Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-1 

Human and Ecological Risk Assessment of Coal 

Combustion Wastes – Executive Summary 

The Executive Summary of EPA’s Human 

and Ecological Risk Assessment of Coal 

Combustion Wastes is organized into four 

parts. First, it presents Background for the 

regulation and study of coal combustion 

wastes. Next, it discusses the Risk 

Assessment Methodology used to evaluate 

these wastes’ potential impact on human 

health and the environment. The Executive 

Summary continues with the presentation of 

the report’s Results and Characterization. 

Finally, it discusses the overall Conclusions

of the report.

Background 

The U.S. Environmental Protection 

Agency (EPA) is evaluating management 

options for solid wastes from coal 

combustion: fly ash, bottom ash, boiler slag, 

flue gas desulphurization (FGD) residues, and 

fluidized bed combustion (FBC) wastes. In 

this report, these five types of coal combustion 

wastes are referred to as coal combustion 

waste (CCW). All coal-fired electric utility 

plants in the United States generate at least 

one of these wastes, and most generate more 

than one. For example, most electric utility 

plants generate fly ash and either bottom ash 

or boiler slag.1

 Some plants also generate 

FGD residues.2

 Coal-fired electric utility 

plants that use FBC technology generate both 

bottom ash (bed ash) and fly ash. 

Depending on the coal-fired power plant 

boiler and air pollution control technologies 

employed at the power plants, these five types 

of CCW might be initially generated either as 

primarily dry or primarily wet material. 

Typically, the dry materials are disposed of in 

landfills, while the wet materials are disposed 

 

1

 U.S. EPA (1999a), Figure 3-2. 

2

 U.S. EPA (1999a), Figure 3-3. 

of, at least initially, in surface impoundments 

(the settled solids can be removed periodically 

and disposed of in landfills). Landfills and 

surface impoundments are referred to as waste 

management units (WMUs). 

Coal-fired power plants typically conduct 

coal preparation activities before burning the 

coal in their boilers. Wastes from these coal 

preparation activities (such as coal handling 

by conveyor systems, coal washing for 

removing mineral matter, and coal “sizing”—

for example, reducing particle sizes of coal for 

firing in a pulverized coal boiler) are not part 

of the Bevill exclusion under the federal 

Resource Conservation and Recovery Act. 

However, in the past, some U.S. coal-fired 

power plants have managed CCW together 

with these coal preparation wastes, or “coal 

refuse,” in the same landfills and surface 

impoundments. Because the chemical 

characteristics of the coal refuse can affect the 

amount and behavior of chemical constituents 

in the CCW,3

 EPA designed this analysis 

specifically to estimate risks from CCW 

management that is conducted separately from 

coal refuse management, as well as to estimate 

risks from CCW that is comanaged with coal 

refuse. 

This report describes the results of a 

national-scale analysis of groundwater 

impacts of managing CCW in five separate 

scenarios: 

 CCW managed alone in landfills 

 CCW managed alone in surface 

impoundments 

 CCW comanaged with coal refuse in 

landfills 

 

3

 U.S. EPA (1999a), page 3-18. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-2 

 CCW comanaged with coal refuse in 

surface impoundments 

 FBC waste managed in landfills. 

This risk assessment was designed and 

implemented to identify and quantify human 

health and ecological risks that may result 

from groundwater contamination from current 

management practices for high-volume 

CCWs. The risk assessment uses 

mathematical models to represent either a 

landfill or a surface impoundment, and to 

represent the movement of chemical 

constituents from the CCW placed into a 

landfill or surface impoundment through the 

environment, up to an exposure point where 

the chemical constituent comes into contact 

with a person (such as in a glass of drinking 

water from a well) or an aquatic organism 

(such as a fish swimming in surface water that 

has become contaminated by groundwater that 

discharges into the stream near a CCW 

landfill). In this analysis, EPA evaluated 

human health exposures that occur by the 

groundwater-to-drinking-water pathway, 

human health exposures that occur by fish 

consumption, ecological exposures of aquatic 

organisms in direct contact with contaminated 

surface water or sediment, and ecological 

exposures of organisms that eat contaminated 

food items from those contaminated nearby 

surface water bodies. 

Because the infiltration from a landfill or 

surface impoundment can significantly 

influence how much, and how quickly, 

leachate flows out of a waste management 

unit, the models also account for three types of 

liner scenarios: unlined, clay-lined, and 

composite-lined. An unlined waste 

management unit has native soils as the 

bottom and sides; a clay-lined unit has a 

certain amount of clay present to slow the 

flow of leachate; and a composite-lined unit is 

constructed from various layers, including 

human-made materials, which are assumed to 

retard the leachate flow to a significantly 

greater extent than a clay liner. 

The risk assessment provides a distribution 

of estimated risks for each of the five 

scenarios and three liner types. EPA modeled 

CCW waste management units that were 

located across the United States, in locations 

that represent a subset of the coal-fired power 

plants that were in use in the mid-1990s. The 

models used to represent the movement of 

chemical constituents from a landfill or 

surface impoundment through the 

environment rely on data such as weather 

patterns, soil types, and subsurface geology, 

which influence the speed and direction in 

which the chemical constituents move. Thus, 

the environmental setting, or geographic 

location, of a landfill or surface impoundment 

can influence the resulting estimated risk. By 

conducting the analysis at a national scale, 

EPA estimated risks at locations across the 

United States. 

Risk Assessment Methodology 

To estimate the risks posed by the onsite 

management of CCW, the risk assessment 

determined the release of CCW constituents 

from landfills and surface impoundments, 

estimated the concentrations of these 

constituents in environmental media, and 

estimated the risks that these concentrations 

pose to human and ecological receptors. To 

evaluate the significance of these risks, they 

were compared with a risk range or single 

criterion as follows: 

 For constituents that cause cancer 

(carcinogens), the typical cancer risk 

evaluated was a range from 1 excess 

lifetime cancer case per 1,000,000 exposed 

individuals (i.e., 10-6 excess cancer risk) to 

1 case per 10,000 exposed individuals 

(i.e., 10-4 excess lifetime cancer risk).4

 

4

 This is the typical cancer risk range used by the 

Office of Solid Waste and Emergency Response - 

(10-6 to 10-4). 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-3 

 For constituents that cause adverse, 

noncancer health effects (noncarcinogens), 

the criterion is a hazard quotient (HQ)5

 of 

greater than 1 

 For constituents that cause adverse 

ecological effects, the criterion is an HQ 

of greater than 1. 

In support of this risk assessment, EPA 

assembled a constituent database that includes 

leachate and porewater waste concentrations 

for 41 CCW constituents taken from more 

than 140 CCW disposal sites around the 

country. The CCW risk assessment subjected 

these waste and leachate constituent 

concentrations to a risk assessment 

methodology that implemented the following 

steps to assess the human and ecological risks 

posed by CCW: 

 Hazard Identification, which collected 

existing human health and ecological 

benchmarks for the 41 CCW constituents 

to identify the 25 chemicals with 

benchmarks for constituent screening 

 Constituent Screening, which compared 

very conservative estimates of exposure 

concentrations (e.g., whole waste 

concentrations, leachate concentrations) to 

health-based concentration benchmarks to 

quickly and simply eliminate constituents 

and exposure pathways that did not require 

further analysis 

 Full-Scale Analysis, which used a Monte 

Carlo probabilistic analysis to characterize 

the risks to human health and ecological 

receptors from onsite disposal of CCW 

constituents that posed the greatest 

potential risks in the screening analysis. 

To select the constituents for full-scale 

modeling, the screening analysis compared 

very conservative estimates of exposure 

 

5

 The HQ is the ratio of the average daily exposure 

level to a protective exposure level corresponding to 

the maximum level at which no appreciable effects 

are likely to occur. 

concentrations (e.g., leachate concentrations) 

to health-based concentration benchmarks to 

quickly and simply identify constituents that 

do not appear to pose human or ecological 

health concerns, so that these constituents 

could be eliminated from further analysis. For 

example, leachate concentrations were 

compared directly to drinking water standards, 

which is equivalent to assuming that human 

receptors are drinking leachate. 

During both the screening and 

probabilistic modeling stages, two exposure 

scenarios were evaluated for humans: 

 Contaminated groundwater being 

transported to drinking water wells from a 

CCW landfill or surface impoundment 

 Contaminated groundwater discharging 

into a surface waterbody where people 

catch and eat fish. 

Constituents addressed in the full-scale 

analysis were those that posed the greatest 

potential risks to human or ecological health. 

The full-scale analysis was designed to 

characterize waste management scenarios 

based on two WMUs (landfills and surface 

impoundments), three waste types (CCW, 

CCW codisposed with coal refuse, and FBC 

waste), and three liner types (unlined, claylined, and composite-lined). Because FBC 

waste is not known to be disposed of in 

surface impoundments, this left 15 possible 

disposal options to model. These options 

provide a good representation of CCW 

disposal practices and waste chemistry 

conditions that affect the release of CCW 

constituents from WMUs. 

The full-scale analysis was implemented 

using a probabilistic approach that produces a 

distribution of risks or hazards for each 

receptor by allowing the values of some of the 

parameters in the analysis to vary. This 

approach is ideal for this risk assessment 

because there are so many CCW facilities 

across the United States, and the approach 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-4 

captures the variability in both waste 

management practices and environmental 

settings (e.g., hydrogeology, climate, 

hydrology). This probabilistic approach was 

implemented through the following steps: 

1. Characterize the CCW constituents and 

waste chemistry, along with the WMUs in 

which each waste stream may be managed 

(i.e., the size and linear status of CCW 

landfills and surface impoundments). 

2. Characterize the environmental settings 

for the sites where CCW landfills and 

surface impoundments are located (i.e., 

locations of coal-fired power plants). 

3. Identify how contaminants are released 

from a WMU through leaching and 

transported to human and ecological 

receptors by groundwater and surface 

water. 

4. Predict the fate, transport, and 

concentration of constituents in 

groundwater and surface water once they 

are released to groundwater from the 

WMUs and travel to receptors at each site. 

5. Quantify the potential exposure of human 

and ecological receptors to the 

contaminant in the environment. 

6. Estimate the potential risk to each receptor 

from the exposure and characterize this 

risk in terms of exposure pathways and 

health effects. 

Based on this approach, EPA 

characterized the potential risks associated 

with the waste disposal scenarios and 

exposure pathways, including the uncertainties 

associated with the results. 

Results and Characterization 

The CCW risk assessment presented 

results at a typical exposure (50th percentile) 

as well as a high-end exposure (90th 

percentile). CCW risk assessment results at 

the 90th percentile suggest that managing 

CCW in unlined or clay-lined WMUs result in 

risks greater than the risk criteria of an HQ 

greater than 1 for noncancer effects to both 

human and ecological receptors (for humans 

drinking groundwater, 90th percentile HQs up 

to 3 for antimony, 7 for boron, 9 for lead, 8 for 

molybdenum, 20 for nitrate, and 4 for 

thallium; for ecological receptors, 90th 

percentile HQs up to 2,000 for boron, 300 for 

lead, 100 for arsenic, 30 for cadmium, and 12 

for selenium). With respect to arsenic in 

CCW, the 90th percentile results suggest that 

managing CCW in unlined or clay-lined 

WMUs results in human excess cancer risks 

within or above a range of 1 in 1 million to 1 

in 10,000 (i.e., ranging from 6 in 100,000 to 1 

in 50 excess cancer risk). Clay-lined units 

tended to have lower risks than unlined units, 

but still had 90th percentile arsenic III excess 

cancer risks ranging from 6 in 100,000 to 7 in 

1,000.However, it was the composite-lined 

units that effectively reduced risks from all 

pathways and constituents, below1 in 100,000 

excess cancer risk or an HQ of one. 

The tables that follow present selected risk 

results only for chemicals that exceed an 

excess cancer risk of 1 in 100,000 (arsenic 

only) or an HQ of 1. 

As shown in Table ES-1, arsenic was the 

constituent with the highest risk for landfills. 

Clay-lined landfills presented 90th percentile 

arsenic III cancer risks as high as 1 in 5,000 

and thallium HQs as high as 2. When landfills 

were unlined, they additionally presented 

arsenic III cancer risks as high as 1 in 2,000 

and a maximum thallium HQ of 3. In addition 

to arsenic and thallium, clay-lined FBC 

landfills also presented 90th percentile risks 

above an HQ of 1 for antimony. However, 

unlined FBC landfills differed in that they 

only exceeded a 1 in 100,000 excess cancer 

risk for arsenic and did not exceed an HQ of 1 

for any of the noncarcinogens modeled.6

 At 

the 50th percentile (see Table ES-2) arsenic 

 

6

 Unlined FBC units showed less risk as modeled. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-5 

III from CCW codisposed with coal refuse 

exceeded an excess cancer risk of 10-5, with 

cancer risks of 1 in 50,000. 

As shown in Table ES-3, arsenic and 

cobalt were the constituents with the highest 

risks for surface impoundments. Clay-lined 

surface impoundments presented 90th 

percentile excess cancer risks above 1 in 

10,000 for arsenic and exceed the HQ 

criterion of 1 for boron, cadmium, cobalt, 

molybdenum, and nitrate. Here, arsenic excess 

cancer risks were as high as 1 in 500, and 

cobalt had HQs as high as 200. When surface 

impoundments were unlined, they also 

showed risk above the HQ criterion for lead 

and selenium. Here, arsenic excess cancer 

risks were as high as 1 in 50, and cobalt had 

HQs as high as 500. As seen in Table ES-4, 

the 50th percentile surface impoundment 

results exceeded a 1 in 100,000 cancer risk for 

arsenic and only cobalt exceeded an HQ of 1. 

Here, unlined units had arsenic excess cancer 

risks as high as 6 in 10,000 while clay-lined 

units had arsenic excess cancer risks as high 

as 1 in 5,000. Cobalt HQs were as high as 20 

and 6 for unlined and clay-lined surface 

impoundments, respectively. 

For the groundwater-to-drinking-water 

pathway, composite liners, as modeled in this 

assessment, effectively reduced risks from all 

constituents to below a 10-5 cancer risk or HQ 

of 1 for both landfills and surface 

impoundments at the 90th and 50th 

percentiles. 

For the groundwater-to-drinking-water 

pathway, arrival times of the peak 

concentrations at a receptor well are much 

longer for landfills (hundreds or thousands of 

years) than for surface impoundments (most 

less than 100 years). 

For humans exposed via the groundwaterto-surface-water (fish consumption) pathway, 

unlined and clay-lined surface impoundments 

posed risks above the HQ criterion and an 

excess cancer risk of 1 in 100,000 at the 90th 

percentile (see Table ES-5). For CCW 

managed alone in surface impoundments, 

these exceedences came from selenium (HQs 

of 3 and 2 for unlined and clay-lined units, 

respectively), while for CCW comanaged with 

coal refuse, these exceedences came from 

arsenic (3 in 100,000 and 2 in 100,000 excess 

cancer risks for unlined and clay-lined units, 

respectively). All 50th percentile surface 

impoundment risks are below an HQ of 1 and 

an excess cancer risk of 1 in 100,000. No 

constituents pose risks above these levels for 

landfills (including FBC landfills) at the 90th 

or 50th percentile. 

Waste type has a much larger effect when 

managed in surface impoundments than when 

managed in landfills. In the case of surface 

impoundments, some constituents presented 

higher risks from CCW managed alone 

(boron, molybdenum, nitrate, and selenium). 

However, others presented higher risks when 

CCW is comanaged with coal refuse (arsenic, 

cadmium, cobalt, and lead). 

The higher risks for surface impoundments 

than landfills are likely due to higher waste 

leachate concentrations and the higher 

hydraulic head from the impounded liquid 

waste. This is consistent with damage cases 

reporting wet handling as a factor that can 

increase risks from CCW management. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-6 

Table ES-1. Selecteda 90th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway 

Chemical 

90th Percentile HQ or Cancer Risk Valueb

Unlined Units Clay-Lined Units

Composite-Lined 

Units

Conventional CCW – 79 landfills 

Cancer 

Arsenic III 4E-04 2E-04 0 

Arsenic V 2E-04 3E-05 0 

Noncancer 

Antimony 2 0.8 0 

Molybdenum 2 0.8 0 

Thallium 3 2 0 

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 5E-04 2E-04 0 

Arsenic V 4E-04 6E-05 0 

Noncancer 

Molybdenum 2 0.6 0 

Thallium 2 1 0 

FBC Waste – 7 landfills 

Cancer 

Arsenic III 3E-05 6E-05 0 

Arsenic V 2E-05 2E-05 0 

Noncancer 

Antimony 0.8 3 0 

Thallium 1 4 0 

a Values are presented only for chemicals that exceed an excess cancer risk of 1 in 100,000 

(arsenic only) or an HQ of 1. 

b Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000-year period of the analysis. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-7 

Table ES-2. Selecteda 50th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway 

Chemical 

50th Percentile HQ or Cancer Risk Valueb

Unlined Units Clay-Lined Units

Composite-Lined 

Units

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 2E-05 6E-06 0 

a Values are presented only for chemicals that exceed an excess cancer risk of 1 in 100,000 

(arsenic only) or an HQ of 1. 

b Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000-year period of the analysis. 

Table ES-3. Selecteda 90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway 

Chemical 

90th Percentile HQ or Cancer Risk Valueb

Unlined Units Clay-Lined Units

Composite-Lined 

Units

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 2E-03 9E-04 2E-07 

Arsenic V 7E-04 2E-04 0 

Noncancer 

Boron 7 4 5E-03

Lead (MCL)c 3 0.7 1E-21

Molybdenum 8 5 7E-03

Nitrate/nitrite (MCL)c 20 10 9E-04 

Selenium VI 2 1 1E-03

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 2E-02 7E-03 4E-06

Arsenic V 2E-02 2E-03 3E-09 

Noncancer 

Cadmium 9 3 5E-05

Cobalt 500 200 3E-06

Lead (MCL)c 9 1 1E-19

Molybdenum 3 2 4E-03

a Values are presented only for chemicals that exceed an excess cancer risk of 1 in 100,000 

(arsenic only) or an HQ of 1. 

b Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero 

results indicate that contaminant infiltration rates were too small for the contaminant 

plume to reach the receptor during the 10,000-year period of the analysis. c Values are ratios of exposure concentration to MCL. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-8 

Table ES-4. Selecteda 50th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway 

Chemical 

50th Percentile HQ or Cancer Risk Valueb

Unlined Units Clay-Lined Units 

Composite–Lined 

Units 

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 1E-04 6E-05 0 

Arsenic V 2E-05 4E-06 0 

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 6E-04 2E-04 0 

Arsenic V 3E-04 4E-05 0 

Noncancer 

Cobalt 20 6 0 

a Values are presented only for chemicals that exceed an excess cancer risk of 1 in 100,000 

(arsenic only) or an HQ of 1. 

b Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. 

Zero results indicate that contaminant infiltration rates were too small for the 

contaminant plume to reach the receptor during the 10,000-year period of the analysis. 

Table ES-5. Selecteda 90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Surface-Water Pathway 

Chemical 

90th Percentile HQ or Cancer Risk Valueb

Unlined Units Clay-Lined Units

Composite-Lined 

Units

Conventional CCW – 44 surface impoundments 

Noncancer 

Selenium VI 3 2 2E-06 

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 3E-05 2E-05 1E-14 

Arsenic V 2E-05 8E-06 6E-19 

a Values are presented only for chemicals that exceed an excess cancer risk of 1 in 100,000 

(arsenic only) or an HQ of 1. 

b Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. 

Zero results indicate that contaminant infiltration rates were too small for the 

contaminant plume to reach the receptor during the 10,000-year period of the analysis. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-9 

For ecological receptors exposed via 

surface water, risks for landfills exceeded an 

HQ of 1 for boron and lead at the 90th 

percentile, but 50th percentile HQs were 

well below 1. For surface impoundments, 

90th percentile risks for several constituents 

exceeded the risk criteria, with boron 

showing the highest risks (HQ = 2,000). 

Only boron exceeded an HQ of 1 at the 50th 

percentile (HQ = 7). Exceedances for boron 

and selenium are consistent with reported 

ecological damage cases, which include 

impacts to waterbodies through the 

groundwater-to-surface-water pathway. 

For ecological receptors exposed via 

sediment, 90th percentile risks for lead, 

arsenic, and cadmium exceeded the risk 

criteria for both landfills and surface 

impoundments because these constituents 

strongly sorb to sediments in the waterbody. 

The 50th percentile risks were generally an 

order of magnitude or more below the risk 

criteria. 

Sensitivity analysis results indicate that 

for more than 70 percent of the scenarios 

evaluated, the risk assessment model was 

most sensitive to parameters related to the 

contaminant source and groundwater flow 

and transport, including WMU infiltration 

rate, leachate concentration, and aquifer 

hydraulic conductivity and gradient. For the 

groundwater-to-surface water pathway, 

another sensitive parameter is the flow rate 

of the waterbody into which the 

contaminated groundwater is discharging. 

For strongly sorbing contaminants (such as 

lead and cadmium), variables related to 

sorption and travel time are also important 

(adsorption coefficient, depth to 

groundwater, and receptor well distance). 

Although the best available data and 

techniques were used, there were several 

uncertainties associated with the CCW risk 

assessment. The major types of uncertainty 

were as follows: 

 Scenario Uncertainty includes the 

assumptions and modeling decisions that 

are made to represent an exposure 

scenario. 

 Model Uncertainty is associated with 

all models used in a risk assessment 

because mathematical expressions are 

simplifications of reality that 

approximate real-world conditions and 

processes. 

 Parameter Uncertainty occurs when 

there is a lack of data about the values 

used in the equations, data available are 

not representative of the instance being 

modeled, or parameter values have not 

been measured precisely because of 

limitations in technology. 

Scenario uncertainty has been minimized 

by basing the risk assessment on conditions 

around existing U.S. coal-fired power plants 

around the United States. Uncertainty in 

environmental setting parameters has been 

incorporated into the risk assessment by 

varying these inputs within reasonable 

ranges when the exact value is not known. 

Uncertainty in human exposure factors (such 

as exposure duration, body weight, and 

intake rates) has also been addressed 

through the use of national distributions. 

Some uncertainties not addressed 

explicitly in the risk assessment have been 

addressed through comparisons with other 

studies and data sources. These include the 

appropriateness of the leachate data used for 

landfills, concentrations of mercury in 

current CCW, and the potential impacts of 

future mercury regulations. 

Other uncertainties are not as easily 

addressed as the ones above. These include 

issues such as receptor well distance, liner 

conditions, ecological benchmarks, 

ecological receptors at risk, and synergistic 

risks. Detailed discussion of all the risk 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-10 

assessment uncertainties is presented in 

Section 4.4 of the report. 

Conclusions 

Given the results and characterization 

above, composite liners, as modeled in this 

risk assessment, effectively reduced risks 

from all pathways and constituents below 

the risk criteria for both landfills and surface 

impoundments. The CCW risk assessment 

suggests that the management of CCW in 

unlined landfills and unlined surface 

impoundments may present risks to human 

health and the environment. Selenium in 

certain types of WMUs managing certain 

types of CCW may present a risk of clinical 

selenosis to highly exposed groundwater 

users or fish consumers, or a risk of adverse 

effects to highly exposed aquatic receptors. 

Arsenic in certain types of WMUs managing 

certain types of CCW may present lifetime 

cancer risks above EPA’s range of concern 

to highly exposed groundwater users. 

Estimated risks from clay-lined units are 

lower than the risks of unlined units, but are 

still above the risk criteria used for this 

analysis. In addition, surface impoundments 

typically showed higher risks than landfills, 

regardless of liner type. Finally, for surface 

impoundments, codisposal of CCW with 

coal refuse results in significantly higher 

risks from arsenic and certain other 

constituents than CCW disposed alone, 

while for other constituents, managing 

CCWs alone results in higher estimated risks 

than codisposed CCW. 

These risk results are in many cases 

consistent with damage cases compiled by 

EPA (U.S. EPA, 2000, 2003e, 2007) and 

others (Lang and Schlictmann, 2004; 

Zillmer and Fauble, 2004; Carlson and 

Adriano, 1993; Rowe et al., 2002; Hopkins 

et al., 2006).7

 For example, the full-scale 

modeling of selenium released from unlined 

surface impoundments into groundwater 

suggests that certain fish consumers may be 

exposed to relatively high levels of 

selenium, consistent with fish consumption 

advisories at some of the proven damage 

case sites. These results suggest that with a 

higher prevalence of composite liners in new 

CCW disposal facilities, along with 

practices to prevent codisposal of coal refuse 

with CCW, future national risks from onsite 

CCW disposal are likely to be lower than 

those presented in this risk assessment 

(which is based on 1995 CCW WMUs). 

References 

Carlson, C.L., and D.C. Adriano. 1993. 

Environmental impacts of coal combustion 

residues. J. Environ. Qual. 22(227-247). 

Hopkins, W.A., S.E. DuRant, B.P. Staub, 

C.L. Rowe, and B.P. Jackson. 2006. 

Reproduction, embryonic development, 

and maternal transfer of contaminants in 

the amphibian Gastrophryne carolinensis. 

Environmental Health Perspectives

114(5):661–666. 

Lang, D.J., and J.R. Schlictmann. 2004. 

Leaching of arsenic from a fly ash dump 

in Beverly, Massachu-setts. In 

Proceedings of State Regulation of Coal 

Combustion By-Product Placement at 

Mine Sites. May 4–6, 2004, Harrisburg, 

Pennsylvania. U.S. Department of the 

Interior, Office of Surface Mining, Alton, 

IL. Available at http://www.mcrcc.osmre

.gov/MCR/Resources/ccb/PDF/State_

Regulation_of_CCB_Placement.pdf. 

Rowe, C.L., W.A. Hopkins, and J.D. 

Congdon. 2002. Ecotoxicological 

 

7

 See Sections 4.1.5 and 4.2.5 for a more complete 

discussion of CCW damage cases and risk 

assessment results. 
Executive Summary Human and Ecological Risk Assessment of Coal Combustion Wastes 

April 2010–Draft EPA document. ES-11 

implications of aquatic disposal of coal 

combustion residues in the United States: 

a review. Environmental Monitoring and 

Assessment 80:207–276. 

U.S. EPA (Environmental Protection 

Agency). 1999a. Report to Congress: 

Wastes from the Combustion of Fossil 

Fuels. EPA 530-R-99-010. Office of Solid 

Waste, Washington, DC. March. 

U.S. EPA (Environmental Protection 

Agency). 2000. Review of Causative 

Factors for Coal Combustion Waste 

Damage Cases. Prepared by SAIC for 

U.S. EPA Office of Solid Waste under 

EPA contract 68-W-99-001, WA No. 121. 

Washington, DC. November 29. Available 

as document ID number EPA-HQ-RCRA2006-0796-0020 at 

http://www.regulations.gov. 

U.S. EPA (Environmental Protection 

Agency). 2003e. Final Revised Report on 

Resolution of 18 Previously Indeterminate 

Candidate Damage Cases. Prepared by 

SAIC for U.S. EPA Office of Solid Waste 

under EPA contract 68-W-02-036, WA 

No. 1-12. Washington, DC. March 5. 

Available as document ID number EPAHQ-RCRA-2003-0003-0420 at 

http://www.regulations.gov. 

U.S. EPA (Environmental Protection 

Agency). 2007. Coal Combustion Waste 

Damage Case Assessments. Available 

from the docket to the Notice of Data 

Availability on the Disposal of Coal 

Combustion Wastes in Landfills and 

Surface Impoundments, Docket # EPAHQ-RCRA-2006-0796. June 19. 

Zilmer, M., and P. Fauble. 2004. 

Groundwater impacts from coal 

combustion ash disposal sites in 

Wisconsin. In: Proceedings of State 

Regulation of Coal Combustion ByProduct Placement at Mine Sites. May 4–

6, 2004, Harrisburg, Pennsylvania. U.S. 

Department of Interior, Office of Surface 

Mining, Alton, IL. Available at 

http://www.mcrcc.osmre.gov/

MCR/Resources/ccb/PDF/State_

Regulation_of_CCB_Placement.pdf. 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-1 

1.0 Introduction 

1.1 Background 

The U.S. Environmental Protection Agency (EPA) has evaluated the human health and 

environmental risks associated with coal combustion waste (CCW) management practices, 

including disposal in landfills and surface impoundments. In May 2000, EPA determined that 

regulation as hazardous wastes under Subtitle C of the Resource Conservation and Recovery Act 

(RCRA) was not warranted for certain CCWs, but that regulation as nonhazardous wastes under 

RCRA Subtitle D was appropriate. However, EPA did not specify regulatory options at that time. 

This risk assessment was designed and implemented to help EPA identify and quantify human 

health and ecological risks that may be associated with current management practices for highvolume CCWs. These wastes are fly ash, bottom ash, boiler slag, and flue gas desulfurization 

(FGD) sludge, along with wastes from fluidized-bed combustion (FBC) units and CCWs 

codisposed with coal refuse. This risk assessment will help EPA develop CCW management 

options for these high-volume waste streams. Details on EPA’s CCW work can be found at 

http://www.epa.gov/epawaste/nonhaz/industrial/special/fossil/index.htm. 

Note that the full-scale risk assessment described in this report was primarily conducted 

in 2003, meaning that the data collection efforts to support the risk assessment were based on the 

best information available to EPA at that time. As a result, more recent Agency efforts to 

characterize CCW wastes and management practices, such as the joint EPA and U.S. Department 

of Energy (DOE) survey of CCW waste management units (WMUs) (U.S. DOE, 2006) and 

EPA’s recent study of CCW chemistries and leaching behavior (U.S. EPA, 2006c, 2008c), were 

not considered in the main analysis phase of this risk assessment. However, these more recent 

efforts are discussed as part of the risk characterization, and EPA is currently evaluating how to 

best incorporate and consider the results and findings of these studies in future efforts to address 

CCW management practices. 

The Agency has revised this risk analysis document to address comments on the 

analytical methodology, data, and assumptions used in the risk assessment from an independent 

scientific peer review by experts outside EPA. Public comments (available in docket number 

EPA-HQ-RCRA-2006-07961

) were made available to the peer reviewers for their consideration 

during the review process. The peer review focused on technical aspects of the analysis, 

including the construction and implementation of the Monte Carlo analysis, the selection of 

models to estimate the release of constituents found in CCW from landfills and surface 

impoundments and their subsequent fate and transport in the environment, and the 

characterization of risks resulting from potential exposures to human and ecological receptors. 

EPA’s responses to the peer-review comments, including descriptions of the revisions 

incorporated in this document to address those comments, are available in a separate response-tocomments document (U.S. EPA, 2009d). 

 

1

 Available at http://www.regulations.gov. 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-2 

1.2 Purpose and Scope of the Risk Assessment 

The purpose of this risk assessment was to identify CCW constituents, waste types, 

exposure pathways, and receptors that may produce risks to human and ecological health, and to 

provide information about those scenarios that EPA could use to develop management options 

for CCW management. 

The scope of this risk assessment was utility CCWs managed onsite at utility power 

plants. EPA’s Report to Congress: Wastes from the Combustion of Fossil Fuels (U.S. EPA, 

1999a) reports that there are 440 coal-fired utility power plants in the United States. Although 

these plants are concentrated in the East, they are found in nearly every state, with facility 

settings ranging from urban to rural. The large volumes of waste generated by these plants are 

typically managed onsite in landfills and surface impoundments. This risk assessment was 

designed to develop national human and ecological risk estimates that are representative of 

onsite CCW management settings throughout the United States. 

1.3 Overview of Risk Assessment Methodology 

To estimate the risks posed by the onsite management of CCW, this risk assessment 

estimated the release of CCW constituents from landfills and surface impoundments, the 

concentrations of these constituents in groundwater and surface water near coal-fired utility 

power plants, and the risks that these concentrations pose to human and ecological receptors. 

1.3.1 Contaminant Sources 

The size, design, and locations of the onsite CCW landfills and surface impoundments 

modeled in this risk assessment were based on data from a national survey of utility CCW 

disposal conducted by the Electric Power Research Institute (EPRI) in 1995 (EPRI, 1997). Data 

from this survey on facility area, volume, and liner characteristics were used in the CCW risk 

assessment because they were the most recent and complete data set available at the time the risk 

assessment was conducted (2003). However, as shown in Table 1-1, the EPA/DOE study 

conducted since then (U.S. DOE, 2006) shows a much higher proportion of lined facilities than 

do the 1995 EPRI data (see further discussion in Section 4.4.1). 

Table 1-1. Liner Prevalence in EPRI and DOE Surveys 

Liner Type Landfills 

Surface 

Impoundments 

1995 EPRI Surveya – 181 facilities 

Unlined 40% 68% 

Lined (compacted clay or composite 

[clay and synthetic]) 60% 32% 

2004 DOE Surveyb – 56 facilities 

Unlined 3% 0% 

Lined (compacted clay or composite 

[clay and synthetic]) 97% 100% 

a EPRI (1997) 

b U.S. DOE (2006) 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-3 

1.3.2 Exposure Pathways 

The releases, and hence media concentrations and risk estimates in this report, were 

based on leaching to groundwater and groundwater transport to nearby wells and surface water 

bodies. This analysis did not address direct releases to surface water, which are permitted under 

the National Pollutant Discharge Elimination System (NPDES) of the Clean Water Act. Thus, 

the estimated media concentrations and risks do not take into account contributions from 

NPDES-permitted releases, including discharges due to flooding or heavy rainfall. Uncertainties 

associated with this decision are described in Section 4.4.1 of this report. 

EPA recognizes that there are exposure pathways in addition to the groundwater 

pathways addressed in this report that could be of concern to human health and ecological 

receptors, including fugitive dust eroded and transported by wind from uncovered CCW 

landfills, and erosion and transport of CCW constituents from uncovered landfills onto adjacent 

land and eventually into downslope waterbodies. These “aboveground” pathways were addressed 

in the 1998 risk assessment, and in 2002, EPA conducted a draft screening analysis (U.S. EPA, 

2002a) to evaluate risks from these pathways. 

1.3.3 Risk Levels 

To evaluate the significance of the estimated risks from the pathways assessed in this 

assessment, EPA compared the risk estimates to a risk range (for carcinogens) or to a specific 

risk criterion (for noncarcinogens) that are protective of human health and the environment: 

 An estimate of the excess lifetime cancer risk for individuals exposed to carcinogenic 

(cancer-causing) contaminants ranging from 1 chance in 1,000,000 (10-6 excess cancer 

risk) to 1 chance in 10,000 (10-4 excess cancer risk). For decisions made to screen out 

certain constituents from further consideration, a 1 in 100,000 (10-5) excess lifetime 

cancer risk was used.2 

 A measure of safe intake levels to predicted intake levels, a hazard quotient (HQ) greater 

than 1 for constituents that can produce noncancer human health effects (an HQ of 1 is 

defined as the ratio of a potential exposure to a constituent to the highest exposure level 

at which no adverse health effects are likely to occur) 

 An HQ greater than 1 for constituents with adverse effects to ecological receptors. 

1.3.4 Methodology 

In 1998, EPA conducted a risk assessment for fossil fuel combustion wastes (which 

include CCWs) to support the May 2000 RCRA regulatory determination (U.S. EPA, 1998a,b). 

Since then, EPA has added to the waste constituent database that was used in that effort, 

expanding the number of leachate and total waste analyses for 41 CCW constituents. The CCW 

risk assessment subjected these waste and leachate constituent concentrations to the tiered risk 

assessment methodology illustrated in Figure 1-1. 

 

2

 The typical cancer risk range used by the Office of Solid Waste and Emergency Response is 10-4 to 10-6. 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-4 

Figure 1-1. Overview of coal combustion waste risk assessment. 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-5 

This methodology implemented the following steps to assess the human and ecological 

risk of CCWs: 

 Hazard Identification, which collected existing human health and ecological 

benchmarks for the CCW constituents. Only constituents with benchmarks moved on to 

the next step, constituent screening. 

 Constituent Screening, which compared very conservative estimates of exposure 

concentrations (e.g., using leachate concentrations) to health-based concentration 

benchmarks to quickly and simply identify constituents with risks below the screening 

criteria. 

 Full-Scale Analysis, which characterized at a national level the human health and 

ecological risks for constituents in CCW disposed onsite in landfills and surface 

impoundments using a site-based probabilistic Monte Carlo risk analysis. 

This document focuses on the full-scale Monte Carlo analysis, but includes a discussion of the 

hazard identification and screening analysis (in U.S. EPA, 2002a) that led to the full-scale 

assessment. 

1.3.5 Waste Management Scenarios Addressed 

The full-scale analysis was designed to characterize waste management scenarios based 

on two waste management options (disposal of CCW onsite in landfills and in surface 

impoundments) and three waste types, as follows: 

 Conventional CCW, which includes fly ash, bottom ash, boiler slag, and FGD sludge 

 Codisposed CCWs and coal refuse,3

 which are more acidic than conventional CCWs 

due to sulfide minerals in the mill rejects 

 FBC wastes, which include fly ash and the fluidized bed ash, and which tend to be more 

alkaline than conventional CCW because of the limestone mixed in during fluidized bed 

combustion. 

Conventional CCW and codisposed CCW and coal refuse are typically disposed of in 

landfills and surface impoundments that can be lined with clay or composite liners. FBC wastes 

are only disposed of in landfills in the United States; therefore, surface impoundment disposal 

was not modeled for FBC waste. 

These three waste types, two waste management options, and three liner conditions 

(unlined, clay lined, composite lined) modeled in this analysis provide a good representation of 

CCW disposal practices and waste chemistry conditions that affect the release of CCW 

constituents from WMUs. 

 

3

 Coal refuse is the waste coal produced from coal handling, crushing, and sizing operations, and tends to have a 

high sulfur content and low pH from high amounts of sulfide minerals (like pyrite). In the CCW constituent 

database, codisposed coal refuse includes “combined ash and coal gob,” “combined ash and coal refuse,” and 

“combined bottom ash and pyrites.” 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-6 

1.3.6 Modeling Approach 

The full-scale analysis was implemented using a site-based probabilistic approach that 

produces a distribution of risks or hazard for each receptor by allowing the values of some of the 

parameters in the analysis to vary. Input parameters were varied in the analysis using data 

collected at or around CCW disposal facilities or, when site-based data were not available, from 

distributions representing the variability of parameters across the United States. This approach 

was ideal for this risk assessment because there are many CCW facilities across the United 

States, and site-based data collection can capture both the variability in waste management 

practices at these facilities and the differences in their environmental settings (e.g., 

hydrogeology, climate, hydrology). 

This probabilistic approach was implemented through seven primary steps: 

Problem Formulation 

1. Characterize the CCW constituents and waste chemistry, along with the size and liner 

status of the WMUs in which each waste stream may be managed 

2. Characterize the environmental settings for the sites where CCW landfills and surface 

impoundments are located 

3. Identify scenarios under which contaminants are released from a WMU and 

transported to a human receptor 

Analysis 

4. Screen risks to select CCW constituents for full-scale analysis 

5. Predict the fate and transport of constituents in the environment once they are 

released from the WMUs at each site 

6. Quantify the exposure of human and ecological receptors to the contaminant in the 

environment and the risk associated with this exposure 

Risk Characterization 

7. Estimate the risk to receptors from the exposure and characterize this risk in terms of 

exposure pathways, health effects, and uncertainties 

8. Identify the waste disposal scenarios and environmental conditions that pose risks of 

potential concern to human health or the environment. Evaluate risks at the 50th and 

90th percentiles. 
Section 1.0 Introduction 

April 2010–Draft EPA document. 1-7 

1.4 Document Organization 

This document is organized into the following sections: 

 Section 2, Problem Formulation, describes how the framework for the full-scale 

analysis was developed, including identification of the waste constituents, exposure 

pathways, and receptors of concern; selection and characterization of waste management 

practices and sites to model; and development of the conceptual site models for the 

modeling effort. 

 Section 3, Analysis, describes the probabilistic modeling framework and the models and 

methods used to (1) screen CCW constituents for the full-scale analysis, (2) estimate 

constituent releases from CCW landfills and surface impoundments (source models), 

(3) model constituent concentrations in the environmental media of concern (groundwater 

and surface water), (4) calculate exposure, and (5) estimate risk to human and ecological 

receptors. 

 Section 4, Risk Characterization, characterizes the human health and environmental 

risks posed by CCW, including (1) discussion of the methods used to account for 

variability and uncertainty and (2) identification of the scenarios and conditions modeled 

that resulted in higher and lower risks. Results are presented as national estimates for 

CCW landfills and CCW surface impoundments, as well as by waste type and liner 

status. This section characterizes the risks posed by CCW constituents and pathways 

under the conditions modeled, including factors (such as liners or facility environmental 

setting) that result in higher or lower risk levels. Finally, the risk characterization 

evaluates the risk results in light of more recent research on CCW waste management 

practices and the environmental behavior of CCW constituents. 

The first three appendices provide detailed information on how wastes, WMUs, and 

settings were characterized for the risk assessment. Appendix A describes the chemical 

characteristics of CCW, including the CCW leachate concentration distributions used to 

represent disposal conditions in landfills and surface impoundments. Appendix B describes how 

EPA characterized the CCW landfills and surface impoundments, including locations, surface 

area, capacities, geometry, and liner status. Appendix C presents the methodologies and data 

used to characterize the environmental setting at each CCW site identified in Appendix B, 

including delineating the site layout and determining the environmental setting (e.g., 

meteorology, climate, soils, aquifers, and waterbodies). 

The next five appendices provide detailed information on the specific models and data 

used to calculate risk, including the nonlinear sorption isotherms (Appendix D), the surface 

water fate and transport and intake equations (Appendix E), the exposure factors (Appendix F), 

benchmarks for human health (Appendix G), and benchmarks for ecological risk (Appendix H). 

The next three appendices provide background and results for the screening analysis, 

including calculation of health-based numbers (HBNs, Appendix I), chemical-specific inputs 

used in the screening analysis (Appendix J), and the screening analysis results (Appendix K). 

Finally, Appendix L provides figures showing, for selected CCW constituents, 

cumulative percentiles of the time it took for the peak concentration to reach a receptor well for 

each source type. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-1 

2.0 Problem Formulation 

The CCW risk assessment was intended to evaluate, at a national level, risk to individuals 

who live near WMUs used for CCW disposal. This section describes how the conceptual 

framework for the full-scale risk assessment was developed, including 

 Constituent selection to identify the CCW constituents, exposure pathways, and receptors 

to address in this analysis (Section 2.1.1) 

 Location and characterization of the CCW landfills and surface impoundments to be 

modeled as the sources of CCW contaminants in the full-scale site-based analysis 

(Section 2.1.2) 

 The conceptual site model used to represent CCW disposal facilities (Section 2.2) 

 The general modeling approach and scope, including constituent screening (Section 2.3), 

and full-scale modeling (Section 2.4) to estimate exposure point concentrations, assess 

exposures, and calculate risks to human and environmental receptors. 

2.1 Source Characterization 

The main technical aspects of the CCW risk assessment were completed in 2003, and the 

waste management scenarios modeled in this assessment were based on the best data on waste 

compositions, industry operations, and waste management practices that were available at that 

time. These data sources included a 1995 industry survey on CCW management practices (the 

EPRI comanagement survey [EPRI, 1997]) and data collected from a variety of sources before 

the 2003 risk assessment (e.g., EPA’s CCW constituent database). Since 2003, DOE and EPA 

have completed a survey to characterize CCW waste disposal practices from 1994 to 2004, with 

a focus on new facilities or facility expansions completed within that same time frame (U.S. 

DOE, 2006). In addition, EPA studies of CCW composition and leaching behavior are ongoing 

(U.S. EPA, 2006c, 2008c). Although these newer data were not available when this risk 

assessment was conducted, they are discussed in the risk characterization (Section 4) as an 

uncertainty with respect to how well the risk assessment represents CCW leachate composition 

and current WMU liner conditions. 

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. This section describes how the coal 

combustion waste streams and management practices were characterized (based on the above 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-2 

data sources) and screened to develop the waste disposal scenarios modeled in the full-scale 

analysis. 

2.1.1 Identification of Waste Types, Constituents, and Exposure Pathways 

To identify the CCW constituents and exposure pathways to be addressed in this risk 

assessment, we relied on a database of CCW analyses that EPA had assembled over the past 

several years to characterize whole waste and waste leachate from CCW disposal sites across the 

country (see Appendix A). The 2003 CCW constituent database includes all of the CCW 

characterization data used by EPA in its previous risk assessments, supplemented with additional 

data collected from public comments, data from EPA Regions and state regulatory agencies, 

industry submittals, and literature searches up to 2003. 

The CCW constituent database represents a significant improvement in the quantity and 

scope of waste characterization data available from the 1998 EPA risk assessment of CCWs 

(U.S. EPA, 1998a,b). For example, the constituent data set used for the previous risk assessments 

(U.S. EPA, 1999a) covered approximately 50 CCW generation and/or disposal sites, while the 

2002 CCW constituent database covers approximately 140 waste disposal sites.1 The 2002 

database also has broader coverage of the major ion concentrations of CCW leachate (e.g., 

calcium, sulfate, pH), that can influence CCW impacts on human health and the environment. 

2.1.1.1 Waste Types 

Table 2-1 shows the waste types included in the 2002 CCW constituent database, along 

with counts of the number of sites with wastes of each type with constituent measurements in 

landfill leachate, surface impoundment porewater, and whole waste.

Comments received by EPA on the previous CCW risk assessment pointed out that the 

analysis did not adequately consider the impacts of CCW leachate on the geochemistry and 

mobility of metal constituents in the subsurface. Commenters stated that given the large size of 

the WMUs and the generally alkaline nature of CCW leachate, it is likely that the leachate 

affects the geochemistry of the soil and aquifers underlying CCW disposal facilities, which can 

impact the migration of metals in the subsurface. To address this concern, EPA statistically 

evaluated major ion porewater data from the CCW constituent database for the waste streams 

shown in Table 2-1. Based on this analysis and prevalent comanagement practices, EPA grouped 

the waste streams into three statistically distinct categories: conventional CCW (fly ash, bottom 

ash, slag, and FGD sludge), which has moderate to high pH; codisposed CCW and coal refuse, 

which tends to have low pH; and FBC waste, which tends to have high pH. As shown in Table 

2-1, each of these waste types included several waste streams that are usually codisposed in 

landfills or surface impoundments. Note that some sites in the CCW database have more than 

one waste stream, so the site counts for the different waste streams in a waste type category sum 

to more than the total site count for that waste type.

 

1

 Although EPA believes that the 140 waste disposal sites do represent the national variability in CCW 

characteristics, they are not the same sites as in the EPRI survey. During full-scale modeling, data from the CCW 

constituent database were assigned to each EPRI site based on the waste types reported in the EPRI survey data. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-3 

Table 2-1. Waste Streams in CCW Constituent Database 

Waste Type 

 Waste Stream

Landfills 

Surface 

Impoundments 

Landfill 

Leachate 

Total 

Wasteb

Pore 

Water 

Conventional CCW 97 62 13 

Ash (not otherwise specified) 43 30 0 

Fly ash 61 33 2 

Bottom ash and slag 24 23 3 

Combined fly and bottom ash 7 4 4 

FGD sludge 4 5 6 

Codisposed Ash & Coal Refuse 9 1 5 

FBC Waste 58 54 0 

Ash (not otherwise specified) 18 10 0 

Fly ash 33 32 0 

Bottom and bed ash 26 25 0 

Combined fly & bottom ash 20 22 0 

a

 For waste types (shaded rows) the table gives the number of sites; for waste streams 

(unshaded rows), the table gives the number of samples. 

b

 Whole waste concentration data. 

Along with the type of WMU (landfill or surface impoundment), the three waste types in 

Table 2-1 defined the basic modeling scenarios to be addressed in the full-scale analysis. To 

characterize these waste types, the CCW constituent database was queried by waste type to 

develop the waste concentration data for the constituents and the major ion and pH conditions 

used to develop waste-type-specific metal sorption isotherms (see Appendix D for a more 

extensive discussion of the development of CCW waste chemistries and metal sorption 

isotherms). 

2.1.1.2 CCW Constituents of Potential Concern 

The CCW constituent database contains data on more than 40 constituents. During the 

hazard identification step of the CCW risk assessment, constituents of potential concern were 

identified from this list of constituents by searching EPA and other established sources for 

human health and ecological benchmarks (e.g., Agency for Toxic Substances and Disease 

Registry [ATSDR]; see Section 3.1 and Appendices G and H for a full list of sources). Table 2-

2 shows the results of that search for each constituent. Benchmarks were found for 24 chemicals 

in the constituent database. The 16 constituents without human health or ecological benchmarks 

were not addressed further in the risk analysis.2

 

2

 The CCW constituents without human health benchmarks are limited to common elements, ions, and compounds 

(e.g., iron, magnesium, phosphate, silicon, sulfate, sulfide, calcium, pH, potassium, sodium, carbon, sulfur). 

These measurements were used to determine overall CCW chemistries modeled in the risk assessment (see 

Section 3). Although some of these chemicals or parameters (e.g., pH, sulfate, phosphate, chloride) can pose an 

ecological hazard if concentrations are high enough, they were not addressed in this risk assessment. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-4 

Table 2-2. Toxicity Assessment of CCW Constituents 

Constituent CAS ID HHBa EcoBb

Metals

Aluminum 7429-90-5 U U

Antimony 7440-36-0 U U

Arsenic 7440-38-2 Uc U

Barium 7440-39-3 U U

Beryllium 7440-41-7 Ud U

Boron 7440-42-8 U U

Cadmium 7440-43-9 Ud U

Chromium 7440-47-3 Uc U

Cobalt 7440-48-4 U U

Copper 7440-50-8 U U

Iron 7439-89-6 

Lead 7439-92-1 Ue U

Magnesium 7439-95-4 

Manganese 7439-96-5 U

Mercury 7439-97-6 U U

Molybdenum 7439-98-7 U U

Nickel 7440-02-0 U U

Selenium 7782-49-2 U U

Silver 7440-22-4 U U

Strontium 7440-24-6 U

Thallium 7440-28-0 U U

Vanadium 7440-62-2 U U

Zinc 7440-66-6 U U

Inorganic Anions

Chloride 16887-00-6 

Cyanide 57-12-5 U

Fluoride 16984-48-8 U

Nitrate/nitrite 14797-55-8/14797-65-0 U

Phosphate 14265-44-2 

Silicon 7631-86-9 

Sulfate 14808-79-8 

Sulfide 18496-25-8 

Inorganic Cations

Ammonia 7664-41-7 U

Calcium 7440-70-2 

pH 12408-02-5 

Potassium 7440-09-7 

Sodium 7440-23-5 

Nonmetallic Elements

Carbon 7440-44-0 

Sulfur 7704-34-9 

(continued) 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-5 

Toxicity Assessment of CCW Constituents (continued) 

Constituent CAS ID HHBa EcoBb

Measurements

Total Dissolved Solids none 

Total Organic Carbon none 

Dissolved Organic Carbon none a

 HHB = human health effect benchmark 

b

 EcoB = ecological benchmark 

c

 Known carcinogen (for chromium VI, inhalation only); although arsenic can act as 

both a carcinogen and a noncarcinogen, the cancer risk exceeds the noncancer risk at 

any concentration, so the more protective cancer benchmark for human health was 

used throughout this assessment. 

d

 Probable carcinogen 

e Safe Drinking Water Act Action Level only

2.1.2 Waste Management Scenarios 

The full-scale CCW risk assessment modeled landfills and surface impoundments 

managing wastes onsite at coal-fired utility power plants. Because EPA selected a site-based 

modeling approach for the full-scale analysis, it was necessary to locate these disposal sites 

across the country to provide the spatial foundation for this analysis. It was also necessary to 

characterize CCW WMUs to define the scope for source modeling. 

Two primary sources of data on these were used to characterize this population: 

 1998 Energy Information Agency (EIA) data on coal-fired power plants, which identifies 

approximately 300 coal-fired power plants with onsite waste management 

 The 1995 EPRI waste comanagement survey (EPRI, 1997), which contains detailed 

WMU data (i.e., area, capacity, liner status, and waste type) for 177 of those facilities. 

Because of the completeness of the WMU data from the EPRI survey, the EPRI data 

were used to establish the plant locations and WMU data for the full-scale modeling effort for 

conventional CCW3

 and CCW codisposed with coal refuse, as well as to help define protective 

waste management settings for the screening analysis. 

Note that although there is overlap, the 140-site CCW constituent database described in 

Appendix A and the EPRI survey used to characterize CCW landfills and surface impoundments 

were assembled under separate efforts and represent different populations of disposal sites. As 

described in Section 3.1.3, these data sets were sampled independently during the Monte Carlo 

analysis, and constituent data were not assigned to particular sites except by waste type. 

Although there is a good amount of FBC data in the constituent database (58 sites; see 

Table 2-1), there were only 3 FBC landfill sites in the EPRI database and 4 additional sites added 

by EPA, for a total of 7 FBC sites with data on onsite WMUs. Because EPA believes that this 

 

3

 Fly ash, bottom ash, boiler slag, and FGD sludge. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-6 

small sample is not sufficient to represent the universe of FBC disposal units and, if included in 

the overall analysis, could bias the Monte Carlo results towards the environmental conditions 

around these few landfill units, FBC wastes were addressed separately from the more 

conventional CCW types in the full-scale analysis and are not included with the conventional 

and codisposal CCW management scenarios in the overall results. Section 4.1.3 compares the 

risk results for each of these waste types, including FBC. 

Table 2-3 shows how the plants were distributed across the waste type/WMU scenarios 

modeled in the full-scale analysis. The distribution across the waste type/WMU scenarios, the 

geographic distribution of these facilities, and the size and liner status of the WMUs were 

assumed to be representative of all onsite CCW landfills and surface impoundments in the 

continental United States as of 1995. As mentioned previously, DOE and EPA have conducted a 

newer survey on CCW disposal facilities (U.S. DOE, 2006), but the scope of this survey was not 

as comprehensive as the EPRI survey (e.g., WMU areas and capacity data were not collected). 

Newer information (U.S. DOE, 2007a,b) suggest that there now may be up to approximately 500 

coal-fired electric utility power plants in the United States, the majority of which would be 

expected to conduct some waste management activities in onsite landfills or surface 

impoundments (U.S. EPA, 2010). 

Table 2-3. Coal Combustion Plants with Onsite CCW WMUs 

Modeled in the Full-Scale Assessment 

Waste Type and Liner Status

Number of Plants in 1995 EPRI Surveya with Onsite:

Landfills

Surface 

Impoundments

Either WMU 

Typeb

Conventional CCWc 

unlined 

clay-lined 

composite-lined 

71 

38 

28 

10

38 

24 

10 

5

103 

60 

38 

15

Codisposed CCW and coal refuse 

unlined 

clay-lined 

composite-lined 

38 

20 

10 

9

65 

52 

11 

2

100 

69 

21 

11

FBC wasted

unlined 

clay-lined 

composite-lined 

7 

3 

3 

1

- 7 

3 

3 

1

All waste types 108 96 181 

a

 EPRI (1997); note that some coal combustion plants have one or more onsite WMUs. 

b Number of coal combustion plants with onsite landfill(s), surface impoundment(s), or both. c

 Fly ash, bottom ash, boiler slag, and FGD sludge. 

d

 Includes 3 EPRI Survey FBC landfills plus 4 additional FBC landfills added by EPA. FBC was 

treated separately in the full-scale assessment because of the small number of FBC sites. 

2.2 Conceptual Model 

The waste stream/WMU combinations discussed above provided the waste management 

scenarios evaluated in the risk assessment. The full-scale assessment used the EPRI survey data 

to place these scenarios at actual onsite CCW disposal sites across the country. These sites were 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-7 

used as the basis for a national-scale site-based Monte Carlo assessment of risks posed by the 

onsite disposal of CCW at utility power plants across the United States. Figure 2-1 maps the 

CCW disposal sites modeled in this analysis against long-term average precipitation levels for 

the country. 

Figure 2-1. Coal combustion plants with onsite waste disposal modeled 

in CCW risk assessment. 

2.2.1 Conceptual Site Model 

Figure 2-2 depicts the conceptual site model for CCW disposal that was the basis for the 

national 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 and subsequent direct contact with contaminated surface 

water and sediments 

 Groundwater to surface water and subsequent ingestion of contaminated aquatic food 

items.
Section 2.0 

April 2010–Draft EPA document. 

Figure 2-2. Conceptual site model of CCW risk assessment.
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-9 

As shown in Figure 2-2, EPA focused full-scale modeling on groundwater-to-drinkingwater and groundwater-to-surface-water exposure pathways for the national risk assessment. 

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. 

2.2.2 Conceptual Site Layouts 

This risk assessment was based on site layouts that are conceptual rather than sitespecific. Although EPA had plant locations and some site-specific data on WMUs, we did not 

have the exact locations of each WMU or the residential wells surrounding each facility. 

Therefore, EPA had to develop conceptual layouts to place receptors around each WMU. 

The conceptual site layouts capture possible relationships between a WMU and human 

and ecological receptors by locating, with respect to the WMU boundary, the geographic 

features (i.e., receptor wells, waterbodies) that are important for determining human and 

ecological exposures to chemicals released from CCW landfills and surface impoundments. 

Two site layouts were used in the full-scale analysis to model the land use scenarios of 

most concern for CCW disposal facilities: 

 Residential groundwater ingestion scenario 

 Recreational fisher and aquatic ecological risk scenario. 

These two conceptual site layouts are shown in the following two subsections, including 

WMU boundaries, waterbodies, and residential wells modeled in this analysis. In the conceptual 

site layouts, the WMU is represented as a square source. The size of the source was determined 

by the surface area of the WMU (CCW WMU areas were collected from the EPRI 

comanagement survey, as described in Appendix B). The WMU was assumed to be located at 

the property line of the facility to which it belongs. 

Adjacent to the WMU is a buffer area within which there was assumed to be no human 

activity that would present human risk (i.e., there are no residences or waterbodies in the buffer). 

The buffer area lies between the WMU boundary and the residential well or waterbody, and 

represents the distance to well or waterbody discharge point modeled by the groundwater model. 

Each site layout must also be oriented in terms of direction. 

2.2.2.1 Residential Groundwater-to-Drinking-Water Scenario 

The residential groundwater-to-drinking-water scenario, shown in Figure 2-3, calculated 

exposure through residential use of well water as drinking water. In the Monte Carlo analysis, 

the receptor well was randomly placed up to 1 mile downgradient from the edge of the WMU 

(this radial well distance is labeled Rrw in Figure 2-3), based on a nationwide distribution of 

nearest downgradient residential wells from Subtitle D municipal landfills (U.S. EPA, 1988a; 

this distribution is provided in Appendix C). EPA assumed that this distribution was relevant to 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-10 

onsite CCW landfills and surface impoundments at coal-fired utility power plants, but does not 

have data on typical distances (or the distributions of distances) of domestic drinking water wells 

from CCW disposal facilities. (The potential impact on the results of this assumption is discussed 

as an uncertainty in Section 4.4.3.3). 

Figure 2-3. Conceptual site layout for residential groundwater ingestion scenario. 

The angle off the contaminant plume centerline (θrw in Figure 2-3) was based on a 

uniform distribution ranging from 0 to 90°. The depth of the well below the water table was set 

within the groundwater model based on assumptions that are generally typical of average 

conditions for surficial aquifers across the United States. These limits are discussed in 

Section 3.6.3. In this assessment, receptors were always located within the lateral extent of the 

plume. 

The soil and aquifer characteristics needed for the groundwater model were based on 

available data on soil and groundwater conditions collected around the 181 modeled sites, as 

described in Appendix C. 

2.2.2.2 Recreational Fisher and Ecological Risk Scenario 

The recreational fisher4

 scenario, shown in Figure 2-4, was used to estimate risks to 

recreational fishers (and their children) who live near the CCW landfills and surface 

impoundments and catch and consume fish from a waterbody located adjacent to the buffer and 

contaminated by CCW constituents through the groundwater to surface water pathway. The 

 

4

 Only recreational fishers were considered as the reasonable maximum exposed individuals. Subsistence receptors 

who eat fish were not modeled, but could be expected to have higher risks than the recreational fishers for whom 

we present results. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-11 

potential for cumulative exposure from both contaminated fish and groundwater was not 

considered in the CCW risk assessment. One reason is that the exposures are likely to occur over 

different timeframes because of differences in transit time of the contaminant plume to wells 

versus surface waterbodies. As described in Section 3.6.3, for each model run in the Monte 

Carlo analysis, the distances to the downgradient well and surface water were independently 

sampled from national distributions presented in Appendix C, Tables C-1 (wells) and C-2 

(surface waterbodies). Also, these exposures may involve different receptors because a resident 

exposed via groundwater may not be a recreational fisher. Thus, adding risks across pathways 

would not likely change the results. 

The waterbody was assumed to be a stream or lake located downgradient from the WMU, 

beginning where the buffer area ends (see Figure 2-4), and was also used as the most impacted 

aquatic system for the ecological risk assessment. Waterbody characteristics were determined 

based on a combination of site-specific, regional, or national data (as described in Appendix C), 

except for the length of the stream impacted by the plume, which was determined by the width of 

the plume as it intersects the waterbody. 

The downgradient distance to the surface water body was determined from a national 

distribution developed by measuring this distance (using scaled U.S. Geological Survey [USGS] 

maps and aerial photographs obtained from the Terraserver Web site [http://terraserver.usa.com/

geographic.aspx]) at 59 CCW landfill and surface impoundment sites randomly selected from a 

larger data set of 204 CCW WMUs, including those modeled in this risk assessment. 

Appendix C presents that distribution and further details on how the distribution was developed 

and the sample of 59 facilities used to develop the distribution. 

Figure 2-4. Conceptual site layout for residential fisher and 

aquatic ecological risk scenario. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-12 

2.3 Screening Analysis 

To assist in selecting constituents for full-scale modeling, a screening analysis was 

conducted in 2002 (U.S. EPA, 2002a) that compared very conservative estimates of exposure 

concentrations (e.g., leachate concentrations) to health-based concentration benchmarks to 

quickly and simply identify constituents with risks that clearly do not exceed the risk criteria so 

that these could be eliminated from further analysis. For example, for the groundwater-todrinking-water pathway, leachate concentrations were compared directly to drinking water 

standards, which is equivalent to assuming that human receptors are drinking leachate.5

 Similarly 

conservative estimates were used for ecological receptors (e.g., fish swimming directly in 

leachate). EPA made use of those screening results in this risk assessment, which was conducted 

in 2003 and documented in the August 6, 2007, draft report and its subsequent revisions, 

including the current document. Section 3.2 provides further detail on how the CCW screening 

analysis was conducted to develop the list of CCW constituents modeled in the full-scale 

analysis. 

2.4 Full-Scale Risk Assessment 

Although the screening analysis identified the potential for risk for a subset of the 

constituents reported in CCW, the conservative assumptions used precluded an accurate 

quantitative estimate of these risks. The screening results were not intended to, and do not, 

characterize the risks that we expect would actually occur, because the purpose is not to 

characterize risks but rather to identify those constituent/pathway/receptor combinations that are 

unlikely to be problematic versus those that are most likely to be problematic. To gain a better 

understanding of the risks that may be posed by the constituents identified as likely to be 

problematic, EPA conducted a full-scale probabilistic (Monte Carlo) risk assessment to estimate 

the national distribution of the risks to human health and the environment posed by CCW 

disposal, and 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 full-scale CCW 

Monte Carlo risk assessment was designed to characterize the national CCW risk profile in terms 

of WMU type, waste type, and constituent, and to use distributions in a probabilistic modeling 

framework to incorporate variability and uncertainty into the analysis. 

The full-scale modeling approach used data about waste management practices and 

environmental conditions at 181 utility CCW disposal sites across the United States.6

 These sites 

were assumed to represent the universe of CCW onsite waste disposal sites at the time of the 

EPRI survey (1995) and defined the national framework for the risk assessment. One question 

related to this risk assessment is how CCW facilities may have changed since the 1995 EPRI 

survey. Although the DOE/EPA survey did not include all of the data needed to conduct a risk 

assessment (WMU area and capacity data were not collected), liner conditions were addressed, 

and by comparing the DOE/EPA survey results to the EPRI data, it is possible to assess how 

 

5

 Note that RCRA waste disposal risk assessments do not address direct discharges from impoundments to surface 

waters because they are regulated as permitted point source discharges under the Clean Water Act by EPA’s 

Office of Water. 6

 These 181 sites include177 sites from the EPRI survey and 4 additional CCW sites added by EPA to better 

represent FBC waste disposal facilities; see Section 2.1.2. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-13 

liner conditions have changed as CCW facilities were built or expanded since 1995. The 56 

WMUs surveyed in the U.S. DOE (2006) study were commissioned between 1994 and 2004. 

Although the actual number of WMUs that were established in that timeframe cannot be verified, 

based on proxy data (i.e., CCW available for disposal in those states with identified new WMUs 

and coal-fired power plant generating capacity), the sample coverage was estimated to be at least 

61 to 63 percent of the total population of the newly commissioned WMUs.7

 With the exception 

of one landfill, the newly constructed facilities are all lined, with either clay, synthetic, or 

composite liners. The single unlined landfill identified in the recent DOE report receives bottom 

ash, which is characterized as an inert waste by the state, and therefore, a liner is not required. 

There has been a marked trend away from unlined WMUs in favor of lined units, with a distinct 

preference for synthetic or composite liners. A comparison of the 26 coal combustion plants in 

both the EPRI survey and the DOE/EPA survey (U.S. DOE, 2006) showed that although most of 

those facilities (17 of 26) were using unlined WMUs in 1995, all 26 are now placing wastes in 

new or expanded landfills or surface impoundments that are lined with clay, synthetic, or 

composite liners. However, it is likely that the older unlined units were closed with wastes in 

place, and that these wastes therefore still pose a threat through groundwater pathways. In 

addition, the available data cannot be used to determine the number of unlined units that 

continue to operate in the United States. See further discussion of the uncertainty posed by the 

use of the EPRI liner data in Section 4.4.1. 

The full-scale 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. 

2.4.1 Data Collection 

For the sites representing each WMU and waste type combination selected for analysis, 

the Monte Carlo analysis began with input files that contain, for each Monte Carlo realization, 

the following variables collected at and around each of the 181 modeled sites: 

 WMU area, depth, and capacity 

 WMU liner status (no liner, clay liner, composite liner) 

 Waste type (conventional CCW, CCW codisposed with coal refuse, FBC wastes) 

 Soil texture (for vadose zone properties and infiltration rates) 

 Soil pH and organic carbon 

 Aquifer type 

 Groundwater temperature 

 Climate center (for infiltration rates) 

 USGS Hydrologic Region (for surface water quality data) 

 Surface water type and flow conditions. 

 

7

 For additional details as to how these estimates were derived, the reader is referred to the DOE study, pages S-2 

to S-3 of the Summary Section and Section 3.1.2. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-14 

Data sources and collection methods for these variables may be found in Appendices B

and C. 

CCW constituent data in the CCW constituent database were used as a national empirical 

distribution of the concentrations of the constituents of concern in CCW landfill leachate and 

surface impoundment porewater. Like the WMU database, the CCW constituent data include 

WMU type and waste type, which enabled constituent concentrations to be assigned to the 181 

CCW sites by waste type and WMU type. The CCW constituent database was also used to assign 

(by waste type) the high, medium, and low leachate pH and ionic strength conditions needed to 

select the appropriate subsurface sorption isotherms for each model run (see Appendix D). 

Because site-specific data were not readily available, national distributions were used to 

populate the following variables by model run: 

 Distance to nearest drinking water well 

 Distance to nearest surface waterbody 

 Aquifer depth, thickness, gradient, and hydraulic conductivity (based on site-specific 

hydrogeologic setting) 

 Soil hydrologic properties (based on site-specific soil type). 

The data sources used to develop national distributions for these variables are described 

in Appendix C. Human exposure factors, such as exposure duration and drinking water and fish 

consumption rates, were also based on national distributions, which are provided in Appendix F. 

2.4.2 Model Implementation 

As a first step in the modeling process, the groundwater model (EPACMTP) read the 

site-based data files to estimate the following for each model run: 

 Drinking water well peak concentration 

 Time to drinking water peak concentration 

 Peak surface water contaminant flux 

 Time to peak surface water contaminant flux. 

The groundwater model was run until contaminant concentrations at the receptor point 

returned to zero after the concentration peak or for the maximum simulation time of 10,000 

years, whichever came first. 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-15 

Groundwater model results were passed to the multimedia modeling system to estimate 

surface water and sediment concentrations and to calculate human and ecological exposure and 

risk. Additional inputs sent to the model at this stage included 

 Site-based surface waterbody type, dimensions, flows, pH, and total suspended solids 

(TSS) concentration 

 Chemical-specific fish bioconcentration factors (BCFs) 

 Human exposure factors (from national distributions) 

 Human and ecological health benchmarks. 

For human health, the multimedia modeling system calculated risk from drinking water 

ingestion and fish consumption for each realization. For ecological risk, the model used surface 

water and sediment concentrations along with ecological benchmarks to estimate the risks to 

ecological receptors. 

2.4.3 Exposure Assessment 

Table 2-4 lists the human and ecological receptors considered in the CCW risk 

assessment, along with the specific exposure pathways that apply to each receptor. All of the 

receptors that EPA considered were assumed to live offsite, at a location near the WMU. 

Table 2-4. Receptors and Exposure Pathways Addressed in the 

Full-Scale CCW Assessment 

Receptor

Ingestion 

of Drinking 

Water

Fish 

Consumption

Direct Contact 

with Surface 

Water and 

Sediment

Ingestion of 

Aquatic 

Organisms

Human Receptors

Adult resident U 

Child resident U

Adult recreational fisher U 

Child recreational fisher U 

Ecological Receptors

Aquatic and sediment organisms U

Mammals and birds U

For human receptors, the exposure assessment estimated the dose to an individual 

receptor by combining modeled CCW constituent concentrations in drinking water or fish with 

intake rates for adult and child receptors. The full-scale CCW risk assessment considered 

exposures due to chemicals leaching from WMUs and contaminating groundwater. The 

groundwater exposures include drinking water ingestion and consumption of recreationally 

caught contaminated fish from surface waterbodies affected by contaminated groundwater. For 

the groundwater-to-drinking-water pathway, it was assumed that well water was the only source 
Section 2.0 Problem Formulation 

April 2010–Draft EPA document. 2-16 

of drinking water (although some households may drink bottled or treated water or may drink 

water outside the home, e.g., at work or at school). 

For ecological receptors, exposure assumptions were incorporated into the development 

of ecological benchmarks (see Appendix H), which were surface water and sediment 

concentrations corresponding to an HQ of 1. 

The time period for the exposure assessment was defined by the peak concentration in the 

media of concern and the exposure duration. For human receptors, annual average media 

concentrations were averaged over the randomly selected exposure duration around the peak 

concentration for each run. To protect against chronic effects to ecological receptors, EPA 

considered the exposure duration over a significant portion of the receptor’s lifetime, and we 

believe that one year is the appropriate period of time for that. To be protective, we used the 

highest (peak) annual average concentration to estimate ecological exposure and risk. 

2.4.4 Risk Estimation 

Risk was estimated using several risk endpoints as particular measures of human health 

risk or ecological hazard. A risk endpoint is a specific type of risk estimate (e.g., an individual’s 

excess cancer risk) that is used as the metric for a given risk category. The CCW risk assessment 

evaluated cancer and noncancer endpoints for humans and noncancer endpoints for ecological 

receptors. For human risk, the availability of toxicological benchmarks for cancer and noncancer 

effects determined which endpoints were evaluated for each constituent. 

EPA used two risk endpoints to characterize risk for the human receptors and a single 

risk endpoint, total HQ, to characterize risk for ecological receptors. These endpoints are 

discussed in Section 3.9; in addition, uncertainty related to these endpoints is discussed in 

Sections 4.4.2 (exposures to multiple constituents) and 4.4.3.4 (benchmark uncertainties). 

From the distribution of risks for each risk endpoint generated by the Monte Carlo 

analysis, the 50th and 90th percentile risks were selected and compared to a risk range of 1 in 

1,000,000 to 1 in 10,000 excess cancers and a hazard quotient greater than 1 for noncarcinogenic 

effects. A hazard quotient greater than 1 was also used for the ecological risk criterion in the fullscale risk assessment. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-1 

3.0 Analysis 

The CCW risk analysis evaluated risks from CCWs disposed of in landfills and surface 

impoundments located onsite at coal-fired utility power plants across the United States based 

primarily on data collected in 1995 by EPRI (1997).1

 Chemical constituents found in CCW can 

be released from these WMUs into the surrounding environment by releases through leachate to 

the subsurface underlying the WMU. Leachate forms in both landfills and surface 

impoundments, migrates from the WMU through soil to groundwater, and is transported in 

groundwater to drinking water wells (groundwater-to-drinking-water pathway) and into surface 

waterbodies near the WMU (groundwater-to-surface-water pathway). 

To select the constituents for full-scale modeling, the screening analysis compared very 

conservative estimates of exposure concentrations (e.g., leachate concentrations) to health-based 

concentration benchmarks to quickly, simply, and safely identify constituents with risks that 

clearly do not exceed the risk criteria so that these could be eliminated from further analysis. For 

example, leachate concentrations were compared directly to drinking water standards, which is 

equivalent to assuming that human receptors are drinking leachate. 

For the full-scale analysis, EPA used computer-based models and sets of equations to 

estimate the risk to human health and the environment from current CCW disposal practices.2

These models included 

 Source models that simulate the release of CCW constituents in leachate from landfills 

and surface impoundments3

 Fate and transport models that estimate contaminant concentrations in environmental 

media such as groundwater and surface water 

 Exposure models that estimate daily contaminant doses for humans and ecological 

receptors exposed to CCW constituents in environmental media that were not screened 

out 

 Risk models that calculate risks to humans and ecological receptors. 

This section describes the data, models, and equations used for CCW constituent 

screening, as well as those used to calculate exposure point concentrations and risk in the full-

 

1

 The selection and characterization of these CCW WMUs are described in more detail in Appendix B. 2

 As discussed in Section 2, the 1995 EPRI survey data was assumed to represent current CCW management 

practices. However, new data from a more recent DOE/EPA survey suggest that liners may be more prevalent in 

new and expanded units built since 1994. Section 4 discusses implications of this uncertainty on the risk 

assessment results. 3

 EPA used source-term models integrated into EPACMTP to estimate environmental releases of constituents in 

leachate from landfills and surface impoundments. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-2 

scale analysis. Section 3.1 describes the health benchmarks used to develop human and 

ecological risk estimates for screening and full-scale analysis. Section 3.2 describes the 

screening analysis, along with how the screening results were used to select constituents for the 

full-scale analysis. Section 3.3 provides the overall structure for the full-scale analysis, including 

the spatial and temporal framework and the probabilistic (Monte Carlo) framework for the model 

runs. Sections 3.4 and 3.5 describe the landfill and surface impoundment source models used to 

predict environmental releases of constituents from CCW. Sections 3.6 and 3.7 describe the fate 

and transport modeling used to predict contaminant concentrations in groundwater and surface 

water. Section 3.8 describes the human exposure calculations and Section 3.9 describes how 

risks were calculated for human and ecological receptors. 

Supporting detail can be found in the following appendices: 

 Appendix A, CCW Constituent Data, provides the CCW constituent concentrations 

used and describes how they were collected and processed for both the screening and 

full-scale analyses 

 Appendix B, Waste Management Unit Data, describes the location and characteristics 

of each landfill and surface impoundment modeled and describes how the source model 

input parameter values were collected for the full-scale analysis 

 Appendix C, Site Data, describes how environmental data around each CCW waste 

disposal site were collected to provide inputs for the groundwater and surface water 

modeling 

 Appendix D, MINTEQA2 Nonlinear Sorption Isotherms, describes the development 

and application of the CCW-specific MINTEQ metal sorption isotherms used to model 

fate and transport in soils and groundwater 

 Appendix E, Surface Water, Fish Concentration, and Contaminant Intake 

Equations, documents the algorithms used to calculate surface water concentrations, fish 

concentrations, and drinking water and fish intake rates 

 Appendix F, Human Exposure Factors, documents the human exposure parameters and 

equations used for calculating the environmental exposure from CCW disposal 

 Appendix G, Human Health Benchmarks, describes how the human toxicity 

benchmarks were selected and developed for CCW constituents 

 Appendix H, Ecological Benchmarks, describes how the ecological toxicity 

benchmarks were selected and developed for CCW constituents 

 Appendix I, Calculation of HBNs, describes how health-based numbers were calculated 

for the screening analysis 

 Appendix J, Chemical-Specific Inputs Used in the Screening Analysis, describes 

additional chemical-specific data used in the screening analysis 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-3 

 Appendix K, Screening Analysis Results, provides the results of the screening analysis 

for human and ecological receptors. 

 Appendix L, Time of Travel to Receptor Well, provides figures showing, for selected 

CCW constituents, cumulative percentiles of the time it took for the peak concentration 

to reach a receptor well for each source type. 

3.1 Toxicity Assessment 

The assessment of human risks from disposal of a waste stream like CCW begins by 

assessing, for constituents in the waste, the ability of each chemical to cause an adverse human 

health effect, which depends on the toxicity of the chemical, the chemical’s route of exposure to 

an individual (ingestion, inhalation, or direct contact), the duration of exposure, and the dose 

received (the amount that a human ingests or inhales). Similar principles apply to ecological 

receptors, although exposure duration is much shorter than for human receptors because humans 

generally live longer than ecological receptors. For a risk assessment, the toxicity of a 

constituent is defined by a human health or ecological benchmark for each route of exposure. A 

benchmark is a quantitative value used to predict a chemical’s possible toxicity and ability to 

induce an adverse effect at certain levels of exposure. Because different chemicals cause 

different health effects at different doses, benchmarks are chemical-specific. 

Appropriate human health and ecological benchmarks for the constituents of potential 

concern in CCW wastes were collected for use in the screening assessment and in the full-scale 

risk assessment. Although these assessments were conducted in 2002 and 2003, the benchmarks 

and risks presented in this 2009 report were updated to reflect current toxicity data.4 The data 

sources and collection methodology for these benchmarks are described briefly in Sections 3.1.1

(human health benchmarks) and 3.1.2 (ecological benchmarks), and in more detail in Appendix 

G (human health benchmarks) and Appendix H (ecological benchmarks). 

3.1.1 Human Health Benchmarks 

Human health benchmarks for chronic oral exposures were needed for the full-scale 

analysis. These health benchmarks were derived from toxicity data based on animal studies or 

human epidemiological studies. Each benchmark represents a dose-response estimate that relates 

the likelihood and severity of adverse health effects to exposure and dose. This section presents 

the noncancer and cancer benchmarks used to evaluate human health effects that may result from 

exposure to the constituents modeled. 

Chronic human health benchmarks were used to evaluate potential noncancer and cancer 

risks. These include reference doses (RfDs) to evaluate noncancer risk from oral exposures and 

oral cancer slope factors (CSFs) to evaluate cancer risk from oral exposures. The benchmarks are 

chemical-specific and do not vary between age groups. 

 

4

 Because the risk calculations are linear and occur at the end of the analysis, all screening and full-scale results can 

be simply scaled to accommodate any changes in human health and ecological benchmarks. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-4 

 The RfD is an estimate (with uncertainty spanning perhaps an order of magnitude) of a 

daily exposure to the human population (including sensitive subgroups) that is likely to 

be without appreciable risk of deleterious noncancer effects during a lifetime. The RfD 

provides a reference point to gauge the potential effects (U.S. EPA, 2002c). At exposures 

increasingly greater than the RfD, the potential for adverse health effects may increase, 

although this potential cannot be quantified. Lifetime exposure above the RfD does not 

imply that an adverse health effect would necessarily occur. 

 The CSF is an upper-bound estimate (approximating a 95 percent confidence limit) of the 

increased human cancer risk from a lifetime exposure to an agent. Because this is an 

upper-bound estimate, true risk is likely lower. This estimate is usually expressed in units 

of proportion (of a population) affected per milligram of agent per kilogram body weight 

per day (mg/kg-d). Unlike RfDs, CSFs do not represent “safe” exposure levels; rather, 

they relate levels of exposure with a probability of effect or risk. 

Human health benchmarks are available from several sources. Health benchmarks 

developed by EPA were used whenever they were available. Sources of human health 

benchmarks were used in the following order of preference: 

 Integrated Risk Information System (IRIS) (U.S. EPA, 2002c) 

 Superfund Technical Support Center Provisional Benchmarks 

 Health Effects Assessment Summary Tables (HEAST) (U.S. EPA, 1997f) 

 Various other EPA health benchmark sources 

 ATSDR minimal risk levels (MRLs) (ATSDR, 2002). 

These sources and the selection hierarchy are described in more detail in Appendix G. 

The chronic human health benchmarks used in the screening and full-scale analysis are 

summarized in Table 3-1. For most constituents, human health benchmarks were available from 

IRIS. Benchmarks for a few constituents were obtained from ATSDR. For chemicals for which 

purely health-based benchmarks were not available (lead), a drinking water action level was used 

(U.S. EPA, 2002d). 

Table 3-1. Human Health Benchmarks Used in the CCW Risk Assessment 

Constituent 

Type of 

Benchmarka Value Units Sourceb

Cancer Benchmark 

Arsenic CSF 1.5E+00 (mg/kg-d)-1 IRIS 

Noncancer Benchmarks 

Aluminum RfD 1.0E+00 mg/kg-d PPRTV 

Antimony RfD 4.0E-04 mg/kg-d IRIS 

Arsenic RfD 3.0E-04 mg/kg-d IRIS 

Barium RfD 2.0E-01 mg/kg-d IRIS 

Beryllium RfD 2.0E-03 mg/kg-d IRIS 

(continued) 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-5 

Human Health Benchmarks Used in the CCW Risk Assessment (continued) 

Constituent 

Type of 

Benchmarka Value Units Sourceb

Boron RfD 2.0E-01 mg/kg-d IRIS 

Cadmium RfD (water)c 5.0E-04 mg/kg-d IRIS 

RfD (food)d 1.0E-03 mg/kg-d IRIS 

Chromium III RfD 1.5E-00 mg/kg-d IRIS 

Chromium VI RfD 3.0E-03 mg/kg-d IRIS 

Cobalt RfD 3.0E-04 mg/kg-d PPRTV 

Copper RfD 1.0E-02 mg/kg-d ATSDR 

Cyanide RfD 2.0E-02 mg/kg-d IRIS 

Fluoride RfD 6.0E-02 mg/kg-d IRIS 

Lead MCL 1.5E-02 mg/L DWAL 

Manganese RfD (food) 1.4E-01 mg/kg-d IRIS 

RfD (water, soil) 4.7E-02 mg/kg-d IRIS 

Mercury (divalent) RfD (food, water, 

soil) 

3.0E-04 mg/kg-d HEAST 

RfD (fish) 1.0E-04 mg/kg-d IRIS 

Molybdenum RfD 5.0E-03 mg/kg-d IRIS 

Nickel RfD 2.0E-02 mg/kg-d IRIS 

Nitrate MCL 1.0E+01 mg/L DWAL 

RfD 1.6E+00 mg/kg-d IRIS 

Nitrite RfD 1.0E-01 mg/kg-d IRIS 

Selenium RfD 5.0E-03 mg/kg-d IRIS 

Silver RfD 5.0E-03 mg/kg-d IRIS 

Strontium RfD 6.0E-01 mg/kg-d IRIS 

Thallium RfD 8.0E-05 mg/kg-d IRIS 

Vanadium RfD 7.0E-03 mg/kg-d HEAST 

Zinc RfD 3.0E-01 mg/kg-d IRIS 

a

 MCL = maximum concentration limit 

b

 References: 

ATSDR: Minimal Risk Levels, ATSDR (2009) 

DWAL: Drinking Water Action Level, U.S. EPA (2002d) 

HEAST: U.S. EPA (1997f) 

IRIS: U.S. EPA (2009a) 

PPRTV: Provisional peer-reviewed toxicity value (U.S. EPA, 2006a, 2006b, 2008a) c

 Used for drinking water ingestion. 

d

 Used for fish ingestion. 

Cadmium has two RfDs, one for exposures via water and one for exposures via food. The 

RfD for water was used for drinking water ingestion and the RfD for food was used for fish 

consumption. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-6 

3.1.2 Ecological Benchmarks 

The ecological risk assessment addressed two routes of exposure for ecological receptors, 

direct contact with contaminated media and ingestion of contaminated food items. For each 

constituent for which ecological effect data were available, HQs were calculated using chemicalspecific media concentrations assumed to be protective of ecological receptors of concern. To 

calculate ecological HQs, these media concentrations (also known as chemical stressor 

concentration limits [CSCLs]) were divided by the estimated media concentrations. The CSCLs 

are media-specific environmental quality criteria intended to represent a protective threshold 

value for adverse effects to various ecological receptors in aquatic ecosystems (surface water and 

sediment). The CSCLs were developed to be protective of the assessment endpoints chosen for 

this assessment. An HQ greater than 1 indicates that the predicted concentration exceeds the 

CSCL, and therefore, the potential for adverse ecological effects exists. In this regard, the use of 

CSCLs to calculate an ecological HQ is analogous to the use of the reference concentration 

(RfC) for human health where the air concentration is compared to the health-based 

concentration (the RfC), and an HQ greater than 1 is considered to indicate the potential for 

adverse health effects. 

Table 3-2 shows the receptor types assessed for each exposure route (direct contact and 

ingestion) in each environmental medium addressed by the full-scale CCW risk assessment. 

Table 3-2. Ecological Receptors Assessed by Exposure Route 

and Medium (Surface Water or Sediment) 

Receptor Type

Surface Water 

(water column)

Surface Water 

Sediment

Direct Contact Exposure 

Aquatic Community U

Sediment Community U

Amphibians U

Aquatic Plants and Algae U

Terrestrial Plants 

Ingestion Exposure 

Mammals U

Birds U

Ecological receptors that live in close contact with contaminated media are considered to 

be potentially at risk. For the screening and full-scale analysis, these receptors are exposed 

through direct contact with contaminants in surface water and sediment. The benchmarks for 

receptor communities (aquatic or sediment communities) are not truly community-level 

concentration limits in that they do not consider predator-prey interactions. Rather, they are 

based on the theory that protection of 95 percent of the species in the community will provide a 

sufficient level of protection for the community (see, for example, Stephan et al., 1985, for 

additional detail). Appendix H summarizes the benchmark derivation methods for each receptor 

assessed for the direct contact route of exposure. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-7 

For surface water and sediments, the ingestion route of exposure addresses the exposure 

of terrestrial mammals and birds through ingestion of aquatic plants and prey. Thus, the 

benchmarks for ingestion exposure represent media concentrations that, based on certain 

assumptions about receptor diet and foraging behavior, are expected to be protective of 

populations of mammals and birds feeding and foraging in contaminated areas. 

