# Sensitivity Analysis for the Fossil Fuel Combustion Waste Risk Assessment

Technical Report

Work Assignment #1-16 Contract #68-W-03-042

Prepared for

U.S. Environmental Protection Agency Office of Solid Waste Research Triangle Park, NC 27709

Prepared by

RTI International Center for Environmental Analysis P.O. Box 12194 Research Triangle Park, NC 27709

June 2005Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

iii 

### Table of Contents

1.0 Summary of Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2.0 Introduction ............................................................2

3.0 Methodology ...........................................................2

4.0 Test Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

5.0 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

6.0 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

7.0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Appendix A Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1 Appendix B Highly Correlated Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1 Appendix C Response Surface Regression Results by Scenario . . . . . . . . . . . . . . . . . . . . . . C-1



### List of Figures

Figure 1. Example Graph of Risk Versus Infiltration Rate ..............................8



### List of Tables

Table 1. Scenarios Included in Sensitivity Analysis (90 th Percentile Risk > Risk Criteria) .....4 Table 2. Percent of Zero Risk Values by Liner Type and Scenario . . . . . . . . . . . . . . . . . . . . . . . 7 Table 3. Comparison of Test Results With and Without Zero Risk Values .................8Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

iv [This page intentionally left blank.]Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

1 

# 1.0 Summary of Key Findings

In 2004, RTI conducted a probabilistic risk assessment to evaluate the human and ecological impacts of fossil fuel combustion wastes (FFCW) disposed in surface impoundments and landfills. A sensitivity analysis was conducted on the results of the probabilistic analysis to determine the factors most important for risk to human receptors. The sensitivity analysis utilized a response surface regression method. In general, this approach derives a statistical model for risk (or hazard quotient) based on the input parameters from the probabilistic analysis. The output from the sensitivity analysis is the goodness-of-fit values for the overall regression models (R 2 ), as well as the relative importance of each input parameter in determining risk.

Prior to running the full sensitivity analysis, the methodology was tested on a subset of scenarios (i.e., a combination of waste management unit, waste type, chemical, exposure pathway, and receptor). Preliminary results indicated that the response surface method was unable to determine which input parameters were the most important drivers of risk. The cause of these poor results was attributed to the high percentage of zero risk values in the analysis, which resulted from a variety of input combinations, including composite liner type, low infiltration rates, and low source concentrations. The methodology was modified to remove all zero risk values prior to developing the regression models. This modified methodology showed much- improved results and is valid for determining the inputs that are the most important drivers of risk.

When the sensitivity analysis was completed for all scenarios under consideration for human receptors, a wide range in goodness-of-fit values for the regression models were observed, with R 2 values ranging from 0.53-0.90. In general, the regression models for the fish consumption pathway tended to have higher R 2 values (0.76-0.90) compared to the drinking water pathway (0.53-0.83).

The sum of percent variation for scenarios ranged from 70% to >100% (due to overlap between individual variables from the second-order interaction terms). In general, the drinking water pathway tended to have a larger number of input parameters that were significant, while the fish consumption pathway had a small number (typically <= 3). The most important input variables by exposure pathway were as follows:

Drinking Water Pathway



### # Infiltration rate within the waste management unit (WMU) footprint



### # Leachate concentration from the WMU



### # Hydraulic conductivity of the aquifer



### # Regional hydraulic gradient in the aquiferSensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

2 Fish Consumption Pathway



### # Infiltration rate within the WMU footprint



### # Leachate concentration from the WMU



### # Waterbody flow rate



# 2.0 Introduction

In 2004, RTI conducted a risk assessment to estimate the impacts of FFCW disposed in landfills and surface impoundments on human health and the environment. The risk assessment consisted of a site-based, probabilistic analysis for the groundwater-to-surface water pathway and the groundwater-to-drinking water pathway. For human receptors, the two exposure routes were consumption of drinking water from a we ll and fish consumption. The output from the probabilistic analysis was a national distribution of the potential human and ecological risks from (WMUs) that accept FFCW.

This report presents the results of a sensitivity analysis that determined which parameters were the most important drivers of human health risk. The basic method employed for the sensitivity analysis was response surface regression. In general, this approach uses the input parameters from the probabilistic analysis to derive statistical models for risk (or hazard quotient). Regression models were developed for each scenario, as defined by a WMU, waste type, chemical, exposure pathway, and receptor. The response surface method (RSM) provides both a goodness-of-fit value for the overall regression model (R 2 ), as well as the percent variation in risk attributable to each significant input. The percent variation is used to rank the input parameters in order of importance.

Section 3 of this report presents a summary of the methodology used; Section 4 contains the results of preliminary runs used to test the RSM; Section 5 presents the results of the full sensitivity analysis; and Section 6 presents recommendations for future work.



# 3.0 Methodology

The goals of the sensitivity analysis were two-fold:

(1) To develop regression models with risk (or hazard quotient) as the dependent variable and inputs from the probabilistic analysis as the independent variables. This was achieved through a two-step process. First, regression models were developed for the relevant environmental concentration (i.e., well concentration for the drinking water pathway and fish concentration for the fish consumption pathway). These models were then used to develop regression models for risk (or hazard quotient).

(2) To rank the independent variables according to their contribution to the variability in risk (or hazard quotient).Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

3 The RSM offers the following advantages:



### # The ranges of the input variables need not be consistent.



### # The input variables may interact with one another via interaction terms in the regression model.



### # Nonlinear effects may be observed via the squared terms in the regression model.

Regression methods are also subject to certain limitations. They are not capable of determining the sensitivity of risk to input variables that are not varied in the analysis (e.g., assumptions) or are not otherwise included within the scope of the analysis (e.g., model-derived variables). If the most important variables do not vary, or their variability is improperly characterized, the sensitivity analysis may not identify them as being important.

The 11 major steps in the sensitivity analysis are summarized below. A complete description of the methodology is contained in the U.S. EPA’s technical background document (2003).

Step 1. Define the independent variables and dependent variables. For the initial regression models, the dependent variable was either well concentration for the drinking water pathway or fish concentration for the fish consumption pathway. The independent variables included waste properties, site/source parameters, chemical properties, meteorological factors, groundwater parameters, well characteristics, and water body characteristics.

The response surface regression included only those inputs that were continuous and numeric. Therefore, some variables of importance, such as coal type, liner type, hydrologic region, groundwater region, soil type, and wa ter body type, were not included. Appendix A contains a list of the input variables from the probabilistic analysis that served as independent variables in the sensitivity analysis.

Step 2. Define the scenarios of interest. For the purposes of this sensitivity analysis, a scenario is a subset of the data for which an individual regression model was developed. A scenario is denoted by the combination of



### # WMU: landfill or surface impoundment;



### # Waste type: conventional fly and bottom ash, co-disposed coal cleaning waste and conventional ash, or fluidized bed combustion waste;



### # Chemical: antimony, arsenic, boron, cadmium, cobalt, mercury, selenium, or thallium;



### # Exposure pathway: drinking water or fish consumption;



### # Receptor: adult or child.Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

4 Scenarios were included in the sensitivity analysis if their 90 th percentile risk or hazard quotient value (as determined in the probabilistic analysis) exceeded the risk criteria of 1 for non-cancer and 10 -5 for cancer. Table 1 lists the 84 scenarios that exceeded the risk criteria and were included in the sensitivity analysis. Aluminum, barium, and nitrate were included in the probabilistic analysis, but not the sensitivity analysis, because they did not exceed the risk criteria for any scenario.

