Human Health Multipathway Residual Risk Assessment 

for the Secondary Lead Smelting Source Category 

Draft Report 

April, 2011 

Prepared for: 

Terri Hollingsworth, Work Assignment Manager 

Air Toxics Assessment Group 

U.S. Environmental Protection Agency 

Research Triangle Park, NC 27711 

Prepared by: 

ICF International 

2222 East NC-54 

Beta Building, Suite 480 

Durham, NC 27713
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TABLE OF CONTENTS 

1 Introduction ............................................................................................................................. 1

1.1 Secondary Lead Smelting .................................................................................................. 1

1.2 Regulatory Context ............................................................................................................ 2

1.3 Purpose of the Multipathway Assessment ......................................................................... 2

2 Scope of the Assessment ...................................................................................................... 3

2.1 Selection of HAPs .............................................................................................................. 3

2.2 Emission Rate Screening Analysis .................................................................................... 4

2.3 PB-HAPs Selected for Detailed Residual Risk Assessment .............................................. 6

2.4 Selection of Relevant Exposure Pathways and Approach to Exposure Assessment ........ 8

2.5 Selection of Facilities ....................................................................................................... 10

3 Methodology .......................................................................................................................... 11

3.1 Fate and Transport Modeling (TRIM.FaTE) ..................................................................... 13

3.1.1 Facility Details ..................................................................................................... 13

3.1.2 Stack Details ....................................................................................................... 18

3.1.3 Chemical Properties ............................................................................................ 19

3.1.4 Meteorological Data ............................................................................................ 19

3.1.5 Extent and Dimensions of Modeled Environment ................................................ 21

3.1.6 TRIM.FaTE Parcel Design................................................................................... 23

3.1.6.1 Modeled Water Bodies ............................................................................ 24

3.1.6.2 Modeled Source and Agricultural Parcels ................................................ 27

3.1.6.3 Layout of Watershed Surface Parcels and Outer Surface Parcels .......... 29

3.1.6.4 Surface Parcel Vegetation Types ............................................................ 30

3.1.6.5 Air Parcel Layout ..................................................................................... 33

3.1.7 Abiotic Environment ............................................................................................ 35

3.1.7.1 Soil Properties ......................................................................................... 35

3.1.7.2 Erosion ..................................................................................................... 36

3.1.7.3 Runoff ...................................................................................................... 37

3.1.7.4 Surface Water and Sediment Properties ................................................. 38

3.1.7.5 Water Transfers ....................................................................................... 38

3.1.7.6 Sediment .................................................................................................. 39

3.1.8 Biotic Environment .............................................................................................. 40

3.1.8.1 Terrestrial Plants ...................................................................................... 40

3.1.8.2 Aquatic Ecosystems ................................................................................ 40

3.2 Exposure and Risk Calculations ...................................................................................... 42

3.2.1 Calculating Concentrations in Farm Food Chain Media ...................................... 42

3.2.2 Ingestion Exposure Assessment ......................................................................... 43

3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake .............................. 43

3.2.2.2 Exposure Scenarios and Corresponding Inputs ...................................... 45

3.2.2.3 Calculating Average Daily Doses ............................................................ 47

3.2.2.4 Infant Ingestion of Breast Milk ................................................................. 48

3.2.3 Risk Characterization .......................................................................................... 48

4 Dose-response Assessment ................................................................................................ 48

5 Modeling Results and Risk Characterization ..................................................................... 49
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5.1 Estimated Media Concentrations ..................................................................................... 49

5.1.1 Exide Frisco Facility ............................................................................................ 49

5.1.2 Revere Middletown Facility.................................................................................. 53

5.2 Human Health Multipathway Risk Assessment Results .................................................. 58

5.2.1 Risk Assessment Summary................................................................................. 58

5.2.2 Risk Assessment Results from Multipathway 2,3,7,8-TCDD Exposure .............. 59

5.2.2.1 Estimated Lifetime Cancer Risks ............................................................. 59

5.2.2.2 Chronic Noncancer Hazard Quotients ..................................................... 62

5.2.3 Risk Assessment Results from Multipathway Mercury Exposure ........................ 64

5.2.4 Risk Assessment Results from Multipathway Cadmium Exposure ..................... 67

5.2.5 Risk Assessment Results from Multipathway Benzo(a)pyrene Exposure ........... 69

5.3 Discussion of Uncertainties and Limitations .................................................................... 70

5.3.1 Uncertainties Related to Fate and Transport Modeling (TRIM.FaTE) ................. 72

5.3.2 Uncertainties Related to Exposure Modeling and Risk Characterization ............ 74

Appendix A: Supplemental Details on Facility Selection, Meteorology Data, and Modeling 

Parcel Design 

Appendix B: TRIM.FaTE Inputs for Secondary Lead Scenario Exide Frisco and Revere 

Middletown 

Appendix C: Exposure Factors and Assumptions 

Appendix D: Chemical Intake and Risk Equations 

Appendix E: Detailed Exposure and Risk Estimates
2/15/2011 iii DRAFT 

 

LIST OF TABLES 

Table 2-1. OAQPS List of PB-HAPs ............................................................................................................ 4

Table 2-2. The Four PB-HAPs with Screening Levels, the Chemicals Constituting those PB-HAPs, and 

the Metal Speciation Factors and Equivalency Factors, where Appropriate ................................... 5

Table 3-1. PB-HAP Emissions for the Exide Frisco and Revere Middletown Facilities ............................. 16

Table 3-2. The Source Groups Used for TRIM.FaTE Modeling, Along with the Emissions and Height of 

Each Source Group, for the Exide Frisco and Revere Middletown Facilities ................................ 19

Table 3-3. Average Meteorological Conditions Near the Exide Frisco and Revere Middletown Facilities. 

Rounding Errors Might be Present ................................................................................................. 20

Table 3-4. Water Bodies Included in the Modeled Regions of the Exide Frisco and Revere Middletown 

Facilities ......................................................................................................................................... 26

Table 3-5. TRIM.FaTE Properties that Vary by Vegetation Type .............................................................. 31

Table 3-6. Soil and Groundwater Compartment Depths ............................................................................ 36

Table 3-7. Selected Properties of Soil and Groundwater .......................................................................... 36

Table 3-8. Selected Surface Water and Sediment Properties ................................................................... 38

Table 3-9. Lake Parameters for Modeled Water Bodies ............................................................................ 39

Table 3-10. Biomass Parameters for Lake Lewisville in the Exide Frisco Facility ..................................... 41

Table 3-11. Biomass Parameters for Fishable Lakes in the Revere Middletown Facility .......................... 42

Table 3-12. Summary of Ingestion Exposure Pathways and Routes of Uptake ........................................ 44

Table 3-13. Overview of Exposure Factors used for Multipathway Analysis for Farm Food Pathway 

Scenariosa,b .................................................................................................................................... 46

Table 3-14. Overview of Exposure Factors used for Multipathway Analysis for Fish Pathway 

Scenariosa,b,c .................................................................................................................................. 47

Table 4-1. Dose-Response Values for Assessed PB-HAPsa .................................................................... 49

Table 5-1. Concentration Estimates and Speciation for Mercury (Exide Frisco) ....................................... 50

Table 5-2. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Exide Frisco) ..................... 52

Table 5-3. Concentration Estimates and Speciation for Mercury (Revere Middletown) ............................ 54

Table 5-4. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Revere Middletown) .......... 56

Table 5-5. 2,3,7,8-TCDD Individual Excess Lifetime Cancer Risks .......................................................... 60

Table 4-6. Uncertainty Factors in the Multipathway Residual Risk Assessment for Secondary Lead 

Smelting ......................................................................................................................................... 71
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LIST OF FIGURES 

Figure 2-1. Conceptual Exposure Model for Farmer Scenario .............................................................. 9

Figure 2-2. Conceptual Exposure Model for Fisher Scenario ............................................................. 10

Figure 3-1. Multipathway Analysis Methodology Overview ................................................................. 11

Figure 3-2. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator ................ 13

Figure 3-3. Location of the Exide Frisco Facility. Surrounding Cities and Water Bodies are 

Shown. Shading Indicates Land Use Type in 2001 (With Simplified Categories) ........... 15

Figure 3-4. Wind Rose Representing Wind Flow Conditions Near the Exide Frisco Facility .............. 15

Figure 3-5. Location of the Revere Middletown Facility. Surrounding Cities and Water Bodies 

are Shown. Shading Indicates Land Use Type in 2001 (With Simplified Categories) ..... 17

Figure 3-6. Wind Rose Representing Wind Flow Conditions Near the Revere Middletown 

Facility ............................................................................................................................... 18

Figure 3-7. Streams, Rivers, and Water Bodies of the Upper Trinity Basin, in which the 

Exide Frisco Facility is Located ......................................................................................... 22

Figure 3-8. Streams, Rivers, and Water Bodies of the Upper Hudson Basin, in which the 

Revere Middletown Facility is Located, as well as the Nearby Lower Hudson 

and Upper Delaware Basins ............................................................................................. 23

Figure 3-9. The Surface Parcels for the Exide Frisco Facility, along with Local Elevation ................. 25

Figure 3-10. The Surface Parcels for the Revere Middletown Facility, along with Local Elevation ...... 26

Figure 3-11. Locations of the Farm Parcels and Source Parcel for the Exide Frisco Facility, 

along with Local Land Use ................................................................................................ 28

Figure 3-12. Locations of the Farm parcels and Source Parcel for the Revere Middletown 

Facility, along with Local Land Use ................................................................................... 29

Figure 3-13. The TRIM Vegetation Types of the Surface Parcels for the Exide Frisco Facility ............ 32

Figure 3-14. The TRIM Vegetation Types of the Surface Parcels for the Revere Middletown 

Facility ............................................................................................................................... 33

Figure 3-15. The Air Parcels for the Exide Frisco Facility, Overlaid with the Surface Parcels ............. 34

Figure 3-16. The Air Parcels for the Revere Middletown Facility, Overlaid with the Surface 

Parcels .............................................................................................................................. 35

Figure 3-17. Aquatic Food Web and Diet Fractions in Exide Frisco and Revere Middletown 

Facilities ............................................................................................................................ 43

Figure 5-1. Mercury Biomagnification in the Eastern Arm of Lake Lewisville (Exide Frisco) .............. 51

Figure 5-2. Mercury Bioaccumulation in the Eastern Arm of Lake Lewisville (Exide Frisco) .............. 52

Figure 5-3. Calculated 2,3,7,8-TCDD, BaP, and Cadmium Bioaccumulation Factors in the 

Eastern Arm of Lake Lewisville (Exide Frisco) ................................................................. 53

Figure 5-4. Mercury Biomagnification in Silver Lake (Revere Middletown) ........................................ 55

Figure 5-5. Calculated Mercury Bioaccumulation Factors in Silver Lake (Revere Middletown) ......... 56

Figure 5-6. 2,3,7,8-TCDD, BaP, and Cadmium Biomagnification in Silver Lake (Revere 

Middletown) ....................................................................................................................... 57

Figure 5-7. Calculated 2,3,7,8-TCDD, BaP, and Cadmium Bioaccumulation Factors in 

Silver Lake (Revere Middletown) ...................................................................................... 58

Figure 5-8. 2,3,7,8-TCDD Individual Lifetime Cancer Risks, RME Ingestion Rate ............................. 61
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Figure 5-9. Pathway Contribution by Ingestion Pathway to 2,3,7,8-TCDD Individual 

Lifetime Cancer Risks for the Farming Scenario .............................................................. 62

Figure 5-10. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Farming Scenarios and 

Recreational Angler Scenario by Location, RME Ingestion Rate ..................................... 63

Figure 5-11. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Recreational Angler 

Populations, RME Ingestion Rate ..................................................................................... 63

Figure 5-12. Hazard Quotients for Exposure to Methyl Mercury for Farming and Recreational 

Angler Scenarios, RME Ingestion Rate ............................................................................ 64

Figure 5-13. Hazard Quotients for Exposure to Methyl Mercury for Recreational Angler 

Populations, RME Ingestion Rate ..................................................................................... 65

Figure 5-14. Hazard Quotients for Exposure to Divalent Mercury for Farming and Recreational 

Angler, RME Ingestion Rate ............................................................................................. 66

Figure 5-15. Hazard Quotients for Exposure to Divalent Mercury for Recreational Angler 

Population, RME Ingestion Rate ....................................................................................... 66

Figure 5-16. Hazard Quotients for Exposure to Cadmium for Farming and Recreational 

Angler Scenarios, RME Ingestion Rate ............................................................................ 67

Figure 5-17. Hazard Quotients for Exposure to Cadmium for Recreational Angler Population, 

RME Ingestion Rate .......................................................................................................... 68

Figure 5-18. Benzo(a)pyrene Individual Lifetime Cancer Risks, RME Ingestion Rate .......................... 69

Figure 5-19. Pathway Contribution by Ingestion Pathway to Benzo(a)pyrene Individual 

Lifetime Cancer Risks ....................................................................................................... 70
2/15/2011 1 DRAFT 

1 Introduction 

This report presents an assessment of “multipathway” (i.e., non-inhalation) human health risks 

from emissions from secondary lead smelting facilities – specifically, emissions of hazardous air 

pollutants (HAPs) known to be persistent and bioaccumulative in the environment (PB-HAPs). 

This assessment was conducted as part of a broader effort to evaluate whether EPA is required 

to adopt further standards in addition to the technology-based standards for hazardous air 

pollutants under Section 112(d) of the Clean Air Act (CAA) in order to protect public health with 

an ample margin of safety. See CAA section 112 (f)(2). . 

For PB-HAP other than lead, EPA assessed multipathway human health risks (i.e., human 

cancer risks and chronic non-cancer hazards) for emissions from secondary lead smelting 

facilities by conducting two site-specific case study risk assessments. The two facilities were 

chosen based on location, the surrounding land use, emissions data, and other criteria. They 

are believed to pose elevated risks within the source category based on the suitability of 

surrounding landscapes for multipathway exposures to occur, and on PB-HAP emission rates. 

However, the facilities and exposure scenarios assessed, while believed to be in the upper end 

of the potential for multipathway risks from this source category, may not represent the highest 

potential human health risks from the source category (see Section 3.11). 

This report presents the approach, data, assumptions, and results of the assessment. The 

remainder of Section 1 provides background information on secondary lead smelting facilities 

and their emissions, the regulatory context of this assessment, and the purpose of the 

multipathway assessment. Section 2 describes the scope of the assessment, including the 

selection of facilities and HAPs. Section 3 is a detailed description of the assessment 

methodology, including modeling to simulate the fate and transport of emitted PB-HAPs in the 

environment, and the estimation of levels of human exposure and associated heath risks. The 

dose-response assessment for the PB-HAPs included in the multipathway assessment is 

presented in Section 4. Results of the assessment are presented in Section 5, along with a 

discussion of sources of uncertainty in the data and methods. 

1.1 Secondary Lead Smelting 

Secondary lead smelting facilities use blast, rotary, reverberatory, and/or electric furnaces to 

recover lead metal from lead-bearing scrap materials, primarily lead-acid batteries. The 

secondary lead smelting process consists of the following: (1) breaking lead-acid batteries and 

separating the lead-bearing materials from the other materials (including the plastic case 

material and acid electrolyte); (2) melting lead metal and reducing lead compounds to lead 

metal in the smelting furnace; and (3) refining and alloying the lead to customer specifications. 

Air pollutants are emitted from secondary lead smelters in the following ways: (1) process 

emissions contained in the primary exhaust of smelting furnaces; (2) process fugitive emissions 

associated with charging and tapping of smelting furnaces and lead refining kettles; and (3) 

fugitive dust emissions from wind or mechanically induced entrainment of dust from stockpiles, 

plant yards, and roadways. At present, EPA has identified fourteen secondary lead smelters 

operating in the U.S. 

Air emissions from secondary lead smelter facilities include a number of toxic air pollutants, 

including metals (e.g., lead, mercury, arsenic, chromium, cadmium) and organic and inorganic 

compounds. Organic toxic air pollutants emitted from secondary lead smelting include 

compounds classified by EPA as polycyclic organic matter (POM), including 1,3-butadiene, 

benzene, and naphthalene, and dioxins/furans. 
2/15/2011 2 DRAFT 

1.2 Regulatory Context 

The CAA requires EPA to develop regulations to control emissions of hazardous air pollutants 

from secondary lead smelters. In particular, the CAA establishes a two-stage regulatory 

process for addressing the risks posed by HAPs from stationary sources. The first stage 

involves the implementation of technology-based standards, also known as maximum 

achievable control technology (MACT) standards, for 174 categories of industrial sources, 

referred to as “source categories,” including secondary lead smelting. EPA issued MACT 

standards for secondary lead smelters on June 23, 1995, that required facilities to reduce 

emissions from a number of sources, including smelting furnaces, kettles, dryers, and fugitive 

sources such as material handling. EPA estimated that MACT standards would reduce 

emissions of air toxics from 23 facilities in operation at the time by about 1,400 tons per year, a 

72 percent reduction from the pre-regulated emissions.1

The second stage of regulating HAP emissions under the CAA involves the following: (i) 

assessing the health and environmental risks that continue even after sources are subject to 

MACT standards, also known as residual risks; and (ii) developing standards to address 

residual risks, if necessary, to protect public health with an ample margin of safety and to 

prevent adverse environmental effects. For each source category subject to MACT standards, 

EPA is required to complete a residual risk assessment within eight years of the promulgation of 

the initial technology-based standard. 

The Risk and Technology Review (RTR) is a combined effort to evaluate both risk and 

technology as required by the CAA after the application of MACT standards. The RTR program 

therefore links the MACT technology review (under Section 112d(6)) to residual risks (under 

Section 112(f)(2)). 

RTR is divided into phases. RTR Phase I consisted of the first eight separate residual risk 

standards completed through April 2007. RTR Phase II streamlines the residual risk standard 

development process by grouping together MACT source categories with compliance dates of 

2002 and earlier. RTR Phase II is divided into seven bins (Bin 1 through Bin 7) that are based 

loosely on current proposal and promulgation schedules. Secondary lead smelters, the source 

category considered in this analysis, is in Bin 4. 

In June of 2010, EPA issued an Information Collection Request (ICR) pursuant to section 114 of 

the CAA to gather facility-specific information including emissions data. EPA is using 

information on HAP emissions collected through this process to conduct human health risk 

assessments. 

1.3 Purpose of the Multipathway Assessment 

To assess the post-MACT residual risk for the secondary lead smelting source category, EPA is 

modeling human exposure through inhalation of HAPs emitted from secondary lead smelting 

facilities. In addition, EPA is performing a “multipathway” assessment of public health risks via 

non-inhalation exposure pathways such as ingestion of contaminated farm produce and 

livestock, and fish. The potential for significant public health risks due to exposures via 

multipathway routes was screened by first determining whether any sources emitted any PBHAPs. EPA previously identified 14 PB-HAP compounds or compound classes, including 

 

1

 National Emission Standards for Hazardous Air Pollutants From Secondary Lead Smelting, Final Rule (40 CFR 

Parts 9 and 63), U.S. Environmental Protection Agency, 60 Federal Register, Volume 60, Pages 32587-32601, June 

23, 1995. 
2/15/2011 3 DRAFT 

cadmium compounds, chlordane, dioxins, DDE, heptachlor, hexachlorobenzene, 

hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlorinated 

biphenyls, polycyclic organic matter (POM), toxaphene, and trifluralin.2

 

EPA does not necessarily conduct a multipathway assessment for each RTR source category. 

To evaluate the potential for human health risks, reported emissions of PB-HAPs are compared 

to conservative screening level emission concentrations established by EPA for the purposes of 

the RTR program. Facilities whose emissions of a PB-HAP are below the screening levels are 

not expected to pose significant non-inhalation human health risks. In other words, if PB-HAP 

emissions at a facility do not exceed the screening level for that PB-HAP, EPA is reasonably 

certain that the resulting exposures and associated risks are negligible. Source categories with 

facilities emitting PB-HAPs in amounts exceeding the screening levels will be the focus of more 

refined multipathway exposure and risk analyses for RTR. A screening level comparison 

conducted by EPA for the secondary lead smelting source category indicated that emissions 

might exceed screening levels for at least one PB-HAP (see section 2.2). Therefore, EPA 

initiated a multipathway assessment for the source category. This report describes the scope, 

data, methodology, and results of that assessment. 

2 Scope of the Assessment 

This multipathway assessment is intended to evaluate the emissions of a subset of PB-HAPs 

that are released by a subset of facilities in the secondary lead smelting source category. 

These facilities must have a combination of significant emissions (i.e., above emission

screening levels) of all or most of these HAPs as well as surrounding land that can support a 

range of agricultural and subsistence fishing uses. This section describes the selection of these 

HAPs, facilities, and other aspects of the assessment scope. 

2.1 Selection of HAPs 

Only a subset of the HAPs emitted from secondary lead smelters are of potential concern for the 

multipathway risk assessment. These include PB-HAPs, which are toxic, accumulate in the 

food chain, and are not easily degradable. Furthermore, they transfer easily between air, water, 

and land. For these reasons, PB-HAPs are expected to pose the greatest risks to human health 

and ecosystems from multipathway exposure. 

