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Draft Residual Risk Assessment for the Primary Lead 

Smelting Source Category 

by 

EPA’s Office of Air Quality Planning and Standards 

Office of Air and Radiation 

December 2010 
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Table of Contents 

Table of Tables ................................................................................................................... 3

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

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

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

2.2 Dispersion modeling for inhalation exposure assessment ........................................ 5

2.3 Estimating human inhalation exposure ..................................................................... 7

2.4 Acute Risk Screening and Refined Assessments ...................................................... 8

2.5 Multipathway and environmental risk screening and refined assessments .............. 9

2.6 Dose-Response Assessment .................................................................................... 11

2.6.1 Sources of chronic dose-response information ................................................ 11

2.6.2 Sources of acute dose-response information .................................................... 15

2.7 Risk Characterization .............................................................................................. 17

2.7.1 General ............................................................................................................. 17

2.7.2 Mixtures ........................................................................................................... 19

2.7.3 Facility-wide Risks .......................................................................................... 20

3. Risk Results for the Primary Lead Smelting Source Category ................................. 20

3.1 Source Category Description and Results ............................................................... 20

3.2 Baseline Risk Characterization ............................................................................... 22

3.3 Assessment of Emission Reductions Scenarios ...................................................... 26

4. General Discussion of Uncertainties and How They Have Been Addressed .............. 26

4.1 Exposure Modeling Uncertainties ........................................................................... 26

4.2 Uncertainties in the Dose-Response Relationships ................................................. 26

5. References .................................................................................................................... 35

 
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Table 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

........................................................................................................................................... 14

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

Noncarcinogens................................................................................................................. 14

Table 2.6-2 Dose-Response Values for Acute Exposure ................................................. 17

Table 3.1-1 Summary of Emissions from the Primary Lead Smelting Source Category 

and Availability of Dose-Response Values ...................................................................... 22

Table 3.2-1 Summary of Source Category Level Inhalation Risks for the Primary Lead 

Smelting Facility – non-lead HAP .................................................................................... 23

Table 3.2-2 Summary of Lead Concentrations Relative to the NAAQS Given Actual 

2008 and MACT Allowable Emissions ............................................................................ 25

 
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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 technologybased 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 to this rulemaking docket [4]. 

This document contains the methods and the results of baseline risk assessments (i.e., 

after the implementation of the respective MACT standards) performed for the Primary 

Lead Smelting source category. The methods discussion includes descriptions of the 

methods used to develop refined estimates of chronic inhalation exposures and human 

health risks for cancer and noncancer endpoints, as well as descriptions of the methods 

used to assess the potential for acute health risks, chronic non-inhalation health risks, and 

adverse environmental effects. 

2. Methods 

2.1 Emissions and source data 

Since there is only one primary lead smelter currently operating in the U.S., the data set 

for the source category is represented by the data set for this one facility. The preliminary 

emissions dataset was based on data in the 2002 National Emissions Inventory (NEI) 
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Final Inventory, Version 1 (made publicly available on February 26, 2006), and the 2005 

National Emissions Inventory (NEI), version 2.0 (made publicly available in October 

2008). The 2005 NEI was updated to develop the 2005 National Air Toxics Assessment 

(NATA) Inventory. NATA inventory updates for the primary lead category included SIP 

data provided by the state of MO to EPA. The 2005 NATA inventory was used with 

updated 2008 data received in an Information Collection Request (ICR) response from 

the facility. In addition, we performed an engineering review of the information using 

EPA engineers who were directly involved in the development of the MACT standards 

for the source category and/or who have extensive knowledge of the characteristics of 

this industry. The goal of the engineering review was to identify readily apparent 

limitations and issues with the emissions data and/or source parameters (particularly 

those that would have the potential to influence risk estimates) and to make changes to 

the data set where possible to address these issues and decrease the uncertainties 

associated with the assessment. 

Details on the development of the emissions and source data for this source category are 

discussed in Section 3. The emissions data and modifications made to the NEI data are 

discussed in Appendix 1, entitled “Emissions Inventory Support Memorandum.” 

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-AERMOD). The approach used in 

applying this modeling system is outlined below, and further details are provided in 

Appendix 2. HEM-AERMOD 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-AERMOD system, AERMOD version 09292, is a 

state-of-the-science Gaussian plume dispersion model that is preferred by EPA for 

modeling point, area, and volume sources of continuous air emissions from facility 

applications [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-AERMOD modeling system is to predict 

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

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

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

Modeling Option Selected Parameter for chronic exposure 

Type of calculations Hourly Ambient Concentration 

Source type Point, area represented as pseudo point source 

Receptor orientation Polar (13 rings and 16 radials) 

Discrete (census block centroids) 

Terrain characterization Actual from USGS 1-degree DEM data 

Building downwash Not Included 

Plume deposition/depletion Not Included 

Urban source option No 

Meteorology 1 year site-specific 

In HEM-AERMOD, meteorological data are ordinarily selected from a list of 158 

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. However, in this 

instance, site-specific meteorological data for the one facility in this source category were 

supplied by the state of Missouri and used for the modeling. The data provided by the 

state of Missouri were for eight quarters (i.e., eight three-month periods) from April 1997 

through June 1999. To obtain one year of meteorological data, we used the middle 

portion of these data, the year 1998, in our modeling. 