Table 1. Scenarios Included in Sensitivity Analysis (90 th Percentile Risk > Risk Criteria)

Chemical Benchmark WMU Waste Type Pathway Total Scenarios 1 Antimony Noncancer SI A DW 2 Arsenic (III) Cancer LF SI SI A, AC, FBC A, AC A, AC DW FC DW 6 4 4 Arsenic (V) Cancer LF SI SI A, AC, FBC A, AC A, AC DW FC DW 6 4 4 Boron Noncancer SI A, AC DW 4 Cadmium Noncancer SI A, AC DW 4 Cobalt Noncancer SI A, AC DW 4 Mercury Noncancer LF SI A, AC, FBC AC FC FC 6 2 Lead Noncancer SI A, AC DW 4 Molybdenum Noncancer SI A, AC DW 4 Selenium (IV) Noncancer SI SI A, AC A, AC FC DW 4 4 Selenium (VI) Noncancer SI SI A, AC A, AC FC DW 4 4 Thallium Noncancer LF SI SI A, AC, FBC A A DW FC DW 6 2 2

1 Each WMU, waste type, and pathway has results for both adult and child receptors.

Step 3. Develop the input data set. The input data set contains one record for each scenario and iteration, with numeric values for all of the independent and dependent variables. This step required extensive quality assurance to ensure that the inputs were assembled correctly, as the data set contains approximately 840,000 records (84 scenarios x 10,000 iterations/scenario) and 150 fields (144 independent and dependent variables, plus six fields to denote the scenario and iteration).

Step 4. Remove any variables that are constants. Any variable that was constant across a scenario does not affect the environmental concentration, and was therefore removed from the data set prior to the regression analysis. Appendix A classifies each input variable as to whether it was included in the sensitivity analysis, and if not, why the variable was dropped.

Step 5. Perform transformations to the input variables so that all input variables will have approximately symmetric distributions. The distributions of the input parameters (both shape and scale) highly influence the results of the regression analysis. To mitigate the adverse effect of skewed distributions, the or iginal values were transformed using power orSensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

5 natural log functions. In most cases, the presence of zero values necessitated that a constant value be added prior to the log transformation. The transformation process forces the distributions to be as symmetric as possible and removes some of the correlation among the input variables.

Step 6. Standardize the transformed variables. The input variables were standardized by subtracting the mean and dividing by the standard deviation. This process allows the regression results to be independent of the magnitude of the value of the input variables. Without standardization, inputs with larger va lues would overwhelm the effects of inputs with smaller values. The combination of transforming and standardizing the input variables created optimal conditions for the regression analysis.

Step 7. Check the correlations of the tr ansformed input variables. Remove any input variables that are highly correlated with other input variables in the data set. If two or more input variables were highly correlated with one another, only one was retained in the regression model. Keeping highly correlated variables will reduce the significance of each in the model, because each is essentially explaining the same linear relationship with the response variable (i.e., the effect of one such variable may mask the effect of another). Appendix B contains a list of all the highly correlated inputs encountered in the analysis as determined by a correlation coefficient >= 0.8. The highlighted member of each group indicates the variable that was retained in the analysis.

Step 8. Determine the form of the regression model. The starting form of the regression model included linear, squared, and second-order interaction terms for each independent variable. More complex terms (e.g., higher-order polynomials or interactions) were not considered in the model. Even given this limitation in model complexity, an example regression model included more than 1,000 terms in its initial form.

Step 9. Use response surface regression methods to eliminate insignificant terms in the model. An iterative process was used to eliminate terms from the model that did not satisfy the significance criterion (p-value <= 0.01). After the response surface regression results were obtained, the model was modified based on the following rules:



### # Any second-order terms that were determined not to have a significant effect were dropped from the model.



### # Any first-order term that was part of a significant second-order term remained in the model, regardless of the level of significance of the first-order term.



### # Any first-order terms that were determined not to be significant and not to have any significant second-order terms were dropped from the model.

A regression analysis was then conducted on the reduced model. This process was repeated until all of the second-order terms in the model had significant effects on risk or hazard quotient, and no more terms could be removed.Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

6 (1)

(2)

(3) Step 10. Use the regression models for environmental concentration to develop regression models for the risk (or hazard quotient). The original methodology included the following equation as a model for risk:

The natural log form of the equation is shown in Equation 2. The environmental concentration term was replaced with the final regression model developed in the previous step.

For the current sensitivity analysis, exposure factors (including risk factors, exposure duration, intake, and body weight) were not included in the model. This decision was made because the values for exposure factors were taken from national distributions and cannot be regulated. Therefore, the risk model reduces to

A regression analysis was performed for risk. Because environmental concentration and risk were highly correlated, the results for the models were very similar. This final regression equation is referred to as the “full regression model.”

Step 11. Rank the independent variables according to the degree that each explains the variation in risk. To determine the degree that each independent variable explains the variation in risk, a regression technique was employed that compares the results of the full regression model (determined in Step 10) to a reduced model in which all terms for a specific independent variable have been eliminated. The degree to which the goodness of the model changes is a measure of the sensitivity of risk to the independent variable that was removed. The sensitivity measure is referred to as the “percent variation,” and was used to assign relative rankings to the inputs.



# 4.0 Test Results

Prior to conducting the full sensitivity analysis, the RSM was applied to a small numberof scenarios to find technical problems and ensure that results showed both a high goodness-offit measure as well as a high percent variation value for the significant terms. Four landfill scenarios were selected for this initial testing procedure:



### # Mercury, Conventional Fly and Bottom Ash, Fish Consumption Pathway;



### # Mercury, Conventional Fly and Bottom Ash, Drinking Water Pathway;Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

7 

### # Arsenic(V), Conventional Fly and Bottom Ash, Drinking Water Pathway; and



### # Arsenic(III), Conventional Fly and Bottom Ash, Drinking Water Pathway.

Preliminary results showed that the regression equations exhibited high goodness-of-fit measures (R 2 values ranging from 0.84-0.89), but that the percent variation results were unsatisfactory, with total percent variation of less than 30% and no single parameter showing a value greater than 8%.

A wide range of actions were taken to investigate this outcome, including quality assurance on the input data, an analysis of the transformation process, a series of regression analyses using intermediate values (e.g., well concentration) as the dependent variables, and graphical analysis of relationships between sp ecific independent variables and risk. The problem was finally traced to the fact that the scenarios being analyzed had a high proportion (27-42%) of zero risk values. The zero risk values resulted from a wide combination of input parameters, including composite liners, low infiltration rates, and low waste concentration. Composite-lined units had a much higher incidence of zero risk values than unlined or clay-lined units, as shown in Table 2.