EPA’s assessment of multipathway human exposures for RTR risk assessment focuses on PBHAPs that OAQPS has identified as candidates for multipathway risk assessments. OAQPS 

previously developed a list of fourteen chemicals and chemical groups that are PB-HAPs based 

on a two-step process taking into account the following: (1) their presence on three previously 

existing EPA lists of persistent, bioaccumulative, and toxic substances; and (2) a semiquantitative ranking of toxicity and bioaccumulation potential of the entire list of HAPs.3

The OAQPS list of fourteen PB-HAPs is presented in Table 2-1. This table also shows which of 

the PB-HAPs are commonly emitted in detectable quantities from secondary lead smelters. For 

this source category, cadmium, dioxins/furans, lead, mercury, and polycyclic organic matter 

[POM]) are candidates for further consideration for the multipathway analysis. With respect to 

 

2

 Persistent and bioaccumulative HAP: EPA’s Air Toxics Risk Assessment Library, Volume 1, EPA-453K-04-001A, as 

referenced in the ANPRM and provided on the EPA’s Technology Transfer Network website for Fate, Exposure, 

and Risk Assessment at http://www.epa.gov/ttn/fera/risk_atra_vol1.html. 3

 Air Toxics Risk Assessment (ATRA) Reference Library: http://www.epa.gov/ttn/fera/risk_atra_main.html 
2/15/2011 4 DRAFT 

mercury, we note that using high-end, worst-case assumptions (e.g., assuming that all nondetect emissions test values are equal to the detection limit), we estimated that mercury 

emissions could theoretically be emitted over the de minimis screening level emissions rate. 

Therefore, to be all inclusive, we also included mercury along with POM, cadmium, and dioxins 

for further multipathway analyses. 

Table 2-1. OAQPS List of PB-HAPs

OAQPS PB-HAP Compounds 

Emitted by 

Secondary 

Lead 

Smelters 

Cadmium Compoundsa 

Chlordane 

Chlorinated Dibenzodioxins and Furansa 

DDE 

Heptachlor 

Hexachlorobenzene 

Hexachlorocyclohexane (all isomers) 

Lead Compounds 

Mercury Compoundsa,b 

Methoxychlor 

Polychlorinated biphenyls 

Polycyclic Organic Matter (POM)a,c 

Toxaphene 

Trifluralin 

a

 Compounds for which EPA has developed screening levels 

– see Section 2.2. 

b Most of the test results for mercury emissions for this industry 

were below detection limit. Mercury emissions estimates are 

based on the assumption that all the non-detect test values 

were at the level of the detection limit. Therefore, these 

estimated emissions for mercury are clear overestimates. 

c

 The screening level for POMs has been developed 

specifically for the benzo[a]pyrene species. 

 

2.2 Emission Rate Screening Analysis 

To simplify the overall RTR approach, EPA previously developed emission screening levels that 

represent the level below which it is reasonably certain that emissions will not result in adverse 

human health impacts through non-inhalation exposures. EPA developed the screening levels 

by identifying exposure concentrations resulting in a 1 in 1 million level of cancer risk or non 

cancer chronic hazard quotient of 1 or greater,and then using a multipathway analysis to backcalculate emissions levels corresponding to those exposure concentrations. EPA uses the 

screening levels in screening analyses that help to determine whether multipathway risk 

assessments should be included in RTR evaluations for individual source categories. 

As shown in Table 2-1, EPA has developed screening levels for five PB-HAPs: cadmium, 

chlorinated dibenzodioxins and furans (as 2,3,7,8-Tetrachlorodibenzo-p-dioxin [2,3,7,8-TCDD]), 

mercury, and POMs (as benzo[a]pyrene [BaP]). These screening levels are shown in Table 2-

2. EPA chose these PB-HAPs based on emissions data and toxicity considerations, which 
2/15/2011 5 DRAFT 

indicated that these four PB-HAPs are likely to pose the vast majority of the non-inhalation risks 

to humans from air emissions at sources subject to the residual risk provisions of the CAA. 

Lead is also of concern, but EPA has not developed a screening level for lead in part because 

potential impacts on human health from non-inhalation exposures to lead are evaluated for RTR 

using the National Ambient Air Quality Standard (NAAQS) for lead, which takes into account 

multipathway risks (73 FR 66964) . Therefore, lead emissions are not considered in this 

analysis because the further analysis would be redundant. Table 2-2 also shows speciation and 

equivalency factors, which are discussed in Section 2-3. 

Table 2-2. The Four PB-HAPs with Screening Levels, the Chemicals Constituting 

those PB-HAPs, and the Metal Speciation Factors and Equivalency Factors, where 

Appropriate 

NEI Pollutant Name 

Metal 

Speciation 

Factor 

Equivalency 

Factor PB-HAP 

Screening 

Level (tons 

year-1) a

Acenaphthylene NA 0.05 

POM as BaP 3.597E-04 

15-PAH NA 0.05 

16-PAH NA 0.05 

2-Chloronaphthalene NA 0.05 

2-Methylnaphthalene NA 0.05 

3-Methylcholanthrene NA 1 

5-Methylchrysene NA 1 

7,12-Dimethylbenz[a]Anthracene NA 100 

7-PAH NA 0.05 

Acenaphthene NA 0.05 

Anthracene NA 0.05 

B[j]Fluoranthen NA 0.10 

Benz[a]Anthraceneb

NA 0.10 

Benzo(c)phenanthrene NA 0.05 

Benzo[a]Pyreneb

NA 1 

Benzo[b]Fluorantheneb

NA 0.10 

Benzo[b+k]Fluoranthene NA 0.10 

Benzo[e]Pyrene NA 0.05 

Benzo[g,h,i,]Perylene NA 0.05 

Benzo[k]Fluorantheneb

NA 0.10 

Chryseneb

NA 0.01 

Dibenzo[a,e]Pyrene NA 1 

Dibenzo[a,h]Anthraceneb

NA 1 

Dibenzo[a,h]Pyrene NA 10 

Dibenzo[a,i]Pyrene NA 10 

Dibenzo[a,j]Acridine NA 0.10 

Dibenzo[a,l]Pyrene NA 0.05 

Fluoranthene NA 0.05 

Fluorene NA 0.05 

Indeno[1,2,3-c,d]Pyreneb

NA 0.10 

PAH, total NA 0.10 
2/15/2011 6 DRAFT 

Table 2-2. The Four PB-HAPs with Screening Levels, the Chemicals Constituting 

those PB-HAPs, and the Metal Speciation Factors and Equivalency Factors, where 

Appropriate 

NEI Pollutant Name 

Metal 

Speciation 

Factor 

Equivalency 

Factor PB-HAP 

Screening 

Level (tons 

year-1) a

Perylene NA 0.05 

Phenanthrene NA 0.05 

Polycyclic Organic Matter NA 0.05 

Pyreneb

NA 0.05 

1,2,3,4,6,7,8-Heptachlorodibenzofuran NA 0.01 

Chlorinated 

Dibenzodioxins and 

Furans as 2,3,7,8-

TCDD 

9.175E-10 

1,2,3,4,7,8,9-Heptachlorodibenzofuran NA 0.01 

1,2,3,4,7,8-Hexachlorodibenzofuran NA 0.1 

1,2,3,6,7,8-Hexachlorodibenzofuran NA 0.1 

1,2,3,7,8,9-Hexachlorodibenzofuran NA 0.1 

2,3,4,6,7,8-Hexachlorodibenzofuran NA 0.1 

1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin NA 0.1 

1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin NA 0.1 

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin NA 0.1 

1,2,3,4,6,7,8-Heptachlorodibenzo-p-dioxin NA 0.01 

1,2,3,4,6,7,8,9-Octachlorodibenzofuran NA 0.0003 

1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin NA 0.0003 

1,2,3,7,8-Pentachlorodibenzofuran NA 0.03 

2,3,4,7,8-Pentachlorodibenzofuran NA 0.3 

1,2,3,7,8-Pentachlorodibenzo-p-dioxin NA 1 

2,3,7,8-Tetrachlorodibenzofuran NA 0.1 

2,3,7,8-Tetrachlorodibenzo-p-dioxin NA 1 

Mercury 1 NA 

Divalent Mercury 1.946E-03 

Mercury & Compounds 1 NA 

Gaseous Divalent Mercuryb

1 NA 

Particulate Divalent Mercuryb

1 NA 

Cadmiumb

1 NA 

Cadmium & Compounds 1 NA Cadmium 3.509E-02 

Cadmium Oxide 0.8753 NA a

 Throughout this document, “tons” refers to imperial short tons (i.e., 2,000 pounds). b

 Emitted by secondary lead smelters. 

2.3 PB-HAPs Selected for Detailed Residual Risk Assessment 

For the screening assessment for secondary lead smelting, EPA compared the screening levels 

to emissions data for all fourteen secondary lead smelting facilities. The emissions data for all 

HAPs were obtained from, and derived from, submittals by each industry source to a recent 

ICR. The procedures listed below were set up to modify (where necessary) the cadmium, 

mercury, dioxin, and POM emissions from the NEI before they were summed together to arrive 

at the total facility-specific emission amount for an individual PB-HAP. 
2/15/2011 7 DRAFT 

 The emission amount of cadmium oxide (the only metal PB-HAP species with known 

composition) was speciated by the molecular weight of cadmium (which drives the 

toxicity of cadmium oxide). This metal speciation is shown in Table 2-2. 

 For chemicals contributing to POM, emissions were scaled by estimated relative 

cancer potency, with BaP used as a surrogate for environmental behavior and 

exposure. These toxicity-weighting factors relative to the cancer slope factor for BaP 

are shown in Table 2-2. 

 Similar to POM, for dioxin and furan congeners, emissions were scaled by estimated 

relative cancer potency, with 2,3,7,8-TCDD used as a surrogate for environmental 

behavior and exposure. These toxicity-weighting factors for 2,3,7,8-TCDD are shown 

in Table 2-2. 

 Emissions of generic mercury (i.e., “mercury” and “mercury and compounds”) were 

converted to divalent and elemental components according to a speciation factor 

assumed for the source category. For secondary lead smelting, the assumed 

speciation in the absence of source-specific data was 20 percent divalent mercury 

and 80 percent elemental mercury. Elemental mercury was not screened because 

the sum of its emissions across all facilities in NEI is less than the elemental mercury 

emission screening level4

. 

The results of the screening analysis are presented in Table 2-3. All of the fourteen secondary 

lead smelting facilities emitted all four of the PB-HAPs of potential concern. PB-HAP-specific 

results include the following: 

 Cadmium – All facilities emitted cadmium and one facility exceeded the cadmium 

screening level. The highest-emitting facility produced 2.71E-01 tons year-1 of 

cadmium (about 7.7 times the screening level). 

 BaP – All facilities emitted POM and eight facilities exceeded the screening level of 

POM as BaP. One facility exceeded the screening level by at least a factor of 10, 

with the highest-emitting facility producing 8.56E-03 tons year-1 of POM as BaP 

(about 24 times the screening level). 

 2,3,7,8-TCDD – All facilities emitted dioxins and all facilities exceeded the screening 

level of chlorinated dibenzodioxins and furans as 2,3,7,8-TCDD. Ten facilities 

exceeded the screening level by at least a factor of 10, with the highest-emitting 

facility producing 2.11E-05 tons year-1 of chlorinated dibenzodioxins and furans as 

2,3,7,8-TCDD (about 23,000 times the screening level). 

 Divalent Mercury – All facilities emitted mercury and nine exceeded the divalent 

mercury screening level based on our conservative estimates of emissions (see 

above, Section 2.1)). The highest-emitting facility produced 7.92E-03 tons year-1 of 

divalent mercury (about 4.1 times the screening level). 

 

4

 As noted above, most of the test results for mercury emissions for this industry were below detection limits. 

Mercury emissions estimates are based on the assumption that all the non-detect test values were at the level of 

the detection limit. Therefore, these estimated emissions for mercury are clear overestimates 
2/15/2011 8 DRAFT 

Multiple facilities exceeded the screening level for 2,3,7,8-TCDD, BaP, and divalent mercury. 

Cadmium was the only remaining PB-HAP currently with a screening level. One facility 

exceeded the screening level for cadmium while several others were at least 0.5 times the 

screening level. Given these findings, all four of these PB-HAPs (dioxins as 2,3,7,8-TCDD, 

POMs as BaP, divalent mercury, and cadmium) were included in modeling for the multipathway 

assessment for secondary lead smelting. All other HAPs commonly emitted from secondary 

lead smelters are not included in the multipathway assessment either because they are not PBHAPs or they are PB-HAPs but do not currently have screening levels. As described above, 

lead was not included in the proposed HAPs because non-inhalation exposures to lead are 

evaluated for RTR using the NAAQS for lead, which takes into account multipathway risks. 

2.4 Selection of Relevant Exposure Pathways and Approach to Exposure 

Assessment 

For an inhalation risk assessment, the MIR can be approximated (taking into account a range of 

assumptions) using modeled long-term average air concentrations associated with a source and 

information on where people reside. For an evaluation of non-inhalation exposures, however, 

estimating the risk to the “most exposed” individual can be more difficult because chemical 

concentrations in environmental media to which people are exposed and individual exposure 

patterns associated with ingestion can vary greatly depending on location, timing, and other 

factors. For example, people can be exposed to chemicals that accumulate in the farm food 

chain by consuming a variety of fruits and vegetables, each of which may or may not be grown 

in the vicinity of the source. The amount of each type of produce consumed can vary widely 

among the individuals in a population, as can the fraction of each type of produce that is actually 

impacted by emissions from a source. 

To simplify the exposure and risk analysis of a multipathway air toxics risk assessment, a 

scenario approach can be employed. This approach, described in more detail in Chapter 15 of 

ATRA,3

 entails evaluating a combination of exposure pathways by which an individual might be 

exposed to PB-HAPs (i.e., a set of “exposure scenarios”). The scenario approach provides a 

systematic method for evaluating the relative importance of exposure pathways (e.g., 

consumption of farm food products versus consumption of fish) that are of potential concern for 

different chemicals and locations. Only scenarios that are plausible for the situation of interest 

are typically evaluated, and the assessment usually focuses on those scenarios that are 

assumed a priori to lead to the highest individual exposure and risks. Risk metrics such as 

incremental lifetime cancer risk and chronic non-cancer hazard quotient are calculated as 

appropriate for each scenario, and information regarding the likelihood of a specific exposure 

scenario actually occurring can be used to develop estimates of uncertainty for each scenario 

and the variations thereof. 

For this RTR case study, exposure estimates and risks were calculated for the following two 

basic scenarios: 

 A farmer scenario, involving an individual living on a farm homestead in the vicinity 

of the source who does the following: (a) consumes produce grown on and meat and 

animal products raised on the farm; and (b) incidentally ingests surface soil at the 

location of the farm homestead; and 

 A recreational fisher scenario, involving an individual who regularly consumes fish 

caught in freshwater lakes in the vicinity of the source of interest. 
2/15/2011 9 DRAFT 

Variations of these two scenarios were evaluated using different assumptions about location of 

the farm homestead or the water body, the age of the individual exposed (for non-cancer 

hazards), the assumed ingestion rate of each food type, and other factors. Exposures were 

calculated for five age groups and three subpopulations of recreational anglers (Hispanic, 

Laotian, and Vietnamese descent) who are culturally or economically disposed to higher rates of 

fish consumption. In addition, exposure estimates and risks to infants via breastfeeding were 

evaluated in the case of 2,3,7,8-TCDD, with the assumption that the nursing mother was 

exposed to chemicals via one of the two basic scenarios listed above. 

Figure 2-1 presents the conceptual exposure model for the farmer scenario. The arrows 

represent the movement of chemical of concern through the environment and farm food chain. 

In this exposure scenario, the hypothetical receptors consume produce, meat, and animal 

products, and incidentally ingest soil. Figure 2-2 presents the conceptual exposure model for 

the fisher scenario. The hypothetical receptor, a recreational fisher, consumes fish from a 

contaminated water body. 

These scenarios are expected to cover most of the highest possible exposures and risks. In 

addition to ingestion, non-inhalation exposure to PB-HAPs can also occur by way of the dermal 

pathway. However, the risk from dermal exposure was expected to be a small fraction of the 

risk from inhalation exposure or ingestion exposure. Therefore, the risk from dermal exposure 

was not calculated. 

Figure 2-1. Conceptual Exposure Model for Farmer Scenario 
2/15/2011 10 DRAFT 

Figure 2-2. Conceptual Exposure Model for Fisher Scenario 

2.5 Selection of Facilities 

The secondary lead smelting source category includes fourteen active facilities. Because it was 

not feasible or necessary to model residual risks for all facilities in the source category, EPA has 

chosen a subset of two sample facilities for inclusion in the multipathway assessment. 

The proposed facilities were identified by a methodology that evaluated data for eight selection 

criteria. These criteria are listed below, and further details about how these criteria were applied 

to select secondary lead smelting facilities for the multipathway assessment are provided in 

Section A-1 of Appendix A. 

1. Emission rates of PB-HAPs; 

2. Proximity to water bodies; 

3. Proximity to farmland; 

4. Average rainfall; 

5. Average wind speed and direction; 

6. Smelting furnace type; 

7. Local change in elevation; and 

8. Geographic representativeness of sites throughout the US. 

Based on these criteria and data described in Section A-1 of Appendix A, the following two 

secondary lead smelting facilities were chosen for the multipathway assessment: 

1. Exide facility (NEI6493) near Frisco, TX 

2. Revere facility (NEI38394) near Middletown, NY 

As part of a model comparison study in 2003,5

 ICF has already run multipathway simulations for 

these two facilities, which reduces the level of time and effort required in the current analysis. 

 

5

 2003 TRIM.FaTE Evaluation Using Secondary Lead Facilities: TRIM.FaTE Application for Two Secondary Lead 

Smelting Facilities And TRIM.FaTE - IEM Model Comparison, Prepared for EPA by ICF Consulting, September 30, 

2003 
2/15/2011 11 DRAFT 

Details about each of these facilities, including emissions, local climatology, land use, 

watershed properties, and elevation, are presented in Section 3. 

3 Methodology 

Figure 3-1 provides an overview of the methodology used to assess multipathway risks from 

secondary lead smelting emissions of PB-HAPs from the 2 case study sources. The approach 

can be divided into the following four steps that correspond to the green boxes in Figure 3-1: 

1. Fate and transport modeling of PB-HAPs emitted to air by the source that partition 

into soil, water, and other environmental media (including fish);6 

2. Modeling of transfer and uptake of PB-HAPs into farm food chain media (produce, 

livestock, dairy products) from soil and air; 

3. Estimating ingestion exposures as a result of contact with the various selected media 

and estimating average daily ingestion doses for a hypothetical human receptor; and 

4. Calculating lifetime cancer risk estimates or chronic non-cancer hazard quotients, as 

appropriate, for each PB-HAP and comparing these metrics to selected risk 

management points of departure used in the RTR. 

The fate and transport modeling step depicted in the first box in Figure 3-1 was implemented 

using the Fate, Transport, and Ecological Exposure module of the TRIM modeling system 

(TRIM.FaTE).7

 In particular, Version 3.6.2 of TRIM.FaTE was used to model the fate and 

 

6 As discussed below, concentrations in fish calculated by the TRIM.FaTE model were used in the current approach 

to estimate ingestion exposures for humans consuming fish. Modeling of fish concentrations is therefore discussed 

in this document as part of the fate and transport modeling. Uptake of PB-HAPs into all other biotic media assumed 

to be ingested is modeled in the second step. 

7 TRIM.FaTE is a spatially explicit, compartmental mass balance model that describes the movement and 

transformation of pollutants over time, through a user-defined, bounded system that includes both biotic and abiotic 

compartments. Outputs include pollutant concentrations in multiple environmental media and biota, which provide 

exposure estimates for ecological receptors (i.e., plants and animals). The output concentrations from TRIM.FaTE 

Figure 3-1. Multipathway Analysis Methodology Overview 
2/15/2011 12 DRAFT 

transport of emitted PB-HAPs and to estimate HAP concentrations in relevant media. Additional 

information about TRIM.FaTE, including support documentation, software, and the TRIM.FaTE 

public reference library, is available on EPA’s TTN.8

 The fate and transport modeling 

methodology, including source characterization, is discussed further in Section 3.1 

The Multimedia Ingestion Risk Calculator (MIRC), a Microsoft Access-based computer 

framework developed by ICF International primarily for use in EPA’s residual risk assessments, 

was used to complete the calculations required for estimating PB-HAP concentrations in farm 

food chain media, average daily ingestion doses, and cancer risks and chronic non-cancer HQs. 

The algorithms included in MIRC that calculate chemical concentrations in farm food chain 

media and ingestion exposures for hypothetical individuals were generally obtained from EPA’s 

Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities, or 

HHRAP.9

 The implementation of these algorithms in MIRC is described in detail in Appendix C 

(Attachment C-2) to EPA’s report to the Science Advisory Board (SAB) on the risk assessment 

methods used in the RTR program.10 These algorithms, and the required exposure factors and 

other parameter values, were compiled into a database. An overview of the computational 

processes this tool carries out and the types of input data it requires is presented in Figure 3-2. 

This figure demonstrates the general relationships between the relevant TRIM.FaTE outputs 

(i.e., chemical concentrations in environmental media and fish) and the ingestion exposure and 

risk calculations carried out using MIRC. A description of the exposure and risk calculations for 

this analysis is presented in Section 3.2. 

 are also intended to be used as inputs to a human ingestion exposure model to estimate human exposures. 

