# Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category

Draft Report April, 2011

Prepared for: Terri Hollingsworth, Wo rk 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 ..................................................................... 492/15/2011 ii DRAFT 

### 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 Multi pathway 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 Estimates2/15/2011 iii DRAFT 

### LIST OF TABLES

Table 2-1. OAQPS List of PB-HAPs ............................................................................................. ............... 4
Table 2-2. The Four PB-HAPs with Screening Lev els, the Chemicals Constituting those PB-HAPs, and the Metal Speciation Factors and Equiva lency Factors, wher e Appropriate ................................... 5

Table 3-1. PB-HAP Emissions for the Exide Frisco and Revere Mi ddletown 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 Fri sco and Revere Middlet own Facilities ................................ 19
Table 3-3. Average Meteorological Conditions Near t he Exide Frisco and Revere Middletown Facilities. Rounding Errors Mi ght be Pr esent .............................................................................................. ... 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 V egetation Type .............................................................. 31

Table 3-6. Soil and Groundwat er Compartment Depths ........................................................................... . 36
Table 3-7. Selected Proper ties of Soil and Groundwater ....................................................................... ... 36

Table 3-8. Selected Surface Water and Sedim ent Properties ................................................................... 38

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

Table 3-10. Biomass Parameters for Lake Lewisville in the Exi de Frisco Fa cility ..................................... 41

Table 3-11. Biomass Parameters for Fishable Lakes in the Revere Middletown Facility .......................... 42
Table 3-12. Summary of Ingestion Expos ure Pathways and Rout es of Uptake ........................................ 44
Table 3-13. Overview of Exposure Factors used fo r Multipathway Analysis for Farm Food Pathway Scenarios a,b

.............................................................................................................................. ...... 46
Table 3-14. Overview of Exposure Factors used for Multipathway Analysis for Fish Pathway Scenarios a,b,c

.............................................................................................................................. .... 47
Table 4-1. Dose-Response Values for Assessed PB-HAPs a .................................................................... 49

Table 5-1. Concentration Estimates and Speciation for Mercur y (Exide Frisco) ....................................... 50
Table 5-2. Concentration Estimates for 2,3, 7,8-TCDD, BaP, and Cadm ium (Exide Frisco) ..................... 52
Table 5-3. Concentration Estimates and Spec iation for Mercury (R evere Middle town) ............................ 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 Ri sks .......................................................... 60
Table 4-6. Uncertainty Factors in the Multipat hway Residual Risk Assessment for Secondary Lead Smelting ...................................................................................................................... ................... 712/15/2011 iv DRAFT 

