Sensitivity Analysis for the

Fossil Fuel Combustion Waste Risk Assessment

Technical Report

Work Assignment #1-16

Contract #68-W-03-042

Prepared for

U.S. Environmental Protection Agency

Office of Solid Waste

Research Triangle Park, NC 27709

Prepared by

RTI International

Center for Environmental Analysis

P.O. Box 12194

Research Triangle Park, NC 27709

June 2005

Sensitivity Analysis for the Fossil Fuel Combustion Waste Risk Assessment

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Table of Contents

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

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

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

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

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

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

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

Appendix A Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B Highly Correlated Input Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

Appendix C Response Surface Regression Results by Scenario . . . . . . . . . . . . . . . . . . . . . . C-1

List of Figures

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

List of Tables

Table 1. Scenarios Included in Sensitivity Analysis (90th Percentile Risk > Risk Criteria) .....4

Table 2. Percent of Zero Risk Values by Liner Type and Scenario . . . . . . . . . . . . . . . . . . . . . . . 7

Table 3. Comparison of Test Results With and Without Zero Risk Values .................8
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1.0 Summary of Key Findings

In 2004, RTI conducted a probabilistic risk assessment to evaluate the human and

ecological impacts of fossil fuel combustion wastes (FFCW) disposed in surface impoundments

and landfills. A sensitivity analysis was conducted on the results of the probabilistic analysis to

determine the factors most important for risk to human receptors. The sensitivity analysis

utilized a response surface regression method. In general, this approach derives a statistical

model for risk (or hazard quotient) based on the input parameters from the probabilistic analysis.

The output from the sensitivity analysis is the goodness-of-fit values for the overall regression

models (R2

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

Prior to running the full sensitivity analysis, the methodology was tested on a subset of

scenarios (i.e., a combination of waste management unit, waste type, chemical, exposure

pathway, and receptor). Preliminary results indicated that the response surface method was

unable to determine which input parameters were the most important drivers of risk. The cause

of these poor results was attributed to the high percentage of zero risk values in the analysis,

which resulted from a variety of input combinations, including composite liner type, low

infiltration rates, and low source concentrations. The methodology was modified to remove all

zero risk values prior to developing the regression models. This modified methodology showed

much- improved results and is valid for determining the inputs that are the most important

drivers of risk.

When the sensitivity analysis was completed for all scenarios under consideration for

human receptors, a wide range in goodness-of-fit values for the regression models were

observed, with R2

 values ranging from 0.53-0.90. In general, the regression models for the fish

consumption pathway tended to have higher R2 values (0.76-0.90) compared to the drinking

water pathway (0.53-0.83). 

The sum of percent variation for scenarios ranged from 70% to >100% (due to overlap

between individual variables from the second-order interaction terms). In general, the drinking

water pathway tended to have a larger number of input parameters that were significant, while

the fish consumption pathway had a small number (typically <= 3). The most important input

variables by exposure pathway were as follows:

Drinking Water Pathway

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

# Leachate concentration from the WMU

# Hydraulic conductivity of the aquifer

# Regional hydraulic gradient in the aquifer 
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Fish Consumption Pathway

# Infiltration rate within the WMU footprint

# Leachate concentration from the WMU

# Waterbody flow rate

2.0 Introduction

In 2004, RTI conducted a risk assessment to estimate the impacts of FFCW disposed in

landfills and surface impoundments on human health and the environment. The risk assessment

consisted of a site-based, probabilistic analysis for the groundwater-to-surface water pathway

and the groundwater-to-drinking water pathway. For human receptors, the two exposure routes

were consumption of drinking water from a well and fish consumption. The output from the

probabilistic analysis was a national distribution of the potential human and ecological risks

from (WMUs) that accept FFCW. 

This report presents the results of a sensitivity analysis that determined which parameters 

were the most important drivers of human health risk. The basic method employed for the

sensitivity analysis was response surface regression. In general, this approach uses the input

parameters from the probabilistic analysis to derive statistical models for risk (or hazard

quotient). Regression models were developed for each scenario, as defined by a WMU, waste

type, chemical, exposure pathway, and receptor. The response surface method (RSM) provides

both a goodness-of-fit value for the overall regression model (R2), as well as the percent

variation in risk attributable to each significant input. The percent variation is used to rank the

input parameters in order of importance. 

Section 3 of this report presents a summary of the methodology used; Section 4 contains

the results of preliminary runs used to test the RSM; Section 5 presents the results of the full

sensitivity analysis; and Section 6 presents recommendations for future work.

3.0 Methodology

The goals of the sensitivity analysis were two-fold:

(1) To develop regression models with risk (or hazard quotient) as the dependent variable

and inputs from the probabilistic analysis as the independent variables. This was achieved

through a two-step process. First, regression models were developed for the relevant

environmental concentration (i.e., well concentration for the drinking water pathway and fish

concentration for the fish consumption pathway). These models were then used to develop

regression models for risk (or hazard quotient).

(2) To rank the independent variables according to their contribution to the variability in

risk (or hazard quotient). 
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The RSM offers the following advantages:

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

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

regression model. 

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

Regression methods are also subject to certain limitations. They are not capable of

determining the sensitivity of risk to input variables that are not varied in the analysis (e.g.,

assumptions) or are not otherwise included within the scope of the analysis (e.g., model-derived

variables). If the most important variables do not vary, or their variability is improperly

characterized, the sensitivity analysis may not identify them as being important.

The 11 major steps in the sensitivity analysis are summarized below. A complete

description of the methodology is contained in the U.S. EPA’s technical background document

(2003).

Step 1. Define the independent variables and dependent variables. For the initial

regression models, the dependent variable was either well concentration for the drinking water

pathway or fish concentration for the fish consumption pathway. The independent variables

included waste properties, site/source parameters, chemical properties, meteorological factors,

groundwater parameters, well characteristics, and water body characteristics. 

The response surface regression included only those inputs that were continuous and

numeric. Therefore, some variables of importance, such as coal type, liner type, hydrologic

region, groundwater region, soil type, and water body type, were not included. Appendix A

contains a list of the input variables from the probabilistic analysis that served as independent

variables in the sensitivity analysis.

Step 2. Define the scenarios of interest. For the purposes of this sensitivity analysis, a

scenario is a subset of the data for which an individual regression model was developed. A

scenario is denoted by the combination of

# WMU: landfill or surface impoundment;

# Waste type: conventional fly and bottom ash, co-disposed coal cleaning waste and

conventional ash, or fluidized bed combustion waste;

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

thallium;

# Exposure pathway: drinking water or fish consumption;

# Receptor: adult or child. 
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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 of 1 for

non-cancer and 10-5 for cancer. Table 1 lists the 84 scenarios that exceeded the risk criteria and

were included in the sensitivity analysis. Aluminum, barium, and nitrate were included in the

probabilistic analysis, but not the sensitivity analysis, because they did not exceed the risk

criteria for any scenario.