Significant features of TRIM.FaTE include: (1) a fully coupled multimedia model; (2) user flexibility in defining 

scenarios, in terms of the links among compartments, and number and types of compartments, as appropriate for 

the application spatial and temporal scale; (3) transparent, user-accessible algorithm and input library that allows the 

user to review and modify how environmental transfer and transformation processes are modeled; (4) a full 

accounting of all of the pollutant as it moves among environmental compartments during simulation; (5) an 

embedded procedure to characterize uncertainty and variability; and (6) the capability to provide exposure estimates 

for ecological receptors. 

8

 Additional TRIM.FaTE Information: http://www.epa.gov/ttn/fera/trim_gen.html 9

 Human Health Risk Assessment Protocol for Hazardous Waste Combustion Facilities (including the Hazardous 

Waste Companion Database of chemical-specific parameter values). U.S. Environmental Protection Agency, Office 

of Solid Waste and Emergency Response, Washington, DC. EPA-530-R-05-006. September. 10 U.S. Environmental Protection Agency (EPA). 2009. Risk and Technology Review (RTR) Risk Assessment 

Methodologies: For Review by the EPA’s Science Advisory Board, Office of Air Quality Planning and Standards, 

Research Triangle Park, NC. EPA-452/R-09-006, June 2009. 
2/15/2011 13 DRAFT 

Figure 3-2. Overview of Process Carried Out in the Multimedia Ingestion Risk Calculator 

3.1 Fate and Transport Modeling (TRIM.FaTE) 

This section describes the TRIM.FaTE modeling conducted for this assessment. Most of the 

material presented here describes the assumptions and data sources used to set TRIM.FaTE 

inputs and settings related to meteorological inputs used by the model, the spatial aspects of 

the modeled regions for each site, characteristics of abiotic environmental compartments and 

plants included in the scenario, and the aquatic ecosystems set up in each water body of 

interest. 

3.1.1 Facility Details 

As discussed in Section 2.5 and Section A-1 of Appendix A, two secondary lead smelting 

facilities were selected based mainly on the following: (1) emission rates as they compare to the 

screening levels; (2) proximity to freshwater lakes of reasonable size; and (3) proximity to land 

used to support a range of agricultural activities. To a lesser extent, a site’s average rainfall, 

wind conditions, process types, and local terrain were also considered in choosing facilities for 

modeling. 

As shown in Figure 3-3, the Exide facility near Frisco, TX (hereafter referred to as the Exide 

Frisco facility) generally meets proximity criteria. The facility is located on the border of Collin 

and Denton Counties (est. 2009 populations 791,631 and 658,616, respectively11), less than 2 

km south of Frisco and about 37 km north of Dallas (est. 2009 populations 102,413 and 

1,299,542, resepectively11). A large lake (Lake Lewisville) is between 8 and 25 km west of the 

facility. Figure 3-3 shows Lake Lewisville, and other nearby water bodies are also labeled but 

were not modeled (see Section 3.1.5 for more discussion on the modeling area). There is 

 

11 US Census: http://www.census.gov 
2/15/2011 14 DRAFT 

farmland and pastureland mostly north and east of the facility, and all eight surrounding counties 

(Collin, Cooke, Denton, Dallas, Grayson, Kaufman, Rockwall, and Tarrant, all in Texas) support 

livestock and crops grown for human and animal consumption.12

The Frisco area is not arid, and the terrain nearby the facility ranges from about 115 m to 250 m 

above sea level (elevation shading is shown in Figure 3-8 in a later section). The abundant 

farmland to the north of the facility was downwind from the facility about 52 percent of the 

modeled hours, and the small farmland areas to the south were downwind from the facility about 

26 percent of the modeled hours. Lake Lewisville to the west of the facility was downwind from 

the facility about 15 percent of the modeled hours (see Figure 3-4). The choices of water 

bodies and farmland to model are discussed further in Sections 3.1.6.1 and 3.1.6.2, 

respectively. 

The Exide Frisco facility has both blast and reverberatory furnaces. As shown in Table 3-1, its 

total emissions of 2,3,7,8-TCDD far exceed the screening level, and its total emissions of 

divalent mercury and BaP also exceed their respective screening levels. Its emissions of 

cadmium are about a factor of 5 below its screening level. These results suggested that it was 

not possible to eliminate emissions of 2,3,7,8-TCDD, divalent mercury, and BaP from concern 

with respect to potential incremental multipathway cancer risks exceeding 1 in 1 million and/or 

incremental multipathway non-cancer hazard quotients exceeding 1. Because the emissions of 

cadmium are below the screening level, they are not expected to lead to cancer risks greater 

than 1 in 1 million and hazard quotients greater than 1. 

The Exide Frisco facility also has the added benefit of having been analyzed in 2003 as part of 

an evaluation of TRIM.FaTE.5

 This provides a “head start” in defining the extent and 

dimensions of the modeling domain, defining meteorological data, and estimating the various 

TRIM.FaTE input settings 

With respect to the selection of these two facilities, we believe that these sites, based on the 

criteria outlined in Section 2.5 represent an upper end estimate with respect to multipathway 

risks associated with this source category. However, there is uncertainty as to whether this 

analysis of these two facilities captures that absolute highest multipathway risk from this source 

category. 

 

12 USDA 2007 Census of Agriculture: http://www.agcensus.usda.gov/Publications/2007/Full_Report/index.asp 
2/15/2011 15 DRAFT 

Figure 3-3. Location of the Exide Frisco Facility. Surrounding Cities and Water Bodies 

are Shown. Shading Indicates Land Use Type in 2001a

 (With Simplified Categories) 

a

 USGS National Land Cover Database 2001: http://www.mrlc.gov/nlcd.php

Figure 3-4. Wind Rose Representing Wind Flow Conditions Near the Exide Frisco Facility 

a

 From the surface hourly meteorological data from 

the Dallas/Ft. Worth Airport (WBAN ID 03927) for 

years 1990-199313

 

13 NOAA Hourly Surface Meteorological Data: http://www.ncdc.noaa.gov 

NORTH

SOUTH

WEST EAST

5%

10%

15%

20%

25%
2/15/2011 16 DRAFT 

Table 3-1. PB-HAP Emissions for the Exide Frisco and Revere Middletown 

Facilities 

Facility PB-HAP Emissions 

(tons year-1) 

Screening 

Level 

(tons year-1) 

Screening Results 

Exide 

Frisco 

Dioxins/Furans as 

2,3,7,8-TCDD 

4.26E-06 1.31E-09 Exceeds screening level 

by x3,249 

Cadmium 

Compounds 

2.03E-02 3.50E-02 Below screening level by 

x0.58 

PAH as BaP 1.90E-03 3.59E-04 Exceeds screening level 

by x5.29 

Divalent Mercury 3.00E-03 1.94E-03 Exceeds screening level 

by x1.55 

Elemental Mercurya 1.20E-02 -- -- 

Revere 

Middletown 

Dioxins/Furans as 

2,3,7,8-TCDD 

7.30E-09 1.31E-09 Exceeds screening level 

by x5.57 

Cadmium 

Compounds 

3.04E-03 3.50E-02 Below screening level by 

x0.09 

PAH as BaP 6.81E-04 3.59E-04 Exceeds screening level 

by x1.90 

Divalent Mercury 2.73E-03 1.94E-03 Exceeds screening level 

by x1.41 

Elemental Mercurya 1.09E-02 -- -- a

 Elemental mercury does not have a screening level. It is included in modeled emissions because a small 

amount of it is involved in transformations into divalent mercury. 

As shown in Figure 3-5, the Revere facility near Middletown, NY (hereafter referred to as the 

Revere Middletown facility) meets the proximity criteria. The facility is located in Orange County 

(est. 2009 population 383,53211), 5 km east of Middletown and 7 km north of Goshen (est. 2009 

population 25,936 and 13,879, resepectively11). Many relatively small lakes are near the facility, 

including Silver Lake, Shawangunk Lake, Glenmere Lake, and Tomahawk Lake (2 km west, 11 

km west, 15 km south, and 12 km west of the facility, respectively). Figure 3-5 shows these 

lakes, and other nearby waterbodies are also labeled but were not modeled (see Section 3.1.5 

for more discussion on the modeling area). There is farmland and pastureland mostly northeast 

and southwest of the facility, and all eleven surrounding counties (Dutchess, Orange, Putnam, 

Sullivan, Ulster, and Westchester in New York; Bergen, Morris, Passaic, and Sussex in New 

Jersey; and Pike in Pennsylvania) support livestock and crops grown for human and animal 

consumption.12

The Middletown area is not arid. The terrain nearby the facility ranges from about 90 m to 

320 m above sea level, though elevations approach 0 m near the Hudson River (30 km east of 

the facility) and exceed 500 m in areas further than 20 km west and north of the facility (e.g., 

Mount Don Bosco and Walnut Mountain). Elevation shading is shown in Figure 3-10 (in a later 

section). The abundant farmland to the north, east, and south of the facility were downwind 

from the facility about 17, 39, and 24 percent of the modeled hours, respectively. Shawangunk 

and Silver Lake (west of the facility), Glenmere Lake (south), and Tomahawk Lake (east) were 

downwind from the facility about 20, 24, and 39 percent of the modeled hours, respectively (see 

Figure 3-6). 
2/15/2011 17 DRAFT 

The Revere Middletown facility has reverberatory furnaces. As shown in Table 3-1, its total 

emissions of divalent mercury, BaP, and especially 2,3,7,8-TCDD exceeds their respective 

screening levels. Its emissions of cadmium are well below the screening level. These results 

suggested that it was not possible to eliminate emissions of divalent mercury, BaP, and 2,3,7,8-

TCDD from concern with respect to potential incremental multipathway cancer risks exceeding 1 

in 1 million and/or incremental multipathway non-cancer hazard quotients exceeding 1. 

Because the emissions of cadmium are below the screening level, they are not expected to lead 

to cancer risks greater than 1 in 1 million and hazard quotients greater than 1. 

As with the Exide Frisco facility, the Revere Middletown facility has the added benefit of having 

been analyzed in 2003 as part of an evaluation of TRIM.FaTE.5

 This provides a “head start” in 

defining the extent and dimensions of the modeling domain, defining meteorological data, and 

estimating the various TRIM.FaTE input settings. 

The modeling scenario duration was 50 years (i.e., sufficient time to achieve steady state 

concentrations in the environment), and emissions of the PB-HAPs were assumed to be 

constant over the course of the simulation 

Figure 3-5. Location of the Revere Middletown Facility. Surrounding Cities and Water 

Bodies are Shown. Shading Indicates Land Use Type in 2001a

 (With Simplified 

Categories) 

a

 USGS National Land Cover Database 2001: http://www.mrlc.gov/nlcd.php
2/15/2011 18 DRAFT 

Figure 3-6. Wind Rose Representing Wind Flow Conditions Near the Revere Middletown 

Facilitya

a

 From the surface hourly meteorological data from the Allentown 

International Airport (WBAN ID 14737) for years 1990-199313 

3.1.2 Stack Details 

The emissions data from EPA’s ICR for this source categoryalso provide the coordinates of 

each emission source as well as the physical parameters of the source (release height, exit gas 

temperature and velocity, release diameter, area source dimensions where appropriate). 

However, TRIM.FaTE only requires source coordinates and release height. TRIM.FaTE does 

not calculate plume rise, which is the vertical movement of emissions due to buoyancy and 

momentum forces; instead, only the height at which a pollutant is emitted (i.e., the source 

height) and the mixing height determine if the emissions are released in the mixing layer or in 

the upper-air layer. In order to account for the effect of elevated stack exit gas temperatures on 

effective source height, hourly plume rise was estimated for each source in the modeling 

scenarios. The plume rise calculation methodology is described in more detail in Section A-2 of 

Appendix A. 

Because using hour-varying source heights adds significant complexity to the modeling 

(compared with a static height), hourly plume rise was only added to a source’s height if it 

caused at least 5 percent of its pollutant mass to be emitted in the upper-air layer. For both 

secondary lead smelting facilities, adding plume rise, reduced the number of hours where the 

pollutants were emitted into the mixing layer by up to about 1 percent. This did not meet the 5 

percent criterion that triggers the modeling of hourly plume rise values, so static source heights 

were modeled for both facilities. 

As described in greater detail in Section A-2 of Appendix A, individual sources emitting PBHAPs were condensed into modeling groups based on having similar stack parameters (with 

special attention to stack heights). For each modeling group, the emissions were summed 

across the component sources and the source height was equal to the shortest component 

source (a health-conservative choice). These modeling groups and their emissions and 

modeling source heights are shown in Table 3-2. 

NORTH

SOUTH

WEST EAST

3%

6%

9%

12%

15%
2/15/2011 19 DRAFT 

Table 3-2. The Source Groups Used for TRIM.FaTE Modeling, Along with the Emissions 

and Height of Each Source Group, for the Exide Frisco and Revere Middletown 

Facilities 

Facility 

Final 

Source 

Group for 

Modeling 

Source 

Height 

(m) 

Emissions of 

Cadmium 

Compounds 

(tons year-1) 

Emissions of 

Dioxins/Furans as 

2,3,7,8-TCDD TEQs 

(tons year-1) 

Emissions of 

Divalent 

Mercury 

(tons year-1) 

Emissions of 

PAHs as BaP 

(tons year-1) 

Exide Frisco 

STKA 3.048 1.91E-02 0.00E+00 1.79E-03 0.00E+00 

STKB 30.63 1.15E-03 4.26E-06 8.45E-04 1.90E-03 

STKC 22.86 7.77E-05 0.00E+00 3.62E-04 0.00E+00 

Revere 

Middletown 

STKA 3.048 2.97E-03 6.00E-09 2.15E-03 5.06E-05 

STKB 60.96 6.57E-05 1.30E-09 5.23E-04 6.30E-04 

STKC 30.48 5.89E-06 0.00E+00 5.55E-05 0.00E+00 

3.1.3 Chemical Properties 

The chemical/physical properties that TRIM.FaTE requires, such as Henry’s law constant and 

molecular weight, were obtained from peer-reviewed and standard reference sources. 

Numerous other chemical-specific properties that are related to a particular abiotic or biotic 

compartment type are discussed in later sections. 

3.1.4 Meteorological Data 

TRIM.FaTE uses several meteorological inputs to determine chemical transfers among the air 

compartments in a scenario via advective transport (i.e., wind-driven physical movement 

through the atmosphere) and from air to underlying soil or water surfaces via deposition 

transfers. These processes determine the long-term spatial patterns of chemical distribution 

within the scenario, and modeled concentrations are highly sensitive to the meteorological 

inputs used in TRIM.FaTE. Consequently, an initial step in developing the TRIM.FaTE 

application for these case studies was to collect meteorological data for the expected modeling 

region (i.e., the areas near Frisco, TX, and the areas near Middletown, NY). The long-term 

trends in these data were then used to inform the development of the modeling layout that was 

the basis of these scenarios. To maintain consistency with the development of the TRIM.FaTE 

application, we present in this section the specifications of the meteorological data and a 

summary of the long-term temporal trends. 

The meteorological inputs that TRIM.FaTE requires include wind speed, wind direction, 

precipitation, ambient air temperature, and mixing height. Because the Exide Frisco and the 

Revere Middletown facilities were analyzed in 2003 as part of an evaluation of TRIM.FaTE 

using secondary lead smelters5

, the same meteorology data used in those previous analyses 

were used in this analysis. There was one exception to this – the previous analyses used 5 

years of meteorological data, but because the data must be repeated to create a 50-year record 

for modeling, and because leap years occur every four years, the meteorological data were 

reduced to four years to make it simpler to repeat. The meteorological data for the Exide Frisco 

facility were the 1990-1993 hourly surface data from the Dallas/Ft. Worth Airport (WBAN ID 

03927; 13 km southwest of the facility)13 and the twice-daily upper-air data from the Stephenville 

Weather Service Meteorological Observatory (WBAN ID 13901; 163 km southwest of the 
2/15/2011 20 DRAFT 

facility; has since relocated to the Dallas/Ft. Worth Airport).14 The meteorological data for the 

Revere Middletown facility were the 1990-1993 hourly surface data from the Allentown 

International Airport (WBAN ID 14737; 133 km southwest of the facility)13 and the twice-daily 

upper-air data from the Albany Airport (WBAN ID 14735; 147 km north-northeast of the 

facility).14 

Newer meteorological data were not pursued for a number of reasons. First, these early 1990s 

meteorological data deviate from the 1971-2000 historical 30-year normal values (Table 3-3) in 

ways that are similar to or smaller than those of the Ravena Portland Cement analysis for the 

2009 EPA SAB review.15 In the natural environment, rainfall amounts that are larger than 

climatologically typical quantities in the Exide Frisco area can have the effect of increasing 

modeled wet deposition. Increased rainfall may also decrease chemical concentrations in water 

bodies by increasing their volumes and flush rates, although the water bodies would also collect 

increased amounts of chemicals from their tributaries. The relatively small deviations from 

normal meteorological conditions (i.e., wetter and slightly warmer for Dallas, warmer and slightly 

drier for Allentown) in the data year used were assumed to be acceptable for this assessment. 

Second, wind flows and mixing heights were not expected to have changed significantly since 

the early 1990s. Finally, weather extremes on the scale of hours or days will not significantly 

affect annualized TRIM.FaTE modeling results. 

Further details about the meteorology data, including wind patterns during precipitation events 

and mixing height distributions, are provided in Section A-3 of Appendix A. 

Table 3-3. Average Meteorological Conditions Near the Exide Frisco and Revere 

Middletown Facilities. Rounding Errors Might be Present 

Exide Frisco Revere Middletown 

Dallas/Ft. Worth Airport (WBAN 

03927) 

Allentown International Airport (WBAN 

ID 14737) 

Daily 

Max. 

Temp. 

(

◦

C) 

Daily 

Min. 

Temp. 

(

◦

C) 

Daily 

Mean 

Temp. 

(

◦

C) 

Total 

Precip. 

(mm) 

Daily 

Max. 

Temp. 

(

◦

C) 

Daily 

Min. 

Temp. 

(

◦

C) 

Daily 

Mean 

Temp. 

(

◦

C) 

Total 

Precip. 

(mm) 

Annual 1971-

2000 Climate 

Normal 

Averagesa

24.3 12.8 18.6 882 15.8 4.8 10.3 1,147 

Annual 

Averages from 

Data Used in 

This Analysis 

24.3 13.5 18.9 1,103 16.6 6.3 11.5 1,081 

Deviation from 

Normal 

Averages 

0 

(-0.2%)

+0.7 

(+5.2%)

+0.3 

(+1.5%)

+221 

(+25%)

+0.8 

(+5.1%)

+1.6 

(+32.9%)

+1.2 

(+11.2%)

-66 

(-5.8%)

a

 1971-2000 Climate Normals – http://cdo.ncdc.noaa.gov/cgibin/climatenormals/climatenormals.pl?directive=prod_select2&prodtype=CLIM81&subrnum

 

14 NOAA/ESRL Radiosonde Database: http://www.esrl.noaa.gov/raobs/ 15 Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by the EPA’s Science Advisory 

Board, EPA-452/R-09-006, June 2009. 
2/15/2011 21 DRAFT 

3.1.5 Extent and Dimensions of Modeled Environment 

This section describes the environment for which media concentrations were estimated using 

TRIM.FaTE and the geographic characteristics of the modeled environment (e.g., layout of the 

modeled domain and geometry of the constituents included). 

The design of the modeling layout was developed based primarily on the physical and 

geographic characteristics of the watershed(s) surrounding the facility as well as the nearby 

land use data. This section provides a brief overview of the features present in the vicinity of 

the modeled facilities. 

As illustrated in Figure 3-7, the Exide Frisco facility lies within the Upper Trinity Basin (HUC120301. Rivers and streams in the Upper Trinity Basin drain southward, eventually into Trinity 

Bay, Galveston Bay, and the Gulf of Mexico. Large and small lakes and reservoirs are located 

throughout the Upper Trinity Basin, including Lake Lewisville (87 km2

 surface area), which was 

between 8 and 25 km west of the facility (labeled in Figure 3-3). 

Based on the 2001 National Land Cover Database (NLCD)16 from the US Geological Survey 

(USGS), land use in the immediate vicinity of the Exide Frisco facility was classified as a mix of 

grasses, land in agricultural use (for pasture and cropland), water, and commercial and 

residential uses, with small amounts of forest (see Figure 3-3). Land use becomes more urban 

proceeding southward from the facility towards the Dallas and Fort Worth metropolitan areas. 

Dallas and Fort Worth are the only major metropolitan areas in the Upper Trinity Basin. A map 

similar to Figure 3-7 but showing land use throughout the basin is provided in Section A-4 of 

Appendix A (Figure A-5). 

As illustrated in Figure 3-8, the Revere Middletown facility is located within the Upper Hudson 

Basin (HUC-020200), though the TRIM.FaTE modeling scenario for this facility also extends into 

the Lower Hudson (HUC-020301) and Upper Delaware (HUC-020401) Basins. Rivers and 

streams within the Upper and Lower Hudson Basins drain into the Hudson River and/or into the 

New York and New Jersey harbors. Rivers and streams within the Upper Delaware Basin drain 

into Delaware Bay. Relatively small lakes and reservoirs are located throughout basins, 

including Silver Lake, Shawangunk Lake, Glenmere Lake, and Tomahawk Lake nearby the 

facility (2 km west of the facility and 0.182 km2

, 11 km west and 0.405 km2, 15 km south and 

0.688 km2

, and 12 km west and 1.314 km2, respectively; labeled in Figure 3-5). 