For birds and mammals, the derivation of ingestion benchmarks required the selection of 

appropriate ecotoxicological data based on a hierarchy of sources. The assessment endpoint 

chosen for birds and mammals was population viability and therefore, the ingestion benchmarks 

were based on study data for physiological effects that are relevant to populations. These data 

included measures of reproductive fitness, developmental success, survival, and other 

toxicological effects that could have an impact on the population rather than just the health of an 

individual animal. Choosing these measures of effect provided the basis to evaluate the potential 

for adverse effects at the population level by inference; this analysis did not evaluate the effects 

on population dynamics in the sense that a reduction in the population was predicted over time in 

response to exposure to constituents released from CCW. Population-level modeling was beyond 

the scope of this risk assessment. 

Once an appropriate ingestion exposure study was identified, a benchmark was 

calculated. Appendix H describes the basic technical approach used to convert avian or 

mammalian benchmarks (in daily doses) to the CSCLs (in units of concentration) used to assess 

ecological risks for contaminated surface water and sediment. The methods reflect exposure 

through the ingestion of contaminated plants, prey, and various media, and include parameters 

on accumulation (e.g., BCFs), uptake (e.g., consumption rates), and dietary preferences. 

Where multiple ecological benchmarks were available for a pathway of interest, the 

benchmark that produced the lowest (most sensitive) CSCL for each chemical in each medium 

was used. For example, several types of receptors (the aquatic community, amphibians, aquatic 

plants, mammals, birds) can be exposed to contaminants in surface water. The surface water 

criterion for a given constituent represents the lowest CSCL for these receptors, and thus gives 

the highest (most protective) HQ. The CSCLs used to assess ecological endpoints in the fullscale analysis and the associated receptor are summarized in Table 3-3. Additional details on the 

CCW ecological benchmarks and CSCLs and their development can be found in Appendix H

and in U.S. EPA (1998a). 

Table 3-3. Ecological Risk Criteria Used for Surface Water and Sediment 

Constituent Mediuma Exposure Route CSCL Units Receptor

Aluminum Surface Water Direct contact 0.09 mg/L Aquatic biota 

Arsenic total Sediment Ingestion 0.51 mg/kg Spotted sandpiper 

Arsenic III Surface Water Direct contact 0.15 mg/L Aquatic biota 

Arsenic IV Surface Water Direct contact 8.10E-03 mg/L Aquatic biota 

Barium Sediment Ingestion 190 mg/kg Spotted sandpiper 

Surface Water Direct contact 4.00E-03 mg/L Aquatic biota 

Boron Surface Water Direct contact 1.60E-03 mg/L Aquatic biota 

 (continued) 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-8 

Ecological Risk Criteria Used for Surface Water and Sediment (continued) 

Constituent Mediuma Exposure Route CSCL Units Receptor

Cadmium Sediment Direct contact 0.68 mg/kg Sediment biota 

Surface Water Direct contact 2.50E-03 mg/L Aquatic biota 

Cobalt Surface Water Direct contact 0.02 mg/L Aquatic biota 

Lead Sediment Ingestion 0.22 mg/kg Spotted sandpiper 

Surface Waterb Ingestion 3.00E-04 mg/L River otter 

Selenium total Surface Water Direct contact 5.00E-03 mg/L Aquatic biota 

Selenium IV Surface Water Direct contact 0.03 mg/L Aquatic biota 

Selenium VI Surface Water Direct contact 9.5E-03 mg/L Aquatic biota 

Source: U.S. EPA (1998a)

a

 If a medium (surface water or sediment) is not listed, there were insufficient data to develop a benchmark 

for it. 

b

 Includes ingestion of fish. 

3.2 Constituent Screening 

The screening risk analysis was designed to select the CCW constituents for full-scale 

exposure modeling. The groundwater pathway screening evaluated exposure through drinking 

and surface water contamination5 from groundwater. The analysis considered risks to both 

human and ecological receptors. Waste constituents that passed the screen (i.e., were below 

target risk/hazard criteria) were assumed to pose de minimis risks and were not addressed in the 

full-scale modeling. 

3.2.1 Waste Constituent Concentrations 

The CCW screening analysis addressed metals and inorganic compounds identified as 

described in Section 2.1.1.2. Waste concentrations were available for most of these constituents 

from the CCW constituent database described in Section 2.1.1 and Appendix A. The CCW 

constituent database includes waste analysis data for CCW leachate, surface impoundment and 

landfill porewater, and whole waste samples, and was used in the screening analysis as follows: 

 Analyte concentrations (in mg/L) porewater sampled from surface impoundment 

sediments represent surface impoundment leachate affecting the groundwater pathways 

 To represent landfill leachate, the different types of landfill leachate and porewater data 

in the CCW constituent database were selected based on a hierarchy developed to best 

represent CCW landfill waste concentrations at a wide variety of sites and waste disposal 

conditions. 

To allow screening decisions to be made by waste constituent, waste stream, and exposure 

pathway, CCW data were processed to produce a single concentration per analyte and waste 

 

5

 For the groundwater-to-surface-water pathway, the analysis assumed that human exposure occurs through the 

consumption of contaminated fish. Ecological exposure occurs through direct contact to contaminated surface 

water and sediment and consumption of aquatic organisms. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-9 

stream (surface impoundment porewater and landfill leachate) for comparison with health-based 

numbers (HBNs) and CSCLs. Data processing to create these analyte concentrations involved 

two steps: 

 Calculation of average constituent concentrations by site for landfill leachate, 

surface impoundment porewater, and total ash concentrations. Site averaging avoids 

potential bias toward sites with many analyses per analyte. During site averaging, 

separate waste disposal scenarios at a site (e.g., non-FBC and FBC ash; FGD sludge and 

ash) were treated as separate “sites” and averaged independently. Nondetects were 

averaged at one-half the reported detection limit.6

 

 Selection of screening concentrations from site-averaged values. For the screening 

calculations, the analysis used the 90th percentile of the site-averaged concentrations 

across all sites for landfill leachate and surface impoundment porewater. 

Appendix A describes the CCW constituent database and how the waste constituent 

concentrations were selected and processed for the screening analysis and full-scale risk 

assessment. 

3.2.2 Media-Specific Exposure Concentrations for Screening 

The screening analysis required media concentrations for groundwater, surface water, 

and sediment to compare with the HBNs and CSCLs. As a simple first screen of risk, the 

analysis used waste concentrations as protective estimates of offsite groundwater and surface 

water concentrations. 

For groundwater-to-drinking-water exposures, the analysis used the 90th percentile waste 

porewater7

 and leachate concentrations to represent groundwater contamination from the surface 

impoundment and landfill, respectively. No dilution or attenuation was assumed between the 

WMU and the drinking water well because the large size range of CCW units precluded the use 

of a dilution attenuation factor (DAF)8

 for a nearby well. Similarly, surface water concentrations 

were assumed to be equivalent to waste leachate and porewater concentrations. 

3.2.3 Screening Methodology 

The CCW screening approach compared protective health-based concentrations in each 

medium of concern with estimated offsite media concentrations of CCW constituents described 

in Section 3.2.2. Both human and ecological receptors were addressed. HBNs are media 

concentrations developed to protect human health, and CSCLs are media concentrations 

developed to protect ecological receptors. HBNs were calculated based on the target risk criteria 

 

6

 Appendix A contains figures showing how site-averaged 90th percentile concentrations and 90th percentile 

concentrations taken across all analyses (nonaveraged concentrations) compare with HBNs for surface 

impoundment porewater, toxicity characteristic leaching procedure (TCLP) leachate, and whole waste 

concentrations. 7

 Although the 95th percentile was used in 1998, the 90th percentile was used in this analysis as a reasonably 

conservative value considering the protective screening analysis assumptions and the larger 2002 constituent data 

set. 8

 A DAF is the waste concentration divided by the media concentration at the point of exposure. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-10 

for the screening analysis: an HQ of 1 (for noncarcinogens) or an excess cancer risk level of 10-5. 

CSCLs were calculated based on an HQ of 1. A full description of the development of the HBNs 

can be found in Appendix I. Development of the CSCLs used for screening based on ecological 

risks is provided in Appendix H. 

Screening involved developing these HBNs and CSCLs, as well as developing the waste 

constituent or media concentrations to be used in the comparison and estimating the risk 

associated with these concentrations. Pathways and waste streams evaluated in the analysis 

include those summarized in Table 3-4, along with the basic assumptions and methods used to 

evaluate each pathway in the screening analysis. 

Table 3-4. Exposure Pathways Evaluated In CCW Constituent Screening 

Exposure Pathway Methodology 

Groundwater-to-drinking-water Compared drinking water HBNs to landfill leachate and surface 

impoundment porewater concentrations 

Groundwater-to-surface-water (fish 

consumption; ecological) 

Compared surface water HBNs and CSCLs to landfill leachate 

and surface impoundment porewater concentrations 

Direct exposure to surface 

impoundment CCW (ecological only) 

Compared surface water CSCLs to CCW surface impoundment 

constituent concentrations from the 1998 CCW risk assessment 

3.2.3.1 HBN Calculations 

HBNs represent media concentrations that are protective of human health from exposure 

pathways that are relevant to that particular medium. The exposure scenarios assumed for CCW 

management (see Section 2.2) defined the media of concern for the analysis. Human exposure 

scenarios included the following: 

 Drinking of groundwater contaminated by leachate from CCW landfills and surface 

impoundments 

 Consumption of fish by recreational fishers fishing in streams and lakes contaminated by 

CCW leachate through the groundwater-to-surface-water pathway 

The CCW screening analysis used HBNs calculated for groundwater and surface water 

exposure. The CCW HBNs represent reasonable maximum exposure (RME) scenarios for an 

offsite receptor: 

 Groundwater HBNs are protective for residential drinking water exposure from a 

domestic well immediately downgradient from a CCW landfill or surface impoundment 

 Surface water HBNs are protective for fish caught (and consumed) by a recreational 

fisher from a river, lake, or stream adjacent to a CCW landfill or surface impoundment. 

Key features and assumptions of the HBN calculations included the following: 

 HBNs were calculated based on a target cancer risk of 10-5 or target HQ of 1 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-11 

 The analysis considered exposures for three child receptor cohorts and one adult receptor 

cohort; exposure for these cohorts was assumed to start at ages 3, 8, 15, and 20, 

respectively 

 Chemical properties (bio-uptake and bioaccumulation factors) were collected from best 

available literature values (see Appendix J) 

 Human exposure factors (e.g., body weight, exposure duration, exposure frequency, 

consumption rates) were set at central tendency values. 

Appendix I describes the methodology used to develop the CCW HBNs and provides the 

HBNs used in the screening analysis. 

3.2.3.2 CSCL Calculations 

The CCW ecological screening analysis paralleled the human health screening analysis 

and addressed two routes of exposure for ecological receptors: direct contact with contaminated 

media and ingestion of contaminated food items. Ecological exposure scenarios occurring near 

CCW landfills or surface impoundments and addressing these exposure routes included the 

following: 

 Direct contact with surface water contaminated by CCW leachate through the 

groundwater-to-surface-water pathway 

 Ingestion of aquatic organisms in streams and lakes contaminated by CCW leachate 

through the groundwater-to-surface-water pathway. 

CSCLs for the contaminated media in each of these exposure scenarios were calculated 

as described in Section 3.1.2 and Appendix H (the same CSCLs were used for both screening 

and the full-scale analysis). As with the HBNs, CSCLs were compared directly to concentrations 

of constituents found in CCW and CCW leachate and porewater, or to protective offsite media 

concentrations to estimate risk for screening. 

3.2.4 Screening Results 

The screening analysis conducted in 2002 (U.S. EPA, 2002a) was used in this risk 

assessment to help narrow the list of constituents to be addressed in the full scale analysis for the 

groundwater-to-drinking-water and groundwater-to-surface-water pathways. Detailed human and 

ecological screening results for these pathways are provided in Appendix K. The groundwaterto-drinking-water and groundwater-to-surface-water pathways (human fish consumption and 

ecological risks) did show risks above the screening criteria for several CCW constituents in the 

screening analysis. Table 3-5 lists the 21 constituents that had 90th percentile screening analysis 

groundwater pathway risks greater than a cancer risk of 1 in 100,000 or a noncancer risk with an 

HQ greater than 1 for human health and 10 for ecological risk.9

 

9

 An HQ of 10 was used for screening ecological risks to account for conservatism of ecological benchmarks and 

exposure estimates used in the screening analysis (see Section 4.4.3.4). 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-12 

Table 3-5. Screening Analysis Results: Selection and Prioritization 

of CCW Constituents for Further Analysisa 

Constituent

Human Health – 

Drinking Water

Human Health – 

Surface Waterb

Ecological Risk - 

Surface Water

LF HQ 

(Cancer

Risk)

SI HQ 

(Cancer 

Risk)

LF HQ 

(Cancer 

Risk)

SI HQ 

(Cancer 

Risk)

LF 

HQ

SI 

HQ

Constituents Modeled in Full-scale Assessment 

Carcinogen 

Arsenicc (1.4x10-3) (1.8x10-2) (2.2x10-4) (1.7x10-5) 49 640 

Noncarcinogens 

Boron 4.0 28 - - 6,600 47,000 

Cadmium 3.4 8.9 1.4 3.7 20 52 

Lead 16 12 - - 790 590 

Selenium 1.2 2.4 4.7 9.5 35 71 

Thallium 21 19 6.3 5.7 - - 

Aluminum - - - - 120 270 

Antimony 22 5.5 - - - - 

Barium - - - - 400 75 

Cobalt - 11 - - - 270 

Molybdenum 4.2 6.8 - - - - 

Nitrate/ Nitrite - /1.2 60/1.2 - - - - 

Constituents Not Modeled in Full-scale Assessmentd

Noncarcinogens

Chromium VI 2.3 4.2 - - 18 33 

Fluoride 1.8 5.2 - - - - 

Manganese 1 5.6 - - - - 

Vanadium 2.2 2.3 - - 23 24 

Beryllium - - - - 24 - 

Copper - - - - 16 31 

Nickel - 1.3 - - - 14 

Silver - - - - 110 14 

Zinc - - - - 16 - 

HQ = screening hazard quotient. 

LF = landfill. 

SI = surface impoundment. 

a

 A dash in a cell indicates that the screening HQ was less than 1 (or 10 for ecological risk), so the 

risk did not exceed the screening criteria for the indicated pathway. 

b

 Fish consumption pathway. 

c Although arsenic can act as both a carcinogen and a noncarcinogen, the cancer risk exceeds the 

noncancer risk at any concentration, so the more protective cancer benchmark for human health 

was used throughout this assessment. 

d These constituents were addressed using risk attenuation factors developed from full-scale results 

from modeled constituents (see Section 4.1.5). 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-13 

Note that although mercury was originally addressed in both the 2002 screening and 

2003 full-scale analyses, results were removed from the 2007 draft and this version of the risk 

assessment report because subsequent evaluation found that the very high proportion of mercury 

nondetects in the CCW constituent database, along with the use of one-half the detection limit 

for the nondetect measurements, led to the results being driven by the detection limit, rather than 

the actual (but unknown) levels in CCW leachate and porewater. Therefore, the results were not 

meaningful in terms of the actual risks mercury in CCW poses to human and ecological health. 

Similarly, a large number of nondetects (or a very small number of measurements) prevented 

accurate screening or full-scale analysis for antimony, thallium, and cobalt in surface 

impoundments. These uncertainties are discussed in Section 4.4.3.1. 

Full-scale modeling was not conducted for all 21 constituents that had 90th percentile 

risks above the screening criteria for the groundwater pathways. Instead, those 21 constituents 

were ranked and divided into two groups to focus the full-scale analysis on the CCW 

constituents that were likely to pose relatively higher risks to human and ecological receptors. 

The ranking was based on the magnitude of the HQs and the number of HQs exceeding the 

screening criteria, and was used to select chemicals for full-scale modeling. Constituents with at 

least one human health HQ greater than 6 or with ecological HQs greater than 100 for both 

landfills and surface impoundments were modeled. Arsenic, with cancer risks greater than 1 in 

1,000, exceeded the cancer risk criterion by a factor of 100 and was also modeled in the fullscale analysis. Constituents with no human health HQs greater than 6 and only one or no 

ecological HQs greater than 100 were not modeled, but were addressed in a separate analysis 

using results from the modeled constituents. 

Table 3-5 shows the 21 constituents and which of these constituents exceeded the 

screening criteria and thus were modeled in the full-scale analysis. As shown, 12 constituents 

were subjected to the full-scale probabilistic risk assessment described in this document. Another 

9 constituents exceeded the screening criteria and were addressed using risk factors developed 

from comparing the screening and full-scale results for the modeled constituents, as described in 

Section 4.1.5 of this document. 

3.3 Full-Scale Modeling Approach 

This section describes the framework, general assumptions, and constraints for the 

full-scale probabilistic analysis. Section 3.3.1 describes the temporal and spatial framework. 

Section 3.3.2 describes the probabilistic framework, and Section 3.3.3 describes how the 

assessment was implemented within the probabilistic framework. 

3.3.1 Spatial and Temporal Framework 

The spatial framework for the analysis was determined by the geographic distribution of 

CCW facilities modeled and by the site layout assumed as the conceptual site model for risk 

assessment. As described in Section 2.1.2, the geographic distribution of landfills and surface 

impoundments managing wastes onsite at coal-fired utility power plants was determined from 

the 177 sites in the 1995 EPRI survey of the onsite management of CCW (EPRI, 1997). The 

assessment assumes that these 177 sites and their locations were representative of the 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-14 

approximately 300 coal-fired power plants identified by EIA data as having onsite waste 

management of conventional CCW and CCW codisposed with coal refuse throughout the United 

States. For FBC wastes, these 177 sites include only 3 FBC landfills. EPA was able to add 4 

additional FBC landfill sites to better represent FBC waste management, for an overall total of 

181 sites in this analysis. 

The conceptual site layouts applied to each of the sites are described and pictured in 

Section 2.2.2. Two site layouts were used to define the relationship between a landfill or surface 

impoundment and (1) a drinking water well (for human risk via the groundwater-to-drinkingwater pathway) and (2) a surface water body (for human and ecological risk via the 

groundwater-to-surface-water pathway). In each case, the receptor point (well or waterbody) was 

assumed to lie within the boundaries of the groundwater contaminant plume. The distance from 

the edge of the WMU to the well or waterbody was varied for each model run based on national 

distributions, with well distance taken from a national distribution for Subtitle D municipal 

landfills (U.S. EPA, 1988a) and distance to surface water taken from a set of measured distances 

for CCW landfills and surface impoundments developed for this assessment. Appendix C

presents additional details on these distributions. 

The temporal framework was mainly defined by the time of travel from the modeled 

WMU to the well or waterbody, which can be up to one mile away from the edge of the unit, and 

the exposure duration over which risks were calculated. The subsurface migration of some CCW 

constituents (e.g., lead) may be very slow; therefore, it may take a long time for the contaminant 

plume to reach the receptor well or nearest waterbody, and the maximum concentration may not 

occur until a very long time after the WMU ceases operations. This time delay may be on the 

order of thousands of years. To avoid excessive model run time while not missing significant 

risk at the receptor point, the groundwater model was run until the observed groundwater 

concentration of a contaminant at the receptor point dropped below a minimum concentration 

(10-16 mg/L) or until the model had been run for a time period of 10,000 years. The minimum 

concentration used for all fate and transport simulations (10-16 mg/L) was at least a million times 

below any risk- or health-based criteria. 

For the groundwater-to-drinking-water pathway (human health risk), risks were 

calculated based on a maximum time-averaged concentration around the peak concentration at 

each receptor well. The exposure duration (which varies from 1 to 50 years)10 was applied 

around the peak drinking well concentration to obtain the maximum time-averaged 

concentration. 

For the groundwater-to-surface-water pathway, the groundwater model produced surface 

water contaminant loads (based on groundwater concentration and flow) for a stream that 

penetrates the aquifer. Because the surface water model is a steady-state model, there is no 

temporal component to it and the receptor is exposed to the same concentration over the entire 

exposure duration. For human health risk, the loadings from groundwater to surface water were 

averaged over the exposure duration, bracketing the time of the peak groundwater concentration. 

 

10 Distributions of exposure duration and other exposure variables were obtained from the Exposure Factors 

Handbook (U.S. EPA, 1997c,d,e) as described in Section 3.8.2 and Appendix F. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-15 

The exposure duration for sensitive ecological receptors was generally a year or less; therefore, 

for ecological risk, a single peak annual average surface water concentration was used. 

For all scenarios, if the groundwater model predicted that the maximum groundwater 

concentration had not yet occurred after 10,000 years, the actual groundwater concentration at 

10,000 years was used in the exposure calculations instead of a maximum time-weighted average 

concentration around the peak. 

3.3.2 Probabilistic Approach 

The full-scale analysis evaluated risk in a probabilistic manner and was based on a Monte 

Carlo simulation that produced a distribution of exposures and risks. The general Monte Carlo 

approach is shown in Figure 3-1. The foundation of the Monte Carlo simulation was the source 

data derived from the EPRI survey. These were combined with data from the national CCW 

constituent database to conduct a Monte Carlo simulation of 10,000 iterations per waste 

type/WMU type/constituent combination. 

Waste Scenarios

Scenario

(waste type x WMU x constituent)

Sample

(constituent data)

Site

(WMU data)

EPACMTP/

Surface Water model

Receptor

Pathway

WMU

Constituents

Isotherms

Randomly located well

10,000

Randomly located waterbody

Groundwater-to- Child

drinking-water risk

Adult

10,000

Fish 

Consumption

Risk

Eco Risk Food chain 

Direct contact

Exposure/Risk

iterations

iterations

Surface water 

concentrations

Receptor well 

concentrations

Child

Adult

Figure 3-1. Overview of the Monte Carlo approach. 

The detailed looping structure for the Monte Carlo analysis is shown in Figure 3-2. For 

each waste type/WMU combination, two separate loops were run. The first loop (shown with 

dashed lines in Figure 3-2) prepared a set of input files containing 10,000 sets of WMU and site 

data (as described in Section 3.3.3). The second loop (shown with solid lines in Figure 3-2) used 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-16 

those input files to run 10,000 iterations of the source, fate and transport, exposure, and risk 

models for each constituent. 

Waste/WMU Scenario Loop

Next Waste/WMU Scenario

Human Receptor Loop

Next Constituent

Constituent Loop [Monte Carlo Loop]

Select 1 Waste type Conventional CCW, Codisposed CCW and coal refuse, FBC)

Select 1 WMU type (landfill, surface impoundment)

Data Preparation Loop:

Select 1 Facility ID (EPRI survey)

Pull data for Facility ID

Select 1 Aquifer Type (empirical distribution)

Select 1 SoilType, with pH, organic matter (empirical distribution)

Pull WMU data (area, depth, liner type)

Select distributed WMU data (missing depth, DBGS)

Pull Climate Center

Select 1 SampleID from Constituent database (pH table)

Pull leachate pH

Pull national data

Pull waste bulk density (=1.19), fraction CCW (=1), SI duration (=75 yr), SI sludge depth (0.2 m)

Select 1 distance to surface water

Select next Facility ID

** Call EPACMTP to select soil data (by SoilType), aquifer data (by Aquifer Type), MINTEQA2 master variables, receptor well 

distance and calculate infiltration rate and vadose zone pH

Loop over 4 receptors: (1) adult resident, (2) child resident, (3) adult fisher, (4) fisher's child

Select pathways, exposure factors based on receptor:

Pull benchmarks

** Call Exposure Module to calculate ADD, LADD for each pathway

** Call Risk Module and calculate cancer risk, HQ for each pathway

RunID Loop (10,000 iterations)

Select 1 Facility ID (with data from data preparation loop)

Pull surface water type, flow data

Select 1 SampleID from Constituent Database

Pull 1 leachate concentration

Pull (or calculate) 1 corresponding total waste concentration (landfills only)

Pull nationally distributed data from data preparation loop

Select 1 surface water TSS

** Call EPACMTP to calculate drinking water concentration (random and 150-m well) and surface water flux 

[output based on exposure durations] 

** Call Surface Water Module to calculate surface water and sediment concentrations

** Call Food Web Module to calculate fish concentration

Next RunID

Next Receptor

Select 1 Constituent (11 priority metals and nitrate/nitrite)

Ecological Risk

Pull ecological benchmarks (CSCLs)

** Calculate surface water and sediment HQs Monte Carlo Loop

Data preparation loop

** indicates model runs

Figure 3-2. Monte Carlo looping structure. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-17 

3.3.3 Implementation of Probabilistic Approach 

Table 3-6 lists the five waste disposal scenarios addressed in the full-scale analysis. FBC 

waste landfills were modeled and treated as a separate scenario in the analysis because of the 

limited number (7) of FBC landfill sites. Each waste disposal scenario modeled in the full-scale 

assessment included unlined, clay-lined, and composite-lined WMUs. Additional detail on these 

scenarios can be found in Section 2.1 and Appendix A. 

Table 3-6. CCW Waste Management Scenarios Modeled 

in Full-Scale Assessment 

WMU Type Waste Type

Conventional CCW and CCW Codisposed with Coal Refuse (main analysis) 

Landfill Conventional CCW (fly ash, bottom ash, boiler slag, FGD sludge) 

Landfill Codisposed CCW and coal refuse 

Surface impoundment Conventional CCW 

Surface impoundment Codisposed CCW and coal refuse 

FBC Waste (separate analysis) 

Landfill FBC waste (fly ash and bottom [bed] ash) 

To capture the national variation in waste management practices for the Monte Carlo 

analysis, an input database was created with approximately 10,000 iterations for each of the 

waste type/WMU combinations. This input database provided the source data for 10,000 

iterations of the source modeling and the fate and transport modeling. Figure 3-3 provides an 

overview of the process used to compile these data, which were organized into source data files. 

As shown in Figure 3-3, seven tasks, some parallel and some sequential, were required to 

construct these data files, one file for each waste management scenario. 

Constructing the source data files for use in the probabilistic analysis involved first 

developing a 10,000-record data file for each waste type-WMU scenario. This was accomplished 

by selecting from the EPRI survey data the landfills and surface impoundments that manage each 

type of waste. Within a scenario, a list of the EPRI plants with that WMU type and waste type 

was repeated to produce around 10,000 records. For each record, site-based, regional, and 

national inputs were randomly selected from distributions developed to characterize the regional 

or national variability in these inputs. Each record in the source data files was identified by a 

model run identification number (RunID). 

The EPRI survey provided most of the WMU data needed, including area, capacity, liner 

type, and waste type. Additional data were collected to characterize the height and depth below 

ground surface of typical CCW landfills and surface impoundments (see Appendix B). 

The environmental setting in which waste disposal occurs was characterized based on the 

location of the 181 power plants used in the full-scale analysis. These locations were used to 

characterize climate, soils, aquifers, and surface water bodies at each site as follows (see 

Appendix C for details): 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-18 

Landfills and Surface Impoundments

Replicate site data to create 

10,000 iterations per scenario

CCW Waste Disposal Practices

Landfills

Surface impoundments

EPRI Survey Data:

108 CCW landfills

96 CCW surface impoundments

181 Plant locations 

Landfills and Surface Impoundments

Waste type

Liner type

Surface area

Depth

Depth below ground surface

Other parameters

Task 1.

Identify waste management scenarios

Task 4.

Select representative disposal sites

Task 6.

Characterize environmental setting

Task 5.

Characterize WMUs

Task 7a.

Construct source data files 

(1 per waste type - WMU scenario)

CCW Waste Types

Conventional CCWs

Codisposed ash and coal refuse

FBC wastes (separate scenario)

For each plant location, 

characterize

Climate

Soil characteristics

Groundwater aquifers

Surface water conditions

Replicate site data file to create 

10,000 iterations per scenario -

constituent combination

(5 scenarios x 12 constituents x 

10,000 = 600,000 iterations)

Task 7b.

Construct source data files 

(add constituent data)

CCW Constituent Database

41 analytes

3 waste forms

> 160 CCW sites

> 35,000 analyses

Task 2.

Characterize CCW

Calculate site-average media 

concentrations

Compare to conservative target levels 

Select pathways, receptors, and 

constituents for full-scale modeling

Task 3.

Screen constituent data

Figure 3-3. Process used to construct the Monte Carlo input database. 

 Climatic data, including precipitation and infiltration rates, were collected by assigning 

each site to a nearby HELP climate center (see Section 3.4.2.1 for a discussion of the 

HELP landfill infiltration model). 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-19 

 Soil and aquifer type were collected within a 5-km radius of each site location to account 

for locational uncertainty for the WMUs (site location are often facility centroids or front 

gate locations). 

 Surface water type and flows were collected using a geographic information system 

(GIS) to identify the nearest stream and by matching plants to the Permit Compliance 

System (PCS) database to get the stream segment for each plant’s NPDES discharge 

point. 

These site-based data were supplemented with regional data on surface water quality and 

with national distributions of receptor distances (i.e., distance to drinking water well and 

distance to nearest surface waterbody). Appendix C describes the site-based approach and data 

sources used for these site-specific, regional, and national-scale data collection efforts. 

The five 10,000-record scenario-specific source data files were then combined with the 

CCW constituent data for each constituent in the appropriate waste type to develop the final 

source data files for each scenario. With 12 constituents modeled for most scenarios, this 

resulted in over 600,000 records in the final input data set. 

3.4 Landfill Model 

Releases from landfills were modeled using a landfill source-term model contained in 

EPACMTP. EPA has used EPACMTP and its predecessor models for almost 20 years to conduct 

groundwater risk assessments in support of regulations for land disposal of hazardous and 

nonhazardous wastes. In that context, EPACMTP has undergone numerous peer reviews, 

including multiple reviews by EPA’s Science Advisory Board (SAB). Each of these reviews has 

supported and approved the use of this model for developing national regulations and guidance, 

including verification that the model and model code are scientifically sound and properly 

executed. Some of the more important reviews include 

 A 1989 review by SAB of the component saturated zone (groundwater) model used in 

EPACMTP 

 A 1993 review by EPA’s Office of Research and Development (ORD) of EPACMTP for 

potential Hazardous Waste Identification Rule applications, which resulted in a number 

of improvements in the computational modules of EPACMTP 

 A 1994 consultation with SAB on the use of EPACMTP for determination of dilutionattenuation factors for EPA’s Soil Screening Guidance

 A 1994 review by expert modelers Dr. Fred Molz (Auburn University) and Mr. Chris 

Neville (SS Papadopoulos & Associates), who verified that the mathematical formulation 

of the model and the code verification testing are scientifically sound 

 The peer-reviewed publication of EPACMTP in the Journal of Contaminant Hydrology

(Kool et al., 1994) 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-20 

 An in-depth review by SAB related to the use of EPACMTP in the proposed/draft 1995 

Hazardous Waste Identification Rule (U.S. EPA, 1995) 

 A 1999 peer review by leading modelers of the implementation of EPACMTP in EPA’s 

multimedia, multiple exposure pathway, multiple receptor risk assessment (3MRA) 

model (U.S. EPA, 1999c) 

 A 2003 SAB review of the 3MRA implementation of EPACMTP (SAB, 2004). 

An overview and statement of assumptions for the landfill model is presented here, 

followed by a listing of inputs to the landfill source-term model and a brief discussion of the 

output generated by the model. 

3.4.1 Conceptual Model 

The landfill model treats a landfill as a permanent WMU with a rectangular footprint and 

a uniform depth (see Figure 3-4). If only the area is known (which is the case for the CCW 

landfills), the landfill source-term model assumes a square footprint. The model assumes that the 

landfill is filled with waste during the unit’s operational life and that upon closure of the landfill, 

the waste is left in place and a final soil cover is installed. 

Figure 3-4. Conceptualization of a landfill 

in the landfill source-term model. 

Three liner scenarios were modeled: a no-liner (unlined) scenario, a compacted clay liner, 

and a composite liner that combines a high-density polyethylene (HDPE) membrane with either 

geosynthetic or natural clays. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-21 

In the unlined scenario, waste is placed directly on local soils, either on grade or 

excavated to some design depth and without a leachate collection system. After the landfill has 

been filled to capacity, a 2-foot native soil cover (the minimum required by Subtitle D 

regulations) is installed and assumed to support vegetation. 

In the clay liner scenario, waste is placed directly on a 3-foot compacted clay liner, which 

is installed on the local soils, either on grade or excavated to some design depth and without a 

leachate collection system. After the landfill has been filled to capacity, a 3-foot clay cover is 

installed and covered with 1 foot of loam to support vegetation and drainage. The hydraulic 

conductivity of both the liner and cover clays is assumed to be 1x10-7 cm/sec, the typical design 

specification for compacted clay liners (U.S. EPA, 1988c). 

In the composite liner scenario, wastes are placed on a liner system that consists of a 

60 mil HDPE membrane with either an underlying geosynthetic clay liner or a 3-foot compacted 

clay liner. A leachate collection system is also assumed to exist between the waste and the liner 

system. After the landfill has been filled to capacity, a 3-foot clay cover is assumed to be 

installed and covered with 1 foot of loam to support vegetation and drainage (U.S. EPA, 2002b). 

As described in Section 3.4.3 (and Appendix B), one of these three liner types was 

assigned to each CCW landfill or surface impoundment modeled based on the liner type data 

from the 1995 EPRI Survey (EPRI, 1997). 

3.4.2 Modeling Approach and Assumptions 

The starting point for the landfill source-term model simulation was the time when the 

landfill is closed (i.e., when the unit is filled with CCW).11 As described in detail below, the fullscale analysis modeled contaminants leaching from CCW into precipitation infiltrating the 

landfill, which exits the landfills as leachate. Contaminant loss in leachate was taken into 

account at closure by subtracting the cumulative amount of contaminant mass loss that occurred 

during the unit’s active life from the amount of contaminant mass present at the time of landfill 

closure. Loss calculations in the landfill source-term model continued after closure until the 

contaminant was depleted from the waste mass in the landfill. This is a conservative assumption, 

as some metal will not leach from the waste mass. 

3.4.2.1 Infiltration and Leaching 

The average rate at which water percolates through a landfill over time (the long-term 

infiltration rate) drives the leaching process in the landfill, which results from partitioning of the 

constituent from the waste into the infiltrating water. The methodology, assumptions, and data 

 

11 The simple landfill model used in this assessment cannot model a landfill as it is being filled prior to closure. 

Although leaching does occur during a landfill’s operating life, risks from these releases are insignificant when 

compared to postclosure releases, given the long time it takes metal-bearing wastes to leach and reach peak 

concentrations in groundwater wells surrounding the landfill. For lined units, the liner system would be functional 

and governing during the active period of the landfill. For the unlined case, the landfill model assumes that the 

cap soils are no less permeable than the ambient soils around and under the landfill. So the majority of the cases 

would not have greater infiltration before closure. For these reasons, EPA does not believe that the additional 

risks from the preclosure period justify the additional complexity, data, and effort required to model an operating 

landfill. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-22 

used to determine infiltration rates for each CCW liner scenario were consistent with the 

approach used in EPA’s Industrial D guidance, as described in Section 4.3 and Appendix A of 

the EPACMTP Parameter/Data Background Document (U.S. EPA, 2003a) and Section 4.2.2 of 

the Industrial Waste Evaluation Model (IWEM) Technical Background Document (U.S. EPA, 

2002b). EPA developed the IWEM model as part of a guide for managing nonhazardous 

industrial wastes in landfills and surface impoundments (http://www.epa.gov/industrialwaste). 

To help ensure that it was technically sound, the model (including the liner scenarios and 

algorithms used in the CCW risk assessment) was developed with a large stakeholder working 

group, including representatives from industry. The model was also subjected to a peer review in 

1999 (64 FR 54889–54890, October 8, 1999, Peer Reviews Associated with the Guide for 

Industrial Waste Management), and the model was updated and improved in response to those 

comments before its final release in 2003. That update included the addition of a more robust 

liner leakage database to support the existing algorithms for calculating infiltration rates through 

composite liner systems. 

No-Liner (Unlined) Scenario. For the no-liner scenario, infiltration rates were selected 

from a database in EPACMTP that contains 306 infiltration rates already calculated using EPA’s 

Hydrologic Evaluation of Landfill Performance (HELP) water balance model (Schroeder, et al., 

1994a, b). HELP is a product of an interagency agreement between EPA and the U.S. Army 

Corps of Engineers Waterways Experiment Station, and was subjected to the Agency’s peer and 

administrative review. All of the infiltration rates were calculated based on the single typical 

landfill design described in Section 3.4.1, with the only variables that changed between HELP 

simulations being the meteorological data associated with 102 nationwide climate centers and 

the type of cover soil applied at closure. Three cover soil categories representing coarse-grained 

soils, medium-grained soils, and fine-grained soils were used. The selection of an infiltration rate 

from the database depends on the type of cover soil selected for the landfill and the assignment 

of the landfill to a HELP climate center. The unlined HELP-derived infiltration rates are 

presented in U.S. EPA (2003a) by climate center. The assignment of HELP climate centers and 

soil categories to each CCW site modeled is described in Appendix C. 

Clay Liner Scenario. The clay liner scenario is very similar to the unlined scenario in 

that previously calculated HELP infiltration rates for a single clay-lined, clay-capped landfill 

design were used. The scenario was based on a typical engineered compacted clay liner that is 3 

feet thick with a design hydraulic conductivity of 1x10-7 cm/sec. The one difference from the 

unlined case is that the clay liner and cover control the rate of water percolation through the 

landfill and thus infiltration rate does not vary with cover soil (i.e., there is one clay liner 

infiltration rate per climate center). The clay liner HELP-derived infiltration rates are provided in 

U.S. EPA (2003a). 

Composite Liner Scenario. Composite liner infiltration rates were compiled from 

monthly average leak detection system (LDS) flow rates for industrial landfill cells reported by 

TetraTech (2001). The liner configurations are consistent with the composite liner design 

assumptions presented in Section 3.4.1 and are the same as those assumed for defaults in EPA’s 

Industrial D landfill guidance (U.S. EPA, 2002b). The LDS flow rates were taken from 27 

municipal landfill cells and used in the IWEM model (U.S. EPA, 2002b). As shown in 

Table 3-7, these LDS flow rates included 22 operating landfill cells and 5 closed landfill cells 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-23 

located in eastern United States: 23 in the northeastern region, 1 in the mid-Atlantic region, and 

3 in the southeastern region. Each of the landfill cells is underlain by a geomembrane/

geosynthetic clay liner which consists of a high-density polyethylene geomembrane of thickness 

between 1 and 1.5 mm, overlying a 6-mm composite geosynthetic clay layer consisting of two 

geotextile outer layers with a uniform core of bentonite clay to form a hydraulic barrier. Each 

liner system is underlain by an LDS. 

Table 3-7. Leak Detection System Flow Rate Data Used to Develop 

Landfill Composite Liner Infiltration Rates 

Cell ID Status Flow Rate (m/y) Location 

G228 Operating 2.1E-04 Mid-Atlantic 

G232 Operating 4.0E-04 Northeast 

G232 Closed 7.3E-05 Northeast 

G233 Operating 0 Northeast 

G233 Closed 0 Northeast 

G234 Operating 7.3E-05 Northeast 

G234 Closed 0 Northeast 

G235 Operating 1.5E-04 Northeast 

G235 Closed 3.7E-05 Northeast 

G236 Operating 3.7E-05 Northeast 

G236 Closed 0 Northeast 

G237 Operating 7.3E-05 Northeast 

G238 Operating 0 Northeast 

G239 Operating 7.3E-05 Northeast 

G240 Operating 0 Northeast 

G241 Operating 0 Northeast 

G242 Operating 0 Northeast 

G243 Operating 0 Northeast 

G244 Operating 0 Northeast 

G245 Operating 0 Northeast 

G246 Operating 0 Northeast 

G247 Operating 0 Northeast 

G248 Operating 0 Northeast 

G249 Operating 7.3E-05 Northeast 

G250 Operating 2.2E-04 Southeast 

G251 Operating 0 Southeast 

G252 Operating 0 Southeast 

Source: U.S. EPA (2002a); original data from TetraTech (2001). 

As described in U.S. EPA (2002b), only a subset of the TetraTech (2001) flow rates were 

used to develop the composite liner infiltration rates. LDS flow rates for geomembrane/ 

compacted clay composite-lined landfill cells were not used in the distribution because 

compacted clay liners (including composite geomembrane/compacted clay liners) can release 

water during consolidation and contribute an unknown amount of water to LDS flow, which 

makes it difficult to determine how much of the LDS flow is due to liner leakage versus clay 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-24 

consolidation. Also, LDS flow rates from three geomembrane/geosynthetic clay lined-cells were 

not used. For one cell, postclosure flow rates were very high, and were more than twice as high 

as those recorded during the cell’s operating period. Data were not used for two other cells 

because of inconsistencies with the data for the 27 landfill cells used to develop composite liner 

infiltration rates (U.S. EPA, 2002b). The composite liner infiltration rates were specified as an 

empirically distributed input to the landfill model (see U.S. EPA, 2003a). 

3.4.2.2 Source Depletion and Mass Balance 

For this assessment, the landfill source-term model represented releases from landfills as 

a finite source where the mass of a constituent in a landfill is finite and depleted over time by 

leaching. The landfill source-term model was set as a pulse source, where the leachate 

concentration is constant over a prescribed period of time and then goes to zero when the 

constituent is depleted from the landfill. A pulse source is appropriate for metals and other 

constituents whose sorption behavior is nonlinear. Because all but one (nitrate/nitrite) of the 

constituents addressed in the full-scale analysis were metals, releases from landfills were 

modeled as pulse sources. 

For a pulse source, basic mass balance considerations require leaching from the landfill 

to stop when all of the constituent mass has leached from the landfill. For the constant 

concentration pulse source condition, the pulse duration is given by 

CZERO SINFIL

CWASTE DEPTH FRACT CTDENS TSOURC

×

× × × = (3-1) 

where 

 TSOURC = Pulse duration (yr) 

 CWASTE = Constituent concentration in the waste (mg/kg) 

 DEPTH = Depth of landfill (m) 

 FRACT = Volume fraction of the landfill occupied by the waste (unitless) 

 CTDENS = Waste density (g/cm3) 

 CZERO = Initial waste leachate concentration (mg/L) 

 SINFIL = Annual areal infiltration rate (m/yr). 

The landfill source-term model uses the above relationship to determine the leaching duration. 

More details regarding the waste concentration and WMU parameters in Equation 3-1 are 

provided below and in Appendices A and B. 

3.4.3 Landfill Model Input Parameters 

Input parameters required by the landfill source-term model are discussed below. 

Additional details on how data for these inputs were collected for the CCW risk assessment are 

provided in Appendix A for leachate and waste concentrations and Appendix B for landfill 

dimensions and characteristics. 

 Landfill Area. The model uses landfill area to determine the area over which infiltration 

rate occurs and, along with landfill depth and waste concentration, to calculate the total 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-25 

contaminant mass in the landfill. CCW landfill area data were obtained from the EPRI 

comanagment survey (EPRI, 1997). The landfill was assumed to be square. 

 Landfill Depth. Landfill depth is one of several parameters used by the landfill sourceterm model to calculate the contaminant mass in the landfill. For CCW landfills, average 

waste depth was estimated by dividing landfill capacity by landfill area. CCW landfill 

capacity data were taken from the EPRI comanagement survey (EPRI, 1997). 

 Depth Below Grade. The depth of the bottom of the landfill below the surrounding 

ground surface is used, along with depth to groundwater, to determine the thickness of 

the unsaturated zone. For CCW landfills, depth below grade was determined from a 

national distribution based on available measurements from a number of CCW landfills 

(see Appendix B). 

 Waste Fraction. The landfills were assumed to be CCW monofills, which corresponds to 

a waste fraction of 1.0. 

 Waste Density. The average waste bulk density, as disposed, is used to convert waste 

volume to waste mass. The waste bulk density for all CCW waste types was assumed to 

be 1.19 g/cm3 (U.S. EPA, 1998b). 

 Leachate Concentration. The concentration of waste constituents in leachate was 

assumed to be constant until all of the contaminant mass initially present in the landfill 

has leached out, after which the leachate concentration was assumed to be zero. The 

constant value used for leachate concentration is from EPA’s CCW Constituent 

Database, described in Appendix A. 

 Waste Concentration. In the finite-source scenario modeled, the total waste 

concentration is used, along with the waste bulk density and landfill area and depth, to 

determine the total amount of a constituent available for leaching. Measured total CCW 

concentrations were paired with leachate concentrations, as described in Appendix A and 

provided in Attachment A-2. 

 Liner Type. The type of liner is used to determine the infiltration/leaching scenario used 

to calculate leachate flux from the landfill. Table 3-8 shows the crosswalk used to assign 

one of the three liner scenarios to each facility based on the liner data in the 1995 EPRI 

survey (EPRI, 1997). Attachment B-2 to Appendix B provides these assignments, along 

with the original EPRI liner type, for each CCW landfill facility modeled. One 

uncertainty in these liner assumptions is how representative the EPRI survey data are of 

current conditions at coal combustion facilities. 

Table 3-8. Crosswalk Between EPRI and CCW Source Model Liner Types 

EPRI Liner Type Model Liner Code Description 

Compacted ash 0 no liner 

Compacted clay 1 clay 

 (continued) 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-26 

Crosswalk Between EPRI and CCW Source Model 

Liner Types (continued) 

EPRI Liner Type Model Liner Code Description 

Composite clay/membrane 2 composite 

Double 2 composite 

Geosynthetic membrane 2 composite 

None/natural soils 0 no liner 

3.4.4 Model Outputs 

For each year in the simulation, the landfill source-term model uses the average annual 

leachate concentration and infiltration rate to calculate a constituent flux through the bottom of 

the landfill. This time series was used as an input for the EPACMTP unsaturated zone model. 

3.5 Surface Impoundment Model 

Releases from surface impoundments were modeled using a surface impoundment 

source-term model contained in EPACMTP. An overview and statement of assumptions for the 

surface impoundment model are presented here, followed by a listing of inputs to the surface 

impoundment source-term model and a brief discussion of the output generated by the model. 

The primary differences between the treatment of landfills and surface impoundments are (1) the 

integration of the surface impoundment source term into the unsaturated flow solution, and 

(2) clean closure of the impoundment after the operating period is over. 

3.5.1 Conceptual Model 

The surface impoundment model treats a surface impoundment as a temporary WMU 

with a prescribed operational life. Unlike the landfill model, clean closure is assumed; that is, at 

the end of the unit’s operational life, the model assumes that all wastes are removed and there is 

no further release of waste constituents to groundwater. Although this simplifying assumption 

limits the length of potential exposure, and is not consistent with the practice to close CCW 

surface impoundments with these wastes in place, the peak annual leachate concentrations on 

which the CCW risk results are based are not likely to be affected, because they are highest when 

the surface impoundment is in operation due to the higher hydraulic head in an operating 

impoundment, which drives leachate into the underlying soil with greater force than infiltration 

after the impoundment is covered and closed. This higher head results in a greater flux of 

contaminants to groundwater during the active life of the surface impoundment, especially in 

unlined units. These assumptions are discussed further in Section 3.5.3.

Following the unit’s closure, the surface impoundment model assumes that the 

contaminated liquid and sediment in the surface impoundment are replaced by uncontaminated 

liquid and sediment with otherwise identical configurations and properties. The contaminants 

that have migrated to the unsaturated zone during operation continue to migrate towards the 

water table with the same infiltration rate as during operation. By continuing infiltration after the 

wastes are removed, the infiltration through the surface impoundment unit can be modeled as a 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-27 

single steady-state flow regime until concentrations in groundwater are no longer affected by 

constituents released from the surface impoundment during its operation. 

The EPACMTP surface impoundment model assumes a square footprint and a constant 

ponding depth during the impoundment’s operational life (Figure 3-5). For an unlined 

impoundment, the model assumes that while the impoundment is in operation, a consolidated 

layer of sediment accumulates at the bottom of the impoundment. The leakage (infiltration) rate 

through the unlined impoundment is a function of the ponding depth in the impoundment and the 

thickness and effective permeability of the consolidated sediment layer at the bottom of the 

impoundment. The rate of leakage is constrained to ensure that there is not a physically 

unrealistic high rate of leakage, which would cause groundwater mounding beneath the unit to 

rise above the ground surface. Underlying the assumption of a constant ponding depth, the 

surface impoundment source-term model assumes that wastewater in the impoundment is 

continually replenished while the impoundment is in operation. It also assumes, from the 

beginning of the unit’s operation, that the sediment is always in equilibrium with the wastewater 

(i.e., the presence of sediment does not alter the concentration of leachate). Accordingly, the 

surface impoundment source-term model also assumes that the leachate concentration is constant 

during the impoundment operational life and equal to the concentration in the porewater in the 

sediments at the bottom of the impoundment. 

Figure 3-5. Schematic cross-section view of surface impoundment. 

Three liner scenarios were modeled: a no-liner (unlined) scenario, a compacted clay liner, 

and a composite liner. 

In the unlined scenario, wastewater is placed directly on local soils and the depth of water 

is constant over the entire life of the impoundment, pre- and post-closure. As described above, 

sediments accumulate and consolidate at the bottom of the impoundment and migrate into the 

underlying native soils, where they clog pore spaces and provide some barrier to flow. The 

surface impoundment model assumes that the thickness of the consolidated sediments is equal to 

one-half of the total sediment thickness, which is an input to the model. The sediment thickness 

was assumed to be 0.2 m for all simulations. The model also assumes that the thickness of the 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-28 

clogged region of native soils is always 0.5 m and has a hydraulic conductivity 10 percent of that 

of the native soil underlying the impoundment. 

In the clay liner scenario, wastewater is placed on a compacted clay liner, which is 

installed on the local soils. The assumptions for an unlined impoundment also apply to the 

compacted clay liner scenario, except that a compacted clay liner filters out the sediments that 

clog the native soils in the unlined case, so the effect of clogging the native materials is not 

included in the calculation of the infiltration rate. The thickness of the compacted clay liner was 

assumed to be 3 feet and the hydraulic conductivity was assumed to be 1x10-7 cm/sec (U.S. EPA, 

1988c). 

In the composite liner scenario, wastewater is placed on a synthetic membrane with an 

underlying geosynthetic or natural compacted clay liner with a hydraulic conductivity of 1x10-7

cm/sec. The membrane liner was assumed to have a number of pinhole leaks of uniform size 

(6 mm2). The distribution of leak densities (expressed as number of leaks per hectare) was 

compiled from 26 leak density values reported in TetraTech (2001), the best available data on 

liner leaks. These leak densities are based on liners installed with formal construction quality 

assurance programs. The 26 sites with leak density data are mostly located outside the United 

States: 3 in Canada, 7 in France, 14 in the United Kingdom, and 2 in unknown locations; EPA 

assumed that these are representative of U.S. conditions. The WMUs at these sites (8 landfills, 4 

surface impoundments, and 14 of unknown type) are underlain by a layer of geomembrane with 

a thickness varying from 1.14 mm to 3 mm. The majority of the geomembranes (23 of 26) are 

made from HDPE, and the remaining 3 are made from prefabricated bituminous geomembrane 

or polypropylene. One of the sites has a layer of compacted clay liner beneath the geomembrane; 

however, for 25 of the 26 sites, material types below the geomembrane layer are not reported. 

The empirical distribution used in the analysis can be found in IWEM (U.S. EPA, 2002b), along 

with a table showing details about the 26 liners used to develop the distribution.

3.5.2 Modeling Approach and Assumptions 

Figure 3-5 illustrates a compartmentalized surface impoundment with stratified sediment. 

Shown in the figure are the liquid compartment, the sediment compartment (with loose and 

consolidated sediments), and the unsaturated zone (with clogged and unaffected native 

materials). The model assumes that all sediment layer thicknesses remain unchanged throughout 

the life of the unit. 

The EPACMTP surface impoundment model uses the unsaturated zone flow model to 

calculate the infiltration rate out of the bottom of the impoundment. This model is designed to 

simulate steady-state downward flow through an unsaturated (vadose) zone consisting of one or 

more soil layers. Steady-state means that the rate of flow does not change with time. In the case 

of flow out of an unlined surface impoundment, the model simulates flow through a system 

consisting of three layers: a consolidated sediment layer, a clogged soil layer, and a native soil 

layer. 

The native unsaturated soil extends downward to the water table. The steady-state 

infiltration rate out of the surface impoundment is driven by the head gradient between the water 

ponded in the impoundment and the head at the water table. The pressure head at the top of the 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-29 

consolidated sediment layer is equal to the water depth in the impoundment plus the thickness of 

the unconsolidated sediment. 

The EPACMTP Technical Background Document (U.S. EPA, 2003c) describes the 

algorithms used in this model to calculate the infiltration rate from surface impoundment units, 

and discusses in detail the maximum allowable infiltration rate based on the groundwater 

mounding condition. This information is summarized here. 

The EPACMTP surface impoundment source-term model calculates infiltration through 

the accumulated sediment at the bottom of an impoundment, accounting for clogging of the 

native soil materials underlying the impoundment, liner conditions, and mounding due to 

infiltration. The modeled infiltration is governed by the depth of liquid in the impoundment and 

the following limiting factors: 

 Effective hydraulic conductivity and thickness of the consolidated sediment layer.

As sediment accumulates at the base of the impoundment, the weight of the liquid and 

upper sediments tends to compress (or consolidate) the lower sediments. The 

consolidation process reduces the hydraulic conductivity of the sediment layer, and the 

layer of consolidated sediment will act as a restricting layer for flow out of the 

impoundment. By contrast, the layer of loose, unconsolidated sediment that overlies the 

consolidated sediment layer is assumed not to restrict the flow rate out of the unit, so it is 

not explicitly considered in the surface impoundment flow model. 

 Effective hydraulic conductivity of the clogged native material. As liquids infiltrate 

soil underlying the impoundment, suspended particulate matter accumulates in the soil 

pore spaces, reducing hydraulic conductivity and lowering infiltration rates. 

 Effective hydraulic conductivity and thickness of a clay liner. When the surface 

impoundment is underlain by a compacted clay liner, the rate of infiltration is also 

determined by simulating flow through a three-layer system, substituting the 

characteristics of the clay liner for those of the clogged soil layer. 

 Leak rate of a composite liner. For cases where the surface impoundment is underlain 

by a composite liner (a geomembrane underlain by a low permeability liner such as a 

compacted clay liner or a geosynthetic clay liner), the surface impoundment source-term 

model uses a modified equation of Bonaparte et al. (1989) to calculate the infiltration 

rate. The equation uses, among other inputs, the head generated by the water and 

unconsolidated sediments in the unit, a leak density selected from an empirical 

distribution derived from a TetraTech (2001) study of liner leakage, a uniform leak size 

of 6 mm2

, and an assumed hydraulic conductivity of 1x10-7 cm/sec for the 3 feet of 

underlying compacted clay material. 

 Limitations on maximum infiltration rate from mounding. If the calculated 

infiltration rate exceeds the rate at which the saturated zone can transport the 

groundwater, the groundwater level will rise into the unsaturated zone. The model 

accounts for groundwater mounding when calculating the infiltration rate from the 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-30 

surface impoundment unit and, if necessary, constrains the value to ensure that the 

groundwater mound does not rise to the bottom of the surface impoundment unit. 

3.5.3 Surface Impoundment Model Input Parameters 

Input parameters required by the surface impoundment source-term model are discussed 

below. Additional details on how data for these inputs were collected for the CCW risk 

assessment are provided in Appendix A for waste concentrations and Appendix B for surface 

impoundment dimensions and characteristics. 

 Surface Impoundment Area. The model uses surface impoundment area to determine 

the area over which infiltration occurs. CCW surface impoundment area data were 

obtained from the EPRI comanagement survey (EPRI, 1997). The impoundment was 

assumed to be square. 

 Areal Infiltration Rate. The surface impoundment leachate infiltration rate (or flux) is 

computed internally by the surface impoundment source-term model, as described in 

Section 3.5.2. 

 Depth Below Grade. The depth of the bottom of the impoundment below the 

surrounding ground surface is used, along with depth to groundwater, to determine the 

thickness of the unsaturated zone beneath the impoundment. For CCW impoundments, 

depth below grade was sampled from an empirical distribution based on available 

measurements from a number of CCW surface impoundments (see Appendix B). 

 Operating Depth. The operating (or ponding) depth is the long-term average depth of 

wastewater and sediment in the impoundment, measured from the base of the 

impoundment. For CCW surface impoundments, depth was estimated by dividing 

impoundment capacity by impoundment area. CCW impoundment capacity data were 

taken from the EPRI comanagement survey (EPRI, 1997). 

 Total Thickness of Sediment. By default, EPACMTP models unlined surface 

impoundments with a layer of “sludge” or sediment above the base of the unit. The 

sediment layer is divided into two sublayers: an upper, loose sediment sublayer and a 

lower, consolidated sediment sublayer. The consolidated sediment has a relatively low 

hydraulic conductivity and acts to impede flow. The calculated infiltration rate is 

inversely related to the thickness of the consolidated sediment sublayer. A thinner 

consolidated sediment layer will result in a higher infiltration rate and a greater rate of 

constituent loss from the impoundment. The surface impoundment source-term model 

uses the total sediment thickness as an input parameter and assumes that it consists of 

equal thicknesses of loose and consolidated material. Because data were not available on 

CCW sediment layer thicknesses, the CCW risk assessment used the Tier 1 IWEM model 

assumption: a total (unconsolidated plus consolidated) sediment layer thickness of 0.2 

meters (U.S. EPA, 2002b). It is not known how representative this assumption is with 

respect to unlined CCW surface impoundments, but it is reasonable to assume that a 

sediment layer would accumulate and restrict flow from the bottom of a CCW 

impoundment.
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-31 

 Distance to the Nearest Surface Water Body. The distance to the nearest waterbody is 

used to determine the location of a fully penetrating surface waterbody at which 

groundwater mass and water fluxes will be calculated and reported. The distance to the 

nearest surface waterbody is also used as a surrogate for the distance to the nearest point 

at which the water table elevation is kept at a fixed value. That distance is used to 

calculate the estimated height of groundwater mounding underneath the impoundment to 

ensure that excessively high infiltration rates, which may be calculated for deep, unlined 

impoundments, do not occur. If necessary, the model reduces the infiltration rate to 

ensure the predicted water table does not rise above the ground surface. For the CCW 

sites, distance to surface water was sampled from an empirical distribution developed 

from aerial photo measurements at 59 coal-fired power plants with onsite landfills or 

surface impoundments (Appendix C). 

 Leachate Concentration. The annual average leachate concentration is modeled as a 

constant concentration pulse with a defined duration. For a particular model run, the 

leachate concentration was assumed to be constant during the operation of the unit; there 

is no reduction in leachate concentration until the impoundment ceases operation. 

Leachate concentrations for CCW impoundments were obtained by waste type from 

surface impoundment porewater data from EPA’s CCW Constituent Database, as 

described in Appendix A. 

 Source Leaching Duration. For surface impoundments, the addition and removal of 

waste during the operational life period are more or less balanced, without significant net 

accumulation of waste. In the finite-source implementation used for CCW surface 

impoundments, the duration of the leaching period is assumed to be the same as the 

operational life of the surface impoundment. Based on industry data (see Appendix B) 

for CCW surface impoundments, EPA used a high-end (90th percentile) fixed surface 

impoundment operating life of 75 years. A high-end value was appropriate because CCW 

surface impoundments are typically closed with waste in place, while the surface 

impoundment source-term model assumes clean closure (waste removed). In addition, 

operating life is not a particularly sensitive parameter in this analysis: the difference 

between the 50th percentile value (40 years) and the 90th percentile value used (75 years) 

is less than a factor of two. 

 Liner Type, Thickness, Hydraulic Conductivity, and Leak Density. The type of liner 

is used to calculate leachate flux from the impoundment. To assign one of the three liner 

scenarios to each facility in the EPRI survey (EPRI, 1997), EPA used the same crosswalk 

as for landfills (see Table 3-7). Attachment B-2 to Appendix B provides these 

assignments, along with the original EPRI liner type, for each CCW surface 

impoundment modeled. 

As with IWEM (U.S. EPA, 2002b), clay liners were assumed to be 3 feet thick and to 

have a constant hydraulic conductivity of 10-7 cm/s, reflecting typical design specifications for 

clay liners. For composite liners, infiltration was assumed to result from defects (pin holes) in 

the geomembrane. The pin holes were assumed to be circular and uniformly sized (6 mm2

). The 

leak density was defined as the average number of circular pin holes per square meter and was 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-32 

obtained from a study of industrial surface impoundment membrane liner leak rates by Tetra 

Tech (2001). 

3.5.4 Surface Impoundment Model Outputs 

For each year in the simulation, the surface impoundment source-term model uses the 

average annual leachate concentration and calculates an infiltration rate to estimate the 

constituent flux through the bottom of the impoundment. This time series was used as an input to 

the EPACMTP unsaturated zone model. 

3.6 Groundwater Model 

This section describes the methodology and the models that were used to predict the fate 

and transport of chemical constituents in soil and groundwater to determine impacts on drinking 

water wells and surface water that is connected to groundwater. The surface water model used to 

address the groundwater-to-surface water pathways is described in Section 3.7. 

3.6.1 Conceptual Model 

The groundwater pathway was modeled to determine the receptor well concentrations 

and contaminant flux to surface water resulting from the release of waste constituents from a 

WMU. The release of a constituent occurs when liquid percolating through the WMU becomes 

leachate as it infiltrates from the bottom of the WMU into the subsurface. For landfills, the liquid 

percolating through the landfill is from water in the waste and precipitation. For surface 

impoundments, the percolating liquid is primarily the wastewater managed in the impoundments. 

Waste constituents dissolved in the leachate are transported through the unsaturated zone 

(the soil layer under the WMU) to the underlying saturated zone (i.e., groundwater). Once in the 

groundwater, contaminants are transported downgradient to a hypothetical receptor well or 

waterbody. For this analysis, the groundwater concentration was evaluated for two receptor 

locations, each at a specified distance from the downgradient edge of the WMU: 

 The intake point of a hypothetical residential drinking water well (the receptor well), 

which was used for the residential drinking water pathway 

 A nearby river, stream, or lake, which is modeled as a fully penetrating surface 

waterbody and was used for the fish ingestion and ecological pathways. 

Figure 3-6 shows the conceptual model of the groundwater fate and transport of contaminant 

releases from a WMU to a downgradient receptor well. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-33 

Figure 3-6. Conceptual model of the groundwater modeling scenario. 

3.6.2 Modeling Approach and Assumptions 

The transport of leachate from the WMU through the unsaturated and saturated zones 

was modeled using EPACMTP (U.S. EPA, 1996a, 1997a, 2003a,c,d). EPACMTP is a composite 

model consisting of two coupled modules: (1) a one-dimensional module that simulates 

infiltration and dissolved contaminant transport through unsaturated soils, and (2) a 

3-dimensional saturated zone flow and transport module to model groundwater fate and 

transport. EPACMTP has been used by EPA to make regulatory decisions for wastes managed in 

land disposal units (including landfills and surface impoundments) for a number of solid waste 

and hazardous waste regulatory efforts, and as noted earlier, has undergone extensive peer 

review. EPACMTP simulates the concentration arriving at a specified receptor location (such as 

a well or stream). 

The primary subsurface transport mechanisms modeled by EPACMTP are (1) downward 

(1-dimensional) movement along with infiltrating water flow in the unsaturated zone soils and 

(2) movement and dispersion along with ambient groundwater flow in the saturated zone. 

EPACMTP models soils and aquifer as uniform porous media and does not account for 

preferential pathways such as fractures and macropores or for facilitated transport, which may 

affect migration of strongly sorbing constituents such as metals. 

In the unsaturated zone, flow is gravity driven and prevails in the downward direction. 

Therefore, the flow is modeled in the unsaturated zone as one-dimensional in the vertical 

direction. The model also assumes that transverse (sideways) dispersion (from both mechanical 

and molecular diffusion processes) is negligible in the unsaturated zone because the scale of 

lateral migration due to transverse dispersion is negligible compared with the size of the WMUs. 

This assumption is also environmentally protective because it allows the leading front of the 

contaminant plume to arrive at the water table with greater peak concentration in the case of a 

finite source. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-34 

In the saturated zone, the EPACMTP model assumes that movement of chemicals is 

driven primarily by ambient groundwater flow, which in turn is controlled by a regional 

hydraulic gradient and hydraulic conductivity in the aquifer formation. The model does take into 

account the effects of infiltration through the WMU, as well as regional recharge into the aquifer 

around the WMU. Infiltration through the WMU increases the groundwater flow in all directions 

under and near the WMU and may result in groundwater mounding. This 3-dimensional flow 

pattern enhances the horizontal and vertical spreading of the contaminant plume. The effect of 

recharge (outside the WMU) is to cause a downward (vertical) movement of the contaminant 

plume as it travels along groundwater flow direction. In addition to advective movement with the 

groundwater flow, the model simulates mixing of contaminants with groundwater due to 

hydrodynamic dispersion, which acts along the groundwater flow direction, as well as vertically 

and in the horizontal transverse direction. 

To model sorption of CCW constituents in the unsaturated zone, soil-water partitioning 

coefficients (Kd values) for metal constituents were selected from nonlinear sorption isotherms 

generated from the equilibrium geochemical speciation model MINTEQA2 (U.S. EPA, 2001a). 

Chemicals with low Kd values will have low retardation factors, which means that they will 

move at nearly the same velocity as the groundwater. Chemicals with high Kd values will have 

high retardation factors and may move many times slower than groundwater. As described in 

Appendix D, CCW-specific partition coefficients were developed with MINTEQA2 considering 

CCW leachate chemistry, including the highly alkaline chemistries that are characteristic of 

some CCWs. Although a complete listing of all Kd values available in the MINTEQA2 isotherms 

used in these analyses would not be practicable, Table D-1 presents a sampling of the Kd values 

used. 

MINTEQA2 is a product of ORD, and like EPACMTP, has a long history of peer- and 

SAB-review during its development, use, and continued improvement for regulatory support 

over the past two decades. These reviews largely focused on the use of MINTEQA2 to generate 

sorption isotherms for metals for EPACMTP, which is how it was used in the CCW risk 

assessment. Two of the more recent peer reviews include one for application within the 3MRA 

model (U.S. EPA, 1999d) and a review of its use and application to RCRA rulemaking and 

guidance support, including revisions made to the model to support IWEM and the CCW 

rulemaking efforts (U.S. EPA, 2003f). In the latter review, three experts found that the revisions 

made to the MINTEQA2 model were appropriate, but also suggested further improvements in 

how the model addresses environments with highly alkaline leachate (such as CCW sites). As 

explained in Appendix D, these comments were addressed in this application of MINTEQA2 to 

CCW waste transport by the development of sorption isotherms that are specific to geochemical 

conditions encountered in CCW landfills and surface impoundments. 

3.6.3 Model Inputs and Receptor Locations 

EPACMTP requires information about soil and aquifer properties as model inputs. For 

soils, EPACMTP uses soil texture to generate consistent hydrological properties for the 

unsaturated zone model, and soil pH and organic matter to select appropriate sorption 

coefficients to model contaminant sorption in the soil. As described in Appendix C, 

Attachment C-2, site-specific soil texture, pH, and organic carbon data were collected around 

each site from the STATSGO soils database. Similarly, the hydrogeological setting around each 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-35 

WMU was used to select appropriate aquifer conditions from EPACMTP’s Hydrogeologic 

Database (HGDB; see Appendix C). 

Recharge is water percolating through the soil to the aquifer outside the footprint of the 

WMU. The recharge rate is determined by precipitation and soil texture. For the CCW landfills 

and surface impoundments, recharge rates were selected by soil texture and meteorological 

station assignment from a database of HELP model–derived recharge rates for climate stations 

across the country that is included in the EPACMTP input files. Further details about how these 

rates were determined and other options for determining recharge rates outside of the EPACMTP 

model can be found in the EPACMTP Parameters/Data Background Document (U.S. EPA, 

2003a). 

One of the most important inputs for EPACMTP is receptor location, which for this risk 

assessment includes residential drinking water wells and surface water bodies. Figure 3-7 shows 

a schematic of how residential well drinking water intakes were defined in terms of their radial 

downgradient distance from the WMU and the angle off the contaminant plume centerline. The 

shaded areas in Figure 3-7 represent the horizontal extent of the contaminant plume. 

Figure 3-7. Schematic plan view showing idealized maximum lateral 

contaminant plume extent and receptor well location. 

In this analysis, receptor wells were located randomly within the contaminant plume, as 

follows: 

 Because residential well distance data are not available for CCW WMUs, EPA based the 

radial downgradient distance on a nationwide distribution of the nearest downgradient 

residential or municipal wells from a survey of Subtitle D municipal solid waste landfills 

(U.S. EPA, 1988a; see Appendix C). The maximum radial distance in this survey was 1 

mile. EPA believes that this distribution is protective of CCW WMUs. A well distance, 

(Rrw in Figure 3-7) was randomly selected from this distribution. 

 The angle off the contaminant plume centerline (θrw in Figure 3-7) was selected from a 

uniform distribution ranging from 0 to 90 degrees. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-36 

 The receptor well was located based on Rrw and θrw as shown in Figure 3-7. 

 The maximum lateral extent of a groundwater plume, based on lateral dispersion, was 

calculated using the dimensions of the WMU sampled for that simulation, a sampled 

value for lateral dispersivity in the groundwater, and the downgradient distance to the 

receptor well. 

 If the receptor well was located inside the idealized maximum plume extent, the shaded 

portion in Figure 3-7 (the distance from the well to the centerline was less than the lateral 

extent of the calculated in the previous step), the well location was used for that 

simulation. Otherwise, new values for Rrw and θrw were sampled and the process repeated 

for the same WMU. The depth of the well intake point was based on a uniform 

distribution with limits of 0 (i.e., well at the water table) to 10 meters (or the total 

saturated aquifer thickness if the aquifer is less than 10 meters thick). 

The location of the surface waterbody intercepting groundwater flow was specified for 

each flow and transport simulation. The waterbody was constrained to lie across the contaminant 

plume centerline and perpendicular to the groundwater flow direction. The waterbody is 

assumed to fully penetrate the aquifer thickness. Downgradient distance to the surface waterbody 

was determined from an empirical distribution of distances measured for CCW landfills and 

surface impoundments (see Appendix C), which was randomly sampled to develop the distances 

used in EPACMTP to calculate groundwater concentrations at those distances in the Monte 

Carlo analysis. 

3.6.4 Groundwater Model Outputs 

The output of EPACMTP is a prediction of the contaminant concentration arriving at a 

downgradient groundwater receptor location (either a well or a surface water body). Because a 

finite-source scenario was used, the concentration is time-dependent. A maximum time-averaged 

concentration was calculated for each constituent across the exposure duration selected in each 

Monte Carlo iteration. 

3.7 Surface Water Models 

For the groundwater-to-surface-water pathway, chemical contaminants leach out of 

WMUs and into groundwater, and this contaminated groundwater then discharges into a surface 

waterbody through groundwater discharge. Once in the waterbody, the continued fate and 

transport of the contaminants is modeled with a surface water model, which uniformly mixes the 

contaminants in a single stream segment. Surface water flows in and out of the stream segment. 

Surface water flowing into the stream segment is assumed to have zero constituent 

concentration, and surface water flowing out has nonzero constituent concentrations due to the 

groundwater contamination. The primary simplifying assumptions in EPACMTP are as follows: 

(1) the groundwater–surface water interface is assumed to be perpendicular to the regional 

groundwater flow direction; (2) the interface is infinite in its lateral extent so as to intercept the 

entire width of the dissolved contaminant plume; and (3) the intercepting surface water body 

fully penetrates the saturated region of the subsurface. Therefore, all of the mass in the 

contaminated groundwater is available to be transferred to the surface water model. If stream 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-37 

flow is greater than the available groundwater flow, then all of the mass available in the 

groundwater is assumed to be transferred to the surface waterbody. It is important to note that 

while a mass transfer is assumed to take place between the two systems, mass is not actually 

removed from the groundwater—it is still available to be observed at a receptor well placed 

beyond the groundwater-surface water interface. 

To ensure that an unrealistic transfer of mass from the contaminated groundwater into the 

surface waterbody does not occur, the available groundwater flow is compared to the stream 

flow. If the groundwater flow exceeds the stream flow, all of the stream flow is assumed to be 

from groundwater discharge and the total concentration in the stream is equal to the groundwater 

concentration. 

The waterbody considered in the CCW risk assessment is a river, stream, or lake located 

downgradient of the WMU. As described in Appendix C, the flow characteristics and 

dimensions for this waterbody were determined by site-specific stream flow data, the width of 

the groundwater contaminant plume as it intersects the waterbody, and established relationships 

between flow and stream depth. The stream segment modeled in this assessment was assumed to 

be homogeneously mixed. 

Simple equilibrium partitioning models were used to estimate contaminant concentrations 

in the water column, suspended and bed sediments (see Section 3.7.1), and aquatic organisms 

(see Section 3.7.2). Special modeling provisions for aluminum are described in Section 3.7.3. 

3.7.1 Equilibrium Partitioning Model 

The primary surface water model used to estimate groundwater impacts on waterbodies is 

a simple steady-state equilibrium-partitioning model adapted from models in EPA’s Indirect 

Exposure Methodology (IEM; U.S. EPA, 1998c) and Human Health Risk Assessment Protocol 

(HHRAP; U.S. EPA, 1998d). This model is based on the concept that dissolved and sorbed 

concentrations can be related through equilibrium partitioning coefficients. This model was used 

for all constituents except aluminum, which was modeled based on a solubility approach (see 

Section 3.7.3). Although these models have not been specifically peer reviewed in this 

application, they have been subject to the Agency’s peer review process as part of the 

development of the IEM and HHRAP. 

The model partitions the total mass of chemical contaminant in the waterbody into four 

compartments: 

 Constituents dissolved in the water column 

 Constituents sorbed onto suspended solids 

 Constituents sorbed onto sediment particles at the bottom of the waterbody 

 Constituents dissolved in porewater in the sediment layer. 

Table 3-9 provides the partitioning coefficients used by the surface water model to estimate 

contaminant partitioning between water and suspended solids in the water column and between 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-38 

sediment and porewater in the sediment layer. These distributions were derived from published 

empirical data as described in U.S. EPA (1999b). 

Table 3-9. Sediment/Water Partition Coefficients: Empirical Distributionsa

Chemical 

Distribution 

Type Minimum Mean Maximum SD 

Aluminum not used 

Antimony log normal 0.6 3.6 4.8 1.8 

Arsenic log normal 1.6 2.4 4.3 0.7 

Barium log normal 0.9 2.5 3.2 0.8 

Boron log normal !0.5 0.8 1.4 0.5 

Cadmium log normal 0.5 3.3 7.3 1.8 

Cobalt log normal 2.2 3.9 5.3 0.8 

Lead log normal 2.0 4.6 7.0 1.9 

Molybdenum log normal 1.3 2.2 3.2 0.9 

Selenium IV log normal 1.0 3.6 4.0 1.2 

Selenium VI log normal !1.4 0.6 3.0 1.2 

Thallium log normal !0.5 1.3 3.5 1.1 

Total Nitrate Nitrogen constant 0 0 0 0 

Source: U.S. EPA (1999b). 

SD = standard deviation. 

a

 All values are log values. 

Following calculation of the constituent loading and loss rates, the surface water model 

estimates steady-state, equilibrium waterbody contaminant concentrations in each compartment 

using equations presented in Attachment E-1 to Appendix E. For evaluating risks to human 

health from fish consumption, the model calculates waterbody concentrations using groundwater 

loadings that are explicitly averaged over the exposure period for the each human receptor (i.e., 

adult and child fishers). These average waterbody concentrations are then used to calculate fish 

concentrations as described in Section 3.7.2. Ecological risks were based on waterbody 

concentrations calculated using the peak annual groundwater loading value from EPACMTP. 

The equilibrium–partitioning model, as implemented, is conservative because there are no loss 

mechanisms (e.g., burial) for any of the constituents. 

3.7.2 Aquatic Food Web Model 

An aquatic food web model was used to estimate the concentration of CCW constituents 

that accumulate in fish. This risk assessment assumed that fish are a food source for a 

recreational fisher. Trophic level three (TL3) and four (TL4) fish12 were considered in this 

analysis because most of the fish that humans eat are T4 fish (e.g., salmon, trout, walleye, bass) 

and medium to large T3 fish (e.g., carp, smelt, perch, catfish, sucker, bullhead, sauger). The 

aquatic food web model has been peer reviewed as part of the 3MRA model development effort 

(see http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/peer03/aquatic/aqtfoods.pdf). 

 

12 TL3 fish are those that consume invertebrates and plankton; TL4 fish are those that consume other fish. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-39 

The aquatic food web model calculates the concentration in fish from the concentration 

calculated for the waterbody downgradient from the CCW disposal site. The contaminants in the 

water column consist of dissolved constituents and constituents sorbed to suspended solids. For 

all constituents, the contaminant concentrations in fish were calculated from the total waterbody 

concentration (i.e., dissolved plus sorbed to suspended solids) using BCFs, which are presented 

in Table 3-10. The equations used to model fish tissue concentrations are provided in 

Attachment E-2 to Appendix E. 

Table 3-10. Bioconcentration Factors for Fish 

CAS Chemical TL3 Value TL4 Value Units Reference 

7429-90-5 Aluminum ND ND L/kg 

7440-36-0 Antimony 0 0 L/kg Barrows et al. (1980) 

22569-72-8 Arsenic (III) 4.0E+00 4.0E+00 L/kg Barrows et al. (1980) 

15584-04-0 Arsenic (V) 4.0E+00 4.0E+00 L/kg Barrows et al. (1980) 

7440-39-3 Barium ND ND L/kg 

7440-42-8 Boron ND ND L/kg 

7440-43-9 Cadmium 2.7E+02 2.7E+02 L/kg Kumada et al. (1972) 

7440-48-4 Cobalt ND ND L/kg 

7439-92-1 Lead 4.6E+01 4.6E+01 L/kg Stephan (1993) 

7439-98-7 Molybdenum 4.0E+00 4.0E+00 L/kg Eisler (1989) 

10026-03-6 Selenium (IV) 4.9E+02 1.7E+03 L/kg Lemly (1985) 

7782-49-2 Selenium (VI) 4.9E+02 1.7E+03 L/kg Lemly (1985) 

7440-28-0 Thallium 3.4E+01 1.3E+02 L/kg T3: Barrows et al. (1980) 

T4: Stephan (1993) 

14797-55-8 Total Nitrate Nitrogen ND ND L/kg 

ND = No Data. Fish concentrations were not calculated for constituents with no BCF data. 

3.7.3 Aluminum Precipitation Model 

Aluminum is generally solubility limited in natural waters; therefore, a simple 

precipitation model was used for aluminum in lieu of the equilibrium-partitioning model. The 

MINTEQA2 model was used to estimate total soluble aluminum concentrations as a function of 

pH for a typical surface waterbody (Stumm and Morgan, 1996; Drever, 1988). By assuming the 

common aluminum silicate mineral gibbsite was the equilibrium solid phase, the computed 

values of total dissolved aluminum were interpreted as the maximum expected for each pH. If 

more aluminum were added to the system, it would be expected to precipitate as the mineral 

gibbsite for the system to maintain equilibrium. Table 3-11 shows the maximum dissolved 

aluminum concentrations as a function of waterbody pH. 

The precipitation model initially calculates the aluminum concentration in the surface 

water column by assuming that all aluminum in the groundwater flux is dissolved. If this 

concentration exceeds the maximum soluble concentration based on pH, the dissolved 

concentration is capped and the excess aluminum is assumed to precipitate as the mineral 

gibbsite and settle to the benthic sediment layer. The equations used in this model are presented 

in Appendix E. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-40 

Table 3-11. Aluminum Solubility as a Function of Waterbody pHa

Minimum pH Maximum pH Solubility (mg/L) 

3.5 4.5 26.2 

4.5 5 1.84 

5 5.5 0.196 

5.5 6 0.0112 

6 6.5 0.00143 

6.5 7 0.000662 

7 7.5 0.000915 

7.5 8 0.00229 

8 8.5 0.00682 

8.5 9 0.0212 

9 9.5 0.0666 

9.5 10 0.211 

10 10.5 0.668 a Computed using MINTEQA2 

Only the water column concentration for aluminum was used in subsequent exposure and 

risk calculations, because there is no available ecological benchmark for aluminum in sediment. 

The water column concentration was used to calculate human exposure via drinking water 

ingestion, as well as risk to ecological receptors exposed via direct contact. 

3.8 Human Exposure Assessment 

The human exposure component of the full-scale analysis assessed the magnitude, 

frequency, duration, and route of exposure to CCW contaminants that an individual may 

experience. The term “exposure,” as defined by the EPA exposure guidelines (U.S. EPA, 1992), 

as the condition that occurs when a contaminant comes into contact with the outer boundary of 

the body. The exposure of an individual to a contaminant completes an exposure pathway (i.e., 

the course a constituent takes from the WMU to an exposed individual). Once the body is 

exposed, the constituent can cross the outer boundary and enter the body. The amount of 

contaminant that crosses and is available for adsorption at internal exchange boundaries is 

referred to as the “dose” (U.S. EPA, 1992). 

This risk assessment evaluated the risk from CCW contaminants to receptors in the 

vicinity of a WMU. The individuals evaluated were those residents closest to the WMU. The 

distances from the WMU to the residents were taken from a distribution of distances to the 

nearest residential drinking water well measured for municipal landfills and, for the recreational 

fisher, a distribution of the distance of the nearest surface water body from CCW landfills and 

surface impoundments (see Appendix C). 

Section 3.8.1 presents an overview of the receptors and selected exposure pathways 

considered for this assessment, including a discussion of how childhood exposure was 

considered in the analysis. Section 3.8.2 presents exposure factors (i.e., values needed to 

calculate human exposure) used in the analysis. Section 3.8.3 describes the methods used to 

estimate dose, including average daily dose (ADD) and lifetime average daily dose (LADD). 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-41 

3.8.1 Receptors and Exposure Pathways 

Human receptors may come into contact with constituents present in environmental 

media through a variety of pathways. The exposure pathways considered in the full-scale 

analysis were ingestion of drinking water from contaminated groundwater sources and ingestion 

of fish from surface water contaminated by groundwater. 

 Ingestion of Drinking Water. Groundwater from an offsite well was assumed to be used 

for drinking water for residents (adult and child). 

 Ingestion of Fish. Fish are exposed to constituents via uptake of contaminants from 

surface water. Adult recreational fishers and their children were assumed to consume fish 

caught in local waterbodies contaminated by CCW constituents through the groundwaterto-surface-water pathway. EPA considers this assumption to be reasonable and protective 

for fishers relying on locally caught fish as a food source. 

Table 3-12 lists each human receptor type considered in this analysis along with the specific 

exposure pathways that apply to that receptor. Both adult and child residents are exposed by 

drinking groundwater, and adult fishers and their children are exposed by eating fish caught in 

streams and lakes impacted by CCW. 

Table 3-12. Receptors and Exposure Pathways 

Receptor

Ingestion of 

Drinking Water

Ingestion of 

Fish

Adult resident T

Child resident T

Adult recreational fisher T

Child of recreational fisher T

Children are an important subpopulation to consider in a risk assessment because they 

may be more sensitive to exposures than adults. Compared with adults, children may eat more 

food and drink more fluids per unit of body weight. This higher intake-rate-to-body-weight ratio 

can result in a higher ADD for children than adults. 

As children mature, their physical characteristics and behavior patterns change. To 

capture these changes in the analysis, the life of a child was considered in stages represented by 

the following cohorts: cohort 1 (ages 1 to 5), cohort 2 (ages 6 to 11), cohort 3 (ages 12 to 19), 

and cohort 4 (ages 20 to 70). Associated with each cohort are distributions of exposure 

parameters that reflect the physical characteristics and behavior patterns of that age range. These 

exposure parameters are required to calculate exposure to an individual. The distributions for the 

20- to 70-year-old cohort were the same as those used for adult receptors. 

To capture the higher intake-rate-to-body-weight ratio of children, a start age of 1 year 

was selected for the child receptors. The exposure duration distribution for cohort 1 (a 1- to 5-

year-old) was used to define exposure duration for the child receptors for each of the 10,000 

iterations in the probabilistic analysis. For each individual iteration, the child receptor was aged 

through the age cohorts as appropriate until the age corresponding to the selected exposure 

duration was reached (e.g., if an exposure duration of 25 years was selected for an iteration, the 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-42 

child was aged from 1 year to 25 years, spending 5 years in cohort 1, 6 years in cohort 2, 8 years 

in cohort 3, and 6 years in cohort 4, for a total of 25 years). 

3.8.2 Exposure Factors 

The exposure factors used in the risk assessment are listed in Table 3-13, along with their 

data sources and variable type (i.e., whether they were represented as a distribution or a fixed 

value in the Monte Carlo analysis). These exposure factors were used to calculate the dose of a 

chemical based on contact with contaminated media or food, the duration of that contact, and the 

body weight of the exposed individuals. 

Table 3-13. Human Exposure Factor Input Parameters and Data Sources 

Parameter Variable Type Data Source

Body weight (adult, child) Distribution U.S. EPA (1997c) 

Ingestion rate: fish (adult, child) Distribution U.S. EPA (1997d) 

Ingestion rate: drinking water (adult, child) Distribution U.S. EPA (1997c) 

Exposure duration (adult, child) Distribution U.S. EPA (1997e) 

Exposure frequency (adult, child) Fixed (constant) U.S. EPA policy 

Fraction contaminated: drinking water Fixed (constant) U.S. EPA policy 

Fraction contaminated: fish Fixed (constant) U.S. EPA policy 

Fraction of TL3 fish consumed Fixed (constant) U.S. EPA (1997d) 

Fraction of TL4 fish consumed Fixed (constant) U.S. EPA (1997d) 

Human lifetime (used in carcinogenic risk calculation) Fixed (constant) U.S. EPA policy 

The primary data source of human exposure model inputs used in this risk assessment 

was EPA’s Exposure Factors Handbook (EFH; U.S. EPA, 1997c-e). The EFH summarizes data 

on human behaviors and characteristics related to human exposure from relevant key studies and 

provides recommendations and associated confidence estimates on the values of exposure 

factors. These data were carefully reviewed and evaluated for quality before being included in 

the EFH. EPA’s evaluation criteria included peer review, reproducibility, pertinence to the 

United States, currency, adequacy of the data collection period, validity of the approach, 

representativeness of the population, characterization of variability, lack of bias in study design, 

and measurement error (U.S. EPA, 1997c-e). For exposure factors that were varied in the Monte 

Carlo analysis, probability distribution functions were developed from the values in the EFH. 

The data sources and assumptions for intake and other human exposure factors used in 

this analysis are described below. Appendix F presents the exposure factors used and describes 

the rationale and data used to select the form of the distributions (e.g., normal, lognormal, 

gamma, Weibull) for those exposure factors that were varied in the probabilistic analysis. Data 

for three child cohorts (ages 1–5, 6–11, and 12–19 years) and adults were used. However, as 

most infants are breastfed and therefore are not exposed to fish or water, they were excluded 

from the risk assessment (i.e., modeling start age for a child is 1 year). 

