## Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

Draft Technical Report

Contract EP-W2-09-004

Work Assignment 0-02

Prepared for

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

Prepared by

RTI International

P.O. Box 12194 Research Triangle Park, NC 27709

September 1, 2009Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

ii Table of Contents

1.0 Introduction .................................................................................................................. ....................... 3

2.0 Methodology ................................................................................................................... .................... 5 Step 1. Define the independent and dependent variables ................................................................ 6 Step 2. Define the scenarios of interest ...................................................................................... ..... 6 Step 3. Develop the input data set ............................................................................................ ....... 7 Step 4. Remove independent variables th at are constants for a given scenario .............................. 8 Step 5. Transform the input variables ......................................................................................... .... 8 Step 6. Standardize the transformed variables ................................................................................ 8 Step 7. Check the correlations of the transformed input variables ................................................. 8 Step 8. Determine the form of the linear regression model with interaction terms ........................ 9 Step 9. Eliminate insignificant terms from the model using modified step-wise regression methods ....................................................................................................................... ........ 9 Step 10. Rank the independent variables accord ing to the degree that each explains the variation in environmental concentration ......................................................................... 10

3.0 Analysis Testing .............................................................................................................. .................. 10

4.0 Results and Discussion ........................................................................................................ .............. 12

5.0 References .................................................................................................................... ..................... 14

Appendix A. Input Variables Appendix B. Highly Correlated Input Variables Appendix C. Response Surface Regression Results by Scenario (spreadsheet: Appendix_C.xls)

List of Figures Figure 1. Conceptual site m odel of CCW risk assessment. ....................................................................... ... 4 Figure 2. Example graph of risk versus infiltration rate. ..................................................................... ....... 11

List of Tables Table 1. Scenarios Included in Sensitivity Analys is (90th Percentile Risk > Risk Criteria) ........................ 7 Table 2. Range of Percent of Zero Risk Valu es Across All Scenarios, by Liner Type .............................. 11 Table 3. Comparison of Test Results Wi th and Without Zero Risk Values ............................................... 12 Table 4. Percentage of Significant Variables Across All Drinking Water Pathway Scenarios .................. 13 Table 5. Percentage of Significant Variables Ac ross All Fish Consumption Pathway Scenarios .............. 13Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

3 

### 1.0 Introduction

This sensitivity analysis was conducted in support of the U.S. Environmental Protection Agency’s (EPA’s) 2009 Human and Ecological Risk Assessment of Coal Combustion Wastes . In 2003, the EPA conducted a probabilistic (Monte Carlo) risk assessment to evaluate the human and ecological impacts of coal combustion wastes (CCW) disposed in surface impoundments and landfills. Reanalyzing the 2003 modeling results, the 2009 risk assessment estimated th e national distribution of risks to human health and the environment so as to provide the informa tion needed to assess future management options for these wastes in the context of their risks to human health and the environment. The CCW Monte Carlo risk assessment was designed to characterize the nationa l CCW risk profile in terms of waste management unit (WMU) type, waste type, and constituent, and to use distributions in a probabilistic modeling framework to incorporate variability and uncertainty in the analysis.

The CCW risk assessment was conducted using several modeling components: (1) EPA’s Composite Model for Leachate Migration with Tr ansformation Products (EPACMTP; U.S. EPA, 1997a) groundwater model, (2) a simple steady-state su rface water and aquatic food web model, and (3) a multipathway exposure and risk modeling system.

This risk assessment provides a national charact erization of waste management scenarios for wastes generated by coal-fired u tility power plants. The sources modeled in these scenarios are onsite landfills and surface impoundments, which are the pr imary means by which CCW is managed in the United States. The characterization of these sources, in terms of their physical dimensions, operating parameters, location, environmental settings, and waste characteristics, is fundamental to the construction of scenarios for modeling.

Figure 1 depicts the conceptual site model for CCW di sposal that was the basis for the national probabilistic CCW risk assessment, including contam inant sources, exposure pathways, and receptors. The CCW conceptual site model includes the following exposure pathways:

Human Health:

 Groundwater to drinking water (drinking water ingestion)

 Groundwater to surface water (fish consumption)

Ecological Risk:

 Groundwater to surface water (sediment and surface water receptors)

This groundwater pathway analysis evaluated exposures through drinking water ingestion and surface water contamination from groundwater dischar ge. For the groundwater-to-surface-water pathway, the analysis assumed that human exposure occurs thr ough the consumption of contaminated fish and that ecological exposure occurs through direct contact wi th contaminated surface water and sediment or from the consumption of aquatic organisms.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment4Figure 1. Conceptual site model of CCW risk assessment.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

5 In planning this assessment, EPA a ddressed as much of the variabilit y as possible, either directly in the Monte Carlo analysis or through aggregation of the data into discrete elements of the analysis. For example, because the exact location of a landfill or surface impoundment at a coal-fired power plant was uncertain, spatial variability in soil and aquifer charac teristics was accounted for by using distributions for soil and aquifer properties around the best available plant location. Variability in waste leachate concentrations was represented by a national datab ase of CCW constituent concentrations from disposal sites around the country. These data were aggregated by waste and WMU types that were defined by statistically significant differences in mean concentration. Variability in human exposure factors (e.g., body weight, ingestion rates) was accounted for using national distributions that represent the range of possible values.

To determine which parameters were the most important drivers of human risk, a sensitivity analysis was conducted on the inpu ts and results of the probabilisti c analysis using regression methods. The basic method employed for the sensitivity anal ysis used regression models developed for each scenario, as defined by a WMU, waste type, chemical, e xposure pathway, and receptor. 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 was goodness-of-fit values for the overall regression models (R 2 ), as well as the relative importance of each input parameter in determining the environmental concentrations used to calculate ri sk (i.e., the percent variation in concentration attributable to each significant input). In each scenar io, percent variation was used to rank the input parameters in order of importance; that is, the i nput with the highest percent variation was the most important.