Based on the 2001 National Land Cover Database (NLCD)16 from the US Geological Survey 

(USGS), land use in the immediate vicinity of the Revere Middletown facility was classified as a 

mix of forest (mostly deciduous), land in agricultural use (for pasture and cropland), water, and 

commercial and residential uses (see Figure 3-5). Land use becomes more urban proceeding 

southeastward from the facility towards the New York City metropolitan area in the Lower 

Hudson Basin. A map similar to Figure 3-8 but showing land use throughout the basins is 

provided in Section A-4 of Appendix A (Figure A-6). 

 

16 NLCD: http://www.mrlc.gov/. The 2001 dataset is the most recent. 
2/15/2011 22 DRAFT 

Figure 3-7. Streams, Rivers, and Water Bodies of the Upper Trinity Basin, in which the 

Exide Frisco Facility is Locateda

I   

a

 Upper Trinity Basin data were obtained from the US Geological Survey (USGS) 

National Hydrography Dataset (NHD).17 These data are based on the content of 

USGS 1:100,000-scale data. One lake (Lake Ray Roberts, the northern-most 

lake visible in the figure) was not in the USGS NHD data, so it is displayed in the 

figure using a water bodies file provided by Environmental Systems Research 

Institute, Inc. (ESRI)18 and based on the US National Atlas Water Feature 

Areas.19

 

17 USGS NHD: http://nhd.usgs.gov/index.html 18 ESRI: http://www.esri.com/ 19 US National Atlas: http://www.nationalatlas.gov/index.html 
2/15/2011 23 DRAFT 

Figure 3-8. Streams, Rivers, and Water Bodies of the Upper Hudson Basin, in which the 

Revere Middletown Facility is Located, as well as the Nearby Lower Hudson and Upper 

Delaware Basinsa

a

 Upper Hudson, Lower Hudson, and Upper Delaware Basin data were obtained 

from the US Geological Survey (USGS) National Hydrography Dataset (NHD).17 

These data are based on the content of USGS 1:100,000-scale data. The 

Hudson River is highlighted in the figure using a water bodies file provided by 

Environmental Systems Research Institute, Inc. (ESRI)18 and based on the US 

National Atlas Water Feature Areas.19

3.1.6 TRIM.FaTE Parcel Design 

The TRIM.FaTE surface parcel layout is the two-dimensional configuration of soil and water 

regions included in the modeled domain; this is overlain by the air parcel layout. These layouts 

provide the spatial reference for three-dimensional compartments that hold the modeled 

chemical mass. 

The spatial extent of a TRIM.FaTE modeling scenario is generally at least a 15-km radius 

around the facility, and it can extend out to perhaps 35 km as necessary to capture important 

water bodies and their watersheds.20 The modeling extent is almost always square in shape 

because of the methodology used to design the air parcels (discussed in Section 3.1.6.5). 

 

20 This proximity is derived from an internal study using the ISCST3 model (“Summary of ISC Analysis at the NY 

TRIM Site”, prepared by ICF International, Draft, May 6, 2002). The results of this study suggest that increasing the 

modeling domain beyond a 15-km radius of the facility generally does not capture enough additional chemical 
2/15/2011 24 DRAFT 

3.1.6.1 Modeled Water Bodies 

For the Exide Frisco facility, one large water body (Lake Lewisville, west of the facility; labeled in 

Figure 3-3) was parameterized in the modeled area for the purposes of modeling fish 

concentrations for the risk assessment. Lake Lewisville is large enough to support large 

populations of fish. Lake Lewisville was downwind from the facility about 15 percent of modeled 

hours (see Figure 3-4 for the wind rose). 

For the Revere Middletown facility, the following three water bodies were parameterized for 

modeling: Glenmere Lake to the south, Tomahawk Lake to the east, and Silver Lake to the west 

(all are labeled in Figure 3-5). Glenmere and Tomahawk Lakes are large enough to support a 

fish population of reasonable size for sustenance fishing, while Silver Lake is small but was 

judged to be large enough to support a fishable aquatic population if it were stocked regularly. 

Among these three water bodies, Tomahawk Lake was most often downwind from the facility 

during modeled hours (39 percent), while Silver Lake and Glenmere Lake were respectively 

downwind of the facility 20 and 24 percent of modeled hours (see Figure 3-6 for the wind rose). 

As shown in Figures 3-9 and 3-10, the surface parcels created to represent the water bodies 

were developed as simplified shapes with the goal of representing the actual water body surface 

areas as accurately as possible (the water body surface area determines the amount of 

chemical mass deposited from air compartments in TRIM.FaTE). Note that Lake Lewisville was 

represented by several adjacent surface parcels due to its irregular shape, with each of the 

arms (“Lake_W”, “Lake_NW”, “Lake_NE”, and “Lake_E”) feeding into the largest of the Lake 

Lewisville parcels (“Lake”). Table 3-4 indicates the actual surface areas of these water bodies 

compared to the surface areas of the parcels representing them. The total surface area of the 

Lake Lewisville surface parcels was within 4 percent of the actual water body surface area 

reported in the USGS NHD.17 The surface areas of the water body surface parcels in the 

Revere Middletown facility are within 1 percent of the actual surface areas. 

Shawangunk Lake was at first intended to be a modeled water body for the Revere Middletown 

facility, but it was later discovered that it has restricted access and may be surrounded by a 

fence that discourages fishing. The parcel for Shawangunk Lake was still modeled, but it was 

modeled as a surface soil parcel instead of surface water. 

 deposition to counterbalance the added computational resources needed to model the larger scenario. However, 

increasing the proximity somewhat (e.g., to 25 or 35 km) in at least one direction may be desired in order to model a 

certain water body and its watershed or to accommodate location-specific wind patterns. 
2/15/2011 25 DRAFT 

Figure 3-9. The Surface Parcels for the Exide Frisco Facility, along with Local Elevationa

a

 USGS National Elevation Dataset: http://ned.usgs.gov/
2/15/2011 26 DRAFT 

Figure 3-10. The Surface Parcels for the Revere Middletown Facility, along with Local 

Elevationa

a

 USGS National Elevation Dataset: http://ned.usgs.gov/

Table 3-4. Water Bodies Included in the Modeled Regions of the Exide Frisco and 

Revere Middletown Facilities 

Facility Water Body 

Name 

Actual Surface 

Area (km2)

a

 

Surface Parcel 

Name 

Modeled 

Surface Area 

of TRIM.FaTE 

Parcel (km2) 

Exide Frisco Lake Lewisville 87.831 Lake 28.571 

Lake_E 6.183 

Lake_NE 11.292 

Lake_NW 28.432 

Lake_W 9.882 

(Total) (84.36) 

Revere 

Middletown 

Glenmere Lake 1.314 Glenmere Lake 1.302 

Tomahawk Lake 0.688 Tomahawk Lake 0.69 

Silver Lake 0.182 Silver Lake 0.183 

a

 Actual surface areas are from the USGS NHD.17
2/15/2011 27 DRAFT 

3.1.6.2 Modeled Source and Agricultural Parcels 

The source parcel for TRIM.FaTE applications was calibrated to roughly capture the actual 

locations of all emissions sources considered in the application. The source parcels for the 

Exide Frisco and Revere Middletown facilities are roughly 380 m and 460 m on a side, 

respectively (Figure 3-11 and Figure 3-12, respectively). 

Agricultural use regions also were included in the modeled domain to estimate soil 

concentrations and other TRIM.FaTE outputs for use in calculating FFC exposures. The goals 

are the following: (1) place two farm regions within about 5 km of the facility (where modeled 

chemical deposition values should be largest); (2) place the two regions in different compass 

directions from the facility (ideally in opposite directions and in directions of favorable wind 

flows); and (3) generally represent actual farmed areas according to land use data. The sizes of 

farm regions do not need to be large. Farm regions were bisected to create two approximately 

equal parcels to accommodate modeling of tilled and untilled surface soil for use in estimating 

various farm food media concentrations. 

For the Exide Frisco facility, all of these goals for placing farm regions were met. As shown in 

Figure 3-11, one farm region was centered approximately 6 km north-northwest of the facility, 

and one farm region was centered approximately 3 km south-southeast of the facility. The wind 

flow in the area was most often towards the north, with a secondary peak towards the south 

(see Figure 3-4 for the wind rose). Consequently, the “northern” farm region was very often 

downwind of the facility, while the “southern” farm region was less often downwind of the facility 

and more often upwind. These farm regions generally represent actual farmland and 

pastureland according to 2001 land use types16, although some parts of the farm parcels 

contain other land use types. 

For the Revere Middletown analysis, all of these goals for placing farm regions were met well or 

reasonably well. As shown in Figure 3-12, one farm region was centered approximately 2.5 km 

north of the facility, and one farm region was centered approximately 2 km southwest of the 

facility. The wind flow in the area was most often towards the eastern half of the compass, with 

a secondary peak towards the southwest (see Figure 3-6 for the wind rose); as such, the 

“northern” farm region was often neither upwind nor downwind of the facility, while the 

“southern” farm region was often split between being upwind and downwind of the facility. 

These farm regions generally represent actual farmland and pastureland according to 2001 land 

use types, although some parts of the farm parcels contain other land use types. 
2/15/2011 28 DRAFT 

Figure 3-11. Locations of the Farm Parcels and Source Parcel for the Exide Frisco 

Facility, along with Local Land Use16
2/15/2011 29 DRAFT 

Figure 3-12. Locations of the Farm parcels and Source Parcel for the Revere Middletown 

Facility, along with Local Land Use16

3.1.6.3 Layout of Watershed Surface Parcels and Outer Surface Parcels 

The chief goal in designing the surface parcel layout was to accurately capture the watersheds 

surrounding the water bodies selected for modeling (i.e., those that contain fish people are 

assumed to eat). In pursuing this goal, surface parcels were generally drawn following 

subwatershed boundaries while also considering topographic data21 and wind flow data. Some 

surface parcels contain multiple subwatersheds. The shapes of surface parcels were kept as 

simple as possible to reduce complexity in the layout and corresponding run time. As required 

by TRIM.FaTE, no surface parcel was fully contained within any other surface parcel; all surface 

parcels share at least one side or corner with another parcel. 

Because the air parcel layout was square and has a predetermined extent irrespective of the 

site (see Section 3.1.6.6 for more detail), it extends beyond the extent of the surface parcels 

that are drawn to subwatershed boundaries (hereafter referred to as “watershed surface 

parcels”). “Outer” surface parcels must be added beyond the extent of the watershed surface 

parcels, out to the extent of the air parcels, so that every air parcel has a surface parcel 

underneath it. These outer surface parcels are not drawn to watershed boundaries; instead, 

their inside boundaries are shared with the watershed surface parcels and their outside 

boundaries are shared with the outer extent of the air parcels. Any watersheds or water bodies 

 

21 USGS National Elevation Dataset: http://ned.usgs.gov/ 
2/15/2011 30 DRAFT 

in these outer surface parcels are not specifically modeled (i.e., surface parcels are not drawn 

specific to the watersheds or water bodies). 

The watershed surface parcels are shown as the polygons inside the full modeling extent 

square in Figure 3-9 for Exide Frisco and Figure 3-10 for Revere Middletown. The full modeling 

extent of both facility scenarios was about 79 km square. 

3.1.6.4 Surface Parcel Vegetation Types 

Calculations of the areal coverage of each land use type (using the 2001 NLCD16) within each 

surface soil (i.e., not water body) parcel were used to set each parcel’s dominant vegetation 

type. This strategy results in some simplification because most parcels are at least several 

square kilometers in area and contain a variety of land use types. However, key parcels such 

as the farms are drawn smaller in order to more accurately represent actual land use. 

The vegetation type determines which algorithms and inputs will be used to model terrestrial 

plants. Table 3-5 summarizes the vegetation types used in this analysis and the corresponding 

inputs and algorithms that vary by land use type. In the current TRIM.FaTE library, only one 

vegetation type can be assigned per surface soil parcel. The four vegetation types that are in 

the current TRIM.FaTE library are: 

 Deciduous Forest: Deciduous tree plant cover (leaf and leaf particle), “regular” 

surface soil depth (i.e., 0.01 m surface soil depth, 0.79 m root zone depth), standard 

site-specific soil erosion parameters, relatively small coverage management factor 

(i.e., 0.039); 

 Coniferous Forest: Same as “Deciduous Forest” except with coniferous tree plant 

cover and a larger coverage management factor (i.e., 0.1); 

 Grasses/Herbs (and Untilled Farmland): Same as “Deciduous Forest” except with 

grasses/herbs plant cover (leaf, leaf particle, root, and stem) and a smaller coverage 

management factor (i.e., 0.003); 

 Agriculture: Currently not used; 

 Untilled Soil (and No Vegetation): Same as “Deciduous Forest” except with no 

plants; and 

 Tilled Soil (and Tilled Farmland): Same “Untilled Soil” and “No Vegetation” except 

with thicker surface soil (i.e., 0.2 m) and thinner root zone depth (i.e., 0.6 m). 

Note that although “Tilled Farmland” contains no plants the tilled soil concentrations are used in 

the exposure assessment methodology (see Section 3.2) to approximate concentrations in 

produce and animal feed (and untilled soil concentrations are used for where animals graze and 

people live). The “No Vegetation” category was used in situations where the area was highly 

developed/paved or where the user wants to omit terrestrial plants from a model run. 
2/15/2011 31 DRAFT 

Table 3-5. TRIM.FaTE Properties that Vary by Vegetation Type 

Vegetation 

Type 

Litter Fall Allow 

Exchange

Coverage 

Management 

Factora

Plants 

Included in 

Parcel 

Surface 

Soil Depth 

(m) 

Root Zone 

Depth (m)

Deciduous 

Forest 

15 percent of 

leaves fall 

each day for 

30 days after 

first autumn 

freeze 

Off after 

first 

autumn 

freeze; On 

after last 

spring 

freeze 

0.039 Leaf and Leaf 

Particle: 

Deciduous 

Forest 

0.01 0.79 

Coniferous 

Forest 

99 percent of 

leaves fall 

over 6 years 

Always On 0.1 Leaf and Leaf 

Particle: 

Coniferous 

Forest 

0.01 0.79 

Grasses/Herbs 

and Untilled 

Farmland 

15 percent of 

leaves fall 

each day for 

30 days after 

first autumn 

freeze 

Off after 

first 

autumn 

freeze; On 

after last 

spring 

freeze 

0.003 Leaf, Leaf 

Particle, Root, 

and Stem: 

Grasses/Herbs

0.01 0.79 

Tilled Soil and 

Tilled Farmland 

N/A N/A 0.2 N/A 0.01 0.79 

Untilled Soil 

and No 

Vegetation 

N/A N/A 0.2 N/A 0.20 0.60 

a Coverage Management Factor Source: Engel, B., 2001. Estimating Soil Loss with USLE. Purdue 

University. http://cobweb.ecn.purdue.edu/~sedspec/sedspec/doc/usleapp.doc. 

The vegetation type for each surface parcel was determined by calculating the most common 

land use type (using the 2001 NLCD16) in each parcel. Further details on determining the 

vegetation type for each surface parcel are provided in Section A-4.2 of Appendix A. 

Figure 3-13 shows the TRIM vegetation type assigned to each surface parcel for the Exide 

Frisco facility. The majority of surface parcels were set as grasses/herbs. Because of the 

heavier urbanization to the south and southwest of the facility, some parcels were set as no 

vegetation. 

Figure 3-14 shows the TRIM vegetation type assigned to each surface parcel for the Revere 

Middletown facility. The majority of surface parcels were set as deciduous forest. A parcel 

nearby the facility (i.e., parcel W1) was set as no vegetation due to high urbanization. For 

several surface parcels (i.e., parcels N, NE, and S) that were not intended to be modeled 

agriculture areas, their most frequent TRIM vegetation type was either tilled or untilled soil. For 

one parcel (i.e., parcel E2), its majority land use type was wetlands, and its most frequent 

vegetation type was deciduous forest, so it was set as wetlands/deciduous forest. Specific 

parameters in the Universal Soil Loss Equation were modified to account for the deciduous 

forest canopy and wetlands floor (see Section 3.1.7 for more information on soil properties). 

Recall that Shawangunk Lake was originally intended to be a water body parcel, but it was later 

discovered that it is fenced and fishing may not be allowed, so its parcel was kept but it was set 

as deciduous forest. 
2/15/2011 32 DRAFT 

Figure 3-13. The TRIM Vegetation Types of the Surface Parcels for the Exide Frisco 

Facility 
2/15/2011 33 DRAFT 

Figure 3-14. The TRIM Vegetation Types of the Surface Parcels for the Revere 

Middletown Facility 

3.1.6.5 Air Parcel Layout 

The design of the air parcel layout for the TRIM.FaTE scenario was simpler than for the surface 

parcel layout because watersheds, terrain, and land use are not taken into account in the 

design. The air parcel layout consists of concentric squares around the source overlaid onto 

lines emanating radially in a regular 45-degree pattern. The distance from the side of a given 

square to the side of the next outward square increases with increasing distance from the 

source, in much the same way that modeled pollutant air concentrations and depositions 

generally decrease rapidly with increasing distance from the source. Radial lines divide each 

concentric square such that eight parcels of equal area can be formed. This radial layout 

minimizes the TRIM.FaTE bias for over-accumulation of mass along the axes of the grid (refer 

to the TRIM.FaTE User's Guide22 for additional discussion of this design). Further details on the 

sizes of the concentric air parcel squares are provided in Section A-4.3 of Appendix A. 

Overall, 33 air parcels, including the source parcel, are included in each air parcel layout for the 

Exide Frisco and Revere Middletown analyses. Figures 3-15 and 3-16 show the air parcel and 

surface parcel layouts overlaid for Exide Frisco and Revere Middletown, respectively. Figures 

showing the modeling names of these air parcels are provided in Section A-4.3 of Appendix A 

(Figures A-8 and A-9). 

 

22 TRIM.FaTE User's Guide: http://www.epa.gov/ttn/fera/trim_fate.html#current_user 
2/15/2011 34 DRAFT 

Figure 3-15. The Air Parcels for the Exide Frisco Facility, Overlaid with the Surface 

Parcels 
2/15/2011 35 DRAFT 

Figure 3-16. The Air Parcels for the Revere Middletown Facility, Overlaid with the Surface 

Parcels 

3.1.7 Abiotic Environment 

TRIM.FaTE requires various abiotic environmental properties for each compartment in the 

scenario. Examples of abiotic environmental properties include the depth of surface soil, soil 

porosity and water content, erosion and runoff rates from surface soil to water bodies, and 

suspended sediment concentration). Where site-specific data were readily available for this 

assessment, they were used. Regional or national defaults were used in numerous instances, 

especially for those parameters that are not expected to influence chemical concentration 

dramatically. This section lists some of the important values used for this application. A 

complete list of TRIM.FaTE inputs for abiotic compartments is provided in Appendix B. 

3.1.7.1 Soil Properties 

For both facilities, soils were modeled as three stacked soil layers (surface, root zone, and 

vadose zone soil) over ground water. For soils that were not tilled, the surface soil layer was 

assumed to be 1 centimeter (cm) deep. For soils that were tilled, a depth of 20 cm for the 

homogeneously mixed “surface soil” layer was assumed. The tilled soil compartments were 

used to estimate concentrations in farmed soil where produce is grown. Depths for surface and 

subsurface soil layers are presented in Table 3-6. Where soils were assumed to be tilled, the 

thickness of the root zone soil was reduced accordingly. Depths to and thicknesses of the 

vadose zone soil and groundwater layers were identical regardless of whether the surface layer 

was tilled. 
2/15/2011 36 DRAFT 

Table 3-6. Soil and Groundwater Compartment Depths

Zone 

Exide Frisco Revere Middletown 

Untilled Soil 

(m)a Tilled Soil (m) a Untilled Soil (m)a Tilled Soil (m) a

Surface soil 0.00 – 0.01 0.00 – 0.20 0.00 – 0.01 0.00 – 0.20 

Root soil 0.01 – 0.80 0.20 – 0.80 0.01 – 0.80 0.20 – 0.80 

Vadose soil 0.80 – 2.50 0.80 – 2.50 0.80 – 2.20 0.80 – 2.20 

Groundwater 2.50 – 5.50 2.50 – 5.50 2.20 – 5.20 2.20 – 5.20 

a Depths are reported as meters from the surface. 

For most of the basic surface soil properties, values were defined using typical regional or state 

values compiled by McKone et al. for use in multipathway modeling.23 A list of selected soil 

properties is shown below in Table 3-7. 