Table 2. Percent of Zero Risk Values by Liner Type and Scenario

Liner Type Minimum % Zero By Scenario Maximum % Zero By Scenario Median % Zero By Scenario

No Liner 0.2 87.0 11.5
Clay Liner 0.2 71.9 5.3
Composite Liner 35.6 100.0 95.0

The fact that a wide variety of input parameters resulted in zero risk values complicated the analysis. For example, Figure 1 shows a graph of risk versus infiltration rate. Although a general relationship between the two variables can be observed, the horizontal line of zero risk values for a wide range of infiltration rates prevents the regression analysis (containing only second-order terms) from observing a significant relationship.

The methodology was modified to remove all zero risk values from the data set prior to conducting the regression analysis. Table 3 shows the difference in results as a result of this modification. The overall goodness-of-fit measures are still relatively high (R 2 values ranging from 0.67-0.88), but the percent variation values are much improved (73-110%). Note that the total percent variation for the regression model can exceed 100% due to the overlap between individual input variables in the second-order interaction terms.Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

8 -80 -70 -60 -50 -40 -30 -20 -10

0 -18 -16 -14 -12 -10 -8 -6 -4 -2 0

LN(SINFIL)LN(RISK)

Figure 1. Example Graph of Risk Versus Infiltration Rate

Table 3. Comparison of Test Results With and Without Zero Risk Values

Scenario All Results No 0 Risk Values

% Zero Risk Values R 2 Sum Percent Variation R 2 Sum Percent Variation Mercury Convention fly and bottom ash Fish consumption Adult 33% 0.89 27% 0.88 73%

Mercury Convention fly and bottom ash Drinking water Adult 32% 0.86 22% 0.67 111%

Arsenic(V) Convention fly and bottom ash Drinking water Adult 42% 0.84 28% 0.83 88%

Arsenic(III) Convention fly and bottom ash Drinking water Adult 27% 0.85 20% 0.72 65%Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

9 

# 5.0 Results

When the sensitivity analysis was completed for all scenarios under consideration for human receptors, it indicated a wide range in goodness-of-fit values for the regression models (R 2 values ranging from 0.53-0.90, with a median of 0.70). In general, the regression models for the fish consumption pathway tended to have higher goodness-of-fit values (0.76-0.90) compared to the drinking water pathway (0.53-0.83). Although these results are deemed acceptable, R 2 values of 0.7 or higher are generally considered good results, with lower values indicating that the model is missing important inputs, requires additional terms, or that implicit assumptions are not being addressed by the model. This may be an area for future investigation.

In examining the percent variation results, inputs with a percent variation value greater than 5% were generally regarded as important. The percent variation is not useful for ranking variables below this threshold because the model results do not change significantly when these variables are removed from the regression model.

The sum of percent variation for the scenarios was excellent, ranging from 70% to >100%. In general, the drinking water pathway tended to have a larger number of input parameters that were significant, while the fish consumption pathway only showed a small number (typically <= 3) as significant. This may explain the difference in goodness-of-fit values observed between the pathways.

The variables that were significant for the drinking water pathway tended to be source characteristics and aquifer variables. The drinking water pathway had seven important parameters, four of which were significant in > 75% of the scenarios, and three that were significant for about one-third of the scenarios:

Drinking Water—Important in most scenarios



### # Infiltration rate within the WMU footprint (SINFIL)



### # Leachate concentration from the WMU (CZERO(1))



### # Hydraulic conductivity of the aquifer (XKX)



### # Regional hydraulic gradient in the aquifer (GRADNT)

Drinking Water—Important in many scenarios



### # Leading Freundlich adsorption isotherm coefficient (SFCOF(1))



### # Depth from ground surface to water table (DSOIL)



### # Distance between well and down-gradient edge of WMU (XWELL(1))

The relative importance of groundwater flow parameters (shown in the first group) and sorption and travel time parameters (shown in the second group) was related to the magnitude of a chemical’s partition coefficient (Kd). For example, arsenic(V) had a much higher Kd value at low concentrations compared to arsenic(III), indicating that sorption was a more dominant process. Accordingly, the most significant variables for arsenic(V) were adsorption isothermSensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

10 coefficient and depth from ground surface to wate r table; the most significant variables for arsenic(III) were infiltration rate and leachate concentration.

The variables that tended to be significant for the fish consumption pathway were source characteristics and waterbody parameters. Unlike the drinking water pathway, only three variables were consistently significant across scenarios:

Fish Consumption



### # Infiltration rate within the WMU footprint (SINFIL)



### # Leachate concentration from the WMU (CZERO(1))



### # Waterbody flow rate (VFX)

Some of the input variables listed above were highly correlated to other input variables. Although these correlated variables were dropped from the regression model, it is important to consider them as related drivers of risk. The specific groups of highly correlated variables varied by scenario, but some of the commonly encountered correlations were:



### # Infiltration rate and WMU area and duration of leaching.



### # Leachate concentration and buffering capacity of the soil column below the WMU.



### # The leading Freundlich adsorption isotherm coefficient and other isotherm parameters and governing pH.

Appendix C contains a summary of the sensitivity results by scenario.



# 6.0 Recommendations

The current sensitivity analysis produced regression models with (1) moderate to excellent goodness-of-fit values, (2) excellent percent variation results, and (3) a rank order of variables that is consistent with chemical properties and exposure pathways.

Future work should focus on improving the goodness-of-fit values for those regression equations with R 2 values less than 0.7, and modifying the RSM (to the extent possible) to address the issue of zero risk values. The following two specific actions should be considered:



### # Conducting a categorical regression analysis: This would permit the inclusion of categorical variables, which may be significant drivers of risk. Liner type, in particular, is likely to be significant given the high proportion of zero infiltration rates and risk values for composite liners.



### # Including higher-order terms in the regression model: Given the many correlations and interactions between input variables, it may be insightful to test aSensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

11 model with higher order terms to determine the impact of model form on the results.



# 7.0 References

U.S. EPA (Environmental Protection Agency). 2003. Technical Background Document for the Sewage Sludge Screening Analysis . Internal Deliberative Draft. Office of Water. Washington, DC.Sensitivity Analysis for the Fossil Fu el Combustion Waste Risk Assessment

12 [This page intentionally left blank.]