This report presents the results of that sens itivity analysis and is organized as follows:

 Section 2 describes the methodology used

 Section 3 explains the results of the preliminar y runs used to test the regression model

 Section 4 presents the findings of the full sensitivity an alysis as well as limitations of the method

 Section 5 provides the references cited.


### 2.0 Methodology

The goals of this sensitivity analysis were two-fold:

1. To develop regression models with risk (or hazard quotient, HQ) as the dependent variable, and inputs from the probabilistic analysis as the independent variables 2. To rank the input variables (i.e., statistically significant independent variables) according to their contribution (i.e., percent variation) to the variability in risk (or HQ).

Risk (or hazard quotient), as calculated in this anal ysis can be defined by the followi ng equation:



### weight body intake duration exposure criteria risk ion concentrat tal environmen Risk × × ×

= (1)

Because exposure factors, such as body weight, exposure duration, and ingestion rate (which determines intake), are lifestyle variables, they are not amenable to regulation to reduce or manage risk. Furthermore, these exposure factors have well-establis hed, peer-reviewed, national distributions (U.S. EPA, 1997b,c,d), which are regularly used in the pr obabilistic national risk analyses conducted by EPA, including this one. Risk criteria were selected as a constant (10 -5 excess cancer risk or an HQ of 1). For these reasons, EPA decided to use environmental concentr ation rather than risk as the dependent variable for the sensitivity analysis.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

6 In the analysis, linear regression models with in teraction terms were deve loped for the relevant environmental concentration (i.e., well concentra tion for the drinking water pathway and fish concentration for the fish consumption pathway).

Regression analysis offers the following advantages:

 The independent variables may interact with one another via interaction terms in the regression model, which may increase goodness-of-fit (R 2 )

 Nonlinear effects may be observed via the squa red terms of independent variables in the regression model.

Some assumptions must be met to use regression analysis. The main assumptions of concern are as follows:

 The dependent variables (i.e., log[concentrati on]) have a normal distribution. Non-normally distributed variables (highly skewed or kurtotic variables, or variables with substantial outliers) can distort relationships and significance tests.

 The sample is representative of the population

 The independent variables are linearly indepe ndent (i.e., no independent variable can be represented by a linear function of the othe r independent variables—no multicolinearity)

 The variance of the error is constant acro ss observations (homoscedasticity). Slight heteroscedasticity (variance of errors differs at different levels of the dependent variable) has little effect on significance tests; however, when he teroscedasticity is severe it can lead to serious distortion of findings and seriously weaken the an alysis, thus increasing th e possibility of a Type I error (Berry and Feldman, 1985).

These assumptions will be addressed throughout th e report in the development of the regression model, as well as in Section 4 , Results and Discussion.

The sensitivity analysis consisted of 10 major steps, summarized below.

Step 1. Define the independent and dependent variables For the initial regression models, the dependent variable was the relevant environmental concentration: well concentration for the drinking wa ter pathway or fish concentration for the fish consumption pathway. A log transformation was applied to the dependent variables (i.e., well concentration and fish concentration) to obtain an almost normal dependent variable.

The independent variables considered include w aste properties (e.g., leachate concentrations), site/source parameters (e.g., infiltration rates), chemical properties (e.g., sorption coefficients), groundwater parameters (e.g., h ydraulic conductivity), well distance, distance to surface water, and waterbody characteristics (e.g., flowrate). Appendix A contains a list of the input variables from the probabilistic analysis that served as independe nt variables in the sensitivity analysis.

Step 2. Define the scenarios of interest For the purposes of this sensitivit y analysis, a “scenario” was a sub set of the data for which an individual regression model was devel oped (i.e., one realization of the model). A scenario was denoted by a combination of the following parameters:

 WMU: landfill or surface impoundmentSensitivity Analysis for the Coal Combustion Waste Risk Assessment

7  Waste type: conventional coal waste (fly and bottom ash; flue gas desulfurization sludge, codisposed coal cleaning waste (coal refuse) a nd conventional ash; or fluidized bed combustion
waste

 Chemical : antimony, arsenic (III), arsenic (V), boron, cadmium, cobalt, lead, molybdenum, selenium (IV), selenium (VI), or thallium

 Exposure pathway: drinking water or fish consumption

 Receptor: adult or child.

Aggregated scenarios were included in the sensitiv ity analysis if their 90th percentile risk or hazard quotient value (as determined in the probab ilistic analysis) exceeded th e risk criteria (1 for noncancer effects and 10 -5 for cancer). Table 1 lists the 76 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 anal ysis, because they did not exceed th e risk criteria for any scenario. However, because these scenarios were based on aggregated results, liner types are not distinguished here as they are in the 2009 risk assessment. Thus, the 2009 risk assessment showed nitrate to present risks in certain combinations of waste type, WMU type, and liner type.

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

Chemical WMU a Waste Type b Pathway c Total Scenarios d Antimony SI A DW 2 Arsenic (III) LF SI SI A, AC, FBC A, AC A, AC DW FC DW 6 4 4 Arsenic (V) LF SI SI A, AC, FBC A, AC A, AC DW FC DW 6 4 4 Boron SI A, AC DW 4 Cadmium SI A, AC DW 4 Cobalt SI A, AC DW 4 Lead SI A, AC DW 4 Molybdenum SI A, AC DW 4 Selenium (IV) SI SI A, AC A, AC FC DW 4 4 Selenium (VI) SI SI A, AC A, AC FC DW 4 4
Thallium LF SI SI A, AC, FBC A A DW FC DW 6 2 2

a LF = landfill, SI = surface impoundment. b A = conventional fly ash, bottom ash, and flue gas desulfurization sludge; AC = codisposed coal cleaning waste (coal refuse) and conventional ash; FBC = fluidized bed combustion waste.

c DW = consumption of drinking water, FC = fish consumption. d Each waste management unit, waste type, and pathway has results for both adult and

child receptors.

