Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 1



## Deliberative Draft Document for EPA Internal Review

Draft

Residual Risk Assessment for th e Secondary Lead Smelting Source Category

by EPA’s Office of Air Qualit y Planning and Standards

Office of Air and Radiation April 2011Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 2



### Table of Contents

1 Introduction .................................................................................................................. ...... 4

2 Methods ....................................................................................................................... ....... 4

2.1 Emissions and source data ........................................................................................... 4

2.2 Dispersion modeling for inhala tion exposure assessment ........................................... 5

2.3 Estimating chronic human inhalation exposure .......................................................... 8

2.4 Screening for potential acute impacts of concern ........................................................ 9

2.5 Multipathway human health and environmental risk screening ................................ 10

2.6 Dose-response assessment ......................................................................................... 13

2.6.1 Sources of chronic dose-response information .................................................. 13

2.6.2 Sources of acute dose-response information ...................................................... 20

2.7 Risk characterization ................................................................................................. 24

2.7.1 General ............................................................................................................... 24

2.7.2 Mixtures ............................................................................................................. 26

2.7.3 Facility-wide risks .............................................................................................. 26

3 Risk results for the secondary lead source category .................................................... 27

3.1 Source category descrip tion and results .................................................................... 27

3.2 Baseline risk characterization .................................................................................... 30

3.3 Post-control risk characterization .............................................................................. 35

4 General discussion of uncertainties a nd how they have been addressed ......................... 36

4.1 Exposure modeling uncertainties .............................................................................. 36

4.2 Uncertainties in the dose -response relationships ....................................................... 39

5 References .................................................................................................................... .... 48

Index of Tables

Table 2.2-1 AERMOD version 09292 m odel options for RTR modeling ................................ 6 Table 2.6-1 (a) Dose-Response Values for Chr onic Inhalation Exposure to Carcinogens .... 17 Table 2.6-1 (b) Dose-Response Values for Ch ronic Oral Exposure to Carcinogens ............. 18 Table 2.6-1 (c) Dose-Response Values for Ch ronic Inhalation Expos ure to Noncarcinogens

.............................................................................................................................. .................... 19 Table 2.6-2 Dose-Response Valu es for Acute Exposure ........................................................ 23 Table 3.1-1 Summary of Em issions from the Secondary Lead Source Category and Availability of Dose-Response Values .................................................................................... 28 Table 3.2-1 Summary of Sour ce Category Level Inhalation Risks for Secondary Lead Smelting ...................................................................................................................... ............. 32 Table 3.2-3 Secondary Lead Smelting Facility Modeled Maximum Ambient Lead

Concentrations ................................................................................................................ ......... 35 (rolling three-month average values) ....................................................................................... 35

Appendices

Appendix 1 Technical Support Docu ment for HEM-AERMOD Modeling Appendix 2 Meteorological Da ta for HEM-AERMOD ModelingDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 3

Appendix 3 Analysis of data on short-term emission rates relative to long-term emission

rates Appendix 4 Technical Support Document fo r TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation Appendix 5 Draft Detailed Risk Modeling Results Appendix 6 Acute Impacts Re fined Analysis Figures

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model AEGL Acute exposure guideline level ASTDR US Agency for Toxic Substances and Disease Registry CalEPA California Environmental Agency CTE Central Tendency Estimate ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant HEM Human Exposure Model

HI Hazard index HQ Hazard quotient IRIS Integrated Risk Information System MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action NAC National Advisory Committee NAAQS National Ambient Air Quality Standards

NATA National Air Toxics Assessment NEI National Emissions Inventory NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP POM Polycyclic organic matter REL Reference exposure level

RfC Reference concentration

RfD Reference dose RME Reasonable Maximum Exposure RTR Risk and Technology TOSHI Target-organ-specific hazard index

URE Unit risk estimateDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 4



### 1 Introduction

Section 112 of the Clean Air Act (CAA) establ ishes a two-stage regu latory process for addressing emissions of hazardous air pollutants ( HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agen cy (EPA, or the Agency) to develop technology-based standards for categorie s of sources (e.g., petr oleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largely completed the initial Maximum Achievable Control Technology (MACT) standards as re quired under this provision. Under section 112(d)(6), EPA must review each of these tec hnology-based standards at least every eight years and revise a standard, as necessary, “tak ing into account developments in practices, processes and control technologi es.” In the second stage, EPA is required under section 112(f)(2) to assess the health and environmental risks that remain after implementation of the

MACT standards. If additional risk reductions ar e necessary to protect public health with an ample margin of safety or to prevent an a dverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of th e regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the residual risk st age must be completed within eight years of promulgation of the initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board (SAB) on the “Risk and Technology Review (RTR ) Assessment Plan,” and in June of 2007,

we received a letter with the results of that c onsultation. Subsequent to the consultation, in June of 2009, a meeting was held with an SAB pa nel for a formal peer review of the “Risk and Technology Review (RTR) Assessment Methodologies” [ 2 ]. We received the final SAB report on this review in May of 2010 [ 3 ]. Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our assessm ents by incorporating updates based on the SAB recommendations as they are developed and be come available. Our responses to the key recommendations of the SAB are outlined in a memo entitled, “EPA’s Actions in Response to Key Recommendations from the SAB Review of RTR risk Assessment Methodologies” [ 4 ].

This document contains the methods and the resu lts of baseline risk assessments performed for the Secondary Lead Smelting source ca tegory. The methods discussion includes descriptions of the methods used to screen fo r acute health risks, chronic non-inhalation health risks, and adverse environm ental effects, as well as descriptions of the methods used to develop refined estimates of chronic inhalati on exposures and human health risks for cancer and noncancer endpoints.



### 2 Methods

2.1 Emissions and source data

Data from the 2005 National Emissions Inventor y (NEI) which were used and published as part of the 2005 National Scale Air Toxics Assessment (NATA) served as the starting point

for this assessment. The 2005 NEI contains in formation on actual emissions during the entireDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 5

2005 base year. Using the process MACT code 1 , we developed a subset of this inventory that contains emissions and facility data for the Secondary Lead Smelting source category. Next, we performed an engineering review of the information using EPA engineers to identify readily apparent limitations and issues with the emissions data In 2010, following this initial engineering review, we sent an information collection reque st (ICR) survey to the six companies which own all 14 facilities in the U.S. of which we are aware. The survey included many questions about emissions, stack parameters, locations, and other important inputs for the risk modeling. We also requested that facilities submit all existing test data. These data included stack test data for lead emissions for multiple years for most emissions points. In 2010, we also requested that most facilities conduct additional emissions testing of various furnaces and other emissions points. The facilities conducted emissions tests in 2010 for all HAP metals, dioxins, and various other HAPs. We received test data from most

facilities in late 2010. We revi ewed and analyzed all the ICR and stack test data and then incorporated these data into the model data set, as appropriate. However, with regard to the estimates of fugitive emissions, the data we received through the ICR were limited and uncertain. To estimate fugitive emissions we ev aluated the ICR responses and other available information and did supplemental analyses to derive estimates of fugitive emissions for all 14

facilities.

Details on the development of the emissions a nd source data for this source category are discussed in: Draft Summary of Secondary Lead RTR Modeling File Development, available in the docket for this rulemaking. Section 3 below provides a summary of the emissions.



### 2.2 Dispersion modeling for in halation exposure assessment

Both long- and short-term inhalation exposure co ncentrations and associated health risk from each facility in the source category of interest were estimated using the Human Exposure

Model in combination with the American Meteorological Society/ EPA Regulatory Model dispersion modeling system (HEM-AERMOD). Th e approach used in applying this modeling system is outlined below, and further deta ils are provided in Appendix 1. HEM-AERMOD performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimatio n of population risks. This section focuses on the dispersion modeling component. The expos ure and risk characterization components are

discussed in other subsecti ons of Sections 2 and 3.

The dispersion model in the HEM-AERMOD sy stem, AERMOD version 09292, is a state-ofthe-science Gaussian plume di spersion model that is prefer red by EPA for modeling point, area, and volume sources of continuous ai r emissions from facility applications [ 5 ]. Further details on AERMOD can be found in the AERMOD Users Guide [ 6 ]. The model is used to develop annual average ambient concentra tions through the simulation of hour-by-hour dispersion from the emission sources into the su rrounding atmosphere. Hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

1 The tagging of data with MACT codes allows EPA to de termine reductions attributable to the MACT program. The NEI associates MACT codes corresponding to MACT source categories with stationary major and area source data. MACT codes are assigned at th e process level for the point source.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 6

The first step in the application of the HEM-AERMOD modeling system is to predict ambient

concentrations at locations of interest. The AERMOD model options employed are

summarized in Table 2.2-1 and are discussed further below.

Table 2.2-1 AERMOD version 09292 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point, area represented as pseudo point source

Receptor orientation Polar (13 rings and 16 radials) Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (158 stations)

In HEM-AERMOD, meteorological data are ordinarily select ed from a list of 158 National Weather Service (NWS) surface observation stat ions across the continental United States, Alaska, Hawaii, and Puerto Rico. In most cases the nearest station is selected as representative of the conditions at the subject facility. Idea lly, when considering off-site meteorological data most site specific disp ersion modeling efforts will employ up to five years of data to capture variability in weathe r patterns from year to year. However, to minimize data processing time and resources fo r this assessment, we modeled only a single year, typically 1991. While the selection of a single year may result in under-prediction of long-term ambient levels at some locations, likew ise it may result in over-prediction at others. For each facility identified by its characteristic latitude and long itude coordinates, the closest meteorological station was used in the disper sion modeling. The average distance between a modeled facility and the applic able meteorological station was 40 miles (72 km). Appendix 2 (Meteorological Data Processing Using AERM ET for HEM-AERMOD) provides a complete listing of stations and assumptions along with further details used in processing the data through AERMET. The sensitivity of model resu lts to the selection of the nearest weather station and the use of one year of meteorologi cal data is discussed in “Risk and Technology Review (RTR) Risk A ssessment Methodologies” [ 2 ].

The HEM-AERMOD system estimates ambient con centrations at the ge ographic centroids of census blocks (using the 2000 Census) and at othe r receptor locations that can be specified by the user. The model accounts for the impacts of multiple facilities when estimating concentration impacts at each block centroid. Typically we combined only the impacts of facilities within the same source category, and assessed chronic exposur e and risk only for census blocks with at least one resident (i .e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambientDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 7

concentrations were calculated as the annual average of all estimated short-term (one-hour)

concentrations at each block centroid. Possible future residential use of currently uninhabited areas was not considered. Census blocks, the fi nest resolution availa ble in the census data, are typically comprised of approximately 40 people or about ten households.

In contrast to the developmen t of ambient concentrations fo r evaluating long-term exposures, which was performed only for occupied censu s blocks, worst-case short-term (one-hour)

concentrations were estimated both at the censu s block centroids and at points nearer the facility that represent locations where people may be present for short periods, but generally

no nearer than 100 meters from the center of the facility (note that for large facilities, this 100-meter ring could still contain locations inside the facility property). Since short-term emission rates were needed to screen for the potential for hazard via acute exposures, and since the NEI contains only annual emission tota ls, we typically apply the general assumption to all source categories that th e maximum one-hour emission rate from any source other than those resulting in fugitive dust emissions was ten times the average annual hourly emission

rate for that source. The factor of ten is not applied to fugitive dust sources because these emissions decrease during the meteorological c onditions associated with the worst-case shortterm impacts (i.e., during low-wi nd, stable atmospheric conditions).

