1

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
2

Table of Contents

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

2 Methods.............................................................................................................................. 5

2.1 Emissions and source data........................................................................................... 5

2.2 Dispersion modeling for inhalation exposure assessment........................................... 6

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

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

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

2.6 Dose-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
3

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
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1 Introduction

Section 112 of the Clean Air Act (CAA) establishes a two-stage regulatory process for 

addressing emissions of hazardous air pollutants (HAPs) from stationary sources. In the first 

stage, section 112(d) requires the Environmental Protection Agency (EPA, or the Agency) to 

develop technology-based standards for categories of sources (e.g., petroleum refineries, pulp 

and paper mills, etc.) [1]. EPA has 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” [2]. We received the final SAB 

report on this review in May of 2010 [3]. Where appropriate, we have responded to the key

messages from this review in developing our current risk assessments and we will be 

continuing our efforts to improve our assessments by incorporating updates based on the SAB 

recommendations as they are developed and become available. Our responses to the key

recommendations of the SAB are outlined in a memo entitled, “EPA’s Actions in Response to 

Key Recommendations from the SAB Review of RTR risk Assessment Methodologies” [4].

This document contains the methods and the results of baseline risk assessments performed 

for the Secondary 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.
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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 code1, 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.

 

1 The tagging of data with MACT codes allows EPA to determine reductions attributable to the MACT program. 

The NEI associates MACT codes corresponding to MACT source categories with stationary major and area 

source data. MACT codes are assigned at the process level for the point source. 
6

2.2 Dispersion modeling for inhalation exposure assessment

Both long- and short-term inhalation exposure concentrations and associated health risk from 

each facility in the source category of interest were estimated using the Human Exposure 

Model in combination with the American Meteorological Society/EPA Regulatory Model 

dispersion modeling 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-thescience Gaussian plume dispersion model that is preferred by EPA for modeling point, area, 

and volume sources of continuous air emissions from facility applications [5]. Further details 

on AERMOD can be found in the AERMOD Users Guide [6]. The model is used to develop 

annual average ambient concentrations through the simulation of hour-by-hour dispersion 

from the emission sources into the surrounding atmosphere. 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 
7

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 shortterm 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)[7]. In 
8

that report, the Texas Environmental Research Consortium Project compared hourly and 

annual emissions data for volatile organic compounds for all facilities in a heavilyindustrialized 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 99th

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 shortand 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.
9

We did not address long-term migration nor population growth or decrease over 70 years, 

instead basing the assessment on the assumption that each person’s predicted exposure is 

constant over the course of their lifetime which is assumed to be 70 years. In assessing cancer 

risk, we 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:
10

 Peak 1-hour emissions were assumed to equal 10 times the average 1-hour emission 

rates except in the case of fugitive dust (see section 2.2)

 For facilities with multiple emission points, peak 1-hour emissions were assumed to 

occur at all emission points at the same time.

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

were assumed to be the sum of the maximum concentrations due to each emission 

point, regardless of whether those maximum concentrations occurred during the same

hour. 

 Worst-case meteorology (from one year at the nearest NWS site) was assumed to 

occur at the same time the peak emission rates occur. The recommended EPA localscale dispersion model, AERMOD, was used for simulating atmospheric dispersion

during this worst-case hour.

 A person was assumed to be located 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)2. The PB-HAP compounds or compound 

classes are identified for the screening from the EPA’s Air Toxics Risk Assessment Library

[8], and they are cadmium compounds, 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 

 

2 Although the two-letter chemical symbol for lead is Pb, in this assessment PB-HAP refers to the many air 

pollutants known to be persistent and bioaccumulative in the environment. In instances where the report is 

specifically referring to lead, it is spelled out (i.e., the two-letter chemical symbol for lead is not used in this 

document). 
11

recently-developed screening scenario which was developed for use with the TRIM.FaTE3