Step 3. Develop the input data set The input data set contained one record for each scenario and iteration, with numeric values for all of the independent and dependent variables. This step required extensive da ta management and qualitySensitivity Analysis for the Coal Combustion Waste Risk Assessment

8 assurance to ensure that the input s were assembled correctly, as the data set contained approximately 760,000 records (76 scenarios × 10,000 iterations/ scenario) and 150 fields (144 independent and dependent variables plus 6 fields to denote the scenario and iteration).

Step 4. Remove independent variables that are constants for a given scenario Any independent variable that is constant across a scenario does not explain any of the variability observed in the environmental concentration; therefor e, such variables were removed from the data set prior to the regression analysis. For example, because average diameter of soil particle (DIAM) is a constant value and did not vary across the scenario, it would have no contribution to the variability in concentration and thus was removed from the model. Appendix A includes each input that was used in the sensitivity analysis, along with the constant inpu ts that were dropped. The appendix also lists the exposure factors that were not included in the sensitivity analysis because of the decision to use environmental concentration rather than risk as the independent variable.

Step 5. Transform the input variables The distributions of the input parameters (both sh ape and scale) highly influence the results of the regression analysis. Skewness is a measure of how sy mmetrical the data are. Skewed distributions produce a tail in one direction of the probability dist ribution and represent relatively few values in that direction. These distributions can cause adverse e ffects such as heteroscedasticity because they are not normally distributed. To alleviate heteroscedasticity of skewed distributions, the original values were transformed using power or natural log functions, wh ich involves taking the natural log of each value.

Some variables in the data set for the sensitivity analysis contained multiple zero values. The log of zero is an undefined mathematical expression, because the log function is defined for positive arguments only, so errors arise if a variable that requires a log transformation includes zero values (as was the case for some variables in this data set). To correct this, we added a negligib le constant value to all values of an input variable that required a log tr ansformation. Adding a constant value to all values preserves the variance of the distribu tion, so the regression analysis is still accurate. This transformation process forced the distributions to be as symmetric as possible, improving one of the key assumptions necessary for regression analysis.

Step 6. Standardize the transformed variables To make the regression results independent of the magnitude of the value of the input variables, the input variables were standardized by subtracting the mean and dividing by the standard deviation. Without standardization, inputs with larger values w ould overwhelm the effects of inputs with smaller values. The combination of transforming and standard izing the input variables created optimal conditions for the regression analysis.

Step 7. Check the correlations of the transformed input variables To conduct a proper regression anal ysis, all independent variables must be linearly independent. Therefore, the transformed input variables were eval uated for correlations. Input variables were defined as highly correlated with one another if they possessed a correlation coefficient ≥ 0.8. If two or more input variables were highly correlated with one anot her, only one was retained in the regression model. Keeping highly correlated variables would reduce the significance of each in the model, because each is essentially explaining the same linear relationship with th e response variable (i.e., the effect of one such variable may mask the effect of another).

In general, high correlation between variables wa s found to result from one variable being an internal function of the other. For example, WMU area, WMU length, and WMU width were all highlySensitivity Analysis for the Coal Combustion Waste Risk Assessment

9 correlated variables in the model. Because area is a product of length and width, these variables are highly correlated. Therefore, we removed length and width fr om the model, leaving only area, to conduct our regression analysis. Among the most used remedies fo r multicollinearity are the following: if one of the variables in the model doesn’t seem essential to the model, removing it may reduce multicollinearity. Examining the correlations between variables and taki ng into account the importance of the variables will help in making a decision about which variables to drop from the model. Another option is to combine the variables using orthogonalizaton methods or principa l component methods (Harvey, 1977). The drawback to this approach is the interpretabilit y of the resulting combined variables.

Appendix B contains a list of all the highly correlated inputs encountered in the analysis. The highlighted member of each pair or group indicates the variable that was retained in the analysis. Examination of the correlation matrix and identificati on of the more important input variable (i.e., the variable used to calculate the others) were used as criteria to determine which of two or more highly correlated variables to keep.

Step 8. Determine the form of the linear regression model with interaction terms Appendix A defines the 64 input variables that were in cluded in the sensitivity analysis. In Step 7, 33 of these terms were removed, leaving 31 inpu t variables used in the linear regression model with interaction terms. From these input variables, th e starting form of the regression model included linear, squared, and second-order interacti on terms for each independent variable. An example of a regression model of this form with only two i ndependent variables is of the form:



### ε β β β β β β + + + + + + = 2 1 12 2
2 4 2 3 2
1 2 1 1 0 

### x x x x x x Y

where Y is the dependent variable and x i is an independent variable. A first-order term is of the form j x i . A second-order term is of the form j x i 2 . The x 1 x 2 term is the second-or der interaction term.

More complex terms (e.g., higher-order polynomials or interactions) were not considered in the model. Even with this limitation in model complex ity, because there were so many independent variables in a scenario, a typical regression model included more than 1,000 terms in its initial form.

Step 9. Eliminate insignificant terms from the model using modified step-wise regression methods An iterative process (stepwise selection) was used to eliminate terms from the model that did not satisfy the significance criterion ( p ≤ 0.01). Stepwise model selection was used to select statistically significant inputs for the final model. In stepwise regressi on, input variables are added one at a time to the model as long as the F-statistic p value is below the specified α (e.g., α = 0.01). After a variable is added, however, the stepwise technique evaluates all of the va riables already included in the model and removes any variable that has an insignificant F-statistic p value exceeding the specified α . Only after this check is made and the identified variables have been removed can another variable be added to the model. The stepwise process ends when none of the variables excl uded from the model has an F-statistic significant at the specified α and every variable included in the model is significant at the specified α

. After the stepwise regression results were obtai ned, the stepwise reduced model was modified based on the following rules:

 Any second-order terms ( x i 2 .) that were determined not to have a significant effect were dropped from the model

 Any first-order term ( j x i .) that was part of a significant second-order term ( x i 2 .) remained in the model, regardless of the level of significance of the first-order termSensitivity Analysis for the Coal Combustion Waste Risk Assessment

10  Any first-order terms ( j x i .) that were determined not to be significant and not to have any significant second-order terms ( x i 2 .) were dropped from the model.