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. 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 generally 

apply the general assumption to all source categories that the maximum one-hour 

emission rate from any source was ten times the average annual hourly emission rate for 
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that source. The average hourly emissions rate is defined as the total emissions for a year 

divided by the total number of operating hours in the year. This choice of a factor of ten 

for screening was originally based on engineering judgment. To develop a more robust 

peak-to-mean emissions factor, and in response to one of the key messages from the SAB 

consultation on our RTR Assessment Plan, we performed an analysis using a short-term 

emissions dataset from a number of sources located in Texas (originally reported by 

Allen et al. 2004)[7]. In that report, the Texas Environmental Research Consortium 

Project compared hourly and annual emissions data for volatile organic compounds for 

all facilities in a heavily-industrialized 4-county area (Harris, Galveston, Chambers, and 

Brazoria Counties, TX) over an eleven-month time period in 2001. We obtained the 

dataset and performed our own analysis, focusing 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 11 times 

the annual average. Based on these results, we chose the factor of ten for all initial 

screening; it is intended to cover routinely 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. 

 

Census block elevations for HEM-AERMOD modeling were determined nationally from 

the US Geological Service 1-degree digital elevation model (DEM) data files, which have 

a spatial resolution of about 90 meters. Elevations of polar grid points used in estimating 

short- and long-term ambient concentrations were assumed to be equal to the highest 

elevation of any census block falling within the polar grid sector corresponding to the 

grid point. If a sector does not contain any blocks, the model defaults the elevation to 

that of the nearest block. If an elevation is not provided for the emission source, the 

model uses the average elevation of all sectors within the innermost model ring. 

In addition to using receptor elevation to determine plume height, AERMOD adjusts the 

plume’s flow if nearby elevated hills are expected to influence the wind patterns. For 

details on how hill heights were estimated and used in the AERMOD modeling see 

Appendix 2. 

2.3 Estimating human inhalation exposure 

We used the annual average ambient air concentrations 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 shortterm 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 1996, 1999, 2002, and 2005 NATA assessments (which modeled daily activity using 

EPA’s HAPEM model) suggests that, given our current understanding of 
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microenvironment concentrations and daily activities, modeling short-term activity 

would, on average, reduce risk estimates about 25% for particulate HAPs; it will also 

reduce risk estimates for gaseous HAPs, but typically by much less. Second, basing 

exposure estimates on average ambient concentrations at census block centroids may 

underestimate or overestimate actual exposure concentrations at some residences. 

Further reducing exposure estimates for the most highly exposed residents by modeling 

their short-term behavior could add a systematic low bias to these results. 

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

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

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

cancer risk, we generally estimated three metrics: the maximum individual risk (MIR), 

which is defined as the risk associated with a lifetime of exposure at the highest 

concentration; the population risk distribution; and the cancer incidence. The assumption 

of not considering short- or long-term population mobility does not bias the estimate of 

the theoretical MIR nor does it affect the estimate of cancer incidence since the total 

population number remains the same. It does, however, affect the shape of the 

distribution of individual risks across the affected population, shifting it toward higher 

estimated individual risks at the upper end and reducing the number of people estimated 

to be at lower risks, thereby biasing the risk estimates high. 

When screening for potentially significant acute exposures, we used an estimate of the 

highest hourly ambient concentration at any off-site location as the surrogate for the 

maximum potential acute exposure concentration for any individual. 

2.4 Acute Risk Screening and Refined Assessments 

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 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. 

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 acute health risks (i.e., it “screens out”), or that it requires further, more refined 

assessment. A refined 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 

would be needed to refine the assessment; in others, lesser amounts of site-specific data 
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could be used to determine that acute exposures are not a concern, and significant 

additional data collection would not be necessary. 

Acute health risk screening was performed as the first step. We used conservative 

assumptions for emission rates, meteorology, and exposure location. We used the 

following worst-case assumptions in our screening approach: 

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

emission rates. 

 Peak 1-hour emissions were assumed to occur at all emission points at the same 

time. 

 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 of local meteorology) was assumed to 

occur at the same time the peak emission rates occur. The recommended EPA 

local-scale dispersion model, AERMOD, is used for simulating atmospheric 

dispersion. 

 A person was assumed to be located downwind at the point of maximum impact 

during this same 1-hour period, but no nearer to the source than 100 meters. 

 The maximum impact was compared to multiple short-term health benchmarks 

for the chemical 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. In this case, this included 

use of a refined emissions multiplier to more accurately estimate the peak hourly 

emission rates from the average rates. 

2.5 Multipathway and environmental risk screening and refined 

assessments 

The potential for significant human health risks due to exposures via routes other than 

inhalation (i.e., multipathway exposures) or adverse environmental impacts was screened 

by first determining whether any sources emitted any hazardous air pollutants known to 

be persistent and bioaccumulative in the environment (PB-HAP). The PB-HAP 

compounds or compound classes are identified for the screening from the EPA’s Air 

Toxics Risk Assessment Library [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 two different PB-HAP were identified in the emissions inventory for the 

Primary Lead Smelting source category: lead and cadmium. 

Cadmium emissions were evaluated for potential non-inhalation risks and adverse 

environmental impacts using our recently developed screening scenario which was 
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developed for use with the TRIM.FaTE1

 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.2

 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 de minimis emission rates developed for screening potentially 

significant multi-pathway impacts. For the purpose of developing de minimis emission 

rates for our multi-pathway screening, we derived emission levels for each PB HAP at 

which the maximum human health risk would be 1 in a million for lifetime cancer risk or 

a hazard quotient of 1.0 for noncancer impacts. 

