical Geology , 136:71-97. [Sh97c] Sharmasarkar, S., G. F. Vance, and F. Cassel-Sharmasarkar, 1998. Analysis and speciation of selenium ions in mine environments. Environmental Geology , 34(1):31-38. Shelton, C. H. and G. M. Lessman, 1978. Quality characteristics of agricultural and waste disposal runoff water. Journal of Soil and Water Conservation , May-June : 134-139. Shen, Z., D. Wenjiang, Z. Licheng, and C. Xibao, 1989. Geochemical characteristics of heavy metals in the Xiangjiang River. Hydrobiologia , 176/177:253-262.

### 4-17 HydroGeoLogic, Inc 11/16/99



### Sheppard, M. I. and D. H. Thibault, 1990. Default soil solid/liquid partition coefficients, K d 

### s, for four major soil types: A compendium. Health Physics , 59(4):471-482. [Sh90] Sheppard, M. I. and D. H. Thibault, 1991. A four-year mobility study of selected trace elements and heavy metals. Journal of Environmental Quality , 20:101-114. [Sh91a] Sheppard, S. C. and W. G. Evenden, 1989. Comparison of partition coefficients for 54 

### Mn and soil-extractable Mn, including relationship to plant uptake. Canadian Journal of Soil Science

, 69:351-365. Sheppard, S. C., W. G. Evenden, and R. J. Pollock, 1989. Uptake of natural radionuclides by field and garden crops. Canadian Journal of Soil Science , 69:751-767. [Sh89b] Sheppard, S. C. and M. I. Sheppard, 1991. Lead in boreal soils and food plants. Water, Air, and Soil Pollution

, 57-58:79-91. [Sh91b] Shiller, A. M. and E. A. Boyle, 1987. Dissolved vanadium in rivers and streams. Earth and Planetary Science Letters , 86:214-224. [Sh87] Sigg L., 1998. Partitioning of metals to suspended particles. In: Metals in Surface Waters
, H. E. Allen, A. W. Garrison, and G. W. Luther, III, eds., (pp.217-255) Ann Arbor Press, Chelsea, Michigan. [Si98] Sinclair, P., R. Beckett, and B. T. Hart, 1989. Trace elements in suspended particulate matter from the Yarra River, Australia. Hydrobiologia , 176/177:239-251. Staritsky, P. H., P. H. M. Sloot, and A. Stein, 1992. Spatial variability and sampling of cyanide polluted soil on former galvanic factory premises. Water, Air, and Soil Pollution , 61:1-16. Stein, E. D., Y. Cohen, and A. M. Winer, 1996. Environmental distribution and transformation of mercury compounds. Critical Reviews in Environmental Science and Technology

, 26(1):1-43. Stordal, M. C., G. A. Gill, L. -S. Wen, and P. H. Santschi, 1996. Mercury phase speciation in the surface waters of three Texas estuaries: Importance of colloidal forms. Limnology and Oceanography , 41(1):52-61. [St96a] Stumm, W. and J. J. Morgan, 1996. Aquatic Chemistry, 3 rd 

### Edition, John Wiley and Sons, New York, 1022 p. Sung, W., 1995. Some observations on surface partitioning of Cd, Cu, and Zn in estuaries. Environmental Science & Technology , 29(5):1303-1312. [Su95]

### 4-18 HydroGeoLogic, Inc 11/16/99



### Szabo, G., J. Guczi, J. Valyon, R. A. Bulman, 1995. Investigations of the sorption characteristics of radiosilver on some natural and artificial soil particles. The Science of the Total Environment , 172:65-78. [Sz94] Theis, T. L. and R. O. Richter, 1979. Chemical speciation of heavy metals in power plant ash pond leachate. Environmental Science & Technology , 13(2):219-224. [Th79] Thibault, D. H., M. I. Sheppard, P. A. Smith, 1990. A Critical Compilation and Review of Default Soil Solid/Liquid Partition Coefficients, K d 

### , for Use in Environmental Assessments

. Atomic Energy of Canada, Ltd. (AECL-10125), Whiteshell Nuclear Research Establishment, 111 p. [Th90] Tipping, E., 1993. Modeling of cation binding by natural organic matter in soils and waters. Analytical Proceedings

, 30:186-189. [Ti93] Tipping, E. and M. A. Hurley, 1992. A unifying model of cation binding by humic substances. Geochimica et Cosmochimica Acta , 56:3627-3641. Tipping, E., 1994. WHAM—A chemical equilibrium model and computer code for waters, sediments, and soils incorporating a discrete site/electrostatic model for ion-binding by humic substances. Computers and Geosciences , 20(6):973-1023.
Turner, A., G. E. Millward, A. J. Bale, and A. W. Morris, 1992. The solid-solution partitioning of trace metals in the southern North Sea -- in situ radiochemical experiments. Continental Shelf Research , 12(11):1311-1329. Turner, A., G. E. Millward, A. J. Bale, and A. W. Morris, 1993. Application of the K d 

