                                       
                                       
                                       
                                       
                                       
                                       
   Residual Risk Assessment for the Secondary Lead Smelting Source Category
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                       
                                      by
             EPA's Office of Air Quality Planning and Standards
                          Office of Air and Radiation
                                 December 2011
                                       
                                       
                                                                               

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-response assessment	13
2.6.1	Sources of chronic dose-response information	13
2.6.2	Sources of acute dose-response information	20
2.7	Risk characterization	24
2.7.1	General	24
2.7.2	Mixtures	26
2.7.3	Facility-wide risks	26
3	Risk results for the secondary lead source category	27
3.1	Source category 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 Lead Source Category and Availability of Dose-Response Values	28
Table 3.2-1  Summary of Source Category Level Inhalation Risks for Secondary Lead Smelting	31
Table 3.2-3  Secondary Lead Smelting Facility Modeled  Maximum Ambient Lead 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 Modeling
Appendix 3	Analysis 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 Analysis 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
IRIS		Integrated Risk Information System
MACT		Maximum Achievable Control Technology
MIR		Maximum Individual Risk
MOA		Mode of action
NAC		National Advisory Committee
NAAQS	National Ambient Air Quality Standards
NATA 	National Air Toxics Assessment
NEI		National Emissions Inventory
NPRM		Notice of Proposed Rulemaking
PB-HAP	Persistent and Bioaccumulative - HAP
POM		Polycyclic organic matter
REL		Reference exposure level
RfC		Reference concentration
RfD		Reference dose
RME		Reasonable Maximum Exposure
RTR		Risk and Technology
TOSHI		Target-organ-specific hazard index
URE		Unit risk estimate
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.) [].  EPA has largely 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 years and revise a standard, as necessary, "taking into account developments in practices, processes and control technologies."  In the second stage, EPA is required under section 112(f)(2) to assess the health and environmental risks that remain after implementation of the MACT standards.  If additional risk reductions are necessary to protect public health with an ample margin of safety 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 residual 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 Plan," 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 Methodologies" [].  We received the final SAB report on this review in May of 2010 [].  Where appropriate, we have responded to the key messages from this review in developing our current risk assessments and we will be continuing our efforts to improve our 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" [].

This document contains the methods and the results of baseline risk assessments performed for the Secondary Lead Smelting source category.  The methods discussion includes descriptions of the methods 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 docket 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 baseline 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.


2 Methods
  2.1 Emissions and source data
Data from the 2005 National Emissions 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.  Using the process MACT code, we developed a subset of this inventory that contains emissions and facility data for the Secondary Lead Smelting source category.  Next, we performed an engineering review of the information using EPA engineers to identify readily apparent limitations and issues with the emissions data  In 2010, following this initial engineering review, we sent an information collection request (ICR) survey to the six companies  which own all 14 facilities in the U.S. of which we are aware.  The survey included many questions about emissions, stack parameters, locations, and other important inputs for the risk modeling.  We also requested that facilities submit all existing test data.  These data included stack test data for lead emissions for multiple years for most emissions points.  In 2010, we also requested that most facilities conduct additional emissions testing of various furnaces and other emissions points.  The facilities conducted emissions tests in 2010 for all HAP metals, dioxins, and various other HAPs.  We received test data from most facilities in late 2010.  We reviewed and analyzed all the ICR and stack test data and then incorporated these data into the model data set, as appropriate.  However, with regard to the estimates of fugitive emissions, the data we received through the ICR were limited and uncertain.  Of the 14 facilities surveyed, five facilities provided no estimate for fugitive emissions and five other facilities provided quite incomplete estimates.  Additional information was later submitted by one of the facilities (East Penn). Rather than rely on the incomplete and often nonexistent estimates of fugitive emissions provided in ICR 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 rule, EPA received some updated facility information, including updated emissions 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 received 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.

  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 system (HEM-3).  The approach used in applying this modeling system is outlined below, and further details 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 component.  The exposure and risk characterization components are discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM-3 system, AERMOD version 11103, is a state-of-the-science Gaussian plume dispersion model that is preferred by EPA for modeling point, area, and volume sources of continuous air emissions from facility applications [].  Further details on AERMOD can be found in the AERMOD Users Guide [].  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.  Hourly emission rates used for this simulation are generated by evenly dividing the total annual emission rate from the inventory into the 8,760 hours of the year.

The first step in the application of the HEM-3 modeling system is to predict ambient concentrations at locations of interest.  The AERMOD model options employed are summarized in Table 2.2-1 and are discussed further below.
                                       
       Table 2.2-1  AERMOD version 09292 model options for RTR modeling

Modeling  Option
Selected Parameter for chronic exposure
Type of calculations
Hourly Ambient Concentration
Source type
Point and area sources
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.  Ideally, when considering off-site meteorological data most site specific dispersion modeling efforts will employ up to five years of data to capture variability in weather patterns from year to year.  However, to minimize data processing time and resources for this assessment, we modeled only a single year, typically 1991.  While the selection of a single year may result in under-prediction of long-term ambient levels at some locations, 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 facility and the applicable meteorological station was 40 miles (72 km).  Appendix 2 (Meteorological 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 weather 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 system estimates ambient concentrations at the geographic centroids of census blocks (using the 2000 Census) and at other receptor locations that can be specified by the user.  The model accounts for the impacts of multiple facilities when estimating concentration impacts at each block centroid.  Typically we combined only the impacts of facilities within the same source category, and assessed chronic exposure and risk only for census blocks with at least one resident (i.e., locations where people may reasonably be assumed to reside rather than receptor points at the fenceline of a facility).  Chronic 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 typically comprised of approximately 40 people or about ten households.   

In contrast to the development of ambient concentrations for evaluating long-term exposures, 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 generally no nearer than 100 meters from the center of the facility (note that for large facilities, this 100-meter ring could still contain locations inside the facility property).  Since short-term emission rates were needed to screen for the potential for hazard via acute exposures, and since the NEI contains only annual emission totals, we typically apply the general assumption to all source categories that the maximum one-hour emission rate from any source other than those resulting in fugitive dust emissions was ten times the average annual hourly emission rate for that source.  The factor of ten is not applied to fugitive dust sources because these emissions decrease during the meteorological conditions associated with the worst-case short-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.  The choice of a factor of ten for acute screening was originally based on engineering judgment.  To develop a more robust peak-to-mean emissions factor, and in response to one of the key messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis using a short-term emissions dataset from a number of sources located in Texas (originally reported on by Allen et al. 2004)[].  In that report, the Texas Environmental Research Consortium Project compared hourly and annual emissions data for volatile organic compounds for all facilities in a heavily-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 analysis, focusing that analysis on sources which reported emitting high quantities of HAP over short periods of time (see Appendix 3, "Analysis 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 factor of 74 times the annual average and the 99[th] percentile ratio of peak hourly emission rate to the annual hourly emission rate was 9.  Based on these results, we chose the factor of ten for all initial screening; it is intended to cover routinely-variable emissions as well as startup, shutdown, and malfunction (SSM) emissions.  While there have been some documented emission excursions above this level, our analysis of the data from the Texas Environmental Research Consortium suggests that this factor should cover more than 99% of the short-term peak gaseous or volatile HAP emissions from typical industrial sources (which were the biggest 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.  If a sector does not contain any blocks, the model defaults the elevation to that of the nearest block.  If an elevation is not provided for the emission source, the model uses the average elevation of all sectors within the innermost model ring.