The average hourly emissions rate is defined as the total emissions for a year divided by the total number of operating hours in the year. This choice of a factor of ten for screening was originally based on engineering judgment. To develop a more robust peak-to-mean emissions factor, and in response to one of the key messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis usin g a short-term emissions dataset from all the major HAP-emitting sources located in an area of Texas near Houston (originally reported on by Allen et al. 2004)[ 7]. In that report, the Texas Environmental Research Consortium Project compared hourly and annual emissions data for volatile organic compounds for all facilities in a heavily-industria lized 4-county area (Harris, Ga lveston, Chambers, and Brazoria Counties, TX) over an eleven-month time period in 2001. We obtained the dataset and performed our own analysis, focusing that analysis on sources which reported emitting high quantities of HAP over short periods of time (s ee Appendix 3, “Analysis of data on short-term emission rates relative to long-term emission ra tes”). Most peak emission events were less than twice the annual average, and the highest was a factor of 11 times the annual average. Based on these results, we chose the factor of ten for all initial screening; it is intended to cover routinely-variab le emissions as well as start up, shutdown, and malfunction (SSM)

emissions. While there have been some docum ented emission excursions above this level, our analysis of the data from the Texas Envir onmental Research Consortium suggests that this

factor should cover more than 99% of the short- term peak gaseous or volatile HAP emissions from typical industrial sources (which were the biggest c ontributor to high short-term emission events) and more than account for pe ak HAP emission events associated with particulates.

Census block elevations for HEM-AERMOD modeling were determined nationally from the US Geological Service 1-degree digital elevation model (DEM ) data files, which have a spatial resolution of about 90 meters. Elevations of polar grid points used in estimating shortand long-term ambient concentrations were assu med to be equal to the highest elevation ofDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 8

any census block falling within the polar grid sector corresponding to the grid point. If a

sector does not contain any blocks, the model defaults the elevation to that of the nearest

block. If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring.

In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume’s flow if nearby elevated hills are expected to influence the wind pa tterns. For details on how hill heights were estimated and used in the AERMOD modeling see Appendix 1.



### 2.3 Estimating chronic human inhalation exposure

We used the annual average ambient air con centration of each HAP at each census block centroid as a surrogate for the lifetime inhalati on exposure concentration of all the people who

reside in the census block. That is, the risk analysis did not cons ider either the short-term or long-term behavior (mobility) of the exposed populations and its poten tial influence on their

exposure.

We did not address short-term human activity for two reasons. First, our experience with the 1996, 1999, 2002, and 2005 NATA assessments (which modeled daily activity using EPA’s HAPEM model) suggests that, given our cu rrent understanding of microenvironment concentrations and daily activ ities, modeling short-term ac tivity would, on average, reduce risk estimates about 25% for particulate HAPs; it will also reduce risk estimates for gaseous HAPs, but typically by much less. Second, ba sing exposure estimates on average ambient

concentrations at census block centroids may underestimate or overestimate actual exposure

concentrations at some residences. Further reducing exposure estimates for the most highly exposed residents by modeling their short-term be havior could add a systematic low bias to

these results.

We did not address long-term migration nor population growth or decrease over 70 years, instead basing the assessment on the assumpti on that each person’s predicted exposure is

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer risk, we generally estimated three metrics; the maximum individual risk (MIR), which is

defined as the risk associated with a lifetime of exposure at the highe st concentration (note

that the MIR can be estimated for each source as well as for the entire source category); the population risk distribution; a nd the cancer incidence. The assumption of not considering short or long-term population mob ility does not bias the estimate of the MIR nor does it affect

the estimate of cancer incidence since the total populat ion number remains the same. It does, however, affect the shape of th e distribution of individual risk s across the affected population, shifting it toward higher estimat ed individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereby biasing the ri sk estimates high.

When screening for potentially significant acute exposures, we used an estimate of the highest hourly ambient concentration at any off-site location as the surrogate for the maximum potential acute exposure concen tration for any individual.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 9



### 2.4 Screening for potential acute impacts of concern

In establishing a scientifically defensible approa ch for the assessment of potential health risks due to acute exposures to HAP, we followed the same general appr oach that has been used for developing chronic health risk assessments for ot her rules under the resi dual risk program. That is, we developed a tiered, iterative approach. This approach to risk assessment was endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to Congress” in 1999.

With respect to screening for potential acute health risks, the assessment methodology is designed to eliminate from furthe r consideration those facilities for which we have confidence

that no acute adverse health effects of concern w ill occur. To do so, we use what is called a tiered, iterative approach to the assessment. This means that we began with a screening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a worst-case exposure. The result of this scr eening process is that either the facility being assessed poses no potential acute health risks (i.e., it “screens out”), or that it requires further, more refined assessment with respect to potential acute exposures from

emitted HAP. A more refined acute assessment could use industry- or site-specific data on the temporal pattern of emissions, the layout of emission points at the facility, the boundaries of the facility, and/or the local meteorology. In some cases, all of these site-specific data are used to refine the assessment; in others, lesse r amounts of site-specific data can be used to determine that acute exposures are not a concern, and significant additional data collection is
not necessary.

Acute health risk screening was performed for each facility as the first step. We used conservative assumptions for emission rates, meteorology, and exposure location. We used the following worst-case assumpti ons in our screening approach:

 Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission rates except in the case of fugitive dust (see section 2.2)

 For facilities with multiple emission points, peak 1-hour emissions were assumed to occur at all emission points at the same time.

 For facilities with multiple emission points, 1-hour concentrations at each receptor

were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same

hour.

 Worst-case meteorology (from one year at the nearest NWS site) was assumed to occur at the same time the peak emissi on rates occur. The recommended EPA localscale dispersion model, AERMOD, was used for simulating atmospheric dispersion during this worst-case hour.

 A person was assumed to be located down wind at the point of maximum modeled impact during this same worst-case 1-hour pe riod, but no nearer to the source than 100 meters. As a result, the maximum modeled 1-hour off-site concentration equals the worst-case acute exposure concentration.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 10

 The maximum impact was compared to multiple short-term health benchmarks for the HAP being assessed to determine if a possibl e acute health risk might exist. These

benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acu te impacts which exceed their relevant benchmarks, we pursue refining our acute screening estimates to the extent possible. In some cases, this may include the use of a facility-specific emissions multiplier to estimate the peak hourly emission rates from the average rates (rathe r than the default factor of 10). In other cases, this may entail determining the actual physical layout and boundaries of a facility to more accurately gauge where people might reasonably be exposed for an hour.



### 2.5 Multipathway human health and environmental risk screening

The potential for significant hum an health risks and adverse environmental impacts due to exposures via routes other than inhalation (i.e ., multipathway exposures) was screened by first determining whether any sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in the environment (PB-HAP) 2 . The PB-HAP compounds or compound classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library [ 8 ], and they are cadmium compounds, chlordan e, chlorinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlor inated biphenyls, polycyclic organic matter (POM), toxaphene, and trifluralin. Emi ssions of 5 different PB-HAP we re identified in the emissions inventory for the Secondary Lead Smelting source category; they are POM, dioxins, cadmium, mercury, and lead. With respect to PB-HAP other than lead, emissions were evaluated for potential non-inha lation risks and adverse environmental impacts using our recently-developed screening scenario which was developed for use with the TRIM.FaTE 3
model. This screening scenar io uses environmental media outputs from the peer-reviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified emission scenario by using a generic farming/fishing e xposure scenario that simulates a subsistence environment. The screening scenario retain s many of the ingestion and scenario inputs developed for EPA’s Human Health Risk Assessment Protocols (HHRAP) for hazardous

waste combustion facilities. 4 In the development of the sc reening scenario a sensitivity analysis was conducted to ensure that its key design parameters were established such that

environmental media concentrations were not underestimated, and to also minimize the occurrence of false positives for human health endpoints. See Appendix 4 for a complete discussion of the development and testing of the screening scenario, as well as for the values of facility-level de minimis emission rates developed for screening potentially significant multi-pathway impacts. For the purpose of developing de minimis emission rates for our multi-pathway screening, we derived emission levels for each PB HAP at which the

2 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this document).

3 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html

4 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities; http://www.epa.gov/epaoswer/hazwa ste/combust/riskvol.htm#volume1Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 11

maximum human health risk would be 1 in a million for lifetime cancer risk or a hazard quotient of 1.0 for noncancer impacts.

For the secondary lead smelting sour ce category, there we re exceedences of de minimis emissions rates at multiple facilities for multiple PB-HAP, and thus a multipathway analysis was performed. Two facilities were chosen as case study analyses to assess potential multipathway risks for mercury, cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions ra tes of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and direction, smelting furnace type, local change in elevati on, and geographic representativeness of sites throughout the US. As a result of our selecti on process, we believe the multipathway risks

associated with these two facilities are in the upper end of the potential for multipathway risks from the source category. Since the modeling used in these case study assessments utilize site specific parameters to describe naturally occurring physical, chemical and biological processes, we believe that the multimedia concentrations of PB-HAPs generated in this analysis are unbiased estimate s of the true impacts.

In general, results of this assessment were designed to characterize multipathway risks associated with high end consumption of PB -HAP predominantly from contaminated food sources. Thus, multipathway exposure and risk estimates were calculated for two basic scenarios, both of which are expected to give rise to high-end expos ures and risks. The

farmer scenario involves an indi vidual living on a farm homest ead in the vicinity of a PBHAP source who consumes contaminated produce grown on the farm, as well as contaminated meat and animal products raised on the farm. The farming scenario also accounts for incidental ingestion of contaminat ed surface soil at the location of the farm

homestead. The recreational fisher scenario i nvolves an individual w ho regularly consumes fish caught in freshwater lakes in the vicinity of a PB -HAP source. In the fishing scenario, in addition to the characterization of exposur e and risks across the broad population of recreational anglers, exposures were also calc ulated for three subpopula tions of recreational anglers (Hispanic, Laotian, and Vietnamese descent) who have higher rates of fish consumption 5 . Furthermore, in order to more fully characterize the modeled potential multipathway risks that may be associated with high-end consumption of PB-HAP contaminated food, we present results based on two ingestion exposure scenarios: 1) a reasonable maximum exposure (RME) scenario that, for example, utilizes 90 th percentile ingestion rates for farmers, recreational angler s, and the three subpopulat ions of recreational anglers (e.g., ingestion rates specific to La otian recreational angler s); and 2) a central tendency exposure (CTE) scenario that for exam ple, utilizes mean ingestion rates for the groups just described. We provide results from both scenar ios to illustrate the range of potential modeled exposures and risks that may exist in the high-end of the complete

5 In both scenarios, exposure via drinking water was not considered because it is unlik ely that humans would use surface waters as a drinking water sour ce. Groundwater, which is a likely source of drinking water, also was not included in the exposure scenarios because contamination of groundwater aquifers by air deposition sources was not expected to be significant. For dioxin, exposure via breast milk was considered in the farming scenario as well as the recreational fishing scenario, but not for th e three recreational fishing subpopulations (Hispanic, Laotian, and Vietnamese descent) since subpopulation ingestion rates were only applicable to adult males. The breast milk pathway was not considered with respect to me rcury exposure due to a current lack of data regarding this pathway.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 12

distribution of potential multipathway risks for this source category.

In evaluating the potential multi-pathway risks fr om emissions of lead compounds, rather than developing a de minimis emission rate, we compared maximum estimated chronic (3-month average) atmospheric concentrations with th e current primary National Ambient Air Quality Standard (NAAQS) for lead. The current pr imary National Ambient Air Quality Standard (NAAQS) for lead, which is set to a level of 0.15 μg/m 3 based on a rolling 3-month averaging period with a not-to-be-exceeded form (s ee 73 FR 66964). Notably, in making these comparisons, we estimated maximum rolling 3-mont h ambient lead concentrations taking into account all of the elements of the NAAQS for le ad. That is, our estimated 3-month lead

concentrations are calculated in a manner that is consistent with the i ndicator, averaging time, and form of the NAAQS for lead, and those estima tes are compared to th e actual level of the lead NAAQS (0.15 μg/m 3 ). Values below the NAAQS we re considered to have a low potential for multi-pathway risks of any significance.