### concept to the study of trace metal removal and desorption during estuarine mixing. Estuarine, Coastal, and Shelf Science , 36:1-13. [Tu93]
U.S. EPA, 1991. Leachability Phenomena: Recommendations and Rationale for Analysis of Contaminant Release by the Environmental Engineering Subcommittee. U. S.Environmental Protection Agency, Science Advisory Board, Washington, D.C., EPASAB-EEC-92-003. [SAB91]
U.S. EPA, 1992. Technical Support Document for Land Application of Sewage Sludge, Volume II , U. S. Environmental Protection Agency, Office of Water, Washington, D. C., EPA 822/R-93-001b, Appendix J. [USEPA92]
U.S. EPA, 1996a. EPA’s composite model for leachate migration with transformation products (EPACMTP): Background document for metals . United States Environmental Protection Agency unpublished report, Office of Solid Waste, Washington, DC, 77p.

### 4-19 HydroGeoLogic, Inc 11/16/99



### U.S. EPA, 1996c. Soil Screening Guidance: Technical Background Document , U. S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, D. C., EPA / 540/R-95/128. [USEPA96c] U.S. EPA, 1996d. Technical Support document for the Round Two Sewage Sludge Pollutants

, U. S. Environmental Protection Agency, Office of Water, Washington, D. C., EPA-822-R-96-003. [USEPA96d]
U.S. EPA, 1998. National Sediment Bioaccumulation Conference . U.S. Environmental Protection Agency, Office of Water, Washington, D.C., EPA 823-R-98-002, 360 p. U.S. EPA, 1998b. Cement Kiln Dust Groundwater Pathway Migration Report . Report prepared for the U.S. Environmental Protection Agency, Washington, D.C., by

HydroGeoLogic, Inc. van der Koolj, L. A., D. van de Meent, C. J. van Leeuwen, and W. A. Bruggeman, 1991. Deriving quality criteria for water and sediment from the results of aquatic toxicity tests and product standards: Application of the equilibrium partitioning method. Water Research

, 25(6):697-705. [Ko91] van der Sloot, H. A., R. N. J. Comans, and O. Hjelmar, 1996. Similarities in the leaching behaviour of trace contaminants from waste, stabilized waste, construction materials, and soils. The Science of the Total Environment , 178:111-126. [Sl96] van Hattum, B., N. M. van Straalen, and H. A. J. Govers, 1996. Trace metals in populations of freshwater isopods: influence of biotic and abiotic variables. Archives of Environmental Contamination and Toxicology , 31:303-318. [Ha96c] Vertacnik, A., D. Barisic, Lj. Musani, E. Prohic, and M. Juracic, 1997. Exchangeable fraction of elements in alluvial sediments under waste disposal site (Zagreb, Croatia). Journal of Radioanalytical and Nuclear Chemistry , 218(1):45-52. Vidal, M. and G. Rauret, 1993. A sequential extraction scheme to ascertain the role of organic matter in radionuclide retention in Mediterranean soils. Journal of Radioanalytical and Nuclear Chemistry, Articles , 173(1):79-86.
Voutsa, D., G. Zachariadis, C. Samara, and Th. Kouimtzis, 1995. Evaluation of chemical parameters in Aliakmon River / Northern Greece, Part II : Dissolved and particulate heavy metals. Journal of Environmental Science and Health , A30(1):1-13. Wang, L., K. J. Reddy, and L. C. Munn, 1994. Geochemical modeling for predicting potential solid phases controlling the dissolved molybdenum in coal overburden, Powder River Basin, WY, U.S.A. Applied Geochemistry , 9:37-43. [Wa94]

### 4-20 HydroGeoLogic, Inc 11/16/99



### Wang, S. Y. and C. Vipulanandan, 1996. Leachability of lead from solidified cement-fly ash binders. Cement and Concrete Research , 26(6):895-905. [Wa96] Warren, L. A. and A. P. Zimmerman, 1994. The influence of temperature and NaCl on cadmium, copper, and zinc partitioning among suspended particulate and dissolved phases in an urban river. Water Research , 28(9):1921-1931. [Wa94a] Watras, C. J., K. A. Morrison, and J. S. Host, 1995. Concentration of mercury species in relationship to other site-specific factors in the surface waters of northern Wisconsin lakes. Limnology and Oceanography , 40(3):556-565. [Wa95]
Wen, L., P. H. Santschi, G. A. Gill, C. L. Paternostro, and D. D. Lehman, 1997. Colloidal and particulate silver in river and estuarine waters of Texas. Environmental Science & Technology