In addition to using receptor elevation to determine plume height, AERMOD adjusts the plume's flow if nearby elevated hills are expected to influence the wind 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
We 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 analysis did not consider either 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 daily 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 also reduce risk estimates for gaseous HAPs, but typically 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 highly exposed residents by modeling their short-term behavior could add a systematic low bias to these results.

We did not address long-term migration nor population growth or decrease over 70 years, instead basing the assessment on the 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 generally 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 estimate of the theoretical MIR 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, thereby 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 any individual.
  2.4 Screening for potential acute impacts of concern
In establishing a scientifically defensible approach for the assessment of potential health risks due to acute exposures to HAP, we followed 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 by the National Academy of Sciences in its 1993 publication "Science and Judgment in Risk Assessment" and subsequently was adopted in the EPA's "Residual Risk Report to Congress" in 1999.  

With respect to screening for potential acute health risks, the assessment methodology is designed to eliminate from further consideration those facilities for which we have confidence that no acute adverse health effects of concern will occur.  To do so, we use what is called a tiered, iterative approach to the assessment.  This means that we began with a screening assessment, which relies on readily available data and uses conservative assumptions that in combination approximate a worst-case exposure.  The result of this 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 exposures from emitted HAP.  A more refined acute assessment could use industry- or site-specific data on the temporal pattern of emissions, the layout of emission points at the facility, the boundaries of the facility, and/or the local meteorology.  In some cases, all of these site-specific data are used to refine the assessment; in others, lesser amounts of site-specific data can be used to determine that acute exposures are not a concern, and significant additional data collection is not necessary.  

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

   * Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission rates except in the case of fugitive dust (see section 2.2)
   * For facilities with multiple emission points, peak 1-hour emissions were assumed to occur at all emission points at the same time.
   * For facilities with multiple emission points, 1-hour concentrations at each receptor were assumed to be the sum of the maximum concentrations due to each emission point, regardless of whether those maximum concentrations occurred during the same hour. 
   * Worst-case meteorology (from one year at the nearest NWS site) was assumed to occur at the same time the peak emission rates occur.  The recommended EPA local-scale dispersion model, AERMOD, was 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 concentration 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 section 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 facility-specific emissions 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 physical layout and boundaries of a facility to more accurately gauge where people might reasonably be exposed for an hour.

  2.5 Multipathway human health and environmental risk screening
The potential for significant human health risks and adverse environmental impacts due to exposures via routes other than inhalation (i.e., multipathway exposures) was screened by first determining whether any sources emitted any hazardous air pollutants known to be persistent and bioaccumulative in the environment (PB-HAP).  The PB-HAP compounds or compound classes are identified for the screening from the EPA's Air Toxics Risk Assessment Library [], and they are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury compounds, methoxychlor, polychlorinated biphenyls, polycyclic organic matter (POM), toxaphene, and trifluralin.  Emissions of 5 different PB-HAP were identified in the emissions inventory for the Secondary Lead Smelting source category; they are POM, dioxins, cadmium, mercury, and lead.  With respect to PB-HAP other than lead, emissions were evaluated for potential non-inhalation risks and adverse environmental impacts using our recently-developed screening scenario which was developed for use with the TRIM.FaTE model.  This screening scenario uses environmental media outputs from the peer-reviewed TRIM.FaTE to estimate the maximum potential ingestion risks for any specified emission scenario by using a generic farming/fishing 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.  In the development of the screening scenario a sensitivity analysis was conducted to ensure that its key design parameters were established such that environmental media concentrations were not underestimated, and to also minimize the occurrence of false positives for human health endpoints.  See Appendix 4 for a complete discussion of the development and testing of the screening scenario, as well as for the values of facility-level 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 analysis was performed.  Two facilities were chosen as case study analyses to assess potential multipathway risks for mercury, cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions rates of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and direction, smelting furnace type, local change in elevation, and geographic representativeness of sites throughout the US.  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 for multipathway risks from the source category. Since the modeling used in these case study assessments utilize site specific parameters to describe naturally occurring physical, chemical and biological processes, we believe that the multimedia concentrations of PB-HAPs generated in this analysis are unbiased 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 vicinity 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 regularly consumes fish caught in freshwater lakes in the vicinity of a PB-HAP source.  In the fishing scenario, in addition to the characterization of exposure and risks across the broad population of recreational anglers, exposures were also calculated for three subpopulations of recreational anglers (Hispanic, Laotian, and Vietnamese descent) who have higher rates of fish consumption.  Furthermore, in order to more fully characterize the modeled potential multipathway risks that may be associated with high-end consumption of PB-HAP contaminated food, we present results based on two ingestion exposure scenarios: 1) a reasonable maximum exposure (RME) scenario that, for example, utilizes 90[th] percentile ingestion rates for farmers, recreational 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 ug/m3 based on a rolling 3-month averaging period with a not-to-be-exceeded form (see 73 FR 66964).  Notably, 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 calculated 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 ug/m[3]).   Values below the NAAQS were considered to have a low potential for multi-pathway risks of any significance.

The NAAQS value, 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 mentioned 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 assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and exposure analyses, and level of external peer and public review that went into the development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion modeling used in this assessment to estimated ambient lead concentrations 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 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 children and other at-risk populations against an array of adverse health effects, most notably including neurological effects, particularly neurobehavioral and neurocognitive effects, in children (73 FR 67007).  The Administrator, in setting the standard, also 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 note 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 to 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 environmental 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 facilities.  Human health dose-response threshold values are generally derived from studies conducted on laboratory animals (such as rodents) and developed with the inclusions of uncertainty factors that could be as high as 3000.  As a result, these human thresholds 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 wildlife.  Thus, if the maximum inhalation hazard in an ecosystem 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 impacts of specific HAP, we note that as an uncertainty and, where possible, refine our analysis by comparing our modeled impacts to available threshold values from the scientific literature.