In evaluating the potential air-related multi-pathway risks from the emissions of lead 

compounds from the one facility in this source category, rather than developing a de 

minimis emission rate, we compared its maximum modeled 3-month rolling average 

atmospheric lead concentration at any off-site location with the current primary National 

Ambient Air Quality Standard (NAAQS) for lead, which is set to a level of 0.15 μg/m3

based on a rolling 3-month period with a not-to-be-exceeded form. 73 FR 66964. Values 

below the primary NAAQS are considered to have a low potential for significant multipathway risks because the primary NAAQS is set as to protect public health with an 

adequate margin of safety.. However, ambient air lead concentrations above the primary 

NAAQS, regardless of whether these concentrations occurred at a populated census 

block, are considered to pose a significant risk to public health. This is because: 1) the 

primary NAAQS for lead is a standard that applies anywhere in the ambient air; 2) the 

primary NAAQS was established to account for the air-related multi-pathway impacts of 

lead sources, which due to the multi-media transport of lead after it is deposited from the 

atmosphere, may be experienced by individuals in areas distal to the original site of 

deposition; and 3) lead levels above the primary NAAQS have been associated with 

significant neurological effects, including loss of IQ points in children. Moreover, we 

also consider this assessment to be a refined risk analysis given the combination of: 1) 

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; and 2) 

the site-specific dispersion modeling performed to develop the ambient concentration 

estimates. It should be noted, however, that this comparison to the NAAQS for lead does 

not account for possible population exposures to lead from sources other than the one 

being modeled; for example, via consumption of water from untreated local sources or 

ingestion of locally grown food. 

 

1

 EPA’s Total Risk Integrated Methodology (General Information) http://epa.gov/ttn/fera/trim_gen.html 2

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

http://www.epa.gov/epaoswer/hazwaste/combust/riskvol.htm#volume1 
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We further note that comparing ambient lead concentrations to the NAAQS for lead 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 ecological exposures to non PB-HAP from 

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 

threshold values 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 HAPs 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 HAPs, 

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 HAPs reported in the emissions inventory for 

the one facility in the Primary Lead Smelting source category was based on the EPA 

Office of Air Quality Planning and Standards’ existing recommendations for HAPs [9], 

also used for NATA 1999, 2002, and 2005 [10]. This information has been obtained 

from various sources and prioritized according to (1) conceptual consistency with EPA 

risk assessment guidelines and (2) level of peer review received. The prioritization 

process was aimed at incorporating into our assessments the best available science with 

respect to dose-response information. The recommendations are based on the following 

sources, in order of priority: 

1) US Environmental Protection Agency (EPA). EPA has developed dose-response 

assessments for chronic exposure for many of the pollutants in this study. These 

assessments typically provide a qualitative 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 
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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 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/m3

 in air.” The SF is “an 

upper bound, approximating a 95% confidence limit, on the increased cancer risk 

from a lifetime exposure to an agent. This estimate, [is] usually expressed in units of 

proportion (of a population) affected per mg/kg-day...” EPA disseminates doseresponse 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. 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 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 

assessments3

: “The guidelines for developing chronic inhalation exposure levels 

 

3

 Air Toxics Hot Spots Program, Risk Assessment Guidelines, Part III - Technical Support Document 
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incorporate many recommendations of the U.S. EPA [13] and NAS [14].” The noncancer 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 antimony 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 the HAP that lack dose-response values may pose 

an unacceptable risk and require further examination, or screen out from further 

assessment. 

2) The IRIS URE for nickel inhalation was derived from evidence of the carcinogenic 

effects of insoluble nickel compounds in crystalline form. Soluble nickel species, and 

insoluble species in amorphous form, do not appear to produce genotoxic effects by 

the same toxic mode of action as insoluble crystalline nickel. Nickel speciation 

information for some of the largest nickel-emitting sources (including oil combustion, 

coal combustion, and others) suggests that at least 35% of total nickel emissions may 

be soluble compounds. The remaining insoluble nickel emissions are not wellcharacterized. Consistent with this limited information, this assessment will 

conservatively assume that 65% of emitted nickel is insoluble and that all insoluble 

nickel is crystalline. The nickel URE (based on nickel subsulfide, and representative 

of pure insoluble crystalline nickel) was adjusted to reflect an assumption that 65% of 

the total mass of nickel may be carcinogenic. The noncancer dose-response values 

were not adjusted because noncancer effects of nickel are not thought to be limited to 

the crystalline, insoluble form. 

The emissions inventory for the primary lead smelting source category includes 

emissions of six 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) and (b). 

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

Carcinogens 

URE (unit risk estimate for cancer)4

 = cancer risk per μg/m3 of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System. 

Pollutant CAS Number5 URE

 (1/μg/m3) 

Source 

Arsenic Compounds 174 4.3E-03 IRIS 

Cadmium Compounds 7440439 1.8E-03 IRIS 

Chromium Compounds 

 Chromium VI Compounds 18540299 0.012 IRIS 

Nickel Compounds 186 4.8E-04 IRIS 

Table 2.6-1 (b) 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 and Disease 

Registry, NAAQS = National Ambient Air Quality Standard. 

Pollutant CAS Number RfC 

(mg/m3) 

Source6

Antimony Compounds 7440360 0.0002 IRIS-M 

Arsenic Compounds 174 0.000015 CAL 

Cadmium Compounds 176 0.00001 ATSDR 

Chromium Compounds 

Chromium VI Compounds 18540299 0.0001 IRIS-M 

Cobalt Compounds 178 0.0001 ATSDR 

Lead & Compounds 182 0.00015 NAAQS 

Nickel Compounds 186 0.00009 ATSDR 

 

4

 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/m3 in air. URE’s are considered upper bound estimates meaning they 

represent a plausible upper limit to the true value. 

5

 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. 

6

 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. 
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2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for the acute inhalation 

exposure assessment are based on the existing recommendations of OAQPS for HAPs 

[17]. 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 a newly released EPA document (Reference: 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.) 