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.4 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 multipathway impacts. For the purpose of developing screening emission rates for our multipathway 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 PBHAP source who consumes contaminated produce grown on the farm, as well as 

contaminated meat and animal products raised on the farm. The farming scenario also 

accounts for incidental ingestion of 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 

 

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

4 EPA’s Human Health Risk Assessment Protocol (HHRAP) for Hazardous Waste Combustion Facilities;

http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume1
12

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

consumption5. 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 90th 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 μg/m3based 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 μg/m3). 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 

 

5

In both scenarios, exposure via drinking water was not considered because it is unlikely that humans would use 

surface waters as a drinking water source. Groundwater, which is a likely source of drinking water, also was not 

included in the exposure scenarios because contamination of groundwater aquifers by air deposition sources was 

not expected to be significant. For dioxin, exposure via breast milk was considered in the farming scenario as 

well as the recreational fishing scenario, but not for the three recreational fishing subpopulations (Hispanic, 

Laotian, and Vietnamese descent) since subpopulation ingestion rates were only applicable to adult males. The 

breast milk pathway was not considered with respect to mercury exposure due to a current lack of data regarding 

this pathway.
13

example, via consumption of water from untreated local sources or ingestion of locally grown 

food. Nevertheless, the Administrator judged that such a standard would protect, with an 

adequate margin of safety, the health of 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

2.6.1 Sources of chronic dose-response information 

Dose-response assessment (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAP reported in the emissions inventory for the Secondary

Lead Smelting sources were based on the EPA Office of Air Quality Planning and Standards’ 

(OAQPS) existing recommendations for HAP [9], also used for NATA [10]. This 

information has been obtained from various sources and prioritized according to (1) 

conceptual consistency with EPA risk assessment guidelines and (2) level of peer review 

received. The prioritization process was aimed at incorporating into our assessments the best 
14

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 μg/m3in 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) [11] 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) [12] 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 
15

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 assessments6: “The 

guidelines for developing chronic inhalation exposure levels incorporate many

recommendations of the U.S. EPA [13] and NAS [14].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [15]. 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 [16], 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 doseresponse 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 

 

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

for the Determination of Non-cancer Chronic Reference Exposure Levels. Air Toxicology and Epidemiology 

Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency. 

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)
16

of 2011 (http://www.nap.edu/catalog.php?record_id=13142). EPA is reviewing the public 

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 into 

individual compounds. As a result, it is necessary to apply the same simplifying 

assumptions to assessments that were used for the 1999 NATA study [17]. The NATA 

approach partitions POM into eight different non-overlapping “groups” that are modeled 

as separate pollutants. Each POM group comprises POM species of similar carcinogenic 

potency, for which we can apply the same URE. 

The emissions inventory for the Secondary 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).
17

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

Carcinogens

URE (unit risk estimate for cancer)7= cancer risk per μg/m3of 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 Number8 URE5

(1/μg/m3)

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 9

EPA OAQPS

 Chrysene 218019 1.1E-05 CAL

 Benz[a]Anthracene 56553 1.1E-04 CAL

 Benzo[b]Fluoranthene 205992 1.1E-04 CAL

 Benzo[k]Fluoranthene 207089 0.00011 CAL

 Indeno[1,2,3-c,d]Pyrene 193395 0.00011 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

 

7 The URE is the upper-bound excess cancer risk estimated to result from continuous lifetime exposure to an 

agent at a concentration of 1 μg/m3in air. URE’s are considered upper bound estimates meaning they represent 

a plausible upper limit to the true value. 

8 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have 

used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAPs.

