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, 2009 
Sensitivity 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 that 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 according 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 model of CCW risk assessment. .......................................................................... 4

Figure 2. Example graph of risk versus infiltration rate. ............................................................................ 11

List of Tables 

Table 1. Scenarios Included in Sensitivity Analysis (90th Percentile Risk > Risk Criteria) ........................ 7

Table 2. Range of Percent of Zero Risk Values Across All Scenarios, by Liner Type .............................. 11

Table 3. Comparison of Test Results With 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 Across All Fish Consumption Pathway Scenarios .............. 13
Sensitivity 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 the national distribution of risks to human health 

and the environment so as to provide the information 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 national 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 Transformation Products (EPACMTP; U.S. EPA, 1997a) 

groundwater model, (2) a simple steady-state surface water and aquatic food web model, and (3) a 

multipathway exposure and risk modeling system. 

This risk assessment provides a national characterization of waste management scenarios for 

wastes generated by coal-fired utility power plants. The sources modeled in these scenarios are onsite 

landfills and surface impoundments, which are the primary 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 disposal that was the basis for the national 

probabilistic CCW risk assessment, including contaminant 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 discharge. For the groundwater-to-surface-water pathway, 

the analysis assumed that human exposure occurs through the consumption of contaminated fish and that 

ecological exposure occurs through direct contact with contaminated surface water and sediment or from 

the consumption of aquatic organisms. 
 Sensitivity Analysis for the Coal Combustion 

 4 

Figure 1. Conceptual site model of CCW risk assessment.
 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

 5 

In planning this assessment, EPA addressed as much of the variability 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 characteristics 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 database 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 inputs and results of the probabilistic analysis using regression methods. 

The basic method employed for the sensitivity analysis used regression models developed for each 

scenario, as defined by a WMU, waste type, chemical, exposure 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 (R2

), as well as the relative importance of each input parameter in determining the 

environmental concentrations used to calculate risk (i.e., the percent variation in concentration 

attributable to each significant input). In each scenario, percent variation was used to rank the input 

parameters in order of importance; that is, the input with the highest percent variation was the most 

important. 

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

 Section 2 describes the methodology used 

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

 Section 4 presents the findings of the full sensitivity analysis 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 analysis can be defined by the following equation: 

body weight

environmental concentration risk criteria exposureduration intake 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-established, peer-reviewed, national distributions (U.S. 

EPA, 1997b,c,d), which are regularly used in the probabilistic 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 concentration 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 interaction terms were developed for the relevant 

environmental concentration (i.e., well concentration 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 (R2

) 

 Nonlinear effects may be observed via the squared 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[concentration]) 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 independent (i.e., no independent variable can be 

represented by a linear function of the other independent variables—no multicolinearity) 

 The variance of the error is constant across observations (homoscedasticity). Slight 

heteroscedasticity (variance of errors differs at different levels of the dependent variable) has 

little effect on significance tests; however, when heteroscedasticity is severe it can lead to serious 

distortion of findings and seriously weaken the analysis, thus increasing the possibility of a Type 

I error (Berry and Feldman, 1985). 

These assumptions will be addressed throughout the 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 water 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 waste properties (e.g., leachate concentrations), 

site/source parameters (e.g., infiltration rates), chemical properties (e.g., sorption coefficients), 

groundwater parameters (e.g., hydraulic 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 independent variables in the sensitivity analysis. 

Step 2. Define the scenarios of interest 

For the purposes of this sensitivity analysis, a “scenario” was a subset of the data for which an 

individual regression model was developed (i.e., one realization of the model). A scenario was denoted by 

a combination of the following parameters: 

 WMU: landfill or surface impoundment 
 Sensitivity 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) and 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 sensitivity analysis if their 90th percentile risk or 

hazard quotient value (as determined in the probabilistic analysis) exceeded the 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 analysis, because they did not exceed the 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 Analysis (90th Percentile Risk > Risk Criteria) 

Chemical WMUa Waste Typeb Pathwayc Total Scenariosd

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 data management and quality 
 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

 8 

assurance to ensure that the inputs 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; therefore, 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 inputs 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 shape and scale) highly influence the results of the 

regression analysis. Skewness is a measure of how symmetrical the data are. Skewed distributions 

produce a tail in one direction of the probability distribution and represent relatively few values in that 

direction. These distributions can cause adverse effects 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, which 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 negligible constant value to all 

values of an input variable that required a log transformation. Adding a constant value to all values 

preserves the variance of the distribution, 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 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 transformed input variables 

To conduct a proper regression analysis, all independent variables must be linearly independent. 