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

Chemical Benchmark WMU Waste Type Pathway

Total

Scenarios1

Antimony Noncancer SI A DW 2

Arsenic (III) Cancer LF

SI

SI

A, AC, FBC

A, AC

A, AC

DW

FC

DW

6

4

4

Arsenic (V) Cancer LF

SI

SI

A, AC, FBC

A, AC

A, AC

DW

FC

DW

6

4

4

Boron Noncancer SI A, AC DW 4

Cadmium Noncancer SI A, AC DW 4

Cobalt Noncancer SI A, AC DW 4

Mercury Noncancer LF

SI

A, AC, FBC

AC

FC

FC

6

2

Lead Noncancer SI A, AC DW 4

Molybdenum Noncancer SI A, AC DW 4

Selenium (IV) Noncancer SI

SI

A, AC

A, AC

FC

DW

4

4

Selenium (VI) Noncancer SI

SI

A, AC

A, AC

FC

DW

4

4

Thallium Noncancer LF

SI

SI

A, AC, FBC

A

A

DW

FC

DW

6

2

2 1

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

Step 3. Develop the input data set. The input data set contains one record for each

scenario and iteration, with numeric values for all of the independent and dependent variables.

This step required extensive quality assurance to ensure that the inputs were assembled correctly,

as the data set contains approximately 840,000 records (84 scenarios x 10,000

iterations/scenario) and 150 fields (144 independent and dependent variables, plus six fields to

denote the scenario and iteration).

Step 4. Remove any variables that are constants. Any variable that was constant

across a scenario does not affect the environmental concentration, and was therefore removed

from the data set prior to the regression analysis. Appendix A classifies each input variable as to

whether it was included in the sensitivity analysis, and if not, why the variable was dropped. 

Step 5. Perform transformations to the input variables so that all input variables

will have approximately symmetric distributions. The distributions of the input parameters

(both shape and scale) highly influence the results of the regression analysis. To mitigate the

adverse effect of skewed distributions, the original values were transformed using power or
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natural log functions. In most cases, the presence of zero values necessitated that a constant

value be added prior to the log transformation. The transformation process forces the

distributions to be as symmetric as possible and removes some of the correlation among the input

variables.

Step 6. Standardize the transformed variables. The input variables were standardized

by subtracting the mean and dividing by the standard deviation. This process allows the

regression results to be independent of the magnitude of the value of the input variables. 

Without standardization, inputs with larger 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. Remove any

input variables that are highly correlated with other input variables in the data set. If two

or more input variables were highly correlated with one another, only one was retained in the

regression model. Keeping highly correlated variables will reduce the significance of each in the

model, because each is essentially explaining the same linear relationship with the response

variable (i.e., the effect of one such variable may mask the effect of another). Appendix B

contains a list of all the highly correlated inputs encountered in the analysis as determined by a

correlation coefficient >= 0.8. The highlighted member of each group indicates the variable that

was retained in the analysis. 

Step 8. Determine the form of the regression model. The starting form of the

regression model included linear, squared, and second-order interaction terms for each

independent variable. More complex terms (e.g., higher-order polynomials or interactions) were

not considered in the model. Even given this limitation in model complexity, an example

regression model included more than 1,000 terms in its initial form.

Step 9. Use response surface regression methods to eliminate insignificant terms in

the model. An iterative process was used to eliminate terms from the model that did not satisfy

the significance criterion (p-value <= 0.01). After the response surface regression results were

obtained, the model was modified based on the following rules:

# Any second-order terms that were determined not to have a significant effect were

dropped from the model. 

# Any first-order term that was part of a significant second-order term remained in

the model, regardless of the level of significance of the first-order term. 

# Any first-order terms that were determined not to be significant and not to have

any significant second-order terms were dropped from the model. 

A regression analysis was then conducted on the reduced model. This process was

repeated until all of the second-order terms in the model had significant effects on risk or hazard

quotient, and no more terms could be removed.
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(1)

(2)

(3)

Step 10. Use the regression models for environmental concentration to develop

regression models for the risk (or hazard quotient). The original methodology included the

following equation as a model for risk:

The natural log form of the equation is shown in Equation 2. The environmental

concentration term was replaced with the final regression model developed in the previous step.

 For the current sensitivity analysis, exposure factors (including risk factors, exposure

duration, intake, and body weight) were not included in the model. This decision was made

because the values for exposure factors were taken from national distributions and cannot be

regulated. Therefore, the risk model reduces to

A regression analysis was performed for risk. Because environmental concentration and

risk were highly correlated, the results for the models were very similar. This final regression

equation is referred to as the “full regression model.”

Step 11. Rank the independent variables according to the degree that each explains

the variation in risk. To determine the degree that each independent variable explains the

variation in risk, a regression technique was employed that compares the results of the full

regression model (determined in Step 10) to a reduced model in which all terms for a specific

independent variable have been eliminated. The degree to which the goodness of the model

changes is a measure of the sensitivity of risk to the independent variable that was removed. The

sensitivity measure is referred to as the “percent variation,” and was used to assign relative

rankings to the inputs.

4.0 Test Results

Prior to conducting the full sensitivity analysis, the RSM was applied to a small number

of scenarios to find technical problems and ensure that results showed both a high goodness-offit measure as well as a high percent variation value for the significant terms. Four landfill

scenarios were selected for this initial testing procedure:

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

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

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

Preliminary results showed that the regression equations exhibited high goodness-of-fit

measures (R2

 values ranging from 0.84-0.89), but that the percent variation results were

unsatisfactory, with total percent variation of less than 30% and no single parameter showing a

value greater than 8%. 

 A wide range of actions were taken to investigate this outcome, including quality

assurance on the input data, an analysis of the transformation process, a series of regression

analyses using intermediate values (e.g., well concentration) as the dependent variables, and

graphical analysis of relationships between specific independent variables and risk. The problem

was finally traced to the fact that the scenarios being analyzed had a high proportion (27-42%) of

zero risk values. The zero risk values resulted from a wide combination of input parameters,

including composite liners, low infiltration rates, and low waste concentration. Composite-lined

units had a much higher incidence of zero risk values than unlined or clay-lined units, as shown

in Table 2. 

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

Liner Type

Minimum % Zero

By Scenario

Maximum % Zero By

Scenario

Median % Zero By

Scenario

No Liner 0.2 87.0 11.5

Clay Liner 0.2 71.9 5.3

Composite Liner 35.6 100.0 95.0

 The fact that a wide variety of input parameters resulted in zero risk values complicated

the analysis. For example, Figure 1 shows a graph of risk versus infiltration rate. Although a

general relationship between the two variables can be observed, the horizontal line of zero risk

values for a wide range of infiltration rates prevents the regression analysis (containing only

second-order terms) from observing a significant relationship. 