9 Assigned the URE associated with a mixture of POM compounds having a similar potency. Details of this 

method, also used in the 1999 National Air Toxics Assessment, are available at 

http://www.epa.gov/ttn/atw/nata1999/99pdfs/pomapproachjan.pdf
18

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

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average 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 Number10

SF

(1/mg/kg/d) Source

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 150000 EPA ORD10

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

 

10 The assessment report for dioxin is available in the docket for this Secondary Lead Smelting rulemaking (see: 

Health Assessment Document for Polychlorinated Dibenzo-p-Dioxins; September, 1985)
19

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

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of 

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 Number6 RfC 

(mg/m3)

Source11

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

 

11 The descriptors L (low), M (medium), and H (high) have been added for IRIS RfC values to indicate the 

overall level of confidence in the RfC value, as reported in the IRIS file.
20

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

RfC (reference inhalation concentration) = an estimate (with uncertainty spanning perhaps an order of 

magnitude) of a continuous inhalation exposure to the human population (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 Number6 RfC 

(mg/m3)

Source11

 Gaseous Divalent Mercury

 Particulate Divalent Mercury

Naphthalene 91203 0.003 IRIS -- M

Nickel 186 0.00009 ATSDR

o-Cresol 95487 0.6 (mixed) CAL

Phenol 108952 0.2 CAL

Propionaldehyde 123386 0.008 IRIS -- L/M 

Selenium 7782492 0.02 CAL

Styrene 100425 1 IRIS -- M

Tetrachloroethylene 79016 0.6 CAL

Trichloroethylene 79016 0.6 CAL

Toluene 108883 5 IRIS -- H

Xylenes (Mixture of o, m, and p Isomers) 1330207 0.1 IRIS

 m-Xylene 108383 0.1 IRIS -- M

 o-Xylene 95476 0.1 IRIS -- M

 p-Xylene 106423 0.1 IRIS -- M

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

inhalation exposure assessments are based on the existing recommendations of OAQPS for 

HAPs [18]. Depending on availability, the results from screening acute assessments are 

compared to both “no effects” reference levels for the general public, such as the California 

Reference Exposure Levels (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.

12

 

12 See: U.S. EPA. Graphical Arrays of Chemical-Specific Health Effect Reference Values for Inhalation 

Exposures [Final Report]. (U.S. Environmental Protection Agency, Washington, D.C., EPA/600/R-09/061, 

2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)
21

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. [19]. 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/m3) 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/m3) of a substance above 

which it is predicted that the general population, including susceptible individuals, could 
22

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) [20] 

for acute exposures at three different levels of severity. These guidelines represent 

concentrations for exposure of the general population (but not particularly sensitive persons) 

for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), 

irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3). 

ERPG values (http://www.aiha.org/1documents/Committees/ERP-erpglevels.pdf) are

described in their supporting documentation as 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.
23

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) 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
24

2.7 Risk characterization

2.7.1 General

The final product of the risk assessment is the risk characterization, in which the information 

from the previous steps is integrated and an overall conclusion about risk is 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 [21] specifies that a risk characterization “be prepared 

in a manner that is clear, transparent, reasonable, and consistent with other risk 

characterizations of similar scope prepared across programs in the Agency.” These principles 

of transparency and consistency have been reinforced by the Agency’s Risk Characterization 

Handbook [22], in 2002 by the Agency’s information quality guidelines [23], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis13, 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 [24], we applied 

EPA’s Supplemental Guidance for Assessing Susceptibility from Early-Life Exposure to 

Carcinogens [25]. This guidance has the effect of adjusting the URE by factors of 10 (for 

children aged 0-1), 3 (for children aged 2-15), or 1.6 (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 

 

13Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis

(September 19, 2007), From Susan E. Dudley, Administrator, Office of Information and Regulatory Affairs, 

Office of Management and Budget; and Sharon L. Hays, Associate Director and Deputy Director for Science, 

Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)
25

expresses carcinogenic potency for compounds in this group in terms of benzo[a]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 [26] 

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.
26

2.7.2 Mixtures

Since most or all receptors in these assessments receive exposures to multiple pollutants 

rather than a single pollutant, we estimated the aggregate health risks associated with all the 

exposures from a particular source category combined.

