1



## Residual Risk Assessm **ent for the Secondary Lead Smelting**

**Source Category**

**by EPA’s Office of Air Quality Planning and Standards** 

**Office of Air and Radiation December**  **2011**2



### Table of Contents 1 Introduction ................................ ................................ ................................ ........................ 4 2 Methods ................................ ................................ ................................ .............................. 5 2.1 Emissions and source data ................................ ................................ ........................... 5 2.2 Dispersion modeling for inhalation exposure assessment ................................ ........... 6 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 - respons e 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 Facilit y - wid e risks ................................ ................................ .............................. 26 3 Risk results for the secondary lead source category ................................ .................... 27 3.1 Source category

description and results ................................ ................................ .... 27 3.2 Baseline risk characterization ................................ ................................ .................... 30 3.3 Post - control risk characterization ................................ ................................ .............. 34 4 General discussion of uncertainties and how they have been addressed ......................... 35 4.1 Exposure modeling uncertainties ................................ ................................ .............. 35 4.2 Uncertainties in the dose - response relationships ................................ ....................... 38 5 References ................................ ................................ ................................ ........................ 47 **Index of Tables** Table 2.2 - 1 AERMOD version 09292 model options for RTR modeling ................................ 6 Table 2.6 - 1 (a) Dose - Response Values for Chronic Inhalation Exposure to Carcinogens .... 17 Table 2.6 - 1 (b) Dose - Response Values for Chronic Oral Exposure to Carcinogens ............. 18 Table 2.6 - 1 (c) Dose - Response Values for Chronic Inhalation Exposure to Noncarcinogens

................................ ................................ ................................ ................................ .................. 19 Table 2.6 - 2 Dose - Response Values for Acute Exposure ................................ ........................ 23 Table 3.1 - 1 Summary of Emissions from the Secondary L ead Source Category

and Availability

of Dose - Response Values ................................ ................................ .................... 28 Table 3.2 - 1 Summary

of Source Cat egory

L evel Inhalation Risks for Secondary L ead Smelting ................................ ................................ ................................ ................................ ... 31 Table 3.2 - 3 Secondary

Lead Smelting Facility

Modeled Maximum Ambient Le ad Concentrations ................................ ................................ ................................ .......................... 34 (rolling three - month average values) ................................ ................................ ....................... 34 **Appendices** Appendix 1 Technical Support Document for HEM - 3 Modeling Appendix 2 Meteorological Data for HEM - 3 Modeling3

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

rates Appendix 4 Technical Support Document for TRIM - Based Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation Appendix 5 Detailed Risk Modeling Results Appendix 6 Acute Impacts Refined Analy sis Figures Appendix 7 Dispersion Model Receptor Revisions and Additions **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 IR IS Integrated Risk Information Sy stem MACT Maximum Achievable Control Technology MIR Maximum Individual Ri sk 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 estimate4



### 1 **Introduction** Section 112 of the Clean Air Act (CAA) establishes a two - stage regulatory process for addressing emissions of hazardous air pollutants (HAPs) from stationary sources. In the first stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to develop technology - based standards for categories of sources (e.g., petroleum refineries, pulp and paper mills, etc.) [ 1 ]. EPA has largel y completed the initial Maximum Achievable Control Technology

( MACT ) standards as required under this provision. Under section 112(d)(6), EPA must review each of these technology - based standards at least every eight ye ars and revise a standard, as necessary , “taking into account developments in practices, processe s and control technologies.” In the secon d 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 are necessary to protect public health with an ample margin of safet y or to prevent an adverse environmental effect, EPA must develop standards to address these remaining risks. This second stage of the regulatory process is known as the residual risk stage. For each source category for which EPA issued MACT standards, the resi dual risk stage 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 Pl an , ” and in June of 2007, we received a letter with the results of that consultation. Subsequent to the consultation, in June of 2009 , a meeting was held with an SAB panel for a formal peer review of the “Risk and Technology

Review (RTR) Assessment Method ologies” [ 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 assessment s and we will be continuing our efforts to improve our assessments by incorporating updates based on the SAB recommendations as they are developed and become 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 results of baseline risk assessments performed for the Secondary

L ead Smelting source category . The methods discussion includes descriptions of the method s used to screen for acute health risks, chronic non - inhalation health risks, and adverse environmental effects, as well as descriptions of the methods used to develop refined estimates of chronic inhalation exposures and human health risks for cancer and noncancer endpoints. During the public comment period for the proposed rule, some facilities in the source category submitted updated emissions, facility boundary , and other relevant information with respect to performing a risk assessment (see doc ket number: EPA - HQ - OAR - 2011 - 0344 for public comments received) . In addition, we also received emissions data for an additional facility that was not included in the modeling to support the proposed rulemaking. Thus, the methods and data used to generate the ba seline and post - control risk results presented in this final risk assessment document consider the updated information received during the public comment period for the proposed rule.5



### 2 **Methods 2.1**  **Emissions and source data** Data from the 2005 National Emission s Inventory (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 information on actual emissions during the entire 2005 base year. Usin g the process MACT code 1 , we developed a subset of this inventory that contains emissions and facility data for the Secondary

L ead Smelting source category . Next, we performed an engineering review of the information using EPA engineers to identify readil y apparent limitations and issues with the emissions data In 2010, following this initial engineering review, we sent an information collection request (ICR) survey to the six companies which own all 14 facilities in the U.S. of which we are aware . The survey included man y 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 mul tiple 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 reviewed and anal yz ed all the I CR and stack test data and then incorporated these data into the model data set, as appropriate. However, with regard to the estimates of fugi tive emissions, the data we received through the ICR were limited and uncertain. Of the 14 facilities surveyed, five facilities provided no estimate for fugitive emissions and five other facilities provided quite incomplete estimates. Additional informat ion was later submitted by one of the facilities (East Penn). Rather than rel y on the incomplete and often nonexistent estimates of fugitive emissions provided in I CR responses, the EPA scaled estimates of fugitive emissions from one facility (Exide Frisco ) that provided a robust estimate of these emissions. We received public comments that led us to revise the estimated fugitive emissions for Exide Frisco. See Development of the RTR Emissions Dataset for the Secondary Lead Smelting Source Category at page 11 for a discussion of the fugitive emission estimates and how these estimates were derived. In addition, as noted above during the public comment period for the proposed r ule, EPA received some updated facil ity information, including updated emi ssions data from some facilities in the source category (see docket number: EPA - HQ - OAR - 2011 - 0344 for public comments) . As a result, the emissions dataset used to generate the risk results presented in this final risk report consider the emissions data re ceived during the public comment period for the proposed rule. In addition, EPA also received emissions information for an additional secondary lead smelting facility located in Puerto Rico. Thus, the total number of facilities in the source category , and the total number of facilities evaluated in this risk report, is 15.

1 The tagging of data with MACT codes allows EPA to determine reductions attributable to the MACT program. The N E I associates MACT codes correspon ding to MACT source categories with stationary major and area source data. MACT codes are assigned at the process level for the point source .6



### 2.2 **Dispersion modeling for inhalation exposure assessment** Both long - and short - term inhalation exposure concentrations 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 s ys tem (HEM - 3 ). The approach used in appl yi ng this modeling s ys tem is outlined below, and further de tails are provided in Appendix 1 . HEM - 3 performs three main operations: atmospheric dispersion modeling, estimation of individual human exposures and health risks, and estimation of population risks. This section focuses on the dispersion modeling compon ent. The exposure and risk characterization components are discussed in other subsections of Sections 2 and 3. The dispersion model in the HEM - 3 sy stem, AERMOD version 11103 , is a state - of - the science Gaussian plume dispersion model that is preferred b y EPA for modeling point, area, and volume sources of continuous air 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 concentrations through the simulation of hour - by - hour dispersion from the emission sources into the surrounding atmosphere. Hourl y emission rates used for this simulation are generated b y evenl y dividing the total annual emission rate from the inventory into the 8,760 hours of the ye ar. The first step in the application of the HEM - 3 modeling s ys tem 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 mo del options for RTR**  **modeling** Modeling Option Selected Parameter for chronic exposure Type of calculations Hourly Ambient Concentration Source type Point and area source s 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 ( over 200 stations) In HEM - AERMOD, meteorological data are ordinarily selected from a list of over 200 National Weather Service (NWS) surface observation stations 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 . Ideall y, when c onsidering off - site7

meteorological data most site specific dispersion modeling efforts will employ up to five ye ars of data to capture variability in weather patterns from ye ar to ye ar. However, to minimize data processing time and resources for this asse ssment , we modeled only a single ye ar, t ypicall y 1991. While the selection of a single year may result in under - prediction of long - term ambient levels at some locations, likewise it may result in over - prediction at others. For each facility

identified by its characteristic latitude and longitude coordinates, the closest meteorological station was used in the dispersion modeling. The average distance between a modeled facilit y and the applicable meteorological station was 40 miles (72 km). Appendix 2 (Me teorological Data Processing Using AERMET 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 results to the selection of the nearest weath er station and the use of one year of meteorological data is discussed in “ Risk and Technology Review (RTR) Risk Assessment Methodologies ” [ 2 ]. The HEM - AERMOD s ys tem estimates ambient concentrations at the geographic centroids of census blocks (using the 2000 Census) and at other receptor locations that can be specified b y the user. The model accounts for the impa cts of multiple facilities when estimating concentration impacts at each block centroid . Ty pically we combined onl y the impacts of facilities within the same source category , and assessed chronic exposure and risk only for census blocks with at least one resident (i.e., locations where people may reasonabl y be assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient 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 finest resolution available in the census data, are t ypicall y comprised of approximately 40 people or about ten households. In contrast to the development of ambient concentrations for evaluating long - term e xposures, which was performed only for occupied census blocks, worst - case short - term (one - hour) concentrations were estimated both at the census block centroids and at points nearer the facility that represent locations where people may be present for short periods, but generall y 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 totals, we ty pica lly appl y the general assumption to all source categories that the maximum one - hour emission rate from an y source other than those resulting in f

ugitive dust emissions w as 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 conditions associated with the worst - case sh ort term impacts (i.e., during low - wind, 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. Th e choice of a factor of ten for acute sc reening 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 anal ysis using a short - term emis sions data set from a number of source s located in Texas (originally reported on b y Allen et al
. 2004)[ 7]. In8

that report, the Texas Environmental Research Consortium Project compared hourly and annual emissions data for volatile organic compound s for all facilities in a heavily industrialized 4 - county

area (Harris, Galveston, Chambers, and Brazoria Counties , TX) over an eleven - month time period in 2001 . We obtained the dataset and performed our own anal ys is, focusing that analy sis on sources which reporte d emitting high quantities of HAP over short periods of time (see Appendix 3 , “Analy sis of data on short - term emission rates relative to long - term emission rates”). Most peak emission events were less than twice the annual average, and the highest was a f actor of 74 times the annual average and the 99 th percentile ratio of peak hourly emission rate to the annual hourl y emission rate was 9 . Based on these results, we chose the factor of ten for all initial screening; it is intended to cover routinely - varia

ble emissions as well as startup, shutdown, and malfunction (SSM) emissions. While there have been some documented emission excursions above this level, our anal ys is of the data from the Texas Environmental Research Consortium suggests that this factor sh ould cover more than 99% of the short - term peak gaseous or volatile HAP emissions from ty pical industrial sources (which were the biggest contributor to high short - term emission events) and more than account for peak 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 short and long - term ambient concentrations were assumed to be equal to the highest elevation of any census block falling within the polar grid sector corresponding to the grid point. I f a sector does not contain any blocks, the model d efaults 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 h eight, AERMOD adjusts the plume’s flow if nearb y el evated hills are expected to influence the wind patterns. For details on how hill heights were estimated and used in the AERMOD modeling see Appendix 1 . 