 Body Weight. Distributions of body weight were developed for adult and child receptors 

based on data from the EFH. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-43 

 Fish Ingestion Rate. Fish ingestion rates were based on a recreational angler who 

catches and eats some fish from a waterbody impacted by contaminants released from 

CCW WMUs. Distributions of fish intake rates were developed for adult fishers based on 

data from the 1997 EFH. At the time the risk assessment was conducted (May-June 

2003), separate fish ingestion rates for children of recreational anglers were not available. 

 Drinking Water Ingestion Rate. Distributions of drinking water intake rates were 

developed for the adult and child resident based on data from the EFH. 

 Exposure Duration. Exposure duration refers to the amount of time that a receptor is 

exposed to a contaminant source. Exposure duration was assumed to correspond with the 

receptor’s residence time in the same house. Exposure durations were determined using 

data on residential occupancy from the EFH. The data used to develop parameter 

information for resident receptors were age-specific. Thus, separate exposure duration 

distributions were developed for adult and child residents. For children, the modeling 

start age is 1 year, and exposure duration was used to determine the amount of time spent 

in each cohort (e.g., if exposure duration was 2 years, consumption rates and body 

weights were based only on cohort 1 data; however, if exposure duration was 21 years, 

the child spends 5 years in cohort 1, 6 years in cohort 2, 8 years in cohort 3, and 2 years 

in cohort 4/adult). Infants between birth and 1 year are not modeled because they are 

assumed to either breastfeed or consume commercial formula. 

 Exposure Frequency. Exposure frequency is the frequency with which the receptor is 

exposed to the contaminated source during the exposure duration. Exposure frequency is 

not expected to vary much, so distributions were not developed for exposure frequency. 

All receptors were assumed to be exposed to the contaminant source 350 days/year. This 

value was based on the assumption that individuals are away from their homes (e.g., on 

vacation) approximately 2 weeks out of the year, but are otherwise exposed daily. 

 Lifetime and Averaging Time. Averaging time is the period of time over which a 

receptor’s dose is averaged. To evaluate carcinogens, total dose was averaged over the 

lifetime of the individual, assumed to be 70 years. To evaluate noncarcinogens, dose was 

averaged over the last year of exposure because noncancer effects may become evident 

during less-than-lifetime exposure durations if toxic thresholds are exceeded. Essentially, 

this amounts to setting exposure duration and averaging time equal so that they cancel 

each other out in the equation for ADD. Thus, neither exposure duration nor averaging 

time is included in the ADD equation. 

3.8.3 Dose Estimates 

An exposure assessment estimates the dose to each receptor from the contaminant 

concentration in the exposure medium (e.g., drinking water, fish) and the intake rate for that 

medium (e.g., ingestion rate of drinking water, ingestion rate of fish). For this assessment, 

exposure estimates were based on the potential dose (e.g., the dose ingested) rather than the 

applied dose (e.g., the dose delivered to the gastrointestinal tract) or the internal dose (e.g., the 

dose delivered to the target organ). Doses from groundwater or fish ingestion were calculated by 

multiplying the contaminant concentration in groundwater or fish by the respective intake rate on 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-44 

a per kilogram body weight basis. Doses were then summed over the exposure duration, 

resulting in an ADD received from ingestion exposure. The ADD was used to assess noncancer 

risk from ingestion exposures and is defined as 

×= IRCADD (3-2) 

where 

 C = average concentration (mass/volume or mass/mass) 

 IR = intake rate (mass/body weight mass/time, or volume/body weight 

mass/time). 

Contaminant concentration represents the concentration of a chemical in a medium that 

contacts the body. The ADD was calculated from concentrations averaged over the exposure 

duration for each receptor. 

For cancer effects, where the biological response is described in terms of lifetime 

probabilities even though exposure may not occur over the entire lifetime, dose is presented as a 

LADD. The LADD was used to assess cancer risks from each exposure route (i.e., ingestion) and 

is defined as 

365AT

EFEDIRC LADD

×

××× = (3-3) 

where 

C = average concentration (mass/mass or mass/volume) 

 IR = intake rate (mass/body weight mass/time, or volume/body weight 

mass/time) 

 ED = exposure duration (yr) 

 EF = exposure frequency (d/yr) 

 AT = averaging time (yr) 

 365 = units conversion factor (d/yr). 

As with the ADD, contaminant concentration represents the concentration of a chemical 

in a medium that contacts the body. Intake rate depends on the route of exposure; for example, it 

might be an inhalation rate or an ingestion rate. Exposure frequency is the number of days per 

year the receptor is exposed to the contaminated source during the exposure duration. 

For cancer effects, biological responses are described in terms of lifetime probabilities, 

even though exposure may not be lifelong; consequently, the exposure duration (the length of 

time of contact with a contaminant) was used to average the ADD over a lifetime (70 years). The 

media concentrations used were averaged over the duration of exposure. 

3.9 Risk Estimation 

The final step of the risk assessment process is to estimate the risk posed to human and 

ecological receptors (e.g., residents, fishers; aquatic organisms). In this step, estimates of toxicity 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-45 

(the human health and ecological benchmarks) and exposure doses or exposure concentrations 

are integrated into quantitative expressions of risk. For the CCW constituents modeled in the 

full-scale assessment, the CCW human risk assessment used estimates of dose and toxicity to 

calculate individual excess lifetime carcinogenic risk estimates and noncancer HQs (Section 

3.9.1). The risk calculations for ecological receptors differ from those for humans because the 

ecological benchmarks are developed as media concentrations (i.e., they are calculated 

considering ecological exposure). Thus the CCW risk assessment used estimates of exposure 

(media) concentrations and toxicity (media-specific concentration limits) to calculate an 

ecological HQ (Section 3.9.2). 

3.9.1 Human Health Risk Estimation 

The full-scale analysis focused on two human health exposure pathways: groundwater-todrinking-water and groundwater-to-surface-water via fish consumption by recreational fishers. 

The cancer and noncancer health impacts of ingesting groundwater and fish contaminated by 

CCW leachate were estimated using the risk endpoints shown in Table 3-14. These endpoints 

were generated for each iteration of the Monte Carlo analysis. Only the cancer endpoint was 

used for arsenic, because it is the more sensitive endpoint compared to noncancer effects. For the 

other 11 constituents, only noncancer HQs were calculated, using the appropriate noncancer 

endpoint. 

Table 3-14. Risk Endpoints Used for Human Health 

Risk Category Risk Endpoints Definition

Cancer Effects 

(arsenic only) 

Lifetime excess cancer risk by pathway 

and chemical 

Lifetime excess cancer risk resulting from 

single pathway exposure 

Noncancer Effects Ingestion HQ by pathway and chemical Ingestion HQ resulting from single 

pathway exposure 

Ingestion HQ based on drinking water 

action level for lead and copper 

Lead and copper ingestion HQ resulting 

from drinking water pathway 

Average daily dose for fish consumption 

for lead 

Lead exposure resulting from fish 

ingestion pathway 

Cancer risks for arsenic were characterized using lifetime excess cancer risk estimates, 

which represent the excess probability of developing cancer over a lifetime as a result of 

exposure to the chemical of interest. Lifetime excess cancer risk estimates use the LADD (see 

Section 3.8.3) as the exposure metric. Lifetime excess cancer risk estimates are the product of 

the LADD for a specific receptor and the corresponding cancer slope factor, as shown in 

Equation 3-4. 

Lifetime excess riskcancer i = LADDi × CSF (3-4) 

where 

 LADD = lifetime average daily dose for ingestion pathway i (mg/kg BW/d) 

 i = pathway index 

 CSF = cancer slope factor (mg/kg BW/d)-1. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-46 

Noncancer risk was characterized through the use of HQs, which are generated by 

dividing an ADD (see Section 3.8.3) for ingestion pathways by the corresponding RfD.13 An HQ 

establishes whether a particular individual has experienced exposure above a threshold for a 

specific health effect. Therefore, unlike cancer risk estimates, HQs are not probability 

statements. Rather, the RfD represents an estimate (with uncertainty spanning perhaps an order 

of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) 

that is likely to be without an appreciable risk of deleterious effects during a lifetime. It can be 

derived from a no observed adverse exposure level (NOAEL), lowest observed adverse exposure 

level (LOAEL), or benchmark dose, with uncertainty factors generally applied to reflect 

limitations of the data used. Equation 3-5 shows the calculation for the ingestion HQ. This 

calculation was completed for each pathway considered (i.e., drinking water ingestion and fish 

consumption). 

RfD

ADD HQ i

i = (3-5) 

where 

 ADDi = average daily dose for ingestion pathway i (mg/kg-d) 

 i = pathway index 

 RfD = reference dose (mg/kg-d). 

The risk results address risk from exposure via the groundwater-to-drinking-water and 

groundwater-to-surface-water pathway separately. This is appropriate because the resident 

consuming contaminated groundwater may not be the recreational fisher who is consuming 

contaminated fish. Also, the arrival time of the contaminant plume to the stream and the human 

receptor may not be the same for a particular iteration.14 However, a resident may consume fish 

caught from a nearby stream or lake and contaminated drinking water if the travel times are 

similar, so that possibility should be considered as an uncertainty in this analysis (see Section 

4.4.1). 

For each receptor type, lifetime excess cancer risk estimates for arsenic were calculated 

separately for the drinking water and fish consumption pathways. 

3.9.2 Ecological Risk Estimation 

The full-scale analysis addressed two routes of exposure for ecological receptors: direct 

contact with contaminated media and ingestion of contaminated food items. HQs were calculated 

using chemical-specific media concentrations assumed to be protective of ecological receptors of 

concern through either exposure route (CSCLs). As described in Section 3.1.2, these ecological 

benchmarks were developed for representative organisms and communities in each 

environmental medium of concern. 

 

13 HQs calculated for lead in drinking water were based on the drinking water action level (0.015 mg/L); lead 

exposures from fish ingestion are reported as an ADD. 14 Stream distance and well distance were sampled independently in the Monte Carlo analysis. 
Section 3.0 Analysis 

April 2010–Draft EPA document. 3-47 

For a particular Monte Carlo iteration, HQs were calculated for sediment and surface 

water as the ratio between the media concentration and the ecological benchmark. Because the 

CSCLs were derived for an HQ of 1 (for relevant ecological endpoints), the ratio of a constituent 

concentration in a media to the media-specific CSCL represents the HQ for that constituent and 

pathway. For surface water, the HQ was calculated as follows: 

 HQsurface water = Csw / CSCLsw (3-6) 

where 

 Csw = total concentration in surface water column (mg/L) 

 CSCLsw = ecological benchmark for surface water (mg/L). 

Similarly, for sediment, the HQ was calculated as 

 HQsediment = Csediment / CSCLsediment (3-7) 

where 

 Csediment = total concentration in sediment (mg/kg) 

 CSCLsediment = ecological benchmark for sediment (mg/kg). 

Because the sediment and surface water benchmarks were based on separate receptor 

communities, it is not appropriate to add HQs across pathways. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-1 

4.0 Risk Characterization 

This section summarizes the results of the full-scale Monte Carlo analysis and 

characterizes those results in terms of significant uncertainties and the scenarios and factors that 

influence risks to human health and the environment. Results are presented at a high-end (90th 

percentile) and typical (50th percentile) exposure for both pathways under each combination of 

WMU type, ash type, and liner type. 

An overview of the assessment on which these results were based (e.g., waste 

management scenarios, analysis framework) is provided in Section 2. Section 3 provides more 

details on analysis methodologies, parameter values, and assumptions. In this section, Section 

4.1 presents results from the human health risk assessment and includes an analysis of how liner 

conditions influence results. Section 4.2 presents the results from the ecological risk assessment. 

Tables summarizing the human and ecological results are presented in each section. Section 4.3

describes the sensitivity analysis conducted for the CCW risk assessment, and Section 4.4

discusses how variability and uncertainty have been addressed, including a semi-quantitative 

review of the potential impact of some of the more significant uncertainties on results. 

The probabilistic results were based on a Monte Carlo simulation in which many model 

input parameter values were varied over 10,000 iterations of the model per waste management 

scenario to yield a statistical distribution of exposures and risks. Per the Guidance for Risk 

Characterization developed by the EPA Science Policy Council in 1995 

(http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf), EPA defined the high end of the risk 

distribution at the 90th percentile risk or hazard estimate generated during the Monte Carlo 

simulation. Thus, the 90th percentile risk results are shown in this section as the high-end 

estimate of the risk distribution generated during the Monte Carlo simulation of constituent 

release, fate and transport, and exposure associated with CCW disposal in landfills and surface 

impoundments. In addition, the 50th percentile results are presented as the central tendency 

estimate of that risk distribution. 

For exposure scenarios describing the waste management unit type (e.g., lined landfill; 

unlined surface impoundment), waste type (e.g., conventional CCW, ash mixed with coal 

refuse), receptor (i.e., child, adult, ecological), and health endpoint (i.e., cancer, noncancer, 

ecological), the 90th percentile risk represents the high-end estimate of cancer or noncancer risk 

that was used to help determine whether CCW disposal practices are protective of public health. 

To evaluate the significance of the estimated cancer risks or noncancer hazards that are 

attributable to CCW disposal for the exposure pathways assessed in this assessment, EPA 

compared the risk estimates to a risk range (for carcinogens) or to a specific risk criterion (for 

noncarcinogens) that are protective of human health and the environment: 

 An estimate of the excess lifetime cancer risk for individuals exposed to carcinogenic 

(cancer-causing) contaminants ranging from 1 chance in 1,000,000 (10-6 excess cancer 

risk) to 1 chance in 10,000 (10-4 excess cancer risk). For decisions made to screen out 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-2 

certain constituents from further consideration, a 1 in 100,000 (10-5) excess lifetime 

cancer risk) was used.1

 

 For constituents that cause adverse, noncancer health effects (noncarcinogens), the 

criterion is an HQ of greater than 1, with the HQ being the ratio of the average daily 

exposure level to a protective exposure level corresponding to the maximum level at 

which no appreciable effects are likely to occur. 

 An HQ greater than 1 for was used to identify constituents with adverse effects to 

ecological receptors. 

In general, the full-scale analysis showed lower risks than the screening analysis, but still 

showed risks within or above the cancer risk range or above an HQ of 1 for certain CCW 

constituents, WMU types, pathways, and receptors at the 90th percentile. At the 50th percentile, 

risks are still above these levels for both WMU types, but for fewer constituents and pathways. 

The results presented herein are subject to further interpretation, as EPA queries the CCW risk 

inputs and outputs to investigate how the results may be affected by (1) waste types and 

environmental and waste management conditions, (2) assumptions made about these conditions 

in designing the probabilistic analysis, and (3) the availability of newer facility data. 

4.1 Human Health Risks 

This section presents the 90th and 50th percentile risk results for the two human exposure 

pathways evaluated in the full-scale analysis: (1) groundwater-to-drinking-water and 

(2) groundwater-to-surface-water (fish consumption). Results are presented for the two WMU 

types addressed in the analysis: landfills and surface impoundments, and show the distribution of 

risks across all waste types by liner type from the EPRI survey data (see Section 4.1.3 for further 

discussion of liners). 

4.1.1 Groundwater-to-Drinking-Water Results by Waste Type/WMU Scenario 

As described in Section 3.3, the CCW risk assessment was organized by waste type so 

that different waste chemistries could be accounted for in the fate and transport modeling. The 

results discussed so far in this report address conventional CCW (fly ash, bottom ash, boiler slag, 

FGD sludge) and conventional CCW codisposed with coal refuse.2 Section 4.1.1.1 presents these 

results by waste type. FBC wastes were also modeled in this assessment. However, there was a 

very small number of FBC waste disposal sites (seven) in the EPRI/EPA database. For this 

reason, the FBC results are treated separately in Section 4.1.1.2. Groundwater results are 

reported for a resident’s child because these consistently led to higher HQs, with the exception of 

arsenic cancer values, which were consistently higher in adults. Thus, the cancer risks reported 

are for adults. 

 

1

 The typical cancer risk range used by the Office of Solid Waste and Emergency Response is 10-4 to 10-6. 2

 Coal refuse is the waste coal produced from coal handling, crushing, and sizing operations, and tends to have a 

high sulfur content and low pH. In the CCW constituent database, codisposed coal refuse includes “combined 

ash and coal gob,” “combined ash and coal refuse,” and “combined bottom ash and pyrites.” 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-3 

Note that only the chemicals for which constituent data were adequate to model and 

assess risks were modeled in the full-scale assessment, and only those modeled 

chemical/pathway/WMU scenarios are shown in the tables and figures. For example, antimony 

and thallium risks are not presented for surface impoundments and mercury is not shown for 

either landfills or surface impoundments because more than 90% of the measurements were 

nondetects. For further discussion of how nondetects were treated, see Section 4.4.3.1. Although 

screening-level human health risks for aluminum and barium were below the screening criteria, 

they were modeled in the risk assessment due to their potential to cause ecological harm. 

Additionally, there were nine constituents that failed the screen but were not modeled. Instead, 

these constituents were dealt with using risk attenuation factors, as described in Section 4.1.4. 

The screening analysis results in Section 3.2.4 and Table 3-6 show which CCW constituents 

were modeled. 

Results for two constituents (arsenic and selenium) also varied based on chemical 

speciation. An earlier draft of this document showed results assuming 100% trivalent arsenic 

(arsenic III) and 100% hexavalent selenium (selenium VI) because these forms are more mobile 

in soil and groundwater, and thus would show higher estimated risks than either arsenic V or 

selenium IV. This revised draft also presents results for arsenic V and selenium IV. The results 

for the two species of arsenic and selenium bound the range of possible risks for these two 

constituents. For further discussion of speciation, see Section 4.4.2. 

4.1.1.1 Conventional CCW and CCW Codisposed with Coal Refuse 

Tables 4-1 and 4-2 show 90th and 50th percentile risk results, respectively, by waste type 

and liner type for CCW landfills for the drinking water pathway. Although some risks were 

higher for conventional CCW and others for codisposed CCW, there was generally little 

difference in results between waste types for landfills. Although risks are greater for unlined 

landfills than for clay-lined landfills, those with composite liners show zero, or near-zero, risks 

for all constituents modeled in this assessment (see Section 4.1.3 for a further discussion of risks 

by liner type). 

Tables 4-3 and 4-4 show the 90th and 50th percentile risk results, respectively, by waste 

type and liner type for CCW surface impoundments for the drinking water pathway. The 

difference in risks between waste types is greater for surface impoundments than for landfills. 

For surface impoundments, some constituents present higher risks from CCW managed alone 

(boron, molybdenum, nitrate, and selenium). However, others presented higher risks when CCW 

is comanaged with coal refuse (arsenic, cadmium, cobalt, and lead). This result is likely due to 

the higher metal concentrations and the acidity of coal refuse leachate3

 for surface 

impoundments in the CCW database, which in turn result from the association of these elements 

(and acidity) with the sulfide minerals4

 that are concentrated in coal refuse (Finkelman, 1995). 

As with landfills, clay-lined units show lower risks than unlined units, and composite liners show 

zero, or near-zero, risks for either waste type. 

 

3

 Many metals tend to be more soluble and mobile in acidic leachate. 4

 Arsenic: pyrite, cadmium: sphalerite, lead: galena, cobalt: pyrite. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-4 

When viewing the results in Tables 4-1 through 4-4, readers should note that these risks 

assume that the contaminated groundwater plume will intercept a receptor well. Because 

approximately two-thirds of the model runs showed surface water bodies intersecting the 

groundwater plume, there could be a significant number of instances where a well is either not 

contaminated or is less contaminated than the results below would indicate. This uncertainty is 

discussed further in Section 4.4.3.3. 

Table 4-1. 90th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway 

Chemical 

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 79 landfills 

Cancer 

Arsenic III 4E-04 2E-04 0 

Arsenic V 2E-04 3E-05 0 

Noncancer 

Aluminum 2E-03 1E-04 0 

Antimony 2 0.8 0 

Barium 3E-03 7E-04 0 

Boron 0.7 0.4 0 

Cadmium 0.7 0.4 0 

Cobalt 1 0.4 0 

Lead (MCL)b 1 0.3 0 

Molybdenum 2 0.8 0 

Nitrate/nitrite (MCL)b 0.1 0.06 2E-06 

Selenium IV 0.01 3E-3 0 

Selenium VI 0.2 0.1 0 

Thallium 3 2 0 

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 5E-04 2E-04 0 

Arsenic V 4E-04 6E-05 0 

Noncancer 

Aluminum 0.02 4E-04 0 

Antimony 0.8 0.3 0 

Barium 0.04 4E-03 0 

Boron 0.3 0.1 0 

Cadmium 0.2 0.07 0 

Cobalt 0.8 0.09 0 

Lead (MCL)b 0.7 0.09 0 

Molybdenum 2 0.6 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-5 

90th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway (continued) 

Chemical 

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Nitrate/nitrite (MCL)b 0.2 0.1 3E-06 

Selenium IV 0.1 0.04 0 

Selenium VI 0.7 0.3 0 

Thallium 2 1 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

Table 4-2. 50th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway 

Chemical 

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 79 landfills 

Cancer 

Arsenic III 6E-06 4E-06 0 

Arsenic V 6E-10 3E-14 0 

Noncancer 

Aluminum 5E-07 3E-07 0 

Antimony 0.04 0.02 0 

Barium 0 0 0 

Boron 0.01 0.01 0 

Cadmium 0.01 8E-03 0 

Cobalt 3E-03 8E-06 0 

Lead (MCL)b 4E-04 2E-08 0 

Molybdenum 0.1 0.04 0 

Nitrate/nitrite (MCL)b 0.004 0.003 0 

Selenium IV 0 0 0 

Selenium VI 9E-03 6E-03 0 

Thallium 0.2 0.1 0 

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 2E-05 6E-06 0 

Arsenic V 6E-06 7E-10 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-6 

50th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Drinking-Water Pathway (continued) 

Chemical 

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Noncancer 

Aluminum 4E-06 2E-09 0 

Antimony 0.05 0.02 0 

Barium 5E-05 7E-07 0 

Boron 8E-03 3E-03 0 

Cadmium 0.02 4E-03 0 

Cobalt 2E-05 0 0 

Lead (MCL)b 0.01 2E-07 0 

Molybdenum 0.02 6E-03 0 

Nitrate/nitrite (MCL)b 0.04 0.009 0 

Selenium IV 2E-09 2E-15 0 

Selenium VI 0.03 0.01 0 

Thallium 0.2 0.07 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

Table 4-3. 90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway 

Chemical 

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 2E-03 9E-04 2E-07 

Arsenic V 7E-04 2E-04 0 

Noncancer 

Aluminum 2E-03 1E-03 2E-07 

Barium 5E-03 3E-03 2E-11 

Boron 7 4 5E-03

Cadmium 0.5 0.3 4E-11

Cobalt 0.9 0.4 0

Lead (MCL)b 3 0.7 1E-21

Molybdenum 8 5 7E-03

Nitrate/nitrite (MCL)b 20 10 9E-04 

Selenium IV 0.4 0.1 1E-04 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-7 

90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway (continued) 

Chemical 

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Selenium VI 2 1 1E-03

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 2E-02 7E-03 4E-06 

Arsenic V 2E-02 2E-03 3E-09 

Noncancer 

Aluminum 0.3 0.07 6E-07 

Barium 7E-03 3E-03 9E-07 

Boron 1 0.5 2E-03

Cadmium 9 3 5E-05

Cobalt 500 200 3E-06

Lead (MCL)b 9 1 1E-19

Molybdenum 3 2 4E-03

Nitrate/nitrite (MCL)b 0.4 0.2 1E-04 

Selenium IV 0.3 0.1 3E-10 

Selenium VI 0.8 0.4 1E-03

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

Table 4-4. 50th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway 

Chemical 

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units 

Composite–Lined 

Units 

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 1E-04 6E-05 0 

Arsenic V 2E-05 4E-06 0 

Noncancer 

Aluminum 2E-05 1E-05 8E-20 

Barium 1E-04 1E-04 0 

Boron 0.4 0.2 3E-11 

Cadmium 0.05 0.02 0 

Cobalt 0.2 0.05 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-8 

50th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Drinking-Water Pathway (continued) 

Chemical 

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units 

Composite–Lined 

Units 

Lead (MCL)b 0.05 0.007 0 

Molybdenum 1 0.5 2E-11 

Nitrate/nitrite (MCL)b 0.1 0.05 7E-08 

Selenium IV 8E-04 4E-10 0 

Selenium VI 0.1 0.07 2E-11 

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 6E-04 2E-04 0 

Arsenic V 3E-04 4E-05 0 

Noncancer 

Aluminum 5E-04 4E-05 0 

Barium 4E-04 2E-04 0 

Boron 0.1 0.06 5E-15 

Cadmium 0.1 0.05 0 

Cobalt 20 6 0 

Lead (MCL)b 0.1 0.01 0 

Molybdenum 0.8 0.3 3E-18 

Nitrate/nitrite (MCL)b 0.03 0.01 4E-08 

Selenium IV 3E-03 9E-05 0 

Selenium VI 0.1 0.03 5E-15 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

4.1.1.2 FBC Wastes 

Tables 4-5 and 4-6 show the 90th and 50th percentile risk results for FBC landfills by 

liner type. These results suggest lower risks than for conventional CCW and CCW codisposed 

with coal refuse. The difference may be attributed to lower FBC leachate concentrations and the 

alkaline nature of FBC waste. Note that clay-lined FBC landfills show higher risks than unlined 

facilities, which is counterintuitive considering how clay-lined and unlined units are designed 

and operated. This result reflects the characteristics of the limited number and locations of FBC 

landfills.5

 When the risk results of an exposure pathway are viewed at a resolution finer than the 

analysis design, a small sample size, along with the interactions of liner type with other site-

 

5

 FBC WMU data were available for only seven landfills (3 unlined, 3 clay-lined, and 1 composite-lined), and it is 

not known how representative these data are with respect to WMU characteristics and locations throughout the 

United States. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-9 

based inputs, can produce unexpected results. In the case of FBC wastes, the characteristics of 

the three unlined landfills (primarily infiltration rate and areas) were such that their risks were 

lower than the three clay-lined FBC landfills. 

Table 4-5. 90th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Drinking-Water Pathway 

90th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

FBC Waste – 7 landfills 

Cancer 

Arsenic III 3E-05 6E-05 0 

Arsenic V 2E-05 2E-05 0 

Noncancer 

Aluminum 4E-06 2E-05 0 

Antimony 0.8 3 0 

Barium 4E-04 2E-03 0 

Boron 0.02 0.07 0 

Cadmium 0.1 0.3 0 

Cobalt 0.4 0.8 0 

Lead (MCL)b 0.4 0.6 0 

Molybdenum 0.2 0.5 0 

Nitrate/nitrite (MCL)b 0.03 0.07 5E-08

Selenium IV 3E-14 0.05 0 

Selenium VI 0.08 0.1 0 

Thallium 1 4 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach the 

receptor during the 10,000-year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

Table 4-6. 50th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Drinking-Water Pathway 

50th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

FBC Waste – 7 landfills 

Cancer 

Arsenic III 0 4E-07 0 

Arsenic V 0 5E-10 0 

Noncancer 

Aluminum 0 0 0 

Antimony 0 0.09 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-10 

50th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Drinking-Water Pathway (continued) 

50th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

Barium 0 0 0 

Boron 0 0.003 0 

Cadmium 0 0.01 0 

Cobalt 0 3E-03 0 

Lead (MCL)b 0 2E-04 0 

Molybdenum 0 0.04 0 

Nitrate/nitrite (MCL)b 3E-08 0.004 0 

Selenium IV 0 5E-15 0 

Selenium VI 0 0.01 0 

Thallium 0 0.2 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach the 

receptor during the 10,000-year period of the analysis. 

b Values are ratios of exposure concentration to MCL. 

4.1.1.3 Comparing Landfills and Surface Impoundments 

The full-scale analysis produced lower risks for landfills than for surface impoundments. 

The higher risks for surface impoundments as compared to landfills reflect higher constituent 

concentrations in the surface impoundment wastes and a higher hydraulic head in an 

impoundment that drives leachate into the underlying soil with greater force than infiltration in 

landfills. This higher head results in a greater flux of contaminants to groundwater during the 

active life of the surface impoundment, especially in unlined units. In combination with the 

higher CCW constituent concentrations in surface impoundment porewater and a greater 

proportion of unlined units, these factors lead to more and higher risk exceedances for surface 

impoundments than for landfills. 

4.1.1.4 The Effect of Liners 

The analysis demonstrates that the presence of liners, especially composite liners, reduce 

leaching and risks from CCW landfills and surface impoundments. Note that 90th percentile 

risks from composite liners are zero for most constituents for landfills, which means that in 90 

percent of the cases, the contaminant did not reach the receptor well in the 10,000 year limit for 

this analysis. Composite liners also reduced risks for surface impoundments for several 

constituents at the 90th percentile by 4 to 10 orders of magnitude and generated risk results well 

below the cancer risk range or noncancer risk criterion used for this analysis. Infiltration rates for 

composite-lined surface impoundments are largely controlled by leak density (see Section 3.5), 

which is an empirical distribution from the same source as the landfill infiltration rates (U.S. 

EPA, 2002b), and are subject to similar uncertainties. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-11 

Zero values reflect the liner leakage rates in the empirical data set used to develop 

composite landfill liner infiltration rates used in this risk assessment (from U.S. EPA, 2002b; see 

Section 3.4.2), which are mostly zero values or very low in terms of infiltration rate. Although 

these infiltration rates are based on the best data available to EPA, these data are not specific to 

CCW facilities. This represents an uncertainty in the analysis (see Sections 3.4.2 and 4.4.3.2). 

4.1.1.5 Modeled Peak Concentration Arrival Times 

Arrival times for the peak well concentrations used to calculate groundwater to drinking 

water risks for selected CCW constituents (arsenic, boron, cobalt, selenium, and thallium) are 

plotted as cumulative distributions for surface impoundments and landfills in the figures in 

Appendix L. These constituents were selected to represent the chemicals with the highest risks 

and to span the range of mobility in the subsurface. Table 4-7 summarizes these time of travel 

results by showing selected percentiles from these distributions for each WMU/liner 

combination modeled in the risk assessment. 

As can be seen in Table 4-7, the peak arrival times for most constituents in unlined 

surface impoundment is less than 100 years (i.e., peak concentration occurs before or shortly 

after surface impoundment closure). The 10th percentile ranges from 70 years (for arsenic III, 

boron, and selenium VI) to 76 years (for selenium IV). The 50th percentile arrival times remain 

under 100 years for most constituents, with only the less mobile forms of arsenic and selenium 

having 50th percentile arrival times later than 100 years. 

Arrival times for unlined landfills are much longer, ranging up to thousands of years. For 

boron and selenium IV, the 50th percentiles are 2,000 and 10,000 years respectively. However, 

even at the 10th percentile, arrival times ranged from 300 years (for boron) to 4,600 years (for 

selenium IV). 

At the higher percentiles, arrival times shown as greater than 10,000 years indicate that 

the contaminant plume did not reach the well before the simulation ended. Although the plume 

might eventually reach the well in these cases, EPA does not believe that extending the 

simulation beyond 10,000 years would have captured any significant risk beyond what was 

captured by the selection of the 90th percentile values, which reflect cases where the plume did 

reach the well. In other words, the 90th percentile values would not be influenced by whether 

lower percentile concentrations were zero or the concentration at a peak beyond 10,000 years. 

Table 4-7. Time to Peak Well Concentration by WMU and Liner Type as Modeled 

Liner 

Time to Peak (years)a,b

Percentile 

Arsenic 

III 

Arsenic 

IV Boron Cobalt 

Selenium 

IV 

Selenium 

VI Thalliumc

Landfills (all waste types) 

Unlined 

10 400 2,000 300 1,200 4,600 400 580 

30 1,100 7,100 880 4,100 9,400 1,000 1,100 

50 2,800 9,700 2,000 7,800 10,000 2,600 2,300 

70 6,400 10,000 4,300 10,000 >10,000 5,500 4,400 

90 >10,000 >10,000 9,400 >10,000 >10,000 10,000 9,700 

(continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-12 

Time to Peak Well Concentration by WMU and Liner Type as Modeled (continued) 

Liner 

Time to Peak (years)a,b

Percentile 

Arsenic 

III 

Arsenic 

IV Boron Cobalt 

Selenium 

IV 

Selenium 

VI Thalliumc

Compacted 

clay 

10 400 1,900 550 1,400 8,100 400 570 

30 1,400 8,200 1,400 5,900 >10,000 1,300 1,200 

50 4,000 10,000 5,600 10,000 >10,000 5,100 4,300 

70 >10,000 >10,000 10,000 >10,000 >10,000 10,000 10,000 

90 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 

Synthetic or 

composite 

(clay and 

synthetic) 

10 10,000 >10,000 9,600 >10,000 10,000 9,000 >10,000 

30 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 

50 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 

70 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 

90 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 >10,000 

Surface Impoundments (all waste types) 

Unlined 

10 70 73 70 71 76 70 N/A 

30 73 97 72 78 610 72 N/A 

50 78 220 74 97 4,400 74 N/A 

70 91 890 80 190 10,000 80 N/A 

90 170 6,500 110 970 >10,000 110 N/A 

Compacted 

clay 

10 75 95 75 86 81 75 N/A 

30 86 350 80 140 3,000 80 N/A 

50 110 1,300 90 270 7,900 90 N/A 

70 150 5,000 110 690 10,000 110 N/A 

90 340 10,000 150 3,100 >10,000 150 N/A 

Synthetic or 

composite 

(clay and 

synthetic) 

10 1,300 10,000 960 9,500 1,900 990 N/A 

30 3,900 >10,000 2,800 >10,000 6,900 2,800 N/A 

50 8,600 >10,000 4,400 >10,000 >10,000 4,600 N/A 

70 >10,000 >10,000 7,000 >10,000 >10,000 7,300 N/A 

90 >10,000 >10,000 10,000 >10,000 >10,000 10,000 N/A a

 Arrival times have been rounded to two significant digits. 

b

 >10,000 indicates that the contaminant plume did not reach the receptor well during the modeled period. c

 N/A = Not Applicable. Thallium was not modeled for surface impoundments (see Section 4.1.1 above). 

As with the higher constituent concentrations that are characteristic of surface 

impoundments, the shorter arrival times for surface impoundments are primarily due to the 

hydraulic head of the waste liquids in the unit; by contrast, landfill leaching is driven solely by 

infiltration of precipitation through the cap and liner of the unit and the peak concentration takes 

much longer to reach the well. 

The arrival times presented in Table 4-7 correspond to the arrival of the maximum 

estimated risks for each model run. However, for model runs where the risk range or HQ 

criterion was exceeded, the first exceedence would sometimes occur earlier than the maximum 

risk arrivals reported in Table 4-7. This is consistent with the appearance of damage cases 

described in U.S. EPA (2007), which were sometimes observed sooner than the time-to-peak 

estimates in Table 4-7. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-13 

4.1.2 Groundwater-to-Surface-Water (Fish Consumption) Pathway 

Like the drinking water results above, the fish consumption results are organized by 

waste type so that different waste chemistries could be accounted for. Section 4.1.2.1 presents 

the results for conventional CCW and codisposed CCW by WMU and liner type. FBC wastes 

were also modeled for the surface water pathway, and these results are treated separately in 

Section 4.1.2.2. Note that only the four constituents that failed the surface water screen were 

probabilistically modeled for this scenario. Of those, thallium risks are not presented for surface 

impoundments because of a high proportion (>90%) of nondetects in the surface impoundment 

data (see Section 4.4.3.1 for further discussion). The screening analysis results in Section 3.2.4

and Table 3-6 show which CCW constituents exceeded the surface water screening criteria. 

4.1.2.1 Conventional CCW and CCW Codisposed with Coal Refuse 

Tables 4-8 and 4-9 present the 90th and 50th percentile risk results, respectively, by 

waste type and liner type for CCW landfills for the fish consumption pathway. The results 

presented are for a fisher’s child because those risks were consistently higher than the risks for 

the adult fisher. As seen in these tables, the results for landfills that codispose of CCW are not 

drastically different from those that handle only conventional CCW. At the 90th percentile, only 

unlined landfills that comanage CCW present risks at an HQ of 1 (for selenium). The remainder 

of the modeled constituents had risks below an excess cancer risk of 1 in 100,000 or an HQ of 1 

at the 90th percentile. 50th percentile results were all well below these levels for both cancer and 

noncancer risks. 

Tables 4-10 and 4-11 present the 90th and 50th percentile risk results, respectively, by 

waste type and liner for CCW surface impoundments for the fish consumption pathway. Again, 

risks are higher for surface impoundments than for landfills because of the higher waste 

concentrations and the higher hydraulic head in these units, as discussed previously for the 

drinking water pathway. Results at that 90th percentile exceeded an HQ of 1 for selenium in 

unlined (HQ of 3) and clay-lined (HQ of 2) impoundments managing conventional CCW, and 

also exhibited excess cancer risks just above 1 in 100,000 for arsenic in unlined (3 in 100,000) 

and clay-lined (2 in 100,000) impoundments comanaging CCW. Fish consumption pathway 50th 

percentile results are well below an excess cancer risk of 1 in 1,000,000 and an HQ of 1 for all 

constituents, waste management scenarios, and liner types. 

Table 4-8. 90th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Surface-Water Pathway 

Chemicalb

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 79 landfills 

Cancer 

Arsenic III 1E-06 1E-07 0 

Arsenic V 4E-07 3E-09 0 

(continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-14 

90th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Surface-Water Pathway (continued) 

Chemicalb

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Noncancer 

Cadmium 0.09 6E-03 0 

Selenium IV 6E-05 1E-04 0 

Selenium VI 0.3 0.04 0 

Thallium 0.4 0.04 0 

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 2E-06 8E-07 0 

Arsenic V 2E-06 2E-07 0 

Noncancer 

Cadmium 0.05 0.01 0 

Selenium IV 0.03 9E-03 0 

Selenium VI 1 0.4 0 

Thallium 0.4 0.2 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis 

as needing further assessment (see Section 3.2.4) were modeled. 

Table 4-9. 50th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Surface-Water Pathway 

Chemicalb

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 79 landfills 

Cancer 

Arsenic III 3E-10 7E-11 0 

Arsenic V 4E-14 1E-18 0 

Noncancer 

Cadmium 2E-05 3E-06 0 

Selenium IV 0 0 0 

Selenium VI 2E-04 4E-05 0 

Thallium 1E-04 5E-05 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-15 

50th Percentile Risk Results by CCW Type: Landfills, 

Groundwater-to-Surface-Water Pathway (continued) 

Chemicalb

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Codisposed CCW and Coal Refuse – 41 landfills 

Cancer 

Arsenic III 4E-09 3E-09 0 

Arsenic V 2E-10 8E-14 0 

Noncancer 

Cadmium 1E-04 6E-05 0 

Selenium IV 6E-10 1E-15 0 

Selenium VI 3E-03 3E-03 0 

Thallium 1E-03 1E-03 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis 

as needing further assessment (see Section 3.2.4) were modeled. 

Table 4-10. 90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Surface-Water Pathway 

Chemicalb

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 8E-06 4E-06 1E-12 

Arsenic V 2E-06 4E-07 0 

Noncancer 

Cadmium 0.09 0.04 2E-15 

Selenium IV 0.6 0.04 1E-07 

Selenium VI 3 2 2E-06 

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 3E-05 2E-05 1E-14 

Arsenic V 2E-05 8E-06 6E-19 

Noncancer 

Cadmium 0.5 0.3 8E-13 

Selenium IV 0.2 0.05 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-16 

90th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Surface-Water Pathway (continued) 

Chemicalb

90th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units

Composite-Lined 

Units 

Selenium VI 1 0.8 7E-10 a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis as 

needing further assessment (see Section 3.2.4) were modeled. 

Table 4-11. 50th Percentile Risk Results by CCW Type: Surface 

Impoundments, Groundwater-to-Surface-Water Pathway 

Chemicalb

50th Percentile HQ or Cancer Risk Valuea

Unlined Units Clay-Lined Units 

Composite–Lined 

Units 

Conventional CCW – 44 surface impoundments 

Cancer 

Arsenic III 4E-08 6E-10 0 

Arsenic V 7E-09 2E-11 0 

Noncancer 

Cadmium 6E-04 6E-06 0 

Selenium IV 5E-05 1E-11 0 

Selenium VI 0.02 3E-04 0 

Codisposed CCW and Coal Refuse – 72 surface impoundments 

Cancer 

Arsenic III 6E-08 1E-08 0 

Arsenic V 3E-08 2E-09 0 

Noncancer 

Cadmium 1E-03 2E-04 0 

Selenium IV 8E-05 4E-07 0 

Selenium VI 3E-03 8E-04 0 a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis 

as needing further assessment (see Section 3.2.4) were modeled. 

As with the groundwater-to-drinking-water pathway analysis, the absence of risk from 

composite-lined units indicates that the liners modeled in this analysis are effective at preventing 

contaminants from reaching the surface waterbodies of interest. One should keep in mind that all 

surface water results are calculated assuming that constituents are being added to the 

waterbodies only via groundwater. However, for surface impoundment operation, effluent is 

constantly being discharged directly into that same waterbody. These discharges are regulated 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-17 

under the Clean Water Act, and although they pose an uncertainty in the analysis, they are 

outside the scope of the risk assessment (see Section 4.4.1 for further discussion). 

4.1.2.2 FBC Wastes 

Tables 4-12 and 4-13 show the 90th and 50th percentile risk results for FBC landfills by 

liner type. These results are much lower than those for conventional CCW and comanaged CCW 

landfills seen above, and suggest that releases from FBC landfills do not present a hazard to 

surface waters. This difference may be attributed to lower FBC leachate concentrations and the 

alkaline nature of FBC wastes. However, as with the FBC results reported for drinking water, the 

results here are strongly influenced by the small sample size of site data available. Thus, the 

limitation of only having seven sites may present an uncertainty in the analysis. 

Table 4-12. 90th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Surface-Water Pathway 

90th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

FBC Waste – 7 landfills 

Cancer 

Arsenic III 4E-12 7E-08 0 

Arsenic V 3E-12 3E-08 0 

Noncancer 

Cadmium 7E-07 0.02 0 

Selenium IV 3E-17 8E-03 0 

Selenium VI 5E-06 0.1 0 

Thallium 5E-06 0.2 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis 

as needing further assessment (see Section 3.2.4) were modeled. 

Table 4-13. 50th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Surface-Water Pathway 

50th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

FBC Waste – 7 landfills 

Cancer 

Arsenic III 0 3E-13 0 

Arsenic V 0 6E-14 0 

 (continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-18 

50th Percentile Risk Results for FBC Wastes: Landfills, 

Groundwater-to-Surface-Water Pathway (continued) 

50th Percentile HQ or Cancer Risk Valuea

Chemical Unlined Units Clay-Lined Units 

Composite-Lined 

Units 

Noncancer 

Cadmium 0 2E-05 0 

Selenium IV 0 8E-16 0 

Selenium VI 0 1E-03 0 

Thallium 0 1E-03 0 

a

 Values are HQs for all chemicals except arsenic; arsenic values are cancer risk. Zero results 

indicate that contaminant infiltration rates were too small for the contaminant plume to reach 

the receptor during the 10,000 year period of the analysis. 

b

 Note that only the chemicals with adequate data that were identified in the screening analysis 

as needing further assessment (see Section 3.2.4) were modeled. 

4.1.3 Results by Liner Type 

The effect of liner type on human health risk for the groundwater-to-drinking-water 

pathways can be seen in Tables 4-1 through 4-6 and for the groundwater-to-surface water 

pathway in Tables 4-8 through 4-13, which present risks for WMUs that are unlined, clay lined, 

and lined with composite liners from the 1995 EPRI survey data (EPRI, 1997). At the 90th 

percentile, lined units produced lower risk estimates than unlined units for all constituents 

modeled. Composite liners produced very low to zero risk estimates as compared to clay liners 

for all constituents modeled for both landfills and surface impoundments. For surface 

impoundments, clay liners produced higher risk estimates for all constituents as compared to 

clay liners in landfills. Similar trends are evident at the 50th percentile, where composite liners 

produced risk estimates of zero or near zero for all constituents for surface impoundments. 

Table 4-14 shows how frequent each of the liner types is in the 1995 EPRI survey data 

modeled in this analysis, and it compares these data with the liner type frequency in the more 

recent DOE/EPA study (U.S. DOE, 2006). The 56 WMUs surveyed in the U.S. DOE 2006 study 

were commissioned between 1994 and 2004. Although the actual number of WMUs that were 

established in that timeframe cannot be verified, based on proxy data (i.e., CCW available for 

disposal in those states with identified, new WMUs and coal-fired power plant generating 

capacity), the sample coverage is estimated to be at least 61–63 percent of the total population of 

the newly commissioned WMUs.6

 With the exception of one landfill, the newly constructed 

facilities are all lined, with either clay, synthetic, or composite liners. The single unlined landfill 

identified in the recent DOE report receives bottom ash, which is characterized as an inert waste 

by the state, and therefore, a liner is not required. 

 

6

 For additional details as to how these estimates were derived, the reader is referred to the DOE study, pages S-2 

– S-3 of the Summary Section and Section 3.1.2.. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-19 

Table 4-14. Liner Types in EPRI Survey 

Liner Type Landfills

Surface 

Impoundments

1995 EPRI Surveya – 181 facilities 

Unlined 40% 68% 

Compacted clay 45% 27% 

Synthetic or composite (clay and synthetic) 16% 5% 

Total 100% 100% 

2004 DOE Surveyb – 56 Facilities 

Unlined 3% 0% 

Compacted clay 29% 17% 

Synthetic or composite (clay and synthetic) 68% 83% 

Total 100% 100% 

a EPRI (1997) 

b U.S.DOE (2006) 

As Table 4-14 shows, there is a marked trend away from unlined WMUs in favor of lined 

units, with a distinct preference for synthetic or composite liners. Comparison of the 26 coal 

combustion plants in both the EPRI survey and the DOE/EPA survey (U.S. DOE, 2006) shows 

that although most of those facilities (17 of 26) were using unlined WMUs in 1995, all 26 are 

now placing wastes in new or expanded landfills or surface impoundments that are lined with 

clay, synthetic, or composite liners. However, it is likely that the older unlined units were closed 

with wastes in place, and that these wastes could therefore still pose a threat through 

groundwater pathways. Also, the number of unlined unit that continue to operate in the United 

States cannot be determined from the available data. 

As described in Sections 3.4.1 and 3.5.1, the characteristics of the liners used in the 

CCW risk were taken from the IWEM model as representative of the general performance of 

each liner type. For landfills, an engineered compacted clay liner (3 feet thick, with a hydraulic 

conductivity of 1 × 10-7 cm/s) reduced the 90th percentile risk by a factor of about 2 to 4 

compared to no liner, but did not change the constituents at or above an excess cancer risk of 1 

in 100,000 (arsenic, excess cancer risk of 1 in 5,000) or an HQ of 1 (thallium, HQ of 2). For 

surface impoundments, clay liners did reduce the risk to just below an HQ of 1 for cobalt, lead, 

and selenium. 

Composite (clay and synthetic) liners, as modeled in this risk assessment (see 

Sections 3.4 and 3.5), were much more effective at reducing risk for all constituents; 90th (and 

50th) percentile risks with composite liners for landfills were zero7 for arsenic and metals and 

very low or zero for reactive nitrogen compounds (nitrate and nitrite), and were well below an 

excess cancer risk of 1 in 100,000 or an HQ of 1 for all constituents for surface impoundments. 

The analysis used data collected for composite liner performance at industrial waste management 

 

7

 The absence of risk indicates that contaminant infiltration rates were too small for the contaminant plume to 

reach the receptor well during the 10,000 year period of the analysis. See Section 3.2.2 for a discussion of the 

empirical liner infiltration data used in this analysis. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-20 

facilities, including liner leakage rate for landfills and the number of liner perforations for 

surface impoundments (TetraTech, 2001). Because data on CCW liner leakage rates were not 

available, there is some uncertainty in applying these Industrial D Guidance liner performance 

data to CCW disposal units. Still, these rates do reflect actual performance data from liners under 

real WMUs. They demonstrate that composite liners can be effective in reducing leaching from 

CCW WMUs and suggest that there will be a decrease in risk from CCW disposal if more 

facilities line their WMUs with composite or clay liners. Information from the more recent 

DOE/EPA study (U.S. DOE, 2006) indicates that composite liners are becoming more prevalent 

in newly constructed facilities, so the risks from CCW disposal should be lower for newer CCW 

landfills and surface impoundments.

4.1.4 Constituents Not Modeled in the Full-Scale Assessment 

As described in Section 3.2.4, full-scale modeling was not conducted for all 21 

constituents that were above the screening criteria in the initial screening analysis; only 

constituents that were judged likely to have generally higher risks to human health and 

ecological health were modeled in the full-scale risk assessment.8 Five chemicals (chromium, 

fluoride, manganese, vanadium, and nickel) had drinking water pathway HQs in the screening 

analysis ranging from 1 to less than 6 for surface impoundments, and three (chromium, fluoride, 

and vanadium) had screening HQs of 2 for landfills. 

To address these unmodeled constituents, EPA developed surrogate risk attenuation 

factors by dividing the screening risk results by the full-scale risk results, across all unit (liner) 

types combined, for the constituents modeled in the full-scale assessment. This comparison was 

done only for the drinking water exposure pathway, the only human health exposure pathway for 

which the risks for these constituents were above the screening criteria. Table 4-15 shows the 

risk attenuation factor statistics for the modeled constituents, and Table 4-16 shows the results 

of applying the median and 10th percentile attenuation factors to the screening risk results for the 

marginal constituents. Differences in attenuation among the modeled constituents reflect 

differences in contaminant sorption and mobility. To be conservative, the 10th percentile 

attenuation factor was selected as a high-end value representing the more mobile constituents, 

such as arsenic, selenium, and molybdenum. The 50th percentile (or median) risk represents a 

central tendency value. 

Table 4-15. Risk Attenuation Factora

 Statistics for Modeled Constituents— 

Groundwater to Drinking Water Pathway 

Statistic Landfill Surface Impoundment 

10th percentile 7 1.6

50th percentile 12 2.6

Average 16 3.3

Maximum 40 9.3

 (continued) 

 

8

 These constituents of concern had human health HQs greater than 6 or both ecological HQs greater than 100 at the 

90th percentile. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-21 

Risk Attenuation Factora Statistics for Modeled 

Constituents— 

Groundwater to Drinking Water Pathway (continued) 

Statistic Landfill Surface Impoundment 

Number of data points 9 8 a

 The risk attenuation factor is the ratio of the full-scale analysis risk and 

screening analysis risk for a constituent modeled in the full-scale analysis. 

Table 4-16. Summary of Risk Screening Values for Unmodeled Constituents Using Risk 

Attenuation Factors—Groundwater-to-Drinking-Water Pathway 

Landfill Surface Impoundment 

WMU/Pathway 

Screening 

HQ 

HQ with 

Median 

Attenuation 

HQ with 

10th 

Percentile 

Attenuation 

Screening 

HQ 

HQ with 

Median 

Attenuation 

HQ with 

10th 

Percentile 

Attenuation 

Chromium VI 2.3 0.2 0.3 4.2 1.6 2.6 

Fluoride 1.8 0.2 0.3 5.2 2.0 3.3 

Manganese 1 0.1 0.1 5.6 2.2 3.5 

Vanadium 2.2 0.2 0.3 2.3 0.9 1.4 

Nickel - - - 1.3 0.5 0.8 

For landfills, the risk attenuation factors ranged from 6 to 40, with the lower attenuation 

factors mainly representing the more mobile constituents (i.e., those with lower soil sorption 

potential). Both the median and 10th percentile risk attenuation factors were adequate to reduce 

risks for all nine constituents below an HQ of 1. 

For surface impoundments, risk attenuation factors were considerably lower, ranging 

from 1 to 9, reflecting higher contaminant mobility due to the higher hydraulic head in surface 

impoundments (as compared to landfills) and a lower proportion of liners. For the same reason, 

the screening HQs for surface impoundments were higher than the landfill HQs. As a result of 

this combination of higher HQs and lower risk attenuation factors, only the HQ for nickel was 

reduced to below 1 by applying the attenuation factors. The other constituents (chromium, 

fluoride, manganese, and vanadium) still show risks slightly above an HQ of 1, with HQs 

ranging from 1.4 to 3.5 at 10th percentile attenuation. This is consistent with the general trend in 

this analysis of surface impoundments showing higher risks than CCW landfills. 

4.1.5 Human Health (Groundwater and Fish Consumption) Damage Case Review 

Table 4-17 summarizes the proven damage cases from U.S. EPA (2007) that showed an 

impact on groundwater, usually through an exceedence of an MCL or state groundwater standard 

for one or more metals. As detailed in U.S. EPA (2007), these facilities represent worst-case 

disposal conditions: all are unlined, several represent fills in old quarries, and many have wastes 

disposed of below the water table. Groundwater standard exceedences are usually onsite or 

closely offsite. As one can see in the table, the same metals showing risk exceedences for 

unlined facilities (arsenic, boron, cadmium, lead, molybdenum, and selenium) in this analysis 

were reported as exceedences in the groundwater damage cases. Other incidents of groundwater 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-22 

contamination supporting the conclusions of this risk assessment can be found in the published 

literature in references such as Lang and Schlictmann (2004) and Zilmer and Fauble (2004). 

Table 4-18 summarizes the five proven damage cases from U.S. EPA (2007) that showed 

a fish consumption advisory for selenium. Although these were all cases where CCW surface 

impoundments directly discharged to a lake, and hence larger fluxes of surface impoundment 

waters into the waterbody of interest than through the groundwater-to-surface-water pathway, 

they do support the finding of this risk assessment that the fish consumption pathway is of 

potential concern for selenium in CCW. 

Table 4-17. Summary of Proven Damage Cases with Groundwater Impacts 

(U.S. EPA, 2007) 

Proven NODA Damage Casea Reported Groundwater Impacts 

1. Salem Acres Site, MA (lagoons and fly ash pile) Minor – As, Cr, Pb 

2. City of Beverly/ Vitale Brothers Fly Ash Pit, MA (quarry 

fill) 

Al, As, Fe, Mn, Se over MCLs 

3. Don Frame Trucking, Inc. Fly Ash Landfill, NY Pb, Mn over MCLs 

4. Virginia Electric Power Co. (VEPCO) Possum Point, VA 

(ash ponds) 

Cd, Ni over MCLs 

5. PEPCO Morgantown Generating Station Faulkner Offsite Disposal Facility, MD (landfills and settling ponds) Low pH, iron staining 

6. Virginia Power Yorktown Power Station Chisman Creek 

Disposal Site, VA (quarry fill) 

Se, sulfate over MCL; green staining; As, Be, Cr, 

Cu, Mo, Ni, V over background 

9. DOE Oak Ridge Y-12 Plant Chestnut Ridge Operable 

Unit 2, TN (ash pond) 

Al, As, Fe, Pb, Mn over MCL 

10. South Carolina Electric & Gas Canadys Plant, SC (ash 

ponds) 

As above MCL outside compliance boundary; NI 

above state standard 

13. Dairyland Power Cooperative E.J. Stoneman Generating 

Station Ash Disposal Pond, WI 

Cd, Cr, sulfate, Mn, Fe, and Zn over MCLs 

onsite; B over background offsite 

14. WEPCO Highway 59 Landfill, WI As, Se, sulfate, B, Mn, Cl-, Fe over state 

standards 

15. Alliant Nelson Dewey Ash Disposal Facility, WI As, Se, sulfate, B, F- over state standards 

16. WEPCO Cedar-Sauk Landfill, WI Se, sulfate over MCLs; B over state standard 

17. WEPCO Port Washington Facility, WI (quarry fill) B over state standard; elevated Se 

18. Lansing Board of Water & Light North Lansing Landfill, 

MI (quarry fill) 

Li, Mn, Se above state standards 

19. Northern Indiana Public Service Corp. Yard 520 Landfill 

Site, IN 

As, B, Mn,, Mo, Pb contaminated residential 

wells 

23. Basin Electric Power Cooperative W.J. Neal Station 

Surface Impoundment, ND 

Al, As, Cd, Cr, Zn above MCL 

24. Cooperative Power Association/United Power Coal 

Creek Station Surface Impoundments, ND 

As, Se, sulfate, Cl above MCL; elevated B 

a Numbers represent original case numbers in U.S. EPA (2007) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-23 

Table 4-18. Summary of Proven Damage Cases with Fish Consumption Advisories 

(U.S. EPA, 2007) 

Proven NODA Damage Case Reported Fish Consumption Advisory 

7. Hyco Lake, Roxboro, North Carolina (surface 

impoundment discharge) 

Selenium fish consumption advisory 

11. Belews Lake, NC (surface impoundment discharge) Selenium fish consumption advisory 

20. Brandy Branch Reservoir, Texas (ash pond discharge) Selenium fish consumption advisory 

21. Southwestern Electric Power Company Welsh Reservoir, 

TX (ash pond discharge) 

Selenium fish consumption advisory 

22. Texas Utilities Electric Martin Lake Reservoir, TX (ash 

pond discharge) 

Selenium fish consumption advisory; elevated 

selenium in birds 

EPA has also found that CCW contaminants of concern in the damage cases agree with 

those exceeding a 1 in 100,000 excess cancer risk or an HQ of 1 in this analysis, building 

confidence that the risk assessment captures national conditions. Table 4-19 compares the 

results from the 2007 draft risk assessment with the damage cases reported in the Coal 

Combustion Waste Damage Case Assessments (U.S. EPA, 2007) for the groundwater pathway. 

Table 4-19. Modeled 90th and 50th Percentile Risk Results vs. 

Reported Groundwater Exceedences 

Constituent 

2007 Risk Assessmenta Damage Casesb

Consistent 

Results as of 

2007f 90th %ile 50th %ile 

Human 

Health 

Effectsc

Cosmetic/ 

Aesthetic 

Effectsd

State 

Standarde

Aluminum – – – T – Yes 

Antimony T – – – – No 

Arsenic T T T – T Yes 

Barium – – – – – Yes 

Beryllium Screened – – – Yes 

Boron T – T – T Yes 

Cadmium T – T – – Yes 

Chloride Not Screened – T T N/A 

Chromium RAF T – – Uncertain 

Cobalt T T – – – No 

Copper Screened – – – Yes 

Fluoride RAF – – T Uncertain 

Iron Not Screened – T T N/A 

Lead T – T – – Yes 

Lithium Not Screened – – T N/A 

Manganese RAF T T T Uncertain 

Molybdenum T – T – – Yes 

Nickel RAF T – T Uncertain 

Nitrate/Nitrite T – – – – No 

Selenium T – T – T Yes 

Silver Screened – – – Yes 

Sulfate Not Screened – T T N/A 

Thallium T – – – – No 

Vanadium RAF – – – Uncertain 

(continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-24 

Modeled 90th and 50th Percentile Risk Results vs. 

Reported Groundwater Exceedences (continued) 

Constituent 

2007 Risk Assessmenta Damage Casesb

Consistent 

Results as of 

2007f 90th %ile 50th %ile 

Human 

Health 

Effectsc

Cosmetic/ 

Aesthetic 

Effectsd

State 

Standarde

Zinc Screened – T – Yes a

 Not Screened = Constituent was not considered due to lack of health-based benchmarks. 

Screened = Constituent showed no risk potential in the screening assessment. 

RAF = Constituent showed risk potential in the screening assessment, and was analyzed with risk 

attenuation factors. 

T = Constituent underwent full probabilistic modeling and was shown to pose a risk to human 

health in the landfill scenario, the surface impoundment scenario, or both. 

– = Constituent underwent full probabilistic modeling and was not shown to pose a risk to human 

health 

b T = At least one proven damage case showed an exceedence of this constituent. 

 – = No proven damage cases have yet shown an exceedence of this constituent. 

c T = Exceedences of primary maximum contaminant levels (MCLs) or other health-based numbers 

published by EPA. 

d T = Exceedences of secondary MCLs, which would not result in harm to human health. 

e T = Exceedences of a relevant state standard. 

f Yes = Results of risk assessment and damage cases either both indicated a risk to human health or 

both indicated no risk to human health. 

No = The risk assessment indicated risks where none have yet been found in a proven damage case. 

Uncertain = It is possible that the results were consistent, but due to lack of probabilistic modeling, no 

definitive conclusion can be made. 

N/A = Constituent was not examined at any stage in the 2007 risk assessment, so it was not possible 

to draw any conclusions as to consistency. 

The first category of constituents is those for which the risk assessment and the damage 

cases agree, either because both the risk assessment results and the damage cases indicated risks, 

or because both the risk assessment results and damage cases did not indicate risks. The former 

group had model results exceeding the cancer risk range or an HQ of 1, and also appeared in the 

damage cases with exceedances of maximum contaminant levels (MCLs), state groundwater 

standards, or other health-based numbers (arsenic, boron, cadmium, lead, molybdenum, and 

selenium). The latter group did not show the potential for risks above an HQ of 1 from the risk 

assessment and did not appear in the damage case literature (aluminum, barium, beryllium, 

copper, silver, and zinc). 

The second category of constituents is those for which the risk assessment and the 

damage cases did not agree. Four modeled constituents (antimony, cobalt, thallium, and 

nitrate/nitrite) showed risk at the 90th percentile but no damage cases had been proven as of 

2007. This could indicate that (1) the risk assessment was conservative for these constituents, (2) 

not enough time has passed to see the remaining constituents appear in damage cases, (3) 

corrective action was taken when the first constituent(s) was observed, so further constituents 

that would have appeared at the same site were never seen, or (4) these constituents are not 

tested for as frequently as the constituents found in the proven damage cases. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-25 

The third category of constituents is those that were not screened out, and were analyzed 

using risk attenuation factors (chromium, fluoride, manganese, nickel, and vanadium). Because 

all that is known is that these constituents have the potential to pose a risk to human health, they 

cannot currently be compared to the damage case results. 

The final category of constituents is those that were not evaluated at either the screening 

or modeling stages because no health-based values were available for comparison. These four 

constituents (chlorine, iron, lithium, and sulfate) appeared in damage cases because of 

exceedences of aesthetic or state levels, not because of a known risk to human health. 

Table 4-20 compares the results from the 2007 draft risk assessment with the damage 

cases reported in U.S. EPA (2007) for the fish consumption pathway. The only fish consumption 

advisories documented in CCW damage cases are for selenium. This is consistent with the risk 

assessment for selenium. The two constituents that do not pose a risk in the risk assessment 

(cadmium and thallium) were also not part of any fish consumption advisories in the damage 

cases. The one inconsistency is arsenic, for which the risk assessment shows a cancer risk of 1 in 

50,000, slightly exceeding an excess cancer risk of 1 in 100,000. However, no arsenic fish 

consumption advisories exist at proven damage case sites. This inconsistency could indicate that 

(1) the risk assessment was conservative with respect to arsenic, (2) not enough time has passed 

to see arsenic appear in fish advisories at these sites, or (3) the arsenic exceedences have not 

been detected in random fish tissue samples thus far. 

Table 4-20. Modeled 90th and 50th Percentile Risk Results vs. 

Reported Fish Consumption Exceedences 

Constituent 

2007 Risk Assessmenta

Damage Casesb Consistent Results as of 2007c 90th %ile 50th %ile 

Arsenic T – – No 

Cadmium – – – Yes 

Selenium T – T Yes 

Thallium – – – Yes 

a T = Constituent underwent full probabilistic modeling and was shown to pose a risk to human health in 

the landfill scenario, the surface impoundment scenario, or both. 

– = Constituent underwent full probabilistic modeling and was not shown to pose a risk to human health 

b T = At least one proven damage case showed a fish consumption advisory for this constituent. 

 – = No proven damage cases have yet shown a fish consumption advisory for this constituent. 

c

 Yes = Results of risk assessment and damage cases either both indicated a risk to human health or both 

indicated no risk to human health. 

 No = The risk assessment indicated risks where none have yet been found in a proven damage case. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-26 

4.2 Ecological Risks 

EPA defines ecological risk characterization in terms of (1) the risk estimation, which 

integrates the exposure and stressor-response profile to estimate the likelihood of adverse 

ecological effects and (2) the risk description, which synthesizes the overall conclusion of the 

assessment and addresses assumptions, uncertainty, and limitations. 

For assessments that are based on a HQ approach, as this one was, the comparison of 

modeled exposure concentrations to CSCLs to estimate risk has a binary outcome: either the 

constituent concentration is above the concentration corresponding to an HQ of 1 or the 

concentration is less than or equal to the concentration corresponding to an HQ of 1. For the fullscale analysis, an ecological HQ greater than 1 was selected by EPA as a criterion for decision 

making. Because the CSCLs were based on de minimis ecological effects, it is generally 

presumed that an HQ at or below 1 indicates a low potential for adverse ecological effects for 

those receptors included in the analysis for which data are available. However, it is important to 

recognize that although this method provides important insight into the potential for adverse 

ecological effects, the results are relevant only to those receptors that were included in the 

assessment and for which data were available. The results have limited utility in interpreting the 

ecological significance of predicted effects, and caution should be exercised in extrapolating to 

ecosystems (e.g., wetlands) and receptors (e.g., threatened and endangered species) not explicitly 

modeled. 

This section presents risk results for direct surface impoundment exposure (as evaluated 

in the 1998 CCW risk assessment, U.S. EPA, 1998a,b), screening results for boron that indicate 

risks to plants from aboveground exposure, and the two groundwater-to-surface-water ecological 

exposure pathways investigated in the full-scale analysis: (1) receptors exposed to CCW 

constituents in the water column (surface water receptors) and (2) receptors exposed to CCW 

constituents in bed sediment (sediment receptors). Results are presented for the two WMU types 

addressed in the analysis: landfills and surface impoundments, and are broken out separately for 

the different unit (liner) types. Finally, ecological damage case reports from U.S. EPA (2007) 

and from the published literature are summarized as field evidence supporting the conclusions of 

this risk assessment. 

The ecological risk results and damage cases suggest the potential for adverse ecological 

effects to plants, terrestrial organisms, and aquatic systems from CCW releases into the 

subsurface and subsequent connection with surface waters, particularly for CCW managed in 

unlined surface impoundments. As with human health risks, the higher prevalence of liners in 

newer facilities should result in lower risks in current and future CCW disposal facilities than 

those presented in this risk assessment. 

4.2.1 Direct Surface Impoundment Exposure 

The current risk assessment addresses exposure to receptors in offsite surface 

waterbodies impacted by groundwater, where both the aquatic communities and upper trophic 

level terrestrial receptors would need to be protected.7

 The 2003 CCW constituent database used 

 

7 The 2002 CCW constituent database does not include impoundment water samples, and the direct exposure 

pathway was not addressed. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-27 

in this analysis does not include impoundment water samples, and the direct exposure pathway 

could not be addressed for ecological risk. However, the CCW risk assessment conducted in 

1998 (U.S. EPA, 1998a,b) did consider direct exposure of ecological receptors to surface 

impoundment waters. The approach in the 1998 study restricted the analysis to terrestrial 

receptors that obtain food and prey from the surface impoundments and excluded aquatic 

receptors living in the water column because surface impoundments are not intended to be a 

habitat for aquatic species. For the terrestrial and aquatic receptors considered, the 1998 analysis 

used the same CSCLs and a similar methodology to that used in the CCW screening analysis 

(e.g., comparison of 90th percentile waste concentrations with CSCLs). 

The 1998 direct exposure results are provided in Figure 4-1 and show HQs greater than 

100 for boron, selenium, lead, barium, and cadmium. This, along with the damage case results 

presented in Section 4.2.4, show a clear likelihood of risks to terrestrial organisms that obtain 

food and prey from CCW surface impoundments. It is probable that ecological receptors eat and 

drink from CCW surface impoundments in some settings. In addition, ecological receptors, 

particularly amphibians who may lay their eggs in surface impoundments, are probably exposed 

through chronic contact with wastewater. Because amphibians are prey to a large variety of 

animals (e.g., raptors; wading birds; mammalian omnivores, such as foxes, raccoons, and 

weasels), this exposure is transferred up the food chain. Aquatic plants, although not often a 

focus of this ecological risk assessment, are directly exposed in surface impoundments. Plants, in 

turn, may be ingested by vertebrates and invertebrates at higher trophic levels. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-28 

0.1 1 10 100 1000 10000 100000 1000000

B

Hg

As V

Pb

Co

Al

Ba

Se, total

Cd

Se VI

As III

Cr VI

Cu

V

Ni

Ag

Se IV

Be

Zn

Cr III

Tl

Mo

Sb

Analytes

Ecological Hazard Quotient (HQ)

2002 SI Porewater (GW-to-SW exposure pathway) 1998 SI Wastewater (direct exposure)

Figure 4-1. CCW surface impoundment ecological screening risks: 

Direct exposure to surface impoundment wastewater. 

4.2.2 Surface Water Receptors (Full-Scale Analysis) 

Tables 4-21 and 4-22 present the 90th and 50th percentile results from the full-scale 

ecological risk assessment of the groundwater-to-surface-water pathway for surface water 

receptors for CCW landfills and surface impoundments. For landfills, boron (HQ of 281) , lead 

(HQ of 8), selenium (HQ of 2), arsenic (HQ of 2), and barium (HQ of 2) show risks above an 

HQ of 1 at the 90th percentile for the unlined units. Clay liners reduce the risks below an HQ of 

1 for all constituents except for boron, which still has a very high HQ (78 for the clay liner 

versus 281 for unlined). For surface impoundments, all modeled constituents except cadmium 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-29 

and aluminum showed 90th percentile risks above the ecological risk criterion, with boron 

showing an HQ over 2,000 for the unlined units, and other HQs ranging from 3 to 22 for unlined 

units. The 50th percentile results are all well below an HQ of 1 for landfills and only exceed an 

HQ of 1 for boron in unlined surface impoundments (HQ = 7). 

As with other pathways and receptors, the difference in the number and magnitude of 

ecological HQs that exceed the risk criterion between landfills and surface impoundments is 

likely the result of (1) higher CCW constituent concentrations in surface impoundment 

porewater and (2) the greater flux of contaminants to groundwater predicted during the active 

life of the surface impoundment. As discussed in Section 4.1, the higher infiltration rates for 

surface impoundments result from a higher hydraulic head in the impoundment and a higher 

proportion of unlined surface impoundments than landfills in the 1995 EPRI survey data used for 

this risk assessment. 

Table 4-21. Summary of 90th Percentile Full-Scale CCW Ecological Risk Results: 

Groundwater-to-Surface-Water Pathway, Aquatic Receptorsa

Chemical 

90th Percentile Ecological HQ 

Exposure 

Unlined Units Pathway Receptor 

Clay-Lined 

Units

CompositeLined Units

Landfills 

Boron 281 78 0.07 direct contact aquatic biota 

Lead 8 0.4 2E-06 ingestion river otter 

Selenium (VI) 2 0.7 3E-04 direct contact aquatic biota 

Arsenic (V) 2 0.1 4E-08 direct contact aquatic biota 

Barium 2 0.2 0 direct contact aquatic biota 

Cadmium 0.5 0.1 3E-05 direct contact aquatic biota 

Aluminum 0.01 0.003 1E-07 direct contact aquatic biota 

Surface Impoundments 

Boron 2,375 854 257 direct contact aquatic biota 

Lead 22 7 2 ingestion river otter 

Arsenic (V) 13 4 5 direct contact aquatic biota 

Selenium (VI) 12 4 1 direct contact aquatic biota 

Cobalt 6 3 5 direct contact aquatic biota 

Barium 3 1 0.8 direct contact aquatic biota 

Cadmium 1 0.7 0.4 direct contact aquatic biota 

Aluminum 0.03 0.01 0.008 direct contact aquatic biota a

 Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to 

reach the receptor during the 10,000 year period of the analysis. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-30 

Table 4-22. Summary of 50th Percentile Full-Scale CCW Ecological Risk Results: 

Groundwater-to-Surface-Water Pathway, Aquatic Receptorsa

Chemical 

50th Percentile Ecological HQ 

Exposure 

Unlined Units Pathway Receptor 

Clay-Lined 

Units

CompositeLined Units

Landfills 

Boron 0.2 0.1 0 direct contact aquatic biota 

Lead 7E-05 4E-08 0 ingestion river otter 

Selenium (VI) 0.002 0.001 0 direct contact aquatic biota 

Arsenic (V) 4E-06 5E-09 0 direct contact aquatic biota 

Barium 1E-10 4E-12 0 direct contact aquatic biota 

Cadmium 2E-04 9E-05 0 direct contact aquatic biota 

Aluminum 3E-07 8E-09 0 direct contact aquatic biota 

Surface Impoundments 

Boron 7 0.4 5E-05 direct contact aquatic biota 

Lead 0.05 0.0008 0 ingestion river otter 

Arsenic (V) 0.03 0.0007 0 direct contact aquatic biota 

Selenium (VI) 0.03 0.002 4E-07 direct contact aquatic biota 

Cobalt 0.01 0.001 0 direct contact aquatic biota 

Barium 0.006 0.0004 0 direct contact aquatic biota 

Cadmium 0.008 0.0003 0 direct contact aquatic biota 

Aluminum 0.0007 4E-05 4E-11 direct contact aquatic biota 

a

 Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to 

reach the receptor during the 10,000 year period of the analysis. 

4.2.3 Sediment Receptors (Full-Scale Analysis) 

Tables 4-23 and 4-24 present the 90th and 50th percentile results of the ground-water-tosurface-water pathway for sediment receptors for landfills and surface impoundments. For 

unlined landfills, lead (HQ of 58), arsenic (HQ of 11), cadmium (HQ of 5), and antimony (HQ of 

2) show 90th percentile risks above the ecological risk criterion. For clay lined landfills, only 

arsenic (HQ of 3) has an ecological HQ greater than 1. For surface impoundments, lead, arsenic, 

and cadmium showed 90th percentile HQs above 1 for unlined, clay-lined, and composite-lined 

units (with HQs ranging from 2 to 311). Although cadmium was not above the risk criterion in 

surface water, it did have an HQ of 30 in sediments at the 90th percentile for unlined surface 

impoundments and HQs of 9 and 2 for clay- and composite-lined impoundments respectively. 

None of the constituents modeled showed sediment risks at or above an HQ of 1 at the 50th 

percentile. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-31 

Table 4-23. Summary of 90th Percentile Full-Scale CCW Ecological Risk Results: 

Groundwater-to-Surface-Water Pathway, Sediment Receptorsa

Chemical 

90th Percentile Ecological HQ 

Exposure 

Unlined Units Pathway Receptor 

Clay-Lined 

Units

CompositeLined Units

Landfills 

Lead 58 1 1E-06 direct contact aquatic biota 

Arsenic (III) 11 3 5E-04 ingestion river otter 

Cadmium 5 1 6E-05 direct contact aquatic biota 

Antimony 2 0.5 7E-05 direct contact aquatic biota 

Molybdenum 0.1 0.03 2E-05 direct contact aquatic biota 

Barium 0.006 6e-04 0 direct contact aquatic biota 

Surface Impoundments 

Lead 311 58 4 direct contact aquatic biota 

Arsenic (III) 127 55 31 ingestion river otter 

Cadmium 30 9 2 direct contact aquatic biota 

Molybdenum 0.9 0.3 0.1 direct contact aquatic biota 

Barium 0.008 0.004 0.002 direct contact aquatic biota a

 Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to 

reach the receptor during the 10,000 year period of the analysis. 

Table 4-24. Summary of 50th Percentile Full-Scale CCW Ecological Risk Results: 

Groundwater-to-Surface-Water Pathway, Sediment Receptorsa

Chemical 

50th Percentile Ecological HQ 

Exposure 

Unlined Units Pathway Receptor 

Clay-Lined 

Units

CompositeLined Units

Landfills 

Lead 6E-05 9E-08 0 ingestion spotted sandpiper 

Arsenic (III) 4E-03 0.002 0 ingestion spotted sandpiper 

Cadmium 5E-04 2E-04 0 direct contact sediment biota 

Antimony 3E-04 1E-04 0 direct contact sediment biota 

Molybdenum 5E-05 3E-05 0 ingestion spotted sandpiper 

Barium 3E-13 8E-15 0 ingestion spotted sandpiper 

Surface Impoundments 

Lead 0.1 0.001 0 ingestion spotted sandpiper 

Arsenic (III) 0.4 0.02 4E-09 ingestion spotted sandpiper 

Cadmium 0.02 0.0007 0 direct contact sediment biota 

Molybdenum 0.004 0.0002 2E-08 ingestion spotted sandpiper 

Barium 1E-05 1E-06 0 ingestion spotted sandpiper a

 Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to 

reach the receptor during the 10,000 year period of the analysis. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-32 

4.2.4 Constituents Not Modeled in the Full-Scale Assessment 

As described in Section 3.2.4, full-scale modeling was not conducted for 6 constituents 

with generally lower risks to ecological receptors.9

 These chemicals (chromium, vanadium, 

beryllium, copper, silver, and zinc), had surface water pathway HQs in the screening analysis 

ranging from 16 to 110 for landfills, and four (chromium, vanadium, copper, and silver) had 

screening HQs ranging from 14 to 33 for surface impoundments. 

These constituents were addressed using risk attenuation factors developed by dividing 

the screening risk results by the full-scale risk results for the constituents that were modeled in 

the full-scale assessment. Tables 4-25 and 4-26 show the results of this comparison for the 

surface water ecological risk exposure pathway. Table 4-23 shows the risk attenuation factors for 

the modeled constituents, and Table 4-24 shows the results of applying the median (central 

tendency) and 10th percentile (conservative) attenuation factors to the screening risk results for 

constituents that were not modeled. 

For landfills, the risk attenuation factors ranged from 50 to 2,000. Both the median and 

10th percentile risk attenuation factors were adequate to reduce risks to an HQ below 1 for all 

constituents except for silver. Although silver shows an HQ of 1.5 using the 10th percentile 

attenuation factor, silver’s low mobility would probably result in a higher attenuation factor (i.e., 

at the median or greater). 

For surface impoundments, risk attenuation factors ranged from 7.1 to 64, reflecting 

higher contaminant mobility from the higher hydraulic head in the surface impoundments and a 

lower prevalence of liners (compared to landfills) in the 1995 EPRI data. HQs were reduced 

below 1 for all four unmodeled constituents with the median attenuation factor (38), and the HQ 

for silver was reduced to 0.8 by applying the 10th percentile attenuation factor (17). The other 

three constituents (chromium, vanadium, and copper) show HQs slightly above 1 with the10th 

percentile attenuation (HQs ranging from 1.4 to 1.9). Note that the risks for chromium are based 

on the protective assumption of 100 percent hexavalent chromium in CCW. 

Table 4-25. Risk Attenuation Factora

 Statistics for Modeled Constituents— 

Ecological Risk, Surface Water Pathway (all unit types combined) 

Statistic Landfill Surface Impoundment 

10th percentile 75 17 

50th percentile 178 38 

Average 483 38 

Maximum 2,000 64 

Number of data points 6 7 

a The risk attenuation factor is the ratio of the full-scale analysis risk and 

screening analysis risk for a constituent modeled in the full-scale analysis. 

 

9

 These constituents had only one or no ecological HQs greater than 100. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-33 

Table 4-26. Summary of Risk Screening Values for Unmodeled Constituents Using Risk 

Attenuation Factors—Ecological Risk, Surface Water Pathway 

WMU/Pathway

Landfill Surface Impoundment 

Screening 

HQ 

HQ with 

Median 

Attenuation 

HQ with 10th 

Percentile 

Attenuation 

Screening 

HQ 

HQ with 

Median 

Attenuation

HQ with 10th 

Percentile 

Attenuation

Chromium VI 18 0.1 0.2 33 0.9 1.9 

Vanadium 23 0.1 0.3 24 0.6 1.4 

Beryllium 24 0.1 0.3 - - - 

Copper 16 0.09 0.2 31 0.8 1.8 

Silver 110 0.6 1.5 14 0.4 0.8 

Zinc 16 0.09 0.2 - - - 

4.2.5 Ecological Damage Cases 

Cases of damages to terrestrial and aquatic organisms from improperly managed CCW 

are common in the literature. For example, Carlson and Adriano (1993) summarize such damage 

incidents, including those resulting from alkaline CCW effluent discharge to surface waterbodies 

and boron toxicity to plants. Rowe et al. (2002) provide a more comprehensive review, 

assessment, and meta-analysis of the ecotoxicity of CCW, focusing on aquatic disposal (i.e., 

CCW surface impoundments) and tabulating damages from over 20 years of field and laboratory 

studies in the published literature. Selenium and arsenic are most commonly associated with 

CCW damages to terrestrial and aquatic organisms. Cadmium, boron, chromium, and lead are 

also associated with CCW ecological risk. Hopkins et al. (2006) show deformities and 

reproductive effects in amphibians living on or near CCW disposal sites in Georgia, which are 

mainly attributed to selenium exposure. 

Table 4-27 summarizes the proven CCW ecological damage cases from U.S. EPA 

(2007). Most of these cases are from surface impoundments and direct discharge into lakes and 

other water bodies. Along with the published results discussed in Section 4.1.5, these cases 

clearly support selenium and arsenic in coal ash as risks to aquatic ecosystems, as well as the 

adverse impacts of coal ash on terrestrial vegetation. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-34 

Table 4-27. Summary of Proven Damage Cases with Ecological Impacts 

(U.S. EPA, 2007) 

Proven NODA Damage Case Reported Ecological Impacts 

2. City of Beverly/ Vitale Brothers Fly Ash Pit, MA 

(quarry fill) 

Contamination of wetlands and surface waters 

5. PEPCO Morgantown Generating Station 

Faulkner Off-site Disposal Facility, MD 

(landfills and settling ponds) 

Vegetative damages, contamination of stream and 

wetland by GW 

6. Virginia Power Yorktown Power Station 

Chisman Creek Disposal Site, VA (quarry fill) 

As, Be, Cr, Cu, Mo, Ni, Se, V contamination of onsite 

ponds and offsite creek 

7. Hyco Lake, Roxboro, North Carolina (surface 

impoundment discharge) 

Se fish advisory; fish reproduction and population effects 

8. Georgia Power Company, Plant Bowen, 

Cartersville, GA (ash pond over sinkhole) 

Ash slurry release damaged creek 

9. DOE Oak Ridge Y-12 Plant Chestnut Ridge 

Operable Unit 2, TN (ash pond) 

Se, As, Tl elevated in bass; As over screening criteria; 

deformed fish; stress on aquatic ecosystem; Se plant and 

mammal uptake 

11. Belews Lake, NC (surface impoundment 

discharge) 

Fish advisory for Se; 16 of 20 fish species eliminated 

from lake 

12. U.S. Department of Energy Savannah River 

Project, SC (landfill) 

Impacts on amphibians (deformities) and snake 

(metabolic effects) 

16. WEPCO Cedar-Sauk Landfill, WI Wetland vegetative damage from B in groundwater 

20. Brandy Branch Reservoir, Texas (ash pond 

discharge) 

Se fish consumption advisory 

21. Southwestern Electric Power Company Welsh 

Reservoir, TX (ash pond discharge) 

Se fish consumption advisory 

22. Texas Utilities Electric Martin Lake Reservoir, 

TX (ash pond discharge) 

Se fish consumption advisory; elevated Se in birds 

4.3 Sensitivity Analysis 

EPA conducted a sensitivity analysis (U.S. EPA, 2009b) on the probabilistic risk 

assessment to determine which model inputs were most important to risk, which in turn helped 

focus additional analyses and data collection efforts on the most important drivers of risk, and 

helped identify the important factors to consider when evaluating regulatory and management 

options for CCW. The sensitivity analysis also helped identify parameters that are both sensitive 

and highly uncertain, which affects the confidence in the results. 

The CCW sensitivity analysis used a response-surface regression method that derives a 

statistical model for risk (as the dependent variable) based on the input parameters from the 

probabilistic analysis (as independent variables). Environmental concentration (rather than risk) 

was chosen as the dependent variable for the sensitivity analysis because (1) there is a direct, 

linear relationship between environmental concentrations and risks and (2) the additional inputs 

used to calculate risk from environmental concentration (i.e., exposure factors, such as body 

weight, ingestion rates) are lifestyle variables that are not amenable to regulation to reduce or 

manage risk. Furthermore, these variables have well-established, peer-reviewed, national 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-35 

distributions, which are regularly used in the probabilistic national risk analyses conducted by 

EPA. Therefore, the contribution of the exposure factors to the variability in risk was not 

particularly useful for the primary purposes of the sensitivity analysis, to better understand 

sources of uncertainty in the CCW risk results and to help focus regulatory development on 

sensitive variables that can be addressed through the RCRA regulatory process. 

The outputs from the sensitivity analysis were goodness-of-fit values for the regression 

models and the relative importance of each input parameter in determining environmental 

concentrations across different WMU, waste type, and constituent scenarios. The goodness-of-fit 

values of the regression models were moderate to very good for the drinking water pathway 

(R2

=0.53–0.90) and good to very good for fish consumption (R2=0.76–0.90). In general, the 

drinking water pathway had more input parameters that were significant (seven) than the fish 

consumption pathway (three). The most sensitive parameters for most (over 70 percent) of the 

drinking water scenarios10 evaluated were parameters impacting groundwater flow: 

 Infiltration rate within the WMU footprint 

 Leachate concentration from the WMU 

 Aquifer hydraulic conductivity and groundwater gradient (i.e., groundwater velocity). 

For many (over 30 percent) of the scenarios, including those corresponding to strongly 

sorbing contaminants (i.e., metals with high soil/water partition coefficients), sorption and travel 

time parameters are also important, including 

 Adsorption isotherm coefficient 

 Depth to groundwater 

 Receptor well distance. 

For the fish consumption pathway, only three variables were consistently significant 

across scenarios: 

 Infiltration rate within the WMU footprint 

 Leachate concentration from the WMU 

 Waterbody flow rate. 

Additional detail on how the CCW sensitivity analysis was conducted can be found in 

U.S. EPA (2009b). In terms of the model inputs, the sensitivity analysis found that the most 

consistent drivers of the risk results were constituent concentration in waste leachate (i.e., the 

source term for the risk assessment and infiltration rate through the WMU), which is largely 

controlled by the liner conditions and, to a lesser extent, soil type and (for landfills only) 

precipitation. These variables and their uncertainties are discussed in the following section. 

 

10 Scenarios represent unique combinations of WMU, waste type, chemical, exposure pathway, and receptor. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-36 

4.4 Variability and Uncertainty 

Variability and uncertainty are different conceptually in their relevance to a probabilistic 

risk assessment. Variability represents true heterogeneity in characteristics, such as body weight 

differences within a population or differences in 

pollutant levels in the environment. It accounts for 

the distribution of risk within the exposed 

population. Although variability may be known 

with great certainty (e.g., age distribution of a 

population may be known and represented by the 

mean age and its standard deviation), it cannot be 

eliminated and needs to be treated explicitly in the 

assessment. Uncertainty is a description of the 

imperfection in knowledge of the true value of a 

particular parameter. In contrast to variability, 

uncertainty can be reduced through additional 

information-gathering or analysis (i.e., better data, better models). EPA typically classifies the 

major areas of uncertainty in risk assessments as scenario uncertainty, model uncertainty, and 

parameter uncertainty. Scenario uncertainty refers to missing or incomplete information needed 

to fully define exposure and dose. Model uncertainty is a measure of how well the model 

simulates reality. Parameter uncertainty is the lack of knowledge regarding the true value of a 

parameter used in the assessment. 