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### LIST OF FIGURES
Figure 2-1. Conceptual Exposure Model for Farmer Scenario .............................................................. 9
Figure 2-2. Conceptual Exposur e Model for Fish er Scenario ............................................................. 10
Figure 3-1. Multipathway Anal ysis Methodology Overview ................................................................. 11
Figure 3-2. Overview of Proc ess Carried Out in the Multimedi a Ingestion Risk Calculator ................ 13
Figure 3-3. Location of the Exide Frisco Fac ility. 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 Facilit y 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 Parcel s for the Exide Frisco Facility, along with Loca l 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 Ex ide Frisco Facility, along with Loca l 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 Vegetati on Types of the Surface Parcels fo r the Exide Frisco Facility ............ 32
Figure 3-14. The TRIM Vegetation Types of t he 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 Midd letown Facility, Overla id with the Surface Parcels ....................................................................................................................... ....... 35
Figure 3-17. Aquatic Food Web and Diet Frac tions in Exide Frisco and Revere Middletown Facilities .................................................................................................................... ........ 43
Figure 5-1. Mercury Biomagnification in the Ea stern Arm of Lake Lewisville (Exide Frisco) .............. 51
Figure 5-2. Mercury Bioaccumul ation in the Eastern Arm of Lak e Lewisville (Exide Frisco) .............. 52
Figure 5-3. Calculated 2,3,7,8-TCDD, BaP, and Cadmium Bioaccumulation Factors in the Eastern Arm of Lake Lewi sville (Exide Frisco) ................................................................. 53
Figure 5-4. Mercury Biomagnification in Silver Lake (Rev ere Middlet own) ........................................ 55
Figure 5-5. Calculated Mercury Bioaccumulati on Factors in Silver Lake (Revere Middletown) ......... 56
Figure 5-6. 2,3,7,8-TCDD, BaP, and Cadmiu m Biomagnification in Silver Lake (Revere Middletown) ................................................................................................................... .... 57 Figure 5-7. Calculated 2,3,7,8-TCDD, Ba P, and Cadmium Bioaccumulation Factors in Silver Lake (Rever e Middletown) ...................................................................................... 58
Figure 5-8. 2,3,7,8-TCDD Individual Life time Cancer Risks, RM E Ingestion Rate ............................. 612/15/2011 vi DRAFT Figure 5-9. Pathway Contribution by Inge stion Pathway to 2,3,7,8-TCDD Individual Lifetime Cancer Risks for the Farming Sc enario .............................................................. 62
Figure 5-10. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Farming Scenarios and Recreational Angler Scenario by Location, RME I ngestion Ra te ..................................... 63
Figure 5-11. Hazard Quotients for Exposure to 2,3,7,8-TCDD for Recreational Angler Populations, RME I ngestion 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 I ngestion Rate ..................................................................................... 65
Figure 5-14. Hazard Quotients for Exposure to Divalent Mercury for Farming and Recreational Angler, RME Inges tion Rate ............................................................................................. 66
Figure 5-15. Hazard Quotients for Exposure to Divalent Mercury for Recreational Angler Population, RME I ngestion Ra te ....................................................................................... 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 Ingestio n Rate .......................................................................................................... 6 8
Figure 5-18. Benzo(a)pyrene Individual Li fetime Cancer Risks, RME Ingestion Rate .......................... 69 Figure 5-19. Pathway Contribution by Ingesti on Pathway to Benzo(a)pyrene Individual Lifetime Canc er Risks ....................................................................................................... 7 02/15/2011 1 DRAFT 

## 1 Introduction

This report presents an assessment of “multipat hway” (i.e., non-inhalation) human health risks from emissions from secondary lead smelting facilit ies – specifically, emissions of hazardous air pollutants (HAPs) known to be persistent and bioa ccumulative 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 mult ipathway 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 smel ting 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 mechanicall y 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,” includi ng 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 assess ment 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 Collect ion 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 “m ultipathway” assessment of public health risks via non-inhalation exposure pathways such as i ngestion 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 det ermining whether any sources emitted any PBHAPs. EPA previously identified 14 PB-HAP compounds or compound classes, including

1 National Emission Standards for Hazardous Air Polluta nts 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, dioxin s, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercur y 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 concen trations 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 multipathw ay human exposures for RTR ri sk assessment focuses on PBHAPs that OAQPS has identified as candidates for multipathway risk assessments. OAQPS previously developed a list of fourteen chemic als and chemical groups that are PB-HAPs based on a two-step process taking into account the foll owing: (1) their presence on three previouslyexisting 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/fur ans, lead, mercury, and polycyclic organic matter [POM]) are candidates for further consideration for the multipathway analysis. With respect to

2 Persistent and bioac cumulative 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/fe ra/risk_atra_vol1.html

. 3 Air Toxics Risk Assessment (ATRA) Reference Library: http://www.epa.gov/ttn/fe ra/risk_atra_main.html2/15/2011 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 Compounds a 
Chlordane
Chlorinated Dibenzodioxins and Furans a 

DDE Heptachlor
Hexachlorobenzene Hexachlorocyclohexane (all isomers) Lead Compounds  Mercury Compounds a,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 merc ury 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 detec tion 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 Sc reening 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 expos ures. EPA developed the screening levels by identifying exposure concentrations resulting in a 1 in 1 million level of cancer risk or noncancer 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, which2/15/2011 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 dev eloped 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]Anthracene b NA 0.10 Benzo(c)phenanthrene NA 0.05 Benzo[a]Pyrene b NA 1 Benzo[b]Fluoranthene b 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]Fluoranthene b NA 0.10 Chrysene b NA 0.01 Dibenzo[a,e]Pyrene NA 1 Dibenzo[a,h]Anthracene b 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]Pyrene b NA 0.10 PAH, total NA 0.102/15/2011 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 Pyrene b 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 Mercury b 1 NA Particulate Divalent Mercury b 1 NA Cadmium b 1 NA
Cadmium 3.509E-02 Cadmium & Compounds 1 NA 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 De tailed Residual Risk Assessment