# Appendix A

Input Variables[This page intentionally left blank.]Appendix A – Input Variables

A-3 Table A-1. Input Variables Included in Sensitivity Analysis

Variable Description AL Longitudinal dispersivity in the aquifer ALPHA Moisture retention parameter (Van Genuchten) AQMIXPH Aquifer pH AREA Area of assumed square-shaped WMU ASNOM Aquifer fraction of organic carbon AT_aq Horizontal transverse dispersivity in the aquifer AV Vertical transverse dispersivity in the aquifer BETA Moisture retention parameter BULKD Aquifer soil bulk density CWASTE Buffering capacity of soil column beneath WMU, used for metal simulations, only CZERO(1) Leachate concentration from WMU DBGS Depth below ground surface of WMU base DEPTH Landfill/Waste Pile depth DISPR Longitudinal dispersivity in unsaturated zone DISSW Distance to nearest surface water body for surface impoundments DSOIL Depth from ground surface to water table dwc Depth of water column dz waterbody depth Facility ID Unique ID for each WMU/plant combination (for tracking sites) FEOX Weight percentage of iron-hydroxide in the soil and aquifer FOC Fraction of organic carbon in saturated soils GOVPH Governing pH GRADNT Regional hydraulic gradient in the aquifer ICLR Index to nearest climate station to WMU IGWTYPE Groundwater chemistry identification number ISTYPE2 Surface soil capping WMU Kdbs Soil-water partition coefficient for bed sediment Kdsw Soil-water partition coefficient for suspended sediment PH_GW Mixed groundwater pH pH_Soil Soil pH pH_sw Waterbody pH PHLEACH Leachate pH; used for metal simulations only Plume_Width Plume width at the waterbody POM Percent organic matter POR Volume fraction of connected pore space in the aquiferAppendix A – Input Variables

Variable Description

A-4 RECHRG Regional infiltration/recharge rate outside the WMU footprint RETARD(1) Retardation coefficient RHOB Bulk density of unsaturated soil SampleID Unique Id for each sample chosen for constituent data SATK Saturated hydraulic conductivity SFCOF(1) Leading Freundlich adsorption isotherm coefficient - for linear isotherms (UFEXP = 1), this is Kd SINFIL Infiltration rate inside the WMU footprint TEMP Ambient groundwater temperature TSOURC Duration of leaching TSS Total suspended solids Twater Temperature of the Waterbody U Current velocity of the waterbody UFCOF(1) Leading Freundlich adsorption isotherm coefficient - for liner isotherms (UFEXP = 1), this is Kd USNOM Unsaturated zone percentage of organic matter uw Mean annual windspeed Vfx Waterbody annual flow mixing volume VXCS Regional groundwater seepage velocity WCR Residual water content WCS Saturated water content (effective porosity) Width_river Width of river XKX Hydraulic conductivity of saturated zone (aquifer) XTRAVAR(1) First extra master variable XW Length of WMU side = sqrt(AREA) XWELL(1) Distance between observation well and down-gradient edge of WMU YD Length of WMU side = sqrt(AREA) YWELL(1) Distance off-centerline to observation well ZB Thickness of saturated zone ZWELL(1) Depth of observation location below water table (as fraction of saturated thickness) ZWELL(2) Depth of observation location below water table (as fraction of saturated thickness)Appendix A – Input Variables

A-5 Table A-2. Variables Not Included in Sensitivity Analysis - Constants

Input Variable Description ANIST Ratio of horizontal to vertical hydraulic conductivity bsc Bed sediment particle concentration bsp Bed sediment porosity CTDENS Density of leachable waste in a landfill db Depth of Upper Benthic Layer DIAM Average soil particle diameter DWS(1) Drinking water standard for species 1 EF Exposure frequency F_dw Fraction of drinking water ingested that is contaminated F_fish Fraction of fish intake from contaminated source F_T3 Fraction of trophic level 3 intake, 0.36 F_T4 Fraction of trophic level 4 intake, 0.64 FRACT Volume fraction of leachable waste in a landfill Kg_river Gas-phase transfer coefficient LOM Concentration of dissolved organic matter in the waste leachate RTEMP Reference temperature Theta_water Temperature correction factor THLC Temperature at which HLC values is given WB Rate of burial XWELL(2) Distance between observation well and downgradient edge of WMU YWELL(2) Distance off-centerline to oberservation well

Table A-3. Variables Not Included in Sensitivity Analysis - Constants by Chemical

Input Variable Description AHYDR(1) Second-order acid catalyzed hydrolysis rate constant at reference temperature for species 1 BCF_T3F Bioconcentration factor for trophic level 3, fish fillet BCF_T4F Bioconcentration factor for trophic level 4, fish fillet BHYDR(1) Second-order base catalyzed hydrolysis rate constant at reference temperature for species 1 BIOS(1) Biological degradation rate coefficient BIOU(1) Biological degradation rate coefficient CMWGT(1) Molecular weight for species 1 CSCL_aquatic Ecological CSCL for surface waterAppendix A – Input Variables

Input Variable Description

A-6 CSCL_sediment Ecological CSCL for sediment CSFOral Oral cancer slope factor CSLAM(1) Chemical degradation rate coefficient Da Diffusivity of chemical in air DSTAR(1) Diffusivity in water for species 1 Dw Diffusion coefficient in water HLC Henry's law constant kh Hydrolysis rate KOC(1) Normalized distribution coefficient for organic carbon for species 1 ksed Degradation rate for sediment ksw Degradation rate for surface-water column METALID Identification number for the metal NHYDR(1) Neutral hydrolysis rate constant at reference temperature for species 1 RfD_fish Noncancer reference dose RfD_food Noncancer reference dose RfD_soil Noncancer reference dose RfD_water Noncancer reference dose RLAM1(1) Dissolved phase hydrolysis decay rate for species 1 RLAM2(1) Sorbed phase hydrolysis decay rate for species 1 SFEXP(1) Freundlich exponent (always 1 for linear isotherm) UCLAM(1) Chemical degradation rate coefficient UFEXP(1) Freundlich exponent (always 1 for linear isotherm)Appendix A – Input Variables

A-7 Table A-4. Variables Not Included in Sensitivity Analysis - Exposure Factors

Input Variable Description AT Averaging time BW_1 Body weight, cohort 1 BW_2 Body weight, cohort 2 BW_3 Body weight, cohort 3 BW_4 Body weight, cohort 4 CARC(1,1) Base exposure period for calculating average receptor well concentration (if specified) for child CARC(1,2) Base exposure period for calculating average receptor well concentration (if specified) for adult CR_dw_1 Consumption rate of water, cohort 1 CR_dw_2 Consumption rate of water, cohort 2 CR_dw_3 Consumption rate of water, cohort 3 CR_dw_4 Consumption rate of water, cohort 4 CR_fish Consumption rate of fish

Table A-5. Variables Not Included in Sensit ivity Analysis - Categorical Variables

Input Variable Description CoalType Coal type HREG Hydrologic region IGWR Index to groundwater region/hydrogeologic environment associated with WMU ISTYPE1 Prevalent regional surface soils around WMU LinerID EPACMTP liner type (0, 1, 2 for no liner, clay liner, composite liner, respectively) Wbtype Waterbody type (L or R)[This page intentionally left blank.]

# Appendix B

Highly Correlated Input Variables[This page intentionally left blank.]Appendix B – Highly Correlated Input Variables

B-3 

# Appendix B Highly Correlated Input Variables

Note: The highlighted variable is the variable that was selected to remain in the analysis. Highly correlated parameters are presented in the results section for any input variable that was deemed significant.