  2.6 Dose-response assessment
0.2.1 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 Quality Planning and Standards' (OAQPS) existing recommendations for HAP [], also used for NATA [].  This information has been obtained from various sources and prioritized according to (1) conceptual consistency with EPA risk assessment guidelines and (2) level of peer review received.  The prioritization process was aimed at incorporating into our assessments the best available science with respect to dose-response information.  The recommendations are based on the following sources, in order of priority: 

   1)    US Environmental Protection Agency (EPA).  EPA has developed dose-response assessments for chronic exposure for many of the pollutants in this study.  These assessments typically provide a qualitative 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 probability of developing cancer.  The RfC is defined as an "estimate (with uncertainty 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 uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects during a lifetime."  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 ug/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 several forms, based on the level of review.  The Integrated Risk Information System (IRIS) [] is an EPA database that contains scientific health assessment information, including dose-response information. All IRIS assessments since 1996 have also undergone independent external peer review.  The current IRIS process includes review by EPA scientists, interagency reviewers from other federal agencies, and the public, and peer review by independent scientists external to EPA.  New IRIS values are developed and old IRIS values are updated as new health effects data become available.  Refer to the "IRIS Track" website for detailed information on status and scheduling of current individual IRIS assessments and updates (http://cfpub.epa.gov/ncea/iristrac/index.cfm).  EPA's science policy approach, under the current carcinogen guidelines, is to use linear low-dose 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 analyses (once they are peer reviewed) in our risk assessments.  At this time, however, there are no available carcinogen dose-response assessments for inhalation exposure that are based on a nonlinear MOA.

   2)    US Agency for Toxic Substances and Disease Registry (ATSDR).  ATSDR, which is part of the US Department of Health and Human Services, develops and publishes Minimum Risk Levels (MRLs) [] for inhalation and oral exposure to many 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 to that of the EPA's Reference Dose (RfD) and Reference Concentration (RfC) for deriving substance specific health guidance levels for non neoplastic endpoints."  The MRL is defined as "an estimate of daily human exposure to a substance that is likely to be without an appreciable risk of adverse effects (other than cancer) over a specified duration of exposure."  ATSDR describes MRLs as substance-specific estimates to be used by health assessors to select environmental contaminants for further evaluation.  Exposures 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 Environmental 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: "The guidelines for developing chronic inhalation exposure levels incorporate many recommendations of the U.S. EPA [] and NAS []."  The non-cancer information includes available inhalation health risk guidance values expressed as chronic inhalation reference exposure levels (RELs) [].  CalEPA defines the REL as "the concentration level at or below 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 [], defined similarly to EPA's URE. 
  
In developing 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 glycol ethers and cyanide compounds, the most conservative dose-response value of the chemical category is used as a surrogate for other compounds in the group for which dose-response values are not available.  This is done in order to examine, under conservative assumptions, whether these HAPs that lack dose-response values may pose an unacceptable risk and require further examination, or screen out from further assessment. 
   
2) Where possible for emissions of unspecified mixtures of HAP categories such as metal compounds and POM (see Tables 2.6-1 (a) to 2.6-1 (c), we apply category-specific chemical speciation profiles appropriate to the source category to develop a composite dose-response value for the category.  

3) 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 from 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 IRIS value  EPA has been working on revising the formaldehyde IRIS assessment and the National Academy of Sciences (NAS) completed its review of the EPA's draft assessment in May of 2011 (http://www.nap.edu/catalog.php?record_id=13142).  EPA is reviewing the public comments 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 uncertainty analyses will be an integral component of implementing the BBDR model.  The draft IRIS assessment will be revised in response to the NAS peer review and public comments and the final assessment will be posted on the IRIS database.  In the interim, we will present findings using the 1991 IRIS value as a primary estimate, and may also consider 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 speciated in[t]o individual compounds.  As a result, it is necessary to apply the same simplifying assumptions to assessments that were used for the 1999 NATA study [].  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 Lead Smelting source category includes emissions of HAP with available chronic 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).

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

URE (unit risk estimate for cancer) = cancer risk per μg/m[3] 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 Planning and Standards. EPA ORD = EPA Office of Research and Development
Pollutant
CAS Number
                                    URE[5]
                                  (1/μg/m[3])
                                    Source
1,1,2,2-Tetrachloroethane
79345
                                   0.000058
IRIS
1,3-Dichloropropene
542756
                                   0.000004
IRIS
1,3-Butadiene
106990
                                    0.00003
IRIS
2,3,7,8-TCDD TEQ
1746016
                                      33
EPA ORD
Acetaldehyde
75070
                                    2.2E-06
IRIS
Acrylonitrile
75058
                                   0.000068
IRIS
Arsenic
7440382
                                    4.3E-03
IRIS
Benzene
71432
                                    7.8E-06
IRIS
Bis(2-Ethylhexyl)Phthalate
117817
                                    2.4E-06
CAL
Beryllium
7440417
                                    0.0024
IRIS
Cadmium
7440439
                                    0.0018
IRIS
Chromium compounds

                                       

     Chromium [(]VI)
18540299
                                     0.012
IRIS
Ethyl Benzene
100414
                                    2.5E-06
CAL
Formaldehyde
50000
                                   0.000013
IRIS
Methylene Chloride
75092
                                    4.7E-07
IRIS
Polycyclic Organic Matter
246
                                       
EPA OAQPS
     Chrysene
218019
                                    1.1E-05
CAL
     Benz[a]Anthracene
56553
                                    1.1E-04
CAL
     Benzo[b]Fluoranthene
205992
                                    1.1E-04
CAL
     Benzo[k]Fluoranthene
207089
                                    0.00011
CAL
     Indeno[1,2,3-c,d]Pyrene
193395
                                    0.00011
CAL
     Dibenzo[a,h]Anthracene
53703
                                    0.0012
CAL
Naphthalene
91203
                                    3.4E-05
CAL
Nickel
7440020
                                    0.00009
ATSDR
Tetrachloroethylene
127184
                                    5.9E-06
CAL
Trichloroethylene
79016
                                    2.0E-06
CAL