The methodology was modified to remove all zero risk values from the data set prior to

conducting the regression analysis. Table 3 shows the difference in results as a result of this

modification. The overall goodness-of-fit measures are still relatively high (R2 values ranging

from 0.67-0.88), but the percent variation values are much improved (73-110%). Note that the

total percent variation for the regression model can exceed 100% due to the overlap between

individual input variables in the second-order interaction terms.
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-80

-70

-60

-50

-40

-30

-20

-10

0

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

LN(SINFIL)

LN(RISK)

Figure 1. Example Graph of Risk Versus Infiltration Rate

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

Scenario

All Results No 0 Risk Values

% Zero Risk

Values R2

Sum

Percent

Variation R2

Sum

Percent

Variation

Mercury

Convention fly and bottom ash

Fish consumption

Adult

33% 0.89 27% 0.88 73%

Mercury

Convention fly and bottom ash

Drinking water 

Adult

32% 0.86 22% 0.67 111%

Arsenic(V)

Convention fly and bottom ash

Drinking water 

Adult

42% 0.84 28% 0.83 88%

Arsenic(III)

Convention fly and bottom ash

Drinking water 

Adult

27% 0.85 20% 0.72 65%
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5.0 Results

When the sensitivity analysis was completed for all scenarios under consideration for

human receptors, it indicated a wide range in goodness-of-fit values for the regression models

(R2

 values ranging from 0.53-0.90, with a median of 0.70). In general, the regression models for

the fish consumption pathway tended to have higher goodness-of-fit values (0.76-0.90)

compared to the drinking water pathway (0.53-0.83). Although these results are deemed

acceptable, R2 values of 0.7 or higher are generally considered good results, with lower values

indicating that the model is missing important inputs, requires additional terms, or that implicit

assumptions are not being addressed by the model. This may be an area for future investigation.

In examining the percent variation results, inputs with a percent variation value greater

than 5% were generally regarded as important. The percent variation is not useful for ranking

variables below this threshold because the model results do not change significantly when these

variables are removed from the regression model. 

The sum of percent variation for the scenarios was excellent, ranging from 70% to

>100%. In general, the drinking water pathway tended to have a larger number of input

parameters that were significant, while the fish consumption pathway only showed a small

number (typically <= 3) as significant. This may explain the difference in goodness-of-fit values

observed between the pathways.

The variables that were significant for the drinking water pathway tended to be source

characteristics and aquifer variables. The drinking water pathway had seven important

parameters, four of which were significant in > 75% of the scenarios, and three that were

significant for about one-third of the scenarios:

Drinking Water—Important in most scenarios

# Infiltration rate within the WMU footprint (SINFIL)

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

# Hydraulic conductivity of the aquifer (XKX)

# Regional hydraulic gradient in the aquifer (GRADNT)

Drinking Water—Important in many scenarios

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

# Depth from ground surface to water table (DSOIL)

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

The relative importance of groundwater flow parameters (shown in the first group) and

sorption and travel time parameters (shown in the second group) was related to the magnitude of

a chemical’s partition coefficient (Kd). For example, arsenic(V) had a much higher Kd value at

low concentrations compared to arsenic(III), indicating that sorption was a more dominant

process. Accordingly, the most significant variables for arsenic(V) were adsorption isotherm
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coefficient and depth from ground surface to water table; the most significant variables for

arsenic(III) were infiltration rate and leachate concentration.

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

characteristics and waterbody parameters. Unlike the drinking water pathway, only three

variables were consistently significant across scenarios:

Fish Consumption

# Infiltration rate within the WMU footprint (SINFIL)

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

# Waterbody flow rate (VFX)

Some of the input variables listed above were highly correlated to other input variables.

Although these correlated variables were dropped from the regression model, it is important to

consider them as related drivers of risk. The specific groups of highly correlated variables varied

by scenario, but some of the commonly encountered correlations were:

# Infiltration rate and WMU area and duration of leaching. 

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

WMU.

# The leading Freundlich adsorption isotherm coefficient and other isotherm

parameters and governing pH.

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

6.0 Recommendations

The current sensitivity analysis produced regression models with (1) moderate to

excellent goodness-of-fit values, (2) excellent percent variation results, and (3) a rank order of

variables that is consistent with chemical properties and exposure pathways.

Future work should focus on improving the goodness-of-fit values for those regression

equations with R2

 values less than 0.7, and modifying the RSM (to the extent possible) to

address the issue of zero risk values. The following two specific actions should be considered:

# Conducting a categorical regression analysis: This would permit the inclusion of

categorical variables, which may be significant drivers of risk. Liner type, in

particular, is likely to be significant given the high proportion of zero infiltration

rates and risk values for composite liners. 

# Including higher-order terms in the regression model: Given the many

correlations and interactions between input variables, it may be insightful to test a
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model with higher order terms to determine the impact of model form on the

results. 

7.0 References

U.S. EPA (Environmental Protection Agency). 2003. Technical Background Document for the

Sewage Sludge Screening Analysis. Internal Deliberative Draft. Office of Water.

Washington, DC.
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Appendix A

Input Variables
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Appendix A – Input Variables

A-3

Table A-1. Input Variables Included in Sensitivity Analysis

Variable Description

AL Longitudinal dispersivity in the aquifer

ALPHA Moisture retention parameter (Van Genuchten)

AQMIXPH Aquifer pH

AREA Area of assumed square-shaped WMU

ASNOM Aquifer fraction of organic carbon

AT_aq Horizontal transverse dispersivity in the aquifer

AV Vertical transverse dispersivity in the aquifer

BETA Moisture retention parameter

BULKD Aquifer soil bulk density

CWASTE Buffering capacity of soil column beneath WMU, used for metal

simulations, only

CZERO(1) Leachate concentration from WMU

DBGS Depth below ground surface of WMU base

DEPTH Landfill/Waste Pile depth

DISPR Longitudinal dispersivity in unsaturated zone

DISSW Distance to nearest surface water body for surface impoundments

DSOIL Depth from ground surface to water table

dwc Depth of water column

dz waterbody depth

Facility ID Unique ID for each WMU/plant combination (for tracking sites)

FEOX Weight percentage of iron-hydroxide in the soil and aquifer

FOC Fraction of organic carbon in saturated soils

GOVPH Governing pH

GRADNT Regional hydraulic gradient in the aquifer

ICLR Index to nearest climate station to WMU

IGWTYPE Groundwater chemistry identification number

ISTYPE2 Surface soil capping WMU

Kdbs Soil-water partition coefficient for bed sediment

Kdsw Soil-water partition coefficient for suspended sediment

PH_GW Mixed groundwater pH

pH_Soil Soil pH

pH_sw Waterbody pH

PHLEACH Leachate pH; used for metal simulations only

Plume_Width Plume width at the waterbody

POM Percent organic matter

POR Volume fraction of connected pore space in the aquifer
Appendix A – Input Variables