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. [18]. 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 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 
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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 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) 

[19] 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 (ERPG1), 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 
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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.” As with other emergency levels, these levels reflect consideration of only 

single exposure events, not repeated exposures. 

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 Primary Lead Smelting source category includes 

emissions of two HAP with relevant and available quantitative acute dose-response 

threshold values. Specifically, REL values for arsenic and nickel are listed below in 

Table 2.6-2. 

Table 2.6-2 Dose-Response Values for Acute Exposure 

Pollutant 

CAS 

Number 

AEGL-1 

(1-hr) 

(mg/m3) 

AEGL-2 

(1-hr) 

(mg/m3) 

ERPG-1 

(mg/m3) 

ERPG-2

(mg/m3) REL 

Arsenic Compounds 7440382 0.0002

Nickel Compounds 7440020 0.006

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 [20] 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 
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the Agency.” These principles of transparency and consistency have been reinforced by 

the Agency’s Risk Characterization Handbook [21], in 2002 by the Agency’s information 

quality guidelines [22], and in the OMB/OSTP September 2007 Memorandum on 

Updated Principles for Risk Analysis7

, 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. 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. 

In addition, 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. 

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. 

 

7

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

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

Regulatory Affairs, Office of Management and Budget; and Sharon L. Hays, Associate Director and 

Deputy Director for Science, Office of Science and Technology Policy 

(http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf) 
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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. In this case, 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 truly a 

potential for significant acute risks. 

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’ [23,24] 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. To conform to 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 
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“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 thru emission 

data updates from industry and refined EPA profiles for the source category. In this 

instance, there are no other HAP emission sources present-- all HAP sources have been 

included in the risk analysis. Therefore, the facility-wide risk is the same as the source 

category risk; no separate facility-wide analysis was necessary. 

3. Risk Results for the Primary Lead Smelting Source Category 

3.1 Source Category Description and Results 

The primary lead smelting source category includes one facility that smelts lead-bearing 

ore concentrates to produce lead metal. This metal is used to make various construction 

and consumer products such as batteries, paint, glass, piping, and filler. Lead sulfide 

(PbS) ore concentrates are the main feed material to primary lead smelters. The primary 

lead smelting process consists of PbS concentrate storage and handling, sintering of ore 

concentrates, sinter crushing and handling, smelting of sinter to lead metal, drossing (i.e., 

removing the solid oxide deposits), refining and alloying of lead metal, and smelting of 

the drosses. Hazardous air pollutants (HAP) can be emitted during these steps as process 

emissions, process fugitive emissions, and fugitive dust emissions. Across all three of 

these types of emission points, lead and lead compounds are the main HAP emitted. 

Other emissions include other metal HAP, such as arsenic, antimony, and cadmium. 

As noted above, there is only one primary lead smelter currently operating in the U.S., 

and the NPRM data set for this source category contains data for this one facility. Figure 

3.1-1 shows the facility, modeling receptor locations along its boundary, as well as the 

locations of nearby ambient lead monitors. Figure 3.1-1 also identifies the census block 

MIR, and the location of the highest ambient lead concentration (see results in section 

3.2). The data set for this facility was developed based on site-specific information and 

represents a base year of 2008 (see section 2.1). A complete discussion of the creation of 

the NPRM data set can be found in the NPRM. 
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Figure 3.1-1 Discrete Receptors and Maximum Risk Receptors for the Doe Run 

Primary Lead Smelter in Herculaneum, Missouri 

The emissions in the primary lead smelting source category NPRM data set are 

summarized in Table 3.1-1. The total HAP emissions for the source category are 

approximately 21 tons per year. Based on these data, lead is the HAP emitted in the 

largest quantity and constitutes more than 99 percent of the total mass emitted of HAP. 

Persistent and bioaccumulative HAP (PB HAP) emissions reported in the NPRM dataset 

include lead and cadmium. 

 
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Table 3.1-1 Summary of Emissions from the Primary Lead Smelting Source Category and 

Availability of Dose-Response Values 

HAPa Emissions 

(tpy) 

Number of 

Facilities 

Reporting 

HAP (1 

facility in 

ANPRM 

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? 

Lead & Compounds 21 1 Yc 

Cadmium Compounds 0.1 1   

Antimony Compounds 0.03 1  

Arsenic Compounds 0.02 1   

Nickel Compounds 0.02 1   

Chromium Compounds 

Chromium III Compounds 0.005 1 

Chromium VI Compounds 0.0002 1  

Cobalt Compounds 0.005 1 

a Note 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. 

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. See tables 2.6-1 and 2.6-2 above. 

c

 In considering the air-related multipathway hazards for lead in this assessment, we used the National Ambient Air 

Quality Standards (NAAQS) for lead, which were adopted in October 2008 (http://www.epa.gov/air/lead/actions/html). 

The primary (health-based) standard is 0.15 μg/m3

, as a maximum or not-to-be-exceeded, rolling three-month average, 

measured as total suspended particles (TSP). The secondary (welfare-based) standard is equal to the primary. 

3.2 Baseline Risk Characterization 

This section presents the results of the preliminary risk assessment for the Primary Lead 

Smelting source category. The basic chronic inhalation risk estimates presented here are 

the maximum individual lifetime cancer risk, the maximum chronic hazard index, and the 

cancer incidence. We also present results from our acute inhalation impact 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, as well as a summary of the results of 

the maximum allowed emissions under the current MACT standards. . 