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



Table 2.6-1  (c)  Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens
RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) that is likely to be without an appreciable risk of deleterious 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
CAS Number[6]
                                     RfC 
                                    (mg/m3)
                                    Source
1,3-Butadiene
106990
                                                                          0.002
IRIS -- M
1,3-Dichloropropene
62737
                                                                         0.0005
IRIS -- H
2,3,7,8-TCDD TEQ
1746016
                                                                          4E-08
CAL
Acetaldehyde
75070
                                                                          0.009
IRIS -- L
Acrylonitrile
107131
                                                                          0.002
IRIS -- M
Acrolein
107028
                                                                        0.00002
IRIS -- L/M
Antimony
1309644 (trioxide)
                                                                         0.0002
IRIS -- L
Arsenic
174
                                                                       0.000015
CAL
Benzene
71432
                                                                           0.03
IRIS -- M
Beryllium
7440417
                                                                        0.00002
IRIS -- M
Bis(2-Ethylhexyl)Phthalate
117817
                                                                           0.01
P-CAL
Cadmium
176
                                                                        0.00001
ATSDR
Carbon Disulfide
75150
                                                                            0.7
IRIS -- M
Chlorine
7782505
                                                                        0.00015
D-ATSDR
Chlorobenzene
108907
                                                                              1
CAL
Chloroform
67663
                                                                          0.098
ATSDR
Chromium compounds

                                                                               

     Chromium (VI)
18540299
                                                                         0.0001
IRIS -- M
Cumene
98828
                                                                            0.4
IRIS -- M
Ethyl Benzene
100414
                                                                              1
IRIS -- L
Ethyl Chloride
75003
                                                                             10
IRIS -- M
Formaldehyde
50000
                                                                         0.0098
ATSDR
Hexane
110543
                                                                            0.7
IRIS -- M
Hydrochloric Acid
7647010
                                                                           0.02
IRIS -- L
Lead
182
                                                                        0.00015
EPA OAQPS
Manganese
183
                                                                        0.00005
IRIS -- M
Methyl Bromide
74873
                                                                           0.09
IRIS -- M/H
Methyl Chloride
74873
                                                                           0.09
IRIS -- M/H
Methylene Chloride
75092
                                                                              1
ATSDR
Methyl Isobutyl Ketone
108101
                                                                              3
IRIS -- L/M
Mercury Compounds

                                                                               

     Elemental Gaseous Mercury
7439976
                                                                         0.0003
IRIS -- M
      Gaseous Divalent Mercury

                                                                               

      Particulate Divalent Mercury

                                                                               

Naphthalene
91203
                                                                          0.003
IRIS -- M
Nickel
186
                                                                        0.00009
ATSDR
o-Cresol
95487
                                                                    0.6 (mixed)
CAL
Phenol
108952
                                                                            0.2
CAL
Propionaldehyde
123386
                                                                          0.008
IRIS -- L/M 
Selenium
7782492
                                                                           0.02
CAL
Styrene
100425
                                                                              1
IRIS -- M
Tetrachloroethylene
79016
                                                                            0.6
CAL
Trichloroethylene
79016
                                                                            0.6
CAL
Toluene
108883
                                                                              5
IRIS -- H
Xylenes (Mixture of o, m, and p Isomers)
1330207
                                                                            0.1
IRIS
     m-Xylene
108383
                                                                            0.1
IRIS -- M
     o-Xylene
95476
                                                                            0.1
IRIS -- M
     p-Xylene
106423
                                                                            0.1
IRIS -- M

0.4.1 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 [].  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 (RELs), as well as emergency response levels, such as Acute Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the recognition that the ultimate interpretation of any potential risks associated with an estimated exceedance of a particular reference level depends on the definition of that level and any 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.

California Acute Reference Exposure Levels (RELs).  The California Environmental Protection Agency (CalEPA) has developed acute dose-response reference values for many 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. [].  RELs are based on the most sensitive, relevant, adverse health effect reported in the medical and toxicological literature.  RELs are designed to protect the most sensitive individuals in the population by the inclusion of margins of safety.  Since margins of safety are incorporated to address data gaps and uncertainties, exceeding the REL does not 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 deriving EPA's Inhalation Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has developed chronic RELs).
    
Acute Exposure Guideline Levels (AEGLs).  AEGLs are developed by the National Advisory Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous Substances, and then reviewed and published by the National Research Council.  As described in the Committee's "Standing Operating Procedures (SOP)" (http://www.epa.gov/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 assessments to aid in the development of emergency preparedness and prevention plans, as well as real time emergency response actions, for accidental chemical releases at fixed facilities and from transport carriers."  The document states that "the primary purpose 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 acutely toxic, high-priority chemicals."  In detailing the intended application of AEGL values, the document states that, "It is 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, and 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 chemical 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 susceptible individuals, could experience notable discomfort, irritation, or certain asymptomatic nonsensory effects.  However, the effects are not disabling and are transient and reversible upon cessation of exposure."
   
   "AEGL-2 is the airborne concentration (expressed as ppm or mg/m[3]) of a substance above which it is predicted that the general population, including susceptible individuals, could 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 progressively increasing but transient and nondisabling odor, taste, and sensory irritation or certain asymptomatic, 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 idiosyncratic responses, could experience the effects described at concentrations below the corresponding AEGL."

Emergency Response Planning Guidelines (ERPGs).  The American Industrial Hygiene Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [] 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 follows: "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 intended 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 nearly 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, objectionable odor." 
   
   "ERPG-2 is the maximum airborne concentration below which it is believed that nearly all individuals could be exposed for up to 1 hour without experiencing or developing irreversible or other serious health effects or symptoms which could impair an individual's ability to take protective action."
   
The emissions inventory for the Secondary Lead 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.

                                       
                                       
                                       
             Table 2.6-2  Dose-Response Values for Acute Exposure
Pollutant
                                  CAS Number
                            AEGL-1
(1-hr)
(mg/m[3])
                            AEGL-2
(1-hr)
(mg/m[3])
                               ERPG-1
(mg/m[3])
                               ERPG-2
(mg/m[3])
                                     REL 
1,3-Butadiene
                                    106990
                                     1500
                                     12000
                                     1500
                                     12000
                                       
Acetaldehyde
                                     75070
                                      81
                                      490
                                      81
                                      490
                                     0.47
Acrolein
                                    107028
                                     0.069
                                     0.23
                                     0.069
                                     0.23
                                    0.0025
Acrylonitrile
                                    107131
                                      10
                                      130
                                      22
                                      77
                                       
Antimony
                                    1309644
                                       
                                       
                                       
                                       
                                       
Arsenic
                                    7440382
                                       
                                       
                                       
                                       
                                    0.0002
Benzene
                                     71432
                                      170
                                     2600
                                      170
                                     2600
                                      1.3
Beryllium
                                    7440417
                                       
                                       
                                       