Uncertainty and variability can be addressed two ways: 

 By varying parameter values in a probabilistic assessment such as a Monte Carlo analysis 

 By comparing the data or results to other data or other studies such as damage cases or 

alternative results based on different assumptions. 

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, spatial variability in soil, aquifer, and climate data was accounted for 

by using distributions for soil and aquifer properties around the facility when the actual 

environmental characteristics around a WMU are uncertain. Conversely, 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 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. 

Because CCW is generated nationwide, its disposal may occur anywhere in the United 

States. Thus, this assessment characterized environmental conditions that influence the fate and 

transport of constituents in the environment using site-specific data collected around coal-fired 

power plants with onsite CCW disposal facilities. Spatial variability in environmental setting 

was accounted for by the site-to-site variables for the 181 CCW disposal sites modeled in the 

Variability arises from true heterogeneity in 

characteristics, such as body weight 

differences within a population or differences 

in contaminant levels in the environment. 

Uncertainty represents a lack of knowledge 

about factors such as the nature of adverse 

effects from exposure to constituents, which 

may be reduced with additional research to 

improve data or models. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-37 

analysis using 41 different climate regions and 9 different hydrologic regions throughout the 

contiguous 48 states. 

In summary, a distribution of exposures was developed that included specific 

consideration of the variability in the following sensitive model parameters 

 WMU characteristics, in particular liner type (which strongly influences infiltration rate) 

 CCW constituent concentrations in waste leachate 

 Distance to nearest well 

 Site-specific environmental conditions (especially groundwater flow conditions) 

 Human exposure factors. 

Uncertainty was also considered in the analysis by using reasonable ranges and 

distributions when variables were not known exactly. For example, when a soil texture or 

groundwater flow conditions could not be precisely assigned at a site, multiple soil types or 

hydrogeologic environments were sampled based on the soil and aquifer types that were likely to 

be present at the site. 

The treatment of variability and uncertainty in model parameters using a Monte Carlo 

simulation formed the basis for the national exposure distributions used in this analysis to 

estimate risk. Previous sections of this document describe how EPA generated distributions and 

estimated input parameter values and then used these values in models to estimate risk. The 

discussion in this section focuses on how this treatment of variability and uncertainty affects the 

analysis results and on various comparisons we performed on the results or critical input data to 

evaluate uncertainty. Table 4-28 lists the more important uncertainties described in this section, 

along with whether the uncertainty is likely to underestimate or overestimate risk, or if its effect 

on the risk results is uncertain. 

Table 4-28. Summary of CCW Uncertainties and Their Effect on Risk Estimates 

Uncertainty Likely Effect on Risk 

Overestimates Uncertain Underestimates 

Scenario Uncertainties 

CCW Management Unit Data (1995 EPRI Survey) T

Liner type (as built, 1995; liners more prevalent today) T 

Direct discharge from CCW impoundments (not addressed 

in CCW risk assessment; covered by NPDES) T

Effect of the 10,000-year timeframe for groundwater 

(complete leaching, long timeframe) T 

Receptor populations evaluated (high-end receptor and 

child living near CCW WMU) T 

Additive risks across pathways (not considered) T

Co-occurrence of ecological receptors and constituents T 

Ecosystems and receptors at risk T

(continued) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-38 

Summary of CCW Uncertainties and Their Effect on Risk Estimates (continued) 

Uncertainty Likely Effect on Risk 

Overestimates Uncertain Underestimates 

Model Uncertainties 

Clean closure of surface impoundments T

Arsenic and selenium speciation T 

100% bioavailability of constituents to ecological 

receptors T 

Compaction of landfilled waste T 

Landfills above water table T

Indirect ecological effects (not considered) T

Full mixing effects on aquifer pH (full mixing assumed; 

effect depends on constituent) T T T

Goethite versus hydrous ferric oxide sorbent T 

Multiple constituent exposures (not considered) T

Parameter Uncertainties 

Waste concentrations (2002 CCW constituent database) T

Appropriateness of leachate data (TCLP results) T

(noncancer) 

T

(cancer) 

Constituents with many nondetect analyses (e.g., mercury) T

Treatment of nondetect analyses at half detection limit T

WMU locations (1995 EPRI survey data) T

WMU characteristics (1995 EPRI survey liner types, unit 

sizes) T 

Well location (MSW landfill survey data) T

Well location (well always within plume) T 

Location and characteristics of waterbodies T

Soil and aquifer characteristics T

Waterbodies intercepting the groundwater plume T 

Human exposure factors T

All drinking water from CCW-contaminated well) T 

Human health benchmarks T

(cancer) 

T

(noncancer) 

Ecological benchmarks T

4.4.1 Scenario Uncertainty 

Sources of scenario uncertainty include the assumptions and modeling decisions that are 

made to represent an exposure scenario. Because this risk assessment attempted to characterize 

current conditions by estimating risks from actual CCW disposal sites across the country, it was 

subject to less scenario uncertainty than risk assessments that rely on hypothetical conceptual 

models. However, certain aspects of the scenario are uncertain. 

CCW Management Unit Data. The landfills and surface impoundments modeled in this 

risk assessment were placed, sized, and lined according to data from the 1995 EPRI survey 

(EPRI, 1997). New data collected by EPA and DOE since this risk assessment was conducted 

(U.S. DOE, 2006) indicate that liners are much more prevalent in WMUs constructed or 

expanded from 1994 through 2004 than in units in place before that. This suggests that the risks 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-39 

may be lower for future CCW disposal facilities (although most of the unlined WMUs have been 

closed with wastes remaining in the units). 

Liner-related questions are especially important because liner configurations greatly 

influence infiltration rates, one of the most sensitive parameters in the risk assessment. In terms 

of risks through groundwater pathways, this risk assessment has shown that liners, in particular 

composite (combined clay and synthetic) liners, can limit risks through subsurface exposure 

pathway, and the DOE/EPA survey shows that liners are more prevalent in newly constructed 

WMUs and WMU expansions. Although the DOE/EPA survey does not shed light on how many 

unlined facilities are still operating today, it does indicate that more units are lined today than 

were in the 1995 EPRI survey data set on which this risk assessment was based. 

Although it would have been possible to address this uncertainty by evaluating different 

hypothetical liner scenarios for each facility, such an approach was outside the original scope of 

this risk assessment, which was to evaluate current CCW management activities, not 

hypothetical management scenarios. Furthermore, this approach likely would not have changed 

the general conclusion of the risk assessment that composite lined landfills pose less risk than 

clay lined landfills and that unlined landfills pose the greatest risk. 

Direct Discharge of CCW Impoundments into Surface Water. Because this risk 

assessment addressed CCW disposal under RCRA, it did not include risks from the direct 

discharge of wastes into waterbodies, which are regulated under the Clean Water Act. Although 

not relevant for the management of RCRA waste disposal, EPA recognizes that CCW surface 

impoundment effluent may pose additional risks. 

Effect of the 10,000-Year Timeframe for Groundwater. The risk assessment assumed 

that contaminant concentrations in the leachate remain constant throughout the 10,000-year 

modeling timeframe, although leaching may or may not persist for 10,000 years, depending on 

model inputs. The waste concentration model input was assumed to be a portion of the total 

waste concentration available to be leached, and it was assumed that 100% of the constituent in 

the waste could leach from the landfill. The nonlinear fate and transport solution used for 

metallic constituents in the unsaturated zone module of EPACMTP is based on the assumption 

that the leachate concentration released from the waste management unit is constant over time 

(see Section 3.3.5.3 of U.S. EPA, 2003b). Although a leaching profile that changes over time 

might be more realistic, the simplified leaching profile used by the model does not lead to a 

poorer estimate of risk associated with groundwater exposures. The adoption of a simplified 

leaching profile to support a non-linear sorption approach in the unsaturated zone offered a 

greater benefit and defensibility to the overall approach than assuming linear partitioning and a 

depleting leachate profile would have. 

Receptor Populations Evaluated. The human receptors evaluated for the CCW risk 

analysis were a family with children residing near the CCW disposal facility, drinking from a 

private well screened in a surficial aquifer or eating fish caught from a nearby stream or lake 

impacted by CCW leachate. Additionally, except for a 15-day vacation, it was assumed that 

adults and children were exposed daily and that the private well was the only source of drinking 

water. Although it is possible for other types of individuals to be exposed, the use of the resident 

adult and child as protective of other receptors and pathways is a high-end, simplifying 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-40 

assumption of the analysis. The lack of information to define and model actual exposure 

conditions also introduces uncertainty into this assessment, but EPA believes that the national 

distribution of exposure factors used is appropriate for a national assessment. 

In addition, not all possible exposure pathways were evaluated. For example, the risk 

assessment did not consider potential indirect exposure to humans through game species that 

may have been exposed to surface impoundment waste (e.g., deer drinking surface impoundment 

water). This represents a potential uncertainty in the analysis. 

Additive Risks Across Pathways. The human receptors evaluated in the CCW risk 

assessment were assumed not to consume both contaminated fish and drinking water from the 

same waterbody because untreated surface water is not considered potable water (municipal 

water treatment facilities were assumed to reduce contaminant levels prior to consumption). EPA 

also did not consider the potential cumulative exposure from contaminated fish and groundwater 

in the CCW risk assessment, because the exposures are likely to occur over different timeframes 

(because of differences in transit time of the contaminant plume to wells versus surface 

waterbodies) and may involve different receptors (because a resident near a CCW surface 

impoundment or landfill and exposed via groundwater may not be a recreational fisher). 

Although this could potentially miss some higher exposures for a maximally exposed individual, 

analysis of the individual pathway results does not indicate that adding such risks would change 

the conclusions of this risk assessment in terms of the constituents exceeding the risk criteria. 

Also, risks were high enough for single chemicals for human exposure pathways (notably 

arsenic) that this would not change the basic conclusion of the risk assessment that there are 

potentially significant risks to human health from CCW disposal in landfills and surface 

impoundments. 

Co-Occurrence of Ecological Receptors and Constituents. As a simplification for 

national-scale analyses in the absence of site-based data, co-occurrence of the ecological 

receptors and the constituents of concern is typically assumed. However, the prior probability 

that a receptor will be found in waterbodies affected by constituent releases from CCW WMUs 

is not known, nor is it known whether a receptor will forage for food in contaminated areas or if 

those areas do, in fact, support the type of habitat needed by the receptor. Although the 

assumption of co-occurrence was necessary for this analysis, relatively few field studies are 

available to demonstrate the relationship between adverse ecological effects and constituent 

releases from CCW as it is currently managed. 

Ecosystems and Receptors at Risk. One challenge in conducting a predictive ecological 

risk assessment intended to reflect risks at a national scale is representing all of the receptors and 

ecosystems at risk. In Wastes from the Combustion of Coal by Electric Utility Power Plants - 

Report to Congress (U.S. EPA, 1988b), the authors pointed out that plants or animals of concern 

were located within a 5-km radius of the CCW WMUs at 12 to 32 percent of the sites. Although 

these figures are of limited spatial resolution, they suggest the possibility that threatened and 

endangered species or critical habitats may be at risk from CCW constituents. Examples of other 

critical assessment endpoints not evaluated in this analysis include the following: 

 Managed Lands: Because protected lands play a critical role in preserving plant and 

animal species, managed areas in the United States represent well-recognized ecological 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-41 

values. Managed lands refer to a variety of lands designated by the federal government as 

worthy of protection, including National Wildlife Refuges, National Forests, Wilderness 

areas, and National Recreation areas. 

 Critical Habitats: Although critical habitats may be defined in a number of ways (e.g., 

presence of threatened species, decreasing habitat area), wetlands are widely recognized 

as serving critical ecological functions (e.g., maintenance of water quality). The U.S. Fish 

and Wildlife Service estimates that approximately 45 percent of the Nation’s threatened 

and endangered species directly depend on aquatic and wetland habitats. Consequently, 

impacts of chemical stressors on wetland habitats may have high ecological (and societal) 

significance. The presence of critical habitats such as wetlands is also used to inform the 

selection of ecological receptors (e.g., amphibians, waterfowl) and the construction of 

appropriate food webs. 

 Threatened and Endangered Species: For most ecological risk assessments of chemical 

stressors, available data on toxicity and biological uptake are sufficient to support the 

evaluation of effects on representative species populations or generalized communities 

(e.g., the aquatic community). However, despite their obvious value, threatened and 

endangered species are frequently excluded from the analytical framework for national 

rulemakings. The assessment of threatened and endangered species requires a sitespecific approach in which locations, habitats, and species of concern are identified and 

characterized with respect to the spatial scale of constituent releases. 

Although these classes of receptors and potential ecological hazards are not explicitly considered 

in the analysis, conditions represented by simulations in the upper end of the risk distribution 

(higher risk scenarios) should reasonably characterize many situations with such sensitive 

species or habitats. 

Impact on Groundwater as a Resource. The risk assessment did not explicitly consider 

potential impacts on the availability of groundwater in the future (e.g., contaminated 

groundwater becoming unsuitable for consumption), but the results do clearly indicate that there 

can be a reduction in resource availability if CCW is improperly disposed. However, the scope of 

the risk assessment was to evaluate human health and ecological effects associated with current 

waste disposal practices and conditions, and a quantitative evaluation of potential future 

reductions in groundwater availability as a consequence of CCW disposal practices was not 

conducted as part of this analysis. 

4.4.2 Model Uncertainty 

Model uncertainty is associated with all models used in a risk assessment because models 

and their mathematical expressions are simplifications of reality that are used to approximate 

real-world conditions and processes and their relationships. Computer models are simplifications 

of reality, requiring exclusion of some variables that influence predictions but that cannot be 

included in models either because of their complexity or because data are lacking on a particular 

parameter. Models do not include all parameters or equations necessary to express reality 

because of the inherent complexity of the natural environment and the lack of sufficient data to 

describe the natural environment. Because this was a probabilistic assessment that predicted 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-42 

what may occur with the management of CCW under actual scenarios, it is possible to compare 

the results of these models to specific situations. 

The risk assessor needs to consider the importance of excluded variables on a case-bycase basis, because a given variable may be important in some instances and not important in 

others. A similar problem can occur when a model that is applicable under one set of conditions 

is used for a different set of conditions. In addition, in some instances, choosing the correct 

model form is difficult when conflicting theories seem to explain a phenomenon equally well. In 

other instances, EPA does not have established model forms from which to choose to address 

certain phenomena, such as facilitated groundwater transport. 

The models used in this analysis were selected based on science, policy, and professional 

judgment. These models were selected because they provide the information needed for this 

assessment and because they are generally considered to reflect the state of the science. Even 

though the models used in this analysis are used widely and have been accepted for numerous 

applications, they each retain significant sources of uncertainty. These limitations are well 

documented in the model development references cited in Section 3. 

Although the sources of model uncertainty in this assessment could result in either an 

overestimation or an underestimation of risk, the models used in this assessment have been 

developed over many years to support regulatory applications. As a result, they have been 

designed to be protective of the impacted populations that they represent. In other words, where 

simplifying assumptions are necessary, the assumptions are made in a way that will not 

underestimate risk. 

Assumption of Clean Closure of Surface Impoundments. As described in Section 

3.5.1, the surface impoundment model treats a surface impoundment as a temporary waste 

management unit with a set operational life. At the end of this life, clean closure is assumed; all 

wastes are removed and there is no further release of waste constituents to groundwater. 

Although this simplifying assumption is not consistent with the practice to close CCW surface 

impoundments with wastes in place, and it limits the length of potential exposure, the peak 

annual leachate concentrations on which the CCW risk results were based are not likely to be 

affected. Releases to groundwater are much higher during surface impoundment operation 

because the higher hydraulic head in an operating impoundment drives wastewater into the 

underlying soil with greater force than infiltration through the impoundment cover after the 

impoundment is closed. This higher head results in a greater flux of contaminants to groundwater 

during the active life of the surface impoundment, especially in unlined units. Thus, even if the 

post-closure period were modeled, the corresponding results would not be as high as the peak 

annual leachate concentrations used in the analysis. 

Arsenic and Selenium Speciation. Because the models used in this assessment do not 

speciate metals during soil or groundwater transport, arsenic and selenium speciation in the 

subsurface is a significant groundwater modeling uncertainty in this analysis. Arsenic can occur 

in either a +3 (arsenic III) or +5 (arsenic V) oxidation state in groundwater, with arsenic III 

being the more mobile form. Selenium can occur in either a +4 (selenium IV) or +6 (selenium 

VI) oxidation state in groundwater, with selenium VI being the more mobile form. Because the 

soil and groundwater models assume one form for each model run, the risk results presented for 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-43 

arsenic and selenium were originally based on 100% arsenic III and selenium VI, which is a 

high-end assumption (i.e., arsenic III has higher risks than arsenic V and selenium VI has higher 

risks than selenium IV). Although arsenic is generally thought to occur in the +3 form in 

leachate, there is evidence from damage cases at CCW disposal sites that suggests that arsenic III 

is converted to arsenic V during subsurface transport at some sites (see, for example, U.S. EPA, 

2000, 2003e; Lang and Schlictmann, 2004; Zillmer and Fauble, 2004). To address the 

uncertainty of running the model with 100% arsenic III and selenium VI, the models were also 

run assuming 100% arsenic V and selenium IV. The results from the two species should bracket 

the results expected given some mixing of oxidation states. 

Bioavailability of Constituents to Ecological Receptors. For the purposes of this 

analysis, the model assumed that all forms of a constituent were equally bioavailable to 

ecological receptors, and therefore, the actual exposures that may occur in the field tend to be 

overestimated, thus making this a high-end assumption. Both the chemical form and the 

environmental conditions influence bioavailability and ultimately the expression of adverse 

effects. For example, as discussed above, the form of arsenic has been shown to profoundly 

influence mobility and toxicity. 

Compaction of Landfilled Waste. The source model did not consider potential 

compaction of CCW waste over time. Such compaction could decrease the hydraulic 

conductivity and the associated water infiltration. However, no readily available data were 

identified to support an analysis of the influence of CCW compaction on infiltration rates. The 

current approach would tend to overestimate infiltration rates compared to a model that would 

adjust the hydraulic conductivity over time due to compaction. EPA believes this is an 

appropriately conservative assumption given the lack of the information needed to accurately 

model the effects of waste compaction. 

Landfills Assumed to be Above Water Table. The landfill source model and 

EPACMTP assume that the source is above the water table. However, some actual CCW 

disposal units do extend below the water table. Because waste intersecting the saturated zone 

may increase groundwater concentrations, the approach may underestimate risk in some cases. 

However, including this effect would strengthen a general conclusion of the analysis that 

potentially unacceptable risks exist in some cases with unlined and clay lined CCW landfills. 

Indirect Ecological Effects. Indirect ecological effects (e.g., depletion of food resources) 

were not considered in the analysis. For any given facility, the spatial scale of potential 

contamination would affect a very small proportion of the home range for typical species; 

determining impacts on food supply and habitat quality with regard to the landscape and overall 

health of the animals is not currently possible in a national-level assessment (and difficult to 

understand or estimate in the majority of site-specific assessments). In addition, many species 

are opportunistic feeders and will seek other areas if food sources decline, regardless of the 

source of the stress to the food supply. For these reasons, EPA does not believe that it is possible 

to consider indirect ecological effects in a national risk assessment like CCW. 

Aquifer pH. As explained in Section 3.4, aquifer pH was used to select the metal 

sorption coefficients that were in turn used to calculate retardation coefficients for groundwater 

transport of the CCW constituents. To estimate pH in an aquifer impacted by CCW leachate, the 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-44 

CCW risk analysis assumed that, after entering the aquifer, the leachate plume thoroughly mixes 

with the ambient, uncontaminated groundwater. However, because this mixing zone is largely at 

the periphery of the groundwater plume, thorough mixing may or may not occur at actual sites. 

The full mixing assumption results in higher receptor point concentrations for most metals, 

because metal sorption and precipitation tend to increase (i.e., Kd goes up) with higher pH and 

full mixing tends to reduce the pH of CCW leachate, which is normally alkaline (i.e., assuming 

full mixing results in a lower groundwater pH and lower sorption). 

To assess the effect of this simplifying assumption on the risk results, we compared two 

landfill Monte Carlo simulations for coal ash waste containing As(III) and coal ash waste 

containing As(V): (1) the fully mixed aquifer assumption and (2) an assumption that no mixing 

occurs in the aquifer and the leachate pH is the governing pH for Kd selection. These two metal 

species were selected because their sorption isotherm behavior with pH change differs; Kds 

derived from As(III) isotherms tend to decrease as pH increases (which is typical of most metal 

species examined in the risk assessment), while Kds derived from As(V) isotherms tend to 

increase with increasing pH. 

Percentiles of peak receptor well concentration from the As (III) and As (V) simulations 

were selected and compared by calculating the percent change with mixing assumption as 

follows: 

100

C

CC

Mix Full

Mix No Mix Full × − Change % =

where 

CNo Mix = Simulated peak receptor well concentration for a select percentile based on a 

no mixing assumption (mg/L) 

CFull Mix = Simulated peak receptor well concentration for a select percentile based on a 

fully mixed assumption (mg/L) 

Table 4-29 compares the percent change in peak receptor well As (III) and As (V) 

concentrations between the well mixed and no mixing scenarios over a range of peak well 

percentiles. The results indicate that As(V) has a sensitivity to pH that leads to increased 

receptor well concentrations under the no mixing assumption (i.e., when the leachate pH is used 

to determine Kd in the saturated zone) relative to the well-mixed assumption used in the risk 

assessment. These results suggest that a change in the complete leachate mixing assumption 

could raise the receptor well concentrations (and therefore risks) for metal constituents whose 

Kd values decrease with increasing pH. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-45 

Table 4-29. Change in Peak Receptor Well Concentrations for Ash Disposed 

in Landfills Assuming Leachate Does Not Mix in Aquifer 

Percentile of Peak 

Concentration 

Percent Change in Peak Concentration 

As(III) As(V) 

10 0.00% 0.00% 

20 0.00% 0.00% 

30 0.91% 0.00% 

40 0.25% 0.00% 

50 0.31% 2.28% 

60 0.00% 15.57% 

70 0.23% 57.97% 

80 0.00% 18.31% 

90 0.00% 11.75% 

Goethite Versus Hydrous Ferric Oxide Sorbent. The choice of iron sorbent is 

important because goethite is a much poorer adsorbent than hydrous ferric oxide and will result 

in larger leachate contaminant concentration. With respect to the use of goethite versus the use of 

hydrous ferric oxide, EPA had discussions with Dr. David Dzombak and Dr. Samir Mathur 

(developer of the goethite database). In these discussions, the group discussed the sorbent 

question extensively, and EPA chose to use goethite rather than hydrous ferric oxide as a best 

estimate that would not underestimate risk. However, because actual CCW disposal sites could 

have hydrous ferric oxide present in their soils, the risks for arsenic could be overestimated. 

Multiple Constituent Exposures. The individual human risk from each CCW 

constituent was considered separately in this analysis. However, the CCW waste constituent 

database and recent field studies such as U.S. EPA (2006c) and U.S. EPA (2008c) suggest that 

exposure to multiple constituents is highly likely. Because multiple constituent exposure may be 

synergistic depending on the constituents, certain constituent combinations may cause adverse 

health impacts that a single-constituent approach may underestimate. However, the quantitative 

human health benchmarks used by EPA are based on the toxicity of individual chemicals. With 

only one carcinogen present in CCW (arsenic), it was not necessary to add carcinogenic risks. 

Noncarcinogenic risks can be added only for chemicals with toxic effects on the same target 

organs, and this could have been done for fish and drinking water ingestion risks by accounting 

for transit time and adding HQs for contaminants with noncancer effects on the same target 

organs that arrive at the same time to the receptor point. 

However additivity across chemicals was not considered in this risk assessment; neither 

was synergism or antagonism. Noncancer hazard may, therefore, be under- or overestimated. 

Nevertheless, risks were high enough from human exposure to single chemicals (notably arsenic, 

the single carcinogen) that this would not have changed the basic conclusion of the risk 

assessment: that there are potentially significant risks to human health from CCW disposal in 

landfills and surface impoundments. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-46 

4.4.3 Parameter Uncertainty and Variability 

Parameter uncertainty occurs when (1) there is a lack of data about the values used in the 

equations, (2) the data that are available are not representative of the particular instance being 

modeled, or (3) parameter values have not been measured precisely or accurately because of 

limitations in measurement technology. Random, or sample, errors are a common source of 

parameter uncertainty that is especially critical for small sample sizes, as illustrated by the FBC 

waste results discussed in Section 4.1.3.2. More difficult to recognize and address are 

nonrandom or systematic errors that can bias the analyses from sampling errors, faulty 

experimental designs, or bad assumptions. 

Spatial and temporal variability in parameters used to model exposure account for the 

distribution in the exposed population. For example, the rainfall or precipitation rates used to 

calculate infiltration and recharge to groundwater are measured daily by the National Weather 

Service at many locations throughout the United States, and statistics about these parameters are 

well documented. Although the distributions of these parameters may be well known, their actual 

values vary spatially and temporally and cannot be predicted exactly. Thus, the annual average 

infiltration rates used in the source model for a particular climate station provide information on 

average conditions appropriate for this analysis. Additionally, using data from multiple climate 

stations located throughout the United States can account for some, but not all, spatial 

variability.

4.4.3.1 Waste Concentrations 

The CCW constituent database used to represent CCW total waste and waste leachate 

concentrations is arguably the most important data set in terms of driving the risk assessment 

results. The constituent data are subject to two primary uncertainties beyond the normal 

sampling and analysis uncertainty associated with environmental measurements: (1) the 

appropriateness of the landfill leachate data used in the analysis and (2) high percentages of 

nondetect analyses for some CCW constituents. 

Appropriateness of Leachate Data. The CCW leachate data were collected from a 

varying number of sites using a variety of methods. The available landfill data were largely 

derived from the TCLP, a laboratory test designed to estimate leachate concentrations in 

municipal solid waste (MSW) landfills. The TCLP has been shown to both over- and 

underpredict leachate concentrations for other waste disposal scenarios, so the use of the TCLP 

data to represent CCW leachate is another source of uncertainty. However, as noted below, the 

TCLP data do appear to encompass the range of variability in CCW leachate concentrations that 

have been measured in more recent studies. 

Surface impoundment leachate is represented by porewater measurements taken beneath 

actual impoundments, which should more closely represent the leachate seeping from the bottom 

of the impoundment than would bulk surface impoundment waste concentrations. The porewater 

is in direct contact with the waste, so these concentrations should typically be at least as great as 

concentrations in the bulk surface impoundment. However, although these porewater data 

arguably should better represent leachate concentrations, they are fewer in number than the 

landfill data and therefore subject to uncertainty as to how representative they are of all CCW 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-47 

wastes. Results for surface impoundments for antimony, mercury, and thallium are not presented 

due to the paucity of leachate data (1 or 2 sites, and 11 or fewer values). 

Since the CCW risk assessment was conducted in 2003, EPA-sponsored research 

conducted by Vanderbilt University has improved the scientific understanding of the generation 

of leachate from CCW, in particular for mercury, arsenic, and selenium (U.S. EPA, 2006c; U.S. 

EPA, 2008c). Figure 4-2 plots the results from U.S. EPA (2006c) for arsenic and selenium, 

along with data from EPA’s Leach2000 database and EPRI (as provided in U.S. EPA, 2006c) 

against the data used for landfills and surface impoundments used in the CCW analysis. 

Arsenic

0.0001

0.001

0.01

0.1

1

10

Brayton

Point

Pleasant

Prairie

Salem

Harbor

Facility C St. Clair

Facility

Facility L CCW -

LF

EPRI -

LF

EPA - LF CCW -

SI

EPRI - SI

Vanderbilt Study Other Data Sets

As (mg/L)

max

Natural pH

min

95th %

50th %

5th %

Selenium

0.0001

0.001

0.01

0.1

1

10

Brayton

Point

Pleasant

Prairie

Salem

Harbor

Facility C St. Clair

Facility

Facility L CCW -

LF

EPRI -

LF

EPA - LF CCW -

SI

EPRI - SI

Vanderbilt Study Other Data Sets

Se (mg/L)

max

Natural pH

min 95th %

50th %

5th %

Key to data sets: 

Vanderbilt = U.S. EPA (2006c) 

 CCW = CCW Constituent Database (this analysis) 

EPRI = EPRI Leachate data (from U.S. EPA, 2006c) 

EPA = Leach 2000 data (from U.S. EPA, 2000) 

 LF = landfills 

 SI = surface impoundments 

Figure 4-2. Comparison of CCW leachate data with other leachate data in 

U.S. EPA (2006c). 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-48 

For the 2006 Vanderbilt leaching study report, data are provided for each ash tested, with 

the minimum, maximum, and value at natural pH plotted on the chart. Percentile values (95th, 

50th, 5th) are plotted for the compiled data sets (EPA, EPRI, and CCW), and mercury was not 

modeled for landfills because of a high number of nondetects. 

For arsenic, the CCW values bracket the range found in the other studies. Selenium 

values also agree fairly well for CCW landfill data, although the CCW landfill values appear to 

be lower than some of the values from the other studies, suggesting that selenium risks may have 

been somewhat underestimated for landfills in this analysis. This is significant even though 

selenium risks from landfills were not above an HQ of 1 in this analysis, because selenium is 

often reported as a constituent of concern (along with arsenic and boron) in CCW damage cases 

(U.S. EPA, 2000, 2003e; Lang and Schlictmann, 2004; Zillmer and Fauble, 2004). 

U.S. EPA (2008c) extends the work in U.S. EPA (2006c) to include laboratory leaching 

studies of 23 CCWs sampled from 8 coal combustion power plants. Wastes tested included fly 

ash, scrubber sludges, and gypsum. All of the metals addressed in this risk assessment were 

measured in the laboratory leaching tests. 

Similar to Figure 4-2 above, Figures 46–59 on pages 77–86 in U.S. EPA (2008c) 

compare constituent concentration ranges in their laboratory CCW extracts to ranges reported by 

other CCW leachate data compilations, including the constituent data from this risk assessment. 

These graphs are not repeated here, but the conclusions are similar to the U.S. EPA (2006c) 

comparisons, in that the ranges of metals concentrations generally plot within the range reported 

for the laboratory tests, especially with fly ash and flue gas desulfurization sludges. For ash 

codisposed with coal refuse metal, concentrations tend to be an order of magnitude or more 

greater than the wastes studied in U.S. EPA (2008c), which did not include such codisposed 

wastes. Only two CCW metals plot largely outside the range for fly ash. Barium fly ash 

concentrations from the CCW risk assessment are an order of magnitude or more lower than 

those reported by U.S. EPA (2008c), and lead concentrations in the fly ash and FGD wastes 

modeled in this risk assessment are one to two orders of magnitude above those plotted in U.S. 

EPA (2008c). The latter may be an artifact of the predominance of TCLP measurements in the 

CCW constituent database, because the acetate buffer in the TCLP can be especially effective in 

complexing lead compounds into the extract solution. Finally, a few of the Vanderbilt 

measurements for molybdenum and selenium are above the range modeled in the CCW risk 

assessment. 

The fact that the 2006 and 2008 Vanderbilt results are in general agreement with the 

CCW arsenic and selenium levels does help allay concerns that the TCLP CCW leachate values 

used in the analysis markedly overestimate or underestimate the concentrations actual CCW 

leachate. 

Mercury and Nondetect Analyses. For certain of the CCW constituents addressed in 

this analysis, the CCW leachate database contains a large number of nondetect measurements 

(concentrations below an analytical instrument’s ability to measure). Table 4-30 illustrates this 

point by showing, by WMU type and chemical, the overall percent of nondetect values for each 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-49 

chemical and the percent of site-averaged values11 that are composed entirely of nondetect 

measurements. Although some constituents have a large number of nondetect values, many of 

those could still be modeled (substituting half the detection limit for nondetect values). Where 

there are detections for a chemical, the specific substitute value used for nondetect values does 

not affect the upper percentile risks, because the upper percentile risks are associated with the 

higher, detectable source concentrations in the distribution rather than the lower source 

concentrations associated with nondetect values. Values for nondetects will be in the lower 

percentiles whether they are half the detection limit or some other value. 

Table 4-30. Proportion of Nondetect Analyses for Modeled CCW Constituents 

Chemicala

Measurements Sites 

Number % nondetects Number % with all nondetects 

Landfills 

Aluminum 397 18% 61 5% 

Antimony 496 50% 66 41% 

Arsenic 1,182 49% 128 20% 

Barium 1,225 11% 126 5% 

Boron 930 8% 83 2% 

Cadmium 1,237 50% 124 31% 

Cobalt 559 56% 52 19% 

Lead 1,109 60% 125 30% 

Mercury 974 91% 101 58% 

Molybdenum 373 24% 58 10% 

Nitrate/Nitrite 141 48% 20 15% 

Selenium 1,227 49% 131 17% 

Thallium 402 60% 40 45% 

Surface Impoundments 

Aluminum 158 10% 16 6% 

Antimony 11 100% 2 100% 

Arsenic 155 16% 16 6% 

Barium 161 14% 16 13% 

Boron 164 7% 171 6% 

Cadmium 164 68% 16 50% 

Cobalt 49 59% 4 50% 

Lead 138 78% 14 36% 

Mercury 1 100% 1 100% 

Molybdenum 161 37% 17 24% 

Nitrate/Nitrite 267 59% 14 7% 

Selenium 140 33% 15 20% 

(continued) 

 

11 As explained in Appendix A, the CCW risk assessment used site-averaged constituent concentrations. That is, 

an average value was used when there were multiple measurements for a chemical at a particular site. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-50 

Proportion of Nondetect Analyses for Modeled CCW Constituents 

(continued) 

Chemicala

Measurements Sites 

Number % nondetects Number % with all nondetects 

Thallium 11 100% 2 100% 

a

 Results for constituents shown in bold italics were not presented in this report because of high 

detection limits or limited data.

Constituents that could not be addressed in this analysis because of a very high number 

of nondetects (i.e., more than 90 percent of measurements) included mercury (for landfills and 

surface impoundments) and thallium and antimony (for surface impoundments only). Mercury is 

of particular interest because it is the only constituent with significant concern through the fish 

consumption pathway, and because there is the potential for mercury concentrations in CCW to 

increase as flue gas mercury controls are installed on coal-fired power plants in response to the 

Clean Air Interstate Rule (CAIR) and the Clean Air Mercury Rule (CAMR). However, analysis 

of the effect of mercury emission controls was outside the scope of the risk assessment, which 

was to evaluate current waste disposal conditions, not potential future changes due to emission 

controls. 

Recent work by Vanderbilt University (U.S. EPA, 2006c, 2008c) sheds some light on 

mercury concentrations in leachate from some CCWs. Figure 4-3 plots the CCW distribution of 

mercury concentrations (assuming half the detection limit for mercury values below detection) 

against results from the Vanderbilt work and recent data collected by EPRI (from U.S. EPA, 

2006c; results are similar in U.S. EPA, 2008c). Assuming half the detection limit, the CCW 

mercury leachate values are about an order of magnitude or more higher than the Vanderbilt or 

EPRI data. With a single CCW leachate analysis available for surface impoundments, it is 

difficult to draw firm conclusions, but the concentration value is above the maximum value 

shown in the other studies. In short, the mercury levels in the CCW database are not useful 

because of high detection limits. In addition, the Vanderbilt study found that older mercury 

analyses, such as the ones in the CCW database, could be biased high because of crosscontamination issues. 

Finally, U.S. EPA (2006c) and preliminary results of ongoing EPA studies (e.g., U.S. 

EPA, 2008c) suggest that both mercury levels and mercury leachability in CCW can vary 

depending on the flue gas mercury controls used at a power plant. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-51 

Mercury

0.0000001

0.000001

0.00001

0.0001

0.001

0.01

0.1

Brayton

Point

Pleasant

Prairie

Salem

Harbor

Facility C St. Clair

Facility

Facility L CCW -

LF

EPRI -

LF

CCW -

SI

EPRI - SI

Vanderbilt Study Other Data Sets

Hg (mg/L)

max

Natural 

pH

min

95th %

50th %

5th %

Key to data sets: 

Vanderbilt = U.S. EPA (2006c) 

CCW = CC Constituent Database (this analysis) 

EPRI = EPRI Leachate data (from U.S. EPA, 2006c) 

EPA = Leach 2000 data (from U.S. EPA, 2000, as cited in U.S. EPA, 2006c) 

 LF = landfills 

 SI = surface impoundments 

Figure 4-3. CCW mercury concentrations compared with other leachate data. 

4.4.3.2 WMU Locations and Characteristics 

The locations of the specific sites in the United States where CCW is disposed are 

known, and EPA used the soil and climatic characteristics of these sites in the Monte Carlo 

analysis. Because most locations were facility front gates or centroids, the exact location of the 

CCW landfill or surface impoundment was not known. To account for this uncertainty, soil data 

were collected for an area around the plant and soil type distributions were sampled in the Monte 

Carlo analysis. Climate center assignments were combined with the soil texture distributions to 

select infiltration and recharge rates to use in the analysis. 

WMU area, depth, volume, and liner type were not varied in the Monte Carlo analysis 

because values for these variables were known from the EPRI survey data. More uncertain 

parameters, such as depth below grade, were varied within reasonable ranges. These data were 

used in the source model calculations to generate the distribution of environmental releases used 

by the fate and transport modeling. 

Three standard WMU liner scenarios (clay, composite, and unlined) were assigned to 

each facility based on best matches to data in the EPRI survey on liner type. Infiltration through 

these liners was then modeled using assumptions, models, and data developed in support of 

EPA’s Industrial Subtitle D guidance. How well these assumptions and models represent the 

performance of CCW WMU landfills and surface impoundments is an uncertainty in this 

analysis. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-52 

With respect to the clay liners, the 2009 risk assessment used the assumption that 

compact clay liners were designed to have a hydraulic conductivity of 1×10-7 cm/sec. This is 

consistent with EPA’s Industrial D Guidance, which states that “clay liners should be at least 

2 feet thick and have a maximum hydraulic conductivity of 1×10-7 cm/sec” (U.S. EPA, 2006d). 

However, clay liners designed to meet a 1×10-7 cm/sec hydraulic conductivity could perform 

differently in practice. In one liner study (Moo-Young et al., 2004), a small set of clay-lined 

landfills were found to have field hydraulic conductivities ranging from 2×10-9 to 

4.4×10-8 cm/sec and a small set of surface impoundments were found to have field hydraulic 

conductivities ranging from 3×10-6 to 3.2×10-5 cm/sec. Thus, the assumption of clay liners 

performing at 1×10-7 cm/sec could lead to an under- or over-estimate of actual risks. 

Composite liners would also not be expected to perform consistently over 10,000 years as 

was assumed in the model. Instead, the liner would eventually perform at the level of the clay 

layer once the synthetic layer had deteriorated. This simplification is likely to lead to an 

underestimate of composite liner risks. 

4.4.3.3 Fate and Transport Model Variables 

The parameter values required to model contaminant fate and transport in groundwater 

were obtained from site-specific, regional, and national databases. Hydrogeologic environment 

was assigned to each site, based on geologic maps and soil conditions; where assignments were 

uncertain, two or three settings might be used in the Monte Carlo analysis. Because aquifer 

properties are highly variable and uncertain, reasonable sets of aquifer properties were selected, 

based on hydrogeologic environment, from a hydrogeologic database. 

Receptor Location (Drinking Water Wells). The sensitivity analysis (Section 4.3) 

showed that distance of a receptor from the contaminant source is an important influence on 

media concentration, especially for contaminants that strongly sorb to soil and aquifer materials. 

For the groundwater-to-drinking-water pathway, receptor location was represented as the 

distance and position, relative to a contaminant plume, of residential drinking water wells from 

the WMU. Because no data were readily available on the distance of CCW disposal sites from 

residential wells, EPA used data from a survey of well distances from MSW landfills. Whether 

or not this is an accurate representation of well distance for CCW landfills and surface 

impoundment is an uncertainty in this analysis. EPA believes that the MSW well distance 

distribution used is protective for CCW landfills and surface impoundments. See Appendix C, 

Section C.2, for more details. 

Location and Characteristics of Waterbodies. One aspect of the site configuration of 

particular relevance to the aquatic food chain modeling is the locations and characteristics of the 

waterbodies. The size of the waterbodies (and the distance from the WMU) affects constituent 

concentrations and loadings predicted for that waterbody. The distance from the WMU to the 

waterbody was based on an empirical distribution of measurements, taken from actual CCW 

sites, of the distance from the edge of the WMU to the nearest stream or lake. The uncertainty 

posed in this analysis is the sampling of this distribution as compared to a more certain 

measurement of the actual distance at each CCW site. Surface water variables, including flow 

and water quality parameters, were collected for the stream reach being modeled, or for a larger 

hydrologic region where data were not available for a particular reach. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-53 

Waterbodies Intercepting the Groundwater Plume. As discussed in Section 3.7, mass 

is not actually removed from the groundwater when the plume is intercepted by a surface 

waterbody. Therefore, in cases where wells are located beyond an intersecting surface water 

body, the draft risk assessment may not account for interactions between surface water and 

groundwater. Examining the input database, EPA notes that approximately two-thirds (69%) of 

the Monte Carlo runs contained such an intersecting surface waterbody. Thus, the 50th percentile 

results may overestimate groundwater risks to these receptors. However, because the WMUs 

with closer receptor wells exhibited higher risks on average, the 90th percentile results are not 

likely to be significantly affected. 

Environmental Parameters. Uncertainties related to environmental parameters (soil, 

aquifer, surface water, climate data) have already been mentioned. The parameters with the 

largest impact on results are aquifer hydraulic conductivity and gradient, which were selected 

from a national database of aquifer properties. 

Fish Bioconcentration and Bioaccumulation Factors. For fish consumption, exposure 

dose was calculated using BCFs to estimate the transfer of pollutants from environmental media 

into fish. Uncertainty is associated with models used to estimate BCFs for aquatic biota. Aquatic 

BCFs are developed by dividing measured concentrations in aquatic biota by total surface water 

concentrations. Appendix J lists the bioconcentration and bioaccumulation parameters used in 

the risk assessment, along with their sources. 

4.4.3.4 Exposure and Risk Modeling Variables 

Exposure parameters and benchmarks for human and ecological risk also contribute to 

parameter variability and uncertainty. 

Human Exposure Factors. Individual physical characteristics, activities, and behavior 

are quite different, and thus the exposure factors that influence the exposure of an individual, 

including ingestion rate, body weight, and exposure duration, are quite variable. Exposure 

modeling relies heavily on default assumptions concerning population activity patterns, mobility, 

dietary habits, body weights, and other factors. The probabilistic assessment for the adult and 

child exposure scenario addressed the possible variability in the exposure modeling by using 

statistical distributions for these variables for each receptor in the assessment: adult and child 

resident and adult and child recreational fisher. Data on fish consumption rates were not 

available for children of recreational anglers; thus the adult recreational angler data were used 

for children in this analysis, which could overestimate risk from this pathway for children. For 

all exposure factors varied, a single exposure factor distribution was used for adults for both 

males and females. For child exposures, one age (age 1) was used to represent the age at the start 

of exposure, because this age group was considered to be most sensitive for most health effects. 

The Exposure Factors Handbook (U.S. EPA, 1997c,d,e) provides the current state of the 

science concerning exposure assumptions and represents EPA’s current guidance on exposure 

data, and it was used throughout this assessment to establish statistical distributions of values for 

each exposure parameter for each receptor. The Exposure Factors Handbook has been carefully 

reviewed and evaluated for quality. EPA’s evaluation criteria included peer review, 

reproducibility, pertinence to the United States, currency, adequacy of the data collection period, 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-54 

validity of the approach, representativeness of the population, characterization of the variability, 

lack of bias in study design, and measurement error. There are some uncertainties, however, in 

the data that were used. 

Site-specific fish consumption rate data were not available, but the Maine study data, 

where anglers fished from streams, rivers, and ponds, were consistent with the modeling 

scenarios used in this risk analysis and provided the detailed percentile data required for a 

probabilistic analysis. However, applying Maine angler consumption rates to other parts of the 

country may under- or overestimate exposures. 

EPA’s child-specific exposure guidance has been recently finalized (U.S. EPA, 2008b) 

but was not used in the risk assessment because the water consumption rates and body weights 

provided in the Child-Specific Exposure Factors Handbook (U.S. EPA, 2008b) do not differ 

significantly from those found in the 1997 Exposure Factors Handbook and would not have 

changed the results, but the use of the 1997 values may contribute some parameter uncertainty. 

One exception is the distribution of child fish consumption rates used. Here, U.S. EPA (2008b) 

consumption rates are higher than the 1997 rates used in the analysis. This introduces uncertainty 

into the analysis, and likely underestimates risks in the fish consumption pathway. 

As is customary for EPA’s RCRA risk assessments, human exposure factor data were not 

correlated (i.e., for each modeling run, each exposure factor was selected from its distribution 

independently), introducing some uncertainty because it is possible to select, for example, a high 

drinking water rate with a small body weight. However, although a specific modeling run may 

have had an unrealistic combination of exposure factors, the large number of Monte Carlo 

iterations performed (10,000) ensures that this is unlikely to significantly affect the risk 

assessment results. 

Diet Assumptions for Ecological Receptors. National-scale assessments often assume 

maximum intake of contaminated prey in the diets of primary and secondary consumers (i.e., 100 

percent of the diet originates from the contaminated area). Under field conditions, many 

receptors are opportunistic feeders with substantial variability in both the type of food items 

consumed and the geospatial patterns of feeding and foraging. The actual proportion of wildlife 

receptors’ diets that would be contaminated depends on a number of factors such as the species’ 

foraging range, quality of food source, season, intra- and interspecies competition. Consequently, 

the exclusive diet of contaminated food items tends to provide a very high-end estimate of 

potential risks. 

Human Health Benchmarks. The uncertainties generally associated with human health 

benchmarks are discussed in detail in EPA’s Guidelines for Carcinogen Risk Assessment (U.S. 

EPA, 2005), and IRIS (U.S. EPA, 2009a). EPA defines the RfD as “an estimate (with uncertainty 

spanning perhaps an order of magnitude) of a daily exposure to the human population (including 

sensitive subgroups) that is likely to be without appreciable risk of deleterious effects during a 

lifetime” (U.S. EPA, 1994, 2009a). RfDs are based on an assumption of lifetime exposure and 

may not be appropriate when applied to less-than-lifetime exposure situations (U.S. EPA, 

2009a). The CSF is an upper-bound estimate of the human cancer risk per mg of chemical per kg 

body weight per day. Because exposures were often less than lifetime, some uncertainty was 

introduced in the noncancer hazard and cancer risk estimates. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-55 

EPA routinely accounts for uncertainty in their development of RfDs and other human 

health benchmarks. Uncertainty and variability in the toxicological and epidemiological data 

from which RfDs were derived are accounted for by applying uncertainty factors. Some of these 

uncertainties include those associated with extrapolation from animals to humans, from LOAELs 

to NOAELs, and from subchronic to chronic data, and to account for sensitive subpopulations. If 

certain toxicological data are missing from the overall toxicological database (e.g., reproductive 

data), EPA accounts for this by applying an uncertainty factor. 

Table 4-31 presents IRIS uncertainty factors for the RfDs for the CCW constituents that 

showed HQs greater than 1 in the risk assessment, along with the highest HQ observed and the 

disposal scenario for which this HQ was observed. IRIS defines uncertainty factors as follows: 

“Uncertainty factors (UFs) are one of several, generally 10-fold, default factors 

used in operationally deriving the RfD from experimental data. The factors are 

intended to account for (1) variation in susceptibility among the members of the 

human population (i.e., inter-individual or intraspecies variability); (2) 

uncertainty in extrapolating animal data to humans (i.e., interspecies uncertainty); 

(3) uncertainty in extrapolating from data obtained in a study with less-thanlifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) 

uncertainty in extrapolating from a LOAEL rather than from a NOAEL; and (5) 

uncertainty associated with extrapolation when the database is incomplete.”12 

The constituent-specific uncertainty factors for the CCW constituents in Table 4-31 are 

discussed further in the source documents (e.g., IRIS) for the individual human health 

benchmarks used in the analysis, which are referenced in Appendix G. In general, EPA human 

health benchmarks are derived using a health-protective approach. These uncertainty factors can 

be considered when evaluating the constituent-specific risks presented in this document, but only 

in the context of the above definitions and the information presented in IRIS for each chemical. 

The hierarchy of data sources that was implemented for this analysis was based largely 

on the rigor of review that a benchmark has received. Methodologies evolve over time, with 

improvements in existing methods and the development of new health benchmark practices (e.g., 

benchmark dose methodology). As a result, the magnitude of a given benchmark can either 

increase or decrease, or a given benchmark can appear or disappear in a toxicity benchmark 

database. An example of the latter situation, disappearance of a toxicity benchmark, occurred 

during the development of this report. The human health benchmark for thallium was withdrawn 

from IRIS in late September 2009. The modeling results, including the noncancer human health 

effects estimates, were retained in this document to reflect the potential for thallium releases 

from CCW WMUs. EPA has decided to retain these estimates, in light of the National Academy 

of Sciences’ (NAS’s) 2008 report entitled Science and Decisions: Advancing Risk Assessment 

(NAS, 2008). In that report’s recommendations, the authors noted that absence of certain 

information from a risk characterization can result in the missing information being overlooked 

during the decision making process. Evidence that relatively small quantities of thallium can be 

 

12 http://www.epa.gov/ncea/iris/help_gloss.htm#u 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-56 

fatal to humans13 leads EPA to conclude that omitting the thallium results from this report might 

cause thallium’s existence in coal combustion residues to be overlooked during the risk 

management decision making, and thus EPA has chosen to retain those modeling results in this 

report. 

Table 4-31. RfD Uncertainty Factors for and Benchmark Confidence for CCW 

Constituents with HQs Over 1 

Constituent 

RfD 

(mg/kg-day) Source 

Uncertainty 

Factor 

Benchmark 

Confidence 

Highest 

CCW 

HQ 

CCW Scenario for 

Highest HQ 

Antimony 4.0E-04 IRIS 1,000 low 3 GW-DW, FBC wastes, 

clay-lined landfills 

Boron 2.0E-01 IRIS 66 high 7 GW-DW, Conventional 

CCW, unlined SIs 

Cadmium 5.0E-04 IRIS 10 high 9 GW-DW, Codisposed 

CCW, unlined SIs 

Cobalt 3.0E-04 PPRTV 1,000 low 500 GW-DW, Codisposed 

CCW, unlined SIs 

Molybdenum 5.0E-03 IRIS 30 medium 8 GW-DW, Conventional 

CCW, unlined SIs 

Selenium 5.0E-03 IRIS 3 high 3 GW-SW, Conventional 

CCW, unlined SIs 

Thallium 8.0E-05 IRIS 3,000 low 4 GW-DW, FBC wastes, 

clay-lined landfills 

Most health benchmarks used in this analysis were from IRIS. Human health benchmarks 

in IRIS have been subjected to rigorous internal and external reviews and represent Agency-wide 

consensus human health risk information. However, some benchmarks in IRIS are quite dated. 

Provisional human health benchmarks derived by the Superfund Technical Support Center have 

been peer reviewed and are used where there is no IRIS value. 

Chemical-specific health benchmarks were used for all constituents assessed in the 

analyses. However, the RfD for fluoride was based on fluorine; the RfDs for mercuric chloride 

and methyl mercury were used as surrogates for elemental mercury from food, soil, and water 

ingestion, and fish ingestion, respectively; and the RfD for thallium was based on thallium 

chloride. The use of these surrogate data is not thought to have introduced any significant 

uncertainty. Human health benchmarks are not age-specific, and therefore, were applied to both 

child and adult receptors, thereby introducing some uncertainty. 

EPA used the drinking water MCL for lead to estimate risks from drinking water 

exposure. The IEUBK model may better quantify risk for a young child exposed to lead; 

therefore, use of the MCL may introduce some uncertainty. However, risks from lead exposure 

were relatively low, well below the risk criterion for landfills and at or slightly above the risk 

criterion for surface impoundments, and did not drive the risk assessment conclusions. 

 

13 “Temporary hair loss, vomiting, and diarrhea can also occur and death may result after exposure to large amounts 

of thallium for short periods. Thallium can be fatal from a dose as low as 1 gram.” (ATSDR, 1992) 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-57 

Ecological Criteria. CSCLs were developed for constituents when sufficient data were 

available. In many cases, sufficient data were unavailable for a receptor/constituent combination, 

and therefore, the potential risk to a receptor could not be assessed. In particular, insufficient 

data were available to derive chronic effects CSCLs for amphibians. Because the risk results can 

only be interpreted within the context of available data, the absence of data cannot be construed 

to mean that adverse ecological effects will not occur. 

In addition to the effects of data gaps on ecological benchmarks, the ecological criteria 

tend to be fairly conservative because the overall approach is based on “no effects” or “lowest 

effects” study data. In site-specific assessments, a de minimis effects approach is often replaced 

with an effects level similar to natural population variability (e.g., sometimes as high as a 20 

percent effects level). As a result, the CSCLs used in this analysis are likely to overestimate risks 

for representative species and communities assumed to live in surface waters impacted by CCW 

WMUs. Because the difference between a LOAEL and a NOAEL is often about a factor of 10, 

an HQ exceedance of roughly 10 may not be ecologically significant. In contrast, CSCLs based 

on no effects data that are developed for the protection of threatened and endangered species are 

presumed to be protective. 

4.5 Summary and Conclusions 

CCW risk assessment results at the 90th percentile suggest that the management of CCW 

in unlined or clay-lined WMUs result in risks greater than 1 in 100,000 for excess cancer risk to 

humans or an HQ greater than 1 for noncancer effects to both human and ecological receptors. 

Key risk findings include the following: 

 For humans exposed via the groundwater-to-drinking-water pathway, risks from claylined units that dispose CCW or CCW comanaged with coal refuse are lower than those 

for unlined units. However, the 90th percentile risks for clay-lined units are still well 

within or above the range of concern (10-6 to 10-4) for cancer risk and above an HQ of 1 

for noncarcinogens. For example, arsenic III cancer risks in clay-lined units range from

1 in 5,000 for landfills to 9 in 10,000 in surface impoundments. The thallium HQ was

as high as 2 for clay-lined landfills, and the clay-lined surface impoundment HQ was as 

high as 200 for cobalt and 4 for boron. 

 Arsenic was the constituent with the highest risk for landfills. Clay-lined landfills 

presented 90th percentile risks above an excess cancer risk of 1 in 100,000 for arsenic 

(risks as high as 1 in 5,000) and an HQ of 1 for thallium (HQ of 2). When landfills are 

unlined, they also present risk above an HQ of 1 for antimony and molybdenum, each 

with an HQ of 2. Here, arsenic cancer risks were as high as 1 in 2,000. Clay-lined FBC 

landfills also presented 90th percentile risks above and HQ of 1 for antimony (HQ = 3) 

and thallium (HQ = 4) and showed excess cancer risks of 3 in 50,000 for arsenic. 

However, unlined FBC landfills differed in that they only exceeded a 1 in 100,000 excess 

cancer risk for arsenic.14 At the 50th percentile, arsenic III from CCW codisposed with 

 

14 As modeled, unlined FBC units showed less risk than clay-line FBC units. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-58 

coal refuse unlined landfills showed an excess cancer risk of 1 in 50,000: all 

noncarcinogenic constituents were well below an HQ of 1. 

 Arsenic and cobalt were the constituents with the highest risks for surface 

impoundments, with risks as high as 1 in 50 and an HQ of 500, respectively, for unlined 

units. Clay-lined surface impoundments presented 90th percentile cancer risks above 1 in 

100,000 for arsenic (7 in 1,000 cancer risk), HQs above 1 for boron (HQs as high as 4), 

cadmium (HQ as high as 3), cobalt (HQ as high as 200), molybdenum (HQ as high as 5), 

and nitrate (an MCL-based HQ as high as 10). When surface impoundments are unlined, 

they also show risk above an HQ of 1 for lead (HQ of 9) and selenium (HQ of 2). Here, 

arsenic cancer risks are as high as 1 in 50, and cobalt had HQs as high as 500. The only 

50th percentile surface impoundment results that exceeded the risk range or HQ criterion 

were arsenicand cobalt. Here, unlined units had arsenic cancer risks as high as 6 in 

10,000 while clay-lined units had arsenic cancer risks as high as 1 in 5,000. Cobalt HQs 

were as high as 20 and 6 for unlined and clay-lined surface impoundments, respectively. 

 For the groundwater-to-drinking-water pathway, composite liners, as modeled in this 

assessment, effectively reduce risks from all constituents to below a cancer risk of 1 in 

100,000 and an HQ of 1 for both landfills and surface impoundments at the 90th and 50th 

percentiles. 

 For the groundwater-to-drinking-water pathway, arrival times of the peak concentrations 

at a receptor well are much longer for landfills (hundreds or thousands of years) than for 

surface impoundments (most less than 100 years). 

 For humans exposed via the groundwater-to-surface-water (fish consumption) pathway, 

unlined and clay-lined surface impoundments posed risks above an excess cancer risk of 

1 in 100,000 and an HQ of 1 at the 90th percentile. For CCW managed alone in surface 

impoundments, these exceedences came from selenium (HQs of 3 and 2), while for CCW 

comanaged with coal refuse these exceedences came from arsenic (3 in 100,000 and 2 in 

100,000 excess cancer risks for unlined and clay-lined units). All 50th percentile surface 

impoundment risks are below a cancer risk of 1 in 100,000 and an HQ of 1. No 

constituents pose risks above these risk levels for landfills (including FBC landfills) at 

the 90th or 50th percentile for the fish consumption pathway. 

 Waste type has a much larger effect when wastes are managed in surface impoundments 

than when they are managed in landfills. In the case of surface impoundments, some 

constituents (boron, molybdenum, nitrate, and selenium) presented higher risks from 

CCW managed alone. However, others (arsenic, cadmium, cobalt, and lead) presented 

higher risks when CCW is comanaged with coal refuse, because of their association with 

the sulfide minerals concentrated in the refuse. 

 The higher risks for surface impoundments than landfills are likely due to higher waste 

leachate concentrations and the higher hydraulic head from the impounded liquid waste. 

This is consistent with damage cases reporting wet handling as a factor that can increase 

risks from CCW management. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-59 

 For ecological receptors exposed via surface water, risks for landfills exceed an HQ of 1 

for boron (HQ of 281 for unlined and 78 for clay-lined), lead (HQ of 8 for unlined), and 

selenium, arsenic, and barium (HQs of 2) at the 90th percentile, but 50th percentile HQs 

are well below 1. For surface impoundments, 90th percentile risks for several 

constituents (boron, lead, arsenic, selenium, cobalt, and barium) exceed an HQ of 1, with 

boron showing the highest risks (HQ over 2,000). Only boron exceeds an HQ of 1 at the 

50th percentile (HQ = 7 for unlined surface impoundments). The HQs over 1 for boron 

and selenium are consistent with reported ecological damage cases, which include 

impacts to waterbodies through the groundwater-to-surface-water pathway. 

 For ecological receptors exposed via sediment, 90th percentile risks exceed an HQ of 1 

for both landfills and surface impoundments because certain CCW constituents strongly 

sorb to sediments in the waterbody. Here, the 90th percentile HQ for lead was 58 for 

unlined landfills and clay-lined surface impoundments, and 311 for unlined surface 

impoundments. For arsenic, HQs were 11 and 3 for unlined and clay-lined landfills, and 

127 and 55 for unlined and clay-lined surface impoundments. Cadmium had HQs of 5 for 

unlined landfills, and 30 and 9 for unlined and clay-lined surface impoundments. 

Antimony had an HQ of 2 for unlined landfills. Composite lined surface impoundments 

also had risks above an HQ of 1 for lead (HQ of 4), arsenic (HQ of 31), and cadmium 

(HQ of 2). The 50th percentile risks are an order of magnitude or more below an HQ of 1 

for ecological receptors exposed via sediments. 

Sensitivity analysis results indicate that for most of the scenarios evaluated (over 70 

percent), the risk assessment model was most sensitive to parameters related to the contaminant 

source and groundwater flow and transport: WMU infiltration rate, leachate concentration, and 

aquifer hydraulic conductivity and gradient. For strongly sorbing contaminants (such as lead and 

cadmium), variables related to sorption and travel time (adsorption coefficient, depth to 

groundwater, receptor well distance) are also important. 

One of the most sensitive parameters in the risk assessment (infiltration rate) is greatly 

influenced by whether and how a WMU is lined. The 1994–2004 DOE/EPA survey results 

(U.S. DOE, 2006) do not include information on how many unlined facilities are still operating 

today, but do indicate that more facilities are lined today than were in the 1995 EPRI survey data 

set on which this risk assessment was based. This suggests that the risks from future CCW 

disposal facilities are likely to be lower than the results presented in this report. 

There are uncertainties associated with the CCW risk assessment, but scenario 

uncertainty (i.e., uncertainty about the environmental setting around the plant) has been 

minimized by basing the risk assessment on conditions around existing U.S. coal-fired power 

plants around the United States. Uncertainty in environmental setting parameters has been 

incorporated into the risk assessment by varying these inputs within reasonable ranges when the 

exact value is not known. Uncertainty in human exposure factors (such as exposure duration, 

body weight, and intake rates) has also been addressed through the use of national distributions. 

Some uncertainties not addressed explicitly in the risk assessment have been addressed 

through comparisons with other studies and data sources. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-60 

 Appropriateness of CCW leachate data. Data on another highly sensitive parameter, 

leachate (porewater) constituent concentration, were available and used for CCW surface 

impoundments. However, available data for landfills were mainly TCLP analyses, which 

may not be representative of actual CCW leachate. Comparisons with recent (2006 and 

2008) studies of coal ash leaching processes show very good agreement for arsenic. 

However, although the selenium CCW data are within the range of the 2006 and 2008 

data, some of the higher concentrations in both Vanderbilt data sets are not represented 

by the TCLP data, and U.S. EPA (2008c) show similar trends for barium and 

molybdenum. This suggests that risks for these metals may be underestimated, which is 

consistent with selenium as a common driver of the damage cases. 

 Impacts of mercury rules (CAIR and CAMR). While CAIR and CAMR will reduce 

emissions of mercury and other metals from coal-fired power plants, mercury and other 

more volatile metals will be transferred from the flue gas to fly ash and other air pollution 

control residues, including the sludge from wet scrubbers. EPA ORD has research 

underway to evaluate changes to CCW characteristics and leaching of mercury and other 

metals from CAIR and CAMR. Data from the first report (U.S. EPA, 2006c) suggest that 

although total mercury will increase in CCW from the use of sorbents as mercury 

controls, the leachability of mercury may be reduced. Data from U.S. EPA (2008c) add to 

this assessment by supporting similar findings. 

 Mercury and nondetect analyses. Because of a high proportion of nondetect values and 

a limited number of measurements, the risks from mercury in CCW could not be 

evaluated for either landfills or surface impoundments and for antimony and thallium in 

surface impoundments. The 2006 leaching study data suggest that mercury levels are 

fairly low in fly ash from coal combustion, a conclusion generally confirmed by the 2008 

study report (U.S. EPA, 2008c), although that study did find higher mercury leachate 

concentrations from scrubber sludge than other coal wastes and found that blending fly 

ash and lime can increase mercury leaching from scrubber sludge. 

Uncertainties that are more difficult to evaluate with respect to CCW risk results include 

the following: 

 Well distance. Nearest well distances were taken from a survey of MSW landfills, as 

data were not available from CCW sites. EPA believes that this is a protective 

assumption because MSW landfills generally tend to be in more populated areas, but 

there are little data available to test this hypothesis. 

 Liner conditions. Liner design and performance for CCW WMUs were based on data 

and assumptions EPA developed to be appropriate for nonhazardous industrial waste 

landfills. EPA believes that CCW landfills should have similar performance 

characteristics, but does not have the quantitative data to verify that. 

 Data gaps for ecological receptors. Insufficient data were available to develop 

screening levels and quantitative risk estimates for terrestrial amphibians, but EPA 

acknowledges that damage cases indicate risk to terrestrial amphibian and plant 

communities through exposure to selenium and boron. 
Section 4.0 Risk Characterization 

April 2010–Draft EPA document. 4-61 

 Ecosystems and receptors at risk. Certain critical assessment endpoints were not 

evaluated in this analysis, including impacts on managed lands, critical habitats, and 

threatened and endangered species. 

 Synergistic risk. The impact of exposures of multiple contaminants to human and 

ecological risks was not evaluated in this analysis. EPA recognizes that a singleconstituent analysis may underestimate risks associated with multiple chemical 

exposures. 

These are potentially the more significant uncertainties associated with the CCW risk 

assessment. Other uncertainties are discussed in Section 4.4. 

Given the results and characterization above, composite liners, as modeled in this risk 

assessment, effectively reduce risks from all pathways and constituents to levels below an excess 

cancer risk of 1 in 100,000 or an HQ of 1 for both landfills and surface impoundments. The 

CCW risk assessment suggests that the management of CCW in unlined landfills and unlined 

surface impoundments may present risks to human health and the environment. From the 

perspective of what is known about toxic effects in humans, arsenic, nitrates, cadmium, and 

selenium appear to be among the constituents that may present risks of concern depending on the 

specific waste management practices employed. From the perspective of what is known about 

toxic effects in ecological receptors, arsenic, boron, lead, and selenium emerge as having 

documented adverse effects on ecological receptors. 

The estimated human health arsenic risks from clay-lined units are lower than the risks of 

unlined units, but are still above a 1 in 100,000 excess cancer risk or an HQ of 1. In addition, 

surface impoundments typically showed higher risks than landfills, regardless of liner type. 

These risk results are largely consistent with damage cases compiled by EPA (U.S. EPA, 2000, 

2003e, 2007) and others (Lang and Schlictmann, 2004; Zillmer and Fauble, 2004; Carlson and 

Adriano, 1993; Rowe et al., 2002; Hopkins et al., 2006). These results suggest that with a higher 

prevalence of composite liners in new CCW disposal facilities, future national risks from onsite 

CCW disposal are likely to be lower than those presented in this risk assessment (which is based 

on 1995 CCW WMUs). 
Section 5.0 References 

April 2010–Draft EPA document. 5-1 

5.0 References 

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Thallium. Available at http://www.atsdr.cdc.gov/toxprofiles/tp54.html. 

Barrows, M.E., S.R. Petrocelli, K.J. Macek, and J.J. Carroll. 1980. Chapter 24: Bioconcentration 

and elimination of selected water pollutants by bluegill sunfish (Lepomis macrochirus). 

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Bonaparte, R., J.P. Giroud, and B.A. Gross. 1989. Rates of leakage through landfill liners. 

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CalEPA (California Environmental Protection Agency). 2000. Air Toxics Hot Spots Program 

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of Noncancer Chronic Reference Exposure Levels. Office of Environmental Health 

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http://www.oehha.org/air/chronic_rels/42kChREL.html

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CalEPA (California Environmental Protection Agency). 2008. Air Toxics Hot Spots Program 

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Carlson, C.L., and D.C. Adriano. 1993. Environmental impacts of coal combustion residues. J. 

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Drever, J.I. 1988. The Geochemistry of Natural Waters, 2d ed. Englewood Cliffs NJ: PrenticeHall. 

Eisler, R. 1989. Molybdenum Hazards to Fish, Wildlife, and Invertebrates: A Synoptic Review. 

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EPRI (Electric Power Research Institute). 1997. Coal Combustion By-Products and Low-Volume 

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Section 5.0 References 

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Finkelman, R.B. 1995. Modes of occurrence of environmentally sensitive trace elements in coal. 

Chapter 3 in: Dalway, J.S., and F. Goodarzi (eds.), Environmental Aspects of Trace 

Elements in Coal. Springer. 

Hopkins, W.A., S.E. DuRant, B.P. Staub, C.L. Rowe, and B.P. Jackson. 2006. Reproduction, 

embryonic development, and maternal transfer of contaminants in the amphibian 

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Kumada, H., S. Kimura, M. Yokote, and Y. Matida. 1972. Acute and chronic toxicity, uptake, 

and retention of cadmium in freshwater organisms. Bulletin of Freshwater Fisheries 

Research Laboratory 22(2):157–165. December 20. 

Lang, D.J., and J.R. Schlictmann. 2004. Leaching of arsenic from a fly ash dump in Beverly, 

Massachusetts. In Proceedings of State Regulation of Coal Combustion By-Product 

Placement at Mine Sites. May 4–6, 2004, Harrisburg, Pennsylvania. U.S. Department of 

the Interior, Office of Surface Mining, Alton, IL. Available at http://www.mcrcc.osmre

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Lemly, A.D. 1985. Toxicology of selenium in a freshwater reservoir: Implications for 

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NAS (National Academies of Science). 2008. Science and Decisions: Advancing Risk 

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Newell, C.J., L.P. Hopkins, and P.B. Bedient. 1989. Hydrogeologic Database for Ground Water 

Modeling. API Publication No. 4476. American Petroleum Institute, Washington, DC. 

Available as document ID number EPA-HQ-RCRA-2001-0022-0604 and also in several 

other dockets at http://www.regulations.gov. 

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Section 5.0 References 

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Final Report. Office of Solid Waste, Washington, DC. June. Available at 

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industrial/special/fossil/ffc2_395.pdf. 

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recordisplay.cfm?deid=55525. 

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wasteid/hwirwste/peer03/vadoze/vz-aquif.pdf. 

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Identification Rule Risk Assessment Model - MINTEQA2/PRODEFA2 Version 4.0. 

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Assignment No. 17, for Office of Solid Waste. Washington, DC. Available at 
Section 5.0 References 

April 2010–Draft EPA document. 5-6 

http://www.epa.gov/osw/hazard/wastetypes/wasteid/hwirwste/peer03/minteqa2/minteqa2.

pdf. 

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Washington, DC. November. Available at http://nepis.epa.gov/EPA/html/Pubs/

pubtitleOSWER.htm. 

U.S. EPA (Environmental Protection Agency). 2000. Review of Causative Factors for Coal 

Combustion Waste Damage Cases. Prepared by SAIC for U.S. EPA Office of Solid 

Waste under EPA contract 68-W-99-001, WA No. 121. Washington, DC. November 29. 

Available as document ID number EPA-HQ-RCRA-2006-0796-0020 at 

http://www.regulations.gov. 

U.S. EPA (Environmental Protection Agency). 2001a. Revisions in Input Data and Modeling 

Procedures for Using MINTEQA2 in Estimating Metal Partition Coefficients. Prepared 

by Allison Geoscience Consultants, Inc., for the Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2001b. WATER9, Air Emission Models for Waste 

and Wastewater. Technology Transfer Network Clearinghouse for Inventories & 

Emission Factors. Available at http://www.epa.gov/ttn/chief/software/water. 

U.S. EPA (Environmental Protection Agency). 2001c. Supplemental Guidance For Developing 

Soil Screening Levels For Superfund Sites. OSWER 9355.4-24. Office of Solid Waste 

and Emergency Response, Washington, DC. December. Available at 

http://www.epa.gov/superfund/health/conmedia/soil/index.htm. 

U.S. EPA (Environmental Protection Agency). 2002a. Constituent Screening for Coal 

Combustion Wastes. Draft Report prepared by Research Triangle Institute for Office of 

Solid Waste, Washington, DC, under EPA Contract No. 68-W-98-085. October. 

U.S. EPA (Environmental Protection Agency). 2002b. Industrial Waste Management Evaluation 

Model (IWEM) Technical Background Document. EPA530-R-02-012. Office of Solid 

Waste, Washington, DC. August. Available at http://nepis.epa.gov/EPA/html/

Pubs/pubtitleOSWER.htm. 

U.S. EPA (Environmental Protection Agency). 2002d. Current Drinking Water Standards. 

National Primary and Secondary Drinking Water Regulations. Office of Ground Water 

and Drinking Water, Washington, DC. Available at http://www.epa.gov/safewater/

mcl.html. 

U.S. EPA (Environmental Protection Agency). 2003a. EPA’s Composite Model for Leachate 

Migration with Transformation Products (EPACMTP): Parameters/Data Background 

Document. Office of Solid Waste, Washington, DC. April. Available at 

http://www.epa.gov/epawaste/nonhaz/industrial/tools/cmtp/index.htm. 
Section 5.0 References 

April 2010–Draft EPA document. 5-7 

U.S. EPA (Environmental Protection Agency). 2003b. Risk Assessment Technical Background 

Document for the Dye and Pigment Industry Hazardous Waste Listing Determination. 

OMB Review Draft. Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2003c. EPACMTP Technical Background 

Document. Office of Solid Waste, Washington, DC. April. Available at 

http://www.epa.gov/epawaste/nonhaz/industrial/tools/cmtp/index.htm. 

U.S. EPA (Environmental Protection Agency). 2003d. Addendum to the EPACMTP Technical 

Background Document. Office of Solid Waste, Washington, DC. Available as document 

ID number EPA-HQ-RCRA-2003-0001-0166 at http://www.regulations.gov. 

U.S. EPA (Environmental Protection Agency). 2003e. Final Revised Report on Resolution of 18 

Previously Indeterminate Candidate Damage Cases. Prepared by SAIC for U.S. EPA 

Office of Solid Waste under EPA contract 68-W-02-036, WA No. 1-12. Washington, 

DC. March 5. Available as document ID number EPA-HQ-RCRA-2003-0003-0420 at 

http://www.regulations.gov. 

U.S. EPA (Environmental Protection Agency). 2003f. Peer Review Of Changes To EPA's 

MINTEQA2 Model. Prepared by Industrial Economics for the Office of Solid Waste, 

Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2005. Guidelines for Carcinogen Risk 

Assessment. EPA/630/P-03/001B. Risk Assessment Forum, U.S. Environmental 

Protection Agency, Washington, DC. March. Available at http://nepis.epa.gov/

EPA/html/Pubs/pubtitleORD.htm. 

U.S. EPA (Environmental Protection Agency). 2006a. Provisional Peer Reviewed Toxicity 

Values for Aluminum (CASRN 7429-90-5). 10-23-2006. National Center for 

Environmental Assessment. Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2006b. Provisional Peer Reviewed Toxicity 

Values for Iron and Compounds (CASRN 7439-89-6). Derivation of Subchronic and 

Chronic Oral RfDs. 9-11-2006. National Center for Environmental Assessment. 

Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2006c. Characterization of Mercury-Enriched 

Coal Combustion Residues from Electric Utilities Using Enhanced Sorbents for Mercury 

Control. EPA 600/R-06/008. Office of Research and Development. Research Triangle 

Park, NC. January. Available at http://nepis.epa.gov/EPA/html/Pubs/pubtitleORD.htm. 

U.S. EPA (Environmental Protection Agency). 2006d. Guide for Industrial Waste Management. 

Office of Solid Waste, Washington, DC. Available at http://www.epa.gov/waste/nonhaz/

industrial/guide/index.htm. 

U.S. EPA (Environmental Protection Agency). 2007. Coal Combustion Waste Damage Case 

Assessments. Available from the docket to the Notice of Data Availability on the 
Section 5.0 References 

April 2010–Draft EPA document. 5-8 

Disposal of Coal Combustion Wastes in Landfills and Surface Impoundments, Docket # 

EPA-HQ-RCRA-2006-0796. June 19. 

U.S. EPA (Environmental Protection Agency). 2008a. Provisional Peer Reviewed Toxicity 

Values for Cobalt (CASRN 7440-48-4). 8-25-2008. National Center for Environmental 

Assessment. Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2008b. Child-Specific Exposure Factors 

Handbook. EPA/600/R-06-096F. National Center for Environmental Assessment, 

Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2008c. Characterization of Coal Combustion 

Residues from Electric Utilities Using Wet Scrubbers for Multi-Pollutant Control. 

EPA/600/R-08/077. Office of Research and Development, Air Pollution Control 

Division. Research Triangle Park, NC. July. Available at 

http://www.epa.gov/nrmrl/pubs/600r08077/600r08077.pdf. 

U.S. EPA (Environmental Protection Agency). 2009a. Integrated Risk Information System 

(IRIS). National Center for Environmental Assessment, Office of Research and 

Development, Washington, DC. Available at http://www.epa.gov/iris/. 

U.S. EPA (Environmental Protection Agency). 2009b. Sensitivity Analysis for the Coal 

Combustion Waste Risk Assessment. Draft Technical Report. Prepared by RTI 

International for U.S. EPA, Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2009c. Inhalation of Fugitive Dust: A Screening 

Assessment of the Risks Posed by Coal Combustion Waste Landfills. Prepared by Office 

of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2009d. Responses to Review Comments on

Human and Ecological Risk Assessment for Coal Combustion Wastes. Prepared by RTI 

International for U.S. EPA, Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2010. Regulatory Impact Analysis For EPA’s 

Proposed Regulation of Coal Combustion Wastes Generated by the Electric Utility 

Industry. Office of Resource Conservation and Recovery, Washington, DC. April. 

Zilmer, M., and P. Fauble. 2004. Groundwater impacts from coal combustion ash disposal sites 

in Wisconsin. In: Proceedings of State Regulation of Coal Combustion By-Product 

Placement at Mine Sites. May 4–6, 2004, Harrisburg, Pennsylvania. U.S. Department of 

Interior, Office of Surface Mining, Alton, IL. Available at http://www.mcrcc.osmre.gov/

MCR/Resources/ccb/PDF/State_Regulation_of_CCB_Placement.pdf. 
Appendix A Constituent Data 

Appendix A. Constituent Data

The coal combustion waste (CCW) risk assessment addressed metals and inorganic 

constituents identified by EPA as potential constituents of concern in CCW (Table A-1). EPAderived waste concentrations for these constituents from the CCW constituent database, which 

includes analyte concentration data in three tables representing different types of waste samples: 

landfill leachate analyses (in mg/L), surface impoundment and landfill porewater analyses (in 

mg/L), and analyses of whole waste samples (in mg/kg). Each database table specifies, for most 

samples, the type of waste sampled and the type of coal burned at the facility. 

Table A-1. Constituents Addressed in the CCW Risk Assessment 

Constituent CAS ID Constituent CAS ID 

Metals Inorganic Anions

Aluminum 7429-90-5 Chloride 16887-00-6 

Antimony 7440-36-0 Cyanide 57-12-5 

Arsenic 7440-38-2 Fluoride 16984-48-8 

Barium 7440-39-3 Total Nitrate Nitrogen 14797-55-8 

Beryllium 7440-41-7 Phosphate 14265-44-2 

Boron 7440-42-8 Silicon 7631-86-9 

Cadmium 7440-43-9 Sulfate 14808-79-8 

Chromium 7440-47-3 Sulfide 18496-25-8 

Cobalt 7440-48-4 Inorganic Cations

Copper 7440-50-8 Ammonia 7664-41-7 

Iron 7439-89-6 Calcium 7440-70-2 

Lead 7439-92-1 pH 12408-02-5 

Magnesium 7439-95-4 Potassium 7440-09-7 

Manganese 7439-96-5 Sodium 7440-23-5 

Mercury 7439-97-6 Nonmetallic Elements

Molybdenum 7439-98-7 Inorganic Carbon 7440-44-0 

Nickel 7440-02-0 Total Elemental Sulfur 7704-34-9 

Selenium 7782-49-2 Measurements

Silver 7440-22-4 Total Dissolved Solids none 

Strontium 7440-24-6 Total Organic Carbon none 

Thallium 7440-28-0 Dissolved Organic Carbon none 

Vanadium 7440-62-2 

Zinc 7440-66-6 

April 2010–Draft EPA document. A-1 
Appendix A Constituent Data 

A.1 Data Sources 

EPA prepared the CCW constituent database in 2002. The 2002 CCW constituent 

database includes all of the waste characterization data used by EPA in its risk assessments in 

support of the March 1999 Report to Congress: Wastes from the Combustion of Fossil Fuels (the 

RTC) (U.S. EPA, 1999). In addition to the data set from the March 1999 RTC, EPA 

supplemented the database with the following data: 

 Data submitted with public comments to EPA on the 1999 RTC 

 Data submitted with public comments to EPA concerning the May 22, 2000, Final 

Regulatory Determination 

 Data collected by and provided to EPA since the end of the public comment period on the 

Final Regulatory Determination 

 Data identified from literature searches. 

The primary sources of these additional data include the electric power industry, state and federal 

regulatory agencies, and scientific literature. Attachment A-1 is a complete list of the sources of 

data contained in the 2002 CCW constituent database. 

The additional data represent a significant expansion in the quantity of characterization 

data available to EPA for analysis. For example, the data set used for the risk assessments 

supporting the RTC covered approximately 50 CCW generation and/or disposal sites. With the 

addition of the supplemental data, the 2002 CCW constituent database now covers more than 

160 sites. The 1999 data set included approximately 10,000 individual samples of CCW. The 

2002 CCW constituent database now includes more than 35,000 individual samples. 

The additional data also represent an expansion in the scope of characterization data 

available to EPA for analysis. The 1999 data were obtained exclusively from the electric power 

industry. As shown in Attachment A-1, the 2002 data set includes data from other sources, such 

as scientific literature and state and federal regulatory agencies. The 1999 data set included 

analyses of whole waste samples, surface impoundment and landfill porewater analyses, and 

analyses of extracts obtained using the Toxicity Characteristic Leaching Procedure (TCLP), the 

Synthetic Precipitation Leaching Procedure (SPLP), and Extraction Procedure (EP) Toxicity 

leaching methods. The 2002 data set added analyses of actual landfill leachate (e.g., obtained 

from leachate collection systems), analyses of extracts obtained using other leaching methods 

(including higher retention time leaching methods), and porewater analyses. 

The 2002 CCW constituent database represents CCW characteristics across a broad 

cross-section of the generating universe. Not only does the database include data from a large 

number of sites, but these sites are distributed throughout the United States, as shown in 

Table A-2. The database includes data for all major types of CCW (i.e., fly ash, bottom ash, flue 

gas desulfurization [FGD] sludge, fluidized bed combustion [FBC] fly ash, and FBC bed ash), 

from mixtures of CCW types that are commonly created during disposal operations (e.g., 

combined fly ash and bottom ash), and from CCW mixed with coal refuse (a common disposal 

practice). Section A.2 discusses waste types in more detail. 

April 2010–Draft EPA document. A-2 
Appendix A Constituent Data 

Table A-2. States Included in the CCW Constituent Database 

Alaska Illinois Maryland 

Arkansas Indiana Michigan 

California Kentucky Ohio 

Colorado Missouri Oklahoma 

Connecticut North Carolina Pennsylvania 

Florida North Dakota Tennessee 

Georgia Nebraska Texas 

Hawaii New Mexico Wisconsin 

Iowa Louisiana West Virginia 

The database also includes data for CCW generated from combustion of all major coal 

ranks: bituminous, sub-bituminous, lignite, and anthracite. Although the database does include 

coal type designations for most of the entries, in many cases the type is not specified. In addition, 

many coal plants mix coal from different sources (e.g., eastern and western coals), depending on 

prices and the need to reduce sulfur levels. As a result, correlations of risk results with coal types 

may be difficult and may not produce significant results. 

A.2 Data Preparation 

Table A-3 lists the waste types evaluated in the CCW risk assessment, along with the 

number of sites representing each waste type in the CCW constituent database. Key steps in 

preparing these data for risk assessment include (1) selection and grouping of waste types to be 

addressed, (2) selection of the analyte data to be used, and (3) processing of these data to develop 

the analyte concentrations for the screening analysis and full-scale risk assessment. 

Table A-3. Waste Streams in CCW Constituent Database 

Waste Type

Waste Streams

Number of Sites by Waste Typea

Landfill 

Leachate

Surface 

Impoundment 

Porewater Total Waste

Conventional Combustion Waste 97 13 62

Ash (not otherwise specified) 43 0 30 

Fly ash 61 2 33 

Bottom ash & slag 24 3 23 

Combined fly & bottom ash 7 4 4 

FGD sludge 4 6 5 

Codisposed Ash & Coal Refuse 9 5 1 

Fluidized Bed Combustion Waste 58 0 54

Ash (not otherwise specified) 18 0 10 

Fly ash 33 0 32 

Bottom and bed ash 26 0 25 

Combined fly & bottom ash 20 0 22 a Site counts by waste type from leachate, porewater, and whole waste data tables in the 2002 CCW 

constituent database. 

April 2010–Draft EPA document. A-3 
Appendix A Constituent Data 

April 2010–Draft EPA document. A-4 

A.2.1 Selection and Grouping of Waste Types of Concern 

The CCW constituent database contains a variety of waste types. Some selection and 

grouping of these types was appropriate so that the risk assessment could evaluate risks 

consistently for groups of wastes that are expected to behave similarly when disposed in landfills 

and surface impoundments. 

Combustion ash types in the CCW constituent database include fly ash, bottom ash, bed 

ash, slag, combined fly and bottom ash, and coal ash not otherwise specified. Based on a 

statistical analysis that showed no significant difference in leachate and porewater chemistry, the 

analysis combines data for these ash types for landfills and surface impoundments. FGD sludge 

was also combined with these conventional combustion ash types based on insignificant 

differences in porewater chemistry and the fact that FGD sludge is usually codisposed with 

varying amounts of fly ash and bottom ash. 

CCW porewater constituent data did show that FBC wastes and codisposed ash and coal 

refuse (coal waste from coal crushers and other coal preparation and handling operations1

) differ 

significantly from coal combustion ash in their composition and leachate chemistry, so these 

wastes were addressed separately in the risk analysis. FBC waste chemistry is impacted by the 

limestone injected with coal in FBC units for sulfur capture and tends to be very alkaline with 

high levels of calcium and sulfate. Coal refuse is high in pyrite, which generates sulfuric acid 

when disposed. As a result, combustion wastes exhibit a lower pH when codisposed with coal 

refuse. 

A.2.2 Selection of Appropriate Analyte Data 

CCW analyte concentration data represent leachate from landfills and surface 

impoundments and whole waste in landfills, as follows: 

 Whole waste analyte concentrations (in mg/kg) represent landfill waste. 

 Analyte concentrations (in mg/L) in porewater sampled from surface impoundment 

sediments represent surface impoundment leachate. 

 Analyte concentrations for extracts from leaching methods, analyses of actual landfill 

leachate, and landfill porewater analyses represent landfill leachate. Because the CCW 

constituent database includes analyte concentrations from several leaching methods, a 

decision hierarchy was used to select leachate analyses to use in the risk assessment 

(Table A-4). 

