# Human and Ecological

Risk Assessment of

Coal Combustion Wastes

April 20 10

DRAFT

U.S. Environmental Protection Agency Office of Solid Waste and Emergency Response

Office of Resource Conservation and Recovery

Regu lation Identifier Number (RIN) 2050

- AE81

## This draft document has been prepared by the Office of Resource Conservation and Recovery in the U.S. Environmental Protection Agency (EPA). Any opinions, findings, conclusions, or

recommendations do not chan ge or substitute for any statutory or regulatory provisions. This document does not impose legally binding requirements, nor does it confer legal rights, impose legal obligations, or implement any statutory or regulatory provisions. Mention of trade names or commercial products is not intended to constitute endorsement or recommendation for use. This document is being provided to other government agencies and to the public. As a draft, EPA may change any part of this document in the future, as appropriate.

Prepared under

Contract EP

- W2

- 09

- 004 Work Assignment 1

- 02

# Human and Ecological

Risk Assessment of

Coal Combustion Wastes

DRAFT

U.S. Environmental Protection Agency Office of Solid Waste and Emergency Re sponse Office of Resource Conservation and Recovery

April 20 10

### CCW Table of Contents



## April 2010

–

Draft EPA document

. v 

# Table of Contents

Executive Summary

................................ ................................ ................................ .................. ES

- 1

1.0 Introduction

................................ ................................ ................................ ...................... 1

- 1

1.1 Background

................................ ................................ ................................ .......... 1

- 1

1.2 Purpose and Scope of the Risk Assessment

................................ ......................... 1

- 2

1.3 Overview of Risk Assessment Methodology

................................ ....................... 1

- 2

1.3.1 Contaminant Sources

................................ ................................ ............... 1

- 2

1.3.2 Exposure Pathways

................................ ................................ .................. 1

- 3

1.3.3 Risk Levels

................................ ................................ ............................... 1

- 3

1.3.4 Methodology

................................ ................................ ............................ 1

- 3

1.3.5 Waste Management Scenarios Addressed

................................ ............... 1

- 5

1.3.6 Modeling Approach

................................ ................................ ................. 1

- 6

1.4 Document Organization

................................ ................................ ....................... 1

- 7

2.0 Problem Formulati on

................................ ................................ ................................ ....... 2

- 1

2.1 Source Characterization

................................ ................................ ....................... 2

- 1

2.1.1 Identification of Waste Types, Constituents, and Exposure Pathways

................................ ................................ ................................ .. 2

- 2

2.1.2 Waste Management Scenarios

................................ ................................ . 2

- 5

2.2 Conceptual Model

................................ ................................ ................................ 2

- 6

2.2.1 Conceptual Site Model

................................ ................................ ............. 2

- 7

2.2.2 Conceptual Site Layouts

................................ ................................ .......... 2

- 9

2.3 Screenin g Analysis

................................ ................................ ............................. 2

- 12

2.4 Full

- Scale Risk Assessment

................................ ................................ ............... 2

- 12

2.4.1 Data Collection

................................ ................................ ...................... 2

- 13

2.4.2 Model Implementation

................................ ................................ ........... 2

- 14

2.4.3 Exposure Assessment

................................ ................................ ............. 2

- 15

2.4.4 Risk Estimation

................................ ................................ ...................... 2

- 16

3.0 Analysis

................................ ................................ ................................ ............................ 3

- 1

3.1 Toxicity Assessment

................................ ................................ ............................ 3

- 3

3.1.1 Human Health Benchmarks

................................ ................................ ..... 3

- 3

3.1.2 Ecological Benchmarks

................................ ................................ ........... 3

- 6

3.2 Constituent Screening

................................ ................................ .......................... 3

- 8

3.2.1 Waste Constituent Concentrations

................................ ........................... 3

- 8

3.2.2 Media

- Specific Exposure Concentrations for Screening

......................... 3

- 9

3.2.3 Screening Methodology

................................ ................................ ........... 3

- 9

3.2.4 Screening Results

................................ ................................ ................... 3

- 11

3.3 Full

- Scale Modeling Approach

................................ ................................ .......... 3

- 13

3.3.1 Spatial and Temporal Framework

................................ .......................... 3

- 13

3.3.2 Probabilistic Approach

................................ ................................ ........... 3

- 15

3.3.3 Implementation of Probabilistic Approach

................................ ............ 3

- 17

3.4 Landfill Model

................................ ................................ ................................ ... 3

- 19

3.4.1 Conceptual Model

................................ ................................ .................. 3

- 20

3.4.2 Modeling Approach and Assumptions

................................ ................... 3

- 21

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. vi 3.4.3 Landfill Model Input Parameters

................................ ........................... 3

- 24

3.4.4 Model Outputs

................................ ................................ ....................... 3

- 26

3.5 Surface Impoundment Model

................................ ................................ ............ 3

- 26

3.5.1 Conceptual Model

................................ ................................ .................. 3

- 26

3.5.2 Modeling Approach and Assumptions

................................ ................... 3

- 28

3.5.3 Surface Impoundment Model Input Parameters

................................ .... 3

- 30

3.5.4 Surface Impoundment Model Outputs

................................ ................... 3

- 32

3.6 Gr oundwater Model

................................ ................................ ........................... 3

- 32

3.6.1 Conceptual Model

................................ ................................ .................. 3

- 32

3.6.2 Modeling Approach and Assumptions

................................ ................... 3

- 33

3.6.3 Model Inputs and Receptor Locations

................................ ................... 3

- 34

3.6.4 Groundwater Model Outputs

................................ ................................ . 3

- 36

3.7 Surface Water Models

................................ ................................ ........................ 3

- 36

3.7.1 Equilibrium Partitioning Model

................................ ............................. 3

- 37

3.7.2 Aquatic Food Web Model

................................ ................................ ...... 3

- 38

3.7.3 Aluminum Precipitation Model

................................ ............................. 3

- 39

3.8 Human Exposure Assessment

................................ ................................ ............ 3

- 40

3.8.1 Receptors and Exposure Pathways

................................ ........................ 3

- 41

3.8.2 Exposure Factors

................................ ................................ .................... 3

- 42

3.8.3 Dose Estimates

................................ ................................ ....................... 3

- 43

3.9 Risk Estimation

................................ ................................ ................................ .. 3

- 44

3.9.1 Human Health Risk Estim ation

................................ ............................. 3

- 45

3.9.2 Ecological Risk Estimation

................................ ................................ .... 3

- 46

4.0 Risk Characterization

................................ ................................ ................................ ....... 4

- 1

4.1 Human Health Risks

................................ ................................ ............................ 4

- 2

4.1.1 Groundwater

- to

- Drinking

- Water Results by Waste Type/WMU

Scenario

................................ ................................ ................................ .... 4

- 2

4.1.2 Groundwater

- to

- Surface

- Water (Fish Consumption) Pathway

............. 4

- 13

4.1.3 Results by Liner Type

................................ ................................ ............ 4

- 18

4.1.4 Constituents Not Modeled in the Full

- Scale Assessment

...................... 4

- 20

4.1.5 Human Health (Groundwater and Fish Consumption) Damage

Case Review

................................ ................................ ........................... 4

- 21

4.2 Ecological Risks

................................ ................................ ................................ . 4

- 26

4.2.1 Direct Surface Impoundment Exposure

................................ ................. 4

- 26

4.2.2 Surfa ce Water Receptors (Full

- Scale Analysis)

................................ .... 4

- 28

4.2.3 Sediment Receptors (Full

- Scale Analysis)

................................ ............ 4

- 30

4.2.4 Constituents Not Modeled in the Full

- Scale Assessment

...................... 4

- 32

4.2.5 Ecological Damage Cases

................................ ................................ ...... 4

- 33

4.3 Sensitivity Analysis

................................ ................................ ........................... 4

- 34

4.4 Variability and Uncertai nty

................................ ................................ ................ 4

- 36

4.4.1 Scenario Uncertainty

................................ ................................ .............. 4

- 38

4.4.2 Model Uncertainty

................................ ................................ ................. 4

- 41

4.4.3 Parameter Uncertainty and Variability

................................ .................. 4

- 46

4.5 Summary and Conclusions

................................ ................................ ................ 4

- 57

5.0 References

................................ ................................ ................................ ........................ 5

- 1

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# List of Figure s

1

- 1. Overview of coal combustion wast e risk assessment.

................................ .......................... 1

- 4

2

- 1. Coal combustion plants with onsite waste disposal modeled in CCW risk assessment.

...... 2

- 7

2

- 2. Conceptual site model of CCW risk assessment.

................................ ................................ . 2

- 8

2

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

............................. 2

- 10

2

- 4. C onceptual site layout for residential fisher and aquatic ecological risk scenario.

............ 2

- 11

3

- 1. Overview of the Monte Carlo approach.

................................ ................................ ............ 3

- 15

3

- 2. Monte Carlo looping structure.

................................ ................................ ........................... 3

- 16

3

- 3. Process used to construct the Monte Carlo input database.

................................ ................ 3

- 18

3

- 4. Concep tualization of a landfill in the landfill source

- term model.

................................ ..... 3

- 20

3

- 5. Schematic cross

- section view of surface impoundment.

................................ .................... 3

- 27

3

- 6. Conceptual model of the groundwater modeling scenario.

................................ ................ 3

- 33

3

- 7. Schematic plan view showing idealized maximum lateral con taminant plume extent and receptor well location.

................................ ................................ ............................. 3

- 35

4

- 1. CCW surface impoundment ecological screening risks: Direct exposure to surface impoundment wastewater.

................................ ................................ ............................. 4

- 28

4

- 2. Comparison of CCW leachate data with other leachate data in U.S. EPA (2006c).

......... 4

- 47

4

- 3. CCW mercury concentrations compared with other leachate data.

................................ .... 4

- 51



# List of Table s

ES

- 1. Selected a 

## 90th Percentile Risk Results by CCW Type: Landfills, Groundwater

- to

Drinking

- Water Pathway

................................ ................................ .............................. ES

- 6

ES

- 4. Selected a 

## 50th Perce ntile Risk Results by CCW Type: Surface Impoundments, Groundwater

- to

- Drinking

- Water Pathway

................................ ......... ES

- 8

ES

- 5. Selected a 

## 90th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater

- to

- Surface

- Water Pathway

................................ ............ ES

- 8

1

- 1. Liner Prevalence in EPRI and DOE Surveys

................................ ................................ ....... 1

- 2

2

- 1. Waste Streams in CCW Constituent Database

................................ ................................ ..... 2

- 3

2

- 2. Toxicity Assessment of CCW Constituents

................................ ................................ ......... 2

- 4

2

- 3. Coal Combustion Plants with Onsite CCW WMUs Modeled in the Full

- Scale

Assessment

................................ ................................ ................................ ....................... 2

- 6

2

- 4. Receptors and Exposure Pathways Addressed in the Full

- Scale CCW Assessment

.......... 2

- 15

3

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

................................ ........ 3

- 4

3

- 2. Ecological Receptors Assessed by Exposure Route and Medium (Surface Water or Sediment)

................................ ................................ ................................ ......................... 3

- 6

3

- 3. Ecological Risk Criteria Used for Surface Water and Sedime nt

................................ .......... 3

- 7

3

- 4. Exposure Pathways Evaluated In CCW Constituent Screening

................................ ......... 3

- 10

3

- 5. Screening Analysis Results: Selection and Prioritization of CCW Constituents for Further Analysis a



## ................................ ................................ ................................ ............ 3

- 12

3

- 6. CCW Waste Management Scenarios Modeled in Full

- Scale Assessment

......................... 3

- 1 7

3

- 7. Leak Detection System Flow Rate Data Used to Develop Landfill Composite Liner

Infiltration Rates

................................ ................................ ................................ ............ 3

- 23

3

- 8. Crosswalk Between EPRI and CCW Source Model Liner Types

................................ ...... 3

- 25

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## April 2010

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- 9. Sediment/Water Partition Coefficients: Empirical Distributions a



## ................................ ...... 3

- 38

3

- 10. Bioconcentration Factors for Fish

................................ ................................ .................... 3

- 39

3

- 11. Aluminum Solubility as a Function of Waterbody pH a



## ................................ .................... 3

- 40

3

- 12. Receptors and Exposure Pathways

................................ ................................ ................... 3

- 41

3

- 13. Human Exposure Factor Input Parameters and Data Sources

................................ .......... 3

- 42

3

- 14. Risk Endpoints Used for Human Health

................................ ................................ .......... 3

- 45

4

- 1. 90th Percentile Risk Results by CCW Type: Landfills, Groundwater

- to

- Drinking

Water Pathway

................................ ................................ ................................ ................. 4

- 4

4

- 2. 50th Percentile Risk Results by CCW Type: Landfills, Groundwater

- to

- Drinking

Water Pathway

................................ ................................ ................................ ................. 4

- 5

4

- 3. 90th Percentile Risk Results by CCW Type: Surfac e Impoundments, Groundwater

to

- Drinking

- Water Pathway

................................ ................................ ............................. 4

- 6

4

- 4. 50th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater

to

- Drinking

- Water Pathway

................................ ................................ ............................. 4

- 7

4

- 5. 90th Percentile Risk Results for FBC Wastes: Landfills, Groundwater

- to

- Drinking

Wa ter Pathway

................................ ................................ ................................ ................. 4

- 9

4

- 6. 50th Percentile Risk Results for FBC Wastes: Landfills, Groundwater

- to

- Drinking

Water Pathway

................................ ................................ ................................ ................. 4

- 9

4

- 7. Time to Peak Well Concentration by WMU and Liner Type as Modeled

......................... 4

- 11

4

- 8. 90th Percentile Risk Results by CCW Type: Landfills, Grou ndwater

- to

- Surface

Water Pathway

................................ ................................ ................................ ............... 4

- 13

4

- 9. 50th Percentile Risk Results by CCW Type: Landfills, Groundwater

- to

- Surface

Water Pathway

................................ ................................ ................................ ............... 4

- 14

4

- 10. 90th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater

to

- Surface

- Water Pathway

................................ ................................ ............................. 4

- 15

4

- 11. 50th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater

to

- Surface

- Water Pathway

................................ ................................ ............................. 4

- 16

4

- 12. 90th Percentile Risk Results for FBC Wastes: Landfills, Groundwater

- to

- Surface

Water Pathway

................................ ................................ ................................ ............... 4

- 17

4

- 13. 50th Percentile Risk Results for FBC Was tes: Landfills, Groundwater

- to

- Surface

Water Pathway

................................ ................................ ................................ ............... 4

- 17

4

- 14. Liner Types in EPRI Survey

................................ ................................ ............................. 4

- 19

4

- 15. Risk Attenuation Factor a 

## Statistics for Modeled Constituents

— Groundwater to Drinking Water Pathway

................................ ................................ ................................ 4

- 20

4

- 16. Summary of Risk Screening Values for Unmode led Constituents Using Risk

Attenuation Factors

— Groundwater

- to

- Drinking

- Water Pathway

................................ . 4

- 21

4

- 17. Summary of Proven Damage Cases with Groundwater Impacts (U.S. EPA, 2007)

....... 4

- 22

4

- 18. Summary of Proven Damage Cases with Fish Consumption Advisories (U.S. EPA

, 2007)

................................ ................................ ................................ .............................. 4

- 23

4

- 19. Modeled 90th and 50th Percentile Risk Results vs. Reported Groundwater

Exceedences

................................ ................................ ................................ ................... 4

- 23

4

- 20. Modeled 90th and 50th Percentile Risk Results vs. Reported Fish Consumption

Exceedences

................................ ................................ ................................ ................... 4

- 25

4

- 21. Summary of 90th Percentile Full

- Scale CCW Ec ological Risk Results:

Groundwater

- to

- Surface

- Water Pathway, Aquatic Receptors a



## ................................ ...... 4

- 29

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## April 2010

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. ix 4

- 22. Summary of 50th Percentile Full

- Scale CCW Ecological Risk Results:

Groundwater

- to

- Surface

- Water Pathway, Aquatic Receptors a



## ................................ ...... 4

- 30

4

- 23. Summary of 90th Percentile F ull

- Scale CCW Ecological Risk Results:

Groundwater

- to

- Surface

- Water Pathway, Sediment Receptors a



## ................................ ... 4

- 31

4

- 24. Summary of 50th Percentile Full

- Scale CCW Ecological Risk Results:

Groundwater

- to

- Surface

- Water Pathway, Sediment Receptors a



## ................................ ... 4

- 31

4

- 25. Risk Atten uation Factor a 

## Statistics for Modeled Constituents

— Ecological Risk, Surface Water Pathway (all unit types combined)

................................ ......................... 4

- 32

4

- 26. Summary of Risk Screening Values for Unmodeled Constituents Using Risk

Attenuation Factors

— Ecological Risk, Surface Water Pathwa y

................................ ... 4

- 33

4

- 27. Summary of Proven Damage Cases with Ecological Impacts (U.S. EPA, 2007)

........... 4

- 34

4

- 28. Summary of CCW Uncertainties and Their Effect on Risk Estimates

............................. 4

- 37

4

- 29. Change in Peak Receptor Well Concentrations for Ash Disposed in Landfills Assuming L eachate Does Not Mix in Aquifer

................................ .............................. 4

- 45

4

- 30. Proportion of Nondetect Analyses for Modeled CCW Constituents

................................ 4

- 49

4

- 31. RfD Uncertainty Factors for and Benchmark Confidence for CCW Constituents with HQs Over 1

................................ ................................ ................................ ............ 4

- 56

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### CCW List of Acronyms



## April 2010

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Draft EPA document

. xi 

# List of Acronyms

Acronym Definition

3MRA multimedia, multiple exposure pathway, multiple receptor risk assessment

ADD average daily dose Ag silver

Al aluminum

As arsenic

ATSDR Agency for Toxic Substances and Disease Registry

B boron

Ba barium

BCF bioconcentration factors

Be beryllium

CAIR Clean Air Interstate Rule

CalEPA California EPA

CAMR Clean Air Mercury Rule

CCW coal combustion waste

Cd cadmium

Co cobalt

Cr chromium

CSCL chemical stressor concentration limit

CSF canc er slope factor

Cu copper DAF dilution attenuation factor

DBGS depth below ground surface

DOE U.S. Department of Energy

DWAK Drinking Water Action Level

EFH Exposure Factors Handbook

EIA Energy Information Agency

EPA U.S. Environmental Protectio n Agency

EPACMTP EPA’s Composite Model for Leachate Migration with Transformation Products

EPRI Electric Power Research Institute

F fluoride

FBC fluidized

- bed combustion

Fe iron

FGD flue gas desulfurization

GIS geographic information system

HB N health

- based number

HDPE high

- density polyethylene

HEAST Health Effects Assessment Summary Tables

HELP Hydrologic Evaluation of Landfill Performance Hg mercury HGDB Hydrogeologic Database

HHRAP Human Health Risk Assessment Protocol HQ hazard quotient

### CCW List of Acronyms



## April 2010

–

Draft EPA document

. xii Acronym Definition

ID identification

IEM Indirect Exposure Methodology

IRIS Integrated Risk Information System

IWEM Industrial Waste Evaluation Model

LADD lifetime average daily dose

LDS leak detection system

LF landfill

LOAEL lowest observed adverse ex posure level

MCL maximum concentration limit

Mn manganese
M I NTEQA2 a geochemical assessment model for environmental systems

Mo molybdenum

MRL minimal risk level

MSW municipal solid waste

Ni nickel

NOAEL no observed adverse exposure level

NPDES National Pollutant Discharge Elimination System

ORD Office of Research and Development (EPA)

Pb lead

PCS Permit Compliance System pH a measure of h ydrogen ion activity

RCRA Resource Conservation and Recovery Act

RCRA Resource Conservation and Recov ery Act

RfD reference dose

RME reasonable maximum exposure

SAB Science Advisory Board (EPA)

Sb antimony

SD standard deviation

Se selenium

SI surface impoundment

Sr strontium

SW solid waste

SWTSS surface water total suspended solids

TCLP toxicit y characteristic leaching procedure

Tl thallium

TL3 trophic level 3

TL4 trophic level 4

TSS total suspended solids

UF uncertainty factor

USGS U.S. Geological Survey

V vanadium

WMU waste management unit

Zn zinc

### CCW List of Acronyms



## April 2010

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. xiii

### 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-f ired 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 material s 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 activit ies before burning the

coal in their boilers. Wa stes 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 analys is 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 moveme nt 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-drinki ng-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 sp eed 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, th e 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., leac hate 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 wate rbody 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 a nd 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 e xposure (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 re ceptors (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 high est 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 ha d arsenic excess cancer risks as high as 6 in 10,000 while clay-lined

units had arsenic exce ss 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. Selected a 

## 90th Percentile Risk Results by CCW Type: Landfills, Groundwater-to-Drinking-Water Pathway

Chemical 90th Percentile HQ or Cancer Risk Value b



### 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 infiltr ation rates were too small for the contaminant plume to reach the receptor during the 10,000-y ear 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. Selected a 

## 50th Percentile Risk Results by CCW Type: Landfills, Groundwater-to-Drinking-Water Pathway

Chemical 50th Percentile HQ or Cancer Risk Value b



### 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 infiltr ation rates were too small for the contaminant plume to reach the receptor during the 10,000-y ear period of the analysis.



## Table ES-3. Selected a 

## 90th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater-to-Drinking-Water Pathway

Chemical 90th Percentile HQ or Cancer Risk Value b



### 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. Selected a 

## 50th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater-to-Drinking-Water Pathway

Chemical 50th Percentile HQ or Cancer Risk Value b



### 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 ar senic; arsenic values are cancer risk.

Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to reach th e receptor during the 10,000-year period of the analysis.



## Table ES-5. Selected a 

## 90th Percentile Risk Results by CCW Type: Surface Impoundments, Groundwater-to-Surface-Water Pathway

Chemical 90th Percentile HQ or Cancer Risk Value b



### 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 ar senic; arsenic values are cancer risk.

Zero results indicate that contaminant infiltration rates were too small for the contaminant plume to reach th e 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 a ssessment 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 a nd 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 da ta 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 natio nal 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, concentratio ns 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 recept or 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 t ypes 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 faci lities, along with practices to prevent codi sposal 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. Availableas 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 StateRegulation of Coal Combustion ByProduct Placement at Mine Sites . May 4– 6, 2004, Harrisburg, Pennsylvania. U.S. Department of Interi or, 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 im poundments. 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, bu t 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 implemen ted to help EPA identify and quantify human health and ecological risks that may be associat ed with current management practices for highvolume CCWs. These wastes are fly ash, bottom as h, 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 a ssessment 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 mana gement practices, such as the joint EPA and U.S. Department of Energy (DOE) survey of CCW waste mana gement units (WMUs) (U.S. DOE, 2006) and EPA’s recent study of CCW chemistries and leach ing behavior (U.S. EPA, 2006c, 2008c), were not considered in the main analysis phase of th is risk assessment. However, these more recent efforts are discussed as part of the risk charac terization, and EPA is curre ntly evaluating how to best incorporate and consider the results and findi ngs of these studies in fu ture efforts to address CCW management practices.

The Agency has revised this risk analysis document to address comments on the analytical methodology, data, and as sumptions used in the risk a ssessment from an independent scientific peer review by expe rts outside EPA. Public comments (available in docket number EPA-HQ-RCRA-2006-0796 1 

## ) 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 subseque nt fate and transport in the environment, and the characterization of risks resulting from potentia l 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 avai lable 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 util ity 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 co al-fired utility power plants in the United States. Although these plants are concentrated in the East, they are found in near ly every state, with facility settings ranging from urban to rural. The larg e volumes of waste genera ted by these plants are typically managed onsite in landfills and su rface impoundments. This risk assessment was designed to develop national human and ecological risk estimates that ar e 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 ma nagement of CCW, this risk assessment estimated the release of CCW constituents from landfills and surface impoundments, the concentrations of these constituents in groundwat er and surface water ne ar coal-fired utility power plants, and the risks that these concentr ations pose to human and ecological receptors.

1.3.1 Contaminant Sources

The size, design, and locations of the ons ite 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 Elect ric 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 co mplete 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 mu ch 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 Survey a 

### – 181 facilities

Unlined 40% 68%

Lined (compacted clay or composite [clay and synthetic]) 60% 32%

2004 DOE Survey b 

### – 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 rel eases to surface water, which are permitted under the National Pollutant Discharge Elimination System (NPDES) of the Clean Water Act. Thus,

the estimated media co ncentrations 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 ar e exposure pathways in addition to the groundwater pathways addressed in this repo rt that could be of concern to human health and ecological receptors, including fugitive dust eroded a nd 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 con ducted 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 carcino gens) 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 ra nging 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 furt her consideration, a 1 in 100,000 (10 -5 

## ) excess lifetime

cancer risk was used. 2



##  A measure of safe intake levels to predicte d intake levels, a hazard quotient (HQ) greater than 1 for constituents that can produce noncan cer 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 fo r fossil fuel combustion wastes (which include CCWs) to support the May 2000 RCRA re gulatory determination (U.S. EPA, 1998a,b). Since then, EPA has added to the waste constitu ent 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 Of fice 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 combus tion waste risk assessment.

### Section 1.0 Introduction



## April 2010–Draft EPA document. 1-5 This methodology implemented the following st eps to assess the human and ecological

risk of CCWs:

 Hazard Identification , which collected existing hu man health and ecological benchmarks for the CCW constituents. Only constituents with benchmarks moved on to the next step, cons tituent 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 nati onal level the human health and ecological risks for constituents in CCW disposed onsite in landfills and surface impoundments using a site-based probabi listic Monte Carl o risk analysis.

This document focuses on the full-scale Monte Carl o 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 char acterize waste management scenarios based on two waste management options (disposal of CCW onsite in landf ills 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 th e 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 manage ment options, and three liner conditions (unlined, clay lined, composite lined) modeled in this analysis provide a good representation of CCW disposal practices and wa ste chemistry conditions that affect the release of CCW

constituents from WMUs.

3 

### Coal refuse is the waste coal produced from coal hand ling, 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 as h 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 probabili stic 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 parame ters 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 para meters 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 co llection can capture both the vari ability in waste management practices at these facilities and the differences in their environmental settings (e.g., hydrogeology, climate, hydrology).

This probabilistic approach was impl emented 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 contam inants 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 const ituents in the environment once they are

released from the WMUs at each site

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

environment and the risk asso ciated with this exposure

Risk Characterization

7. Estimate the risk to receptors from the expos ure and characterize th is 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 en vironment. 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, incl uding identification of the waste constituents, exposure pathways, and receptors of concern; selecti on and characterization of waste management practices and sites to model; and developmen t of the conceptual site models for the modeling effort.

 Section 3, Analysis, describes the probabilistic mode ling 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) identifica tion of the scenarios and conditions modeled that resulted in higher and lo wer risks. Results are presen ted as national estimates for CCW landfills and CCW surface impoundments, as well as by waste type and liner

status. This section characterizes the ri sks posed by CCW constituents and pathways under the conditions modeled, including factors (such as liners or facility environmental setting) that result in high er or lower risk levels. Fina lly, the risk characterization evaluates the risk results in light of mo re recent research on CCW waste management practices and the environmental behavior of CCW constituents.

The first three appendices provide detail ed information on how wastes, WMUs, and settings were characterized for the risk assessment. Appendix A describes the chemical characteristics of CCW, including the CCW l eachate concentration distributions used to represent disposal conditions in landfills and surface impoundments. Appendix B describes how

EPA characterized the CCW landfills and surf ace 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 identi fied in Appendix B, including delineating the site layout and determining the environmental setting (e.g., meteorology, climate, soils, a quifers, and waterbodies).

The next five appendices provide detailed information on the specific models and data
used to calculate ri sk, including the nonlinea r 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 h ealth-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,

cumu lative percentiles of the time it took for the peak concentrati on 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 fr om 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 ge neration 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 diffe rent 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 base d 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

Waste b 

### 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 benchmar ks are limited to common elements, ions, and compounds (e.g., iron, magnesium, phosphate, silicon, sulfate, sulf ide, 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 addr essed 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 HHB a 

### EcoB b



### Metals

Aluminum 7429-90-5 U U
Antimony 7440-36-0 U U

Arsenic 7440-38-2 U c 

### U

Barium 7440-39-3 U U
Beryllium 7440-41-7 U d 

### U

Boron 7440-42-8 U U

Cadmium 7440-43-9 U d 

### U

Chromium 7440-47-3 U c 

### U

Cobalt 7440-48-4 U U
Copper 7440-50-8 U U

Iron 7439-89-6

Lead 7439-92-1 U e 

### 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 HHB a 

### EcoB b



### 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 can cer risk exceeds the noncancer risk at any concentration, so the more protectiv e 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 CCW 3 

## 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 represen t 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 Survey a 

### with Onsite:

Landfills Surface Impoundments Either WMU Type b



### Conventional CCW c



### 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 waste d



### 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 ons ite 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 assessmen t 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 2.0Problem FormulationApril 2010–Draft EPA document.2-8Figure 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 fu ll-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 o ccurs 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 so me 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 R

rw 

## 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 distributi ons 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 ( θ

rw 

## 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 fisher 4 

## 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 r easonable maximum exposed indivi duals. 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 risk s that clearly do not exceed the risk criteria sothat 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 discharg es 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 file s 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 de rived, 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 expos ure 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 waterbodi es 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 sp ecific 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-t o-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 impoundments 3



##  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 c ontact), the duration of exposure, and the dose received (the amount that a human ingests or i nhales). 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 dos es, 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

Benchmark a 

### Value Units Source b



### 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

Benchmark a 

### Value Units Source b



### 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 inge stion 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 eachconstituent for which ecological effect data were available, HQs were calculated using chemicalspecific media concentrations assumed to be pr otective 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 givesthe 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 Medium a 

### Exposure Route CSCL Units Receptor

Aluminum Surface Water Direct c ontact 0.09 mg/L Aquatic biota

Arsenic total Sediment Ingestion 0.51 mg/kg Spotted sandpiper

Arsenic III Surface Water Direct c ontact 0.15 mg/L Aquatic biota

Arsenic IV Surface Water Direct c ontact 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 Medium a 

### 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 cont act 0.02 mg/L Aquatic biota

Lead Sediment Ingestion 0.22 mg/kg Spotted sandpiper

Surface Water b 

### 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 sedime nt) is not listed, there were insuffi cient 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 sc reening evaluated exposure through drinking

and surface water contamination 5 

## 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 dir ect contact to contaminated surface

water and sediment and consum ption 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 s ite (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

porewater 7 

## 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 (nonavera ged concentrations) compare with HBNs for surface impoundment porewater, toxicity characteristic leachi ng procedure (TCLP) leachate, and whole waste

concentrations.

7 

### Although the 95th percentile was used in 1998, the 90th pe rcentile was used in this analysis as a reasonably

conservative value considering the prot ective 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 Co mpared 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 CSCL s to CCW surface impoundment

constituent concentrations fro m 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 fi shing 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 resi dential 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 bioaccumu lation 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-wate r 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 Analysis a



### Constituent Human Health – Drinking Water Human Health –

Surface Water b 

### 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

Arsenic c 

### (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 Assessment d



### 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 cr iteria 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 concentra tion, 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 in1,000, 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 surfaceimpoundment 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 wa ter 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 excessi ve 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 othe r 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

Child Groundwater-to- 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, bo ttom 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 dist ributions developed to characterize the regional or national variability in these inputs. Each reco rd 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 Adviso ry 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 opera tional 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 la ndfill’s operating life, risks from thes e 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 sy stem would be functional and governing during the active period of the landfill. For the unlined case, the landf ill model assumes that the cap soils are no less permeable than th e ambient soils around and under the landf ill. 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 comp lexity, 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 m odel 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 Fl ow 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



## SINFIL CZERO CTDENS FRACT DEPTH CWASTE 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/cm 3 

## ) 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/cm 3 

## (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)

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## 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 c onfigurations 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 concentra tions 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 mm 2 

## ). 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 botto m 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 mm 2 

## , 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 mm 2 

## ). 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 ma ke 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 spr eading 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 (K d 

## 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 K d 

## values will have low retardation factors, which means that they will move at nearly the same velocity as the groundwater. Chemicals with high K d 

## 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 K d 

## values available in the MINTEQA2 isotherms used in these analyses would not be practicable, Table D-1 presents a sampling of the K d 

## 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 review s 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 latte r 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 aqui fer 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 mode l–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, ( 

## R

rw 

## in Figure 3-7) was randomly selected from this distribution.

 The angle off the contaminant plume centerline ( 

## θ

rw 

## 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 

## R

rw 

## and 

## θ

rw 

## 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 

## R

rw 

## and 

## θ

rw 

## 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 infin ite 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 s ubsurface. Therefore, all of the mass in the contaminated groundwater is available to be tran sferred 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 fl ow, 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 Co efficients: Empirical Distributions a



### 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 calcu lates 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) fish 12 

## 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/wa steid/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 pH a



### 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 fishcaught 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 wei ght. 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 medi a 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) Fi xed (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, characterizati on 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 de veloped 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 rate s 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; conse quently, 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 vi a 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 i ngestion 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.

CSF LADD riskcancer excess Lifetime
i i 

## × = 

## (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 le vel (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 consid ered (i.e., drinking water ingestion and fish consumption).



## RfD ADD HQ i

i 

## = (3-5)

where

ADD i 

## = 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 expos ure 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 assu med 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 base d 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:

HQ surface water 

## = C

sw 

## / CSCL

sw 

## (3-6)

where

C

sw 

## = total concentration in surface water column (mg/L)

CSCL

sw 

## = ecological benchmark for surface water (mg/L).

Similarly, for sediment, the HQ was calculated as

HQ sediment 

## = C sediment 

## / CSCL sediment 

## (3-7)

where

C sediment 

## = total concentration in sediment (mg/kg)

CSCL sediment 

## = 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 account ed 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 hand ling, 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). Howe ver, 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 leachate 3 

## for surface impoundments in the CCW database, which in turn result from the association of these elements (and acidity) with the sulfide minerals 4 

## 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 sol uble 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 Value a



### 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 contamin ant 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 Value a



### 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 Value a



### 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 contamin ant 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 Concentrat ion 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 Thallium c


### 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 Thallium c



### 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 recep tor 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

Chemical b 

### 90th Percentile HQ or Cancer Risk Value a



### 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)

Chemical b 

### 90th Percentile HQ or Cancer Risk Value a



### 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 arseni c; 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

Chemical b 

### 50th Percentile HQ or Cancer Risk Value a



### 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)

Chemical b 

### 50th Percentile HQ or Cancer Risk Value a



### 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 arseni c; 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

Chemical b 

### 90th Percentile HQ or Cancer Risk Value a



### 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)

Chemical b 

### 90th Percentile HQ or Cancer Risk Value a



### Unlined Units Clay-Lined Units Composite-Lined

Units

Selenium VI 1 0.8 7E-10

a 

### Values are HQs for all chemicals except arseni c; 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

Chemical b 

### 50th Percentile HQ or Cancer Risk Value a



### 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 arseni c; 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 Value a



### 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 arseni c; 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 Value a



### 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 Value a



### 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 arseni c; 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 de rived, 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 Survey a 

### – 181 facilities

Unlined 40% 68%

Compacted clay 45% 27%

Synthetic or composite (clay and synthetic) 16% 5%

Total 100% 100%

2004 DOE Survey b 

### – 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 zero 7 

## 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 th e 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 Factor a 

## 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 Factor a 

## 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 ra tio 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 Case a 

### 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 discha rge) Selenium fish consumption advisory

20. Brandy Branch Reservoir, Texas (ash pond di scharge) 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 Assessment a 

### Damage Cases b



### Consistent

Results as of

2007 f 

### 90th %ile 50th %ile Human

Health

Effects c 

### Cosmetic/

Aesthetic

Effects d 

### State

Standard e



### 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 Assessment a 

### Damage Cases b



### Consistent

Results as of

2007 f 

### 90th %ile 50th %ile Human

Health

Effects c 

### Cosmetic/

Aesthetic

Effects d 

### State

Standard e



### 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 modeli ng and was shown to pose a risk to human

health in the landfill s cenario, the surface impoundmen t 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 show ed an exceedence of this constituent.

– = No proven damage cases have yet shown an exceedence of this constituent.

c T = Exceedences of primary maxi mum contaminant levels (MCLs) or other health-based numbers published by EPA.

d 

### T = Exceedences of secondary MCLs, which w ould 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 a ssessment, 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, fluorid e, 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 Assessment a


### Damage Cases b 

### Consistent Results as of 2007 c 

### 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 theconcentration 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, ecologi cal 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 im poundment 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 e xposure 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 A s V

Pb Co A l Ba Se, total Cd Se VI A s III Cr VI

Cu V Ni A g Se I V

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 Receptors a



### Chemical 90th Percentile Ecological HQ

Exposure Pathway Receptor Unlined Units 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 Receptors a



### Chemical 50th Percentile Ecological HQ

Exposure Pathway Receptor Unlined Units 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 Receptors a



### Chemical 90th Percentile Ecological HQ

Exposure Pathway Receptor Unlined Units Clay-Lined

Units CompositeLined Units

Landfills

Lead 58 1 1E-06 direct contact aquatic biota

Arsenic (III) 11 3 5E-04 i ngestion 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 Receptors a



### Chemical 50th Percentile Ecological HQ

Exposure Pathway Receptor Unlined Units 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 i ngestion 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 i ngestion 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 10t h 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 Factor a 

## 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 exposur e 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
(R 2 

## =0.53–0.90) and good to very good for fish consumption (R 2 

## =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 scenarios 10 

## 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, wa ste 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 c ountry. These data were aggregated by waste and WMU types that were defined by statistically si gnificant differences in concentration. Variability in human exposure factors (e.g., body weight, inge stion 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 precise ly 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 documen t 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; lin ers 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 evaluate d (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 E ffect 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 effect s (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 wa ste 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 groundw ater 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., amphibi ans, 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 nationalrulemakings. 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 risk s 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 influe nce 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 Full Mix No 

## × −

= Change %

where

C No Mix 

## = Simulated peak receptor well concentration for a select percentile based on a no mixing assumption (mg/L)

C Full 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 e xposure 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 mercur y, 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.

A rsenic

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 SetsAs (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 SetsSe (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 combus tion 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 as h 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 out side 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 th e 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 values 11 

## 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 Anal yses for Modeled CCW Constituents

Chemical a 

### 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)

Chemical a 

### 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 mercuryanalyses, 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 SetsHg (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 woul d 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, a nd 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, reasona ble 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 distributi on 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 not es 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 expos ure 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 fact or 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 assessm ents, human exposure factor data were not correlated (i.e., for each modeling run, each exposur e 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 feedi ng 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 defi nes 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 base d 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 fr om 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, th e 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/ir is/help_gloss.htm#u

### Section 4.0 Risk Characterization



## April 2010–Draft EPA document. 4-56 fatal to humans 13 

## 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 base d 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 fa tal 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 ecologi cally significant. In contrast, CSCLs based on no effects data that are developed for the prot ection 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 l ead (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 addr essed 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 ot her 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 andecological 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. Fr om 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 risk s 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

ATSDR (Agency for Toxic Substances and Disease Registry). 1992. Toxicological Profile for

Thallium . Available at http://www.at sdr.cdc.gov/toxprofiles/tp54.html.

Barrows, M.E., S.R. Petrocelli, K.J. Macek, a nd 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 . Edited by R. Haque. Ann Arbor, MI: Ann Arbor Science.

Bonaparte, R., J.P. Giroud, and B.A. Gross. 1989. Rates of leakage through landfill liners. Geosynthetics 1989 Conference . San Diego, CA.

CalEPA (California Environmen tal Protection Agency). 2000. Air Toxics Hot Spots Program

Risk Assessment Guidelines: Part III. T echnical Support Document for the Determination of Noncancer Chronic Reference Exposure Levels . Office of Environmental Health Hazard Assessment, Berkeley, CA. Av ailable (in four sections) at http://www.oehha.org/air/ chronic_rels/22RELS2k.html http://www.oehha.org/air/ch ronic_rels/42kChREL.html http://www.oehha.org/air/ch ronic_rels/Jan2001ChREL.html http://www.oehha.org/air/ch ronic_rels/1201Crels.html

.