### 2.3 **Estimating chronic**  **human inhalation exposure** W e used the annual average ambient air concentration of each HAP at each census block centroid as a surrogate for the lifetime inhalation exposure concentration of all the people who reside in the census block. That is, the risk anal ysis did not consider e ither the short - term or long - term behavior (mobility ) of the exposed populations and its potential influence on their exposure. We did not address short - term human activity for two reasons. First, our experience with the NATA assessments (which modeled dail y activity using EPA’s HAPEM model) suggests that, given our current understanding of microenvironment concentrations and daily activities, modeling short - term activity would, on average, reduce risk estimates about 25% for particulate HAPs; it will a lso reduce risk estimates for gaseous HAPs, but ty pically by much less. Second, basing 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 highl y exposed residents b y modeling their short - term behavior could add a sy stematic low bias to these results.9

We did not address long - term migration nor population growth or decrease over 70 years, instead basing the assessment on the assumption 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 generall y estimated three metrics; the maximum individual risk (MIR), which is defined as the risk associated with a lifetime of exposure at the highest concentration; the population risk distribution; and the cancer incidence. The assumption of not considering short or long - term population mobility does not bias the estim ate of the theoretical MI R nor does it affect the estimate of cancer incidence since the total population number remains the same. It does, however, affect the shape of the distribution of individual risks across the affected population, shifting it toward higher estimated individual risks at the upper end and reducing the number of people estimated to be at lower risks, thereb y increasing the estimated number of people at specific risk levels . 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 concentration for an y individual. 

### 2.4 **Screening for potential acute impacts of concern** In establishing a scienti fically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we f ollowed the same general approach that has been used for developing chronic health risk assessments for other rules under the residual risk program. That is, we developed a tiered, iterative approach. This approach to risk assessment was endorsed b y the National Academ y 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 risk s , the assessment methodology is designed to eliminate from further consideration those facilities for which we have confidence that no acute adverse health effect s of concern will occur. To do so, we use what is called a tiered, iterative approach to the asse ssment. This means that we began with a screening assessment, which relies on readil y available data and uses conservative assumptions that in combination ap proximate a worst - case exposure . The result of this screening 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 exposure s from emitted HAP . A more refined acute assessment could u s e 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, lesser amount s 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 assumptions in our screening approach:10

 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 ye ar at the nearest NWS site ) was assumed to occur at the same time the peak emission rates occur. The recommended EPA local scale dispersion model, AERMOD, wa s used for simulating atmospheric dispersion during this worst - case hour .  A person was assumed to be located downwind at the point of maximum modeled impact during this same worst - case 1 - hour period, but no nearer to the source than 100 meters. As a result, the maximum modeled 1 - hour off - site conce ntration equals the worst - case acute exposure concentration.  The maximum impact was compared to multiple short - term health benchmarks for the HAP being assessed to determine if a possible acute health risk might exist. These benchmarks are described in se ction 2.6 of this report. As mentioned above, when we identify acute 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 facilit y - specific e missions multiplier to estimate the peak hourly emission rates from the average rates (rather than the default factor of 10) . In other cases, this may

entail determining the actual ph ys ical layout and boundaries of a facility to more accurately

gauge wher e people might reasonably be exposed for an hour. 

### 2.5 **Multipathway human health**  **and env**  **ironmental risk screening**  The potential for significant human health risks and adverse environmental impacts due to exposures via routes other than inhalation (i.e., multip athway exposures) was screened b y first determining whether an y sources emitted an y 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 L ibrary [ 8 ], and they

are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlori nated biphenyls, pol yc yclic organic matter (POM) , toxaphene, and trifluralin . Emissions of 5 different PB - HAP were identified in the emissions inventory for the Secondary

L ead Smelting source category ; they are POM, dioxins, cadmium, mercury , and lead. W ith respect to PB - HAP other than lead, emissions were evaluated for potential non - inhalation risks and adverse environmental impacts using our

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).11

recentl y - developed screening scenario which was developed for use with the TRIM.FaTE 3 model. This screening scen ario uses environmental media outputs from the peer - reviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified emission scenario b y using a generic farming/fishing exposure scenario that simulates a subsistence environment. The screening scenario retains 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 screening scenario a sensitivity anal ys is was co nducted 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 discus sion of the development and testing of the screening scenario, as well as for the values of facilit y - level screening emission rates developed for screening potentially significant multi pathway impacts. For the purpose of developing screening emission rates for our multi pathway screening, we derived emission levels for each PB HAP at which the 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 source category , there were exceedences of screening emissions rates at multiple facilities for multiple PB - HAP, and thus a multipathway analy sis was performed. Two facilities were chosen as case study anal ys es to assess potential multipathway

ri sks for mercury , cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions rates of PB - HAPs, proximity to water bodies, proximity

to farmland, average rainfall, average wind speed and direction, smelt ing furnace t ype, local change in elevation, and geographic representativeness of sites throughout the US. As a result of our selection process, we believe the multipathway risks associated with these two facilities are in the upper end of the potential f or multipathway

risks from the source category . Since the modeling used in these case study assessments utilize site specific parameters to describe naturally occurring ph ys ical, chemical and biological processes, we believe that the multimedia concentrati ons of PB - HAPs generated in this anal ys is are unbiased estimates 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 exposures and risks. The farmer scenario involves an individual living on a farm homestead in the vic inity of a PB HAP 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 contaminated surface soil at the location of the farm homestead. The recreational fisher scenario involves an individual who regularl y consumes fish caught in freshwater lakes in the vicinity of a PB - HAP source. In the fishing scenario, in addition to the characterization of exposure and risks acros s the broad population of recreational anglers, exposures were also calculated for three subpopulations of recreational

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/hazwaste/combust/riskvol.htm#v olume112

anglers (Hispanic, Laotian, and Vietnamese descent) who have higher rates of fish consumption 5

. Furthermore, in order to more full y 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 anglers, and the three subpopulations of recreational anglers (e.g., ingestion rates specific to Laotian recreational anglers); and 2) a central tendency exposure (CTE) scenario that for example, utilizes mean ingestion rates for the groups just described. 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 . In evaluating the potential multi - pathway risks from emissions of lead compounds, rather than developing a screening emission rate, we compared maximum estimated chronic (3 - month average) atmospheric concentrations with the current primary National Ambient Air Quality Standard (NAAQS) for lead. The current primary National Ambient Air Quality Standard (NAAQS) for lead, which is set to a level of 0.15 μ g/m 3 based on a rolling 3 - month averaging period with a not - to - be - ex ceeded form (see 73 FR 66964 ) . Notabl y, in making these comparisons, we estimated maximum rolling 3 - month ambient lead concentrations taking into account all of the elements of the NAAQS for lead. That is, our estimated 3 - month lead concentrations are ca lculated in a manner that is consistent with the indicator, averaging time, and form of the NAAQS for lead, and those estimates are compared to the actual level of the lead NAAQS (0.15 μg/m 3 ). Values below the NAAQS were considered to have a low potentia l for multi - pathway risks of any

significance. The NAAQS va lue, a public health policy judgment, incorporated the Agency ’s most recent health evaluation of air effects of lead exposure for the purposes of setting a national ambient air quality standard. In setting this value, the Administrator promulgated a standard that was requisite to protect public health with an adequate margin of safety. We consider values below the level of the primary NAAQS to protect against multipathway risks because as mention ed above, the primary NAAQS is set as to protect public health with an adequate margin of safety . However, ambient air lead concentrations above the NAAQS are considered to pose the potential for increased risk to public health. We consider this NAAQS as sessment to be a refined anal ys is given : 1) the numerous health studies, detailed risk and exposure anal ys es, and level of external peer and public review that went into the development of the primary NAAQS for lead, combined with : 2) the sit e - specific dis persion modeling used in this assessment to estimated ambient lead concentration s due to secondary lead smelting emissions . It should be noted, however, that this comparison does not account for possible population exposures to lead from sources other than the one being modeled; for
5 In both scenarios, exposure via drinking water was not considered because it is unlikely that humans would use surface waters as a drinking water source. Groundwater, which is a likely source of drinking water, also w as 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 the 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 mercury exposure due to a current lack of data regarding this pathway.13

example, via consumption of water from untreated local sources or ingestion of locally grown food. Nevertheless, the Administrator judged that such a standard would protect, with an adequate margin of safety , the health of chi ldren and other at - risk populations against an array of adverse health effects, most notably including neurological effects, particularly neurobehavioral and neurocognitive effects, i n children (73 FR 67007). The Administrator, in setting the standard, al so recognized 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 range of reasonable values (73 FR 67006). We further n ote that comparing ambient lead concentrations to the NAAQS for lead , considering the level, averaging time, form and indicator, also informs whether there is the potential for adverse environmental effects. This is because the secondary lead NAAQS, set t o protect against adverse welfare effects (including adverse environmental effects), has the same averaging time, form, and level as the primary standard. Thus, ambient lead concentrations above the NAAQS for lead also indicate the potential for adverse e nvironmental effects. We also evaluated the potential for significant direct environmental exposures to HAP (other than lead) by

evaluating the potential for exceedances of chronic human health inhalation thresholds in the ambient air near these facilitie s. Human health dose - response threshold values are generall y derived from studies conducted on laboratory animals (such as rodents) and developed with the inclusions of uncertaint y factors that could be as high as 3000. As a result, these human threshold s are often significantly lower than the level expected to cause an adverse effect in an exposed 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 wildl ife. Thus, if the maximum inhalation hazard in an ecosy stem is below the level of concern for humans , we have generally concluded that mammalian receptors should be at no risk of adverse effects due to inhalation exposures from non PB - HAP, and have assurance that other ecological receptors are also not at any significant risk from direct atmospheric impact. In some isolated cases where we have data indicating potential adverse impacts on plants, birds, or other wildlife due to the direct atmospheric impac ts of specific HAP, we note that as an uncertaint y and, where possible, refine our anal ys is by comparing our modeled impacts to available threshold values from the scientific literature. 