                                     0.025
                                       
Biphenyl
                                     92524
                                       
                                      61
                                       
                                       
                                       
Carbon Disulfide
                                     75150
                                      40
                                      500
                                      40
                                      500
                                      6.2
Chlorine
                                    7782505
                                      1.5
                                      5.8
                                      1.5
                                      5.8
                                     0.21
Chlorobenzene
                                    108907
                                      46
                                      690
                                       
                                       
                                       
Chloroform
                                     67663
                                       
                                      310
                                       
                                      310
                                     0.15
Cumene
                                     98828
                                      250
                                     1500
                                       
                                       
                                       
Elemental Gaseous Mercury
                                    7439976
                                       
                                      1.7
                                       
                                       2
                                     .0006
Ethyl Benzene
                                    100414
                                      140
                                     4800
                                       
                                       
                                       
Formaldehyde
                                     50000
                                      1.1
                                      17
                                      1.1
                                      17
                                     0.055
Hexane
                                    110543
                                       
                                     12000
                                       
                                       
                                       
Hydrochloric Acid
                                    7647010
                                      2.7
                                      33
                                      2.7
                                      33
                                      2.1
Methyl Bromide
                                     74839
                                       
                                      820
                                       
                                      820
                                      3.9
Methyl Chloride
                                     74873
                                       
                                       
                                       
                                       
                                       
Methylene Chloride
                                     75092
                                      690
                                     1900
                                      690
                                     1900
                                      14
Methyl Iodide
                                     74884
                                       
                                       
                                      150
                                      290
                                       
Nickel
                                    7440020
                                       
                                       
                                       
                                       
                                     0.006
Phenol
                                    108952
                                      58
                                      89
                                      58
                                      89
                                      5.8
Propionaldehyde
                                    123386
                                      110
                                      620
                                       
                                       
                                       
Styrene
                                    100425
                                      85
                                      550
                                      85
                                      550
                                      21
Tetrachloroethylene
                                    127184
                                      240
                                     1600
                                      240
                                     1600
                                      20
Toluene
                                    108883
                                      750
                                     4500
                                      750
                                     1900
                                      37
Trichloroethylene
                                     79016
                                      700
                                     2400
                                      700
                                     2400
                                       
Xylenes (Mixed)
                                    1330207
                                      560
                                     4000
                                       
                                       
                                      22
     m-Xylene
                                    108383
                                       
                                       
                                       
                                       
                                      22
  1.1 Risk characterization
0.4.2 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 synthesized that is complete, informative, and useful for decision makers.  In general, 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 identified and discussed.  Further, the EPA Administrator's March 1995 Policy for Risk Characterization [] 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 consistency have been reinforced by the Agency's Risk Characterization Handbook [], in 2002 by the Agency's information quality guidelines [], and in the OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis, and are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data and methodology.  Through our tiered, iterative analytical approach, we have attempted to reduce both uncertainty and bias to the greatest degree 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 discussion 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, individual and population cancer risks were 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-yr period (i.e., the assumed human lifespan) at that exposure.  Because UREs for most HAPs are upper-bound estimates, actual risks at a given exposure level may be lower than predicted, and could be zero.

For EPA's list of carcinogenic HAPs that act by a mutagenic mode-of-action [], we applied EPA's Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to Carcinogens [].  This guidance has the effect of adjusting the URE by factors of 10 (for children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at birth), as needed in risk assessments.  In this case, this has the effect of increasing the estimated life time risks for these pollutants by a factor of 1.6.  In addition, although only a small fraction of the total POM emissions may be reported as individual compounds, EPA expresses carcinogenic potency for compounds in this group in terms of benzo[a]pyrene equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism of action as does benzo[a]pyrene.  For this reason, EPA implementation policy [] recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk estimates are based on relative potency.  Accordingly, we applied the Supplemental Guidance to all unspeciated POM mixtures.

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

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

Unlike linear dose-response assessments for 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 divided by a reference level (e.g., the RfC).  For a given HAP, exposures at or below the reference level (HQ<=1) are not likely to cause adverse health effects.  As exposures increase above the reference level (HQs increasingly greater than 1), the potential for adverse effects increases.  For exposures predicted to be above the RfC, the risk characterization 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 impact 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 generally 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 seen, additional information is used to determine whether there is a potential for significant acute risks.

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

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines' [,] 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 completely different modes of action, it may be inappropriate to aggregate HQs.  In consideration of these mode-of-action differences, the mixtures guidelines support aggregating effects of different substances in specific and limited ways.  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 system.  All 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 systems that occur above the critical dose have not been included in the TOSHI calculations, some TOSHIs may have been underestimated.  As with the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict delineation of "safe" and "unsafe" levels.  Rather, the TOSHI is another measure of the 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 risks
To help place the source category risks in context, we typically examine "facility-wide" risks using facility-wide and source category data files developed by EPA through emission data updates from industry and refined EPA profiles for the source category.  In this instance, there are no other significant HAP emission sources present-- all significant HAP sources have been included in the source category risk analysis.  Therefore, the facility-wide risk is essentially the same as the source category risk; no separate facility-wide analysis was necessary.

 Risk results for the secondary lead source category
  3.1 Source category description and results
                                       
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, primarily 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 electrolyte); (2) melting lead metal and reducing lead compounds to lead metal in the smelting furnace; and (3) refining and alloying the lead to customer specifications.  HAP are emitted from secondary lead smelters as: (1) process emissions contained in the primary exhaust of smelting furnaces; (2) process fugitive emissions associated with charging and tapping of smelting furnaces and lead refining kettles; and (3) fugitive dust emissions from wind or mechanically induced entrainment of dust from stockpiles, plant yards, and roadways.  A complete description of the secondary lead smelting source category can be found in the text of the Notice of Proposed Rulemaking (NPRM): National Emissions Standards for Hazardous Air Pollutants: Secondary Lead Smelting. 

There are currently 15 secondary lead smelters operating in the U.S. and Puerto Rico. The NPRM data set for the secondary lead smelting category contains 15 facilities identified with a secondary lead smelting MACT code in the 2005 National-Scale Air Toxics Assessment (NATA) National Emissions Inventory (NEI), September 2010 version.    