CalEPA (California Environmen tal 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 Environmen tal Health Hazard Assessment, Berkeley, CA. Available at http://www.oehha.org/air/hot_spo ts/2008/NoncancerTSD_final.pdf

.

Carlson, C.L., and D.C. Adriano. 1993. Environm ental impacts of coal combustion residues. J. Environ. Qual. 22(227-247).

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.

Contaminant Hazard Reviews , Report No. 19, Biological Re port 85(1.19). Laurel, MD. August. Available at http://www.pwrc.usgs.gov/infoba se/eisler/CHR_19_Molybdenum.pdf

.

EPRI (Electric Power Research Institute). 1997. Coal Combustion By-Products and Low-Volume Wastes Comanagement Survey . Palo Alto, CA. June.

### Section 5.0 References



## April 2010–Draft EPA document. 5-2 Finkelman, R.B. 1995. Modes of occurrence of envir onmentally sensitive tra ce 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 transf er of contaminants in the amphibian Gastrophryne carolinensis. Environmental Health Perspectives

. 114 (5):661–666.

Kool, J.B., P.S. Huyakorn, E.A. Sudicky, and Z.A. Saleem. 1994. A composite modeling approach for subsurface transport of degradi ng contaminants from land-disposal sites. J. Contaminant Hydrology 17 :69–90.

Kumada, H., S. Kimura, M. Yokote, and Y. Ma tida. 1972. Acute and chro nic 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 Mi ning, Alton, IL. Available at http://www.mcrcc.osmre .gov/MCR/Resources/ccb/PDF/State _Regulation_of_CCB_Placement.pdf

.

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.

Moo-Young, H., B. Johnson, A. Johnson, D. Ca rson, C. Lew, S. Liu, and K. Hancock. 2004.

Characterization of infiltration rates fr om landfills: supporting groundwater modeling

efforts. Environmental Monitoring and Assessment 96:283–311.

NAS (National Academies of Science). 2008. Science and Decisions: Advancing Risk

Assessment . National Academy Press, Washington, DC.

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

.

Newell, C,J., L.P. Hopkins, and P.B. Bedient. 1990. A hydrogeologic database for ground water modeling. Ground Water 28 (5):703–714.

Rowe, C.L., W.A. Hopkins, and J.D. Congdon. 2002. Ecotoxicological implications of aquatic disposal of coal combustion residue s in the United States: a review. Environmental Monitoring and Assessment. 80:207–276.

SAB (Science Advisory Board). 2004. Review of EPA’s Multimed ia, Multipathway, and Multireceptor Risk Assessmen t (3MRA) Modeling System. Report of the 3MRA Review

Panel of the EPA Science Adviso ry Board. EPA-SAB-05-003, November.

### Section 5.0 References



## April 2010–Draft EPA document. 5-3 Schroeder, P.R., T.S. Dozier, P.A. Zappi, B.M. McEnroe, J.W. Sjostrom, and R.L. Peton. 1994a. The Hydrologic Evaluation of Landfill Perf ormance Model (HELP) : User’s Guide for

Version 3 . EPA/600/R-94/168a. U.S. EPA Risk Reduction Engineering Laboratory, Cincinnati, OH. Available at http://nepis.epa.gov/EPA/html/Pubs/pubtitleORD.htm

.

Schroeder, P.R., T.S. Dozier, P.A. Zappi, B.M. McEnroe, J.W. Sjostrom, and R.L. Peton. 1994b. The Hydrologic Evaluation of Landfill Pe rformance (HELP) Model: Engineering Documentation for Version 3 . EPA/600/R-94/168b. U.S. EPA Risk Reduction Engineering Laboratory, Cinc innati, OH. Available at http://nepis.epa.gov/EPA/html/ Pubs/pubtitleORD.htm

.

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. U.S. Environmental Protection Agency, Office of Research and Development, Springfield,

VA.

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 Res earch and Development, Duluth, MN. March.

Stumm, W., and J.J. Morgan. 1996. Aquatic Chemistry, Chemical Equilibria and Rates in

Natural Waters. 3d ed. New York: John Wiley & Sons, Inc.

TetraTech. 2001. Characterization of Infiltration Rate Data to Support Groundwater Modeling Efforts (Draft). Prepared for the Office of Solid Wa ste, U.S. Environmental Protection Agency, Washington, DC. Contract No. 68- W6-0061. May. Available as document ID numbers EPA-HQ-RCRA-2003-0001-0115 and EPA-HQ-RCRA-2003-0001-0116 at http://www.regulations.gov

.

U.S. DOE (Department of Energy). 2006. Coal Combustion Waste Management at Landfills and Surface Impoundments, 1994-2004 . DOE/PI-0004, ANL-EVS/06-4. Office of Scientific and Technical Information, Oak Ridge, TN. August. Available at http://www.ipd.anl.gov/ anlpubs/2006/09/57081.pdf

.

U.S. DOE (Department of Energy). 2007a. Elect ric Generating Capacity: Existing Generating Units in the United States, 2007. Excel sp readsheet file named existingunits2007.xls.

Available at http://www.eia.doe.gov/cneaf/electricity/page/capac ity/capacity.html

. Washington DC: U.S. Department of En ergy, Energy Information Administration.

U.S. DOE (Department of Energy). 2007b. Form EIA-860 Database Annual Electric Generator Report for 2007. Available at http://www.eia.doe.gov/cneaf/ele ctricity/page/eia860.html

. Washington DC: U.S. Department of En ergy, Energy Information Administration.

U.S. EPA (Environmental Protection Agency). 1988a. National Survey of Solid Waste (Municipal) Landfill Facilities (Draft). EPA/530-SW88-034. Office of Solid Waste, Washington, DC. September. Available as document ID number EPA-HQ-RCRA-2003-

0004-0466 and also in other dockets at http://www.regulations.gov

.

### Section 5.0 References



## April 2010–Draft EPA document. 5-4 U.S. EPA (Environmental Protection Agency). 1988b. Wastes from the Combustion of Coal by Electric Utility Power Plants—Report to Congress . EPA 530-SW-88-002. U.S. EPA Office of Solid Waste and Emergency Remediation. Washington, DC. February.

U.S. EPA (Environmental Protection Agency). 1988c. Design, Construction and Evaluation of Clay Liners for Waste Management Facilities . EPA-530-SW-86-007F. Office of Solid Waste and Emergency Response. Wash ington, DC. November. Available at http://nepis.epa.gov/EPA/html/Pubs/pubtitleOSWER.htm

.

U.S. EPA (Environmental Protection Agency). 1992. Guidelines for expos ure assessment. Final guidelines. Federal Register 57 FR 22888-22893. Washington, DC. May 29.

U.S. EPA (Environmental Protection Agency). 1995. Review of EPA’s Composite Model for Leachate Migration with Transformation Products-EPACMTP . EPA-SAB-EEC-95-010. Science Advisory Board Exposure Mode l Subcommittee of the Environmental Engineering Committee. Washington, DC.

U.S. EPA (Environmental Protection Agency). 1996a. EPA’s Composite Model for Leachate Migration with Transformation Products. EPACMTP: Background Document . Office of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 1996b. Soil Screening Guidance: Technical Background Document . EPA/540/R-95/128. Office of So lid Waste and Emergency Response, Washington, DC. July. Available at http://www.epa.gov/ superfund/health/

conmedia/soil/index.htm

.

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

U.S. EPA (Environmental Protection Agency). 1997b. 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.

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

### Section 5.0 References



## April 2010–Draft EPA document. 5-5 U.S. EPA (Environmental Protection Agency). 1997f. Health Effects Assessment Summary Tables (HEAST). EPA-540-R-97-036. FY 1997 Update. Office of Solid Waste and Emergency Response, Washington, DC. Available at http://nepis.epa.gov/EPA/html/ Pubs/pubtitleOSWER.htm

.

U.S. EPA (Environmental Protection Agency). 1998a. Non-Groundwater Pathways, Human Health and Ecological Risk Analysis for Fossil Fuel Combustion P hase 2 (FFC2): Draft Final Report . Office of Solid Waste, Washi ngton, DC. June. Available at http://www.epa.gov/waste/nonhaz/indus trial/special/fossil/fsltech.htm

.

U.S. EPA (Environmental Protection Agency). 1998b. Technical Background Document for the Supplemental Report to Congress on Remain ing Fossil Fuel Combustion Wastes: Ground-Water Pathway Human Health Ri sk Assessment. Revised Draft Final . Office of Solid Waste, Washington, DC. June. Available at http://www.epa.gov/waste/nonhaz/ industrial/speci al/fossil/ffc2_395.pdf

.

U.S. EPA (Environmental Protection Agency). 1998c. Methodology for Assessing Health Risks Associated with Multiple Pathways of Exposure to Combustor Emissions. Update to Methodology for Assessing Health Risks Associ ated with Indirect Exposure to Combustor

Emissions . EPA-600/R-98/137. National Center for Environmental Assessment, Cincinnati, OH. Dece mber. Available at http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=55525

.

U.S. EPA (Environmental Protection Agency). 1998d. Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, Volume One . EPA-530-D-98-001A. Office of Solid Waste and Emergency Response, Washington, DC. Available at http://nepis.epa.gov/EPA/html/Pubs/pubtitleOSWER.htm

.

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). 1999b. Partition Coefficients for Metals in Surface Water, Soil, and Waste . Office of Solid Waste, Washington, DC. Available at http://www.epa.gov/osw/hazard/wastetypes/wa steid/hwirwste/pdf/risk/reports/s0524.pdf

.

U.S. EPA (Environmental Protection Agency). 1999c. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment Model - The Vadose Zone and Saturated Zone Modules Extracted From EPACMTP for HWIR99 . Prepared by Eastern Research Group under EPA Contract No. 68-W-99-001, Work Assignment No. 17, for Office of Solid Waste. Washington, DC. Available at http://www.epa.gov/osw/hazard/wastetypes/ wasteid/hwirwste/pee r03/vadoze/vz-aquif.pdf

.

U.S. EPA (Environmental Protection Agency). 1999d. Peer Review of EPA’s Hazardous Waste Identification Rule Risk Assessment Model - MINTEQA2/PRODEFA2 Version 4.0

. Prepared by Eastern Research Group unde r EPA Contract No. 68-W-99-001, Work Assignment No. 17, for Office of Solid Wa ste. Washington, DC. Available at

### Section 5.0 References



## April 2010–Draft EPA document. 5-6 http://www.epa.gov/osw/hazard/wastetypes/wa steid/hwirwste/peer03/minteqa2/minteqa2. pdf

.

U.S. EPA (Environmental Protection Agency). 1999e. Revised Risk Assessment for the Air Characteristic Study. EPA-530-R-99-019b. Volume 2. Office of Solid Waste, 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 th e 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/c hief/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 EP A 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, Washi ngton, 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 Lis ting 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, Washi ngton, 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 contra ct 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 Tec hnical 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, Cinc innati, OH.

U.S. EPA (Environmental Protection Agency). 2006c. Characterization of Mercury-Enriched Coal Combustion Residues from Electric Ut ilities Using Enhanced Sorbents for Mercury

Control . EPA 600/R-06/008. Office of Research a nd 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 We t 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 Environmenta l 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 Assessme nt 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 Wast es 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_R egulation_of_CCB_Placement.pdf

.

### Appendix A Constituent Data



# Appendix A. Constituent Data

The coal combustion waste (CCW) risk as sessment addressed metals and inorganic constituents identified by EPA as poten tial 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 di fferent types of waste samples: landfill leachate analyses (in mg/L), surface im poundment and landfill porewater analyses (in mg/L), and analyses of whole wa ste samples (in mg/kg). Each data base table specifies, for most samples, the type of waste sampled and th e type of coal burne d 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 da tabase 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 th e 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 incl ude the electric power in dustry, 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 e xpansion 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 ge neration 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 approxima tely 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 expa nsion 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 da ta set includes data from other sources, such

as scientific literatur e and state and federal regulatory agencies. The 1999 data set included analyses of whole waste samples, surface im poundment and landfill porewater analyses, and analyses of extracts obtained using the Toxicity Characteristi c Leaching Procedure (TCLP), the Synthetic Precipitation Leaching Procedure (SPL P), and Extraction Procedure (EP) Toxicity leaching methods. The 2002 data set added anal yses 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 represen ts CCW characteristics across a broad cross-section of the generating uni verse. Not only does the database include data from a large number of sites, but these si tes are distributed throughout th e United States, as shown in

Table A-2 . The database includes data for all major types of CCW (i.e., fl y ash, bottom ash, flue gas desulfurization [FGD] sludge, fluidized be d combustion [FBC] fly ash, and FBC bed ash), from mixtures of CCW types that are commonly created dur ing 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 ge nerated from combustion of all major coal ranks: bituminous, sub-bituminous, lignite, and an thracite. 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 sour ces (e.g., eastern and west ern 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 anal yte data to be used, and (3) proc essing 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 Type a



### 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 wast e 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 vari ety of waste types. Some selection and grouping of these types was appropr iate so that the risk asse ssment could evaluate risks consistently for groups of wastes that are expected to behave sim ilarly 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 co al ash not otherwise specified. Based on a statistical analysis that showed no significant difference in leacha te 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 operations 1 

## ) differ significantly from coal combustion ash in thei r 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 e xhibit a lower pH when codisposed with coal

refuse.

A.2.2 Selection of Appropriate Analyte Data

CCW analyte concentration data repres ent 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 por ewater sampled from surface impoundment sediments represent surface impoundment leachate.

 Analyte concentrations for extracts from l eaching methods, analyses of actual landfill leachate, and landfill porewater analyses represent landfill leachate. Because the CCW constituent database includes analyte concen trations from several leaching methods, a decision hierarchy was used to select leachat e 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 equ ilibration 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 wi de variety of sites, the hierarchy rank shown

in Table A-4 was used to select the best method fo r 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 highes t analyte concentrations. This ensured that the

data used in the risk assessment were the best th at 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 leacha te, and landfill whole waste), an alyte, and site for use in the risk assessment. Data processing to prepar e these analyte concentrations for the CCW risk assessment involved two steps:

1. Calculation of average constituent concen trations by site for landfill leachate, surface impoundment porewater, and total ash concentrations. Site averaging avoids potential bias toward sites with many anal yses 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 a pproach is consistent

with that used in the 1998 CCW risk anal ysis. 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 concentratio ns from site-averaged values. For the Monte Carlo analysis, the analysis randomly selected, by waste type/waste management unit (WMU) scenario, site-averaged leachate concentratio ns. 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 constituen t data used (sampled) in the fullscale CCW risk assessment by waste type/WMU scenario. Attachment A-3 presents summary statistics, by constituent and WM U type, including the 90th percen tile 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), la ndfill 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 So lid Waste and Emergency Response, Washington, DC. March.

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 1 

# Attachment A

- 1: Sources of Data

General

Alaska Department of Environmental Conservation. 2000. Comments on Regulatory Determination on Wastes from the Combustion of Fossil Fuels, Docket No.

F

- 2000

- FF2F

- FFFFF and attachments.

ARIPPA ( Anthracite Region Indep endent P ower Producers Association ) . 2000. Occur re nce and Fate of Selected Trace Elements in Circulating Fluidized Bed Combustion Byproducts,

Volume II of II, Prepared by Earthtech Inc., Johnstown, PA , for ARIPPA

. Volume I is

available in EPA docket EPA

- HQ

- RCRA

- 2008

- 0329 a s document number EPA

- HQ

RCRA

- 2008

- 0329

- 0324

. Bhattacharja, S., J.L. Evanko, J.J. Gajda, and R.H. Carty. 2001. Ceramic t iles u sing Illinois f ly

a sh. In Proceedings of the American Coal Ash Association 14th International Symposium

on Mana gement & Use of Coal Combustion Products (CCPs) , January 22

– 26, 2001, San Antonio, TX. Palo Alto, CA : Electric Power Research Institute, EPRI 1001183, Vol ume 1: Management and Use of Coal Combustion Products (CCPs)

. Available at http://my.epri.com/portal/server.pt?space=CommunityPage&cached=true&parentname = ObjMgr&parentid=2&control=SetCommunity&CommunityID=404&RaiseDocID=0000 00000001001158&RaiseDocType=Abstract_id

. Bland, A.E., T.H. Brown, and D.O. Steen. 2001. Flowable f ill m aterials m ade from l ignite f ired

f luidized b ed c ombus t ion a sh. In Proceedings of the American Coal Ash Association 14th International Symposium on Management & Use of Coal Combustion Products (CCPs)

, January 22

– 26, 2001, San Antonio, TX. Palo Alto, CA : Electric Power Research Institute, EPRI 1001183, Vol ume 1: Management and Use of Coa l Combustion Products (CCPs). Available at http://my .epri.com/portal/server.pt?space=CommunityPage&cached =true&parentname=ObjMgr&parentid=2&control=SetCommunity&CommunityID=404 &RaiseDocID=000000000001001158&RaiseDocType=Abstract_id

. Butler, R.D., D.F. Pflughoeft

- Hassett, B.A. Dock ter, and H.J. Foster. 199 5. Stabilization of Underground Mine Voids by Filling with Coal Conversion Residuals

. Energy & Environmental Research Center, University of North Dakota, Grand Forks, ND. 43 pp.

Available in EPA docket EPA

- HQ

- RCRA

- 2003

- 0003 a s document number EPA

- HQ

RCRA

- 2 003

- 0003

- 0123

. Dairyland Power Cooperative. 1991. Letter to Wisconsin Department of Natural Resources. May 22, 1991. Available in EPA docket EPA

- HQ

- RCRA

- 2003

- 0003 a s document number

EPA

- HQ

- RCRA

- 2003

- 0003

- 0111

. Electric Power Research Institute ( EPRI ) . 1983

. Pilot Study of Time Variability of Elemental Ash

Concentrations in Power

- Plant Ash

. EPRI

- EA

- 2959 , EPRI, Palo Alto, CA. Available in

RCRA docket number F

- 2003

- FF4P

- FFFFF.

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 2 Electric Power Research Institute (EPRI). 1995. Laboratory Characterization of Atmos pheric

Fluidized

- Bed Combustion By

- Products, Final Report

. EPRI

- TR

- 105527, EPRI, Palo Alto, CA

. Available at http://my.epri.com/portal/server.pt?Abstract_id=TR

- 105527

.
Electric Powe r Research Institute (EPRI). 1995. Land Application of Coal Combustion By

- Products: Use in Agriculture and Land Reclamation, Final Report

. EPRI

- TR

- 103298

, EPRI, Palo Alto, CA

. Available in EPA docket EPA

- HQ

- RCRA

- 1999

- 0022 as document

number EPA

- HQ

- RCRA

- 199 9

- 0022

- 0931

. Electric Power Research Institute (EPRI). 1996. Fluid Placement of Fixated Scrubber Sludge in Abandoned Deep Mines to Abate Surface Subsidence and Reduce Acid Mine Drainage

. EPRI

- TR

- 107053, EPRI, Palo Alto, CA

. Available for viewing in EPA doc ket EPA

- HQ

RCRA

- 2003

- 0003 as document number EPA

- HQ

- RCRA

- 2003

- 0003

- 0127

. Electric Power Research Institute (EPRI). 1996. Mixtures of a Coal Combustion By

- Product and Composted Yard Wastes for Use as Soil Substitutes and Amendments, Final Report

. EPRI

- TR

- 10 6682 , EPRI, Palo Alto, CA

. Available at http://my.epri.com/portal/server.pt?Abstract_id=TR

- 106682

Electric Power Research Institute (EPRI). 1998. Identification of Arsenic Species i n Coal Ash

Particles

. EPRI

- TR

- 109002 , EPRI, Palo Alto, CA

. Available at http://www.regulations.gov as document # EPA

- HQ

- RCRA

- 2003

- 0003

- 0406

. Electric Power Research Institute (EPRI). 1999. Investigation of Ammoni a Adsorption on Fly Ash and Potential Impacts of Ammoniated Ash

. EPRI

- TR

- 113777, EPRI, Palo Alto, CA

. Available for viewing in EPA docket #F

- 2003

- FF4P

- FFFFF.

Electric Power Research Institute (EPRI). 1999. Utilization of Coal Combustion By

- Products in Agri culture and Land Reclamation, Final Report

. EPRI

- TR

- 112746 , EPRI, Palo Alto,

CA

. Available in EPA docket EPA

- HQ

- RCRA

- 1999

- 0022 as document number EPA

HQ

- RCRA

- 1999

- 0022

- 0074

. Electric Power Research Institute (EPRI). 1999

. Guidance for Comanagement of Mill Rejects at

Coal

- Fired Power Plants . EPRI

- TR

- 108994, EPRI, Palo Alto, CA. Available for viewing

in EPA docket number EPA

- HQ

- RCRA

- 1999

- 0022 as document number EPA

- HQ

RCRA

- 1999

- 0022

- 1078

. Electric Power Research Institute (EPRI). 2001. Environmental Evaluatio n for Use of Ash in Soil Stabilization Applications

. WO9227

- 01 , EPRI, Palo Alto, CA

. Available in EPA docket

number F

- 2003

- FF4P

- FFFFF.

Fink el man, R.B., C.A. Palmer, and C.F. Eble. 1998. Characterization of h azardous t race

e lements in s olid w aste p roducts f rom a c oal b urning p ower p lant in Kentucky. In Characterization of Coal and Coal Combustion Products from a Coal Burning Power

Plant

– Preliminary Report and Results of Analysis

. Edited by G.N. Breit and R.B. Finkelman. U.S. Geological Survey, Open File Re port 98

- 342 , 101 pp

. Available at http://pubs.er.usgs.gov/usgspubs/ofr/ofr98342

. Freeman United Coal Mining Company. 1991

– 2001. Quarterly Ash Analyses. Available in EPA

docket number F

- 2003

- FF4 P

- FFFFF and at http://www.regulations.gov as document

numbers EPA

- HQ

- RCRA

- 2003

- 0003

- 0119 , EPA

- HQ

- RCRA

- 2003

- 0003

- 0411, EPA

-

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 3 HQ

- RCRA

- 2003

- 0003

- 0412, EPA

- HQ

- RCRA

- 2003

- 0003

- 0413, EPA

- HQ

- RCRA

- 2003

- 0003

- 0414, EPA

- HQ

- RC RA

- 2003

- 0003

- 0415, EPA

- HQ

- RCRA

- 2003

- 0003

- 0416.

Haefner, R.J. 2000. W ater quality at an abandoned mine site treated with coal combustion by

- products

. In Proceedings of the Use and Disposal of Coal Combustion By

- Products at

Coal Mines: Technical Interactive Forum , K.C. Vories and D. Throgmorton, eds. Morgantown, WV, April 10

– 13, 2000. Available at http://www.techtransfer.osmre.gov/ NTTMainSite/Library/proceed/ccb2000/front.pdf

. Hassett, D.J., D.F. Pflughoeft

- Hassett, D.L. Laudal, and J.H. Pavlish. 1999. Mercury r elease

from c oal c ombustion b y

- p roducts to the e nvironment. In Mercury in the Environment, Proceedings of a Specialty Co nference

, Minneapolis, MN, September 15

– 17, Air & Waste Management Association, pp. 85

– 93. Available in EPA docket EPA

- HQ

- RCRA

2003

- 0003

- 0114 a s document number EPA

- HQ

- RCRA

- 2003

- 0003

- 0114

. Hower, J.C., J.D. Robertson, and J.M. Roberts. 2001. Coal c ombustio n b y p roducts from the

c o

- c ombustion of c oal, t ire

- d erived f uel, and p etroleum c oke at a w estern Kentucky

c yclon e

- f ired u nit. In Proceedings of the American Coal Ash Association 14th International Symposium on Management & Use of Coal Combustion Products ( CCPs)

, January 22

– 26, 2001, San Antonio, TX. Palo Alto, CA : Electric Power Research Institute, EPRI 1001183, Volume 1 : Management and Use of Coal Combustion Products (CCPs)

. Available at http://my.epri.com/portal/server.pt?space=CommunityPage& cached=true&parentname=ObjMgr&parentid=2&control=SetCommunity&C ommunityI D=404&RaiseDocID=000000000001001158&RaiseDocType=Abstract_id

. Hower, J.C., J.D. Robertson, U.M. Graham, G.A. Thomas, A.S. Wong, and W.H. Schram. 1993. Characterization of Kentucky coal

- c ombustion by

- products: Compositional variations

based on sul fur content of feed c oal. Journal of Coal Quality 12 : 150

– 155.

Hower, J.C., A.S. Trimble, C.F. Eble, C.A. Palmer, and A. Kolker. 199 7 . Characterization of f ly

a sh from l ow

- s ulfur and h igh

- s ulfur c oal s ources: Partitioning of c arbon and t race

e lements with p article s ize. Energy Sources Part A: Recovery, Utilization and Environmental Effects 21(6):511

– 525.

Indiana Department of Natural Resources. 1999. Response to the U.S. Environmental Protection Agency’s March 1999 Report to Congress : Wastes from the Combus tion of Fossil Fuels

, Volume 2 of 3. Available in EPA Docket number EPA

- HQ

- RCRA

- 1999

- 0022 as

document number EPA

- HQ

- RCRA

- 1999

- 0022

- 0147.

Kim, A.G. , and G. Kazonich. 2001. Release of t race e lements from CCB: Maximum e xtractable

f raction. 2001. In Proceeding s of the American Coal Ash Association 14th International Symposium on Management & Use of Coal Combustion Products (CCPs) , January 22

– 26, 2001, San Antonio, TX. Palo Alto, CA : Electric Power Research Institute, EPRI 1001183, Vol ume 1: Management and Use o f Coal Combustion Products (CCPs)

. Available at http ://my.epri.com/portal/server.pt?space=CommunityPage&cached= true&parentname=ObjMgr&parentid=2&control=SetCommunity&CommunityID=404& RaiseDocID=000000000001001158&RaiseDocType=Abstract_id

. Kost, D.A., J.P. Vimmerstedt, and R.C. Stehouwer. 1997. Reclamation of Acid, Toxic Coal Spoils Using Wet Flue Gas Desulfurization By

- Product, Fly Ash and Sewage Sludge.

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 4 Available in EPA docket number EPA

- HQ

- RCRA

- 2003

- 0003 as document number EPA

HQ

- RCRA

- 2003

- 0003

- 0113

. Longley, R.D. Jr., Silsbee, M.R., and Elsworth, D. 1997

. Acid mine drainage remediation utilizing fly ash based grouts. Presented at the '97 International Ash Utilization Symposium. Available at http://w ww.ems.psu.edu/~elsworth/publications/proc/ 1997_p_ash_utilization_conf_longley.pdf

. Loop, C.M. 2000. The Impact of Ash Placement in a Surface Mine Pool on the Chemistry of the

Silverbrook Basin. M.S. Thesis, University Park, PA: The Pennsylvania State Un iversity.

Mafi, S., M.T. Damian, R.E. Senita, W.C. Jewitt, S. Bair, Y

- C. Chin, E. Whitlatch, S. Traina,

and W. Wolfe

. 1997. Injection of FGD G rout to A bate A cid M ine D rainage in Underground Coal Mi nes

. Presented at the Advanced Coal

- Based Power and

Enviro nmental Systems ‘97 Conference , Pittsburgh, PA, July 22

– 24. Publication Number

DOE/MC/33092

- 98/C0946 . Washington, DC: U.S. Department of Energy. Available at http:// www.osti.gov/bridge/purl.cover.jsp?purl=/611785

- iz9wzM/webviewable/

. Naik, T.R., R.N. Kraus, R. F . Sturzl, and B.W. Ramme. 1998. Design and t esting c ontrolled

l ow

- s trength m aterials (CLSM) u sing c lean c oal a sh. In The Design and Application of

Controlled L ow

- Strength Materials (Flowable Fill)

, Edited by A.K. Howard and J.L.

Hitch

, ASTM STP 1331.

Pennsylvania Department of Environmental Protection (PADEP ). 1999. Pennsylvania Department of Environmental Quality Comments on EPA Report to Congress

, Volume 2:

Co al Ash Used as Minefill

– Placement Not in Contact with Groundwater

. A vailable

from Roderick A. Fletcher, Director, Bureau of Mining and Reclamation, PADEP, Rachel Carson State Office Building, P.O. Box 8461, Harrisburg, PA 17105

- 8461

. Pennsylvania Departm ent of Environmental Protection (PADEP) . 1999. Pennsylvania Department of Environmental Quality Comments on EPA Report to Congress

, Volume 3: Coal Ash Used as Minefill

– Placement in Contact with Groundwater

. A vailable from

R oderick A. Fletcher, Director, Bureau of Mining and Reclamation, PADEP, Rachel Carson State Office Building, P.O. Box 8461, Harrisburg, PA 17105

- 8461

. Pennsylvania Department of Environmental Protection (PADEP) . 2001. Coal Ash Data CD.

Pflughoeft

- Hassett, D.F. 1997. IGCC and PFBC By

- Pro ducts: Generation, Characteristics, and Management Practices

. Report Number 97

- EERC

- 09

- 06. Prepared by the Energy & Environmental Research Center, University of North Dakota, for the U.S. Environmental Protection Agency, Office of Solid Waste. Available at http://www.undeerc.org/carrc/Assets/IGCCandPFBC.pdf

. Pflughoeft

- Hassett, D.F., B.A. Dockter, D.J. Hassett, K.E. Eylands, and L.L. Hamre. 2000. Use of Bottom Ash and Fly Ash in Rammed

- Ear th Construction . Report No. 2000

- EERC

- 09

- 03 , Energy & Environmental Research Center, University of North Dakota, Grand Forks,

ND. Available at http://www.undeerc.org/carrc/Assets/RammedEar th.pdf

. PPL Generation, LLC. 1994

– 2001. Coal Ash Quality Results. Available at http://www.regulations.gov as document number EPA

- HQ

- RCRA

- 2003

- 0003

- 0110

.

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 5 Radian Corporation. 1994. Field Study for Disposal of Sol id Wastes from Advanced Coal

Processes: Ohio LIMB Site Assessment

, Volume I: Text, Final Report

. Available at http://regulations.gov as document number EPA

- HQ

- RCRA

- 2003

- 0002

- 0442

. Radian Corporation. 1994. Field Stud y of Circulating Fluidized

- Bed Ash, Final Report

. Available at http://www.regulations.gov as document numbers EPA

- HQ

- RCRA

- 2003

- 0002

- 0035

, EPA

- HQ

- RCRA

- 2003

- 0002

- 0436 , and EPA

- HQ

- RCRA

- 2003

- 0002

- 0437.

Radian Corpora tion. 1997. Field Monitoring of Advanced Coal By

- Products: Illinois Site

, Volume II: Appendices, Final Report

. Available at http://www.regulations.gov as document numbers EPA

- HQ

- RCRA

- 2003

- 0002

- 0 040, EPA

- HQ

- RCRA

- 2 003

- 0002

- 0423

, and EPA

- HQ

- RCRA

- 2003

- 0002

- 0424

Reliant Energy. 2001. Coal Ash Analyses for Conemaugh, Keystone, Portland, Titus, Seward, and Shawville Facilities & Groundwater Assessments for Disposal Sites at Keystone, Conemaugh, Portland, and Shawville

.
S cience Applications International Corporation ( SAIC ) . 1999. Memorandum to ERG, Regarding Maryland/West Virginia Mine Site Visit Summary. Available in EPA docket EPA

- HQ

RCRA

- 2003

- 0003

- 0107 as document number EPA

- HQ

- RCRA

- 2003

- 0003

- 0107

. Sellakumar, K.M., R. Conn, and A. Bland. 1999. A c omparison s tudy of ACFB and PCFB a sh

c haracteristics. Paper p resented at the 6th International Conference on Circulating Fluidized Beds, Wurzburg, Germany, August 22

– 27, 1999. Available at http://www.fwc.com/publications/tech_papers/powgen/pdfs/cfb6ash.pdf

. Turris Coal Company. 1993

– 2001. Quarterly Ash Analyses. Available at http://www.regulations.gov as document numbers EPA

- HQ

- RCRA

- 2003

- 0120, EPA

HQ

- RCRA

- 2003

- 0120, EPA

- HQ

- RCRA

- 2003

- 0409, and EPA

- HQ

- RCRA

- 2003

- 0410.

Zeigler Coal Holding Company. 1994

– 2000. Quarterly Ash Analyses.



# Report to Congress

FBC Leachate:

Council of Industrial Boiler Owners. 1997 . Responses to Fossil Fuel Fluidized Bed Combustion By

- Products Survey. November.

FBC Total:

Council of Industrial Boiler Owners. 1997. Responses to Fossil Fuel Fluidized Bed Combustion By

- Products Survey. November.

Porew ater:

Electric Power Research Insti tute (EPRI). 1991. Comanagement of Coal Combustion By

- Products and Low

- Volume Wastes: A Southeastern Site. Final Report

. EPRI

- EN

- 7545. EPRI, Palo Alto, CA. November. ( “ L ” Site).

Electric Power Research Institute (EPRI). 1992. Comanagement of Coal Combustio n By

- Products and Low

- Volume Wastes: A Midwestern Site. Interim Report

. EPRI

- TR

- 100955 , EPRI, Palo Alto, CA . August. ( “C” Site).

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 6 Electric Power Research Institute (EPRI). 1996. Comanagement of Coal Combustion By

- Products and Low

- Volume Wastes: A Southweste rn Site. Final Report

. EPRI

- TR

- 105673

, EPRI, Palo Alto, CA. June. ( “O” Site).

Electric Power Research Institute (EPRI). 1996. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: CL Site. Draft Report

. EPRI,

Palo Alto, CA. May (updated December).

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: MO Site. Draft Report

, EPRI, Palo Alto, CA. April.

Electric Power Research I nstitute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: AP Site. Draft Report

. EPRI, Palo Alto, CA. June.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of Hig h Volume Combustion By

- Products with Other Utility Wastes: SX Site. Draft Report

. EPRI

- TR

- 001015 , EPRI, Palo Alto, CA. June.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Ut ility Wastes: P4 Site. Final Report

. EPRI

- TR

- 108420. July.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: OK Site. Draft Report

. EPRI

- TR

- 108421. EPRI, Palo Al to, CA. July.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: HA Site. Draft Report

. EPRI, Palo Alto, CA. August.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: FC Site. Draft Report

. EPRI

- TR

- 108425. EPRI, Palo Alto, CA. August.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of Hi gh Volume Combustion By

- Products with Other Utility Wastes: LS Site. Final Report

. EPRI

- TR

- 108422. EPRI, Palo Alto, CA. August.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: PA Site. Final Report

. EPRI

- TR

- 108427. EPRI, Palo Alto, CA. August.

Electric Power Research Institute (EPRI). 1997. Evaluation of the Co

- Management of High Volume Combustion By

- Products with Other Utility Wastes: CY Site. Final Report

. EPR I

- TR

- 108426. EPRI, Palo Alto, CA. August.

Electric Power Research Institute (EPRI). 1994. Calcium

- Based Flue Gas Desulfurization Sludge Disposal Sites. Final Report

. EPRI

- TR

- 103914. EPRI, Palo Alto, CA. April.

Electric Power Research Institute (EPRI). 1994

. Sodium

- Based Flue Gas Desulfurization Sludge Disposal Sites. Final Report

. EPRI

- TR

- 103915. EPRI, Palo Alto, CA. April.

### Appendix A Attachment A

- 1: Sources of Data



## April 2010

–

Draft EPA document. A

- 1

- 7 TCLP&SPLP:

Electric Power Research Institute (EPRI). 1998. TCLP & SPLP Data for Comanaged Coal

Combustion Wastes

. EPRI, Palo Alto, CA. August.