This set of rules was intended to capture and main tain in the model some of the inputs considered relevant in explaining the variability of the log(con centration). This process was repeated until all of the second-order terms in the model had significant effect s on risk or hazard quotient and no more terms could be removed.

Step 10. Rank the independent variables according to the degree that each explains the variation in environmental concentration Terms in the final model were ranked according to their “percent variation” (PV) in the model. Percent variation is the proportion of variabilit y observed in the dependent variable (e.g., log concentration) explained by the model term and can be calculated as follows:



### 100 T 

### SSFM SRM PV SST − 

### − ⎛ ⎞

= − ⎜ ⎟ ⎝ ⎠ (2)

where

SSFM = Sum of squares for the full model SSRM -Ti = Regression sum of squares for the reduced model with model term T i missing SST = Total sum of squares. (Gardner and Trabalka, 1985).

The adequacy of the regression model can be assessed using the coefficient of multiple determination, R 2 , which is a measure of the amount of vari ance in the dependent variable (e.g., log concentration) explained by the variation in th e model terms (Draper and Smith, 1981). A high R 2 value indicates a monotonic relationship.


### 3.0 Analysis Testing

Prior to conducting the full sen sitivity analysis, the regression analysis was applied to a small number of scenarios to uncover any problems with the proposed methodology. Two landfill scenarios were selected for this initial testing procedure:

 Arsenic(V), conventional fly and bo ttom ash, drinking water pathway

 Arsenic(III), conventional fly and bo ttom ash, drinking water pathway.

In these preliminary test results, the regression equations exhibited high goodness-of-fit measures (R 2 = 0.84–0.89), but the total percent variation (sum of individual percent variations of significant variables) was less than 30%, and no single paramete r showed a percent variation greater than 8%. We were not satisfied with the low explanation of varia tion in risk. The small change in variation made it difficult to accurately predict the most important variables.

Upon investigation, this outcome was attribut ed to the high proportion of zero concentration values (27–42%) in the input data set . As explained in Section 4 of the risk assessment report, the zero concentration values represented one of two situ ations: (1) zero infiltration rates from the WMU associated with the probability di stribution of infiltration rates used for composite liners, or (2) simulations where the contaminant does not reach th e well or surface water body before the simulation was terminated (at 10,000 years) because of a combina tion of variables that lead to slow groundwater transport (such as low infiltration rates, low w aste concentrations, high sorption rates, and low groundwater velocities). Table 2 shows the minimum, maximum, and median percent of zero values bySensitivity Analysis for the Coal Combustion Waste Risk Assessment

11 liner type for the scenarios. Composite-lined units ha d a much higher incidence of zero risk values than unlined or clay-lined units, b ecause the zero infiltration cases are limited to composite liners.

Table 2. Range of Percent of Zero Risk Values Across All Scenarios, by Liner Type

Liner Type Minimum % Zero Maximum % Zero Median % Zero
No Liner 0.2 87.0 11.5 Clay Liner 0.2 71.9 5.3 Composite Liner 35.6 100.0 95.0

This large number of zero concentration values in the dependent variable distribution complicated the analysis. For example, Figure 2 shows a graph of risk versus in filtration 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.

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

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

LN(SINFIL)LN(RISK)

Figure 2. Example graph of risk versus infiltration rate.

To correct this problem in a simple fashion, the methodology was modified to remove all zero risk values from the data set prior to conducting the regression analysis (through Steps 4–10). This decision was based on the project objective of determin ing model sensitivities to parameters in the region of most interest to the decision makers: positive concentrations and risks. Table 3 shows the difference in results as a consequence of removing the zero r esults. The overall goodness-of-fit measures are still relatively high (R 2 = 0.72–0.83 with the revised data set, vs . 0.84–0.85 with the original data set). However, the total percent variation values are much higher (65–88% with the revised data set, vs. 20– 28% with the original data set).Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

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

1Scenario A ll Results No Zero Risk V alues
% Zero

Risk Values R 2 Sum Percent Variation R 2 Sum Percent Variation 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%



### 4.0 Results and Discussion

When the sensitivity analysis was completed for all scenarios under consideration for human receptors, it indicated a wide range in g oodness-of-fit values for the regression models (R 2 =0.53–0.83, median R 2 =0.68). In general, the regression models te nded to produce higher goodness-of-fit values for the fish consumption pathway (R 2 =0.76–0.89) compared to the drinking water pathway (R 2 =0.53–0.83). Although these results are acceptable, R 2 values of 0.7 or higher are generally considered good results, with lower values indicating that the model is missi ng important terms or requires additional terms, or that implicit assumptions are not being addressed by the model.

In examining the percent variation results of individual variables, inputs with an individual percent variation value greater than 5% were generall y regarded as important. The percent variation is not useful for ranking variables below this threshold, be cause the model results do not change significantly when these variables are removed from the regression model.

We ranked significant input variables by percent variation. The total percent variation for scenarios ranged from 70% to >100% (due to overla p between individual variables from the second-order interaction terms). In general, the drinking wate r pathway tended to have a larger number of input parameters that were significant, while the fish consumption pathway showed only a small number (typically ≤ 3) as significant. This may explain the di fference 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 pa rameters that were found to be significant more often than other variables. F our of the variables were significant in more than 72% of the scenarios, and three were significant for over 30% of the scenarios. The four variables that were important in most scenarios for the drinking water pathway were

 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].