The emissions in the secondary lead smelting source category NPRM data set (of 15 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 15 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, cadmium) and organic and inorganic compounds.  Organic toxic air pollutants emitted from secondary lead smelting include compounds such as polycyclic organic matter (POM), styrene, carbon disulfide, and dioxins/furans.
                                       
Table 3.1-1  Summary of Emissions from the Secondary Lead Source Category and Availability of Dose-Response Values

                                    HAP[a]
                           Emission Estimates (tpy)
Number of Facilities where HAP Reported 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 Benchmark 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-Xylene
                                       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 compounds
                                      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
                                       
                                       
                                       
                                       
o-Cresol
                                      0.3
                                      14
                                       
                                       
                                       
                                       
1,3-Dichloropropene
                                      0.3
                                      14
                                       
                                       
                                       
                                       
Formaldehyde
                                      0.2
                                      14
                                       
                                       
                                       
                                       
Nickel compounds
                                      0.1
                                      15
                                       
                                       
                                       
                                       
Mercury compounds
                                       
                                       
                                       
                                       
                                       
                                       
     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
                                       
                                       
                                       
                                       
Dibutylphthalate
                                     0.04
                                      14
                                       
                                       
                                       
                                       
Beryllium compounds
                                     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) compounds" 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 particulate 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/net/2005inventory.html#inventorydata. 
* For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were grouped into POM subgroups as found on EPA's Technology Transfer Network website for the 2002 National-Scale Air Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom. (Approach to Modeling POM).

[b] Specific dose-response values for each chemical are identified on EPA's Technology Transfer Network website for air toxics at http://www.epa.gov/ttn/atw/toxsource/summary.html.

[c] There is no reference concentration for lead.  In considering noncancer hazards for lead in this assessment, we compared rolling three-month average exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead (0.15 ug/m[3]).  These NAAQS 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, 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 emissions of mercury from this source category are much lower than shown here, but we are not able to quantify precisely how much lower.    

  3.2 Baseline risk characterization

This section presents the results of the risk assessment for the secondary lead smelting source category based on the modeling methods described in the previous sections.  All baseline risk results are developed using the best estimates of actual HAP emissions summarized in the previous section.  The basic chronic inhalation risk estimates presented here are the maximum individual lifetime cancer risk, the maximum 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.

Tables 3.2-1 and 3.2-2 summarize the preliminary chronic and acute inhalation risk results for this source category.  The results of the chronic inhalation cancer risk assessment estimate the maximum lifetime individual cancer risk, considering both fugitive and stack emissions could be as high as 50 in a million.  The maximum cancer risks for this source category were equal to or exceeded a cancer risk of 1 in a million at all 15 facilities, with fugitive dust emissions of arsenic and cadmium dominating the risk.  The total estimated cancer incidence from this source category based on actual emission levels is 0.02 excess cancer cases per year, or one excess case in every 50 years.  Approximately 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 source category.  Considering MACT allowable emissions, the MIR could be as high as 200 driven by arsenic emissions from stacks.

With respect to chronic inhalation noncancer risk, we estimate a maximum TOSHI value of 0.6 for the secondary lead source category, with fugitive dust emissions of cadmium and arsenic dominating those impacts.  In contrast, we note that considering MACT allowable emissions, we estimate a maximum TOSHI value of 3, driven primarily by emissions of arsenic from stacks.





Table 3.2-1  Summary of Source Category Level Inhalation Risks for Secondary Lead Smelting
                                    Result
                               HAP "Drivers"
Facilities in Source Category
Number of Facilities Estimated to be in Source Category
                                      15
                                      n/a
Number of Facilities Identified in NEI and Modeled in Risk Assessment
                                      15
                                      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 compounds
                              arsenic compounds 
Acute Noncancer Screening Results
Maximum Acute Hazard Quotient
                                      20
                            arsenic compounds (REL)
                                       
Number of Facilities With Potential for Acute Effects
                                       9
                               arsenic compounds
Population Exposure
Number of People Living Within 50 Kilometers of Facilities Modeled
                                  26,000,000
                                      n/a
Number of People Exposed to Cancer Risk:
	Greater than or equal to 100 in 1 million
                                       0
                                      n/a
	Greater than or equal to 10 in 1 million
                                      700
                                      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
                                       
                                       
                                       

Based upon the REL, the only available acute health benchmark value for emissions of arsenic, screening-level acute HQ values could exceed a value of 1 at 9 facilities.  In addition, this worst-case screening analysis estimates that the maximum acute HQ value for a facility in this source category could be up to 20 (based on the REL for arsenic), at two facilities in this source category largely 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. 
  
In the risk assessment to support the proposed rule, to identify potential multipathway health risks from PB-HAP other than lead we first performed a screening analysis that compared emissions of PB-HAP emitted from the secondary lead source category to 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 screening level emissions rate.  Therefore, to be all inclusive, we also included mercury along with POM, cadmium and dioxins for the detailed multipathway analyses to support the proposed rule.  More specifically, in the multipathway analyses to support the proposed rule, we performed two detailed case study analyses for these 4 PB-HAP in areas near the Exide Technologies (Frisco, TX) and Revere Smelting & Refining (Middletown, NY) facilities.  In these case study analyses, in order to more fully characterize the potential multipathway risks associated with high end consumption of PB-HAP contaminated food, we presented results based on RME and CTE scenarios.  The RME scenario utilized 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 utilized mean ingestion rates for each of these groups.  In the detailed multipathway analysis and risk assessment to support the proposed rule, we provided 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 the 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 analysis to support the proposed rule) submitted updated emissions information relevant to the multipathway analysis.  To update the multipathway case study assessment for the Exide, Frisco facility we 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 case study 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 greater 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 multipathway effects from emissions of lead, modeled maximum 3-month rolling average lead concentrations were compared to the NAAQS for lead.   Results of this analysis, presented in Table 3.2-3, estimate that the NAAQS for lead could be exceeded at about 9 of 15 facilities, largely due to fugitive dust emissions.  At all 9 facilities, fugitive dust emissions account for about 94 to 99% of the estimated 3-month maximum lead concentrations.   Moreover, this analysis estimates that 3-month maximum lead concentrations could be up to about 20 times the NAAQS for lead 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 analysis estimates that approximately 200 people around 3 secondary lead smelting facilities live in areas where 3-month maximum lead concentrations are estimated to be about 1 to 3 times the lead NAAQS.  Finally as mentioned above, to evaluate the potential for adverse environmental effects, we also compared maximum 3-month rolling average atmospheric concentrations with the current secondary NAAQS for lead (which is identical to the primary, health-based standard).  Thus, the analyses 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 lead levels could be about 8 and 10 times above the NAAQS at a couple of facilities.