## April 2010–Draft EPA document. [This page intentionally left blank.]

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

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- 2

- 1 

# Attachment A

- 2: CCW Constituent Data

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

11

- FBC LF Arsenic 0.002916667 3 3 51

11

- FBC LF Barium 0.339166667 3 3 174.5

11

- FBC LF Ca dmium 0.0005 4 4 6.91875

11

- FBC LF Lead 0.0025 4 4 39.5

11

- FBC LF Mercury 0.00125 4 4 0.1325

11

- FBC LF Selenium 0.00225 4 2 45.5

12

- FBC LF Aluminum 3.4 1 0 35874.6

12

- FBC LF Antimony 0.27 1 0 18

12

- FBC LF Arsenic 0.02205 2 0 57.64333333

12

- FBC LF Barium 0.196 2 1 203.805

12

- FBC LF Boron 0.05 1 1 20.324

12

- FBC LF Cadmium 0.005625 2 1 0.279375

12

- FBC LF Lead 0.025 1 1 45.66666667

12

- FBC LF Mercury 0.00005 2 2 1.2575

12

- FBC LF Molybdenum 0.21 1 0 15.5

12

- FBC LF Selenium 0 .04355 2 0 7.365833333

17

- FBC LF Aluminum 4.788 5 0 46194.8

17

- FBC LF Antimony 0.0708 5 2 14.60333333

17

- FBC LF Arsenic 0.1378 5 0 71.46666667

17

- FBC LF Barium 0.3512 5 1 134.975

17

- FBC LF Boron 0.4404 5 1 34.06333333

17

- FBC LF Cadmium 0. 0434 5 2 3.058333333

17

- FBC LF Lead 0.2372 5 2 49.65

17

- FBC LF Mercury 0.01022 5 5 1.60345

17

- FBC LF Molybdenum 0.097 5 1 3.515

17

- FBC LF Selenium 0.06315 5 2 3.301666667

18

- FBC LF Aluminum 1.333333333 3 0 23501.33333

18

- FBC LF Antimony 0 .025 3 3 5

18

- FBC LF Arsenic 0.025 3 3 53.33333333

18

- FBC LF Barium 0.175 3 1 211.3333333

18

- FBC LF Boron 1.341666667 3 1 532.3333333

18

- FBC LF Cadmium 0.025 3 3 2.5

18

- FBC LF Cobalt 0.025 3 3 11

18

- FBC LF Lead 0.025 3 3 22

18

- FBC LF M ercury 0.0005 3 2 0.268333333

18

- FBC LF Molybdenum 0.175 3 1 7.666666667

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 2 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

18

– FB C LF Selenium 0.108333333 3 1 0.5

18

- FBC LF Thallium 0.025 3 3 1

19

- FBC LF Arsenic 0.0875 2 1 6.25

19

- FBC LF Barium 0.27 2 1 39.2

19

- FBC LF Cadmium 0.01375 2 2 2.5

19

- FBC LF Lead 0.0675 2 2 3.75

19

- FBC LF Mercury 0.00125 2 1 0.125

19

- F BC LF Selenium 0.06875 2 2 6.25

20

- FBC LF Aluminum 10.81 12 0 34329.16522

20

- FBC LF Antimony 0.787 10 0 46.28125

20

- FBC LF Arsenic 0.035 12 0 15.03130435

20

- FBC LF Barium 0.381818182 11 0 255.4608696

20

- FBC LF Boron 0.457142857 7 0 28.0025

20

- FBC LF Cadmium 0.03625 8 0 2.089166667

20

- FBC LF Lead 0.301111111 9 0 36.20052632

20

- FBC LF Mercury 0.29 1 0 0.454

20

- FBC LF Molybdenum 0.392857143 7 0 12.10111111

20

- FBC LF Selenium 0.088571429 7 0 4.177333333

21

- FBC LF Aluminum 1.91 3 0 14677.33167

21

- FBC LF Antimony 0.001833333 3 3 1.083333333

21

- FBC LF Arsenic 0.012 3 0 10.76666667

21

- FBC LF Barium 0.022333333 3 2 176.2666667

21

- FBC LF Boron 0.036666667 3 2 14.38333333

21

- FBC LF Cadmium 0.002083333 3 3 0.145833333

21

- FBC LF Cobalt 0.008333333 3 2 5.756666667

21

- FBC LF Lead 0.009166667 3 3 27.3

21

- FBC LF Mercury 0.000133333 3 2 0.431666667

21

- FBC LF Molybdenum 0.0125 3 3 3.708333333

21

- FBC LF Selenium 0.016666667 3 0 10.9

2

- 18

- Ash LF Arsenic 0.41794375 16 3

2

- 18

- Ash LF Barium 0.4305625 16 0

2

- 18

- Ash LF Boron 1.0160625 16 0

2

- 18

- Ash LF Cadmium 0.05825 16 11

2

- 18

- Ash LF Lead 0.2819375 16 11

2

- 18

- Ash LF Mercury 0.000115625 16 16

2

- 18

- Ash LF Selenium 0.01534375 16 8

22

- FBC LF Arse nic 0.055 5 3

22

- FBC LF Barium 0.5405 5 1

22

- FBC LF Cadmium 0.003 5 5

22

- FBC LF Lead 0.015 5 5

22

- FBC LF Mercury 0.0002 5 3

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 3 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

22

- FBC LF Molybdenum 0.0125 2 2

(continued)

22

- FBC LF Selenium 0.032 5 5

23

- FBC LF Barium 0.81 4 0

25

- FBC LF Arsenic 0.125 1 1

25

- FBC LF Barium 2.5 1 1

25

- FBC LF Cadmium 0.025 1 1

25

- FBC LF Lead 0.125 1 1

25

- FBC LF Mercury 0.005 1 1

25

- FBC LF Selenium 0.025 1 1

28

- FBC LF Barium 2.525 2 0 235.11875

30

- FBC LF Alumin um 6.894555556 18 7 28246.46923

30

- FBC LF Antimony 0.548082353 17 2 61.49315385

30

- FBC LF Arsenic 0.050694444 18 3 48.55980769

30

- FBC LF Barium 0.286388889 18 6 120.0687692

30

- FBC LF Boron 0.31759375 16 7 30.83913462

30

- FBC LF Cadmium 0.0231 25 14 3 1.916230769

30

- FBC LF Lead 0.240805556 18 4 39.36092308

30

- FBC LF Mercury 0.000744444 18 17 10.91689923

30

- FBC LF Molybdenum 0.138125 16 10 14.50257692

30

- FBC LF Selenium 0.10475 16 10 5.603596154

31

- FBC LF Aluminum 0.28 1 0 29437.5

31

- FBC LF Antimony 0.00065 1 1 5.0325

31

- FBC LF Arsenic 0.0687 4 2 26.825

31

- FBC LF Barium 0.58275 4 0 170.25

31

- FBC LF Boron 26.7 1 0 930

31

- FBC LF Cadmium 0.02775 4 3 5.45

31

- FBC LF Cobalt 0.0065 1 0 6.42

31

- FBC LF Lead 0.03025 4 3 1 .19

31

- FBC LF Mercury 0.00095 4 1 0.61

31

- FBC LF Molybdenum 0.085 1 0 8

31

- FBC LF Selenium 0.06485 4 2 7.54

32

- FBC LF Arsenic 0.35 1 1 1.4

32

- FBC LF Barium 0.085 1 0

32

- FBC LF Cadmium 0.005 1 1 0.009

32

- FBC LF Lead 0.05 1 1 0.45

32

- FBC LF Mercury 0.0001 1 1 0.03

32

- FBC LF Selenium 0.175 1 1 3.5

33

- FBC LF Arsenic 0.015 1 1

33

- FBC LF Barium 42 1 0

33

- FBC LF Boron 0.06 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 4 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

33

- FBC LF Cadmium 0.00125 1 1

33

- FBC LF Cobalt 0.0025 1 1

33

- FBC LF Mercury 0.00005 1 1

33

- FBC LF Selenium 0.01 1 1

35

- FBC LF Arsenic 0.015 1 1

35

- FBC LF Barium 2.6 1 0

35

- FBC LF Cadmium 0.009 1 0

35

- FBC LF Lead 0.035 1 1

35

- FBC LF Mercury 0.00025 1 1

35

- FBC LF Selenium 0.2 1 0

37

- FBC LF Arsenic 0. 011102941 17 9 5.79

37

- FBC LF Barium 2.104705882 17 2

37

- FBC LF Boron 1.125 5 1 15.9

37

- FBC LF Cadmium 0.046176471 17 4 4.183333333

37

- FBC LF Cobalt 0.246 5 0

37

- FBC LF Lead 0.287352941 17 6 55

37

- FBC LF Mercury 0.001314706 17 4 0.01125

37

- FBC LF Selenium 0.01075 17 9 3.42

38

- FBC LF Aluminum 2.256666667 9 2 26711.25

38

- FBC LF Antimony 0.213069444 9 6 11.27770833

38

- FBC LF Arsenic 0.024554444 9 3 25.136075

38

- FBC LF Barium 0.178888889 9 4 181.0083333

38

- FBC LF Boron 0.34 6555556 9 2 26.98916667

38

- FBC LF Cadmium 0.007388889 9 5 0.71625

38

- FBC LF Cobalt 0.008566667 3 2 4.515

38

- FBC LF Lead 0.0565 9 6 28.54166667

38

- FBC LF Mercury 0.000344444 9 8 0.18195

38

- FBC LF Molybdenum 0.177375 8 2 14.1875

38

- FBC LF S elenium 0.088561111 9 4 7.682450833

39

- FBC LF Arsenic 0.075 1 1 14.5

39

- FBC LF Barium 0.395 2 1 590

39

- FBC LF Boron 0.76 1 0

39

- FBC LF Cadmium 0.005 1 1 0.5

39

- FBC LF Lead 0.025 1 1 15

39

- FBC LF Mercury 0.00025 1 1 0.17

39

- FBC LF Moly bdenum 0.14 1 0 13.5

39

- FBC LF Selenium 0.025 1 1 21.5

4

- FBC LF Aluminum 13.556 5 0 16084.68429

4

- FBC LF Antimony 0.2236 5 2 26.78817857

4

- FBC LF Arsenic 0.271 5 0 28.03585714

4

- FBC LF Barium 0.6346 5 1 154.95

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 5 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

4

- FBC LF Boron 0.693 4 0 13.026

4

- FBC LF Cadmium 0.0115 5 2 0.646539286

4

- FBC LF Mercury 0.00005 5 5 0.087192857

4

- FBC LF Lead 0.1834 5 1 18.35671429

4

- FBC LF Molybdenum 0.286666667 3 0 16.18257143

4

- FBC LF Selenium 0.0620625 4 2 1.505421429

41

- FBC LF A ntimony 0.025 5 5 1.551333333

41

- FBC LF Arsenic 0.035471698 53 50 13.72255319

41

- FBC LF Barium 0.095694444 54 25 19.05490196

41

- FBC LF Cadmium 0.022355769 52 51 0.427826087

41

- FBC LF Lead 0.017548077 52 51 0.935208333

41

- FBC LF Mercury 0.000 596154 52 50 0.119542553

41

- FBC LF Selenium 0.024433962 53 51 1.505744681

41

- FBC LF Thallium 0.031 5 4 3.662790698

42

- FBC LF Arsenic 0.0125 2 2

42

- FBC LF Barium 0.1625 2 1

42

- FBC LF Cadmium 0.005 2 2

42

- FBC LF Lead 0.0075 2 2

42

- FB C LF Mercury 0.0005 2 2

42

- FBC LF Selenium 0.0125 2 2

43

- FBC LF Arsenic 0.0125 2 2

43

- FBC LF Barium 0.0875 2 1

43

- FBC LF Cadmium 0.005 2 2

43

- FBC LF Lead 0.0075 2 2

43

- FBC LF Mercury 0.0005 2 2

43

- FBC LF Selenium 0.08625 2 1

6

- FBC LF Aluminum 0.1525 2 1 42736.5

6

- FBC LF Antimony 0.05 2 2 16.25

6

- FBC LF Arsenic 0.09125 2 1 126.6

6

- FBC LF Barium 0.285 2 0 221.5

6

- FBC LF Boron 0.1425 2 1 73.8

6

- FBC LF Cadmium 0.0025 2 2 1.29625

6

- FBC LF Lead 0.01375 2 2 8.1125

6

- FBC LF Mercury 0.00005 2 2 1.16

6

- FBC LF Molybdenum 0.09 2 0 1.425

6

- FBC LF Selenium 0.1025 2 1 84.5625

Amerikohl

- FBC LF Aluminum 0.753333333 3 0 51600

Amerikohl

- FBC LF Antimony 0.345 3 3 20

Amerikohl

- FBC LF Arsenic 0.024166667 3 3 114

Amerikohl

- FBC LF Barium 0.1 3 3 140

Amerikohl

- FBC LF Boron 0.346666667 3 1 60

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 6 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Amerikohl

- FBC LF Cadmium 0.004166667 3 3 0.15

Amerikohl

- FBC LF Cobalt 0.175 3 3 30

Amerikohl

- FBC LF Lead 0.009166667 3 3 23

Amerikohl

- FBC LF Mercur y 0.0005 3 3 0.15

Amerikohl

- FBC LF Molybdenum 0.266666667 3 1 10

Amerikohl

- FBC LF Nitrate/Nitrite 3.15 3 3

Amerikohl

- FBC LF Selenium 0.044166667 3 3 3.5

Arkwright

- Ash LF Arsenic 0.07 1 0

Arkwright

- Ash LF Barium 0.4 1 0

Arkwright

- Ash LF Cadmium 0.01 1 0

Arkwright

- Ash LF Lead 0.04 1 0

Arkwright

- Ash LF Selenium 0.02 1 0

Barry

- Ash LF Arsenic 1 1 0

Barry

- Ash LF Barium 0.7 1 0

Barry

- Ash LF Cadmium 0.005 1 0

Barry

- Ash LF Lead 0.04 1 0

Barry

- Ash LF Selenium 0.07 1 0

Belle Ayr

- Ash LF Aluminum 0.036666667 3 0

Belle Ayr

- Ash LF Antimony 0.021 2 0

Belle Ayr

- Ash LF Arsenic 0.181 3 0

Belle Ayr

- Ash LF Barium 1.163333333 3 0

Belle Ayr

- Ash LF Cobalt 0.0075 2 0

Belle Ayr

- Ash LF Molybdenum 0.325 3 0

Bell e Ayr

- Ash LF Selenium 0.652333333 3 0

Big Gorilla Pit

- FBC LF Aluminum 3.774166667 12 0 18440.58824

Big Gorilla Pit

- FBC LF Antimony 0.037166667 12 1 1.244485294

Big Gorilla Pit

- FBC LF Arsenic 0.023181818 22 21 7.534117647

Big Gorilla Pit

- FBC LF Barium 0.243636364 11 3 147.7320588

Big Gorilla Pit

- FBC LF Boron 0.677916667 12 2 29.64058824

Big Gorilla Pit

- FBC LF Cadmium 0.015227273 22 22 0.58728125

Big Gorilla Pit

- FBC LF Cobalt 0.008553571 14 11 2.374214286

Big Gorilla Pit

- FBC LF Lead 0.08125 12 7 19.51823529

Big Gorilla Pit

- FBC LF Mercury 0.001704545 22 19 0.302990909

Big Gorilla Pit

- FBC LF Molybdenum 0.1202 10 1 6.429333333

Big Gorilla Pit

- FBC LF Nitrate/Nitrite 1.755857143 14 3

Big Gorilla Pit

- FBC LF Selenium 0.10975 12 1 7.159397059

Bowen

- Ash LF Arsenic 0.6 1 0 68

Bowen

- Ash LF Barium 0.3 1 0 974

Bowen

- Ash LF Cadmium 0.01 1 0 0.7

Bowen

- Ash LF Lead 0.04 1 0 63.9

Bowen

- Ash LF Selenium 0.1 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 7 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Branch

- Ash LF Arsenic 0.04 1 0

Branch

- Ash LF Barium 0.5 1 0

Branch

- Ash LF Cadmium 0.01 1 0

Branch

- Ash LF Lead 0.04 1 0

Branch

- Ash LF Selenium 0.06 1 0

Buckheart Mine

- Ash LF Antimony 0.01854 40 14

Buckheart Mine

- Ash LF Arsenic 0.122357143 42 13

Buckheart Mine

- Ash LF Barium 0.36 4809524 42 0

Buckheart Mine

- Ash LF Boron 9.998738095 42 0

Buckheart Mine

- Ash LF Cadmium 0.0235 42 8

Buckheart Mine

- Ash LF Cobalt 0.048047619 42 17

Buckheart Mine

- Ash LF Lead 0.27887619 42 9

Buckheart Mine

- Ash LF Mercury 0.000107143 42 4 0

Buckheart Mine

- Ash LF Selenium 0.118266667 42 26

Buckheart Mine

- Ash LF Thallium 0.017875 40 10

Buckheart Mine

- FBC LF Antimony 0.0018125 8 8

Buckheart Mine

- FBC LF Arsenic 0.0465 8 5

Buckheart Mine

- FBC LF Barium 0.560125 8 1

Buckheart Mine

- FBC LF Boron 3.157 8 0

Buckheart Mine

- FBC LF Cadmium 0.0033125 8 7

Buckheart Mine

- FBC LF Cobalt 0.02875 8 7

Buckheart Mine

- FBC LF Lead 0.036 8 4

Buckheart Mine

- FBC LF Mercury 0.0005 8 4

Buckheart Mine

- FBC LF Selenium 0.050625 8 5

Buckheart Mine

- FBC LF Thallium 0.001 8 8

CAER

- Ash LF Arsenic 1.132 5 0 77.32222222

CAER

- Ash LF Barium 0.315 5 0 537.6666667

CAER

- Ash LF Cadmium 0.0942 5 0

CAER

- Ash LF Lead 0.1 5 2 73.62375

CAER

- Ash LF Mercury 0.00025 5 5

CAER

- Ash LF Selenium 0.103 5 0

Canton Site

- Ash LF Aluminum 9.818127778 36 0

Canton Site

- Ash LF Arsenic 0.0025 2 2

Canton Site

- Ash LF Barium 3.0156 10 0

Canton Site

- Ash LF Boron 18.62468571 35 0

Canton Site

- Ash LF Cadmium 0.0005 2 2

Canton Sit e

- Ash LF Cobalt 0.02 1 1

Canton Site

- Ash LF Lead 0.1865 2 0

Canton Site

- Ash LF Mercury 0.0001 1 1

Canton Site

- Ash LF Molybdenum 30.9359 20 0

Canton Site

- Ash LF Nitrate/Nitrite 0.095 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 8 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Canton Site

- Ash LF Selenium 0.0374 1 0

Canton Site

- FBC LF Aluminum 2.461866667 24 0

Canton Site

- FBC LF Arsenic 0.005 1 1

Canton Site

- FBC LF Barium 0.02 1 0

Canton Site

- FBC LF Boron 1.5602625 16 0

Canton Site

- FBC LF Cadmium 0.066 1 0

Canton Site

- FBC LF Lead 0.062 1 0

Canton Site

– FBC LF Mercury 0.0005 1 1

Canton Site

– FBC LF Molybdenum 1.768009524 21 0

Canton Site

– FBC LF Selenium 0.005 1 1

Central Cleaning Plant

- Ash LF Antimony 0.008205882 17 17

Central Cleaning Plant

- Ash LF Arsenic 0.005 17 17

Cent ral Cleaning Plant

- Ash LF Barium 0.168164706 17 0

Central Cleaning Plant

- Ash LF Boron 7.213823529 17 0

Central Cleaning Plant

- Ash LF Cadmium 0.004117647 17 16

Central Cleaning Plant

- Ash LF Cobalt 0.019588235 17 15

Central Cleaning Plant

- A sh LF Lead 0.022782353 17 11

Central Cleaning Plant

- Ash LF Mercury 0.000568824 17 11

Central Cleaning Plant

- Ash LF Selenium 0.040211765 17 0

Central Cleaning Plant

- Ash LF Thallium 0.005 17 17

CL

- Ash and Coal Refuse LF Aluminum 2.58 3 0

CL

- Ash and Coal Refuse LF Antimony 0.0041 3 0

CL

- Ash and Coal Refuse LF Arsenic 0.121266667 3 0

CL

- Ash and Coal Refuse LF Barium 3.63 3 0

CL

- Ash and Coal Refuse LF Boron 0.103133333 3 0

CL

- Ash and Coal Refuse LF Cadmium 0.001 3 0

CL

- Ash and Coal Refuse LF Cobalt 0.006066667 3 1

CL

- Ash and Coal Refuse LF Lead 0.003533333 3 0

CL

- Ash and Coal Refuse LF Mercury 0.00005 6 6

CL

- Ash and Coal Refuse LF Selenium 0.0452 3 0

CL

- Ash and Coal Refuse LF Thallium 0.003483333 3 1

Coal Creek

- Ash LF Arsenic 0.0109 2 0 0.086

Coal Creek

- Ash LF Barium 0.6105 2 0 4.76

Coal Creek

- Ash LF Boron 6.22 2 0 1.1105

Coal Creek

- Ash LF Cadmium 0.00015 2 2 0.00045

Coal Creek

- Ash LF Lead 0.001 2 2 0.02025

Coal Creek

- Ash LF Mercury 0.00000 5 2 2 0.0006

Coal Creek

- Ash LF Selenium 0.0555 2 1 0.00505

Colver Site

- FBC LF Aluminum 0.248333333 6 1 78878.83333

Colver Site

- FBC LF Antimony 0.196666667 6 2 166.5

Colver Site

- FBC LF Arsenic 0.0875 6 1 124.2

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 9 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Colver Site

- FBC LF Barium 0.291666667 6 0 443.8333333

Colver Site

- FBC LF Boron 0.261666667 6 1 62.6

Colver Site

- FBC LF Cadmium 0.016666667 6 2 9.994166667

Colver Site

- FBC LF Lead 0.190833333 6 2 192.075

Colver Site

- FBC LF Mercury 0.00015 6 5 0.586666667

Colver S ite

- FBC LF Molybdenum 0.143333333 6 0 30.65833333

Colver Site

- FBC LF Selenium 0.48 6 1 68.70833333

Conemaugh

- Ash LF Aluminum 1.245 2 0

Conemaugh

- Ash LF Antimony 0.075 1 1

Conemaugh

- Ash LF Arsenic 0.388333333 3 1

Conemaugh

- Ash LF Barium 0.331666667 3 0

Conemaugh

- Ash LF Boron 0.91 1 0

Conemaugh

- Ash LF Cadmium 0.01 3 0

Conemaugh

- Ash LF Cobalt 0.026 1 0

Conemaugh

- Ash LF Lead 0.1 2 2

Conemaugh

- Ash LF Mercury 0.00055 2 2

Conemaugh

- Ash LF Molybdenum 0.355 2 0

Conemaugh

- Ash LF Selenium 0.295 2 1

Conemaugh

- Ash LF Thallium 0.024 1 0

Conemaugh

- Ash and Coal

Refuse LF Aluminum 1.467666667 3 0

Conemaugh

- Ash and Coal

Refuse LF Antimony 0.075 3 3

Conemaugh

- Ash and Coal

Refuse LF Arsenic 0.625 2 2

Conemaugh

- Ash and Coal

Refuse LF Barium 0.145666667 3 0

Conemaugh

- Ash and Coal

Refuse LF Boron 0.095 2 0

Conemaugh

- Ash and Coal

Refuse LF Cadmium 0.002 3 3

Conemaugh

- Ash and Coal

Refuse LF Cobalt 0.009 1 0

Conemaugh

- Ash and Coal

Refuse LF Lead 0.0733 33333 3 2

Conemaugh

- Ash and Coal

Refuse LF Mercury 0.0004 3 2

Conemaugh

- Ash and Coal

Refuse LF Molybdenum 0.01 1 0

Conemaugh

- Ash and Coal

Refuse LF Selenium 0.179833333 3 1

Conemaugh

- Ash and Coal

Refuse LF Thallium 0.005 1 0

Crist

- Ash L F Arsenic 0.02 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 10 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Crist

- Ash LF Barium 0.1 1 0

Crist

- Ash LF Cadmium 0.02 1 0

Crist

- Ash LF Lead 0.003 1 0

Crist

- Ash LF Selenium 0.05 1 0

Crown III

- Ash LF Antimony 0.071159259 54 10

Crown III

- Ash LF Arsenic 0.352503226 62 29

Crown III

- Ash LF Barium 0.279112903 62 3

Crown III

- Ash LF Boron 22.93277419 62 0

Crown III

- Ash LF Cadmium 0.128258065 62 3

Crown III

- Ash LF Cobalt 0.101225806 62 17

Crown III

- Ash LF Lead 0.605616935 62 19

Crown III

- Ash LF Mercur y 0.000104839 62 61

Crown III

- Ash LF Molybdenum 0.588888889 9 4

Crown III

- Ash LF Selenium 0.03946129 62 46

Crown III

- Ash LF Thallium 0.0645 54 18

Crown III

- FBC LF Antimony 0.0135 17 9

Crown III

- FBC LF Arsenic 0.034822581 31 26 3.7666666 67

Crown III

- FBC LF Barium 0.346774194 31 2 150

Crown III

- FBC LF Boron 2.815296296 27 1

Crown III

- FBC LF Cadmium 0.011241935 31 22 2.17

Crown III

- FBC LF Cobalt 0.02475 24 16

Crown III

- FBC LF Lead 0.068645161 31 17 8.233333333

Crown III

- FBC LF Mercury 0.000164516 31 27 0.381

Crown III

- FBC LF Molybdenum 0.1522 10 2

Crown III

- FBC LF Selenium 0.061467742 31 27 3.3

Crown III

- FBC LF Thallium 0.004941176 17 11

CTL

- V

- Ash LF Antimony 0.26 1 0

CTL

- V

- Ash LF Arsenic 0.037 1 0

CTL

- V

- Ash LF Barium 0.247 1 0

CTL

- V

- Ash LF Cadmium 0.04 1 0

CTL

- V

- Ash LF Lead 0.072 1 0

CTL

- V

- Ash LF Mercury 0.001 1 0

CTL

- V

- Ash LF Selenium 0.014 1 0

CTL

- V

- Ash LF Thallium 0.01 1 0

CY

- Ash LF Aluminum 4.735 2 0

CY

- Ash LF Antimony 0.0078 2 0

CY

- Ash LF Arsenic 0.04825 2 0

CY

- Ash LF Barium 1.2395 2 0

CY

- Ash LF Boron 6.13 2 0

CY

- Ash LF Cadmium 0.0002075 2 1

CY

- Ash LF Cobalt 0.001915 4 4

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 11 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

CY

- Ash LF Lead 0.003555 2 1

CY

- Ash LF Mercury 0.000265 2 0

CY

- Ash LF Selenium 0.004825 2 1

CY

- Ash LF Thallium 0.00196 4 4

Dairyland Power Coop

- Ash LF Arsenic 0.0328625 8 0

Dairyland Power Coop

- Ash LF Barium 0.058740741 27 0

Dairyland Power Coop

- Ash LF Boron 68.03979592 49 0

Dairyland Power C oop

- Ash LF Cadmium 0.00539 34 0

Dairyland Power Coop

- Ash LF Lead 0.0046 7 2

Dairyland Power Coop

- Ash LF Mercury 0.000223 2 1

Dairyland Power Coop

- Ash LF Selenium 0.0696375 8 0

Daniel

- Ash LF Arsenic 0.2 1 0

Daniel

- Ash LF Barium 0.4 1 0

Daniel

- Ash LF Cadmium 0.001 1 1

Daniel

- Ash LF Lead 0.001 1 1

Daniel

- Ash LF Selenium 0.001 1 1

Deer Ridge Mine

- Ash LF Aluminum 0.5941 10 1 64681.487

Deer Ridge Mine

- Ash LF Arsenic 0.0029 10 6 21.29419

Deer Ridge Mine

- Ash LF Barium 0.1 448 10 2 258.468

Deer Ridge Mine

- Ash LF Boron 1.228 10 2 179.354

Deer Ridge Mine

- Ash LF Cadmium 0.01365 10 1 0.94425

Deer Ridge Mine

- Ash LF Lead 0.0253 10 2 58.48

Deer Ridge Mine

- Ash LF Mercury 0.00011025 10 10 0.1158

Deer Ridge Mine

- Ash LF Molybdenum 0.0756 10 4 6.6287

Deer Ridge Mine

- Ash LF Nitrate/Nitrite 0.095 3 2

Deer Ridge Mine

- Ash LF Selenium 0.01022 10 2 13.1061

DPC

- Ash LF Antimony 0.04 2 1 0.475

DPC

- Ash LF Arsenic 0.051 2 0 55.085

DPC

- Ash LF Barium 0.28 2 0 37.7

DPC

- Ash LF Boron 27.945 2 0 404.05

DPC

- Ash LF Cadmium 0.005 4 4 0.56

DPC

- Ash LF Lead 0.025 4 4 28.7

DPC

- Ash LF Mercury 0.001 2 2 0.127

DPC

- Ash LF Nitrate/Nitrite 2.5 2 0 0.2425

DPC

- Ash LF Selenium 0.046 2 0 3.4445

EERC

- Ash LF Mercury 0.0000 25 4 4

Elkhart Mine

- Ash LF Antimony 0.025192308 52 46

Elkhart Mine

- Ash LF Arsenic 0.043571429 77 71

Elkhart Mine

- Ash LF Barium 0.495324675 77 23

Elkhart Mine

- Ash LF Boron 6.88961039 77 0

Elkhart Mine

- Ash LF Cadmium 0.022551948 77 41

( continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 12 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Elkhart Mine

- Ash LF Cobalt 0.012785714 77 57

Elkhart Mine

- Ash LF Lead 0.027987013 77 66

Elkhart Mine

- Ash LF Mercury 0.000148052 77 68

Elkhart Mine

- Ash LF Selenium 0.036649351 77 64

Elkhart Mine

- Ash LF Thallium 0.015942308 52 4 8

Elkhart Mine

- FBC LF Antimony 0.021875 16 15

Elkhart Mine

- FBC LF Arsenic 0.034512195 41 37

Elkhart Mine

- FBC LF Barium 0.525365854 41 5

Elkhart Mine

- FBC LF Boron 13.13829268 41 0

Elkhart Mine

- FBC LF Cadmium 0.003536585 41 41

Elkhart M ine

- FBC LF Cobalt 0.007219512 41 39

Elkhart Mine

- FBC LF Lead 0.017195122 41 34

Elkhart Mine

- FBC LF Mercury 0.000104878 41 40

Elkhart Mine

- FBC LF Selenium 0.035365854 41 33

Elkhart Mine

- FBC LF Thallium 0.02390625 16 15

FBX

- Ash LF Arsen ic 0.0025 2 2

FBX

- Ash LF Barium 29.6225 2 1

FBX

- Ash LF Cadmium 0.2 2 2

FBX

- Ash LF Lead 0.5 2 2

FBX

- Ash LF Mercury 0.00025 2 2

FBX

- Ash LF Selenium 0.01375 2 2

FC

- Ash and Coal Refuse LF Aluminum 13.8 2 0

FC

- Ash and Coal Refuse LF Antimony 0.00105 4 4

FC

- Ash and Coal Refuse LF Arsenic 0.005 2 0

FC

- Ash and Coal Refuse LF Barium 0.602 2 0

FC

- Ash and Coal Refuse LF Boron 2.54 2 0

FC

- Ash and Coal Refuse LF Cadmium 0.00015 4 4

FC

- Ash and Coal Refuse LF Cobalt 0.0029 2 0

FC

- Ash and Coal Refuse LF Lead 0.00345 2 0

FC

- Ash and Coal Refuse LF Mercury 0.00005 4 4

FC

- Ash and Coal Refuse LF Selenium 0.01765 2 0

FC

- Ash and Coal Refuse LF Thallium 0.00185 4 4

Florence Mine

- Ash LF Aluminum 0.03 1 0

Florence Mine

- Ash LF Antimony 0.005 1 1

Florence Mine

- Ash LF Arsenic 0.07 1 0

Florence Mine

- Ash LF Barium 2.23 1 0

Florence Mine

- Ash LF Boron 0.01 1 1

Florence Mine

- Ash LF Cadmium 0.01 1 1

Florence Mine

- Ash LF Lead 0.001 1 0

Florence Mine

- Ash LF Mercury 0.002 1 0

Florence Mine

- Ash LF Molybdenum 0.01 1 1

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 13 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Florence Mine

- Ash LF Nitrate/Nitrite 1.2 1 0

Florence Mine

- Ash LF Selenium 0.06 1 0

Fran Site

- FBC LF Aluminum 0.32 1 0

Fran Site

- FBC LF Antimony 0.005 1 1

Fran Site

- FBC LF Arsenic 0.02 1 0

Fran Site

- FBC LF Barium 0.08 1 0

Fran Site

- FBC LF Boron 0.43 1 0

Fran Site

- FBC LF Cadmium 0.005 1 1

Fran Site

- FBC LF Lead 0.005 1 1

Fran Site

- FBC LF Nitrate/Nitrite 1.22 1 0

Fran Site

- FBC LF Selen ium 0.03 1 0

FW

- FBC LF Arsenic 0.02525 4 3

FW

- FBC LF Barium 0.304 4 0

FW

- FBC LF Cadmium 0.005 4 4

FW

- FBC LF Lead 0.05 4 4

FW

- FBC LF Mercury 0.001 4 4

FW

- FBC LF Selenium 0.1 4 4

Gadsden

- Ash LF Arsenic 0.2 1 0

Gadsden

- Ash LF B arium 0.3 1 0

Gadsden

- Ash LF Cadmium 0.01 1 0

Gadsden

- Ash LF Lead 0.04 1 0

Gadsden

- Ash LF Selenium 0.03 1 0

Gale

- Ash LF Aluminum 3.1 1 0 13630

Gale

- Ash LF Antimony 0.03 1 0 3

Gale

- Ash LF Arsenic 0.42 1 0 51.5

Gale

- Ash LF Barium 1.7 1 0 143

Gale

- Ash LF Boron 0.22 1 0 25

Gale

- Ash LF Cadmium 0.01 1 0 1

Gale

- Ash LF Lead 0.23 1 0 21

Gale

- Ash LF Molybdenum 0.05 1 0 5

Gale

- Ash LF Selenium 0.1 1 0 4.4

Gaston

- Ash LF Arsenic 1.8 1 0

Gaston

- Ash LF Barium 0.3 1 0

Gaston

- Ash LF Cadmium 0.01 1 0

Gaston

- Ash LF Lead 0.05 1 0

Gaston

- Ash LF Selenium 0.003 1 0

Gorgas

- Ash LF Arsenic 1.6 1 0

Gorgas

- Ash LF Barium 0.3 1 0

Gorgas

- Ash LF Cadmium 0.01 1 0

Gorgas

- Ash LF Lead 0.04 1 0

Gorgas

- Ash LF Selenium 0.002 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 14 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Greene Co

- Ash LF Arsenic 1.1 1 0

Greene Co

- Ash LF Barium 0.4 1 0

Greene Co

- Ash LF Cadmium 0.01 1 0

Greene Co

- Ash LF Lead 0.04 1 0

Greene Co

- Ash LF Selenium 0.003 1 0

HA

- Ash and Coal Refuse LF Aluminum 1.71925 4 0 5666.666667

HA

- Ash and Coal Refuse LF Antimony 0.003905 4 2

HA

- Ash and Coal Refuse LF Arsenic 0.024975 4 0 9.666666667

HA

- Ash and Coal Refuse LF Barium 1.01675 4 0 186.6666667

HA

- Ash and Coal Refuse LF Boron 0.64545 4 0 14

HA

- Ash and Co al Refuse LF Cadmium 0.0039275 4 0 0.25

HA

- Ash and Coal Refuse LF Cobalt 0.01517875 4 1

HA

- Ash and Coal Refuse LF Lead 0.00378 4 2 8.7

HA

- Ash and Coal Refuse LF Mercury 0.0001 4 0 0.065

HA

- Ash and Coal Refuse LF Selenium 0.005025 4 0 0.5341666 67

HA

- Ash and Coal Refuse LF Thallium 0.00196 8 8

Hammond

- Ash LF Arsenic 0.1 1 0

Hammond

- Ash LF Barium 0.3 1 0

Hammond

- Ash LF Cadmium 0.01 1 0

Hammond

- Ash LF Lead 0.05 1 0

Hammond

- Ash LF Selenium 0.02 1 0

Harrim 3019

- Ash LF Alumi num 5.21 1 0 46577

Harrim 3019

- Ash LF Antimony 0.0058 1 0 646.4

Harrim 3019

- Ash LF Arsenic 0.178 1 0 50.43172727

Harrim 3019

- Ash LF Barium 0.32 1 0 319.89

Harrim 3019

- Ash LF Molybdenum 0.594 1 0 17.9

Harrim 3019

- Ash LF Nitrate/Nitrite 1.99 1 0

Harrim 3019

- Ash LF Selenium 0.0468 1 0 1.405714286

Harrim 3019

- FBC LF Aluminum 0.67375 8 0

Harrim 3019

- FBC LF Antimony 0.002 1 0

Harrim 3019

- FBC LF Barium 0.465888889 9 0

Harrim 3019

- FBC LF Boron 0.07 1 0

Harrim 3019

- FBC LF Cobalt 0.1385 6 0

Harrim 3019

- FBC LF Lead 0.24 5 0

Harrim 3019

- FBC LF Molybdenum 0.347714286 7 0

Harrim 3019

- FBC LF Nitrate/Nitrite 0.199333333 3 0

Harrim 3019

- FBC LF Selenium 0.019 2 0

Industry Mine

- Ash LF Antimony 0.031597143 70 12

Indust ry Mine

- Ash LF Arsenic 0.050248454 97 51

Industry Mine

- Ash LF Barium 0.328329897 97 13

Industry Mine

- Ash LF Boron 4.719969072 97 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 15 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Industry Mine

- Ash LF Cadmium 0.059061856 97 7

Industry Mine

- Ash LF Cobalt 0.120010309 97 30

Industry Mine

- Ash LF Lead 3.610544845 97 16

Industry Mine

- Ash LF Mercury 0.000284536 97 92

Industry Mine

- Ash LF Selenium 0.052408247 97 64

Industry Mine

- Ash LF Thallium 0.016984286 70 12

Industry Mine

- FBC LF Antimony 0.017077778 9 4

Ind ustry Mine

- FBC LF Arsenic 0.031111111 9 7

Industry Mine

- FBC LF Barium 9.515666667 9 0

Industry Mine

- FBC LF Boron 2.813888889 9 2

Industry Mine

- FBC LF Cadmium 0.015888889 9 7

Industry Mine

- FBC LF Cobalt 0.029333333 9 8

Industry Mine

- FB C LF Lead 0.051877778 9 6

Industry Mine

- FBC LF Mercury 0.000222222 9 8

Industry Mine

- FBC LF Selenium 0.080388889 9 4

Industry Mine

- FBC LF Thallium 0.002288889 9 6

Key West

- Ash LF Arsenic 0.005 1 1

Key West

- Ash LF Barium 1 2 0

Key West

- Ash LF Boron 0.2 1 0

Key West

- Ash LF Cadmium 0.07 1 0

Key West

- Ash LF Lead 0.4 1 0

Key West

- Ash LF Mercury 0.18 1 0

Key West

- Ash LF Selenium 0.005 1 1

Keystone

- Ash LF Aluminum 2.059 4 0

Keystone

- Ash LF Antimony 0.036 1 0

Keysto ne

- Ash LF Arsenic 0.30925 4 0

Keystone

- Ash LF Barium 0.40375 4 0

Keystone

- Ash LF Boron 0.72 1 0

Keystone

- Ash LF Cadmium 0.009625 4 1

Keystone

- Ash LF Cobalt 0.023 1 0

Keystone

- Ash LF Lead 0.045375 4 1

Keystone

- Ash LF Mercury 0.001 1 1

Keystone

- Ash LF Molybdenum 0.32 1 0

Keystone

- Ash LF Selenium 0.0525 4 2

Keystone

- Ash LF Thallium 0.083 1 0

Keystone

- Ash and Coal

Refuse LF Aluminum 0.842 4 0

Keystone

- Ash and Coal

Refuse LF Antimony 0.0015 2 2

Keystone

- Ash and C oal

Refuse LF Arsenic 0.01875 4 4

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 16 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Keystone

- Ash and Coal

Refuse LF Barium 0.1925 4 0

Keystone

- Ash and Coal

Refuse LF Boron 0.06 1 0

Keystone

- Ash and Coal

Refuse LF Cadmium 0.00225 4 4

Keystone

- Ash and Coal

Refuse LF Cobalt 0.022 1 0

Keystone

- Ash and Coal

Refuse LF Lead 0.01875 4 4

Keystone

- Ash and Coal

Refuse LF Mercury 0.001 1 1

Keystone

- Ash and Coal

Refuse LF Molybdenum 0.01 2 2

Keystone

- Ash and Coal

Refuse LF Selenium 0.02 4 4

Keystone

- Ash and Coal

Refuse L F Thallium 0.028 1 0

Kraft

- Ash LF Arsenic 0.02 1 0

Kraft

- Ash LF Barium 0.3 1 0

Kraft

- Ash LF Cadmium 0.01 1 0

Kraft

- Ash LF Lead 0.04 1 0

Kraft

- Ash LF Selenium 0.04 1 0

LIMB Site

- Ash LF Aluminum 0.102894737 38 37

LIMB Site

- Ash LF Antimony 0.29 5 1 25

LIMB Site

- Ash LF Arsenic 0.033594737 38 6 63

LIMB Site

- Ash LF Barium 0.036552632 38 0 255

LIMB Site

- Ash LF Boron 0.521842105 38 31 400

LIMB Site

- Ash LF Cadmium 0.001031579 38 33 0.31

LIMB Site

- Ash LF Cobalt 0.005131579 3 8 37

LIMB Site

- Ash LF Lead 0.012789474 38 25 14.5

LIMB Site

- Ash LF Mercury 0.0001 2 2

LIMB Site

- Ash LF Molybdenum 1.527342105 38 1 2.5

LIMB Site

- Ash LF Nitrate/Nitrite 26 2 0

LIMB Site

- Ash LF Selenium 0.0199 38 24 0.25

LIMB Site

- Ash LF Thallium 0.05 5 5

Little Sandy #10 Mine

- Ash LF Aluminum 1.078 6 2 4541.666667

Little Sandy #10 Mine

- Ash LF Arsenic 0.032336364 11 8 38.293

Little Sandy #10 Mine

- Ash LF Barium 0.264454545 11 6 48.81

Little Sandy #10 Mine

- Ash LF Boron 2.6309090 91 11 3 157.76

Little Sandy #10 Mine

- Ash LF Cadmium 0.008290909 11 9 1.198

Little Sandy #10 Mine

- Ash LF Lead 0.022009091 11 10 56.84

Little Sandy #10 Mine

- Ash LF Mercury 0.000486364 11 10 0.24435

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 17 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Little Sandy #10 Mine