Variable Description

A-4

RECHRG Regional infiltration/recharge rate outside the WMU footprint

RETARD(1) Retardation coefficient

RHOB Bulk density of unsaturated soil

SampleID Unique Id for each sample chosen for constituent data

SATK Saturated hydraulic conductivity

SFCOF(1) Leading Freundlich adsorption isotherm coefficient - for linear isotherms

(UFEXP = 1), this is Kd

SINFIL Infiltration rate inside the WMU footprint

TEMP Ambient groundwater temperature

TSOURC Duration of leaching

TSS Total suspended solids

Twater Temperature of the Waterbody

U Current velocity of the waterbody

UFCOF(1) Leading Freundlich adsorption isotherm coefficient - for liner isotherms

(UFEXP = 1), this is Kd

USNOM Unsaturated zone percentage of organic matter

uw Mean annual windspeed

Vfx Waterbody annual flow mixing volume

VXCS Regional groundwater seepage velocity

WCR Residual water content

WCS Saturated water content (effective porosity)

Width_river Width of river

XKX Hydraulic conductivity of saturated zone (aquifer)

XTRAVAR(1) First extra master variable

XW Length of WMU side = sqrt(AREA)

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

YD Length of WMU side = sqrt(AREA)

YWELL(1) Distance off-centerline to observation well

ZB Thickness of saturated zone

ZWELL(1) Depth of observation location below water table (as fraction of saturated

thickness)

ZWELL(2) Depth of observation location below water table (as fraction of saturated

thickness)
Appendix A – Input Variables

A-5

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

Input Variable Description

ANIST Ratio of horizontal to vertical hydraulic conductivity

bsc Bed sediment particle concentration

bsp Bed sediment porosity

CTDENS Density of leachable waste in a landfill

db Depth of Upper Benthic Layer

DIAM Average soil particle diameter

DWS(1) Drinking water standard for species 1

EF Exposure frequency

F_dw Fraction of drinking water ingested that is contaminated

F_fish Fraction of fish intake from contaminated source

F_T3 Fraction of trophic level 3 intake, 0.36

F_T4 Fraction of trophic level 4 intake, 0.64

FRACT Volume fraction of leachable waste in a landfill

Kg_river Gas-phase transfer coefficient

LOM Concentration of dissolved organic matter in the waste leachate

RTEMP Reference temperature

Theta_water Temperature correction factor

THLC Temperature at which HLC values is given

WB Rate of burial

XWELL(2) Distance between observation well and downgradient edge of WMU

YWELL(2) Distance off-centerline to oberservation well

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

Input Variable Description

AHYDR(1) Second-order acid catalyzed hydrolysis rate constant at reference

temperature for species 1

BCF_T3F Bioconcentration factor for trophic level 3, fish fillet

BCF_T4F Bioconcentration factor for trophic level 4, fish fillet

BHYDR(1) Second-order base catalyzed hydrolysis rate constant at reference

temperature for species 1

BIOS(1) Biological degradation rate coefficient

BIOU(1) Biological degradation rate coefficient

CMWGT(1) Molecular weight for species 1

CSCL_aquatic Ecological CSCL for surface water
Appendix A – Input Variables

Input Variable Description

A-6

CSCL_sediment Ecological CSCL for sediment

CSFOral Oral cancer slope factor

CSLAM(1) Chemical degradation rate coefficient

Da Diffusivity of chemical in air

DSTAR(1) Diffusivity in water for species 1

Dw Diffusion coefficient in water

HLC Henry's law constant

kh Hydrolysis rate

KOC(1) Normalized distribution coefficient for organic carbon for species 1

ksed Degradation rate for sediment

ksw Degradation rate for surface-water column

METALID Identification number for the metal

NHYDR(1) Neutral hydrolysis rate constant at reference temperature for species 1

RfD_fish Noncancer reference dose

RfD_food Noncancer reference dose

RfD_soil Noncancer reference dose

RfD_water Noncancer reference dose

RLAM1(1) Dissolved phase hydrolysis decay rate for species 1

RLAM2(1) Sorbed phase hydrolysis decay rate for species 1

SFEXP(1) Freundlich exponent (always 1 for linear isotherm)

UCLAM(1) Chemical degradation rate coefficient

UFEXP(1) Freundlich exponent (always 1 for linear isotherm)
Appendix A – Input Variables

A-7

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

Input Variable Description

AT Averaging time

BW_1 Body weight, cohort 1

BW_2 Body weight, cohort 2

BW_3 Body weight, cohort 3

BW_4 Body weight, cohort 4

CARC(1,1) Base exposure period for calculating average receptor well concentration

(if specified) for child

CARC(1,2) Base exposure period for calculating average receptor well concentration

(if specified) for adult

CR_dw_1 Consumption rate of water, cohort 1

CR_dw_2 Consumption rate of water, cohort 2

CR_dw_3 Consumption rate of water, cohort 3

CR_dw_4 Consumption rate of water, cohort 4

CR_fish Consumption rate of fish

Table A-5. Variables Not Included in Sensitivity Analysis - Categorical Variables

Input Variable Description

CoalType Coal type

HREG Hydrologic region

IGWR Index to groundwater region/hydrogeologic environment associated with

WMU

ISTYPE1 Prevalent regional surface soils around WMU

LinerID EPACMTP liner type (0, 1, 2 for no liner, clay liner, composite liner,

respectively)

Wbtype Waterbody type (L or R)
[This page intentionally left blank.]
Appendix B

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

B-3

Appendix B

Highly Correlated Input Variables

Note: The highlighted variable is the variable that was selected to remain in the analysis. Highly

correlated parameters are presented in the results section for any input variable that was deemed

significant.

1)

AREA WMU area

YD WMU length

XW WMU width

Plume_Width

2)

USNOM percent organic matter (unsaturated zone)

POM percent organic matter (unsaturated zone)

3)

ALPHA soil moisture parameter alpha

BETA soil moisture parameter beta

RHOB soil bulk density

4)

DSOIL vadose zone thickness / depth to water

DISPR dispersivity in vadose zone

5)

DIAM aquifer particle diameter

BULKD aquifer bulk density

6)

POR aquifer effective porosity

RETARD(1) retardation coefficient

7)

AL longitudinal dispersivity

AV vertical dispersivity

AT_aq transverse dispersivity
Appendix B – Highly Correlated Input Variables

B-4

8)

XWELL(1) well distance

RADIS(1) radial well distance

9)

CZERO(1) leachate concentration from WMU

CWASTE Buffering capacity of soil column beneath WMU, used for metal simulations, only

LeachateMax - Peak leachate concentration, well - intermediate??