As shown in Table A-4, the methods thought to best represent long-term waste monofill 

porewater composition (i.e., methods with long equilibration times and low liquid-to-solid ratios) 

represent only a few sites, with most sites having TCLP and/or SPLP measurements. To best 

represent CCW landfill waste concentration at a wide variety of sites, the hierarchy rank shown 

in Table A-4 was used to select the best method for a particular site. For sites where two or more 

 

1

 Coal refuse is the waste coal produced from coal handling, crushing, and sizing operations. In the CCW 

constituent database, codisposed coal refuse includes “combined ash and coal gob”, “combined ash and coal 

refuse”, and “combined bottom ash and pyrites”. 
Appendix A Constituent Data 

methods were available in the same rank (which often occurs for SPLP and TCLP analyses), the 

screening analysis used the method with the highest analyte concentrations. This ensured that the 

data used in the risk assessment were the best that were available and represent a broad variety of 

waste disposal conditions. 

Table A-4. Comparison/Hierarchy of Leaching Methods for Landfills 

Represented in CCW Constituent Database 

Method (Rank) Description Advantages Disadvantages

Landfill leachate (1) Direct samples of 

landfill leachate 

Most representative 

of leachate chemistry 

Low number of sites represented 

Landfill porewater (1) Direct porewater 

samples from landfill 

Most representative 

of leachate chemistry 

Low number of sites represented 

High retention time and 

low liquid-to-solid ratio 

(L:S) methods (2) 

Waste extractions with 

long equilibration times 

(days to weeks) and low 

L:S 

Better representation 

of landfill 

equilibration times 

and L:S 

Low number of sites represented 

Low L:S methods (3) Waste extractions with 

low L:S 

Better representation 

of landfill L:S 

Low number of sites represented; 

equilibrium times relatively short 

High retention time 

methods (3) 

Waste extractions with 

long equilibration times 

(days to weeks) 

Better representation 

of landfill 

equilibration times 

Low number of sites represented; 

L:S relatively high 

TCLP (4) Toxicity Characteristic 

Leaching Procedure 

waste extractions 

Most representative 

in terms of number of 

sites, waste types 

covered 

High L:S (20:1) can dilute leachate 

concentrations; short equilibration 

time (18 hours) may not allow 

equilibrium to develop; Na-acetate 

buffer can overestimate leaching for 

some constituents (e.g., Pb) 

SPLP (4) Synthetic Precipitation 

Leaching Procedure and 

other dilute water waste 

extractions 

More representative 

in terms of number of 

sites, waste types 

covered; extract 

similar to 

precipitation 

High L:S (20:1) can dilute leachate 

concentrations; short equilibration 

time (18 hours) may not allow 

equilibrium to develop 

A.2.3 Development of Waste Constituent Concentrations 

To allow risk assessment results to be organized by waste constituent and waste type, 

CCW data were processed to produce a single concentration per waste stream (surface 

impoundment porewater, landfill leachate, and landfill whole waste), analyte, and site for use in 

the risk assessment. Data processing to prepare these analyte concentrations for the CCW risk 

assessment involved two steps: 

1. Calculation of average constituent concentrations by site for landfill leachate, 

surface impoundment porewater, and total ash concentrations. Site averaging avoids 

potential bias toward sites with many analyses per analyte. During site averaging, any 

separate waste disposal scenarios occurring at a site (e.g., non-FBC and FBC ash) were 

treated as separate “sites” and were averaged independently. This approach is consistent 

with that used in the 1998 CCW risk analysis. As in 1998, nondetects were averaged at 

one-half the reported detection limit. 

April 2010–Draft EPA document. A-5 
Appendix A Constituent Data 

April 2010–Draft EPA document. A-6 

2. Selection of waste concentrations from site-averaged values. For the Monte Carlo 

analysis, the analysis randomly selected, by waste type/waste management unit (WMU) 

scenario, site-averaged leachate concentrations. For landfills, a corresponding total waste 

analysis was pulled from the database or calculated from a constituent-specific 

relationship between landfill leachate and total waste analyses. 

A.3 Results 

Attachment A-2 provides the site-averaged constituent data used (sampled) in the fullscale CCW risk assessment by waste type/WMU scenario. Attachment A-3 presents summary 

statistics, by constituent and WMU type, including the 90th percentile waste concentrations used 

in the screening analysis. Attachment A-3 also includes figures (Figures A-3-1 to A-3-3) that 

illustrate the differences between site-averaged and non-site-averaged waste concentrations for 

surface impoundment porewater (Figure A-3-1), landfill leachate (Figure A-3-2), and total waste 

analyses (Figure A-3-3). 

A.4 References 

U.S. EPA (Environmental Protection Agency). 1999. Report to Congress: Wastes from the 

Combustion of Fossil Fuels. EPA 530-5-99-010. Office of Solid Waste and Emergency 

Response, Washington, DC. March. 







April 2010–Draft EPA document. 

[This page intentionally left blank.] 

































April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix A Attachment A-3 

Attachment A-3: CCW Constituent Data 

Used in Screening Analysis 

Table A-3-1. CCW Landfill Waste Analyte Concentrations 

Used in Screening: Total Waste Analyses (mg/kg) 

Analyte Sites1 ND Sites2

2002 CCW Total Waste Concentrations 1998 Total 

Waste 

Minimum Maximum 50th 75th 90th 95th 95th 

Aluminum 71 0 1.45E+01 1.37E+05 2.53E+04 4.17E+04 8.57E+04 9.76E+04 1.43E+05 

Antimony 64 19 1.25E-01 3.10E+02 1.56E+01 2.94E+01 4.62E+01 7.93E+01 4.67E+01 

Arsenic 111 3 4.70E-02 3.70E+02 2.79E+01 6.18E+01 1.05E+02 1.25E+02 1.54E+02 

Barium 94 1 4.76E+00 7.14E+03 2.22E+02 4.49E+02 1.05E+03 2.59E+03 8.38E+03 

Beryllium 37 6 1.19E-01 2.85E+01 4.10E+00 1.00E+01 1.76E+01 2.25E+01 1.56E+01 

Boron 70 4 2.50E-02 2.47E+03 5.35E+01 1.50E+02 3.46E+02 5.54E+02 4.17E+02 

Cadmium 102 21 1.65E-04 7.60E+02 1.08E+00 2.26E+00 5.43E+00 1.12E+01 2.37E+01 

Chromium 108 2 5.00E-03 1.38E+03 4.45E+01 7.62E+01 1.66E+02 1.81E+02 2.91E+02 

Cobalt 67 8 5.00E-03 1.35E+02 1.02E+01 3.26E+01 6.22E+01 7.93E+01 4.16E+01 

Copper 95 3 5.00E-03 8.90E+02 3.61E+01 8.24E+01 2.28E+02 2.99E+02 1.55E+02 

Cyanide 2 1 1.25E-01 2.48E-01 1.86E-01 2.17E-01 2.35E-01 2.41E-01 - 

Fluoride 8 0 2.50E+00 7.61E+02 1.08E+01 2.07E+01 2.49E+02 5.05E+02 - 

Lead 107 6 1.30E-02 1.37E+03 2.87E+01 4.97E+01 8.06E+01 1.25E+02 1.52E+02 

Manganese 87 2 5.00E-02 9.81E+03 1.11E+02 2.41E+02 5.10E+02 6.37E+02 8.17E+02 

Mercury 86 12 6.00E-04 6.43E+01 3.28E-01 6.00E-01 1.63E+00 8.22E+00 - 

Molybdenum 73 7 4.43E-02 1.26E+02 1.20E+01 2.23E+01 3.47E+01 5.38E+01 4.31E+01 

Nickel 106 5 4.90E-02 5.41E+04 4.23E+01 1.30E+02 3.29E+02 6.79E+02 1.55E+02 

Nitrate 1 1 2.43E-01 2.43E-01 2.43E-01 2.43E-01 2.43E-01 2.43E-01 - 

Nitrite 0 0 - - - - - - - 

Selenium 94 11 5.05E-03 6.73E+02 5.12E+00 1.03E+01 2.14E+01 4.79E+01 3.24E+02 

Silver 69 26 5.00E-02 1.90E+03 1.72E+00 3.30E+00 1.37E+01 2.66E+01 1.36E+01 

Strontium 15 1 5.60E+00 1.23E+03 2.63E+02 7.63E+02 1.05E+03 1.20E+03 4.76E+03 

Thallium 20 10 9.00E-02 1.00E+02 3.23E+00 1.05E+01 2.08E+01 4.21E+01 4.80E+01 

Vanadium 43 1 3.30E+00 4.55E+03 2.24E+02 3.48E+02 9.07E+02 2.95E+03 3.46E+02 

Zinc 98 1 3.40E-02 1.82E+04 4.58E+01 1.44E+02 2.93E+02 1.43E+03 8.56E+02 

1

 Number of sites with analyte data (2002) 

2

 Number of sites with only nondetect analyte data (2002) 

April 2010–Draft EPA document. A-3-1 
Appendix A Attachment A-3 

Table A-3-2. CCW Surface Impoundment Waste Analyte Concentrations 

Used in Screening: Porewater Analyses (mg/L) 

Analyte Sites1 ND Sites2

2002 SI Porewater Concentrations 1998 

Porewater 

95th Minimum Maximum 3 50th 75th 90th 95th 

Aluminum 17 2 1.50E-02 8.91E+01 1.18E+00 4.68E+00 2.30E+01 5.02E+01 2.70E+02 

Antimony 2 2 1.00E-02 7.00E-02 4.00E-02 5.50E-02 6.40E-02 6.70E-02 - 

Arsenic 17 2 7.50E-03 6.77E+00 1.80E-01 4.94E-01 5.18E+00 5.65E+00 9.64E+00 

Barium 17 2 1.00E-03 5.50E-01 1.10E-01 1.59E-01 3.02E-01 4.88E-01 2.74E+01 

Beryllium 2 1 1.00E-03 6.20E-03 3.60E-03 4.90E-03 5.68E-03 5.94E-03 - 

Boron 18 1 2.50E-02 3.37E+02 5.01E+00 2.92E+01 7.52E+01 1.44E+02 3.42E+02 

Cadmium 17 9 1.00E-03 2.50E-01 2.50E-03 1.56E-02 1.31E-01 1.90E-01 1.56E-01 

Chromium 18 8 9.00E-04 5.78E-01 3.56E-02 1.13E-01 3.66E-01 5.29E-01 7.46E-01 

Cobalt 4 2 5.00E-03 8.87E+00 1.07E-01 2.37E+00 6.27E+00 7.57E+00 - 

Copper 16 5 6.40E-04 7.22E-01 3.63E-02 1.26E-01 2.84E-01 4.90E-01 6.90E-01 

Cyanide 0 0 - - - - - - - 

Fluoride 15 2 5.05E-02 4.10E+02 8.96E-01 4.99E+00 1.91E+01 1.39E+02 4.10E+02 

Lead 14 5 1.23E-03 2.28E-01 5.90E-03 4.53E-02 1.77E-01 2.16E-01 4.68E-01 

Manganese 16 2 4.24E-03 1.82E+02 1.69E-01 1.20E+00 7.67E+00 5.15E+01 1.03E+02 

Mercury 1 1 2.50E-04 2.50E-04 2.50E-04 2.50E-04 2.50E-04 2.50E-04 7.96E-04 

Molybdenum 18 6 1.79E-02 1.14E+01 4.73E-01 6.55E-01 1.00E+00 2.70E+00 1.14E+01 

Nickel 17 4 5.00E-03 1.23E+01 4.61E-02 2.75E-01 7.49E-01 3.09E+00 8.33E+00 

Nitrate 13 3 8.05E-02 1.17E+03 1.85E+00 4.73E+00 6.02E+02 9.17E+02 1.17E+03 

Nitrite 15 4 7.00E-03 4.61E+02 1.89E-01 1.39E+00 5.22E+00 1.43E+02 4.61E+02 

Selenium 15 3 2.50E-03 1.03E+00 6.97E-02 1.93E-01 3.56E-01 5.92E-01 1.03E+00 

Silver 8 8 5.00E-05 5.00E-03 2.06E-03 4.25E-03 5.00E-03 5.00E-03 

Strontium 17 0 4.20E-01 1.61E+01 4.25E+00 7.00E+00 8.74E+00 1.06E+01 1.61E+01 

Thallium 2 2 2.50E-03 5.00E-02 2.63E-02 3.81E-02 4.53E-02 4.76E-02 

Vanadium 15 1 1.25E-03 6.61E-01 1.03E-01 3.15E-01 4.78E-01 5.81E-01 8.00E-01 

Zinc 17 5 1.16E-02 2.34E+01 1.00E-01 1.20E-01 6.70E-01 5.40E+00 2.31E+01 

1

 Number of sites with analyte data (2002) 

2

 Number of sites with only nondetect analyte data (2002) 

3

 Includes both landfill and surface impoundment (SI) porewater data 

April 2010–Draft EPA document. A-3-2 
Appendix A Attachment A-3 

Table A-3-3. CCW Landfill Waste Analyte Concentrations 

Used in Screening: Leachate Analyses (mg/L)

Analyte Sites1 ND Sites2

Leachate Concentrations 1998 

TCLP 

Minimum Maximum 50th 75th 90th 95th 95th 

Aluminum 54 3 3.00E-02 2.86E+01 2.06E+00 4.47E+00 1.05E+01 1.36E+01 - 

Antimony 60 27 6.50E-04 7.87E-01 2.19E-02 7.50E-02 2.61E-01 2.98E-01 - 

Arsenic 119 26 1.00E-03 1.80E+00 3.65E-02 1.31E-01 3.94E-01 1.01E+00 2.40E-01 

Barium 115 7 2.00E-02 4.20E+01 3.04E-01 5.71E-01 1.60E+00 2.55E+00 - 

Beryllium 47 15 5.00E-05 2.80E-01 2.14E-03 5.37E-03 1.58E-02 2.96E-02 - 

Boron 72 3 1.00E-02 2.79E+01 1.07E+00 4.57E+00 1.06E+01 2.07E+01 - 

Cadmium 117 38 1.50E-04 6.00E-01 1.00E-02 2.24E-02 4.94E-02 9.00E-02 - 

Chromium 118 17 1.00E-03 7.64E-01 3.40E-02 1.00E-01 2.00E-01 3.50E-01 5.90E-02 

Cobalt 51 10 1.92E-03 2.46E-01 1.52E-02 2.55E-02 8.25E-02 1.31E-01 - 

Copper 72 13 1.60E-03 3.27E+00 4.14E-02 9.46E-02 1.50E-01 4.55E-01 - 

Cyanide 24 14 3.50E-03 1.20E-01 7.23E-03 2.03E-02 6.32E-02 8.67E-02 - 

Fluoride 33 1 8.00E-02 5.99E+01 8.19E-01 1.90E+00 6.34E+00 3.09E+01 - 

Lead 116 38 1.00E-03 3.61E+00 3.23E-02 7.23E-02 2.39E-01 2.90E-01 - 

Manganese 72 13 1.25E-03 3.27E+00 1.63E-01 4.39E-01 1.37E+00 1.56E+00 - 

Mercury 97 60 5.00E-06 2.90E-01 2.89E-04 1.00E-03 2.69E-03 1.32E-02 - 

Molybdenum 49 5 1.00E-02 3.09E+01 1.77E-01 3.40E-01 6.16E-01 1.27E+00 - 

Nickel 80 19 2.00E-03 3.88E+00 5.17E-02 1.41E-01 3.09E-01 5.70E-01 5.00E-02 

Nitrate 17 3 1.75E-02 2.60E+01 1.59E+00 2.50E+00 2.83E+00 7.72E+00 - 

Nitrite 5 4 5.00E-02 5.00E+00 8.33E-01 1.17E+00 3.47E+00 4.23E+00 - 

Selenium 119 23 1.00E-03 1.05E+00 4.87E-02 8.74E-02 1.76E-01 2.06E-01 4.40E-01 

Silver 109 60 0.00E+00 2.50E-01 8.70E-03 1.75E-02 3.95E-02 5.02E-02 - 

Strontium 20 0 6.35E-02 4.28E+01 2.95E+00 4.87E+00 9.70E+00 1.36E+01 - 

Thallium 40 18 1.00E-03 1.97E-01 8.29E-03 2.34E-02 5.00E-02 6.54E-02 - 

Vanadium 40 5 1.00E-03 1.20E+01 1.07E-01 1.82E-01 4.50E-01 1.50E+00 - 

Zinc 75 9 2.00E-03 5.83E+01 1.30E-01 6.09E-01 1.94E+00 1.01E+01 - 

1

 Number of sites with analyte data (2002) 

2

 Number of sites with only nondetect analyte data (2002) 

TCLP = Toxicity Characteristic Leaching Procedure 

April 2010–Draft EPA document. A-3-3 
Appendix A Attachment A-3 

0.001 0.01 0.1 1 10 100 1000 10000

As, Cancer

As

NO3 (MCL)

B

Tl

Pb (MCL)

Co

Cd

Mo

Mn

Sb

FCr VI

Se

V

NO2

Ni

Sr

Al

Cu (MCL)

Ba

Be

Zn

Ag

Hg

Cr III

Analytes

90th percentile concentration/health-based number (HBN)

Non-averaged Site-averaged

Figure A-3-1. Comparison of site-averaged and nonaveraged results for surface 

impoundment porewater screening, groundwater-to-drinking-water pathway. 

April 2010–Draft EPA document. A-3-4 
Appendix A Attachment A-3 

April 2010–Draft EPA document. A-3-5 

0.001 0.01 0.1 1 10 100 1000

As, Cancer

As

Sb

Tl

Pb (MCL)

B

V

FCd

Cr VI

Mo

Mn

Se

Ba

Zn

Hg

Ni

Be

Ag

Cu (MCL)

NO3 (MCL)

Co

Sr

Al

CN

Cr III

Analytes

95th percentile concentration/health-based number (HBN)

Non-averaged Site-averaged

Figure A-3-2. Comparison of site-averaged and nonaveraged results for landfill leachate 

screening, groundwater-to-drinking-water pathway. 
Appendix A Attachment A-3 

0.0000001 0.000001 0.00001 0.0001 0.001 0.01 0.1 1 10

As, Cancer

Tl

B

Ag

Sb

As

Hg

Sr

Zn

Mo

Cr VI

V

Cd

Ni

Ba

Al

Se

Pb (SSL/PRG)

Mn

Co

Cr VI, Cancer

Be

FCo, Cancer

Be, Cancer

Cr III

Cd, Cancer

CN

NO3

Analytes

90th percentile concentration/health-based number (HBN)

Non-averaged Site-averaged

Figure A-3-3. Comparison of site-averaged and nonaveraged results for landfill 

total waste screening, aboveground pathways. 

April 2010–Draft EPA document. A-3-6 
Appendix B Waste Management Units 

Appendix B. Waste Management Units

The source models supporting the CCW risk assessment require inputs describing the 

characteristics of CCW waste management units (WMUs). To satisfy this requirement, the 

assessment used a data set of WMU area, capacity, liner type, geometry, and waste type 

managed for a set of individual CCW landfills and surface impoundments that are representative 

of the national population of coal combustion facilities that are managing their wastes onsite. 

The sources for these data sets were responses to two voluntary industry surveys: an 

Electric Power Research Institute (EPRI) comanagement survey (for conventional utility coal 

combustion WMUs units) and a Council of Industrial Boiler Owners (CIBO) fluidized bed 

combustion (FBC) survey (for FBC WMUs). In addition to the individual WMU data, certain 

assumptions were required regarding (1) liner types and characteristics, (2) surface impoundment 

operating life, and (3) above- and below-grade geometries for WMUs. The sections below 

describe the two industry surveys, then discuss the data sources and assumptions made.

Attachment B-1 lists the 181 CCW disposal sites modeled in this risk assessment and their 

locations. Attachment B-2 presents the WMU data used in the CCW risk assessment for each of 

the 108 landfills and 96 surface impoundments at these coal combustion facilities. 

B.1 EPRI Comanagement Survey 

For conventional utility coal combustion WMUs, the source of data for area, capacity, 

liner type, and waste type managed was the EPRI Coal Combustion By-Products and 

Low-Volume Wastes Comanagement Survey (EPRI, 1997a). In 1995, EPRI sent a 4-page 

questionnaire to all electric utilities with more than 100 megawatts (MW) of coal-fired 

generating capacity. The survey gathered data on the design of coal combustion management 

units and the types and volumes of waste managed. From the survey responses, EPRI prepared 

an electronic database and provided it to EPA in support of the March 1999 Report to Congress: 

Wastes from the Combustion of Fossil Fuels (the RTC) (U.S. EPA, 1999a). EPRI also published 

a report (EPRI, 1997a) documenting the survey format and providing a brief summary of the 

results. 

The EPRI survey responses include information on 323 waste management facilities 

serving 238 power plants located in 36 states. The total annual volume of CCW reported 

disposed by respondents to the EPRI comanagement survey was nearly 62 million tons. This 

quantity was two-thirds of the total generation of CCW in 1995. Therefore, the survey sample 

encompasses the majority of CCW disposed in terms of volume. Based on comparison with data 

from other sources, the EPRI survey sample appears representative of the population of coal 

combustion WMUs in terms of the types of units included (i.e., landfills and surface 

impoundments). The EPRI survey sample also is believed to be generally geographically 

representative of the population of conventional utility WMUs, although it may under-represent 

certain management practices in a few states. The EPA document, Technical Background 

Document for the Supplemental Report to Congress on Remaining Fossil Fuel Combustion 

April 2010–Draft EPA document. B-1 
Appendix B Waste Management Units 

Wastes: Industry Statistics and Waste Management Practices (U.S. EPA, 1999b), discusses the 

representativeness of the EPRI survey in greater detail and provides extensive summary statistics 

on the survey responses. 

The EPRI comanagement survey included questions requesting the respondent to report 

the location of the WMU (by state) and the WMU area, capacity, liner type, and waste type 

managed. Therefore, the data set used for modeling these variables was extracted directly from 

the EPRI database for all active landfills and surface impoundments responding to the EPRI 

survey. Mine placement sites and closed WMUs were excluded from the data set. Also excluded 

from the data set were three responding WMUs that managed FBC waste. Data for these units 

were instead combined with the data set for FBC WMUs from the CIBO FBC survey (described 

below). 

The EPRI survey data were provided in blinded form. That is, the original database did 

not report the identity of each respondent and identified WMU location only by state. To provide 

a more complete identification of the EPRI waste management locations, each unit in the EPRI 

database had to be matched with a specific electric utility facility. This matching was 

accomplished by applying professional judgment in comparing the state, waste quantity, and 

waste management practice information in the EPRI database with similar data from responses to 

the U.S. Department of Energy’s Energy Information Administration (EIA) Form EIA-767 

(Steam-Electric Plant Operation and Design Report) for the same year as the EPRI survey 

(1995). The latitude and longitude plant locations in the EIA database allowed the pairing of the 

EPRI WMU data with environmental setting information. 

B.2 CIBO Fluidized Bed Combustion Survey 

For FBC WMUs, the primary source of data for area, capacity, liner type, and waste type 

managed was the CIBO Fossil Fuel Fluidized Bed Combustion (FBC) Survey. In 1996, CIBO 

sent a voluntary questionnaire to every fossil-fuel-fired FBC plant, both utility and nonutility, in 

the United States. This survey collected general facility information, characterized process inputs 

and outputs, gathered data on waste generation and characteristics, and captured details of FBC 

waste management practices. From the survey responses, CIBO prepared an electronic database 

and provided it to EPA in support of the March 1999 RTC. CIBO also published a report (CIBO, 

1997) that includes documentation of the survey format and provides a brief summary of the 

results. 

CIBO reports a total of 84 facilities using FBC technology. Forty-five of these responded 

to the CIBO FBC survey, with 20 of the respondents providing information about waste 

management practices. The facilities with waste management data cover 24 percent of all U.S. 

facilities using FBC. The CIBO sample is geographically representative of the full population, 

with the exception of two states that appear under-represented in the sample—Pennsylvania and 

Illinois. EPA’s technical background document on industry statistics and waste management 

practices (U.S. EPA, 1999b) discusses the representativeness of the EPRI survey in greater detail 

and provides extensive summary statistics on the survey responses. 

The CIBO survey includes questions requesting the respondent to report WMU area, 

capacity, liner type, and waste type managed. Therefore, the data set used for modeling these 

April 2010–Draft EPA document. B-2 
Appendix B Waste Management Units 

variables was extracted directly from the CIBO database. The CIBO respondents included both 

utility and nonutility (i.e., industrial or institutional facilities that burn coal, but are not primarily 

engaged in the business of selling electricity) facilities. Because nonutilities are outside the scope 

of this risk assessment, nonutilities were excluded from the data set. Three additional utility 

facilities were excluded from the data set because their responses contained insufficient data on 

the variables of interest (area, capacity, liner type, and waste type). Mine placement sites also 

were excluded from the data set. Data for the FBC units responding to the EPRI survey (see 

Section B.1) were added to the data set. This resulted in a sample of seven FBC landfills and one 

FBC surface impoundment for modeling. Table B-1 compares this sample to the waste 

management practices of the full utility FBC population. 

As shown in Table B-1, FBC facilities frequently avoid waste disposal units by directing 

all of their waste to mine placement or beneficial use. Therefore, although only 8 of the 41 utility 

FBC facilities were included in the model data set, these 8 facilities represent nearly all of the 

known FBC landfills and surface impoundments. 

Table B-1. Utility FBC Waste Management Practices and Units Modeled 

Number of Facilities... Total Landfill

Surface 

Impoundment

Minefill or 

Beneficial Use Unknown

in the full population 41 11 1 16 13 

modeled 8 7 1 Not applicable Not applicable 

The CIBO survey database identified the location of each WMU in detail (latitude and 

longitude). Therefore, no additional analysis was necessary to pair the WMU data with 

environmental setting information. 

B.3 Liner Type 

The EPRI survey data included information on the liner (if any) for each WMU. For this 

assessment, the WMUs were assigned to one of three liner scenarios based on the EPRI liner 

data: an unlined (no liner) scenario, a compacted clay liner, and a composite liner that combines 

a plastic (e.g., high-density polyethylene (HDPE) membrane) over either geosynthetic or natural 

clays. These three scenarios correspond to the following conceptual liner scenarios, developed in 

support of EPA’s Industrial Subtitle D guidance (U.S. EPA, 2002), which can be selected in the 

landfill and surface impoundment models used in this assessment. 

 Unlined Scenario. For landfills, waste is placed directly on local soils, either on grade or 

excavated to some design depth and without a leachate collection system. After the 

landfill has been filled to capacity, a 2-foot native soil cover (the minimum required by 

Subtitle D regulations) is installed and assumed to support vegetation. For surface 

impoundments, wastewater is placed directly on local soils, and the depth of water is 

constant over the entire life of the impoundment, pre- and post-closure. Sediments 

accumulate and consolidate at the bottom of the impoundment and migrate into the 

underlying native soils, where they clog pore spaces and provide some barrier to flow. 

April 2010–Draft EPA document. B-3 
Appendix B Waste Management Units 

 Clay Liner Scenario. For landfills, waste is placed directly on a 3-foot compacted clay 

liner, which is installed on the local soils, either on grade or excavated to some design 

depth and without a leachate collection system. After the landfill has been filled to 

capacity, a 3-foot clay cover is installed and covered with 1 foot of loam to support 

vegetation and drainage. The hydraulic conductivity of both the liner and cover clays is 

assumed to be 1x10-7 cm/sec. For surface impoundments, wastewater is placed on a 

compacted clay liner, which is installed on the local soils. The assumptions for an unlined 

impoundment also apply to the compacted clay liner scenario, except that a compacted 

clay liner filters out the sediments that clog the native soils in the unlined case, so the 

effect of clogging the native materials is not included in the calculation of the infiltration 

rate. The thickness of the compacted clay liner was assumed to be 3 feet and the 

hydraulic conductivity was assumed to be 1x10-7 cm/sec. 

 Composite Liner Scenario. For landfills, wastes are placed on a liner system that 

consists of a 60 mil HDPE membrane with either an underlying geosynthetic clay liner 

with a maximum hydraulic conductivity of 5x10-9 cm/sec, or a 3-foot compacted clay 

liner with a maximum hydraulic conductivity of 1x10-7 cm/sec. A leachate collection 

system is also assumed to exist between the waste and the liner system. After the landfill 

has been filled to capacity, a 3-foot clay cover is assumed to be installed and covered 

with 1 foot of loam to support vegetation and drainage. For surface impoundments, 

wastewater is placed on a synthetic membrane with an underlying geosynthetic or natural 

compacted clay liner with a hydraulic conductivity of 1x10-7 cm/sec. The membrane liner 

was assumed to have a number of pinhole leaks of uniform size (6 mm2). The number of 

these leaks was based on an empirical distribution of membrane leak density values 

obtained from TetraTech (2001), as described in the IWEM Technical Background 

Document (U.S. EPA, 2002). 

Table B-2 shows the crosswalk used to assign one of the three liner scenarios to each 

facility based on the liner data in the EPRI survey data (EPRI, 1997a). Attachment B-2 provides 

these assignments, along with the original EPRI liner type, for each CCW landfill facility 

modeled. 

Table B-2. Crosswalk Between EPRI and 

CCW Source Model Liner Types 

EPRI Liner Type 

Model Liner 

Code Description 

Compacted ash 0 no liner 

Compacted clay 1 clay 

Composite clay/membrane 2 composite 

Double 2 composite 

Geosynthetic membrane 2 composite 

None/natural soils 0 no liner 

April 2010–Draft EPA document. B-4 
Appendix B Waste Management Units 

April 2010–Draft EPA document. B-5 

B.4 Surface Impoundment Operating Life 

The model runs for surface impoundments required a general assumption about the length 

of the operating life for these WMUs. Of the surface impoundments in the EPRI comanagement 

survey, 86 provided responses to questions about both the unit’s opening date and expected 

closure date. From these two dates, an expected operating life for each impoundment could be 

calculated. An additional 30 impoundments provided an opening date, but no closure date. One 

possible interpretation of these responses is that these facilities do not expect to close in the 

foreseeable future, corresponding to a very long or indefinite operating life with dredging of 

waste to maintain capacity. Figure B-1 shows the distribution of the calculated operating lives, 

along with a bar showing the facilities with no closure date. 

3

10

17 17 17

9

6 5

1 1

30

Expected Operating Life (years)

10 or less 11 to 20 21 to 30 31 to 40 41 to 50 51 to 60 61 to 70 71 to 80 81 to 90 91 to 100 No closure date

0

5

10

15

20

25

30

35

Figure B-1. Operating life of impoundments in the EPRI survey. 

Based on these data, a 75-year operating life was chosen. This value corresponds to the 

95th percentile of the observed distribution. While the use of a 95th percentile value may appear 

conservative, if many of the facilities with no closure date do, in fact, plan to operate 

indefinitely, 75 years would correspond to a much lower percentile in the distribution. More 

significantly, many CCW surface impoundments close with wastes in place. The selection of 75 

years minimizes the underestimation of chronic risks for this scenario, given that EPA’s 

Composite Model for Leachate Migration with Transformation Products (EPACMTP) surface 

impoundment model assumes clean closure after the operating life. 

B.5 Above- and Below-Grade Geometry 

The model runs for surface impoundments and landfills required general assumptions 

about the geometry of these units with respect to the ground surface (i.e., how much of the unit’s 

depth is below grade). The CIBO FBC survey included data on this geometry, so, for FBC units, 

these data were extracted directly from the database along with the other individual WMU data 
Appendix B Waste Management Units 

(e.g., capacity). The EPRI comanagement survey did not contain data describing above- and 

below-grade geometry. Therefore, for conventional utility coal combustion WMUs, EPA 

reviewed 17 site-characterization reports published by EPRI (EPRI 1991; 1992; 1994a,b; 

1996a,b; 1997b-k) and determined an above- versus below-grade geometry for each unit 

described in those reports based on schematic diagrams and site descriptions. EPA also extracted 

data from another CIBO voluntary survey that covered conventional (non-FBC) nonutility coal 

combustors. Figures B-2 and B-3 display the distributions of the data thus collected. 

3

1

0 0

1

3

Percent above grade

0% 1 to 25% 26 to 50% 51 to 75% 76 to 99% 100%

0

1

2

3

4

5

6

7

8

9

Figure B-2. Above- and below-grade geometry for landfills. 

8

1

4

2

0

1

Percent above grade

0% 1 to 25% 26 to 50% 51 to 75% 76 to 99% 100%

0

1

2

3

4

5

6

7

8

9

8

1

4

2

0

1

Percent above grade

0% 1 to 25% 26 to 50% 51 to 75% 76 to 99% 100%

0

1

2

3

4

5

6

7

8

9

Figure B-3. Above- and below-grade geometry for impoundments. 

April 2010–Draft EPA document. B-6 
Appendix B Waste Management Units 

For landfills, because the data were limited (8 sites), the model runs assumed that the 

percent below grade ranged from 1 to 100 and was uniformly distributed. For each landfill 

iteration, a random value for percent below grade was picked and applied to the landfill depth to 

determine depth below ground surface. This value was constrained to be no deeper than the 

water table and was checked to see that EPACMTP groundwater mounding constraints were not 

violated. 

For surface impoundments, more data were available (16 sites), with 8 sites being 

constructed entirely below grade and the remaining 8 sites ranging from 7.5 to 45 feet above 

grade. For each surface impoundment iteration, height above grade at these 15 sites was 

randomly sampled as an empirical distribution and applied to the overall surface impoundment 

depth to determine depth below ground surface. 

B.6 Calculation of WMU Depth and Imputation of Missing WMU Data 

The EPRI survey includes information on the total area and total waste capacity of each 

landfill and surface impoundment included in the survey. To calculate average depth for each 

WMU (a necessary EPACMTP model input), the total waste capacity was divided by the area. 

The resulting depths were then checked for reasonableness. For surface impoundments, one 

depth (1 foot) was culled as being unrealistically low and one (700 feet) as too high. Two landfill 

depths less than 2 feet and one depth greater than 350 feet were also removed from the database. 

In these cases the EPRI waste capacity data were culled and replaced using the regressions 

described below (i.e., WMU areas are considered more reliable than the capacity estimates in the 

survey data), and new capacities were estimated as described below. 

In addition, four landfills and six surface impoundments had neither area nor capacity 

data in the EPRI survey. In these cases, the EIA facility locations were used to find the plants 

and their WMUs on aerial photos from the Terraserver Web site (http://terraserver-usa.com/ 

geographic.aspx), and a geographic information system (GIS) was used to measure the areas of 

the units in question. Capacities were then estimated as described below. 

To impute data for facilities missing either area or capacity data in the EPRI survey, 

linear regression equations were developed based on WMUs with both area and capacity data, 

one to predict area from capacity, and one to predict capacity from area. The final regression 

equations are shown in Figures B-4 and B-5 for landfills and Figures B-6 and B-7 for surface 

impoundments. In each case, a standard deviation around the regression line was also computed 

and used during source data file preparation to randomly vary the area or capacity from iteration 

to iteration within the bounds of the existing data set. 

April 2010–Draft EPA document. B-7 
Appendix B Waste Management Units 

April 2010–Draft EPA document. B-8 

y = 0.6437x - 2.3704

R2 = 0.6671

Std. Dev = 0.28

0

0.5

1

1.5

2

2.5

3

0123456789

log(capacity, cubic yards)

Log(area, acres) 3.5

Figure B-4. Linear regression to impute landfill area from capacity. 

y = 1.0363x + 4.6299

R2 = 0.6671

Std. Dev. = 0.35

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5 3 3.5

Log(area, acres)

Log(capacity, cubic yards) 9

Figure B-5. Linear regression to impute landfill capacity from area. 
Appendix B Waste Management Units 

April 2010–Draft EPA document. B-9 

y = 0.6854x - 2.4976

R2 = 0.5867

Std. Dev. = 0.32

0

0.5

1

1.5

2

2.5

3

3.5

0123456789

log(capacity, cubic yards)

Log(area, acres)

Figure B-6. Linear regression to impute surface impoundment area from capacity. 

y = 0.8559x + 4.8166

R2 = 0.5867

Std. Dev. = 0.35

0

1

2

3

4

5

6

7

8

0 0.5 1 1.5 2 2.5 3 3.5

Log(area, acres)

Log(disposal capacity, cubic yards) 9

Figure B-7. Linear regression to impute surface impoundment capacity from area. 
Appendix B Waste Management Units 

B.7 Results 

Attachment B-1 lists the 181 CCW disposal sites modeled in this risk assessment and 

their locations. The WMU data used in the CCW risk assessment for each of the 108 landfills 

and 96 surface impoundments at these coal combustion facilities are presented in 

Attachment B-2. Missing data that were randomly replaced as described above are not 

represented in the table (i.e., the fields are left blank). 

B.8 References 

CIBO (Council of Industrial Boiler Owners). 1997. Report to the U.S. Environmental Protection 

Agency on Fossil Fuel Combustion Byproducts from Fluidized Bed Boilers. Prepared by 

the CIBO Special Project on Non-Utility Fossil Fuel Ash Classification and ICF Kaiser 

Consulting Group. November. 

EPRI (Electric Power Research Institute). 1991. Comanagement of Coal Combustion ByProducts and Low-Volume Wastes: A Southeastern Site. Final Report. EN-7545. EPRI, 

Palo Alto, CA. November 1991. (“L” Site). 

EPRI (Electric Power Research Institute). 1992. Comanagement of Coal Combustion 

By-Products and Low-Volume Wastes: A Midwestern Site. Interim Report. TR-100955. 

EPRI, Palo Alto, CA. August 1992. (“C” Site). 

EPRI (Electric Power Research Institute). 1994a. Calcium-Based Flue Gas Desulfurization 

Sludge Disposal Sites. Final Report. TR-103914. EPRI, Palo Alto, CA. April 1994. 

EPRI (Electric Power Research Institute). 1994b. Sodium-Based Flue Gas Desulfurization 

Sludge Disposal Sites. Final Report. TR-103915. EPRI, Palo Alto, CA. April 1994. 

EPRI (Electric Power Research Institute). 1996a. Comanagement of Coal Combustion ByProducts and Low-Volume Wastes: A Southwestern Site. Final Report. TR-105673. EPRI, 

Palo Alto, CA. June 1996. (“O” Site). 

EPRI (Electric Power Research Institute). 1996b. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: CL Site. Draft Report. EPRI, 

Palo Alto, CA. May 1996 (updated December). 

EPRI (Electric Power Research Institute). 1997a. Coal Combustion By-Products and 

Low-Volume Wastes Comanagement Survey. EPRI, Palo Alto, CA. June. 

EPRI (Electric Power Research Institute). 1997b. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: AP Site. Draft Report. EPRI, 

Palo Alto, CA. June 1997. 

EPRI (Electric Power Research Institute). 1997c. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: CY Site. Final Report. 

TR-108426. EPRI, Palo Alto, CA. August 1997. 

April 2010–Draft EPA document. B-10 
Appendix B Waste Management Units 

April 2010–Draft EPA document. B-11 

EPRI (Electric Power Research Institute). 1997d. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: FC Site. Draft Report. 

TR-108425. EPRI, Palo Alto, CA. August 1997. 

EPRI (Electric Power Research Institute). 1997e. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: HA Site. Draft Report. EPRI, 

Palo Alto, CA. August 1997. 

EPRI (Electric Power Research Institute). 1997f. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: LS Site. Final Report. 

TR-108422. EPRI, Palo Alto, CA. August 1997. 

EPRI (Electric Power Research Institute). 1997g. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: MO Site. Draft Report. 

EPRI, Palo Alto, CA. April 1997. 

EPRI (Electric Power Research Institute). 1997h. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: OK Site. Draft Report. 

TR-108421. EPRI, Palo Alto, CA. July 1997. 

EPRI (Electric Power Research Institute). 1997i. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: P4 Site. Final Report. 

TR-108420. EPRI, Palo Alto, CA. July 1997. 

EPRI (Electric Power Research Institute). 1997j. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: PA Site. Final Report. 

TR-108427. EPRI, Palo Alto, CA. August 1997. 

EPRI (Electric Power Research Institute). 1997k. Evaluation of the Co-Management of High 

Volume Combustion By-Products with Other Utility Wastes: SX Site. Draft Report. 

TR-001015. EPRI, Palo Alto, CA. June 1997. 

TetraTech. 2001. Characterization of infiltration rate data to support groundwater modeling 

efforts (Draft). Prepared for the Office of Solid Waste, U.S. Environmental Protection 

Agency, Contract No. 68-W6-0061. May. 

U.S. EPA (Environmental Protection Agency). 1999a. Report to Congress: Wastes from the 

Combustion of Fossil Fuels. EPA 530-5-99-010. Office of Solid Waste and Emergency 

Response, Washington, DC. March. 

U.S. EPA (Environmental Protection Agency). 1999b. Technical Background Document for the 

Supplemental Report to Congress on Remaining Fossil Fuel Combustion Wastes: 

Industry Statistics and Waste Management Practices. Office of Solid Waste, Washington, 

DC. March 15, 1999. Docket number F-1999-FF2P-S0398. 

U.S. EPA (Environmental Protection Agency). 2002. Industrial Waste Management Evaluation 

Model (IWEM) Technical Background Document. EPA 530-R-02-012. Office of Solid 

Waste, Washington, DC. August. 
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix B Attachment B-1: CCW Disposal Sites (Plants) 

Attachment B-1: CCW Disposal Sites (Plants) 

Plant Name Utility Name County State Latitude Longitude

A B Brown Southern Indiana Gas & Elec. Co. Posey IN 37.9053 87.715 

A/C Power - Ace 

Operations 

A.C.E. Cogeneration Co. San Bernardino CA 35.75 117.3667 

Allen Tennessee Valley Authority Shelby TN 35.0742 90.1492 

Alma Dairyland Power Coop Buffalo WI 44.3078 91.905 

Antelope Valley Basin Electric Power Coop Mercer ND 47.37 101.8353 

Arkwright Georgia Power Co. Bibb GA 32.9269 83.6997 

Asheville Carolina Power & Light Co. Buncombe NC 35.4714 82.5431 

Baldwin Illinois Power Co. Randolph IL 38.205 89.8544 

Barry Alabama Power Co. Mobile AL 31.0069 88.0103 

Bay Front Northern States Power Co. Ashland WI 43.4833 89.4 

Bay Shore Toledo Edison Co. Lucas OH 41.6925 83.4375 

Belews Creek Duke Power Co. Stokes NC 36.2811 80.0603 

Ben French Black Hills Corp. Pennington SD 44.0872 103.2614 

Big Cajun 2 Cajun Electric Power Coop, Inc. Pointe Coupee LA 30.7283 91.3686 

Big Sandy Kentucky Power Co. Lawrence KY 38.1686 82.6208 

Big Stone Otter Tail Power Co. Grant SD 45.3047 96.5083 

Black Dog Steam 

Plant 

Northern States Power Company Dakota MN 44.8167 93.25 

Blue Valley Independence, City of Jackson MO 39.0919 94.3364 

Bowen Georgia Power Co. Bartow GA 34.1256 84.9192 

Brandon Shores Baltimore Gas & Electric Co. Anne Arundel MD 39.18 76.5333 

Buck Duke Power Co. Rowan NC 35.7133 80.3767 

Bull Run Tennessee Valley Authority Anderson TN 36.0211 84.1567 

C D McIntosh Jr. Lakeland, City of Polk FL 28.075 81.9292 

C P Crane Baltimore Gas & Electric Co. Baltimore City MD 39.2845 76.6207 

Cape Fear Carolina Power & Light Co. Chatham NC 35.5989 79.0492 

Carbon PacifiCorp Carbon UT 39.7264 110.8639 

Cardinal Cardinal Operating Co. Jefferson OH 40.2522 80.6486 

Cayuga PSI Energy, Inc. Vermillion IN 39.9008 87.4136 

Chalk Point Potomac Electric Power Co. Prince Georges MD 38.5639 76.6806 

Cholla Arizona Public Service Co. Navajo AZ 34.9414 110.3003 

Cliffside Duke Power Co. Cleveland NC 35.22 81.7594 

Clover Virginia Electric & Power Co. Halifax VA 36.8667 78.7 

Coal Creek Coop Power Assn. McLean ND 47.3789 101.1572 

Coleto Creek Central Power & Light Co. Goliad TX 28.7128 97.2142 

(continued)

April 2010–Draft EPA document. B-1-1 
Appendix B Attachment B-1: CCW Disposal Sites (Plants) 

CCW Disposal Sites (Plants) (continued) 

Plant Name Utility Name County State Latitude Longitude

Colstrip Montana Power Co. Rosebud MT 45.8844 106.6139 

Conemaugh GPU Service Corporation Indiana PA 40.3842 79.0611 

Conesville Columbus Southern Power Co. Coshocton OH 40.1842 81.8811 

Council Bluffs MidAmerican Energy Co. Pottawattamie IA 41.18 95.8408 

Crawford Commonwealth Edison Co. Cook IL 39.8225 90.5681 

Crist Gulf Power Co. Escambia FL 30.5658 87.2239 

Cross South Carolina Pub Serv. Auth. Berkeley SC 33.3694 80.1119 

Cumberland Tennessee Valley Authority Stewart TN 36.3942 87.6539 

Dale East Kentucky Power Coop, Inc. Clark KY 37.875 84.25 

Dallman Springfield, City of Sangamon IL 39.7547 89.6008 

Dan E Karn Consumers Energy Co. Bay MI 43.645 83.8414 

Dan River Duke Power Co. Rockingham NC 36.4861 79.7244 

Danskammer Central Hudson Gas & Elec. Corp. Orange NY 41.5719 73.9664 

Dave Johnston PacifiCorp Converse WY 42.8333 105.7667 

Dickerson Potomac Electric Power Co. Montgomery MD 39.144 77.2059 

Dolet Hills CLECO Corporation De Soto LA 32.0308 93.5644 

Duck Creek Central Illinois Light Co. Fulton IL 40.4644 89.9825 

Dunkirk Niagara Mohawk Power Corp. Chautauqua NY 42.4919 79.3469 

E D Edwards Central Illinois Light Co. Peoria IL 40.5961 89.6633 

E W Brown Kentucky Utilities Co. Mercer KY 37.7911 84.7147 

Eckert Station Lansing, City of Ingham MI 42.7189 84.5583 

Edgewater Wisconsin Power & Light Co. Sheboygan WI 43.7181 87.7092 

Elmer W Stout Indianapolis Power & Light Co. Marion IN 39.7122 86.1975 

F B Culley Southern Indiana Gas & Elec. Co. Warrick IN 37.91 87.3267 

Fayette Power Prj. Lower Colorado River Authority Fayette TX 29.9172 96.7506 

Flint Creek Southwestern Electric Power Co. Benton AR 36.2625 94.5208 

Fort Martin Monongahela Power Co. Monongalia WV 39.7 79.9167 

Frank E Ratts Hoosier Energy R E C, Inc. Pike IN 38.5186 87.2725 

G G Allen Duke Power Co. Gaston NC 35.1897 81.0122 

Gadsden Alabama Power Co. Etowah AL 34.0136 85.9703 

Gallatin Tennessee Valley Authority Sumner TN 36.3156 86.4006 

Gen J M Gavin Ohio Power Co. Gallia OH 38.9358 82.1164 

Genoa Dairyland Power Coop Vernon WI 43.5592 91.2333 

Gibson PSI Energy, Inc. Gibson IN 38.3589 87.7783 

Gorgas Alabama Power Co. Walker AL 33.5111 87.235 

Green River Kentucky Utilities Co. Muhlenberg KY 37.3636 87.1214 

Greene County Alabama Power Co. Greene AL 32.6 87.7667 

H B Robinson Carolina Power & Light Co. Darlington SC 34.4 80.1667 

Hammond Georgia Power Co. Floyd GA 34.3333 85.2336 

(continued)

April 2010–Draft EPA document. B-1-2 
Appendix B Attachment B-1: CCW Disposal Sites (Plants) 

April 2010–Draft EPA document. B-1-3 

CCW Disposal Sites (Plants) (continued) 

Plant Name Utility Name County State Latitude Longitude

Harllee Branch Georgia Power Co. Putnam GA 33.1942 83.2994 

Harrison Monongahela Power Co. Harrison WV 39.3833 80.3167 

Hatfield's Ferry West Penn Power Co. Greene PA 39.85 79.9167 

Hennepin Illinois Power Co. Putnam IL 41.3028 89.315 

Heskett Montana-Dakota Utilities Co. Morton ND 46.8669 100.8839 

Holcomb Sunflower Electric Power Corp. Finney KS 37.9319 100.9719 

Homer City GPU Service Corporation Indiana PA 40.5142 79.1969 

Hoot Lake Otter Tail Power Co. Otter Tail MN 46.29 96.0428 

Hugo Western Farmers Elec. Coop, Inc. Choctaw OK 34.0292 95.3167 

Hunter PacifiCorp Emery UT 39.1667 111.0261 

Huntington PacifiCorp Emery UT 39.3792 111.075 

Intermountain Los Angeles, City of Millard UT 39.5108 112.5792 

J H Campbell Consumers Energy Co. Ottawa MI 42.9103 86.2031 

J M Stuart Dayton Power & Light Co. Adams OH 38.6364 83.7422 

J R Whiting Consumers Energy Co. Monroe MI 41.7914 83.4486 

Jack McDonough Georgia Power Co. Cobb GA 33.8244 84.475 

Jack Watson Mississippi Power Co. Harrison MS 30.4392 89.0264 

James H Miller Jr. Alabama Power Co. Jefferson AL 33.6319 87.0597 

Jim Bridger PacifiCorp Sweetwater WY 41.75 108.8 

John E Amos Appalachian Power Co. Putnam WV 38.4731 81.8233 

John Sevier Tennessee Valley Authority Hawkins TN 36.3767 82.9639 

Johnsonville Tennessee Valley Authority Humphreys TN 36.0278 87.9861 

Joliet 29 Commonwealth Edison Co. Will IL 41.4892 88.0844 

Keystone GPU Service Corporation Armstrong PA 40.6522 79.3425 

Killen Station Dayton Power & Light Co. Adams OH 38.6903 83.4803 

Kingston Tennessee Valley Authority Roane TN 35.8992 84.5194 

Kraft Savannah Electric & Power Co Chatham GA 32.1333 81.1333 

L V Sutton Carolina Power & Light Co. New Hanover NC 34.2831 77.9867 

Lansing Interstate Power Co. Allamakee IA 43.3386 91.1667 

Laramie R Station Basin Electric Power Coop Platte WY 42.1086 104.8711 

Lawrence EC KPL Western Resources Co. Douglas KS 39.0078 95.2681 

Lee Carolina Power & Light Co. Wayne NC 35.3778 78.1 

Leland Olds Basin Electric Power Coop Mercer ND 47.2833 101.4 

Lon Wright Fremont, City of Dodge NE 41.45 96.5167 

Louisa MidAmerican Energy Co. Louisa IA 41.3181 91.0931 

Marion Southern Illinois Power Coop Williamson IL 37.6167 88.95 

Marshall Duke Power Co. Catawba NC 35.5975 80.9658 

Martin Lake Texas Utilities Electric Co. Rusk TX 32.2606 94.5708 

Mayo Carolina Power & Light Co. Person NC 36.5278 78.8919 

Meramec Union Electric Co. St Louis MO 38.6522 90.2397 

(continued)
Appendix B Attachment B-1: CCW Disposal Sites (Plants) 

April 2010–Draft EPA document. B-1-4 

CCW Disposal Sites (Plants) (continued) 

Plant Name Utility Name County State Latitude Longitude

Merom Hoosier Energy R E C, Inc. Sullivan IN 39.0694 87.5108 

Miami Fort Cincinnati Gas & Electric Co. Hamilton OH 39.1111 84.8042 

Milton R Young Minnkota Power Coop, Inc. Oliver ND 47.0664 101.2139 

Mitchell - PA West Penn Power Co. Washington PA 40.2167 79.9667 

Mitchell - WV Ohio Power Co. Marshall WV 39.8297 80.8153 

Mohave Southern California Edison Co. Clark NV 35.1667 114.6 

Monroe Detroit Edison Co. Monroe MI 41.8911 83.3444 

Morgantown Potomac Electric Power Co. Charles MD 38.3611 76.9861 

Mountaineer (1301) Appalachian Power Co. Mason WV 38.9794 81.9344 

Mt Storm Virginia Electric & Power Co. Grant WV 39.2014 79.2667 

Muscatine Plant #1 Muscatine, City of Muscatine IA 41.3917 91.0569 

Muskogee Oklahoma Gas & Electric Co. Muskogee OK 35.7653 95.2883 

Neal North MidAmerican Energy Co. Woodbury IA 42.3167 96.3667 

Neal South MidAmerican Energy Co. Woodbury IA 42.3022 96.3622 

Nebraska City Omaha Public Power District Otoe NE 40.625 95.7917 

New Castle Pennsylvania Power Co. Lawrence PA 40.9383 80.3683 

Newton Central Illinois Pub Serv. Co. Jasper IL 38.9364 88.2778 

North Omaha Omaha Public Power District Douglas NE 41.33 95.9467 

Northeastern Public Service Co. of Oklahoma Rogers OK 36.4222 95.7047 

Nucla Tri-State G & T Assn., Inc. Montrose CO 38.2386 108.5072 

Oklaunion West Texas Utilities Co. Wilbarger TX 34.0825 99.1753 

Paradise Tennessee Valley Authority Muhlenberg KY 37.2608 86.9783 

Petersburg Indianapolis Power & Light Co. Pike IN 38.5267 87.2522 

Pleasant Prairie Wisconsin Electric Power Co. Kenosha WI 42.5381 87.9033 

Port Washington Wisconsin Electric Power Co. Ozaukee WI 43.3908 87.8686 

Portland Metropolitan Edison Co. Northampton PA 40.7525 75.3324 

Possum Point Virginia Electric & Power Co. Prince William VA 38.5367 77.2806 

Potomac River Potomac Electric Power Co. Alexandria VA 38.8078 77.0372 

Presque Isle Wisconsin Electric Power Co. Marquette MI 46.5694 87.3933 

R Gallagher PSI Energy, Inc. Floyd IN 38.2631 85.8378 

R M Schahfer Northern Indiana Pub. Serv. Co. Jasper IN 41.2167 87.0222 

Reid Gardner Nevada Power Co. Clark NV 36.6606 114.625 

Richard Gorsuch American Mun. Power-Ohio, Inc. Washington OH 39.3672 81.5208 

Riverbend Duke Power Co. Gaston NC 35.36 80.9742 

Rodemacher CLECO Corporation Rapides LA 31.395 92.7167 

Roxboro Carolina Power & Light Co. Person NC 36.4831 79.0711 

Sandow Texas Utilities Electric Co. Milam TX 30.5642 97.0639 

Scherer Georgia Power Co. Monroe GA 33.0583 83.8072 

Shawnee Tennessee Valley Authority McCracken KY 37.1517 88.775 

Shawville GPU Service Corporation Clearfield PA 41.0681 78.3661 

(continued)
Appendix B Attachment B-1: CCW Disposal Sites (Plants) 

April 2010–Draft EPA document. B-1-5 

CCW Disposal Sites (Plants) (continued) 

Plant Name Utility Name County State Latitude Longitude

Sheldon Nebraska Public Power District Lancaster NE 40.5589 96.7842 

South Oak Creek Wisconsin Electric Power Co. Milwaukee WI 42.8014 87.8314 

Springerville Tucson Electric Power Co Apache AZ 34.3186 109.1636 

St Johns River Power JEA Duval FL 30.4308 81.5508 

Stanton Energy Ctr. Orlando Utilities Comm. Orange FL 28.4822 81.1678 

Stockton Cogen 

Company 

Stockton Cogen Co (operator: Air 

Products) 

San Joaquin CA 37.9778 121.2667 

Syl Laskin Minnesota Power, Inc. St Louis MN 47.53 92.1617 

Tecumseh EC KPL Western Resources Co. Shawnee KS 39.0528 95.5683 

Texas-New Mexico Texas-New Mexico Power 

Company/Sempra Energy 

Robertson TX 31.0928 96.6933 

Titus Metropolitan Edison Co. Berks PA 40.3047 75.9072 

Trimble County Louisville Gas & Electric Co. Trimble KY 38.5678 85.4139 

Tyrone Kentucky Utilities Co. Woodford KY 38.0213 84.7456 

Valley Wisconsin Electric Power Co. Milwaukee WI 43.0303 87.925 

Vermilion Illinois Power Co. Vermilion IL 40.1781 87.7481 

Victor J Daniel Jr. Mississippi Power Co. Jackson MS 30.5322 88.5569 

W A Parish Houston Lighting & Power Co. Fort Bend TX 29.4833 95.6331 

W H Weatherspoon Carolina Power & Light Co. Robeson NC 34.5889 78.975 

W S Lee Duke Power Co. Anderson SC 34.6022 82.435 

Wabash River PSI Energy, Inc. Vigo IN 39.5278 87.4222 

Walter C Beckjord Cincinnati Gas & Electric Co. Clermont OH 38.9917 84.2972 

Wansley Georgia Power Co. Heard GA 33.4167 85.0333 

Warrick Southern Indiana Gas & Elec. Co. Warrick IN 37.915 87.3319 

Waukegan Commonwealth Edison Co. Lake IL 42.3833 87.8083 

Weston Wisconsin Public Service Corp. Marathon WI 44.8617 89.655 

Widows Creek Tennessee Valley Authority Jackson AL 34.8825 85.7547 

Will County Commonwealth Edison Co. Will IL 38.8639 90.1347 

Wyodak PacifiCorp Campbell WY 44.2833 105.4 

Yates Georgia Power Co. Coweta GA 33.4631 84.955 
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix B Attachmen

Attachment B-2: CCW WMU Data 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

A B Brown 42 LF 176 10360000 Ash compacted clay A/C Power - Ace Operations 3000 LF 18 1030815 FBC none/natural soiAllen 293 SI 85 1500000 Ash none/natural soAlma 7 LF 85 2000000 Ash and Coal Waste composite clay/Antelope Valley 57 LF 27 3500000 Ash none/natural soArkwright 198 LF 54 415907 Ash and Coal Waste none/natural soiAsheville 159 SI 140 3200000 Ash none/natural soBaldwin 2 SI 107 4000000 Ash and Coal Waste none/natural soiBarry 301 SI 63 1900000 Ash and Coal Waste none/natural soiBay Front 81 LF 10 350000 Ash none/natural soiBay Shore 32 LF 85 Ash none/natural soiBelews Creek 167 SI 512 2200000 Ash and Coal Waste none/natural soBelews Creek 168 LF 315 14000000 Ash compacted ash Ben French 14 LF 4.61 Ash compacted clay Big Cajun 2 186 SI 241 4990003 Ash compacted clay Big Sandy 138 SI 115 12052100 Ash and Coal Waste none/natural soiBig Stone 15 LF 3.4 80000 Ash compacted clay Big Stone 41 LF 106 8000000 Ash none/natural soiBlack Dog Steam Plant 2700 LF 96 8936296 FBC compacted clay Blue Valley 176 SI 23.1 372000 Ash and Coal Waste compacted clay Bowen 143 LF 25.24 491400 Ash compacted ash Bowen 144 LF 25.77 406971 Ash compacted ash Brandon Shores 339 LF 246 5600000 Ash and Coal Waste none/natural soi

April 2010–Draft EPA document. 
Appendix B Attachmen

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Buck 235 SI 90 4840000 Ash and Coal Waste none/natural soiBull Run 296 SI 41 650000 Ash and Coal Waste none/natural soiC D McIntosh Jr. 223 LF 26 Ash and Coal Waste compacted ash C P Crane 338 LF 35 800000 Ash none/natural soiCape Fear 161 SI 60 2300000 Ash none/natural soCarbon 263 lf 11.7739066 Ash and Coal Waste none/natural soiCardinal 126 SI 123 8437500 Ash none/natural soiCayuga 325 SI 280 25000000 Ash and Coal Waste none/natural soiChalk Point 292 LF 596 4634000 Ash and Coal Waste none/natural soiCholla 107 SI 171 2600000 Ash none/natural soCliffside 163 SI 82 2200000 Ash compacted clay Clover 139 LF 22 1000000 Ash geosynthetic meCoal Creek 29 LF 70 4700000 Ash compacted clay Coal Creek 30 LF 220 23000000 Ash composite clay/Coleto Creek 190 si 314.6135409 Ash and Coal Waste compacted clay Colstrip 89 LF 9 Ash none/natural soiConemaugh 101 LF 434 82000000 Ash geosynthetic meConesville 250 LF 300 10000000 Ash compacted clay Conesville 251 LF 100 2500000 Ash and Coal Waste none/natural soiCouncil Bluffs 94 SI 200 Ash none/natural soCrawford 272 SI 24.5 642000 Ash and Coal Waste compacted clay Crist 157 LF 12 Ash and Coal Waste none/natural soCross 264 LF 320 Ash compacted ash Cross 265 LF 30 Ash and Coal Waste none/natural soiCross 266 LF 30 Ash and Coal Waste none/natural soiCross 267 LF 230 Ash and Coal Waste none/natural soiCross 268 LF 60 Ash and Coal Waste compacted clay 

April 2010–Draft EPA document. 
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Cumberland 294 SI 75 1750000 Ash and Coal Waste none/natural soiCumberland 303 SI 295 9500000 Ash none/natural soiDale 151 SI 115 7408274 Ash and Coal Waste none/natural soiDallman 178 LF 22 1800000 Ash compacted clay Dallman 179 SI 417 3800000 Ash none/natural soiDan E Karn 6 LF 40 1650000 Ash and Coal Waste geosynthetic meDan River 234 SI 72 2097000 Ash and Coal Waste none/natural soiDanskammer 24 LF 14 517265 Ash and Coal Waste geosynthetic meDave Johnston 13 LF 45 296100 Ash compacted clay Dickerson 290 LF 206 12600000 Ash none/natural soDolet Hills 245 SI 66 850000 Ash and Coal Waste none/natural soiDolet Hills 246 LF 109 8500000 Ash compacted clay Duck Creek 11 LF 21.3 1500000 Ash compacted clay Dunkirk 49 LF 12 1126080 Ash compacted clay E D Edwards 276 SI 145 11000000 Ash and Coal Waste none/natural soiE W Brown 313 SI 33 1000000 Ash none/natural soiE W Brown 314 SI 84 2710000 Ash none/natural soiEckert Station 113 LF 174 6460000 Ash none/natural soiEckert Station 114 SI 151 7200000 Ash none/natural soiEdgewater 289 LF 25 1655700 Ash and Coal Waste none/natural soiElmer W Stout 130 SI 10 3420000 Ash geosynthetic meF B Culley 183 SI 82 2600000 Ash and Coal Waste none/natural soiFayette Power Prj. 195 SI 190 4351644 Ash compacted clay Fayette Power Prj. 196 LF 23 890560 Ash geosynthetic meFlint Creek 191 LF 40 1508250 Ash and Coal Waste none/natural soiFlint Creek 192 si 35.73857178 Ash and Coal Waste none/natural soiFort Martin 213 LF 17 1900000 Ash none/natural so
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Fort Martin 214 LF 61 1400000 Ash double Fort Martin 215 LF 121 3700000 Ash and Coal Waste composite clay/Frank E Ratts 182 SI 39 1250000 Ash and Coal Waste none/natural soiG G Allen 237 SI 210 6545000 Ash and Coal Waste none/natural soiGadsden 283 SI 60 484000 Ash and Coal Waste compacted clay Gallatin 304 SI 341 4300000 Ash and Coal Waste none/natural soiGen J M Gavin 135 LF 255 50000000 Ash composite clay/Gen J M Gavin 136 SI 300 30000000 Ash and Coal Waste none/natural soiGen J M Gavin 137 LF 99 12000000 Ash compacted clay Genoa 244 LF 100 Ash and Coal Waste none/natural soiGibson 327 SI 875 55000000 Ash and Coal Waste none/natural soiGibson 329 LF 85 20000000 Ash compacted clay Gorgas 280 SI 250 Ash and Coal Waste compacted clay Gorgas 281 SI 283 24100000 Ash and Coal Waste compacted clay Gorgas 282 SI 1500 15000000 Ash and Coal Waste compacted clay Green River 147 SI 36 2331219 Ash and Coal Waste none/natural soiGreene County 279 SI 480 5000000 Ash compacted clay H B Robinson 169 SI 30 Ash and Coal Waste none/natural soiHammond 203 SI 56 576256 Ash and Coal Waste none/natural soHarllee Branch 204 SI 324 7898277 Ash and Coal Waste none/natural soHarllee Branch 205 SI 203 7634000 Ash and Coal Waste none/natural soHarrison 211 LF 79 18000000 Ash and Coal Waste composite clay/Harrison 330 SI 300 28000000 Ash none/natural soiHatfield's Ferry 112 LF 20 790000 Ash and Coal Waste compacted ash Hennepin 274 SI 150 3460600 Ash and Coal Waste none/natural soHeskett 87 LF 58 1550000 FBC compacted clay Holcomb 65 LF 8 Ash compacted ash 
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Homer City 118 LF 247 29636550 Ash and Coal Waste geosynthetic meHoot Lake 40 LF 72 800000 Ash and Coal Waste none/natural soiHugo 193 LF 40 4000000 Ash compacted ash Hugo 194 si 151.0232271 Ash and Coal Waste compacted clay Hunter 256 LF 280 12000000 Ash none/natural soHuntington 255 LF 70 11400000 Ash none/natural soIntermountain 224 SI 105 4840000 Ash and Coal Waste geosynthetic meIntermountain 225 LF 339 17800000 Ash compacted ash Intermountain 226 SI 180 5200000 Ash geosynthetic meJ H Campbell 115 SI 267 6900000 Ash and Coal Waste none/natural soiJ M Stuart 125 SI 88 8357000 Ash none/natural soiJ R Whiting 129 SI 6 140000 Ash none/natural soiJack McDonough 202 SI 73 1531893 Ash and Coal Waste none/natural soJack Watson 220 SI 100 Ash none/natural soiJames H Miller Jr. 300 SI 200 5500000 Ash compacted clay Jim Bridger 257 LF 120 7940941 Ash none/natural soJim Bridger 258 LF 241 24000000 Ash and Coal Waste none/natural soiJim Bridger 259 SI 140 3400000 Ash and Coal Waste none/natural soiJim Bridger 262 SI 125 6500000 Ash and Coal Waste none/natural soiJohn E Amos 120 SI 100 13000000 Ash none/natural soiJohn E Amos 121 LF 200 14000000 Ash and Coal Waste compacted clay John E Amos 122 SI 10 3078000 Ash none/natural soiJohn Sevier 297 SI 57 1600000 Ash and Coal Waste none/natural soiJohn Sevier 298 LF 51 4800000 Ash compacted clay John Sevier 309 SI 105 7000000 Ash and Coal Waste none/natural soiJohnsonville 306 SI 91 2900000 Ash and Coal Waste none/natural soiJoliet 29 275 SI 63.1 1012000 Ash and Coal Waste none/natural soi
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Keystone 106 LF 155 22663120 Ash and Coal Waste none/natural soiKillen Station 254 SI 99935 Ash and Coal Waste compacted clay Kingston 311 SI 41 11000000 Ash and Coal Waste none/natural soiKingston 312 SI 275 8900000 Ash and Coal Waste none/natural soiKraft 206 si 59.87027428 Ash and Coal Waste none/natural soiL V Sutton 231 SI 162 7696000 Ash and Coal Waste none/natural soiLansing 64 SI 15 Ash compacted clay Laramie R Station 260 SI 10.7 464156 Ash and Coal Waste compacted clay Laramie R Station 261 SI 38 939605 Ash geosynthetic meLawrence EC 109 LF 825 34300000 Ash compacted clay Lawrence EC 110 LF 22 1360000 Ash compacted clay Lawrence EC 111 LF 30 1000000 Ash compacted clay Lee 240 SI 35 1936000 Ash and Coal Waste none/natural soiLeland Olds 103 LF 37 1800000 Ash compacted clay Leland Olds 104 LF 20 458000 Ash and Coal Waste none/natural soiLon Wright 98 LF 170000 Ash none/natural soiLouisa 63 SI 30 500000 Ash compacted clay Marion 52 LF 105 2200000 Ash none/natural soiMarion 53 LF 38 1000000 Ash compacted clay Marshall 232 LF 110 7826000 Ash none/natural soMarshall 233 SI 340 19689000 Ash and Coal Waste none/natural soiMartin Lake 152 LF 290 30000000 Ash compacted clay Mayo 171 SI 30 185000 Ash none/natural soiMayo 172 SI 65 2400000 Ash none/natural soMeramec 175 SI 61.1 591200 Ash and Coal Waste none/natural soiMerom 184 LF 65 8500000 Ash none/natural soMiami Fort 39 LF 80 4000000 Ash compacted clay 
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Milton R Young 100 LF 80 6500000 Ash compacted clay Mitchell - PA 208 LF 70 5600000 Ash none/natural soiMitchell - WV 131 SI 12030000 Ash and Coal Waste none/natural soiMohave 72 LF 250 21500000 Ash none/natural soiMonroe 26 LF 400 20000000 Ash and Coal Waste none/natural soiMonroe 27 SI 400 15000000 Ash none/natural soiMorgantown 291 LF 212 7700000 Ash and Coal Waste none/natural soiMountaineer (1301) 212 LF 60 9700000 Ash composite clay/Mt Storm 73 LF 125 18920000 Ash composite clay/Mt Storm 134 LF 900 8800000 Ash and Coal Waste compacted clay Muscatine Plant #1 70 LF 36 2000000 Ash compacted clay Muskogee 51 LF 36 1247112 Ash compacted clay Neal North 92 SI 150 Ash and Coal Waste none/natural soNeal North 93 LF 200 Ash none/natural soiNeal South 284 LF 150 Ash none/natural soiNebraska City 20 LF 17 600000 Ash and Coal Waste compacted clay New Castle 66 LF 27 1100000 Ash and Coal Waste geosynthetic meNewton 180 LF 309 Ash none/natural soiNorth Omaha 17 LF 13 105000 Ash and Coal Waste compacted clay Northeastern 142 LF 69 3185190 Ash none/natural soNucla 96 LF 41.2 1500000 FBC none/natural soiOklaunion 228 SI 11 408940 Ash and Coal Waste none/natural soiOklaunion 229 SI 19.4 718060 Ash none/natural soiOklaunion 230 SI 290.8 6056820 Ash none/natural soParadise 146 SI 85 7582510 Ash composite clay/Paradise 316 SI 200 5000000 Ash none/natural soiPetersburg 155 LF 250 19750000 Ash compacted clay 
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Petersburg 156 si 156.6901408 Ash none/natural soiPleasant Prairie 243 LF 26 6500000 Ash and Coal Waste geosynthetic mePort Washington 242 LF 300 1900000 Ash and Coal Waste compacted clay Portland 67 LF 15 2200000 Ash and Coal Waste none/natural soPossum Point 77 SI 56 Ash and Coal Waste none/natural soiPotomac River 140 LF 33 802000 Ash geosynthetic mePresque Isle 116 LF 292 14200000 Ash none/natural soR Gallagher 326 SI 170 20000000 Ash and Coal Waste compacted clay R M Schahfer 84 SI 80 1030000 Ash and Coal Waste none/natural soiR M Schahfer 85 LF 200 17200000 Ash none/natural soiReid Gardner 95 LF 112.5 4520000 Ash none/natural soRichard Gorsuch 36 LF 3003600 Ash compacted clay Riverbend 165 SI 143 3200000 Ash none/natural soRodemacher 247 SI 36 1200000 Ash compacted clay Rodemacher 248 SI 109 2500000 Ash compacted clay Roxboro 239 LF 55 4165000 Ash none/natural soSandow 153 LF 125 1300000 Ash compacted clay Sandow 187 LF 48 903467 Ash and Coal Waste none/natural soiSandow 188 SI 45 1351973 Ash and Coal Waste none/natural soiScherer 199 SI 490 22262030 Ash and Coal Waste none/natural soiShawnee 317 SI 180 5810000 Ash and Coal Waste none/natural soShawnee 318 LF 96 6100000 FBC none/natural soiShawville 209 LF 68 8000000 Ash none/natural soSheldon 23 LF 9 375000 Ash compacted clay South Oak Creek 3 LF 45 4050000 Ash and Coal Waste compacted clay South Oak Creek 4 LF 130 4600000 Ash none/natural soiSpringerville 154 LF 57 6400000 Ash none/natural so
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

St Johns River Power 158 lf 128.624166 Ash and Coal Waste compacted clay Stanton Energy Ctr. 117 LF 312 Ash none/natural soiStockton Cogen Company 2000 LF 4 533333 FBC composite clay/Syl Laskin 68 SI 75 726000 Ash and Coal Waste none/natural soiTecumseh EC 177 LF 540 Ash compacted clay Texas-New Mexico 3900 LF 61 6142473 FBC compacted clay Titus 207 LF 39 3000000 Ash and Coal Waste composite clay/Trimble County 69 SI 115 6856667 Ash compacted clay Tyrone 148 SI 5.5 351699 Ash none/natural soiTyrone 149 SI 5 327500 Ash and Coal Waste none/natural soiTyrone 150 SI 7.75 500123 Ash and Coal Waste none/natural soiValley 8 LF 16.4 534000 Ash and Coal Waste compacted clay Vermilion 55 SI 43 8100000 Ash and Coal Waste none/natural soiVictor J Daniel Jr 287 lf 49.20163084 Ash compacted clay Victor J Daniel Jr 288 si 20.03879417 Ash and Coal Waste composite clay/W A Parish 189 lf 28.68322214 Ash compacted clay W H Weatherspoon 236 SI 26 1200000 Ash and Coal Waste none/natural soiW S Lee 238 SI 41 1634000 Ash and Coal Waste none/natural soiWabash River 324 SI 120 14000000 Ash and Coal Waste none/natural soiWalter C Beckjord 123 LF 14 1000000 Ash compacted ash Walter C Beckjord 124 SI 2000000 Ash none/natural soWansley 200 SI 330 18712850 Ash and Coal Waste none/natural soiWansley 201 SI 43 Ash none/natural soiWarrick 181 SI 140 4500000 Ash and Coal Waste compacted clay Waukegan 54 LF 60 4000000 Ash and Coal Waste compacted clay Weston 241 LF 18 600000 Ash none/natural soWidows Creek 320 SI 110 3500000 Ash and Coal Waste none/natural so
Appendix B Attachmen

April 2010–Draft EPA document. 

CCW WMU Data (continued) 

Plant 

Facility 

ID 

WMU 

Type 

Area 

(acres) 

Capacity 

(cubic yards) Waste Type Original 

Widows Creek 321 SI 222 12400000 Ash compacted clay Will County 277 SI 60 599256 Ash and Coal Waste compacted clay Wyodak 71 LF 68 3500000 Ash geosynthetic meYates 197 SI 4.7 115000 Ash composite clay/
Appendix C Site Data 

April 2010–Draft EPA document. C-1 

Appendix C. Site Data

The site characteristics used in this analysis were based on site-specific, regional, and 

national data sources to provide the environmental parameters necessary for modeling the fate 

and transport of coal combustion waste (CCW) constituents released in landfill or surface 

impoundment leachate. Site-specific data were collected for the area in the immediate vicinity of 

the waste management unit (WMU), and included the geographic relationship among important 

features such as the WMU boundary, residential well location, and streams and lakes. These data 

were collected at each of the 181 coal-fired power plants selected for the analysis. These 181 

locations across the continental United States were intended to represent the geographic 

distribution of onsite WMUs used for disposal of CCW and were used to capture national 

variability in meteorology, soils, climate, aquifers, and surface waterbodies at the disposal sites. 

C.1 Data Collection Methodology 

The CCW risk assessment employed site-specific, regional, and national data. Sitespecific data were collected around CCW plant locations from the Energy Information 

Administration (EIA) database to obtain data for each facility that were representative of the 

environment immediately surrounding the plant. When site-specific data were not available, 

regional or national scale data sources were used. Where appropriate, distributions were used in 

the Monte Carlo analysis to capture site-to-site, within-site, and national variability in the 

parameters collected. 

Data were collected around each CCW site using a geographic information system (GIS) 

that allowed (1) site-specific data to be assembled from the area immediately surrounding the 

facility and (2) the site to be assigned to a region to collect regional data. To account for 

locational uncertainty for the CCW WMUs1

, a 5-km radius was used to define the data collection 

area for aquifer type and soil data. If multiple soil or aquifer types occurred within this radius, 

multiple types were sent to the model, weighted by the fraction of the collection area that they 

occupied. Surface waterbody type and stream flows also were collected for each site by 

identifying the nearest stream segment. 

Climate and water quality data were collected by assigning each site to a Hydrologic 

Evaluation of Landfill Performance (HELP) model climate station and a U.S. Geological Survey 

(USGS) hydrologic region. The EPA STOrage and RETrieval (STORET) database was used as 

the source for water quality data, with parameters selected from distributions queried from this 

database for each region. 

Because the EIA locations were not exact for the WMUs being modeled, a national 

distribution of stream distances was developed by manually measuring the distance between the 

 

1

 The EIA latitudes and longitudes usually represent a facility centroid or front-gate location for each power plant. 

Because these facilities are often large, the WMUs are frequently located some distance from the plant itself and 

not at the EIA location. 
Appendix C Site Data 

WMU and the waterbody at a random sample of the CCW sites. Similarly, a national distribution 

was used to represent the distance of the nearest residential wells from the CCW WMUs being 

modeled. 

C.2 Receptor Location (National Data) 

The residential scenario for the CCW groundwater pathway analysis calculates exposure 

through use of well water as drinking water. During the Monte Carlo analysis, the receptor well 

was placed at a distance of up to 1 mile from the edge of the WMU, by sampling a nationwide 

distribution of nearest downgradient residential well distances taken from a survey of municipal 

solid waste landfills (U.S. EPA, 1988). 

EPA believes that this MSW well-distance distribution (presented in Table C-1) is 

protective for onsite CCW landfills and surface impoundments at coal-fired utility power plants, 

but recognizes that this is an uncertainty in this analysis. Because CCW plants tend to be in more 

isolated areas than MSW landfills and because CCW WMUs tend to be larger than municipal 

landfills, EPA believes that the MSW well distance distribution is a protective representation of 

actual well distances at CCW disposal sites. As discussed in Section 3.4.3, the groundwater 

model used in the CCW risk assessment placed limits on the lateral direction from the plume 

centerline (i.e., angle off plume centerline) and depth below the water table to ensure that the 

well remained within the plume and at a depth appropriate for surficial aquifers across the United 

States. These limits were consistent with other recent national risk assessments conducted by 

EPA OSW and provided a protective approach to siting wells for this analysis. 

Table C-1. Distribution of Receptor Well Distance 

Percentile x-distance (m)

Minimum 0.6 

10 104 

20 183 

30 305 

40 366 

50 (Median) 427 

60 610 

70 805 

80 914 

90 1,220 

Maximum 1,610 

Source: U.S. EPA (1988). 

C.2.1 Recreational Fisher and Ecological Risk Scenario (Distance to Waterbody) 

The recreational fisher scenario was used to estimate risks to recreational fishers and their 

children who live in the vicinity of the CCW landfills and surface impoundments and catch and 

consume fish from a waterbody located adjacent to the buffer. The waterbody was assumed to be 

a stream or lake located downwind of the WMU, beginning where the buffer area ends (see 

Figure 2-4), and was also used as the reasonable worst case aquatic system for the ecological risk 

April 2010–Draft EPA document. C-2 
Appendix C Site Data 

assessment. Waterbody characteristics were determined based on site-specific, regional, or 

national data (as described in Section C.6), except for stream length, which was determined by 

the width of the plume as it intersects the waterbody. 

The downgradient distance to the surface water body was determined from a national 

distribution developed by measuring this distance at 59 CCW landfill and surface impoundment 

sites randomly selected from the 204 WMUs modeled in this risk assessment. Table C-2

presents this distribution. Figure C-1 provides a map and aerial photo of one of the facilities 

used to develop this distribution. The development of this distribution is described in 

Section C.6.4. 

Table C-2. Distribution of Surface Water Distances 

Percentile Distance (m)

Minimum 10 

0.03 10 

0.05 20 

0.07 20 

0.09 20 

0.10 20 

0.13 20 

0.15 30 

0.20 40 

0.25 50 

0.30 50 

0.35 60 

0.40 70 

0.45 100 

0.50 (Median) 120 

0.55 130 

0.60 150 

0.65 250 

0.70 400 

0.75 440 

0.80 500 

0.85 700 

0.87 775 

0.90 800 

0.91 1,000 

0.93 1,500 

0.95 2,125 

0.97 2,750 

Maximum 3,000 

April 2010–Draft EPA document. C-3 
Appendix C Site Data 

Figure C-1. Example CCW site used to develop waterbody distance distribution. 

April 2010–Draft EPA document. C-4 
Appendix C Site Data 

April 2010–Draft EPA document. C-5 

C.3 Soil Data 

The groundwater model used in the CCW risk assessment—EPA’s Composite Model for 

Leachate Migration with Transformation Products (EPACMTP)—requires soil properties for the 

entire soil column to model leachate transport through the vadose zone to groundwater. As with 

aquifer type, soil data were collected within a 5-km radius of each CCW plant. A GIS was used 

to identify soil map units within a 20-mile radius around each meteorological station. Database 

programs were then used to assemble and process soil texture, pH, and soil organic matter data 

for these map units from the State Soil Geographic (STATSGO) database. Both pH and soil 

organic matter were processed and indexed by the soil textures present within the 5-km radius. 

Soil properties are listed by texture for each of the 181 CCW plants in Attachment C-1. 

C.3.1 Data Sources 

The primary data source for soil properties was the STATSGO database. STATSGO is a 

repository of nationwide soil properties compiled primarily by the U.S. Department of 

Agriculture (USDA) from county soil survey data (USDA, 1994). STATSGO includes a 

1:250,000-scale GIS coverage that delineates soil map units and an associated database 

containing soil data for each STATSGO map unit. (Map units are areas used to spatially 

represent soils in the database.) 

In addition, two compilations of STATSGO data, each keyed to the STATSGO map unit 

GIS coverage, were used in the analysis as a convenient source of average soil properties: 

 USSOILS. The USSOILS data set (Schwarz and Alexander, 1995) averages STATSGO 

data over the entire soil column for each map unit. 

 CONUS. The Conterminous United States Multi-Layer Soil Characteristics (CONUS) 

data set (Miller and White, 1998) provides average STATSGO data by map unit and a set 

of 11 standardized soil layers. 

Soil organic matter and pH were derived directly from USSOILS and STATSGO data. A 

complete set of hydrological soil properties2

 was not available from STATSGO. To ensure 

consistent and realistic values, EPACMTP relies on established, nationwide relationships 

between hydrologic properties and soil texture. Peer-reviewed publications by Carsel and Parrish 

(1988) and Carsel et al. (1988) provide a consistent set of correlated hydrologic properties for 

each soil texture. Soil texture data for the entire soil column were collected from the CONUS 

database. 

C.3.2 Methodology 

The soil data collection methodology began with GIS programs (in Arc Macro Language 

[AML]). These programs overlaid a 5-km radius around each CCW plant location on the 

STATSGO map unit coverage to determine the STATSGO map units and their area within the 

radius. These data were then passed to data processing programs that derive soil properties for 

 

2

 Hydrological soil properties required by EPACMTP include bulk density, saturated water content, saturated 

hydraulic conductivity, and the van Genuchten soil moisture retention parameters alpha and beta. 
Appendix C Site Data 

each site, either through direct calculations or by applying established relationships in lookup 

tables. 

EPACMTP utilizes three soil textures to represent variability in hydrologic soil properties 

and (along with climate data) to assign infiltration rates to each site. Because STATSGO soils 

are classified into the 12 U.S. Soil Conservation Service (SCS) soil textures, the crosswalk 

shown in Table C-3 was used to assign the SCS textures to the EPACMTP megatextures and to 

calculate the percentage of each megatexture within the 5-km data collection radius. These 

percentages were sampled for each site when preparing the source data file for each site. 

Both soil pH and soil organic matter were derived for each EPACMTP soil megatexture 

at a site. During source data file preparation, when a megatexture was picked for a particular 

iteration of a site, the corresponding pH and organic matter values were selected as well. 

Table C-3. EPACMTP Soil Texture Crosswalk 

STATSGO Texture EPACMTP Megatexture

Sand Sandy loam 

Loamy sand 

Sandy loam 

Silt loam Silt loam 

Silt 

Loam 

Sandy clay loam 

Clay loam 

Silty clay loam Silty clay loam 

Sandy clay 

Silty clay 

Clay 

C.3.3 Results 

Attachment C-1 lists the STATSGO soil textures and EPACMTP megatexture 

assignments and percentages for each CCW disposal site. 

C.4 Hydrogeologic Environments (Aquifer Type) 

To assign aquifer properties used by EPACMTP, it was necessary to designate 

hydrogeologic environments (or aquifer types) for each of the locations modeled so that 

correlated, national aquifer property data could be used in the analysis. EPACMTP uses the 

Hydrogeologic Database (HGDB) developed by the American Petroleum Institute (API) (Newell 

et al., 1989; Newell et al., 1990) to specify correlated probability distributions, which were used 

to populate the following four hydrogeologic parameters during the Monte Carlo analysis: 

 Unsaturated zone thickness 

 Aquifer thickness 

April 2010–Draft EPA document. C-6 
Appendix C Site Data 

April 2010–Draft EPA document. C-7 

 Hydraulic gradient 

 Saturated hydraulic conductivity. 

The HGDB provides correlated data on these hydrogeologic parameters and an aquifer 

classification for approximately 400 hazardous waste sites nationwide, grouped according to 12 

hydrogeologic environments described in Newell et al. (1990). The EPACMTP User’s Guide

(U.S. EPA, 1997) provides the empirical distributions of the four hydrogeologic parameters for 

each of the hydrogeologic environments. 

Average aquifer/vadose zone temperature was also required for the groundwater model 

and was obtained from a digitized map of groundwater temperatures for the continental United 

States from the Water Encyclopedia (van der Leeden et al., 1990). 

The hydrogeologic environment approach to assigning EPACMTP aquifer variables 

relied upon a hydrogeologic framework originally developed for an attempt by EPA to classify 

and score groundwater environments according to their potential to be polluted by pesticide 

application. Although this DRASTIC3

 scoring system was not widely applied to determining 

groundwater vulnerability to pesticide pollution, the hydrogeologic framework established for 

the effort has proven very useful in categorizing geologic settings in terms of the aquifer 

characteristics needed for groundwater modeling. The major components of this modeling 

framework are Groundwater Regions, hydrogeologic settings, and hydrogeologic environments, 

as described below: 

 The fifteen Groundwater Regions, defined by Heath (1984), provide a regional 

framework that groups hydrogeologic features (i.e., nature and extent of dominant 

aquifers and their relationship to other geologic units) that influence groundwater 

occurrence and availability. 

 Hydrogeologic settings were developed within each Heath region by Aller et al. (1987)4

to create mappable geological units that are at the proper scale to capture differences in 

aquifer conditions. Note that there may be the same or similar settings across different 

regions (e.g., the alluvial settings). Within each region, Aller et al. (1987) describe each 

setting with a written narrative and provide a block diagram to visualize the geology, 

geomorphology, and hydrogeology. 