- Ash LF Molybd enum 0.177272727 11 5 6.354

Little Sandy #10 Mine

- Ash LF Selenium 0.059527273 11 9 6.531

Lone Mtn

- Ash LF Aluminum 28.615 2 0

Lone Mtn

- Ash LF Antimony 0.033 2 0

Lone Mtn

- Ash LF Arsenic 0.185 2 0 76

Lone Mtn

- Ash LF Barium 0.167 2 0 1483.2

L one Mtn

- Ash LF Cadmium 0.572 2 0 11.86

Lone Mtn

- Ash LF Cobalt 0.142 2 0 87.3

Lone Mtn

- Ash LF Mercury 0.0019 1 0

Lone Mtn

- Ash LF Molybdenum 0.4295 2 0

Lone Mtn

- Ash LF Selenium 0.328 2 0

LS

- Ash and Coal Refuse LF Aluminum 1.18 7 0

LS

- Ash and Coal Refuse LF Antimony 0.0107 4 0

LS

- Ash and Coal Refuse LF Arsenic 0.0104525 16 3

LS

- Ash and Coal Refuse LF Barium 0.13220625 16 0

LS

- Ash and Coal Refuse LF Boron 18.93125 16 0

LS

- Ash and Coal Refuse LF Cadmium 0.00148 16 15

LS

- Ash and Coal Refuse LF Cobalt 0.011125 4 0

LS

- Ash and Coal Refuse LF Lead 0.0025 16 16

LS

- Ash and Coal Refuse LF Mercury 0.00007 4 3

LS

- Ash and Coal Refuse LF Molybdenum 0.886875 16 0

LS

- Ash and Coal Refuse LF Nitrate/Nitrite 3.045 32 16

LS

- Ash and Coal Refuse LF Selenium 1.05343125 16 0

LS

- Ash and Coal Refuse LF Thallium 0.00185 8 8

Martins Creek

- Ash LF Aluminum 3.18335 20 2 114229.3889

Martins Creek

- Ash LF Antimony 0.005021053 19 11 10.315

Martins Creek

- Ash LF Arsenic 0 .2314 20 1 50.50530556

Martins Creek

- Ash LF Barium 0.1969 20 2 641.5466667

Martins Creek

- Ash LF Boron 3.5089 20 1 304.1266667

Martins Creek

- Ash LF Cadmium 0.0032 20 20 2.025

Martins Creek

- Ash LF Cobalt 0.024722222 18 18 66.37611111

Martins Cre ek

- Ash LF Lead 0.014 20 19

Martins Creek

- Ash LF Mercury 0.0001 19 19

Martins Creek

- Ash LF Molybdenum 0.195157895 19 10

Martins Creek

- Ash LF Nitrate/Nitrite 0.636428571 14 9

Martins Creek

- Ash LF Selenium 0.05717 20 8 4.043888889

Martins C reek

- Ash LF Thallium 0.003263158 19 19

McCloskey Site

- FBC LF Aluminum 0.5 2 2 27450

McCloskey Site

- FBC LF Arsenic 0.001 2 2 45.355

McCloskey Site

- FBC LF Barium 0.1 2 2 32.55

McCloskey Site

- FBC LF Boron 0.022 2 1 0.092

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 18 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

McCloskey Site

- FBC LF Cadmium 0.0375 2 1 0.025

McCloskey Site

- FBC LF Lead 0.05 2 2 50

McCloskey Site

- FBC LF Mercury 0.25 2 2 0.4465

McCloskey Site

- FBC LF Molybdenum 0.15 2 2 0.15

McCloskey Site

- FBC LF Selenium 0.0515675 2 2 52.315

McDonough

- Ash LF Arsenic 0.9 1 0

McDonough

- Ash LF Barium 0.5 1 0

McDonough

- Ash LF Cadmium 0.01 1 0

McDonough

- Ash LF Lead 0.04 1 0

McDonough

- Ash LF Selenium 0.2 1 0

McIntosh

- Ash LF Arsenic 0.09 1 0

McIntosh

- Ash LF Barium 0.2 1 0

McIntosh

- Ash LF C admium 0.6 1 0

McIntosh

- Ash LF Lead 0.03 1 0

McIntosh

- Ash LF Selenium 0.03 1 0

McKay Site

- FBC LF Aluminum 0.105 2 0 30000

McKay Site

- FBC LF Antimony 0.01 2 2 2.5

McKay Site

- FBC LF Arsenic 0.025 2 2 51.5

McKay Site

- FBC LF Barium 0.27 2 0 215

McKay Site

- FBC LF Boron 0.265 2 0 41.5

McKay Site

- FBC LF Cadmium 0.005 2 2 2.5

McKay Site

- FBC LF Lead 0.03 2 1 49

McKay Site

- FBC LF Mercury 0.0001 2 2 0.345

McKay Site

- FBC LF Molybdenum 0.13 2 0 6.25

McKay Site

- FBC LF Nitrate/Nitrit e 0.0175 2 1

McKay Site

- FBC LF Selenium 0.0355 2 1 1

Miller

- Ash LF Arsenic 1.3 1 0 18

Miller

- Ash LF Barium 0.1 1 0 7140

Miller

- Ash LF Cadmium 0.09 1 0 1.6

Miller

- Ash LF Lead 0.002 1 0 38

Miller

- Ash LF Selenium 0.03 1 0

Miller Creek Min e

- Ash LF Aluminum 4.78597619 42 4 22486.5969

Miller Creek Mine

- Ash LF Arsenic 0.075817021 47 16 60.54551064

Miller Creek Mine

- Ash LF Barium 0.147255319 47 0 87.49382979

Miller Creek Mine

- Ash LF Boron 2.343829787 47 3 167.0508511

Miller Creek Mi ne

- Ash LF Cadmium 0.009771277 47 31 1.850959894

Miller Creek Mine

- Ash LF Lead 0.034382979 47 24 51.50851064

Miller Creek Mine

- Ash LF Mercury 0.000255319 47 46 0.06780663

Miller Creek Mine

- Ash LF Molybdenum 0.166808511 47 17 9.819680851

Miller C reek Mine

- Ash LF Selenium 0.047102128 47 23 6.492617021

Mine 26

- Ash LF Antimony 0.0125 6 6

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 19 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Mine 26

- Ash LF Arsenic 0.022333333 9 8

Mine 26

- Ash LF Barium 0.388111111 9 1

Mine 26

- Ash LF Boron 9.266666667 9 0

Mine 26

- Ash LF Ca dmium 0.008555556 9 4

Mine 26

- Ash LF Cobalt 0.021744444 9 5

Mine 26

- Ash LF Lead 0.148111111 9 6

Mine 26

- Ash LF Mercury 0.0003 9 9

Mine 26

- Ash LF Selenium 0.026388889 9 6

Mine 26

- Ash LF Thallium 0.006833333 6 5

Mine 26

- Ash and Coal

R efuse LF Antimony 0.01 2 2

Mine 26

- Ash and Coal

Refuse LF Arsenic 0.054285714 7 5

Mine 26

- Ash and Coal

Refuse LF Barium 0.615714286 7 0

Mine 26

- Ash and Coal

Refuse LF Boron 3.504285714 7 0

Mine 26

- Ash and Coal

Refuse LF Cadmium 0.010142857 7 4

Mine 26

- Ash and Coal

Refuse LF Cobalt 0.032857143 7 2

Mine 26

- Ash and Coal

Refuse LF Lead 0.047142857 7 4

Mine 26

- Ash and Coal

Refuse LF Mercury 0.0001 7 7

Mine 26

- Ash and Coal

Refuse LF Selenium 0.02 7 7

Mine 26

- Ash and Coal

Refuse LF Thallium 0.005 2 2

Mine 26

- FBC LF Arsenic 0.03 1 1

Mine 26

- FBC LF Barium 0.51 1 0

Mine 26

- FBC LF Boron 1.3 1 0

Mine 26

- FBC LF Cadmium 0.0025 1 1

Mine 26

- FBC LF Cobalt 0.005 1 1

Mine 26

- FBC LF Lead 0.01 1 1

Mine 26

- FBC LF Mer cury 0.0001 1 1

Mine 26

- FBC LF Selenium 0.08 1 0

Mitchell

- Ash LF Arsenic 1.3 1 0

Mitchell

- Ash LF Barium 0.3 1 0

Mitchell

- Ash LF Cadmium 0.01 1 0

Mitchell

- Ash LF Lead 0.06 1 0

Mitchell

- Ash LF Selenium 0.06 1 0

MO

- Ash and Coal Ref use LF Aluminum 4.49 2 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 20 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

MO

- Ash and Coal Refuse LF Antimony 0.0125 2 0

MO

- Ash and Coal Refuse LF Arsenic 0.2855 2 0

MO

- Ash and Coal Refuse LF Barium 1.845 2 0

MO

- Ash and Coal Refuse LF Boron 0.219 2 0

MO

- Ash and Coal Refuse LF Cadmium 0.006 2 0

MO

- Ash and Coal Refuse LF Cobalt 0.012 2 0

MO

- Ash and Coal Refuse LF Lead 0.0065 2 0

MO

- Ash and Coal Refuse LF Mercury 0.00005 4 4

MO

- Ash and Coal Refuse LF Selenium 0.1312 2 0

MO

- Ash and Coal Refuse LF Thallium 0. 01415 2 0

Murdock Mine

- Ash LF Antimony 0.0076875 8 8

Murdock Mine

- Ash LF Arsenic 0.0080875 8 6

Murdock Mine

- Ash LF Barium 0.258625 8 0

Murdock Mine

- Ash LF Boron 9.38775 8 0

Murdock Mine

- Ash LF Cadmium 0.0458 8 2

Murdock Mine

- Ash LF Cobalt 0.0225625 8 2

Murdock Mine

- Ash LF Lead 0.00555 8 2

Murdock Mine

- Ash LF Mercury 0.0004375 8 8

Murdock Mine

- Ash LF Selenium 0.0053875 8 4

Murdock Mine

- Ash LF Thallium 0.02325 8 2

Murdock Mine

- FBC LF Antimony 0.004 3 3

Murdock Min e

- FBC LF Arsenic 0.005 3 3

Murdock Mine

- FBC LF Barium 0.368333333 3 0

Murdock Mine

- FBC LF Boron 0.436666667 3 0

Murdock Mine

- FBC LF Cadmium 0.0015 3 3

Murdock Mine

- FBC LF Cobalt 0.0025 3 3

Murdock Mine

- FBC LF Lead 0.0015 3 3

Murdock Mine

- FBC LF Mercury 0.0004 3 3

Murdock Mine

- FBC LF Selenium 0.003533333 3 2

Murdock Mine

- FBC LF Thallium 0.005 3 3

Nepco

- FBC LF Arsenic 0.025 2 2 21

Nepco

- FBC LF Cadmium 0.01 1 0 0.5

Nepco

- FBC LF Lead 0.025 2 2 39

Nepco

- FBC LF Mercu ry 0.0002 2 2 0.01

Nepco

- FBC LF Selenium 0.05 2 2 12.6

No. 1 Contracting Corp

- FBC LF Aluminum 0.935 2 0

No. 1 Contracting Corp

- FBC LF Antimony 0.018 1 0

No. 1 Contracting Corp

- FBC LF Arsenic 0.046 2 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 21 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

No. 1 Contracting Corp

- F BC LF Barium 0.1315 2 0

No. 1 Contracting Corp

- FBC LF Boron 0.05 1 0

No. 1 Contracting Corp

- FBC LF Cadmium 0.005 1 0

No. 1 Contracting Corp

- FBC LF Lead 0.06 1 0

No. 1 Contracting Corp

- FBC LF Mercury 0.0002 1 0

No. 1 Contracting Corp

- FBC LF Molybdenum 0.105 2 0

No. 1 Contracting Corp

- FBC LF Selenium 0.1395 2 0

Northampton40000201

- Ash LF Aluminum 0.38 1 0 24500

Northampton40000201

- Ash LF Antimony 0.01 1 0 20

Northampton40000201

- Ash LF Arsenic 0.005 1 0 40.6

Northampton400002 01

- Ash LF Barium 0.21 1 0 242

Northampton40000201

- Ash LF Boron 0.2 1 0 17.3

Northampton40000201

- Ash LF Cadmium 0.012 1 0 0.5

Northampton40000201

- Ash LF Lead 0.1 1 0 18

Northampton40000201

- Ash LF Mercury 0.0002 1 0 0.535

Northampton40000201

- Ash LF Molybdenum 0.1 1 0 10

Northampton40000201

- Ash LF Selenium 0.015 1 0 8.9

Nucla

- FBC LF Aluminum 0.1 2 2 110050

Nucla

- FBC LF Arsenic 0.0025 4 4 7.4

Nucla

- FBC LF Barium 0.08 2 1 190

Nucla

- FBC LF Boron 0.485 2 1 57.5

Nucla

- FBC LF Cadmi um 0.00055 2 2 1.95

Nucla

- FBC LF Cobalt 0.005 2 2 10

Nucla

- FBC LF Lead 0.0016 2 1 35.5

Nucla

- FBC LF Mercury 0.0001 2 2

Nucla

- FBC LF Molybdenum 0.2045 2 0 83

Nucla

- FBC LF Nitrate/Nitrite 0.1125 2 2

Nucla

- FBC LF Selenium 0.00485 2 1 9.35

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 22 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Nucla2

- FBC LF Aluminum 7.18 3 0 100000

Nucla2

- FBC LF Antimony 0.1 6 6 46

Nucla2

- FBC LF Arsenic 0.00375 6 5 27.93333333

Nucla2

- FBC LF Barium 0.093 3 0 246

Nucla2

- FBC LF Boron 3.1 3 1 69.16666667

Nucla2

- FBC LF Cadmium 0.000475 6 4 0.263333333

Nucla2

- FBC LF Cobalt 0.012 3 1 6.1

Nucla2

- FBC LF Lead 0.0062 3 0 8.296666667

Nucla2

- FBC LF Mercury 0.000566667 6 5 0.214166667

Nucla2

- FBC LF Molybdenum 0.303333333 3 0 3.316666667

Nucla2

- FBC LF Nitrate/Nitrite 6.591666667 6 4

Nucla2

- FBC LF Selenium 0.048666667 6 2 1.395

Nucla2

- FBC LF Thallium 0.05 3 3 6.416666667

OK

- Ash LF Aluminum 11.895 2 0

OK

- Ash LF Antimony 0.001575 2 1

OK

- Ash LF Arsenic 0.003225 2 1

OK

- Ash LF Barium 0.686 2 0

OK

- Ash LF Boron 2.68 2 0

OK

- Ash LF Cadmium 0.00027 2 1

OK

- Ash LF Cobalt 0.00745 2 0

OK

- Ash LF Lead 0.00355 2 0

OK

- Ash LF Mercury 0.0001 2 1

OK

- Ash LF Selenium 0.037 2 0

OK

- Ash LF Thallium 0.00185 4 4

P4

- Ash LF Aluminum 6.2196875 8 0

P4

- Ash LF A ntimony 0.00105 4 4

P4

- Ash LF Arsenic 0.00420375 8 5

P4

- Ash LF Barium 0.254375 8 0

P4

- Ash LF Boron 1.142697917 8 0

P4

- Ash LF Cadmium 0.00125 8 8

P4

- Ash LF Cobalt 0.00315 2 0

P4

- Ash LF Lead 0.0025 8 8

P4

- Ash LF Mercury 0.00005 4 4

P4

- Ash LF Molybdenum 0.2114375 8 4

P4

- Ash LF Nitrate/Nitrite 1.92075 16 8

P4

- Ash LF Selenium 0.01 8 8

P4

- Ash LF Thallium 0.002775 2 2

PA

- Ash LF Aluminum 26.16153846 13 0

PA

- Ash LF Antimony 0.0031 2 0

PA

- Ash LF Arsenic 0.005991 923 13 9

PA

- Ash LF Barium 1.043838462 13 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 23 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

PA

- Ash LF Boron 0.736153846 13 0

PA

- Ash LF Cadmium 0.001758462 13 12

PA

- Ash LF Cobalt 0.001915 2 2

PA

- Ash LF Lead 0.005993077 13 10

PA

- Ash LF Mercury 0.000175 2 0

PA

- Ash LF Molybdenum 0.138461538 13 4

PA

- Ash LF Nitrate/Nitrite 2.544596154 26 15

PA

- Ash LF Selenium 0.084376923 13 5

PA

- Ash LF Thallium 0.00196 4 4

Pitt

- FBC LF Antimony 0.0219 1 0

Pitt

- FBC LF Arsenic 0.05 1 1

Pitt

- FBC LF Barium 1.167333333 3 1

Pitt

- FBC LF Cadmium 0.033333333 3 3

Pitt

- FBC LF Lead 0.183333333 3 3

Pitt

- FBC LF Mercury 0.005 1 1

Pitt

- FBC LF Selenium 0.05 1 1

Pitt

- FBC LF Thallium 0.0025 3 3

Plant 10

- FBC LF Arsenic 0.14875 4 0 71.3

Plant 10

- FBC LF Cadmium 0.05425 4 1 2.418181818

Plant 10

- FBC LF Lead 0.2965 4 1 39.63636364

Plant 10

- FBC LF Mercury 0.05005 4 4 1.174

Plant 10

- FBC LF Selenium 0.1285 4 0 4.011818182

Plant 12

- FBC LF Arsenic 0.004125 8 4 98.62222222

Plant 12

- FBC LF Cadmium 0.02 8 8 2.188888889

Plant 12

- FBC LF Lead 0.28375 8 2 47.83333333

Plant 12

- FBC LF Mercury 0.0004 8 8 1.047777778

Plant 12

- FBC LF Selenium 0.006125 8 8 4.263888889

Plant 8

- FBC LF Arsenic 0.019868421 19 18 42.04210526

Plant 8

- FBC LF Cadmium 0.016826923 52 43 2.288947368

Plant 8

- FBC LF Lead 0.007211538 52 37 27.62105263

Plant 8

- FBC LF Mercury 0.000289474 19 19 0.065789474

Plant 8

- FBC LF Selenium 0.053026316 19 9 33.02263158

Plant 9

- FBC LF Arsenic 0.058666667 3 0 2.8

Plant 9

- FBC LF Lead 0.1 05454545 11 8 57.67142857

Plant 9

- FBC LF Mercury 0.00025 11 11 0.604285714

Plant 9

- FBC LF Selenium 0.065333333 3 0 5.115714286

Portland

- Ash LF Aluminum 2.648555556 9 0

Portland

- Ash LF Antimony 0.075 2 2

Portland

- Ash LF Arsenic 0.178666667 9 6

Portland

- Ash LF Barium 0.28475 8 0

Portland

- Ash LF Boron 4.799333333 3 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 24 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Portland

- Ash LF Cadmium 0.006 9 7

Portland

- Ash LF Cobalt 0.014 2 1

Portland

- Ash LF Lead 0.058333333 9 8

Portland

- Ash LF Mercury 0.001 4 4

Po rtland

- Ash LF Molybdenum 0.178666667 3 1

Portland

- Ash LF Selenium 0.25625 4 4

Portland

- Ash LF Thallium 0.005 4 4

PP

- Ash LF Aluminum 2.422 2 0

PP

- Ash LF Antimony 0.00245 2 0

PP

- Ash LF Arsenic 0.0273375 2 1

PP

- Ash LF Barium 0.2435 2 0

PP

- Ash LF Boron 6.605 2 0

PP

- Ash LF Cadmium 0.0023975 2 1

PP

- Ash LF Cobalt 0.0049575 2 1

PP

- Ash LF Lead 0.001155 2 1

PP

- Ash LF Mercury 0.00028 2 0

PP

- Ash LF Selenium 0.0364 2 0

PP

- Ash LF Thallium 0.01518 2 1

Revloc Site

- F BC LF Aluminum 0.58 2 1

Revloc Site

- FBC LF Antimony 0.002 2 2

Revloc Site

- FBC LF Arsenic 0.002 2 1

Revloc Site

- FBC LF Barium 0.44 2 2

Revloc Site

- FBC LF Boron 0.2585 2 1

Revloc Site

- FBC LF Cadmium 0.02 2 2

Revloc Site

- FBC LF Cobalt 0.0825 2 1

Revloc Site

- FBC LF Lead 0.25 2 0

Revloc Site

- FBC LF Mercury 0.0005 2 2

Revloc Site

- FBC LF Molybdenum 0.0545 2 1

Revloc Site

- FBC LF Selenium 0.0025 2 1

Scherer

- Ash LF Arsenic 0.01 1 0

Scherer

- Ash LF Barium 0.7 1 0

Schere r

- Ash LF Cadmium 0.001 1 0

Scherer

- Ash LF Lead 0.001 1 0

Scherer

- Ash LF Selenium 0.06 1 0

Scholz

- Ash LF Arsenic 0.02 1 0

Scholz

- Ash LF Barium 0.2 1 0

Scholz

- Ash LF Cadmium 0.04 1 0

Scholz

- Ash LF Lead 0.04 1 0

Scholz

- Ash LF Sel enium 0.02 1 0

Scrubgrass

- FBC LF Arsenic 0.025 2 2 59

Scrubgrass

- FBC LF Cadmium 0.0025 1 0 0.7

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 25 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Scrubgrass

- FBC LF Lead 0.025 2 2 50

Scrubgrass

- FBC LF Mercury 0.0002 2 2 0.01

Scrubgrass

- FBC LF Selenium 0.05 2 2 21.7

Seward

- Ash LF Aluminum 2.965 2 0

Seward

- Ash LF Antimony 0.075 2 2

Seward

- Ash LF Arsenic 0.288666667 3 2

Seward

- Ash LF Barium 0.473333333 3 0

Seward

- Ash LF Boron 0.57 1 0

Seward

- Ash LF Cadmium 0.005833333 3 1

Seward

- Ash LF Cobalt 0.014 1 0

S eward

- Ash LF Lead 0.1875 1 1

Seward

- Ash LF Mercury 0.003733333 3 3

Seward

- Ash LF Molybdenum 0.53 1 0

Seward

- Ash LF Selenium 0.196666667 3 2

Seward

- Ash LF Thallium 0.012 1 0

Shawnee

- FBC LF Aluminum 0.231 5 3 38240

Shawnee

- FBC LF Ant imony 0.296 5 2 15.6

Shawnee

- FBC LF Arsenic 0.219 10 6 17.3

Shawnee

- FBC LF Barium 2.001 10 0 799.4

Shawnee

- FBC LF Boron 0.97 5 3 116.2

Shawnee

- FBC LF Cadmium 0.005555 10 7 0.622

Shawnee

- FBC LF Cobalt 0.07 5 2 2.75

Shawnee

- FBC LF Lead 0.08 97 10 5 6.4

Shawnee

- FBC LF Mercury 0.00029 10 8 0.365

Shawnee

- FBC LF Molybdenum 0.382 5 0 6.4

Shawnee

- FBC LF Nitrate/Nitrite 3.786666667 8 4

Shawnee

- FBC LF Selenium 0.13005 10 6 0.73

Shawnee

- FBC LF Thallium 0.197 5 3 8.9

Shawville

- Ash LF Aluminum 2.0958 5 0

Shawville

- Ash LF Antimony 0.075 2 2

Shawville

- Ash LF Arsenic 0.4384 5 1

Shawville

- Ash LF Barium 0.2172 5 0

Shawville

- Ash LF Boron 0.56 1 0

Shawville

- Ash LF Cadmium 0.0059 5 2

Shawville

- Ash LF Cobalt 0.021 1 0

Shawville

- Ash LF Lead 0.1875 1 1

Shawville

- Ash LF Mercury 0.001 2 2

Shawville

- Ash LF Molybdenum 0.09 1 0

Shawville

- Ash LF Selenium 0.191 5 2

Shawville

- Ash LF Thallium 0.005 2 2

Sibley Quarry

- Ash LF Aluminum 0.6 4 4

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 26 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Sibl ey Quarry

- Ash LF Arsenic 0.018 4 0

Sibley Quarry

- Ash LF Barium 0.265 4 4

Sibley Quarry

- Ash LF Cadmium 0.00114125 4 2

Sibley Quarry

- Ash LF Lead 0.00305 4 4

Sibley Quarry

- Ash LF Mercury 0.0001 4 4

Sibley Quarry

- Ash LF Molybdenum 0.725 3 1

Sibley Quarry

- Ash LF Selenium 0.18425 4 1

Silverton

- Ash LF Aluminum 3.1 1 0 16870

Silverton

- Ash LF Arsenic 0.375 2 0 48.5

Silverton

- Ash LF Barium 1.7 1 0 181.5

Silverton

- Ash LF Boron 0.22 1 0 20.5

Silverton

- Ash LF Lead 0.23 1 0 29.5

Silverton

- Ash LF Molybdenum 0.1 1 0 5

Silverton

- Ash LF Selenium 0.12 2 0 6.7

Smith

- Ash LF Arsenic 0.02 1 0

Smith

- Ash LF Barium 0.2 1 0

Smith

- Ash LF Cadmium 0.04 1 0

Smith

- Ash LF Lead 0.01 1 0

Smith

- Ash LF Selenium 0.01 1 0

SW

- A sh LF Arsenic 0.006679487 195 53 29.495189

SW

- Ash LF Barium 0.81082716 243 0 2538.862069

SW

- Ash LF Cadmium 0.003400769 195 47 1.230670103

SW

- Ash LF Lead 0.001570707 99 97 35.39886598

SW

- Ash LF Mercury 0.000217677 99 98 0.039255034

SW

- Ash LF Selenium 0.003534884 172 46 0.6

SX

- Ash LF Aluminum 1.862 2 0

SX

- Ash LF Antimony 0.003275 2 1

SX

- Ash LF Arsenic 0.0365 2 0

SX

- Ash LF Barium 0.959 2 0

SX

- Ash LF Boron 4.5223 2 0

SX

- Ash LF Cadmium 0.04425 2 0

SX

- Ash LF Cobalt 0.0167 2 0

SX

- Ash LF Lead 0.00675 2 0

SX

- Ash LF Mercury 0.00005 4 4

SX

- Ash LF Selenium 0.048725 2 1

SX

- Ash LF Thallium 0.013625 2 1

Tidd

- FBC LF Aluminum 0.105 3 1

Tidd

- FBC LF Antimony 0.03 5 5

Tidd

- FBC LF Arsenic 0.028333333 3 2

Tid d

- FBC LF Barium 0.184 2 0

Tidd

- FBC LF Boron 0.82 3 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 27 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Tidd

- FBC LF Cadmium 0.0015 3 3

Tidd

- FBC LF Cobalt 0.021 3 0

Tidd

- FBC LF Lead 0.015833333 3 3

Tidd

- FBC LF Mercury 0.006733333 3 3

Tidd

- FBC LF Molybdenum 0.082 3 0

Tidd

- FBC LF Selenium 0.101666667 3 2

Titus

- Ash LF Aluminum 4.4135 4 0

Titus

- Ash LF Antimony 0.04375 4 4

Titus

- Ash LF Arsenic 0.346 2 1

Titus

- Ash LF Barium 0.3 4 0

Titus

- Ash LF Boron 7.345 2 0

Titus

- Ash LF Cadmium 0.0115 4 0

Titu s

- Ash LF Cobalt 0.027 2 0

Titus

- Ash LF Lead 0.19375 2 2

Titus

- Ash LF Mercury 0.001 2 2

Titus

- Ash LF Molybdenum 0.34 2 0

Titus

- Ash LF Selenium 0.144 4 3

Titus

- Ash LF Thallium 0.01 2 0

Tracy Vein Slope

- Ash LF Aluminum 0.533833333 6 0 11090

Tracy Vein Slope

- Ash LF Antimony 0.05 5 0 24.215

Tracy Vein Slope

- Ash LF Arsenic 0.065166667 6 0 61.33333333

Tracy Vein Slope

- Ash LF Barium 0.148833333 6 0 99.31666667

Tracy Vein Slope

- Ash LF Boron 1.4486 5 0 122.4333333

Tracy Vein Slo pe

- Ash LF Cadmium 0.044833333 6 0 1.070166667

Tracy Vein Slope

- Ash LF Lead 0.075 6 0 18.90833333

Tracy Vein Slope

- Ash LF Mercury 0.001 2 0 1.5888

Tracy Vein Slope

- Ash LF Molybdenum 0.1662 5 0 7.721666667

Tracy Vein Slope

- Ash LF Selenium 0.052 4 5 0 8.608

Tracy Vein Slope

- FBC LF Aluminum 1.32 1 0 7240

Tracy Vein Slope

- FBC LF Arsenic 0.052 1 0 6.97

Tracy Vein Slope

- FBC LF Barium 0.056 1 0 68.9

Tracy Vein Slope

- FBC LF Boron 0.043 1 0 7.43

Tracy Vein Slope

- FBC LF Molybdenum 0.027 1 0 0.84

Tracy Vein Slope

- FBC LF Selenium 0.039 1 0 3.22

UAPP

- Ash LF Arsenic 0.0025 2 2

UAPP

- Ash LF Barium 0.4 2 1

UAPP

- Ash LF Cadmium 0.04 2 2

UAPP

- Ash LF Lead 0.1 2 2

UAPP

- Ash LF Mercury 0.025 2 2

UAPP

- Ash LF Selenium 0.00275 2 1

Universal

- Ash LF Aluminum 2.057777778 9 0 6000.222222

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 28 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Universal

- Ash LF Arsenic 0.277818182 11 2 41.50909091

Universal

- Ash LF Barium 0.090181818 11 1 71

Universal

- Ash LF Boron 2.754545455 11 0 180.2954545

Universal

- Ash LF Cadmium 0.003227273 11 9 2.115909091

Universal

- Ash LF Lead 0.022145455 11 7 33.00909091

Universal

- Ash LF Mercury 0.000386364 11 11 0.137272727

Universal

- Ash LF Molybdenum 0.134363636 11 1 3.554545455

Universal

- Ash LF Selenium 0.160090909 11 2 7.106363 636

Wansley

- Ash LF Arsenic 0.05 1 0

Wansley

- Ash LF Barium 0.2 1 0

Wansley

- Ash LF Cadmium 0.09 1 0

Wansley

- Ash LF Lead 0.02 1 0

Wansley

- Ash LF Selenium 0.06 1 0

WEPCO CALEDONIA

LANDFILL

- Ash LF Barium 0.225 2 0

WEPCO CALEDONIA

LANDFI LL

- Ash LF Boron 16.90454545 22 0

WEPCO CALEDONIA

LANDFILL

- Ash LF Cadmium 0.000045 3 3

WEPCO CALEDONIA

LANDFILL

- Ash LF Lead 0.003566667 3 3

WEPCO CALEDONIA

LANDFILL

- Ash LF Molybdenum 0.77500575 4 3

WEPCO CALEDONIA

LANDFILL

- Ash LF Selenium 0.046794118 34 0

WEPCO HWY 32

LANDFILL

- Ash LF Boron 83.41666667 12 0

WEPCO HWY 32

LANDFILL

- Ash LF Selenium 0.006675 12 4

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Arsenic 0.0055 2 0

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Barium 0.1195 2 0

WEPC O SYSTEMS

CONTROL CENTER A

- Ash LF Boron 14.02134483 29 0

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Cadmium 0.010266667 3 1

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Lead 0.00625 2 1

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 29 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Mercury 0.0002 1 0

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Molybdenum 0.000022375 4 4

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Nitrate/Nitrite 1.866666667 3 0

WEPCO SYSTEMS

CONTROL CENTER A

- Ash LF Selenium 0.06332275 28 0

Wilton Site

- Ash LF Aluminum 3 1 0

Wilt on Site

- Ash LF Arsenic 0.027 1 0

Wilton Site

- Ash LF Barium 0.51 1 0

Wilton Site

- Ash LF Boron 25 1 0

Wilton Site

- Ash LF Cadmium 0.0025 2 2

Wilton Site

- Ash LF Lead 0.0025 2 2

Wilton Site

- Ash LF Mercury 0.001 2 2

Wilton Site

- Ash LF M olybdenum 0.34 1 0

Wilton Site

- Ash LF Nitrate/Nitrite 0.5 1 1

Wilton Site

- Ash LF Selenium 0.09 1 0

WIS PUBLIC SERV CORP

WESTON AS

- Ash LF Arsenic 0.0014 3 2

WIS PUBLIC SERV CORP

WESTON AS

- Ash LF Barium 0.183025 4 1

WIS PUBLIC SERV CORP

WES TON AS

- Ash LF Boron 6.363333333 21 1

WIS PUBLIC SERV CORP

WESTON AS

- Ash LF Cadmium 0.0047595 8 0

WIS PUBLIC SERV CORP

WESTON AS

- Ash LF Lead 0.00668375 8 0

WIS PUBLIC SERV CORP

WESTON AS

- Ash LF Mercury 0.000082 5 5

WIS PUBLIC SERV CORP

WESTO N AS

- Ash LF Selenium 0.011077619 21 1

Yates1

- Ash LF Arsenic 0.1 1 0

Yates1

- Ash LF Barium 0.3 1 0

Yates1

- Ash LF Cadmium 0.02 1 0

Yates1

- Ash LF Lead 0.05 1 0

Yates1

- Ash LF Selenium 0.02 1 0

Yates2

- Ash LF Arsenic 0.09 1 0

Yates2

- Ash LF Barium 0.2 1 0

Yates2

- Ash LF Cadmium 0.02 1 0

Yates2

- Ash LF Lead 0.03 1 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 30 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

Yates2

- Ash LF Selenium 0.05 1 0

AP

- Ash SI Aluminum 0.553384615 13 0

AP

- Ash SI Antimony 0.01 1 1

AP

- Ash SI Arsenic 0.070933333 15 0

AP

- Ash SI Barium 0.063066667 15 1

AP

- Ash SI Boron 12.50986667 15 0

AP

- Ash SI Cadmium 0.001042857 14 7

AP

- Ash SI Cobalt 0.01 1 1

AP

- Ash SI Lead 0.001723333 15 14

AP

- Ash SI Molybdenum 0.486733333 15 2

AP

- Ash SI Nitrate/Nitrite 0.25480952 4 29 22

AP

- Ash SI Selenium 0.044326667 15 1

AP

- Ash SI Thallium 0.0025 1 1

BR

- Ash and Coal Refuse SI Aluminum 89.12777778 18 0

BR

- Ash and Coal Refuse SI Arsenic 0.775383333 15 4

BR

- Ash and Coal Refuse SI Barium 0.188055556 18 14

BR

- A sh and Coal Refuse SI Boron 3.857694444 18 2

BR

- Ash and Coal Refuse SI Cadmium 0.175 18 7

BR

- Ash and Coal Refuse SI Cobalt 0.204722222 18 11

BR

- Ash and Coal Refuse SI Molybdenum 0.5 18 18

C

- Ash SI Aluminum 4.192307692 13 0

C

- Ash SI Anti mony 0.07 10 10

C

- Ash SI Arsenic 0.15 10 0

C

- Ash SI Barium 0.113769231 13 0

C

- Ash SI Boron 10.96428571 14 0

C

- Ash SI Cadmium 0.0025 10 10

C

- Ash SI Cobalt 0.005 10 10

C

- Ash SI Lead 0.00229 10 5

C

- Ash SI Molybdenum 0.585384615 13 0

C

- Ash SI Nitrate/Nitrite 10.85474359 16 3

C

- Ash SI Selenium 0.0175 10 2

C

- Ash SI Thallium 0.05 10 10

CADK

- Ash SI Aluminum 0.165 2 0

CADK

- Ash SI Arsenic 0.0075 2 2

CADK

- Ash SI Barium 0.02 2 2

CADK

- Ash SI Boron 60.05 2 0

CADK

- Ash SI Cadmium 0.001 2 2

CADK

- Ash SI Lead 0.1 2 2

CADK

- Ash SI Molybdenum 1.165 2 0

CADK

- Ash SI Nitrate/Nitrite 11.135 4 0

CADK

- Ash SI Selenium 0.125 2 0

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 31 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

CASJ

- Ash SI Aluminum 0.1108 5 4

CASJ

- Ash SI Arsenic 5.37225 4 0

CASJ

- Ash SI Barium 0.0214 5 2

CASJ

- Ash SI Boron 46.02 5 0

CASJ

- Ash SI Cadmium 0.0156 5 3

CASJ

- Ash SI Lead 0.21 5 4

CASJ

- Ash SI Molybdenum 0.13 5 5

CASJ

- Ash SI Nitrate/Nitrite 1.882 10 8

CASJ

- Ash SI Selenium 0.40575 4 0

CATT

- Ash SI Aluminum 0.28 2 0

CATT

- Ash SI Arsenic 0.206 2 0

CATT

- Ash SI Barium 0.085 2 0

CATT

- Ash SI Boron 110.5 2 0

CATT

- Ash SI Cadmium 0.002 2 1

CATT

- Ash SI Lead 0.2275 2 0

CATT

- Ash SI Molybdenum 0.655 2 0

CATT

- Ash SI Nitrate/Nitri te 0.01 2 0

CATT

- Ash SI Selenium 1.025 2 0

CL

- Ash and Coal Refuse SI Aluminum 4.680970556 30 2

CL

- Ash and Coal Refuse SI Arsenic 0.493663408 30 2

CL

- Ash and Coal Refuse SI Barium 0.550251717 30 0

CL

- Ash and Coal Refuse SI Boron 1.092075 30 0

CL

- Ash and Coal Refuse SI Cadmium 0.001680507 30 27

CL

- Ash and Coal Refuse SI Lead 0.003384333 30 29

CL

- Ash and Coal Refuse SI Molybdenum 0.377590556 30 0

CL

- Ash and Coal Refuse SI Nitrate/Nitrite 0.6303 60 13

CL

- Ash and Coal Refu se SI Selenium 0.147525085 30 9

CY

- Ash SI Aluminum 6.0975 4 0

CY

- Ash SI Arsenic 0.1975 4 0

CY

- Ash SI Barium 0.179725 4 0

CY

- Ash SI Boron 0.025 4 4

CY

- Ash SI Cadmium 0.0040625 4 4

CY

- Ash SI Lead 0.008125 4 4

CY

- Ash SI Molybdenum 0.655 4 0

CY

- Ash SI Nitrate/Nitrite 750.2625 8 5

CY

- Ash SI Selenium 0.086575 4 1

FC

- Ash and Coal Refuse SI Aluminum 11.433 10 0

FC

- Ash and Coal Refuse SI Arsenic 0.00752 10 8

FC

- Ash and Coal Refuse SI Barium 0.14918 10 0

FC

- Ash and Coal Refuse SI Boron 0.7445 10 1

FC

- Ash and Coal Refuse SI Cadmium 0.001956 10 9

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 32 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

FC

- Ash and Coal Refuse SI Lead 0.0025 10 10

FC

- Ash and Coal Refuse SI Molybdenum 0.2275 10 10

FC

- Ash and Coal Refuse SI Nitrate/Nitrite 0.2 20 20

FC

- Ash and Coal Refuse SI Selenium 0.02174 10 0

HA

- Ash SI Aluminum 2.830833333 9 2

HA

- Ash SI Arsenic 0.086774333 9 2

HA

- Ash SI Barium 0.471945556 9 0

HA

- Ash SI Boron 2.283583333 9 0

HA

- Ash SI Cadmium 0.00125 9 9

HA

- Ash SI Lead 0 .003503333 9 8

HA

- Ash SI Molybdenum 0.107333333 9 4

HA

- Ash SI Nitrate/Nitrite 1.968222222 18 10

HA

- Ash SI Selenium 0.01 9 9

HA

- Ash and Coal Refuse SI Aluminum 0.65 1 0

HA

- Ash and Coal Refuse SI Arsenic 0.18 1 0

HA

- Ash and Coal Refus e SI Barium 0.11 1 0

HA

- Ash and Coal Refuse SI Boron 1.7 1 0

HA

- Ash and Coal Refuse SI Cadmium 0.0025 1 1

HA

- Ash and Coal Refuse SI Lead 0.025 1 1

HA

- Ash and Coal Refuse SI Mercury 0.00025 1 1

HA

- Ash and Coal Refuse SI Molybdenum 0.075 1 1

HA

- Ash and Coal Refuse SI Selenium 0.0025 1 1

L

- Ash SI Aluminum 0.015 2 2

L

- Ash SI Barium 0.001 2 2

L

- Ash SI Boron 0.62 2 0

L

- Ash SI Cadmium 0.001 2 2

L

- Ash SI Molybdenum 0.1675 2 1

MO

- Ash SI Aluminum 0.894458333 6 0

MO

- Ash SI Arsenic 0.011755993 6 3

MO

- Ash SI Barium 0.019379487 6 0

MO

- Ash SI Boron 0.085041667 6 2

MO

- Ash SI Cadmium 0.00125 6 6

MO

- Ash SI Lead 0.003666667 6 5

MO

- Ash SI Molybdenum 0.928770833 6 3

MO

- Ash SI Nitrate/Nitrite 0.1205 12 10

MO

- Ash SI Selenium 0.005 6 6

MO

- Ash and Coal Refuse SI Aluminum 296.2888026 19 6

MO

- Ash and Coal Refuse SI Arsenic 11.67554177 20 0

MO

- Ash and Coal Refuse SI Barium 0.039930301 20 1

MO

- Ash and Coal Refuse SI Boron 15.49313158 19 2

MO

- Ash and Coal Refuse SI Cadmium 0.124406392 27 9

(continued)

### Appendix A Attachment A

- 2: CCW Constituent Data



## April 2010

–

Draft EPA document. A

- 2

- 33 CCW Constituent Data (continued)

Site/Waste Type WMU Type Chemical Leachate (mg/L) No. of

Leachate

Measure

ments No. of

Leachate

Non

detects Total (mg/kg)

MO

- Ash and Coal Refuse SI Cobalt 4.8377 20 7

MO

- Ash and Coal Refuse SI Lead 0.321181411 20 11

MO

- Ash and Coal Refuse SI Molybdenum 0.402184211 19 15

MO

- Ash and Coal Refuse SI N itrate/Nitrite 5.165 39 37

MO

- Ash and Coal Refuse SI Selenium 0.103823054 20 9

O

- Ash SI Arsenic 0.234766667 3 0

O

- Ash SI Boron 6.166666667 3 0

O

- Ash SI Molybdenum 0.0179 1 0

O

- Ash SI Nitrate/Nitrite 461 1 0

O

- Ash SI Selenium 0.0029 3 0

OK

- Ash SI Aluminum 40.45955556 9 0

OK

- Ash SI Arsenic 0.060628889 9 2

OK

- Ash SI Barium 0.159055556 9 1

OK

- Ash SI Boron 3.148333333 9 0

OK

- Ash SI Cadmium 0.01 9 9

OK

- Ash SI Lead 0.02 9 9

OK

- Ash SI Molybdenum 0.721694444 9 0

OK

- Ash SI Nitrate/Nitrite 7.62 18 17

OK

- Ash SI Selenium 0.282377778 9 2

SX

- Ash SI Aluminum 3.866609827 15 0

SX

- Ash SI Arsenic 0.054834273 15 2

SX

- Ash SI Barium 0.079191593 15 0

SX

- Ash SI Boron 32.70433889 15 0

SX

- Ash SI Cadmium 0. 019243353 15 5

SX

- Ash SI Lead 0.001228153 15 5

SX

- Ash SI Molybdenum 11.40518778 15 0

SX

- Ash SI Nitrate/Nitrite 1.6328 30 12

SX

- Ash SI Selenium 0.239368793 15 6

## 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 Sites 1 ND Sites 2 2002 CCW Total Waste Concentrations 1998 Total

Waste

95th Minimum Maximum 50th 75th 90th 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 Sites 1 ND Sites 2 2002 SI Porewater Concentrations 1998

Porewater

95th 3 Minimum Maximum 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 Sites 1 ND Sites 2 Leachate Concentrations 1998

TCLP

95th Minimum Maximum 50th 75th 90th 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-0 2 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-0 2 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-0 2 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 SbFCr VI

Se V NO2

Ni Sr Al Cu (MCL) Ba Be Zn Ag Hg Cr IIIAnalytes

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 VFCd Cr VI

Mo Mn Se Ba Zn Hg Ni Be Ag Cu (MCL) NO3 (MCL) Co Sr Al CN Cr IIIAnalytes

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 BeFCo, Cancer Be, Cancer Cr III Cd, Cancer CN NO3Analytes

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 ma nagement units (WMUs). To sa tisfy this requirement, the assessment used a data set of WMU area, cap acity, 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 ar e managing their wastes onsite.