### 2.6 **Dose -**  **r esponse**  **a ssessment**  **2.6.1 Sources of chronic dose**  **- 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 Office of Air Q uality Planning and Standards’ (OAQPS) existing recommendations for HAP [ 9 ], also used for NATA [ 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 best14

available science with respect to dose - response information. The recommendations are based on the following sources, in order of priori ty : 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 ty

pically provide a qualitative statement regarding the strength of scientific data and specify

a reference concentration (RfC, for inhalation) or reference dose (RfD, for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, for inhalation) or slope factor (SF, for ingestion) to estimate the probabi lity of developing cancer. The RfC is defined as an “ estimate (with uncertaint y spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. ” The RfD is “an estimate (with uncertaint y spanning perhaps an order of magnitude) of a daily

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

to be without an appreciable risk of deleterious effects during a lifetime. ” The URE is defined as “ the upper - bound excess cancer risk estimated to result from continuous lifetime 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 agent. This estimate, [is] usually expressed in units of proportion (of a population) affected per mg/kg - day ...” EPA disseminates dose - response assessment information in seve ral forms, based on the level of review. The Integrated Risk Information Sy stem (IRI S) [ 11 ] is an EPA database that contains scientific health assessment information, including dose - response information. All IRI S assessments since 1996 have also undergone independent external peer review. The current IRI S process includes review b y EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scientists external to EPA. New IRI S values are developed and old IRI S values are updated as new health effects data become available. Refer to the “IRI S Track” website for detailed information on status and scheduling of current individual IRI S 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 extrapolation 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 analy ses (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 Human Services, develops and publishes Minimum Risk L evel s (MRLs) [ 12 ] for inhalation and oral exposure to man y toxic substances. As stated on the ATSDR web site: “ Following discussions with scientists within the Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a practice similar t o that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints .” The MRLis defined as “an estimate of dail y human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified15

duration of exposure.” ATSDR describes MRL s as substance - specific estimates to be used by health assessors to select environmental contaminants for further evaluation. E xposures above an MRL do not necessarily represent 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 Envi ronmental Health Hazard Assessment has developed dose - response assessments for many substances, based both on carcinogenicity and health effects other than cancer. The process for developing these assessments is similar to that used by EPA to develop IRIS values and incorporates significant external scientific peer review. As cited in the CalEPA Technical Support Document for developing their chronic assessments 6 : “The guidelines for developing chronic inhalation exposure levels incorporate many recommend ations of the U.S. EPA [ 13 ] and NAS [ 14 ].” The non - cancer information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (REL s) [ 15 ]. CalEPA defines the RELas “the concentration level at or b elow which no health effects are anticipated in the general human population.” CalEPA's quantitative dose - response information on carcinogenicity by inhalation exposure is expressed in terms of the URE [ 16], defined similarly to EPA's URE. In developin g chronic risk estimates, we adjusted dose - response values for some HAPs based on professional judgment, as follows: 1) In the case of HAP categories such as gl ycol ethers and cyanide compounds, the most conservative dose - response value of the chemical cate gory i s used as a surrogate for other compounds in the group f or which dose - response values are not available. This i s done in order to examine, under conservative assumptions, whether these HAPs that lack dose response values may

pose an unacceptable ris k and require further examination, or screen out from further assessment. 2) Where possible for emissions of unspecified mixtures of HAP categories such as met al compounds and POM (see Tables 2.6 - 1 (a) to 2.6 - 1 (c) , we appl y category - specific chemical speci ation profiles appropriate to the source category to develop a composite dose - response value for the category . 3) In 2004, the EPA determined that the Chemical Industry Institute of Toxicology

(CIIT) cancer dose - response value for formaldehyde (5.5 x 10 - 9 per μg/m3) was based on better science than the IRIS dose - response value (1.3 x 10 - 5 per μg/m3), and we switched f rom using the IRIS value to the CIIT value in risk assessments supporting regulatory actions. Based on subsequent published research, however, EPA changed its determination regarding the CIIT model and in 2010 the EPA returned to using 1991 IR IS value EP A has been working on revising the formaldeh yde IR IS assessment and the National Academ y of Sciences (NAS) completed its review of the EPA’s draft assessment in May

6 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document for the Determination of Non - cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology Section, Office o f Environmental Health Hazard Assessment, California Environmental Protection Agency. February 2000 ( http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf )16

of 2011 (http://www.nap.edu/catalog.php?record_id=13142). EPA is reviewing the public comm ents and the NAS independent scientific peer review. EPA will follow the NAS Report recommendations and will present results obtained by implementing the CIIT BBDR model for formaldehyde. EPA will compare these estimates with those currently presented in the External Review draft of the assessment and will discuss their strengths and weaknesses. As recommended by the NAS committee, appropriate sensitivity and uncertaint y analyses will be an integral component of implementing the BBDR model. The draft IR IS assessment will be revised in response to the NAS peer review and public comments and the final assessment will be posted on the IRI S database. In the interim, we will present findings using the 1991 IRIS value as a primary estimate, and may

also consi der other information as the science evolves. 4) Emissions from secondary lead smelting facilities include POM. A substantial proportion of POM reported to EPA’s national emission inventory (NEI) are not s pe ciated int o individual compounds. As a result, it is necessary to appl y the same s implifying a ssumptions to assessments that were used for the 1999 NATA study [ 17]. The NATA approach partitions POM into eight different non - overlapping “groups” that are modeled as separate pollutants. Each POM group comprises POM species of similar carcinogenic potency , for which we can apply the same URE. The emissions inventory

for the Secondary

L ead Smelting source category includes emissions of HAP with availa

b le chr onic quantitative inhalation dose - response values. These HAP, their dose - response values, and the source of the values are listed in Tables 2.6 - 1 (a) through (c).17 **Table 2.6 -**  **1 (a) Dose -**  **Response Values for Chronic Inhalation Exposure to Carcinogens**  URE (unit risk estimate for cancer) 7 = cancer risk p er μg/m 3 of a verage lifetime exposure. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health Hazard Assessm e nt, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality Planning and Standards. EPA ORD = EPA Office of Research and Development **Pollutant CA** **S Number 8**  **URE 5**  **(1/μg/m 3**  **) Source**  1,1,2,2 - Tetrachloroethane 79345 0.000058 IR IS 1,3 - Dichloropropene 542756 0.000004 IR IS 1,3 - Butadiene 106990 0.00003 IR IS 2,3,7,8 - TCDD TEQ 1746016 33 EPA ORD Acetaldehyde 75070 2.2E - 06 IR IS Acrylonitrile 75058 0.000068 IR IS Arsenic 7440382 4.3E - 03 IR IS Benzene 71432 7.8E - 06 IR IS Bis(2 - Ethylhexyl)Phthalate 117817 2.4E - 06 CAL Beryllium 7440417 0.0024 IR IS Cadmium 7440439 0.0018 IR IS Chromium com

pounds Chromi um (VI ) 18540299 0.012 IR IS Ethyl Benzene 100414 2.5E - 06 CAL Formaldehyde 50000 0.000013 IR IS Methylene Chloride 75092 4.7E - 07 IR IS Polycyclic Organic M att e r 246 9 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 0.00 011 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

7 The URE is the upper - bound excess cancer risk estimated to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m 3 in air. URE’s are considered upper bound estimates meaning they represent a plausible upper limit to the true value. 8 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. 9 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/nata1999/99pdfs/pomapproachjan.pdf18

**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 lifetime exposure. Sources : IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality P lanning and Standards. **Pollutant CA** **S Number** 10 **SF (1/mg/kg/d)**  **Source** 2,3,7,8 - Tetrachlorodibenzo - p - dioxin 1746016 150000 EPA ORD 10 Benzo(k)fluoranthene 207089 1.2 CAL Polycyclic Organic Matter Dibenzo[a,h]Anthracene 53703 4.1 CAL In deno [1,2,3 - c,d]Py

rene 193395 1.2 CAL

10 The assessment report for diox in is available in the docket for this Secondary Lead Smelting rulemaking (see: Health Assessment Document for Polychlorinated Dibenzo - p - Dioxins; September, 1985)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 ma gnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of de leterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry. **Pollutant CA** **S Number 6**  **RfC (mg/m3)**  **Source 11**  1,3 - Butadiene 106990 0.002 IR IS -- M 1,3 - Dichloropropene 62737 0.0005 IR IS -- H 2,3,7,8 - TCDD TEQ 1746016 4E - 08 CAL Acetaldeh yd e 75070 0.009 IR IS -- L Acrylonitrile 107131 0.002 IR IS -- M Acrolein 107028 0.00002 IR IS -- L/M Antimony 1309644 (trioxide) 0.0002 IR IS -- L Arsenic 174 0.000015 CAL Benzene 71432 0.03 IR IS -- M Beryl lium 7440417 0.00002 IR IS -- M Bis(2 - Ethylhex yl )Phthalate 117817 0.01 P - CAL Cadmium 176 0.00001 ATSDR Carbon Disulfide 75150 0.7 IR IS -- M Chlorine 7782505 0.00015 D - ATSDR Chlorobenzene 108907 1 CAL Chloroform 67663 0.098 ATSDR Chromium compounds Chromium (VI) 18540299 0.0001 IR IS -- M Cumene 98828 0.4 IR IS -- M Ethyl Benzene 100414 1 IR IS -- L Ethyl Chloride 75003 10 IR IS -- M Formaldeh yd e 50000 0.0098 ATSDR Hexane 110543 0.7 IR IS -- M Hydrochloric Acid 7647010 0.02 IR IS -- L Lead 182 0.00015 EPA OAQPS Manganese 183 0.00005 IR IS -- M Methyl Bromide 74873 0.09 IR IS -- M/H Methyl Chloride 74873 0.09 IR IS -- M/H Methylene Chloride 75092 1 ATSDR Methyl Isobut ylKetone 108101 3 IR IS -- L/M Mercury Compounds Elemental Gaseous Mercury 7439976 0.0003 IR IS -- M