1) AREA WMU area YD WMU length XW WMU width Plume_Width

2) USNOM percent organic matter (unsaturated zone) POM percent organic matter (unsaturated zone)

3) ALPHA soil moisture parameter alpha BETA soil moisture parameter beta RHOB soil bulk density

4) DSOIL vadose zone thickness / depth to water DISPR dispersivity in vadose zone

5) DIAM aquifer particle diameter BULKD aquifer bulk density

6) POR aquifer effective porosity RETARD(1) retardation coefficient

7) AL longitudinal dispersivity AV vertical dispersivity AT_aq transverse dispersivityAppendix B – Highly Correlated Input Variables

B-4 8) XWELL(1) well distance RADIS(1) radial well distance

9) CZERO(1) leachate concentration from WMU CWASTE Buffering capacity of soil column beneath WMU, used for metal simulations, only LeachateMax - Peak leachate concentration, well - intermediate??

10) dwc dz U Width_river Vfx

11) dwc, depth of water column dz, waterbody depth

12) TEMP, Ambient groundwater temperature Twater, Temperature of the Waterbody

13) RECHRG, Regional infiltration/recharge rate outside the WMU footprint pH_Soil, Soil pH

14) RETARD(1) SFCOF(1)

15) PHLEACH GOVPH

16) AQMIXPH PH_GW

17) GOVPH SFCOF(1)Appendix B – Highly Correlated Input Variables

B-5 18) PH_SOIL PH_SW

19) DWC VFX

20) TSOURC SINFIL

21) USNOM TWATER

22) AREA SINFIL

23) GOVPH AQMIXPH

24) SINFIL PH_SOIL

25) RECHRG SINFIL[This page intentionally left blank.]

# Appendix C

Response Surface Regression Results by Scenario[This page intentionally left blank.]Appendix C – Response Surface Regression Results by Scenario

C-3 Table C-1. Goodness-of-fit Values for the Final Regression Models by Scenario

Chemical Benchmark WMU Pathway Waste Receptor Full R 2 Antimony Noncancer SI DW A Adult 0.62 Antimony Noncancer SI DW A Child 0.62 Arsenic (III) Cancer LF DW A Adult 0.72 Arsenic (III) Cancer LF DW A Child 0.73 Arsenic (III) Cancer LF DW AC Adult 0.68 Arsenic (III) Cancer LF DW AC Child 0.68 Arsenic (III) Cancer LF DW FBC Adult 0.59 Arsenic (III) Cancer LF DW FBC Child 0.60 Arsenic (III) Cancer SI DW A Adult 0.62 Arsenic (III) Cancer SI DW A Child 0.62 Arsenic (III) Cancer SI FC A Adult 0.80 Arsenic (III) Cancer SI FC A Child 0.80 Arsenic (III) Cancer SI DW AC Adult 0.66 Arsenic (III) Cancer SI DW AC Child 0.68 Arsenic (III) Cancer SI FC AC Adult 0.82 Arsenic (III) Cancer SI FC AC Child 0.83 Arsenic (V) Cancer LF DW A Adult 0.83 Arsenic (V) Cancer LF DW A Child 0.83 Arsenic (V) Cancer LF DW AC Adult 0.77 Arsenic (V) Cancer LF DW AC Child 0.77 Arsenic (V) Cancer LF DW FBC Adult 0.65 Arsenic (V) Cancer LF DW FBC Child 0.66 Arsenic (V) Cancer SI DW A Adult 0.74 Arsenic (V) Cancer SI DW A Child 0.74 Arsenic (V) Cancer SI FC A Adult 0.77 Arsenic (V) Cancer SI FC A Child 0.78 Arsenic (V) Cancer SI DW AC Adult 0.66 Arsenic (V) Cancer SI DW AC Child 0.67 Arsenic (V) Cancer SI FC AC Adult 0.76 Arsenic (V) Cancer SI FC AC Child 0.77 Boron NC SI DW A Adult 0.69 Boron NC SI DW A Child 0.69 Boron Noncancer SI DW AC Adult 0.55 Boron Noncancer SI DW AC Child 0.55 Cadmium Noncancer SI DW A Adult 0.66 Cadmium Noncancer SI DW A Child 0.66 Cadmium Noncancer SI DW AC Adult 0.64 Cadmium Noncancer SI DW AC Child 0.64 Cobalt Noncancer SI DW A Adult 0.58 Cobalt Noncancer SI DW A Child 0.59 Cobalt Noncancer SI DW AC Adult 0.58 Cobalt Noncancer SI DW AC Child 0.58 Divalent Mercury Noncancer LF FC A Adult 0.88 Divalent Mercury Noncancer LF FC A Child 0.88 Divalent Mercury Noncancer LF FC AC Adult 0.89 Divalent Mercury Noncancer LF FC AC Child 0.89 Divalent Mercury Noncancer LF FC FBC Adult 0.90Table C-1. (Continued) Appendix C – Response Surface Regression Results by Scenario