The sources for these data sets were res ponses to two voluntary industry surveys: an Electric Power Research Institute (EPRI) comanage ment survey (for conve ntional utility coal combustion WMUs units) and a Council of Indus trial Boiler Owners (C IBO) 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 th e EPRI Coal Combustion By-Products and Low-Volume Wastes Comanagement Survey (EPRI, 1997a). In 1995, EPRI sent a 4-page questionnaire to all electric u tilities with more than 100 me gawatts (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 manage d. 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 info rmation on 323 waste management facilities serving 238 power plants located in 36 stat es. The total annual vol ume of CCW reported disposed by respondents to the EPRI comanageme nt 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 a ppears 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 ge nerally geographically representative of the population of conventi onal 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 C ongress 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 WM U area, capacity, liner type, and waste type managed. Therefore, the data set used for modeli ng these variables was ex tracted 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 th at managed FBC waste. Data for these units

were instead combined with the data set for F BC WMUs from the CIBO FBC survey (described below).

The EPRI survey data were provided in blinde d form. That is, the original database did not report the identity of each re spondent and identified WMU location only by state. To provide a more complete identification of the EPRI wast e 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 In formation Administration (EIA) Form EIA-767 (Steam-Electric Plant Operation and Design Repor t) 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 envir onmental setting information.



# B.2 CIBO Fluidized Bed Combustion Survey

For FBC WMUs, the primary source of data fo r 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 f acility information, characterized process inputs and outputs, gathered data on wast e generation and characteristics, and captured details of FBC waste management practices. From the survey res ponses, CIBO prepared an electronic database and provided it to EPA in support of the March 1 999 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 faciliti es with waste management data cover 24 percent of all U.S. facilities using FBC. The CIBO sample is geogr aphically 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) discus ses the representativeness of the EPRI survey in greater detail and provides extensive summary stat istics on the survey responses.

The CIBO survey includes questions reque sting the respondent to report WMU area, capacity, liner type, and waste t ype 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 institu tional facilities that burn coal, but are not primarily engaged in the business of selling electricity) fac ilities. Because nonutilities are outside the scope of this risk assessment, nonutilities were exclude d 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 resulte d 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 th e model data set, these 8 faciliti es 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 lo cation 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) scenar io, a compacted clay liner, and a composite liner that combines a plastic (e.g., high-density polye thylene (HDPE) membrane) over either geosynthetic or natural clays. These three scenarios correspond to the foll owing conceptual liner scenarios, developed in support of EPA’s Industrial Subtit le D guidance (U.S. EPA, 2002), wh ich 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 s upport vegetation. For surface impoundments, wastewater is pl aced 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 sp aces 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 dire ctly on a 3-foot compacted clay liner, which is installed on the local soils, e ither on grade or excavated to some design depth and without a leachate collection syst em. After the landfill has been filled to capacity, a 3-foot clay cover is installed a nd covered with 1 foot of loam to support vegetation and drainage. The hydraulic conductiv ity 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 materi als is not included in the ca lculation 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 ei ther 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 hydr aulic 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 c over is assumed to be installed and covered with 1 foot of loam to support vegeta tion 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 mm 2 

## ). The number of these leaks was based on an empirical dist ribution 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 requi red 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 abou t 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 re sponses 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 impo undments 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 f acilities 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 w ith 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 Produc ts (EPACMTP) surface impoundment model assumes clean cl osure after the operating life.



# B.5 Above- and Below-Grade Geometry

The model runs for surface impoundments a nd 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 incl uded data on this geometry, so, for FBC units,

these data were extracted dire ctly 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 conve ntional utility coal combustion WMUs, EPA

reviewed 17 site-character ization 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-grad e 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-grad e 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 wa s picked and applied to the landfill depth to determine depth below ground surface. This valu e was constrained to be no deeper than the

water table and was checked to see that EP ACMTP groundwater mounding constraints were not

violated.

For surface impoundments, more data were av ailable (16 sites), with 8 sites being constructed entirely below grad e and the remaining 8 sites ranging from 7.5 to 45 feet above grade. For each surface impoundme nt 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 su rvey. To calculate average depth for each WMU (a necessary EPACMTP model input), the to tal 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 unrealistica lly 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 we re estimated as described below.

In addition, four landfills a nd six surface impoundments ha d 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 Terrase rver Web site (http://terraserver-usa.com/ geographic.aspx), and a geographic information syst em (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 ba sed on WMUs with both area and capacity data, one to predict area from capacit y, 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 deviatio n 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

R 2

= 0.6671

Std. Dev = 0.28

0 0.5

1 1.5

2 2.5

3

0123456789
log(capacity, cubic yards) L o g ( a r e a , a c r e s ) 3.5



## Figure B-4. Linear regression to im pute 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) L o g ( c a p a c it y , c u b ic y a r d s ) 9



## Figure B-5. Linear regression to im pute 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 su rface 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) L o g ( d is p o s a l c a p a c it y , c u b ic y a r d s ) 9



## Figure B-7. Linear regression to impute su rface impoundment capacity from area.

### Appendix B Waste Management Units



# B.7 Results

Attachment B-1 lists the 181 CCW dispos al 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 co mbustion 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 Industr ial Boiler Owners). 1997. Report to the U.S. Environmental Protection Agency on Fossil Fuel Combustion Bypr oducts from Fluidized Bed Boilers. Prepared by the CIBO Special Project on Non-Utility Foss il Fuel Ash Classifica tion 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 So lid Waste and Emergency Response, Washington, DC. March.

U.S. EPA (Environmental Protection Agency). 1999b. Technical Background Document for the Supplemental Report to Congress on Remain ing Fossil Fuel Combustion Wastes: Industry Statistics and Wa ste 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 (P lants) (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 (P lants) (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 (P lants) (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 (P lants) (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 Powe r 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 & Electri c 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 BAttachment B-2: CCW WMU DataAttachment B-2: CCW WMU DataPlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeA B Brown42LF17610360000Ashcompacted clayclayA/C Power - Ace Operations3000LF181030815FBCnone/natural soilsno linerAllen293SI851500000Ashnone/naturalsoilsnolinerAlma7LF852000000Ash and Coal Wastecomposite clay/membranecompositeAntelope Valley57LF273500000Ashnone/natural soilsno linerArkwright198LF54415907Ash and Coal Wastenone/natural soilsno linerAsheville159SI1403200000Ashnone/naturalsoilsnolinerBaldwin2SI1074000000Ash and Coal Wastenone/natural soilsno linerBarry301SI631900000Ash and Coal Wastenone/natural soilsno linerBay Front81LF10350000Ashnone/natural soilsno linerBay Shore32LF85Ashnone/natural soilsno linerBelews Creek167SI5122200000Ash and Coal Wastenone/natural soilsno linerBelews Creek168LF31514000000Ashcompacted ashno linerBen French14LF4.61AshcompactedclayclayBig Cajun 2186SI2414990003Ashcompacted clayclayBig Sandy138SI11512052100Ash and Coal Wastenone/natural soilsno linerBig Stone15LF3.480000AshcompactedclayclayBig Stone41LF1068000000Ashnone/natural soilsno linerBlack Dog Steam Plant2700LF968936296FBCcompacted clayclayBlue Valley176SI23.1372000Ash and Coal Wastecompacted clayclayBowen143LF25.24491400Ashcompacted ashno linerBowen144LF25.77406971Ashcompacted ashno linerBrandon Shores339LF2465600000Ash and Coal Wastenone/natural soilsno liner(continued)April 2010–Draft EPA document.B-2-1Appendix BAttachment B-2: CCW WMU DataCCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeBuck235SI904840000Ash and Coal Wastenone/natural soilsno linerBull Run296SI41650000Ash and Coal Wastenone/natural soilsno linerC D McIntosh Jr.223LF26Ash and Coal Wastecompacted ashno linerC P Crane338LF35800000Ashnone/natural soilsno linerCape Fear161SI602300000Ashnone/natural soilsno linerCarbon263lf11.7739066Ash and Coal Wastenone/natural soilsno linerCardinal126SI1238437500Ashnone/naturalsoilsnolinerCayuga325SI28025000000Ash and Coal Wastenone/natural soilsno linerChalk Point292LF5964634000Ash and Coal Wastenone/natural soilsno linerCholla107SI1712600000Ashnone/naturalsoilsnolinerCliffside163SI822200000AshcompactedclayclayClover139LF221000000AshgeosyntheticmembranecompositeCoal Creek29LF704700000Ashcompacted clayclayCoal Creek30LF22023000000Ashcomposite clay/membranecompositeColeto Creek190si314.6135409Ashand Coal Wastecompacted clayclayColstrip89LF9Ashnone/naturalsoilsnolinerConemaugh101LF43482000000AshgeosyntheticmembranecompositeConesville250LF30010000000AshcompactedclayclayConesville251LF1002500000Ash and Coal Wastenone/natural soilsno linerCouncil Bluffs94SI200Ashnone/natural soilsno linerCrawford272SI24.5642000Ashand Coal Wastecompacted clayclayCrist157LF12Ash and Coal Wastenone/natural soilsno linerCross264LF320Ashcompacted ashno linerCross265LF30Ash and Coal Wastenone/natural soilsno linerCross266LF30Ash and Coal Wastenone/natural soilsno linerCross267LF230Ash and Coal Wastenone/natural soilsno linerCross268LF60Ash and CoalWastecompacted clayclay(continued)April 2010–Draft EPA document.B-2-2Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-3CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeCumberland294SI751750000Ash and Coal Wastenone/natural soilsno linerCumberland303SI2959500000Ashnone/naturalsoilsnolinerDale151SI1157408274Ash and Coal Wastenone/natural soilsno linerDallman178LF221800000AshcompactedclayclayDallman179SI4173800000Ashnone/naturalsoilsnolinerDan E Karn6LF401650000Ash and Coal Wastegeosynthetic membranecompositeDan River234SI722097000Ash and Coal Wastenone/natural soilsno linerDanskammer24LF14517265Ash and Coal Wastegeosynthetic membranecompositeDave Johnston13LF45296100Ashcompacted clayclayDickerson290LF20612600000Ashnone/naturalsoilsnolinerDolet Hills245SI66850000Ash and Coal Wastenone/natural soilsno linerDolet Hills246LF1098500000Ashcompacted clayclayDuck Creek11LF21.31500000Ashcompacted clayclayDunkirk49LF121126080AshcompactedclayclayE D Edwards276SI14511000000Ash and Coal Wastenone/natural soilsno linerE W Brown313SI331000000Ashnone/natural soilsno linerE W Brown314SI842710000Ashnone/natural soilsno linerEckert Station113LF1746460000Ashnone/natural soilsno linerEckert Station114SI1517200000Ashnone/natural soilsno linerEdgewater289LF251655700Ash and Coal Wastenone/natural soilsno linerElmer W Stout130SI103420000Ashgeosynthetic membranecompositeF B Culley183SI822600000Ash and Coal Wastenone/natural soilsno linerFayette Power Prj.195SI1904351644Ashcompacted clayclayFayette Power Prj.196LF23890560Ashgeosynthetic membranecompositeFlint Creek191LF401508250Ash and Coal Wastenone/natural soilsno linerFlint Creek192si35.73857178Ash and Coal Wastenone/natural soilsno linerFort Martin213LF171900000Ashnone/natural soilsno liner(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-4CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeFort Martin214LF611400000AshdoublecompositeFort Martin215LF1213700000Ash and Coal Wastecomposite clay/membranecompositeFrank E Ratts182SI391250000Ash and Coal Wastenone/natural soilsno linerG G Allen237SI2106545000Ash and Coal Wastenone/natural soilsno linerGadsden283SI60484000AshandCoal Wastecompacted clayclayGallatin304SI3414300000Ash and Coal Wastenone/natural soilsno linerGen J M Gavin135LF25550000000Ashcomposite clay/membranecompositeGen J M Gavin136SI30030000000Ash and Coal Wastenone/natural soilsno linerGen J M Gavin137LF9912000000Ashcompacted clayclayGenoa244LF100Ash and Coal Wastenone/natural soilsno linerGibson327SI87555000000Ash and Coal Wastenone/natural soilsno linerGibson329LF8520000000AshcompactedclayclayGorgas280SI250Ash and Coal Wastecompacted clayclayGorgas281SI28324100000AshandCoal Wastecompacted clayclayGorgas282SI150015000000Ash and Coal Wastecompacted clayclayGreen River147SI362331219Ash and Coal Wastenone/natural soilsno linerGreeneCounty279SI4805000000AshcompactedclayclayH B Robinson169SI30Ash and Coal Wastenone/natural soilsno linerHammond203SI56576256Ash and Coal Wastenone/natural soilsno linerHarllee Branch204SI3247898277Ash and Coal Wastenone/natural soilsno linerHarllee Branch205SI2037634000Ash and Coal Wastenone/natural soilsno linerHarrison211LF7918000000Ash and Coal Wastecomposite clay/membranecompositeHarrison330SI30028000000Ashnone/naturalsoilsnolinerHatfield's Ferry112LF20790000Ashand Coal Wastecompacted ashno linerHennepin274SI1503460600Ash and Coal Wastenone/natural soilsno linerHeskett87LF581550000FBCcompactedclayclayHolcomb65LF8Ashcompacted ashno liner(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-5CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeHomer City118LF24729636550Ash and Coal Wastegeosynthetic membranecompositeHoot Lake40LF72800000Ash and Coal Wastenone/natural soilsno linerHugo193LF404000000Ashcompacted ashno linerHugo194si151.0232271Ash and Coal Wastecompacted clayclayHunter256LF28012000000Ashnone/naturalsoilsnolinerHuntington255LF7011400000Ashnone/naturalsoilsnolinerIntermountain224SI1054840000Ash and Coal Wastegeosynthetic membranecompositeIntermountain225LF33917800000Ashcompacted ashno linerIntermountain226SI1805200000AshgeosyntheticmembranecompositeJ H Campbell115SI2676900000Ash and Coal Wastenone/natural soilsno linerJ M Stuart125SI888357000Ashnone/natural soilsno linerJ R Whiting129SI6140000Ashnone/natural soilsno linerJack McDonough202SI731531893Ash and Coal Wastenone/natural soilsno linerJack Watson220SI100Ashnone/natural soilsno linerJames H Miller Jr.300SI2005500000Ashcompacted clayclayJim Bridger257LF1207940941Ashnone/natural soilsno linerJim Bridger258LF24124000000Ash and Coal Wastenone/natural soilsno linerJim Bridger259SI1403400000Ash and Coal Wastenone/natural soilsno linerJim Bridger262SI1256500000Ash and Coal Wastenone/natural soilsno linerJohn E Amos120SI10013000000Ashnone/natural soilsno linerJohn E Amos121LF20014000000Ashand Coal Wastecompacted clayclayJohn E Amos122SI103078000Ashnone/natural soilsno linerJohn Sevier297SI571600000Ash and Coal Wastenone/natural soilsno linerJohn Sevier298LF514800000Ashcompacted clayclayJohn Sevier309SI1057000000Ash and Coal Wastenone/natural soilsno linerJohnsonville306SI912900000Ash and Coal Wastenone/natural soilsno linerJoliet 29275SI63.11012000Ash and Coal Wastenone/natural soilsno liner(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-6CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeKeystone106LF15522663120Ash and Coal Wastenone/natural soilsno linerKillen Station254SI99935Ash and Coal Wastecompacted clayclayKingston311SI4111000000Ash and Coal Wastenone/natural soilsno linerKingston312SI2758900000Ash and Coal Wastenone/natural soilsno linerKraft206si59.87027428Ash and Coal Wastenone/natural soilsno linerL V Sutton231SI1627696000Ash and Coal Wastenone/natural soilsno linerLansing64SI15AshcompactedclayclayLaramie R Station260SI10.7464156Ash and Coal Wastecompacted clayclayLaramie R Station261SI38939605Ashgeosynthetic membranecompositeLawrence EC109LF82534300000Ashcompacted clayclayLawrence EC110LF221360000Ashcompacted clayclayLawrence EC111LF301000000Ashcompacted clayclayLee240SI351936000Ash and Coal Wastenone/natural soilsno linerLeland Olds103LF371800000Ashcompacted clayclayLeland Olds104LF20458000Ash and Coal Wastenone/natural soilsno linerLon Wright98LF170000Ashnone/natural soilsno linerLouisa63SI30500000AshcompactedclayclayMarion52LF1052200000Ashnone/naturalsoilsnolinerMarion53LF381000000AshcompactedclayclayMarshall232LF1107826000Ashnone/naturalsoilsnolinerMarshall233SI34019689000Ash and Coal Wastenone/natural soilsno linerMartin Lake152LF29030000000Ashcompacted clayclayMayo171SI30185000Ashnone/naturalsoilsnolinerMayo172SI652400000Ashnone/naturalsoilsnolinerMeramec175SI61.1591200Ash and Coal Wastenone/natural soilsno linerMerom184LF658500000Ashnone/naturalsoilsnolinerMiami Fort39LF804000000Ashcompacted clayclay(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-7CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeMilton R Young100LF806500000Ashcompacted clayclayMitchell - PA208LF705600000Ashnone/natural soilsno linerMitchell - WV131SI12030000Ash and Coal Wastenone/natural soilsno linerMohave72LF25021500000Ashnone/naturalsoilsnolinerMonroe26LF40020000000Ash and Coal Wastenone/natural soilsno linerMonroe27SI40015000000Ashnone/naturalsoilsnolinerMorgantown291LF2127700000Ash and Coal Wastenone/natural soilsno linerMountaineer (1301)212LF609700000Ashcomposite clay/membranecompositeMt Storm73LF12518920000Ashcomposite clay/membranecompositeMt Storm134LF9008800000Ash and Coal Wastecompacted clayclayMuscatine Plant #170LF362000000Ashcompacted clayclayMuskogee51LF361247112AshcompactedclayclayNeal North92SI150Ash and Coal Wastenone/natural soilsno linerNeal North93LF200Ashnone/natural soilsno linerNeal South284LF150Ashnone/natural soilsno linerNebraska City20LF17600000Ashand Coal Wastecompacted clayclayNew Castle66LF271100000Ash and Coal Wastegeosynthetic membranecompositeNewton180LF309Ashnone/naturalsoilsnolinerNorth Omaha17LF13105000Ash and Coal Wastecompacted clayclayNortheastern142LF693185190Ashnone/naturalsoilsnolinerNucla96LF41.21500000FBCnone/naturalsoilsnolinerOklaunion228SI11408940Ash and Coal Wastenone/natural soilsno linerOklaunion229SI19.4718060Ashnone/naturalsoilsnolinerOklaunion230SI290.86056820Ashnone/naturalsoilsnolinerParadise146SI857582510Ashcompositeclay/membranecompositeParadise316SI2005000000Ashnone/naturalsoilsnolinerPetersburg155LF25019750000Ashcompactedclayclay(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-8CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypePetersburg156si156.6901408Ashnone/naturalsoilsnolinerPleasant Prairie243LF266500000Ash and Coal Wastegeosynthetic membranecompositePort Washington242LF3001900000Ash and Coal Wastecompacted clayclayPortland67LF152200000Ash and Coal Wastenone/natural soilsno linerPossum Point77SI56Ash and Coal Wastenone/natural soilsno linerPotomac River140LF33802000Ashgeosynthetic membranecompositePresque Isle116LF29214200000Ashnone/natural soilsno linerR Gallagher326SI17020000000Ash and Coal Wastecompacted clayclayR M Schahfer84SI801030000Ash and Coal Wastenone/natural soilsno linerR M Schahfer85LF20017200000Ashnone/natural soilsno linerReid Gardner95LF112.54520000Ashnone/natural soilsno linerRichard Gorsuch36LF3003600Ashcompacted clayclayRiverbend165SI1433200000Ashnone/naturalsoilsnolinerRodemacher247SI361200000AshcompactedclayclayRodemacher248SI1092500000AshcompactedclayclayRoxboro239LF554165000Ashnone/naturalsoilsnolinerSandow153LF1251300000AshcompactedclayclaySandow187LF48903467Ash and Coal Wastenone/natural soilsno linerSandow188SI451351973Ash and Coal Wastenone/natural soilsno linerScherer199SI49022262030Ash and Coal Wastenone/natural soilsno linerShawnee317SI1805810000Ash and Coal Wastenone/natural soilsno linerShawnee318LF966100000FBCnone/naturalsoilsnolinerShawville209LF688000000Ashnone/naturalsoilsnolinerSheldon23LF9375000AshcompactedclayclaySouth Oak Creek3LF454050000Ash and Coal Wastecompacted clayclaySouth Oak Creek4LF1304600000Ashnone/natural soilsno linerSpringerville154LF576400000Ashnone/naturalsoilsnoliner(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-9CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeSt Johns River Power158lf128.624166Ash and Coal Wastecompacted clayclayStanton Energy Ctr.117LF312Ashnone/natural soilsno linerStockton Cogen Company2000LF4533333FBCcomposite clay/membranecompositeSyl Laskin68SI75726000Ash and Coal Wastenone/natural soilsno linerTecumseh EC177LF540AshcompactedclayclayTexas-New Mexico3900LF616142473FBCcompacted clayclayTitus207LF393000000Ash and Coal Wastecomposite clay/membranecompositeTrimble County69SI1156856667Ashcompacted clayclayTyrone148SI5.5351699Ashnone/naturalsoilsnolinerTyrone149SI5327500Ash and Coal Wastenone/natural soilsno linerTyrone150SI7.75500123Ash and Coal Wastenone/natural soilsno linerValley8LF16.4534000Ash and Coal Wastecompacted clayclayVermilion55SI438100000Ash and Coal Wastenone/natural soilsno linerVictor J Daniel Jr287lf49.20163084Ashcompacted clayclayVictor J Daniel Jr288si20.03879417Ash and Coal Wastecomposite clay/membranecompositeW A Parish189lf28.68322214Ashcompacted clayclayW H Weatherspoon236SI261200000Ash and Coal Wastenone/natural soilsno linerW S Lee238SI411634000Ash and Coal Wastenone/natural soilsno linerWabash River324SI12014000000Ash and Coal Wastenone/natural soilsno linerWalter C Beckjord123LF141000000Ashcompacted ashno linerWalter C Beckjord124SI2000000Ashnone/natural soilsno linerWansley200SI33018712850Ash and Coal Wastenone/natural soilsno linerWansley201SI43Ashnone/naturalsoilsnolinerWarrick181SI1404500000Ash and Coal Wastecompacted clayclayWaukegan54LF604000000Ash and Coal Wastecompacted clayclayWeston241LF18600000Ashnone/naturalsoilsnolinerWidows Creek320SI1103500000Ash and Coal Wastenone/natural soilsno liner(continued)Appendix BAttachment B-2: CCW WMU DataApril 2010–Draft EPA document.B-2-10CCW WMU Data (continued)PlantFacilityIDWMUTypeArea(acres)Capacity(cubic yards)Waste TypeOriginal LinerLinerTypeWidows Creek321SI22212400000Ashcompacted clayclayWill County277SI60599256Ash and Coal Wastecompacted clayclayWyodak71LF683500000AshgeosyntheticmembranecompositeYates197SI4.7115000Ashcompositeclay/membranecomposite

### Appendix C Site Data



## April 2010–Draft EPA document. C-1 

# Appendix C. Site Data

The site characteristics used in this analys is were based on site-s pecific, 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 relati onship among important features such as the WMU boundary, residential we ll location, and streams a nd lakes. These data were collected at each of the 181 coal-fired pow er plants selected fo r 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 we re 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-speci fic, regional, and national data. Sitespecific data were collected around CCW plan t 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, distri butions were used in the Monte Carlo analysis to captu re 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 a ssembled 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 WMUs 1 

## , 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 fl ows also were collected for each site by identifying the nearest stream segment.

Climate and water quality data were collect ed by assigning each site to a Hydrologic Evaluation of Landfill Performance (HELP) model cl imate station and a U.S. Geological Survey (USGS) hydrologic region. The EPA STOrage and R ETrieval (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 meas uring the distance between the

1 

### The EIA latitudes and longitudes usually represent a fac ility centroid or front-gate lo cation 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 th e CCW sites. Similarly, a national distribution was used to represent the distance of the near est residential wells fr om the CCW WMUs being

modeled.



# C.2 Receptor Location (National Data)

The residential scenario for the CCW groundwat er pathway analysis calculates exposure through use of well water as drinki ng 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 downgradie nt residential well distances take n 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 de pth below the water table to ensure that the well remained within the plume and at a depth appr opriate for surficial aquifers across the United

States. These limits were consistent with ot her recent national risk assessments conducted by EPA OSW and provided a prot ective 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 Ri sk Scenario (Distance to Waterbody)

The recreational fisher scenario was used to es timate 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 dow nwind of the WMU, beginning wh ere 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 dete rmined 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 th e waterbody.

The downgradient distance to the surface water body was determined from a national distribution developed by measuring this dist ance 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 devel opment 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 de velop 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 ri sk assessment—EPA’s Composite Model for Leachate Migration with Transf ormation Products (EPACMTP)—requires soil properties for the entire soil column to model leachate transpor t through the vadose zone to groundwater. As with aquifer type, soil data were coll ected 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 meteorologi cal station. Database programs were then used to assemble and proces s soil texture, pH, and soil organic matter data for these map units from the State Soil Geogr aphic (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 comp iled 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 an d Alexander, 1995) averages STATSGO

data over the entire soil colu mn for each map unit.

 CONUS. The Conterminous United States Mul ti-Layer Soil Char acteristics (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 di rectly from USSOILS and STATSGO data. A complete set of hydrological soil properties 2 

## was not available from STATSGO. To ensure consistent and realistic values, EPACMTP re lies on established, natio nwide relationships between hydrologic properties a nd soil texture. Peer-reviewed pub lications by Carsel and Parrish (1988) and Carsel et al. (1988) provide a consistent set of co rrelated hydrologic properties for

each soil texture. Soil texture data for the en tire soil column were collected from the CONUS

database.

C.3.2 Methodology

The soil data collection methodology began wi th GIS programs (in Arc Macro Language [AML]). These programs overlaid a 5-km ra dius around each CCW plant location on the STATSGO map unit coverage to determine the ST ATSGO map units and their area within the radius. These data were then passed to data proc essing 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 text ures to represent variability in hydrologic soil properties and (along with climate data) to assign infiltration rates to ea ch site. Because STATSGO soils

are classified into the 12 U.S. Soil Conserva tion 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 wi thin the 5-km data collection radius. These percentages were sampled for each site when pr eparing 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, wh en a megatexture was picked for a particular iteration of a site, the correspondi ng 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 EP ACMTP, 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 In stitute (API) (Newell et al., 1989; Newell et al., 1990) to specify correl ated probability distributions, which were used to populate the following four hydrogeologic pa rameters during the M onte 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 describe d in Newell et al. (1990). The EPACMTP User’s Guide (U.S. EPA, 1997) provides the empirical distribu tions of the four hydrogeologic parameters for each of the hydrogeologic environments.

Average aquifer/vadose zone temperature wa s also required for th e groundwater model and was obtained from a digitized map of groundw ater 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 DRASTIC 3 

## 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 categoriz ing geologic settings in terms of the aquifer characteristics needed for groundwater mode ling. The major components of this modeling framework are Groundwater Regions, hydrogeolog ic 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 ex tent of dominant aquifers and their relationship to other geologic units) that influence groundwater occurrence and availability.

 Hydrogeologic settings were developed within each H eath region by Aller et al. (1987) 4


## to create mappable geological un its that are at the proper scale to capture differences in aquifer conditions. Note that there may be th e 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 th e 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 hydrolog ic 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 n ecessary to assign the CCW site s to a hydrogeologic environment

so that the correct HGDB data set would be us ed for modeling each site. The data sources and methodology used to make these a ssignments are described below.

C.4.1 Data Sources

Data sources used to make hydrogeologi c assignments for the sites included:

 A USGS inventory of state groundw ater resources (Heath, 1985)

 GIS coverages from Digital Data Sets Describing Prin cipal Aquifers, Su rficial 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 (d escribed in Section C.3.2).

These coverages were used in a GIS overlay process to determine the principal aquifers, surficial geologic units, groundw ater region, productive aquife rs, and general hydrogeologic settings for a 5-km radius around each CCW fac ility location. Attributes for each of these items were passed to a database for use in assigning hydrogeolog ic environments.

C.4.2 Assignment Methodology

For each CCW site, hydrogeologic environmen ts were assigned by a professional geologist as follows:

 Determine Heath Groundwater Region (for th e Alluvial Valleys region, determine the region in which the alluvial valley is located)

 Assign hydrogeologic setting using state geol ogical descriptions from Heath (1985); aquifer, soil, and surficial geology informa tion 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 be tter resolution than the aquifer coverages and was used to develop setting pe rcentages for the 5-km radius. In most cases, there were two settings per site. In cas es where a single setting accounted fo r over 80 percent of the 5-km area, a single setting was assigned.

Because Newell et al. (1990) define two alluvi al environments (6, River alluvium with overbank deposits, and 7, River alluvium wit hout overbank deposits), it was necessary to

determine which environment an alluvial site fe ll into. The survey soil layer information was used to distinguish between these two settings by determining whether there were significant fine-grained overbank depos its in the soil column.

April 2010–Draft EPA document. C-8

### Appendix C Site Data



## Quality assurance/quality control (QA/QC) m easures included independent review of the assignments by other geologists with expertise in assigning settings.

C.4.3 Data Processing

HGDB hydrogeologic environment fr actions (i.e., the portion of the region assigned to each of the 12 hydrogeological environments) we re defined and used in the CCW risk

assessment as follows. If the 5-km radius aroun d a site contained only one HGDB environment, the fraction assigned was 1.0 and al l groundwater model runs for th at location were associated with that hydrological environm ent. If more than one HGDB environment was present, each environment was assigned a fraction based on the areal percentages of eac h 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 environm ent and half with the second.

Once the hydrogeologic environments were assigned, a preprocessing run of EPACMTP was conducted to construct a set of randomly ge nerated but correlated hydrogeologic parameter

values for each occurrence of th e hydrogeologic environments in the source data files. Missing values in the HGDB data set were filled using correlations, as describe d 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, showi ng the crosswalk between Groundwater Regions, hydrogeologic settings, an d 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 Environmen ts: 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 Environm ents 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 follow ing data were compiled:

 Mean annual windspeed

 Mean annual air temperature

 Mean annual precipitation.

With respect to precipitation, EPACMTP uses th e climate station, along w ith soil texture, to

select the HELP-modeled infiltration rates to us e in the landfill source m odel 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 volat ilization 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 st ations 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 fo r 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 th ose 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 cent er was examined and matched based on:

 A 5-year precipitation average within or clos e to the site’s predicted precipitation range

April 2010–Draft EPA document. C-11

### Appendix C Site Data



## Figure C-2. EPACMTP climate centers, precipit ation ranges, and CCW disposal sites.

 Confirmation of a site’s av erage annual rainfall on http://www.weather.com and van der Leeden et al. (1990)

 Geographic similarities between pl ant and climate center locations

 Best professional judgment.

In a few cases, the three closest climate cente rs 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 si milar geography and averag e precipitation rates. Each assignment was independently checked for accuracy. Attachment C-3 lists the climate center assigned to each CCW disposal site, along w ith 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 A ssigned 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 di stance between the wa terbody and the WMU being modeled. Surface waterbody data were collected on a site-based, regional, or national basis depending on the variable and da ta availability. Collection met hods 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 estima ted in the early 1980s. St atistics developed for each flowing reach were mean annual flow, low flow (approximately 7Q10), 5 

## and mean monthly

flow. RF1 also contains veloci ties corresponding to mean annual and low flow, estimated from a compendium of time-of-travel studies. For stream s 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 ar e indexed by USGS catal oging unit and stream segment (CUSEG).

To assign the CCW plants to the nearest dow ngradient reach (i.e., the nearest waterbody in the direction of groundwater flow), a GIS was used to identify the clos est RF1 stream segment to each CCW plant location. Because of several uncertainties in th e nearest reach approach (i.e.,

5 

### The 7Q10 is the minimum 7-day average flow expected to occur within a 10-y ear return period (i.e., at least once

in 10 years).

### Appendix C Site Data



## inaccurate WMU location, unknown direction of gr oundwater flow, and limited lake coverages), the CCW plants also were matched to standard industrial classification (S IC) 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 Elimina tion System (NPDES) disc harge 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 th at the NPDES receiving waterbody would also be receiving loads from the WMU through the grou ndwater-to-surface-water pathway. CCW plants

that could not be matched to the PCS database were simply assigned th e 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, a nd waterbody types for the CCW disposal sites.

With respect to waterbody type, the RF1 da ta include several types of waterbodies, including streams and rivers, and types with zero fl ows such as lakes, Great Lakes, wide rivers,

and coastline features. Each of th ese waterbody types needed to be de signated 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 featur es 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 Assi gned to CCW Disposal Sites

RF1

Code RF1Name Description Reach

Model Type a 

### 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 down stream 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 (forexample, 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 CC W Disposal Sites. (continued)

RF1

Code RF1Name Description Reach

Model Type a 

### Number

of CCW

Plants

Reaches with Zero RF1 Flow

C Coastal/Continental Shoreline Segment A reach that represents a segm ent of a shoreline of a gulf,

sea, or ocean. L 3

G Great Lakes Shoreline Segment A reach that represents a se gment 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 segmen t of the left or right bank

of a stream. L 5

a 

### R = river; L = lake.



## Stream dimensions were calcula ted from the flow data as follows. First, the length of the modeled stream segment was set to be the wi dth of the groundwater plume as it enters the waterbody. Stream width was then determined fr om flow (Q) using a liner regression equation derived from empirical data by Kocher and Sartor (1997):

(C-1) 0.4559 

## 5.1867Q Width =

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 assign ed to the CCW plant sites were not readily available from RF1, Reach File Version 3 (RF3 ), the National Hydrogr aphy Dataset (NHD), or other sources. In addition, many plants were lo cated on very large wate rbodies (e.g., the Great Lakes, wide rivers, or coastlin es), 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 a ll 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 rese rvoir 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 an alysis. Given that many of the lakes assigned to CCW plants were much larger than 13 acres, th is produced high-end risk results. However, given that many of the plants were located on very la rge waterbodies, this nece ssary 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 data base. EPA’s STORET system is the largest single source of water quality da ta in the country. The Legacy ST ORET 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 mu st 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 techni ques were employed taking into account the above issues (including coordination with gage statistical analysis and Reach F iles, 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 techniqu es, which can be thought of as extracting the underlying “signal” of wate r quality from the inhe rent “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 ( E C) 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, th e 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 prepara tion 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 me an 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 Susp ended Solids (TSS) Distributions

Hydrologic

Region Number

of CCW

Plants No. of

Measurements No. of

Annual

Medians Annual Median TSS (log triangular distribution)

Geometric

Mean Minimum Maximum 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

Mean Minimum Maximum 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 triangula r distributions for each hydrologic region. Table C-11 provides these regional statis tics, 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 Ta bles C-10 and C-11. During source data file preparation, TSS data were pulled from the appropriate regional da ta set sequentially for

each iteration at a site.

Table C-11. Regional Surfa ce Water pH Distributions

Hydrologic

Region Number of

CCW

Plants No. of

Measurements No. of Annual

Median

Values Annual Median pH (triangular distribution)

Average Median pH Minimum Maximum 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 Di stributions. (continued)

Hydrologic

Region Number of

CCW

Plants No. of

Measurements No. of Annual

Median

Values Annual Median pH (triangular distribution)

Average Median pH Minimum Maximum 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 su rface impoundment or landfill in question, along with the nearest downgradient waterbody.

The nearest waterbody matching one of the fo llowing 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 great er (i.e., fishable waterbodies).

Stream order was determined by tracing the conver gence of tributaries with order 1 assigned to the furthest upstream segment indicated on the ma p (both ephemeral and perennial streams were assigned as order 1). Topography on the map wa s used to determine if the waterbody was downgradient of the plant. Many CCW WMUs in th e 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 im poundments) were statistically compared using (1) a Wilcoxon Rank Sum Test (to determine whether one distributi on is shifted to the right or left of the othe r distribution) and (2) a Quantile Test (to test for differences, that is, differing numbers of observations) between the tw o distributions for the values above a given percentile. The results of the Wilcoxon test s howed 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 17 th percentile (p value = 0.066), but the remainder of the tests showed no significant differences. Ba sed 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 (previou sly shown in Table C-2) that was applied nationally to both landfil ls 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 Poten tial Using Hydrogeologic Settings. EPA-600/2-87- 035. U.S. Environmental Protection Agency. O ffice 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 Descri bing 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 co nterminous United States

with unconsolidated watercourses. Edition: 1.0 (map). In: Open-File Report 99-77 (Clawges and Price, 1999a). U.S. Geologi cal 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(C lawges 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 c onterminous 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. Geol ogical Survey, Washington, DC.