Therefore, the transformed input variables were evaluated 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 another, 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 the response variable (i.e., the effect of one such 

variable may mask the effect of another). 

In general, high correlation between variables was found to result from one variable being an 

internal function of the other. For example, WMU area, WMU length, and WMU width were all highly 
 Sensitivity 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 from the model, leaving only area, to conduct our 

regression analysis. Among the most used remedies for 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 taking 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 principal component methods (Harvey, 1977). The drawback 

to this approach is the interpretability 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 identification 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 included in the sensitivity analysis. In Step 

7, 33 of these terms were removed, leaving 31 input variables used in the linear regression model with 

interaction terms. From these input variables, the starting form of the regression model included linear, 

squared, and second-order interaction terms for each independent variable. An example of a regression 

model of this form with only two independent variables is of the form: 

= β +β + β + β + β + β + ε 12 1 2

2

3 2 4 2

2

0 1 1 2 1 Y x x x x x x 

where Y is the dependent variable and xi is an independent variable. A first-order term is of the form jxi. A 

second-order term is of the form jxi

2

. The x1x2 term is the second-order interaction term. 

More complex terms (e.g., higher-order polynomials or interactions) were not considered in the 

model. Even with this limitation in model complexity, 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 regression, 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 variables 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 excluded 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 obtained, the stepwise reduced model was modified 

based on the following rules: 

 Any second-order terms (xi

2

.) that were determined not to have a significant effect were dropped 

from the model 

 Any first-order term (jxi.) that was part of a significant second-order term (xi

2

.) remained in the 

model, regardless of the level of significance of the first-order term 
 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

 10 

 Any first-order terms (jxi.) that were determined not to be significant and not to have any 

significant second-order terms (xi

2

.) were dropped from the model. 

This set of rules was intended to capture and maintain in the model some of the inputs considered 

relevant in explaining the variability of the log(concentration). 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. 

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 variability 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 Ti 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, R2

, which is a measure of the amount of variance in the dependent variable (e.g., log 

concentration) explained by the variation in the model terms (Draper and Smith, 1981). A high R2

 value 

indicates a monotonic relationship. 

3.0 Analysis Testing 

Prior to conducting the full sensitivity 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 bottom ash, drinking water pathway 

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

In these preliminary test results, the regression equations exhibited high goodness-of-fit measures 

(R2

 = 0.84–0.89), but the total percent variation (sum of individual percent variations of significant 

variables) was less than 30%, and no single parameter showed a percent variation greater than 8%. We 

were not satisfied with the low explanation of variation in risk. The small change in variation made it 

difficult to accurately predict the most important variables. 

Upon investigation, this outcome was attributed 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 situations: (1) zero infiltration rates from the WMU 

associated with the probability distribution of infiltration rates used for composite liners, or (2) 

simulations where the contaminant does not reach the well or surface water body before the simulation 

was terminated (at 10,000 years) because of a combination of variables that lead to slow groundwater 

transport (such as low infiltration rates, low waste concentrations, high sorption rates, and low 

groundwater velocities). Table 2 shows the minimum, maximum, and median percent of zero values by 
 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

 11 

liner type for the scenarios. Composite-lined units had a much higher incidence of zero risk values than 

unlined or clay-lined units, because 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 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. 

-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 determining 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 results. The overall goodness-of-fit measures are still 

relatively high (R2

 = 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 

All Results No Zero Risk Values

% Zero 

Risk 

Values R2

Sum 

Percent 

Variation R2

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 goodness-of-fit values for the regression models (R2

=0.53–0.83, 

median R2

=0.68). In general, the regression models tended to produce higher goodness-of-fit values for 

the fish consumption pathway (R2

=0.76–0.89) compared to the drinking water pathway (R2=0.53–0.83). 

Although these results are acceptable, R2

 values of 0.7 or higher are generally considered good results, 

with lower values indicating that the model is missing 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 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. 

We ranked significant input variables by percent variation. The total 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 showed only 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 parameters that were found 

to be significant more often than other variables. Four 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. From this value, we calculate the percentage of 

significance across all scenarios for the drinking water 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 parameters (four most important variables) and 

sorption and travel time parameters (second tier of important parameters) 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 the adsorption isotherm coefficient and 

the depth from ground surface to water table; the most significant variables for arsenic(III) were the 

infiltration rate and the leachate concentration. 