Table 3-7. Selected Properties of Soil and Groundwater 

Property Surface Soil Root Zone 

Soil Vadose Soil Groundwater 

Exide Frisco 

pH 6.8 6.8 6.8 6.8 

Organic carbon content 0.007 0.007 0.004 0.004 

Volume fraction, vapor (air content) 0.23 0.19 0.16 -- 

Volume fraction, liquid (water content) 0.22 0.25 0.26 -- 

Average downwind vertical velocity of 

water infiltrating the soil (m day-1) 6.03E-4 6.03E-4 6.03E-4 -- 

Revere Middletown 

pH 6.8 6.8 6.8 6.8 

Organic carbon content 0.013 0.013 0.004 0.004 

Volume fraction, vapor (air content) 0.36 0.31 0.21 -- 

Volume fraction, liquid (water content) 0.13 0.17 0.17 -- 

Average downwind vertical velocity of 

water infiltrating the soil (m day-1) 5.92E-4 5.92E-4 5.92E-4 -- 

3.1.7.2 Erosion 

Erosion rates for each surface parcel for all facilities were estimated using the Universal Soil 

Loss Equation (USLE), with a sediment delivery (SD) ratio adjustment. The USLE was intended 

to predict the long-term average soil losses from individual field areas24 and represents the 

sheet and rill erosion from a small plot or agricultural field. Application of the USLE to an entire 

watershed requires modification of the equation result to account for subsequent re-deposition 

of eroded soil before it reaches the water body. The SD ratio was developed for this purpose: it 

estimates the fraction of sediment that reaches a water body based on the size of the 

watershed. 

The USLE and SD equations use only a few inputs; representative site-specific values were 

developed to estimate erosion for this application with readily available data. Rainfall/erosivity 

values were estimated from the isoerodent gradient map of the continental U.S. presented in 

Wischmeier 1978. In cases where the facility location fell between lines of equal erosivity, linear 

 

23 Basic surface soil properties: McKone, TE. Bodnar, AB. Hertwich, EG. 2001. Development and Evaluation of StateSpecific Landscape Data Sets for Multimedia Source-to-Dose Models. University of California, Berkeley School of 

Public Health. Available online: http://repositories.cdlib.org/lbnl/LBNL-43722/ 24 Wischmeier, W. H., and D.D. Smith. 1978. Predicting rainfall erosion losses – a guide to conservation planning. 

U.S. Department of Agriculture, Agriculture Handbook No. 537. 
2/15/2011 37 DRAFT 

interpolation was used. Soils data were obtained from the Soil Survey Geographic 

(SSURGO 2.2) database for the counties of interest (obtained from the USDA Natural 

Resources Conservation Service) to calculate site-specific soil erodibility factors. ArcMap was 

used to generate K values for each parcel with the same land use type (see Section 3.1.6.5). 

Different cover management factors were used for farm parcels and natural forests and grasses 

and herbs. Calculated erosion rates for each surface soil parcel ranged from 6.89E-7 to 3.56E4 kg m-2

 day-1 for the Exide Frisco facility and from 3.81E-6 to 5.66E-4 kg m-2 day-1 for the 

Revere Middletown facility. 

The USLE is an empirical model, and therefore modeled conditions must be similar to 

conditions for which the model has been calibrated to output useful results. In particular, the 

USLE was designed for application to a single slope or field, rather than to an entire watershed. 

Using average values across a watershed parcel would likely introduce uncertainties in the 

prediction; predictions are improved when individual analyses of the slopes within the watershed 

are conducted. We note that the EPA’s HHRAP documentation states that using the USLE to 

calculate sediment load to a lake from the surrounding watershed can sometimes lead to 

overestimates.9

 The use of area-weighted averages for some of the USLE variables helps to 

avoid under- or over-estimating by assuming uniformity across the watershed. The areaweighted soil erodibility factor (K) and cover management factor (C) are not expected to 

contribute significantly to inaccurate soil erosion estimates. 

Estimating the length-slope (LS) factor was more challenging than any other factor for the 

USLE25, especially for complex watersheds. In actual watersheds, the entire watershed has 

neither uniform slope length nor uniform slope steepness. Also, due to nonlinearities in the 

equation to calculate the LS factor, the assumption of uniformity can result in underestimates or 

overestimates of the LS factor. The use of average slope likely would underpredict the LS 

factor. A representative slope-length was selected for each scenario using Google Terrain 

software.26 Finally, uncertainty was introduced when using the SD ratio to account for the redeposition of soil before it reaches the water body. The degree by which the SD ratio 

underpredicts or overpredicts actual sediment delivery was unknown. 

3.1.7.3 Runoff 

Runoff from surface parcels into water bodies was calculated by subtracting the annual 

evaporation (0.0030 and 0.0021 m year-1 for Exide Frisco and Revere Middletown, 

respectively)23 from the annual precipitation (1.10 and 1.08 m year-1 for Exide Frisco and Revere 

Middletown, respectively).27 The resulting total runoff values include interflow and ground water 

recharge; to estimate surface runoff only, total runoff was reduced by the amount expected to 

infiltrate the groundwater28. Total runoff for all surface parcels into the each lake is reported in 

Table 3-9 (source parcel excluded). The source parcels were not included in runoff because the 

facilities are assumed to have different containment configurations than the rest of the modeled 

areas. 

 

25 Universal Soil Loss Equation: Moore, I. D., and J. P. Wilson. 1992. Length-slope factors for the Revised Universal 

Soil Loss Equation: simplified method of estimation. Soil and Water Cons. 47(5): 423-428. 

26 Google Terrain: Google Inc. 2010. Google Terrain as part of Google Maps. Available at www.maps.google.com 27 Annual average precipitation: Meteorology data is consistent with the previous iteration of this analysis. National 

Oceanic and Atmospheric Administration, National Climatic Data Center. Surface Hourly Meteorological Data: 

Available at http://www.ncdc.noaa.gov 28 USGS water summary table: U.S. Geological Survey. 1985. National Water Summary 1985 – Hydrologic Events 

and Surface-Water Resources. Water-Supply Paper 2300. 
2/15/2011 38 DRAFT 

3.1.7.4 Surface Water and Sediment Properties 

Important surface water and sediment properties for all lakes and the river are shown in 

Table 3-8; sources for these properties and values for all other inputs are listed in Appendix B. 

Table 3-8. Selected Surface Water and Sediment Properties 

Property Exide Frisco Revere 

Middletown 

Temperature (K) 298 298 

Suspended sediment concentration 

(kg[sediment]/ kg[water]) 0.05 0.05 

Water column and sediment organic carbon 

content (kg[organic carbon]/ kg[solid wet 

weight]) 

0.02 0.02 

Water column pH 8.229 7.7a

Sediment pH 7.3 7.3 

Chlorophyll concentration (mg L-1) 0.0029 0.0029 

Chloride concentration (mg L-1) 8 8 

Algae density in water column (g L-1) 0.0205 0.010087 a

Multiple sources were available for this variable.30,31,32,33 Values was selected based on 

the most reliable and, when available, site-specific sources. 

3.1.7.5 Water Transfers 

A water balance within each modeled lake was assumed to estimate annual flush rates. Inputs 

to each water body included runoff from the surrounding watershed and direct precipitation to 

the lake. Outputs from the water body included flushing through the lake outlet and evaporation 

from the lake surface. 

Long-term average precipitation rates used to calculate the water balances for each lake were 

obtained from nearby meteorological stations identified in Section 3.1.4. For each water body, 

this precipitation was added as a water input, based on surface area of the water body. Runoff 

from the watershed was calculated by subtracting annual average evapotranspiration and 

groundwater infiltration from annual average precipitation and multiplying the difference by the 

total watershed area. Evapotranspiration data for each state were obtained from the USGS 

Water Summary Table.28

Evaporation from each water body’s surface was subtracted from the water inputs to estimate 

the volumetric flow of water leaving the water bodies. Using surface areas and mean depths to 

calculate lake volumes, turnover rates in flushes per year were calculated. The values of these 

 

29 Lake Lewisville water data: U.S. EPA. 1977. National Eutrophication Survey Working Paper Series Report on Lake 

Lewisville (Garza-Little Elm) Denton County, Texas EPA Region VI. Working Paper 650. 

30 New York water data(a): New York CSLAP. 2009. Downstate Region Report. New York Department of 

Environmental Conservation. 

31 New York water data(b): New York CSLAP. 2009. Annual Report. New York Department of Environmental 

Conservation. 

32 New York water data(c): Allied Biological. 2007. Glenmere Lake Northern Cricket Frog Survey. Glenmere 

Conservation Coalition. Accessed online at http://sandrasdtsestudentblog.files.wordpress.com/2010/02/northerncricket-frog-survey-report.pdf. (12/14/10) 

33 New York water data(d): Kishbaugh, Scott. 2010. Personal communication - Adirondack Lake Survey Corporation 

1987 raw data. 
2/15/2011 39 DRAFT 

turnover rates are presented in Table 3-9. For the Exide Frisco facility, Lake Lewisville was split 

up into four arms and a central lake in order to control for varying flow characteristics throughout 

the lake. The central part of the lake was used in developing risk values. Table 3-9 presents 

the results for each arm of the lake. 

Table 3-9. Lake Parameters for Modeled Water Bodies 

Water Body Name Area 

(km2) 

Turnover 

Rate 

(flushes 

per year) 

Average 

Depth 

(m) 

Discharge 

Rate (m3

s-1) 

Sediment Resuspension 

Rate 

(m[sediment] 

day-1) 

Surface 

Runoff 

from 

Land (m3

yr-1) 

Exide Frisco 

Lake Lewisville 28.57 7.64 6a

 41.55 9.64E-5 5.46E6 

E Arm 6.18 1.01 6a

 1.18 9.59E-5 7.63E6 

NE Arm 11.29 7.47 6a

 16.05 1.02E-4 1.23E8 

W Arm 9.88 2.49 6a

 4.68 9.81E-5 3.42E7 

NW Arm 28.43 3.31 6a

 17.95 9.88E-5 1.34E8 

Revere Middletown 

Silver Lake 0.18 16.76 3 b

 0.29 1.03E-4 6.72E6 

Glenmere Lake 1.30 3.99 1 b

 0.16 9.65E-5 2.83E6 

Tomahawk Lake 0.69 33.43 2.4b

 1.75 1.06E-5 4.07E7 a

 Multiple sources were available for this variable.29,34 Value was selected based on the most reliable source. b

 Multiple sources were available for this variable.30,33 Value was selected based on the most reliable source.

3.1.7.6 Sediment 

The sediment balance of each watershed/water body system modeled was estimated by 

accounting for sediment inputs to the water body based on the erosion calculations and the 

removal of sediment from the modeled system via benthic burial and outflow of suspended 

sediment in the water column. In this scenario, assumptions about the physical environment 

were used to calculate sediment input through erosion and sediment removal through 

suspended sediment flushing. All sediment inputs to the watershed are derived from the 

erosion calculations. 

For this assessment, all sediment that was not flushed out as suspended sediment was 

assumed to be buried (i.e., removed from the modeled system by transfer to the consolidated 

benthic sediment layer, where it was assumed to no longer interact with the overlying water 

column). Suspended sediment depositional velocity was used to calculate total deposition to 

the lake bottom, and the difference between deposition and burial was then used to calculate 

the sediment that was resuspended. In TRIM.FaTE, the processes of burial and resuspension 

are defined to be equal and opposite. The suspended sediment concentration for all water 

bodies was 0.05 kg[sediment] m-3[water]. 

 

34 Lake Lewisville visitor’s data: Lewisville Convention and Visitors Bureau. 2009. Lake Lewisville. Accessed online at 

http://www.visitlewisville.com/where-to-play/lake-and-outdoors/lewisville-lake/. (11-30-2010) 
2/15/2011 40 DRAFT 

3.1.8 Biotic Environment 

3.1.8.1 Terrestrial Plants 

Modeling plants in TRIM.FaTE requires two properties: (1) an “allow exchange” property that is 

used in TRIM.FaTE algorithms to determine whether plants are actively growing (and thus able 

to exchange chemical mass to and from the ambient air and take up chemical mass from soil); 

and (2) a litterfall rate property that dictates when and how fast chemical mass accumulated by 

a leaf is transferred to underlying surface soil (to account for chemical transfers to soil from 

leaves dropped by deciduous trees and plants). For this assessment, the dates at which these 

seasonal events occur were based on the dates of the first and last frosts reported for the 

regions in which the three facilities are located.35 Litterfall was assumed to begin on the first 

day of frost and to end 30 days later, with a litterfall rate of 15 percent of the remaining detritus 

falling per day. 

Modeling terrestrial plants also requires an assigned vegetation type for each surface parcel. 

See Section 3.1.6.5 for details on how each parcel was assigned a vegetation type. 

3.1.8.2 Aquatic Ecosystems 

The aquatic food web was an important part of the multipathway assessment because the 

chemical concentrations modeled in fish are used to calculate human ingestion exposure and 

risks associated with eating contaminated fish. A biokinetic approach to modeling 

bioaccumulation in fish was used in the assessment. The primary producers (first trophic level) 

in the TRIM.FaTE aquatic ecosystems are algae and macrophytes. The scenario used for all 

aquatic food webs in this assessment includes a benthic invertebrate compartment to represent 

the primary invertebrate consumers (second trophic level) in the benthic environment, and 

zooplankton to represent the primary invertebrate consumers in the water column. The benthic 

and water column fish compartments represent the higher tropic levels in the aquatic system. 

For TRIM.FaTE to provide reasonable predictions of the distribution of a chemical across biotic 

and abiotic compartments in aquatic systems, the biomass of the aquatic biotic compartments 

must represent all biota in the system and the distribution of biomass among the trophic levels 

and groups must be as realistic as possible. 

To support the development of a relatively generic freshwater aquatic ecosystem in which to 

model bioaccumulation in fish, a literature search, review, and analysis was conducted in 

support of developing and parameterizing aquatic biotic compartments for TRIM.FaTE.36 This 

research demonstrated that the diversity of species and food webs across U.S. aquatic 

ecosystems is substantial, reflecting the wide range of sizes, locations, and physical/chemical 

attributes of both flowing (rivers, streams) and low-flow (ponds, lakes, reservoirs) water bodies. 

In general, lentic bodies of water (lakes and ponds) are at a higher risk of accumulating 

contaminants in both sediments and biota than are lotic systems (rivers, streams). Also, the 

previous research suggested that a lake of at least 60 hectares (ha) likely would be sufficient to 

support higher trophic level predatory fish, with some fraction of their diet comprising smaller 

fish.36

 

35 Frost/Freeze Data: Koss, W; Owenby, J; Steurer, P, Ezell, D. (1988). Freeze/Frost Data. Climatology of the U.S. 

No. 20. Supplement No. 1. National Climatic Data Center. Available at: 

http://www.ncdc.noaa.gov/oa/climate/freezefrost/freezefrost.pdf 36 Memorandum from ICF to Deirdre Murphy and Terri Hollingsworth, U.S. EPA. TRIM.FaTE Screening Scenario: 

Aquatic Food Web Analysis. October 15, 2005. 
2/15/2011 41 DRAFT 

For Lake Lewisville in the Exide Frisco modeling zone, ICF verified from available literature that 

the lake supported a range of biotic components consistent with the default aquatic biota 

compartments in the TRIM.FaTE model.37 These components included macrophytes, algae, 

zooplankton, benthic invertebrates, benthic omnivores, benthic carnivores, water column 

planktivores, water column omnivores and water column carnivores. However, site-specific data 

on the precise biomass densities of these biotic components in Lake Lewisville were not 

available. ICF therefore used empirical equations to predict the biomass densities of the 

modeled biotic compartments on the basis of the annual average total phosphorus levels in the 

lake.38 The relative biomass densities of different types of fish were calibrated within the 

predicted empirical ranges to reflect the appropriate level of bioaccumulation and 

biomagnification in the food chain. The biomass densities for the various aquatic biota 

compartments included in TRIM.FaTE for Lake Lewisville are presented in Table 3-10. 

Table 3-10. Biomass Parameters for Lake Lewisville 

in the Exide Frisco Facility

Biotic Compartment Biomass Density Units 

Macrophytes 0.5 kg[ww] m-2

Algae 0.0205 g[ww] L-1

Zooplankton 0.0379 kg[ww] m-2

Benthic Invertebrates 0.0182 kg[ww] m-2

Benthic Omnivores 0.00466 kg[ww] m-2

Benthic Carnivores 0.00233 kg[ww] m-2

Water Column 

Planktivores 

0.00466 kg[ww] m-2

Water Column 

Omnivores 

0.001169 kg[ww] m-2

Water Column 

Carnivores 

0.00047 kg[ww] m-2

Annual Average Total Phosphorus Level = 80.37 μg L-1 39

For the Revere Middletown site, several lakes were encompassed by the modeling zone, 

including Shawangunk Lake, Tomahawk Lake, Silver Lake and Glenmere Lake. Although 

considerable site-specific data were available on Shawangunk Lake, this lake is a designated 

no-fishing zone and therefore could not contribute to fish consumption-related exposures in the 

model. In the absence of site-specific data on annual average total phosphorus in the other 

lakes, however, we used total phosphorus data from Lake Shawangunk to predict biotic 

densities in the other lakes.40 The biomass densities for the various aquatic biota compartments 

in TRIM.FaTE for Lakes Tomahawk, Silver and Glenmere are presented in Table 3-11. 

 

37 Lake Lewisville environmental assessment: Tiered Environmental Assessment To The Lake Lewisville 

Programmatic Environmental Assessment, U.S. Army Corps of Engineers, Fort Worth District, May 2005. 38 Phosphorus: Peters, R.H. 1986. The role of prediction in limnology. Limnological Oceanography 31(5): 1143-1159. 39 Texas lake use assessment: Texas Commission on Environmental Quality. Reservoir and Lake Use Support 

Assessment -- accessed online at 

http://www.tceq.state.tx.us/assets/public/compliance/monops/water/02twqmar/14_res&lkass.pdf 

40 New York State Department of Environmental Conservation. New York Citizen’s Statewide Lake Assessment Program (CSLAP),

2009. Appendix A, Lake Water Quality Summary ‐‐ accessed online at

http://www.cslap.net/2009/Shawangunk%20Lake%20Appendix.pdf
2/15/2011 42 DRAFT 

Table 3-11. Biomass Parameters for Fishable Lakes 

in the Revere Middletown Facility 

Biotic 

Compartment 

Biomass Value Units 

Macrophytes 0.5 kg[ww] m-2

Algae 0.010087 g[ww] L-1

Zooplankton 0.0263 kg[ww] m-2

Benthic 

Invertebrates 

0.00845 kg[ww] m-2

Benthic Omnivores 0.00216 kg[ww] m-2

Benthic Carnivores 0.00108 kg[ww] m-2

Water Column 

Planktivores 

0.00216 kg[ww] m-2

Water Column 

Omnivores 

0.000542 kg[ww] m-2

Water Column 

Carnivores 

0.000216 kg[ww] m-2

Annual Average Total Phosphorus Level = 27.2 ug/L 

For both the Exide Frisco and Revere Middletown facilities, the aquatic food web (predator-prey) 

relationships between the biotic components were conservatively structured to simulate 

maximum exposure and risk to human consumers of fish. In the water column, each trophic 

level exclusively consumed prey from the trophic level directly below it, thus receiving (and 

transmitting) the maximum exposure to bioaccumulative contaminants. Figure 3-17 illustrates 

the structure and diet fractions of the aquatic food web modeled in both scenarios. 

3.2 Exposure and Risk Calculations 

This section describes the approach for modeling chemical concentrations in farm food chain 

(FFC) media (Section 3.2.1); estimating human exposures associated with ingestion of FFC 

media, incidental ingestion of soil, consumption of fish, and infant consumption of breast milk 

(Section 3.2.2); and calculating human health screening risk metrics associated with these 

exposure pathways (Section 3.2.3). All of these calculations are conducted using the MIRC 

exposure model. 

3.2.1 Calculating Concentrations in Farm Food Chain Media 

MIRC was compiled to calculate concentrations of PB-HAPs in foodstuffs that are part of the 

farm food chain. The FFC media included in this multipathway assessment are as follows: 

 Exposed and protected fruit, 

 Exposed and protected vegetables, 

 Root vegetables, 

 Beef, 

 Total dairy products, 

 Pork, and 

 Poultry and eggs. 
2/15/2011 43 DRAFT 

Figure 3-17. Aquatic Food Web and Diet Fractions in Exide Frisco and Revere 

Middletown Facilities 

The algorithms used in MIRC were obtained from HHRAP.9

 These algorithms model the 

transfer of concentrations of PB-HAPs in FFC media using empirical biotransfer factors. 

Environmental media concentrations were obtained from TRIM.FaTE. Specifically, TRIM.FaTE 

outputs included as inputs to MIRC are the following: 

 PB-HAP concentrations in air; 

 Air-to-surface deposition rates for PB-HAPs in both particle and vapor phases; and 

 PB-HAP concentrations in surface soil and root zone soil. 

In general, plant- and animal-specific parameter values, including chemical-specific transfer 

factors for FFC media, were obtained from the Hazardous Waste Companion Database 

included in HHRAP9

. 

3.2.2 Ingestion Exposure Assessment 

The following four subsections detail the process of building an ingestion exposure scenario and 

generating hazard and risk estimates for each of the PB-HAPs of interest. 

3.2.2.1 Ingestion Exposure Pathways and Routes of Uptake 

MIRC was used to estimate ingestion rates as average daily doses (ADDs) normalized to body 

weight for a range of exposure pathways. Exposure pathways included are incidental ingestion 
2/15/2011 44 DRAFT 

of soil and consumption of fish, produce, and farm animals and related products. The ingestion 

exposure pathways included in the site-specific evaluations and the environmental media 

through which these exposures occur are summarized in Table 3-12. 