Jones, R.D., S.W. Abel, W. Effl and, 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 F ile, 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 Cont erminous United States Multi-Layer Soil Characteristics Data Set for Regi onal 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 (STATS GO) Data Base for the

Conterminous United States. Edition: 1.1. Reston, VA. Available at http://water.usgs.gov/GIS/me tadata/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. Of fice of Solid Waste and Emergency Response, Washington, DC. Sept ember. Available in docket number EPAHQ-RCRA-2003-0004 as document nu mber 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 0.48Appendix CAttachment C-2: Hydrogeologic EnvironmentAttachment C-2: Hydrogeologic EnvironmentPlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionBig Cajun 210BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting (100%alluvium); soils have significant fines(SCL+SLT = 95%)A B Brown6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting; soils havesignificant fines (SCL+SLT = 95%)A/C PowerAce Operations2CAlluvial Fans5Alluvial Basins Valleys andFans100Based on surficial geology; consistent withalluvial fan settingAllen10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on aquifer coverages, surficialgeology; Heath (1985) and soils indicateoverbank depositsAlma7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage based on SNL/SCL soils; settingbased on productive aquifers and surficialgeologyAlma7EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Percentage based on SNL/SCL soils; settingbased on productive aquifers and surficialgeologyAntelope Valley7GThin Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on principal aquifer and surficialgeology coveragesArkwright8DRegolith1Metamorphic and Igneous100Most common Piedmont setting (residuum)Asheville8BAlluvial Mountain Valleys5Alluvial Basins Valleys andFans100Appropriate for alluvial blue ridge valley(colluvium)Baldwin7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geology (74%Floodplain and alluvium gravel terraces)Baldwin7GThin Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock30Percentage based on surficial geology (74%Floodplain and alluvium gravel terraces)(continued)April 2010–Draft EPA document.C-2-1Appendix CAttachment C-2: Hydrogeologic EnvironmentHydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionBarry10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting, significant finegrained soils = overbank depositsBay Front7BbOutwash Over BeddedSedimentary Rock2Bedded Sedimentary Rock70Percentage based on productive aquifersBay Front7DBuried Valley4Sand and Gravel30Percentage based on productive aquifersBay Shore7AcGlacial Till Over SolutionLimestone12Solution Limestone100Closest setting considering carbonateaquifers, high SCL soils, and lake depositssurficial geologyBelews Creek6HTriassic Basins2Bedded Sedimentary Rock50Sources somewhat dissimilar; fraction basedon surficial geology; Triassic basinBelews Creek8DRegolith1Metamorphic and Igneous50Sources somewhat dissimilar; fraction basedon surficial geologyBen French6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock60Percentage, thin soils based on surficialgeologyBen French6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geology;significant fine soils (25% SCL)Big Sandy6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock50Percentage based on surficial geology; thinsoils inferred from colluviumBig Sandy6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage based on surficial geology; soilshave significant fines (SCL+SLT = 95%)Big Stone7BaOutwash8Outwash100Based on surficial geologyBlack DogSteam Plant7BbOutwash Over BeddedSedimentary Rock2Bedded Sedimentary Rock100Based on surficial geology, aquifer coveragesBlue Valley7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock80Percentage based on Heath (1985),productive aquifers(continued)April 2010–Draft EPA document.C-2-2Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-3Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionBlue Valley7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit20Percentage based on Heath (1985),productive aquifersBowen6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock100Based on aquifers, surficial residuum(massive red clay); metamorphic surficialgeology not consistent with Valley and RidgeBrandon Shores10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Assigned based on location and aquifer andsurficial geology coverages; Heath regionincorrect (it's Atlantic Coastal Plain, notPiedmont)Buck8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit100Based on productive aquifer & Heath regioncoveragesBull Run6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock60Percentage based on surficial geologyBull Run6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geology; highSCL (77%) = overbank depositsC D McIntoshJr11ASolution Limestone andShallow Surficial Aquifers12Solution Limestone100Based on both aquifer coveragesC P Crane10AaRegional Aquifers4Sand and Gravel50Appears to be on border between Piedmontand Coastal PlainC P Crane8DRegolith1Metamorphic and Igneous50Appears to be on border between Piedmontand Coastal PlainCape Fear6HTriassic Basins2Bedded SedimentaryRock20Percentage based on productive aquifer &Heath region coverages; Triassic basinCape Fear8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit80Percentage based on productive aquifer &Heath region coveragesCarbon4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Setting based on aquifer and surficialgeology coverages(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-4Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionCardinal6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock30Percentage based on surficial geology; soilswith low (<1%) SNLCardinal6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geology; soilswith low (<1%) SNLCayuga7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer overlaid by alluvialdepositsChalk Point10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Predominant settingCholla4BConsolidated SedimentaryRock2Bedded Sedimentary Rock20Percentage based on surficial geology (83%Floodplain and alluvium gravel terraces)Cholla4CRiver Alluvium7River Valleys and Floodplainswithout Overbank Deposits80Percentage based on surficial geology (83%Floodplain and alluvium gravel terraces)Cliffside8DRegolith1Metamorphic andIgneous100Based on surficial geologyClover6HTriassic Basins2Bedded Sedimentary Rock20Percentage based on surficial geology;Triassic Basin from Heath (1985) andprincipal aquifer coverageClover8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geologyCoal Creek7GThin Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on principal aquifer and surficialgeology coveragesColeto Creek10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifer and surficialgeology coveragesColstrip6daAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Based on all coverages(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-5Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionConemaugh6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on aquifer coverages & Heath(1985); thin regolith inferred from colluviumConesville6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology; soilswith low (10%) SNLConesville6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology; soilswith low (10%) SNLCouncil Bluffs7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on productive aquifersCrawford7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Assigned based on predominant surficialgeology (98% Floodplain and alluviumgravel terraces), productive aquifer coverageCrist10BbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits100Assigned based on predominant surficialgeology (96% Floodplain and alluviumgravel terraces), coarse-grained soil (49%SNL)Cross10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifers, surficial geology,soils, Heath (1985)Cumberland6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on surface geology; high (61%)SCL = overbank depositsDale6ESolution Limestone12Solution Limestone20Percentage based on surficial geology;setting from principal aquifers (carbonate)Dale6FaRiverAlluviumWithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology; soilshave significant fines (SNL = 0.1%)Dallman7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on soils, surficial geology, principalaquiferDan E Karn7FGlacial Lake Deposits4Sand and Gravel100Based on surficial geology, soils(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-6Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionDan River6HTriassic Basins2Bedded Sedimentary Rock100Based on surfucial geology, principalaquifers; Triassic basinDanskammer7DBuried Valley4Sand and Gravel100Based on predominant Heath region,productive aquifers; little coarse-grainedsoilsDave Johnston4CRiver Alluvium7River Valleys and Floodplainswithout Overbank Deposits100Based on aquifer and surficial geologycoverages, Heath (1985)Dickerson8DRegolith1Metamorphicand Igneous100Predominant settingDolet Hills10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Predominant shallow unconsolidated aquifersystemDuck Creek7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Assigned based on predominant surficialgeology (100% Floodplain and alluviumgravel terraces), Heath Alluvial ValleyRegionDunkirk7HBeaches, Beach Ridges andSand Dunes11Coastal Beaches100Based on location, surficial geologyE D Edwards7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock20Percentage based on surficial geology (83%Floodplain and alluvium gravel terraces)E D Edwards7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology (83%Floodplain and alluvium gravel terraces)E W Brown6ESolution Limestone12Solution Limestone20Percentage based on surficial geology (76%alluvium, 23% clay); soils have significantfine-grained (0% SNL)E W Brown6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology (76%alluvium, 23% clay); soils have significantfine-grained (0% SNL)Eckert Station7BbOutwash Over BeddedSedimentary Rock2Bedded Sedimentary Rock30Percentage based on productive aquifercoverage, Heath regions(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-7Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionEckert Station7EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits70Percentage based on productive aquifercoverage, Heath regionsEdgewater7BcOutwash Over SolutionLimestone12Solution Limestone100Setting based on aquifer and surficialgeology coveragesElmer W Stout7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer overlaid by alluvialdepositsF B Culley6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting; soils havesignificant fines (SCL+SLT = 94%)Fayette PowerPrj10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifer and surficialgeology coveragesFlint Creek6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock100Ozark plateau; Heath (1985) indicatesdolomite, sandy dolomite, sandstone, with noindication of solutioning. Surficial geology(cherty red clay) noted as thick regolith inAller et al. (1987)Fort Martin6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on surficial geology; low SNL(< 1%) = overbank depositsFrank E Ratts7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer in alluvial valley region(99%)G G Allen8DRegolith1Metamorphic andIgneous100Based on surficial geologyGadsden6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock30Percentage assigned based on productiveaquifer coverageGadsden6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage assigned based on productiveaquifer coverage; soils have significant fines(SCL+SLT > 25%)Gallatin6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on surface geology; high (56%)SCL = overbank deposits(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-8Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionGen J M Gavin6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on productive aquifers,surficial geologyGenoa6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage based on SNL/SCL soils; settingbased on surficial geology and productiveaquifersGenoa6FbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Percentage based on SNL/SCL soils; settingbased on surficial geology and productiveaquifersGibson7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting; soils havesignificant fines (SCL+SLT = 99%)Gorgas6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock30Percentage based on surficial geology;alluvial setting with coarser soils (= nooverbank deposits)Gorgas6FbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits70Percentage based on surficial geology;alluvial setting with coarser soils (= nooverbank deposits)Green River6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting (>85%alluvium); soils have significant fines (SNL= 0%)Greene County10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers30Percentage based on surficial geology; soilshave significant fines (SCL+SLT > 90%)Greene County10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geology; soilshave significant fines (SCL+SLT > 90%)H B Robinson10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifers, surficial geology,soils, Heath (1985); Heath region coverageincorrect (Coastal Plain, not Piedmont)(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-9Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionHammond6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock100Based on aquifers, surficial residuum(massive red clay)Harllee Branch8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit100Assigned based on predominant surficialgeology (99% floodplain and alluvium gravelterraces)Harrison6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock20Percentage based on surficial geology; thinsoils inferred from surficial geologyHarrison6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology;0%SNL = overbank depositsHatfield's Ferry6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology; thinregolith inferred from colluviumHatfield's Ferry6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology; soils< 1% SNLHennepin7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock30Percentage to capture uncertainty in soils,surficial geology, principal aquiferHennepin7BbOutwash Over BeddedSedimentary Rock2Bedded Sedimentary Rock30Percentage to capture uncertainty in soils,surficial geology, principal aquiferHennepin7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage to capture uncertainty in soils,surficial geology, principal aquiferHeskett7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvium surficialgeology(96%); mixed soilsHolcomb5GbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits100Alluvial valley with very coarse soilsHomer City6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on aquifer coverages & Heath(1985); thin regolith inferred from colluvium(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-10Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionHoot Lake9EOutwash8Outwash100Based on productive aquifer, soils, surficialgeologyHugo6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology;soil/regolith thickness inferred from Heath(1985)Hugo6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology; finesoils with about 10% SNLHunter4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Setting based on aquifer and surficialgeology coveragesHuntington4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Setting based on aquifer and surficialgeology coveragesIntermountain2EPlaya Lakes5Alluvial Basins Valleys andFans100Setting based on surficial geology coverage,Heath (1985)J H Campbell7FGlacial Lake Deposits4Sand and Gravel100Based on surficial geology, soilsJ M Stuart6ESolution Limestone12Solution Limestone50Percentage based on surficial geology; low(< 2%) SNLJ M Stuart6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage based on surficial geology; low(< 2%) SNLJ R Whiting7FGlacial Lake Deposits4Sand and Gravel100Based on surficial geologyJackMcDonough8CMountain Flanks2Bedded Sedimentary Rock100Assigned based on predominant surficialgeology (94% stony colluvium onmetamorphic rocks; less silt and clay than incolluvium over limestone)Jack Watson10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Based on all coveragesJames H MillerJr6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock20Percentage based on surficial geology; soilshave significant fines (SCL+SLT > 25%)(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-11Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionJames H MillerJr6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology; soilshave significant fines (SCL+SLT > 25%)Jim Bridger4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Based on aquifer and surficial geologycoverages, Heath (1985)John E Amos6daAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology; thinsoils inferred from surficial geologyJohn E Amos6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology;0%SNL = overbank depositsJohn Sevier6ESolution Limestone12Solution Limestone50Percentagebased on surface geology; settingbased on surface geology and aquifer type,with possibility of solution limestone fromHeath (1985)John Sevier6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage, setting based on surface geology;low (<1%) SNL = overbank depositsJohnsonville6ESolution Limestone12Solution Limestone30Percentage based on surface geology; settingbased on aquifer coverages, Heath (1985);placed in NonglaciatedCentral region basedon aquifer coverages and Heath (1985)Johnsonville6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage, setting based on surface geology;low (3%) SNL = overbank deposits; placedin Nonglaciated Central region based onaquifer coverages and Heath (1985)Joliet 297AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on aquifers, soils; soils don't suggestoutwash like surficial geology doesKeystone6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on aquifer coverages & Heath(1985); thin regolith inferred from colluviumKillen Station6ESolution Limestone12Solution Limestone30Percentage based on surficial geology; low(< 2%) SNL(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-12Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionKillen Station6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geology; low(< 2%) SNLKingston6ESolution Limestone12Solution Limestone20Percentage based on surface geology; settingbased on surface geology and aquifer type,with possibility of solution limestone fromHeath (1985)Kingston6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage, setting based on surface geology;high (67 %) SCL = overbank depositsKraft11ASolution Limestone andShallow Surficial Aquifers12Solution Limestone100Only possible assignment; predominantalluvium (84%) not well representedL V Sutton10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers20Percentage based on surficial geology; sandysoilsL V Sutton10BbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits80Percentage based on surficial geology; sandysoilsLansing6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology,productive aquifers; loess = thin soilsLansing6FbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits60Percentage based on surficial geology,productive aquifers; coarse-grained soilsLaramie RStation6FbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits100Based on aquifer and surficial geologycoverages, Heath (1985)Lawrence EC7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial valley with low coarse soils (<1%SNL)Lee10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers30Percentage based on surficial geology; sandysoilsLee10BbRiverAlluviumWithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits70Percentage based on surficial geology; sandysoils(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-13Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionLeland Olds7EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Percentage based on surficial geology;assumed coarse soilsLeland Olds7GThin Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock50Percentage based on surficial geology;assumed coarse soilsLon Wright7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit30Alluvial based on predominant Heath,productive aquifer; percentage based on soiltexturesLon Wright7EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits70Alluvial based on predominant Heath,productive aquifer; percentage based on soiltexturesLouisa7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Alluvial Valley; significant coarse-graineddepositsLouisa7EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Alluvial Valley; significant coarse-graineddepositsMarion7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Assigned to Glaciated Central region basedon surficial geology (pre-Wisconsin drift)Marshall8DRegolith1Metamorphic andIgneous100Based on surficial geologyMartin Lake10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifer and surficialgeology coveragesMayo8DRegolith1Metamorphic and Igneous100Based on surficial geologyMeramec7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Based on surficial, predominant HeathMerom7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer overlaid by alluvialdepositsMiami Fort7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Assigned based on productive aquifers,surficial geology and soil (3% SNL)(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-14Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionMilton R Young7GThin Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on principal aquifer and surficialgeology coveragesMitchell6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock60Percentage based on surficial geology; thinregolith inferred from colluviumMitchell6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geology; soils 0% SNLMitchell6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on surficial geology; low SNL(< 1%) = overbank depositsMohave2HaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on predominant surficialgeology, Heath (1985)Monroe7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Based on Heath region, productive aquifers,soilsMorgantown10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Assigned based on location and aquifer andsurficial geology coverages; Heath regionincorrect (it's Atlantic Coastal Plain, notPiedmont)Mountaineer(1301)6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Setting based on surficial geology; low SNL(10%) = overbank depositsMt Storm6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on surficial geology, aquifercoverages; thin soils inferred from surficialgeologyMuscatine Plant#17EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Alluvial Valley; significant coarse-graineddepositsMuscatine Plant#17EbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Alluvial Valley; significant coarse-graineddepositsMuskogee6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Surficial geology indicatesalluvium/colluvium; Heath (1985) indicatesfine soils over sands and gravels(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-15Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionNeal North7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial Valley settingNeal South7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial Valley settingNebraska City7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial based on predominant Heath,productive aquifer, soil texturesNew Castle7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock20Percentage and setting based on Heath region& surficial geology; thin regolith inferredfrom colluviumNew Castle7DBuried Valley4Sand and Gravel80Percentage and setting based on Heath region& bookNewton7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on soils, surficial geology, aquifercoveragesNorth Omaha7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial based on predominant Heath,productive aquifer; soil texture (28% SCL,10% SNL) = overbank depositsNortheastern6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock40Percentage based on surficial geology, whichindicates thin residual soilsNortheastern6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology; soils< 2% SNLNucla4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Based on surficial geology, aquifer coveragesOklaunion6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on surficial geology; thin soilinferredParadise6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting (93% alluvium);soils have significant fines (SNL = 0%)Petersburg7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer in alluvial valley region(similar to 1043)(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-16Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionPleasant Prairie7AcGlacial Till Over SolutionLimestone12Solution Limestone100Setting based on aquifer and soil coverages(high SCL soils)PortWashington7AcGlacial Till Over SolutionLimestone12Solution Limestone100Setting based on aquifer and soil coverages(high SCL soils)Portland7AcGlacial Till Over SolutionLimestone12Solution Limestone100Setting based on aquifer and surficialgeology coveragePossum Point10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Based on productive aquifer coverage; Heathregion incorrectPotomac River10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers50Percentage based on surficial geologycoverage; Heath region incorrectPotomac River10BbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits50Percentage based on surficial geologycoverage; sandy soils (51% SNL) = nooverbank deposits; Heath region incorrectPresque Isle9FMoraine4Sand and Gravel100Based on surficial geology, Heath region,soilsR Gallagher6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting; soils havesignificant fines (SCL+SLT = 99%)R M Schahfer7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer in alluvial valley regionReid Gardner2CAlluvial Fans5Alluvial Basins Valleys andFans100Based on surficial geology; consistent withproductive aquifersRichardGorsuch6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Assigned based on productive aquifers,surficial geology and soil (3% SNL)Riverbend8DRegolith1Metamorphic andIgneous100Based on surficial geologyRodemacher10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers50Setting percentage determined from Heath,productive aquifer, and surficial geologycoverages(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-17Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionRodemacher10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Setting percentage determined from Heath,productive aquifer, and surficial geologycoveragesRoxboro8DRegolith1Metamorphic and Igneous100Based on surficial geology, productiveaquifersSandow10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Setting based on aquifer and surficialgeology coverages; Heath region coverage isincorrect (based on Heath [1985] and aquifercoverages)Scherer8DRegolith1Metamorphic and Igneous100Most common Piedmont setting (residuum)Shawnee10BbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits100Predominant alluvial setting (100%alluvium); soils have low fines (SCL = 9%)Shawville6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Setting based on aquifer coverages & Heath(1985); thin regolith inferred from colluviumSheldon7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock30Percentage based on productive aquifercoverage; buried valley indicated by Heath(1985)Sheldon7DBuried Valley4Sand and Gravel70Percentage based on productive aquifercoverage; buried valley indicated by Heath(1985)South OakCreek7AcGlacial Till Over SolutionLimestone12Solution Limestone100Setting based on aquifer and soil coverages(high SCL soils)Springerville4BConsolidated SedimentaryRock2Bedded Sedimentary Rock100Assigned based on productive aquifers(consolidated sandstone)St Johns RiverPower11BCoastal Deposits4Sand and Gravel100Based on sea island surficial geologyStanton EnergyCtr11ASolution Limestone andShallow Surficial Aquifers12Solution Limestone100Based on both aquifer coverages(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-18Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionStockton CogenCompany2CAlluvial Fans5Alluvial Basins Valleys andFans50Percentage based on surficial geology;Central Valley soils show significant finesStockton CogenCompany2HaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit50Percentage based on surficial geology;Central Valley soils show significant finesSyl Laskin9EOutwash8Outwash60Percentage based on surficial geologySyl Laskin9GaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geologyTecumseh EC7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Alluvial valley with low coarse soils (<3%SNL)Texas-NewMexico10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers100Based on productive aquifers, Heath (1985)(Heath region coverage is incorrect)Titus6DbAlternating Sandstone,Limestone and Shale - DeepRegolith2Bedded Sedimentary Rock100Setting based on aquifer and surficialgeology coverage; deep regolith inferredfrom red, massive clayTrimble County6ESolution Limestone12Solution Limestone40Heath incorrect; Percentage based onsurficial geology (56% alluvium, 44% clay);soils have significant fine-grained (1% SNL)Trimble County6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Heath incorrect; Percentage based onsurficial geology (56% alluvium, 44% clay);soils have significant fine-grained (1% SNL)Tyrone6ESolution Limestone12Solution Limestone100Based on principal aquifer coverageValley7AcGlacial Till Over SolutionLimestone12Solution Limestone100Setting based on aquifer and soil coverages(high SCL soils)Vermilion7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock100Based on aquifers, soils; soils don't suggestoutwash like surficial geology doesVictor J DanielJr10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers60Percentage based on surficial geology(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-19Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionVictor J DanielJr10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geology, soilsW A Parish10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers30Percentage based on surficial geology andproductive aquifer coveragesW A Parish10BaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geology andproductive aquifer coverages; high SCL(96%) = overbank depositsW HWeatherspoon10AbUnconsolidated and SemiConsolidated ShallowSurficial Aquifer10Unconsolidated andSemiconsolidated ShallowAquifers30Percentage based on surficial geology; sandysoilsW HWeatherspoon10BbRiver Alluvium WithoutOverbank Deposits7River Valleys and Floodplainswithout Overbank Deposits70Percentage based on surficial geology; sandysoilsW S Lee8DRegolith1Metamorphic and Igneous100Setting based on aquifers, surficial geology,soils, Heath (1985)Wabash River7DBuried Valley4Sand and Gravel100Glaciofluvial aquifer in Alluvial ValleyregionWalter CBeckjord7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock60Percentage based on surficial geology; placedin glaciated central based on Heath (1985);soils 2% SNLWalter CBeckjord7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit40Percentage based on surficial geology; placedin glaciated central based on Heath (1985);soils 2% SNLWansley8CMountain Flanks2Bedded Sedimentary Rock30Percentage based on surficial geologyWansley8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit70Percentage based on surficial geologyWarrick6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit100Predominant alluvial setting; soils havesignificant fines (SCL+SLT = 94%)Waukegan7BcOutwash Over SolutionLimestone12Solution Limestone100Based on soils, surficial geology, aquifercoverages(continued)Appendix CAttachment C-2: Hydrogeologic EnvironmentApril 2010–Draft EPA document.C-2-20Hydrogeologic Environment (continued)PlantHydrogeologic SettingHydrogeologic EnvironmentPercentageCommentCodeDescriptionCodeDescriptionWeston9EOutwash8Outwash100Setting based on productive aquifer, surficialgeology coveragesWidows Creek6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock20Percentage based on surficial geology; thinsoils inferred from colluviumWidows Creek6FaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit80Percentage based on surficial geology; soilshave significant fines (SCL+SLT > 25%)Will County7AaGlacial Till Over BeddedSedimentary Rock3Till Over Sedimentary Rock40Percentage based on surficial geology (65%Floodplain and alluvium gravel terraces)Will County7EaRiver Alluvium WithOverbank Deposits6River Valleys and Floodplainswith Overbank Deposit60Percentage based on surficial geology (65%Floodplain and alluvium gravel terraces)Wyodak6DaAlternating Sandstone,Limestone and Shale - ThinSoil2Bedded Sedimentary Rock100Based on aquifer and surficial geologycoverages, Heath (1985)Yates8DRegolith1Metamorphic and Igneous40Percentage assigned based on surficialgeology (59% alluvium/colluvium, 42%residuum)Yates8ERiver Alluvium6River Valleys and Floodplainswith Overbank Deposit60Percentage assigned based on surficialgeology (59% alluvium/colluvium, 42%residuum)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 Using HydrogeologicSettings.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. Geological Survey, Washington,DC.Appendix CAttachment C-3: Climate Center AssignmentsAttachment C-3: Climate Center AssignmentsPlantClimate CenterExplanation If Not Closest Climate CenterA B BrownIndianapolis, INClosest Met Station (Nashville) receives much more precipitation (12.26” out of range) than the sitelocation.Used second closest because only slightly below (1.3) expected precipitation range for plant.A/C Power- AceOperationsLas Vegas, NVAllenLittleRock,ARAlmaMadison, WIClosest Met Station (St. Cloud) receives lessrain than plant location. Used second closest Met Stationbecause 5-year averages fell within expected precipitation range for the plant.Antelope ValleyBismarck, NDArkwrightWatkinsville, GAClosest Met Station (Atlanta) receives 6.96” more precipitation than plant location. Used second closest MetStation because 5-year averages are only slightly above (0.2) expected precipitation range for the plant.AshevilleKnoxville,TNBaldwinEast St. Louis, ILBarryTallahassee, FLClosest Met Station (New Orleans) receivesmuch more precipitation (5.06”out of range) than the sitelocation. Used second closest because only slightly above (3.4) expected precipitation range for plant.Bay FrontMadison, WIBay ShorePut-in-Bay, OHBelews CreekGreensboro, NCBen FrenchRapid City, SDBig Cajun 2Lake Charles, LAClosest Met Station (New Orleans)receives much more precipitation (5.06” out of range) than the sitelocation. Used second closest because only slightly below (2.77) expected precipitation range for plant.Big SandyCincinnati, OHClosest Met Station(Lexington) receives much more precipitation (8.35” out of range) than plant location.Used second closest Met Station because 5-year averages fell within expected precipitation range for theplant.Big StoneSt. Cloud, MN(continued)April 2010–Draft EPA document.C-3-1Appendix CAttachment C-3: Climate Center AssignmentsClimate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterBlack Dog Steam PlantMadison, WIClosest Met Station (St Cloud) is dryer (<27.5”) than the 28-33” that the site receives. Madison fits inprecipitation range (32.5”) and is second closest.Blue ValleyTopeka, KSBowenAtlanta,GABrandon ShoresSeabrook, NJBuckGreensboro,NCBull RunKnoxville, TNC D McIntosh JrOrlando, FLClosest Met Station (Tampa) receives less precipitation (5.31” out of range) than site location. Used secondclosest Met Station because 5-year averages fell within expected precipitation range for the plant.C P CraneSeabrook, NJCape FearGreensboro, NCCarbonSalt Lake City, UTCardinalPittsburgh,PACayugaIndianapolis,INChalk PointSeabrook, NJChollaPhoenix, AZClosest Met Station (Flagstaff) receives muchmore precipitation (13.92” out ofrange) than plant location.Used second closest Met Station because 5-year averages were close (.31 higher) than the expectedprecipitation range for the plant.CliffsideGreensboro,NCCloverLynchburg,VACoal CreekBismarck, NDColeto CreekSan Antonio, TXColstripGlasgow,MTConemaughPittsburgh,PAConesvilleColumbus,OHCouncil BluffsNorth Omaha, NECrawfordEast St. Louis, ILCristTallahassee,FL(continued)April 2010–Draft EPA document.C-3-2Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-3Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterCrossCharleston,SCCumberlandNashville,TNDaleLexington,KYDallmanEast St. Louis, ILDan E KarnEast Lansing, MIDan RiverGreensboro, NCDanskammerBridgeport,CTDave JohnstonCheyenne, WYDickersonSeabrook,NJDolet HillsShreveport, LADuck CreekEast St. Louis, ILDunkirkIthaca,NYE D EdwardsChicago, ILE W BrownLexington, KYEckert StationEast Lansing, MIEdgewaterMadison,WIElmer W StoutIndianapolis, INF B CulleyIndianapolis, INClosest Met Station (Nashville) receives much more precipitation (12.26” out of range) than plant location.Used second closest Met Station because 5-year & 30-year averages fellwithin expected precipitation rangefor the plant.Fayette Power PrjSan Antonio, TXFlint CreekColumbia, MOUsed http://www.weather.com and Envirofacts to determine that avg. precipitation for site was ~47”. Theclosest Met Station (Tulsa) receives much less (~17”) precipitation per year. Used second closest station.Fort MartinPittsburgh, PAFrank E RattsIndianapolis, ING G AllenGreensboro, NCGadsdenAtlanta,GA(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-4Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterGallatinNashville,TNGen J M GavinCincinnati, OHClosest Met Station (Columbus) receives less rain than plant location. Used second closest Met Stationbecause 5-year averages fell within expected precipitation range forthe plant. Also average precipitation forthe second closest Met Station wasnearest to http://www.weather.comGenoaMadison,WIGibsonIndianapolis,INGorgasAtlanta,GAGreen RiverIndianapolis, INClosest Met Station (Nashville) receives much more precipitation (12.26” out of range) than plantlocation.Used third closest Met Station because 5-year averagesfell within expected precipitation range for the plant.Greene CountyAtlanta, GAH B RobinsonCharleston, SCHammondAtlanta,GAHarllee BranchWatkinsville, GAHarrisonPittsburgh,PAHatfield’s FerryPittsburgh, PAHennepinChicago,ILHeskettBismarck,NDHolcombDodge City, KSHomer CityPittsburgh, PAHoot LakeSt. Cloud, MNHugoShreveport, LAClosest Met Station (Dallas) receives less precipitation (6.45” out of range) than plantlocation. Used secondclosest because only slightly above (2.07)expected precipitation range for plant.HunterGrandJunction,COClosest Met Station (Salt Lake City)receives 8.6” more precipitation thanplant location. Used second closestMet Station because 5-year averages fell withinexpected precipitation range for the plant.HuntingtonCedar City, UTTwo closest Met Stations are out of range. Used second closest Met Station because 5-year averages fellwithin expected precipitation range for the plant.IntermountainEly, NVClosest Met Station (Salt Lake City) receives 6.1” more precipitation thanplant location. Used second closestMet Station because 5-year averages fell withinexpected precipitation range for the plant.J H CampbellEast Lansing, MI(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-5Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterJ M StuartCincinnati, OHJ R WhitingPut-in-Bay, OHJack McDonoughAtlanta, GAJack WatsonTallahassee, FLClosest Met Station (New Orleans) receives much more precipitation (5.06” out of range) than the sitelocation. http://www.weather.com predicted average precipitation at plant location to be 65.2. Used thirdclosest because its average was closest.James H Miller JrAtlanta, GAJim BridgerLander, WYJohn E AmosCincinnati, OHThe two closest Met Stations are outof the site’s precipitation range. Used third closest Met Station because5-year averages fell within expectedprecipitation range for the plant. Also average precipitation for thesecond closest Met Station was nearest to http://www.weather.com average.John SevierKnoxville, TNJohnsonvilleNashville,TNJoliet 29Chicago, ILKeystonePittsburgh,PAKillen StationCincinnati, OHKingstonKnoxville,TNKraftCharleston,SCL V SuttonCharleston, SCLansingMadison,WILaramie R StationCheyenne, WYLawrence ECTopeka, KSLeeGreensboro,NCLeland OldsBismarck, NDLon WrightNorth Omaha, NELouisaDes Moines, IAMarionEast St. Louis, ILMarshallGreensboro,NC(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-6Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterMartin LakeShreveport, LAMayoLynchburg,VAMeramecEast St. Louis, ILMeromIndianapolis,INMiami FortCincinnati, OHMilton R YoungBismarck, NDMitchell - PAPittsburgh, PAMitchell - WVPittsburgh, PAMohaveLasVegas,NVMonroePut-in-Bay,OHMorgantownNorfolk,VAMountaineer (1301)Cincinnati, OHClosest Met Station (Columbus) receives more rain than plant location. Although second closest site also fallswithin range, used third closest Met Station becausesite geography was similarand the station’s 5-yearaverages fell within expected precipitation range for the plant.Mt StormPittsburgh, PAMuscatine Plant #1Des Moines, IAMuskogeeTulsa,OKNeal NorthNorth Omaha, NENeal SouthNorth Omaha, NENebraska CityNorth Omaha, NENew CastlePittsburgh, PANewtonIndianapolis, INClosest Met Station (East St. Louis) receives less rain thanplant location. Used second closest Met Stationbecause 5-year averages fell within expected precipitation range forthe plant. Also average precipitation forthe second closest Met Station was nearest to http://www.weather.comNorth OmahaNorth Omaha, NENortheasternTulsa,OKNuclaGrandJunction,CO(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-7Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterOklaunionOklahomaCity,OKParadiseCincinnati, OHClosest Met Station (Nashville) receives much more precipitation (12.26” outof range) than plant location.Used third closest Met Station because 5-year averagesfell within expected precipitation range for the plant.PetersburgIndianapolis,INPleasant PrairieChicago, ILPort WashingtonMadison, WIPortlandPhiladelphia,PAPossum PointNorfolk, VAPotomac RiverSeabrook, NJPresque IsleSault Ste. Marie,MIR GallagherCincinnati, OHClosest Met Station (Lexington) receivesmuch more precipitation (8.35”out of range) than plant location.Used second closest Met Station because 5-year & 30-year averages fellwithin expected precipitation rangefor the plant.R M SchahferChicago, ILReid GardnerLas Vegas, NVRichard GorsuchColumbus, OHRiverbendGreensboro,NCRodemacherLakeCharles,LARoxboroGreensboro,NCSandowSanAntonio,TXSchererWatkinsville, GAClosest Met Station (Atlanta) receives 6.96” more precipitation than plant location. Used second closest MetStation because 5-year averages fell withinexpected precipitationrange for the plant.ShawneeEast St. Louis, ILShawvillePittsburgh,PASheldonNorthOmaha,NE(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-8Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterSouth Oak CreekChicago, ILSpringervilleAlbuquerque,NMClosest Met Station (Flagstaff) receives muchmore precipitation (8.92”out of range) than plantlocation.Used second closest Met Station because 5-year averages were within the expected precipitation range for theplant.St Johns River PowerJacksonville, FLStanton Energy CtrOrlando, FLStockton Cogen CompanySacramento, CASyl LaskinSt. Cloud, MNTecumseh ECTopeka, KSTexas-New MexicoSan Antonio, TXClosest Met Station (Dallas) received less precipitation than site location. Used second closest Met Stationbecause 5-year averages fell within expected precipitation range forthe plant. Also average precipitation forthe second closest Met Station was nearest to http://www.weather.comTitusPhiladelphia,PATrimble CountyCincinnati, OHTyroneLexington,KYValleyMadison,WIVermilionChicago, ILClosest Met Station (Indianapolis) receives more precipitation thanplant location. Used second closest MetStation because 5-year averages fell withinexpected precipitationrange for the plant.Victor J Daniel JrTallahassee, FLClosestMet Station (New Orleans) receives much more precipitation (5.06”out of range) thanthe sitelocation. Used second closest because only slightly above (3.4) expected precipitation range for plant.W A ParishShreveport, LA2 Closest Met Stations (Lake Charles & San Antonio) are more than 4” out of range. Used third closestbecause only slightly above (1.65”) expected precipitation range for plant.W H WeatherspoonGreensboro, NCW S LeeWatkinsville, GAWabash RiverIndianapolis, INWalter C BeckjordCincinnati, OHWansleyAtlanta,GAWarrickIndianapolis, INClosest Met Station (Nashville) receives 12.2” more precipitation than plant location. Used second closestMet Station because 5-year averages fell withinexpected precipitation range for the plant.(continued)Appendix CAttachment C-3: Climate Center AssignmentsApril 2010–Draft EPA document.C-3-9Climate Center Assignments (continued)PlantClimate CenterExplanation If Not Closest Climate CenterWaukeganChicago,ILWestonMadison,WIWidows CreekNashville, TNWill CountyEast St. Louis, ILWyodakRapid City, SDYatesAtlanta,GA