The variables that tended to be significant for the fish consumption pathway were source 

characteristics and waterbody parameters. 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 consumption pathway in which a variable was 

found to be significant based on percent variation. From this value, we calculate the percentage of 

significance across all scenarios for the fish consumption 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 sensitivity 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 will 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 transformation 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 values, 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 selected 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 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. 

5.0 References 

Berry, W. D., and S. Feldman. 1985. Multiple Regression in Practice. Sage University Paper Series on 

Quantitative Applications in the Social Sciences, 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. 

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 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

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 Sensitivity Analysis for the Coal Combustion Waste Risk Assessment 

 16 
Appendix A—Input Variables 

Appendix A 

Input Variables 
Appendix A—Input Variables 

 
Appendix A—Input Variables 

A-1 

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 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 coefficient - for linear isotherms (UFEXP = 1), this is Kd 

SINFIL Infiltration rate inside the WMU footprint 

TEMP Ambient groundwater temperature 

TSOURC Duration of leaching 
Appendix A—Input Variables 

A-2 

Variable Description

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-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 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 observation well 

 
Appendix A—Input Variables 

A-4 

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 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 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-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 fish 
Appendix A—Input Variables 

A-6 
Appendix B—Highly Correlated Input Variables 

Appendix B 

Highly Correlated Input Variables 
Appendix 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/recharge 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 pH 
Appendix 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/recharge rate outside the WMU footprint 

 
Appendix C— Response Surface Regression Results by Scenario 

Appendix C 

Response Surface Regression Results by Scenario 
Chemical CAS Benchmark WMU Pathway Waste Receptor Full R2 VAR1 PV1 VAR2 PV2 VAR3 PV3 VAR4 PV4 VAR5 PV5 VAR6 PV6 VA

Antimony 7440-36-0 Noncancer SI DW A Adult 0.62 SINFIL 35% XKX 10% CZERO(1) 9% GRADNT 6% Antimony 7440-36-0 Noncancer SI DW A Child 0.62 SINFIL 35% XKX 10% CZERO(1) 9% GRADNT 6% Arsenic (III) 22569-72-8 Cancer LF DW A Adult 0.72 CZERO(1) 16% XKX 11% GRADNT 6% 

Arsenic (III) 22569-72-8 Cancer LF DW A Child 0.73 CZERO(1) 17% XKX 10% GRADNT 6% 

Arsenic (III) 22569-72-8 Cancer LF DW AC Adult 0.68 TSOURC 32% CZERO(1) 15% XKX 10% DEPTH 9% GRADNT 6% Arsenic (III) 22569-72-8 Cancer LF DW AC Child 0.68 TSOURC 32% CZERO(1) 15% XKX 10% DEPTH 9% GRADNT 6% Arsenic (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% ZWArsenic (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% ZWArsenic (III) 22569-72-8 Cancer SI DW A Adult 0.62 SINFIL 42% CZERO(1) 22% XKX 8% GRADNT 5% Arsenic (III) 22569-72-8 Cancer SI DW A Child 0.62 SINFIL 43% CZERO(1) 22% XKX 7% GRADNT 5% Arsenic (III) 22569-72-8 Cancer SI FC A Adult 0.80 VFX 21% SINFIL 20% CZERO(1) 9% 

Arsenic (III) 22569-72-8 Cancer SI FC A Child 0.80 VFX 21% SINFIL 19% CZERO(1) 9% 

Arsenic (III) 22569-72-8 Cancer SI DW AC Adult 0.66 CZERO(1) 53% SINFIL 13% XKX 6% GRADNT 6% TWATER 5% Arsenic (III) 22569-72-8 Cancer SI DW AC Child 0.68 CZERO(1) 54% SINFIL 13% XKX 6% GRADNT 5% TWATER 5% Arsenic (III) 22569-72-8 Cancer SI FC AC Adult 0.82 CZERO(1) 22% VFX 16% SINFIL 7% 

Arsenic (III) 22569-72-8 Cancer SI FC AC Child 0.83 CZERO(1) 22% VFX 15% SINFIL 7% 

Arsenic (V) 15584-04-0 Cancer LF DW A Adult 0.83 SFCOF(1) 33% DSOIL 24% SINFIL 17% CZERO(1) 5% Arsenic (V) 15584-04-0 Cancer LF DW A Child 0.83 SFCOF(1) 33% DSOIL 24% SINFIL 17% CZERO(1) 5% Arsenic (V) 15584-04-0 Cancer LF DW AC Adult 0.77 SFCOF(1) 28% DSOIL 16% Arsenic (V) 15584-04-0 Cancer LF DW AC Child 0.77 SFCOF(1) 28% DSOIL 16% Arsenic (V) 15584-04-0 Cancer LF DW FBC Adult 0.65 SFCOF(1) 44% VFX 19% DSOIL 17% XKX 6% Arsenic (V) 15584-04-0 Cancer LF DW FBC Child 0.66 SFCOF(1) 44% VFX 19% DSOIL 17% XKX 6% Arsenic (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% 