The NAAQS value, a public hea lth policy judgment, incorporated the Agency’s most recent

health evaluation of air effects of lead exposure for the purpose s of setting a national ambient air quality standard. In setting this value, th e Administrator promulgated a standard that was requisite to protect public health with an ade quate margin of safety. We consider values below the level of the primary NAAQS to protect against multipathway risks because as mentioned above, the primary NAAQS is set as to protect public health with an adequate margin of safety. However, ambient ai r lead concentrations above the NAAQS are considered to pose the potential for increased ri sk to public health. We consider this NAAQS assessment to be a refined analysis given: 1) th e numerous health studies, detailed risk and exposure analyses, and level of external pe er and public review that went into the development of the primary NAAQS for lead, comb ined with: 2) the si te-specific dispersion modeling used in this assessmen t to estimated ambient lead c oncentrations due to secondary lead smelting emissions. It should be noted, however, that this comparison does not account for possible population exposures to lead from so urces other than the one being modeled; for example, via consumption of water from untreated local sources or inge stion of locally grown food. Nevertheless, the Administ rator judged that such a standa rd would protect, with an adequate margin of safety, the health of children and other at-r isk populations against an array of adverse health effects, most notably in cluding neurological e ffects, particularly neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in setting the standard, also rec ognized that no evidence-or risk based bright line indicated a single appropriate level. Instead a collection of scientific evidence and other information was

used to select the standard from a ra nge of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, considering the level, averaging time, form a nd indicator, also informs whether there is the potential for adverse environmental effects. This is because the secondary lead NAAQS, set to protect against adverse welfare effects (inc luding adverse environmen tal effects), has the same averaging time, form, and level as th e primary standard. Thus, ambient lead concentrations above the NAAQS for lead also indicate the po tential for adverse

environmental effects.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 13

We also evaluated the potential for significant direct environmental exposures to HAP (other than lead) by evaluating the potential for ex ceedances of chronic human health inhalation

reference values in the ambi ent air near these facilities. Human health dose-response reference values are generally derived from studies conducted on laboratory animals (such as rodents) and developed with the inclusions of uncertainty factor s that could be as high as 3000. As a result, these human reference values are often significantly lower than the level expected to cause an adverse effect in an expos ed rodent. It should be noted that there is a scarcity of data on the direct atmospheric impact of these HAP on other receptors, such as plants, birds, and wildlife. Thus, if the maxi mum inhalation hazard in an ecosystem is below the level of concern for humans (i.e., is below the human health reference value which is itself a lower reference value than would be expected for an ecologic effect), we have generally concluded that mammalian receptors should be at no risk of adve rse effects due to inhalation exposures from non PB-HAP, and have assurance th at other ecological r eceptors are also not at any significant risk from dir ect atmospheric impact. In some isolated cases where we have data indicating potential adverse impacts on plants, birds, or ot her wildlife due to the direct atmospheric impacts of specific HAP, we note th at as an uncertainty and, where possible, refine our analysis by comparing our modeled im pacts to available reference values from the

scientific literature.



### 2.6 Dose-response assessment

2.6.1 Sources of chronic do se-response information

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either by inhalation or ingestion) for the HAP reported in the emissions inventory for the Secondary Lead Smelting sources were based on the EPA Of fice of Air Quality Planning and Standards’ (OAQPS) existing recommendations for HAP [ 9 ], also used for NATA 1999, 2002, and 2005 [ 10 ]. This information has been obtained from various sources and prioritized according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of peer review received. The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose-response information. The recommendations are based on the following sources, in order of priority:

1) US Environmental Protection Agency (EPA). EPA has developed dose-response assessments for chronic exposure for many of the pollutants in this study. These assessments typically provide a qualitative stat ement regarding the st rength of scientific data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects othe r than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimat e the probability of developing cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation e xposure to the human population (including sensitive subgroups) that is likely to be without an appreciabl e risk of deleterious effects during a lifetime.” The URE is defined as “the upper-bound excess ca ncer risk estimated

to result from continuous lifet ime exposure to an agent at a concentration of 1 μg/m 3 in air.” The SF is “an upper bound, approximating a 95% confidence limit, on the increased cancer risk from a lifetime exposure to an agen t. This estimate, [is] usually expressed inDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 14

units of proportion (of a populat ion) affected per mg/kg-da y...” EPA disseminates doseresponse assessment information in several fo rms, based on the level of review. The Integrated Risk Information System (IRIS) [ 11 ] is an EPA database that contains scientific health assessment information, including dose -response information. All IRIS assessments since 1996 have also undergone independent ex ternal peer review. The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by inde pendent scientists external to EPA. New IRIS values are developed and old IRIS valu es are updated as new health effects data

become available. Refer to the “IRIS Track ” website for detailed information on status and scheduling of current indivi dual IRIS assessments and updates ( http://cfpub.epa.gov/ncea/iristrac/index.cfm )

. EPA’s science policy approach, under the current carcinogen guidelines, is to use linear low-dose extrap olation as a default option for carcinogens for which the mode of action (MOA) has not been identified. We expect future EPA dose-response assessments to identify nonlinear MOAs where appropriate, and we will use those analyses (once they are peer reviewed) in our risk assessments. At this time, however, there are no available carcinogen dose-response assessments for inhalation exposure that are based on a nonlinear MOA

.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part of the US Department of Health and Huma n Services, develops and publishes Minimum Risk Levels (MRLs) [ 12 ] for inhalation and oral exposur e to many toxic substances. As stated on the ATSDR web site: “Following di scussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar to that of the EPA's Refere nce Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” The MRL is defined as “an estimate of daily human exposure to a subs tance that is likely to be without an appreciable risk of advers e effects (other than cancer) over a specified duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be used by health assessors to select environm ental contaminants for further evaluation. Exposures above an MRL do not necessarily re present a threat, and MRLs are therefore not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of

Environmental Health Hazard Assessment ha s developed dose-response assessments for many substances, based both on carcinogenicity a nd health effects other than cancer. The process for developing these assessments is si milar to that used by EPA to develop IRIS values and incorporates signifi cant external scientific peer review. As cited in the CalEPA Technical Support Document fo r developing their chronic assessments 6 : “The guidelines for developing chronic inhala tion exposure levels incorporate many recommendations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The non-cancer information

includes available inhalation hea lth risk guidance values expr essed as chronic inhalation reference exposure levels (RELs) [ 15 ]. CalEPA defines the R EL as “the concentration

6 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document

for the Determination of Non-cancer Chronic Referen ce Exposure Levels. Air Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 15

level at or below which no health effects are anticipated in the gene ral human population.” CalEPA's quantitative dose-response info rmation on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 16 ], defined similarly to EPA's URE.

In developing chronic risk estimates, we adju sted dose-response values for some HAPs based on professional judgment, as follows:

1) In the case of HAP categories such as glyc ol ethers and cyanide compounds, the most conservative dose-response value of the chemical category is used as a surrogate for other compounds in the group for which dose-response valu es are not available. This is done in order to examine, under conservative assump tions, whether these HAPs that lack doseresponse values may pose an unacceptable risk and require further examination, or screen out from further assessment. For example, with respect to antimony compounds, in this assessment the most conservative dose-respons e value of the chemical category was used as a surrogate for other compounds in the group for which dose-response values are not

available.

2) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM (see Tables 2.6-1 (a) to 2.6-1 (c), we apply category-specific chemical speciation profiles appropriate to the source category to develop a composite dose-response value for the category.

3) Emissions from secondary lead smelting f acilities include formaldehyde. In 2004, EPA

determined that the CIIT dose-res ponse value for formaldehyde (5.5 x 10 -9 per μ g/m 3 ) was

based on better science than the IR IS dose-response value (1.3 x 10 -5 per μ g/m 3 ), and we switched from using the IRIS value to the CIIT value in risk assessments supporting regulatory actions. This determination was based on a substantial body of research on the inhalation dosimetry for formaldehyde in rode nts and primates by the CIIT Centers for Health Research (formerly the CIIT), with a fo cus on use of rodent data for refinement of the quantitative cancer dose-response assessment [ 17

, 18

, 19 ]. The CIIT’s risk assessment of formaldehyde incorporated mechanistic and dosimetric information on formaldehyde. However, recent research published by EPA i ndicates that, when the CIIT’s two-stage modeling assumptions are varied, resulting do se-response estimates can vary by several orders of magnitude [ 18, 19, 20 ]. These findings are not s upportive of interpreting the CIIT model results as providing a conservative (health-protective) estimate of human risk [ 20 ]. The recent EPA research also examined the contribution of the two-stage modeling for formaldehyde towards characterizing the rela tive weights of key events in the modeof-action of a carcinogen. For example, in the EPA research, the model-based inference in the published CIIT study that formaldehyde’s direct mutageni c action is not relevant to the compound’s tumorigenicity was found not to hold under variations of modeling assumptions [ 21 ]. Additionally, in 2007, the California Environmental Protection Agency considered a petition to use the CIIT URE valu e rather than its 1992 cancer risk value of 6

x 10 -6 per μg/m 3 . After consideration and consultation with their Scientific Review Panel of Toxic Air Contaminants, they decide d not to change from their 1992 value [ 22

, 23 ]. EPA no longer considers the CIIT URE value to be health protective, and we are again using the Agency’s current va lue on IRIS, which was last revised in 1991, and which isDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 16

more than 2000 times greater than the CIIT value. A new draft IRIS reassessment for formaldehyde was recently reviewed by the NAS, and EPA is currently forming a response to the recommendations and issues raised during the NAS review 7

.

4) Emissions from secondary lead smelting fac ilities include POM. A substantial proportion of POM reported to EPA’s national emissi on inventory (NEI) are not speciated in t o individual compounds. As a result, it is necessary to apply the same simplifying assumptions to assessments that were used for the 1999 NATA study [ 24 ]. The NATA approach partitions POM into eight different non-overlapping “groups” that are modeled as separate pollutants. Each POM group co mprises POM species of similar carcinogenic potency, for which we can apply the same URE.

The emissions inventory for the Secondary L ead Smelting source cate gory includes emissions of HAP with available chronic quantitative inhalation dose-response values. These HAP, their dose-response values, and th e source of the values are listed in Tables 2.6-1 (a) through (c).

7 The draft IRIS assessment for formaldehyde is available at: http://cfpub.epa.gov/ncea/iris _drafts/recordisplay.cfm?deid=223614, an d the NAS review of the draft IRIS assessment is available at: http://www.nap.edu/catalog.php?record_id=13142.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 17

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer) 8 = cancer risk per μ g/m 3 of a verage lifetime exposure. Sources: IRIS = EPA Integrated Risk Informat ion System, CAL = California EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim val ue recommended by the EPA Office of Air Quality Planning and Standar ds. EPA ORD = EPA Office of Research and Development

Pollutant CAS Number 9 URE 5
(1/ μ g/m 3 ) Source

1,1,2,2-Tetrachloroethane 79345 0.000058 IRIS
1,3-Dichloropropene 542756 0.000004 IRIS
1,3-Butadiene 106990 0.00003 IRIS
2,3,7,8-TCDD TEQ 1746016 33 EPA ORD
Acetaldehyde 75070 2.2E-06 IRIS
Acrylonitrile 75058 0.000068 IRIS

Arsenic 7440382 4.3E-03 IRIS

Benzene 71432 7.8E-06 IRIS
Bis(2-Ethylhexyl)Phthalate 117817 2.4E-06 CAL
Beryllium 7440417 0.0024 IRIS

Cadmium 7440439 0.0018 IRIS
Chromium compounds

Chromium ( VI) 18540299 0.012 IRIS
Ethyl Benzene 100414 2.5E-06 CAL
Formaldehyde 50000 0.000013 IRIS
Methylene Chloride 75092 4.7E-07 IRIS
Polycyclic Organic Matter 246 10 EPA OAQPS
Chrysene 218019 1.1E-05 CAL
Benz[a]Anthracene 56553 1.1E-04 CAL
Benzo[b]Fluoranthene 205992 1.1E-04 CAL
Benzo[k]Fluoranthene 207089 0.00011 CAL
Indeno[1,2,3-c,d]Pyrene 193395 0.00011 CAL
Dibenzo[a,h]Anthracene 53703 0.0012 CAL
Naphthalene 91203 3.4E-05 CAL

Nickel 7440020 0.00009 ATSDR
Tetrachloroethylene 127184 5.9E-06 CAL
Trichloroethylene 79016 2.0E-06 CAL

8 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μ g/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value.

9 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.

10 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this method, also used in the 1999 National Air Toxics Assessment, are available at http://www.epa.gov/ttn/atw/nata1 999/99pdfs/pomapproachjan.pdfDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 18

Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lif etime exposure. Sources : IRIS = EPA Integrated Risk Information System, CAL = Ca lifornia EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards.