, 31(3):723-731. [We97] Wieland, E., P. H. Santschi, P. Hohener, and M. Strum, 1993. Scavenging of Chernobyl

137 

### Cs and natural 210



### Pb in Lake Sempach, Switzerland. Geochimica et Cosmochimica Acta , 57:2959-2979. Wiles, C. C., 1996. Municipal solid waste combustion ash: state-of-the-knowledge. Journal of Hazardous Materials

, 47:325-344. [Wi96b] Wood, T. M., A. M. Baptista, J. S. Kuwabara, and A. R. Flegal, 1995. Diagnostic modeling of trace element partitioning in south San Francisco Bay. Limnology and Oceanography

, 40(2):345-358. Yan, J. and I. Neretnieks, 1995. Is the glass phase dissolution rate always a limiting factor in the leaching processes of combustion residues? The Science of the Total Environment

, 172:95-118. Young, P., G. Baldwin, and D. C. Wilson, 1984. Attenuation of heavy metals within municipal waste landfill sites. Hazardous and Industrial Waste Management and Testing: Third Symposium, ASTM STP 851 , L. P. Jackson, A. R. Rohlik, and R. A. Conway, Eds., American Society for Testing and Materials, Philadelphia, pp. 193- 212. [Yo84]

# APPENDIX A

METAL PARTITION COEFFICIENTS USED IN SOME PREVIOUS U.S. EPA RISK ASSESSMENTS

### A-1 HydroGeoLogic, Inc 11/16/99 Metal Risk Assessment Type of Kd Kd(L/kg) Single Value Range Ag US EPA Region 6

Combustion [USEPA96b] soil/water

--- 0.1 - 110
US EPA Region 6 Combustion [USEPA96b] suspended matter /water --- 0.1 - 110

US EPA Region 6 Combustion [USEPA96b] sediment/water

--- 0.1 - 110 Soil-Screening Guidance [USEPA96c] soil/water

--- 0.1 - 110
Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 290

---

As US EPA Region 6

Combustion [USEPA96b] soil/water

--- 25 - 31
US EPA Region 6 Combustion [USEPA96b] suspended matter /water --- 25 - 31

US EPA Region 6 Combustion [USEPA96b] sediment/water

--- 25 - 31 Sewage Sludge Rule [USEPA92] waste/leachate 20

--- Sewage Sludge Rule [USEPA92] suspended matter /water 63,700

---
Soil-Screening Guidance [USEPA96c] soil/water

--- 25 - 31
Chlorinated Aliphatics Listing [USEPA?] soil/water 29

---

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/water

--- 11 - 52

Soil-S creening Guidance [USEPA96c] soil/water

--- 11 - 52 Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 6

--- Chlorinated 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/water

--- 23 - 100,000

Soi l-Screening Guidance [USEPA96c] soil/water

--- 23 - 100,000
Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 43

---Metal Risk Assessment Type of Kd Kd(L/kg) Single Value Range



### A-2 HydroGeoLogic, Inc 11/16/99 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/leachate 431

--- Sewage Sludge Rule [USEPA92] suspended matter /water 174,000

---
Soil-Screening Guidance [USEPA96c] soil/water

--- 15 - 4300
Chlorinated Aliphatics Listing [USEPA?] soil/water 162

---

Co Chlorinated 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/leachate 59

--- Sewage Sludge Rule [USEPA92] suspended matter /water 255,000

---

Soi l-Screening Guidance [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 Guidance [USEPA96c] soil/water

--- 14 - 31 Chlorinated Aliphatics Listing [USEPA?] soil/water 18

---

Cu Sewage Sludge Rule

[USEPA92] waste/leachate 98

--- Sewage Sludge Rule [USEPA92] suspended matter /water 132,000

---
Chlorinated 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

---Metal Risk Assessment Type of Kd Kd(L/kg) Single Value Range



### A-3 HydroGeoLogic, Inc 11/16/99 US EPA Region 6 Combustion [USEPA96b] sediment /water 3000

--- Sewage Sludge Rule [USEPA92] waste/leachate 330

--- Sewage Sludge Rule [USEPA92] suspended matter /water 125,000

---
Soil-Screening Guidance [USEPA96c] soil/water

--- 0.04 - 200

Mo Chlorinated Aliphatics

Listing [USEPA?] soil/water 20

---

Ni US EPA Region 6

Combustion [USEPA96b] soil/water

--- 16 - 1900 US EPA Region 6 Combustion [USEPA96b] suspended matter /water --- 16 - 1900