The three variables that were important in many, but not most, scenarios were

 Leading Freundlich adsorption isotherm coefficient [SFCOF(1)]

 Depth from ground surface to water table [DSOIL]Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

13  Distance between well and down-gradient edge of WMU [XWELL(1)].

Table 4 shows the number of scenarios for the drinking water pathway in which a variable was found to be significant based on percent variation. Fr om this value, we calculate the percentage of significance across all scenarios for the drinking wate r pathway (58 scenarios in total) included in the sensitivity analysis

Table 4. Percentage of Significant Variables Across All Drinking Water Pathway Scenarios

Variable Number of Significant Scenarios Percentage of Significant Scenarios (58 total)
SINFIL 46 79.3% CZERO(1) 46 79.3%
XKX 45 77.6%
GRADNT 42 72.4% SFCOF(1) 22 37.9%
DSOIL 20 34.5% XWELL(1) 18 31.0%

The relative importance of groundwater flow para meters (four most important variables) and sorption and travel time parameters (second tier of im portant parameters) was related to the magnitude of a chemical’s partition coefficient (Kd). For exampl e, 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 arsen ic(V) were the adsorption isotherm coefficient and the depth from ground surface to water table; the mo st significant variables for arsenic(III) were the infiltration rate and the leachate concentration.

The variables that tended to be significant fo r the fish consumption pathway were source characteristics and waterbody parame ters. Only three variables were consistently significant across scenarios for the fish consumption pathway:

 Infiltration rate within the WMU footprint [SINFIL]

 Leachate concentration from the WMU [CZERO(1)]

 Waterbody flow rate [VFX].

Table 5 shows the number of scenarios for the fish co nsumption pathway in which a variable was found to be significant based on percent variation. Fr om this value, we calculate the percentage of significance across all scenarios for the fish consumpti on pathway (18 scenarios in total) included in the sensitivity analysis.

Table 5. Percentage of Significant Variables Across All Fish Consumption Pathway Scenarios

Variable Number of Significant Scenarios Percentage of Significant Scenarios (18 total)
VFX 18 100%
SINFIL 14 77.8% CZERO(1) 12 66.7%

Appendix C contains a summary of the sen sitivity results by scenario.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

14 There are some general concerns with the use of a linear regression model with interaction terms for the sensitivity analysis based on assumptions listed in the methodology section, as well as the methodology in general. The step-by-step method w ill be evaluated to find potential pitfalls and comments will be made on how this could be improved.

In Step 5 of the regression analysis, a log tran sformation was performed for all variables. As mentioned in Section 2 , Methodology, a log transformation is performed when a distribution is skewed. Because a large portion of the data consists of zero va lues, a data set produces a right-skewed distribution. Linear regression requires an assumption of normal distribution, so skewed distributions must be transformed to accommodate this assumption.

Step 7 in the methodology involved removing input variables that are highly correlated. This step is necessary to make the regression model accurate due to the assumption that all input variables in the regression model must be linearly independent. If there is correlation between input variables, the results will be significantly biased. Thus, if two or more input variables were found to be correlated, all but one variable was removed. The removed variables were se lected by the risk assessment experts based on the subjective matter. If a variable could be represented as a function of other correlated variables, then removal of all variables used in the calculation is acceptable. However, the creation of a combined variable may complicate interpretation of model results

In conclusion, the sensitivity analysis produced re gression 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 pr operties and exposure pathways.



### 5.0 References

Berry, W. D., and S. Feldman. 1985. Multiple Regression in Practice. Sage University Paper Series on Quantitative Applications in the Social Scien ces, Series no. 07-050. Newbury Park, CA: Sage.

Draper N.R., and H. Smith. 1981. Applied Regression Analysis . Second Edition. John Wiley and Sons: New York.

Gardner, R.H., and J.R. Trabalka. 1985. Methods of Uncertainty Analysis for a Global Carbon Dioxide Model. DOE/OR/21400-4. US Department of Energy, Washington, DC.

Graybill, F.A. 1961. An Introduction to Linear Statistical Models, Volume I . New York: McGraw-Hill Book Co.

Harvey, A.C. 1977. Some Comments on Multicollinearity in Regression. Journal of the Royal Statistical Society. Series C (Applied Statistics) 26(2):188–191.

U.S. EPA (Environmental Protection Agency). 1997a . EPA’s Composite Model for Leachate Migration with Transformation Products. EPACMTP: User’s Guide. Office of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1997b. Exposure Factors Handbook, Volume I, General Factors. EPA/600/P-95/002Fa. Office of Research and Development, Washington, DC. August.

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume II, Food Ingestion Factors. EPA/600/P-95/002Fa. Office of Research and Development, Washington, DC. August.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

15 U.S. EPA (Environmental Protection Agency). 1997d. Exposure Factors Handbook, Volume III, Activity Factors. EPA/600/P-95/002Fa. Office of Research and Development, Washington, DC. August.

Xu C., and G.Z. Gertner. 2008. Uncertainty and sen sitivity analysis for models with correlated parameters. Reliability Eengineering and System Safety 93(10):1563–1573.Sensitivity Analysis for the Coal Combustion Waste Risk Assessment

16Appendix A—Input Variables



# Appendix A
Input VariablesAppendix A—Input VariablesAppendix A—Input Variables

A-1 Table A-1. Input Variables Incl uded in Sensitivity Analysis

V ariable 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/pl ant 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 connec ted pore space in the aquifer 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 coeffici ent - for linear isotherms (UFEXP = 1), this is Kd
SINFIL Infiltration rate inside the WMU footprint TEMP Ambient groundwater temperature TSOURC Duration of leachingAppendix A—Input Variables

A-2 V ariable Description TSS Total suspended solids Twater Temperature of the Waterbody U Current velocity of the waterbody UFCOF(1) Leading Freundlich adsorption isotherm coeffici ent - 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 wa ter table (as fraction of saturated thickness) ZWELL(2) Depth of observation location below wa ter table (as fraction of saturated thickness)Appendix A—Input Variables