 Hydrogeologic environments were developed by Newell et al. (1990) as the geologic 

framework for the API’s HGDB. To create the 12 environments, Newell et al. rolled up 

similar hydrologic settings across the Groundwater Regions to group settings with similar 

aquifer characteristics (hydraulic conductivity, gradient, thickness, and depth-to-water). 

Table C-4 shows the crosswalk between hydrologic environment and hydrogeologic 

setting, organized by Groundwater Region. 

 

3

 The DRASTIC scoring factors are Depth to water, net Recharge, Aquifer media, Soil media, Topography, Impact 

of the vadose zone media, and aquifer hydraulic Conductivity. 4

 Aller et al. (1987, p. 14) did not develop settings for Region 15 (Puerto Rico and the Virgin Islands) and 

reincorporated Region 12 (Alluvial Valleys) into each of the other regions as “river alluvium with overbank 

deposits” and “river alluvium without overbank deposits.” 
Appendix C Site Data 

Because EPACMTP uses the HGDB for national and regional analyses (using a regional 

site-based approach), it was necessary to assign the CCW sites to a hydrogeologic environment 

so that the correct HGDB data set would be used for modeling each site. The data sources and 

methodology used to make these assignments are described below. 

C.4.1 Data Sources 

Data sources used to make hydrogeologic assignments for the sites included: 

 A USGS inventory of state groundwater resources (Heath, 1985) 

 GIS coverages from Digital Data Sets Describing Principal Aquifers, Surficial Geology, 

and Ground-Water Regions of the Conterminous United States (Clawges and Price, 

1999a-d) 

 GIS coverages of principal aquifers from the USGS Groundwater Atlas (Miller, 1998) 

 STATSGO soil texture data (described in Section C.3.2). 

These coverages were used in a GIS overlay process to determine the principal aquifers, 

surficial geologic units, groundwater region, productive aquifers, and general hydrogeologic 

settings for a 5-km radius around each CCW facility location. Attributes for each of these items 

were passed to a database for use in assigning hydrogeologic environments. 

C.4.2 Assignment Methodology 

For each CCW site, hydrogeologic environments were assigned by a professional 

geologist as follows: 

 Determine Heath Groundwater Region (for the Alluvial Valleys region, determine the 

region in which the alluvial valley is located) 

 Assign hydrogeologic setting using state geological descriptions from Heath (1985); 

aquifer, soil, and surficial geology information obtained using GIS; and narratives and 

block diagrams from Aller et al. (1987) 

 Using the look-up table from Newell et al. (1990), determine hydrogeologic environment 

from hydrogeologic setting. 

In general, the surficial geology coverage had better resolution than the aquifer coverages and 

was used to develop setting percentages for the 5-km radius. In most cases, there were two 

settings per site. In cases where a single setting accounted for over 80 percent of the 5-km area, a 

single setting was assigned. 

Because Newell et al. (1990) define two alluvial environments (6, River alluvium with 

overbank deposits, and 7, River alluvium without overbank deposits), it was necessary to 

determine which environment an alluvial site fell into. The survey soil layer information was 

used to distinguish between these two settings by determining whether there were significant 

fine-grained overbank deposits in the soil column. 

April 2010–Draft EPA document. C-8 
Appendix C Site Data 

Quality assurance/quality control (QA/QC) measures included independent review of the 

assignments by other geologists with expertise in assigning settings. 

C.4.3 Data Processing 

HGDB hydrogeologic environment fractions (i.e., the portion of the region assigned to 

each of the 12 hydrogeological environments) were defined and used in the CCW risk 

assessment as follows. If the 5-km radius around a site contained only one HGDB environment, 

the fraction assigned was 1.0 and all groundwater model runs for that location were associated 

with that hydrological environment. If more than one HGDB environment was present, each 

environment was assigned a fraction based on the areal percentages of each setting within the 

5-km radius. 

These fractions were used to generate the hydrogeologic environment for that location for 

each iteration of the Monte Carlo groundwater modeling analysis. For example, if two 

hydrogeologic environments were assigned to a CCW site with a fraction of 0.5, half of the 

realizations were modeled with the first hydrogeologic environment and half with the second. 

Once the hydrogeologic environments were assigned, a preprocessing run of EPACMTP 

was conducted to construct a set of randomly generated but correlated hydrogeologic parameter 

values for each occurrence of the hydrogeologic environments in the source data files. Missing 

values in the HGDB data set were filled using correlations, as described in U.S. EPA (1997). 

C.4.4 Results 

Attachment C-2 lists the hydrogeologic environment assignments for each CCW 

disposal site. Table C-4 summarizes these results, showing the crosswalk between Groundwater 

Regions, hydrogeologic settings, and hydrogeologic environments used to make the assignments, 

along with the number of CCW sites for each setting. Table C-5 totals the number of CCW 

disposal sites for each hydrogeologic environment sent to EPACMTP. 

Table C-4. Groundwater Regions, Hydrogeologic Settings, and 

Hydrogeologic Environments: CCW Disposal Sites 

Hydrogeologic Setting

Hydrogeologic 

Environment

Number of 

CCW Sites

Alluvial Basins

2C Alluvial Fans 5 1 

2E Playa Lakes 5 1 

2Ha River Alluvium With Overbank Deposits 6 1 

Colorado Plateau and Wyoming Basin

4B Consolidated Sedimentary Rock 2 7 

4C River Alluvium 7 3 

High Plains

5Gb River Alluvium Without Overbank Deposits 7 1 

(continued) 

April 2010–Draft EPA document. C-9 
Appendix C Site Data 

Groundwater Regions, Hydrogeologic Settings, and 

Hydrogeologic Environments: CCW Disposal Sites. (continued) 

Hydrogeologic Setting

Hydrogeologic 

Environment

Number of 

CCW Sites

Nonglaciated Central Region

6Da Alternating Sandstone, Limestone, and Shale – Thin Soil 2 22 

6Db Alternating Sandstone, Limestone, and Shale – Deep Regolith 2 6 

6E Solution Limestone 12 9 

6Fa River Alluvium With Overbank Deposits 6 37 

6Fb River Alluvium Without Overbank Deposits 7 4 

6H Triassic Basins 2 4 

Glaciated Central Region

7Aa Glacial Till Over Bedded Sedimentary Rock 3 12 

7Ac Glacial Till Over Solution Limestone 12 6 

7Ba Outwash 8 1 

7Bb Outwash Over Bedded Sedimentary Rock 2 3 

7Bc Outwash Over Solution Limestone 12 2 

7D Buried Valley 4 11 

7Ea River Alluvium With Overbank Deposits 6 24 

7Eb River Alluvium Without Overbank Deposits 7 6 

7F Glacial Lake Deposits 4 3 

7G Thin Till Over Bedded Sedimentary Rock 3 5 

7H Beaches, Beach Ridges, and Sand Dunes 11 1 

Piedmont and Blue Ridge

8B Alluvial Mountain Valleys 5 1 

8C Mountain Flanks 2 2 

8D Regolith 1 13 

8E River Alluvium 6 6 

Northeast and Superior Uplands

9E Outwash 8 3 

9F Moraine 4 1 

9Ga River Alluvium With Overbank Deposits 6 1 

Atlantic and Gulf Coastal Plain

10Aa Regional Aquifers 4 1 

10Ab Unconsolidated/Semiconsolidated Shallow Surficial Aquifers 10 20 

10Ba River Alluvium With Overbank Deposits 6 7 

10Bb River Alluvium Without Overbank Deposits 7 6 

Southeast Coastal Plain

11A Solution Limestone and Shallow Surficial Aquifers 12 3 

11B Coastal Deposits 4 1 

April 2010–Draft EPA document. C-10 
Appendix C Site Data 

Table C-5. Hydrogeologic Environments for CCW Disposal Sites 

Hydrogeologic Environment Number of CCW Sites

1 Metamorphic and Igneous 13 

2 Bedded Sedimentary Rock 44 

3 Till Over Sedimentary Rock 17 

4 Sand and Gravel 17 

5 Alluvial Basins Valleys and Fans 3 

6 River Valleys and Floodplains With Overbank Deposit 76 

7 River Valleys and Floodplains Without Overbank Deposits 20 

8 Outwash 4 

9 Till and Till Over Outwash 0 

10 Unconsolidated and Semiconsolidated Shallow Aquifers 20 

11 Coastal Beaches 1 

12 Solution Limestone 20 

C.5 Climate Data 

The CCW risk assessment selected EPACMTP meteorological (or climate) stations for 

each CCW disposal site to collect the climatic data necessary for fate and transport modeling. 

For each station, the following data were compiled: 

 Mean annual windspeed 

 Mean annual air temperature 

 Mean annual precipitation. 

With respect to precipitation, EPACMTP uses the climate station, along with soil texture, to 

select the HELP-modeled infiltration rates to use in the landfill source model and recharge rates 

to use in EPACMTP (see Section 3.2.2). The surface water model uses mean annual windspeed 

and average air temperature to estimate volatilization losses from the surface waterbodies 

modeled in the analysis. 

To assign the EPACMTP/HELP climate centers to each CCW site, a GIS was used to 

determine the three meteorological stations closest to the plant. These assignments were passed 

to a meteorologist, who reviewed the closest stations against plots of the CCW sites and the 

climate centers on a downloadable map (http://www.nationalatlas.gov) of annual average 

precipitation rates for the period from 1961 to 1990 across the contiguous United States. 

(Figure C-2). The meteorologist compared the 5-year average precipitation range for each 

EPACMTP climate center to precipitation ranges for each plant from the map. In most cases, the 

precipitation rate for the nearest climate center fell within the site’s expected precipitation range, 

and the nearest climate center was assigned in those cases. In some cases, the precipitation rates 

from the nearest climate center did not fall within the site’s expected range. When this occurred, 

the second or third closest climate center was examined and matched based on: 

 A 5-year precipitation average within or close to the site’s predicted precipitation range 

April 2010–Draft EPA document. C-11 
Appendix C Site Data 

Figure C-2. EPACMTP climate centers, precipitation ranges, and CCW disposal sites. 

 Confirmation of a site’s average annual rainfall on http://www.weather.com and van der 

Leeden et al. (1990) 

 Geographic similarities between plant and climate center locations 

 Best professional judgment. 

In a few cases, the three closest climate centers did not reflect the average precipitation 

rates for a plant’s location. In these cases, other nearby stations were examined and the plant was 

assigned to the closest climate center with similar geography and average precipitation rates. 

Each assignment was independently checked for accuracy. Attachment C-3 lists the climate 

center assigned to each CCW disposal site, along with notes for plants not assigned to the nearest 

center. Table C-6 lists all the climate centers used in the CCW risk assessment along with the 

number of CCW sites assigned to each station. 

April 2010–Draft EPA document. C-12 
Appendix C Site Data 

Table C-6. EPACMTP Climate Centers Assigned to CCW Disposal Sites 

Climate Center State

Number of 

CCW Sites

4 Grand Junction CO 2 

6 Glasgow MT 1 

7 Bismarck ND 5 

10 Cheyenne WY 2 

11 Lander WY 1 

13 Sacramento CA 1 

16 Ely NV 1 

17 Rapid City SD 2 

18 Cedar City UT 1 

19 Albuquerque NM 1 

20 Las Vegas NV 3 

21 Phoenix AZ 1 

26 Salt Lake City UT 1 

29 Dodge City KS 1 

31 St. Cloud MN 3 

32 East Lansing MI 3 

33 North Omaha NE 7 

34 Tulsa OK 2 

37 Oklahoma City OK 1 

39 Pittsburgh PA 12 

42 Chicago IL 8 

48 Sault Ste. Marie MI 1 

49 Put-in-Bay OH 3 

50 Madison WI 9 

51 Columbus OH 2 

53 Des Moines IA 2 

54 East St. Louis IL 8 

55 Columbia MO 1 

56 Topeka KS 3 

58 San Antonio TX 4 

66 Ithaca NY 1 

69 Lynchburg VA 2 

71 Philadelphia PA 2 

72 Seabrook NJ 5 

73 Indianapolis IN 12 

74 Cincinnati OH 11 

75 Bridgeport CT 1 

76 Orlando FL 2 

77 Greensboro NC 11 

(continued) 

April 2010–Draft EPA document. C-13 
Appendix C Site Data 

April 2010–Draft EPA document. C-14 

EPACMTP Climate Centers Assigned to CCW Disposal 

Sites. (continued) 

Climate Center State

Number of 

CCW Sites

78 Jacksonville FL 1 

79 Watkinsville GA 4 

80 Norfolk VA 2 

81 Shreveport LA 4 

85 Knoxville TN 4 

87 Lexington KY 3 

89 Nashville TN 4 

90 Little Rock AR 1 

91 Tallahassee FL 4 

93 Charleston SC 4 

95 Atlanta GA 9 

96 Lake Charles LA 2 

C.6 Surface Water Data 

The surface water model used in the CCW risk assessment requires information on 

surface waterbody type (river or lake), flow conditions, dimensions, and water quality. In 

addition, the groundwater model requires the distance between the waterbody and the WMU 

being modeled. Surface waterbody data were collected on a site-based, regional, or national basis 

depending on the variable and data availability. Collection methods are described below by data 

source. Attachment C-4 provides a summary of waterbody assignments, waterbody types, and 

flow conditions. 

C.6.1 Waterbody Type, Stream Flow Conditions, and Dimensions 

Waterbody type and flow parameters were obtained by matching the CCW plants to 

stream segments in the Reach File Version 1.0 (RF1) database (U.S. EPA, 1990). Stream flow 

estimates for all RF1 flowing reaches were estimated in the early 1980s. Statistics developed for 

each flowing reach were mean annual flow, low flow (approximately 7Q10),5

 and mean monthly 

flow. RF1 also contains velocities corresponding to mean annual and low flow, estimated from a 

compendium of time-of-travel studies. For streams and rivers, the CCW risk assessment used the 

low flow statistic and the corresponding flow velocity, along with a waterbody type also 

included in the RF1 database. All RF1 data are indexed by USGS cataloging unit and stream 

segment (CUSEG). 

To assign the CCW plants to the nearest downgradient reach (i.e., the nearest waterbody 

in the direction of groundwater flow), a GIS was used to identify the closest RF1 stream segment 

to each CCW plant location. Because of several uncertainties in the nearest reach approach (i.e., 

 

5

 The 7Q10 is the minimum 7-day average flow expected to occur within a 10-year return period (i.e., at least once 

in 10 years). 
Appendix C Site Data 

inaccurate WMU location, unknown direction of groundwater flow, and limited lake coverages), 

the CCW plants also were matched to standard industrial classification (SIC) code 4911 facilities 

in EPA’s Permit Compliance System (PCS) database (http://www.epa.gov/enviro/html/pcs/

index.html), to obtain the PCS information (e.g., name, CUSEG) on the receiving waterbody for 

the plants’ National Pollutant Discharge Elimination System (NPDES) discharge point(s). When 

the two sources matched, the reach was selected for modeling. When they differed, the PCS data 

were used, because it was judged more likely that the NPDES receiving waterbody would also be 

receiving loads from the WMU through the groundwater-to-surface-water pathway. CCW plants 

that could not be matched to the PCS database were simply assigned the nearest RF1 waterbody. 

The next step in the assignment process was to review the waterbody names (especially 

those from PCS) to identify lakes and reservoirs. Finally, visual review, using aerial photos and 

topographic maps from the Terraserver Web site (http://www.terraserver.com), was used to 

check all low-flow streams and RF1 reaches whose identity was not clear. Attachment C-4

provides the RF1 stream assignments, flows, and waterbody types for the CCW disposal sites. 

With respect to waterbody type, the RF1 data include several types of waterbodies, 

including streams and rivers, and types with zero flows such as lakes, Great Lakes, wide rivers, 

and coastline features. Each of these waterbody types needed to be designated as a river or a lake 

for the simple waterbody model used in the full-scale CCW risk assessment. Because only the 

streams and rivers have flow data in RF1 (i.e., are flowing reaches), all other types were assigned 

to the lake modeling category. Modeling these features as a simple model lake is an uncertainty 

in the CCW risk assessment Table C-7 lists the RF1 waterbody types for the waterbodies 

assigned to the CCW disposal sites, along with the number of CCW plants assigned to each type 

and the crosswalk to the river (R) or lake (L) waterbody type used in this risk assessment. 

Table C-7. RF1 Reach Types Assigned to CCW Disposal Sites 

RF1 

Code RF1Name Description

Reach

Model 

Typea

Number 

of CCW 

Plants

Flowing Reaches

M Artificial Open 

Water Reach 

An artificial reach within any open water, other than a lake 

or reservoir, to provide connection between input and 

output reaches of the open water. 

R 1 

R Regular Reach A reach that has upstream and downstream reaches 

connected to it and that is not classified as another type of 

reach. 

R 106 

S Start Reach A headwater reach that has no reaches above it and either 

one or two transport reaches connected to its downstream 

end. 

R 16 

T Terminal Reach A reach downstream of which there is no other reach (for 

example, a reach that terminates into an ocean, a landlocked lake, or the ground). This type of reach has either 

one or two reaches connected to its upstream end. 

R 2 

(continued) 

April 2010–Draft EPA document. C-15 
Appendix C Site Data 

RF1 Reach Types Assigned to CCW Disposal Sites. (continued) 

RF1 

Code RF1Name Description

Reach

Model 

Typea

Number 

of CCW 

Plants

Reaches with Zero RF1 Flow

C Coastal/Continental 

Shoreline Segment 

A reach that represents a segment of a shoreline of a gulf, 

sea, or ocean. 

L 3 

G Great Lakes 

Shoreline Segment 

A reach that represents a segment of a shoreline of the 

Great Lakes. 

L 12 

L Lake Shoreline 

Segment 

A segment that follows the shoreline of a lake other than 

one of the Great Lakes. 

L 36 

W Wide-River 

Shoreline Segment 

A reach that represents a segment of the left or right bank 

of a stream. 

L 5 

a

 R = river; L = lake. 

Stream dimensions were calculated from the flow data as follows. First, the length of the 

modeled stream segment was set to be the width of the groundwater plume as it enters the 

waterbody. Stream width was then determined from flow (Q) using a liner regression equation 

derived from empirical data by Kocher and Sartor (1997): 

 (C-1) 0.4559 Width = 5.1867Q

Water column depth (dwc) was derived from width, velocity (V), and flow using the continuity 

equation: 

Widthv

Q dwc

× = (C-2) 

C.6.2. Lake Flow Conditions and Dimensions 

Areas and depths for many of the lakes assigned to the CCW plant sites were not readily 

available from RF1, Reach File Version 3 (RF3), the National Hydrography Dataset (NHD), or 

other sources. In addition, many plants were located on very large waterbodies (e.g., the Great 

Lakes, wide rivers, or coastlines), where applying the simple steady-state, single-compartment 

model used in this analysis to the entire lake would not be appropriate. For these reasons, a 

model lake approach was used to represent all lakes and other nonflowing waterbodies assigned 

to the CCW disposal site. 

The model lake chosen was Shipman City Lake in Illinois, a well-characterized 13-acre 

lake that EPA has chosen as the index reservoir for modeling drinking water exposures to 

pesticides (Jones et al., 1998). The parameter values shown in Table C-8 for Shipman City Lake 

were used to model all lakes in this initial analysis. Given that many of the lakes assigned to 

CCW plants were much larger than 13 acres, this produced high-end risk results. However, given 

that many of the plants were located on very large waterbodies, this necessary simplification is 

an uncertainty in defining the environmental settings for the CCW risk assessment. 

April 2010–Draft EPA document. C-16 
Appendix C Site Data 

Table C-8. Model Lake Used in CCW Risk Assessment 

Parameter Value

Area a 13 acres 

Water column depth (dwc)a 9 feet 

Hydraulic residence time (HRT) Random, triangular distribution: 

Minimum = 1 month 

Mean = 6 months 

Maximum = 24 months 

Annual flow mixing volume = (Area × dwc) / HRT 

a

 Source: Shipman City Lake, IL (Jones et al., 1998). 

C.6.3 Water Quality Data 

Surface water temperature, total suspended solids (TSS), and pH data were collected by 

USGS hydrologic region from the STORET database. EPA’s STORET system is the largest 

single source of water quality data in the country. The Legacy STORET database contains over 

275 million analyses performed on more than 45 million samples collected from 800,000 stations 

across the United States for the period 1960 through 1998. STORET can be accessed from the 

Web at http://www.epa.gov/STORET. 

STORET water quality data are notoriously “noisy” because they are influenced by 

hydrology, point sources, nonpoint sources, stream/lake morphology, and varying data quality. 

The following issues in using STORET data must be considered before using the data: 

 Not all of the data have undergone rigorous QA/QC. 

 STORET site locations can be biased, especially to known “problem” waters. 

 The sample times are often at critical periods, such as summer low flows. 

Statistical analysis techniques were employed taking into account the above issues 

(including coordination with gage statistical analysis and Reach Files, the use of median values 

to avoid bias in central tendency estimates, and specification of a minimum number of 

measurements to estimate median values). As a result of these techniques, which can be thought 

of as extracting the underlying “signal” of water quality from the inherent “noise” of water 

quality data, the above issues were manageable. 

Surface water temperature data were collected as median values for each hydrologic 

region. These data are shown in Table C-9 along with the number of the modeled CCW plants in 

each region. 

April 2010–Draft EPA document. C-17 
Appendix C Site Data 

Table C-9. Regional Surface Water Temperatures: 

CCW Disposal Sites 

Hydrologic 

Region

Surface Water 

Temperature (EC)

Number of CCW 

Plants

2 16 12 

3 21 37 

4 14 14 

5 17 43 

6 18 6 

7 15 20 

8 20 2 

9 10 1 

10 13 20 

11 17 8 

12 21 6 

14 9 5 

15 17 4 

16 9 1 

18 15 2 

Data source: Legacy STORET database. 

Total suspended solids data were collected separately for streams/rivers and lakes 

because lakes tend to have lower TSS levels. Annual median values were used to develop 

statistics. For rivers, the minimum, maximum, and geometric mean values were used to define 

log triangular distributions for each hydrologic region (Table C-10); these distributions were 

then sampled during the preparation of the source data files. (The geometric means were 

weighted by the annual number of measurements.) For lakes, data were limited and national 

statistics were developed, with the geometric mean of the median values being weighted by the 

number of measurements per year and the number of annual values in each region. 

Table C-10. Surface Water Total Suspended Solids (TSS) Distributions 

Hydrologic 

Region

Number 

of CCW 

Plants

No. of 

Measurements

No. of 

Annual 

Medians

Annual Median TSS 

(log triangular distribution)

Geometric 

Minimum Maximum Mean

Weighted 

Geometric 

Mean

1 0 9,007 33 3.2 40 8.0 6.0 

2 12 47,202 38 10 316 32 40 

3 37 43,395 36 6.3 79 25 25 

4 14 29,577 37 6.3 794 25 25 

5 43 39,900 38 4.0 100 25 25 

6 6 4,137 28 5.0 316 16 20 

(continued)

April 2010–Draft EPA document. C-18 
Appendix C Site Data 

Surface Water Total Suspended Solids (TSS) Distributions. (continued) 

Hydrologic 

Region

Number 

of CCW 

Plants

No. of 

Measurements

No. of 

Annual 

Medians

Annual Median TSS 

(log triangular distribution)

Geometric 

Minimum Maximum Mean

Weighted 

Geometric 

Mean

7 20 34,494 37 32 1,585 63 100 

8 2 46,231 38 50 316 158 126 

9 1 3,254 35 13 3,162 32 63 

10 20 62,791 38 10 398 126 126 

11 8 48,969 38 25 794 200 126 

12 6 7,280 35 40 1,995 79 126 

13 0 13,974 37 32 79,433 200 398 

14 5 26,699 38 16 5,012 158 251 

15 4 9,162 37 20 19,953 200 398 

16 1 19,965 33 4 2,512 16 25 

17 0 173,136 37 2 316 6.0 10 

18 2 42,022 37 13 398 63 50 

Lakes 

(national) 

56 4,360 99 1 398 25 25 

Data source: Legacy STORET database. 

For surface water pH, the minimum, maximum, and weighted average annual median 

values were used to specify triangular distributions for each hydrologic region. Table C-11

provides these regional statistics, which were applied to both rivers and lakes. 

To prepare the water quality data for the source datafile, the 181 CCW disposal sites were 

assigned to a hydrogeologic region using a GIS. For each region, 10,000-record TSS and pH data 

sets were created by sampling the distributions shown in Tables C-10 and C-11. During source 

data file preparation, TSS data were pulled from the appropriate regional data set sequentially for 

each iteration at a site. 

Table C-11. Regional Surface Water pH Distributions 

Hydrologic 

Region 

Number of 

CCW 

Plants 

No. of 

Measurements

No. of Annual 

Median 

Values 

Annual Median pH 

(triangular distribution) 

Average 

Minimum Maximum Median pH

Weighted 

Average 

1 0 232,025 38 5.9 7.7 6.5 6.8 

2 12 447,166 39 7.2 7.6 7.4 7.4 

3 37 1,595,237 39 6.3 7.2 7.0 7.0 

4 14 335,261 39 7.6 8.2 8.1 8.0 

5 43 684,235 41 3.5 7.5 7.2 7.1 

6 6 382,915 39 6.3 7.7 7.2 7.4 

(continued)

April 2010–Draft EPA document. C-19 
Appendix C Site Data 

Regional Surface Water pH Distributions. (continued) 

Hydrologic 

Region 

Number of 

CCW 

Plants 

No. of 

Measurements

No. of Annual 

Median 

Values 

Annual Median pH 

(triangular distribution) 

Average 

Minimum Maximum Median pH

Weighted 

Average 

7 20 234,589 39 7.6 8.1 7.9 7.8 

8 2 171,643 39 6.9 7.8 7.1 7.2 

9 1 23,038 38 7.5 8.4 7.9 7.9 

10 20 269,570 39 7.6 8.2 8.0 8.0 

11 8 311,768 39 7.4 8.1 7.8 7.8 

12 6 178,990 39 7.0 7.9 7.8 7.6 

13 0 35,355 39 7.0 8.1 8.0 7.9 

14 5 77,041 39 7.9 8.3 8.1 8.1 

15 4 75,145 38 7.7 8.3 8.0 8.0 

16 1 68,581 38 7.5 8.3 8.0 8.0 

17 0 293,909 39 6.9 8.0 7.5 7.4 

18 2 182,049 38 7.4 8.6 7.8 7.8 

Data source: Legacy STORET database. 

C.6.4 Distance to Surface Water 

Because the CCW plant locations were not accurate in terms of locating the WMUs, a 

national empirical distribution of distances between the WMU and the nearest downgradient 

surface waterbodies (discussed in Section C.2.1) was developed using manual measurements on 

online maps and aerial photographs for a random selection of 30 CCW landfills and 29 CCW 

surface impoundments. Scaled USGS maps and aerial photographs were obtained from the 

Terraserver Web site (http://www.terraserver.com) by entering each plant’s longitude and 

latitude. Labels on the maps, features on the photographs, and best professional judgment were 

used to identify the power plant and the surface impoundment or landfill in question, along with 

the nearest downgradient waterbody. 

The nearest waterbody matching one of the following descriptions was used in the 

analysis: 

 Lakes or rivers beyond the facility boundary 

 Streams originating in or passing through the facility boundary and then coursing 

downstream beyond the property boundary 

 Streams with an order of 3 or greater (i.e., fishable waterbodies). 

Stream order was determined by tracing the convergence of tributaries with order 1 assigned to 

the furthest upstream segment indicated on the map (both ephemeral and perennial streams were 

assigned as order 1). Topography on the map was used to determine if the waterbody was 

downgradient of the plant. Many CCW WMUs in the sample were located on a large waterbody. 

April 2010–Draft EPA document. C-20 
Appendix C Site Data 

Once the waterbody was identified, the scale provided on the maps and photos was used 

to measure the horizontal distance between the CCW impoundment or landfill and the 

waterbody. All assignments and measurements were independently checked for accuracy. 

The two distributions (landfills and surface impoundments) were statistically compared 

using (1) a Wilcoxon Rank Sum Test (to determine whether one distribution is shifted to the 

right or left of the other distribution) and (2) a Quantile Test (to test for differences, that is, 

differing numbers of observations) between the two distributions for the values above a given 

percentile. The results of the Wilcoxon test showed a p value of 0.64, indicating no significant 

difference in the shape of the distributions. The Quantile Test evaluated every decile from 0.1 to 

0.9, with adjustments to the lower percentiles to be estimated for large numbers of ties in the 

ranks for the lower end of the data. The nonsignificant p values ranged from 0.33 (for 90th 

percentile) to 0.17 (for the 40th percentile). One significant p value indicating differences 

between the two distributions occurred at the 17th percentile (p value = 0.066), but the remainder 

of the tests showed no significant differences. Based on these results, the distributions were 

judged to be similar and combined to produce the single distribution of 59 values used to 

produce a single empirical distribution (previously shown in Table C-2) that was applied 

nationally to both landfills and surface impoundments at the CCW sites. 

C.5 References 

Aller, L., T. Bennett, J.H. Lehr, R.J. Perry, and G. Hackett. 1987. DRASTIC: A Standardized 

System for Evaluating Pollution Potential Using Hydrogeologic Settings. EPA-600/2-87-

035. U.S. Environmental Protection Agency. Office of Research and Development. Ada, 

OK. April. 

Carsel, R.F., and R.S. Parrish. 1988. Developing joint probability distributions of soil water 

retention characteristics. Water Resources Research 24(5):755–769. 

Carsel, R.F., R.S. Parrish, R.L. Jones, J.L. Hansen, and R.L. Lamb. 1988. Characterizing the 

uncertainty of pesticide leaching in agricultural soils. Journal of Contaminant Hydrology

2:111–124. 

Clawges, R.M., and C.V. Price. 1999a. Digital Data Sets Describing Principal Aquifers, 

Surficial Geology, and Ground-Water Regions of the Conterminous United States. 

Open-File Report 99-77. U.S. Geological Survey, Rapid City, SD. Available at 

http://pubs.usgs.gov/of/1999/ofr99-77/. 

Clawges, R.M., and C.V. Price. 1999b. Ground-water regions of the conterminous United States 

with unconsolidated watercourses. Edition: 1.0 (map). In: Open-File Report 99-77 

(Clawges and Price, 1999a). U.S. Geological Survey, Rapid City, SD. Available at 

http://water.usgs.gov/lookup/getspatial?ofr99-77_gwreguw. 

Clawges, R.M., and C.V. Price. 1999c. Productive aquifers of the conterminous United States. 

Version 1.0 (map). In Open-File Report 99-77(Clawges and Price, 1999a). U.S. 

Geological Survey, Rapid City, SD. Available at http://water.usgs.gov/lookup/

getspatial?ofr99-77_aquif75m. 

April 2010–Draft EPA document. C-21 
Appendix C Site Data 

Clawges, R.M., and C.V. Price. 1999d. Surficial geology of the conterminous United States. 

Version 1.0. In Open-File Report 99-77 (Clawges and Price, 1999a). U.S. Geological 

Survey, Rapid City, SD. Available at http://water.usgs.gov/lookup/getspatial?ofr99-

77_geol75m. 

Heath, R.C. 1984. Ground-Water Regions of the United States. Water-Supply Paper 2242, U.S. 

Geological Survey, Washington, DC. Available at http://pubs.usgs.gov/wsp/wsp2242/. 

Heath, R.C. 1985. National Water Summary 1984. State Summaries of Groundwater Resources. 

Water-Supply Paper 2275. U.S. Geological Survey, Washington, DC. 

Jones, R.D., S.W. Abel, W. Effland, R. Matzner, and R. Parker. 1998. An index reservoir for use 

in assessing drinking water exposures. Chapter IV in Proposed Methods for Basin-Scale 

Estimation of Pesticide Concentrations in Flowing Water and Reservoirs for Tolerance 

Reassessment, presented to the FIFRA Scientific Advisory Panel. July. Available at 

http://www.epa.gov/scipoly/sap/meetings/1998/july/1part4.pdf. 

Kocher, K., and R. Sartor. 1997. Derivation and use of data and table to determine stream flow 

values for water bodies. Memorandum to File, RTI Project 6720-005. Research Triangle 

Institute, Research Triangle Park, NC. June 16. 

Miller, J.A. 1998. Principal Aquifers of the 48 Contiguous United States. Version 1.0. United 

States Geological Survey, Madison, WI. Available at http://nationalatlas.gov/. 

Miller, D.A., and R.A. White. 1998. A Conterminous United States Multi-Layer Soil 

Characteristics Data Set for Regional Climate and Hydrology Modeling. Earth 

Interactions 2(2): 1–26. 

Newell, C.J., L.P. Hopkins, and P.B. Bedient. 1989. Hydrogeologic Database for Ground Water 

Modeling. API Publication No. 4476. American Petroleum Institute, Washington, DC. 

Newell, C,J., L.P. Hopkins, and P.B. Bedient. 1990. A hydrogeologic database for ground water 

modeling. Ground Water 28(5):703–714. 

Schwarz, G.E., and R.B. Alexander. 1995. State Soil Geographic (STATSGO) Data Base for the 

Conterminous United States. Edition: 1.1. Reston, VA. Available at 

http://water.usgs.gov/GIS/metadata/usgswrd/XML/muid.xml. 

USDA (U.S. Department of Agriculture). 1994. State Soil Geographic (STATSGO) Data Base. 

Data Use Information. Miscellaneous Publication No. 1492. Natural Resources 

Conservation Service, Fort Worth, TX. Available at http://www.nrcs.usda.gov/

technical/techtools/statsgo_db.pdf. 

U.S. EPA (Environmental Protection Agency). 1988. National Survey of Solid Waste 

(Municipal) Landfill Facilities (Draft). EPA/530-SW88-034. Office of Solid Waste and 

Emergency Response, Washington, DC. September. Available in docket number EPAHQ-RCRA-2003-0004 as document number EPA-HQ-RCRA-2003-0004-0466. 

April 2010–Draft EPA document. C-22 
Appendix C Site Data 

April 2010–Draft EPA document. C-23 

U.S. EPA (Environmental Protection Agency). 1990. Office of Water Environmental and 

Program Information Systems Compendium. FY 1990. EPA 500/9-90-002. Office of 

Water, August. 

U.S. EPA (Environmental Protection Agency). 1997. EPA’s Composite Model for Leachate 

Migration with Transformation Products (EPACMTP) User’s Guide. Office of Solid 

Waste, Washington, DC. Available at http://www.epa.gov/waste/nonhaz/industrial/tools/

cmtp/index.htm. 

van der Leeden, F., F.L. Troise, and D.K. Todd. 1990. The Water Encyclopedia. 2nd Ed. 

Chelsea, MI: Lewis Publishers. 176 pp. 
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-1 

Attachment C-1: Soil Data 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

A B Brown 43.9 SCL 6.0 1.2 

A B Brown 51.1 SLT 6.5 1.6 

A B Brown 5.0 SNL 6.9 1.4 

A/C Power- Ace Operations 8.9 SCL 8.9 0.21 

A/C Power- Ace Operations 32.0 SLT 8.4 0.46 

A/C Power- Ace Operations 59.1 SNL 8.0 0.46 

Allen 48.9 SCL 7.1 0.98 

Allen 19.2 SLT 6.2 1.1 

Allen 32.0 SNL 7.1 1.1 

Alma 18.9 SCL 6.6 1.7 

Alma 59.4 SLT 6.5 3.4 

Alma 21.7 SNL 5.6 0.69 

Antelope Valley 8.4 SCL 7.6 3.2 

Antelope Valley 68.5 SLT 7.6 1.7 

Antelope Valley 23.1 SNL 7.8 2.4 

Arkwright 50.7 SCL 5.4 0.5 

Arkwright 24.7 SLT 5.6 0.88 

Arkwright 24.5 SNL 5.4 0.64 

Asheville 6.3 SCL 5.4 0.43 

Asheville 77.8 SLT 5.2 0.99 

Asheville 15.8 SNL 5.4 1 

Baldwin 39.5 SCL 6.2 1.3 

Baldwin 58.6 SLT 6.0 1.6 

Baldwin 1.9 SNL 6.5 1.4 

Barry 35.8 SCL 4.8 3.6 

Barry 23.5 SLT 4.8 7 

Barry 40.7 SNL 4.8 4.4 

Bay Front 11.7 SCL 7.3 4 

Bay Front 21.1 SLT 7.1 3.8 

Bay Front 67.2 SNL 7.1 1.4 

Bay Shore 90.8 SCL 7.1 4.1 

Bay Shore 4.3 SLT 7.2 2.6 

Bay Shore 4.9 SNL 7.7 9.3 

Belews Creek 69.2 SCL 5.2 0.34 

Belews Creek 14.0 SLT 5.4 1 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-2 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Belews Creek 16.8 SNL 5.2 0.4 

Ben French 25.3 SCL 8.0 0.87 

Ben French 59.7 SLT 7.7 1.8 

Ben French 15.0 SNL 7.1 1.7 

Big Cajun 2 66.4 SCL 7.1 1.1 

Big Cajun 2 28.4 SLT 6.3 1.2 

Big Cajun 2 5.2 SNL 6.0 1.3 

Big Sandy 54.8 SCL 5.4 1.6 

Big Sandy 41.5 SLT 5.3 1.9 

Big Sandy 3.7 SNL 5.1 2.6 

Big Stone 7.3 SCL 7.5 5.7 

Big Stone 45.0 SLT 7.7 3.1 

Big Stone 47.7 SNL 7.5 1.1 

Black Dog Steam Plant 8.2 SCL 6.9 4.2 

Black Dog Steam Plant 41.4 SLT 6.8 2.5 

Black Dog Steam Plant 50.4 SNL 6.9 1.8 

Blue Valley 63.8 SCL 6.3 1.5 

Blue Valley 31.6 SLT 6.6 2.8 

Blue Valley 4.6 SNL 6.5 1.1 

Bowen 18.1 SCL 5.0 1.2 

Bowen 81.9 SLT 5.0 0.74 

Brandon Shores 18.2 SCL 4.5 0.47 

Brandon Shores 16.8 SLT 4.6 3.4 

Brandon Shores 64.9 SNL 4.8 0.88 

Buck 79.1 SCL 5.4 0.39 

Buck 18.9 SLT 5.6 1 

Buck 2.0 SNL 5.3 0.6 

Bull Run 76.7 SCL 5.2 0.92 

Bull Run 18.2 SLT 5.6 1.7 

Bull Run 5.1 SNL 5.0 0.67 

C D McIntosh Jr 6.5 SCL 8.1 2.3 

C D McIntosh Jr 93.5 SNL 5.5 1.8 

C P Crane 34.1 SCL 4.8 0.52 

C P Crane 34.3 SLT 4.7 1 

C P Crane 31.6 SNL 4.9 1.1 

Cape Fear 67.6 SCL 5.1 0.97 

Cape Fear 24.7 SLT 5.4 1.5 

Cape Fear 7.7 SNL 5.2 0.66 

Carbon 0.4 SCL 6.3 7.4 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-3 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Carbon 95.8 SLT 7.8 3.4 

Carbon 3.8 SNL 8.2 1.4 

Cardinal 69.1 SCL 5.8 1 

Cardinal 30.4 SLT 5.7 1.7 

Cardinal 0.5 SNL 6.4 2 

Cayuga 32.3 SCL 6.6 1.9 

Cayuga 48.7 SLT 7.1 1.4 

Cayuga 19.0 SNL 6.8 1.1 

Chalk Point 6.9 SCL 4.6 0.58 

Chalk Point 16.4 SLT 4.8 8.8 

Chalk Point 76.7 SNL 4.6 1.1 

Cholla 27.3 SCL 8.4 1.9 

Cholla 61.0 SLT 8.1 0.62 

Cholla 11.6 SNL 8.3 0.75 

Cliffside 66.4 SCL 5.2 0.31 

Cliffside 13.6 SLT 5.5 0.77 

Cliffside 20.0 SNL 5.2 0.27 

Clover 71.0 SCL 5.3 0.71 

Clover 23.3 SLT 5.3 1.3 

Clover 5.7 SNL 5.1 0.65 

Coal Creek 6.1 SCL 6.8 3 

Coal Creek 82.7 SLT 7.6 1.7 

Coal Creek 11.2 SNL 8.2 2.8 

Coleto Creek 12.1 SCL 7.0 1.1 

Coleto Creek 86.0 SLT 7.4 0.78 

Coleto Creek 1.8 SNL 6.2 0.75 

Colstrip 9.0 SCL 8.0 0.79 

Colstrip 63.0 SLT 8.2 0.73 

Colstrip 27.9 SNL 8.3 0.54 

Conemaugh 11.8 SCL 5.0 2.7 

Conemaugh 81.4 SLT 4.8 1.3 

Conemaugh 6.8 SNL 4.5 1.8 

Conesville 44.0 SCL 5.4 2.2 

Conesville 45.5 SLT 5.6 1.9 

Conesville 10.5 SNL 5.0 2.2 

Council Bluffs 43.3 SCL 7.5 1.5 

Council Bluffs 47.2 SLT 7.6 1.2 

Council Bluffs 9.6 SNL 7.7 0.74 

Crawford 48.4 SCL 6.8 1.9 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-4 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Crawford 23.6 SLT 6.7 1.4 

Crawford 28.0 SNL 6.7 0.82 

Crist 18.8 SCL 5.4 4.5 

Crist 32.3 SLT 5.3 1.1 

Crist 48.8 SNL 5.4 3.3 

Cross 3.0 SCL 5.0 1.3 

Cross 46.0 SLT 4.6 0.58 

Cross 51.0 SNL 4.9 1.2 

Cumberland 61.1 SCL 5.3 1.6 

Cumberland 34.2 SLT 5.7 0.98 

Cumberland 4.8 SNL 5.2 1.3 

Dale 91.7 SCL 6.4 1.9 

Dale 8.2 SLT 6.4 2 

Dale 0.1 SNL 6.7 1.3 

Dallman 66.2 SCL 6.4 1.8 

Dallman 33.3 SLT 6.7 1.2 

Dallman 0.5 SNL 7.0 1.1 

Dan E Karn 0.01 SCL 7.0 3 

Dan E Karn 53.6 SLT 7.9 4.2 

Dan E Karn 46.3 SNL 7.8 5.4 

Dan River 73.3 SCL 5.0 0.39 

Dan River 12.0 SLT 5.3 1.4 

Dan River 14.7 SNL 5.1 0.6 

Danskammer 89.8 SLT 5.8 2.9 

Danskammer 10.2 SNL 6.9 2.8 

Dave Johnston 2.2 SCL 8.9 0.96 

Dave Johnston 36.6 SLT 8.2 1.2 

Dave Johnston 61.2 SNL 8.2 1.1 

Dickerson 6.1 SCL 5.1 0.52 

Dickerson 93.9 SLT 5.2 0.68 

Dolet Hills 65.7 SCL 4.8 0.97 

Dolet Hills 21.6 SLT 5.0 0.77 

Dolet Hills 12.7 SNL 5.1 1.1 

Duck Creek 65.5 SCL 6.4 0.82 

Duck Creek 33.6 SLT 6.5 0.6 

Duck Creek 0.9 SNL 7.0 0.98 

Dunkirk 8.8 SCL 7.3 5.4 

Dunkirk 79.6 SLT 6.9 4.6 

Dunkirk 11.6 SNL 6.5 2.7 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-5 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

E D Edwards 49.5 SCL 6.4 1.1 

E D Edwards 29.8 SLT 6.3 1.2 

E D Edwards 20.6 SNL 6.8 1.1 

E W Brown 92.9 SCL 6.4 3.7 

E W Brown 7.1 SLT 6.6 3.8 

Eckert Station 4.8 SCL 7.2 4.5 

Eckert Station 82.0 SLT 6.9 1.2 

Eckert Station 13.2 SNL 6.7 0.5 

Edgewater 58.5 SCL 7.3 3.3 

Edgewater 3.7 SLT 7.3 1.2 

Edgewater 37.8 SNL 6.8 2.2 

Elmer W Stout 29.9 SCL 6.7 1.9 

Elmer W Stout 56.7 SLT 7.0 1.2 

Elmer W Stout 13.3 SNL 6.8 0.8 

F B Culley 45.3 SCL 5.9 0.93 

F B Culley 48.9 SLT 6.5 2 

F B Culley 5.8 SNL 6.9 1.1 

Fayette Power Prj 51.9 SCL 7.7 3.8 

Fayette Power Prj 35.7 SLT 7.6 1.2 

Fayette Power Prj 12.5 SNL 7.1 1 

Flint Creek 62.2 SCL 4.9 0.87 

Flint Creek 37.8 SLT 5.3 0.69 

Fort Martin 45.9 SCL 5.6 1.2 

Fort Martin 54.1 SLT 5.2 1.9 

Fort Martin 0.04 SNL 4.6 2.5 

Frank E Ratts 30.9 SCL 5.8 1.5 

Frank E Ratts 58.0 SLT 6.3 1.1 

Frank E Ratts 11.1 SNL 7.0 0.73 

G G Allen 85.9 SCL 5.3 0.36 

G G Allen 11.9 SLT 5.6 1.1 

G G Allen 2.2 SNL 5.2 0.28 

Gadsden 45.2 SCL 4.8 0.68 

Gadsden 46.4 SLT 5.3 1.3 

Gadsden 8.5 SNL 5.1 0.97 

Gallatin 56.1 SCL 5.6 0.94 

Gallatin 43.9 SLT 5.4 0.94 

Gen J M Gavin 35.9 SCL 6.0 1.4 

Gen J M Gavin 46.1 SLT 5.6 2.1 

Gen J M Gavin 18.0 SNL 5.1 1.3 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-6 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Genoa 14.3 SCL 6.1 2.3 

Genoa 64.6 SLT 6.6 1.8 

Genoa 21.0 SNL 6.1 0.97 

Gibson 55.3 SCL 6.6 1.5 

Gibson 43.2 SLT 6.4 1.1 

Gibson 1.5 SNL 7.3 0.67 

Gorgas 17.0 SCL 4.6 0.42 

Gorgas 53.0 SLT 5.1 0.77 

Gorgas 30.0 SNL 5.2 0.73 

Green River 48.4 SCL 5.9 1 

Green River 51.6 SLT 6.0 1.4 

Greene County 19.5 SCL 5.1 1.8 

Greene County 72.6 SLT 5.2 1.4 

Greene County 7.9 SNL 4.9 1.6 

H B Robinson 0.1 SCL 5.2 0.75 

H B Robinson 32.6 SLT 4.8 1 

H B Robinson 67.3 SNL 5.3 0.6 

Hammond 54.7 SCL 5.1 0.74 

Hammond 33.8 SLT 5.3 1.3 

Hammond 11.5 SNL 5.0 0.75 

Harllee Branch 54.7 SCL 5.3 0.49 

Harllee Branch 15.3 SLT 5.6 0.97 

Harllee Branch 30.0 SNL 5.3 0.47 

Harrison 48.8 SCL 5.6 1 

Harrison 51.2 SLT 5.0 2.1 

Hatfield's Ferry 39.3 SCL 5.7 1.8 

Hatfield's Ferry 60.4 SLT 5.3 1.6 

Hatfield's Ferry 0.3 SNL 4.6 2.5 

Hennepin 44.6 SCL 6.4 1.5 

Hennepin 38.2 SLT 6.7 1.1 

Hennepin 17.2 SNL 7.0 1.3 

Heskett 39.9 SCL 8.0 2.1 

Heskett 44.1 SLT 7.6 2.4 

Heskett 16.0 SNL 7.7 1.9 

Holcomb 4.4 SLT 7.9 0.67 

Holcomb 95.6 SNL 7.3 0.75 

Homer City 11.0 SCL 4.9 2.9 

Homer City 84.5 SLT 4.8 1.6 

Homer City 4.5 SNL 4.5 2.1 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-7 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Hoot Lake 3.1 SCL 7.5 5.4 

Hoot Lake 38.9 SLT 7.7 2.6 

Hoot Lake 58.1 SNL 7.5 1.3 

Hugo 55.1 SCL 6.6 1.4 

Hugo 35.8 SLT 6.7 1.6 

Hugo 9.2 SNL 5.3 0.7 

Hunter 90.8 SCL 8.3 0.73 

Hunter 3.5 SLT 8.2 2 

Hunter 5.7 SNL 8.5 0.75 

Huntington 4.5 SCL 8.6 1.5 

Huntington 79.5 SLT 8.0 2.4 

Huntington 15.9 SNL 8.6 1.3 

Intermountain 46.9 SCL 8.6 0.7 

Intermountain 8.3 SLT 8.9 0.51 

Intermountain 44.8 SNL 8.8 0.44 

J H Campbell 5.0 SLT 7.1 1.8 

J H Campbell 95.0 SNL 5.9 1.2 

J M Stuart 73.5 SCL 6.5 1.6 

J M Stuart 24.8 SLT 6.8 2.4 

J M Stuart 1.7 SNL 5.5 2 

J R Whiting 80.6 SCL 7.1 4.2 

J R Whiting 17.1 SLT 7.1 2.1 

J R Whiting 2.3 SNL 6.8 2.8 

Jack McDonough 58.9 SCL 5.2 0.46 

Jack McDonough 7.8 SLT 5.6 1.1 

Jack McDonough 33.3 SNL 5.3 0.37 

Jack Watson 20.5 SCL 6.7 11 

Jack Watson 46.8 SLT 4.8 3 

Jack Watson 32.8 SNL 4.9 3.8 

James H Miller Jr 17.0 SCL 4.6 0.42 

James H Miller Jr 53.0 SLT 5.1 0.77 

James H Miller Jr 30.0 SNL 5.2 0.73 

Jim Bridger 1.4 SCL 8.7 0.75 

Jim Bridger 37.9 SLT 8.6 0.52 

Jim Bridger 60.6 SNL 8.2 0.64 

John E Amos 35.8 SCL 6.3 1.6 

John E Amos 64.2 SLT 5.1 2.2 

John Sevier 43.2 SCL 6.2 1.6 

John Sevier 56.7 SLT 5.8 1.2 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-8 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

John Sevier 0.2 SNL 5.0 0.67 

Johnsonville 39.2 SCL 5.1 1.7 

Johnsonville 57.3 SLT 5.2 1.3 

Johnsonville 3.5 SNL 4.7 1.5 

Joliet 29 52.8 SCL 7.1 2.7 

Joliet 29 43.5 SLT 7.0 2.1 

Joliet 29 3.7 SNL 7.1 1.8 

Keystone 7.7 SCL 4.9 2.8 

Keystone 90.1 SLT 4.9 1.4 

Keystone 2.2 SNL 4.5 2.2 

Killen Station 74.3 SCL 6.0 1.9 

Killen Station 24.0 SLT 6.3 2.2 

Killen Station 1.8 SNL 6.2 1.7 

Kingston 66.7 SCL 5.0 1.2 

Kingston 21.0 SLT 5.5 1.7 

Kingston 12.3 SNL 5.0 0.67 

Kraft 57.1 SCL 7.2 11 

Kraft 22.8 SLT 5.0 1.3 

Kraft 20.1 SNL 5.0 1.4 

L V Sutton 18.0 SCL 6.1 3.9 

L V Sutton 32.4 SLT 5.0 3.7 

L V Sutton 49.6 SNL 5.0 1.6 

Lansing 9.0 SCL 5.8 2.6 

Lansing 67.7 SLT 6.8 2.1 

Lansing 23.3 SNL 6.2 1.4 

Laramie R Station 41.1 SLT 8.1 0.87 

Laramie R Station 58.9 SNL 7.9 1.2 

Lawrence EC 51.5 SCL 6.6 1.9 

Lawrence EC 47.7 SLT 6.8 2.9 

Lawrence EC 0.8 SNL 7.5 0.75 

Lee 16.4 SCL 5.0 1.3 

Lee 51.1 SLT 5.0 1.3 

Lee 32.5 SNL 5.1 0.96 

Leland Olds 13.5 SCL 7.8 2.6 

Leland Olds 52.9 SLT 7.6 1.9 

Leland Olds 33.6 SNL 7.5 2 

Lon Wright 25.7 SCL 7.5 1.5 

Lon Wright 8.4 SLT 7.0 2.1 

Lon Wright 65.9 SNL 7.8 1.4 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-9 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Louisa 35.5 SCL 6.7 1.8 

Louisa 16.6 SLT 6.3 1.5 

Louisa 47.9 SNL 6.6 0.96 

Marion 10.9 SCL 5.6 0.96 

Marion 88.8 SLT 5.2 0.95 

Marion 0.3 SNL 6.6 1 

Marshall 72.1 SCL 5.2 0.33 

Marshall 12.9 SLT 5.5 0.87 

Marshall 15.0 SNL 5.2 0.27 

Martin Lake 34.3 SCL 4.9 1 

Martin Lake 25.1 SLT 5.1 0.8 

Martin Lake 40.6 SNL 5.1 0.73 

Mayo 71.9 SCL 5.6 0.61 

Mayo 27.9 SLT 5.6 1 

Mayo 0.2 SNL 5.2 0.76 

Meramec 87.9 SCL 6.4 1.3 

Meramec 12.1 SLT 6.5 1.3 

Merom 30.2 SCL 5.5 0.84 

Merom 59.2 SLT 5.8 0.96 

Merom 10.6 SNL 6.4 0.77 

Miami Fort 69.6 SCL 6.5 1.7 

Miami Fort 27.3 SLT 6.8 2 

Miami Fort 3.1 SNL 6.7 1.2 

Milton R Young 4.6 SCL 7.6 3.1 

Milton R Young 92.9 SLT 7.7 1.5 

Milton R Young 2.5 SNL 7.5 1.8 

Mitchell - PA 19.1 SCL 5.9 2.1 

Mitchell - PA 80.9 SLT 5.5 1.4 

Mitchell - WV 39.9 SCL 6.0 1.7 

Mitchell - WV 59.9 SLT 5.2 2 

Mitchell - WV 0.2 SNL 6.0 1.3 

Mohave 29.0 SLT 8.1 0.26 

Mohave 71.0 SNL 8.1 0.31 

Monroe 38.5 SCL 7.0 3 

Monroe 49.5 SLT 7.2 3.1 

Monroe 12.0 SNL 6.8 3.5 

Morgantown 21.7 SCL 4.6 1.2 

Morgantown 39.3 SLT 4.7 3.2 

Morgantown 39.0 SNL 4.9 1.3 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-10 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Mountaineer (1301) 56.1 SCL 6.0 1.6 

Mountaineer (1301) 34.2 SLT 5.9 2.2 

Mountaineer (1301) 9.8 SNL 4.9 2.5 

Mt Storm 4.1 SCL 5.0 2.9 

Mt Storm 65.3 SLT 4.7 1.4 

Mt Storm 30.6 SNL 4.4 1 

Muscatine Plant #1 46.8 SCL 6.6 1.8 

Muscatine Plant #1 27.4 SLT 6.4 1.4 

Muscatine Plant #1 25.8 SNL 6.6 0.84 

Muskogee 30.9 SCL 6.5 1.7 

Muskogee 53.1 SLT 6.8 1.1 

Muskogee 16.0 SNL 6.7 1 

Neal North 36.7 SCL 7.9 1.1 

Neal North 46.5 SLT 7.9 0.67 

Neal North 16.9 SNL 7.7 0.73 

Neal South 34.0 SCL 7.8 1.1 

Neal South 50.7 SLT 7.8 0.69 

Neal South 15.3 SNL 7.7 0.73 

Nebraska City 55.5 SCL 7.4 1.4 

Nebraska City 35.5 SLT 7.3 1.7 

Nebraska City 9.0 SNL 7.7 0.74 

New Castle 5.1 SCL 7.7 0.73 

New Castle 81.6 SLT 5.9 2.8 

New Castle 13.2 SNL 6.1 1.5 

Newton 37.9 SCL 5.5 0.54 

Newton 61.3 SLT 5.5 0.53 

Newton 0.7 SNL 6.5 0.85 

North Omaha 29.0 SCL 7.4 1.5 

North Omaha 60.1 SLT 7.7 0.82 

North Omaha 11.0 SNL 7.7 0.74 

Northeastern 76.9 SCL 6.7 2.1 

Northeastern 21.3 SLT 6.3 2.2 

Northeastern 1.8 SNL 5.6 2 

Nucla 61.2 SLT 7.9 0.98 

Nucla 38.8 SNL 8.1 0.55 

Oklaunion 92.2 SCL 8.0 1.7 

Oklaunion 7.0 SLT 7.9 0.94 

Oklaunion 0.7 SNL 7.3 1.5 

Paradise 14.8 SCL 5.6 1.4 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-11 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Paradise 85.2 SLT 5.9 1.2 

Petersburg 29.7 SCL 5.9 1.5 

Petersburg 62.9 SLT 6.3 1.2 

Petersburg 7.5 SNL 7.2 0.59 

Pleasant Prairie 97.2 SCL 7.1 1.7 

Pleasant Prairie 2.8 SNL 7.3 1.5 

Port Washington 86.3 SCL 7.3 3.3 

Port Washington 7.7 SLT 7.5 0.68 

Port Washington 6.1 SNL 7.3 3 

Portland 8.7 SCL 5.8 0.58 

Portland 90.8 SLT 5.5 1.1 

Portland 0.5 SNL 6.0 1.8 

Possum Point 6.3 SCL 4.6 0.58 

Possum Point 43.0 SLT 4.9 3 

Possum Point 50.7 SNL 4.9 0.8 

Potomac River 13.3 SCL 4.5 0.56 

Potomac River 35.5 SLT 4.9 2.8 

Potomac River 51.2 SNL 5.0 1.1 

Presque Isle 18.7 SLT 5.2 2.5 

Presque Isle 81.3 SNL 5.3 3.1 

R Gallagher 40.4 SCL 5.6 1.5 

R Gallagher 59.0 SLT 5.9 2.1 

R Gallagher 0.5 SNL 6.9 1.4 

R M Schahfer 2.1 SCL 7.1 3.8 

R M Schahfer 6.5 SLT 6.9 2.9 

R M Schahfer 91.4 SNL 6.6 1.5 

Reid Gardner 13.3 SCL 8.4 0.29 

Reid Gardner 21.6 SLT 8.3 0.58 

Reid Gardner 65.1 SNL 8.4 0.34 

Richard Gorsuch 69.9 SCL 6.1 1.7 

Richard Gorsuch 27.0 SLT 5.9 2.4 

Richard Gorsuch 3.0 SNL 5.1 2.6 

Riverbend 77.4 SCL 5.3 0.37 

Riverbend 20.1 SLT 5.7 1.1 

Riverbend 2.5 SNL 5.2 0.45 

Rodemacher 42.9 SCL 6.5 0.96 

Rodemacher 51.4 SLT 6.5 0.92 

Rodemacher 5.7 SNL 5.3 0.85 

Roxboro 40.3 SCL 5.5 0.47 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-12 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Roxboro 55.7 SLT 6.0 0.79 

Roxboro 4.0 SNL 5.5 1.4 

Sandow 0.8 SCL 6.9 0.5 

Sandow 37.4 SLT 6.3 0.66 

Sandow 61.8 SNL 6.3 0.64 

Scherer 58.5 SCL 5.3 0.39 

Scherer 12.8 SLT 5.5 0.97 

Scherer 28.7 SNL 5.3 0.42 

Shawnee 9.5 SCL 5.8 1 

Shawnee 84.2 SLT 5.6 1.4 

Shawnee 6.3 SNL 6.5 1.1 

Shawville 5.2 SCL 5.0 3 

Shawville 82.6 SLT 4.9 1.1 

Shawville 12.2 SNL 4.4 1.2 

Sheldon 62.7 SCL 6.8 2.3 

Sheldon 33.2 SLT 7.0 1.6 

Sheldon 4.1 SNL 6.9 2 

South Oak Creek 95.5 SCL 7.1 1.9 

South Oak Creek 4.5 SNL 7.3 1.6 

Springerville 10.0 SLT 8.1 0.79 

Springerville 90.0 SNL 7.9 0.79 

St Johns River Power 27.1 SCL 6.9 49 

St Johns River Power 0.4 SLT 5.0 1.3 

St Johns River Power 72.5 SNL 5.2 1.1 

Stanton Energy Ctr 0.8 SCL 7.0 10 

Stanton Energy Ctr 2.4 SLT 7.7 1 

Stanton Energy Ctr 96.8 SNL 5.3 4.8 

Stockton Cogen Company 89.9 SCL 7.6 1.8 

Stockton Cogen Company 6.6 SLT 7.5 1.5 

Stockton Cogen Company 3.5 SNL 6.8 0.51 

Syl Laskin 8.5 SCL 6.5 3.2 

Syl Laskin 4.6 SLT 6.3 6.3 

Syl Laskin 86.9 SNL 5.8 3.1 

Tecumseh EC 55.2 SCL 6.6 2 

Tecumseh EC 41.9 SLT 6.9 2.6 

Tecumseh EC 2.9 SNL 7.6 0.62 

Texas-New Mexico 4.4 SCL 7.0 0.61 

Texas-New Mexico 43.5 SLT 6.3 0.67 

Texas-New Mexico 52.1 SNL 6.0 0.77 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-13 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Titus 31.8 SCL 6.0 0.76 

Titus 63.6 SLT 5.6 1.4 

Titus 4.6 SNL 5.0 0.98 

Trimble County 57.3 SCL 6.3 2 

Trimble County 41.9 SLT 6.5 1.9 

Trimble County 0.8 SNL 5.9 1.7 

Tyrone 92.1 SCL 6.3 3.7 

Tyrone 7.9 SLT 6.6 3.9 

Valley 98.5 SCL 6.9 1.2 

Valley 0.2 SLT 7.5 0.45 

Valley 1.3 SNL 7.4 1.3 

Vermilion 82.5 SCL 6.9 1.3 

Vermilion 16.6 SLT 7.0 1.2 

Vermilion 0.8 SNL 7.2 1.1 

Victor J Daniel Jr 46.2 SCL 4.6 2.2 

Victor J Daniel Jr 27.7 SLT 4.7 2.3 

Victor J Daniel Jr 26.1 SNL 4.7 16 

W A Parish 95.8 SCL 7.4 1.4 

W A Parish 4.2 SLT 7.9 0.74 

W H Weatherspoon 7.4 SCL 5.5 1.9 

W H Weatherspoon 50.4 SLT 4.7 2.2 

W H Weatherspoon 42.2 SNL 4.8 1.3 

W S Lee 68.0 SCL 5.3 0.48 

W S Lee 9.0 SLT 5.7 1 

W S Lee 23.0 SNL 5.3 0.41 

Wabash River 22.0 SCL 6.4 1.6 

Wabash River 48.5 SLT 6.9 1.2 

Wabash River 29.5 SNL 6.7 1.2 

Walter C Beckjord 71.6 SCL 6.3 1.4 

Walter C Beckjord 26.5 SLT 6.7 2 

Walter C Beckjord 1.9 SNL 6.6 1.1 

Wansley 46.3 SCL 5.2 0.52 

Wansley 18.1 SLT 5.6 1.2 

Wansley 35.5 SNL 5.4 0.5 

Warrick 45.8 SCL 6.0 0.95 

Warrick 48.6 SLT 6.5 1.9 

Warrick 5.6 SNL 7.0 1.1 

Waukegan 43.9 SCL 6.6 1 

Waukegan 18.1 SLT 6.6 1.4 

(continued)
Appendix C Attachment C-1: Soil Data 

April 2010–Draft EPA document. C-1-14 

Soil Data (continued) 

Plant 

Percent 

Composition 

Megatexture 

Code Average pH 

Average % 

Organic Material

Waukegan 38.0 SNL 6.7 0.8 

Weston 33.5 SLT 5.6 1.7 

Weston 66.5 SNL 6.0 1.4 

Widows Creek 64.5 SCL 5.3 0.88 

Widows Creek 20.0 SLT 5.2 1.4 

Widows Creek 15.5 SNL 5.4 1.2 

Will County 40.0 SCL 6.8 1.8 

Will County 52.7 SLT 7.0 0.96 

Will County 7.2 SNL 7.1 0.98 

Wyodak 1.3 SCL 8.1 0.38 

Wyodak 40.2 SLT 7.9 1.1 

Wyodak 58.5 SNL 7.9 0.93 

Yates 47.8 SCL 5.2 0.48 

Yates 17.7 SLT 5.6 1.2 

Yates 34.5 SNL 5.3 0.48 
Appendix C Attachment C-2: Hyd

Attachment C-2: Hydrogeologic Environment 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

 Big Cajun 2 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

alluvium); soils hav(SCL+SLT = 95%) 

A B Brown 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

significant fines (SC

A/C PowerAce Operations 2C Alluvial Fans 5 Alluvial Basins Valleys and Fans 100 Based on surficial g

alluvial fan setting 

Allen 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on aqu

geology; Heath (198overbank deposits 

Alma 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage based on 

based on productive geology 

Alma 7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Percentage based on 

based on productive geology 

Antelope Valley 7G Thin Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on principal a

geology coverages 

Arkwright 8D Regolith 1 Metamorphic and Igneous 100 Most common PiedAsheville 8B Alluvial Mountain Valleys 5 Alluvial Basins Valleys and 

Fans 

100 Appropriate for allu

(colluvium) 

Baldwin 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

Floodplain and alluv

Baldwin 7G Thin Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 30 Percentage based on 

Floodplain and alluv

April 2010–Draft EPA document. 
Appendix C Attachment C-2: Hyd

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Barry 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

grained soils = over

Bay Front 7Bb Outwash Over Bedded 

Sedimentary Rock 

2 Bedded Sedimentary Rock 70 Percentage based on 

Bay Front 7D Buried Valley 4 Sand and Gravel 30 Percentage based on Bay Shore 7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Closest setting cons

aquifers, high SCL surficial geology 

Belews Creek 6H Triassic Basins 2 Bedded Sedimentary Rock 50 Sources somewhat d

on surficial geology

Belews Creek 8D Regolith 1 Metamorphic and Igneous 50 Sources somewhat d

on surficial geology 

Ben French 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 60 Percentage, thin soi

geology 

Ben French 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

significant fine soils 

Big Sandy 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 50 Percentage based on 

soils inferred from c

Big Sandy 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage based on 

have significant fine

Big Stone 7Ba Outwash 8 Outwash 100 Based on surficial gBlack Dog Steam Plant 

7Bb Outwash Over Bedded 

Sedimentary Rock 

2 Bedded Sedimentary Rock 100 Based on surficial g

Blue Valley 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 80 Percentage based on 

productive aquifers 

April 2010–Draft EPA document. 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Blue Valley 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

20 Percentage based on 

productive aquifers 

Bowen 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 100 Based on aquifers, s

(massive red clay); geology not consist

Brandon Shores 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Assigned based on l

surficial geology coincorrect (it's AtlantPiedmont) 

Buck 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

100 Based on productiv

coverages 

Bull Run 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 60 Percentage based on 

Bull Run 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

SCL (77%) = overb

C D McIntosh Jr 

11A Solution Limestone and 

Shallow Surficial Aquifers 

12 Solution Limestone 100 Based on both aquif

C P Crane 10Aa Regional Aquifers 4 Sand and Gravel 50 Appears to be on bo

and Coastal Plain 

C P Crane 8D Regolith 1 Metamorphic and Igneous 50 Appears to be on bo

and Coastal Plain 

Cape Fear 6H Triassic Basins 2 Bedded Sedimentary Rock 20 Percentage based on 

Heath region covera

Cape Fear 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

Heath region covera

Carbon 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Setting based on aq

geology coverages 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Cardinal 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 30 Percentage based on 

with low (<1%) SN

Cardinal 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

with low (<1%) SN

Cayuga 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

deposits 

Chalk Point 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Predominant setting 

Cholla 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 20 Percentage based on 

Floodplain and alluv

Cholla 4C River Alluvium 7 River Valleys and Floodplains 

without Overbank Deposits 

80 Percentage based on 

Floodplain and alluv

Cliffside 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial gClover 6H Triassic Basins 2 Bedded Sedimentary Rock 20 Percentage based on 

Triassic Basin from principal aquifer co

Clover 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

Coal Creek 7G Thin Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on principal a

geology coverages 

Coleto Creek 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

geology coverages 

Colstrip 6da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Based on all covera
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Conemaugh 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on aqu

(1985); thin regolith 

Conesville 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

with low (10%) SN

Conesville 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

with low (10%) SN

Council Bluffs 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on pro

Crawford 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Assigned based on p

geology (98% Floodgravel terraces), pro

Crist 10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

100 Assigned based on p

geology (96% Floodgravel terraces), coaSNL) 

Cross 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

soils, Heath (1985) 

Cumberland 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on sur

SCL = overbank de

Dale 6E Solution Limestone 12 Solution Limestone 20 Percentage based on 

setting from princip

Dale 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

have significant fine

Dallman 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on soils, surf

aquifer 

Dan E Karn 7F Glacial Lake Deposits 4 Sand and Gravel 100 Based on surficial g
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Dan River 6H Triassic Basins 2 Bedded Sedimentary Rock 100 Based on surfucial g

aquifers; Triassic ba

Danskammer 7D Buried Valley 4 Sand and Gravel 100 Based on predomin

productive aquiferssoils 

Dave Johnston 4C River Alluvium 7 River Valleys and Floodplains 

without Overbank Deposits 

100 Based on aquifer an

coverages, Heath (1

Dickerson 8D Regolith 1 Metamorphic and Igneous 100 Predominant setting Dolet Hills 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Predominant shallow 

system 

Duck Creek 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Assigned based on p

geology (100% Floogravel terraces), HeRegion 

Dunkirk 7H Beaches, Beach Ridges and 

Sand Dunes 

11 Coastal Beaches 100 Based on location, s

E D Edwards 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 20 Percentage based on 

Floodplain and alluv

E D Edwards 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

Floodplain and alluv

E W Brown 6E Solution Limestone 12 Solution Limestone 20 Percentage based on 

alluvium, 23% clayfine-grained (0% SN

E W Brown 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

alluvium, 23% clayfine-grained (0% SN

Eckert Station 7Bb Outwash Over Bedded 

Sedimentary Rock 

2 Bedded Sedimentary Rock 30 Percentage based on 

coverage, Heath reg
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Eckert Station 7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

70 Percentage based on 

coverage, Heath reg

Edgewater 7Bc Outwash Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

geology coverages 

Elmer W Stout 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

deposits 

F B Culley 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

significant fines (SC

Fayette Power Prj 

10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

geology coverages 

Flint Creek 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 100 Ozark plateau; Heat

dolomite, sandy dolindication of solutio(cherty red clay) noAller et al. (1987) 

Fort Martin 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on sur

(< 1%) = overbank 

Frank E Ratts 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

(99%) 

G G Allen 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial gGadsden 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 30 Percentage assigned 

aquifer coverage 

Gadsden 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage assigned 

aquifer coverage; so(SCL+SLT > 25%) 

Gallatin 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on sur

SCL = overbank de
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Gen J M Gavin 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on pro

surficial geology 

Genoa 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage based on 

based on surficial gaquifers 

Genoa 6Fb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Percentage based on 

based on surficial gaquifers 

Gibson 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

significant fines (SC

Gorgas 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 30 Percentage based on 

alluvial setting with overbank deposits) 

Gorgas 6Fb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

70 Percentage based on 

alluvial setting with overbank deposits) 

Green River 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

alluvium); soils hav= 0%) 

Greene County 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

30 Percentage based on 

have significant fine

Greene County 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

have significant fine

H B Robinson 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

soils, Heath (1985); incorrect (Coastal P
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Hammond 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 100 Based on aquifers, s

(massive red clay) 

Harllee Branch 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

100 Assigned based on p

geology (99% floodterraces) 

Harrison 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 20 Percentage based on 

soils inferred from s

Harrison 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

0%SNL = overbank 

Hatfield's Ferry 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

regolith inferred fro

Hatfield's Ferry 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

< 1% SNL 

Hennepin 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 30 Percentage to captu

surficial geology, pr

Hennepin 7Bb Outwash Over Bedded 

Sedimentary Rock 

2 Bedded Sedimentary Rock 30 Percentage to captu

surficial geology, pr

Hennepin 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage to captu

surficial geology, pr

Heskett 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluviu

geology(96%); mix

Holcomb 5Gb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

100 Alluvial valley with 

Homer City 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on aqu

(1985); thin regolith 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Hoot Lake 9E Outwash 8 Outwash 100 Based on productiv

geology 

Hugo 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

soil/regolith thickne(1985) 

Hugo 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

soils with about 10% 

Hunter 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Setting based on aq

geology coverages 

Huntington 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Setting based on aq

geology coverages 

Intermountain 2E Playa Lakes 5 Alluvial Basins Valleys and 

Fans 

100 Setting based on sur

Heath (1985) 

J H Campbell 7F Glacial Lake Deposits 4 Sand and Gravel 100 Based on surficial gJ M Stuart 6E Solution Limestone 12 Solution Limestone 50 Percentage based on 

(< 2%) SNL 

J M Stuart 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage based on 

(< 2%) SNL 

J R Whiting 7F Glacial Lake Deposits 4 Sand and Gravel 100 Based on surficial gJack McDonough 

8C Mountain Flanks 2 Bedded Sedimentary Rock 100 Assigned based on p

geology (94% stony metamorphic rocks; colluvium over lime

Jack Watson 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Based on all covera

James H Miller Jr 

6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 20 Percentage based on 

have significant fine
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

James H Miller Jr 

6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

have significant fine

Jim Bridger 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Based on aquifer an

coverages, Heath (1

John E Amos 6da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

soils inferred from s

John E Amos 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

0%SNL = overbank 

John Sevier 6E Solution Limestone 12 Solution Limestone 50 Percentage based on 

based on surface gewith possibility of sHeath (1985) 

John Sevier 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage, setting b

low (<1%) SNL = o

Johnsonville 6E Solution Limestone 12 Solution Limestone 30 Percentage based on 

based on aquifer coplaced in Nonglacia

on aquifer coverage

Johnsonville 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage, setting b

low (3%) SNL = ovin Nonglaciated Cenaquifer coverages an

Joliet 29 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on aquifers, s

outwash like surfici

Keystone 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on aqu

(1985); thin regolith 

Killen Station 6E Solution Limestone 12 Solution Limestone 30 Percentage based on 

(< 2%) SNL 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Killen Station 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

(< 2%) SNL 

Kingston 6E Solution Limestone 12 Solution Limestone 20 Percentage based on 

based on surface gewith possibility of sHeath (1985) 

Kingston 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage, setting b

high (67 %) SCL = 

Kraft 11A Solution Limestone and 

Shallow Surficial Aquifers 

12 Solution Limestone 100 Only possible assign

alluvium (84%) not 

L V Sutton 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

20 Percentage based on 

soils 

L V Sutton 10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

80 Percentage based on 

soils 

Lansing 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

productive aquifers

Lansing 6Fb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

60 Percentage based on 

productive aquifers

Laramie R Station 

6Fb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

100 Based on aquifer an

coverages, Heath (1

Lawrence EC 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial valley with 

SNL) 

Lee 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

30 Percentage based on 

soils 

Lee 10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

70 Percentage based on 

soils 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Leland Olds 7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Percentage based on 

assumed coarse soil

Leland Olds 7G Thin Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 50 Percentage based on 

assumed coarse soil

Lon Wright 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

30 Alluvial based on pr

productive aquifer; textures 

Lon Wright 7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

70 Alluvial based on pr

productive aquifer; textures 

Louisa 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Alluvial Valley; sig

deposits 

Louisa 7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Alluvial Valley; sig

deposits 

Marion 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Assigned to Glaciat

on surficial geology 

Marshall 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial gMartin Lake 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

geology coverages 

Mayo 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial gMeramec 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Based on surficial, p

Merom 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

deposits 

Miami Fort 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Assigned based on p

surficial geology an
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Milton R Young 7G Thin Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on principal a

geology coverages 

Mitchell 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 60 Percentage based on 

regolith inferred fro

Mitchell 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

% SNL 

Mitchell 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on sur

(< 1%) = overbank 

Mohave 2Ha River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on pre

geology, Heath (198

Monroe 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Based on Heath reg

soils 

Morgantown 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Assigned based on l

surficial geology coincorrect (it's AtlantPiedmont) 

Mountaineer (1301) 

6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Setting based on sur

(10%) = overbank d

Mt Storm 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on sur

coverages; thin soilgeology 

Muscatine Plant #1 

7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Alluvial Valley; sig

deposits 

Muscatine Plant #1 

7Eb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Alluvial Valley; sig

deposits 

Muskogee 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Surficial geology in

alluvium/colluviumfine soils over sands 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Neal North 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial Valley sett

Neal South 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial Valley sett

Nebraska City 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial based on pr

productive aquifer, 

New Castle 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 20 Percentage and setti

& surficial geologyfrom colluvium 

New Castle 7D Buried Valley 4 Sand and Gravel 80 Percentage and setti

& book 

Newton 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on soils, surf

coverages 

North Omaha 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial based on pr

productive aquifer; 10% SNL) = overba

Northeastern 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 40 Percentage based on 

indicates thin residu

Northeastern 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

< 2% SNL 

Nucla 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Based on surficial g

Oklaunion 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on sur

inferred 

Paradise 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

soils have significan

Petersburg 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

(similar to 1043) 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Pleasant Prairie 7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

(high SCL soils) 

Port Washington 

7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

(high SCL soils) 

Portland 7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

geology coverage 

Possum Point 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Based on productiv

region incorrect 

Potomac River 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

50 Percentage based on 

coverage; Heath reg

Potomac River 10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

50 Percentage based on 

coverage; sandy soioverbank deposits; H

Presque Isle 9F Moraine 4 Sand and Gravel 100 Based on surficial g

soils 

R Gallagher 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

significant fines (SC

R M Schahfer 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquifeReid Gardner 2C Alluvial Fans 5 Alluvial Basins Valleys and 

Fans 

100 Based on surficial g

productive aquifers 

Richard Gorsuch 

6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Assigned based on p

surficial geology an

Riverbend 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial gRodemacher 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

50 Setting percentage d

productive aquifer, coverages 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Rodemacher 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Setting percentage d

productive aquifer, coverages 

Roxboro 8D Regolith 1 Metamorphic and Igneous 100 Based on surficial g

aquifers 

Sandow 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Setting based on aqu

geology coverages; incorrect (based on coverages) 

Scherer 8D Regolith 1 Metamorphic and Igneous 100 Most common PiedShawnee 10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

100 Predominant alluvia

alluvium); soils hav

Shawville 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Setting based on aqu

(1985); thin regolith 

Sheldon 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 30 Percentage based on 

coverage; buried va(1985) 

Sheldon 7D Buried Valley 4 Sand and Gravel 70 Percentage based on 

coverage; buried va(1985) 

South Oak Creek 

7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

(high SCL soils) 

Springerville 4B Consolidated Sedimentary 

Rock 

2 Bedded Sedimentary Rock 100 Assigned based on p

(consolidated sands

St Johns River Power 

11B Coastal Deposits 4 Sand and Gravel 100 Based on sea island 

Stanton Energy Ctr 

11A Solution Limestone and 

Shallow Surficial Aquifers 

12 Solution Limestone 100 Based on both aquif
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Stockton Cogen Company 

2C Alluvial Fans 5 Alluvial Basins Valleys and 

Fans 

50 Percentage based on 

Central Valley soils 

Stockton Cogen Company 

2Ha River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

50 Percentage based on 

Central Valley soils 

Syl Laskin 9E Outwash 8 Outwash 60 Percentage based on Syl Laskin 9Ga River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

Tecumseh EC 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Alluvial valley with 

SNL) 

Texas-New Mexico 

10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

100 Based on productiv

(Heath region cover

Titus 6Db Alternating Sandstone, 

Limestone and Shale - Deep Regolith 

2 Bedded Sedimentary Rock 100 Setting based on aq

geology coverage; dfrom red, massive c

Trimble County 6E Solution Limestone 12 Solution Limestone 40 Heath incorrect; Per

surficial geology (5soils have significan

Trimble County 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Heath incorrect; Per

surficial geology (5soils have significan

Tyrone 6E Solution Limestone 12 Solution Limestone 100 Based on principal aValley 7Ac Glacial Till Over Solution 

Limestone 

12 Solution Limestone 100 Setting based on aqu

(high SCL soils) 

Vermilion 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 100 Based on aquifers, s

outwash like surfici

Victor J Daniel Jr 

10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

60 Percentage based on 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Victor J Daniel Jr 

10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

W A Parish 10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

30 Percentage based on 

productive aquifer c

W A Parish 10Ba River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

productive aquifer c(96%) = overbank d

W H Weatherspoon 

10Ab Unconsolidated and SemiConsolidated Shallow 

Surficial Aquifer 

10 Unconsolidated and 

Semiconsolidated Shallow Aquifers 

30 Percentage based on 

soils 

W H Weatherspoon 

10Bb River Alluvium Without 

Overbank Deposits 

7 River Valleys and Floodplains 

without Overbank Deposits 

70 Percentage based on 

soils 

W S Lee 8D Regolith 1 Metamorphic and Igneous 100 Setting based on aq

soils, Heath (1985) 

Wabash River 7D Buried Valley 4 Sand and Gravel 100 Glaciofluvial aquife

region 

Walter C Beckjord 

7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 60 Percentage based on 

in glaciated central soils 2% SNL 

Walter C Beckjord 

7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

40 Percentage based on 

in glaciated central soils 2% SNL 

Wansley 8C Mountain Flanks 2 Bedded Sedimentary Rock 30 Percentage based on Wansley 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

70 Percentage based on 

Warrick 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

100 Predominant alluvia

significant fines (SC

Waukegan 7Bc Outwash Over Solution 

Limestone 

12 Solution Limestone 100 Based on soils, surf

coverages 
Appendix C Attachment C-2: Hyd

April 2010–Draft EPA document. 

Hydrogeologic Environment (continued) 

Plant 

Hydrogeologic Setting Hydrogeologic Environment 

Code Description Code Description Percentage Co

Weston 9E Outwash 8 Outwash 100 Setting based on pro

geology coverages 

Widows Creek 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 20 Percentage based on 

soils inferred from c

Widows Creek 6Fa River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

80 Percentage based on 

have significant fine

Will County 7Aa Glacial Till Over Bedded 

Sedimentary Rock 

3 Till Over Sedimentary Rock 40 Percentage based on 

Floodplain and alluv

Will County 7Ea River Alluvium With 

Overbank Deposits 

6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage based on 

Floodplain and alluv

Wyodak 6Da Alternating Sandstone, 

Limestone and Shale - Thin Soil 

2 Bedded Sedimentary Rock 100 Based on aquifer an

coverages, Heath (1

Yates 8D Regolith 1 Metamorphic and Igneous 40 Percentage assigned 

geology (59% alluvresiduum) 

Yates 8E River Alluvium 6 River Valleys and Floodplains 

with Overbank Deposit 

60 Percentage assigned 

geology (59% alluvresiduum) 

SCL = silty clay loam; SNL = sandy loam; SLT = silt loam. Aller, L., T. Bennett, J.H. Lehr, R.J. Perry, and G. Hackett. 1987. DRASTIC: A Standardized System for Evaluating Pollution Potential 

Settings. EPA-600/2-87-035. U.S. Environmental Protection Agency. Office of Research and Development. Ada, OK. April. 

Heath, R.C. 1985. National Water Summary 1984. State Summaries of Groundwater Resources. Water-Supply Paper 2275. U.S. Geolo

DC. 
Appendix C Attachment C-3: Clim

Attachment C-3: Climate Center Assignments 

Plant Climate Center Explanation If Not Closest Climate Center A B Brown Indianapolis, IN Closest Met Station (Nashville) receives much more precipitation (12.26” out of rang

Used second closest because only slightly below (1.3) expected precipitation range f

A/C Power- Ace Operations 

Las Vegas, NV 

Allen Little Rock, AR Alma Madison, WI Closest Met Station (St. Cloud) receives less rain than plant location. Used second cl

because 5-year averages fell within expected precipitation range for the plant. 

Antelope Valley Bismarck, ND Arkwright Watkinsville, GA Closest Met Station (Atlanta) receives 6.96” more precipitation than plant location. U

Station because 5-year averages are only slightly above (0.2) expected precipitation 

Asheville Knoxville, TN Baldwin East St. Louis, IL Barry Tallahassee, FL Closest Met Station (New Orleans) receives much more precipitation (5.06” out of ra

location. Used second closest because only slightly above (3.4) expected precipitatio

Bay Front Madison, WI Bay Shore Put-in-Bay, OH Belews Creek Greensboro, NC Ben French Rapid City, SD Big Cajun 2 Lake Charles, LA Closest Met Station (New Orleans) receives much more precipitation (5.06” out of ra

location. Used second closest because only slightly below (2.77) expected precipitati

Big Sandy Cincinnati, OH Closest Met Station (Lexington) receives much more precipitation (8.35” out of rang

Used second closest Met Station because 5-year averages fell within expected precipplant. 

Big Stone St. Cloud, MN 

April 2010–Draft EPA document. 
Appendix C Attachment C-3: Clim

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Black Dog Steam Plant Madison, WI Closest Met Station (St Cloud) is dryer (<27.5”) than the 28-33” that the site receive

precipitation range (32.5”) and is second closest. 

Blue Valley Topeka, KS Bowen Atlanta, GA Brandon Shores Seabrook, NJ Buck Greensboro, NC Bull Run Knoxville, TN C D McIntosh Jr Orlando, FL Closest Met Station (Tampa) receives less precipitation (5.31” out of range) than site 

closest Met Station because 5-year averages fell within expected precipitation range 

C P Crane Seabrook, NJ Cape Fear Greensboro, NC Carbon Salt Lake City, UTCardinal Pittsburgh, PA Cayuga Indianapolis, IN Chalk Point Seabrook, NJ Cholla Phoenix, AZ Closest Met Station (Flagstaff) receives much more precipitation (13.92” out of rang

Used second closest Met Station because 5-year averages were close (.31 higher) thaprecipitation range for the plant. 

Cliffside Greensboro, NC Clover Lynchburg, VA Coal Creek Bismarck, ND Coleto Creek San Antonio, TX Colstrip Glasgow, MT Conemaugh Pittsburgh, PA Conesville Columbus, OH Council Bluffs North Omaha, NE Crawford East St. Louis, IL Crist Tallahassee, FL 

April 2010–Draft EPA document. 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Cross Charleston, SC Cumberland Nashville, TN Dale Lexington, KY Dallman East St. Louis, IL Dan E Karn East Lansing, MI Dan River Greensboro, NC Danskammer Bridgeport, CT Dave Johnston Cheyenne, WY Dickerson Seabrook, NJ Dolet Hills Shreveport, LA Duck Creek East St. Louis, IL Dunkirk Ithaca, NY E D Edwards Chicago, IL E W Brown Lexington, KY Eckert Station East Lansing, MI Edgewater Madison, WI Elmer W Stout Indianapolis, IN F B Culley Indianapolis, IN Closest Met Station (Nashville) receives much more precipitation (12.26” out of rang

Used second closest Met Station because 5-year & 30-year averages fell within expe

for the plant. 