For the screening assessment for secondary l ead smelting, EPA compared the screening levels to emissions data for all fourteen secondary lead sm elting 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 molecula r 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., “mer cury” 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 level 4



### . 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 s creening 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 di oxins and all facilities exceeded the screening level of chlorinated dibenzodioxins and fu rans 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 re sults 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 overestimates2/15/2011 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 multipathw ay assessment either because they are not PBHAPs or they are PB-HAPs but do not current ly 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 eval uation of non-inhalation exposures, however, estimating the risk to the “most exposed” indi vidual 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 v egetables, 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 expos ure 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 relati ve 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 us ually 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 conc ern 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 exposur e was expected to be a small fraction of the risk from inhalation exposure or ingestion expos ure. Therefore, the risk from dermal exposure was not calculated.

Figure 2-1. Conceptual Exposure Model for Farmer Scenario2/15/2011 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 Facilit ies: TRIM.FaTE Application for Two Secondary Lead

Smelting Facilities And TRIM.FaTE - IEM Model Comparis on, Prepared for EPA by ICF Consulting, September 30, 20032/15/2011 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 met hodology used to assess multipathway risks from secondary lead smelting emissions of PB-HAPs fr om 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 Expos ure 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 concentrat ions 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 ma ss balance model that describes the movement and

transformation of pollutants over time, through a user-defi ned, bounded system that includes both biotic and abiotic compartments. Outputs include pollutant concentrations in multiple environmental m edia 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 Overview2/15/2011 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 primar ily 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 Sci ence 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 relations hips 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 incl ude: (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, us er-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 am ong 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-s pecific parameter values). U.S. En vironmental Protection Agency, Office of Solid Waste and Emergency Response, Wa shington, 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, characterist ics 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, respectively 11 

### ), less than 2 km south of Frisco and about 37 km north of Dallas (est. 2009 populations 102,413 and 1,299,542, resepectively 11 

### ). 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.gov2/15/2011 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 multipathw ay 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 abs olute highest multipathway risk from this source category.

12 USDA 2007 Census of Agriculture: http://www.agcensus.usda.gov/Publications/2007/Full_Report/index.asp2/15/2011 15 DRAFT 

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

are Shown. Shading Indicates Land Use Type in 2001 a 

### (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 1990-1993 13

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 Mercury a 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 Mercury a 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 383,532 11 

### ), 5 km east of Middletown and 7 km north of Goshen (est. 2009 population 25,936 and 13,879, resepectively 11 

### ). Many relatively small lakes are near the facility, including Silver Lake, Shawangunk Lake, Glenm ere Lake, and Tomahawk Lake (2 km west, 11 km west, 15 km south, and 12 km west of the fa cility, 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), Glenm ere Lake (south), and Tomahawk Lake (east) were downwind from the facility about 20, 24, and 39 pe rcent 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 espec ially 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 incr emental multipathway cancer risks exceeding 1 in 1 million and/or incremental multipathw ay 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 mi llion 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 2001 a 

### (With Simplified Categories)

a USGS National Land Cover Database 2001: http://www. mrlc.gov/nlcd.php2/15/2011 18 DRAFT 

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

a From the surface hourly meteor ological data from the Allentown

International Airport (WBAN ID 14737) for years 1990-1993 13



### 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 bas ed 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-te rm 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 smelters 5 

### , 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 the2/15/2011 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 tribut aries. The relatively small deviations from normal meteorological conditions (i.e., wetter and sli ghtly 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 Internati onal 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 Averages a 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?dire ctive=prod_select2&prodtype=CLIM81&subrnum

14 NOAA/ESRL Radiosonde Database: http://www.esrl.noaa.gov/raobs/

15 Risk and Technology Review (RTR) Risk Assessment Methodo logies: 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 dev eloped 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 overvi ew 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 km 2 

### 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 throug hout 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, Gl enmere Lake, and Tomahawk Lake nearby the facility (2 km west of the facility and 0.182 km 2 

### , 11 km west and 0.405 km 2 

### , 15 km south and 0.688 km 2 

### , and 12 km west and 1.314 km 2 

### , 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 t he 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 Located a



### 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.html2/15/2011 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 Basins a

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 t he 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 ov erlain by the air parcel layout. These layouts provide the spatial reference for three-di mensional compartments that hold the modeled chemical mass.