A-3 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 organ ic 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 observatio n well and downgradient edge of WMU YWELL(2) Distance off-centerline to observation wellAppendix A—Input Variables

A-4 Table A-3. Variables Not Included in Sens itivity 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 water 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 coe fficient 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-5 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 fishAppendix A—Input Variables

A-6Appendix B—Highly Correlated Input Variables



# Appendix B Highly Correlated Input VariablesAppendix B—Highly Correlated Input Variables

B-1 Highly Correlated Input Variables Note: In each correlated set, the bold 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 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 dispersivity

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) Vfx waterbody annual flow mixing volume dwc depth of water column dz waterbody depth U current velocity of the waterbody Width_river width of stream or river

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

12) TEMP ambient groundwater temperature Twater, waterbody temperature

13) pH_Soil Soil pH RECHRG regional infiltration/rechar ge rate outside the WMU footprint

14) SFCOF(1) Freundlich adsorption isotherm coefficient - for linear isotherms this is Kd RETARD(1) retardation coefficient

15) GOVPH governing pH PHLEACH leachate pH

16) AQMIXPH aquifer pH PH_GW mixed groundwater pHAppendix B—Highly Correlated Input Variables

B-2 17) GOVPH governing pH SFCOF(1) Freundlich adsorption isotherm coefficient - for linear isotherms this is Kd

18) pH_SOIL soil pH PH_SW surface water pH

19) VFX waterbody annual flow mixing volume DWC depth of water column

20) SINFIL infiltration rate inside the WMU footprint TSOURC duration of leaching

21) USNOM unsaturated zone percentage of organic matter TWATER surface water temperature

22) SINFIL infiltration rate inside the WMU footprint AREA area of WMU

23) GOVPH governing pH AQMIXPH aquifer pH

24) SINFIL infiltration rate inside the WMU footprint PH_SOIL soil pH

25) SINFIL infiltration rate inside the WMU footprint RECHRG regional infiltration/rechar ge rate outside the WMU footprintAppendix C— Response Surface Regression Results by Scenario