Table 3-12. Summary of Ingestion Exposure Pathways and Routes of Uptake 

Ingestion 

Exposure 

Pathway 

Medium Ingested Intermediate 

Exposure 

Pathway – Farm 

Animals a

Environmental Uptake Route 

Medium Processb

Incidental 

ingestion of soil Surface soil N/A Surface soil 

Deposition; transfer through 

plants; transfer via erosion and 

runoffc

Consumption of 

fish 

Fish from local water 

body N/A Fish tissue 

Direct uptake from water and 

consumption of food 

compartments modeled in 

TRIM.FaTEc

Consumption of 

breast milkd Breast milk N/A Breast 

milk 

Ingested by mother and then 

partition to breast milk 

Consumption of 

produce 

Aboveground produce, 

exposed fruits and 

vegetables 

N/A 

Air 

Air 

Soil 

Deposition to leaves/plants 

Vapor transfer 

Root uptake 

Aboveground produce, 

protected fruits and 

vegetables 

N/A Soil Root uptake 

Belowground produce N/A Soil Root uptake 

Consumption of 

farm animals and 

related food 

products 

Beef 

Ingestion of 

forage Air 

Air 

Soil 

Direct deposition to plant 

Vapor transfer to plant 

Root uptake Ingestion of 

silage 

Ingestion of grain Soil Root uptake 

Ingestion of soil Soil Ingestion from surface 

Dairy (milk) 

Ingestion of 

forage Air 

Air 

Soil 

Direct deposition to plant 

Vapor transfer to plant 

Root uptake Ingestion of 

silage 

Ingestion of grain Soil Root uptake 

Ingestion of soil Soil Ingestion from surface 

Pork 

Ingestion of 

silage 

Air 

Air 

Soil 

Direct deposition to plant 

Vapor transfer to plant 

Root uptake 

Ingestion of grain Soil Root uptake 

Ingestion of soil Soil Ingestion from surface 

Poultry Ingestion of grain Soil Root uptake 

Ingestion of soil Soil Ingestion from surface 

Eggs Ingestion of grain Soil Root uptake 

Ingestion of soil Soil Ingestion from surface a

 Calculation of intermediate exposure concentrations were required only for the farm animal/animal product ingestion 

pathways. 

b Process by which PB-HAP enters medium ingested by humans. 

c Modeled in TRIM.FaTE. 

d The consumption of breast milk exposure scenario is discussed in Section 3.2.2. 
2/15/2011 45 DRAFT 

3.2.2.2 Exposure Scenarios and Corresponding Inputs 

Specific exposure scenarios are developed by defining the ingestion activity patterns (i.e., 

estimating how much of each medium was consumed and the fraction of the consumed medium 

that was grown in or obtained from contaminated areas) and the characteristics of the 

hypothetical human exposed (e.g., age and body weight). MIRC computes exposure doses and 

risks for each ingestion pathway separately, enabling the pathway(s) of interest for each PBHAP to be determined. Data related to exposure factors and receptor characteristics were 

obtained primarily from EPA’s Exposure Factors Handbook.41

For the secondary lead smelting multipathway assessment, variants of three exposure 

scenarios were evaluated: 

1. Consumption of farm-grown fruits, vegetables, and animal products, and incidental 

ingestion of soil; 

2. Consumption of self-caught fish from local water bodies; and 

3. Ingestion of contaminated breast milk by an infant. 

For the first of these scenarios, exposed individuals were assumed to consume locally grown 

produce and livestock products. Fruits and vegetables were assumed to be contaminated from 

air and soil at an agricultural parcel in the modeling domain. Contaminant uptake by plants 

included deposition to leaves/plants, vapor transfer, and root uptake. Livestock were assumed 

to ingest contaminants in locally-grown feed plants (i.e., forage, silage, and grain), as well as in 

soil incidentally ingested during grazing. 

In the fisher scenario, the exposed individuals were assumed to eat fresh-water game fish 

caught in a local lake within the modeling domain. This scenario represents subsistence fishing. 

This scenario was varied to include multiple subpopulations that may be economically or 

culturally disposed to higher fish ingestion rates than the general population. 

In the breast-milk scenario, an infant was assumed to be exposed to contaminants ingested in 

the fat-content (i.e., lipid phase) of the mother’s breast milk. Exposure from the aqueous phase 

has not yet been developed in MIRC. However, contaminants assessed using the breast-milk 

scenario are strongly lipophilic, and exposures are expected to be insignificant in the aqueous 

phase. 

In estimating contaminant concentrations in breast milk for the farmer scenario, the mother was 

assumed to be exposed through consumption of farm-grown fruits, vegetables, and animal 

products; and incidental ingestion of soil. For the fisher scenario, maternal exposure was 

assumed to include only consumption of self-caught fish from local water bodies. 

The drinking water exposure pathway was not included in the proposed scenarios because it is 

unlikely that humans would use surface waters as a drinking water source. Groundwater, which 

is a likely source of drinking water, also was not included in the exposure scenarios because 

contamination of groundwater aquifers by air deposition sources was not expected to be 

significant. 

 

41 Exposure Factors Handbook, Volume I, General Factors. EPA/600/P-95/002Fa. U.S. EPA Office of Research and 

Development, Washington, DC, 1997. August. Available at: http://www.epa.gov/nceawww1/efh/. 
2/15/2011 46 DRAFT 

Two variants of each of the three exposure scenarios were modeled. These variants were 

named in keeping with nomenclature and precedent set by risk analyses performed under the 

purview of the Superfund programs: 

 CTE – Central tendency estimate; and 

 RME—Reasonable maximum exposure 

Additionally, CTE and RME estimates for subpopulations with higher fish ingestion rates were 

modeled. In particular, risks were estimated for three subpopulations of recreational anglers 

(Hispanic, Laotian, and Vietnamese descent) who are culturally or economically disposed to 

higher rates of fish consumption. Exposures for these subpopulations were estimated by 

varying only the assumed intake and ingestion rates for an individual; the values remained the 

same for both CTE and RME estimates for other exposure factors (i.e., body weight, exposure 

frequency, fraction of food from contaminated sources, and cooking loss). 

An overview of some of the exposure characteristics selected for the ingestion screening 

scenario is provided in Tables 3-13 and 3-14. Quantitative input values corresponding to these 

parameters are presented in Appendix C. 

Table 3-13. Overview of Exposure Factors used for Multipathway Analysis for Farm Food 

Pathway Scenariosa,b

Exposure Factor Selection for Screening Assessment 

Age group evaluated 

Infants under 1 year (breast milk only) 

Children 1 to 2 years of age 

Children 3 to 5 years of age 

Children 6 to 11 years of age 

Children 12 to 19 years of age 

Adult (20 to 70 years) 

Body weight (BW; varies by age) Weighted mean of national distribution 

Intake rate and ingestion rate (IR) for farm produce and animal 

products (varies by age and media consumed) 

RME Scenarios: 90th percentile of 

distribution of consumers who produce own 

food 

CTE Scenarios: mean of distribution of 

consumers who produce own food

Incidental soil ingestion rate (varies by age) 

RME Scenarios: 90th percentile of general 

population 

 CTE Scenarios: mean of general 

population 

Exposure frequency (EF) 365 days/year 

Exposure duration 

Lifetime, for estimating cancer risk; 

varies by chemical for chronic noncancer evaluation 

Fraction contaminated (FC) (varies by media consumed)c 1 

Cooking loss d 

Assumed to be “typical;” varies 

depending on food product (see 

Appendix C) a Values for exposure characteristics are presented in Appendix C. Exposure parameter values were based on data 

obtained from the Exposure Factors Handbook41. See Appendix C for details. b

 Exposure factor inputs are used in calculating average daily dose (ADD) estimates for each exposure pathway. 

ADD equations for each pathway evaluated in this screening assessment are provided in Appendix C. c

 Fraction contaminated represents the fraction of food product that was derived from the environment included in the 
2/15/2011 47 DRAFT 

Table 3-13. Overview of Exposure Factors used for Multipathway Analysis for Farm Food 

Pathway Scenariosa,b

Exposure Factor Selection for Screening Assessment 

screening scenario (e.g., produce grown on soil impacted by PB-HAPs). This parameter was defined separately for 

each FFC medium. 

d Cooking loss inputs were included to simulate the amount of a food product that was not ingested due to loss during 

preparation or cooking, or after cooking. 

Table 3-14. Overview of Exposure Factors used for Multipathway Analysis for Fish 

Pathway Scenariosa,b,c

Exposure Factor Selection for Screening Assessment 

Age group evaluated 

Infants under 1 year (breast milk only) 

Children 1 to 2 years of age (Recreational fisher only) 

Children 3 to 5 years of age(Recreational fisher only) 

Children 6 to 11 years of age(Recreational fisher only) 

Children 12 to 19 years of age(Recreational fisher only) 

Adult (20 to 70 years) 

Body weight (BW; varies by age) Weighted mean of national distribution 

Ingestion rate for fish (varies by age and 

subpopulation)

RME Scenarios: 90th percentile of distribution of subpopulation 

of interest (recreational, Hispanic, Laotian, and Vietnamese 

fishersc

) 

CTE Scenarios: mean of distribution of subpopulation of 

interest (recreational, Hispanic, Laotian, and Vietnamese 

fishersc

) 

Cooking correction factor (CCF)d 1.5 for Methyl Mercury; otherwise 1 

Exposure frequency (EF) 365 days/year 

Exposure duration Lifetime, for estimating cancer risk; varies by chemical for 

chronic non-cancer evaluation a Values for exposure characteristics are presented in Appendix C. Exposure parameter values were based on data 

obtained from the Exposure Factors Handbook41. See Appendix C for details. b

 Exposure factor inputs are used in calculating average daily dose (ADD) estimates for each exposure pathway. 

ADD equations for each pathway evaluated in this screening assessment are provided in Appendix D. c

 Fraction contaminated represents the fraction of food product that was derived from the environment included in the 

screening scenario (e.g., produce grown on soil impacted by PB-HAPs). This parameter was defined separately for 

each FFC medium. 

d EPA-recommended CCF which accounts for the difference between ingestion rates for cooked fish and calculated 

mercury concentrations in whole fish. Cooking fish tends to reduce the overall weight of fish, and volatilization of 

mercury is unlikely to occur during cooking, thereby increasing the concentration of mercury by a factor of roughly 

1.5.42

3.2.2.3 Calculating Average Daily Doses 

MIRC calculates chemical-specific ADDs of chemicals normalized to body weight (mg PB-HAP 

per kg of body weight per day). The equations to calculate ADDs for each ingestion pathway 

are provided in Appendix D. 

 

42 Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. “Effects of Commonly Used Cooking Practices on Total Mercury 

Concentration in Fish and Their Impact on Exposure Assessments.” Journal of Exposure Analysis and 

Environmental Epidemiology 7(1):119-133. 
2/15/2011 48 DRAFT 

3.2.2.4 Infant Ingestion of Breast Milk 

The magnitude of the nursing infant’s exposure via ingestion of contaminated breast milk can be 

estimated from information on the mother’s exposure, data on the partitioning of the chemical 

into various compartments of the mother’s body and into breast milk, and information on the 

infant’s consumption of milk and absorption of the chemical. This pathway was generally of 

most concern for lipophilic bioaccumulative chemicals (e.g., dioxins) that can cause 

developmental effects. The methodology and algorithms used for the breast milk consumption 

scenario for this case study are presented in Appendix D. 

3.2.3 Risk Characterization 

MIRC was used to calculate excess lifetime cancer risk (ELCR) and non-cancer hazard 

(expressed as an HQ) using the calculated ADDs and ingestion dose-response values. 

Chemical dose-response data include CSFs for ingestion and non-cancer oral RfDs for chronic 

exposures. The CSFs and RfDs for the PB-HAPs included in this screening scenario are 

presented in the dose-response assessment in Section 4. Equations used to estimate cancer 

risk and non-cancer hazard are provided in Appendix D. 

Estimated individual cancer risks for the POM included in the screening scenario were adjusted 

upward to account for the higher mutagenic cancer potency of these compounds during 

childhood, as specified by EPA in supplemental guidance for cancer risk assessment.43 

Specifically, cancer potency was assumed to be tenfold greater for the first 2 years of life and 

threefold greater for the next 14 years for PAHs. These factors were incorporated into a timeweighted total increase in potency over a lifetime of 70 years. The cancer potency adjustment 

for chemicals with a mutagenic mode of action was discussed in detail in Appendix D. 

4 Dose-response Assessment 

Estimates of exposure (as ADDs, discussed in Section 3.2.3) for each PB-HAP and doseresponse data were used to calculate excess lifetime cancer risk and non-cancer hazard for 

chronic exposures. Table 4-1 provides chemical dose-response data for the PB-HAPs 

evaluated in this assessment. 

The carcinogenic potency slope factors (CSFs) for ingestion and non-cancer oral reference 

doses (RfDs) for chronic exposures for the PB-HAP included in this assessment are provided in 

Table 4-1. The dose-response values were obtained from tabulated dose-response data that 

OAQPS uses for risk assessments of hazardous air pollutants.44 In general, OAQPS chose 

these values based on the following hierarchy of sources: EPA’s Integrated Risk Information 

System (IRIS); the Centers for Disease Control’s Agency for Toxic Substances and Disease 

Registry (ATSDR); and the California Environmental Protection Agency (CalEPA). 

 

43 U.S. Environmental Protection Agency. 2005. Supplemental Guidance for Assessing Susceptibility from Early-Life 

Exposure to Carcinogens. EPA-630/R-03-003F. Risk Assessment Forum: Washington, DC. March. 44 EPA Dose-Response Table: U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning and 

Standards (OAQPS). 2007. Prioritized Chronic Dose-Response Values for Screening Risk Assessments (Table 1). 

June 12, 2007. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html. 
2/15/2011 49 DRAFT 

Table 4-1. Dose-Response Values for Assessed PB-HAPsa

PB-HAP CSF 

([mg/kg-day]-1) Source RfD (mg/kg-day) Source

Inorganics 

Cadmium not available 5E-4 IRIS 

Elemental Mercury NA not available 

Divalent Mercury not available 3E-4 IRIS 

Methyl Mercury not available 1E-4 IRIS 

Organics 

BaPb 10 IRIS not available 

2,3,7,8-TCDD 1.5E+5 EPA ORD 1E-9 ATSDR 

ATSDR = Agency for Toxic Substances and Disease Registry 

IRIS = Integrated Risk Information System 

EPA ORD = EPA’s Office of Research and Development a Values presented here are consistent with those defined by OAQPS for evaluation of HAPs.44 

Sources listed in this table are the original references cited by EPA. b The method to assign oral cancer slope factors for polycyclic organic matter (POM) was the same as 

was used in the 1999 National Air Toxics Assessment.45. 

5 Modeling Results and Risk Characterization 

The results presented and discussed in this section include estimated concentrations of PBHAPs in environmental media and biota (Section 5.1) and estimates of PB-HAP exposures and 

associated cancer and noncancer human health risks (Section 5.2). Section 5.3 provides a 

discussion of the sources of uncertainty in the results. 

5.1 Estimated Media Concentrations 

For each of the abiotic and biotic compartments described in Section 3.1, TRIM.FaTE predicts 

annual average PB-HAP concentrations for each year in the 50 year modeling period. To 

simplify the presentation of estimated media concentrations, this section presents only those 

results that are used to estimate human health risks. In particular, results are only presented for 

the 50th (i.e., final) year of the modeling period and only for those compartments associated with 

direct human exposure or exposure via the farm food chain and fish consumption. These 

estimates are presented separately for the Exide Frisco and Revere Middletown sites in 

Sections 5.1.1 and 5.1.2, respectively. 

5.1.1 Exide Frisco Facility 

Table 5-1 presents media concentrations of divalent mercury, elemental mercury, and methyl 

mercury at the end of the 50-year modeling period for the Exide Frisco facility. The relative 

speciation results indicate that divalent mercury is the dominant species in surface water, 

sediment, and surface soil. Methyl mercury, however, is the dominant species in the higher 

trophic levels of the aquatic biota, progressively bioaccumulating up the food chain until it 

 

45 Approach for POM modeling in the 1999 National Air Toxics Assessment: 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf 
2/15/2011 50 DRAFT 

represents over 95 percent of total mercury in game fish. These speciation trends are 

consistent with the literature. 46

Mercury partitioning between surface water and sediment, surface water and the various biotic 

compartments, and between the biota themselves are also consistent with a range of empirical 

data. 47 Figures 5-1 and 5-2 illustrate biomagnification and bioaccumulation in the eastern arm 

of the modeled lake (the arm closest to the source). 

Table 5-1. Concentration Estimates and Speciation for Mercury (Exide Frisco) 

Food Web 

Compartment 

Units Total Hg 

Conc. 

Divalent Hg Elemental Hg Methyl Hg 

Conc. % 

Total 

Hg 

Conc. % 

Total 

Hg 

Conc. % 

Total 

Hg 

Surface water (Lake_E) mg L-1

3.5E-08 2.6E-08 75.7% 7.2E-09 20.6% 1.3E-09 3.7% 

Algae (Lake_E) mg kg-1[wet 

weight] 5.1E-05 3.7E-05 73.8% 0.0E+00 0.0% 1.3E-05 26.2%

Macrophyte (Lake_E) mg kg-1[wet 

weight] 1.1E-08 9.8E-09 92.8% 1.0E-18 0.0% 7.6E-10 7.2% 

Zooplankton (Lake_E) mg kg-1[wet 

weight] 9.5E-05 2.8E-05 29.1% 0.0E+00 0.0% 6.7E-05 70.9%

Water Column 

Carnivore (Lake_E) 

mg kg-1[wet 

weight] 6.4E-03 2.4E-06 0.0% 0.0E+00 0.0% 6.4E-03 100.0%

Water Column 

Herbivore (Lake_E) 

mg kg-1[wet 

weight] 2.6E-04 1.1E-05 4.2% 0.0E+00 0.0% 2.5E-04 95.8%

Water Column 

Omnivore (Lake_E) 

mg kg-1[wet 

weight] 1.2E-03 4.6E-06 0.4% 0.0E+00 0.0% 1.2E-03 99.6%

Sediment (Lake_E) μg g-1[dry weight] 4.4E-04 4.1E-04 94.6% 7.1E-06 1.6% 1.7E-05 3.8% 

Benthic Invertebrate 

(Lake_E) 

mg kg-1[wet 

weight] 7.0E-05 2.1E-05 30.4% 3.7E-07 0.5% 4.9E-05 69.1%

Benthic Omnivore 

(Lake_E) 

mg kg-1[wet 

weight] 1.9E-04 9.5E-06 5.0% 9.1E-13 0.0% 1.8E-04 95.0%

Benthic Carnivore 

(Lake_E) 

mg kg-1[wet 

weight] 1.5E-03 8.3E-06 0.5% 6.5E-13 0.0% 1.5E-03 99.5%

Untilled Surface Soil in 

Farm_N 

μg g-1[dry weight]

2.1E-03 2.0E-03 98.3% 5.7E-07 0.0% 3.4E-05 1.6% 

Tilled Surface Soil in 

Farm_N 

μg g-1[dry weight] 2.3E-04 2.1E-04 93.4% 1.2E-05 5.1% 3.5E-06 1.6% 

Untilled Surface Soil in 

Farm_S 

μg g-1[dry weight] 2.9E-03 2.9E-03 98.3% 8.2E-07 0.0% 4.8E-05 1.6% 

Tilled Surface Soil in 

Farm_S 

μg g-1[dry weight] 3.3E-04 3.1E-04 93.4% 1.7E-05 5.1% 5.2E-06 1.6% 

 

46 Raymond, B. and R. Rossmann. 2009. Total and Methyl Mercury Accumulation in 1994-1995 Lake Michigan Lake 

Trout and Forage Fish. J. Great Lakes Res. 35:438-446. 47 Driscoll, C.T., Han, Y., et al. 2007. Mercury Contamination in Forest and Freshwater Ecosystems in the 

Northeastern United States. Bioscience. 57 (1):1-12. 
2/15/2011 51 DRAFT 

Figure 5-1. Mercury Biomagnification in the Eastern Arm of Lake Lewisville (Exide Frisco) 

5.1E+00

3.7E+00

4.9E+00

5.2E+00

7.4E‐01

4.0E‐01

4.2E‐01

5.3E‐01

1.9E+00

2.7E+00

4.8E+00

5.2E+00

0.00E+00

1.00E+00

2.00E+00

3.00E+00

4.00E+00

5.00E+00

6.00E+00

Zooplankton:Algae Planktivore:

Zooplankton

Omnivore:Planktivore Carnivore:Omnivore

Biomagnification Factor

Biota Concentration Ratios in the Water Column

Methyl Mercury

Divalent Mercury

Total Mercury
2/15/2011 52 DRAFT 

Figure 5-2. Mercury Bioaccumulation in the Eastern Arm of Lake Lewisville (Exide Frisco) 

Table 5-2 presents media concentrations of 2,3,7-8 TCDD, BaP, and cadmium for the Exide 

Frisco facility. The partitioning trends predicted by the model for these chemicals are also 

consistent with the literature (Figure 5-3). 

Table 5-2. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Exide Frisco) 

Food Web Compartment Units 2,3,7,8-

TCDD Conc.

BaP Conc. Cadmium 

Conc. 