The spatial extent of a TRIM.FaTE modeling scenar io is generally at least a 15-km radius around the facility, and it can extend out to perha ps 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 In ternational, Draft, May 6, 2002). The result s of this study sugges t that increasing the modeling domain beyond a 15-km radius of the facility g enerally does not capture enough additional chemical2/15/2011 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 co mputational resources needed to mode l 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 Elevation a

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 Elevation a

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 (km 2 ) a Surface Parcel Name Modeled Surface Area of TRIM.FaTE Parcel (km 2 ) Exide Frisco Lake Lewisv ille 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. 172/15/2011 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 types 16 

### , 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 Use 162/15/2011 29 DRAFT 

### Figure 3-12. Locations of the Farm parcels and Source Parcel for the Revere Middletown Facility, along with Local Land Use 16



### 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 data 21 

### 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 NLCD 16 

### ) 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, re latively 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 Sect ion 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 om it 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 Factor a 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.purd ue.edu/~sedspec/seds pec/doc/usleapp.doc

. 

### The vegetation type for each surface parcel was determined by calculating the most common land use type (using the 2001 NLCD 16 

### ) 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 a ssigned 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 assi gned 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 Facility2/15/2011 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 Guide 22 

### 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#cur rent_user2/15/2011 34 DRAFT 

### Figure 3-15. The Air Parcels for the Exide Frisco Facility, Overlaid with the Surface Parcels2/15/2011 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 environment al properties for each compartment in the scenario. Examples of abiotic environmental pr operties 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 a ssumed. 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 me ters 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 areas 24 

### 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 grassesand 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 doc umentation 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 assumi ng 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 USLE 25 

### , 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 s ediment 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 groundwater 28 

### . 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 factor s 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 Te rrain as part of Google Maps. Available at www.maps.google.com

27 Annual average precipitation: Meteorology data is consistent with the pr evious iteration of this analysis. National

Oceanic and Atmospheric Administration, National Climat ic 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 8.2 29 7.7 a 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 availa ble, 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 precip itation 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 Eutr ophication 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.f iles.wordpress.com/2010/02/northerncricket-frog-survey-report.pdf. (12/14/10)

33 New York water data(d): Kishbaugh, Scott. 2010. Pers onal 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 (km 2 ) Turnover Rate (flushes per year) Average Depth

(m) Discharge Rate (m 3

s -1 ) Sediment Resuspension Rate (m[sediment] day -1 ) Surface Runoff

from Land (m 3

yr -1 )
Exide Frisco Lake Lewisville 28.57 7.64 6 a 41.55 9.64E-5 5.46E6
E Arm 6.18 1.01 6 a 1.18 9.59E-5 7.63E6
NE Arm 11.29 7.47 6 a 16.05 1.02E-4 1.23E8
W Arm 9.88 2.49 6 a 4.68 9.81E-5 3.42E7
NW Arm 28.43 3.31 6 a 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 2.4 b 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/wate r 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 mo deled 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 proper ties: (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, t he 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, Eze ll, D. (1988). Freeze/Frost Da ta. Climatology of the U.S.

No. 20. Supplement No. 1. National Climatic Data Center. Available at: http://www.ncdc.noaa.gov/oa/clim ate/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 z one, 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 Co rps 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/mono ps/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/Shaw angunk%20Lake%20Appendix.pdf2/15/2011 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 obtai ned 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 va lues, including chemical-specific transfer factors for FFC media, were obtained from the Hazardous Waste Companion Database included in HHRAP 9



### .

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. Ex posure pathways included are incidental ingestion2/15/2011 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 Process b

Incidental ingestion of soil Surface soil N/A Surface soil Deposition; transfer through plants; transfer via erosion and runoff c

Consumption of fish Fish from local water body N/A Fish tissue Direct uptake from water and consumption of food compartments modeled in TRIM.FaTE c Consumption of breast milk d 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 cons umed 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 we ight). MIRC computes exposure doses andrisks 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 vegetabl es were assumed to be contaminated from air and soil at an agricultural parcel in the m odeling 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 ai r 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. Q uantitative 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 Scenarios a,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: 90 th 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: 90 th 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 Appe ndix C. Exposure parameter values were based on data

obtained from the Exposure Factors Handbook 41 . See Appendix C for details.
b Exposure factor inputs are used in calculating average daily dose (ADD) es timates for each ex posure pathway.