Table 3.2-1 summarizes the preliminary chronic and acute inhalation risk results for this 

source category for all HAP except for lead. The results of the chronic inhalation cancer 

risk assessment indicate the maximum lifetime individual cancer risk could be as high as 
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30 in a million with fugitive dust emissions of cadmium dominating the risk. The total 

estimated cancer incidence from this source category based on actual emission levels is 

0.0008 excess cancer cases per year, or one case in every 1,250 years. Approximately 

200 people were estimated to have cancer risks above 10 in a million, and approximately 

4,900 people were estimated to have cancer risks above 1 in a million. Finally, when 

considering MACT allowable emissions, the MIR remains 30 in a million. This is 

because fugitive dust emissions, which are driving the risk, are covered by a work 

practice standard under the MACT and therefore, MACT-allowable and actual fugitive 

dust emissions are equal. 

Table 3.2-1 Summary of Source Category Level Inhalation Risks for the Primary 

Lead Smelting Facility – non-lead HAP 

Result HAP “Drivers” 

Cancer Risks 

Maximum Individual Lifetime Cancer Risk 

(in 1 million) 30 cadmium compounds, arsenic compounds, nickel compounds 

Chronic Noncancer Risks 

Maximum Kidney Hazard Index 1 cadmium compounds 

Acute Noncancer Risks 

Maximum Acute Hazard Quotient 6 

0.4 

arsenic compounds (REL) 

nickel compounds (REL) 

Refined Acute Noncancer Risk 

Maximum Refined Acute Hazard Quotient 0.6 arsenic compounds (REL) 

Population Exposure 

Number of People Living Within 50 

Kilometers of the Facility Modeled 1,509,000 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 200 n/a 

Greater than or equal to 1 in 1 

million 4900 n/a 

Number of People Exposed to Noncancer Kidney Hazard Index:

 Greater than 1 0 n/a 

Estimated Cancer Incidence (excess cancer 

cases per year) 0.0008 n/a 

Contribution of HAP to Cancer Incidence 

 cadmium compounds 82% n/a 

 arsenic compounds 16% n/a 

 nickel compounds 2% n/a 

The maximum chronic noncancer TOSHI value is 1, with fugitive dust emissions of 

cadmium dominating those impacts. When considering MACT allowable emissions, for 

the reason provided above, the maximum chronic noncancer TOSHI value remains 1. 

Taken together, these results indicate no significant potential for chronic noncancer 

impacts. 
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Based on the acute REL value for arsenic, our screening analysis determined that the 

maximum off-site acute HQ value from this facility could be as high as 6, again 

dominated by the emissions of fugitive dust. Our refined acute assessment reduced the 

estimate to a maximum off-site acute HQ value of 0.6. The refined assessment took into 

consideration that an emissions factor of 10 times the average hourly emissions rate (see 

above, section 2.4) is not appropriate in this instance, given that fugitive emissions are 

minimized during the meteorological conditions associated with the worst-case shortterm impacts (i.e., during low-wind, stable atmospheric conditions). 

Table 3.2-2 presents the results of our lead impact analysis broken down by emission 

point considering actual 2008 emissions as well as the maximum emissions of lead that 

the MACT standard would have allowed for the 2008 base year. Results indicate that 

estimated 3-month rolling average lead concentrations resulting from this facility’s 

fugitive dust emissions could exceed the primary NAAQS for lead by as much as 50-fold. 

This analysis also indicates that emissions from the main stack do not result in estimated 

ambient lead concentrations that exceed the primary NAAQS anywhere within 50 km of 

the facility (likely due to stack height), and that emissions from furnace area stacks could 

result in estimated ambient lead concentrations that exceed the NAAQS under both actual 

2008 and MACT-allowable emissions scenarios. In addition, although refining operations 

were not subject to the emission limit under the MACT, results of this analysis indicate 

that actual emissions from the refining stacks could result in estimated ambient lead 

concentrations that exceed the NAAQS. This analysis also indicates that within 50 km of 

this facility, approximately 1,900 people could be exposed to ambient lead concentrations 

exceeding the primary NAAQS for lead. 

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 3.2-2 also indicate the potential for adverse 

environmental effects from emissions of lead. 

 
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Table 3.2-2 Summary of Lead Concentrations Relative to the NAAQS Given Actual 

2008 and MACT Allowable Emissions 

Emission Point

Actual 2008 

Emissions (tpy)

Maximum 

Impact- Actual 

Emissions

MACT Allowable 

Emissions1

Maximum 

ImpactAllowable 

Emissions

Main stack2 13.31 0.05 times the 

NAAQS 

65.8 0.25 times the 

NAAQS 

Refining stacks 2.74 3 times the 

NAAQS 

NA NA 

Furnace area: 

(controlled blast and 

drossing fugitives) 

1.81 2 times the 

NAAQS 

8.94 10 times the 

NAAQS 

Fugitive dust3 2.85 50 times the 

NAAQS 

2.85 50 times the 

NAAQS 

1 Allowables for the main stack and furnace area emission points are based on 1 lb of Pb/ton 

production (MACT limit that is also codified in the state permit); refining operations were not 

subject to the emission limit under the MACT. 

2 Main stack is the emission point for sinter machine, blast furnace and drossing operations. 

3 Fugitives covered by work practices under current MACT and were calculated via emission factors 

assuming compliance with the MACT.