10)

dwc

dz

U

Width_river

Vfx

11)

dwc, depth of water column

dz, waterbody depth

12)

TEMP, Ambient groundwater temperature

Twater, Temperature of the Waterbody

13) 

RECHRG, Regional infiltration/recharge rate outside the WMU footprint

pH_Soil, Soil pH

14)

RETARD(1)

SFCOF(1)

15)

PHLEACH

GOVPH

16)

AQMIXPH

PH_GW

17)

GOVPH

SFCOF(1)
Appendix B – Highly Correlated Input Variables

B-5

18)

PH_SOIL

PH_SW

19)

DWC

VFX

20)

TSOURC

SINFIL

21) 

USNOM

TWATER

22)

AREA

SINFIL

 23)

GOVPH

AQMIXPH

24)

SINFIL

PH_SOIL

25)

RECHRG

SINFIL
[This page intentionally left blank.]
Appendix C

Response Surface Regression

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

C-3

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

Chemical Benchmark WMU Pathway Waste Receptor Full R2

Antimony Noncancer SI DW A Adult 0.62

Antimony Noncancer SI DW A Child 0.62

Arsenic (III) Cancer LF DW A Adult 0.72

Arsenic (III) Cancer LF DW A Child 0.73

Arsenic (III) Cancer LF DW AC Adult 0.68

Arsenic (III) Cancer LF DW AC Child 0.68

Arsenic (III) Cancer LF DW FBC Adult 0.59

Arsenic (III) Cancer LF DW FBC Child 0.60

Arsenic (III) Cancer SI DW A Adult 0.62

Arsenic (III) Cancer SI DW A Child 0.62

Arsenic (III) Cancer SI FC A Adult 0.80

Arsenic (III) Cancer SI FC A Child 0.80

Arsenic (III) Cancer SI DW AC Adult 0.66

Arsenic (III) Cancer SI DW AC Child 0.68

Arsenic (III) Cancer SI FC AC Adult 0.82

Arsenic (III) Cancer SI FC AC Child 0.83

Arsenic (V) Cancer LF DW A Adult 0.83

Arsenic (V) Cancer LF DW A Child 0.83

Arsenic (V) Cancer LF DW AC Adult 0.77

Arsenic (V) Cancer LF DW AC Child 0.77

Arsenic (V) Cancer LF DW FBC Adult 0.65

Arsenic (V) Cancer LF DW FBC Child 0.66

Arsenic (V) Cancer SI DW A Adult 0.74

Arsenic (V) Cancer SI DW A Child 0.74

Arsenic (V) Cancer SI FC A Adult 0.77

Arsenic (V) Cancer SI FC A Child 0.78

Arsenic (V) Cancer SI DW AC Adult 0.66

Arsenic (V) Cancer SI DW AC Child 0.67

Arsenic (V) Cancer SI FC AC Adult 0.76

Arsenic (V) Cancer SI FC AC Child 0.77

Boron NC SI DW A Adult 0.69

Boron NC SI DW A Child 0.69

Boron Noncancer SI DW AC Adult 0.55

Boron Noncancer SI DW AC Child 0.55

Cadmium Noncancer SI DW A Adult 0.66

Cadmium Noncancer SI DW A Child 0.66

Cadmium Noncancer SI DW AC Adult 0.64

Cadmium Noncancer SI DW AC Child 0.64

Cobalt Noncancer SI DW A Adult 0.58

Cobalt Noncancer SI DW A Child 0.59

Cobalt Noncancer SI DW AC Adult 0.58

Cobalt Noncancer SI DW AC Child 0.58

Divalent Mercury Noncancer LF FC A Adult 0.88

Divalent Mercury Noncancer LF FC A Child 0.88

Divalent Mercury Noncancer LF FC AC Adult 0.89

Divalent Mercury Noncancer LF FC AC Child 0.89

Divalent Mercury Noncancer LF FC FBC Adult 0.90
Table C-1. (Continued)