Pollutant CAS Number 10 SF (1/mg/kg/d) Source 2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 150000 EPA ORD 11
Benzo(k)fluoranthene 207089 1.2 CAL Polycyclic Organic Matter Dibenzo[a,h]Anthracene 53703 4.1 CAL Indeno[1,2,3-c,d]Pyrene 193395 1.2 CAL

11 The assessment report for dioxin is available in the docket for this Secondary Lead Smelting proposed rulemaking (see: Health Assessment Document for Polychlorinated Dibenzo-p-Dioxins; September, 1985)Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 19

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (incl uding sensitive subgroups) that is likely to be without an appreciable risk of delet erious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for To xic Substances Disease Registry.

Pollutant CAS Number 6 RfC (mg/m3) Source 12

1,3-Butadiene 106990 0.002 IRIS -- M 1,3-Dichloropropene 62737 0.0005 IRIS -- H 2,3,7,8-TCDD TEQ 1746016 4E-08 CAL Acetaldehyde 75070 0.009 IRIS -- L Acrylonitrile 107131 0.002 IRIS -- M

Acrolein 107028 0.00002 IRIS -- L/M Antimony 1309644 (trioxide) 0.0002 IRIS -- L

Arsenic 174 0.000015 CAL

Benzene 71432 0.03 IRIS -- M Beryllium 7440417 0.00002 IRIS -- M Bis(2-Ethylhexyl)Phthalate 117817 0.01 P-CAL

Cadmium 176 0.00001 ATSDR

Carbon Disulfide 75150 0.7 IRIS -- M

Chlorine 7782505 0.00015 D-ATSDR

Chlorobenzene 108907 1 CAL

Chloroform 67663 0.098 ATSDR Chromium compounds Chromium (VI) 18540299 0.0001 IRIS -- M

Cumene 98828 0.4 IRIS -- M Ethyl Benzene 100414 1 IRIS -- L Ethyl Chloride 75003 10 IRIS -- M Formaldehyde 50000 0.0098 ATSDR

Hexane 110543 0.7 IRIS -- M Hydrochloric Acid 7647010 0.02 IRIS -- L Lead 182 0.00015 EPA OAQPS Manganese 183 0.00005 IRIS -- M Methyl Bromide 74873 0.09 IRIS -- M/H Methyl Chloride 74873 0.09 IRIS -- M/H Methylene Chloride 75092 1 ATSDR Methyl Isobutyl Ketone 108101 3 IRIS -- L/M Mercury Compounds Elemental Gaseous Mercury 7439976 0.0003 IRIS -- M

12 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the overall level of confidence in the RfC value, as reported in the IRIS file.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 20

Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (incl uding sensitive subgroups) that is likely to be without an appreciable risk of delet erious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Informatio n System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for To xic Substances Disease Registry.

Pollutant CAS Number 6 RfC (mg/m3) Source 12

Gaseous Divalent Mercury

Particulate Divalent Mercury Naphthalene 91203 0.003 IRIS -- M

Nickel 186 0.00009 ATSDR o-Cresol 95487 0.6 (mixed) CAL

Phenol 108952 0.2 CAL Propionaldehyde 123386 0.008 IRIS -- L/M

Selenium 7782492 0.02 CAL Styrene 100425 1 IRIS -- M Tetrachloroethylene 79016 0.6 CAL Trichloroethylene 79016 0.6 CAL

Toluene 108883 5 IRIS -- H Xylenes (Mixture of o, m, and p Isomers) 1330207 0.1 IRIS m-Xylene 108383 0.1 IRIS -- M o-Xylene 95476 0.1 IRIS -- M p-Xylene 106423 0.1 IRIS -- M

2.6.2 Sources of acute dose-response information

Hazard identification and dose-response assessment information for preliminary acute inhalation exposure assessments are based on the existing recommenda tions of OAQPS for HAPs [ 25 ]. Depending on availability, the result s from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular refe rence level depends on the definition of that level and any limitations expressed therei n. Comparisons among different available

inhalation health effect refere nce values (both acute and chro nic) for selected HAPs can be

found in an EPA document. 13

13 See: U.S. EPA. Graphical Arrays of Chemical-Specific Health Eff ect Reference Values for Inhalation Exposures [Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 21

California Acute Reference Exposure Levels (RELs ). The California Environmental Protection Agency (CalEPA) has developed acu te dose-response refe rence values for many substances, expressing the results as Acute i nhalation Reference Exposure Levels (RELs).

The acute REL ( http://www.oehha.ca.gov/air/pdf/acuterel.pdf ) is defined by CalEPA as

“the concentration level at or below which no adverse health effects are anticipated for a specified exposure duration. [ 26 ]. RELs are based on the most sensitive, relevant, adverse health effect reported in the medi cal and toxicological literature. RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety. Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not auto matically indicate an adverse health impact.” Acute RELs are developed fo r 1-hour (and 8-hour) exposures. The values incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs ). AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guide line Levels (NAC/AEGL) for Hazardous Substances, and then reviewed and published by the National Research Council. As described in the Committee’s “Standing Operating Procedures (SOP)” ( http://www.epa.gov/op ptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emer gency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixe d facilities and from tran sport carriers.” The document states that “the primary purpos e of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once-in-a-lifetim e, short-term exposures to

airborne concentrations of acute ly toxic, high-priority chemi cals.” In detailing the intended application of AEGL values, the document states that, “It is antic ipated that the AEGL values will be used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and possibly the international community in c onjunction with chemical emergency response, planning, and prevention programs. More spec ifically, the AEGL values will be used for conducting various risk assessments to aid in the development of emergency preparedness and prevention plans, as well as real-time emerge ncy response actions, for accidental chemical releases at fixed facilities and from transpor t carriers.” The NAC/A EGL defines AEGL-1 and

AEGL-2 as:

“AEGL-1 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. However, the effects are not disabling and ar e transient and revers ible upon cessation of exposure.”

“AEGL-2 is the airborne concentr ation (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that th e general population, including su sceptible individuals, couldDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 22

experience irreversible or other serious, long-lasting adverse h ealth effects or an impaired ability to escape.”

“Airborne concentrations below AEGL-1 repr esent exposure levels that can produce mild and progressively increasing but transient and nondisabli ng odor, taste, and sensory irritation or certain asymptomatic, nonsenso ry effects. With increasing airborne concentrations above each AEGL, there is a progressive increase in the likelihood of occurrence and the severity of effects described for each corresponding AEGL. Although the AEGL values represent threshold levels for the general public, including susceptible subpopulations, such as infants, children, the el derly, persons with asthma, and those with other illnesses, it is recognized that indivi duals, subject to unique or idiosyncratic responses, could experience the effects de scribed at concentr ations below the corresponding AEGL.”

Emergency Response Planni ng Guidelines (ERPGs). The American Industrial Hygiene Association (AIHA) has developed Emergenc y Response Planning Guidelines (ERPGs) [ 27] for acute exposures at three different levels of severity. Thes e guidelines represent concentrations for exposure of the general population (but not pa rticularly sensitive persons) for up to 1 hour associated with effects expe cted to be mild or transient (ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3).

ERPG values ( http://www.aiha.org/1documents /Committees/ERP-erpglevels.pdf ) are described in their supporting documentation as follows: “Emergency Response Planning Guidelines (ERPGs) were developed for emerge ncy planning and are intended as health based guideline concentrations for single exposures to chemicals. These gui delines (i.e., the ERPG Documents and ERPG values) are intended fo r use as planning tools for assessing the adequacy of accident prevention and emergenc y response plans, including transportation emergency planning and for developing community emergency response plans. The emphasis is on ERPGs as planning values: When an actua l chemical emergency occurs there is seldom

time to measure airborne concentrations and then to take action.” ERPG-1 and ERPG-2 values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing other than mild

transient adverse health eff ects or without perceiving a clearly defined, objectionable

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing

irreversible or other serious h ealth effects or symptoms which could impair an individual's ability to take protective action.”

The emissions inventory for the Secondary L ead Smelting source cate gory includes emissions

of numerous HAP with releva nt and available quantitative acute dose-res ponse threshold values. These HAPs, their acute threshold values , and the source of the value are listed below

in Table 2.6-2.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 23

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number AEGL-1 (1-hr) (mg/m 3 ) AEGL-2 (1-hr) (mg/m 3 ) ERPG-1 (mg/m 3 ) ERPG-2 (mg/m 3 ) REL 1,3-Butadiene 106990 1500 12000 1500 12000 Acetaldehyde 75070 81 490 81 490 0.47

Acrolein 107028 0.069 0.23 0.069 0.23 0.0025 Acrylonitrile 107131 10 130 22 77 Antimony 1309644

Arsenic 7440382 0.0002

Benzene 71432 170 2600 170 2600 1.3 Beryllium 7440417 0.025 Biphenyl 92524 61

Carbon Disulfide 75150 40 500 40 500 6.2

Chlorine 7782505 1.5 5.8 1.5 5.8 0.21

Chlorobenzene 108907 46 690

Chloroform 67663 310 310 0.15

Cumene 98828 250 1500

Elemental Gaseous Mercury 7439976 1.7 2 .0006 Ethyl Benzene 100414 140 4800 Formaldehyde 50000 1.1 17 1.1 17 0.055

Hexane 110543 12000 Hydrochloric Acid 7647010 2.7 33 2.7 33 2.1 Methyl Bromide 74839 820 820 3.9 Methyl Chloride 74873 Methylene Chloride 75092 690 1900 690 1900 14 Methyl Iodide 74884 150 290

Nickel 7440020 0.006

Phenol 108952 58 89 58 89 5.8 Propionaldehyde 123386 110 620 Styrene 100425 85 550 85 550 21 Tetrachloroethylene 127184 240 1600 240 1600 20

Toluene 108883 750 4500 750 1900 37 Trichloroethylene 79016 700 2400 700 2400 Xylenes (Mixed) 1330207 560 4000 22 m-Xylene 108383 22Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 24



### 2.7 Risk characterization

2.7.1 General

The final product of the risk as sessment is the risk characteri zation, in which the information from the previous steps is integrated and an overa ll conclusion about risk is synthesized that is complete, informative, and useful for decision ma kers. In general, the nature of this risk characterization depends on the in formation available, the application of the risk information and the resources available. In all cases, majo r issues associated with determining the nature

and extent of the risk are identified and disc ussed. Further, the EPA Administrator’s March

1995 Policy for Risk Characterization [ 28 ] specifies that a risk ch aracterization “be prepared in a manner that is clear, transparent, r easonable, and consistent with other risk characterizations of similar scope prepared acro ss programs in the Agency.” These principles of transparency and consistency have been reinforced by the Agency’s Risk Characterization

Handbook [ 29 ], in 2002 by the Agency’s information quality guidelines [ 30 ], and in the OMB/OSTP September 2007 Memorandum on U pdated Principles for Risk Analysis 14 , and are incorporated in these assessments.

Estimates of health risk are pres ented in the context of uncertainties and limitations in the data and methodology. Through our tiered, iterative an alytical approach, we have attempted to reduce both uncertainty and bias to the greate st degree possible in these assessments, within

the limitations of available time and resour ces. We provide summari es of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussion of the major uncertainties associated with their derivation to provide decision ma kers with the fullest pictur e of the assessment and its

limitations.

For each carcinogenic HAP included in an assess ment that has a potency estimate available, individual and population cancer risks were calculated by multiplying the corresponding lifetime average exposure estimate by the approp riate URE. This calculated cancer risk is defined as the upper-bound probability of de veloping cancer over a 70- yr period (i.e., the assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound estimates, actual risks at a given exposure le vel may be lower than predicted, and could be

zero.

For EPA’s list of carcinogenic HAPs th at act by a mutagenic mode-of-action [ 31 ], we applied

EPA’s Supplemental Guidance for Asse ssing Susceptibility from Early-Life Exposure to Carcinogens [ 32 ]. This guidance has the effect of adjusting the URE by factors of 10 (for children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments. In th is case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a

small fraction of the total POM emissions may be reported as individual compounds, EPA

14 Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis (September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Depu ty Director for Science, Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 25

expresses carcinogenic potency for compounds in this group in term s of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene. For this reason, EPA implementation policy [ 33 ] recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency. Acco rdingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population with in the area of analysis was estimated by multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the results for the entire modeled domain. This lifetime populati on incidence estimate was divide d by 70 years to obtain an estimate of the number of cancer cases per year.