In addition to the results presented above, for calendar year 2008, we also compared 

maximum AERMOD estimates of ambient lead concentrations with those measured at 4 

monitors in close proximity to the only facility in this source category. Specifically, we 

compared the maximum 3-month rolling average lead concentrations reported at the 

Main Street, Circle Street, South Cross, and Church Street monitors to the maximum 3-

month rolling average lead concentrations at model receptor locations at or near these 

monitoring sites. Results of this analysis indicate that with respect to the Main Street and 

Circle Street monitors, AERMOD underestimates 3-month maximum lead concentrations 

by approximately 4.6- and 3-fold respectively. With respect to the South Cross and 

Church Street monitors, AERMOD overestimates 3-month maximum lead concentrations 

by approximately 2.8- and 4.2-fold, respectively. Moreover, the maximum 3-month 

average lead concentration reported by any of these monitors is approximately equal to 

the maximum 3-month average lead concentration estimated by AERMOD at any of 

these monitoring sites (i.e., both the South Cross monitor and the Main Street AERMOD 

estimates indicate maximum 3-month average lead concentrations of 2.1 μg/m3

). Taken 

together, these results indicate that AERMOD estimates of ambient lead concentration 

provide a reasonable representation of the measured maximum lead concentrations 

present in the ambient air near this facility. 
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 3.3 Assessment of Emission Reductions Scenarios 

To inform possible risk management actions, we considered specific emission limits for 

those emission points that were modeled to result in lead concentrations in excess of the 

NAAQS (see Table 3-2.2). In so doing, we found that in order to meet the NAAQS for 

lead, fugitive dust emissions would have to be reduced by approximately 98% to 0.064 

tpy, refinery emissions would have to be reduced by approximately 66% to 0.94 tpy, and 

furnace area emissions would have to be reduced by approximately 52% to 0.86 tpy. 

Further, because the ambient impacts of each of the separate emission points do not 

overlap significantly on each other, we estimate that lead emissions from all emission 

points other than the main stack would have to be limited to a total of approximately 1.86 

tpy in order to ensure 3-month rolling average ambient lead concentrations do not exceed 

the primary lead NAAQS level of 0.15 μg/m3. Finally as noted above, emissions from 

the main stack (i.e., emission point for sinter machine, blast furnace and drossing 

operations) did not result in ambient lead concentrations in excess of the lead NAAQS at 

modeled locations within 50 km of the fenceline and thus, we are not proposing any 

reductions at the main stack in order to meet the NAAQS for lead. 

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 emission points, as well 

as to develop accurate estimates of the annual emission rates for all relevant HAP, the 

uncertainties in our emission inventory likely dominate the uncertainties in our exposure 

modeling estimates. The chronic exposure modeling uncertainties are considered 

relatively small in comparison, since we are using EPA’s refined local dispersion model 

with site-specific parameters and 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. 

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. 

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 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 
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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 [25] (herein referred to as Cancer Guidelines). “The primary goal of EPA 

actions is protection of human health; accordingly, as an Agency policy, risk assessment 

procedures, including default options that are used in the absence of scientific data to the 

contrary, should be health protective.” The approach adopted in this document is 

consistent with this approach as described in the Cancer Guidelines. 

For cancer endpoints EPA usually derives an oral slope factor for ingestion and a unit 

risk value for inhalation exposures. These values allow estimation of a lifetime 

probability of developing cancer given long-term exposures to the pollutant. Depending 

on the pollutant being evaluated, EPA relies on both animal bioassay and epidemiological 

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

Cancer Guidelines, EPA uses animal cancer bioassays as indicators of potential human 

health risk when other human cancer risk data are unavailable. 

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

EPA’s assessment of the scientific database for a pollutant using EPA’s guidance 

documents and other peer-reviewed methodologies. The EPA Cancer Guidelines

describes the Agency’s recommendations for methodologies for cancer risk assessment. 

EPA believes that cancer risk estimates developed following the procedures described in 

the Cancer Guidelines and outlined below generally provide an upper bound estimate of 
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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).8

 In 

some circumstances, the true risk could be as low as zero; however, in other 

circumstances the risk could also be greater.9

 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.10 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 [26]. 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 are a source of 

uncertainty in cancer risk assessment. In general, EPA does not assume that tumor sites 

observed in an experimental animal bioassay are necessarily predictive of the sites at 

which tumors would occur in humans.11 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. 

 

8

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

9

 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. 10 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. 

11 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.” 
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(2) Uncertainties regarding the most appropriate dose metric for an assessment can also 

lead to differences in risk predictions. For example, the measure of dose is commonly 

expressed in units of mg/kg/d ingested or the inhaled concentration of the pollutant. 

However, data may support development of a pharmacokinetic model for the absorption, 

distribution, metabolism, and excretion of an agent, which may result in improved dose 

metrics (e.g., average blood concentration of the pollutant or the quantity of agent 

metabolized in the body). Quantitative uncertainties result when the appropriate choice 

of a dose metric is uncertain or when dose metric estimates are themselves uncertain 

(e.g., as can occur when alternative pharmacokinetic models are available for a 

compound). Uncertainty in dose estimates may lead to either over or underestimation of 

risk. 

(3) For the quantitative extrapolation of cancer risk estimates from experimental animals 

to humans, EPA uses scaling methodologies (relating expected response to differences in 

physical size of the species), which introduce another source of uncertainty. These 

methodologies are based on both biological data on differences in rates of process 

according to species size and empirical comparisons of toxicity between experimental 

animals and humans. For a particular pollutant, the quantitative difference in cancer 

potency between experimental animals and humans may be either greater than or less 

than that estimated by baseline scientific scaling predictions due to uncertainties 

associated with limitations in the test data and the correctness of scaled estimates. 

(4) EPA cancer risk estimates, whether based on epidemiological or experimental animal 

data, are generally developed using a benchmark dose (BMD) analysis to estimate a dose 

at which there is a specified excess risk of cancer, which is used as the point of departure 

(or POD) for the remainder of the calculation. Statistical uncertainty in developing a 

POD using a benchmark dose (BMD) approach is generally addressed though use of the 

95% lower confidence limit on the dose at which the specified excess risk occurs (the 

BMDL), decreasing the likelihood of understating risk. EPA has generally utilized the 

multistage model for estimation of the BMDL using cancer bioassay data (see further 

discussion below). 