Appendix C – Response Surface Regression Results by Scenario

Chemical Benchmark WMU Pathway Waste Receptor Full R2

C-4

Divalent Mercury Noncancer LF FC FBC Child 0.90

Divalent Mercury Noncancer SI FC AC Adult 0.83

Divalent Mercury Noncancer SI FC AC Child 0.83

Lead Noncancer SI DW A Adult 0.67

Lead Noncancer SI DW A Child 0.67

Lead Noncancer SI DW AC Adult 0.61

Lead Noncancer SI DW AC Child 0.61

Molybdenum Noncancer SI DW A Adult 0.66

Molybdenum Noncancer SI DW A Child 0.66

Molybdenum Noncancer SI DW AC Adult 0.53

Molybdenum Noncancer SI DW AC Child 0.53

Selenium (IV) Noncancer SI DW A Adult 0.78

Selenium (IV) Noncancer SI DW A Child 0.78

Selenium (IV) Noncancer SI FC A Adult 0.80

Selenium (IV) Noncancer SI FC A Child 0.80

Selenium (IV) Noncancer SI DW AC Adult 0.80

Selenium (IV) Noncancer SI DW AC Child 0.80

Selenium (IV) Noncancer SI FC AC Adult 0.79

Selenium (IV) Noncancer SI FC AC Child 0.80

Selenium (VI) Noncancer SI DW A Adult 0.66

Selenium (VI) Noncancer SI DW A Child 0.66

Selenium (VI) Noncancer SI FC A Child 0.81

Selenium (VI) Noncancer SI FC A Adult 0.82

Selenium (VI) Noncancer SI DW AC Adult 0.61

Selenium (VI) Noncancer SI DW AC Child 0.61

Selenium (VI) Noncancer SI FC AC Adult 0.82

Selenium (VI) Noncancer SI FC AC Child 0.82

Thallium Noncancer LF DW A Adult 0.70

Thallium Noncancer LF DW A Child 0.70

Thallium Noncancer LF DW AC Adult 0.61

Thallium Noncancer LF DW AC Child 0.61

Thallium Noncancer LF DW FBC Adult 0.63

Thallium Noncancer LF DW FBC Child 0.63

Thallium Noncancer SI DW A Adult 0.68

Thallium Noncancer SI DW A Child 0.68

Thallium Noncancer SI FC A Adult 0.82

Thallium Noncancer SI FC A Child 0.82
C-5

Table C-2. Percent Variation Results by Scenario

Chemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 Antimony Noncancer SI DW A Adult SINFIL 35% XKX 10% CAntimony Noncancer SI DW A Child SINFIL 35% XKX 10% CArsenic (III) Cancer LF DW A Adult CZERO(1) 16% XKX 11% GArsenic (III) Cancer LF DW A Child CZERO(1) 17% XKX 10% GArsenic (III) Cancer LF DW AC Adult TSOURC 32% CZERO(1) 15% Arsenic (III) Cancer LF DW AC Child TSOURC 32% CZERO(1) 15% Arsenic (III) Cancer LF DW FBC Adult XKX 20% TSOURC 18% CArsenic (III) Cancer LF DW FBC Child TSOURC 19% XKX 19% CArsenic (III) Cancer SI DW A Adult SINFIL 42% CZERO(1) 22% Arsenic (III) Cancer SI DW A Child SINFIL 43% CZERO(1) 22% Arsenic (III) Cancer SI FC A Adult VFX 21% SINFIL 20% CArsenic (III) Cancer SI FC A Child VFX 21% SINFIL 19% CArsenic (III) Cancer SI DW AC Adult CZERO(1) 53% SINFIL 13% Arsenic (III) Cancer SI DW AC Child CZERO(1) 54% SINFIL 13% Arsenic (III) Cancer SI FC AC Adult CZERO(1) 22% VFX 16% Arsenic (III) Cancer SI FC AC Child CZERO(1) 22% VFX 15% Arsenic (V) Cancer LF DW A Adult SFCOF(1) 33% DSOIL 24% Arsenic (V) Cancer LF DW A Child SFCOF(1) 33% DSOIL 24% Arsenic (V) Cancer LF DW AC Adult SFCOF(1) 28% DSOIL 16%Arsenic (V) Cancer LF DW AC Child SFCOF(1) 28% DSOIL 16%Arsenic (V) Cancer LF DW FBC Adult SFCOF(1) 44% VFX 19% Arsenic (V) Cancer LF DW FBC Child SFCOF(1) 44% VFX 19% Arsenic (V) Cancer SI DW A Adult SFCOF(1) 32% SINFIL 26% Arsenic (V) Cancer SI DW A Child SFCOF(1) 31% SINFIL 26% Arsenic (V) Cancer SI FC A Adult SINFIL 23% SFCOF(1) 22% Arsenic (V) Cancer SI FC A Child SINFIL 21% SFCOF(1) 20% Arsenic (V) Cancer SI DW AC Adult CZERO(1) 19% SINFIL 19% SArsenic (V) Cancer SI DW AC Child CZERO(1) 20% SINFIL 19% SArsenic (V) Cancer SI FC AC Adult VFX 16% SINFIL 14% CArsenic (V) Cancer SI FC AC Child VFX 16% SINFIL 14% C
C-6

Table C-2. (Continued)

Chemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 Boron NC SI DW A Adult CZERO(1) 28% SINFIL 27%Boron NC SI DW A Child CZERO(1) 28% SINFIL 27% Boron Noncancer SI DW AC Adult SINFIL 21% CZERO(1) 20% Boron Noncancer SI DW AC Child SINFIL 22% CZERO(1) 20% Cadmium Noncancer SI DW A Adult SINFIL 49% CZERO(1) 10% Cadmium Noncancer SI DW A Child SINFIL 49% CZERO(1) 10% Cadmium Noncancer SI DW AC Adult CZERO(1) 46% SINFIL 13% GCadmium Noncancer SI DW AC Child CZERO(1) 46% SINFIL 14% GCobalt Noncancer SI DW A Adult SINFIL 27% SFCOF(1) 20% Cobalt Noncancer SI DW A Child SINFIL 27% SFCOF(1) 20% Cobalt Noncancer SI DW AC Adult CZERO(1) 23% SINFIL 21% Cobalt Noncancer SI DW AC Child CZERO(1) 23% SINFIL 21% Divalent Mercury Noncancer LF FC A Adult VFX 46% CZERO(1) 9%Divalent Mercury Noncancer LF FC A Child VFX 46% CZERO(1) 9%Divalent Mercury Noncancer LF FC AC Adult VFX 19% UFCOF(1) 10% Divalent Mercury Noncancer LF FC AC Child VFX 19% UFCOF(1) 10% Divalent Mercury Noncancer LF FC FBC Adult TSOURC 8% CWASTE 6%Divalent Mercury Noncancer LF FC FBC Child TSOURC 8% CWASTE 6%Divalent Mercury Noncancer SI FC AC Adult VFX 33% SINFIL 5% Divalent Mercury Noncancer SI FC AC Child VFX 31%Lead Noncancer SI DW A Adult SINFIL 36% CZERO(1) 21% Lead Noncancer SI DW A Child SINFIL 36% CZERO(1) 21% Lead Noncancer SI DW AC Adult CZERO(1) 31% SINFIL 22% SLead Noncancer SI DW AC Child CZERO(1) 31% SINFIL 23% SMolybdenum Noncancer SI DW A Adult SINFIL 37% CZERO(1) 14% Molybdenum Noncancer SI DW A Child SINFIL 35% CZERO(1) 14% Molybdenum Noncancer SI DW AC Adult SINFIL 21% XKX 10% GMolybdenum Noncancer SI DW AC Child SINFIL 22% XKX 10% CSelenium (IV) Noncancer SI DW A Adult SINFIL 19% FEOX 17% Selenium (IV) Noncancer SI DW A Child SINFIL 19% FEOX 17% Selenium (IV) Noncancer SI FC A Adult SINFIL 23% FEOX 13% Selenium (IV) Noncancer SI FC A Child SINFIL 23% FEOX 13% 
C-7

Table C-2. (Continued)

Chemical Benchmark WMU Pathway Waste Receptor VAR1 PV1 VAR2 PV2 Selenium (IV) Noncancer SI DW AC Adult SFCOF(1) 35% SINFIL 15% XSelenium (IV) Noncancer SI DW AC Child SFCOF(1) 35% SINFIL 15% XSelenium (IV) Noncancer SI FC AC Adult SFCOF(1) 32% SINFIL 19% Selenium (IV) Noncancer SI FC AC Child SFCOF(1) 31% SINFIL 19% Selenium (VI) Noncancer SI DW A Adult SINFIL 32% CZERO(1) 18% Selenium (VI) Noncancer SI DW A Child SINFIL 32% CZERO(1) 19% Selenium (VI) Noncancer SI FC A Child VFX 18% SINFIL 17% CSelenium (VI) Noncancer SI FC A Adult SINFIL 17% VFX 16% CSelenium (VI) Noncancer SI DW AC Adult CZERO(1) 36% SINFIL 15% Selenium (VI) Noncancer SI DW AC Child CZERO(1) 36% SINFIL 15% Selenium (VI) Noncancer SI FC AC Adult VFX 27% CZERO(1) 13% Selenium (VI) Noncancer SI FC AC Child VFX 27% CZERO(1) 13% Thallium Noncancer LF DW A Adult SINFIL 21% XKX 14% Thallium Noncancer LF DW A Child SINFIL 21% XKX 13% Thallium Noncancer LF DW AC Adult TSOURC 35% XKX 21% GThallium Noncancer LF DW AC Child TSOURC 35% XKX 21% GThallium Noncancer LF DW FBC Adult CZERO(1) 16% XKX 15% GThallium Noncancer LF DW FBC Child CZERO(1) 16% XKX 15% GThallium Noncancer SI DW A Adult SINFIL 26% CZERO(1) 12% Thallium Noncancer SI DW A Child SINFIL 26% CZERO(1) 12% Thallium Noncancer SI FC A Adult VFX 16% SINFIL 13% CThallium Noncancer SI FC A Child VFX 16% SINFIL 13% C
C-8