Surface water (Lake_E) mg L-1

4.0E-14 1.9E-12 8.3E-07

Algae (Lake_E) mg kg-1[wet weight] 1.0E-11 4.5E-09 1.3E-03

Macrophyte (Lake_E) mg kg-1[wet weight] 2.3E-10 1.9E-09 7.0E-05

Zooplankton (Lake_E) mg kg-1[wet weight] 7.9E-10 9.6E-06 5.7E-02

Water Column Carnivore (Lake_E) mg kg-1[wet weight] 2.8E-07 8.6E-07 1.4E-02

Water Column Herbivore (Lake_E) mg kg-1[wet weight] 4.4E-09 5.7E-06 4.8E-02

Water Column Omnivore (Lake_E) mg kg-1[wet weight] 1.4E-08 2.4E-06 2.8E-02

Sediment (Lake_E) μg g-1[dry weight] 4.3E-10 8.5E-09 1.9E-03

Benthic Invertebrate (Lake_E) mg kg-1[wet weight] 3.2E-11 3.5E-09 3.2E-04

Benthic Omnivore (Lake_E) mg kg-1[wet weight] 4.7E-10 1.3E-08 2.5E-04

Benthic Carnivore (Lake_E) mg kg-1[wet weight] 5.2E-09 1.3E-08 1.7E-03

7.7E+04

3.9E+05

4.4E+00

1.4E+06

7.1E+06

3.7E+07

8.8E+03 6.5E+03

2.3E+00

2.6E+03

1.1E+03

5.6E+02

4.5E+03

8.4E+03

9.4E‐01

2.3E+04

1.1E+05

5.7E+05

1.00E‐01

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

Bioaccumulation Factor

Ratio of  Biota to Dissolved Water Concentration

Methyl Mercury

Divalent Mercury

Total Mercury
2/15/2011 53 DRAFT 

Table 5-2. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Exide Frisco) 

Food Web Compartment Units 2,3,7,8-

TCDD Conc.

BaP Conc. Cadmium 

Conc. 

Untilled Surface Soil in Farm_N μg g-1[dry weight] 3.6E-08 3.7E-06 8.7E-04

Tilled Surface Soil in Farm_N μg g-1[dry weight] 1.7E-09 4.4E-07 3.0E-04

Untilled Surface Soil in Farm_S μg g-1[dry weight] 4.0E-08 7.5E-06 1.5E-03

Tilled Surface Soil in Farm_S μg g-1[dry weight] 1.7E-09 7.7E-07 4.7E-04

Figure 5-3. Calculated 2,3,7,8-TCDD, BaP, and Cadmium Bioaccumulation Factors in the 

Eastern Arm of Lake Lewisville (Exide Frisco) 

5.1.2 Revere Middletown Facility 

Table 5-3 presents media concentrations of divalent mercury, elemental mercury, and methyl 

mercury at the end of the 50-year modeling period for the Revere Middletown facility. 

Speciation and partitioning trends (Figures 5-4 and 5-5) in these results are similar to the Exide 

Frisco facility and consistent with the literature. Silver Lake is shown here because it is the 

water body closest to the source. 

1.0E+03

8.1E+04

2.3E+04

4.5E+05 1.5E+06

2.9E+07

3.6E+03

7.6E+06

1.5E+03

4.5E+06

1.9E+06

6.9E+05

1.8E+03

8.2E+04

1.0E+02

6.9E+04

4.0E+04

2.1E+04

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

Bioaccumulation Factor

Ratio of Biota to Dissolved Water Concentration

TCDD

BaP

Cd
2/15/2011 54 DRAFT 

Table 5-3. Concentration Estimates and Speciation for Mercury (Revere Middletown) 

Food Web Compartment Units Total Hg 

Conc. 

Divalent Hg Elemental Hg Methyl Hg 

Conc. % 

Total 

Hg 

Conc. % 

Total 

Hg 

Conc. % 

Total 

Hg 

Surface water (Silver Lake) mg L-1

4.4E-08 3.9E-08 88.5% 3.4E-09 7.7% 1.7E-09 3.7% 

Algae (Silver Lake) mg kg-1[wet 

weight] 7.5E-05 5.7E-05 75.0% 0.0E+00 0.0% 1.9E-05 25.0%

Macrophyte (Silver Lake) mg kg-1[wet 

weight] 9.7E-09 8.6E-09 88.9% 4.8E-19 0.0% 1.1E-09 11.1%

Zooplankton (Silver Lake) mg kg-1[wet 

weight] 1.4E-04 4.2E-05 29.8% 0.0E+00 0.0% 9.8E-05 70.2%

Water Column Carnivore 

(Silver Lake) 

mg kg-1[wet 

weight] 9.6E-03 3.7E-06 0.0% 0.0E+00 0.0% 9.6E-03 100.0%

Water Column Herbivore 

(Silver Lake) 

mg kg-1[wet 

weight] 3.8E-04 1.7E-05 4.4% 0.0E+00 0.0% 3.6E-04 95.6%

Water Column Omnivore 

(Silver Lake) 

mg kg-1[wet 

weight] 1.8E-03 7.0E-06 0.4% 0.0E+00 0.0% 1.8E-03 99.6%

Sediment (Silver Lake) μg g-1[dry 

weight] 6.4E-04 6.1E-04 95.6% 3.5E-06 0.5% 2.4E-05 3.8% 

Benthic Invertebrate (Silver 

Lake) 

mg kg-1[wet 

weight] 1.0E-04 3.2E-05 30.5% 1.8E-07 0.2% 7.2E-05 69.4%

Benthic Omnivore (Silver 

Lake) 

mg kg-1[wet 

weight] 2.8E-04 1.4E-05 4.9% 3.2E-11 0.0% 2.7E-04 95.1%

Benthic Carnivore (Silver 

Lake) 

mg kg-1[wet 

weight] 2.3E-03 1.2E-05 0.5% 1.5E-11 0.0% 2.3E-03 99.5%

Untilled Surface Soil in 

Farm_N 

μg g-1[dry 

weight] 4.3E-03 4.2E-03 98.4% 4.2E-07 0.0% 7.1E-05 1.6% 

Tilled Surface Soil in 

Farm_N 

μg g-1[dry 

weight] 1.3E-04 1.2E-04 96.2% 2.9E-06 2.2% 2.1E-06 1.6% 

Untilled Surface Soil in 

Farm_S 

μg g-1[dry 

weight] 1.7E-02 1.6E-02 98.4% 1.6E-06 0.0% 2.7E-04 1.6% 

Tilled Surface Soil in 

Farm_S 

μg g-1[dry 

weight] 8.6E-04 8.3E-04 96.2% 1.9E-05 2.2% 1.4E-05 1.6% 
2/15/2011 55 DRAFT 

Figure 5-4. Mercury Biomagnification in Silver Lake (Revere Middletown) 

5.2E+00

3.7E+00

5.0E+00

5.3E+00

7.4E‐01

4.0E‐01

4.2E‐01

5.3E‐01

1.9E+00

2.7E+00

4.8E+00

5.3E+00

0.00E+00

1.00E+00

2.00E+00

3.00E+00

4.00E+00

5.00E+00

6.00E+00

Zooplankton:Algae Planktivore:

Zooplankton

Omnivore:Planktivore Carnivore:Omnivore

Biomagnification Factor

Biota Concentration Ratios in the Water Column

Methyl

Mercury

Divalent

Mercury

Total

Mercury
2/15/2011 56 DRAFT 

Figure 5-5. Calculated Mercury Bioaccumulation Factors in Silver Lake (Revere 

Middletown) 

Table 5-4 presents media concentrations of 2,3,7-8 TCDD, BaP, and cadmium for the Revere 

Middletown facility. The predicted partitioning trends for these chemicals (Figures 5-6 and 5-7) 

are similar to the Exide Frisco facility and also consistent with the literature. Differences in 

absolute values are attributable to differences in the relative emission rates for the two facilities. 

Table 5-4. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Revere 

Middletown) 

Food Web Compartment Units 2,3,7,8-

TCDD 

Conc. 

BaP 

Conc. 

Cadmium 

Conc. 

Surface water (Silver Lake) mg L-1

3.11E-16 7.24E-12 4.23E-08

Algae (Silver Lake) mg kg-1[wet weight] 7.90E-14 1.76E-08 7.22E-05

Macrophyte (Silver Lake) mg kg-1[wet weight] 1.85E-12 7.35E-09 3.87E-06

Zooplankton (Silver Lake) mg kg-1[wet weight] 1.00E-11 4.37E-05 3.23E-03

Water Column Carnivore (Silver Lake) mg kg-1[wet weight] 3.72E-09 4.05E-06 8.26E-04

Water Column Herbivore (Silver Lake) mg kg-1[wet weight] 5.66E-11 2.61E-05 2.73E-03

Water Column Omnivore (Silver Lake) mg kg-1[wet weight] 1.91E-10 1.12E-05 1.60E-03

Sediment (Silver Lake) μg g-1[dry weight] 3.55E-12 4.12E-08 5.97E-05

7.2E+04

4.0E+05

4.4E+00

1.5E+06

7.4E+06

3.9E+07

8.7E+03 6.5E+03

1.3E+00

2.6E+03

1.1E+03

5.8E+02

7.6E+03 1.4E+04

9.7E‐01

3.8E+04

1.8E+05

9.6E+05

1.00E‐01

1.00E+00

1.00E+01

1.00E+02

1.00E+03

1.00E+04

1.00E+05

1.00E+06

1.00E+07

1.00E+08

Bioaccumulation Factor

Ratio of Biota to Dissolved Water Concentration

Methyl Mercury

Divalent Mercury

Total Mercury
2/15/2011 57 DRAFT 

Table 5-4. Concentration Estimates for 2,3,7,8-TCDD, BaP, and Cadmium (Revere 

Middletown) 

Food Web Compartment Units 2,3,7,8-

TCDD 

Conc. 

BaP 

Conc. 

Cadmium 

Conc. 

Benthic Invertebrate (Silver Lake) mg kg-1[wet weight] 2.66E-13 1.70E-08 9.97E-06

Benthic Omnivore (Silver Lake) mg kg-1[wet weight] 3.92E-12 5.01E-08 8.64E-06

Benthic Carnivore (Silver Lake) mg kg-1[wet weight] 4.47E-11 4.97E-08 6.46E-05

Untilled Surface Soil in Farm_N μg g-1[dry weight] 1.00E-10 3.13E-06 1.55E-04

Tilled Surface Soil in Farm_N μg g-1[dry weight] 1.58E-12 9.63E-08 2.74E-05

Untilled Surface Soil in Farm_S μg g-1[dry weight] 2.78E-10 1.42E-05 6.65E-04

Tilled Surface Soil in Farm_S μg g-1[dry weight] 7.63E-12 7.10E-07 1.76E-04

Figure 5-6. 2,3,7,8-TCDD, BaP, and Cadmium Biomagnification in Silver Lake (Revere 

Middletown) 

1.3E+02

5.7E+00

3.4E+00

2.0E+01

2.5E+03

6.0E‐01

4.3E‐01

3.6E‐01

4.5E+01

8. 5E‐01 5.9E‐01 5.2E‐01

‐5.00E+00

5.00E+00

1.50E+01

2.50E+01

3.50E+01

4.50E+01

5.50E+01

6.50E+01

7.50E+01

Zoop:Alg Pvore: Zoop Ovore:Pvore Cvore:Ovore

Biomagnification Factor

Biota Concentration Ratios in the Water Column

TCDD

BaP

Cd
2/15/2011 58 DRAFT 

Figure 5-7. Calculated 2,3,7,8-TCDD, BaP, and Cadmium Bioaccumulation Factors in 

Silver Lake (Revere Middletown) 

5.2 Human Health Multipathway Risk Assessment Results 

This section presents the multipathway human health risk assessment results for secondary 

lead smelting. Section 5.2.1 provides an overview of the risk assessment results for the 

exposure scenarios evaluated. Sections 5.2.2 through 5.2.5 provide further details about the 

risk estimates for each PB-HAP assessed. 

5.2.1 Risk Assessment Summary 

The annually-averaged concentrations estimated by TRIM.FaTE for the 50th year of the 

modeling period were used to calculate lifetime individual incremental cancer risks and chronic 

noncancer hazard quotients (HQs) attributes to source emissions only based on two scenarios: 

a farmer who grows and consumes food on a farm near the facility and a recreational angler 

who ingests self-caught fish from nearby lakes. As described in Section 3.2, these scenarios 

were modeled using assumed ingestion rates for fish, dairy, beef, and other foods. Ingestion 

rates were assumed to vary by age, and exposures were calculated for five age groups and 

three populations of recreational anglers (Hispanic, Laotian, and Vietnamese descent) who are 

culturally or economically disposed to higher rates of fish consumption. Because dioxin 

exposure can occur via consumption of breast milk by nursing infants, this pathway was 

included in the lifetime cancer risk. 
2/15/2011 59 DRAFT 

Four PB-HAPs were included in the multipathway risk assessment: 2,3,7,8-TCDD (dioxin), 

mercury (including both methyl mercury and divalent mercury), cadmium, and benzo(a)pyrene 

(representative of POM). Using the toxicity reference values identified in Section 4, we 

estimated cancer risk for 2,3,7,8-TCDD and benzo(a)pyrene and noncancer chronic HQs for 

cadmium, mercury, and 2,3,7,8-TCDD. 

Cancer risk results for 2,3,7,8-TCDD include an excess lifetime cancer risk greater than one-ina-million at the Exide Frisco facility in Texas for all populations of recreational anglers who 

fished in Lake Lewisville, as well as for farmers consuming products from both farm parcels 

modeled. The contribution of excess cancer risk associated with infant consumption of breast 

milk contributed approximately 25% to the excess lifetime cancer risk for both farmer and angler 

scenarios. These results are discussed in more detail in Section 5.2.2. All lifetime cancer risk 

estimates were below one-in-a-million for benzo(a)pyrene emissions from the Exide Frisco 

facility and for both 2,3,7,8-TCDD and benzo(a)pyrene emissions from the Revere Middletown 

facility. 

For noncancer hazard estimates, HQs were below one for all scenarios and PB-HAPs at both 

facilities. With the exception of 2,3,7,8-TCDD, HQs were at least two orders of magnitude 

below one for all scenarios. In the case of 2,3,7,8-TCDD, the highest HQ was 0.78 for infants 

via the exposure pathway of breast milk. 

More detailed results for each PB-HAP are presented in the following four subsections. 

Estimated excess individual cancer risk and noncancer hazard quotients, as well as exposure 

dose estimates, are presented in Appendix E for all scenarios and PB-HAPs evaluated at each 

facility. 

5.2.2 Risk Assessment Results from Multipathway 2,3,7,8-TCDD Exposure 

Exposure to 2,3,7,8-TCDD is associated with both increased cancer risk and adverse 

noncancer effects. Consumption of breast milk by infants can also be a significant contributor to 

lifetime 2,3,7,8-TCDD exposure. Lifetime cancer risks and hazard quotients by age group are 

presented in Sections 5.2.2.1 to 5.2.2.2. 

5.2.2.1 Estimated Lifetime Cancer Risks 

Table 5-5 presents estimated excess lifetime cancer risks for each exposure scenario and 

ingestion rate. All scenarios that exceed the one-in-a-million lifetime cancer risk are highlighted 

in blue. No scenario associated with the Revere Middletown smelter exceeds one in a million; 

however, all scenarios at the Exide Frisco facility, at both CTE and RME ingestion rates, do 

exceed this threshold. 

In Figure 5-8, the one-in-a-million and 100-in-a-million cancer risk levels are shown for 

comparison to the estimated excess individual lifetime cancer risks of 2,3,7,8-TCDD exposure 

for the farmer and angler scenarios. The risk estimates shown in Figure 5-8 were calculated 

using the 90th percentile (i.e., RME) ingestion rates for all age groups. Risks for all scenarios 

were below one-in-one-million for the Revere Middletown facility. For the Exide Frisco facility, 

one-in-one-million was exceeded for each scenario. 
2/15/2011 60 DRAFT 

Table 5-5. 2,3,7,8-TCDD Individual Excess Lifetime Cancer Risks 

        

CTE Ingestion

Rate

RME Ingestion

Rate

Exposure Scenario Lifetime Risk Lifetime Risk

Revere  

North Farm Parcel

   2.5E‐09 5.3E‐09

Middletown, NY

South Farm Parcel

   9.2E‐09 2.0E‐08

   Glenmere Lake Recreational Angler 1.7E‐08 4.7E‐08

      Hispanic Anglera 4.1E‐08 1.6E‐07

      Laotian Anglera 4.3E‐08 2.3E‐07

      Vietnamese Anglera 4.3E‐08 1.6E‐07

   Silver Lake Recreational Angler 3.5E‐08 8.5E‐08

      Hispanic Anglera 7.6E‐08 2.9E‐07

      Laotian Anglera 7.9E‐08 4.2E‐07

      Vietnamese Anglera 7.9E‐08 2.9E‐07

   Tomahawk Lake Recreational Angler 2.1E‐08 5.2E‐08

      Hispanic Anglera 4.6E‐08 1.7E‐07

      Laotian Anglera 4.8E‐08 2.6E‐07

      Vietnamese Anglera 4.8E‐08 1.8E‐07

Exide  

North Farm Parcel

   1.1E‐06 2.2E‐06

Frisco, TX

South Farm Parcel

   1.2E‐06 2.6E‐06

   Lewisville Lake Recreational Angler 3.0E‐06 7.3E‐06

      Hispanic Anglera 5.7E‐06 2.2E‐05

      Laotian Anglera 6.0E‐06 3.2E‐05

      Vietnamese Anglera 6.0E‐06 2.2E‐05 a

Does not include breast milk exposure pathway. Ingestion rate data is only applicable to adult

males.  
2/15/2011 61 DRAFT 

Figure 5-8. 2,3,7,8-TCDD Individual Lifetime Cancer Risks, RME Ingestion Rate 

Cancer risks estimated with RME ingestion rates were between 2- and 2.5-times greater than 

cancer risks estimated with CTE ingestion rates consistent with the increase in ingestion rate. 

At both facilities, estimated cancer risks were slightly higher when products from the southern 

farm parcel were consumed rather than the northern farm parcel. This is likely due to the closer 

proximity of the southern farm parcels to the source in both the Exide and Revere scenarios. 

When only fish consumption is considered, risks were twice as large for Silver Lake versus 

Glenmere or Tomahawk Lake. Again, this is likely due to the much closer proximity of Silver 

Lake to the source than the other lakes assessed. 

Figure 5-9 presents the pathway contribution to 2,3,7,8-TCDD lifetime average daily dose 

(LADD) for the farmer scenario. In Figure 5-9, exposure scenarios are presented from left to 

right in order of increasing cancer risk. For the farming scenario, total dairy is the largest 

contributor to risk, followed by breast milk, beef, and pork. Eggs, poultry, fruits, vegetables, and 

incidental soil ingestion do not make large contributions to total 2,3,7,8-TCDD exposure. 

For the angler scenario, the excess lifetime cancer risk for the general recreational angler 

population was calculated based on fish ingestion for ages 2 through 70 and breast milk 

ingestion by infants. Breast milk ingestion during the first year of life represented approximately 

25% of the excess lifetime cancer risk for the angler scenario, similar to the contribution of 

breast milk to the farmer scenario. Estimated exposures for Hispanic, Laotian, and Vietnamese 

adult male anglers included fish ingestion only; the breast milk pathway was not included in the 

angler scenario for these populations. 
2/15/2011 62 DRAFT 

Figure 5-9. Pathway Contribution by Ingestion Pathway to 2,3,7,8-TCDD Individual 

Lifetime Cancer Risks for the Farming Scenario 

5.2.2.2 Chronic Noncancer Hazard Quotients 

Chronic noncancer hazard quotients (HQs) for 2,3,7,8-TCDD were also estimated using 

modeled TRIM.FaTE environmental concentrations and exposure estimates. Results are 

presented for the farmer and angler scenarios in Figures 5-10 and 5-11, corresponding RME 

ingestion rates, both farm locations, and four water bodies. The highest HQs were estimated for 

infants via the breast milk pathway, and the lowest HQs were estimated for adolescents aged 

12 to 19. 

With the exception of infant exposures, 2,3,7,8-TCDD HQs for all scenarios generally were at 

least an order of magnitude lower than 1. Tables of all results for the calculated HQs for 

2,3,7,8-TCDD are provided in Appendix E. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

North Farm

Parcel

South Farm

Parcel

North Farm

Parcel

South Farm

Parcel

Revere Middletown, NY Exide Frisco, TX

Percent Contribution to LIfetime

Cancer Risk

Total Dairy

Breast milk

Beef

Pork

Eggs & Poultry

Fruits,Vegetables,

& Soil

Increasing Lifetime Cancer Risk
2/15/2011 63 DRAFT 

Figure 5-10. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Farming Scenarios and 

Recreational Angler Scenario by Location, RME Ingestion Rate 

Figure 5-11. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Recreational Angler 

Populations, RME Ingestion Rate 

1.0E‐05

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

North Farm Parcel

South Farm Parcel

Glenmere Lake

Silver Lake

Tomahawk Lake

North Farm Parcel

South Farm Parcel

Lewisville Lake

Revere, Middletown, NY Exide, Frisco, TX

Noncancer Hazard Quotient

Infant

Child1‐2

Child3‐5

Child6‐11

Child12‐19

Adult

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

Recreational

Angler

Hispanic Adult Laotian Adult Vietnamese

Adult

Noncancer Hazard Quotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX
2/15/2011 64 DRAFT 

5.2.3 Risk Assessment Results from Multipathway Mercury Exposure 

Noncancer HQs for methyl mercury are presented in Figures 5-12 and 5-13 for the farming and 

angler scenarios, respectively. As described in Section 3, for methyl mercury, an EPArecommended cooking conversion factor (CCF) of 1.5 was utilized to account for the fact that 

cooking fish reduces the overall mass of the fish without a reduction in the mercury level, which 

effectively increases the mercury concentration in the cooked fish.48 Results are presented for 

the RME ingestion rate, which is associated with approximately two-fold increase in HQs over 

CTE ingestion rates. All HQs are more than one order of magnitude below 1. HQs for the 

angler scenario are higher than the HQs for farming scenario. This is expected because fish 

ingestion is the primary pathway for methyl mercury exposure. HQs are highest for populations 

that consume higher amounts of fish. Further results for methyl mercury are provided in 

Appendix E. 