# Appendix C
Response Surface Regression Results by ScenarioChemical CAS Benchmark WMU Pathway Waste ReceptorFull R2VAR1 PV1 VAR2 PV2 VAR3 PV3 VAR4 PV4 VAR5 PV5 VAR6 PV6 VAR7 PV7 High Correlations in Sig VariablesAntimony 7440-36-0 Noncancer SI DW A Adult 0.62 SINFIL 35% XKX 10% CZERO(1) 9% GRADNT 6% NoneAntimony 7440-36-0 Noncancer SI DW A Child 0.62 SINFIL 35% XKX 10% CZERO(1) 9% GRADNT 6% NoneArsenic (III)22569-72-8 Cancer LF DW A Adult 0.72 CZERO(1) 16% XKX 11% GRADNT 6%CZERO/CWASTEArsenic (III)22569-72-8 Cancer LF DW A Child 0.73 CZERO(1) 17% XKX 10% GRADNT 6%CZERO/CWASTEArsenic (III)22569-72-8 Cancer LF DW AC Adult 0.68 TSOURC 32% CZERO(1) 15% XKX 10% DEPTH 9% GRADNT 6% CZERO/CWASTEArsenic (III)22569-72-8 Cancer LF DW AC Child 0.68 TSOURC 32% CZERO(1) 15% XKX 10% DEPTH 9% GRADNT 6% CZERO/CWASTEArsenic (III)22569-72-8 Cancer LF DW FBC Adult 0.59 XKX 20% TSOURC 18% CWASTE 15% GRADNT 13% CZERO(1) 9% XWELL(1) 7% ZWELL(1) 7% NoneArsenic (III)22569-72-8 Cancer LF DW FBC Child 0.60 TSOURC 19% XKX 19% CWASTE 16% GRADNT 12% CZERO(1) 9% XWELL(1) 7% ZWELL(1) 6% NoneArsenic (III)22569-72-8 Cancer SI DW A Adult 0.62 SINFIL 42% CZERO(1) 22% XKX 8% GRADNT 5% NoneArsenic (III)22569-72-8 Cancer SI DW A Child 0.62 SINFIL 43% CZERO(1) 22% XKX 7% GRADNT 5% NoneArsenic (III)22569-72-8 Cancer SI FC A Adult 0.80 VFX 21% SINFIL 20% CZERO(1) 9%VFX->U,WIDTH_RIVERArsenic (III)22569-72-8 Cancer SI FC A Child 0.80 VFX 21% SINFIL 19% CZERO(1) 9%VFX->U,WIDTH_RIVERArsenic (III)22569-72-8 Cancer SI DW AC Adult 0.66 CZERO(1) 53% SINFIL 13% XKX 6% GRADNT 6% TWATER 5% NoneArsenic (III)22569-72-8 Cancer SI DW AC Child 0.68 CZERO(1) 54% SINFIL 13% XKX 6% GRADNT 5% TWATER 5% NoneArsenic (III)22569-72-8 Cancer SI FC AC Adult 0.82 CZERO(1) 22% VFX 16% SINFIL 7%VFX->U,WIDTH_RIVERArsenic (III)22569-72-8 Cancer SI FC AC Child 0.83 CZERO(1) 22% VFX 15% SINFIL 7%VFX->U,WIDTH_RIVERArsenic (V) 15584-04-0 Cancer LF DW A Adult 0.83 SFCOF(1) 33% DSOIL 24% SINFIL 17% CZERO(1) 5% CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Arsenic (V) 15584-04-0 Cancer LF DW A Child 0.83 SFCOF(1) 33% DSOIL 24% SINFIL 17% CZERO(1) 5% CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Arsenic (V) 15584-04-0 Cancer LF DW AC Adult 0.77 SFCOF(1) 28% DSOIL 16%DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1)Arsenic (V) 15584-04-0 Cancer LF DW AC Child 0.77 SFCOF(1) 28% DSOIL 16%DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1)Arsenic (V) 15584-04-0 Cancer LF DW FBC Adult 0.65 SFCOF(1) 44% VFX 19% DSOIL 17% XKX 6% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVER, DWC, DZ, UWArsenic (V) 15584-04-0 Cancer LF DW FBC Child 0.66 SFCOF(1) 44% VFX 19% DSOIL 17% XKX 6% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U,WIDTH_RIVER, DWC, DZ, UWArsenic (V) 15584-04-0 Cancer SI DW A Adult 0.74 SFCOF(1) 32% SINFIL 26% DSOIL 13% XWELL(1) 5% CZERO(1) 5% GRADNT 5% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI DW A Child 0.74 SFCOF(1) 31% SINFIL 26% DSOIL 12% XWELL(1) 5% GRADNT 5% CZERO(1) 5% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI FC A Adult 0.77 SINFIL 23% SFCOF(1) 22% VFX 12% DSOIL 11% DISSW 7% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI FC A Child 0.78 SINFIL 21% SFCOF(1) 20% DSOIL 11% VFX 10% DISSW 7% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI DW AC Adult 0.66 CZERO(1) 19% SINFIL 19% SFCOF(1) 16% DSOIL 5% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX-> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI DW AC Child 0.67 CZERO(1) 20% SINFIL 19% SFCOF(1) 16% DSOIL 5% DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOF(1); VFX-> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI FC AC Adult 0.76 VFX 16% SINFIL 14% CZERO(1) 11% SFCOF(1) 9% SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERArsenic (V) 15584-04-0 Cancer SI FC AC Child 0.77 VFX 16% SINFIL 14% CZERO(1) 11% SFCOF(1) 9% SFCOF(1)->RETARD(1), UFCOF(1); VFX -> U, WIDTH_RIVERBoron 7440-42-8 Noncancer SI DW A Adult 0.69 CZERO(1) 28% SINFIL 27%NoneBoron 7440-42-8 Noncancer SI DW A Child 0.69 CZERO(1) 28% SINFIL 27% XKX 5%NoneBoron 7440-42-8 Noncancer SI DW AC Adult 0.55 SINFIL 21% CZERO(1) 20% XKX 12% TWATER 10% GRADNT 9% DWC 7% TEMP 6% DWC->WIDTH_RIVER,DZBoron 7440-42-8 Noncancer SI DW AC Child 0.55 SINFIL 22% CZERO(1) 20% XKX 12% TWATER 9% GRADNT 9% DWC 7% TEMP 5% DWC->WIDTH_RIVER,DZCadmium 7440-43-9 Noncancer SI DW A Adult 0.66 SINFIL 49% CZERO(1) 10% AREA 7% GRADNT 6% XKX 5% AREA->XW,YDCadmium 7440-43-9 Noncancer SI DW A Child 0.66 SINFIL 49% CZERO(1) 10% AREA 6% GRADNT 6% XKX 5% AREA->XW,YDCadmium 7440-43-9 Noncancer SI DW AC Adult 0.64 CZERO(1) 46% SINFIL 13% GRADNT 7% XKX 6% TWATER 5% DWC 5% DWC->WIDTH_RIVER,DZCadmium 7440-43-9 Noncancer SI DW AC Child 0.64 CZERO(1) 46% SINFIL 14% GRADNT 7% XKX 7% TWATER 5% DWC 5% DWC->WIDTH_RIVER,DZCobalt 7440-48-4 Noncancer SI DW A Adult 0.58 SINFIL 27% SFCOF(1) 20% XKX 12% GRADNT 8% XWELL(1) 8% ZB 6% AREA 5% SFCOF(1)->RETARD(1), UFCOF(1); AREA->XW,YDCobalt 7440-48-4 Noncancer SI DW A Child 