ADD equations for each pathway evaluated in this scr eening assessment are provided in Appendix C.

c Fraction contaminated represents the fr action of food product that was derived from the environment included in the2/15/2011 47 DRAFT 

### Table 3-13. Overview of Exposure Factors used for Multipathway Analysis for Farm Food Pathway Scenarios a,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 Scenarios a,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: 90 th percentile of distri bution of subpopulation of interest (recreational, Hispanic, Laotian, and Vietnamese fishers c ) CTE Scenarios: mean of distribution of subpopulation of interest (recreational, Hispani c, Laotian, and Vietnamese fishers c ) 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 Appe ndix C. Exposure parameter values were based on data

obtained from the Exposure Factors Handbook 41 . See Appendix C for details.
b Exposure factor inputs are used in calculating average daily dose (ADD) es timates for each ex posure pathway.

ADD equations for each pathway evaluated in this scr eening assessment are provided in Appendix D.

c Fraction contaminated represents the fr action 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 in creasing 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 c hemicals 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. “Effe cts 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 algor ithms 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 andthreefold 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-H AP 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 Cont rol’s Agency for Toxic Substances and Disease Registry (ATSDR); and the California Environmental Protection Agency (CalEPA).

43 U.S. Environmental Protection Agency. 2005. Supplem ental Guidance for Assessing Susceptibility from Early-Life

Exposure to Carcinogens. EPA-630/R-03-003F. Ri sk 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-Respons e 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-HAPs a

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
BaP b 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 Re search and Development

a Values presented here are consistent with t hose 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 compartment s 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 50 th 

### (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 dival ent 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/nat a1999/99pdfs/poma pproachjan.pdf2/15/2011 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 sedi ment, surface water and the various biotic compartments, and between the biota themselves ar e 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 Me rcury 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. 200 7. 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:OmnivoreBiomagnificationFactor

Biota Concentration Ratios in the Water Column Methyl Mercury

Divalent Mercury

Total Mercury2/15/2011 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+08BioaccumulationFactor

Ratio of Biota to Dissolved Water Concentration Methyl Mercury Divalent Mercury Total Mercury2/15/2011 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 dival ent 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+08BioaccumulationFactor

Ratio of Biota to Dissolved Water Concentration TCDD

BaP

Cd2/15/2011 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:OmnivoreBiomagnificationFactor

Biota Concentration Ratios in the Water Column Methyl Mercury Divalent Mercury Total Mercury2/15/2011 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+08BioaccumulationFactor

Ratio of Biota to Dissolved Water Concentration Methyl Mercury

Divalent Mercury

Total Mercury2/15/2011 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:OvoreBiomagnificationFactor

Biota Concentration Ratios in the Water Column TCDD

BaP

Cd2/15/2011 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 Multipathw ay 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 i ndividual 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 ex posures 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 di valent 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 farm ers 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 exce ss 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 90 th 

### 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 Angler a 

### 4.1E

‐ 08 1.6E

‐ 07

Laotian Angler a 

### 4.3E

‐ 08 2.3E

‐ 07

Vietnamese Angler a 

### 4.3E

‐ 08 1.6E

‐ 07
Silver Lake Recreational Angler 3.5E

‐ 08 8.5E

‐ 08 Hispanic Angler a 

### 7.6E

‐ 08 2.9E

‐ 07

Laotian Angler a 

### 7.9E

‐ 08 4.2E

‐ 07

Vietnamese Angler a 

### 7.9E

‐ 08 2.9E

‐ 07
Tomahawk Lake Recreational Angler 2.1E

‐ 08 5.2E

‐ 08 Hispanic Angler a 

### 4.6E

‐ 08 1.7E

‐ 07

Laotian Angler a 

### 4.8E

‐ 08 2.6E

‐ 07

Vietnamese Angler a 

### 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 Angler a 

### 5.7E

‐ 06 2.2E

‐ 05

Laotian Angler a 

### 6.0E

‐ 06 3.2E

‐ 05

Vietnamese Angler a 

### 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 t he 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, TXPercentContributiontoLIfetimeCancerRisk Total Dairy