To combine risks across multiple carcinogens, our assessments use the mixtures guidelines’ 

[27,28] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

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

cause adverse health effects via 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.
27

3 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.
28

Table 3.1-1 Summary of Emissions from the Secondary Lead Source Category and Availability of 

Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(15 facilities 

in NPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? 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   
29

Table 3.1-1 Summary of Emissions from the Secondary Lead Source Category and Availability of 

Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(15 facilities 

in NPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

o-Cresol 0.3 14 

1,3-Dichloropropene 0.3 14  

Formaldehyde 0.2 14   

Nickel compounds 0.1 15   

Mercury 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 μg/m3). 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, 
30

rolling three-month average, measured as total suspended particles (TSP). The secondary (welfare-based) standard is 

identical to the primary standard. 

d Most of the test results for mercury emissions for this industry were below detection limit. The emissions estimates 

presented in this table are based on the assumption that all the non-detect test values were at the level of the detection limit. 

Therefore, these estimated emissions for mercury are clear overestimates. We conclude that the true amounts of 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.
31

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)14

Number of Facilities With Potential for Acute 

Effects 9 arsenic compounds

Population Exposure

Number of People Living Within 50 

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

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1

million 0 n/a

Greater than or equal to 10 in 1 

million 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

 

14 The REL is the only available acute health benchmark value for arsenic. All other pollutants had HQ values 

less than or equal to 1.
32

Based upon the REL, the only available acute health benchmark value for emissions of 

arsenic, screening-level acute HQ 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.

15

 

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 nondetect 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) facilities16. 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 90th 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 

 

15 Individual facility acute HQ values for all facilities can be found in Appendix 5, Table 3. Acute HQ values 

exceeding a value of 1 were as follows: 2, 2, 2, 2, 3, 4, 5, 20, and 20

16 Site selection criteria is described in detail in section 2.5.1 of the document Human Health Multipathway 

Residual Risk Assessment for the Secondary Lead Smelting Source Category, which can be found in the docket 

for the proposed rule. In brief, the criteria for selecting these two facilities as case studies included emission 

rates of PB-HAPs, proximity to water bodies, proximity to farmland, average rainfall, average wind speed and 

direction, smelting furnace type, local change in elevation, and geographic representativeness of sites throughout 

the US.
33

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

updated facility emissions information. As a result, we now estimate for both the RME and 

CTE scenarios, that the MIR cancer risk from POM and dioxins is less than 1 in a million for 

both 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.

17

 Moreover, this analysis estimates that 3-

month maximum lead concentrations could be up to about 20 times the NAAQS for lead18 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 

NAAQS19. 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.

 

17 For all facilities, the percent contribution of fugitive and stack emissions to modeled ambient lead 

concentrations has only been estimated for the model receptor representing the site of maximum lead impact. 

18 Fugitive emissions are estimated to account for about 99% of this facility’s modeled lead concentration at the 

site of maximum lead impact (see previous footnote).

19 The highest modeled 3-month ambient lead concentration at census blocks around the Sanders Lead Co., 

Buick Resource Recycling Facility, and Exide Technologies, Reading Recycling are estimated to be up to 3, 2, 

and 2 times the NAAQS for lead respectively. All other facilities had 3-month ambient lead concentrations at all 

census blocks below the NAAQS for lead.
34

Table 3.2-3 Secondary Lead Smelting Facility Modeled Maximum Ambient Lead Concentrations

(rolling three-month average values)

Facility Name City State

Highest 

Modeled Lead 

Concentration 

(μg/m3)

Concentration is 

X times higher 

than the NAAQS20

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

3.3 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 

 

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

effective work practices. We also considered a reduction of the MACT limit that would result 

in reduced actual stack emissions for two of these facilities: Doe Run and Exide Technologies 

Baton Rouge Smelter. Based on this scenario, we estimated (based on use of the same

modeling assumptions and methodologies used for the baseline risk assessment) that all but 2

facilities would meet the NAAQS for lead. Of these 2 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 μgs/m3). 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 μgs/m3). 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).