11 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to in dicate the overall level of confidence in the RfC value, as reported in the IRIS file.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 ma gnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of de leterious effects during a lifetime. Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease Registry. **Pollutant CA** **S Number 6**  **RfC (mg/m3)**  **Source 11**  Gaseous Divalent Mercury Particulate Divalent Mercury Naphthalene 91203 0.003 IR IS -- M Nickel 186 0.00009 ATSDR o - Cresol 95487 0.6 (mixed) CAL Phenol 108952 0.2 CAL Propionaldehyde 123386 0.008 IR IS -- L/M Selenium 7782492 0.02 CAL Sty rene 100425 1 IR IS -- M Tetrachloroeth yl ene 79016 0.6 CAL Trichloroeth yl ene 79016 0.6 CAL Toluene 108883 5 IR IS -- H Xylenes (Mixture of o, m, and p Isomers) 1330207 0.1 IR IS m - Xylene 108383 0.1 IR IS -- M o - Xylene 95476 0.1 IR IS -- M p - Xylene 106423 0.1 IR IS -- 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 recommendations of OAQPS for HAPs [ 18 ] . Depending on availability , the results from screening acute assessments are compared to both “no effects” reference levels for the general public, such as the California Reference Exposure Levels (REL s), as well as emergency response levels, such as Acut e Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of an y potential risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and an y limitations expressed therein. Comparisons among different available inhalation health effect reference values (both acute and chronic) for selected HAPs can be found in an EPA document . 12

12 See : U.S. EPA. Graphical Arrays of Chemical - Specific Health Effect Reference Values for Inhalation Exposures [Final Report]. (U.S. Environmental Protection Agency, W ashington, D.C., EPA/600/R - 09/061, 2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)21

**California Acute Reference Exposure L evels (RELs**  **).** The California Environmental Protection Agency

(CalEPA) has developed acute dose - response reference values for man y substances, expressing the results as Acute inhalation 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 . [ 19 ] . REL s are based on the most sensitive, relevant, adverse health effect reported in the medical and toxicological literature. REL s are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety . Since margins of safety are inco rporated to address data gaps and uncertainties, exceeding the REL does not automatically

indicate an adverse health impact. ” Acute RELs are developed for 1 - hour (and 8 - hour) exposures. The values incorporate uncertainty

factors similar to those used in d eriving EPA’s Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs). **Acute Exposure Guideline Levels (AEGLs** ). AEGL s are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) f or Hazardous Substances , and then reviewed and published b y the National Research Council . A s described in the Committee’s “Standing Operating Procedures (SOP) ” ( http://www.epa.gov/opptintr/aegl/pubs/sop.pdf ), AEGLs “represent threshold exposure limits for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.” Their intended application is “for conducting risk asses sments to aid in the development of emergency

preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers.” The document states that “the primary purp ose of the AEGL program and the NAC/AEGL Committee is to develop guideline levels for once - in - a - lifetime , short - term exposures to airborne concentrations of acutel y toxic, high - priority chemicals.” In detailing the intended application of AEGL values, the document states that , “ Itis anticipated 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 conjunction with chemical emergency response, planning, an d prevention programs. More specifically , 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 emergency response actions, for accidental chemic al releases at fixed facilities and from transport carriers.” The NAC/AEGL defines AEGL - 1 and AEGL - 2 as:

“ AEGL - 1 is the airborne concentration (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general population, including s usceptible individuals, could experience notable discomfort, irritation, or certain asy mptomatic nonsensory

effects. However, the effects are not disabling and are transient and reversible upon cessation of exposure. ”

“ AEGL - 2 is the airborne concentratio n (expressed as ppm or mg/m 3 ) of a substance above which it is predicted that the general population, including susceptible individuals, could22

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

“ Airborne concentrations below AEGL - 1 represent exposure levels that can produce mild and progressivel y increasing but transient and nondisabling odor, taste, and sensory irritation or certain as ymptomatic, nonsensory 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 elderly , persons with asthma, and those with other illnesses, it is recognized that individuals, subject to unique or idiosy ncratic responses, could experience the effects described at concentration s below the corresponding AEGL.” **Emergency Response Planning Guidelines (ERPGs).** The American Industrial Hygiene Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [ 20 ] for acute exposures at three different levels of severity . These guidelines represent concentrations for exposure of the general population (but not particularly sensitive persons) for up to 1 hour associated with effects expected 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 fol lows: “ Emergency Response Planning Guidelines (ERPGs) were developed for emergency planning and are intended as health based guideline concentrations for single exposures to chemicals. These guidelines (i.e., the ERPG Documents and ERPG values) are intend ed for use as planning tools for assessing the adequacy of accident prevention and emergency response plans, including transportation emergency planning and for developing community emergency

response plans. The emphasis is on ERPGs as planning values: When an actual 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 nearl y all individuals could be exposed for up to 1 hour without experiencing other than mild transient adverse health effects or without perceiving a clearly defined, objectionab le odor. ”

“ ERPG - 2 is the maximum airborne concentration below which it is believed that nearl y all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or s ymptoms which could impai r an individual's ability to take protective action. ” The emissions inventory

for the Secondary

L ead Smelting source category includes emissions of numerous HAP with relevant and available quantitative acute dose - response threshold values. These HAPs, their acute threshold values, and the source of the value are listed below in Table 2.6 - 2.23

**Table 2.6 -**  **2 Dose -**  **Response Values for Acute Exposure Pollutant**  **CA S 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 Acetaldeh yd e 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 Beryl lium 7440417 0.025 Biphen yl 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 Formaldeh yd e 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 Sty rene 100425 85 550 85 550 21 Tetrachloroeth yl ene 127184 240 1600 240 1600 20 Toluene 108883 750 4500 750 1900 37 Trichloroeth yl ene 79016 700 2400 700 2400 Xylenes (Mixed) 1330207 560 4000 22 m - Xylene 108383 2224



### 2.7 **Risk c**  **haracterization 2.7.1**  **General** The final product of the risk assessment is the risk characterization, in which the information from the previous steps is integrated and an overall conclusion about risk is sy nthesized that is complete, informative, and useful for decision makers. In gen eral, the nature of this risk characterization depends on the information available, the application of the risk information and the resources available. In all cases, major issues associated with determining the nature and extent of the risk are identifi ed and discussed. Further, the EPA Administrator’s March 1995 Policy for Risk Characterization [ 21 ] specifies that a risk characterization “be prepared in a manner that is clear, transparent, reasonable, and consistent with other risk characterizations of similar scope prepared across programs in the Agency .” These principles of transparency

and consi stency

have been reinforced by

the Agency’s Risk Characterization Handbook [ 22 ], in 2002 by the Agency ’s information quality guidelines [ 23 ], and in the OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analy sis 13 , and are incorporated in thes e assessments. Estimates of health risk are presented in the context of uncertainties and limitations in the data and methodology . Through our tiered, iterative analy tical approach, we have attempted to reduce both uncertainty

and bias to the greatest de gree possible in these assessments, within the limitations of available time and resources. We provide summaries of risk metrics (including maximum individual cancer risks and noncancer hazards, as well as cancer incidence estimates) along with a discussi on of the major uncertainties associated with their derivation to provide decision makers with the fullest picture of the assessment and its limitations. For each carcinogenic HAP included in an assessment that has a potency estimate available, individu al and population cancer risks we re calculated by multiplying the corresponding lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is defined as the upper - bound probability of developing cancer over a 70 - y r 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 level may be lower than predicted, and could be zero. For EPA’s list of carcinogenic HAPs that act b y a mutagenic mode - of - action [ 24 ], we applied EPA’s Supplemental Guidance for Assessing Susceptibility from Early
- Life Exposure to Carcinogens [ 25 ] . 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 (f or 70 ye ars of exposure beginning at birth), as needed in risk assessments . In this case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6. Inaddition, although onl y a small fraction of the total PO M emissions may be reported as individual compounds, EPA

13 Memorandum f

or the Heads o f Executive Departments and Agencies - Updated Principles for Risk Analysis ( September 19, 2007 ), From Susan E. Dudley, Admini strator, Office of Information and Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, Office of Science and Technology Policy ( http://www.whitehouse.gov/omb/memoranda/fy2007/m07 - 24 .pdf )25

expresses carcinogenic potency for compounds in this group in terms of benzo[a]py rene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a] py rene. For this reason , EPA implem entation policy [ 26] recommends appl yi ng the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency . Accordingl y, we applied the Supplemental Guidance to all unspeciated POM mixtures. In creased cancer incidence for the entire receptor population within the area of anal ys is wa s estimated by

multiplying the estimated lifetime cancer risk for each census block by the number of people residing in that block, then summing the res ults for the entire modeled domain. This lifetime population incidence estimate wa s divided by 70 years to obtain an estimate of the number of cancer cases per ye ar. In the case of benzene, the high end of the reported cancer URE range was used in our as sessments 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 estimated benzene

– associated risks exceed 1 in a million, we also evaluate d the impact of using the low end of the URE range on our risk results. Unlike linear dose - response assessments for cancer, noncancer health hazards generally are not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects is expressed by

comparing an exposure to a reference level as a ratio. The “hazard quotient” (HQ) is the estimated exposure divid ed b y a reference level (e.g., the RfC). For a given HAP, exposures at or below the reference level (HQ ≤1) are not likel y to cause adverse health effects. As exposures increase above the reference level (HQs increasingl y greater than 1), the potential fo r adverse effects increases. For exposures predicted to be above the RfC, the risk characterization includes the degree of confidence ascribed to the RfC values for the compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impac t of this on possible health interpretations. The risk characterization for chronic effects other 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 gen erally conservative uncertainty 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 adverse health effect outcomes in a population . Screening for potentially

significant 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 values relative to the various acute endpoints and thresholds. In general, when none of these HQ values are greater than one, there is no potential for acute risk. In those cases where HQ values above one are se en, additional information is used to determine whether there is a potential for significant acute risks.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 a ggregate health risks associated with all the exposures from a particular source category combined. To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ [ 27 , 28 ] default assumption of additivity of effects, and combine risks by summing them using the independence formula in the mixtures guidelines. In assessing noncancer hazard from chronic exposures, in cases where different pollutants cause adverse health effects via completel y different modes of action, it may be inap propriate to aggregate HQs. In consideration of these mode - of - action differences, the mixtures guidelines support aggregating effects of different substances in specific and limited way s. Consistent with these guidelines, we aggregated 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 individual HAPs that affect the same organ or organ s ys tem. A ll TOSHI calculations presented here were based exclusively on effects occurring at the “ critical dose” (i.e., the lowest dose that produces adverse health effects). Although HQs associated with some pollutants have been aggregated into more than one TOSHI , this has been done only in cases where the critical dose affects more than one target organ . Because impacts on organs or s ys tems 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. Rather, the TOSHI is another measure of th e potential for adverse health outcomes associated with pollutant exposure, 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 impacts were screened on an individual pollutant basis, not using the TOSHI approach. **2.7.3 Facility**  **- wide**  **r isks**  To help place the source category risks in context, we ty

picall y examine “facility - wide” risks using facility - wide and source category data files developed by