Breast milk

Beef

Pork

Eggs & Poultry

Fruits,Vegetables,
& Soil 

### Increasing Lifetime Cancer Risk2/15/2011 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+00NorthFarmParcelSouthFarmParcelGlenmereLakeSilverLakeTomahawkLakeNorthFarmParcelSouthFarmParcelLewisvilleLake

Revere, Middletown, NY Exide, Frisco, TXNoncancerHazardQuotient
Infant

Child 1

‐ 2

Child 3

‐ 5

Child 6

‐ 11

Child 12

‐ 19

Adult

1.0E

‐ 04 1.0E

‐ 03 1.0E

‐ 02 1.0E

‐ 01 1.0E+00

Recreational Angler Hispanic Adult Laotian Adult Vietnamese AdultNoncancerHazardQuotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX2/15/2011 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 andangler 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 merc ury 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. “E ffects 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+00NorthFarmParcelSouthFarmParcelGlenmereLakeSilverLakeTomahawkLakeNorthFarmParcelSouthFarmParcelLewisvilleLake

Revere Middletown NY Exide Frisco TXNoncancerHazardQuotient Child 1

‐ 2

Child 3

‐ 5

Child 6

‐ 11

Child 12

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Adult2/15/2011 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. HQ s 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 AdultNoncancerHazardQuotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX2/15/2011 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

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‐ 01 1.0E+00NorthFarmParcelSouthFarmParcelGlenmereLakeSilverLakeTomahawkLakeNorthFarmParcelSouthFarmParcelLewisvilleLake

Revere Middletown NY Exide Frisco TXNoncancerHazardQuotient Child 1

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1.0E

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Recreational Angler Hispanic Adult Laotian Adult Vietnamese AdultNoncancerHazardQuotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX2/15/2011 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

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‐ 01 1.0E+00NorthFarmParcelSouthFarmParcelGlenmereLakeSilverLakeTomahawkLakeNorthFarmParcelSouthFarmParcelLewisvilleLake

Revere, Middletown, NY Exide, Frisco, TXNoncancerHazardQuotient

Child 1

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Child 3

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Adult2/15/2011 68 DRAFT 

### Figure 5-17. Hazard Quotients for Exposure to Cadmium for Recreational Angler Population, RME Ingestion Rate

1.0E

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Recreational Angler Hispanic Adult Laotian Adult Vietnamese AdultNoncancerHazardQuotient

Glenmere Lake, NY

Silver Lake, NY

Tomahawk Lake, NY

Lewisville Lake, TX2/15/2011 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 90 th 

### percentile RME ingestion rates for all age groups.

Figure 5-18. Benzo(a)pyrene Individual Li fetime 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 c ontribute significantly to benzo(a)pyrene exposure and risk. Further results for benzo(a)pyrene are provided in Appendix E. 1.0E

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‐ 05NorthFarmParcelSouthFarmParcelRecreationalFisherHispanicFisherLaotianFisherVietnameseFisherRecreationalFisherHispanicFisherLaotianFisherVietnameseFisherRecreationalFisherHispanicFisherLaotianFisherVietnameseFisherNorthFarmParcelSouthFarmParcelRecreationalFisherHispanicFisherLaotianFisherVietnameseFisher

Glenmere Lake Silver Lake Tomahawk Lake Lewisville Lake

Revere, Middletown, NY Exide, Frisco, TXLifetimeCancerRisk2/15/2011 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 relative ly 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 whos e 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 ParcelPercentContributiontoLIfetimeCancerRisk Total Dairy

Beef

Pork

Eggs & Poultry

Fruits,Vegetable

s, & Soil 

### Increasing Lifetime Cancer Risk2/15/2011 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 assim ilation efficiencies and elimination rates), topographic parameters (such as lake depth, runoff rates, and erosion rates), meteorological parameters (such as evaporation and precipitati on 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 val ue, but estimate includes inherent variability and uncertainty. Low-Medium2/15/2011 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 models2/15/2011 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 cycli ng 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 efficien cies, 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 concen trations 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. Cert ain 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 demethylation2/15/2011 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 hydr odynamic 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 c oncentration 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 elimi nation, 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 are2/15/2011 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 sa mple 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 in cremental 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. “E ffects 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.