(5) Extrapolation from high to low doses is an important, and potentially large, source of 

uncertainty in cancer risk assessment. EPA uses different approaches to low dose risk 

assessment (i.e., developing estimates of risk for exposures to environmental doses of an 

agent from observations in experimental or epidemiological studies at higher dose) 

depending on the available data and understanding of a pollutant’s mode of action (i.e., 

the manner in which a pollutant causes cancer). EPA’s Cancer Guidelines express a 

preference for the use of reliable, compound-specific, biologically based risk models 

when feasible; however, such models are rarely available. The mode of action for a 

pollutant (i.e., the manner in which a pollutant causes cancer) is a key consideration in 

determining how risks should be estimated for low-dose exposure. A reference value is 

calculated when the available mode of action data show the response to be nonlinear 

(e.g., as in a threshold response). A linear low-dose (straight line from POD) approach is 

used when available mode of action data support a linear (e.g., nonthreshold) response or 

as the most common default approach when a compound’s mode of action is unknown. 
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Linear extrapolation can be supported by both pollutant-specific data and broader 

scientific considerations. For example, EPA’s Cancer Guidelines generally consider a 

linear dose-response to be appropriate for pollutants that interact with DNA and induce 

mutations. Pollutants whose effects are additive to background biological processes in 

cancer development can also be predicted to have low-dose linear responses, although the 

slope of this relationship may not be the same as the slope estimated by the straight line 

approach. 

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline 

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

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

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

appropriately applied as part of EPA’s cancer risk assessment process, provide an upper 

bound estimate of risk and generally provide a health protective approach. Note that 

another source of uncertainty is the characterization of low-dose nonlinear, non-threshold 

relationships. The National Academy of Sciences has encouraged the exploration of 

sigmoidal type functions (e.g., log-probit models) in representing dose response 

relationships due to the variability in response within human populations. A recent 

National Research Council (NRC) report [27] 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) [28]. It is important to note that earlier EPA assessments, including the majority of 

those for which risk values exist today, were generally developed using the multistage 

model to extrapolate down to environmental dose levels and did not involve the use of a 

POD. Subramaniam et al. (2006) also provide comparisons indicating that slopes based 

on straight line extrapolation from a POD do not show large differences from those based 

on the upper confidence limit of the multistage model. 

(6) Cancer risk estimates do not generally make specific adjustments to reflect the 

variability in response within the human population — resulting in another source of 
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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 values12

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.

To address multipathway 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, uncertainties associated with the health evidence and risk and exposure 

analyses were explicitly considered during the standard-setting process. More 

 

12 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. 
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specifically, these uncertainties were carefully considered when selecting the appropriate 

indicator, averaging time, form, and level of the NAAQS for lead. A description of the 

uncertainties and how they were considered in setting the NAAQS can be found in EPA’s 

final rule establishing the lead NAAQS (73FR66964). In general, the uncertainties 

associated with the establishment of the NAAQS are small relative to those for doseresponse values developed for many of the other HAPs. This is because the development 

of the lead NAAQS used the results from numerous human epidemiological studies rather 

than relying solely on the extrapolation of animal studies. In addition, because of the 

multi-pathway, multi-media impacts of lead, the development of the NAAQS considered 

direct inhalation exposures and indirect air-related multi-pathway exposures from 

industrial sources like primary and secondary lead smelting operations, as well as 

background lead exposures from all other sources (like contaminated drinking water and 

exposure to lead-based paints) when deriving an ambient concentration level which is 

considered to protect human health with an adequate margin of safety. 

For non-cancer endpoints related to chronic exposures, EPA derives a Reference Dose 

(RfD) for exposures via ingestion and a Reference Concentration (RfC) for inhalation 

exposures. These values provide an estimate (with uncertainty spanning perhaps an order 

of magnitude) of a daily oral exposure (RfD) or 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.13 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., inter-individual variability); (2) uncertainty in extrapolating from 

experimental animal data to humans (i.e., interspecies differences); (3) uncertainty in 

extrapolating from data obtained in a study with less-than-lifetime exposure (i.e., 

extrapolating from subchronic to chronic exposure); (4) uncertainty in extrapolating from 

a LOAEL in the absence of a NOAEL; and (5) uncertainty when the database is 

 

13 See IRIS glossary 
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incomplete or there are problems with applicability of available studies. When 

scientifically sound, peer-reviewed assessment-specific data are not available, default 

adjustment values are selected for the individual UFs. For each type of uncertainty 

(when relevant to the assessment), EPA typically applies an UF value of 10 or 3 with the 

cumulative UF value leading to a downward adjustment of 10-3000 fold from the 

selected POD. An UF of 3 is used when the data do not support the use of a 10-fold 

factor. If an extrapolation step or adjustment is not relevant to an assessment (e.g., if 

applying human toxicity data and an interspecies extrapolation is not required) the 

associated UF is not used. The major adjustment steps are described more fully below. 

 1) Heterogeneity among humans is a key source of variability as well as 

uncertainty. Uncertainty related to human variation is considered in extrapolating doses 

from a subset or smaller-sized population, often of one sex or of a narrow range of life 

stages (typical of occupational epidemiologic studies), to a larger, more diverse 

population. In the absence of pollutant-specific data on human variation, a 10-fold UF is 

used to account for uncertainty associated with human variation. Human variation may 

be larger or smaller; however, data to examine the potential magnitude of human 

variability are often unavailable. In some situations, a smaller UF of 3 may be applied to 

reflect a known lack of significant variability among humans. 