Arsenic (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% Arsenic (V) 15584-04-0 Cancer SI FC A Adult 0.77 SINFIL 23% SFCOF(1) 22% VFX 12% DSOIL 11% DISSW 7% Arsenic (V) 15584-04-0 Cancer SI FC A Child 0.78 SINFIL 21% SFCOF(1) 20% DSOIL 11% VFX 10% DISSW 7% Arsenic (V) 15584-04-0 Cancer SI DW AC Adult 0.66 CZERO(1) 19% SINFIL 19% SFCOF(1) 16% DSOIL 5% Arsenic (V) 15584-04-0 Cancer SI DW AC Child 0.67 CZERO(1) 20% SINFIL 19% SFCOF(1) 16% DSOIL 5% Arsenic (V) 15584-04-0 Cancer SI FC AC Adult 0.76 VFX 16% SINFIL 14% CZERO(1) 11% SFCOF(1) 9% Arsenic (V) 15584-04-0 Cancer SI FC AC Child 0.77 VFX 16% SINFIL 14% CZERO(1) 11% SFCOF(1) 9% Boron 7440-42-8 Noncancer SI DW A Adult 0.69 CZERO(1) 28% SINFIL 27% Boron 7440-42-8 Noncancer SI DW A Child 0.69 CZERO(1) 28% SINFIL 27% XKX 5% Boron 7440-42-8 Noncancer SI DW AC Adult 0.55 SINFIL 21% CZERO(1) 20% XKX 12% TWATER 10% GRADNT 9% DWC 7% TEBoron 7440-42-8 Noncancer SI DW AC Child 0.55 SINFIL 22% CZERO(1) 20% XKX 12% TWATER 9% GRADNT 9% DWC 7% TECadmium 7440-43-9 Noncancer SI DW A Adult 0.66 SINFIL 49% CZERO(1) 10% AREA 7% GRADNT 6% XKX 5% Cadmium 7440-43-9 Noncancer SI DW A Child 0.66 SINFIL 49% CZERO(1) 10% AREA 6% GRADNT 6% XKX 5% Cadmium 7440-43-9 Noncancer SI DW AC Adult 0.64 CZERO(1) 46% SINFIL 13% GRADNT 7% XKX 6% TWATER 5% DWC 5% Cadmium 7440-43-9 Noncancer SI DW AC Child 0.64 CZERO(1) 46% SINFIL 14% GRADNT 7% XKX 7% TWATER 5% DWC 5% Cobalt 7440-48-4 Noncancer SI DW A Adult 0.58 SINFIL 27% SFCOF(1) 20% XKX 12% GRADNT 8% XWELL(1) 8% ZB 6% ARCobalt 7440-48-4 Noncancer SI DW A Child 0.59 SINFIL 27% SFCOF(1) 20% XKX 12% GRADNT 8% XWELL(1) 8% ZB 6% ARCobalt 7440-48-4 Noncancer SI DW AC Adult 0.58 CZERO(1) 23% SINFIL 21% XKX 7% GRADNT 6% Cobalt 7440-48-4 Noncancer SI DW AC Child 0.58 CZERO(1) 23% SINFIL 21% XKX 7% GRADNT 7% Lead 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% XKLead 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% XKLead 7439-92-1 Noncancer SI DW AC Adult 0.61 CZERO(1) 31% SINFIL 22% SFCOF(1) 10% XKX 7% GRADNT 7% DSOIL 6% XWLead 7439-92-1 Noncancer SI DW AC Child 0.61 CZERO(1) 31% SINFIL 23% SFCOF(1) 10% XKX 7% GRADNT 7% DSOIL 6% XWMolybdenum 7439-98-7 Noncancer SI DW A Adult 0.66 SINFIL 37% CZERO(1) 14% XKX 6% Molybdenum 7439-98-7 Noncancer SI DW A Child 0.66 SINFIL 35% CZERO(1) 14% XKX 6% Molybdenum 7439-98-7 Noncancer SI DW AC Adult 0.53 SINFIL 21% XKX 10% GRADNT 8% CZERO(1) 8% TWATER 8% DWC 6% TEMolybdenum 7439-98-7 Noncancer SI DW AC Child 0.53 SINFIL 22% XKX 10% CZERO(1) 8% GRADNT 8% TWATER 8% TEMP 6% DWSelenium (IV) 10026-03-6 Noncancer SI DW A Adult 0.78 SINFIL 19% FEOX 17% GOVPH 14% DSOIL 9% XWELL(1) 7% GRADNT 7% XTSelenium (IV) 10026-03-6 Noncancer SI DW A Child 0.78 SINFIL 19% FEOX 17% GOVPH 14% DSOIL 9% XWELL(1) 7% GRADNT 7% XTSelenium (IV) 10026-03-6 Noncancer SI FC A Adult 0.80 SINFIL 23% FEOX 13% GOVPH 12% DSOIL 10% DISSW 8% VFX 5% Selenium (IV) 10026-03-6 Noncancer SI FC A Child 0.80 SINFIL 23% FEOX 13% GOVPH 12% DSOIL 10% DISSW 8% VFX 5% Selenium (IV) 10026-03-6 Noncancer SI DW AC Adult 0.80 SFCOF(1) 35% SINFIL 15% XWELL(1) 8% GRADNT 7% DSOIL 7% Selenium (IV) 10026-03-6 Noncancer SI DW AC Child 0.80 SFCOF(1) 35% SINFIL 15% XWELL(1) 8% GRADNT 7% DSOIL 7% Selenium (IV) 10026-03-6 Noncancer SI FC AC Adult 0.79 SFCOF(1) 32% SINFIL 19% DISSW 8% DSOIL 8% VFX 7% 
Chemical CAS Benchmark WMU Pathway Waste Receptor Full R2 VAR1 PV1 VAR2 PV2 VAR3 PV3 VAR4 PV4 VAR5 PV5 VAR6 PV6 VA