Figure 5-12. Hazard Quotients for Exposure to Methyl Mercury for Farming and 

Recreational Angler Scenarios, RME Ingestion Rate 

 

48 Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. “Effects of Commonly Used Cooking Practices on Total Mercury 

Concentration in Fish and Their Impact on Exposure Assessments.” Journal of Exposure Analysis and 

Environmental Epidemiology 7(1):119-133. 

1.0E‐06

1.0E‐05

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

North Farm ParcelSouth Farm Parcel

Glenmere Lake

Silver Lake

Tomahawk Lake

North Farm ParcelSouth Farm Parcel

Lewisville Lake

Revere Middletown NY Exide Frisco TX

Noncancer Hazard Quotient

Child1‐2

Child3‐5

Child6‐11

Child12‐19

Adult
2/15/2011 65 DRAFT 

Figure 5-13. Hazard Quotients for Exposure to Methyl Mercury for Recreational Angler 

Populations, RME Ingestion Rate 

Noncancer HQs for divalent mercury exposure are presented in Figures 5-14 and 5-15 for the 

farming and angler scenarios, respectively. HQs are presented for RME exposures. HQs for 

RME exposures are approximately two times the HQs for CTE exposures. All HQs for divalent 

mercury are at least two orders of magnitude below one. In this case, the divalent mercury 

partitions more readily to products in the farm food chain than to fish, and farming scenarios 

result in higher HQs than angler scenarios. Further results are presented in Appendix E. 

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

Recreational

Angler

Hispanic

Adult

Laotian Adult Vietnamese

Adult

Noncancer Hazard Quotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX
2/15/2011 66 DRAFT 

Figure 5-14. Hazard Quotients for Exposure to Divalent Mercury for Farming and 

Recreational Angler, RME Ingestion Rate 

Figure 5-15. Hazard Quotients for Exposure to Divalent Mercury for Recreational Angler 

Population, RME Ingestion Rate 

1.0E‐07

1.0E‐06

1.0E‐05

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

North Farm Parcel

South Farm Parcel

Glenmere Lake

Silver Lake

Tomahawk Lake

North Farm Parcel

South Farm Parcel

Lewisville Lake

Revere Middletown NY Exide Frisco TX

Noncancer Hazard Quotient

Child1‐2

Child3‐5

Child6‐11

1.0E‐06

1.0E‐05

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

Recreational

Angler

Hispanic Adult Laotian Adult Vietnamese

Adult

Noncancer Hazard Quotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX
2/15/2011 67 DRAFT 

5.2.4 Risk Assessment Results from Multipathway Cadmium Exposure 

All noncancer HQs for cadmium exposure were below one. Fish consumption was estimated to 

be the largest contributor to cadmium exposure. As expected, HQs for the angler scenario at 

both sites were higher than the HQs for farming scenario. For Lake Lewisville near the Exide 

Frisco facility, the HQs approach 0.1 for populations for which higher fish consumption rates 

were assumed (Hispanic, Laotian, and Vietnamese.) Results are presented in Figures 5-16 and 

5-17 for RME exposures. HQs for RME exposures were approximately two-fold higher than the 

HQs for CTE exposure. Further results for cadmium are presented in Appendix E. 

Figure 5-16. Hazard Quotients for Exposure to Cadmium for Farming and Recreational 

Angler Scenarios, RME Ingestion Rate 

1.0E‐05

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

North Farm Parcel

South Farm Parcel

Glenmere Lake

Silver Lake

Tomahawk Lake

North Farm Parcel

South Farm Parcel

Lewisville Lake

Revere, Middletown, NY Exide, Frisco, TX

Noncancer Hazard Quotient

Child1‐2

Child3‐5

Child6‐11

Child12‐19

Adult
2/15/2011 68 DRAFT 

Figure 5-17. Hazard Quotients for Exposure to Cadmium for Recreational Angler 

Population, RME Ingestion Rate 

1.0E‐04

1.0E‐03

1.0E‐02

1.0E‐01

1.0E+00

Recreational

Angler

Hispanic

Adult

Laotian

Adult

Vietnamese

Adult

Noncancer Hazard Quotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX
2/15/2011 69 DRAFT 

5.2.5 Risk Assessment Results from Multipathway Benzo(a)pyrene Exposure 

Figure 5-18 presents the individual lifetime cancer risks estimates from exposure to 

benzo(a)pyrene for all scenarios. The one-in-a-million cancer risk level is highlighted for 

reference. The results were calculated using the TRIM.FaTE results and the 90th percentile 

RME ingestion rates for all age groups. 

Figure 5-18. Benzo(a)pyrene Individual Lifetime Cancer Risks, RME Ingestion Rate 

Figure 5-19 presents the pathway contribution to benzo(a)pyrene LADD for the farmer scenario. 

Angler exposures were comprised solely of fish ingestion. In Figure 5-19, exposure results are 

presented from left to right in order of increasing cancer risk. For the farming scenario, fruits, 

vegetables, and incidental soil ingestion were the largest contributors to risk, followed by total 

dairy and beef. Eggs, poultry, and pork did not contribute significantly to benzo(a)pyrene 

exposure and risk. Further results for benzo(a)pyrene are provided in Appendix E. 

1.0E‐09

1.0E‐08

1.0E‐07

1.0E‐06

1.0E‐05

North Farm Parcel

South Farm Parcel

Recreational Fisher

Hispanic Fisher

Laotian Fisher

Vietnamese Fisher

Recreational Fisher

Hispanic Fisher

Laotian Fisher

Vietnamese Fisher

Recreational Fisher

Hispanic Fisher

Laotian Fisher

Vietnamese Fisher

North Farm Parcel

South Farm Parcel

Recreational Fisher

Hispanic Fisher

Laotian Fisher

Vietnamese Fisher

Glenmere Lake Silver Lake Tomahawk Lake Lewisville Lake

Revere, Middletown, NY Exide, Frisco, TX

Lifetime Cancer Risk
2/15/2011 70 DRAFT 

Figure 5-19. Pathway Contribution by Ingestion Pathway to Benzo(a)pyrene Individual 

Lifetime Cancer Risks 

5.3 Discussion of Uncertainties and Limitations 

The exposure and risk modeling process attempts to describe naturally occurring physical, 

chemical and biological processes in terms of mathematical algorithms. In a risk assessment, 

the modeling process generally involves a relatively simplistic representation of highly complex 

processes for computational tractability. This simplification introduces uncertainty that must be 

considered when evaluating the risk assessment results and other model output. Furthermore, 

algorithms that describe the movement of pollutants in the natural environment depend on a 

large number of environmental parameters whose values may be inherently variable and whose 

variability may not be well characterized in the literature for the specific sites being modeled. 

While these unavoidable issues do not invalidate the integrity of the modeling process, the 

evaluation of model results must be informed by consideration of model uncertainties, 

parameter variability, and related issues. 

The media concentration and risk results presented in Sections 5.1 and 5.2 must therefore be 

interpreted in light of the uncertainties associated with the model outputs. The behavior of 

mercury, cadmium, POM and BaP in the environment is highly complex, and a range of natural 

processes is represented in a simplified manner by TRIM.FaTE, including, but not limited to: 

 Gaseous and particulate deposition from air; 

 Biogeochemical cycling in the aquatic environment, and especially mercury 

transformation through methylation and demethylation at the sediment-surface interface; 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Revere

Middletown,

NY, North

Farm Parcel

Exide Frisco,

TX, North

Farm Parcel

Exide Frisco,

TX, South

Farm Parcel

Revere

Middletown,

NY, South

Farm Parcel

Percent Contribution to LIfetime Cancer Risk

Total Dairy

Beef

Pork

Eggs & Poultry

Fruits,Vegetable

s, & Soil

Increasing Lifetime Cancer Risk
2/15/2011 71 DRAFT 

 Mixing processes and suspended and benthic sediment dynamics in lakes; and 

 Biotic processes such as growth, reproduction, and predation. 

In addition, the toxicology of the modeled chemicals is complex and uncertain, as are the 

empirical estimates of human exposure to these chemicals. 

Examples of parameters and model inputs for which there is variability and uncertainty include 

aquatic food web parameters (such as assimilation efficiencies and elimination rates), 

topographic parameters (such as lake depth, runoff rates, and erosion rates), meteorological 

parameters (such as evaporation and precipitation rates), chemical transformation parameters 

(such as methylation and demethylation rates, in the case of mercury), human exposure 

parameters (especially fish consumption rates), and the reference dose/cancer slope factor 

used to assess potential adverse effects. 

Because of the restrictions inherent to the modeling approach and the parameters of this 

evaluation, this assessment did not attempt to quantitatively estimate the full range of 

uncertainty associated with results (such as might be obtained in a probabilistic analysis). 

Instead, this analysis depended on central tendency and reasonable maximum exposures to 

bound risk estimates. 

The scope of this analysis encompassed only facility-attributable sources of chemical exposure, 

and the omission of background levels of the modeled chemicals from the estimation of 

potential health impacts should be considered when interpreting the results of this assessment. 

No quantitative health impact of the number or fraction of people potentially affected in the 

modeling zones was estimated, and no estimates of specific impacts like IQ decrements or 

cardio-vascular effects were made. This does not represent a source of uncertainty affecting 

the modeling results presented here, but it is an important consideration that must be taken into 

account. 

Table 4-6 presents major sources of uncertainty affecting model results, describes the potential 

impact of these factors on model results in general, offers a brief comment on the treatment of 

the issue in the current assessment, and provides a qualitative assessment of the uncertainty in 

the current results attributable to the various factors. Each of the uncertainty factors is also 

discussed further below. 

Table 4-6. Uncertainty Factors in the Multipathway Residual Risk Assessment for 

Secondary Lead Smelting 

Uncertainty Factor Sensitivity of 

Risk Results Comment 

Estimated 

Impact of 

Uncertainty on 

Results 

Aquatic food web 

parameters High 

Limited data on chemical and speciesspecific parameters such as assimilation 

efficiency, elimination rates, etc. 

Conservative assumptions used. 

Medium 

Depth of lakes High 

Based on limited data. Averaged over lake 

area and time. Perfect mixing assumed in 

estimating concentrations. 

Medium 

Toxicity reference 

value (RfD) and High Used EPA recommended value, but estimate includes inherent variability and uncertainty. Low-Medium 
2/15/2011 72 DRAFT 

Table 4-6. Uncertainty Factors in the Multipathway Residual Risk Assessment for 

Secondary Lead Smelting 

Uncertainty Factor Sensitivity of 

Risk Results Comment 

Estimated 

Impact of 

Uncertainty on 

Results 

cancer slope factors 

Fate and transport 

modeling process High 

TRIM.FaTE model may not capture all natural 

processes or describe them precisely for 

particular sites. 

Low-Medium 

Ingestion exposure 

parameters High 

Used EPA recommended values, but high 

end ingestion rates are likely based on 

extrapolation from survey data. 

Low-Medium 

Methylation and 

demethylation rates in 

sediment and surface 

water (mercury only) 

High 

Model uses fixed rate constants for 

methylation and demethylation in abiotic 

media. Model does not capture complex 

dependence of rate constants on 

environmental conditions. 

Medium-High 

Retention time/flush 

rate Medium-High 

Calculated from depth. Independent data 

would help validate inflows and runoff 

fractions. 

Medium 

Modeling resolution 

and lay out Medium 

Resolution of compartments in modeling 

zone is relatively coarse. Higher area 

averaging may dilute exposure point 

concentrations. 

Low-Medium 

Runoff rates Medium 

Based on subjective judgment in absence of 

scientific method. Could not validate owing to 

lack of data. 

Low-Medium 

Erosion rates Medium Based on USLEs. State-specific erosion data 

may differ. Low-Medium 

Precipitation rate Medium 

Data for four year period may not represent 

true average. Affects deposition quantity and 

type. 

Low-Medium 

Evaporation rate Medium Limited site specific data. Impacts lake flush 

rates and concentrations. Low-Medium 

Wind speed and 

direction Medium 

Data for four year period may not represent 

average conditions. Excessive dispersion 

may underestimate risk. 

Low-Medium 

Sediment dynamics Medium Model uses simplistic algorithms to describe 

deposition and resuspension of sediment. Low-Medium 

Other soil, surface 

water, air & vegetation 

physico-chemical 

parameters 

Medium 

Limited site-specific data. Default 

assumptions often used. Low-Medium 

5.3.1 Uncertainties Related to Fate and Transport Modeling (TRIM.FaTE) 

The algorithms representing the transport and eventual fate of PB-HAPs in air, surface water, 

sediment, and biotic media are simplified representations of complex natural processes. 

Estimated deposition rates and concentrations may vary across different environmental models 
2/15/2011 73 DRAFT 

and may be most accurate in specific conditions that meet restrictive assumptions. The 

TRIM.FaTE model represents all fate and transport processes in terms of first-order differential 

equations; however, some processes like chemical diffusion are known to follow second-order 

dynamics. Other algorithms -- like those dealing with mercury methylation and demethylation in 

the case of mercury, biotic chemical cycling and sediment dynamics -- do not consider all the 

factors known to affect these processes, or the processes may not be well understood. This 

section expands on some of the major uncertainties included in the previous table that are 

specific to the fate and transport modeling conducted for the case studies. 

Aquatic Food Web Parameters. Estimates of pollutant concentrations in fish consumed by 

people are extremely sensitive to aquatic food web parameters. Limited chemical-, site- and 

species-specific data were available on a range of aquatic food web parameters, including 

biomass, food web structure, assimilation efficiencies, elimination rates, ingestion rates, and gill 

absorption rates. Site-specific data were used when feasible, but conservative assumptions 

based on the literature and professional judgment were used in the absence of specific data. 

Depth of Lakes. The concentration of pollutants transported into lakes (and thereafter up the 

aquatic food chain) has a sharp dependence on the depth of the lake assumed in the model. 

The assessment used site-specific average depth data for all lakes, but limited data were 

available for cross-verification. Averaging concentrations over space and time implies perfect 

mixing in the water body and may not account for local pockets or periods of high concentration. 

Surface Water Retention Time/Flush Rate. Retention time, which is inversely proportional to 

flush rate, determines how quickly pollutants are passed out of a lake. A flush rate that is too 

high (or retention time that is too low) could result in an underestimate of pollutant 

concentrations in surface water and in the aquatic food chain. For this assessment, retention 

time was calculated based on information and assumptions regarding inflow into the lake, 

evaporation, and depth. Lack of data prevented cross-verification of the computed retention 

time/flush rates. 

Methylation and Demethylation Rates (Mercury). Methylation and demethylation in sediment 

and surface water are key processes governing biogeochemical cycling of mercury in the 

aquatic environment. The rate of occurrence of these processes has a great influence on the 

sensitivity of an aquatic ecosystem to mercury inputs and specifically influences the amount of 

methyl mercury available for accumulation. Some water bodies that are efficient at methylating 

inorganic mercury can show significant methyl mercury concentrations in biota despite a 

relatively small mercury input into the system. The representation of these processes in the 

TRIM.FaTE model does not explicitly account for known dependencies of transformation rates 

on redox potential, pH, sulfite concentration, dissolved organic carbon content, and 

hydrodynamics at the sediment-surface water interface. Both methylation and demethylation 

can occur either biotically or abiotically. Certain conditions, like specific ranges of chloride, 

sulfide, and dissolved organic matter concentrations, can increase the bioavailability of divalent 

mercury for methylation. Redox conditions can influence the rate of abiotic demethylation. 

These process mechanics, potentially antagonistic interactions with heavy metals like selenium, 

and the potential for photodegradation of methyl mercury are not captured in the TRIM.FaTE 

model. Instead, user-supplied first-order rate constants were used to model methylation and 

demethylation in surface water and sediment. Site-specific data were limited and the regional 

default rate constants used may not represent conditions at the modeled lakes. 

Sediment Dynamics. The suspension and burial of sediment can have a significant impact on 

surface water concentration and speciation by influencing the methylation and demethylation 
2/15/2011 74 DRAFT 

process. Suspended solid concentrations also affect the amount of mercury transported out of 

the water body during flushing. Resuspension of buried sediment could remobilize previously 

deposited mercury into the water body. In the TRIM.FaTE model, these processes are 

simplistically represented by default sediment deposition rates and suspended solids 

concentrations, which do not account for hydrodynamic sediment cycling processes. These 

parameters were not based on site-specific data. 

Runoff Rates. For pollutants whose risks are transmitted chiefly by the fish consumption 

pathway, the amount of pollutant entering lakes is a significant variable. Because runoff can 

account for a significant portion of the pollutant transported into the lake in some locations, 

runoff rate from the watershed is a potentially sensitive parameter in the model. These runoff 

rates were estimated based on information about the surrounding topography and local 

precipitation data. 

Erosion Rates. Similar to runoff rates, erosion rates can affect the quantity of pollutants 

transported into a water body. Erosion rates were estimated using the universal soil loss 

equation, which is a generalized estimate that is dependent on local topography, land use, and 

climate. It is possible that local erosion rates may differ from the USLE estimate. 

Precipitation Rate. The precipitation rate in the model potentially affects the rate at which 

pollutants are transported between surface soil compartments and water bodies and also the 

rate at which pollutants are flushed out of water bodies. This assessment used rainfall data for a 

four-year period, which may not represent average or future conditions. 

Evaporation Rate. The evaporation rate affects flush rate computation in the model, and 

consequently lake concentration and aquatic biota concentration estimates. Limited site-specific 

data were available and regional estimates had to be used in the model. 

Wind Speed and Direction. Wind speed and direction affect dispersion of the pollutant in the 

model. Because these data were derived from a single-four year period, they may not be 

representative of average or future conditions. 

Other Soil, Surface Water, Air & Vegetation Physico-Chemical Parameters. Due to a lack of 

easily accessible site-specific data, default or national-average values were used for a number 

of soil, surface water, and air and vegetation physical and chemical parameters. 

Algorithm Uncertainty. The TRIM.FaTE model represents all fate and transport processes in 

terms of first-order differential equations. However, some processes like diffusion are known to 

follow second-order dynamics. TRIM.FaTE also does not explicitly deal with lateral or vertical 

dispersion in the air compartments. As noted earlier, some algorithms like methylation and 

sediment transport do not consider all the factors known to affect the process. Biotic processes 

including chemical absorption, chemical elimination, growth, reproduction, predation and death 

have been represented relatively simplistically in the model. While the model’s algorithms have 

been validated and are based on professional judgment, some level of uncertainty may result 

from such simplifications. 

5.3.2 Uncertainties Related to Exposure Modeling and Risk Characterization 

Ingestion Exposure Parameters. Although the fish ingestion rates used in this evaluation for 

the general population were obtained from recommended EPA exposure factors, and more 

specific data were provided by EPA for some high-exposure populations, there is likely to be 

uncertainty associated with some of the upper percentile estimates of these rates that are 
2/15/2011 75 DRAFT 

derived using statistical inference from limited survey data. Also, there may be regional 

differences in these parameter values not captured in the model. It is possible that not all of the 

populations modeled in the analysis are present in the modeling zone of the two facilities 

considered. 

Cooking Correction Factor. As described in Section 3, for methyl mercury, an EPArecommended cooking conversion factor (CCF) of 1.5 was utilized to account for the fact that 

cooking fish reduces the overall mass of the fish without a reduction in the mercury level, which 

effectively increases the mercury concentration in the cooked fish.49 The CCF assumption was 

developed using the cooking practices of a small sample size of Native Americans in Wisconsin. 

This may not be representative of the cooking patterns of all angler populations or all selfcaught fish species. For the preparation methods presented the CCF ranged from 1.3 to 1.9, 

suggesting uncertainty even within the study. 

Breast Milk Exposure Parameters. Exposure via the breast milk pathway was estimated using 

algorithms and assumptions to simulate the partitioning behavior of the PB-HAP in breast milk 

and the rate and duration of breast milk consumption. While the methods and assumptions are 

supported by scientific literature, they have not been evaluated against empirical biomonitoring 

data or other models. 

Toxicity Reference Value. Incremental hazard quotients and incremental lifetime cancer risks 

were calculated using EPA’s recommended oral reference doses and cancer slope factors. 

Reference doses and cancer slope factors are typically estimated after building in uncertainty 

factors for pharmacokinetic variability and uncertainty, pharmacodynamic variability and 

uncertainty, inter- or intra-species variability, and potentially other factors. An awareness of the 

values and ranges of these uncertainty factors (documented in EPA’s IRIS data base) could 

help inform risk management decisions. 

 

49 Morgan, J.N., M.R. Berry, and R.L. Graves. 1997. “Effects of Commonly Used Cooking Practices on Total Mercury 

Concentration in Fish and Their Impact on Exposure Assessments.” Journal of Exposure Analysis and 

Environmental Epidemiology 7(1):119-133.