## April 2010–Draft EPA document. [This page intentionally left blank.]Appendix CAttachment C-4: Waterbody Assignments and FlowAttachment C-4: Waterbody Assignments and FlowPlantCUSEGNearest ReachReach TypeQLOWQMEANA B Brown05140202014OHIO RRegular Reach9167.38965150031.6875A/C Power- Ace Operations18090205005SEARLES LLake ShorelineAllen08010211007HORN LAKE CUTOFFLake ShorelineAlma07040003009MISSISSIPPI RRegular Reach5683.0200225397.4707Antelope Valley10130201005ANTELOPE CRStart Reach096.87Arkwright03070103007OCMULGEE RRegular Reach428.799992708.53003Asheville06010105026FRENCH BROAD RRegular Reach412.049991722.34998Baldwin07140204004KASKASKIARRegular Reach351.723832.12012Barry03160204014MOBILERRegular Reach7561.1401463275.23828Bay Front07070005036L SUPERIORGreat Lakes ShorelineBay Shore04100010003L ERIE, U.S. SHOREGreat Lakes Shoreline00Belews Creek03010103098BELEWS LLake ShorelineBen French10120110010CASTLE CRStart Reach2.9618.62Big Cajun 208070100005MISSISSIPPI RRegular Reach100937.8125466865.5625Big Sandy05070204008BIG SANDY RRegular Reach152.025746.95996Big Stone07020001033BIG STONE LAKELake ShorelineBlack Dog Steam Plant07020012001BLACK DOG LAKELake ShorelineBlue Valley10300101034LITTLE BLUE RRegular Reach23.2141.75Bowen03150104008ETOWAHRRegular Reach413.132294.86011Brandon Shores02060003037CURTIS BAYCoastal Shoreline00Buck03040103040YADKIN RRegular Reach912.729984722.54004Bull Run06010207015CLINCH RRegular Reach102.464732.3501C D McIntosh Jr03100205014NO LAKE PARKERLake Shoreline(continued)April 2010–Draft EPA document.C-4-1Appendix CAttachment C-4: Waterbody Assignments and FlowWaterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANC P Crane02060003025CURTIS BAYCoastal Shoreline00Cape Fear03030002001HAW RRegular Reach58.981584.83997Carbon14060007018PRICERRegularReach1.9277Cardinal05030106033OHIO RRegular Reach3391.6201237533.17188Cayuga05120108001WABASHRRegular Reach965.0900310100.21973Chalk Point02060006009PATUXENT RWide-River Shoreline00Cholla15020008017CHOLLA COOLING PONDLake ShorelineCliffside03050105031BROAD RRegular Reach332.170011510.08997Clover03010102027ROANOKE RRegular Reach408.640012702.59009Coal Creek10130101018UNKNOWN LAKELake ShorelineColeto Creek12100303014MARCELINAS CRStart Reach1.113.79Colstrip10100001108ARMELLS CR, E FKStart Reach018.64Conemaugh05010007002CONEMAUGH RRegular Reach194.539991553.52002Conesville05040004071MUSKINGUM RRegularReach447.980014707.08008Council Bluffs10230006004MISSOURI RRegular Reach4402.5898431444.83008Crawford07130011018ILLINOIS RRegular Reach3444.6699220788.71094Crist03140305001ESCAMBIA RTerminal Reach845.460026772.5498Cross03050201022DIVERS CANAL TO LAKE MOULake ShorelineCumberland05130205017CUMBERLAND RRegularReach536.4799825322.66016Dale05100205047KENTUCKYRRegular Reach35.325213.06982Dallman07130007003LAKESPRINGFIELDLakeShorelineDan E Karn04080103005L HURON U.S. SH SAGINAW BAYGreat Lakes Shoreline00Dan River03010103014DAN RRegular Reach358.121954.15002Danskammer02020008022HUDSON RWide-River Shoreline00Dave Johnston10180007005N PLATTE RRegular Reach65.24502.87Dickerson02070008013POTOMAC RRegular Reach895.5700110528.36035Dolet Hills11140206019BAYOU PIERRE LAKELake Shoreline(continued)April 2010–Draft EPA document.C-4-2Appendix CAttachment C-4: Waterbody Assignments and FlowApril 2010–Draft EPA document.C-4-3Waterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANDuck Creek07130003010L CHAUTAUQUALake ShorelineDunkirk04120101003L ERIE, U.S. SHOREGreat Lakes Shoreline00E D Edwards07130003018ILLINOIS RRegular Reach2998.3200713899.62988E W Brown05100205015HERRINGTON LAKELake ShorelineEckert Station04050004003GRAND RRegular Reach73.47484.28Edgewater04030101002L MICHIGANGreat Lakes Shoreline00Elmer W Stout05120201005WHITERRegularReach70.171429.92004F B Culley05140201001OHIO RRegular Reach8728.7002131543.0625Fayette Power Prj12090301003CEDAR CREEK RESERVOIRLake ShorelineFlint Creek11110103031SWEPCO RSRVR,LT FLINT CKLake ShorelineFort Martin05020003001MONONGAHELA RRegularReach293.664497.75Frank E Ratts05120202003WHITE RRegular Reach343.5911525.13965G G Allen03050101009CATAWBA RRegular Reach462.920012958.09009Gadsden03150106041COOSARRegular Reach1096.109999468Gallatin05130201006OLD HICKORY LLake ShorelineGen J M Gavin05030202005OHIO RRegular Reach4258.1201255143.35938Genoa07060001017MISSISSIPPI RRegular Reach6434.1801829379.25Gibson05120113013WABASHRRegular Reach2247.600126799.73047Gorgas03160109002BLACK WARRIOR R, MULBERRY FLake ShorelineGreen River05110003001GREENRRegularReach320.069752Greene County03160113011BLACK WARRIOR RRegular Reach304.730019820.04004H B Robinson03040201042L ROBERTSONLake ShorelineHammond03150105025COOSARRegular Reach1196.829966569.95996Harllee Branch03070101006L SINCLAIRLake ShorelineHarrison05020002008WEST FORK RRegular Reach33.031038.32996Hatfield's Ferry05020005026MONONGAHELA RRegularReach479.799998278.94043Hennepin07130001026ILLINOIS RRegular Reach3233.2399913146.83984Heskett10130101001MISSOURIRRegular Reach3461.5500522744.26953(continued)Appendix CAttachment C-4: Waterbody Assignments and FlowApril 2010–Draft EPA document.C-4-4Waterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANHolcomb11030001001ARKANSAS RRegular Reach0197.92999Homer City05010007015TWO LICKCRRegular Reach4.53295.22Hoot Lake09020103002OTTER TAILRRegular Reach12.45271.35999Hugo11140105041KIAMICHICR,N FKStart Reach2.5553.16Hunter14060009034ROCK CANYON CRStart Reach00.1Huntington14060009020HUNTINGTONCRRegular Reach10.7591.1Intermountainnone00J H Campbell04050002001L MICHIGANGreat Lakes Shoreline00J M Stuart05090201024OHIO RRegular Reach6767.4702192214.6875J R Whiting04100001002L ERIE, U.S. SHOREGreat Lakes Shoreline00Jack McDonough03130002044CHATTAHOOCHEE RRegularReach726.450012952.18994Jack Watson03170009034BILOXI BAYCoastal Shoreline00James H Miller Jr03160111005BLACK WARRIOR R, LOCUST FKLake ShorelineJim Bridger14040105011UNKNOWN LAKELake ShorelineJohn E Amos05050008007KANAWHA RRegular Reach1390.2299814930.83984John Sevier06010104011HOLSTONRRegular Reach6334079.15991Johnsonville06040005007KENTUCKY LLake ShorelineJoliet 2907120004004DES PLAINS RRegularReach1029.930053809.69995Keystone05010006002CROOKED CRRegular Reach30.72422.14999Killen Station05090201024OHIO RRegular Reach6767.4702192214.6875Kingston06010207001CLINCH RRegular Reach266.359997347.89014Kraft03060109007SAVANNAH RRegular Reach3570.5200212365L V Sutton03030005011CAPE FEAR RRegular Reach619.950018594.57031Lansing07060001009MISSISSIPPI RRegular Reach7684.0200232253.15039Laramie R Station10180011002LARAMIE RRegular Reach28.5390.8Lawrence EC10270104021KANSAS RRegular Reach403.816720.29004Lee03020201007NEUSERRegular Reach76.181657.39001Leland Olds10130101020MISSOURI RRegular Reach4270.450221650.67969(continued)Appendix CAttachment C-4: Waterbody Assignments and FlowApril 2010–Draft EPA document.C-4-5Waterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANLon Wright10220003048RAWHIDE CRStart Reach0.9411.59Louisa07080101003MISSISSIPPI RRegular Reach15067.9296954665.96094Marion05140204030L OF EGYPTLake ShorelineMarshall03050101015LNORMANLakeShorelineMartin Lake12010002050MARTIN LAKELake ShorelineMayo03010104045MAYO CRStart Reach5.9961.03Meramec07140101014MISSISSIPPI RRegular Reach33305177021.1875Merom05120111011TURTLE CR RESERVOIRLake ShorelineMiami Fort05090203012OHIO RRegular Reach6516.1899498615.0625Milton R Young10130101024NELSON LAKE AND MISSOURI RIVERLake ShorelineMitchell - PA05020005002MONONGAHELA RRegular Reach848.580029284.13965Mitchell - WV05030106013OHIO RRegular Reach3419.2099638713.19922Mohave15030101011COLORADO RRegular Reach1916.7299812134.36035Monroe04100001002L ERIE, U.S. SHOREGreat Lakes Shoreline00Morgantown02070011051POTOMAC RWide-River Shoreline00Mountaineer (1301)05030202008OHIO RRegularReach4242.5898454823.21094Mt Storm02070002027STONY R RESLake ShorelineMuscatine Plant #107080101005MISSISSIPPI RRegular Reach14573.7197354469.48047Muskogee11110102012ARKANSAS RRegularReach227.5700121258.39062Neal North10230001021MISSOURI RRegular Reach4217.799829486.82031Neal South10230001021MISSOURI RRegular Reach4217.799829486.82031Nebraska City10240001002MISSOURIRRegularReach5807.7700236764.01172New Castle05030104002BEAVER RRegular Reach268.480012425.32007Newton05120114006NEWTON LAKELake ShorelineNorth Omaha10230006009MISSOURI RRegular Reach4365.649931400.93945Northeastern11070105012VERDIGRIS RRegular Reach3.852168.47998Nucla14030003012SANMIGUEL RRegular Reach8.1307.64001Oklaunion11130302061BOGGY CRStart Reach0.0914.93(continued)Appendix CAttachment C-4: Waterbody Assignments and FlowApril 2010–Draft EPA document.C-4-6Waterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANParadise05110003003GREENRRegular Reach316.599663.71973Petersburg05120202003WHITE RRegular Reach343.5911525.13965Pleasant Prairie07120004012L MICHIGAN AND JLake ShorelinePort Washington04030101002L MICHIGANGreat Lakes Shoreline00Portland02040105012DELAWARE RRegular Reach1995.129089.00977Possum Point02070011074POTOMAC RWide-River Shoreline00Potomac River02070010025POTOMAC RArtificial Open Water Reach919.8900111721.87988Presque Isle04020105002L SUPERIOR, U.S. SHOREGreat Lakes Shoreline00R Gallagher05140101001OHIO RRegular Reach7634.39014119152.1875R M Schahfer07120001012KANAKEE RRegular Reach458.920011410.56006Reid Gardner15010012006MUDDY RRegular Reach0.6819.22Richard Gorsuch05030202039OHIO RRegular Reach4079.8100648956.14062Riverbend03050101012CATAWBARRegular Reach412.282623.09009Rodemacher11140207020RODEMACHERLAKELakeShorelineRoxboro03010104034HYCOLLakeShorelineSandow12070102012ALCOALAKELakeShorelineScherer03070103012OCMULGEERStart Reach655.489992490.72998Shawnee05140206009OHIORRegularReach21748.59961288452.1875Shawville02050201002SUSQUEHANNA R, W BRRegular Reach96.91947.33997Sheldon10240008030UNKNOWNLAKELakeShorelineSouth Oak Creek04040002004L MICHIGANGreat Lakes Shoreline00Springerville15020002025*AStart Reach02.49St Johns River Power03080103003ST JOHNS RWide-River Shoreline00Stanton Energy Ctr03080101036ECOHLOCKHATCHEE RStart Reach5.95131.42999Stockton Cogen Company18040002005LITTLEJOHNS CRStart Reach0.2150.61Syl Laskin04010201034COLBY L AND PARTRIDGE RLake ShorelineTecumseh EC10270102003KANSAS RRegular Reach388.519995923.74023Texas-New Mexico12070101008LITTLEBRAZOS RStart Reach0.55139.05(continued)Appendix CAttachment C-4: Waterbody Assignments and FlowApril 2010–Draft EPA document.C-4-7Waterbody Assignments and Flow (continued)PlantCUSEGNearest ReachReach TypeQLOWQMEANTitus02040203010SCHUYLKILLRRegular Reach91.251880.77002Trimble County05140101007OHIO RRegular Reach7524.29004117896.3125Tyrone05100205013KENTUCKYRRegular Reach154.367097.54004Valley04040003001MILWAUKEE RTerminal Reach10.71540.60999Vermilion05120109006VERMILIONR,M FKRegular Reach3.45340.35999Victor J Daniel Jr03170006007PASCAGOULA RRegular Reach1256.5500512878.25W A Parish12070104021SMITHERS LLake ShorelineW H Weatherspoon03040203016LUMBER RRegular Reach97.9865.13W S Lee03050109066SALADA RRegular Reach20.68461.51001Wabash River05120111018WABASH RRegular Reach985.5399810551.67969Walter C Beckjord05090201001OHIORRegularReach6416.7700292084.0625Wansley03130002032CHATTAHOOCHEERRegular Reach702.710024400.72021Warrick05140201022LITTLE PIGEON CRRegular Reach61.571149.60999Waukegan04040002002L MICHIGANGreat Lakes Shoreline00Weston07070002023WISCONSINRRegular Reach1069.300053484.32007Widows Creek06030001049TENNESSEE RRegularReach7221.9599638237.07031Will County07110009002WOOD RStart Reach2987.81Wyodak10120201038DONKEY CRStart Reach04.4Yates03130002061CHATTAHOOCHEERRegular Reach702.219974063.29004

## 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 MI NTEQA2 Modeling

Chemicals in leachate can be subject to complex geochemical interactions in soil and groundwater, which can strongly a ffect their rate of transpor t 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 instantane ously, or at least very quickly relative to the time scale of constituent transport. Although so rption—or the attachment of leach ate 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 (K d 

## ). The retardation factor (R) accounts for the effects of equilibrium sorp tion of dissolved constituents onto the solid phase, removing them from solution and reducing th e available mass in the dissolved phase. R, a function of the constituent-specific K d 

## and the soil or aquifer prope rties, is calculated as:



## Φ ρ 1 d b 

## K R × += (D-1)

where

R = Retardation factor

D b 

## = Soil or aquifer bulk density (g/cm 3 

## )

K d 

## = Solid-water partiti on coefficient (g/cm 3 

## ) 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 genera te 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 con centration of dissolved and particulate natural organic matter), and the MIN TEQA2 modeling was repeated (s eparately for each metal) for

numerous combinations of mast er variable settings. This pr ocedure resulted in nonlinear K d 

## versus aqueous metal concentration curves for comb inations of master vari able 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, hereaf ter 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 a ppropriate adsorption isotherm.

In modeling metals transport in the unsa turated zone, EPACMTP uses a range of K d 

## values from the nonlinear sorption isotherms. Ho wever, in modeling metals transport in the saturated zone, EPACMTP selects th e lowest from all available K d 

## 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 opt ions and is based on the assumption that, after dilution of the leachate plume in groundwater, the c oncentrations 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 envi ronment 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 assessme nt for fossil fuel combustion wastes (FFCWs) conducted in 1998 (U.S. EPA, 1998), sorption isotherms gene rated using MINTEQA2 were used in EPACMTP to account for metal partitioning. However, these isotherms were not calculated specifically for use in FFCW modeling—they ha d 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 i ndustrial Resource Conser vation and Recovery Act (RCRA) Subtitle D nonhazardous waste landfi ll. In fact, the MINTEQA2 modeling that produced the isotherms had originally been design ed 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 modeli ng. For the HWIR analysis, the scenario was changed to indus trial Subtitle D, and only th e isotherms corresponding to low concentrations of the leachate organic acids we re 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 concer ning the use of the industrial Subtitle D metal partitioning isotherms in the 1998 risk a ssessment. The most comprehensive review was prepared by Charles Norris and Christina Hubbard on behalf of the Environmental Defense Fund and other environmental advocacy groups (No rris 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 invol ved in the MINTEQA2 modeling. EPA responded by evaluating each of th ese 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 strate gy, 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 co mposition on metals mobility, EPA (U.S. EPA, 2001b)

also determined that if MINTE QA2 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 elevat ed groundwater pH

 

## Compete with the contaminant metal for sorpti on sites and thus result in reduced metal sorption (e.g., Ca, Mg, SO 4 

## , other metals)

 

## Complex with the contaminant metal so that th e metal is less likely to be sorbed (e.g.,

SO 4 

## , CO 3 

## , organic ligands)

 

## Precipitate with the contaminant metal (e.g., SO 4 

## , CO 3 

## ).



# D.3 MINTEQA2 Modeling Revisi ons for CCW Risk Assessment

Many of the suggested revisi ons from U.S. EPA (2001b) were implemented in the MINTEQA2 modeling for the curre nt CCW risk assessment. Some of the suggested revisions were not implemented, either because they were not applicable (e.g., or ganic carbon assumptions were not changed, because CCW leachate has neg ligible organic carbon) or because models or data were not adequate to carry forth the r ecommendation. These revisions are discussed in greater detail in U.S. EPA (2003c).

In addition to revising the MINTEQA2 mode l, EPA compiled leachate characteristics into the CCW constituent database (see Appendix A 

## ) and statistically analyzed these data to identify three chemically distinct CCW leachat e types: conventional CCW (including ash and flue gas desulfurization [FGD] sludge), codisposed CCW and coal cleaning wastes, and fluidized bed combustion (FBC) waste. Leachate concen tration ranges for major ions (e.g., Ca, SO 4 

## , Mg, Na, Cl, etc.) and pH were developed for each of these waste types and were used to represent CCW leachate during MI NTEQA2 modeling.

As needed, sorption reactions were included for those CCW constituents known to undergo significant sorption. Including elevated con centrations of leachate constituents and their corresponding sorption reactions in the MINTEQA2 model allowed for full competition with the contaminant metal for sorption sites. The me tal solubilizing effect through complexation between the contaminant metal and dissolved liga nds 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 EP ACMTP. The precipitated fraction was used to develop a solubility limit th at 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 K d 

## for a particular metal was represented during the MINTEQA2 modeling effort by varying the input parameters that most impact K d 

## : groundwater type (carbonate or noncarbonate), pH, concen tration of aquifer sorbents, composition and concentration level of CCW leachate, and concen tration of the contaminant metal. The natural pH range for the two groundwater types was sampled from a ra nge of 7 to 8 for carbonate aquifers and 4 to 10 for noncarbonate aquifers (U.S. EPA, 2003c).

In addition, CCW leachate ranges from acidi c (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 se ries of pH values that spanned the range of expected variability in mixed CCW leachat e-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 c oncentrations of two commonly occurring natural sorbents: fe rric (iron) oxyhydroxide (FeOx) a nd particulate organic matter (POM). CCW leachate can include elevated concen trations 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 CC W waste types (conventional CC Ws, codisposed CCW and coal cleaning wastes, and FBC wastes). This new MI NTEQA2 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, sorb ed, 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 K d 

## 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 regardi ng the impacts of leachate pH on ambient soil and aquifer pH, and to address issues regarding solu bility limits for metals in solution. A brief description of these model change s 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 represen tative isotherm from the databa se for both the unsaturated and

saturated zones.

Leachate Effects on Ge ochemical Environment. 

## These effects were addressed in EPACMTP under the following constr aints: (1) no significant impairment of the computational efficiency for probabilistic appl ications; (2) data requirements lim ited 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 subsurf ace. Two modifications to the EPACMTP were considered: (1) determine the govern ing pH in the soil column (eit her the pH of the leachate or the native soils); and (2) determin e the pH of the saturated zone as a result of the infiltrating

leachate.

The approach selected for determining the gove rning 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 hi gh likelihood that the leachate permeates the soil column and that the pH environment is governed by the leachate. Convers ely, a relatively short operational life and retarded contaminant migrat ion would favor ambient soil pH conditions. An analysis of the relationship betw een 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 l eachate pH governs versus cases where soil pH governs over approximately 10,000 Monte Carlo iterations.

For each iteration of EPACMTP, the operati onal life was compared to a travel-time

estimate based on a K d 

## 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 assume d to govern, and the pH of the

leachate was used to select the is otherm for transport in the unsatu rated zone. If the ratio was less than 5, the soil pH was used to select the isotherm.

In the saturated zone, the impacts of l eachate 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 isot herm for transport in the saturated zone with one limitation: in carbonate environments, the mixed pH in the aqui fer 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 so rption isotherm comprises equilibrium concentrations of the three contamin ant phases (dissolved, sorbed, and precipitated) over a range of total concentration values. An ex amination of the change in the dissolved-phase concentrations relative to changes in the total co ncentration 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 ach ieved. If, however, the dissolved component increases along with the total con centration, then there is capacity for more dissolved mass in the groundwater or so il 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 th e dissolved concentration changes more than one tenth of one percent over the la st 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 K d 

# s from MINTEQA2 Isotherms by EPACMTP

As described above, a range of K d 

## s from an isotherm were used by EPACMTP in each unsaturated zone transport simulation. To simplify the presentation of K d 

## here, an effective K d 

## value was calculated and reporte d by EPACMTP. An effective K d 

## was determined by first estimating the value of the retardation fact or, described in Equa tion D-1, as follows:

soil 

## d t q R WT 

## ×

=

where

t wt 

## = time for leachate to reach the water table (yr)

q = seepage velocity of leachate through the unsaturated soil column (m/yr)

d dsoil 

## = 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 K d 

## yields an effective K d 

## that is based on the first arrival of th e leachate front at the water table.

Table D-1 

## presents selected percentiles of K d 

## 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 saturate d zone are taken from the set of actual K d 

## values used in each modeling scenario.



## April 2010–Draft EPA document. D-6Appendix DMINTEQA2 Nonlinear Sorption IsothermsTable D-1. Select Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface DomainWaste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%AshAluminum7429905LFSaturated8.5E-203.0E-165.1E-105.0E-037.1E-029.2E-011.6E+002.8E+00AshAluminum7429905SISaturated8.6E-202.5E-102.6E-049.4E-012.2E+002.7E+002.8E+006.2E+00AshAluminum7429905LFUnsaturated6.9E-031.0E-012.0E-011.8E+015.9E+011.0E+021.8E+023.7E+02AshAluminum7429905SIUnsaturated2.0E-113.7E-021.6E-011.1E+001.6E+002.8E+003.0E+006.6E+00AshAntimony7440360LFSaturated4.3E-032.5E-027.5E-022.0E-012.1E-013.0E-013.4E-017.1E-01AshAntimony7440360SISaturated2.8E-031.9E-029.6E-022.0E-012.1E-013.3E-013.5E-015.9E-01AshAntimony7440360LFUnsaturated9.1E-022.9E-019.6E-017.6E+001.0E+011.4E+012.2E+014.9E+01AshAntimony7440360SIUnsaturated0.0E+001.1E-013.4E-016.8E-017.9E-019.5E-011.3E+001.6E+00AshArsenic322569728LFSaturated5.2E-021.5E-014.1E-016.6E-018.0E-019.2E-011.0E+001.1E+00AshArsenic322569728SISaturated4.1E-021.4E-014.1E-016.8E-018.1E-019.2E-011.0E+001.1E+00AshArsenic322569728LFUnsaturated1.5E-015.0E-011.2E+002.2E+002.7E+003.7E+005.8E+001.2E+01AshArsenic322569728SIUnsaturated5.7E-053.1E-017.4E-011.3E+001.5E+001.6E+001.8E+002.1E+00AshArsenic515584040LFSaturated1.1E+007.0E+003.4E+019.8E+011.2E+021.6E+022.1E+026.3E+02AshArsenic515584040SISaturated1.2E+006.7E+002.9E+018.9E+011.1E+021.5E+022.9E+025.9E+02AshArsenic515584040LFUnsaturated0.0E+004.2E+003.6E+011.0E+021.3E+021.6E+022.1E+024.2E+02AshArsenic515584040SIUnsaturated2.9E-013.2E+002.1E+017.6E+019.5E+011.3E+022.0E+024.7E+02AshBarium7440393LFSaturated2.4E-014.2E-015.6E-019.2E-011.1E+001.2E+001.6E+002.3E+00AshBarium7440393SISaturated2.5E-014.4E-015.7E-019.3E-011.1E+001.2E+001.6E+002.4E+00AshBarium7440393LFUnsaturated0.0E+007.1E+002.0E+026.6E+027.9E+021.0E+031.4E+032.2E+03AshBarium7440393SIUnsaturated0.0E+008.7E-011.8E+005.9E+008.0E+001.2E+011.9E+015.3E+01AshBoron7440428LFSaturated3.8E-114.3E-101.7E-072.8E-063.6E-065.4E-067.1E-061.0E-05AshBoron7440428SISaturated2.6E-103.2E-081.7E-066.5E-067.7E-068.9E-061.1E-051.3E-05AshBoron7440428LFUnsaturated2.6E-031.3E-014.4E-011.8E+002.2E+002.8E+003.9E+006.2E+00AshBoron7440428SIUnsaturated0.0E+002.5E-021.1E-012.2E-012.5E-013.4E-016.6E-011.6E+00AshCadmium7440439LFSaturated1.6E-014.0E-017.7E-011.8E+002.1E+003.4E+005.1E+007.0E+00AshCadmium7440439SISaturated1.6E-014.0E-017.1E-011.7E+002.0E+003.4E+005.1E+007.3E+00AshCadmium7440439LFUnsaturated2.2E-019.8E-012.0E+004.3E+005.2E+007.1E+009.4E+001.3E+01AshCadmium7440439SIUnsaturated-4.1E-023.1E-017.8E-012.0E+002.7E+004.0E+006.3E+001.0E+01(continued)April 2010–Draft EPA document.D-7Appendix DMINTEQA2 Nonlinear Sorption IsothermsSelect Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued)Waste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%AshCobalt7440484LFSaturated5.2E-011.0E+004.0E+001.6E+011.8E+013.5E+016.1E+011.1E+02AshCobalt7440484SISaturated6.5E-019.6E-012.7E+001.1E+011.6E+012.9E+016.7E+011.2E+02AshCobalt7440484LFUnsaturated2.4E-012.6E+008.7E+002.8E+013.5E+014.6E+017.1E+011.1E+02AshCobalt7440484SIUnsaturated-2.9E-021.3E+003.3E+001.0E+011.3E+011.9E+013.9E+018.7E+01AshLead7439921LFSaturated9.0E+001.6E+012.5E+013.9E+014.3E+015.0E+019.7E+011.7E+02AshLead7439921SISaturated8.3E+001.6E+012.2E+013.5E+014.0E+014.5E+016.3E+011.8E+02AshLead7439921LFUnsaturated0.0E+002.0E+013.3E+015.2E+015.9E+017.0E+019.6E+011.6E+02AshLead7439921SIUnsaturated-1.1E-011.3E+001.9E+013.3E+013.6E+014.1E+014.9E+011.3E+02AshMercury7439976LFSaturated2.0E-051.0E-044.4E-042.1E-032.4E-034.6E-035.6E-039.5E-03AshMercury7439976LFUnsaturated8.9E-022.0E+005.4E+001.1E+011.3E+011.7E+012.3E+013.7E+01AshMolybdenum7439987LFSaturated7.8E-073.0E-051.8E-036.0E-021.2E-012.3E-013.7E-015.9E-01AshMolybdenum7439987SISaturated3.2E-073.0E-054.3E-036.0E-021.1E-012.1E-012.4E-014.0E-01AshMolybdenum7439987LFUnsaturated2.3E-021.6E-013.4E-019.1E-011.1E+001.5E+002.0E+003.1E+00AshMolybdenum7439987SIUnsaturated6.0E-115.0E-021.6E-014.0E-015.3E-017.0E-011.3E+001.6E+00AshNitrate/Nitrite14797558LFSaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00AshNitrate/Nitrite14797558SISaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00AshNitrate/Nitrite14797558LFUnsaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00AshNitrate/Nitrite14797558SIUnsaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00AshSelenium410026036LFSaturated3.3E-017.3E+001.5E+021.2E+032.0E+032.9E+033.6E+035.8E+03AshSelenium410026036SISaturated4.5E-022.9E+002.4E+021.3E+032.0E+033.0E+033.6E+035.8E+03AshSelenium410026036LFUnsaturated1.8E-015.4E+001.3E+028.6E+021.5E+032.3E+033.2E+035.2E+03AshSelenium410026036SIUnsaturated0.0E+002.0E+001.6E+029.4E+021.7E+032.5E+033.4E+035.6E+03AshSelenium67782492LFSaturated5.1E-136.3E-113.0E-072.7E-046.7E-041.8E-032.7E-034.9E-03AshSelenium67782492SISaturated7.4E-096.8E-078.4E-059.5E-041.4E-032.2E-033.2E-034.5E-03AshSelenium67782492LFUnsaturated3.3E-031.1E-012.0E-019.1E-011.3E+001.7E+002.5E+004.0E+00AshSelenium67782492SIUnsaturated0.0E+002.2E-021.1E-012.1E-012.3E-012.8E-015.0E-011.6E+00AshThallium7440280LFSaturated1.1E-021.7E-022.7E-024.3E-025.2E-026.3E-028.1E-021.1E-01AshThallium7440280SISaturated1.2E-021.7E-022.5E-024.4E-025.2E-026.6E-028.5E-021.6E-01(continued)April 2010–Draft EPA document.D-8Appendix DMINTEQA2 Nonlinear Sorption IsothermsApril 2010–Draft EPA document.D-9Select Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued)Waste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%AshThallium7440280LFUnsaturated5.5E-022.0E-014.0E-011.5E+001.8E+002.4E+003.2E+004.8E+00AshThallium7440280SIUnsaturated0.0E+008.1E-021.7E-012.9E-013.5E-014.9E-019.3E-011.6E+00Ash & CoalAluminum7429905LFSaturated1.8E-071.8E-074.0E-071.3E-011.3E-011.3E-013.5E-012.8E+00Ash & CoalAluminum7429905SISaturated1.8E-074.0E-077.3E-031.9E-012.0E-018.8E-015.8E+001.4E+01Ash & CoalAluminum7429905LFUnsaturated0.0E+001.2E-011.1E+014.7E+015.9E+017.6E+019.8E+011.5E+02Ash & CoalAluminum7429905SIUnsaturated1.4E-021.1E-012.6E-019.4E-011.5E+001.7E+002.9E+006.3E+00Ash & CoalAntimony7440360LFSaturated1.8E-035.4E-031.7E-027.3E-028.0E-021.5E-013.2E-015.5E-01Ash & CoalAntimony7440360LFUnsaturated0.0E+001.8E-011.4E+006.4E+008.2E+001.1E+011.4E+012.2E+01Ash & CoalArsenic322569728LFSaturated5.5E-031.7E-026.4E-021.9E-012.7E-014.6E-017.4E-019.9E-01Ash & CoalArsenic322569728SISaturated4.6E-031.6E-025.1E-021.4E-012.6E-014.5E-017.1E-011.1E+00Ash & CoalArsenic322569728LFUnsaturated0.0E+001.6E-013.8E-011.9E+002.6E+003.6E+005.4E+009.8E+00Ash & CoalArsenic322569728SIUnsaturated3.8E-021.1E-012.3E-016.3E-018.8E-011.3E+001.6E+002.1E+00Ash & CoalArsenic515584040LFSaturated3.3E-027.2E-016.3E+003.3E+014.6E+015.4E+019.5E+013.2E+02Ash & CoalArsenic515584040SISaturated3.3E-023.5E-012.3E+001.4E+012.1E+012.9E+014.8E+011.5E+02Ash & CoalArsenic515584040LFUnsaturated0.0E+004.8E-016.4E+003.4E+014.6E+016.3E+019.8E+012.2E+02Ash & CoalArsenic515584040SIUnsaturated1.9E-019.6E-013.5E+001.6E+012.2E+013.2E+015.1E+011.3E+02Ash & CoalBarium7440393LFSaturated3.7E-024.5E-022.3E-011.3E+001.3E+001.5E+001.9E+002.2E+00Ash & CoalBarium7440393SISaturated1.3E-024.5E-021.8E-011.3E+001.3E+001.5E+002.1E+002.2E+00Ash & CoalBarium7440393LFUnsaturated0.0E+004.6E-011.5E+015.0E+016.2E+017.4E+011.0E+021.6E+02Ash & CoalBarium7440393SIUnsaturated5.2E-025.0E-012.4E+009.6E+001.4E+012.1E+013.7E+015.4E+01Ash & CoalBoron7440428LFSaturated7.1E-082.1E-078.1E-072.4E-063.5E-065.9E-069.5E-061.3E-05Ash & CoalBoron7440428SISaturated6.0E-082.1E-076.3E-071.8E-063.6E-066.4E-061.0E-051.5E-05Ash & CoalBoron7440428LFUnsaturated0.0E+005.2E-021.5E-012.2E-012.5E-013.2E-014.4E-016.9E-01Ash & CoalBoron7440428SIUnsaturated7.9E-073.1E-021.1E-012.0E-012.3E-012.8E-015.6E-011.6E+00Ash & CoalCadmium7440439LFSaturated1.7E-034.6E-021.5E-017.7E-011.0E+002.1E+002.9E+004.7E+00Ash & CoalCadmium7440439SISaturated1.7E-034.6E-028.5E-026.1E-011.0E+002.1E+003.2E+004.5E+00Ash & CoalCadmium7440439LFUnsaturated0.0E+002.6E-017.9E-012.2E+002.7E+003.9E+005.9E+009.0E+00Ash & CoalCadmium7440439SIUnsaturated6.4E-021.7E-014.0E-011.6E+002.2E+003.3E+004.4E+007.1E+00(continued)Appendix DMINTEQA2 Nonlinear Sorption IsothermsApril 2010–Draft EPA document.D-10Select Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued)Waste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%Ash & CoalCobalt7440484LFSaturated7.6E-036.6E-025.8E-012.8E+003.2E+005.4E+008.5E+002.9E+01Ash & CoalCobalt7440484SISaturated7.6E-036.5E-022.1E-012.4E+002.9E+004.1E+006.1E+001.1E+01Ash & CoalCobalt7440484LFUnsaturated0.0E+007.9E+003.1E+019.3E+011.1E+021.5E+022.9E+025.7E+02Ash & CoalCobalt7440484SIUnsaturated2.3E-014.5E-011.7E+004.9E+005.9E+007.3E+001.1E+012.1E+01Ash & CoalLead7439921LFSaturated1.1E-012.3E+006.6E+002.1E+012.9E+013.8E+014.1E+016.3E+01Ash & CoalLead7439921SISaturated1.1E-012.3E+003.7E+002.2E+012.9E+013.9E+014.4E+016.3E+01Ash & CoalLead7439921LFUnsaturated0.0E+004.4E+001.4E+014.1E+014.9E+015.6E+017.1E+011.2E+02Ash & CoalLead7439921SIUnsaturated-6.3E-039.5E-014.5E+002.0E+013.0E+013.9E+014.6E+016.4E+01Ash & CoalMercury7439976LFSaturated9.0E-042.1E-031.3E-027.9E-023.3E-016.8E-012.9E+004.3E+00Ash & CoalMercury7439976SISaturated1.7E-033.3E-036.2E-028.8E-011.5E+002.8E+002.9E+004.3E+00Ash & CoalMercury7439976LFUnsaturated0.0E+001.6E+006.3E+001.5E+011.8E+012.2E+013.0E+014.4E+01Ash & CoalMercury7439976SIUnsaturated4.8E-021.5E-013.6E-011.6E+001.6E+002.1E+003.0E+004.4E+00Ash & CoalMolybdenum7439987LFSaturated3.4E-066.7E-052.5E-034.6E-027.5E-021.6E-012.7E-017.1E-01Ash & CoalMolybdenum7439987SISaturated3.4E-063.1E-041.1E-027.5E-027.5E-021.6E-013.1E-019.4E-01Ash & CoalMolybdenum7439987LFUnsaturated0.0E+007.3E-022.1E-017.7E-011.0E+001.4E+002.0E+002.9E+00Ash & CoalMolybdenum7439987SIUnsaturated4.7E-038.3E-022.0E-015.0E-017.6E-011.3E+001.6E+002.2E+00Ash & CoalNitrate/Nitrite14797558LFSaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00Ash & CoalNitrate/Nitrite14797558SISaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00Ash & CoalNitrate/Nitrite14797558LFUnsaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00Ash & CoalNitrate/Nitrite14797558SIUnsaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00Ash & CoalSelenium410026036LFSaturated3.7E-016.1E+005.3E+015.3E+028.5E+021.1E+031.9E+036.5E+03Ash & CoalSelenium410026036SISaturated3.7E-011.6E+011.2E+027.7E+029.1E+021.4E+033.3E+037.3E+03Ash & CoalSelenium410026036LFUnsaturated0.0E+002.9E+004.4E+013.2E+025.5E+028.7E+021.5E+035.1E+03Ash & CoalSelenium410026036SIUnsaturated4.7E-011.0E+011.0E+026.1E+028.6E+021.1E+032.9E+037.0E+03Ash & CoalSelenium67782492LFSaturated2.9E-087.7E-081.8E-056.4E-041.1E-031.4E-033.2E-038.8E-03Ash & CoalSelenium67782492SISaturated4.7E-083.2E-061.1E-049.6E-041.1E-031.6E-035.1E-031.0E-02Ash & CoalSelenium67782492LFUnsaturated0.0E+005.9E-021.6E-017.0E-011.0E+001.5E+002.3E+003.7E+00Ash & CoalSelenium67782492SIUnsaturated1.7E-035.2E-021.3E-012.6E-013.4E-015.6E-011.2E+001.6E+00(continued)Appendix DMINTEQA2 Nonlinear Sorption IsothermsApril 2010–Draft EPA document.D-11Select Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued)Waste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%Ash & CoalThallium7440280LFSaturated2.9E-041.9E-039.8E-032.0E-022.4E-023.7E-025.0E-027.7E-02Ash & CoalThallium7440280LFUnsaturated0.0E+001.2E-013.9E-011.3E+001.7E+002.1E+002.7E+003.9E+00FBCAluminum7429905LFSaturated4.1E-254.6E-251.4E-175.2E-081.7E-071.7E-075.3E-077.1E-03FBCAluminum7429905LFUnsaturated0.0E+001.5E-012.5E-014.6E+028.6E+021.9E+033.9E+038.7E+03FBCAntimony7440360LFSaturated3.0E-041.2E-022.0E-026.9E-028.7E-021.1E-011.4E-012.3E-01FBCAntimony7440360LFUnsaturated0.0E+002.1E-014.1E-012.2E+003.0E+004.3E+007.2E+001.8E+01FBCArsenic322569728LFSaturated4.5E-021.0E-013.9E-015.0E-016.1E-016.4E-016.6E-016.7E-01FBCArsenic322569728LFUnsaturated0.0E+004.6E-011.1E+002.1E+007.0E+001.6E+013.4E+011.5E+02FBCArsenic515584040LFSaturated3.3E+001.0E+013.4E+017.3E+019.3E+019.4E+011.6E+022.4E+02FBCArsenic515584040LFUnsaturated0.0E+001.1E+013.8E+018.2E+019.9E+011.1E+021.7E+022.5E+02FBCBarium7440393LFSaturated1.6E-012.1E-014.6E-011.4E+005.7E+007.8E+009.3E+001.1E+01FBCBarium7440393LFUnsaturated0.0E+007.7E+007.1E+012.7E+023.7E+024.7E+026.3E+021.0E+03FBCBoron7440428LFSaturated5.5E-071.3E-064.9E-066.3E-067.8E-068.1E-068.2E-068.7E-06FBCBoron7440428LFUnsaturated0.0E+001.6E-012.6E-012.0E+002.5E+003.2E+004.6E+008.2E+00FBCCadmium7440439LFSaturated2.0E-012.5E-015.9E-012.7E+003.3E+003.4E+004.0E+005.3E+00FBCCadmium7440439LFUnsaturated0.0E+009.5E-012.4E+006.3E+007.3E+008.5E+001.0E+012.1E+01FBCCobalt7440484LFSaturated4.7E-011.1E+005.8E+004.4E+014.6E+017.0E+017.3E+019.5E+01FBCCobalt7440484LFUnsaturated0.0E+002.2E+008.9E+004.1E+015.5E+017.3E+019.0E+011.1E+02FBCLead7439921LFSaturated6.8E+009.9E+002.1E+016.9E+011.1E+021.2E+021.7E+021.8E+02FBCLead7439921LFUnsaturated0.0E+001.3E+012.5E+018.3E+011.0E+021.3E+021.5E+022.0E+02FBCMercury7439976LFSaturated1.6E-054.2E-052.2E-041.6E-033.1E-034.4E-035.8E-037.0E-03FBCMercury7439976LFUnsaturated0.0E+001.1E+006.7E+001.9E+012.8E+014.5E+017.8E+012.1E+02FBCMolybdenum7439987LFSaturated7.5E-078.0E-061.3E-043.1E-037.8E-031.3E-022.7E-024.5E-02FBCMolybdenum7439987LFUnsaturated0.0E+001.5E-012.3E-016.7E-019.6E-011.3E+001.9E+003.7E+00FBCNitrate/Nitrite14797558LFSaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00FBCNitrate/Nitrite14797558LFUnsaturated0.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+000.0E+00FBCSelenium410026036LFSaturated6.4E-021.2E+001.8E+011.2E+022.8E+024.8E+028.8E+021.4E+03FBCSelenium410026036LFUnsaturated0.0E+002.1E+001.9E+011.5E+022.9E+024.8E+027.9E+021.3E+03(continued)Appendix DMINTEQA2 Nonlinear Sorption IsothermsApril 2010–Draft EPA document.D-12Select Percentiles of KdSampled from MINTEQA2 Isotherms by Updated EPACMTPby Waste Type, Metal Species, WMU Type, and Subsurface Domain (continued)Waste StreamMetalCASIDWMUZonePercentiles of Kd10%25%50%75%80%85%90%95%FBCSelenium67782492LFSaturated1.7E-081.8E-072.9E-065.9E-051.4E-042.3E-043.8E-046.9E-04FBCSelenium67782492LFUnsaturated0.0E+001.4E-012.3E-011.6E+002.3E+003.5E+007.6E+002.1E+01FBCThallium7440280LFSaturated9.5E-031.5E-022.3E-025.1E-025.3E-026.2E-021.1E-012.3E-01FBCThallium7440280LFUnsaturated0.0E+002.3E-016.2E-011.9E+002.2E+002.7E+003.3E+005.0E+00

### Appendix D MINTEQA2 Nonlinear Sorption Isotherms



# D.7 References

Norris, C.H., and C.E. Hubbard. 1999. Use of MINTEQA2 and EPACMTP to Estimate Groundwater Pathway Risks from the L and Disposal of Metal-Bearing Wastes . Prepared for The Environmental Defense Fund, Friends of the Earth, Hoosier Environmental Council, and Mineral Policy Center. Av ailable 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 Envi ronmental Systems: Ve rsion 3.0 User’s Manual . EPA/600/3-

91/021. Office of Research and Devel opment, 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 Associ ated with Indirect Exposure to Combustor

Emissions . EPA-600/R-98/137. Nationa l Center for Environmental Assessment, Cincinnati, OH.

U.S. EPA (Environmental Protection Agen cy). 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 Of fice of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agen cy). 2001a. Modeling Sensitivity Analyses Concerning Comments Received by the U.S. EPA on the MINTEQA2 Model as Used in Modeling Support for FFCW Rulemaking. Prepar ed 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 th e Office of Solid Waste, Washington, DC.