Table C-2. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 

Antimony Noncancer SI DW A Adult GRADNT 6%Antimony Noncancer SI DW A Child GRADNT 6%Arsenic (III) Cancer LF DW A AdultArsenic (III) Cancer LF DW A ChildArsenic (III) Cancer LF DW AC Adult DEPTH 9% GRADNT 6%Arsenic (III) Cancer LF DW AC Child DEPTH 9% GRADNT 6%Arsenic (III) Cancer LF DW FBC Adult GRADNT 13% CZERO(1) 9% XWELL(1) 

Arsenic (III) Cancer LF DW FBC Child GRADNT 12% CZERO(1) 9% XWELL(1) Arsenic (III) Cancer SI DW A Adult GRADNT 5%Arsenic (III) Cancer SI DW A Child GRADNT 5%Arsenic (III) Cancer SI FC A AdultArsenic (III) Cancer SI FC A ChildArsenic (III) Cancer SI DW AC Adult GRADNT 6% TWATER 5%Arsenic (III) Cancer SI DW AC Child GRADNT 5% TWATER 5%Arsenic (III) Cancer SI FC AC AdultArsenic (III) Cancer SI FC AC ChildArsenic (V) Cancer LF DW A Adult CZERO(1) 5%Arsenic (V) Cancer LF DW A Child CZERO(1) 5%Arsenic (V) Cancer LF DW AC AdultArsenic (V) Cancer LF DW AC ChildArsenic (V) Cancer LF DW FBC Adult XKX 6%Arsenic (V) Cancer LF DW FBC Child XKX 6%Arsenic (V) Cancer SI DW A Adult XWELL(1) 5% CZERO(1) 5% GRADNT Arsenic (V) Cancer SI DW A Child XWELL(1) 5% GRADNT 5% CZERO(1) Arsenic (V) Cancer SI FC A Adult DSOIL 11% DISSW 7%Arsenic (V) Cancer SI FC A Child VFX 10% DISSW 7%Arsenic (V) Cancer SI DW AC Adult DSOIL 5%Arsenic (V) Cancer SI DW AC Child DSOIL 5%Arsenic (V) Cancer SI FC AC Adult SFCOF(1) 9%Arsenic (V) Cancer SI FC AC Child SFCOF(1) 9%Boron Noncancer SI DW A Adult
C-9

Table C-2. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 

Boron Noncancer SI DW A ChildBoron Noncancer SI DW AC Adult TWATER 10% GRADNT 9% DWC Boron Noncancer SI DW AC Child TWATER 9% GRADNT 9% DWC Cadmium Noncancer SI DW A Adult GRADNT 6% XKX 5%Cadmium Noncancer SI DW A Child GRADNT 6% XKX 5%Cadmium Noncancer SI DW AC Adult XKX 6% TWATER 5% DWC Cadmium Noncancer SI DW AC Child XKX 7% TWATER 5% DWC Cobalt Noncancer SI DW A Adult GRADNT 8% XWELL(1) 8% ZB Cobalt Noncancer SI DW A Child GRADNT 8% XWELL(1) 8% ZB Cobalt Noncancer SI DW AC Adult GRADNT 6%Cobalt Noncancer SI DW AC Child GRADNT 7%Divalent Mercury Noncancer LF FC A AdultDivalent Mercury Noncancer LF FC A ChildDivalent Mercury Noncancer LF FC AC AdultDivalent Mercury Noncancer LF FC AC ChildDivalent Mercury Noncancer LF FC FBC AdultDivalent Mercury Noncancer LF FC FBC ChildDivalent Mercury Noncancer SI FC AC AdultDivalent Mercury Noncancer SI FC AC ChildLead Noncancer SI DW A Adult SFCOF(1) 13% XWELL(1) 6% GRADNT Lead Noncancer SI DW A Child SFCOF(1) 13% GRADNT 6% XWELL(1) Lead Noncancer SI DW AC Adult XKX 7% GRADNT 7% DSOIL Lead Noncancer SI DW AC Child XKX 7% GRADNT 7% DSOIL Molybdenum Noncancer SI DW A AdultMolybdenum Noncancer SI DW A ChildMolybdenum Noncancer SI DW AC Adult CZERO(1) 8% TWATER 8% DWC Molybdenum Noncancer SI DW AC Child GRADNT 8% TWATER 8% TEMP Selenium (IV) Noncancer SI DW A Adult DSOIL 9% XWELL(1) 7% GRADNT 

Selenium (IV) Noncancer SI DW A Child DSOIL 9% XWELL(1) 7% GRADNT 
C-10

Table C-2. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor VAR4 PV4 VAR5 PV5 VAR6 

Selenium (IV) Noncancer SI FC A Adult DSOIL 10% DISSW 8% VFX Selenium (IV) Noncancer SI FC A Child DSOIL 10% DISSW 8% VFX Selenium (IV) Noncancer SI DW AC Adult GRADNT 7% DSOIL 7%Selenium (IV) Noncancer SI DW AC Child GRADNT 7% DSOIL 7%Selenium (IV) Noncancer SI FC AC Adult DSOIL 8% VFX 7%Selenium (IV) Noncancer SI FC AC Child DISSW 8% VFX 7%Selenium (VI) Noncancer SI DW A AdultSelenium (VI) Noncancer SI DW A ChildSelenium (VI) Noncancer SI FC A ChildSelenium (VI) Noncancer SI FC A AdultSelenium (VI) Noncancer SI DW AC Adult GRADNT 7% TWATER 5%Selenium (VI) Noncancer SI DW AC Child GRADNT 7% TWATER 6%Selenium (VI) Noncancer SI FC AC AdultSelenium (VI) Noncancer SI FC AC ChildThallium Noncancer LF DW A Adult GRADNT 8% CZERO(1) 6% RECHRG Thallium Noncancer LF DW A Child GRADNT 8% CZERO(1) 6% RECHRG Thallium Noncancer LF DW AC Adult CZERO(1) 11% DEPTH 10% XWELL(1) Thallium Noncancer LF DW AC Child CZERO(1) 12% DEPTH 11% XWELL(1) Thallium Noncancer LF DW FBC Adult SINFIL 8% DSOIL 7% XWELL(1) Thallium Noncancer LF DW FBC Child SINFIL 8% DSOIL 7% XWELL(1) Thallium Noncancer SI DW A AdultThallium Noncancer SI DW A ChildThallium Noncancer SI FC A AdultThallium Noncancer SI FC A Child
C-11