Fayette Power Prj San Antonio, TX Flint Creek Columbia, MO Used http://www.weather.com and Envirofacts to determine that avg. precipitation fo

closest Met Station (Tulsa) receives much less (~17”) precipitation per year. Used se

Fort Martin Pittsburgh, PA Frank E Ratts Indianapolis, IN G G Allen Greensboro, NC Gadsden Atlanta, GA 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Gallatin Nashville, TN Gen J M Gavin Cincinnati, OH Closest Met Station (Columbus) receives less rain than plant location. Used second c

because 5-year averages fell within expected precipitation range for the plant. Also athe second closest Met Station was nearest to http://www.weather.com 

Genoa Madison, WI Gibson Indianapolis, IN Gorgas Atlanta, GA Green River Indianapolis, IN Closest Met Station (Nashville) receives much more precipitation (12.26” out of rang

Used third closest Met Station because 5-year averages fell within expected precipita

Greene County Atlanta, GA H B Robinson Charleston, SC Hammond Atlanta, GA Harllee Branch Watkinsville, GA Harrison Pittsburgh, PA Hatfield’s Ferry Pittsburgh, PA Hennepin Chicago, IL Heskett Bismarck, ND Holcomb Dodge City, KS Homer City Pittsburgh, PA Hoot Lake St. Cloud, MN Hugo Shreveport, LA Closest Met Station (Dallas) receives less precipitation (6.45” out of range) than plan

closest because only slightly above (2.07) expected precipitation range for plant. 

Hunter Grand Junction, 

CO 

Closest Met Station (Salt Lake City) receives 8.6” more precipitation than plant locaMet Station because 5-year averages fell within expected precipitation range for the 

Huntington Cedar City, UT Two closest Met Stations are out of range. Used second closest Met Station because 

within expected precipitation range for the plant. 

Intermountain Ely, NV Closest Met Station (Salt Lake City) receives 6.1” more precipitation than plant loca

Met Station because 5-year averages fell within expected precipitation range for the 

J H Campbell East Lansing, MI 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center J M Stuart Cincinnati, OH J R Whiting Put-in-Bay, OH Jack McDonough Atlanta, GA Jack Watson Tallahassee, FL Closest Met Station (New Orleans) receives much more precipitation (5.06” out of ra

location. http://www.weather.com predicted average precipitation at plant location to closest because its average was closest. 

James H Miller Jr Atlanta, GA Jim Bridger Lander, WY John E Amos Cincinnati, OH The two closest Met Stations are out of the site’s precipitation range. Used third clos

5-year averages fell within expected precipitation range for the plant. Also average psecond closest Met Station was nearest to http://www.weather.com average. 

John Sevier Knoxville, TN Johnsonville Nashville, TN Joliet 29 Chicago, IL Keystone Pittsburgh, PA Killen Station Cincinnati, OH Kingston Knoxville, TN Kraft Charleston, SC L V Sutton Charleston, SC Lansing Madison, WI Laramie R Station Cheyenne, WY Lawrence EC Topeka, KS Lee Greensboro, NC Leland Olds Bismarck, ND Lon Wright North Omaha, NE Louisa Des Moines, IA Marion East St. Louis, IL Marshall Greensboro, NC 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Martin Lake Shreveport, LA Mayo Lynchburg, VA Meramec East St. Louis, IL Merom Indianapolis, IN Miami Fort Cincinnati, OH Milton R Young Bismarck, ND Mitchell - PA Pittsburgh, PA Mitchell - WV Pittsburgh, PA Mohave Las Vegas, NV Monroe Put-in-Bay, OH Morgantown Norfolk, VA Mountaineer (1301) Cincinnati, OH Closest Met Station (Columbus) receives more rain than plant location. Although sec

within range, used third closest Met Station because site geography was similar and 

averages fell within expected precipitation range for the plant. 

Mt Storm Pittsburgh, PA Muscatine Plant #1 Des Moines, IA Muskogee Tulsa, OK Neal North North Omaha, NE Neal South North Omaha, NE Nebraska City North Omaha, NE New Castle Pittsburgh, PA Newton Indianapolis, IN Closest Met Station (East St. Louis) receives less rain than plant location. Used seco

because 5-year averages fell within expected precipitation range for the plant. Also athe second closest Met Station was nearest to http://www.weather.com 

North Omaha North Omaha, NE Northeastern Tulsa, OK Nucla Grand Junction, 

CO 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Oklaunion Oklahoma City, 

OK 

Paradise Cincinnati, OH Closest Met Station (Nashville) receives much more precipitation (12.26” out of rang

Used third closest Met Station because 5-year averages fell within expected precipita

Petersburg Indianapolis, IN Pleasant Prairie Chicago, IL Port Washington Madison, WI Portland Philadelphia, PA Possum Point Norfolk, VA Potomac River Seabrook, NJ Presque Isle Sault Ste. Marie, 

MI 

R Gallagher Cincinnati, OH Closest Met Station (Lexington) receives much more precipitation (8.35” out of rang

Used second closest Met Station because 5-year & 30-year averages fell within expe

for the plant. 

R M Schahfer Chicago, IL Reid Gardner Las Vegas, NV Richard Gorsuch Columbus, OH Riverbend Greensboro, NC Rodemacher Lake Charles, LA Roxboro Greensboro, NC Sandow San Antonio, TX Scherer Watkinsville, GA Closest Met Station (Atlanta) receives 6.96” more precipitation than plant location. U

Station because 5-year averages fell within expected precipitation range for the plant

Shawnee East St. Louis, IL Shawville Pittsburgh, PA Sheldon North Omaha, NE 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center South Oak Creek Chicago, IL Springerville Albuquerque, NM Closest Met Station (Flagstaff) receives much more precipitation (8.92” out of range

Used second closest Met Station because 5-year averages were within the expected p

plant. 

St Johns River Power Jacksonville, FL Stanton Energy Ctr Orlando, FL Stockton Cogen Company Sacramento, CA Syl Laskin St. Cloud, MN Tecumseh EC Topeka, KS Texas-New Mexico San Antonio, TX Closest Met Station (Dallas) received less precipitation than site location. Used seco

because 5-year averages fell within expected precipitation range for the plant. Also a

the second closest Met Station was nearest to http://www.weather.com 

Titus Philadelphia, PA Trimble County Cincinnati, OH Tyrone Lexington, KY Valley Madison, WI Vermilion Chicago, IL Closest Met Station (Indianapolis) receives more precipitation than plant location. U

Station because 5-year averages fell within expected precipitation range for the plant

Victor J Daniel Jr Tallahassee, FL Closest Met Station (New Orleans) receives much more precipitation (5.06” out of ra

location. Used second closest because only slightly above (3.4) expected precipitatio

W A Parish Shreveport, LA 2 Closest Met Stations (Lake Charles & San Antonio) are more than 4” out of range. 

because only slightly above (1.65”) expected precipitation range for plant. 

W H Weatherspoon Greensboro, NC W S Lee Watkinsville, GA Wabash River Indianapolis, IN Walter C Beckjord Cincinnati, OH Wansley Atlanta, GA Warrick Indianapolis, IN Closest Met Station (Nashville) receives 12.2” more precipitation than plant location

Met Station because 5-year averages fell within expected precipitation range for the 
Appendix C Attachment C-3: Clim

April 2010–Draft EPA document. 

Climate Center Assignments (continued) 

Plant Climate Center Explanation If Not Closest Climate Center Waukegan Chicago, IL Weston Madison, WI Widows Creek Nashville, TN Will County East St. Louis, IL Wyodak Rapid City, SD Yates Atlanta, GA 
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix C Attachment C-4: Waterbod

Attachment C-4: Waterbody Assignments and Flow 

Plant CUSEG Nearest Reach Reach Type Q

A B Brown 05140202014 OHIO R Regular Reach 91A/C Power- Ace Operations 18090205005 SEARLES L Lake Shoreline Allen 08010211007 HORN LAKE CUTOFF Lake Shoreline Alma 07040003009 MISSISSIPPI R Regular Reach 56Antelope Valley 10130201005 ANTELOPE CR Start Reach Arkwright 03070103007 OCMULGEE R Regular Reach 4Asheville 06010105026 FRENCH BROAD R Regular Reach 4Baldwin 07140204004 KASKASKIA R Regular Reach Barry 03160204014 MOBILE R Regular Reach 75Bay Front 07070005036 L SUPERIOR Great Lakes Shoreline Bay Shore 04100010003 L ERIE, U.S. SHORE Great Lakes Shoreline Belews Creek 03010103098 BELEWS L Lake Shoreline Ben French 10120110010 CASTLE CR Start Reach Big Cajun 2 08070100005 MISSISSIPPI R Regular Reach 100Big Sandy 05070204008 BIG SANDY R Regular Reach Big Stone 07020001033 BIG STONE LAKE Lake Shoreline Black Dog Steam Plant 07020012001 BLACK DOG LAKE Lake Shoreline Blue Valley 10300101034 LITTLE BLUE R Regular Reach Bowen 03150104008 ETOWAH R Regular Reach Brandon Shores 02060003037 CURTIS BAY Coastal Shoreline Buck 03040103040 YADKIN R Regular Reach 9Bull Run 06010207015 CLINCH R Regular Reach C D McIntosh Jr 03100205014 NO LAKE PARKER Lake Shoreline 

April 2010–Draft EPA document. 
Appendix C Attachment C-4: Waterbod

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

C P Crane 02060003025 CURTIS BAY Coastal Shoreline Cape Fear 03030002001 HAW R Regular Reach Carbon 14060007018 PRICE R Regular Reach Cardinal 05030106033 OHIO R Regular Reach 33Cayuga 05120108001 WABASH R Regular Reach 9Chalk Point 02060006009 PATUXENT R Wide-River Shoreline Cholla 15020008017 CHOLLA COOLING POND Lake Shoreline Cliffside 03050105031 BROAD R Regular Reach 3Clover 03010102027 ROANOKE R Regular Reach 4Coal Creek 10130101018 UNKNOWN LAKE Lake Shoreline Coleto Creek 12100303014 MARCELINAS CR Start Reach Colstrip 10100001108 ARMELLS CR, E FK Start Reach Conemaugh 05010007002 CONEMAUGH R Regular Reach 1Conesville 05040004071 MUSKINGUM R Regular Reach 4Council Bluffs 10230006004 MISSOURI R Regular Reach 44Crawford 07130011018 ILLINOIS R Regular Reach 34Crist 03140305001 ESCAMBIA R Terminal Reach 8Cross 03050201022 DIVERS CANAL TO LAKE MOU Lake Shoreline Cumberland 05130205017 CUMBERLAND R Regular Reach 5Dale 05100205047 KENTUCKY R Regular Reach Dallman 07130007003 LAKE SPRINGFIELD Lake Shoreline Dan E Karn 04080103005 L HURON U.S. SH SAGINAW BAY Great Lakes Shoreline Dan River 03010103014 DAN R Regular Reach Danskammer 02020008022 HUDSON R Wide-River Shoreline Dave Johnston 10180007005 N PLATTE R Regular Reach Dickerson 02070008013 POTOMAC R Regular Reach 8Dolet Hills 11140206019 BAYOU PIERRE LAKE Lake Shoreline 

April 2010–Draft EPA document. 
Appendix C Attachment C-4: Waterbod

April 2010–Draft EPA document. 

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

Duck Creek 07130003010 L CHAUTAUQUA Lake Shoreline Dunkirk 04120101003 L ERIE, U.S. SHORE Great Lakes Shoreline E D Edwards 07130003018 ILLINOIS R Regular Reach 29E W Brown 05100205015 HERRINGTON LAKE Lake Shoreline Eckert Station 04050004003 GRAND R Regular Reach Edgewater 04030101002 L MICHIGAN Great Lakes Shoreline Elmer W Stout 05120201005 WHITE R Regular Reach F B Culley 05140201001 OHIO R Regular Reach 8Fayette Power Prj 12090301003 CEDAR CREEK RESERVOIR Lake Shoreline Flint Creek 11110103031 SWEPCO RSRVR,LT FLINT CK Lake Shoreline Fort Martin 05020003001 MONONGAHELA R Regular Reach Frank E Ratts 05120202003 WHITE R Regular Reach G G Allen 03050101009 CATAWBA R Regular Reach 4Gadsden 03150106041 COOSA R Regular Reach 10Gallatin 05130201006 OLD HICKORY L Lake Shoreline Gen J M Gavin 05030202005 OHIO R Regular Reach 42Genoa 07060001017 MISSISSIPPI R Regular Reach 64Gibson 05120113013 WABASH R Regular Reach 2Gorgas 03160109002 BLACK WARRIOR R, MULBERRY F Lake Shoreline Green River 05110003001 GREEN R Regular Reach Greene County 03160113011 BLACK WARRIOR R Regular Reach 3H B Robinson 03040201042 L ROBERTSON Lake Shoreline Hammond 03150105025 COOSA R Regular Reach 11Harllee Branch 03070101006 L SINCLAIR Lake Shoreline Harrison 05020002008 WEST FORK R Regular Reach Hatfield's Ferry 05020005026 MONONGAHELA R Regular Reach 4Hennepin 07130001026 ILLINOIS R Regular Reach 32Heskett 10130101001 MISSOURI R Regular Reach 34
Appendix C Attachment C-4: Waterbod

April 2010–Draft EPA document. 

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

Holcomb 11030001001 ARKANSAS R Regular Reach Homer City 05010007015 TWO LICK CR Regular Reach Hoot Lake 09020103002 OTTER TAIL R Regular Reach Hugo 11140105041 KIAMICHI CR, N FK Start Reach Hunter 14060009034 ROCK CANYON CR Start Reach Huntington 14060009020 HUNTINGTON CR Regular Reach Intermountain none J H Campbell 04050002001 L MICHIGAN Great Lakes Shoreline J M Stuart 05090201024 OHIO R Regular Reach 67J R Whiting 04100001002 L ERIE, U.S. SHORE Great Lakes Shoreline Jack McDonough 03130002044 CHATTAHOOCHEE R Regular Reach 7Jack Watson 03170009034 BILOXI BAY Coastal Shoreline James H Miller Jr 03160111005 BLACK WARRIOR R, LOCUST FK Lake Shoreline Jim Bridger 14040105011 UNKNOWN LAKE Lake Shoreline John E Amos 05050008007 KANAWHA R Regular Reach 13John Sevier 06010104011 HOLSTON R Regular Reach Johnsonville 06040005007 KENTUCKY L Lake Shoreline Joliet 29 07120004004 DES PLAINS R Regular Reach 10Keystone 05010006002 CROOKED CR Regular Reach Killen Station 05090201024 OHIO R Regular Reach 67Kingston 06010207001 CLINCH R Regular Reach 2Kraft 03060109007 SAVANNAH R Regular Reach 35L V Sutton 03030005011 CAPE FEAR R Regular Reach 6Lansing 07060001009 MISSISSIPPI R Regular Reach 76Laramie R Station 10180011002 LARAMIE R Regular Reach Lawrence EC 10270104021 KANSAS R Regular Reach Lee 03020201007 NEUSE R Regular Reach Leland Olds 10130101020 MISSOURI R Regular Reach 4
Appendix C Attachment C-4: Waterbod

April 2010–Draft EPA document. 

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

Lon Wright 10220003048 RAWHIDE CR Start Reach Louisa 07080101003 MISSISSIPPI R Regular Reach 150Marion 05140204030 L OF EGYPT Lake Shoreline Marshall 03050101015 L NORMAN Lake Shoreline Martin Lake 12010002050 MARTIN LAKE Lake Shoreline Mayo 03010104045 MAYO CR Start Reach Meramec 07140101014 MISSISSIPPI R Regular Reach Merom 05120111011 TURTLE CR RESERVOIR Lake Shoreline Miami Fort 05090203012 OHIO R Regular Reach 65Milton R Young 10130101024 NELSON LAKE AND MISSOURI RIVER Lake Shoreline Mitchell - PA 05020005002 MONONGAHELA R Regular Reach 8Mitchell - WV 05030106013 OHIO R Regular Reach 34Mohave 15030101011 COLORADO R Regular Reach 19Monroe 04100001002 L ERIE, U.S. SHORE Great Lakes Shoreline Morgantown 02070011051 POTOMAC R Wide-River Shoreline Mountaineer (1301) 05030202008 OHIO R Regular Reach 42Mt Storm 02070002027 STONY R RES Lake Shoreline Muscatine Plant #1 07080101005 MISSISSIPPI R Regular Reach 145Muskogee 11110102012 ARKANSAS R Regular Reach 2Neal North 10230001021 MISSOURI R Regular Reach 4Neal South 10230001021 MISSOURI R Regular Reach 4Nebraska City 10240001002 MISSOURI R Regular Reach 58New Castle 05030104002 BEAVER R Regular Reach 2Newton 05120114006 NEWTON LAKE Lake Shoreline North Omaha 10230006009 MISSOURI R Regular Reach 4Northeastern 11070105012 VERDIGRIS R Regular Reach Nucla 14030003012 SAN MIGUEL R Regular Reach Oklaunion 11130302061 BOGGY CR Start Reach 
Appendix C Attachment C-4: Waterbod

April 2010–Draft EPA document. 

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

Paradise 05110003003 GREEN R Regular Reach Petersburg 05120202003 WHITE R Regular Reach Pleasant Prairie 07120004012 L MICHIGAN AND J Lake Shoreline Port Washington 04030101002 L MICHIGAN Great Lakes Shoreline Portland 02040105012 DELAWARE R Regular Reach Possum Point 02070011074 POTOMAC R Wide-River Shoreline Potomac River 02070010025 POTOMAC R Artificial Open Water Reach 9Presque Isle 04020105002 L SUPERIOR, U.S. SHORE Great Lakes Shoreline R Gallagher 05140101001 OHIO R Regular Reach 76R M Schahfer 07120001012 KANAKEE R Regular Reach 4Reid Gardner 15010012006 MUDDY R Regular Reach Richard Gorsuch 05030202039 OHIO R Regular Reach 40Riverbend 03050101012 CATAWBA R Regular Reach Rodemacher 11140207020 RODEMACHER LAKE Lake Shoreline Roxboro 03010104034 HYCO L Lake Shoreline Sandow 12070102012 ALCOA LAKE Lake Shoreline Scherer 03070103012 OCMULGEE R Start Reach 6Shawnee 05140206009 OHIO R Regular Reach 217Shawville 02050201002 SUSQUEHANNA R, W BR Regular Reach Sheldon 10240008030 UNKNOWN LAKE Lake Shoreline South Oak Creek 04040002004 L MICHIGAN Great Lakes Shoreline Springerville 15020002025 *A Start Reach St Johns River Power 03080103003 ST JOHNS R Wide-River Shoreline Stanton Energy Ctr 03080101036 ECOHLOCKHATCHEE R Start Reach Stockton Cogen Company 18040002005 LITTLEJOHNS CR Start Reach Syl Laskin 04010201034 COLBY L AND PARTRIDGE R Lake Shoreline Tecumseh EC 10270102003 KANSAS R Regular Reach 3Texas-New Mexico 12070101008 LITTLE BRAZOS R Start Reach 
Appendix C Attachment C-4: Waterbod

April 2010–Draft EPA document. 

Waterbody Assignments and Flow (continued) 

Plant CUSEG Nearest Reach Reach Type Q

Titus 02040203010 SCHUYLKILL R Regular Reach Trimble County 05140101007 OHIO R Regular Reach 75Tyrone 05100205013 KENTUCKY R Regular Reach Valley 04040003001 MILWAUKEE R Terminal Reach Vermilion 05120109006 VERMILION R, M FK Regular Reach Victor J Daniel Jr 03170006007 PASCAGOULA R Regular Reach 12W A Parish 12070104021 SMITHERS L Lake Shoreline W H Weatherspoon 03040203016 LUMBER R Regular Reach W S Lee 03050109066 SALADA R Regular Reach Wabash River 05120111018 WABASH R Regular Reach 9Walter C Beckjord 05090201001 OHIO R Regular Reach 64Wansley 03130002032 CHATTAHOOCHEE R Regular Reach 7Warrick 05140201022 LITTLE PIGEON CR Regular Reach Waukegan 04040002002 L MICHIGAN Great Lakes Shoreline Weston 07070002023 WISCONSIN R Regular Reach 10Widows Creek 06030001049 TENNESSEE R Regular Reach 72Will County 07110009002 WOOD R Start Reach Wyodak 10120201038 DONKEY CR Start Reach Yates 03130002061 CHATTAHOOCHEE R Regular Reach 7
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

Appendix D. MINTEQA2 Nonlinear Sorption Isotherms 

D.1 Overview of MINTEQA2 Modeling 

Chemicals in leachate can be subject to complex geochemical interactions in soil and 

groundwater, which can strongly affect their rate of transport in the subsurface. EPA’s 

Composite Model for Leachate Migration with Transformation Products (EPACMTP) treats 

these interactions as equilibrium sorption processes. The equilibrium assumption means that the 

sorption process occurs instantaneously, or at least very quickly relative to the time scale of 

constituent transport. Although sorption—or the attachment of leachate constituents to solid soil 

or aquifer particles—may result from multiple chemical processes, EPACMTP lumps these 

processes together into an effective soil-water partition coefficient (Kd). The retardation factor 

(R) accounts for the effects of equilibrium sorption of dissolved constituents onto the solid 

phase, removing them from solution and reducing the available mass in the dissolved phase. R, a 

function of the constituent-specific Kd and the soil or aquifer properties, is calculated as: 

Φ

ρ 1 b Kd R

×

+= (D-1) 

where 

 R = Retardation factor 

Db = Soil or aquifer bulk density (g/cm3) 

 Kd = Solid-water partition coefficient (g/cm3) 

N = Water content (in unsaturated zone) or porosity (in saturated zone). 

An isotherm is an expression of the equilibrium relationship between the aqueous 

concentration and the sorbed concentration of a metal (or other constituent) at a constant 

temperature. For metals, EPACMTP accounts for more complex geochemical reactions by using 

effective sorption isotherms generated using EPA’s geochemical equilibrium speciation model 

for dilute aqueous systems, MINTEQA2 (U.S. EPA, 1991). 

The MINTEQA2 model was used to generate one set of isotherms for each metal 

reflecting the range in geochemical environments expected at waste sites across the nation. The 

variability in geochemical environments at CCW sites across the country was represented by five 

geochemical master variables (groundwater composition, pH, concentration of iron oxide 

adsorption sites, leachate ionic strength, and concentration of dissolved and particulate natural 

organic matter), and the MINTEQA2 modeling was repeated (separately for each metal) for 

numerous combinations of master variable settings. This procedure resulted in nonlinear Kd

versus aqueous metal concentration curves for combinations of master variable settings spanning 

the range of reasonable values (U.S. EPA 2003a). 

April 2010–Draft EPA document. D-1 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

For each metal, the resulting set of isotherms was tabulated into a supplementary input 

data file for use by the EPACMTP model, hereafter referred to as an “empirical nonlinear 

isotherm.” In the fate and transport modeling for a particular metal, EPACMTP was executed, 

and the national probability distributions for these five master variables formed the basis for the 

Monte Carlo selection of the appropriate adsorption isotherm. 

In modeling metals transport in the unsaturated zone, EPACMTP uses a range of Kd

values from the nonlinear sorption isotherms. However, in modeling metals transport in the 

saturated zone, EPACMTP selects the lowest from all available Kd values corresponding to 

concentrations less than or equal to the maximum water table concentration. For more details see 

the EPACMTP Technical Background Document (U.S. EPA, 2003b). This simplification in the 

saturated zone is required for all solution options and is based on the assumption that, after 

dilution of the leachate plume in groundwater, the concentrations of metals will typically be in a 

range where the isotherm is approximately linear. However, this assumption may not be valid 

when the metal concentrations in the leachate are exceedingly high. Although EPACMTP is able 

to account for the effect of the geochemical environment at a site on the mobility of metals, the 

model assumes that the geochemical environment at a site is constant and not affected by the 

presence of the leachate plume. In reality, the presence of a leachate plume may alter the ambient 

geochemical environment. 

D.2 Previous CCW Metals Modeling Effort 

In a previous risk assessment for fossil fuel combustion wastes (FFCWs) conducted in 

1998 (U.S. EPA, 1998), sorption isotherms generated using MINTEQA2 were used in 

EPACMTP to account for metal partitioning. However, these isotherms were not calculated 

specifically for use in FFCW modeling—they had been computed using MINTEQA2 in 1995 for 

use in modeling support for the Hazardous Waste Identification Rule (HWIR). 

The disposal scenario for HWIR was the industrial Resource Conservation and Recovery 

Act (RCRA) Subtitle D nonhazardous waste landfill. In fact, the MINTEQA2 modeling that 

produced the isotherms had originally been designed to represent municipal solid waste landfills, 

and leachate from those landfills had been sampled so that appropriate forms of leachate organic 

acids at various concentrations could be included in the modeling. For the HWIR analysis, the 

scenario was changed to industrial Subtitle D, and only the isotherms corresponding to low 

concentrations of the leachate organic acids were used for HWIR modeling. The same isotherms 

were used in the 1998 FFCW risk assessment. As in the HWIR modeling, only the isotherms 

corresponding to the lowest setting of leachate organic carbon were used. 

In 1999, EPA received review comments concerning the use of the industrial Subtitle D 

metal partitioning isotherms in the 1998 risk assessment. The most comprehensive review was 

prepared by Charles Norris and Christina Hubbard on behalf of the Environmental Defense Fund 

and other environmental advocacy groups (Norris and Hubbard, 1999). The Norris and Hubbard 

report criticized the 1998 risk assessment for using MINTEQA2 isotherms designed for a 

different scenario (nonhazardous industrial landfills). Norris and Hubbard also offered 20 

specific criticisms on the input parameters and other factors involved in the MINTEQA2 

modeling. EPA responded by evaluating each of these criticisms through review and assessment 

of MINTEQA2 input values, model sensitivity tests, and consultations with experts. This review 

April 2010–Draft EPA document. D-2 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

is documented in U.S. EPA (2000, 2001a). The evaluation of the Norris and Hubbard comments 

resulted in suggested revisions in the MINTEQA2 modeling strategy, as described in U.S. EPA 

(2001b). 

Based on a review of available information on CCW leachate composition and an 

analysis of the potential effects of this composition on metals mobility, EPA (U.S. EPA, 2001b) 

also determined that if MINTEQA2 is to be used at CCW sites, leachate from CCW facilities 

should be studied to look for trends in composition, especially with regard to the concentrations 

of constituents that may 

 Contribute to elevated groundwater pH 

 Compete with the contaminant metal for sorption sites and thus result in reduced metal 

sorption (e.g., Ca, Mg, SO4, other metals) 

 Complex with the contaminant metal so that the metal is less likely to be sorbed (e.g., 

SO4, CO3, organic ligands) 

 Precipitate with the contaminant metal (e.g., SO4, CO3). 

D.3 MINTEQA2 Modeling Revisions for CCW Risk Assessment 

Many of the suggested revisions from U.S. EPA (2001b) were implemented in the 

MINTEQA2 modeling for the current CCW risk assessment. Some of the suggested revisions 

were not implemented, either because they were not applicable (e.g., organic carbon assumptions 

were not changed, because CCW leachate has negligible organic carbon) or because models or 

data were not adequate to carry forth the recommendation. These revisions are discussed in 

greater detail in U.S. EPA (2003c). 

In addition to revising the MINTEQA2 model, EPA compiled leachate characteristics 

into the CCW constituent database (see Appendix A) and statistically analyzed these data to 

identify three chemically distinct CCW leachate types: conventional CCW (including ash and 

flue gas desulfurization [FGD] sludge), codisposed CCW and coal cleaning wastes, and fluidized 

bed combustion (FBC) waste. Leachate concentration ranges for major ions (e.g., Ca, SO4, Mg, 

Na, Cl, etc.) and pH were developed for each of these waste types and were used to represent 

CCW leachate during MINTEQA2 modeling. 

As needed, sorption reactions were included for those CCW constituents known to 

undergo significant sorption. Including elevated concentrations of leachate constituents and their 

corresponding sorption reactions in the MINTEQA2 model allowed for full competition with the 

contaminant metal for sorption sites. The metal solubilizing effect through complexation 

between the contaminant metal and dissolved ligands was also included, as was the potential for 

metal precipitation. Because precipitation of the metal can serve to attenuate the transportable 

concentration, the equilibrium fraction in all three phases (dissolved, sorbed, and precipitated) 

were stored and made available for use by EPACMTP. The precipitated fraction was used to 

develop a solubility limit that was used during EPACMTP modeling (U.S. EPA, 2003c). 

April 2010–Draft EPA document. D-3 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

D.4 MINTEQA2 Modeling for CCW Risk Assessment 

The expected natural variability in Kd for a particular metal was represented during the 

MINTEQA2 modeling effort by varying the input parameters that most impact Kd: groundwater 

type (carbonate or noncarbonate), pH, concentration of aquifer sorbents, composition and 

concentration level of CCW leachate, and concentration of the contaminant metal. The natural 

pH range for the two groundwater types was sampled from a range of 7 to 8 for carbonate 

aquifers and 4 to 10 for noncarbonate aquifers (U.S. EPA, 2003c). 

In addition, CCW leachate ranges from acidic (pH < 2) to highly alkaline (pH > 12), and 

it can impact unsaturated zone and groundwater pH. To account for this possibility, the CCW 

leachate/ groundwater system was equilibrated at a series of pH values that spanned the range of 

expected variability in mixed CCW leachate-groundwater systems (U.S. EPA, 2003c). 

To account for the variability in the sorption capacity of soil and aquifer materials, the 

soil and groundwater systems were equilibrated with various concentrations of two commonly 

occurring natural sorbents: ferric (iron) oxyhydroxide (FeOx) and particulate organic matter 

(POM). CCW leachate can include elevated concentrations of inorganic constituents such as 

calcium, sulfate, sodium, potassium, and chloride, which may reduce sorption of metals due to 

competition for sorption sites or complexation with metals in solution. To account for this effect, 

these leachate components were added to the MINTEQA2 model inputs at concentrations 

representative of the three CCW waste types (conventional CCWs, codisposed CCW and coal 

cleaning wastes, and FBC wastes). This new MINTEQA2 master variable is termed leachate 

“richness” or ionic strength (U.S. EPA, 2003c). 

The results of each MINTEQA2 model run were compiled as the equilibrium distribution 

of the contaminant metal among dissolved, sorbed, and precipitated fractions for each metal 

concentration, and were saved in a separate file indexed with the settings of all variables used to 

define the system. These files were produced for all possible values for the variables defining the 

system, and were compiled into a database of indexed Kd values for use in the EPACMTP fate 

and transport model (U.S. EPA, 2003c). 

D.5 EPACMTP Modeling Revisions to Accommodate MINTEQA2 Updates 

EPA updated EPACMTP to support the new system variable (leachate ionic strength) for 

isotherm selection, to address issues regarding the impacts of leachate pH on ambient soil and 

aquifer pH, and to address issues regarding solubility limits for metals in solution. A brief 

description of these model changes are discussed below, with more detail provided in U.S. EPA 

(2003d). 

Ionic Strength. A new system or “master” variable was added to include ionic strength 

as a key for choosing the representative isotherm from the database for both the unsaturated and 

saturated zones. 

Leachate Effects on Geochemical Environment. These effects were addressed in 

EPACMTP under the following constraints: (1) no significant impairment of the computational 

efficiency for probabilistic applications; (2) data requirements limited to readily available data; 

April 2010–Draft EPA document. D-4 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

and (3) a scientifically defensible approach, given significant uncertainties with respect to the 

true impacts of leachate pH on the subsurface. Two modifications to the EPACMTP were 

considered: (1) determine the governing pH in the soil column (either the pH of the leachate or 

the native soils); and (2) determine the pH of the saturated zone as a result of the infiltrating 

leachate. 

The approach selected for determining the governing pH of the soil column (unsaturated 

zone) beneath the waste management unit (WMU) compares the operational life of the WMU 

(the duration of leaching) to an estimate of the first arrival time of the contaminant front at the 

water table (a surrogate for the residence time of the contaminant in the soil column). If the 

operational life of the WMU is relatively long compared to the time required for the contaminant 

to migrate to the water table, there is a high likelihood that the leachate permeates the soil 

column and that the pH environment is governed by the leachate. Conversely, a relatively short 

operational life and retarded contaminant migration would favor ambient soil pH conditions. An 

analysis of the relationship between operational life and travel time indicated that a ratio of 

approximately 5 (operational life over travel time) would, in many cases, result in a balanced 

selection of cases where leachate pH governs versus cases where soil pH governs over 

approximately 10,000 Monte Carlo iterations. 

For each iteration of EPACMTP, the operational life was compared to a travel-time 

estimate based on a Kd averaged from isotherms selected based on the leachate pH and soil pH. 

If the ratio was greater than 5, the pH of the leachate was assumed to govern, and the pH of the 

leachate was used to select the isotherm for transport in the unsaturated zone. If the ratio was less 

than 5, the soil pH was used to select the isotherm. 

In the saturated zone, the impacts of leachate pH were handled using a simple 

homogeneous mixing calculation. The volume of leachate released from the WMU was mixed 

with the volume of the aquifer that was likely to be impacted by a plume. The resulting mixed 

pH was used to select the isotherm for transport in the saturated zone with one limitation: in 

carbonate environments, the mixed pH in the aquifer was not allowed to drop below a pH of 6. 

Such acid conditions would likely result in significant dissolution of the soil matrix. 

Metal Solubility Limits. As mentioned above, each sorption isotherm comprises 

equilibrium concentrations of the three contaminant phases (dissolved, sorbed, and precipitated) 

over a range of total concentration values. An examination of the change in the dissolved-phase 

concentrations relative to changes in the total concentration in any isotherm reveals solubility 

behavior for that contaminant: if the dissolved component does not change with increasing total 

concentration, a solubility limit has been achieved. If, however, the dissolved component 

increases along with the total concentration, then there is capacity for more dissolved mass in the 

groundwater or soil porewater. 

EPACMTP uses this information (contained in each isotherm file) to determine if a 

solubility limit should be imposed in the saturated zone. Once an isotherm has been selected 

(after pH considerations have been addressed), the equilibrium states corresponding to the three 

highest total concentrations are examined. If the dissolved concentration changes more than one 

tenth of one percent over the last three points, then EPACMTP assumes there is no solubility 

limit. If the change in dissolved concentration is less than one tenth of one percent, EPACMTP 

April 2010–Draft EPA document. D-5 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

assumes a solubility limit has been reached and caps the concentration of the leachate entering 

the saturated zone at the water table to that limit. 

D.6 Sampled Kds from MINTEQA2 Isotherms by EPACMTP 

As described above, a range of Kds from an isotherm were used by EPACMTP in each 

unsaturated zone transport simulation. To simplify the presentation of Kd here, an effective Kd

value was calculated and reported by EPACMTP. An effective Kd was determined by first 

estimating the value of the retardation factor, described in Equation D-1, as follows: 

dsoil

t q R WT × =

where 

 twt = time for leachate to reach the water table (yr) 

 q = seepage velocity of leachate through the unsaturated soil column (m/yr) 

 ddsoil = depth to the water table or length of the unsaturated soil column (m) 

Substituting this value for R in Equation D-1 and solving for Kd yields an effective Kd

that is based on the first arrival of the leachate front at the water table. 

Table D-1 presents selected percentiles of Kd sampled from the MINTEQA2 isotherms 

for every waste management modeling scenario conducted in the CCW risk assessment for the 

groundwater pathway. Each scenario corresponds to a unique combination of waste type, metal 

species, waste management unit type, and subsurface domain (unsaturated zone or saturated 

zone). The values presented for the saturated zone are taken from the set of actual Kd values used 

in each modeling scenario.

April 2010–Draft EPA document. D-6 
Appendix D MINTEQA2 Nonli

Table D-1. Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPAC

by Waste Type, Metal Species, WMU Type, and Subsurface Domain 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

Ash Aluminum 7429905 LF Saturated 8.5E-20 3.0E-16 5.1E-10 5.0E-03 7.1E-02 9.2E-0Ash Aluminum 7429905 SI Saturated 8.6E-20 2.5E-10 2.6E-04 9.4E-01 2.2E+00 2.7E+0Ash Aluminum 7429905 LF Unsaturated 6.9E-03 1.0E-01 2.0E-01 1.8E+01 5.9E+01 1.0E+0Ash Aluminum 7429905 SI Unsaturated 2.0E-11 3.7E-02 1.6E-01 1.1E+00 1.6E+00 2.8E+0Ash Antimony 7440360 LF Saturated 4.3E-03 2.5E-02 7.5E-02 2.0E-01 2.1E-01 3.0E-0Ash Antimony 7440360 SI Saturated 2.8E-03 1.9E-02 9.6E-02 2.0E-01 2.1E-01 3.3E-0Ash Antimony 7440360 LF Unsaturated 9.1E-02 2.9E-01 9.6E-01 7.6E+00 1.0E+01 1.4E+0Ash Antimony 7440360 SI Unsaturated 0.0E+00 1.1E-01 3.4E-01 6.8E-01 7.9E-01 9.5E-0Ash Arsenic3 22569728 LF Saturated 5.2E-02 1.5E-01 4.1E-01 6.6E-01 8.0E-01 9.2E-0Ash Arsenic3 22569728 SI Saturated 4.1E-02 1.4E-01 4.1E-01 6.8E-01 8.1E-01 9.2E-0Ash Arsenic3 22569728 LF Unsaturated 1.5E-01 5.0E-01 1.2E+00 2.2E+00 2.7E+00 3.7E+0Ash Arsenic3 22569728 SI Unsaturated 5.7E-05 3.1E-01 7.4E-01 1.3E+00 1.5E+00 1.6E+0Ash Arsenic5 15584040 LF Saturated 1.1E+00 7.0E+00 3.4E+01 9.8E+01 1.2E+02 1.6E+0Ash Arsenic5 15584040 SI Saturated 1.2E+00 6.7E+00 2.9E+01 8.9E+01 1.1E+02 1.5E+0Ash Arsenic5 15584040 LF Unsaturated 0.0E+00 4.2E+00 3.6E+01 1.0E+02 1.3E+02 1.6E+0Ash Arsenic5 15584040 SI Unsaturated 2.9E-01 3.2E+00 2.1E+01 7.6E+01 9.5E+01 1.3E+0Ash Barium 7440393 LF Saturated 2.4E-01 4.2E-01 5.6E-01 9.2E-01 1.1E+00 1.2E+0Ash Barium 7440393 SI Saturated 2.5E-01 4.4E-01 5.7E-01 9.3E-01 1.1E+00 1.2E+0Ash Barium 7440393 LF Unsaturated 0.0E+00 7.1E+00 2.0E+02 6.6E+02 7.9E+02 1.0E+0Ash Barium 7440393 SI Unsaturated 0.0E+00 8.7E-01 1.8E+00 5.9E+00 8.0E+00 1.2E+0Ash Boron 7440428 LF Saturated 3.8E-11 4.3E-10 1.7E-07 2.8E-06 3.6E-06 5.4E-0Ash Boron 7440428 SI Saturated 2.6E-10 3.2E-08 1.7E-06 6.5E-06 7.7E-06 8.9E-0Ash Boron 7440428 LF Unsaturated 2.6E-03 1.3E-01 4.4E-01 1.8E+00 2.2E+00 2.8E+0Ash Boron 7440428 SI Unsaturated 0.0E+00 2.5E-02 1.1E-01 2.2E-01 2.5E-01 3.4E-0Ash Cadmium 7440439 LF Saturated 1.6E-01 4.0E-01 7.7E-01 1.8E+00 2.1E+00 3.4E+0Ash Cadmium 7440439 SI Saturated 1.6E-01 4.0E-01 7.1E-01 1.7E+00 2.0E+00 3.4E+0Ash Cadmium 7440439 LF Unsaturated 2.2E-01 9.8E-01 2.0E+00 4.3E+00 5.2E+00 7.1E+0Ash Cadmium 7440439 SI Unsaturated -4.1E-02 3.1E-01 7.8E-01 2.0E+00 2.7E+00 4.0E+0

April 2010–Draft EPA document. 
Appendix D MINTEQA2 Nonli

Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPACMTP 

by Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued) 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

Ash Cobalt 7440484 LF Saturated 5.2E-01 1.0E+00 4.0E+00 1.6E+01 1.8E+01 3.5E+0Ash Cobalt 7440484 SI Saturated 6.5E-01 9.6E-01 2.7E+00 1.1E+01 1.6E+01 2.9E+0Ash Cobalt 7440484 LF Unsaturated 2.4E-01 2.6E+00 8.7E+00 2.8E+01 3.5E+01 4.6E+0Ash Cobalt 7440484 SI Unsaturated -2.9E-02 1.3E+00 3.3E+00 1.0E+01 1.3E+01 1.9E+0Ash Lead 7439921 LF Saturated 9.0E+00 1.6E+01 2.5E+01 3.9E+01 4.3E+01 5.0E+0Ash Lead 7439921 SI Saturated 8.3E+00 1.6E+01 2.2E+01 3.5E+01 4.0E+01 4.5E+0Ash Lead 7439921 LF Unsaturated 0.0E+00 2.0E+01 3.3E+01 5.2E+01 5.9E+01 7.0E+0Ash Lead 7439921 SI Unsaturated -1.1E-01 1.3E+00 1.9E+01 3.3E+01 3.6E+01 4.1E+0Ash Mercury 7439976 LF Saturated 2.0E-05 1.0E-04 4.4E-04 2.1E-03 2.4E-03 4.6E-0Ash Mercury 7439976 LF Unsaturated 8.9E-02 2.0E+00 5.4E+00 1.1E+01 1.3E+01 1.7E+0Ash Molybdenum 7439987 LF Saturated 7.8E-07 3.0E-05 1.8E-03 6.0E-02 1.2E-01 2.3E-0Ash Molybdenum 7439987 SI Saturated 3.2E-07 3.0E-05 4.3E-03 6.0E-02 1.1E-01 2.1E-0Ash Molybdenum 7439987 LF Unsaturated 2.3E-02 1.6E-01 3.4E-01 9.1E-01 1.1E+00 1.5E+0Ash Molybdenum 7439987 SI Unsaturated 6.0E-11 5.0E-02 1.6E-01 4.0E-01 5.3E-01 7.0E-0Ash Nitrate/Nitrite 14797558 LF Saturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash Nitrate/Nitrite 14797558 SI Saturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash Nitrate/Nitrite 14797558 LF Unsaturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash Nitrate/Nitrite 14797558 SI Unsaturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash Selenium4 10026036 LF Saturated 3.3E-01 7.3E+00 1.5E+02 1.2E+03 2.0E+03 2.9E+0Ash Selenium4 10026036 SI Saturated 4.5E-02 2.9E+00 2.4E+02 1.3E+03 2.0E+03 3.0E+0Ash Selenium4 10026036 LF Unsaturated 1.8E-01 5.4E+00 1.3E+02 8.6E+02 1.5E+03 2.3E+0Ash Selenium4 10026036 SI Unsaturated 0.0E+00 2.0E+00 1.6E+02 9.4E+02 1.7E+03 2.5E+0Ash Selenium6 7782492 LF Saturated 5.1E-13 6.3E-11 3.0E-07 2.7E-04 6.7E-04 1.8E-0Ash Selenium6 7782492 SI Saturated 7.4E-09 6.8E-07 8.4E-05 9.5E-04 1.4E-03 2.2E-0Ash Selenium6 7782492 LF Unsaturated 3.3E-03 1.1E-01 2.0E-01 9.1E-01 1.3E+00 1.7E+0Ash Selenium6 7782492 SI Unsaturated 0.0E+00 2.2E-02 1.1E-01 2.1E-01 2.3E-01 2.8E-0Ash Thallium 7440280 LF Saturated 1.1E-02 1.7E-02 2.7E-02 4.3E-02 5.2E-02 6.3E-0Ash Thallium 7440280 SI Saturated 1.2E-02 1.7E-02 2.5E-02 4.4E-02 5.2E-02 6.6E-0

April 2010–Draft EPA document. 
Appendix D MINTEQA2 Nonli

April 2010–Draft EPA document. 

Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPACMTP 

by Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued) 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

Ash Thallium 7440280 LF Unsaturated 5.5E-02 2.0E-01 4.0E-01 1.5E+00 1.8E+00 2.4E+0Ash Thallium 7440280 SI Unsaturated 0.0E+00 8.1E-02 1.7E-01 2.9E-01 3.5E-01 4.9E-0Ash & Coal Aluminum 7429905 LF Saturated 1.8E-07 1.8E-07 4.0E-07 1.3E-01 1.3E-01 1.3E-0Ash & Coal Aluminum 7429905 SI Saturated 1.8E-07 4.0E-07 7.3E-03 1.9E-01 2.0E-01 8.8E-0Ash & Coal Aluminum 7429905 LF Unsaturated 0.0E+00 1.2E-01 1.1E+01 4.7E+01 5.9E+01 7.6E+0Ash & Coal Aluminum 7429905 SI Unsaturated 1.4E-02 1.1E-01 2.6E-01 9.4E-01 1.5E+00 1.7E+0Ash & Coal Antimony 7440360 LF Saturated 1.8E-03 5.4E-03 1.7E-02 7.3E-02 8.0E-02 1.5E-0Ash & Coal Antimony 7440360 LF Unsaturated 0.0E+00 1.8E-01 1.4E+00 6.4E+00 8.2E+00 1.1E+0Ash & Coal Arsenic3 22569728 LF Saturated 5.5E-03 1.7E-02 6.4E-02 1.9E-01 2.7E-01 4.6E-0Ash & Coal Arsenic3 22569728 SI Saturated 4.6E-03 1.6E-02 5.1E-02 1.4E-01 2.6E-01 4.5E-0Ash & Coal Arsenic3 22569728 LF Unsaturated 0.0E+00 1.6E-01 3.8E-01 1.9E+00 2.6E+00 3.6E+0Ash & Coal Arsenic3 22569728 SI Unsaturated 3.8E-02 1.1E-01 2.3E-01 6.3E-01 8.8E-01 1.3E+0Ash & Coal Arsenic5 15584040 LF Saturated 3.3E-02 7.2E-01 6.3E+00 3.3E+01 4.6E+01 5.4E+0Ash & Coal Arsenic5 15584040 SI Saturated 3.3E-02 3.5E-01 2.3E+00 1.4E+01 2.1E+01 2.9E+0Ash & Coal Arsenic5 15584040 LF Unsaturated 0.0E+00 4.8E-01 6.4E+00 3.4E+01 4.6E+01 6.3E+0Ash & Coal Arsenic5 15584040 SI Unsaturated 1.9E-01 9.6E-01 3.5E+00 1.6E+01 2.2E+01 3.2E+0Ash & Coal Barium 7440393 LF Saturated 3.7E-02 4.5E-02 2.3E-01 1.3E+00 1.3E+00 1.5E+0Ash & Coal Barium 7440393 SI Saturated 1.3E-02 4.5E-02 1.8E-01 1.3E+00 1.3E+00 1.5E+0Ash & Coal Barium 7440393 LF Unsaturated 0.0E+00 4.6E-01 1.5E+01 5.0E+01 6.2E+01 7.4E+0Ash & Coal Barium 7440393 SI Unsaturated 5.2E-02 5.0E-01 2.4E+00 9.6E+00 1.4E+01 2.1E+0Ash & Coal Boron 7440428 LF Saturated 7.1E-08 2.1E-07 8.1E-07 2.4E-06 3.5E-06 5.9E-0Ash & Coal Boron 7440428 SI Saturated 6.0E-08 2.1E-07 6.3E-07 1.8E-06 3.6E-06 6.4E-0Ash & Coal Boron 7440428 LF Unsaturated 0.0E+00 5.2E-02 1.5E-01 2.2E-01 2.5E-01 3.2E-0Ash & Coal Boron 7440428 SI Unsaturated 7.9E-07 3.1E-02 1.1E-01 2.0E-01 2.3E-01 2.8E-0Ash & Coal Cadmium 7440439 LF Saturated 1.7E-03 4.6E-02 1.5E-01 7.7E-01 1.0E+00 2.1E+0Ash & Coal Cadmium 7440439 SI Saturated 1.7E-03 4.6E-02 8.5E-02 6.1E-01 1.0E+00 2.1E+0Ash & Coal Cadmium 7440439 LF Unsaturated 0.0E+00 2.6E-01 7.9E-01 2.2E+00 2.7E+00 3.9E+0Ash & Coal Cadmium 7440439 SI Unsaturated 6.4E-02 1.7E-01 4.0E-01 1.6E+00 2.2E+00 3.3E+0
Appendix D MINTEQA2 Nonli

April 2010–Draft EPA document. 

Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPACMTP 

by Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued) 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

Ash & Coal Cobalt 7440484 LF Saturated 7.6E-03 6.6E-02 5.8E-01 2.8E+00 3.2E+00 5.4E+0Ash & Coal Cobalt 7440484 SI Saturated 7.6E-03 6.5E-02 2.1E-01 2.4E+00 2.9E+00 4.1E+0Ash & Coal Cobalt 7440484 LF Unsaturated 0.0E+00 7.9E+00 3.1E+01 9.3E+01 1.1E+02 1.5E+0Ash & Coal Cobalt 7440484 SI Unsaturated 2.3E-01 4.5E-01 1.7E+00 4.9E+00 5.9E+00 7.3E+0Ash & Coal Lead 7439921 LF Saturated 1.1E-01 2.3E+00 6.6E+00 2.1E+01 2.9E+01 3.8E+0Ash & Coal Lead 7439921 SI Saturated 1.1E-01 2.3E+00 3.7E+00 2.2E+01 2.9E+01 3.9E+0Ash & Coal Lead 7439921 LF Unsaturated 0.0E+00 4.4E+00 1.4E+01 4.1E+01 4.9E+01 5.6E+0Ash & Coal Lead 7439921 SI Unsaturated -6.3E-03 9.5E-01 4.5E+00 2.0E+01 3.0E+01 3.9E+0Ash & Coal Mercury 7439976 LF Saturated 9.0E-04 2.1E-03 1.3E-02 7.9E-02 3.3E-01 6.8E-0Ash & Coal Mercury 7439976 SI Saturated 1.7E-03 3.3E-03 6.2E-02 8.8E-01 1.5E+00 2.8E+0Ash & Coal Mercury 7439976 LF Unsaturated 0.0E+00 1.6E+00 6.3E+00 1.5E+01 1.8E+01 2.2E+0Ash & Coal Mercury 7439976 SI Unsaturated 4.8E-02 1.5E-01 3.6E-01 1.6E+00 1.6E+00 2.1E+0Ash & Coal Molybdenum 7439987 LF Saturated 3.4E-06 6.7E-05 2.5E-03 4.6E-02 7.5E-02 1.6E-0Ash & Coal Molybdenum 7439987 SI Saturated 3.4E-06 3.1E-04 1.1E-02 7.5E-02 7.5E-02 1.6E-0Ash & Coal Molybdenum 7439987 LF Unsaturated 0.0E+00 7.3E-02 2.1E-01 7.7E-01 1.0E+00 1.4E+0Ash & Coal Molybdenum 7439987 SI Unsaturated 4.7E-03 8.3E-02 2.0E-01 5.0E-01 7.6E-01 1.3E+0Ash & Coal Nitrate/Nitrite 14797558 LF Saturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash & Coal Nitrate/Nitrite 14797558 SI Saturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash & Coal Nitrate/Nitrite 14797558 LF Unsaturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash & Coal Nitrate/Nitrite 14797558 SI Unsaturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0Ash & Coal Selenium4 10026036 LF Saturated 3.7E-01 6.1E+00 5.3E+01 5.3E+02 8.5E+02 1.1E+0Ash & Coal Selenium4 10026036 SI Saturated 3.7E-01 1.6E+01 1.2E+02 7.7E+02 9.1E+02 1.4E+0Ash & Coal Selenium4 10026036 LF Unsaturated 0.0E+00 2.9E+00 4.4E+01 3.2E+02 5.5E+02 8.7E+0Ash & Coal Selenium4 10026036 SI Unsaturated 4.7E-01 1.0E+01 1.0E+02 6.1E+02 8.6E+02 1.1E+0Ash & Coal Selenium6 7782492 LF Saturated 2.9E-08 7.7E-08 1.8E-05 6.4E-04 1.1E-03 1.4E-0Ash & Coal Selenium6 7782492 SI Saturated 4.7E-08 3.2E-06 1.1E-04 9.6E-04 1.1E-03 1.6E-0Ash & Coal Selenium6 7782492 LF Unsaturated 0.0E+00 5.9E-02 1.6E-01 7.0E-01 1.0E+00 1.5E+0Ash & Coal Selenium6 7782492 SI Unsaturated 1.7E-03 5.2E-02 1.3E-01 2.6E-01 3.4E-01 5.6E-0
Appendix D MINTEQA2 Nonli

April 2010–Draft EPA document. 

Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPACMTP 

by Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued) 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

Ash & Coal Thallium 7440280 LF Saturated 2.9E-04 1.9E-03 9.8E-03 2.0E-02 2.4E-02 3.7E-0Ash & Coal Thallium 7440280 LF Unsaturated 0.0E+00 1.2E-01 3.9E-01 1.3E+00 1.7E+00 2.1E+0FBC Aluminum 7429905 LF Saturated 4.1E-25 4.6E-25 1.4E-17 5.2E-08 1.7E-07 1.7E-0FBC Aluminum 7429905 LF Unsaturated 0.0E+00 1.5E-01 2.5E-01 4.6E+02 8.6E+02 1.9E+0FBC Antimony 7440360 LF Saturated 3.0E-04 1.2E-02 2.0E-02 6.9E-02 8.7E-02 1.1E-0FBC Antimony 7440360 LF Unsaturated 0.0E+00 2.1E-01 4.1E-01 2.2E+00 3.0E+00 4.3E+0FBC Arsenic3 22569728 LF Saturated 4.5E-02 1.0E-01 3.9E-01 5.0E-01 6.1E-01 6.4E-0FBC Arsenic3 22569728 LF Unsaturated 0.0E+00 4.6E-01 1.1E+00 2.1E+00 7.0E+00 1.6E+0FBC Arsenic5 15584040 LF Saturated 3.3E+00 1.0E+01 3.4E+01 7.3E+01 9.3E+01 9.4E+0FBC Arsenic5 15584040 LF Unsaturated 0.0E+00 1.1E+01 3.8E+01 8.2E+01 9.9E+01 1.1E+0FBC Barium 7440393 LF Saturated 1.6E-01 2.1E-01 4.6E-01 1.4E+00 5.7E+00 7.8E+0FBC Barium 7440393 LF Unsaturated 0.0E+00 7.7E+00 7.1E+01 2.7E+02 3.7E+02 4.7E+0FBC Boron 7440428 LF Saturated 5.5E-07 1.3E-06 4.9E-06 6.3E-06 7.8E-06 8.1E-0FBC Boron 7440428 LF Unsaturated 0.0E+00 1.6E-01 2.6E-01 2.0E+00 2.5E+00 3.2E+0FBC Cadmium 7440439 LF Saturated 2.0E-01 2.5E-01 5.9E-01 2.7E+00 3.3E+00 3.4E+0FBC Cadmium 7440439 LF Unsaturated 0.0E+00 9.5E-01 2.4E+00 6.3E+00 7.3E+00 8.5E+0FBC Cobalt 7440484 LF Saturated 4.7E-01 1.1E+00 5.8E+00 4.4E+01 4.6E+01 7.0E+0FBC Cobalt 7440484 LF Unsaturated 0.0E+00 2.2E+00 8.9E+00 4.1E+01 5.5E+01 7.3E+0FBC Lead 7439921 LF Saturated 6.8E+00 9.9E+00 2.1E+01 6.9E+01 1.1E+02 1.2E+0FBC Lead 7439921 LF Unsaturated 0.0E+00 1.3E+01 2.5E+01 8.3E+01 1.0E+02 1.3E+0FBC Mercury 7439976 LF Saturated 1.6E-05 4.2E-05 2.2E-04 1.6E-03 3.1E-03 4.4E-0FBC Mercury 7439976 LF Unsaturated 0.0E+00 1.1E+00 6.7E+00 1.9E+01 2.8E+01 4.5E+0FBC Molybdenum 7439987 LF Saturated 7.5E-07 8.0E-06 1.3E-04 3.1E-03 7.8E-03 1.3E-0FBC Molybdenum 7439987 LF Unsaturated 0.0E+00 1.5E-01 2.3E-01 6.7E-01 9.6E-01 1.3E+0FBC Nitrate/Nitrite 14797558 LF Saturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0FBC Nitrate/Nitrite 14797558 LF Unsaturated 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+00 0.0E+0FBC Selenium4 10026036 LF Saturated 6.4E-02 1.2E+00 1.8E+01 1.2E+02 2.8E+02 4.8E+0FBC Selenium4 10026036 LF Unsaturated 0.0E+00 2.1E+00 1.9E+01 1.5E+02 2.9E+02 4.8E+0
Appendix D MINTEQA2 Nonli

April 2010–Draft EPA document. 

Select Percentiles of Kd Sampled from MINTEQA2 Isotherms by Updated EPACMTP 

by Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued) 

Waste Stream Metal CASID WMU Zone 

Percentiles of Kd

10% 25% 50% 75% 80% 85% 

FBC Selenium6 7782492 LF Saturated 1.7E-08 1.8E-07 2.9E-06 5.9E-05 1.4E-04 2.3E-0FBC Selenium6 7782492 LF Unsaturated 0.0E+00 1.4E-01 2.3E-01 1.6E+00 2.3E+00 3.5E+0FBC Thallium 7440280 LF Saturated 9.5E-03 1.5E-02 2.3E-02 5.1E-02 5.3E-02 6.2E-0FBC Thallium 7440280 LF Unsaturated 0.0E+00 2.3E-01 6.2E-01 1.9E+00 2.2E+00 2.7E+0
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

D.7 References 

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Groundwater Pathway Risks from the Land Disposal of Metal-Bearing Wastes. Prepared 

for The Environmental Defense Fund, Friends of the Earth, Hoosier Environmental 

Council, and Mineral Policy Center. Available in docket number EPA-HQ-RCRA-2001-

0022 as document number EPA-HQ-RCRA-2001-0022-0610. 

U.S. EPA (Environmental Protection Agency). 1991. MINTEQ2A/PRODEF2A, A Geochemical 

Assessment Model for Environmental Systems: Version 3.0 User’s Manual. EPA/600/3-

91/021. Office of Research and Development, Athens, GA. Available at 

http://nepis.epa.gov/EPA/html/Pubs/pubtitleORD.htm. 

U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks 

Associated with Multiple Pathways of Exposure to Combustor Emissions. Update to 

Methodology for Assessing Health Risks Associated with Indirect Exposure to Combustor 

Emissions. EPA-600/R-98/137. National Center for Environmental Assessment, 

Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2000. Discussion and Recommendations 

Concerning Comments Received by the U.S. EPA on the EPACMTP and MINTEQA2 

Models as Used in Modeling Support for FFCW Rulemaking. Prepared by Allison 

Geoscience Consultants, Inc. for the Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2001a. Modeling Sensitivity Analyses 

Concerning Comments Received by the U.S. EPA on the MINTEQA2 Model as Used in 

Modeling Support for FFCW Rulemaking. Prepared by Allison Geoscience Consultants, 

Inc., for the Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2001b. Revisions in Input Data and Modeling 

Procedures for Using MINTEQA2 in Estimating Metal Partition Coefficients. Prepared 

by Allison Geoscience Consultants, Inc., for the Office of Solid Waste, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 2003a. EPACMTP Parameters/Data Document. 

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http://www.epa.gov/waste/nonhaz/industrial/tools/cmtp/index.htm. 

U.S. EPA (Environmental Protection Agency). 2003b. EPACMTP Technical Background 

Document. Office of Solid Waste, Washington, DC. Available at 

http://www.epa.gov/waste/nonhaz/industrial/tools/cmtp/index.htm. 

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for Fossil Fuel Combustion Waste Risk Assessment. Prepared by Allison Geoscience 

Consultants, Inc. (under subcontract to RTI International) for U.S. EPA. June. 

April 2010–Draft EPA document. D-13 
Appendix D MINTEQA2 Nonlinear Sorption Isotherms 

April 2010–Draft EPA document. D-14 

U.S. EPA (Environmental Protection Agency). 2003d. Addendum to the EPACMTP Technical 

Background Document. Office of Solid Waste, Washington, DC. 
Appendix E Equations 

Appendix E. Surface Water, Fish Concentration, 

and Contaminant Intake Equations 

This appendix presents the equations used to model surface water and fish concentrations 

and intake of drinking water and fish. These equations are presented in the following 

attachments: 

 Attachment E-1 provides the equations comprising the surface water equilibrium 

partitioning model, including equations that estimate steady state concentrations in 

the water column (dissolved and total) and sediments. 

 Attachment E-2 provides the equations that use bioconcentration factors (BCFs) to 

calculate fish tissue concentrations from total. 

 Attachment E-3 provides the equations used to calculate daily contaminant intake 

rates from drinking water and fish consumption. 

E.1 Aluminum Surface Water Precipitation 

Because the fate and transport of aluminum is controlled more by solubility than by 

sorption in surface water, the surface water model includes algorithms to estimate aluminum 

concentrations in the water column and sediments by accounting for precipitation and fallout of 

aluminum in the water column. These calculations proceed in a stepwise fashion, as follows. 

Step 1. Initially, assume all influent aluminum is dissolved in the water column. 

 Fraction in water column (fwater) = 1 

 Fraction in sediment layer (fbenth) = 0 

 Fraction dissolved (fd) = 1 

Total water column concentration (Cwctot) = dissolved water column concentration (Cwd). 

Step 2. Compare the dissolved water column concentration (Cwd) to the maximum 

soluble concentration (Csol) calculated in MINTEQA2 for the waterbody pH (see Section 3.5.4, 

Table 3-6 for aluminum solubilities and Section C.6.3, Table C-11 for waterbody pH). 

Step 3. If the dissolved water concentration (Cwd) is greater than the solubility limit 

(Csol), reset the dissolved water concentration to the solubility limit, and precipitate and settle 

out the excess aluminum to the benthic sediment layer. 

April 2010–Draft EPA document. E-1 
Appendix E Equations 

April 2010–Draft EPA document. E-2 

If Cwctot > Csol, then 

 Fwater = Csol / Cwctot 

 Fbenth = (Cwctot - Csol) / Cwctot 

 Cwbs = (Cwctot - Csol) * dwc / db 

 Cwtot = Cwctot * dwc / dz 

 Cdw = Csol 

 Cwctot = Csol 

Else 

 Cdw = Cwctot 

 Cwbs = 0 

 Cwtot = Cwctot * rsParam!dwc / rsParam!dz 

End If 

where: 

 Cdw = issolved waterbody concentration 

 Csol = maximum soluble concentration 

 Cwbs = total concentration in bed sediment 

 Cwtot = total waterbody concentration from loading 

 db = depth of the upper benthic layer 

 dwc = depth of the water column 

 dz = depth of the waterbody 

 fbenth = fraction in sediment layer 

 fd = fraction dissolved 

 fwater = fraction in water column. 
Table E-1-1. Fraction of Contaminant in Water Column (Unitless)

Name Description Value

Appendix E

fWater

Attachment E-1: Surface Water Concentrations

bzw −= ddd

[ ( )]

[ ( )] ( ) 











 ××++











××+ ×

××+ ×

=

z

b

dbs

z

w

dsw

z

w

dsw

Water

d

d

bscKbsp

d

d

TSSK

d

d

TSSK

f

1 000001.0

1 000001.0

bsc Bed sediment particle concentration (g/cm^3) or (kg/L) 1

bsp Bed sediment porosity (cm^3/cm^3) 0.6

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

dz Depth of the waterbody (m) Calculated

Kdbs Sediment-water partition coefficient (mL/g) Chemical Data; See Section 3

Kdsw Suspended sediment-water partition coefficient (mL/g) Chemical Data; See Section 3

TSS Total suspended solids (mg/L) Site Data; See Appendix C

0.000001 Conversion factor (L/mL)(g/mg)

April 2010–Draft EPA document

 E-1-1 .
Table E-1-2. Fraction of Contaminant in Benthic Sediments (Unitless)

Name Description Value

Appendix E

fBenth

Attachment E-1: Surface Water Concentrations

( )

( ) ( ) 











 ××++











××+ ×

××+

=

z

b

dbs

z

W

dsw

z

b

dbs

Benth

d

d

bscKbsp

d

d

TSSK

d

d

bscKbsp

f

1 000001.0

bsc Bed sediment particle concentration (g/cm^3) or (kg/L) 1

bsp Bed sediment porosity (cm^3/cm^3) 0.6

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

dz Depth of the waterbody (m) Calculated

Kdbs Sediment-water partition coefficient (mL/g) Chemical Data; See Section 3

Kdsw Suspended sediment-water partition coefficient (mL/g) Chemical Data; See Section 3

TSS Total suspended solids (mg/L) Site Data; See Appendix C

0.000001 Conversion factor (L/mL)(g/mg)

April 2010–Draft EPA document. 

 E-1-2
Table E-1-3. Dissolved Fraction (Unitless)

Name Description Value

Appendix E

fd

Attachment E-1: Surface Water Concentrations

1 000001.0

1

××+

=

TSSK

f

dsw

d

Kdsw Suspended sediment-water partition coefficient (mL/g) Chemical Data; See Section 3

TSS Total suspended solids (mg/L) Site Data; See Appendix C

0.000001 Conversion factor (L/mL)(g/mg)

April 2010–Draft EPA document.

 E-1-3
Table E-1-4. Liquid-Phase Transfer Coefficient - Lakes (m/day) (Mercury Only)

Name Description Value

Appendix E

KLlakes

Attachment E-1: Surface Water Concentrations

86400

67.0

2

33.0

× 





	











×

××××=

−

ww

w

w

a

Lakes wd DRL

k

R

R

KL uC

μ

Cd Drag coefficient (unitless) 0.0011

Dw Diffusivity in water (cm^2/s) Chemical Data; See Section 3, Table 3-4

k von Karman's constant (unitless) 0.4

L2 Viscous sublayer thickness (unitless) 4

μw Viscosity of water ( g/cm-s) 0.0169

Ra Density of air (g/cm^3) 0.0012

Rw Density of water (g/cm^3) 1

uw Mean annual wind speed (m/sec) Site Data; See Appendix C

86400 Conversion factor (sec/day)

April 2010–Draft EPA document. 

 E-1-4
Table E-1-5. Liquid-Phase Transfer Coefficient - Rivers (m/day) (Mercury Only)

Name Description Value

Appendix E

KLrivers

Attachment E-1: Surface Water Concentrations

86400 0001.0×

××

=

z

w

Rivers d

UD

KL

Dw Diffusivity in water (cm^2/s) Chemical Data; See Section 3, Table 3-4

dz Depth of the waterbody (m) Calculated

U Current velocity of the waterbody (m/s) Site Data; See Appendix C

86400 Conversion factor (sec/day)

April 2010–Draft EPA document. 

 E-1-5
Table E-1-6. Gas-Phase Transfer Coefficient - Lakes (m/day) (Mercury Only)

Name Description Value

Appendix E

Kgas

Attachment E-1: Surface Water Concentrations

86400

67.0

2

33.0

× 





	











×

×××=

−

aa

a

gas wd DRL

k

uCK

μ

Cd Drag coefficient (unitless) 0.0011

Da Diffusivity of chemical in air (cm^2/s) Chemical Data; See Section 3, Table 3-4

k von Karman's constant (unitless) 0.4

L2 Viscous sublayer thickness (unitless) 4

μA Viscosity of air (g/cm-s) 0.000181

Ra Density of air (g/cm^3) 0.0012

uw Mean annual wind speed (m/sec) Site Data; See Appendix C

86400 Conversion factor (sec/day)

April 2010–Draft EPA document.

 E-1-6
Table E-1-7. Diffusion Transfer Rate (m/day) (Mercury Only)

Name Description Value

Appendix E

Kv

Attachment E-1: Surface Water Concentrations

( ) hlcw

water

TT

Tempadjust −= θ

TempAdjust

HKK

K

gL

v ×

× ′

+

=

11

1

TR w

HLC H

×

′ =

HLC Henry's Law constant (atm-m^3/mole Chemical Data; See Section 3, Table 3-4

H’ Dimensionless Henry's Law constant (unitless) Calculated

Kg Gas-Phase Transfer Coefficient (m/d) Calculated

KL Liquid-Phase Transfer Coefficient (m/d) Calculated

R Ideal Gas Constant (atm-m^3/K-mole) 0.00008205

θwater Temperature correction (unitless) 1.026

Thlc Temperature of HLC (K) 298

Tw Temperature of the waterbody (K) Site Data; See Appendix C

Note: Drawn from U.S. EPA, 1998 (EPA-530-D-98-001A and EPA-600/R-98/137).

April 2010–Draft EPA document. 

 E-1-7
Table E-1-8. Water Concentration Dissipation Rate Constant (1/day)

Name Description Value

Appendix E

Kwt

Attachment E-1: Surface Water Concentrations

( ) ( ) ( ) ( ) wt Water vold benth b Water sw benth sed h +×+×+×+××= kkfkfKfkffK

b

b

d

WB K =

w

v

vol d

K

k =

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

Fbenth Fraction of contaminant in benthic sediments (unitless) Calculated

fd Dissolved fraction (unitless) Calculated

fWater Fraction of contaminant in water column (unitless) Calculated

Kb Benthic burial rate constant (1/day) Calculated

kh Hydrolysis rate (1/day) 0

ksed Degradation rate for sediment (1/day) 0

ksw Degradation rate for water column (1/day) 0

Kv Diffusion transfer rate (m/day) Calculated (mercury only)

kvol Water column volatilization rate constant (1/day) Calculated (mercury only)

WB Rate of Burial (m/day) 0

April 2010–Draft EPA document. 

 E-1-8
Table E-1-7. Diffusion Transfer Rate (m/day) (Mercury Only)

Name Description Value

Appendix E

Kv

Attachment E-1: Surface Water Concentrations

( ) hlcw

water

TT

Tempadjust −= θ

TempAdjust

HKK

K

gL

v ×

× ′

+

=

11

1

TR w

HLC H

×

′ =

HLC Henry's Law constant (atm-m^3/mole Chemical Data; See Section 3, Table 3-4

H’ Dimensionless Henry's Law constant (unitless) Calculated

Kg Gas-Phase Transfer Coefficient (m/d) Calculated

KL Liquid-Phase Transfer Coefficient (m/d) Calculated

R Ideal Gas Constant (atm-m^3/K-mole) 0.00008205

θwater Temperature correction (unitless) 1.026

Thlc Temperature of HLC (K) 298

Tw Temperature of the waterbody (K) Site Data; See Appendix C

Note: Drawn from U.S. EPA, 1998 (EPA-530-D-98-001A and EPA-600/R-98/137).

April 2010–Draft EPA document. 

 E-1-9
Table E-1-10. Total Water Column Concentration (g/m^3 or mg/L)

Name Description Value

Appendix E

CwcTot

Attachment E-1: Surface Water Concentrations

bzw −= ddd

w

z

wcTot wTot water d

d

fCC ××=

CwTot Total Waterbody Concentration from Loading (g/m^3 or mg/L) Calculated

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

dz Depth of the waterbody (m) Calculated

fWater Fraction of contaminant in water column (unitless) Calculated

April 2010–Draft EPA document.

 E-1-10
Table E-1-11. Dissolved Waterbody Concentration (mg/L)

Name Description Value

Appendix E

Cdw

Attachment E-1: Surface Water Concentrations

bzw −= ddd

w

Z

dw Tot Water d

d

d

ffCwC ×××=

CwTot Total Waterbody Concentration from Loading (g/m^3 or mg/L) Calculated

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

dz Depth of the waterbody (m) Calculated

fd Dissolved fraction (unitless) Calculated

fWater Fraction of contaminant in water column (unitless) Calculated

April 2010–Draft EPA document.

 E-1-11
Table E-1-12. Total Concentration in Bed Sediment (g/m^3 or mg/L)

Name Description Value

Appendix E

Cwbs

Attachment E-1: Surface Water Concentrations

z w += ddd b

b

z

wbs Tot benth d

d

fCC ××=

CwTot Total Waterbody Concentration from Loading (g/m^3 or mg/L) Calculated

db Depth of upper benthic layer (m) 0.03

dw Depth of water column (m) Site Data; See Appendix C

dz Depth of the waterbody (m) Calculated

April 2010–Draft EPA document.

 E-1-12
Table E-2-1. Concentration in Fish at Different Trophic Levels (mg/kg)

Name Description Value

Appendix E

Cfish 

Attachment E-2: Fish Concentrations

For Mercury: 

C fish = *15.0 dw × BCFC

For Non-Volatile Metals: 

fish CwC tot ×= BCF

BCF Bioconcentration factor for specified trophic level (L/kg) Chemical Data; See Section 3

C Dissolved waterbody concentration (mg/L) Calculated dw

CwTot Total waterbody concentration from loading (g/m^3 or mg/L) Calculated

0.15 Fraction of dissolved mercury assumed to be methyl mercury 

(unitless)

April 2010–Draft EPA document.

 E-2-1
Table E-2-2. Average Fish Fillet Concentration Ingested by Humans (mg/kg)

Name Description Value

Appendix E

Cfish_fillet 

Attachment E-2: Fish Concentrations

C _ filletfish T 3 3 TFfishT 4 ×+×= CFCF ishT 4Ff

Concentration of contaminant in fish at different trophic levels Calculated

(mg/kg)

CfishT3F

Concentration of contaminant in fish at different trophic levels Calculated

(mg/kg)

CfishT4F

FT3 Fraction of trophic level 3 intake (unitless) 0.36

FT4 Fraction of trophic level 4 intake (unitless) 0.64

April 2010–Draft EPA document. 

 E-2-2
Table E-3-1. Contaminant Intake from Drinking Water (mg/kg-d)

Name Description Value

Appendix E

Idw 

Attachment E-3: Intake Rates

BW 1000*

CRC F

I

dw dw dw

dw

××

=

BW Body weight (kg) Exposure Data; See Appendix F

C Dissolved waterbody concentration (mg/L) Calculated dw

CRdw Consumption rate of water (mL/day) Exposure Data; See Appendix F

Fraction of drinking water ingested that is contaminated 1

(unitless)

Fdw

1000 Conversion factor (mL/L)

April 2010–Draft EPA document. 

 E-3-1
Table E-3-2. Daily Intake of Contaminant from Fish Ingestion (mg/kg BW/day)

Name Description Value

Appendix E

Ifish

Attachment E-3: Intake Rates

BW

C FCR

I

filletfish fish fish

fish

×

××

=

1000

_

BW Body weight (kg) Exposure Data; See Appendix F

C Average fish fillet concentration ingested by humans (mg/kg) Calculated fish_fillet

CRfish Consumption rate of fish (g WW/day) Exposure Data; See Appendix F

Ffish Fraction of fish intake from contaminated source (unitless) 1

1000 Conversion factor (g/kg)

April 2010–Draft EPA document.

 E-3-2
Appendix F Human Exposure Factors 

Appendix F. Human Exposure Factors

Exposure factors are data that quantify human behavior patterns (e.g., ingestion rates of 

fish and drinking water) and characteristics (e.g., body weight) that affect a person’s exposure to 

environmental contaminants. These data can be used to construct realistic assumptions 

concerning an individual’s exposure to and subsequent intake of a contaminant in the 

environment. The exposure factors data also enable EPA to differentiate the exposures of 

individuals of different ages (e.g., a child vs. an adult). The derivation and values used for the 

human exposure factors in this risk assessment are described below, and the exposure factors 

selected for the probabilistic analyses are also presented. 

F.1 Exposure Parameters Used in Probabilistic Analysis

F.1.1 Introduction 

The general methodology for collecting human exposure data for the probabilistic 

analysis relied on the Exposure Factors Handbook, or EFH (U.S. EPA, 1997a-c), which was 

used in one of three ways: 

1. When EFH percentile data were adequate (most input variables), maximum likelihood 

estimation was used to fit selected parametric models (gamma, lognormal, Weibull, and 

generalized gamma) to the EFH data. The chi-square measure of goodness of fit was then 

used to choose the best distribution. Parameter uncertainty information (e.g., for 

averages, standard deviations) also was derived using the asymptotic normality of the 

maximum likelihood estimate or a regression approach. 

2. When EFH percentile data were not adequate for statistical model fitting (a few 

variables), models were selected on the basis of results for other age cohorts or, if no 

comparable information was available, by assuming lognormal as a default distribution 

and reasonable coefficients of variation (CVs). 

3. When data were not adequate for either 1 or 2 above, variables were fixed at 

EFH-recommended mean values or according to established EPA policy. 

Table F-1 lists all of the parameters used in the probabilistic analysis. Both fixed 

variables and the values used to define distributed data are provided. 

Probabilistic risk analyses involve “sampling” values from probability distribution 

functions (PDFs) and using the values to estimate risk. In some cases, distributions are infinite, 

and there is a probability, although very small, that very large or very small values might be 

selected from the distributions. Because selecting extremely large or extremely small values is 

unrealistic (e.g., the range of adult body weights is not infinite), maximum and minimum values 

April 2010–Draft EPA document. F-1 
Appendix F Human Exposure Factors 

April 2010–Draft EPA document. F-2 

were imposed on the distributions. The minimum and maximum values are included in 

Table F-1. 

F.1.2 Exposure Parameter Distribution Methodology 

This section describes how stochastic or distributed input data for each exposure factor 

were collected and processed. Exposure parameter distributions were developed for use in the 

Monte Carlo analysis. For most variables for which distributions were developed, exposure 

factor data from the EFH were analyzed to fit selected parametric models (i.e., gamma, 

lognormal, Weibull). Steps in the development of distributions included preparing data, fitting 

models, assessing fit, and preparing parameters to characterize distributional uncertainty in the 

model inputs. 

For many exposure factors, EFH data include sample sizes and estimates of the following 

parameters for specific receptor types and age groups: mean, standard deviation, standard error, 

and percentiles corresponding to a subset of the following probabilities: 0.01, 0.02, 0.05, 0.10, 

0.15, 0.25, 0.50, 0.75, 0.85, 0.90, 0.95, 0.98, and 0.99. These percentile data, where available, 

were used as a basis for fitting distributions. Although in no case were all of these percentiles 

actually provided for a single factor, seven or more are typically present in the EFH data. 

Therefore, using the percentiles was a fuller use of the available information than fitting 

distributions simply based on the method of moments (e.g., selecting models that agree with the 

data mean and standard deviation). For some factors, certain percentiles were not used in the 

fitting process because sample sizes were too small to justify their use. Percentiles were used 

only if at least one data point was in the tail of the distribution. If the EFH data repeated a value 

across several adjacent percentiles, only one value (the most central or closest to the median) was 

used in most cases (e.g., if both the 98th and 99th percentiles had the same value, only the 98th 

percentile value was used). 

The EFH does not use standardized age cohorts across exposure factors. Data for 

different exposure factors are reported for different age categories. Therefore, to obtain the 

percentiles for fitting the four standardized age cohorts (i.e., ages 1 to 5, 6 to 11, 12 to 19, and 

more than 20), each EFH cohort-specific value for a given exposure factor was assigned to one 

of these four cohorts. When multiple EFH cohorts fitted into a single CCW cohort, the EFH 

percentiles were averaged within each CCW cohort (e.g., data on 1- to 2-year-olds and 3- to 5-

year-olds from EFH were averaged for the CCW 1- to 5-year-old cohort). If sample sizes were 

available, weighted averages were used, with weights proportional to sample sizes. If sample 

sizes were not available, equal weights were assumed (i.e., the percentiles were simply 

averaged). 
Appendix F H

Table F-1. Summary of Exposure Parameters Used in Probabilistic Analysis 

Units

Variable 

Type Constants

Mean 

(or shape)

Std Dev 

Parameter (or scale) Minimum Maximum 

Averaging time for carcinogens yr Constant 7.00E+01 U.S. EBody weight (adult) kg Lognormal 7.12E+01 1.33E+01 1.50E+01 3.00E+02 U.S. E

7-4, 7-

Body weight (child 1) kg Lognormal 1.55E+01 2.05E+00 4.00E+00 5.00E+01 U.S. E

7-6, 7-

Body weight (child 2) kg Lognormal 3.07E+01 5.96E+00 6.00E+00 2.00E+02 U.S. E

7-6, 7-

Body weight (child 3) kg Lognormal 5.82E+01 1.02E+01 1.30E+01 3.00E+02 U.S. E

7-6, 7-

Consumption rate: fish (adult, child) g/d Lognormal 6.48E+00 1.99E+01 0.00E+00 1.50E+03 U.S. EExposure duration (adult resident) yr Weibull 1.34E+00 1.74E+01 1.00E+00 5.00E+01 U.S. EExposure duration (child) yr Weibull 1.32E+00 7.06E+00 1.00E+00 5.00E+01 U.S. EExposure frequency (adult resident) d/yr Constant 3.50E+02 U.S. EFraction contaminated: drinking water Fraction Constant 1.00E+00 U.S. EFraction contaminated: fish Fraction Constant 1.00E+00 U.S. EFraction of fish consumed that is trophic level (T3) fish 

Fraction Constant 3.60E-01 U.S. E

Fraction of fish consumed that is trophic level 4 (T4) fish 

Fraction Constant 6.40E-01 U.S. E

Ingestion rate: drinking water (adult resident) 

mL/d Gamma 3.88E+00 3.57E+02 1.04E+02 1.10E+04 U.S. E

Ingestion rate: drinking water (child 1 resident) 

mL/d Gamma 2.95E+00 2.37E+02 2.60E+01 3.84E+03 U.S. E

Ingestion rate: drinking water (child 2 resident) 

mL/d Gamma 3.35E+00 2.35E+02 3.40E+01 4.20E+03 U.S. E

Ingestion rate: drinking water (child 3 resident) 

mL/d Gamma 2.82E+00 3.42E+02 3.30E+01 5.40E+03 U.S. E

April 2010–Draft EPA document. 
Appendix F Human Exposure Factors 

April 2010–Draft EPA document. F-4 

Because the EFH data are always positive and are almost always skewed to the right (i.e., 

have a long right tail), three two-parameter probability models commonly used to characterize 

such data (gamma, lognormal, and Weibull) were selected. In addition, a three-parameter model 

(generalized gamma) was used that unifies them1

 and allows for a likelihood ratio test of the fit 

of the two-parameter models. However, only the two-parameter models were selected for use in 

the analysis because the three-parameter generalized gamma model did not significantly improve 

the goodness of fit over the two-parameter models. This simple setup constitutes a considerable 

improvement over the common practice of using a lognormal model in which adequate EFH data 

are available to support maximum likelihood estimation. 

Lognormal, gamma, Weibull, and generalized gamma distributions were fit to each factor 

data set using maximum likelihood estimation (Burmaster and Thompson, 1998). When sample 

sizes were available, the goodness of fit was calculated for each of the four models using the 

chi-square test (Bickel and Doksum, 1977). When percentile data were available but sample 

sizes were unknown, a regression F-test for the goodness of fit against the generalized gamma 

model was used. For each of the two-parameter models, parameter uncertainty information (i.e., 

mean, standard deviation, scale, and shape) was provided as parameter estimates for a bivariate 

normal distribution that could be used for simulating parameter values (Burmaster and 

Thompson, 1998). The information necessary for such simulations includes estimates of the two 

model parameters, their standard errors, and their correlation. To obtain this parameter 

uncertainty information, the asymptotic normality of the maximum likelihood estimate 

(Burmaster and Thompson, 1998) was used when sample sizes were available, and a regression 

approach was used when sample sizes were not available (Jennrich and Moore, 1975; Jennrich 

and Ralston, 1979). In either case, uncertainty can be expressed as a bivariate normal distribution 

for the model parameters. 

The parameter values selected are described in more detail in the following subsections. 

Section F.1.3 discusses fixed parameters. Section F.1.4 describes, for each exposure factor, the 

EFH data used to develop the distributions, along with the final distributional statistics. 

F.1.3 Fixed Parameters 

Certain parameters were fixed, based on central tendency values from the best available 

source (usually EFH recommendations), either because no variability was expected or because 

the available data were not adequate to generate distributions. Fixed (constant) parameters are 

shown in Table F-2 along with the value selected for the risk analysis and the data source. These 

constants included variables for which limited or no percentile data were provided in the EFH: 

exposure frequency, fractions of T3 and T4 fish consumed, and fraction contaminated for the 

various media. Most of these values were extracted directly from the EFH. When evaluating 

carcinogens, total dose was averaged over the lifetime of the individual, assumed to be 70 years. 

 

1

 Gamma, Weibull, and lognormal distributions are all special cases of the generalized gamma distribution. 
Appendix F Human Exposure Factors 

Table F-2. Summary of Human Exposure Factor Data Used in Modeling: Constants 

Description Value Units Source

Fraction contaminated: drinking water 1 Fraction EPA policy 

Fraction contaminated: fish 1 Fraction EPA policy 

Fraction of T3 fish consumed 0.36 Fraction U.S. EPA (1997b); Table 10-66 

Fraction of T4 fish consumed 0.64 Fraction U.S. EPA (1997b); Table 10-66 

Exposure frequency (adult, child) 350 d/yr EPA policy 

Averaging time for carcinogens (adult, child) 70 yr U.S. EPA (1989) 

The fraction contaminated for drinking water was assumed to be 1 (i.e., all drinking water 

available for consumption at a site is potentially contaminated), with actual concentrations 

depending on fate and transport model results. Thus, households for which the drinking water 

pathway was analyzed were assumed to get 100 percent of their drinking water from 

groundwater. Exposure frequency was set to 350 days per year in accordance with EPA policy, 

assuming that residents take an average of 2 weeks’ vacation time away from their homes each 

year. 

F.1.4 Variable Parameters 

F.1.4.1 Fish Consumption 

Table F-3 presents fish consumption data and distributions. Fish consumption data were 

obtained from Table 10-64 of the EFH (U.S. EPA, 1997b). Data (in g/d) were available for adult 

freshwater anglers in Maine. The Maine fish consumption study was one of four recommended 

freshwater angler studies in the EFH (U.S. EPA, 1997b). The other recommended fish 

consumption studies (i.e., Michigan and New York) had large percentages of anglers who fished 

from Great Lakes, which is not consistent with the modeling scenarios used in this risk analysis. 

The anglers in the Maine study fished from streams, rivers, and ponds; these data were more 

consistent with the CCW modeling scenarios. Although the Maine data have a lower mean than 

the Michigan data, the Maine data compared better with a national U.S. Department of 

Agriculture (USDA) study. Also, the Maine study included percentile data, which were 

necessary to develop a distribution. 

Percentile data were used to fit parametric models (gamma, lognormal, and Weibull), and 

measures of goodness of fit were used to select lognormal as the most appropriate model. The 

fraction of fish intake that is locally caught was assumed to be 1 (in accordance with EPA 

policy). The fraction of consumed T3 and T4 fish was 0.36 and 0.64, respectively (Table 10-66, 

U.S. EPA, 1997b). 