 2) Extrapolation from results of studies in experimental animals to humans is a 

necessary step for the majority of chemical risk assessments. When interpreting animal 

data, the concentration at the POD (e.g. NOAEL, BMDL) in an animal model (e.g. 

rodents) is extrapolated to estimate the human response. While there is long-standing 

scientific support for the use of animal studies as indicators of potential toxicity to 

humans, there are uncertainties in such extrapolations. In the absence of data to the 

contrary, the typical approach is to use the most relevant endpoint from the most sensitive 

species and the most sensitive sex in assessing risks to the average human. Typically, 

compound specific data to evaluate relative sensitivity in humans versus rodents are 

lacking, thus leading to uncertainty in this extrapolation. Size-related differences 

(allometric relationships) indicate that typically humans are more sensitive than rodents 

when compared on a mg/kg/day basis. The default choice of 10 for the interspecies UF is 

consistent with these differences. For a specific chemical, differences in species 

responses may be greater or less than this value. 

Pharmacokinetic models are useful to examine species differences in 

pharmacokinetic processing and associated uncertainties; however, such dosimetric 

adjustments are not always possible. Information may not be available to quantitatively 

assess toxicokinetic or toxicodynamic differences between animals and humans, and in 

many cases a 10-fold UF (with separate factors of 3 for toxicokinetic and toxicodynamic 

components) is used to account for expected species differences and associated 

uncertainty in extrapolating from laboratory animals to humans in the derivation of a 

reference value. If information on one or the other of these components is available and 

accounted for in the cross-species extrapolation, a UF of 3 may be used for the remaining 

component. 
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 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. 

Acute noncancer assessment

Many of the UFs used to account for variability and uncertainty in the development of 

acute reference values are quite similar to those developed for chronic durations, but 

more often using individual UF values that may be less than 10. UFs are applied based 

on chemical-specific or health effect-specific information (e.g., simple irritation effects 

do not vary appreciably between human individuals, hence a value of 3 is typically used), 

or based on the purpose for the reference value (see the following paragraph). The UFs 

applied in acute reference value derivation include: 1) heterogeneity among humans; 2) 

uncertainty in extrapolating from animals to humans; 3) uncertainty in LOAEL to 

NOAEL adjustments; and 4) uncertainty in accounting for an incomplete database on 

toxic effects of potential concern. Additional adjustments are often applied to account for 

uncertainty in extrapolation from observations at one exposure duration (e.g., 4 hours) to 

arrive at a POD for derivation of an acute reference value at another exposure duration 

(e.g., 1 hour). 

 

Not all acute reference values are developed for the same purpose, and care must be taken 

when interpreting the results of an acute assessment of human health effects relative to 

the reference value or values being exceeded. Where relevant to the estimated exposures, 

the lack of threshold values at different levels of severity should be factored into the risk 

characterization as potential uncertainties. 
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5. References 

 

1. US EPA, 2006. 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/$Fi

le/EPA-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/Day09/a21627.htm

6. US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA454/B-03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf. 

7. Allen, D., C. Murphy, Y. Kimura, W. Vizuete, T. Edgar, H. Jeffries, B.-U. Kim, M. 

Webster, and M. Symons, 2004. Variable industrial VOC emissions and their impact on 

ozone formation in the Houston Galveston Area. Final Report: Texas Environmental 

Research Consortium Project H-13. 

http://files.harc.edu/Projects/AirQuality/Projects/H013.2003/H13FinalReport.pdf

8. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K04-001A. http://www.epa.gov/ttn/fera/risk_atra_vol1.html. 

9. US EPA, 2005. Table 1. Prioritized Chronic Dose-Response Values (2/28/05). Office 

of Air Quality Planning and Standards. 

http://www.epa.gov/ttn/atw/toxsource/table1.pdf

10. US EPA, 2005. 1999 National Air Toxics Risk Assessment. 

http://www.epa.gov/ttn/atw/nata1999

11. US EPA, 2006. Integrated Risk Information System. http://www.epa.gov/iris/index.html. 

12. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels 

(MRLs) for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html. 

13. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of 

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 
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EPA/600/8-90/066F. Office of Research and Development. Washington, DC: 

U.S.EPA.

14. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press. 

15. CA Office of Environmental Health Hazard Assessment, 2005. Chronic Reference 

Exposure Levels Adopted by OEHHA as of December 2008. 

http://www.oehha.ca.gov/air/chronic_rels/AllChrels.html. 

16. CA Office of Environmental Health Hazard Assessment, 2005. Technical Support 

Document for Describing Available Cancer Potency Factors, May 2005. 

http://www.oehha.ca.gov/air/hot_spots/may2005tsd.html. 

17. US EPA, 2005. Table 2. Acute Dose-Response Values for Screening Risk Assessments 

(6/02/2005). Office of Air Quality Planning and Standards. 

http://www.epa.gov/ttn/atw/toxsource/table2.pdf

18. CA Office of Environmental Health Hazard Assessment, 2000. All Acute Reference 

Exposure Levels developed by OEHHA as of May 2000. 

http://www.oehha.ca.gov/air/acute_rels/allAcRELs.html

19. American Industrial Hygiene Association , 2009. Current AIHA ERPG Values. 

http://www.aiha.org/insideaiha/volunteergroups/Documents/ERP-erpglevels.pdf. 

20. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. 

http://www.epa.gov/OSA/spc/pdfs/rcguide.pdf. 

21. US EPA, 2000. Risk Characterization Handbook. EPA 100-B-00-002. 

22. US EPA, 2002. EPA’s Guidelines for Ensuring and Maximizing the Quality, Objectivity, 

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