0.59 SINFIL 27% SFCOF(1) 20% XKX 12% GRADNT 8% XWELL(1) 8% ZB 6% AREA 5% SFCOF(1)->RETARD(1), UFCOF(1); AREA->XW,YDCobalt 7440-48-4 Noncancer SI DW AC Adult 0.58 CZERO(1) 23% SINFIL 21% XKX 7% GRADNT 6% NoneCobalt 7440-48-4 Noncancer SI DW AC Child 0.58 CZERO(1) 23% SINFIL 21% XKX 7% GRADNT 7% NoneLead 7439-92-1 Noncancer SI DW A Adult 0.67 SINFIL 36% CZERO(1) 21% DSOIL 14% SFCOF(1) 13% XWELL(1) 6% GRADNT 6% XKX 6% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead 7439-92-1 Noncancer SI DW A Child 0.67 SINFIL 36% CZERO(1) 21% DSOIL 14% SFCOF(1) 13% GRADNT 6% XWELL(1) 6% XKX 5% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead 7439-92-1 Noncancer SI DW AC Adult 0.61 CZERO(1) 31% SINFIL 22% SFCOF(1) 10% XKX 7% GRADNT 7% DSOIL 6% XWELL(1) 5% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Lead 7439-92-1 Noncancer SI DW AC Child 0.61 CZERO(1) 31% SINFIL 23% SFCOF(1) 10% XKX 7% GRADNT 7% DSOIL 6% XWELL(1) 5% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Molybdenum 7439-98-7 Noncancer SI DW A Adult 0.66 SINFIL 37% CZERO(1) 14% XKX 6%NoneMolybdenum 7439-98-7 Noncancer SI DW A Child 0.66 SINFIL 35% CZERO(1) 14% XKX 6%NoneMolybdenum 7439-98-7 Noncancer SI DW AC Adult 0.53 SINFIL 21% XKX 10% GRADNT 8% CZERO(1) 8% TWATER 8% DWC 6% TEMP 5% DWC->WIDTH_RIVER,DZMolybdenum 7439-98-7 Noncancer SI DW AC Child 0.53 SINFIL 22% XKX 10% CZERO(1) 8% GRADNT 8% TWATER 8% TEMP 6% DWC 6% DWC->WIDTH_RIVER,DZSelenium (IV) 10026-03-6 Noncancer SI DW A Adult 0.78 SINFIL 19% FEOX 17% GOVPH 14% DSOIL 9% XWELL(1) 7% GRADNT 7% XTRAVAR(1) 5% DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1)Selenium (IV) 10026-03-6 Noncancer SI DW A Child 0.78 SINFIL 19% FEOX 17% GOVPH 14% DSOIL 9% XWELL(1) 7% GRADNT 7% XTRAVAR(1) 5% DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1)Selenium (IV) 10026-03-6 Noncancer SI FC A Adult 0.80 SINFIL 23% FEOX 13% GOVPH 12% DSOIL 10% DISSW 8% VFX 5% DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (IV) 10026-03-6 Noncancer SI FC A Child 0.80 SINFIL 23% FEOX 13% GOVPH 12% DSOIL 10% DISSW 8% VFX 5% DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (IV) 10026-03-6 Noncancer SI DW AC Adult 0.80 SFCOF(1) 35% SINFIL 15% XWELL(1) 8% GRADNT 7% DSOIL 7% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Selenium (IV) 10026-03-6 Noncancer SI DW AC Child 0.80 SFCOF(1) 35% SINFIL 15% XWELL(1) 8% GRADNT 7% DSOIL 7% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1)Selenium (IV) 10026-03-6 Noncancer SI FC AC Adult 0.79 SFCOF(1) 32% SINFIL 19% DISSW 8% DSOIL 8% VFX 7% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1); VFX->U,WIDTH_RIVERChemical CAS Benchmark WMU Pathway Waste ReceptorFull R2VAR1 PV1 VAR2 PV2 VAR3 PV3 VAR4 PV4 VAR5 PV5 VAR6 PV6 VAR7 PV7 High Correlations in Sig VariablesSelenium (IV) 10026-03-6 Noncancer SI FC AC Child 0.80 SFCOF(1) 31% SINFIL 19% DSOIL 8% DISSW 8% VFX 7% DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOF(1); VFX->U,WIDTH_RIVERSelenium (VI) 7782-49-2 Noncancer SI DW A Adult 0.66 SINFIL 32% CZERO(1) 18% XKX 5%NoneSelenium (VI) 7782-49-2 Noncancer SI DW A Child 0.66 SINFIL 32% CZERO(1) 19% XKX 5%NoneSelenium (VI) 7782-49-2 Noncancer SI FC A Child 0.81 VFX 18% SINFIL 17% CZERO(1) 9%VFX->U,WIDTH_RIVERSelenium (VI) 7782-49-2 Noncancer SI FC A Adult 0.82 SINFIL 17% VFX 16% CZERO(1) 9%VFX->U,WIDTH_RIVERSelenium (VI) 7782-49-2 Noncancer SI DW AC Adult 0.61 CZERO(1) 36% SINFIL 15% XKX 9% GRADNT 7% TWATER 5% NoneSelenium (VI) 7782-49-2 Noncancer SI DW AC Child 0.61 CZERO(1) 36% SINFIL 15% XKX 10% GRADNT 7% TWATER 6% NoneSelenium (VI) 7782-49-2 Noncancer SI FC AC Adult 0.82 VFX 27% CZERO(1) 13% SINFIL 8%VFX->U,WIDTH_RIVERSelenium (VI) 7782-49-2 Noncancer SI FC AC Child 0.82 VFX 27% CZERO(1) 13% SINFIL 8%VFX->U,WIDTH_RIVERThallium 7440-28-0 Noncancer LF DW A Adult 0.70 SINFIL 21% XKX 14% UW 11% GRADNT 8% CZERO(1) 6% RECHRG 5% CZERO(1)/CWASTE; SINFIL/TSOURCThallium 7440-28-0 Noncancer LF DW A Child 0.70 SINFIL 21% XKX 13% UW 10% GRADNT 8% CZERO(1) 6% RECHRG 5% CZERO(1)/CWASTE; SINFIL/TSOURCThallium 7440-28-0 Noncancer LF DW AC Adult 0.61 TSOURC 35% XKX 21% GRADNT 12% CZERO(1) 11% DEPTH 10% XWELL(1) 7% ZWELL(1) 5% CZERO(1)/CWASTEThallium 7440-28-0 Noncancer LF DW AC Child 0.61 TSOURC 35% XKX 21% GRADNT 12% CZERO(1) 12% DEPTH 11% XWELL(1) 7% ZWELL(1) 5% CZERO(1)/CWASTEThallium 7440-28-0 Noncancer LF DW FBC Adult 0.63 CZERO(1) 16% XKX 15% GRADNT 12% SINFIL 8% DSOIL 7% XWELL(1) 6% ZB 5% CZERO(1)/CWASTE; DSOIL/DISPR;SINFIL->AREA,XW,YD,PH_SWThallium 7440-28-0 Noncancer LF DW FBC Child 0.63 CZERO(1) 16% XKX 15% GRADNT 11% SINFIL 8% DSOIL 7% XWELL(1) 6% ZB 5% CZERO(1)/CWASTE; DSOIL/DISPR;SINFIL->AREA,XW,YD,PH_SWThallium 7440-28-0 Noncancer SI DW A Adult 0.68 SINFIL 26% CZERO(1) 12% XKX 5%NoneThallium 7440-28-0 Noncancer SI DW A Child 0.68 SINFIL 26% CZERO(1) 12% XKX 5%NoneThallium 7440-28-0 Noncancer SI FC A Adult 0.82 VFX 16% SINFIL 13% CZERO(1) 6%VFX->U,WIDTH_RIVERThallium 7440-28-0 Noncancer SI FC A Child 0.82 VFX 16% SINFIL 13% CZERO(1) 6%VFX->U,WIDTH_RIVER