Table F-3. Fish Consumption Data and Distribution 

EFH Data (g/d) Distribution

 Age 

 Cohort N

Data 

Mean

Data 

SD P50 P66 P75 P90 P95 Distribution

Pop-Estd 

Mean

Pop-Estd 

SD

All ages 1,053 6.4 2 4 5.8 13 26 Lognormal 6.48 19.9 

N = Number of samples; P50–P95 = Percentiles; Pop-Estd = Population-estimated; SD = Standard deviation. 

April 2010–Draft EPA document. F-5 
Appendix F Human Exposure Factors 

April 2010–Draft EPA document. F-6 

F.1.4.2 Drinking Water Intake 

Table F-4 presents drinking water intake data and distributions. Drinking water intake 

data were obtained from Table 3-6 of the EFH (U.S. EPA, 1997a). Data (in mL/d) were 

presented by age groups. Weighted averages of percentiles, means, and standard deviations were 

calculated for the three child age groups and adults. Percentile data were used to fit parametric 

models (gamma, lognormal, and Weibull) using maximum likelihood estimation. Measures of 

goodness of fit were used to select the most appropriate model. The fraction of drinking water 

contaminated was assumed to be 1 (in accordance with EPA policy). 
Appendix F H

Table F-4. Drinking Water Intake Data and Distributions 

EFH Data (mL/d) 

Age 

Cohort N

Data 

Mean

Data 

SD P01 P05 P10 P25 P50 P75 P90 P95 P99 Distribu

1–5 3,200 697.1 401.5 51.62 187.6 273.5 419.2 616.5 900.8 1,236 1,473 1,917 Gamma 6–11 2,405 787 417 68 241 318 484 731 1,016 1,338 1,556 1,998 Gamma 12–19 5,801 963.2 560.6 65.15 241.4 353.8 574.4 868.5 1,247 1,694 2,033 2,693 Gamma 20+ 13,394 1,384 721.6 207.6 457.5 607.3 899.6 1,275 1,741 2,260 2,682 3,737 Gamma N = Number of samples; P01–P99 = Percentiles; Pop-Estd = Population-estimated; SD = Standard deviation. 

April 2010–Draft EPA document. 
Appendix F Human Exposure Factors 

F.1.4.3 Body Weight 

Table F-5 presents body weight data and distributions. Body weight data were obtained 

from Tables 7-2 through 7-7 of the EFH (U.S. EPA, 1997a). Data (in kg) were presented by age 

and gender. Weighted averages of percentiles, means, and standard deviations were calculated 

for 1- to 5-year-olds, 6- to 11-year-olds, 12- to 19-year olds, and adult age groups; male and 

female data were weighted and combined for each age group. These percentile data were used as 

the basis for fitting distributions. These data were analyzed to fit parametric models (gamma, 

lognormal, and Weibull) using maximum likelihood estimation. Measures of goodness of fit 

were used to select the most appropriate model. 

April 2010–Draft EPA document. F-8 
Appendix F H

Table F-5. Body Weight Data and Distributions 

EFH Data (kg) 

Age 

Cohort N

Data 

Mean

Data 

SD P05 P10 P15 P25 P50 P75 P85 P90 P95 Distribut

1–5 3,762 15.52 3.719 12.5 13.1 13.45 14.03 15.26 16.67 17.58 18.32 19.45 Lognorm6–11 1,725 30.84 9.561 22.79 24.05 25.07 26.44 29.58 33.44 36.82 39.66 43.5 Lognorm12–19 2,615 58.45 13.64 43.84 46.52 48.31 50.94 56.77 63.57 68.09 71.98 79.52 Lognorm20+ 12,504 71.41 15.45 52.86 55.98 58.21 61.69 69.26 78.49 84.92 89.75 97.64 LognormN = Number of samples; P05–P95 = Percentiles; Pop-Estd = Population-estimated; SD = Standard deviation. 

April 2010–Draft EPA document. 
Appendix F Human Exposure Factors 

F.1.4.4 Exposure Duration 

Table F-6 presents exposure duration data and distributions. Exposure duration was 

assumed to be equivalent to the average residence time for each receptor. Exposure durations for 

adult and child residents were determined using data on residential occupancy from the EFH 

Table 15-168 (U.S. EPA, 1997c). The data represent the total time a person is expected to live at 

a single location, based on age. The table presents male and female data combined. Adult 

residents aged 21 to 90 were pooled. For child residents, the 3-year-old EFH age group was used 

for the 1- to 5-year-old CCW cohort. The 6- and 9-year-old EFH age groups were pooled for the 

6- to 11-year-old CCW cohort. 

Table F-6. Exposure Duration Data and Distributions 

EFH Data Distributions

Data Mean 

Age Cohort (yr) Distribution

Pop-Estd Shape 

(yr)a

Pop-Estd Scale 

(yr)

1–5 6.5 Weibull 1.32 7.059 

6–11 8.5 Weibull 1.69 9.467 

Adult 16.0 Weibull 1.34 17.38 

Pop-Estd = Population-estimated.

a

 Distributions used in risk assessment. 

In an analysis of residential occupancy data, Myers et al. (U.S. EPA, 2000) found that the 

data, for most ages, were best fit by a Weibull distribution. The Weibull distribution as 

implemented in Crystal Ball is characterized by three parameters: location, shape, and scale. 

Location is the minimum value and, in this case, was presumed to be 0. Shape and scale were 

determined by fitting a Weibull distribution to the pooled data, as follows. To pool residential 

occupancy data for the age cohorts, an arithmetic mean of data means was calculated for each 

age group. Then, assuming a Weibull distribution, the variance within each age group (e.g., 6-

year-olds) was calculated in the age cohort. These variances in turn were pooled over the age 

cohort using equal weights. This is not the usual type of pooled variance, which would exclude 

the variation in the group means. However, this way, the overall variance reflected the variance 

of means within the age groups (e.g., within the 6-year-old age group). The standard deviation 

was estimated as the square root of the variance. The coefficient of variation was calculated as 

the ratio of the standard deviation divided by the Weibull mean. For each cohort, the populationestimated parameter uncertainty information (e.g., shape and scale) was calculated based on a 

Weibull distribution, the calculated data mean for the age cohort, and the CV. 

F.2 Exposure Parameters Used in Screening Analysis

The 50th percentile values used for the human exposure factors in the screening analysis 

are presented in Table F-7. 

April 2010–Draft EPA document. F-10 
Appendix F H

Table F-7. 50th Percentile Exposure Data Used in the Screening Analysis 

Units Reference 

Age Cohort 

Parameter Value 1–5 yr 6–11 yr 12–19 yr 20+ yr 

Body weight 15.3 29.6 56.8 69.3 kg U.S. EPA (1997a) T7-2Consumption rate of fish 2 2 2 2 g WW/d U.S. EPA (1997b) T10Exposure duration 5 7.5 8 10 yr U.S. EPA (1997c) T15Ingestion rate of drinking water 

0.6165 0.731 0.8685 1.275 L/d U.S. EPA (1997a) T3-6 

April 2010–Draft EPA document. 
Appendix F Human Exposure Factors 

F.3 References

Bickel, P.J., and K.A. Doksum. 1977. Mathematical Statistics. San Francisco, CA: Holden Bay. 

Burmaster, D.E, and K.M. Thompson. 1998. Fitting second-order parametric distributions to data 

using maximum likelihood estimation. Human and Ecological Risk Assessment 4(2):319–

339. 

Jennrich, R.I., and R.H. Moore. 1975. Maximum likelihood estimation by nonlinear least 

squares. Pp. 57–65 in Statistical Computing Section Proceedings of American Statistical 

Association. American Statistical Association, Alexandria, VA. 

Jennrich, R.I., and M.L. Ralston. 1979. Fitting nonlinear models to data. Annual Review 

Biophysical Bioengineering 8:195–238. 

U.S. EPA (Environmental Protection Agency). 1989. Risk Assessment Guidance for Superfund. 

Volume I: Human Health Evaluation Manual (Part A) (Interim Final). EPA/540/1-

89/002. Prepared by Office of Emergency and Remedial Response, Washington, DC. 

December. 

U.S. EPA (Environmental Protection Agency). 1997a. Exposure Factors Handbook, Volume I, 

General Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1997b. Exposure Factors Handbook, Volume II, 

Food Ingestion Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume III, 

Activity Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1999. Revised Risk Assessment for the Air 

Characteristic Study. Volume II: Technical Background Document. EPA/530/R-99/019b. 

Office of Solid Waste, Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 2000. Options for Development of Parametric 

Probability Distributions for Exposure Factors. EPA/600/R-00/058. National Center for 

Environmental Assessment, Office of Research and Development, Washington, DC. July. 

April 2010–Draft EPA document. F-12 
Appendix G Human Health Benchmarks 

Appendix G. Human Health Benchmarks 

The CCW screening analysis and risk assessment require human health benchmarks to 

assess potential risks from chronic oral exposures. EPA uses reference doses (RfDs) to evaluate 

noncancer risk from oral exposures. Oral cancer slope factors (CSFs) are used to evaluate risk for 

carcinogens. This appendix provides the human health benchmarks used in the CCW screening 

and risk assessment. Section G.1 describes the data sources and general hierarchy used to collect 

these benchmarks. Section G.2 provides the benchmarks along with discussions of individual 

human health benchmarks extracted from a variety of sources. 

G.1 Methodology and Data Sources

Several sources of health benchmarks are available. The hierarchy used health 

benchmarks developed by EPA to the extent that they were available. The analysis used 

available benchmarks from non-EPA sources for chemicals for which EPA benchmarks were not 

available, and ranked human health benchmark sources in the following order of preference: 

 Integrated Risk Information System (IRIS) 

 Superfund Technical Support Center Provisional Benchmarks 

 Health Effects Assessment Summary Tables (HEAST) 

 EPA health assessment documents 

 Various other EPA health benchmark sources 

 Agency for Toxic Substances and Disease Registry (ATSDR) minimal risk levels 

(MRLs) 

 California Environmental Protection Agency (CalEPA) chronic inhalation reference 

exposure levels (RELs) and cancer potency factors. 

G.1.1 Integrated Risk Information System (IRIS) 

Benchmarks in IRIS are prepared and maintained by EPA, and RTI used values from 

IRIS whenever available. IRIS is EPA’s electronic database containing information on human 

health effects (U.S. EPA, 2009). Each chemical file contains descriptive and quantitative 

information on potential health effects. Health benchmarks for chronic noncarcinogenic health 

effects include RfDs and inhalation reference concentrations (RfCs). Cancer classification, oral 

CSFs, and inhalation unit risk factors (URFs) are included for carcinogenic effects. IRIS is the 

official repository of Agency-wide consensus of human health risk information. 

April 2010–Draft EPA document. G-1 
Appendix G Human Health Benchmarks 

G.1.2 Superfund Provisional Benchmarks 

The Superfund Technical Support Center (EPA’s National Center for Environmental 

Assessment [NCEA]) derives provisional RfCs, RfDs, and CSFs for certain chemicals. Some of 

the provisional values have been externally peer reviewed. These provisional values have not 

undergone EPA’s formal review process for finalizing benchmarks and do not represent 

Agency-wide consensus information. 

G.1.3 Health Effects Assessment Summary Tables 

HEAST is a listing of provisional noncarcinogenic and carcinogenic health toxicity 

values (RfDs, RfCs, URFs, and CSFs) derived by EPA (U.S. EPA, 1997). Although the health 

toxicity values in HEAST have undergone review and have the concurrence of individual EPA 

program offices, either they have not been reviewed as extensively as those in IRIS or their data 

set is not complete enough to be listed in IRIS. HEAST benchmarks have not been updated in 

several years and do not represent Agency-wide consensus information. 

G.1.4 Other EPA Health Benchmarks 

EPA has also derived health benchmark values in other risk assessment documents, such 

as Health Assessment Documents (HADs), Health Effects Assessments (HEAs), Health and 

Environmental Effects Profiles (HEEPs), Health and Environmental Effects Documents 

(HEEDs), Drinking Water Criteria Documents, and Ambient Water Quality Criteria Documents. 

Evaluations of potential carcinogenicity of chemicals in support of reportable quantity 

adjustments were published by EPA’s Carcinogen Assessment Group (CAG) and may include 

cancer potency factor estimates. Health benchmarks derived by EPA for listing determinations 

(e.g., solvents) or studies (e.g., Air Characteristic Study) are also available. Health toxicity 

values identified in these EPA documents are usually dated and are not recognized as 

Agency-wide consensus information or verified benchmarks. 

G.1.5 ATSDR Minimal Risk Levels 

The ATSDR MRLs are substance-specific health guidance levels for noncarcinogenic 

endpoints (ATSDR, 2009). An MRL is an estimate of the daily human exposure to a hazardous 

substance that is likely to be without appreciable risk of adverse noncancer health effects over a 

specified duration of exposure. MRLs are based on noncancer health effects only and are not 

based on a consideration of cancer effects. MRLs are derived for acute, intermediate, and chronic 

exposure durations for oral and inhalation routes of exposure. Inhalation and oral MRLs are 

derived in a manner similar to EPA’s RfCs and RfDs, respectively (i.e., ATSDR uses the no 

observed adverse effect level/uncertainty factor [NOAEL/UF] approach); however, MRLs are 

intended to serve as screening levels and are exposure duration specific. Also, ATSDR uses 

EPA’s (U.S. EPA, 1994) inhalation dosimetry methodology in the derivation of inhalation 

MRLs. 

G.1.6 CalEPA Cancer Potency Factors and Reference Exposure Levels 

CalEPA has developed cancer potency factors for chemicals regulated under California’s 

Hot Spots Air Toxics Program (CalEPA, 1999a). The cancer potency factors are analogous to 

EPA’s oral and inhalation CSFs. CalEPA has also developed chronic inhalation RELs, analogous 

April 2010–Draft EPA document. G-2 
Appendix G Human Health Benchmarks 

to EPA’s RfC, for 120 substances (CalEPA, 1999b, 2000, 2008). CalEPA used EPA’s inhalation 

dosimetry methodology (U.S. EPA, 1994) in the derivation of inhalation RELs. The cancer 

potency factors and inhalation RELs have undergone internal peer review by various California 

agencies and have been the subject of public comment. 

G.1.7 Surrogate Health Benchmarks 

If no human health benchmarks were available from EPA or alternative sources, we 

sought benchmarks for similar chemicals to use as surrogate data. For example, the health 

benchmark of a mixture could serve as the surrogate benchmark for its components or a 

benchmark of a metal salt could serve as the surrogate for an elemental metal. 

G.2 Human Health Benchmarks

The chronic human health benchmarks used to calculate the health-based numbers 

(HBNs) in the CCW screening analysis and risk assessment are summarized in Table G-1, 

which provides the Chemical Abstract Service Registry Number (CASRN), constituent name, 

RfD (mg/kg-d), oral CSF (mg/kg-d-1), and reference for each benchmark. A key to the references 

cited and abbreviations used is provided at the end of the table. 

For a majority of constituents, human health benchmarks were available from IRIS (U.S. 

EPA, 2009), Superfund Provisional Benchmarks, or HEAST (U.S. EPA, 1997). Benchmarks also 

were obtained from ATSDR (2009) or CalEPA (1999a, 1999b, 2000, 2008). This section 

describes benchmarks obtained from other sources, along with the Superfund Provisional 

Benchmarks values and special uses of IRIS benchmarks. 

Table G-1. Human Health Benchmarks Used in CCW Risk Assessment 

Constituent Name CASRN

RfD 

(mg/kg-d) Ref

CSFo 

(per 

mg/kg-d) Ref

MCL 

(mg/L) Notes

Aluminum 7429-90-5 1.0E+00 P 

Antimony 7440-36-0 4.0E-04 I 

Arsenic, inorganic 7440-38-2 3.0E-04 I 1.5E+0 I 

Barium 7440-39-3 2.0E-01 I 

Beryllium 7440-41-7 2.0E-03 I 

Boron 7440-42-8 2.0E-01 I 

Cadmium 7440-43-9 5.0E-04 I RfD for H2O (food = 1E-3) 

Chloride 16887-00-6 250 

Chromium (III), 

insoluble salts 

16065-83-1 1.5E+00 I 

Chromium (VI) 18540-29-9 3.0E-03 I 

Cobalt (and 

compounds) 

7440-48-4 3.0E-04 P 

Copper 7440-50-8 1.0E-02 A 1.3 RfD is the intermediate oral MRL 

Cyanide (amenable) 57-12-5 2.0E-02 I 

(continued)

April 2010–Draft EPA document. G-3 
Appendix G Human Health Benchmarks 

Human Health Benchmarks Used in CCW Risk Assessment (continued) 

Constituent Name CASRN

RfD 

(mg/kg-d) Ref

CSFo 

(per 

mg/kg-d) Ref

MCL 

(mg/L) Notes

Divalent mercury 3.0E-04 H RfD is for mercuric chloride; used 

for food, water, soil 

1.0E-04 I RfD is for methyl mercury; used 

for fish only 

Fluoride 16984-48-8 1.2E-01 I RfD is for fluorine; the alternative 

IRIS value (for skeletal, rather 

than dental, fluorosis) was used 

Iron 7439-89-6 7.0E-01 P 

Lead and compounds 

(inorganic) 

7439-92-1 0.015 

Manganese 7439-96-5 1.4E-01 I RfD for food; H2O and soil = 

4.7E-2 mkd 

Molybdenum 7439-98-7 5.0E-03 I 

Nickel, soluble salts 7440-02-0 2.0E-02 I 

Nitrate 14797-55-8 1.6E+00 I 10 

Nitrite 14797-65-0 1.0E-01 I 

Selenium 7782-49-2 5.0E-03 I 

Silver 7440-22-4 5.0E-03 I 

Strontium 7440-24-6 6.0E-01 I 

Sulfate 14808-79-8 250 

Thallium, elemental 7440-28-0 8.0E-05 I RfD is for thallium chloride 

Vanadium 7440-62-2 7.0E-03 H 

Zinc 7440-66-6 3.0E-01 I 

Key: 

CASRN = Chemical Abstract Service registry number. 

CSFo = Oral cancer slope factor. 

RfD = Reference dose. 

MCL = Maximum Contaminant Level. 

Sources: 

 A = ATSDR MRLs (ATSDR, 2009) 

 H = HEAST (U.S. EPA, 1997) 

 I = IRIS (U.S. EPA, 2009) 

 P = PPRTV (U.S. EPA, 2006a, 2006b, 2008)

The provisional RfD of 1 mg/kg-d developed by NCEA for the Superfund Technical 

Support Center (U.S. EPA, 2006a) was used for aluminum. 

The provisional RfD of 0.0003 mg/kg-d developed by NCEA for the Superfund 

Technical Support Center (U.S. EPA, 2008) was used for cobalt. 

The provisional RfD of 0.7 mg/kg-d developed by NCEA for the Superfund Technical 

Support Center (U.S. EPA, 2006b) was used for iron. 

April 2010–Draft EPA document. G-4 
Appendix G Human Health Benchmarks 

For several constituents, IRIS benchmarks for similar chemicals were used as surrogate 

data. The rationale for these recommendations is as follows: 

 Fluoride was based on fluorine. The IRIS RfD for fluorine is based on soluble fluoride. 

The primary RfD cited in IRIS (6E-02 mg/kg-d) is for dental fluorosis, a cosmetic effect. 

In this analysis, an alternative IRIS value (1.2E-01 mg/kg-d) for skeletal fluorosis in 

adults was used instead. 

 Thallium was based on thallium chloride. IRIS contains RfDs for several thallium salts. 

The lowest value among the thallium salts (8E-05 mg/kg-d) is routinely used to represent 

thallium in risk assessments. 

G.3 References

ATSDR (Agency for Toxic Substances and Disease Registry). 2009. Minimal Risk Levels 

(MRLs) for Hazardous Substances. Available at http://www.atsdr.cdc.gov/mrls.html. 

CalEPA (California Environmental Protection Agency). 1999a. Air Toxics Hot Spots Program 

Risk Assessment Guidelines: Part II. Technical Support Document for Describing 

Available Cancer Potency Factors. Office of Environmental Health Hazard Assessment, 

Berkeley, CA. Available at http://www.oehha.org/air/cancer_guide/hsca2.html. 

CalEPA (California Environmental Protection Agency). 1999b. Air Toxics Hot Spots Program 

Risk Assessment Guidelines: Part III. Technical Support Document for the Determination 

of Noncancer Chronic Reference Exposure Levels. SRP Draft. Office of Environmental 

Health Hazard Assessment, Berkeley, CA. Available (in two sections) at 

http://www.oehha.org/air/chronic_rels/ragsii.htmlhttp://www.oehha.org/air/chronic_rels/

RAGSp3draft.html. 

CalEPA (California Environmental Protection Agency). 2000. Air Toxics Hot Spots Program 

Risk Assessment Guidelines: Part III. Technical Support Document for the Determination 

of Noncancer Chronic Reference Exposure Levels. Office of Environmental Health 

Hazard Assessment, Berkeley, CA. Available (in four sections) at 

http://www.oehha.org/air/chronic_rels/22RELS2k.html

http://www.oehha.org/air/chronic_rels/42kChREL.html

http://www.oehha.org/air/chronic_rels/Jan2001ChREL.html

http://www.oehha.org/air/chronic_rels/1201Crels.html. 

CalEPA (California Environmental Protection Agency). 2008. Air Toxics Hot Spots Program 

Risk Assessment Guidelines. Technical Support Document for the Determination of 

Noncancer Chronic Reference Exposure Levels. Office of Environmental Health Hazard 

Assessment, Berkeley, CA. Available at 

http://www.oehha.org/air/hot_spots/2008/NoncancerTSD_final.pdf. 

U.S. EPA (Environmental Protection Agency). 1997. Health Effects Assessment Summary Tables 

(HEAST). EPA-540-R-97-036. FY 1997 Update. Office of Solid Waste and Emergency 

Response, Washington, DC. 

April 2010–Draft EPA document. G-5 
Appendix G Human Health Benchmarks 

April 2010–Draft EPA document. G-6 

U.S. EPA (Environmental Protection Agency). 2006a. Provisional Peer Reviewed Toxicity 

Values for Aluminum (CASRN 7429-90-5). 10-23-2006. National Center for 

Environmental Assessment. Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2006b. Provisional Peer Reviewed Toxicity 

Values for Iron and Compounds (CASRN 7439-89-6). Derivation of Subchronic and 

Chronic Oral RfDs. 9-11-2006. National Center for Environmental Assessment. 

Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2008. Provisional Peer Reviewed Toxicity Values 

for Cobalt (CASRN 7440-48-4). 8-25-2008. National Center for Environmental 

Assessment. Superfund Technical Support Center, Cincinnati, OH. 

U.S. EPA (Environmental Protection Agency). 2009. Integrated Risk Information System (IRIS). 

National Center for Environmental Assessment, Office of Research and Development, 

Washington, DC. Available at http://www.epa.gov/iris/. 
Appendix H Ecological Benchmarks 

Appendix H. Ecological Benchmarks 

Both the screening and full-scale CCW assessments included an ecological risk 

assessment that paralleled the human health risk assessment. The ecological risk assessment 

addressed two routes of exposure for ecological receptors: direct contact with contaminated 

media and ingestion of contaminated food items. For each CCW chemical for which ecological 

effect data were available, hazard quotients (HQs) were calculated using chemical-specific media 

concentrations assumed to be protective of ecological receptors of concern. 

This appendix provides the ecological benchmarks used in both the CCW screening and 

full-scale risk assessment. Section H.1 describes the data sources and methods used to develop 

these benchmarks. Additional details can be found in U.S. EPA (1998). Section H.2 provides the 

benchmarks. 

H.1 Data Sources and Methodology 

To calculate ecological HQs, the concentration-based ecological benchmarks (also known 

as chemical stressor concentration limits, or CSCLs) were divided by the estimated 

concentrations of constituents in environmental media contaminated by CCW. The CSCLs are 

environmental quality criteria intended to represent a protective threshold value for adverse 

effects to various ecological receptors in aquatic ecosystems (surface water and sediment). An 

HQ greater than target of 1 indicates that the predicted concentration will be above the CSCL 

and, therefore, the potential for adverse ecological effects exists. In this regard, the use of CSCLs 

to calculate an ecological HQ is analogous to the use of the reference concentration (RfC) for 

human health where the air concentration is compared to the health-based concentration (the 

RfC), and an HQ greater than the target value of 1 is considered to indicate the potential for 

adverse health effects. Table H-1 shows the receptor types assessed for each exposure route in 

each environmental medium addressed by the CCW risk assessment. 

Table H-1. Ecological Receptors Assessed by Medium Impacted by CCW 

Receptor Type Surface Water Sediment

Direct Contact Exposure 

Aquatic Community U

Sediment Community U

Amphibians U

Aquatic Plants and Algae U

Ingestion Exposure 

Mammals U

Birds U

Ecological benchmarks for the CCW risk assessment were taken directly from the 1998 

fossil fuel combustion risk analysis, Non-Groundwater Pathways, Human Health and Ecological 

April 2010–Draft EPA document. H-1 
Appendix H Ecological Benchmarks 

Risk Analysis for Fossil Fuel Combustion Phase 2 (FFC2) (U.S. EPA, 1998). The receptors and 

endpoints selected for the 1998 analysis were evaluated and considered appropriate for the goals 

of this risk assessment. The benchmarks were derived for each chemical and receptor to the 

extent that supporting data were available. 

As in 1998, the lowest (most sensitive) benchmark for each chemical in each medium 

was selected to calculate HQs in the CCW risk assessment. For example, several receptors 

(aquatic invertebrates, mammals, and birds) may be exposed to constituents in surface water. The 

surface water HQ for a given chemical was calculated using whichever benchmark was lowest 

and would thus give the highest (most protective) HQ. 

H.1.1 Direct Contact Exposure 

Ecological receptors that live in close contact with contaminated media are considered to 

be potentially at risk. These receptors are exposed through direct contact with contaminants in 

surface water and sediment. The receptors selected to assess the direct contact exposure route for 

each medium are summarized in Table H-1. The benchmarks for receptor communities are not 

truly community-level concentration limits in that they do not consider predator-prey interactions. 

Rather, they are based on the theory that protection of 95 percent of the species in the community 

will provide a sufficient level of protection for the community (see, for example, Stephan et al., 

1985, for additional detail). The following sections summarize the benchmark derivation 

methods for each receptor assessed for the direct contact route of exposure. 

Aquatic Community Benchmarks 

The aquatic community receptor comprises fish and aquatic invertebrates exposed 

through direct contact with constituents in surface water. For the aquatic community, the final 

chronic value (FCV), developed either for the Great Lakes Water Quality Initiative (U.S. EPA, 

1993) or the National Ambient Water Quality Criteria (NAWQC) (U.S. EPA, 1995a,b), was the 

preferred source for the benchmark. If an FCV was unavailable and could not be calculated from 

available data, a secondary chronic value (SCV) was estimated using methods developed for 

wildlife criteria for the Great Lakes Initiative (e.g., 58 FR 20802; U.S. EPA, 1993). The SCV 

methodology is based on the original species data set established for the NAWQC; however, it 

requires fewer data points and includes statistically derived adjustment factors. For benchmark 

derivation, the minimum data set required at least one data point. 

Amphibian Benchmarks 

For amphibian populations, data availability severely limited benchmark development. A 

review of several compendia presenting amphibian ecotoxicity data (e.g., U.S. EPA, 1996; 

Power et al., 1989), as well as primary literature sources, found a lack of standard methods on 

endpoints, species, and test durations necessary to derive a chronic benchmark for amphibians. 

Consequently, an acute benchmark was derived for aqueous exposures in amphibians by taking a 

geometric mean of LC50 (i.e., concentration lethal to 50 percent of test subjects) data identified in 

studies with exposure durations less than 8 days. Although the use of acute effects levels 

produced a benchmark that was not consistent with the other (chronic) ecological benchmarks, 

the sensitivity of these receptors warranted the use of acute effects levels in the absence of 

chronic concentration limits. Recent studies (Hopkins and Rowe, 2004; Hopkins et al., 2006) 

April 2010–Draft EPA document. H-2 
Appendix H Ecological Benchmarks 

have confirmed that amphibians are among the most sensitive taxa to metals found in CCW, and 

selenium appears to be a significant stressor in CCW disposal scenarios. The endpoints 

considered in these studies were related to population sustainability and, consequently, are highly 

relevant to ecological risk assessment. However, these field studies were confounded by the fact 

that wildlife were exposed to multiple chemical pollutants (including radionuclides) and, as a 

result, acute effects data on individual metals remain the most appropriate source for quantitative 

benchmarks to assess the potential for adverse effects in amphibians. 

Sediment Community Benchmarks 

For the sediment community, benchmarks were selected based on a complete assessment 

of several sources proposing sediment benchmark values. Primary sources evaluated for 

developing sediment community benchmarks are shown in Table H-2. 

Table H-2. Primary Sources Evaluated for Developing Sediment Community Benchmarks 

Long, E.R., and L.G. Morgan. 1991. The Potential for Biological Effects of Sediment-Sorbed Contaminants 

Tested in the National Status and Trends Program. Technical Memorandum NOS OMA 52. National Oceanic 

and Atmospheric Administration (NOAA), Washington, DC. 

Jones, D.S., G.W. Suter, II, and R.N. Hull. 1997. Toxicological Benchmarks for Screening Contaminants of 

Potential Concern for Effects on Sediment-Associated Biota: 1997 Revision. Oak Ridge National Laboratory, Oak 

Ridge, TN. 

U.S. EPA (Environmental Protection Agency). 1997. Protocol for Screening Level Ecological Risk Assessment at 

Hazardous Waste Combustion Facilities. Internal Review Draft, February 28. Office of Solid Waste, 

Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1995. Technical Support Document for the Hazardous Waste 

Identification Rule: Risk Assessment for Human and Ecological Receptors. Office of Solid Waste, Washington, 

DC. 

MacDonald, D.D. 1994. Approach to the Assessment of Sediment Quality in Florida Coastal Waters. Volume 1. 

Florida Department of Environmental Protection, Tallahassee, FL. 

Algae and Aquatic Plant Benchmarks 

For algae and aquatic plants, adverse effects concentrations were identified in the open 

literature or from a data compilation presented in Toxicological Benchmarks for Screening 

Potential Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision (Suter and Tsao, 

1996). For most contaminants, studies were not available for aquatic vascular plants, and lowest 

effects concentrations were identified for algae. The benchmark for algae and aquatic plants was 

based on (1) an LOEC for vascular aquatic plants or (2) an effective concentration (ECxx) for a 

species of freshwater algae, frequently a species of green algae (e.g., Selenastrum 

capricornutum). Because of the lack of data for this receptor group and the differences between 

vascular aquatic plants and algae sensitivity, the lowest value of those identified was usually 

chosen. 

April 2010–Draft EPA document. H-3 
Appendix H Ecological Benchmarks 

H.1.2 Ingestion Exposure 

The ingestion route of exposure addresses the exposure of terrestrial mammals and birds 

through ingestion of aquatic plants and prey. Thus, the CCW ecological benchmarks for 

ingestion exposure express media concentrations that, based on certain assumptions about 

receptor diet and foraging behavior, were expected to be protective of populations of mammals 

and birds feeding and foraging in contaminated surface water bodies. 

The derivation of ingestion benchmarks began with the selection of appropriate 

ecotoxicological data based on a hierarchy of data sources. The assessment endpoint for the 

CCW ecological risk assessment was population viability; therefore, ecological benchmarks 

were developed from measures of reproductive/developmental success or, if unavailable, from 

other effects that could conceivably impair population dynamics. Population-level benchmarks 

were preferred over benchmarks for individual organisms; however, very few population-level 

benchmarks have been developed. Therefore, the CCW risk assessment used benchmarks 

derived from individual organism studies, and protection was inferred at the population level. 

Once an appropriate ingestion exposure study was identified, a benchmark was calculated 

using a three-step process. The remainder of this section outlines the basic technical approach 

used to convert avian or mammalian benchmarks (in daily doses) to the media concentration 

benchmarks (in units of concentration) used to assess ecological risks for surface water and 

sediment contaminated by CCW waste constituents. The methods reflect exposure through the 

ingestion of contaminated plants, prey, and media, and include parameters on accumulation (e.g., 

bioconcentration factors), uptake (e.g., consumption rates), and dietary preferences. 

Step 1: Scale Benchmark 

The benchmarks derived for test species can be extrapolated to wildlife receptor species 

within the same taxon using a cross-species scaling equation (Equation H-1) (Sample et al., 

1996). This is the default methodology EPA proposed for carcinogenicity assessments and 

reportable quantity documents for adjusting animal data to an equivalent human dose (57 FR 

24152). 

1/4

w

t

w t bw

bw Benchmark LOAEL ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = × (H-1) 

where 

 Benchmarkw = scaled ecological benchmark for species w (mg/kg/d) 

 LOAELt = lowest observed adverse effects level for test species (mg/kg/d) 

 bwt = body weight of the surrogate test species (kg) 

 bww = body weight of the representative wildlife species (kg). 

Step 2: Identify Bioconcentration Factors/Bioaccumulation Factors 

For metal constituents, whole-body bioconcentration factors (BCFs) and bioaccumulation 

factors (BAFs) were identified for aquatic organisms that could be used as food sources (e.g., 

fish). The Oak Ridge National Laboratory has proposed methods and data that are useful in 

predicting bioaccumulation (Sample et al. 1998a,b). These values were typically identified in the 

open literature and EPA references. 

April 2010–Draft EPA document. H-4 
Appendix H Ecological Benchmarks 

April 2010–Draft EPA document. H-5 

Step 3: Calculate Benchmarks 

The following equation provided the basis for calculating surface water benchmarks 

using a population-inference benchmark (e.g., endpoint on fecundity). 

[ ( )] ( ) Benchmark

I BAF C I C

bw

fish w w w

= × +× (H-2) 

where 

 Ifish = intake of contaminated fish (kg/d) 

 BAF = whole-body bioaccumulation factor (L/kg) 

 bw = weight of the representative species (kg) 

 Iw = intake of contaminated water (L/d) 

 Cw = total concentration in the water (mg/L). 

For chemicals that bioaccumulate significantly in fish tissue, the ingestion of 

contaminated food tends to dominate the exposure (i.e., [Ifish × Cfish] >> [Iw Cw]), and the water 

term (i.e., [Iw × Cw]) can be dropped from Equation H-2, resulting in Equation H-3: 

( )

bw

I CBAF

Benchmark fish × × w = (H-3) 

At the benchmark dose (mg/kg/d), the concentration in water is equivalent to the chemical 

stressor concentration limit for that receptor as a function of body weight, ingestion rate, and the 

bioaccumulation potential for the chemical of concern. Hence, Equation H-3 can be rewritten to 

solve for the surface water (CSCLsw) as follows: 

( BAFII )

benchmark bw CSCL

w fish

sw ×+

× = (H-4) 

H.2 Ecological Benchmarks 

The ecological benchmarks used to calculate ecological HQs in the CCW risk assessment 

are summarized in Table H-3, which provides the constituent name; the criterion and receptor 

for sediment and aquatic receptors; and the source for each benchmark. 
Appendix H Ecological Benchmarks 

Table H-3. Ecological Benchmarks Used in the CCW Risk Assessment 

Constituent

Sediment 

Criterion 

(mg/kg)

Sediment 

Receptor

Aquatic 

Criterion 

(mg/L)

Aquatic 

Receptor Source

Aluminum ID -- 0.09 Aquatic Biota U.S. EPA (1998) 

Antimony 2 Sediment biota 0.03 Aquatic Biota U.S. EPA (1998) 

Arsenic total 0.51 Spotted sandpiper ID -- U.S. EPA (1998) 

Arsenic III ID -- 0.15 Aquatic Biota U.S. EPA (1998) 

Arsenic IV ID -- 8.10E-03 Aquatic Biota U.S. EPA (1998) 

Barium 190 Spotted sandpiper 4.00E-03 Aquatic Biota U.S. EPA (1998) 

Beryllium ID -- 6.60E-04 Aquatic Biota U.S. EPA (1998) 

Boron ID -- 1.60E-03 Aquatic Biota U.S. EPA (1998) 

Cadmium 0.68 Sediment biota 2.50E-03 Aquatic Biota U.S. EPA (1998) 

Chromium 

total 

16.63 Spotted sandpiper ID -- U.S. EPA (1998) 

Chromium IV ID -- 0.09 Aquatic Biota U.S. EPA (1998) 

Chromium VI ID -- 0.01 Aquatic Biota U.S. EPA (1998) 

Cobalt ID -- 0.02 Aquatic Biota U.S. EPA (1998) 

Copper 18.7 Sediment biota 9.30E-03 Aquatic Biota U.S. EPA (1998) 

Lead 0.22 Spotted sandpiper 3.00E-04 River Otter U.S. EPA (1998) 

Mercury 0.11 Spotted sandpiper 1.90E-07 Kingfisher U.S. EPA (1998) 

Molybdenum 34 Spotted sandpiper 0.37 Aquatic Biota U.S. EPA (1998) 

Nickel 15.9 Sediment biota 0.05 Aquatic Biota U.S. EPA (1998) 

Selenium 

total 

ID -- 5.00E-03 Aquatic Biota U.S. EPA (1998) 

Selenium IV ID -- 0.03 Aquatic Biota U.S. EPA (1998) 

Selenium VI ID -- 9.50E-03 Aquatic Biota U.S. EPA (1998) 

Silver 0.73 Sediment biota 3.60E-04 Aquatic Biota U.S. EPA (1998) 

Thallium ID -- 0.01 Aquatic Biota U.S. EPA (1998) 

Vanadium 18 Spotted sandpiper 0.02 Aquatic Biota U.S. EPA (1998) 

Zinc 120 Sediment biota 0.12 Aquatic Biota U.S. EPA (1998) 

ID = insufficient data. 

April 2010–Draft EPA document. H-6 
Appendix H Ecological Benchmarks 

H.3 References 

Hopkins, W.A., and C.L. Rowe. 2004. Impacts of aquatic disposal of coal combustion 

by-products on vertebrates on the Savannah River Site. p. 141–147, In Proceedings of 

State Regulation of Coal Combustion By-Product Placement at Mine Sites. May 4–6, 

2004, Harrisburg, PA. U.S. Department of Interior, Office of Surface Mining, Alton, IL. 

Hopkins, W.A., S.E. DuRant, B.P. Staub, C.L. Rowe, and B.P. Jackson. 2006. Reproduction, 

embryonic development, and maternal transfer of contaminants in the amphibian 

Gastrophryne carolinensis. Environmental Health Perspectives 114(5):661-666. 

Power, T., K.L. Clark, A. Harfenist, and D.B. Peakall. 1989. A Review and Evaluation of the 

Amphibian Toxicological Literature. Technical Report Series No. 61. Canadian Wildlife 

Service, Environment Canada, Hull, Quebec. 

Sample, B.E., D.M. Opresko, and G.W. Suter, II. 1996. Toxicological Benchmarks for Wildlife: 

1996 Revision. ES/ER/TM-86/R3. Oak Ridge National Laboratory, Oak Ridge, TN. 

Sample, B.E., J.J. Beauchamp, R.A. Efroymson, and G.W. Suter, II. 1998b. Development and 

Validation of Bioaccumulation Models for Small Mammals. ES/ER/TM-219. Oak Ridge 

National Laboratory, Oak Ridge, TN. 

Sloof, W. 1992. Ecotoxicological Effect Assessment: Deriving Maximum Tolerable 

Concentrations (MTC) from Single Species Toxicity Data. Guidance Document. Report 

No. 719102.018. National Institute of Public Health and Environmental Protection 

(RIVM), Hilversum, the Netherlands. 

Stephan, C.E., D.I. Mount, D.J. Hansen, J.H. Gentile, G.A. Chapman, and W.A. Brungs. 1985. 

Guidelines for Deriving Numerical National Ambient Water Quality Criteria for the 

Protection of Aquatic Organisms and Their Use. NTIS PB85-220749. Office of Research 

and Development, U.S. Environmental Protection Agency, Springfield, VA. 

Suter, G.W., and C.L. Tsao. 1996. Toxicological Benchmarks for Screening Potential 

Contaminants of Concern for Effects on Aquatic Biota: 1996 Revision. U.S. Department 

of Energy, Oak Ridge National Laboratory, Oak Ridge, TN. June. 

U.S. EPA (Environmental Protection Agency). 1993. Water Quality Guidance for the Great 

Lakes System and Correction. Proposed Rule. 58 FR 20802. 

U.S. EPA (Environmental Protection Agency). 1995a. Great Lakes Water Quality Initiative 

Criteria Documents for the Protection of Aquatic Life in Ambient Water. EPA-820-B-95-

004. Office of Water, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1995b. Great Lakes Water Quality Initiative 

Criteria Documents for the Protection of Wildlife: DDT, Mercury, 2,3,78-TCDD, and 

PCBs. EPA-820-B-95-008. Office of Water, Washington, DC. 

April 2010–Draft EPA document. H-7 
Appendix H Ecological Benchmarks 

April 2010–Draft EPA document. H-8 

U.S. EPA (Environmental Protection Agency). 1996. Water Quality Criteria Documents for the 

Protection of Aquatic Life in Ambient Water: 1995 Updates. EPA-820-B-96-001. Office 

of Water, Washington, DC. 

U.S. EPA (Environmental Protection Agency). 1997b. Protocol for Screening Level Ecological 

Risk Assessment at Hazardous Waste Combustion Facilities (Volumes 1 and 2). Office of 

Solid Waste. Internal Review Draft. February 28, 1997. 

U.S. EPA (Environmental Protection Agency). 1998. Non-Groundwater Pathways, Human 

Health and Ecological Risk Analysis for Fossil Fuel Combustion Phase 2 (FFC2). Office 

of Solid Waste, Washington, DC. 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

Appendix I. Calculation of Health-Based Numbers (HBNs) 

for CCW Constituent Screening 

Management of CCW can result in 

contaminants moving from a waste management 

unit (WMU) and contaminating groundwater, and 

surface water via groundwater transport from a 

CCW WMU. Under these scenarios, individuals 

living near WMUs may then come into contact 

with chemicals via ingestion of contaminated 

drinking water or ingestion of fish contaminated 

via chemical uptake and accumulation. 

Health-based numbers (HBNs) for 

groundwater (as drinking water) and surface water 

were used in this analysis to consider risks and 

hazards to human receptors from chemicals that 

are released from CCW management units and 

move through the subsurface. HBNs represent concentrations in environmental media that will 

not cause an exceedance of a target cancer risk of 10-5 or a hazard quotient (HQ) of 1. 

Key Features of HBN Calculations 

▪ HBNs calculated for groundwater (mg/L) and 

surface water (mg/L) 

▪ HBNs based on a target cancer risk of 10-5 and 

a HQ of 1 

▪ Groundwater HBNs based on a residential 

drinking water scenario 

▪ Surface water HBNs based on a recreational 

fisher scenario 

▪ Adult and child receptors first exposed at ages 

3, 8, 15 and 20 

▪ Exposure factors set at central tendency 

values 

▪ Source size set at the 95th percentile of CCW 

landfills. 

The pathways included in the HBN calculations are summarized in Table I-1. The HBN 

for groundwater was based on domestic use of groundwater as drinking water. Surface water 

HBNs were based on a recreational fisher scenario in which the receptor was assumed to live at a 

different off-site location and to be exposed only to fish caught recreationally. 

Table I-1. Pathways Included in HBN Calculations 

HBN Calculation 

Drinking

Water 

Ingestion 

Fish 

Ingestion 

Groundwater HBN T

Surface water HBN T

I.1 Methodology 

All HBNs considered human receptors exposed to contaminated media and/or food items 

at different ages to take into account changing exposure patterns with age. The specific receptors 

considered were individuals exposed starting at ages 3, 8, 15, and 20. Depending on the start age, 

an appropriate exposure duration was selected for each receptor based on residency data. Each 

receptor was exposed for the period of time determined by the exposure duration, and the model 

accounted for changes in exposure patterns as a person ages. All exposure parameters selected 

for this analysis were based on 50th percentile values. Once the cancer risks and HQs were 

calculated for each receptor, HBNs were calculated based on total cancer risk, noncancer 

inhalation, and noncancer ingestion. The most protective HBN (i.e., the lowest across all age 

April 2010–Draft EPA document. I-1 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

groups) was selected. In all cases, the HBN calculations used central tendency exposure factors 

(e.g., body weight, exposure duration, exposure frequency, consumption rates). 

The equations used to calculate the HBNs are provided at the end of this appendix; Table 

I-2 lists the tables of equations by exposure pathway (Tables I-4 through I-8). Data used in these 

equations to calculate the CCW HBNs can be found in the other appendices to this report, as 

well as in Table I-9, which provides the age cohort–specific human exposure factors used in the 

HBN calculations. 

Table I-2. Key to Tables of Equations Used to Calculate HBNs 

Equation for Fish Concentrations

I-4 Concentration in Fish at Different Trophic Levels (mg/kg)

Equations for Human Exposure

I-5 Daily Intake of Contaminant from Consumption of Fish (mg/kg BW/day) 

I-6 Daily Intake of Contaminant from Consumption of Drinking Water (mg/kg BW/day)

Equations for Unit Risk Calculations and Health-based Numbers

I-7 Cancer Risk and Hazard Quotient Due to Ingestion (unitless) 

I-8 Health-Based Concentration (mg/L)

Groundwater HBNs were based on standard residential exposure assumptions for 

drinking water consumption, using equations from (U.S. EPA, 1998a). The surface water HBNs 

were based on concentrations in fish estimated using an aquatic food chain model; that 

methodology is described in the rest of this section. 

The methodology used for estimating concentrations in fish was based on EPA’s 

Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to 

Combustor Emissions (U.S. EPA, 1998a). An aquatic food chain model was used to estimate the 

concentration of constituent that may accumulate in fish. It was assumed for this analysis that 

fish are a food source for a recreational fisher. Trophic level three (T3) and four (T4) fish were 

considered in this analysis. T3 fish are those that consume invertebrates and plankton. T4 fish 

are those that consume other fish. Most of the fish that humans eat are T4 fish (e.g., salmon, 

trout, walleye, bass) and medium to large T3 fish (e.g., carp, smelt, perch, catfish, sucker, 

bullhead, sauger). For metals other than mercury, the calculation of contaminants in fish was 

based on the total concentration of contaminants in the waterbody (i.e., dissolved and suspended 

solids). For mercury, the calculation of contaminants in fish was based on the dissolved 

concentration of methyl mercury in the waterbody. 

Fish tissue concentrations are dependent on a bioconcentration factor (BCF), which is 

used to estimate the amount of constituent being transferred from the waterbody into the fish 

tissue. Specifically, BCFs reflect the ratio between the tissue concentration in fish and the 

appropriate waterbody concentration. BCFs were developed for each constituent to reflect 

accumulation in each trophic level considered. They were also developed to estimate the 

concentration in the fish filet versus the total fish. Human receptors consume only the filet 

portion of the fish, which has a lower lipid content. Because some constituents tend to 

accumulate in the fatty tissue, the concentration in the filet portion of the fish is sometimes lower 

than the concentration in the whole fish. 

April 2010–Draft EPA document. I-2 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

April 2010–Draft EPA document. I-3 

I.2 Health-Based Numbers 

Table I-3 provides the HBNs for surface water and groundwater. 

Table I-3. Groundwater and Surface Water HBNs 

Chemical Benchmark Type 

Groundwater HBN 

(mg/L)1

Surface Water HBN2

(mg/L) 

Aluminum Noncancer 29.4 NA 

Antimony Noncancer 0.012 NA 

Arsenic Cancer 0.0029 0.23 

Arsenic Noncancer 0.0088 0.71 

Barium Noncancer 5.89 NA 

Beryllium Noncancer 0.059 1.0 

Boron Noncancer 5.87 NA 

Cadmium Noncancer 0.015 0.035 

Chromium (III) Noncancer 44 23,700 

Chromium (VI) Noncancer 0.088 47 

Cobalt Noncancer 0.0088 NA 

Copper3 Noncancer (GW)/AWQ (SW) 0.29 1.3 

Cyanide Noncancer 0.59 NA 

Fluoride Noncancer 3.52 NA 

Lead MCL 0.015 NA 

Manganese Noncancer 1.4 NA 

Mercury Noncancer 0.0088 3.85E-06 

Molybdenum Noncancer 0.147 12 

Nickel Noncancer 0.59 237 

Nitrate MCL 10 NA 

Nitrate Noncancer 47 NA 

Nitrite Noncancer 2.9 NA 

Selenium Noncancer 0.147 0.038 

Silver Noncancer 0.147 NA 

Strontium Noncancer 17.6 NA 

Thallium Noncancer 0.0024 0.008 

Vanadium Noncancer 0.21 NA 

Zinc Noncancer 8.8 8.13 

1

 Based on domestic drinking water ingestion. 2

 Based on fish consumption by a recreational fisher. 

3

 Fish bioconcentration factor values for copper are zero. HBN based on National Ambient Water Quality Criteria. 

AWQ = National Ambient Water Quality Criteria 

MCL = maximum contaminant level or drinking water action level (for lead and copper) 

NA = not available 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

Table I-4. Concentration in Fish at Different Trophic Levels (mg/kg) 

Cfish_T 

For Mercury: 

CfishT = Cdiss × BCFT

Cdiss = 05.0 ×Cwt

For NonvolatileMetals: 

CfishT = × BCFCwt T

Name Description Value 

Cdiss Concentration in surface water (dissolved) (mg/L) Calculated 

Cwt Concentration in surface water (total) (mg/L) Set equal to 1 for HBN calculation 

0.05 Fraction of total mercury as dissolved methyl mercury Derived from U.S. EPA, 1997a

BCF_T3F Bioconcentration factor for trophic level 3, fish filet (L/kg) Chemical-specific (see App. J) 

BCF_T3W Bioconcentration factor for trophic level 3, fish whole (L/kg) Chemical-specific (see App. J) 

BCF_T4F Bioconcentration factor for trophic level 4, fish filet (L/kg) Chemical-specific (see App. J) 

BCF_T4W Bioconcentration factor for trophic level 4, fish whole (L/kg) Chemical-specific (see App. J) 

Source: U.S. EPA, 1998a. 

Table I-5. Daily Intake of Contaminant from Consumption of Fish (mg/kg BW/day) 

Ifish 

= TT 3 × 3 + TFfishT 4 ×CFCFCfish 4FfishT

BW

F

CRCfishIfish fish

T fish ×

××=

000,1

Name Description Value 

1000 Conversion factor (g/kg) 

C_fishT3F Concentration of contaminant in fish at different trophic levels (mg/kg) Calculated (Table I-4) 

C_fishT4F Concentration of contaminant in fish at different trophic levels (mg/kg) Calculated (Table I-4) 

CfishT Concentration of contaminant in fish (mg/kg) Calculated (Table I-4) 

BW Body weight (kg) Age-cohort-specific (Table I-9)

CR_fish Consumption rate of fish (g WW/day) Age-cohort-specific (Table I-9)

F_fish Fraction of fish intake from contaminated source (unitless) 1 (protective value) 

F_T3 Fraction of trophic level 3 intake (unitless) 0.36 (U.S. EPA, 1997d) 

F_T4 Fraction of trophic level 4 intake (unitless) 0.64 (U.S. EPA, 1997d) 

Source: U.S. EPA, 1998a. 

April 2010–Draft EPA document. I-4 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

Table I-6. Daily Intake of Contaminant from Consumption 

of Drinking Water (mg/kg BW/day) 

Idw 

BW

F CrCIdw dw

dw dw ××=

Name Description Value

1000 Conversion factor (mL/L) 

Cdw Concentration of contaminant in drinking water (mg/L) Set equal to 1 for HBN calculation

BW Body weight (kg) Age-cohort-specific (Table I-9) 

CR_dw Consumption rate of water (L/day) Age-cohort-specific (Table I-9) 

F_dw Fraction of drinking water ingested that is contaminated (unitless) 1 (protective value) 

Source: U.S. EPA, 1998a. 

Table I-7. Cancer Risk and Hazard Quotient Due to Ingestion (unitless) 

Risk_Oral 

RfD

I HQOral =

×365

× × × = AT

CSFEFEDI Risk Oral

Oral

Name Description Value

365 Conversion factor (days/yr) 

I Intake rate from fish or drinking water (mg/kg/day) Calculated (Tables I-5 and I-6) 

CSFOral Oral cancer slope factor (mg/kg/day)-1 Chemical-specific (see Appendix G) 

RfD Noncancer reference dose (mg/kg/day) Chemical-specific (see Appendix G) 

AT Averaging time (yr) 70 (U.S. EPA, 1991) 

ED Exposure duration for oral ingestion (yr) Age-cohort-specific (Table I-9) 

EF Exposure frequency (days/yr) 350 (U.S. EPA, 1991) 

April 2010–Draft EPA document. I-5 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

Table I-8. Health-Based Concentration (ppm) 

CalcHBN 

THQ HQ

C HBN

Oral

NCOral ×=

TR

Risk

C HBNRisk ×=

Name Description Value

THQ Target noncancer hazard quotient (unitless) 1 

TR Target cancer risk (unitless) 1.00E-5 

HQ_Oral Noncancer hazard quotient for ingestion (unitless) Calculated (Table I-7) 

Risk Total cancer risk (unitless) Calculated (Table I-7) 

C Constituent concentration in media (mg/L or mg/kg) Value set to unit concentration of 1 

Back calculation assuming linearity. 

Table I-9. Age Cohort-Specific Human Exposure Factors 

Parameter Cohort_1 Cohort_2 Cohort_3 Cohort_4 

Body weight (BW) (kg) 15.3 29.6 56.8 69.3 

Start year (SY) (yr) 3 8 15 20 

Fish consumption rate (CR_fish) 

(g WW/day) 

2 2 2 2 

Exposure duration (ED) (yr) 5 7.5 8 10 

Drinking water consumption rate 

(CR_dw) (L/day) 

0.6165 0.731 0.8685 1.275 

I.3 References 

Bidleman, T.F. 1988. Atmospheric Processes. Environmental Science and Technology 22(4). 

EPRI (Electric Power Research Institute). 1997. Coal Combustion and Low Volume Wastes 

Comanagement Survey. EPRI. Palo Alto, CA. June. 

U.S. EPA (Environmental Protection Agency). 1996. Soil Screening Guidance Technical 

Background Document. EPA/540/R/95/128. Office of Solid Waste and Emergency 

Response, Washington, DC. May. 

U.S. EPA (Environmental Protection Agency). 1997a. Exposure Factors Handbook, Volume I, 

General Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

April 2010–Draft EPA document. I-6 
Appendix I Calculation of Health-Based Numbers (HBNs) for CCW Constituent Screening 

April 2010–Draft EPA document. I-7 

U.S. EPA (Environmental Protection Agency). 1997b. Exposure Factors Handbook, Volume II, 

Food Ingestion Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1997c. Exposure Factors Handbook, Volume III, 

Activity Factors. EPA/600/P-95/002Fa. Office of Research and Development, 

Washington, DC. August. 

U.S. EPA (Environmental Protection Agency). 1997d. The Parameter Guidance Document. A 

Companion Document to the Methodology for Assessing Health Risks Associated with 

Multiple Pathways Exposure to Combustor Emissions (Internal Draft). NCEA-0238. 

National Center for Environmental Assessment, Cincinnati, OH, pp. 1-281. March. 

U.S. EPA (Environmental Protection Agency). 1998a. Methodology for Assessing Health Risks 

Associated with Multiple Pathways of Exposure to Combustor Emissions. EPA 600/R98/137. National Center for Environmental Assessment, Cincinnati, OH. December. 

U.S. EPA (Environmental Protection Agency). 1998b. Technical Background Document for the 

Supplemental Report to Congress on Remaining Fossil-Fuel Combustion Wastes— 

Ground-Water Pathway Human Health Risk Assessment. Revised Draft Final. Office of 

Solid Waste, Washington, DC. June. 

U.S. EPA (Environmental Protection Agency). 2001. Supplemental Guidance for Developing 

Soil Screening Levels for Superfund Sites: Peer Review Draft. OSWER 9355.4-24. Office 

of Solid Waste and Emergency Response. March. 

U.S. EPA (Environmental Protection Agency). 2005. Human Health Risk Assessment Protocol 

for Hazardous Waste Combustion Facilities. Final. EPA530-R-05-006. Office of Solid 

Waste. September. 
April 2010–Draft EPA document. 

[This page intentionally left blank.] 
Appendix J Chemical-Specific Inputs for CCW Constituent Screening 

Appendix J. Chemical-Specific Inputs Used in 

CCW Constituent Screening 

Chemical-specific inputs used to develop the CCW HBNs include the bioconcentration 

factors needed to estimate exposure concentrations in fish. Values for these inputs are obtained 

from the best available literature source. Table J-1 provides, for each chemical in the CCW 

screening analysis, the values used in the analysis along with the source of each value. 

Table J-1. Fish Bioconcentration Factors 

Parametera

Value

(L/kg) Reference Comment 

Aluminum (7429905)

No Data 

Antimony (7440360)

BCF_T3F 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T3W 0 Barrows et al. (1980) Species was sunfish. 

BCF_T4F 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T4W 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

Arsenic (7440382)

BCF_T3F 4 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T3W 4 Barrows et al. (1980) Species was sunfish. 

BCF_T4F 4 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T4W 4 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

Barium (7440393)

No Data 

Beryllium (7440417)

BCF_T3F 19 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T3W 19 Barrows et al. (1980) Species was sunfish. 

BCF_T4F 19 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T4W 19 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

Boron (7440428)

No Data 

Cadmium (7440439)

BCF_T3F 270 Kumada et al. (1972) BCF_T3W was used as a surrogate. 

BCF_T3W 270 Kumada et al. (1972) Geomean of 3 data points in Table 2. 

BCF_T4F 270 Kumada et al. (1972) BCF_T4W was used as a surrogate. 

BCF_T4W 270 Kumada et al. (1972) Geomean of 3 data points in Table 2. Species 

were doce and rainbow trout. 

(continued) 

April 2010–Draft EPA document. J-1 
Appendix J Chemical-Specific Inputs for CCW Constituent Screening 

Fish Bioconcentration Factors (continued) 

Parametera

Value

(L/kg) Reference Comment 

Chromium(III) (16065831)

BCF_T3F 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T3W 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T4F 0.6 Stephan (1993) Geomean (as cited in Stephan, 1993) based 

on Buhler et al. (1977) and Calamari et al. 

(1982). Used chromium as a surrogate. 

BCF_T4W 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

Chromium(VI) (18540299)

BCF_T3F 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T3W 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T4F 0.6 Stephan (1993) Geomean (as cited in Stephan, 1993) based 

on Buhler et al. (1977) and Calamari et al. 

(1982). Used chromium as a surrogate. 

BCF_T4W 0.6 Stephan (1993) BCF_T4F was used as a surrogate. 

Copper (7440508)

BCF_T3F 0 Stephan (1993) 

BCF_T3W 0 Stephan (1993) 

BCF_T4F 0 Stephan (1993) 

BCF_T4W 0 Stephan (1993) 

Cobalt (7440484)

No Data 

Cyanide (57125)

No Data 

Fluoride (16984488)

No Data 

Manganese (7439965)

No Data 

Molybdenum (7439987)

BCF_T3F 4 Eisler (1989) BCF_T4F was used as a surrogate. 

BCF_T3W 4 Eisler (1989) BCF_T4F was used as a surrogate. 

BCF_T4F 4 Eisler (1989) Geomean of values found on pages 27 and 

28. Species were rainbow trout and steelhead 

trout. 

BCF_T4W 4 Eisler (1989) BCF_T4F was used as a surrogate. 

Nickel (7440020)

BCF_T3F 0.8 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T3W 0.8 Stephan (1993) BCF_T4F was used as a surrogate. 

BCF_T4F 0.8 Stephan (1993) Derived from Calamari et al. (1982) (as cited 

in Stephan, 1993). 

BCF_T4W 0.8 Stephan (1993) BCF_T4F was used as a surrogate. 

(continued) 

April 2010–Draft EPA document. J-2 
Appendix J Chemical-Specific Inputs for CCW Constituent Screening 

April 2010–Draft EPA document. J-3 

Fish Bioconcentration Factors (continued) 

Parametera

Value

(L/kg) Reference Comment 

Selenium (7782492)

BCF_T3F 490 Lemly (1985) Based on threadfin shad and blueback 

herring. Units corrected. 

BCF_T3W 490 Lemly (1985) BCF_T3F was used as a surrogate. 

BCF_T4F 1,700 Lemly (1985) Based on threadfin shad and blueback 

herring. Units corrected. 

BCF_T4W 1,700 Lemly (1985) BCF_T4F was used as a surrogate. 

Silver (7440224)

BCF_T3F 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T3W 0 Barrows et al. (1980) Species was sunfish. 

BCF_T4F 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T4W 0 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

Strontium (7440246)

No Data 

Thallium (7440280)

BCF_T3F 34 Barrows et al. (1980) BCF_T3W was used as a surrogate. 

BCF_T3W 34 Barrows et al. (1980) Species was sunfish. 

BCF_T4F 130 Stephan (1993) Derived from Zitko et al. (1975) (as cited in 

Stephan, 1993). 

BCF_T4W 130 Stephan (1993) BCF_T4F was used as a surrogate. 

Total Nitrate Nitrogen (14797558)

No Data 

Vanadium (7440622)

No Data 

Zinc (7440666)

BCF_T3F 350 Murphy et al. (1978) BCF_T3W was used as a surrogate. 

BCF_T3W 350 Murphy et al. (1978) Geomean of converted dry weight 

concentration in Table 1 of bluegills at Site A 

and B. 

BCF_T4F 350 Murphy et al. (1978) BCF_T3W was used as a surrogate. 

BCF_T4W 350 Murphy et al. (1978) BCF_T3W was used as a surrogate. 

a

 BCF_T3F = Bioconcentration factor for trophic level 3 fish, filet 

 BCF_T3W = Bioconcentration factor for trophic level 3 fish, whole 

 BCF_T4F = Bioconcentration factor for trophic level 4 fish, filet 

 BCF_T4W = Bioconcentration factor for trophic level 4 fish, whole 
Appendix J Chemical-Specific Inputs for CCW Constituent Screening 

References 

Barrows, M.E., S.R. Petrocelli, K.J. Macek, and J.J. Carroll. 1980. Chapter 24: Bioconcentration 

and elimination of selected water pollutants by bluegill sunfish (Lepomis macrochirus). 

In: Dynamics, Exposure and Hazard Assessment of Toxic Chemicals, R. Haque (ed.), 

Ann Arbor Science Publishers Inc., Ann Arbor, MI. pp. 379-392. 

Eisler, R. 1989. Molybdenum Hazards to Fish, Wildlife, and Invertebrates: A Synoptic Review. 

Contaminant Hazard Reviews, Report No. 19, Biological Report 85(1.19). Laurel, MD. 

August. 

Kumada, H., S. Kimura, M. Yokote, and Y. Matida. 1972. Acute and chronic toxicity, uptake, 

and retention of cadmium in freshwater organisms. Bulletin of Freshwater Fisheries 

Research Laboratory 22(2):157-165. December 20. 

Lemly, A.D. 1985. Toxicology of selenium in a freshwater reservoir: Implications for 

environmental hazard evaluation and safety. Ecotoxicology and Environmental Safety

10:314-338. 

Murphy, B.R., G.J. Atchison, and A.W. McIntosh. 1978. Cadmium and zinc in muscle of 

bluegill (Lepomis macrochirus) and largemouth bass (Micropterus salmoides) from an 

industrially contaminated lake. Environmental Pollution 17:253-257. 

Stephan, C.E. 1993. Derivation of Proposed Human Health and Wildlife Bioaccumulation 

Factors for the Great Lakes Initiative (Draft). PB93-154672. Environmental Research 

Laboratory, Office of Research and Development, Duluth, MN. March. 

U.S. EPA (Environmental Protection Agency). 1997. Mercury Study Report to Congress. 

Volume III - Fate and Transport of Mercury in the Environment. EPA 452/R-97/005. 

Office of Air Quality Planning and Standards and Office of Research and Development, 

Washington, DC. 

April 2010–Draft EPA document. J-4 
Appendix K Screening Analysis Results 

Appendix K. Screening Analysis Results 

Table K-1. CCW Surface Impoundment (SI) Human Health Screening Results: 

Groundwater-to-Drinking-Water Pathway 

Chemical Benchmark Type HBN (mg/L) 

2002 SI Porewater 

90th Percentile 

HQ(Cancer 

Risk) 

Analytes Exceeding Risk Criteria1

Antimony Noncancer 1.17E-02 6.40E-02 5.45E+00 

Arsenic Cancer 2.86E-03 5.18E+00 (1.81E-02) 

Arsenic Noncancer 8.81E-03 5.18E+00 5.88E+02 

Boron Noncancer 5.87E+00 7.52E+01 1.28E+01 

Cadmium Noncancer 1.47E-02 1.31E-01 8.91E+00 

Chromium (VI) Noncancer 8.81E-02 3.66E-01 4.15E+00 

Cobalt Noncancer 8.81E-03 6.27E+00 7.13E+02 

Fluoride Noncancer 3.52E+00 1.91E+01 5.42E+00 

Lead MCL 1.50E-02 1.77E-01 1.18E+01 

Manganese Noncancer 1.38E+00 7.67E+00 5.56E+00 

Molybdenum Noncancer 1.47E-01 1.00E+00 6.81E+00 

Nickel Noncancer 5.87E-01 7.49E-01 1.27E+00 

Nitrate MCL 1.00E+01 6.02E+02 6.02E+01 

Nitrite Noncancer 2.94E+00 5.22E+00 1.78E+00 

Selenium Noncancer 1.47E-01 3.56E-01 2.43E+00 

Thallium Noncancer 2.35E-03 4.52E-02 1.93E+01 

Vanadium Noncancer 2.06E-01 4.78E-01 2.33E+00 

Analytes Below Risk Criteria1

Aluminum Noncancer 2.94E+01 2.30E+01 7.84E-01 

Barium Noncancer 5.89E+00 3.02E-01 5.15E-02 

Beryllium Noncancer 5.87E-02 5.68E-03 9.67E-02 

Chromium (III) Noncancer 4.40E+01 3.66E-01 8.31E-03 

Copper Noncancer 2.93E-01 2.84E-01 9.69E-01 

Mercury Noncancer 8.81E-03 2.50E-04 2.84E-02 

Silver Noncancer 1.47E-01 5.00E-03 3.41E-02 

Strontium Noncancer 1.76E+01 8.74E+00 4.96E-01 

Zinc Noncancer 8.81E+00 6.70E-01 7.60E-02 

1

 Risk criteria are 1E-05 cancer risk or a hazard quotient (HQ) of 1E+00 for noncancer endpoints. 

HBN = health-based number 

90th percentile = 90th percentile SI porewater concentration 

HQ = hazard quotient 

MCL = maximum contaminant level 

April 2010–Draft EPA document. K-1 
Appendix K Screening Analysis Results 

Table K-2. CCW Surface Impoundment (SI) Human Health Screening Results: 

Groundwater-to-Surface-Water (Fish Ingestion) Pathway 

Chemical Benchmark Type HBN (mg/L) 

2002 SI Porewater 

90th Percentile HQ (Cancer Risk) 

Analytes Exceeding Risk Criteria1

Arsenic Cancer 0.23 5.18E+00 (2.24E-04) 

Arsenic Noncancer 0.71 5.18E+00 7.28E+00 

Cadmium Noncancer 0.035 1.31E-01 3.73E+00 

Mercury Noncancer 3.85E-06 2.50E-04 6.50E+01 

Selenium Noncancer 0.038 3.56E-01 9.50E+00 

Thallium Noncancer 0.008 4.52E-02 5.69E+00 

Analytes Below Risk Criteria1

Antimony AWQ 4.3 6.40E-02 1.49E-02 

Beryllium Noncancer 1.00 5.68E-03 5.69E-03 

Chromium (III) Noncancer 23,700 3.66E-01 1.54E-05 

Chromium (VI) Noncancer 47 3.66E-01 7.72E-03 

Copper2 AWQ 1.3 2.84E-01 2.18E-01 

Molybdenum Noncancer 12 1.00E+01 8.43E-02 

Nickel Noncancer 237 7.49E-01 3.16E-03 

Zinc Noncancer 8.13 6.70E-01 8.24E-02 

1 Risk criteria are 1E-05 cancer risk or a hazard quotient (HQ) of 1E+00 for noncancer endpoints. 

2

 Fish bioconcentration factor values for copper are zero. HBN based on National Ambient Water Quality Criteria. 

HBN = health-based number 

90th percentile = 90th percentile SI porewater concentration 

HQ = hazard quotient 

AWQ = National Ambient Water Quality Criteria 

MCL = maximum contaminant level 

April 2010–Draft EPA document. K-2 
Appendix K Screening Analysis Results 

Table K-3. CCW Landfill Leachate Human Health Screening Results: 

Groundwater-to-Drinking-Water Pathway

Chemical Benchmark Type HBN (mg/L) 

2002 Landfill Leachate 

90th Percentile 

HQ(Cancer 

Risk) 

Analytes Exceeding Risk Criteria1

Antimony Noncancer 1.17E-02 2.61E-01 2.22E+01 

Arsenic Cancer 2.86E-03 3.94E-01 (1.38E-03) 

Arsenic Noncancer 8.81E-03 3.94E-01 4.48E+01 

Boron Noncancer 5.87E+00 1.06E+01 1.80E+00 

Cadmium Noncancer 1.47E-02 4.94E-02 3.37E+00 

Chromium (VI) Noncancer 8.81E-02 2.00E-01 2.27E+00 

Cobalt Noncancer 8.81E-03 8.25E-02 9.33E+00 

Fluoride Noncancer 3.52E+00 6.34E+00 1.80E+00 

Lead MCL 1.50E-02 2.39E-01 1.59E+01 

Molybdenum Noncancer 1.47E-01 6.16E-01 4.20E+00 

Nitrite Noncancer 2.94E+00 3.47E+00 1.18E+00 

Selenium Noncancer 1.47E-01 1.76E-01 1.20E+00 

Thallium Noncancer 2.35E-03 5.00E-02 2.13E+01 

Vanadium Noncancer 2.06E-01 4.50E-01 2.19E+00 

Analytes Below Risk Criteria1

Aluminum Noncancer 2.94E+01 1.05E+01 3.58E-01 

Barium Noncancer 5.89E+00 1.60E+00 2.73E-01 

Beryllium Noncancer 5.87E-02 1.58E-02 2.70E-01 

Chromium (III) Noncancer 4.40E+01 2.00E-01 4.54E-03 

Copper Noncancer 2.93E-01 1.50E-01 5.12E-01 

Cyanide Noncancer 5.87E-01 6.32E-02 1.08E-01 

Manganese Noncancer 1.38E+00 1.37E+00 9.92E-01 

Mercury Noncancer 8.81E-03 2.69E-03 3.06E-01 

Nickel Noncancer 5.87E-01 3.09E-01 5.27E-01 

Nitrate MCL 1.00E+01 2.83E+00 2.83E-01 

Silver Noncancer 1.47E-01 3.95E-02 2.69E-01 

Strontium Noncancer 1.76E+01 9.70E+00 5.51E-01 

Zinc Noncancer 8.81E+00 1.94E+00 2.20E-01 

1

 Risk criteria are 1E-05 cancer risk or a hazard quotient (HQ) of 1E+00 for noncancer 

endpoints, applied to 90th percentile concentrations. 

HBN = health-based number 

90th percentile = 90th percentile concentration 

HQ = hazard quotient 

MCL = maximum contaminant level 

April 2010–Draft EPA document. K-3 
Appendix K Screening Analysis Results 

Table K-4. CCW Landfill Leachate Human Health Screening Results: 

Groundwater-to-Surface-Water Pathway 

Chemical Benchmark Type 

HBN 

(mg/L) 

2002 Landfill Leachate 

90th Percentile HQ (Cancer Risk) 

Analytes Exceeding Risk Criteria1

Arsenic Cancer 0.23 3.94E-01 (1.71E-05) 

Cadmium Noncancer 0.035 4.94E-02 1.41E+00 

Mercury Noncancer 3.85E-06 2.69E-03 7.00E+02 

Selenium Noncancer 0.038 1.76E-01 4.69E+00 

Thallium Noncancer 0.008 5.00E-02 6.29E+00 

Analytes Below Risk Criteria1

Antimony AWQ 4.3 2.61E-01 6.07E-02 

Arsenic Noncancer 0.71 3.94E-01 5.54E-01 

Beryllium Noncancer 1.00 1.58E-02 1.59E-02 

Chromium (III) Noncancer 23,700 2.00E-01 8.44E-06 

Chromium (VI) Noncancer 47 2.00E-01 4.22E-03 

Copper2 AWQ 1.3 1.50E-01 1.15E-01 

Cyanide AWQ 222 6.32E-02 2.85E-04 

Molybdenum Noncancer 12 6.16E-01 5.20E-02 

Nickel Noncancer 237 3.09E-01 1.30E-03 

Zinc Noncancer 8.13 1.94E+00 2.38E-01 

1

 Risk criteria are 1E-05 cancer risk or a hazard quotient (HQ) of 1E+00 for noncancer endpoints, applied to 90th percentile 

concentrations. 2

 Fish bioconcentration factor values for copper are zero. HBN based on National Ambient Water Quality Criteria. 

HBN = health-based number90th percentile = 90th percentile concentration 

HQ = hazard quotient 

AWQ = National Ambient Water Quality Criteria 

MCL = maximum contaminant level 

April 2010–Draft EPA document. K-4 
Appendix K Screening Analysis Results 

Table K-5. Surface Impoundment Ecological Screening Results: Direct Surface 

Impoundment and Groundwater-to-Surface-Water Pathways 

Chemical 

CSCL 2002 SI Porewater 1998 SI Water 

(mg/L) 

90th Percentile 

(mg/L) HQ 

95th Percentile 

(mg/L) HQ 

Analytes Exceeding Risk Criterion1

Aluminum 8.70E-02 2.30E+01 2.65E+02 5.11E+00 5.87E+01 

Arsenic III 1.50E-01 5.18E+00 3.45E+01 5.50E-01 3.67E+00 

Arsenic IV 8.10E-03 5.18E+00 6.39E+02 5.50E-01 6.79E+01 

Barium 4.00E-03 3.02E-01 7.54E+01 7.12E-01 1.78E+02 

Boron 1.60E-03 7.52E+01 4.70E+04 4.60E+02 2.88E+05 

Cadmium 2.50E-03 1.31E-01 5.23E+01 2.50E-01 1.00E+02 

Chromium VI 1.10E-02 3.66E-01 3.33E+01 2.67E-02 2.43E+00 

Cobalt 2.30E-02 6.27E+00 2.73E+02 1.00E-02 4.35E-01 

Copper 9.30E-03 2.84E-01 3.05E+01 3.90E-01 4.19E+01 

Lead 3.01E-04 1.77E-01 5.88E+02 2.50E-01 8.31E+02 

Mercury 1.90E-07 2.50E-04 1.32E+03 1.50E-03 7.89E+03 

Nickel 5.20E-02 7.49E-01 1.44E+01 6.00E-01 1.15E+01 

Selenium IV 2.80E-02 3.56E-01 1.27E+01 7.80E+00 2.79E+02 

Selenium total 5.00E-03 3.56E-01 7.13E+01 7.80E+00 1.56E+03 

Selenium VI 9.50E-03 3.56E-01 3.75E+01 7.80E+00 8.21E+02 

Silver 3.60E-04 5.00E-03 1.39E+01 5.00E-03 1.39E+01 

Vanadium 2.00E-02 4.78E-01 2.39E+01 8.00E-01 4.00E+01 

Analytes Not Exceeding Risk Criterion1

Antimony 3.00E-02 6.40E-02 2.13E+00 1.37E-01 4.57E+00 

Beryllium 6.60E-04 5.68E-03 8.61E+00 1.00E-03 1.52E+00 

Chromium III 8.60E-02 3.66E-01 4.26E+00 4.00E-01 4.65E+00 

Molybdenum 3.70E-01 1.00E+00 2.70E+00 5.00E-01 1.35E+00 

Thallium 1.20E-02 4.52E-02 3.77E+00 5.00E-02 4.17E+00 

Zinc 1.20E-01 6.70E-01 5.58E+00 6.70E-01 5.58E+00 

1

 Risk criterion is a hazard quotient (HQ) of 10 (for direct exposure to impoundment waters). 

SI = surface impoundment 

CSCL = chemical stressor concentration level 

April 2010–Draft EPA document. K-5 
Appendix K Screening Analysis Results 

April 2010–Draft EPA document. K-6 

Table K-6. Landfill Ecological Screening Results: 

Groundwater-to-Surface-Water Pathway 

Chemical 

CSCL 

(mg/L) 

2002 - Landfill Leachate 

90th Percentile (mg/L) HQ 

Analytes Exceeding Risk Criterion1

Aluminum 8.70E-02 1.05E+01 1.21E+02 

Arsenic IV 8.10E-03 3.94E-01 4.87E+01 

Barium 4.00E-03 1.60E+00 4.01E+02 

Beryllium 6.60E-04 1.58E-02 2.40E+01 

Boron 1.60E-03 1.06E+01 6.61E+03 

Cadmium 2.50E-03 4.94E-02 1.98E+01 

Chromium VI 1.10E-02 2.00E-01 1.82E+01 

Copper 9.30E-03 1.50E-01 1.61E+01 

Lead 3.01E-04 2.39E-01 7.94E+02 

Mercury 1.90E-07 2.69E-03 1.42E+04 

Selenium total 5.00E-03 1.76E-01 3.52E+01 

Selenium VI 9.50E-03 1.76E-01 1.85E+01 

Silver 3.60E-04 3.95E-02 1.10E+02 

Vanadium 2.00E-02 4.50E-01 2.25E+01 

Zinc 1.20E-01 1.94E+00 1.61E+01 

Analytes Not Exceeding Risk Criterion1

Antimony 3.00E-02 2.61E-01 8.70E+00 

Arsenic III 1.50E-01 3.94E-01 2.63E+00 

Chromium III 8.60E-02 2.00E-01 2.33E+00 

Cobalt 2.30E-02 8.25E-02 3.59E+00 

Molybdenum 3.70E-01 6.16E-01 1.67E+00 

Nickel 5.20E-02 3.09E-01 5.95E+00 

Selenium IV 2.80E-02 1.76E-01 6.28E+00 

Thallium 1.20E-02 5.00E-02 4.17E+00 

1

 Risk criterion is a hazard quotient (HQ) of 10 

CSCL = chemical stressor concentration level 
Appendix L Time to Peak Concentration at Receptor Well 

April 2010–Draft EPA document. L-1 

Appendix L. Time to Peak Concentration at Receptor Well 

for Selected CCW Constituents 

L.1 Introduction 

This appendix presents plots of arrival times for the peak well concentrations used to 

calculate groundwater-to-drinking-water risks for selected CCW constituents (arsenic III and V, 

boron, cobalt, selenium IV and VI, and thallium1

). The arrival times are plotted as cumulative 

distributions for surface impoundments and landfills. These constituents were selected to 

represent the chemicals with the highest estimated risks and to span the range of mobility in the 

subsurface. 

Groundwater pathway modeling conducted in support of the CCW risk assessment 

consisted of probabilistic fate and transport simulations of mostly metal constituents present in 

three different waste types (ash, ash and coal, and fluidized bed combustion wastes) managed in 

landfills and surface impoundments. Three liner designs were also considered: no liner; a 3-foot 

clay liner; and a composite liner (a composite of geomembrane, geosynthetic clays, and/or 

compacted clays), assigned to each CCW waste management unit (WMU) based on liner type 

data in the EPRI database (see Appendix B). The predicted groundwater concentrations were 

used to estimate potential risks to humans and the environment exposed to the modeled CCW 

constituents. 

Among the inputs to the model were distributions of infiltration rates of water through the 

landfills and surface impoundments corresponding to each of the three liner types. Among the 

outputs generated by the groundwater pathway fate and transport model were the peak 

concentration observed at the receptor well and the time at which the peak was observed. For 

each probabilistic simulation scenario (a constituent in a particular waste type managed in a 

particular type of WMU), approximately 10,000 sets of model inputs generated an equivalent 

number of groundwater observations. Some were non-zero concentrations, others were zero. For 

these zero-value observations, the model also assigned a value of zero to arrival time. Zero-value 

observations can be attributed to zero-value infiltration rates (which occur only for WMUs with 

composite liners); in that case, no mass leaves the WMU and there is no time of travel. Zerovalue observations can also be attributed to fate and transport conditions that retard the 

movement of a constituent from the WMU through the subsurface to the extent that the dissolved 

component was not observed within the established maximum allowable timeframe (10,000 

years). In this case, the time of travel is greater than 10,000 years. 

To better understand the time frames in which risks associated with exposures to 

contaminated groundwater may occur, an analysis was performed to graphically represent 

distributions of arrival time of the peak groundwater concentrations at the nearby drinking water 

well. The analysis was performed across all waste types with respect to liner and WMU type. 

 

1

 Thallium was not modeled in the surface impoundment scenario, and thus no arrival times were calculated here. 
Appendix L Time to Peak Concentration at Receptor Well 

What follows is a description of how the peak concentrations and their arrival times were treated 

to create the plots presented in this appendix, including the treatment of zero-value observations. 

L.2 Methodology 

Given a constituent managed in a particular type of WMU (e.g., arsenic in landfills), all 

infiltration rates and their corresponding peak concentrations and arrival times predicted by the 

model were extracted from the input/output data for simulations across all waste types in which 

the selected constituent was found. The triplets of data needed to prepare the graphs—infiltration 

rate, peak concentration, and arrival time—were then filtered from the data and segregated by 

liner type. Zero-value observed concentrations were treated in the following manner: 

 Zero-value observations corresponding to zero-value infiltration rates were assigned an 

arbitrary value of -1, effectively excluding those data from the graphs. This was 

appropriate, because when infiltration is zero, there is no plume and no contaminants 

enter or are transported in groundwater. Only the composite liner scenarios produced 

zero-value infiltration rates. 

 Zero-value observations corresponding to non-zero infiltration rates were assigned an 

arbitrary arrival time greater than (>) 10,000 years, the maximum simulation timeframe. 

These data points are also not shown in the plots, as only times up to 10,000 years were 

visible. 

Table L-1 shows the distribution of zero-value concentration observations by WMU and 

composite liner scenario. The total observations in this table include data points with a modeled 

arrival time of >10,000 years and those with zero infiltration rates. Note that for surface 

impoundments, there are fewer model runs (observations) for thallium because thallium results 

are not available for ash and coal waste streams because of very limited data in the CCW 

constituent database. 

Table L-1. Distribution of Zero-Value Concentrations 

WMU Type 

Total 

Observationsa

(Number) 

Zero 

Infiltration 

Rates 

(Number) 

Zero 

Infiltration 

Ratesb 

(%) 

Composite 

Liner 

Observations 

(Number) 

Composite Liner 

Zero Infiltration 

Rates 

(%) 

Landfill 29,717 3,538 11.9% 4,847 73.0% 

Surface Impoundment 

(As, B, Se)c

19,825 500 2.5% 1,406 35.6% 

Surface Impoundment 

(Tl)d

9,905 389 3.9% 1,130 34.4% 

a

 Per constituent across all waste types and liners. 

b

 Out of all observations. 

c

 Observations for arsenic, boron, cobalt, and selenium; all were modeled in both ash and ash and coal waste 

streams managed in surface impoundments. 

d

 Observations for thallium only, which was detected only in ash waste streams managed in surface 

impoundments. 

After zero infiltration rate observations were filtered from each data set, percentiles of 

arrival time of the peak observed concentration were plotted on the y-axis by liner type and 

WMU (Figures L-1 through L-21). The x-axis range for landfills is 0 to 10,000 years. For 

April 2010–Draft EPA document. L-2 
Appendix L Time to Peak Concentration at Receptor Well 

surface impoundments, plots are provided on both the full 0 to 10,000-year time frame and a 

shorter time frame, so that the shape of the cumulative distribution can be seen for the lower 

time-of-travel range characteristic of these facilities. 

The figures are organized alphabetically by metal, and there are three figures for each 

metal: landfills, surface impoundments (0–10,000 years), and surface impoundments (shorter 

time frame). 

The shorter arrival times for clay-lined landfills compared to unlined landfills are an 

artifact of the fact that liners were modeled at each landfill as reported in the EPRI survey, and 

each landfill location has a different subsurface geology. The shorter arrival times mainly reflect 

more transmissive soils and aquifer materials at the clay-lined facility locations. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-1. Time to peak distribution for arsenic III: landfills, all waste types. 

April 2010–Draft EPA document. L-3 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-2. Time to peak distribution for arsenic III: surface impoundments, all waste 

types, full 10,000 year time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 500 1000 1500 2000 2500 3000 3500 4000 4500

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-3. Time to peak distribution for arsenic III: surface impoundments, all waste 

types, shorter time frame. 

April 2010–Draft EPA document. L-4 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-4. Time to peak distribution for arsenic V: landfills, all waste types. 

0

20

40

60

80

100

120

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-5. Time to peak distribution for arsenic V: surface impoundments, all waste 

types, full 10,000 year time frame. 

April 2010–Draft EPA document. L-5 
Appendix L Time to Peak Concentration at Receptor Well 

0

20

40

60

80

100

120

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

Clay Liner

No Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-6. Time to peak distribution for arsenic V: surface impoundments, all waste 

types, shorter time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-7. Time to peak distribution for boron: landfills, all waste types. 

April 2010–Draft EPA document. L-6 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-8. Time to peak distribution for boron: surface impoundments, all waste types, 

full 10,000 year time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-9. Time to peak distribution for boron: surface impoundments, all waste types, 

shorter time frame. 

April 2010–Draft EPA document. L-7 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-10. Time to peak distribution for cobalt: landfills, all waste types. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-11. Time to peak distribution for cobalt: surface impoundments, all waste types, 

full 10,000 year time frame. 

April 2010–Draft EPA document. L-8 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-12. Time to peak distribution for cobalt: surface impoundments, all waste types, 

shorter time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-13. Time to peak distribution for selenium IV: landfills, all waste types. 

April 2010–Draft EPA document. L-9 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-14. Time to peak distribution for selenium IV: surface impoundments, all waste 

types, full 10,000 year time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-15. Time to peak distribution for selenium IV: surface impoundments, all waste 

types, shorter time frame. 

April 2010–Draft EPA document. L-10 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-16. Time to peak distribution for selenium VI: landfills, all waste types. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-17. Time to peak distribution for selenium VI: surface impoundments, all waste 

types, full 10,000 year time frame. 

April 2010–Draft EPA document. L-11 
Appendix L Time to Peak Concentration at Receptor Well 

0

10

20

30

40

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

35.6% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-18. Time to peak distribution for selenium VI: surface impoundments, all waste 

types, shorter time frame. 

0

10

20

30

40

50

60

70

80

90

100

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000

Time to Peak Concentration (years)

Percentile

No Liner

Clay Liner

Composite Liner

73% of composite liner results have no infiltration and are not plotted on this graph.

Figure L-19. Time to peak distribution for thallium: landfills, all waste types. 

April 2010–Draft EPA document. L-12