U.S. EPA (Environmental Protection Agency). 2003a. EPACMTP Parameters/Data Document

. Office of Solid Waste, Wash ington, DC. Available at http://www.epa.gov/waste/nonhaz/in dustrial/tools/cmtp/index.htm

.

U.S. EPA (Environmental Protection Agency). 2003b. EPACMTP Technical Background

Document . Office of Solid Waste, Wa shington, DC. Available at http://www.epa.gov/waste/nonhaz/in dustrial/tools/cmtp/index.htm

.

U.S. EPA (Environmental Protection Agency). 2003c. Estimation of Metal Partition Coefficients for Fossil Fuel Combustion Waste Risk Assessment . Prepared by Allison Geoscience Consultants, Inc. (under subc ontract 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. Thes e 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 bi oconcentration factors (BCFs) to

calculate fish tissue concentrations from total.

 Attachment E-3 provides the equations used to ca lculate 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 mode l includes algorithms to estimate aluminum

concentrations in the water column and sedime nts by accounting for precipitation and fallout of

aluminum in the water column. These calculation s 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 concentr ation 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 ( U nitless)

Name Descript i on Value Appendix E



# f Water 

### 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

000001.0 1 000001.0

1

bsc Bed sediment particle concentration (g/cm^3) or (kg/L) 1

bsp Bed sediment porosity (cm^3/cm^3) 0.6

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Depth of the waterbody (m) d

z Calculated

Sed i ment-water partition coefficient (mL/ g) K db s Chemical Data; See Section 3

Suspended sediment-water partition coefficient (mL/g) K dsw Chemical Data; See Section 3

Total suspended solids (mg/L) TSS Site Data; See Appendix C

0.000001 Conversion factor (L/m L )(g/mg)



### E-1-1

## Table E-1-2. Fraction of Contaminant in Benthic Sediments ( U nitless)

Name Descrip t ion Value Appendix E



# f Benth 

### 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

000001.0 1

bsc Bed sediment particle concentration (g/cm^3) or (kg/L) 1

bsp Bed sediment porosity (cm^3/cm^3) 0.6

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Depth of the waterbody (m) d

z Calculated

Sed i ment-water partition coefficient (mL/g ) K dbs Chemica l Data; See Section 3

Suspended sediment-water partition coefficient (mL/g) K dsw Chemical Data; See Section 3

Total suspended solids (mg/L) TSS Site Data; See Appendix C

0.000001 Conversion factor (L/m L )(g/mg)



### E-1-2

## Table E-1-3. Dissolved Fraction ( U nitless)

Name Descrip tion Value Appendix E



# f d 

### Attachment E-1: Surface Water Concentrations



## 000001.0 1 1

××+ = TSSK f

dsw d

Suspended sediment-water partition coefficient (mL/g) K dsw Chemical Data; See Section 3

Total suspended solids (mg/L) TSS Site Data; See Appendix C

0.000001 Conversion factor (L/m L )(g/mg)



### E-1-3

## Table E-1-4. Liquid

- Phase Transfer Coefficient - Lakes (m/d ay ) ( M e r c u r y O nly)

Name Descri p tion Value Appendix E



# KL lakes 

### Attachment E-1: Surface Water Concentrations



## 86400 67.0

2 33.0



## ×

   × ××××= −

ww w

w a wd Lakes 

## DRL k

R R uC KL μ

Drag coefficient (unitless) C d 0.0011

Diffusivity in water (cm^2/s) D

w Chemical Data; See Section 3, Table 3-4

von Karman' s constant (unitless ) k 0.4

Viscous sublayer thickness (unitless) L 2 4

Viscosity of water ( g/cm-s) μ w 0.0169

Density of air (g/cm^3) R

a 0.0012

Density of water (g/cm^3) R

w 1

Mean annual wind speed (m/sec) u w Site Data; See Appendix C

86400 Conversion factor (sec/day)



### E-1-4

## Table E-1-5. Liquid

- Phase Transfer Coefficient - Rivers (m/d ay ) ( M erc ury O nly)

Name Descri ption Value Appendix E



# KL rivers 

### Attachment E-1: Surface Water Concentrations



## 86400 0001.0

× ××

=

z

w Rivers 

## d UD KL

Diffusivity in water (cm^2/s) D

w Chemical Data; See Section 3, Table 3-4

Depth of the waterbody (m) d

z Calculated

Current velocity of the waterbody (m/s) U Site Data; See Appendix C

86400 Conversion factor (sec/day)



### E-1-5

## Table E-1-6. Gas

- Phase Transfer Coefficient - Lakes (m/d ay ) ( M er c ur y O nly)

Name Descr i ption Value Appendix E



# K gas 

### Attachment E-1: Surface Water Concentrations



## 86400 67.0

2 33.0



## ×

   

## × ×××= −

aa a wd gas 

## DRL

k uCK μ

Drag coefficient (unitless) C d 0.0011

Diffusivity of chemical in air (cm^2/s) D

a Chemical Data; See Section 3, Table 3-4

von Karman' s constant (unitless ) k 0.4

Viscous sublayer thickness (unitless) L 2 4

Viscosity of air (g/cm-s) μ A 0.000181

Density of air (g/cm^3) R

a 0.0012

Mean annual wind speed (m/sec) u w Site Data; See Appendix C

86400 Conversion factor (sec/day)



### E-1-6

## Table E-1-7. Diffusion Transfer Rate (m/d ay ) ( M ercury O nly)

Name Descr i ption Value Appendix E



# K

v 

### Attachment E-1: Surface Water Concentrations

( ) hlcw

water TT 

## Tempadjust
−



## = θ



## TempAdjust

HKK K

gL v 

## ×

′ × + = 11 1

w 

## TR HLC H

× = ′

HLC Henry's L aw constant (atm-m^3/mol e Chemic al Data; See Section 3, Table 3-4

Dimensionless Henry's L aw constant (unitless) H’ Calculated

Gas

- Phase Transfer Coefficient (m/d) K g Calcul ated

Liquid

- Phase Transfer Coefficient (m/d) K L Calcul ated

Ideal Gas Constant (atm-m^3/K-mole) R 0.00008205

Temperature correction (unitless) θ water 1.026

Temperature of HLC (K) T hlc 298

Temperature of the waterbody (K) T

w Site Data; See Appendix C



### Note: Drawn from U.S. EPA, 1998 (EPA-530-D-98-001A and EPA-600/R-98/137).

E-1-7

## Table E-1-8. Water Concentration Dissipation Rate Constant (1/d ay )

Name Descr i ption Value Appendix E



# K wt 

### Attachment E-1: Surface Water Concentrations



# ( ) ( ) ( ) ( )

h sed benth sw Water b benth vold Water wt 

## kkfkfKfkffK +×+×+×+××=

b b 

## d WB K =

w

v vol 

## d K k =

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Fraction of contaminant in benthic sediments (unitless) F b enth Calcul a ted

Dissolved fraction (unitless) f d Calculated

Fraction of contaminant in water column (unitless) f Water Calculated

Benthic burial rate constant (1/day) K b Calculated

Hydrolysis rate (1/day) k h 0

Degradation rate for sediment (1/day) k sed 0

Degradation rate for water column (1/day) k

sw 0

Diffusion transfer rate (m/day) K

v Calculated (mercury only)

Water column volatilization rate constant (1/day) k vol Calculated (mercury only)

WB Rate of Burial (m/day) 0



### E-1-8

## Table E-1-7. Diffusion Transfer Rate (m/d ay ) ( M ercury O nly)

Name Descrip t ion Value Appendix E



# K

v 

### Attachment E-1: Surface Water Concentrations

( ) hlcw

water TT 

## Tempadjust
−



## = θ



## TempAdjust

HKK K

gL v 

## ×

′ × + = 11 1

w 

## TR HLC H

× = ′

HLC Henry's L aw constant (atm-m^3/mol e Chemical Data; See Section 3, Table 3-4

Dimensionless Henry's L aw constant (unitless) H’ Calculated

Gas

- Phase Transfer Coefficient (m/d) K g Calculat ed

Liquid

- Phase Transfer Coefficient (m/d) K L Calculat ed

Ideal Gas Constant (atm-m^3/K-mole) R 0.00008205

Temperature correction (unitless) θ water 1.026

Temperature of HLC (K) T hlc 298

Temperature of the waterbody (K) T

w Site Data; See Appendix C



### Note: Drawn from U.S. EPA, 1998 (EPA-530-D-98-001A and EPA-600/R-98/137).

E-1- 9

## Table E-1-10. Total Water Column Concentration (g/m^3 or mg/L)

Name Description Value Appendi x E



# C wcTot 

### Attachment E-1: Surface Water Concentrations

bzw 

## ddd −=

w z

water wTot wcTot 

## d

d fCC ××=

Total Waterbody Concentration from Loading (g/m^3 or mg/L) Cw Tot Calculated

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Depth of the waterbody (m) d

z Calculated

Fraction of contaminant in water column (unitless) f Water Calculated



### E-1-10

## Table E-1-11. Dissolved Waterbody Concentration (mg/L)

Name Description Value Appendi x E



# C dw 

### Attachment E-1: Surface Water Concentrations

bzw 

## ddd −=

w Z
d Water Tot dw 

# d

d ffCwC ×××=

Total Waterbody Concentration from Loading (g/m^3 or mg/L) Cw Tot Calculated

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Depth of the waterbody (m) d

z Calculated

Dissolved fraction (unitless) f d Calculated

Fraction of contaminant in water column (unitless) f Water Calculated



### E-1-11

## Table E-1-12. Total Concentration in Bed Sediment (g/m^3 or mg/L)

Name Description Value Append ix E



# C wbs 

### Attachment E-1: Surface Water Concentrations

b w z 

# ddd +=

b z benth Tot wbs 

### d

d fCC ××=

Total Waterbody Concentration from Loading (g/m^3 or mg/L) Cw Tot Calculated

Depth of upper benthic layer (m) d b 0.03

Depth of water column (m) d

w Site Data; See Appendix C

Depth of the waterbody (m) d

z Calculated



### E-1-12

## Table E-2-1. Concentration in Fish at Different Trophic Levels (mg/kg)

Name Description Value Ap p endix E



# C fish 

### Attachment E-2: Fish Concentrations



## Fo r Mercury:



## BCFC C dw fish 

## × = *15.0

For Non-Volatile Metals:

B C F CwC

tot fish 

## ×=

BCF Chemical Data; See Section 3

Bioconcentration factor for specified trophic level (L/kg)

Calculated Dissolved waterbody concentration (mg/L) 

## C dw

Calculated

Total waterbody concentration from loading (g/m^3 or mg/L) Cw Tot

0.15 Fraction of dissolved mercury assumed to be methyl mercury

(unitless)



### E-2-1

## Table E-2-2. Average Fish Fillet Concentration Ingested by Humans (mg/kg)

Name Description Value Ap p endix E



# C fish_fillet 

### Attachment E-2: Fish Concentrations

Ff i shT TFfishT T filletfish 

## CFCF C 4 4 3 3

_ 

## ×+×=

Calculated Concentration of contaminant in fish at different trophic levels (mg/kg) C fishT3F

Calculated

Concentration of contaminant in fish at different trophic levels (mg/kg) C fishT4F

0.36

Fraction of trophic level 3 intake (unitless) F T3

0.64

Fraction of trophic level 4 intake (unitless) F T4



### E-2-2

## Table E-3-1. Contaminant I ntake from D ri n king W ater (mg/kg-d)

Name De s cription Value Appendix E



## Idw Attachment E-3: Intake Rates



## 1000* BW F C RC I dw dw dw

dw 

## ××

=

BW Exposure Data; See Appendix F

Body weight (kg)

Calculated Dissolved waterbody concentration (mg/L) 

## C dw

Exposure Data; See Appendix F

Consumption rate of water (mL/day) CR dw

1

Fraction of drinking water ingested that is contaminated (unitless) F dw

1000 Conversion factor (mL/L)



### E-3-1

## Table E-3-2. Daily I ntake of C on t aminant from F ish I ngestion (mg/kg BW/d ay )

Name De s cription Value Appendix E



## I fish 

### Attachment E-3: Intake Rates



# BW FCR C I fish fish filletfish
fish 

# ×

××

= 1000

_

BW Exposure Data; See Appendix F

Body weight (kg)

Calculated Average fish fillet concentration ingested by humans (mg/kg) 

## C fish_fillet

Exposure Data; See Appendix F

Consumption rate of fish (g WW/day) CR fish

1

Fraction of fish intake from contaminated source (unitless) F fish

1000 Conversion factor (g/kg)



### E-3-2

### Appendix F Human Exposure Factors



# Appendix F. Human Exposure Factors

Exposure factors are data that quantify huma n behavior patterns (e.g., ingestion rates of fish and drinking water) and ch aracteristics (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 expos ure to and subsequent intake of a contaminant in the environment. The exposure factors data also en able EPA to differentiate the exposures of individuals of different ages (e .g., a child vs. an adult). The deri vation and values used for the human exposure factors in this risk assessment are described below, and the exposure factors selected for the probabilistic an alyses are also presented.



# F.1 Exposure Parameters Used in Probabilistic Analysis

F.1.1 Introduction

The general methodology for collecting huma n 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 parametr ic 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. Pa rameter 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 select ed on the basis of results fo r 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 a ccording to established EPA policy.

Table F-1 lists all of the parameters used in the probabilistic an alysis. Both fixed

variables and the values used to define distribu ted data are provided.

Probabilistic risk analyses involve “sampling” values fr om probability distribution functions (PDFs) and using the values to estimate risk. In some cases, di stributions are infinite, and there is a probability, although very small, that very large or very small values might be

selected from the distributions. Because selectin g extremely large or extremely small values is unrealistic (e.g., the range of adult body weights is not infinite), maximu m 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 dist ributed 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 prepari ng parameters to characterize di stributional uncertainty in the model inputs.

For many exposure factors, EFH data include sa mple 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 a ll 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 mome nts (e.g., selecting models that agree with the data mean and standard deviation). For some fact ors, certain percentiles were not used in the fitting process because sample sizes were too sma ll 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 (t he most central or clos est to the median) was used in most cases (e.g., if both the 98th and 99t h percentiles had the same value, only the 98th percentile value was used).

The EFH does not use standardized age c ohorts across exposure factors. Data for different exposure factors are re ported for different age categories. Therefore, to obtain the percentiles for fitting the four standardized ag e cohorts (i.e., ages 1 to 5, 6 to 11, 12 to 19, and more than 20), each EFH cohort-specific value fo r a given exposure factor was assigned to one of these four cohorts. When multiple EFH cohor ts fitted into a singl e CCW cohort, the EFH percentiles were averag ed 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 FHuman Exposure FactorsTable F-1. Summary of Exposure Parameters Used in Probabilistic AnalysisUnitsVariableTypeConstantsMean(or shape)Std Dev(or scale)MinimumMaximumReferenceParameterAveraging time for carcinogensyrConstant7.00E+01U.S.EPA(1989)Body weight (adult)kgLognormal7.12E+011.33E+011.50E+013.00E+02U.S. EPA (1997a); Tables 7-2,7-4, 7-5Body weight (child 1)kgLognormal1.55E+012.05E+004.00E+005.00E+01U.S. EPA (1997a); Tables 7-3,7-6, 7-7Body weight (child 2)kgLognormal3.07E+015.96E+006.00E+002.00E+02U.S. EPA (1997a); Tables 7-3,7-6, 7-7Body weight (child 3)kgLognormal5.82E+011.02E+011.30E+013.00E+02U.S. EPA (1997a); Tables 7-3,7-6, 7-7Consumption rate: fish (adult, child)g/dLognormal6.48E+001.99E+010.00E+001.50E+03U.S. EPA (1997b); Table 10-64Exposure duration (adult resident)yrWeibull1.34E+001.74E+011.00E+005.00E+01U.S. EPA (1999) (ACS)Exposure duration (child)yrWeibull1.32E+007.06E+001.00E+005.00E+01U.S. EPA (1999) (ACS)Exposure frequency (adult resident)d/yrConstant3.50E+02U.S.EPAPolicyFraction contaminated: drinking waterFractionConstant1.00E+00U.S.EPAPolicyFractioncontaminated:fishFractionConstant1.00E+00U.S.EPAPolicyFraction of fish consumed that istrophic level (T3) fishFractionConstant3.60E-01U.S. EPA (1997b); Table 10-66Fraction of fish consumed that istrophic level 4 (T4) fishFractionConstant6.40E-01U.S. EPA (1997b); Table 10-66Ingestion rate: drinking water (adultresident)mL/dGamma3.88E+003.57E+021.04E+021.10E+04U.S. EPA (1997a); Table 3-6Ingestion rate: drinking water (child 1resident)mL/dGamma2.95E+002.37E+022.60E+013.84E+03U.S. EPA (1997a); Table 3-6Ingestion rate: drinking water (child 2resident)mL/dGamma3.35E+002.35E+023.40E+014.20E+03U.S. EPA (1997a); Table 3-6Ingestion rate: drinking water (child 3resident)mL/dGamma2.82E+003.42E+023.30E+015.40E+03U.S. EPA (1997a); Table 3-6April 2010–Draft EPA document.F-3

### Appendix F Human Exposure Factors



## April 2010–Draft EPA document. F-4 Because the EFH data are always positive and ar e 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 them 1 

## and allows for a likeliho od ratio test of the fit of the two-parameter models. However, only the tw o-parameter models were selected for use in the analysis because the three-parameter genera lized gamma model did not significantly improve the goodness of fit over the two-pa rameter 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 ga mma distributions were fit to each factor data set using maximum likeli hood estimation (Burmaster and Thompson, 1998). When sample sizes were available, the goodness of fit was calcu lated 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 m odels, 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 sa mple sizes were available, and a regression approach was used when sample sizes were not available (Jennrich a nd 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, al ong 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 b ecause no variability was expected or because the available data were not adequate to genera te distributions. Fixed (c onstant) parameters are

shown in Table F-2 along with the value selected for the ri sk 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 extr acted directly from the EFH. When evaluating carcinogens, total dose was averaged over the lifetime of the individua l, 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 (adu lt, child) 70 yr U.S. EPA (1989)



## The fraction contaminated for drinking water wa s assumed to be 1 (i.e., all drinking water available for consumption at a s ite 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 da ys 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 di stributions. 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 stu dy was one of four recommended freshwater angler studies in the EFH (U .S. EPA, 1997b). The other recommended fish consumption studies (i.e., Michig an and New York) had large per centages of anglers who fished from Great Lakes, which is not consistent with th e modeling scenarios used in this risk analysis. The anglers in the Maine study fished from str eams, rivers, and ponds; these data were more consistent with the CCW modeling scenarios. A lthough 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 st udy 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 = Pe rcentiles; Pop-Estd = P opulation-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 a nd 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 ch ild age groups and adults. Percentile data were used to fit parametric models (gamma, lognormal, and Weibull) usi ng maximum likelihood estimation. Measures of goodness of fit were used to select the most a ppropriate model. The fraction of drinking water contaminated was assumed to be 1 (in accordance with EPA policy).Appendix FHuman Exposure FactorsTable F-4. Drinking Water Intake Data and DistributionsEFH Data (mL/d)DistributionsAgeCohortNDataMeanDataSDP01P05P10P25P50P75P90P95P99DistributionPopEstdMeanPopEstdSD1–53,200697.1401.551.62187.6273.5419.2616.5900.81,2361,4731,917Gamma6984066–112,405787417682413184847311,0161,3381,5561,998Gamma78743012–195,801963.2560.665.15241.4353.8574.4868.51,2471,6942,0332,693Gamma96557420+13,3941,384721.6207.6457.5607.3899.61,2751,7412,2602,6823,737Gamma1,383703N = Number of samples; P01–P99 = Percentiles; Pop-Estd = Population-estimated; SD =Standard deviation.April 2010–Draft EPA document.F-7

### Appendix F Human Exposure Factors



## F.1.4.3 Body Weight

Table F-5 presents body weight data and distri butions. Body weight data were obtained from Tables 7-2 through 7-7 of the EFH (U.S. EP A, 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 eac h 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 like lihood estimation. Measures of goodness of fit were used to select the most appropriate model.

April 2010–Draft EPA document. F-8Appendix FHuman Exposure FactorsTable F-5. Body Weight Data and DistributionsEFH Data (kg)DistributionsAgeCohortNDataMeanDataSDP05P10P15P25P50P75P85P90P95DistributionPopEstdMeanPopEstdSD1–53,76215.523.71912.513.113.4514.0315.2616.6717.5818.3219.45Lognormal15.52.056–111,72530.849.56122.7924.0525.0726.4429.5833.4436.8239.6643.5Lognormal30.75.9612–192,61558.4513.6443.8446.5248.3150.9456.7763.5768.0971.9879.52Lognormal58.210.220+12,50471.4115.4552.8655.9858.2161.6969.2678.4984.9289.7597.64Lognormal71.213.3N = Number of samples; P05–P95 = Percentiles; Pop-Estd = Population-estimated; SD =Standard deviation.April 2010–Draft EPA document.F-9

### Appendix F Human Exposure Factors



## F.1.4.4 Exposure Duration

Table F-6 presents exposure duration data a nd distributions. Exposure duration was assumed to be equivalent to the average reside nce time for each receptor. Exposure durations for

adult and child residents were determined using data on reside ntial 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 re sidents, 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 Wei bull distribution. The We ibull 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 th e 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 Weibul l distribution, the variance w ithin each age group (e.g., 6- year-olds) was calculated in th e age cohort. These variances in turn were pooled over the age cohort using equal weights. This is not the usua l 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 ag e 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 devi ation divided by the Weibull mea n. 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 Us ed 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-10Appendix FHuman Exposure FactorsTable F-7. 50th Percentile Exposure Data Used in the Screening AnalysisUnitsReferenceTableAge CohortParameterValue6–11 yr12–19 yr1–5 yr20+ yrBody weight15.329.656.869.3kgU.S. EPA (1997a)T7-2, 7-3, 7-4, 7-5, 7-6, 7-7Consumption rate of fish2222g WW/dU.S. EPA (1997b)T10-64Exposure duration57.5810yrU.S. EPA (1997c)T15-164, 15-168Ingestion rate of drinkingwater0.61650.7310.86851.275L/dU.S. EPA (1997a)T3-6April 2010–Draft EPA document.F-11

### 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 se cond-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. Maxi mum likelihood estimation by nonlinear least squares. Pp. 57–65 in Statistical Computing Section Proceedings of American Statistical

Association . American Statistical As sociation, 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 a nd 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. Nati onal Center for Environmental Assessment, Office of Resear ch 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 assessm ent require human health benchmarks to assess potential risks from chronic oral exposures. EPA uses refere nce 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 h ealth benchmarks used in the CCW screening

and risk assessment. Section G.1 describes the data sources and ge neral hierarchy used to collect

these benchmarks. Section G.2 provides the benchmarks along with discussions of individual

human health benchmarks extrac ted 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 Cent er 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 Info rmation System (IRIS)

Benchmarks in IRIS are prepared and main tained by EPA, and RTI used values from

IRIS whenever available. IRIS is EPA’s elect ronic database containing information on human health effects (U.S. EPA, 2009). Each chemical file contains descri ptive and quantitative information on potential health effects. Health benchmarks for chronic noncarcinogenic health

effects include RfDs and inhalation reference con centrations (RfCs). Canc er classification, oral CSFs, and inhalation unit risk factors (URFs) are included for carcinogenic effects. IRIS is the official repository of Agency-wide consen sus 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 provisi onal RfCs, RfDs, and CSFs for certain chemicals. Some of the provisional values have been externally peer reviewed. Th ese provisional values have not undergone EPA’s formal review process for fi nalizing benchmarks and do not represent Agency-wide consensus information.

G.1.3 Health Effects Assess ment Summary Tables

HEAST is a listing of provisional noncarci nogenic and carcinogenic health toxicity values (RfDs, RfCs, URFs, and CSFs) derive d 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), Heal th Effects Assessments (HEAs), Health and Environmental Effects Profiles (HEEPs), H ealth and Environmental Effects Documents (HEEDs), Drinking Water Criteria Documents, and Ambient Water Quality Criteria Documents. Evaluations of potential carcinoge nicity of chemicals in s upport of reportable quantity adjustments were published by EPA’s Carcinog en 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., Ai r Characteristic Study) are also available. Health toxicity

values identified in these EPA documents ar e usually dated and are not recognized as Agency-wide consensus information or verified benchmarks.

G.1.5 ATSDR Minima l Risk Levels

The ATSDR MRLs are substance-specific he alth 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 adve rse noncancer health effects over a specified duration of exposure. MRLs are base d on noncancer health e ffects only and are not

based on a consideration of cancer effects. MRLs are derived for acute, in termediate, 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 a nd RfDs, respectively (i.e., ATSDR uses the no observed adverse effect level/uncertainty fact or [NOAEL/UF] approach); however, MRLs are intended to serve as screening levels and ar e exposure duration specif ic. Also, ATSDR uses EPA’s (U.S. EPA, 1994) inhalation dosimetry methodology in the derivation of inhalation

MRLs.

G.1.6 CalEPA Cancer Potency Factor s and Reference Exposure Levels

CalEPA has developed cancer potency factor s 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 al so developed chronic i nhalation 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 availa ble 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 pr ovided 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 H 2 O (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 Rf D 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; H 2 O 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 devel oped 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. EP A, 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 fo r 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 alterna tive 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 Environmen tal 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 H ealth Hazard Assessment, Berkeley, CA. Available at http://www.oehha.org/air/cancer_guide/hsca2.html

.

CalEPA (California Environmen tal Protection Agency). 1999b. Air Toxics Hot Spots Program

Risk Assessment Guidelines: Part III. T echnical 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/ch ronic_rels/ragsii.html http://www.oehha.org/ air/chronic_rels/ RAGSp3draft.html

.

CalEPA (California Environmen tal Protection Agency). 2000. Air Toxics Hot Spots Program

Risk Assessment Guidelines: Part III. T echnical Support Document for the Determination of Noncancer Chronic Reference Exposure Levels . Office of Environmental Health Hazard Assessment, Berkeley, CA. Av ailable (in four sections) at http://www.oehha.org/air/ chronic_rels/22RELS2k.html http://www.oehha.org/air/ch ronic_rels/42kChREL.html http://www.oehha.org/air/ch ronic_rels/Jan2001ChREL.html http://www.oehha.org/air/ch ronic_rels/1201Crels.html

.

CalEPA (California Environmen tal 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 Environmen tal Health Hazard Assessment, Berkeley, CA. Available at http://www.oehha.org/air/hot_spo ts/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 Tec hnical 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, Cinc innati, 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 Assessmen t, 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 a ssessments included an ecological risk assessment that paralleled the human health ri sk assessment. The ecological risk assessment addressed two routes of exposure for ecological receptors: direct contac t with contaminated media and ingestion of contaminated food items . For each CCW chemical for which ecological effect data were available, hazard quotients (H Qs) were calculated using chemical-specific media concentrations assumed to be protective of ecological receptors of concern.

This appendix provides the ecological benchm arks 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 concentra tion-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 repr esent 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 indi cates that the predicted concen tration 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 th e use of the reference concentration (RfC) for human health where the air concentration is comp ared 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 assesse d for each exposure route in each environmental medium addressed by the CCW risk assessment.

Table H-1. Ecological Receptors Asse ssed 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 as sessment 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 Fu el Combustion Phase 2 (FFC2) (U.S. EPA, 1998). The receptors and endpoints selected for the 1998 analysis were eval uated and considered appropriate for the goals

of this risk assessment. The benchmarks were derived for each chemical and receptor to the extent that supporting da ta were available.

As in 1998, the lowest (most sensitive) be nchmark for each chemical in each medium was selected to calculate HQs in the CCW risk assessment. For example, several receptors (aquatic invertebrates, mammals, a nd birds) may be exposed to cons tituents in surf ace water. The surface water HQ for a given chemical was calcul ated using whichever benchmark was lowest and would thus give the highe st (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 expos ed 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 protec tion 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 th e direct contact route of exposure.

Aquatic Community Benchmarks

The aquatic community receptor comprises fish and aquatic invertebrates exposed through direct contact with constituents in su rface water. For the aquatic community, the final chronic value (FCV), developed either for the Gr eat Lakes Water Quality Initiative (U.S. EPA, 1993) or the National Ambient Water Quality Cr iteria (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 th e NAWQC; however, it requires fewer data points and incl udes statistically derived adju stment factors. For benchmark derivation, the minimum data set requ ired at least one data point.

Amphibian Benchmarks

For amphibian populations, data availability severely limited benchmark development. A review of several compendia presenting amphibi an ecotoxicity data (e.g., U.S. EPA, 1996; Power et al., 1989), as well as primary literatur e 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 LC 50 

## (i.e., concentration lethal to 50 percen t of test subjects) data identified in studies with exposure durations less than 8 da ys. Although the use of acute effects levels produced a benchmark that was not consistent w ith 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 stresso r in CCW disposal scenarios. The endpoints considered in these studies were related to popula tion 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 indivi dual metals remain the most a ppropriate 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 De veloping 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 Ec ological Risk Assessment at
Hazardous Waste Combustion Facilities . Internal Review Draft, Februa ry 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 avai lable for aquatic vascular plants, and lowest

effects concentrations were iden tified for algae. The benchmark fo r algae and aquatic plants was based on (1) an LOEC for vascular aquatic pl ants or (2) an effective concentration (EC

xx 

## ) for a species of freshwater algae, freque ntly 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, th e 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 exposur e addresses the exposure of terrestrial mammals and birds through ingestion of aquatic plants and pre y. Thus, the CCW ecological benchmarks for ingestion exposure express media concentratio ns that, based on certain assumptions about receptor diet and foraging behavior, were expect ed to be protective of populations of mammals and birds feeding and foraging in contaminated surface water bodies.

The derivation of ingestion benchmarks be gan 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 vi ability; 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 infe rred at the population level.

Once an appropriate ingestion exposure study wa s identified, a benchmark was calculated using a three-step process. The remainder of th is section outlines the basic technical approach

used to convert avian or mammalian benchmarks (in daily doses) to th e media concentration benchmarks (in units of concentration) used to assess ecological risks for surface water and sediment contaminated by CCW waste constitu ents. The methods reflect exposure through the ingestion of contaminated plants, prey, and me dia, and include parameters on accumulation (e.g., bioconcentration factors), upt ake (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 s caling equation (Equation H-1) (Sample et al., 1996). This is the default methodology EPA propos ed for carcinogenicity assessments and reportable quantity documents for adjusting anim al data to an equiva lent human dose (57 FR 24152).

1/4

w t

t w 

## bw bw LOAEL Benchmark ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

× = (H-1)

where

Benchmark

w 

## = scaled ecological benchmark for species 

## w (mg/kg/d)

LOAEL t 

## = lowest observed adverse effects level for test species (mg/kg/d)

bw t 

## = body weight of the surrogate test species (kg)

bw

w 

## = body weight of the representative wildlife species (kg).

Step 2: Identify Bioconcentratio n Factors/Bioaccumulation Factors

For metal constituents, whole-body bioconcentration factors (BCFs) and bioaccumulation factors (BAFs) were identified for aquatic orga nisms that could be used as food sources (e.g., fish). The Oak Ridge National Laboratory has pr oposed methods and data that are useful in predicting bioaccumulation (Sample et al. 1998a,b). Th ese 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 benchm ark (e.g., endpoint on fecundity).



# ( ) 

# [ ] ( )

Benchmark I BAF C I C

bw fish w w w



### = × +× 

## (H-2)

where

I fish 

## = intake of contaminated fish (kg/d) BAF = whole-body bioaccumulation factor (L/kg) bw = weight of the repr esentative species (kg)

I

w 

## = intake of contaminated water (L/d)

C

w 

## = total concentration in the water (mg/L).

For chemicals that bioaccumulate signifi cantly in fish tissue, the ingestion of contaminated food tends to dominate the exposure (i.e., [I fish 

## × C fish 

## ] >> [I

w 

## C

w 

## ]), and the water term (i.e., [I

w 

## × C

w 

## ]) can be dropped from Equation H-2, resulting in Equation H-3:



# ( )

bw CBAF I Benchmark w fish 

## × ×

= (H-3)

At the benchmark dose (mg/kg/d), the concentrati on 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 co ncern. Hence, Equation H-3 can be rewritten to solve for the surface water (CSCL

sw 

## ) as follows:



# ( ) BAFII bw benchmark CSCL

fish w sw 

## ×+ ×

= 

## (H-4)



# H.2 Ecological Benchmarks

The ecological benchmarks used to calculat e 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 Us ed 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 Inte rior, 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 transf er 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 Na tional 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 Assessme nt: 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. Environmenta l Protection Agency, Springfield, VA.

Suter, G.W., and C.L. Tsao. 1996. 

## Toxicological Benchmarks for Screening Potential Contaminants of Concern for Eff ects on Aquatic Biota: 1996 Revision . U.S. Department of Energy, Oak Ridge National La boratory, 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 Wi ldlife: 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 Revi ew Draft. February 28, 1997.

U.S. EPA (Environmental Protection Agen cy). 1998. Non-Groundwater Pathways, Human Health and Ecological Risk Analysis for Fo ssil Fuel Combustion Ph ase 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 e xposed 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, expos ure 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 Quotie nt 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 fr om (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 HBN 2


### (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

Copper 3 

### 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 dri nking water ingestion.

2 Based on fish consumption by a recreational fisher.

3 Fish bioconcentration factor values for copper are zer o. HBN based on National Ambi ent Water Quality Criteria. AWQ = National Ambient Water Quality Criteria

MCL = maximum contaminant level or drinki ng 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:

T T 

## BCF Cdiss Cfish × =



## Cwt Cdiss × = 05.0

For NonvolatileMetals:

T T 

## BCFCwt Cfish × =

Name Description Value

Cdiss Concentration in surface wate r (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 fr om Consumption of Fish (mg/kg BW/day)

Ifish

FfishT TFfishT TT 

## CFCFCfish 4 4 3 3 

## × + × =

BW F CRCfishIfish fish
fish T 

## × ××= 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)

Cfish T 

### 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 Quot ient Due to Ingestion (unitless)

Risk_Oral



## RfD

I HQ Oral 

## =

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)

CSF Oral 

### 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 NC Oral 

## ×=



## TR Risk

C HBN Risk 

## ×=

Name Description Value

THQ Target noncancer hazard quotient (unitless) 1

TR Target cancer risk (unitless) 1.00E-5

HQ_Oral Noncancer hazard quotient for inge stion (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

Parameter a 

### 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) Geom ean 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)

Parameter a 

### 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_T 4F 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_T 4F was used as a surrogate.

BCF_T3W 0.8 Stephan (1993) BCF_T 4F was used as a surrogate.

BCF_T4F 0.8 Stephan (1993) Derived fro m Calamari et al. (1982) (as cited in Stephan, 1993).

BCF_T4W 0.8 Stephan (1993) BCF_T 4F 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)

Parameter a 

### 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) Ge omean 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. Ma tida. 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 Criteria 1

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 Criteria 1

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 Criteria 1

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 Criteria 1

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

Copper 2 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 zer o. HBN based on National Ambi ent 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 Criteria 1

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 Criteria 1

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

90 th Percentile HQ (Cancer Risk)

Analytes Exceeding Risk Criteria 1

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 Criteria 1

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

Copper 2 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 zer o. HBN based on National Ambi ent 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) 90 th 

### Percentile (mg/L) HQ 95th Percentile (mg/L) HQ

Analytes Exceeding Risk Criterion 1



### 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 Criterion 1



### 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 Criterion 1



### 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 Criterion 1



### 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 se lected CCW constituents (arsenic III and V, boron, cobalt, selenium IV and VI, and thallium 1 

## ). 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 ra nge 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 (as h, ash and coal, and fluidized be d combustion wastes) managed in landfills and surface impoundments. Three liner desi gns were also considered: no liner; a 3-foot clay liner; and a composite liner (a composite of geomembrane, geos ynthetic clays, and/or compacted clays), assigned to each CCW wast e 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 rate s of water through the landfills and surface impoundments corresponding to each of the three liner types. Among the outputs generated by the groundwater pathway fa te 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 se ts of model inputs generated an equivalent number of groundwater observati ons. Some were non-zero concentr ations, others were zero. For these zero-value observations, the model also assigned a value of zer o to arrival time. Zero-value

observations can be attributed to zero-value in filtration 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 condi tions that retard the movement of a constituent from the WMU through th e subsurface to the extent that the dissolved component was not observed w ithin the established maximum allowable timeframe (10,000 years). In this case, the time of travel is greate r than 10,000 years.

To better understand the time frames in which risks associated with exposures to contaminated groundwater may occur, an analys is was performed to graphically represent

distributions of arrival time of the peak groundwater concentrati ons at the nearby drinking water well. The analysis was performed across all wast e 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 co ncentrations and their arrival times were treated to create the plots presented in this appendix, including the treatme nt of zero-value observations.



# L.2 Methodology

Given a constituent managed in a particular type of WMU (e.g., arse nic in landfills), all

infiltration rates and their corr esponding peak concentrations a nd arrival times predicted by the

model were extracted from the i nput/output data for simulations across all waste types in which

the selected constituent was found. The triplets of data needed to prepare the gra phs—infiltration rate, peak concentration, and arrival time—were then filtered from the data and segregated by liner type. Zero-value observed concentra tions were treated in the following manner:

 Zero-value observations corresponding to zero- value infiltration rate s were assigned an arbitrary value of -1, effectively excludi ng 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. On ly the composite line r scenarios produced

zero-value infiltration rates.

 Zero-value observations corresponding to non- zero infiltration rate s were assigned an arbitrary arrival time greater than (>) 10,000 years, the maximum simulation timeframe. These data points are also not shown in th e 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 th ose with zero infiltration rates. Note that for surface impoundments, there are fewer model runs (obser vations) for thallium because thallium results

are not available for ash and coal waste stream s because of very limited data in the CCW

constituent database.

Table L-1. Distribution of Z ero-Value Concentrations

WMU Type Total

Observations a



### (Number) Zero

Infiltration

Rates (Number) Zero

Infiltration

Rates b



### (%) 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 detect ed only in ash waste streams managed in surface impoundments.



## After zero infiltration rate observations were f iltered 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 bot h the full 0 to 10,000-year time frame and a shorter time frame, so that th e shape of the cumulative distribution can be seen for the lower time-of-travel range characteris tic of these facilities.

The figures are organized alphabetically by me tal, 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 landf ills 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 ge ology. The shorter arrival times mainly reflect more transmissive soils and aquifer materi als 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 inf iltration 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 in filtration and are not plotted on this graph.



## Figure L-2. Time to peak distribution for ar senic III: surface impo undments, 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 in filtration and are not plotted on this graph.



## Figure L-3. Time to peak distribution for ar senic III: surface impo undments, 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 inf iltration 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 in filtration and are not plotted on this graph.



## Figure L-5. Time to peak di stribution 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 in filtration and are not plotted on this graph.



## Figure L-6. Time to peak di stribution 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 inf iltration 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 in filtration and are not plotted on this graph.



## Figure L-8. Time to peak dist ribution 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 in filtration and are not plotted on this graph.



## Figure L-9. Time to peak dist ribution 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 inf iltration 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 in filtration and are not plotted on this graph.



## Figure L-11. Time to peak distribution for co balt: 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 in filtration and are not plotted on this graph.



## Figure L-12. Time to peak distribution for co balt: 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 inf iltration and are not plotted on this graph.



## Figure L-13. Time to peak distribution fo r 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 in filtration and are not plotted on this graph.



## Figure L-14. Time to peak distribution for se lenium IV: surface im poundments, 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 in filtration and are not plotted on this graph.



## Figure L-15. Time to peak distribution for se lenium IV: surface im poundments, 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 inf iltration and are not plotted on this graph.



## Figure L-16. Time to peak distribution fo r 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 in filtration 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 in filtration 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 inf iltration and are not plotted on this graph.



## Figure L-19. Time to peak distribution fo r thallium: landfills, all waste types.

April 2010–Draft EPA document. L-12