Table 3.2-3  Secondary Lead Smelting Facility Modeled  Maximum Ambient Lead Concentrations
                     (rolling three-month average values)
                                       
                                 Facility Name
                                     City
                                     State
                 Highest Modeled Lead Concentration (ug/m[3])
                Concentration is X times higher than the NAAQS
Buick Resource Recycling Facility, 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 Facility 
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.
ARECIBO
                                      PR
                                     0.76
                                       5

.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 primarily driving exceedences of the lead NAAQS, as well as the cancer MIR value of 50, 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 specifically, at each of these facilities, we estimated risks considering a decrease in fugitive dust emissions resulting primarily from the assumption that all facilities would be fully enclosed with negative pressure air inflow to a control device plus implementation of effective work practices.  We also considered a reduction of the MACT limit that would result in reduced actual stack emissions for two of these facilities: Doe 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 facilities, 1 is projected to be very close to the NAAQS for lead (i.e., 3-month maximum modeled lead concentration is estimated to be 0.17 ugs/m[3]).  The other facility (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 ugs/m[3]).  At both these facilities, 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 NAAQS for lead.

With respect to cancer risk, given the fugitive controls mentioned above, the cancer MIR would be reduced from 50 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 year.   

We finally note that post-control, the maximum worst-case acute screening HQ value would be reduced from a potential high of 20 (i.e., pre-controls, based on the REL for arsenic) to 5 (i.e., post-controls, based on the REL for arsenic).  In addition, post-control there would only be 1 other facility in the source category with a maximum worst-case acute refined HQ value greater than 1 (i.e., based on the REL for arsenic, Doe Run would have an estimated acute HQ value of approximately 2).
2 General discussion of uncertainties and how they have been addressed
  3.1 Exposure modeling uncertainties

Although every effort has been made to identify all the relevant 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 likely dominate the uncertainties in our exposure modeling estimates.  The chronic exposure modeling uncertainties are considered relatively small in comparison, since we are using EPA's refined local dispersion model with site-specific parameters and reasonably representative meteorology.  If anything, the population exposure estimates are biased high by not accounting for short- or long-term population mobility, and by neglecting processes like deposition, plume depletion, and atmospheric degradation.  Additionally, estimates of the maximum individual risk (MIR) contain uncertainty, because they are derived 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 to 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-year chronic exposure period, leading to a potential upward bias in both the MIR and population risk estimates; nor did we factor in the possibility of population growth during the 70-year chronic exposure period, leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis 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 translates 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 purposely 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-case 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 year) to occur at the default worst-case meteorological conditions (low winds with a stable atmosphere). It is 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. Therefore, for fugitive dusts the worst-case meteorology may not be the same as for other process emissions. Additionally, the level of hourly fugitive dusts emissions during this alternate worst-case condition compared to the average hourly emissions is unknown. The actual risks may be 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 values, 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 understanding of the geography of the facility relative to potential exposure locations.  In this case, refining the assessment for the two facilities that had HQ values greater than 1 post-controls resulted in lower acute risks. 
 
As noted in section 2.5, two facilities were chosen as case study analyses to assess potential multipathway risks for mercury, cadmium, POM, and dioxins and furans. The selection criteria for modeling these two facilities included emissions rates of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and direction, smelting furnace type, local change in elevation, and geographic representativeness of sites throughout the US.  However, there is uncertainty as to whether these two facilities represent the highest potential for multipathway human health risks from the source category. 

Since the modeling used in these case study assessments utilize site specific parameters to describe naturally occurring physical, chemical and biological processes, we believe that the multimedia concentrations of PB-HAPs generated in this analysis are unbiased estimates of the true impacts.  

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

To estimate potential high-end multipathway exposures and risks, in addition to utilizing fish consumption rate data for the general US population of recreational anglers, 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).   In this publication, the authors provide fish consumption information for different ethnic groups including Hispanics, Laotians, and Vietnamese surveyed in California's Central Valley Delta based on sample sizes of 45, 33, and 30, respectively.   We note that there is 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 American subsistence populations recommended by the EPA's 1997 Exposure Factors Handbook 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] percentile fish ingestion rate recommended by 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 how the values in the Shilling study compare to other existing data.  All of these fish ingestion rates are significantly greater than the 90[th] percentile fish ingestion rate across all recreational anglers.

We further note that high-end fisher populations could display considerable variability both in terms of the degree to which they frequent specific waterbodies or watersheds and the degree to which they target specific types of fish (or at least sizes of fish). Both of these factors can impact estimates of exposure. If a fisher population distributes their activity across a range of waterbodies and harvests a variety 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 analysis and its associated uncertainties is presented in section 5.3 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.


  3.2 Uncertainties in the dose-response relationships

 In the 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 days), and subchronic (>30 days 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 rely on both chronic (cancer and noncancer) and acute (noncancer) benchmarks, which are described in more detail below.
 
Although every effort is made to identify peer-reviewed 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 ingestion).  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 included in the assessment may be under EPA IRIS 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 enough to indicate that the risk assessment may significantly 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 specific approach currently used by the EPA to develop cancer potency factors.  In general, these same uncertainties attend the development of cancer potency factors by CalEPA, the 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 [] (herein referred to as Cancer Guidelines).  "The primary goal of EPA actions is protection of human health; accordingly, 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 described 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 Cancer 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 using 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 estimate of risk.  That is, EPA's upper bound estimates represent a "plausible upper limit to the true value of a quantity" (although this is usually not a true statistical confidence limit).   In some circumstances, the true risk could be as low as zero; however, in other circumstances the risk could also be greater.   When developing an upper bound estimate of risk and to provide risk values that do not underestimate risk, EPA generally relies on conservative default approaches.   EPA also uses the upper bound (rather than lower bound or central) estimates in its assessments, although it is noted that this approach can have limitations for some uses (e.g. priority setting, expected benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered quantitatively, and others which generally are expressed qualitatively.  Uncertainties may vary substantially among cancer risk assessments associated with exposures to different pollutants, since the assessments employ different databases with different strengths and limitations and the procedures employed may differ in how well they represent actual biological processes for the assessed substance.  EPA's Risk Characterization Handbook also recommends that risk characterizations present estimates demonstrating the impact on the assessment of alternative choices, data, models and assumptions [].  Some of the major sources of uncertainty and variability in deriving cancer risk values are described more fully below.  

(1) The qualitative similarities or differences between tumor responses observed in experimental animal bioassays and those which would occur in humans is a source of uncertainty 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 tumors would occur in humans.   However, unless scientific support is available to show otherwise, EPA assumes that 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, data 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).  Uncertainty in dose estimates may lead to either over or underestimation of risk.

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals to humans, EPA uses scaling methodologies (relating expected response to differences in physical size of the species), which introduce another source of uncertainty.  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 associated with limitations in the test data and the correctness of scaled estimates.  