EPA thr ough emission data updates from industry

and refined EPA profiles for the source category . In this instance, there are no other significant HAP emission sources present -- all significant HAP source s have been included in the source category risk anal ys is. Therefore, the facilit y - wide risk is essentially the same as the source category risk; no separate facility - wide analy sis was necessary .27



### 3 **Risk r** **esults for the s**  **econdary l**  **ead s**  **ource c**  **ategory 3.1**  **Source c**  **ategory d**  **escription and r**  **esults** The secondary

lead smelting source category consists of facilities that use blast, rotary , reverberatory , and/or electric furnaces to recover lead metal from lead - bearing scrap materials , primaril y lead - acid batteries. The secondary lead smelting process consists of: (1) breaking lead - acid batteries and separating the lead - bearing materials from the other materials (including the plastic case material and acid electroly te); (2) melting lead metal and reducing lead compo unds to lead metal in the smelting furnace; and (3) refining and alloying the le ad to customer specifications. HAP are emitted from secondary lead smelters as: (1) process emissions contained in the primary exhaust of smelting furnaces; (2) process fugiti ve emissions associated with charging and tapping of smelting furnaces and lead refining kettles; and (3) fugitive dust emissions from wind or mechanicall y induced entrainment of dust from stockp iles, plant yards, and roadway s. A complete description of the secondary lead smelting source category

can be found in the text of the N otice of Proposed Rulemaking (NP RM ): National Emissions Standards for Hazardous Air Pollutants: Secondary L ead Smelting . Th ere are currently 1 5 secondary lead smelters operating in the U.S. and Puerto Rico. The NPRM data set for the secondary lead smelting category

contains 1 5 facilities identified with a secondary

lead smelting MACT code in the 2005 National - Scale Air Toxics Assessment (NATA) National Emissions Inventory (NE I), S eptember 2010 v ersion . The emissions in the secondary lead smelting source category NPRM data set (of 1 5 facilities) are summarized in Table 3.1 - 1 . The total HAP emissions for the source category are approximately

670 tons per year. Based on these data, 22 HAP account for 99 percent of the mass of HAP emitted across the 1 5 facilities in the NPRM data set . Air emissions from secondary lead smelter facilities include a number of toxic air pollutants, including metals (e.g., lead, arsenic, chromium, c admium) and organic and inorganic compounds. Organic toxic air pollutants emitted from secondary lead smelting inc lude compounds such as polycyclic organic matter (POM), sty rene, carbon disulfide, and dioxins/furans.28

**Table 3.1 -**  **1 Summary of Emissions f rom the Secondary Lead Source Category and Availability of**  **Dose -**  **Response Values**

**HAP a**  **Emission**

**Estimates (tpy)**  **Number of**

**Facilities where HAP**  **Repor ted or**  **Estimated (**  **15 facilities**  **in NPRM**  **data set) Prioritized Inhalation Dose**  **- Response Value**  **Identified by OAQPS**  **b PB**  **** **HAP? Unit Risk**  **Estimate for Cancer?**  **Reference**

**Concentration for Noncancer?**  **Health**

**Benchm ark**  **Values for Acute Noncancer?**  Styrene 361 14   Carbon Disulfide 96 14   Methyl Isobutyl Ketone 50 14  p - Xylene 36 14   Methyl Chloride 33 14   Lead compounds 20 15 c  Benzene 11 14    Toluene 11 14   Bis(2 - Ethylhexyl)Phthalate 8 14   1,3 - Butadiene 5 14    Acetaldehyde 4 14    Biphenyl 4 14  m - Xy lene 3 14   Arsenic compounds 3 15    n - Hexane 3 14   Ethyl Benzene 2 14    Phenol 2 14   Hydrochloric Acid 2 7   Naphthalene 2 14   Ethyl Chloride 2 14  Acrolein 2 14   o - Xylene 1 14   Methyl Bromide 0.9 14   Antimony com

pounds 0.8 15  Acetophenone 0.8 14 Chlorobenzene 0.7 14   Methylene Chloride 0.5 14    Selenium compounds 0.5 15  Polycyclic Organic Matter POM 77002 0.5 14   POM 76002 0.2 14   POM 75002 0.0009 14   POM 71002 0.0003 1   POM 72002 0.000003 4   POM 73002 0.0000007 3   Methyl Iodide 0.4 14  Manganese compounds 0.4 15  Cadmium compounds 0.4 15   29

**Table 3.1 -**  **1 Summary of Emissions f rom the Secondary Lead Source Category and Availability of**  **Dose -**  **Response Values**

**HAP a**  **Emission**

**Estimates (tpy)**  **Number of**

**Facilities where HAP**  **Repor ted or**  **Estimated (**  **15 facilities**  **in NPRM**  **data set) Prioritized Inhalation Dose**  **- Response Value**  **Identified by OAQPS**  **b PB**  **** **HAP? Unit Risk**  **Estimate for Cancer?**  **Reference**

**Concentration for Noncancer?**  **Health**

**Benchm ark**  **Values for Acute Noncancer?**  o - Cresol 0.3 14  1,3 - Dichloropropene 0.3 14   Formaldehyde 0.2 14    Nickel compounds 0.1 15    Mercury c ompounds Mercury (elemental) 0.1 15    Mercuric Chloride 0.04 15   Chloroform 0.1 14   Cumene 0.1 14   Acrylonitrile 0.08 14    Chromium compounds Chromium ( III ) 0.08 15 Chromium (VI) 0.002 15   Dibutylp hthalate 0.04 14 Beryllium com

pounds 0.001 15    Xylenes ( mixed) 0.0004 2   p - Dichlorobenzene 0.00005 3   2,3,7,8 - TCDD TEQ 0.00004 14    Cobalt compounds 0.00001 5 

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 information, 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 “chromium (III) compound s” and about 1 percent “chromium (VI) compounds.” Chromium speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/net/2002inventory.htm;#inventorydata .  For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this category as 80 percent “mercury (elemental)” 10 percent “mercuric chloride , ” and 10 “percent parti culate bound mercury” Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions inventories at http://www.epa.gov/ttn/chief/n et/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 Transfer Network website for the 2002 National - Sca le 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 Air Quality Standards (NAAQS) for lead (0.15 μg/m3 ). These NAA QS for lead were recently reviewed 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,30

rolling three - month average, measured as total suspended particles (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 limit. The emissions estimates presented in this table are based on the assumption that all the non - detect test values were at the level of the detection limit. Therefore, these estimated emissions for mercury are clear overestimates. We conclude that the true amounts of emi ssions 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 r**  **isk c**  **haracterization** This section presents the results of the risk assessment for the secondary lead smelt ing 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 target organ specific chronic hazard index, and the cancer incidence. We also present results from our acute inhalation impact screening assessments in the form of maximum hazard quotients, as well as the results of our screen for potential non - inhalation risks from PB HAP. Also presented are the HAP “drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer risk or maximum hazard at the highest exposure location, A detailed summary of the facility - specific risk assessment results is available in Appendix 5 . Table s 3 .2 - 1 and 3.2 - 2 summarize the preliminary chronic and acute inhalation risk results for this source category . T he results of the chronic inhalation cancer risk assessment estimate the maximum lifetime individual cancer risk , considering both fugitive and stack emissions could be as high as 5 0 i n a m illion. The maximum cancer risks for this source category were equal to or exceeded a cancer risk of 1 in a million at all 1 5 facilities, with fugitive dust emissions of arsenic and cadmium dominating the risk. The total estimated cancer incidence from th is source category based on actual emission levels is 0.0 2 e xcess cancer cases per year, or one excess case in eve ry 50 years. Approximately 700 people were estimated to have cancer risks above 10 in a million and approximately 80 ,000 people were estimated to have cancer risks above 1 in a million considering all facilities in this sourc e category . Considering MACT allowable emissions, the MIR could be as high as 200 driven b y arsenic emissions from stacks . With respect to chronic inhalation non cancer risk, we estimate a maximum TOSHIvalue of 0. 6 for the secondary

lead source category , with fugitive dust emissions of cadmium and arsenic dominating those impacts. Incontrast, we note that considering MACT allowable emissions, we estimate a maximum TOSHIvalue of 3 , driven primaril y b y emissions of arsenic from stacks.31

**Table 3.2 -**  **1 Summary of Source Category Level Inhalation Risks f**  **or Secondary Lead Smelting**  **Result HAP “Drivers”**  **Facilities in Source Category** Number of Facilities Estimated to be i n Source Category 1 5 n/a Number of Facilities Identified in NEI and Modeled in Risk Assessment 1 5 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 5 arsenic compounds, cadmium compounds, 2,3,7,8 - tetrachlorodibenzo -p - dioxin Greater than or equal to 1 in 1 million 15 arsenic compounds, cadmium compounds, 2,3,7,8 - tetrachlorodibenzo -p - dioxin, 1,3 -

butadiene **Non -**  **lead Chronic Noncancer Risks**  Non - lead Maximum Hazard Index 0.6 0.6 cadmium com pounds arsenic compounds **Acute Noncancer Screening Results** Maximum Acute Hazard Quotient 20 arsenic compounds (REL) 14 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 7 00 n/a Greater than or equal to 1 in 1 million 80 ,000 n/a Estimated Cancer Incidence (excess cancer cases per year) 0.02 n/a Contribution of HAP to Cancer Incidence arsenic compounds 74 % n/a cadmium compounds 13% n/a 2,3,7,8 - tetrachlorodibenzo - p - dioxin 7% n/a

14 The REL is the only available acute health benchmark value for arsenic. All other pollutants had HQ values less than or equal to 1.32

Based upon the REL , the only available acute health benchmark value for emissions of arsenic, screening - level acute HQ value s could exceed a value of 1 at 9 facilities. In addition, this worst - case screening anal ys is estimates that the maximum acute HQ value for a facility in this source category

could be up to 2 0 (based on the RELfor arsenic) , at two facilities in this source category largel y due to stack emissions of arsenic at the Exide Baton Rouge facility and due to a combination of stack and fugitive dust emissions of arsenic at the Doe Run facility . 15 In the risk assessment to support the proposed rule, to identif y potential multipathway health risks from PB - HAP other than lead we first performed a screening analy sis that compared emissions of PB - HAP emitted from the secondary lead source category to multipathway screening emission rates (see section 2.5). In doing so, we estimated that emissions of cadmium , POM, and dioxin were above their respective screening emission rates . In addition, based on using high - end, worst - case assumptions for mercury (e.g., assuming that all non detect emissions test values are equal to the detection limit) we estimated that mercury emissions could theoretically be emitted over the s creening level emissions rate. Therefore, to be all inclusive, we also included mercury along with POM, cadmium and dioxins for the detailed multipathway

analy ses to support th e proposed rule . More specifically , in the multipathway

anal ys es to support the proposed rule, we performed two detailed case study anal ys es for these 4 PB - HAP in areas near the Exide Technologies (Frisco, TX) and Revere Smelting & Refining (Middletown, NY) facilities 16