Chemical Benchmark WMU Pathway Waste Receptor Full R 2

C-4 Divalent Mercury Noncancer LF FC FBC Child 0.90 Divalent Mercury Noncancer SI FC AC Adult 0.83 Divalent Mercury Noncancer SI FC AC Child 0.83 Lead Noncancer SI DW A Adult 0.67 Lead Noncancer SI DW A Child 0.67 Lead Noncancer SI DW AC Adult 0.61 Lead Noncancer SI DW AC Child 0.61 Molybdenum Noncancer SI DW A Adult 0.66 Molybdenum Noncancer SI DW A Child 0.66 Molybdenum Noncancer SI DW AC Adult 0.53 Molybdenum Noncancer SI DW AC Child 0.53 Selenium (IV) Noncancer SI DW A Adult 0.78 Selenium (IV) Noncancer SI DW A Child 0.78 Selenium (IV) Noncancer SI FC A Adult 0.80 Selenium (IV) Noncancer SI FC A Child 0.80 Selenium (IV) Noncancer SI DW AC Adult 0.80 Selenium (IV) Noncancer SI DW AC Child 0.80 Selenium (IV) Noncancer SI FC AC Adult 0.79 Selenium (IV) Noncancer SI FC AC Child 0.80 Selenium (VI) Noncancer SI DW A Adult 0.66 Selenium (VI) Noncancer SI DW A Child 0.66 Selenium (VI) Noncancer SI FC A Child 0.81 Selenium (VI) Noncancer SI FC A Adult 0.82 Selenium (VI) Noncancer SI DW AC Adult 0.61 Selenium (VI) Noncancer SI DW AC Child 0.61 Selenium (VI) Noncancer SI FC AC Adult 0.82 Selenium (VI) Noncancer SI FC AC Child 0.82 Thallium Noncancer LF DW A Adult 0.70 Thallium Noncancer LF DW A Child 0.70 Thallium Noncancer LF DW AC Adult 0.61 Thallium Noncancer LF DW AC Child 0.61 Thallium Noncancer LF DW FBC Adult 0.63 Thallium Noncancer LF DW FBC Child 0.63 Thallium Noncancer SI DW A Adult 0.68 Thallium Noncancer SI DW A Child 0.68 Thallium Noncancer SI FC A Adult 0.82 Thallium Noncancer SI FC A Child 0.82C-5Table C-2. Percent Variation Results by ScenarioChemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 VAR3 PV3Antimony Noncancer SI DW A Adult SINFIL 35% XKX 10% CZERO(1) 9%Antimony Noncancer SI DW A Child SINFIL 35% XKX 10% CZERO(1) 9%Arsenic (III) Cancer LF DW A Adult CZERO(1) 16% XKX 11% GRADNT 6%Arsenic (III) Cancer LF DW A Child CZERO(1) 17% XKX 10% GRADNT 6%Arsenic (III) Cancer LF DW AC Adult TSOURC 32% CZERO(1) 15% XKX 10%Arsenic (III) Cancer LF DW AC Child TSOURC 32% CZERO(1) 15% XKX 10%Arsenic (III) Cancer LF DW FBC Adult XKX 20% TSOURC 18% CWASTE 15%Arsenic (III) Cancer LF DW FBC Child TSOURC 19% XKX 19% CWASTE 16%Arsenic (III) Cancer SI DW A Adult SINFIL 42% CZERO(1) 22% XKX 8%Arsenic (III) Cancer SI DW A Child SINFIL 43% CZERO(1) 22% XKX 7%Arsenic (III) Cancer SI FC A Adult VFX 21% SINFIL 20% CZERO(1) 9%Arsenic (III) Cancer SI FC A Child VFX 21% SINFIL 19% CZERO(1) 9%Arsenic (III) Cancer SI DW AC Adult CZERO(1) 53% SINFIL 13% XKX 6%Arsenic (III) Cancer SI DW AC Child CZERO(1) 54% SINFIL 13% XKX 6%Arsenic (III) Cancer SI FC AC Adult CZERO(1) 22% VFX 16% SINFIL 7%Arsenic (III) Cancer SI FC AC Child CZERO(1) 22% VFX 15% SINFIL 7%Arsenic (V) Cancer LF DW A Adult SFCOF(1) 33% DSOIL 24% SINFIL 17%Arsenic (V) Cancer LF DW A Child SFCOF(1) 33% DSOIL 24% SINFIL 17%Arsenic (V) Cancer LF DW AC Adult SFCOF(1) 28% DSOIL 16%Arsenic (V) Cancer LF DW AC Child SFCOF(1) 28% DSOIL 16%Arsenic (V) Cancer LF DW FBC Adult SFCOF(1) 44% VFX 19% DSOIL 17%Arsenic (V) Cancer LF DW FBC Child SFCOF(1) 44% VFX 19% DSOIL 17%Arsenic (V) Cancer SI DW A Adult SFCOF(1) 32% SINFIL 26% DSOIL 13%Arsenic (V) Cancer SI DW A Child SFCOF(1) 31% SINFIL 26% DSOIL 12%Arsenic (V) Cancer SI FC A Adult SINFIL 23% SFCOF(1) 22% VFX 12%Arsenic (V) Cancer SI FC A Child SINFIL 21% SFCOF(1) 20% DSOIL 11%Arsenic (V) Cancer SI DW AC Adult CZERO(1) 19% SINFIL 19% SFCOF(1) 16%Arsenic (V) Cancer SI DW AC Child CZERO(1) 20% SINFIL 19% SFCOF(1) 16%Arsenic (V) Cancer SI FC AC Adult VFX 16% SINFIL 14% CZERO(1) 11%Arsenic (V) Cancer SI FC AC Child VFX 16% SINFIL 14% CZERO(1) 11%C-6Table C-2. (Continued)Chemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 VAR3 PV3Boron NC SI DW A Adult CZERO(1) 28% SINFIL 27%Boron NC SI DW A Child CZERO(1) 28% SINFIL 27% XKX 5%Boron Noncancer SI DW AC Adult SINFIL 21% CZERO(1) 20% XKX 12%Boron Noncancer SI DW AC Child SINFIL 22% CZERO(1) 20% XKX 12%Cadmium Noncancer SI DW A Adult SINFIL 49% CZERO(1) 10% AREA 7%Cadmium Noncancer SI DW A Child SINFIL 49% CZERO(1) 10% AREA 6%Cadmium Noncancer SI DW AC Adult CZERO(1) 46% SINFIL 13% GRADNT 7%Cadmium Noncancer SI DW AC Child CZERO(1) 46% SINFIL 14% GRADNT 7%Cobalt Noncancer SI DW A Adult SINFIL 27% SFCOF(1) 20% XKX 12%Cobalt Noncancer SI DW A Child SINFIL 27% SFCOF(1) 20% XKX 12%Cobalt Noncancer SI DW AC Adult CZERO(1) 23% SINFIL 21% XKX 7%Cobalt Noncancer SI DW AC Child CZERO(1) 23% SINFIL 21% XKX 7%Divalent Mercury Noncancer LF FC A Adult VFX 46% CZERO(1) 9%Divalent Mercury Noncancer LF FC A Child VFX 46% CZERO(1) 9%Divalent Mercury Noncancer LF FC AC Adult VFX 19% UFCOF(1) 10% DSOIL 8%Divalent Mercury Noncancer LF FC AC Child VFX 19% UFCOF(1) 10% DSOIL 8%Divalent Mercury Noncancer LF FC FBC Adult TSOURC 8% CWASTE 6%Divalent Mercury Noncancer LF FC FBC Child TSOURC 8% CWASTE 6%Divalent Mercury Noncancer SI FC AC Adult VFX 33% SINFIL 5% AREA 5%Divalent Mercury Noncancer SI FC AC Child VFX 31%Lead Noncancer SI DW A Adult SINFIL 36% CZERO(1) 21% DSOIL 14%Lead Noncancer SI DW A Child SINFIL 36% CZERO(1) 21% DSOIL 14%Lead Noncancer SI DW AC Adult CZERO(1) 31% SINFIL 22% SFCOF(1) 10%Lead Noncancer SI DW AC Child CZERO(1) 31% SINFIL 23% SFCOF(1) 10%Molybdenum Noncancer SI DW A Adult SINFIL 37% CZERO(1) 14% XKX 6%Molybdenum Noncancer SI DW A Child SINFIL 35% CZERO(1) 14% XKX 6%Molybdenum Noncancer SI DW AC Adult SINFIL 21% XKX 10% GRADNT 8%Molybdenum Noncancer SI DW AC Child SINFIL 22% XKX 10% CZERO(1) 8%Selenium (IV) Noncancer SI DW A Adult SINFIL 19% FEOX 17% GOVPH 14%Selenium (IV) Noncancer SI DW A Child SINFIL 19% FEOX 17% GOVPH 14%Selenium (IV) Noncancer SI FC A Adult SINFIL 23% FEOX 13% GOVPH 12%Selenium (IV) Noncancer SI FC A Child SINFIL 23% FEOX 13% GOVPH 12%C-7Table