In the case of benzene, the high end of th e reported cancer URE range was used in our assessments to provide a conservative estimate of potential cancer risks. Use of the high end of the range provides risk estimates that are approximately 3.5 times higher than use of the equally-plausible low end value. If the esti mated benzene–associated risks exceed 1 in a million, we also evaluated the impact of using the low end of the URE range on our risk

results.

Unlike linear dose-response assessments for can cer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Inst ead, “risk” for noncancer effects is expressed by comparing an exposure to a refe rence level as a ratio. The “hazard quotient” (HQ) is the estimated exposure divided by a re ference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ ≤ 1) are not likely to cause adverse health effects. As exposures increase above the reference level (HQs increasingly greater than 1), the potential for adverse effects increases. For exposures predicted to be above the RfC, the

risk characterization in cludes the degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., high, medium, or lo w confidence) and discusses the impact of this on possible health interpretations.

The risk characterization for chronic effects othe r than cancer is expressed in terms of the HQ for inhalation, calculated for each HAP at each census block centroid. As discussed above, RfCs incorporate generally conservative uncer tainty factors in the face of uncertain extrapolations, such that an HQ greater than one does not necessarily suggest the onset of adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, and is unlikely to be proportional to advers e health effect outcomes in a population.

Screening for potentially signi ficant acute inhalation exposures also followed the HQ approach. We divided the maximum estimated acute exposure by each available short-term threshold value to develop an array of HQ valu es relative to the various acute endpoints and thresholds. In general, when none of these HQ values are greater th an one, there is no potential for acute risk. In those cases where HQ values above one are seen, additional

information is used to determine whether ther e is a potential for significant acute risks.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 26

2.7.2 Mixtures

Since most or all receptors in these assessments receive exposures to multiple pollutants rather than a single pollutant, we estimated the aggregate health risks a ssociated with all the exposures from a particular source category combined.

To combine risks across multiple carcinogens, ou r assessments use the mixtures guidelines’ [ 34

, 35 ] default assumption of additivity of effect s, and combine risks by summing them using the independence formula in the mixtures guidelines.

In assessing noncancer hazard from chronic expo sures, in cases where different pollutants

cause adverse health effects via completely different modes of action, it may be inappropriate to aggregate HQs. In consideration of th ese mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specif ic and limited ways. Consistent with these guidelines, we aggregat ed non-cancer HQs of HAPs that act by similar toxic modes of action, or (where this information is absent) that affect the same target organ. This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), defined as the sum of hazard quotients for indi vidual HAPs that affect the same organ or organ system. All TOSHI calcu lations presented here were based exclusively on effects occurring at the “critica l dose” (i.e., the lowest dose that pr oduces adverse health effects). Although HQs associated with some pollutants ha ve been aggregated into more than one TOSHI, this has been done only in cases where th e critical dose affects more than one target organ. Because impacts on organs or systems that occur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may have been underestimated. As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict

delineation of “safe” and “unsafe” levels. Ra ther, the TOSHI is another measure of the potential for adverse health outcomes associated with pollutant exposur e, and needs to be interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of emissions and potential exposures, acute impact s were screened on an individual pollutant basis, not using the TOSHI approach.

2.7.3 Facility-wide risks

To help place the source category risks in context, we typicall y examine “facility-wide” risks using facility-wide and source category data files developed by EPA through emission data updates from industry and refined EPA profiles for the source category. In this instance, there are no other significant HAP em ission sources present-- all significant HAP sources have been included in the source category risk analys is. Therefore, the f acility-wide risk is essentially the same as the source category ri sk; no separate facility-wide analysis was

necessary.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 27



### 3 Risk results for the secondary lead source category

3.1 Source category desc ription and results

The secondary lead smelting sour ce category consists of faciliti es that use blast, rotary, reverberatory, and/or electric furnaces to recover lead metal from lead-bearing scrap materials, primarily lead-acid batteries. The se condary lead smelting process consists of: (1) breaking lead-acid batteries and separating the le ad-bearing materials from the other materials (including the plastic case material and acid el ectrolyte); (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. HAP are emitted from secondary lead smelters as: (1) process emissions contained in the primary exhaust of smelting furnaces; (2) process fugitive

emissions associated with char ging and tapping of smelting furnaces and lead refining kettles; and (3) fugitive dust emissions from wind or me chanically induced entrainment of dust from stockpiles, plant yards, and roadways. A comple te description of the secondary lead smelting source category can be found in the text of the Notice of Proposed Rulemaking (NPRM): National Emissions Standards for Hazardous Air Pollutants: Secondary Lead Smelting.

There are currently 14 secondary lead smelters op erating in the U.S. The NPRM data set for the secondary lead smelting category contains 14 facilities identified w ith a secondary lead smelting MACT code in the 2005 National-Scal e Air Toxics Assessment (NATA) National Emissions Inventory (NEI), September 2010 versi on. All 14 of these f acilities are identified as major sources in the NEI.

The emissions in the secondary lead smelting s ource category NPRM data set (of 14 facilities)

are summarized in Table 3.1-1. The total HAP emissions for the source category are approximately 670 tons per year. Based on th ese data, 22 HAP account for 99 percent of the

mass of HAP emitted across the 14 facilities in the NPRM data set. Air emissions from secondary lead smelter faciliti es include a number of toxic ai r pollutants, including metals (e.g., lead, arsenic, chromium, cadmium) and organic and inorganic compounds. Organic toxic air pollutants emitted from secondary lead smelting include compounds such as polycyclic organic matter (POM), styrene, carbon disulfide, and dioxins/furans.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 28

Table 3.1-1 Summary of Emissions from the Seco ndary Lead Source Category and Availability of Dose-Response Values

HAP a Emission

Estimates (tpy) Number of

Facilities

where HAP Reported or

Estimated (14 facilities

in NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk

Estimate for

Cancer? Reference

Concentration

for Noncancer? Health

Benchmark

Values for Acute

Noncancer?

Styrene 361 14 U U

Carbon Disulfide 96 14 U U

Methyl Isobutyl Ketone 50 14 U
p-Xylene 36 14 U
Methyl Chloride 33 14 U U

Lead 22 14 c U

Benzene 12 14 U U U

Toluene 11 14 U U
Bis(2-Ethylhexyl)Phthalate 8 14 U U
1,3-Butadiene 6 14 U U U
Acetaldehyde 4 14 U U U
Biphenyl 4 14 U
m-Xylene 3 14 U U

Arsenic 3 14 U U U
Ethyl Benzene 2 14 U U U

Hexane 2 14 U U

Phenol 2 14 U U
Hydrochloric Acid 2 14 U U
Naphthalene 2 14 U U
Ethyl Chloride 2 14 U

Acrolein 2 14 U U
o-Xylene 1 14 U
Antimony 0.9 14 U U
Methyl Bromide 0.9 14 U U
Acetophenone 0.8 14

Chlorobenzene 0.7 14 U U
Methylene Chloride 0.5 14 U U U
Polycyclic Organic Matter Chrysene 0.5 14 U U
Benz[a]Anthracene 0.1 14 U U Benzo[b]Fluoranthene 0.04 14 U U
Benzo[k]Fluoranthene 0.007 14 U U Indeno[1,2,3-c,d]Pyrene 0.002 14 U U Dibenzo[a,h]Anthracene 0.0005 14 U U
Benzo[a]Pyrene 0.0004 14 U U
Pyrene 0 13 U

Selenium 0.5 14 UDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 29

Table 3.1-1 Summary of Emissions from the Seco ndary Lead Source Category and Availability of Dose-Response Values

HAP a Emission

Estimates (tpy) Number of

Facilities

where HAP Reported or

Estimated (14 facilities

in NPRM data set) Prioritized Inhalation Dose-Response Value Identified by OAQPS b

PBHAP? Unit Risk

Estimate for

Cancer? Reference

Concentration

for Noncancer? Health

Benchmark

Values for Acute

Noncancer?

Methyl Iodide 0.4 14 U

Cadmium 0.4 14 U U U Manganese 0.4 14 U

o-Cresol 0.3 14 U
1,3-Dichloropropene 0.3 14 U U
Formaldehyde 0.2 14 U U U

Nickel 0.2 14 U U U
Mercury Compounds

Elemental Gaseous Mercury 0.1 d 14 U U U

Gaseous Divalent Mercury 0.02 d 14 U U

Particulate Divalent Mercury 0.02 d 14 U U

Chloroform 0.1 14 U U

Cumene 0.1 14 U U
Acrylonitrile 0.08 14 U U U
Chromium compounds

Chromium III 0.08 14 Chromium (VI) 0.001 14 U U
Dibutyl Phthalate 0.04 14 Beryllium 0.002 14 U U U
2,3,7,8-TCDD TEQ 0.00004 14 U U U 1,1,2,2-Tetrachloroethane 0 14 U

Chlorine 0 14 U U
Propionaldehyde 0 14 U U
Tetrachloroethylene 0 14 U U U
Trichloroethylene 0 14 U U U
Xylenes (Mixture of o, m, and p Isomers) 0 14 U U

a Notes for how HAP were speciated for risk assessment:

 For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g.,

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation in formation, we assume the reported mass is 100 percent metal.

 For emissions reported generically as “chromium” or “chromium & compounds,” emissions are speciated for this category as about 99 percent “chr omium (III) compounds” and about 1 perc ent “chromium (VI) compounds.” Chromium speciation profiles can be found on the EPA’ s Technology Transfer Netw ork website for emissions

inventories at http://www.epa.gov/ttn/chief/net/2 002inventory.htm;#inventorydata

.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 30

 For emissions reported generically as “m ercury” or “mercury & compounds,” emi ssions are speciated for this category as 80 percent “mercury (elemental)” 10 percent “mercuric chloride,” and 10 “percent particulate bound mercury” Mercury speciation profiles can be found on the EPA’s Tec hnology Transfer Network website for emissions inventories

at http://www.epa.gov/ttn/chief/net/ 2005inventory.html#inventorydata

.

 For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were grouped into POM subgroups as found on EPA’s Technology Tr ansfer Network website for the 2002 National-Scale Air

Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom . ( Approach to Modeling POM ).

b Specific dose-response values for each chemical are identified on EPA’s Technology Transfer Network website for air

toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html

.

c There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared rolling three-month average exposure estimates to the National Ambient Ai r Quality Standards (NAAQS) for lead (0.15 μg/m 3 ). These NAAQS for lead were recently revi ewed with revisions adopted in October 2008 ( http://www.epa.gov/air/ lead/actions.html ). The primary (health-based) standard is a maximum or not-to-be-exceeded, rolling three-month average, measured as total suspended pa rticles (TSP). The secondary (welfare-based) standard is identical to the primary standard.

d Most of the test results for mercury emissions for this industry were below detection lim it. The emissi ons estimates presented in this table are based on the assumption that all the non-detect test values were at the level of the detection limi t. Therefore, these estim ated emissions for mercury are clear overestimates. We conclude that the true amounts of emissions of mercury from this source category are much lower than shown here, but we are not able to quantify precisely how much

lower.



### 3.2 Baseline risk characterization

This section presents the results of the risk assessment for the secondary lead smelting source category based on the modeling methods described in the previous sections . All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section. The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum ta rget organ specific chronic hazard index, and the cancer incidence. We also present resu lts from our acute inha lation impact screening assessments in the form of maximum hazard quotie nts, as well as the results of our screen for potential non-inhalation risks fr om PB HAP. Also presented are the HAP “drivers,” which are the HAP that collectively contribute 90 pe rcent of the maximum cancer risk or maximum hazard at the highest exposure location, A deta iled summary of the facility-specific risk

assessment results is av ailable in Appendix 5.