Selenium (IV) 10026-03-6 Noncancer SI FC AC Child 0.80 SFCOF(1) 31% SINFIL 19% DSOIL 8% DISSW 8% VFX 7% Selenium (VI) 7782-49-2 Noncancer SI DW A Adult 0.66 SINFIL 32% CZERO(1) 18% XKX 5% Selenium (VI) 7782-49-2 Noncancer SI DW A Child 0.66 SINFIL 32% CZERO(1) 19% XKX 5% Selenium (VI) 7782-49-2 Noncancer SI FC A Child 0.81 VFX 18% SINFIL 17% CZERO(1) 9% Selenium (VI) 7782-49-2 Noncancer SI FC A Adult 0.82 SINFIL 17% VFX 16% CZERO(1) 9% Selenium (VI) 7782-49-2 Noncancer SI DW AC Adult 0.61 CZERO(1) 36% SINFIL 15% XKX 9% GRADNT 7% TWATER 5% Selenium (VI) 7782-49-2 Noncancer SI DW AC Child 0.61 CZERO(1) 36% SINFIL 15% XKX 10% GRADNT 7% TWATER 6% Selenium (VI) 7782-49-2 Noncancer SI FC AC Adult 0.82 VFX 27% CZERO(1) 13% SINFIL 8% Selenium (VI) 7782-49-2 Noncancer SI FC AC Child 0.82 VFX 27% CZERO(1) 13% SINFIL 8% Thallium 7440-28-0 Noncancer LF DW A Adult 0.70 SINFIL 21% XKX 14% UW 11% GRADNT 8% CZERO(1) 6% RECHRG 5% Thallium 7440-28-0 Noncancer LF DW A Child 0.70 SINFIL 21% XKX 13% UW 10% GRADNT 8% CZERO(1) 6% RECHRG 5% Thallium 7440-28-0 Noncancer LF DW AC Adult 0.61 TSOURC 35% XKX 21% GRADNT 12% CZERO(1) 11% DEPTH 10% XWELL(1) 7% ZWThallium 7440-28-0 Noncancer LF DW AC Child 0.61 TSOURC 35% XKX 21% GRADNT 12% CZERO(1) 12% DEPTH 11% XWELL(1) 7% ZWThallium 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 Thallium 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 Thallium 7440-28-0 Noncancer SI DW A Adult 0.68 SINFIL 26% CZERO(1) 12% XKX 5% Thallium 7440-28-0 Noncancer SI DW A Child 0.68 SINFIL 26% CZERO(1) 12% XKX 5% Thallium 7440-28-0 Noncancer SI FC A Adult 0.82 VFX 16% SINFIL 13% CZERO(1) 6% Thallium 7440-28-0 Noncancer SI FC A Child 0.82 VFX 16% SINFIL 13% CZERO(1) 6%