C-2. (Continued)Chemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 VAR3 PV3Selenium (IV) Noncancer SI DW AC Adult SFCOF(1) 35% SINFIL 15% XWELL(1) 8%Selenium (IV) Noncancer SI DW AC Child SFCOF(1) 35% SINFIL 15% XWELL(1) 8%Selenium (IV) Noncancer SI FC AC Adult SFCOF(1) 32% SINFIL 19% DISSW 8%Selenium (IV) Noncancer SI FC AC Child SFCOF(1) 31% SINFIL 19% DSOIL 8%Selenium (VI) Noncancer SI DW A Adult SINFIL 32% CZERO(1) 18% XKX 5%Selenium (VI) Noncancer SI DW A Child SINFIL 32% CZERO(1) 19% XKX 5%Selenium (VI) Noncancer SI FC A Child VFX 18% SINFIL 17% CZERO(1) 9%Selenium (VI) Noncancer SI FC A Adult SINFIL 17% VFX 16% CZERO(1) 9%Selenium (VI) Noncancer SI DW AC Adult CZERO(1) 36% SINFIL 15% XKX 9%Selenium (VI) Noncancer SI DW AC Child CZERO(1) 36% SINFIL 15% XKX 10%Selenium (VI) Noncancer SI FC AC Adult VFX 27% CZERO(1) 13% SINFIL 8%Selenium (VI) Noncancer SI FC AC Child VFX 27% CZERO(1) 13% SINFIL 8%Thallium Noncancer LF DW A Adult SINFIL 21% XKX 14% UW 11%Thallium Noncancer LF DW A Child SINFIL 21% XKX 13% UW 10%Thallium Noncancer LF DW AC Adult TSOURC 35% XKX 21% GRADNT 12%Thallium Noncancer LF DW AC Child TSOURC 35% XKX 21% GRADNT 12%Thallium Noncancer LF DW FBC Adult CZERO(1) 16% XKX 15% GRADNT 12%Thallium Noncancer LF DW FBC Child CZERO(1) 16% XKX 15% GRADNT 11%Thallium Noncancer SI DW A Adult SINFIL 26% CZERO(1) 12% XKX 5%Thallium Noncancer SI DW A Child SINFIL 26% CZERO(1) 12% XKX 5%Thallium Noncancer SI FC A Adult VFX 16% SINFIL 13% CZERO(1) 6%Thallium Noncancer SI FC A Child VFX 16% SINFIL 13% CZERO(1) 6%C-8Table C-2. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 PV6 VAR7 PV7Antimony Noncancer SI DW A Adult GRADNT 6%Antimony Noncancer SI DW A Child GRADNT 6%Arsenic (III) Cancer LF DW A AdultArsenic (III) Cancer LF DW A ChildArsenic (III) Cancer LF DW AC Adult DEPTH 9% GRADNT 6%Arsenic (III) Cancer LF DW AC Child DEPTH 9% GRADNT 6%Arsenic (III) Cancer LF DW FBC Adult GRADNT13% CZERO(1) 9% XWELL(1) 7% ZWELL(1) 7%Arsenic (III) Cancer LF DW FBC Child GRADNT 12% CZERO(1) 9% XWELL(1) 7% ZWELL(1) 6%Arsenic (III) Cancer SI DW A Adult GRADNT 5%Arsenic (III) Cancer SI DW A Child GRADNT 5%Arsenic (III) Cancer SI FC A AdultArsenic (III) Cancer SI FC A ChildArsenic (III) Cancer SI DW AC Adult GRADNT 6% TWATER 5%Arsenic (III) Cancer SI DW AC Child GRADNT 5% TWATER 5%Arsenic (III) Cancer SI FC AC AdultArsenic (III) Cancer SI FC AC ChildArsenic (V) Cancer LF DW A Adult CZERO(1) 5%Arsenic (V) Cancer LF DW A Child CZERO(1) 5%Arsenic (V) Cancer LF DW AC AdultArsenic (V) Cancer LF DW AC ChildArsenic (V) Cancer LF DW FBC Adult XKX 6%Arsenic (V) Cancer LF DW FBC Child XKX 6%Arsenic (V) Cancer SI DW A Adult XWELL(1) 5% CZERO(1) 5% GRADNT 5%Arsenic (V) Cancer SI DW A Child XWELL(1) 5% GRADNT 5% CZERO(1) 5%Arsenic (V) Cancer SI FC A Adult DSOIL 11% DISSW 7%Arsenic (V) Cancer SI FC A Child VFX 10% DISSW 7%Arsenic (V) Cancer SI DW AC Adult DSOIL 5%Arsenic (V) Cancer SI DW AC Child DSOIL 5%Arsenic (V) Cancer SI FC AC Adult SFCOF(1) 9%Arsenic (V) Cancer SI FC AC Child SFCOF(1) 9%Boron Noncancer SI DW A AdultC-9Table C-2. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 PV6 VAR7 PV7Boron Noncancer SI DW A ChildBoron Noncancer SI DW AC Adult TWATER 10% GRADNT 9% DWC 7% TEMP 6%Boron Noncancer SI DW AC Child TWATER 9% GRADNT 9% DWC 7% TEMP 5%Cadmium Noncancer SI DW A Adult GRADNT 6% XKX 5%Cadmium Noncancer SI DW A Child GRADNT 6% XKX 5%Cadmium Noncancer SI DW AC Adult XKX 6% TWATER 5% DWC 5%Cadmium Noncancer SI DW AC Child XKX 7% TWATER 5% DWC 5%Cobalt Noncancer SI DW A Adult GRADNT 8% XWELL(1) 8% ZB 6% AREA 5%Cobalt Noncancer SI DW A Child GRADNT 8% XWELL(1) 8% ZB 6% AREA 5%Cobalt Noncancer SI DW AC Adult GRADNT 6%Cobalt Noncancer SI DW AC Child GRADNT 7%Divalent Mercury Noncancer LF FC A AdultDivalent Mercury Noncancer LF FC A ChildDivalent Mercury Noncancer LF FC AC AdultDivalent Mercury Noncancer LF FC AC ChildDivalent Mercury Noncancer LF FC FBC AdultDivalent Mercury Noncancer LF FC FBC ChildDivalent Mercury Noncancer SI FC AC AdultDivalent Mercury Noncancer SI FC AC ChildLead Noncancer SI DW A Adult SFCOF(1) 13% XWELL(1) 6% GRADNT 6% XKX 6%Lead Noncancer SI DW A Child SFCOF(1) 13% GRADNT 6% XWELL(1) 6% XKX 5%Lead Noncancer SI DW AC Adult XKX 7% GRADNT 7% DSOIL 6% XWELL(1) 5%Lead Noncancer SI DW AC Child XKX 7% GRADNT 7% DSOIL 6% XWELL(1) 5%Molybdenum Noncancer SI DW A AdultMolybdenum Noncancer SI DW A ChildMolybdenum Noncancer SI DW AC Adult CZERO(1) 8% TWATER 8% DWC 6% TEMP 5%Molybdenum Noncancer SI DW AC Child GRADNT 8% TWATER 8% TEMP 6% DWC 6%Selenium (IV) Noncancer SI DW A Adult DSOIL 9% XWELL(1) 7% GRADNT 7% XTRAVAR(1)5%Selenium (IV) Noncancer SI DW A Child DSOIL 9% XWELL(1) 7% GRADNT 7% XTRAVAR(1)5%C-10Table C-2. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 PV6 VAR7 PV7Selenium (IV) Noncancer SI FC A Adult DSOIL 10% DISSW 8% VFX 5%Selenium (IV) Noncancer SI FC A Child DSOIL 10% DISSW 8% VFX 5%Selenium (IV) Noncancer SI DW AC Adult GRADNT 7% DSOIL 7%Selenium (IV) Noncancer SI DW AC Child GRADNT 7% DSOIL 7%Selenium (IV) Noncancer SI FC AC Adult DSOIL 8% VFX 7%Selenium (IV) Noncancer SI FC AC Child DISSW 8% VFX 7%Selenium (VI) Noncancer SI DW A AdultSelenium (VI) Noncancer SI DW A ChildSelenium (VI) Noncancer SI FC A ChildSelenium (VI) Noncancer SI FC A AdultSelenium (VI) Noncancer SI DW AC Adult GRADNT 7% TWATER 5%Selenium (VI) Noncancer SI DW AC Child GRADNT 7% TWATER 6%Selenium (VI) Noncancer SI FC AC AdultSelenium (VI) Noncancer SI FC AC ChildThallium Noncancer LF DW A Adult GRADNT 8% CZERO(1) 6% RECHRG 5%Thallium Noncancer LF DW A Child GRADNT 8% CZERO(1) 6% RECHRG 5%Thallium Noncancer LF DW AC Adult CZERO(1) 11% DEPTH 10% XWELL(1) 7% ZWELL(1) 5%Thallium Noncancer LF DW AC Child CZERO(1) 12% DEPTH 11% XWELL(1) 7% ZWELL(1) 5%Thallium Noncancer LF DW FBC Adult SINFIL 8% DSOIL 7% XWELL(1) 6% ZB 5%Thallium Noncancer