Table C-3. Highly-correlated Variable Groups by Scenario

Chemical

Benchmark WMU Pathway Waste Receptor High Correlations in Significant 

Antimony Noncancer SI DW A Adult NoneAntimony Noncancer SI DW A Child NoneArsenic (III) Cancer LF DW A Adult CZERO/CWASTEArsenic (III) Cancer LF DW A Child CZERO/CWASTEArsenic (III) Cancer LF DW AC Adult CZERO/CWASTEArsenic (III) Cancer LF DW AC Child CZERO/CWASTEArsenic (III) Cancer LF DW FBC Adult NoneArsenic (III) Cancer LF DW FBC Child NoneArsenic (III) Cancer SI DW A Adult NoneArsenic (III) Cancer SI DW A Child NoneArsenic (III) Cancer SI FC A Adult VFX->U,WIDTH_RIVERArsenic (III) Cancer SI FC A Child VFX->U,WIDTH_RIVERArsenic (III) Cancer SI DW AC Adult NoneArsenic (III) Cancer SI DW AC Child NoneArsenic (III) Cancer SI FC AC Adult VFX->U,WIDTH_RIVERArsenic (III) Cancer SI FC AC Child VFX->U,WIDTH_RIVERArsenic (V) Cancer LF DW A Adult CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->

UFCOF(1)

Arsenic (V) Cancer LF DW A Child CZERO/CWASTE; DSOIL/DISPR; SFCOF(1)->

UFCOF(1)

Arsenic (V) Cancer LF DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOArsenic (V) Cancer LF DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCOArsenic (V) Cancer LF DW FBC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER, DWC, DZ, UW

Arsenic (V) Cancer LF DW FBC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER, DWC, DZ, UW

Arsenic (V) Cancer SI DW A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER
C-12

Table C-3. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor High Correlations in Significant 

Arsenic (V) Cancer SI DW A Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER

Arsenic (V) Cancer SI FC A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER

Arsenic (V) Cancer SI FC A Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER

Arsenic (V) Cancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER

Arsenic (V) Cancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1), UFCO

WIDTH_RIVER

Arsenic (V) Cancer SI FC AC Adult SFCOF(1)->RETARD(1), UFCOF(1); VFX -> UArsenic (V) Cancer SI FC AC Child SFCOF(1)->RETARD(1), UFCOF(1); VFX -> UBoron NC SI DW A Adult NoneBoron NC SI DW A Child NoneBoron Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZBoron Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZCadmium Noncancer SI DW A Adult AREA->XW,YDCadmium Noncancer SI DW A Child AREA->XW,YDCadmium Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZCadmium Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZCobalt Noncancer SI DW A Adult SFCOF(1)->RETARD(1), UFCOF(1); AREA->XCobalt Noncancer SI DW A Child SFCOF(1)->RETARD(1), UFCOF(1); AREA->XCobalt Noncancer SI DW AC Adult NoneCobalt Noncancer SI DW AC Child NoneDivalent Mercury Noncancer LF FC A Adult CZERO/CWASTE; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC A Child CZERO/CWASTE; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC AC Adult DSOIL/DISPR; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC AC Child DSOIL/DISPR; VFX->U,WIDTH_RIVERDivalent Mercury Noncancer LF FC FBC Adult NoneDivalent Mercury Noncancer LF FC FBC Child NoneDivalent Mercury Noncancer SI FC AC Adult VFX->U,WIDTH_RIVER; AREA->XW,YDDivalent Mercury Noncancer SI FC AC Child VFX->U,WIDTH_RIVER
C-13

Table C-3. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor High Correlations in Significant 

Lead Noncancer SI DW A Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOLead Noncancer SI DW A Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOLead Noncancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOLead Noncancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOMolybdenum Noncancer SI DW A Adult NoneMolybdenum Noncancer SI DW A Child NoneMolybdenum Noncancer SI DW AC Adult DWC->WIDTH_RIVER,DZMolybdenum Noncancer SI DW AC Child DWC->WIDTH_RIVER,DZSelenium (IV) Noncancer SI DW A Adult DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(Selenium (IV) Noncancer SI DW A Child DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(Selenium (IV) Noncancer SI FC A Adult DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(

>U,WIDTH_RIVER

Selenium (IV) Noncancer SI FC A Child DSOIL/DISPR; GOVPH->RETARD(1),SFCOF(

>U,WIDTH_RIVER

Selenium (IV) Noncancer SI DW AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOSelenium (IV) Noncancer SI DW AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCOSelenium (IV) Noncancer SI FC AC Adult DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCO

>U,WIDTH_RIVER

Selenium (IV) Noncancer SI FC AC Child DSOIL/DISPR; SFCOF(1)->RETARD(1),UFCO

>U,WIDTH_RIVER

Selenium (VI) Noncancer SI DW A Adult NoneSelenium (VI) Noncancer SI DW A Child NoneSelenium (VI) Noncancer SI FC A Child VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI FC A Adult VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI DW AC Adult NoneSelenium (VI) Noncancer SI DW AC Child NoneSelenium (VI) Noncancer SI FC AC Adult VFX->U,WIDTH_RIVERSelenium (VI) Noncancer SI FC AC Child VFX->U,WIDTH_RIVERThallium Noncancer LF DW A Adult CZERO(1)/CWASTE; SINFIL/TSOURCThallium Noncancer LF DW A Child CZERO(1)/CWASTE; SINFIL/TSOURCThallium Noncancer LF DW AC Adult CZERO(1)/CWASTE
C-14

Table C-3. (Continued)

Chemical

Benchmark WMU Pathway Waste Receptor High Correlations in Significant 

Thallium Noncancer LF DW AC Child CZERO(1)/CWASTEThallium Noncancer LF DW FBC Adult CZERO(1)/CWASTE; DSOIL/DISPR; SINFIL->AThallium Noncancer LF DW FBC Child CZERO(1)/CWASTE; DSOIL/DISPR; SINFIL->AThallium Noncancer SI DW A Adult NoneThallium Noncancer SI DW A Child NoneThallium Noncancer SI FC A Adult VFX->U,WIDTH_RIVERThallium Noncancer SI FC A Child VFX->U,WIDTH_RIVER