. Inthese case study anal yses , in order to more fully

char acterize the potential multipathway risks associated with high end consumption of PB - HAP contaminated food , we present ed results based on RME and CTE scenarios. The RME scenario ut ilize d 90 th percentile ingestion rates for farmers, recreational anglers, and for three subpopulations of recreational anglers) who have higher rates of fish consumption (Hispanic, Laotian, and Vietnamese descent), while the CTE scenario utilize d mean inge stion rates for each of these groups . In the detailed multipathway anal ys is and risk assessment to support the proposed rule , w e provide d results from both scenarios to illustrate the range of potential modeled exposures and risks that may exist in the hi gh - end of the complete distribution of potential multipathway risks for th e secondary

lead smelting source category . During the public comment period for the proposed rule, the Exide Frisco facility (i.e., one of the two case study facilities included in the detailed multipathway anal ys is to support the proposed rule) submitted updated emissions information relevant to the multipathway anal ys is. To update the multipathway case study

assessment for the Exide, Frisco facility we
15 Individual facility acute HQ values for all facilities can be found in Appendix 5, Table 3. Acute HQ values exce eding a value of 1 were as follows: 2, 2, 2, 2, 3, 4, 5, 20 , and 20 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 selecting 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 ele vation, and geographic representativeness of sites throughout the US.33

scaled the cancer and non - cancer multipathway risks from the initial assessment based on the updated facility

emissions information. As a result, we now estimate for both the RME and CTE scenarios, that the MIR cancer risk from POM and dioxins is less than 1 in a million for both cas e stud y facilities (note: there is no cancer slope factor for mercury and cadmium and thus, no cancer MIR was calculated). With respect to chronic noncancer risk, in both case studies, using both exposure scenarios, we did not estimate chronic HQ values g reater than 1 for dioxin, mercury

(even using the conservative emission assumptions just mentioned above) or cadmium (note: there is no chronic noncancer reference value for POM and thus, a chronic noncancer HQ value was not calculated). As noted above (see section 2.5), in evaluating the potential for mul ti pathway effects from emissions of lead, modeled maximum 3 - month rolling average lead concentrations were compared to the NAAQS for lead. Results of this anal ys is , presented in Table 3.2 - 3 , estimate that the NAAQS for lead could be exceeded at about 9 of 1 5 facilities, largely due to fugitive dust emissions. At all 9 facilities , fugitive dust emissions ac count for about 94 to 99 % of the estimated 3 - month maximum lea d concentrations . 17 Moreover, this analy sis estimates that 3 - month maximum lead concentrations could be up to about 20 times the NAAQS for lead 18 and that more than half these facilities have estimated 3 - month maximum lead concentrations that are at least 2 times higher than the NAAQS lead . In addition, our anal ys i s estimates that approximately

2 00 people around 3 secondary lead smelting facilities live i n areas where 3 - month maximum lead concentrations are estimated to be about 1 to 3 times the lead NAAQS 19 . Finally

a s mentio ned 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 - based standard). Thus, the analy ses 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 concentrations associated with MACT allowable stack emissions would be significantly higher than those just discussed . For example, we estimate that based on allowable emissions from stacks alone , estimated le ad levels could be about 8 and 10 times above the NAAQS at a couple of facilities.

17 For all facilities, the percent contribution of fugitive and stack emissions to modeled ambient lead concentrations has only been estimated for the model receptor representing the site of maximum lead impact. 18 Fugitive emissions are estimated to account for about 99% of this facility’s modeled lead concentration at the site of maximum lead impact (see previous footnote). 19 The highest modeled 3 - month ambient lead concentration at c ens us blocks around the Sanders Lead Co., Buick Resource Recycling Facility, and Exide Technologies, Reading Recycling are estimated to be up to 3, 2, and 2 times the NAAQS for lead respectively. All other facilities had 3 - mo nth ambient lead concentrations at all census blocks below the NAAQS for lead .34

**Table 3.2 -**  **3 Secondary Lead Smelting Facility Modeled**  **Maximum Ambient Lead Concentrations (rolling three**  **- month average values)** 
**Facility Nam e**  **City State**  **Highest Modeled**  **Lead Concentration**  **(μg/m 3**  **) Concentration is** 

**X tim es higher**  **than the NAAQS 20**  Buick Resource Recycling Facilit y, LLC Buick Mill BOSS MO 2. 36 20 Sanders Lead Co. TROY AL 2.16 10 Exide Technologies Vernon Ca VERNON CA 1.14 8 Exide Technologies, Baton Rouge, La BATON ROUGE LA 0.14 0.9 EnviroFocus Technologies, LLC TAMPA FL 0.38 3 Exide Technologies, Cannon Hollow Recycling Facilit y FOREST CITY MO 0.47 3 Exide Technologies, Reading Recycling READING PA 0.25 2 Gopher Resource , LLC EAGAN MN 0 .35 2 Exide Technologies, Frisco TX FRISCO TX 0. 23 2 Quemetco, Inc. City

of Industry , Ca INDUSTRY CA 0.17 1 Revere Smelting and Refining corporation MIDDLETOWN NY 0.10 0.7 Exide Technologies, Muncie, In. MUNCIE IN 0.15 1 East Penn Mfg. Co Inc LYON STATION PA 0.02 0.1 Quemetco, Inc, Indianapolis In INDIANAPOLIS IN 0.07 0.5 The Battery

Recycling Co. A RECIBO PR 0.76 5 

### 3.3 **Post -**  **control risk characterization** As noted above, modeled lead concentrations exceed the NAAQS for lead at 9 of the 15 facilities in this source category . Fugitive dust emissions are primaril y driving exceedences of the lead NAAQS, as well as the cancer MIR value of 5 0, and substantially contributing to acute HQ values greater than 1 (both based primarily 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 adopted control measures to limit fugitive dust emissions. More specifica lly , at each of these facilities, we estimated risks considering a decrease in fugitive dust emissions resulting primaril y from the assumption that all facilities would be fully enclosed with negative pressure air inflow to a control device plus implementation of

20 Results in this column are presented to 1 significant figure.35

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 Run and Exide Technologies Baton Rouge Smelter. Based on this scenario, we estimated (based on use of the same modeling assumptions and methodologies used for the baseline risk assessment) that all but 2 facilities would meet the NAAQS for lead. Of these 2 facil ities, 1 is projected to be very close to the NAAQS for lead (i.e., 3 - month max imum modeled lead concentration is estimated to be 0.17 μgs/m 3 ) . The other facilit y ( Exide , CA) is estimated to be about 3 times above the NAAQS for lead (i.e., 3 - month maximum modeled lead concentration is estimated to be 0.47 μgs/m 3 ) . At both these fac ilities, emissions of fugitive dust account for about 99% of the estimated lead concentrations at the site of maximum lead impact (see footnote 17). Moreover, post - control we estimate that it is likely that no people would be living in areas above the NAA QS for lead. With respect to cancer risk, given the fugitive controls mentioned above, the cancer MI R would be reduced from 5 0 in a million (i.e., pre - controls) to approximately 7 in a million (i.e., post - controls), with an estimated reduction in cancer incidence to 0.01 excess cancer cases per ye ar. We finally

note that post - control, the maximum worst - case acute screening HQ value would be reduced from a potential high of 2 0 (i.e., pre - controls , based on the REL for arsenic ) to 5 (i.e., post - controls , based on the RELfor arsenic ). Inaddition , post - control th ere would onl y be 1 other facility

in the source category

with a max imum worst - case acute refined HQ value greater than 1 (i.e., base d on the RELfor arsenic, Doe Run would have an estimated acute HQ value of approximately 2) . 

### 4 **General d**  **iscussion of u**  **ncertainties and h**  **ow t**  **hey h**  **ave b**  **een a**  **ddressed 4.1**  **E xposure**  **m odeling**  **u ncertainties**  Although every

effort has been made to identify all the rele vant facilities 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 likel y dominate the uncertainties in our exposure modeling estimates. The chronic exposure modeling uncertainties are considered relativel y small in comparison, since we are using EPA’s refined local dispersion model with site specific parameters and reasonabl y representative meteorology. If an ything, the population exposure estimates are biased high b y not accounting for short - or long - term population mobility , and b y neglecting processes like deposition, plume depletion, and atmospheric degradation. Additionally , estimates of the maximum individual risk (MIR) contain uncertaint y , because they are deriv ed at census block centroid locations rather than actual residences. This uncertainty is known to create potential underestimates and overestimates of the actual MIR values for individual facilities, but, overall, it is not thought t o have a significant impact on the estimated MIR for a source category . Finally , we did not factor in the possibility

of a source closure occurring during the 70 - ye ar chronic exposure period, leading to a potential upward bias in both the MIR and populati on risk estimates; nor did we36

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 population risk estimates. A sensitivity

anal ys is performed for the 1999 NATA found that the selection of the meteorology

dataset location could result in a range of chronic ambient concentrations which varied from as much as 17% below the predicted value to as much as 84% higher than the predicted value. This variability translat es directly 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 purposel y biased the acute screening results high, considering that they depend upon the joint occurrence of independent factors, such as hourly emissions rates, meteorology and human activity

patterns. However, we note that there is an unquantified level of uncertainty regarding the emission estimates and the nature of the worst - ca se conditions for estimating the worst - case impacts of acute emissions of fugitive dusts. The current set of assumptions used in deriving the estimate assumes the average hourly emission level (annual emissions divided by 8760 hours per y ear) to occur at the default worst - case meteorological conditions (low winds with a stable atmosphere). Itis acknowledged that the combination of average emissions during low winds would be an overestimate of the fugitive dust emission rate during those low wind periods. T herefore, 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 higher 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 va lues , erring on the side of overestimating potential health risks from acute exposures. In the cases where these results indicated the potential for exceeding short - term health thresholds post controls , we refined our assessment by developing a better und erstanding of the geography of the facility

relative to potential exposure locations. In this case, refining the assessment for the two facilit ies that had HQ values greater than 1 post - controls resulted in lower acute risks. As noted in section 2.5, two facilities were chosen as case study anal ys es to assess potential multipathway

risks for mercury , cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions rates of PB - HAPs, proximity to water bodi es, proximity

to farmland, average rainfall, average wind speed and direction, smelting furnace t ype, local change in elevation, and geographic representativeness of sites throughout the US. However, there is uncertaint y as to whether these two facilities represent the highest potential for multipathway human health risks from the source category . S ince the modeling used in these case study assessments utilize site specific parameters to describe naturall y occurring ph ys ical, chemical and biological proc esses, we believe that the multimedia concentrations of PB - HAPs generated in this analy sis are unbiased estimates of the true impacts. With respect to the risk estimates generated fro m this analy sis, we present results based on two ingestion exposure sc enarios: the RME and CTE scenarios (see section 2.5) . As noted37