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal data, are generally developed using a  benchmark dose (BMD) analysis to estimate a dose at which there is a specified excess risk of cancer, which is used as the point of departure (or POD) for the remainder of the calculation.  Statistical uncertainty in developing a POD using a benchmark dose (BMD) approach is generally addressed though use of the 95% lower confidence limit on the dose at which the specified excess risk occurs (the BMDL), decreasing the likelihood of understating risk.  EPA has generally utilized the multistage model for estimation of the BMDL using cancer bioassay data (see further discussion below).

(5) Extrapolation from high to low doses is an important, and potentially large, source of uncertainty in cancer risk assessment.  EPA uses different approaches to low dose risk assessment (i.e., developing estimates of risk for exposures to environmental doses of an agent from observations in experimental or 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 causes cancer).  EPA's Cancer Guidelines express a preference for the use of reliable, compound-specific, biologically-based risk models when feasible; however, such models are rarely available.  The mode of action for a pollutant (i.e., the manner in which a pollutant causes cancer) is a key 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.  Linear 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 whose 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.  

EPA 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 uncertainty 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 generally provide a health protective approach.  Note that another source of uncertainty is the characterization of low-dose nonlinear, non-threshold relationships.  The National Academy of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit models) in representing dose response relationships due to the variability in response within human populations.  Another National Research Council (NRC) report [] suggests that models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped dose-response functions for a population.  This report notes sources of variability in the human population:  "One might expect these individual tolerances to vary extensively in humans depending on genetics, coincident exposures, nutritional status, and various other susceptibility factors..."   Thus, if a distribution of thresholds approach is considered for a carcinogen risk assessment, application would depend on ability of modeling to reflect the degree of variability in response in human populations (as opposed to responses in bioassays with genetically more uniform rodents).  Note also that low dose linearity 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 chemical exposures and biological processes.

As noted above, EPA's current approach to cancer risk assessment typically utilizes a straight line approach from the BMDL.  This is equivalent to using an upper confidence limit on the slope of the straight line extrapolation.  The impact of the choice of the BMDL on bottom line risk estimates can be quantified by comparing risk estimates using the BMDL value to central estimate BMD values, although these differences are generally not a large contributor to uncertainty in risk assessment (Subramaniam et. al., 2006) [].  It is important to note that earlier EPA assessments, including the majority of those for which risk values exist today, were generally developed using the multistage model to extrapolate down to environmental dose levels and did not involve the use of a POD.  Subramaniam et. al. (2006) also provide comparisons indicating that slopes based on straight line extrapolation from a POD do not show large differences from those based on the upper confidence limit of the multistage model.

(6) Cancer risk estimates do not generally make specific adjustments to reflect the variability in response within the human population  --  resulting in another source of uncertainty in assessments.  In the diverse human population, some individuals are likely to be more sensitive to the action of a carcinogen than the typical individual, although compound-specific data to evaluate this variability are generally not available.  There may also be important life stage differences in the quantitative potency of carcinogens and, with the exception of the recommendations in EPA's Supplemental Cancer Guidance for carcinogens with a mutagenic mode of action, risk assessments do not generally quantitatively address life stage differences.  However, one approach used commonly in EPA assessments that may help address variability in response is to extrapolate human response from results observed in the most sensitive species and sex tested, resulting typically in the highest URE which can be supported by reliable data, thus supporting estimates that 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 intended 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 of 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 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.  However, 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.  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.  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 uncertainty and variability.
   
EPA begins by 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 typically chooses the relevant 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 significantly elevated (no observed adverse effect level --  NOAEL); or (3) use of the lowest observed adverse effect level (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 extrapolating 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 extrapolating 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, default adjustment values are selected for the individual UFs. For each type of uncertainty (when relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the cumulative UF value leading to a downward adjustment of 10-3000 fold from the selected POD.  An UF of 3 is used when the 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) Heterogeneity among humans is a key source of variability as well as uncertainty.  Uncertainty related to human variation is considered in extrapolating doses from a subset or smaller-sized population, often of one sex or of a narrow range of life stages (typical of occupational epidemiologic studies), to a larger, more diverse population.  In the absence of pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty associated with human variation.  Human 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 applied to reflect a known lack of significant variability among humans.

	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, there are uncertainties in such extrapolations.  In the absence of data to the contrary, the typical approach is to use the most relevant endpoint from the most sensitive species and the most sensitive sex in assessing risks to the average human.  Typically, compound specific data to evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty in this extrapolation.  Size-related differences (allometric relationships) indicate that typically humans are more sensitive than rodents when compared on a mg/kg/day basis.  The default choice of 10 for the interspecies UF is consistent with these differences.  For a specific chemical, differences in species responses may be greater or less than this value.

      Pharmacokinetic models are useful to examine species differences in pharmacokinetic processing and associated uncertainties; however, such dosimetric adjustments are not always possible.  Information may not be available to quantitatively assess toxicokinetic or toxicodynamic differences between animals and humans, and in many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to account for expected species differences and associated uncertainty in extrapolating from laboratory animals to humans in the derivation of a reference value.  If information on one or the other of these components is 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 reference values for chronic exposures where only 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 typically applied unless the available scientific information supports use of a different value.

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

There is no RfD or other comparable chronic health benchmark value for lead compounds.  Thus, to address multipathway human health and environmental risks associated with emissions of lead from this facility, 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 exposure, 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 analyses 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 levels and loss of IQ points in children (73FR at 66981).  In considering the evidence and risk analyses and their associated uncertainties, the EPA Administrator noted his view that there is no evidence- or risk-based bright line that indicates a single appropriate 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 indicator, 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 analyses, and their associated 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 with dose-response values developed for many of the other HAP, particularly those HAP for which no human health data exist.   In 1988, EPA's IRIS program reviewed the health effects data regarding lead and its inorganic compounds and determined that it would 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 development of air and water quality criteria by the Agency's Office of Health and Environmental Assessment (OHEA) in support of regulatory decision-making by the Office of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water (ODW). By comparison 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, particularly changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral development, may occur at blood lead levels so low as to be essentially 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 IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-92-1), http://www.epa.gov/iris/subst/0277.htm. 

We note further that because of the multi-pathway, multi-media impacts of lead, the risk assessment supporting the NAAQS considered direct inhalation exposures and indirect air-related multi-pathway exposures from industrial sources like primary and secondary lead smelting operations.  It also considered 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 results 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 protect public health with an adequate margin of safety, 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 variability and uncertainty 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 based 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 typically 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.  

5 References