Tables 3.2-1 and 3.2-2 summarize the preliminary chronic and acute inhalation risk results for this source category. The results of the chronic inhalation cancer risk assessment estimate the maximum lifetime individual cancer risk, consid ering both fugitive and stack emissions could be as high as 50 in a million. The maximum can cer risks for this source category exceeded a

cancer risk of 1 in a million at all 14 facilities , with fugitive dust emissions of arsenic and cadmium dominating the risk. The total estimated cancer incidence from this source category

based on actual emission levels is 0.02 excess cancer cases per year, or one excess case in every 50 years. Approximately 1,500 people were estimated to have cancer risks above 10 in a million and approximately 128,000 people were estimated to have cancer risks above 1 in a million considering all facilities in this source category. Considering MACT allowable emissions, the MIR could be as high as 200.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 31

With respect to chronic inhalation noncancer risk, we estimate a maximum TOSHI value of 0.6 for the secondary lead source category, with fugitive dust emissions of arsenic again dominating those impacts. In contrast, we not e that considering MACT allowable emissions, we estimate a maximum TOSHI value of 3, driv en primarily by emissions of arsenic from

stacks.

Taken together, these results suggest that there is not an appreciable risk of chronic noncancer

inhalation health effects due to the actual HAP emissions but some risk of chronic noncancer

health effects at MACT allowable emissions.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 32

Table 3.2-1 Summary of Source Category Leve l Inhalation Risks for Secondary Lead Smelting

Result HAP “Drivers”
Facilities in Source Category

Number of Facilities Estimated to be in Source Category 14 n/a

Number of Facilities Identified in NEI and Modeled in Preliminary Risk Assessment 14 n/a

Cancer Risks Maximum Individual Lifetime Cancer Risk (in 1 million) 50 arsenic compounds, cadmium compounds Number of Facilities with Maximum Individual Lifetime Cancer Risk: Greater than or equal to 100 in 1

million 0 n/a

Greater than or equal to 10 in 1

million 6 arsenic compounds, cadmium compounds, 2,3,7,8- tetrachlorodibenzo-p-dioxin

Greater than or equal to 1 in 1 million 14 arsenic compounds, cadmium compounds, 2,3,7,8- tetrachlorodibenzo-p-dioxin, 1,3-

butadiene

Chronic Noncancer Risks Maximum Developmental Hazard Index 0.6 arsenic compounds Number of Facilities with Maximu m Developmental Hazard Index:

Greater than 1 0 n/a Acute Noncancer Screening Results

Maximum Acute Hazard Quotient 30 arsenic compounds (REL)

Number of Facilities With Potential for Acute

Effects 9 arsenic compounds

Population Exposure Number of People Living Within 50

Kilometers of Facilities Modeled 26,000,000 n/a

Number of People Exposed to Cancer Risk: Greater than or equal to 100 in 1

million 0 n/a

Greater than or equal to 10 in 1

million 1,500 n/a

Greater than or equal to 1 in 1 million 128,000 n/a Number of People Exposed to Noncancer Developmental Hazard Index:

Greater than 1 0 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.02 n/a

Contribution of HAP to Cancer Incidence arsenic compounds 73% n/a cadmium compounds 15% n/a 2,3,7,8-tetrachlorodibenzo-p-dioxin 8% n/aDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 33

Based upon the REL as the acute assessment value for emissions of arsenic, screening-level acute HQ values could exceed a value of 1 at 9 facilities. In add ition, this worst-case screening analysis estimates that the maximum acute HQ value for a facility in this source category could be as high as 30, largely due to stack emissions of ar senic at this facility 15

. These results indicate that there may be some potential for acute health effects from emissions of arsenic from facilities in this source category.

To identify potential multipathway health risk s from PB-HAP other than lead, we first performed a screening analysis that compared emissions of PB-HAP emitted from the secondary lead source category to de minimis emission rates (see section 2.5). In doing so, we

estimated that emissions of cadmium, POM, and dioxin were above their respective de

minimis emission rates. In addition, based on usi ng high-end, worst-case assumptions for mercury (e.g., assuming that all non-detect emissi ons test values are e qual to the detection limit) we estimated that mercury emissions could theoretically be emitted over the de minimis screening level emissions rate. Therefore, to be a ll inclusive, we also included mercury along with POM, cadmium and dioxins for further multipathway analyses. More specifically, we performed two detailed case st udy multipathway analyses for these 4 PB-HAP in areas near the Frisco Battery Recycling (Frisco, TX) and Revere Smelting & Refining (Middletown, NY) facilities 16 . Moreover, as previously mentioned a bove (see section 2.5), in order to more fully characterize the potential multipathway risks associated with high end consumption of PB-HAP contaminated food, we present result s based on RME and CTE scenarios. The RME

scenario utilizes 90 th percentile ingestion rates for farmer s, recreational angl ers, and for three subpopulations of recreational anglers) who have higher rates of fish consumption (Hispanic, Laotian, and Vietnamese descent), while the CTE scenario utilizes mean ingestion rates for each of these groups. We provide results from both scenarios to illustrate the range of potential modeled exposures and risks that may exist in the high-end of the complete distribution of potential multipathway risks for this source category.

Considering the RME scenario, results of this an alysis estimate the MIR for dioxin to be 30 in a million (based on Laotian anglers near the Frisco, TX facility). Using the CTE scenario, the

maximum individual cancer risk from dioxins is estimated to be 6 in a million (also for Laotian anglers near the Frisco, TX facility). We note that, for the entire distribution of recreational anglers, the indivi dual risk estimates for the CTE and RME scenarios ranged from 3 to 7 in a million. Considering both exposure scenarios, the MIR for POM was less than 1 in a million (note: there is no cancer slope factor for mercury and cadmium and thus, no MIR was calculated). With respect to chr onic noncancer risk, in bot h case studies, using

15 Individual facility acute HQ values for all facilities can be found in Appendix 5, Table 3. Acute HQ values exceeding a value of 1 were as follows: 2, 2, 2, 3, 4, 5, 6, 20 and 30.

16 Site selection criteria is described in detail in section 2.5.1 of the document Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category, which can be found in the docket for the proposed rule. In brief, the criteria for sel ecting these two facilities as case studies included emission rates of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and direction, smelting furnace type, local change in elevation, and geographic representativeness of sites throughout

the US.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 34

both exposure scenarios, we did not estimate chronic HQ values greater than 1 for dioxin, mercury (even using the conservative emissi on assumptions just mentioned above) or cadmium (note: there is no chronic noncancer reference value for POM and thus, an HQ value was not calculated). Detailed methods and result s of the multipathway analysis are presented in the document Human Health Multipathway Residual Risk Assessment for the Secondary Lead Smelting Source Category, which can be f ound in the docket for the proposed rule.

As noted above, considering the RME scenario for the Frisco Battery Recycling facility (Frisco, TX), the multipathway MIR for dioxin was estimated to be approximately 30 in a million. In addition, the chronic inhalation cance r risk assessment estim ated inhalation cancer risk around this same facility to be approxi mately 40 in a million, due in large part to emissions of arsenic and cadmium. Thus, alt hough highly unlikely, if around this facility the person with the highest chronic i nhalation cancer risk is also the same person with the highest individual multipathway cancer risk (and the RME scenario is considered), then the combined, worst-case MIR for that facility (and therefore for the entire source category) could theoretically be up to 70 in a million.

As noted above (see section 2.5), in evaluating the potential for multipathway effects from emissions of lead, modeled maximum 3-month rolling average lead concentrations were compared to the NAAQS for lead. Results of this analysis, presented in Table 3.2-3, estimate that the NAAQS for lead could be exceeded at 12 of 14 facilities, largely due to fugitive dust emissions. Moreover, this analysis estimates that 3-month maximum lead concentrations could be as high as about 20 times the NAAQS for l ead and that more than half these facilities

have estimated 3-month maximum lead concentrati ons that are at least 2 times higher than the NAAQS lead (again, largely due to fugitive dust emissions). As mentioned above, to evaluate the potential for adverse environmental effects, we also compared maximum 3-month rolling average atmospheric concentrations with the current secondary NAAQS for lead (which is identical to the primary, health-b ased standard). Thus, the an alyses presented in Table 3.2-3 indicate the potential for both adverse human health impacts and adverse environmental

effects from emissions of lead.

Finally, with respect to lead we note that modeled lead concentr ations associat ed with MACT allowable stack emissions would be significan tly higher than those just discussed. For example, we estimate that based on allowable emissions from stacks alone, estimated lead

levels could be about 10 times above the NAAQS at a c ouple facilities.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 35

Table 3.2-3 Secondary Lead Smelting Facility Modeled Maximum Ambient Lead Concentrations (rolling three-month average values)

Facility Name City State Highest Lead

Concentration (μg/m 3 ) Concentration is X times higher than the NAAQS

DOE RUN COMPANY-BUICK MILL BOSS MO 3.50 23

SANDERS LEAD CO TROY AL 2.79 19

EXIDE CORPORATION VERNON CA 1.58 10

EXIDE TECHNOLOGIES/B R SMELTER BATON ROUGE LA 0.96 6

GULF COAST RECYCLING, IN C. TAMPA FL 0.71 5

EXIDE TECHNOLOGIES-CANON HOLLOW PLANT FOREST CITY MO 0.65 4

EXIDE TECH/READING SMELTER READING PA 0.54 4

GOPHER RESOURCE CORP EAGAN MN 0.49 3

FRISCO BATTERY RECYCLING FRISCO TX 0.43 3

QUEMETCO INC INDUSTRY CA 0.23 1.5

REVERE SMELTING & REFINING CO RP MIDDLETOWN NY 0.21 1.4

EXIDE TECHNOLOGIES MUNCIE IN 0.20 1.3

EAST PENN MFG CO INC/SMELTER PL T LYON STATION PA 0.10 0.7

QUEMETCO, INC. INDIANAPOLIS IN 0.09 0.6



### 3.3 Post-control risk characterization

As noted above, modeled lead concentrations exceed the NAAQS for lead at almost all facilities in this source category. Fugitive dust emissions are primarily driving exceedences of the lead NAAQS, as well as substantially contributing to the cancer MIR value of 50, and to acute HQ values greater than 1 (both based pr imarily on emissions of arsenic). Given this, using the same methodology described above, we estimated what the risks would be if the

facilities noted in Table 3.2-3 a dopted control measures to lim it fugitive dust emissions. More specifically, at each of these facilities, we estimated risks considering a decrease in fugitive dust emissions resulting primarily from the assumption that all facilities would be fully enclosed with negative pressure air infl ow to a control device plus implementation of effective work practices. We also considered a reduction of the MACT limit that would result

in reduced actual stack emissions for two of these facilities: Doe R un and Exide Technologies Baton Rouge Smelter. Based on this scenari o, we estimated (based on use of the same modeling assumptions and methodologies used for the baseline risk assessment) that all but 4 facilities would meet the NAAQS for lead. Of th ese 4 facilities, 3 were projected to be very close to the NAAQS for lead and may be able to meet the standard with little or no additional controls (i.e., controls in addition to those just discussed). The fourth facility, Exide (CA) isDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 36

still estimated to be about 4 times the NAAQS for lead (primarily driven by fugitive emissions) and will likely need additional controls in order to meet the NAAQS for lead based on our analysis.

With respect to cancer risk, given the fugitive controls mentioned above, the cancer MIR would be reduced from 50 in a million (i.e., pr e-controls) to approximately 10 in a million (i.e., post-controls), w ith an estimated reduction in cancer incidence to 0.01 excess cancer cases per year. In addition, only 20 people woul d be estimated to have a cancer risk greater than or equal to 10 in a million.

Considering multipathway post-control cancer ri sk with respect to dioxin emissions, we estimate about a 10 percent redu ction in cancer risk. Howeve r, due to rounding conventions, the multipathway MIR remains approximately 30 in a million (i.e., post controls) considering

the RME scenario for the Frisco Battery Recycling facility and for the source category. Considering the CTE scenario, cancer risk woul d be decreased from 6 in a million (i.e., precontrols) to 5 in a million (i.e., post controls) fo r the Frisco Battery Recycling facility and for the source category. Moreover, for this facili ty (i.e., the Frisco Battery Recycling facility)

inhalation cancer risk due to arsenic and cadmium would be lowered from an estimated 40 in a million (i.e., pre-controls) to an estimated 9 in a million (i.e., post controls) . Thus, post

controls the combined worst-case cancer risk (in the highly unlikely event the same person experiences the inhalation and multipathway MIR) is theoretically up to 40 in a million (i.e., considering post control inhalation cancer risk results and post control multipathway cancer risk results under the RME scenario).