above, w e 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 expo sure s and risk s, in addition to utilizing fish consumption rate data for the general US population of recreational anglers 21 , we used fish consumption information for distinct fisher subpopulations that are known to have higher fish consumption rates. The data were obtained from Shilling , et al. ( 2010 ) . 22 In this publication, the authors provide fish consumption information for different ethnic groups including Hi spanics, Laotians, and Vietnamese survey ed in California’s Central Valley Delta based on sample sizes of 45, 33, and 30, respectivel y . We note that there is uncertainty 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 capita fish ingestion rate (47.2 g/day) is somewhat less than the mean fish ingestion rate (70 g/day) for native Am erican subsistence populations recommended by

the EPA’s 1997 Exposure Factors Handbook 23 and the 90 th percentile per capita fish ingestion rate calculated from the Shilling study and used in this RTR assessment (144.8 g/day) is somewhat less than the 95 th p ercentile fish ingestion rate recommended b y the EPA’s 1997 Exposure Factors Handbook for native American subsistence populations . While comparing local per capita fish ingestion to total ingestion is a somewhat imperfect comparison, it does help us gauge ho w the values in the Shilling study compare to other existing data. All of these fish ingestion rates are significantl y greater than the 90 th percentile fish ingestion rate acro ss all recreational anglers. We further note that h igh - end fisher populatio ns could display considerable variability

both in terms of the degree to which they frequent specific waterbodies or watersheds and the degree to which they

target specific ty

pes of fish (or at least sizes of fish). Both of these factors can impact estimat es of exposure. If a fisher population distributes their activity across a range of waterbodies and harvests a variet y of fish species (and sizes) than the distribution of exposure and risk across that population will be smaller compared with a population that focuses activity at individual waterbodies and tends to focus on larger fish. A more detailed discussion of the multipathway analy sis and its associated uncertainties is presented in section 5.3 of the document Human Health Multipathway Residual Risk Assessment for the Secondary L ead Smelting Source Category , which can be found in the docket for th e proposed rule.

21 Data for the general U.S. population of recreational 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. 22 Shilling et., al. 2010 is available in the docket for this rulemaking 23 EPA’s 1997 Exposure Factors Handbook is available at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=1246438



### 4.2 **Uncertainties in the**  **d ose**  **- r** **esponse r** **elationships** Inthe sections that follow, separate discussions 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 day s), and subchronic (>30 day s up to 10% of lifetime) exposure durations, all of which are derived based on an assumption of continuous exposure throughout the duration specified. For the purposes of assessing all potential health risks associated with the emissions included in an assessment, we rel y on both chronic (cancer and noncancer) and acute (noncancer) benchmarks, which are described in more detail below. Although every

effort is made to identify peer - review ed dose - response 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 acute effects (inhalation or inges tion) . Since exposures to these pollutants cannot be included in a quantitative risk estimate, an understatement of risk for these pollutants at environmental exposure levels is possible. Additionally , chronic dose - response values for certain compounds i ncluded in the assessment may be under EPA IRI S review and revised assessments 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 e nough to indicate that the risk assessment may significantl y mischaracterize 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 currentl y used by the EPA to develop cancer potency

factors. In general, these same uncertainties attend the development of cancer potency

factors by

CalEPA, the source of peer - reviewed cancer potency factors used where EPA - developed values are not yet available. To place this discussion in context, we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [ 29 ] (herein referred to as Cancer Guidelines ). “The primary goal of EPA actions is protection of human health; accordingl y, as an Agency policy , risk assessment procedures, including default options that are used in the absence of scientific data to the contrary , should be health protective.” The approach adopted in this document is consistent with this approach as describe d in the Cancer Guidelines . For cancer endpoints EPA usually derives an oral slope factor for ingestion 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 Cancer39

Guidelines , EPA uses animal cancer bioassays as indicators of potential human health risk when other human cancer risk data are unavailable. Extrapolation of study

data to estimate potential risks to human populations is based upon EPA’s assessment of the scientific database for a pollutant us ing EPA’s guidance documents and other peer - reviewed methodologies. The EPA Cancer Guidelines describes the Agency ’s recommendations for methodologies for cancer risk assessment. EPA believes that cancer risk estimates developed following the procedures described in the Cancer Guidelines and outlined below generally

provide an upper bound estimat e 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). 24 In some circumstances, the true risk could be as low as zero; howeve r, in other circumstances the risk could also be greater. 25 When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generall y relies on conservative default approaches. 26 EPA also uses the upper boun d (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 anal ys is). Such health risk assessments have associated uncertainties, some which may be considered quantitatively , and others which generally are expressed qualitativel y. Uncertainties may vary substantially

among cancer risk assessments associated with exposures t o different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures emplo yedmay differ in how well they

represent actual biological processes for the assessed substance. EPA’s Risk Characte rization Handbook also recommends that risk characterizations present estimates demonstrating the impact on the assessment of alternative choices, data, models and assumptions [ 30 ]. Some of the major sources of uncertaint y and variability

in deriving cance r risk values are described more full y below. (1) The qualitative similarities or differences between tumor responses observed in experimental animal bioassay s and those which would occur in humans is a source of uncertaint y in cancer risk assessment. In general, EPA does not assume that tumor sites observed in an experimental animal bioassay are necessarily

predictive of the sites at which

24 IRIS glossary (www.epa.gov/NCEA/iris/help_gloss.htm). 25 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, and which is based on maximum likelihood estimates. 26 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowledge 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 underestimated (although defaults are n ot intended to overtly overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, EPA/100/B - 04/001 available at: http://www.epa.gov/osa/pdfs/ratf - final.pdf.40

tumors would occur in humans. 27 However, unless scientific support is available to show otherwise, EPA assumes th at tumors in animals are relevant in humans, regardless of target organ concordance. For a specific pollutant, qualitative differences in species responses can lead to either under - estimation or over - estimation of human cancer risks. (2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead to differences in risk predictions. For example, the measure of dose is commonly expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, dat a may support development of a pharmacokinetic model for the absorption, distribution, metabolism and excretion of an agent, which may result 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 choice of a dose metric is uncertain or when dose metric estimates are themselves uncertain (e.g., as can occur when alternative pharmacokinetic models are available for a compound). Un certainty 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 methodologies (relating expected response to differences in phy sical size of the species), which introduce another source of uncertaint y. These methodologies are based on both biological data on differences in rates of process according to species size and empirical comparisons of toxicity between experimental animals and humans. For a particular pollutant, the quantitative 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 as sociated 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) anal ys is 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 calculation. Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed thou gh use of the 95% lower confidence limit on the dose at which the specified excess risk occurs (the BMDL), decreasing the likelihood of understating risk. EPA has generall y utilized the multistage model for estimation of the BMDL using cancer bioassay dat a (see further discussion below). (5) Extrapolation from high to low doses is an important, and potentially large, source of uncertaint y 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 epidemiological studies at higher dose) depending on the available data and understanding of a pollutant’s mode of action (i.e., the manner in which a pollutant cause s cancer). EPA’s Cancer Guidelines express a preference for the use of reliable, compound - specific, biologicall y - based risk models when feasible; however, such models are rarel y available. The mode of action for a pollutant (i.e., the manner in which a

27 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.”41

p ollutant causes cancer) is a key consideration in determining how risks 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 linear (e.g., nonthreshold response) or as the most common default approach when a compound’s mode of action is unknown. L inear extrapolation can be supported by both pollutant - specific data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally consider a linear dose - response to be appropriate for pollutants that interact with DNA and induce mutations. Pollutants whos e effects are 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 estimated by the straight line approach. EP A most frequently

utilizes a linear low - dose extrapolation approach as a baseline science policy 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 un certainty and variability . EPA believes that linear dose - response models, when appropriately applied as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and generall y provide a health protective approach. Note that an other source of uncertaint y is the characterization of low - dose nonlinear, non - threshold relationships. The National Academy of Sciences has encouraged the exploration of sigmoidal ty pe functions (e.g., log - probit models) in representing dose response rel ationships due to the variability in response within human populations. Another National Research Council (NRC) report [ 31 ] suggests that models based on distributions of individual thresholds are likel y 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 individual tolerances to vary extensively

in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibil ity

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 response in human populations (as opposed to responses in bioassay s with geneticall y more uniform rodents). Note also that low dose linearit y in risk can arise for reasons separate from population variability : due to the nature of a mode of action and additivity of a chemical’s effect on top of background che mical exposures and biological processes. As noted above, EPA’s current approach to cancer risk assessment ty pically utilizes a straight line approach from the BMDL . This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation. The impact of the choice of the BMDLon bottom line risk estimates can be quantified by comparing risk estimates using the BMDLvalue to central estimate BMD values, although these differences are generall y not a large contributor to uncert aint y in risk assessment (Subramaniam et. al., 2006) [ 32 ]. 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 e nvironmental dose levels and did not involve the use of a POD. Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on straight line extrapolation from a POD do not42

show large differences from those based on the upper confidenc e limit of the multistage model. (6) Cancer risk estimates do not generall y make specific adjustments to reflect the variability in response within the human population

— resulting in another source of uncertainty in assessments. In the diverse human pop ulation, some individuals are likely to be more sensitive to the action of a carcinogen than the typ ical individual, although compound - specific data to evaluate this variability are generally

not available. There may also be important life stage differenc es 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 generall y quantitatively address life stage differen ces. However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response from results observed in the most sensitive species and sex tested, resulting ty pically in the highest URE which can be supported by reliable data, thus supporting estimates that are designed not to underestimate risk in the face of uncertainty

and variability . **Chronic noncancer assessment** Chronic noncancer reference values represent chronic exposure levels that are i ntended to be health - protective. That is, EPA and other organizations 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 o f uncertainty and variability . When there are gaps in the available information, 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 28 e.g., factors of 10 or 3, used in the absence of compound - specific data; where data are available, uncertainty factors may also be developed using compound - specific information. When data are limited, more assumptions are needed and more default factors are used. Thus there may be a greater tendency to overestimate risk

—in the sense that further study might support development of reference values that are higher (i.e., less potent) because fewer default assumptions are needed. Ho wever, for some pollutants it is possible that risks may be underestimated. For non - cancer endpoints related to chronic exposures, EPA derives a Reference Dose (RfD) for exposures via ingestion, and a Reference Concentration (RfC) for inhalation exposures . 28 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are generic approaches, based on general scientific knowledge and policy judgment, that are applied to various elements of the risk - assessment process when the correc t 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 underestim ated (although defaults are not intended to overtly overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices, EPA/100/B - 04/001 available at: http://www.epa.gov/osa/pdfs/ratf - final.pdf.43