LF DW FBC Child SINFIL 8% DSOIL 7% XWELL(1) 6% ZB 5%Thallium Noncancer SI DW A AdultThallium Noncancer SI DW A ChildThallium Noncancer SI FC A AdultThallium Noncancer SI FC A ChildC-11Table C-3. Highly-correlated Variable Groups by ScenarioChemicalBenchmark WMU Pathway Waste Receptor High Correlations in Significant VariablesAntimony Noncancer SI DW A Adult NoneAntimony Noncancer SI DW A Child NoneArsenic (III) Cancer LF DW A Adult CZERO/CWASTEArsenic (III) Cancer LF DW A Child CZERO/CWASTEArsenic (III) Cancer LF DW AC Adult CZERO/CWASTEArsenic (III) Cancer LF DW AC Child CZERO/CWASTEArsenic (III) Cancer LF DW FBC Adult NoneArsenic (III) Cancer LF DW FBC Child NoneArsenic (III) Cancer SI DW A Adult NoneArsenic (III) Cancer SI DW A Child NoneArsenic (III) Cancer SI FC A Adult VFX->U,WIDTH_RIVERArsenic (III) Cancer SI FC A Child VFX->U,WIDTH_RIVERArsenic (III) Cancer SI DW AC Adult NoneArsenic (III) Cancer SI DW AC Child NoneArsenic (III) Cancer SI FC AC Adult VFX->U,WIDTH_RIVERArsenic (III) Cancer SI FC AC Child VFX->U,WIDTH_RIVERArsenic (V) Cancer LF DW A Adult CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Arsenic (V) Cancer LF DW A Child CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Arsenic (V) Cancer LF DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1)Arsenic (V) Cancer LF DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1)Arsenic (V) Cancer LF DW FBC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVER, DWC, DZ, UWArsenic (V) Cancer LF DW FBC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVER, DWC, DZ, UWArsenic (V) Cancer SI DW A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERC-12Table C-3. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor High Correlations in Significant VariablesArsenic (V) Cancer SI DW A Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERArsenic (V) Cancer SI FC A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERArsenic (V) Cancer SI FC A Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERArsenic (V) Cancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERArsenic (V) Cancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVERArsenic (V) Cancer SI FC AC Adult SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) Cancer SI FC AC Child SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERBoron NC SI DW A Adult NoneBoron NC SI DW A Child NoneBoron Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZBoron Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZCadmium Noncancer SI DW A Adult AREA->XW,YDCadmium Noncancer SI DW A Child AREA->XW,YDCadmium Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZCadmium Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZCobalt Noncancer SI DW A Adult SFCOF(1)->RETARD(1), UFCOF(1); AREA->XW,YDCobalt Noncancer SI DW A Child SFCOF(1)->RETARD(1), UFCOF(1); AREA->XW,YDCobalt Noncancer SI DW AC Adult NoneCobalt Noncancer SI DW AC Child NoneDivalent Mercury Noncancer LF FC A Adult CZERO/CWASTE; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC A Child CZERO/CWASTE; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC AC Adult DSOIL/DISPR; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC AC Child DSOIL/DISPR; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC FBC Adult NoneDivalent Mercury Noncancer LF FC FBC Child NoneDivalent Mercury Noncancer SI FC AC Adult VFX->U,WIDTH_RIVER; AREA->XW,YDDivalent Mercury Noncancer SI FC AC Child VFX->U,WIDTH_RIVERC-13Table C-3. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor High Correlations in Significant VariablesLead Noncancer SI DW A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead Noncancer SI DW A Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead Noncancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead Noncancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Molybdenum Noncancer SI DW A Adult NoneMolybdenum Noncancer SI DW A Child NoneMolybdenum Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZMolybdenum Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZSelenium (IV) Noncancer SI DW A Adult DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1)Selenium (IV) Noncancer SI DW A Child DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1)Selenium (IV) Noncancer SI FC A Adult DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (IV) Noncancer SI FC A Child DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (IV) Noncancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Selenium (IV) Noncancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Selenium (IV) Noncancer SI FC AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (IV) Noncancer SI FC AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI DW A Adult NoneSelenium (VI) Noncancer SI DW A Child NoneSelenium (VI) Noncancer SI FC A Child VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI FC A Adult VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI DW AC Adult NoneSelenium (VI) Noncancer SI DW AC Child NoneSelenium (VI) Noncancer SI FC AC Adult VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI FC AC Child VFX->U,WIDTH_RIVERThallium Noncancer LF DW A Adult CZERO(1)/CWASTE; SINFIL/TSOURCThallium Noncancer LF DW A Child CZERO(1)/CWASTE; SINFIL/TSOURCThallium Noncancer LF DW AC Adult CZERO(1)/CWASTEC-14Table C-3. (Continued)ChemicalBenchmark WMU Pathway Waste Receptor High Correlations in Significant VariablesThallium Noncancer LF DW AC Child CZERO(1)/CWASTEThallium Noncancer LF DW FBC Adult CZERO(1)/CWASTE; DSOIL/DISPR; SINFIL->AREA,XW,YD,PH_SWThallium Noncancer LF DW FBC Child CZERO(1)/CWASTE; DSOIL/DISPR; SINFIL->AREA,XW,YD,PH_SWThallium Noncancer SI DW A Adult NoneThallium Noncancer SI DW A Child NoneThallium Noncancer SI FC A Adult VFX->U,WIDTH_RIVERThallium Noncancer SI FC A Child VFX->U,WIDTH_RIVER