4 General discussion of uncertainties and how they have been 

addressed

4.1 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 sitespecific 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 
36

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 postcontrols, 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 
37

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 anglers21, we used fish 

consumption information for distinct fisher subpopulations that are known to have higher fish 

consumption rates. The data were obtained from Shilling, et al. (2010).

22

 In this publication, 

the authors provide fish consumption information for different ethnic groups including 

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 Handbook23 and the 90th 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 95th 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 90th 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.

 

21

 Data for the general U.S. population of recreational anglers was obtained from: EPA 2002, “Estimated Per 

Capita Fish Consumption in the United States, Office of Water, Office of Science and Technology, Washington, 

D.C., EPA-821-C-02-003. August 2002.

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

23 EPA’s 1997 Exposure Factors Handbook is available at 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464
38

4.2 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 [29] (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 
39

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).24 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.25 

When developing an upper bound estimate of risk and to provide risk values that do not 

underestimate risk, EPA generally relies on conservative default approaches.26 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 [30]. 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 

 

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

25 The exception to this is the URE for benzene, which is considered to cover a range of values, each end of 

which is considered to be equally plausible, and which is based on maximum likelihood estimates.

26 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An Examination of EPA Risk Assessment Principles and Practices, 

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
40

tumors would occur in humans.27 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 

 

27 Per the EPA Cancer Guidelines: “The default option is that positive effects in animal cancer studies indicate 

that the agent under study can have carcinogenic potential in humans.” and “Target organ concordance is not a 

prerequisite for evaluating the implications of animal study results for humans.”
41

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 sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

determination. This approach is designed to not underestimate risk in the face of uncertainty

and variability. EPA believes that linear dose-response models, when appropriately applied 

as part of EPA’s cancer risk assessment 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 [31] 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) [32]. It is important to note that 

earlier EPA assessments, including the majority of those for which risk values exist today, 

were generally developed using the multistage model to extrapolate down to 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 
42

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 values28 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. 

 

28

 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf. 
43

These values provide an estimate (with uncertainty spanning perhaps an order of magnitude) 

of daily oral exposure (RfD) or of a continuous inhalation exposure (RfC) to the human 

population (including sensitive subgroups) that is likely to be without an appreciable risk of 

deleterious effects during a lifetime.29 To derive values that are intended to be “without 

appreciable risk,” EPA’s methodology relies upon an uncertainty factor (UF) approach (U.S. 

EPA, 1993, 1994) which includes consideration of both uncertainty and variability.

 

EPA begins by evaluating all of the available peer-reviewed literature to determine noncancer 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., interindividual 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.

 

29 See IRIS glossary
44

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.
45

There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with 

emissions of lead from this facility, ambient lead concentrations were compared to the 

NAAQS for lead. In developing the NAAQS for lead, EPA considered human health 

evidence reporting adverse health effects associated with lead 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/m3, 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 airrelated multi-pathway exposures from industrial sources like primary and secondary lead 
46

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 chemicalspecific 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. 
47

5 References

 

1. US EPA. National Emission Standards for Hazardous Air Pollutants. 

http://www.epa.gov/ttn/atw/mactfnlalph.html. 

2 US EPA, 2009. Risk and Technology Review (RTR) Risk Assessment Methodologies: For 

Review by the EPA’s Science Advisory Board with Case Studies – MACT I Petroleum Refining 

Sources and Portland Cement Manufacturing. EPA-452/R-09-006. 

http://www.epa.gov/ttn/atw/rrisk/rtrpg.html.

3. US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EP

A-SAB-10-007-unsigned.pdf

4. US EPA, 2010. Memorandum from Dave Guinnup to RTR Wood Furniture Docket, entitled, 

“EPA’s Actions in Response to Key Recommendations of the SAB Review of RTR Risk 

Assessment Methodologies.

5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final Rule. 

40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005/November/Day-09/a21627.htm

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