We finally note that post-cont rol, the maximum worst-case acute screening HQ value would be reduced from a potential high of 30 (i.e., pre-c ontrols) to 5 (i.e., post-controls). Moreover, post-control this would be the only facility in the source category with a maximum worst-case acute refined HQ value greater than 1.



### 4 General discussion of uncertainties and how they have been

addressed

4.1 Exposure modeling uncertainties

Although every effort has been made to identify a ll the relevant faciliti es and emission points, as well as to develop accurate estimates of the annual emission rates for all relevant HAP, the uncertainties in our emission inventory likely dominate the uncertainties in our exposure modeling estimates. The chronic exposure modeli ng uncertainties are considered relatively small in comparison, since we are using EPA’ s refined local disper sion model with sitespecific parameters and reasonably representa tive meteorology. If anything, the population exposure estimates are biased high by not accounting for short- or long-term population mobility, and by neglecting processes like de position, plume depletion, and atmospheric degradation. Additionally, estimates of the maximum i ndividual risk (MIR) contain uncertainty, because they are derived at census block centroid locations rather than actual residences. This uncertainty is known to create potential under estimates and overestimates of the actual MIR values for individual facilities, but, overall, it is not thought to have aDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 37

significant impact on the estimated MIR for a sour ce category. Finally, we did not factor in the possibility of a source closure occurri ng during the 70-year chronic exposure period, leading to a potential upward bias in both the MIR and populati on risk estimates; nor did we factor in the possibility of population growth during the 70- year chronic exposure period, leading to a potential downward bias in both the MIR and populat ion risk estimates.

A sensitivity analysis performed for the 1999 NATA found that th e selection of the meteorology dataset location could result in a ra nge of chronic ambient concentrations which varied from as much as 17% below the predic ted value to as much as 84% higher than the predicted value. This va riability translates dir ectly to the predicted exposures and risks in our assessment, indicating that the actual risks could vary from 17 % lower to 84% higher than the predicted values.

We have purposely biased the acute screening results high, consideri ng that they depend upon the joint occurrence of independent factors, su ch as hourly emissions rates, meteorology and human activity patterns. However, we note that there is an u nquantified level of uncertainty regarding the emission estimates and the nature of the worst-case conditions for estimating the worst-case impacts of acute emissions of fugitiv e dusts. The current set of assumptions used in deriving the estimate assumes the average hou rly emission level (annual emissions divided by 8760 hours per year) to occur at the default worst-case meteorologic al conditions (low winds with a stable atmosphe re). It is acknowledged that the combination of average emissions during low winds would be an overe stimate of the fugitive dust emission rate during those low wind periods. Therefore, for fugitive dusts the worst-case meteorology may not be the same as for other process emissions. Additionally, the level of hourly fugitive dusts emissions during this alternate worst-case condition compared to the average hourly emissions is unknown. The actual risks may be hi gher or lower than the estimates based upon the current set of assumptions. In cases where multiple acute threshold values are considered scientifically acceptable we have chosen the most conservative of these threshold values, erring on the side of overestimating potential heal th risks from acute expo sures. In the caseswhere these results indicated the potential for exceeding short-term health thresholds postcontrols, we refined our assessment by developi ng a better understandin g of the geography of the facility relative to potential exposure loca tions. In this case, refining the assessment for the two facilities that had HQ values greater than 1 post-controls resulted in lower acute risks.

As noted in section 2.5, two facilities were chos en as case study analyses to assess potential multipathway risks for mercury, cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions ra tes of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and direction, smelting furnace type, local change in elevati on, and geographic representativeness of sites throughout the US. However, there is uncertainty as to whether these two facilities represent the highest potential for multipathway human health risks from the source category.

Since the modeling used in these case study asse ssments utilize site specific parameters to describe naturally occurring physical, chemical and biological processes, we believe that the multimedia concentrations of PB-HAPs generated in this analysis are unbiased estimates of the true impacts.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 38

With respect to the risk estimates generated fr om this analysis, we present results based on two ingestion exposure scenario s: the RME and CTE scenarios (see section 2.5). As noted above, we believe that these scenarios illustrate the range of potential modeled exposures and risks that may exist in the high-end of the complete distribution of potential multipathway risks for this source category.

To estimate potential high-end multipathway exposu res and risks, in addition to utilizing fish consumption rate data for the general US population of recreational anglers 17 , we used fish consumption information for distinct fisher subp opulations that are known to have higher fish consumption rates. The data were obtained from Shilling, et al. (2010). 18 In this publication, the authors provide fish consumption informa tion for different ethn ic groups including Hispanics, Laotians, and Vietnamese surveyed in California’s Central Valley Delta based on sample sizes of 45, 33, and 30, respectively. We note that there is un certainty based on the limited sample sizes and in the extrapolation of these fish consumption rates to other parts of the United States. However, we note that for the highest fish -consuming subpopulation (Laotian), the mean local per cap ita fish ingestion rate (47.2 g/da y) is somewhat less than the mean fish ingestion rate (70 g/day) for native American subsistence populations recommended by the EPA’s 1997 Exposure Factors Handbook 19 and the 90 th percentile per capita fish ingestion rate calculated from th e Shilling study and used in this RTR assessment (144.8 g/day) is somewhat less than the 95 th percentile fish ingestion rate recommended by the EPA’s 1997 Exposure Factors Handbook for na tive American subsistence populations. While comparing local per capita fish ingestion to total ingestion is a somewhat imperfect comparison, it does help us gauge how the valu es in the Shilling study compare to other existing data. All of these fish ingesti on rates are significantly greater than the 90 th percentile fish ingestion rate across all recreational anglers.

We further note that high-end fisher populations could display considerable variability both in terms of the degree to which they frequent spec ific waterbodies or watersheds and the degree to which they target specific types of fish (or at least sizes of fish). Bo th of these factors can impact estimates of exposure. If a fisher populat ion distributes their ac tivity across a range of

waterbodies and harvests a variet y of fish species (and sizes) th an the distribution of exposure and risk across that population will be sma ller compared with a population that focuses activity at individual waterbodies an d tends to focus on larger fish.

A more detailed discussion of the multipathway analysis and its associated uncertainties is presented in section 5.3 of the document Hu man Health Multipat hway Residual Risk Assessment for the Secondary Lead Smelting Source Category, which can be found in the docket for the proposed rule.

17 Data for the general U.S. population of recreationa l anglers was obtained from: EPA 2002, “Estimated Per Capita Fish Consumption in the United States, Office of Water, Office of Science and Technology, Washington, D.C., EPA-821-C-02-003. August 2002.

18 Shilling et., al. 2010 is available in the docket for this rulemaking

19 EPA’s 1997 Exposure Factors Handbook is available at http://cfpub.epa.gov/ncea/cfm/ recordisplay.cfm?deid=12464Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 39



### 4.2 Uncertainties in the dose-response relationships

In the sections that follow, separate discussi ons are provided on uncertainty associated with cancer potency factors and for noncancer reference values. Cancer potency values are derived for chronic (lifetime) exposures. Noncancer reference values are generally derived for chronic exposures (up to a lifetime), but may also be derived for acute (

 <24 hours), short-term (>24 hours up to 30 days), and subchronic (> 30 days up to 10% of lifetime) exposure durations, all of which are derived based on an assumption of con tinuous exposure throughout the duration specified. For the purposes of assess ing all potential health risks associated with the emissions included in an assessment, we rely on both chronic (cancer and noncancer) and acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify p eer-reviewed dose-respons e values for all HAPs emitted by the source category included in an assessment, some HAP have no peer-reviewed cancer potency values or reference values for chronic non-cancer or acu te effects (inhalation or ingestion). Since exposures to these pollu tants cannot be included in a quantitative risk estimate, an understatement of risk for these po llutants at environmental exposure levels is possible.

Additionally, chronic dose -response values for certain compo unds included in the assessment may be under EPA IRIS review and revised asse ssments may determine that these pollutants are more or less potent than currently thought. We will re-evaluate risks if, as a result of these reviews, a dose-response metric changes enough to indicate that the risk assessment may significantly mischaracter ize human health risk

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses on the uncertainties associated with the specifi c approach currently used by the EPA to develop cancer potency factors. In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the sour ce of peer-reviewed cancer potency factors used where EPA-developed values are not yet ava ilable. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 36 ] (herein

referred to as Cancer Guidelines ). “The primary goal of EPA actions is prot ection of human health; accordingly, as an Agency policy, ri sk assessment procedures, including default options that are used in the absence of scien tific data to the cont rary, should be health protective.” The approach adopt ed in this document is consis tent with this approach as

described in the Cancer Guidelines

.

For cancer endpoints EPA usually derives an oral slope factor for inge stion and a unit risk value for inhalation exposures. These values allow estimation of a lifetime probability of developing cancer given long-term exposures to the pollutant. Depending on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological studies to

characterize cancer risk. As a science policy approach, consistent with the Cancer

Guidelines , EPA uses animal cancer bioassays as i ndicators of potential human health risk

when other human cancer risk data are unavailable.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 40

Extrapolation of study data to estimate poten tial risks to human populations is based upon

EPA’s assessment of the scientific database for a pollutant using EPA’s guidance documents and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s recommendations for methodologies for cancer ri sk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper bound estimates represent a “plausible upper limit to the true value of a quantity” (although this is usually not a true statistical confidence limit). 20 In some circumstances, the true risk could be as low as zero; howev er, in other circumstances the risk could also be greater. 21
When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches. 22 EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is noted that this approach can have limitations for some uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some wh ich may be considered quantitatively, and others which generally are ex pressed qualitatively. Uncertainties may vary substantially among cancer risk a ssessments associated with expos ures to different pollutants, since the assessments employ different database s with different strengt hs and limitations and the procedures employed may differ in how well they represent actual biological processes for

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk characterizations present estimat es demonstrating the impact on the assessment of alternative choices, data, models and assumptions [ 37]. Some of the major sources of uncertainty and variability in deriving cancer risk valu es are described more fully below.

(1) The qualitative similarities or differen ces between tumor responses observed in experimental animal bioassays and those wh ich would occur in humans is a source of uncertainty in cancer risk assessment. In ge neral, EPA does not assume that tumor sites observed in an experimental an imal bioassay are necessarily predictive of the sites at which

tumors would occur in humans. 23 However, unless scientific support is ava ilable to show

20 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm).

21 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of which is considered to be equally plausible, an d which is based on maximum likelihood estimates.

22 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions

are used to ensure that risk to chemicals is not under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Asse ssment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

23 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a prerequisite for evaluating the implications of animal study results for humans.”Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 41

otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qua litative differences in species responses can

lead to either under-estimation or over-e stimation of human cancer risks.

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For exam ple, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled con centration of the pollutant. However, data may support development of a pharmacokinetic mode l for the absorption, distribution, metabolism and excretion of an agent, which may resu lt in improved dose metrics (e.g., average blood concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative uncertainties result when the appropriate choi ce of a dose metric is uncertain or when dose

metric estimates are themselves uncerta in (e.g., as can occur when alternative pharmacokinetic models are available for a co mpound). Uncertainty in dose estimates may

lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling met hodologies (relating expected response to differences in physical size of the species), which introdu ce another source of uncertainty. These methodologies are based on both biol ogical data on differences in rates of process according to species size and empirical comparisons of toxicity between expe rimental animals and humans. For a particular pollutant, the quant itative difference in cancer potency between experimental animals and humans may be either greater than or less than that estimated by baseline scientific scaling predictions due to uncertainties associated with limitations in the

test data and the correctness of scaled estimates.

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) for

the remainder of the calculat ion. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95% lower confidence limit on the dose at which the sp ecified excess risk occurs (the BMDL), decreasing the likelihood of understating risk. EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see furt her discussion below).