US EPA Region 6 Combustion [USEPA96b] sediment /water

--- 16 - 1900 Sewage Sludge Rule [USEPA92] waste/leachate 63

--- Sewage Sludge Rule [USEPA92] suspended matter /water 100,000

---

S oil-Screening Guidance [USEPA96c] soil/water

--- 16 - 1900 Chlorinated Aliphatics Listing [USEPA?] soil/water 82

---

Pb US EPA Region 6

Combustion [USEPA96b] soil/water 900

--- US EPA Region 6 Combustion [USEPA96b] suspended matter /water 900

---
US EPA Region 6 Combustion [USEPA96b] sediment /water 900

--- Sewage Sludge Rule [USEPA92] waste/leachate 621

--- Sewage Sludge Rule [USEPA92] suspended matter /water 185,000

---
Chlorinated Aliphatics Listing [USEPA?] soil/water 280,000

---

Sb US EPA Region 6

Combustion [USEPA96b] soil/water 45

--- US EPA Region 6 Combustion [USEPA96b] suspended matter /water 45

---
US EPA Region 6 Combustion [USEPA96b] sediment /water 45

--- Soil-Screening Guidance [USEPA96c] soil/water 45

--- Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 6

---

Se US EPA Region 6

Combustion [USEPA96b] soil/water

--- 2.2 - 18Metal Risk Assessment Type of Kd Kd(L/kg) Single Value Range



### A-4 HydroGeoLogic, Inc 11/16/99 US EPA Region 6 Combustion [USEPA96b] suspended matter /water --- 2.2 -18

US EPA Region 6 Combustion [USEPA96b] sediment /water

--- 2.2 - 18 Soil-Screening Guidance [USEPA96c] soil/water

--- 2.2 - 18

Tl US EPA Region 6

Combustion [USEPA96b] soil/water

--- 44 - 96
US EPA Region 6 Combustion [USEPA96b] suspended matter /water --- 44 - 96

US EPA Region 6 Combustion [USEPA96b] sediment /water

--- 44 - 96

Soil-S creening Guidance [USEPA96c] soil/water

--- 44 - 96

V Soil-Screening Guidance

[USEPA96c] soil/water 1000

--- Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 39

--- Chlorinated Aliphatics Listing [USEPA?] soil/water 50

---

Zn US EPA Region 6

Combustion [USEPA96b] soil/water 62

--- US EPA Region 6 Combustion [USEPA96b] suspended matter /water 62

---
US EPA Region 6 Combustion [USEPA96b] sediment /water 62

--- Soil-Screening Guidance [USEPA96c] soil/water

--- 16 - 530
Chlorinated Aliphatics Listing [USEPA?] soil/water 40

---

CN S oil-Screening Guidance

[USEPA96c] soil/water 9.9

--- Sewage Sludge Rule, Round 2 [USEPA96d] soil/water 0.0014

---

# APPENDIX B

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO ESTIMATE PARTITIONING IN SOIL

### B-1 HydroGeoLogic, Inc 11/16/99

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 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

### B-2 HydroGeoLogic, Inc 11/16/99

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 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

### B-3 HydroGeoLogic, Inc 11/16/99

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 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

### B-4 HydroGeoLogic, Inc 11/16/99

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 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

### B-5 HydroGeoLogic, Inc 11/16/99

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 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

### B-6 HydroGeoLogic, Inc 11/16/99

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 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

# APPENDIX C

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO ESTIMATE PARTITIONING WITH DOC

### C-1 HydroGeoLogic, Inc 11/16/99

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 0.00 0 0 0 330 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+1 492 6.010E+00 -3.00 y /NO3- 140 5.200E+01 -2.51 y /CO3-2 180 5.700E+00 -3.37 y /Cl-1 732 6.600E+00 -3.58 y /SO4-2 144 0.000E+00 -6.00 y /DOM1

3 1 330 7.3000 0.0000 /H+1

# APPENDIX D

EXAMPLE INPUT FILE FOR THE MINTEQA2 MODEL USED TO ESTIMATE PARTITIONING WITH WASTE SYSTEMS

### D-1 HydroGeoLogic, Inc 11/16/99

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)

### D-2 HydroGeoLogic, Inc 11/16/99

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+01 -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)

### D-3 HydroGeoLogic, Inc 11/16/99

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)

### D-4 HydroGeoLogic, Inc 11/16/99

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)

### 3-10 HydroGeoLogic, Inc 11/16/99