These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime. 29 To derive values that are intended to be “without appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach (U.S. EPA, 1993, 1994) which includes consideration of both uncertaint y and variability . EPA begins b y evaluating all of the available peer - reviewed literature to determine non cancer endpoints of concern, evaluating the quality, strengths and limitations of the available studies. EPA ty

picall y chooses the rel evant endpoint that occurs at the lowest dose, often using statistical modeling of the available data, and then determines the 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 dose (BMD) approach; (2) use of the dose or concentration at which the toxic response was not significantl y elevated (no observed adverse effect level

— NOAEL); or (3) use of the lowest observed adverse effect l evel (LOAEL). A series of downward adjustments using default UFs is then applied to the POD to estimate the reference value (U.S. EPA 1994, 2002). While collectively termed “UFs”, these factors account for a number of different quantitative considerations 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 the members of the human population (i.e., inter individual variability); (2) uncertainty in extrapo lating from experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with less - than - lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) uncertainty in e xtrapolating from 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, de fault adjustment values are selected for the individual UFs. For each t yp e of uncertaint y (when relevant to the assessment) , EPA ty pically 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 data do not support the use of a 10 - fold factor. If an extrapolation step or adjustment is not relevant 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) Heterogeneit y among humans is a key source of variability

as well as uncertaint y. Uncertaint y related to human variation is considered in extrapolating doses f rom a subset or smaller - sized population, often of one sex or of a narrow range of life stages (ty pical of occupational epidemiologic studies), to a larger, more diverse population. In the absence of pollutant - specific data on human variation, a 10 - fold U F is used to account for uncertainty associated with human variation. Human variation may be larger or smaller; however, data to examine the potential magnitude of human variability are often unavailable. In some situations, a smaller UF of 3 may be appl ied to reflect a known lack of significant variability among humans. 29 See IRIS glossary44 2) Extrapolation from results of studies in experimental animals to humans is a necessary step for the majority

of chemical risk assessments . When interpreting animal data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. rodents) is extrapolated to estimate the human response. While there is long - standing scientific support for the use of animal studies as indicators of potential toxicity to humans , ther e are uncertainties in such extrapolations. Inthe absence of data to the contrary , the ty pical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the average human. Ty picall y , c ompound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertaint y in this extrapolation. Size - related differences (allometric relationships) indicate that ty picall y humans are more sensitive than rode nts when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is consistent with these differences. For a s pecific chemical, differences in species responses may be greater or less than this value . Pharmacokinetic models are u seful to examine species differences in pharmacokinetic processing and associated uncertainties; however, such dosimetric adjustments are not alway s possible. Information may not be available to quantitatively assess toxicokinetic or toxicody namic differe nces between animals and humans, and in many cases a 10 - fold UF (with separate factors of 3 for toxicokinetic and toxicody namic components) is used to account for expected species differences and associated uncertainty in extrapolating from laboratory

anim als to humans in the derivation of a reference value. If information on one or the other of these components is available and accounted for in the cross - species extrapolation, a UF of 3 may be used for the remaining component. 3) In the case of referenc e values for chronic exposures where onl y data from shorter durations are available (e.g., 90 - day subchronic studies in rodents) or when such data are judged more appropriate for development of an RfC, an additional UF of 3 or 10 - fold is ty picall y applied unless the available scientific information supports use of a different value. 4) Toxicity

data are t ypically limited as to the dose or exposure levels that have been tested in individual studies; in an animal study , for example, treatment groups may diff er in exposure by

up to an order of magnitude. The preferred approach to arrive at a POD is to use BMD analy sis; however, this approach requires adequate quantitative results for a meaningful anal ys is, which is not alway s possible. Use of a NOAEL is the next preferred approach after BMD analy sis in determining a POD for deriving a health effect reference value. However, many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a NOAEL is not identified). When using dat a limited to a LOAEL, a UF of 10 or 3 - fold is often applied. 5) The database UF is intended to account for the potential for deriving an underprotective RfD/RfC due to a data gap preventing complete characterization of the chemical’s toxicity . In the ab sence of studies for a known or suspected endpoint of concern, a UF of 10 or 3 - fold is ty

pically applied.45

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 , ambient lead concentrations were compared to the NAAQS for lead. In developing the NAAQS for lead, EPA considered human health evidence reporting adverse health effects associated with lead expos ure, as well as an EPA conducted multipathway

risk assessment that applied models to estimate human exposures to air - related lead and the associated risk (73FR at 66979). EPA also explicitly considered the uncertainties associated with both the human health evidence and the exposure and risk anal ys es when developing the NAAQS for lead. For example, EPA considered uncertainties in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as uncertainties between blood lead lev els and loss of IQ points in children (73FR at 66981). In considering the evidence and risk anal ys es and their associated uncertainties, the EPA Administrator noted his view that there is no evidence - or risk - based bright line that indicates a single appr opriate level. Instead, he noted, there is a collection of scientific evidence and judgments and other information, including information about the uncertainties inherent in many relevant factors, which needs to be considered 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 μg/m 3 , in combination with the specified choice of indi cator, averaging time, and form, is requisite to protect public health, including the health of sensitive groups, with an adequate margin of safety

(73FR at 67006). A thorough discussion of the health evidence, risk and exposure anal ys es, and their associ ated 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 IR IS program reviewed the health effects data regarding lead and its inorganic compounds and determined that it woul d be inappropriate to develop an RfD for these compounds, saying, “A great deal of information on the health effects of lead has been obtained through decades of medical observation and scientific research. This information has been assessed in the develop ment of air and water qualit y criteria by the Agency's Office of Health and Environmental Assessment (OHEA) in support of regulatory decision - making by

the Office of Air Qualit y Planning and Standards (OAQPS) and by the Office of Drinking Water (ODW). By

c omparison to most other environmental toxicants, the degree of uncertainty about the health effects of lead is quite low. It appears that some of these effects, particularl y changes in the levels of certain blood enzy mes and in aspects of children's neurob ehavioral development, may

occur at blood lead levels so low as to be essentiall y without a threshold. The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for inorganic lead.” EPA’s IRI S 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 direct inhalation exposures and indirect air related multi - pathway

exposures from industrial sources like primary and secondary

lead46

smelting operations. It also considere d background lead exposures from other sources (like contaminated drinking water and exposure to lead - based paints). In revising the NAAQS for lead, we note that the Administrator placed more weight on the evidence - based framework and less weight on the r esults from the risk assessment, although he did find the risk estimates to be roughly

consistent with and generally supportive of the evidence - based framework applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for lead to pr otect public health with an adequate margin of safet y, EPA considered both the evidence - based framework and the risk assessment, albeit to different extents. **Acute noncancer assessment** Many of the UFs used to account for variabilit y and uncertaint y in the development of acute reference values are quite similar to those developed for chronic durations, but more often using individual UF values that may be less than 10. UFs are applied ba sed on chemical specific or health effect - specific information (e.g., simple irritation effects do not vary appreciably

between human individuals, hence a value of 3 is ty picall y used), or based on the purpose for the reference value (see the following paragraph). The UFs applied in acute reference value derivation include: 1) heterogeneity among humans; 2) uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 4) uncertainty

in accounting for an incomplete database on toxic effects of potential concern. Additional adjustments are often applied to account for uncertainty in extrapolation from observations at one exposure duration (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 assessment of human health effects relative to the reference value or values being exceeded. Where relevant to the estimated exposures, the lack of threshold values at different levels of severity should be factored into the risk characterization as potential uncertainties.47



### 5 **References** 1 . US EPA. National Emission Standards for Hazardous Air Pollutants . http://www.epa.gov/ttn/atw/mactfnlalph.html . 2 US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For Review by

the EPA’s Science Advisory Board with Case Studies

– MACT I Petroleum Refining Sources and Portland Cement Manufacturing . EPA - 452/R- 09 - 006. http://www.epa.gov/ttn/atw/rrisk/rtrpg.html. 3 . US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessmen t Methodologies. http://y osemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EP A - SAB - 10 - 007 -unsigned.pdf 4 . US EPA, 2010. Memorandum from Dave Guinnup to RTR Wood Furniture Docket, entitled, “EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk Assessment Methodologies. 5 . US EPA, 2005. Revision to the Guideline on Air Quality Mode ls: Adoption of a Preferred General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 40 CFR Part 51. http://www.epa.gov/EPA - AI R/2005/November/Day- 09/a21627.htm 6 . US EPA, 2004. Users’ guide for the AMS/EPA regulatory model

– AERMOD. EPA - 454/B 03 - 001 . http://www.epa.gov/scram001/7thc onf/aermod/aermodugb.pdf . 7 . Allen , D. , C . Murphy, Y . Kimura, W . Vizuete, T . Edgar , H . Jeffries, B . - U . Kim, M . Webster , and M . Sy mons , 2004. Variable industrial VOC emissions and their impact on ozone formation in the Houston Galveston Area. Final Report: Texas Environmental Research Consortium Project H - 13. http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf 8 . US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA - 453 - K- 04 - 001A. http://www.epa.gov/ttn/fera/risk_atra_vol1.html. 9 . US EPA, 2005. Table 1. Prioritized Chronic Dose - Response Values (2/28/05). Office of Air Quality

Planning and Standards . http://www.epa.gov/ttn/atw/toxsource/table1.pdf 10 . US EPA, 2005. 1999 National Air Toxics Risk Assessment. http://www.epa.gov/ttn/atw/nata1999 11
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ntegrated Risk I

nformation System. http://www.epa.gov/iris/index.html . 12
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for Toxic Substances and Disease Registry. 2006. Minimum Risk Lev els (MRL s) for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html . 13
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15
. CA Office of Environmental Health Hazard Assessment, 2005. Chronic Reference Exposure L evels Adopted by OEHHA as of December 2008. http://www.oehha.ca.gov/air/chronic_rels/AllChrels.html . 16 . CA Office of Environmental Health Hazard Assessment, 2005. Technical Support Document for Describing Available Cancer Potency Factors, May 2005 . http://www.oehha.ca.gov/air/hot_spots/may 2005tsd.html . 17 . US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 National Air Toxics Assessment. http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf . 18 . US EPA, 2005. Table 2. Acute Dose - Response Values for Screening Risk Assessments (6/02/2005). Office of Air Quality Planning and Standards. http://www.epa.gov/ttn/atw/toxsource/table2.pdf 19 . CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference Exposure Levels developed by OEHHA as of May

2000. http://www.oehha.ca.gov/air/acute_rels/allAcREL s.html 20 . American I

ndustrial Hy

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