(5) Extrapolation from high to low doses is an important, and potential ly large, source of uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or ep idemiological studies at higher dose) depending

on the available data and understa nding of a pollutant’s mode of action (i.e., the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use of reliable, compound-specific, bi ologically-based risk models when feasible; however, such models are rarely available. The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key considerati on in determining how ri sks should be estimated for low-dose exposure. A reference value is calculated when the available mode of action data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose (straight line from POD) approach is used when available mode of action data support a linearDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 42

(e.g., nonthreshold response) or as the most common default approach when a compound’s

mode of action is unknown. Lin ear extrapolation can be suppor ted by both pollutant-specific

data and broader scientific cons iderations. For example, EPA’s Cancer Guidelines generally consider a linear dose-response to be appropria te for pollutants that interact with DNA and

induce mutations. Pollutants whose effects ar e additive to background biological processes in cancer development can also be predicted to have low-dose linear responses, although the slope of this relationship may not be the same as the slope estimate d by the straight line approach.

EPA most frequently utilizes a linear low-dose extrapolation appr oach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific determination. This approach is designed to not underestimate risk in the face of uncertainty and variability. EPA believes that linear dose-response models , when appropriately applied as part of EPA’s cancer risk assessment proce ss, provide an upper bound estimate of risk and generally provide a health protec tive approach. Note that anothe r source of uncertainty is the

characterization of low-dose non linear, non-threshold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose res ponse relationships due to the variability in response within human populations. Another National Research Council (NRC) report [ 38 ] suggests that

models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped dose-response functions for a population. This report notes sources of variability in the human population: “One might expect these in dividual tolerances to vary extensively in humans depending on genetics, co incident exposures, nutritional status, and various other susceptibility factors...” Thus , if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on ability of modeling to reflect the degree of variability in respons e in human populations (as opposed to responses in bioassays with genetically more uniform rodents). Note al so that low dose linearity in risk can arise for reasons separate from population variability: du e to the nature of a mode of action and additivity of a chemical’s effect on top of background chemical exposures and biological

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL. This is equiva lent to using an upper confidence limit on the slope of the straight line extr apolation. The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing ri sk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [ 39 ]. It is important to note that earlier EPA assessments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental

dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on st raight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage

model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population — resu lting in another source of uncertainty inDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 43

assessments. In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than the typical individual , although compound-specific data to evaluate this variability are generally not available. There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences. However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response fr om results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that ar e designed not to underestimate risk in the face of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure leve ls that are intended to be health-protective. That is, EPA and other or ganizations which develop noncancer reference values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an approach that is intended not to underestimate risk in the face of uncertainty and variability. When there are gaps in the available informa tion, uncertainty factors (UFs) are applied to

derive reference values that are intended to be protective against appreciable risk of deleterious effects. Uncertainty factors are commonly default values 24 e.g., factors of 10 or 3, used in the absence of compound-specific data; wh ere data are available, uncertainty factors may also be developed using compound-specific information. When data are limited, more assumptions are needed and more default fact ors are used. Thus there may be a greater tendency to overestimate risk—in the sense th at further study might support development of reference values that are higher (i.e., less pot ent) because fewer default assumptions are needed. However, for some pollutants it is possible that risks ma y be underestimated.

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference C oncentration (RfC) for inhalation exposures. These values provide an estimate (with uncerta inty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a conti nuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is lik ely to be without an appreciable risk of deleterious effects during a lifetime. 25 To derive values that are intended to be “without

24 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowle dge and policy judgment, that are applied to various elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions

are used to ensure that risk to chemicals is not under estimated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assess ment Principles and Practices, EPA/100/B-04/001 available at: http://w ww.epa.gov/osa/pdfs/ratf-final.pdf.

25 See IRIS glossaryDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 44

appreciable risk,” EPA’s methodol ogy relies upon an uncertainty factor (UF) approach (U.S. EPA, 1993, 1994) which includes considerati on of both uncertainty and variability.

EPA begins by evaluating all of the availabl e peer-reviewed literature to determine noncancer endpoints of concern, eval uating the quality, strengths and limitations of the available studies. EPA typically chooses th e relevant endpoint that occu rs at the lowest dose, often using statistical modeling of the available data, and then determines th e appropriate point of departure (POD) for derivation of the reference value. A POD is determined by (in order of preference): (1) a statistical estimation using the benchmark dos e (BMD) approach; (2) use of

the dose or concentration at which the toxic response was not signi ficantly elevated (no observed adverse effect level— NOAEL); or (3) use of the lowest observed adverse effect level (LOAEL).

A series of downward adjustments using defau lt UFs is then applied to the POD to estimate the reference value (U.S. EPA 1994, 2002). Whil e collectively termed “UFs”, these factors account for a number of different quantitative co nsiderations when utilizing observed animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation in susceptibility among th e members of the human population (i.e., interindividual variability); (2) uncer tainty in extrapolating from experimental animal data to humans (i.e., interspecies diffe rences); (3) uncertainty in extr apolating from data obtained in a study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating fr om a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is incomplete or there are problems with applicability of available studies. When scientifically sound, peer-reviewed assessment- specific data are not available, default adjustment values are sele cted for the individual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the

selected POD. An UF of 3 is used when the da ta do not support the use of a 10-fold factor. If an extrapolation step or adjustment is not re levant to an assessment (e.g., if applying human toxicity data and an interspecies extrapolation is not required) the associated UF is not used. The major adjustment steps are described more fully below.

1) Heterogeneity among humans is a key sour ce of variability as well as uncertainty. Uncertainty related to human variation is consid ered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger , more diverse population. In the absence of pollutant-specific data on human variation, a 10 -fold UF is used to account for uncertainty

associated with human variation. Human variatio n may be larger or smaller; however, data to examine the potential magnitude of human va riability are often una vailable. In some situations, a smaller UF of 3 ma y be applied to reflect a known l ack of significant variability among humans.

2) Extrapolation from results of studie s in experimental animals to humans is a necessary step for the majority of chemical ri sk assessments. When interpreting animal data, the concentration at the POD (e.g. NOAEL, BM DL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long-sta nding scientific supportDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 45

for the use of animal studies as indicators of potential toxicity to humans, there are uncertainties in such extrapolat ions. In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the aver age human. Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacki ng, thus leading to uncertainty in this extrapolation. Size-related differences (allometric relationships) indicate that typically

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consis tent with these differences. For a specific chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however , such dosimetric adjustments are not always possible. Information may not be available to quantitatively a ssess toxicokinetic or toxicodynamic differences between animals a nd humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodyna mic components) is used to account for expected species differences and as sociated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value. If information on one or the other of these components is availabl e and accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for ch ronic exposures where only data from shorter durations are available (e.g., 90-da y subchronic studies in rodent s) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is typically applied unless th e available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been tested in individual studies; in an animal study, for example, treatment groups may differ in exposure by up to an order of magnitude. The pref erred approach to arrive at a POD is to use BMD analysis; however, this approach requires ad equate quantitative results for a meaningful analysis, which is not always possible. Use of a NOAEL is the next preferred approach after BMD analysis in determining a POD for deriving a health effect reference value. However, many studies lack a dose or exposure level at wh ich an adverse effect is not observed (i.e., a NOAEL is not identified). When using data lim ited to a LOAEL, a UF of 10 or 3-fold is often applied.

5) The database UF is intended to a ccount for the potential for deriving an underprotective RfD/RfC due to a data gap pr eventing complete char acterization of the chemical’s toxicity. In the absence of studies for a known or suspecte d endpoint of concern, a UF of 10 or 3-fold is typically applied.

There is no RfD or other comparable chronic health benchmark value for lead compounds. Thus, to address multipathway human health and environmental risks associated with emissions of lead from this facility, ambien t lead concentrations were compared to the NAAQS for lead. In devel oping the NAAQS for lead, EPA considered human health evidence reporting adverse health effects associ ated with lead exposure, as well as an EPA conducted multipathway risk assessment that app lied models to estimate human exposures toDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 46

air-related lead and th e associated risk (73FR at 66979). EP A also explicitly considered the

uncertainties associated with both the human health evidence and th e exposure and risk analyses when developing the NAAQS for lead. For example, EPA considered uncertainties in the relationship between ambi ent air lead and blood lead leve ls (73FR at 66974), as well as

uncertainties between blood lead levels and lo ss of IQ points in children (73FR at 66981). In considering the evidence and risk analyses and their associated uncertainties, the EPA

Administrator noted his view that there is no evidence- or risk-b ased bright line that indicates a single appropriate level. Instea d, he noted, there is a collectio n of scientific evidence and judgments and other information, including inform ation about the uncertainties inherent in many relevant factors, which needs to be cons idered together in making this public health policy judgment and in selecting a standard level from a range of reasonable values (73FR at 66998). In so doing, the Administrator decided that , a level for the primary lead standard of

0.15 μ g/m 3 , in combination with the specified choice of indicator, averaging time, and form, is requisite to protect public health, including th e health of sensitive groups, with an adequate margin of safety (73FR at 67006). A thorough di scussion of the health evidence, risk and exposure analyses, and their asso ciated uncertainties can be found in EPA’s final rule revising the lead NAAQS (73 FR 66970-66981, November 12, 2008).

We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are likely less than the uncertainties associated w ith dose-response values developed for many of the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s IRIS program reviewed the hea lth effects data regarding l ead and its inorganic compounds and determined that it would be inappropria te to develop an RfD for these compounds, saying, “A great deal of information on the heal th effects of lead has been obtained through

decades of medical observation and scientific re search. This information has been assessed in the development of air and water quality cr iteria by the Agency's Office of Health and Environmental Assessment (OHEA) in suppor t of regulatory decisi on-making by the Office of Air Quality Planning and Standards ( OAQPS) and by the Office of Drinking Water (ODW). By comparison to most other environm ental toxicants, the de gree of uncertainty

about the health effects of lead is quite low. It appear s that some of these effects, particularly changes in the levels of certain blood enzyme s and in aspects of children's neurobehavioral development, may occur at blood lead levels so low as to be essentia lly without a threshold. The Agency's RfD Work Group discussed inor ganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and consider ed it inappropriate to develop an RfD for inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439- 92-1), http://www.epa.gov/iris/subst/0277.htm

.

We note further that because of the multi-pathway, multi-media impacts of lead, the risk assessment supporting the NAAQS considered dir ect inhalation exposures and indirect airrelated multi-pathway exposures from industr ial sources like primary and secondary lead smelting operations. It also considered backgr ound lead exposures from other sources (like contaminated drinking water and exposure to l ead-based paints). In revising the NAAQS for lead, we note that the Administrator placed mo re weight on the evidence-based framework and less weight on the results from the risk asse ssment, although he did find the risk estimates to be roughly consistent with and generall y supportive of the evidence-based framework applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS forDraft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 47

lead to protect public health with an adequa te margin of safety, EPA considered both the

evidence-based framework and the risk a ssessment, albeit to different extents.

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of acute reference values are quite similar to those de veloped for chronic durations, but more often using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific informati on (e.g., simple irritation effects do not vary appreciably between human indivi duals, hence a value of 3 is t ypically used), or based on the purpose for the reference value (see the followi ng paragraph). The UFs applied in acute reference value derivation include: 1) he terogeneity among humans ; 2) uncertainty in extrapolating from animals to humans; 3) unc ertainty in LOAEL to NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on toxic effect s of potential concern. Additional adjustments are often applied to a ccount for uncertainty in extrapolation from observations at one exposure dura tion (e.g., 4 hours) to arrive at a POD for derivation of an

acute reference value at another exposure duration (e.g., 1 hour).

Not all acute reference values are developed for the same purpose and care must be taken when interpreting the results of an acute asse ssment of human health effects relative to the reference value or values being exceeded. Wh ere relevant to the estimated exposures, the

lack of threshold values at di fferent levels of severity shoul d be factored into the risk characterization as poten tial uncertainties.Draft Risk Assessment for the Secondary Lead Smelting Source Category -- FOR PUBLIC COMMENT, DO NOT CITE OR QUOTE 48



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