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Draft Residual Risk Assessment for the Secondary Aluminum 

Production Source Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

January 2012
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Table of Contents

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 chronic human inhalation exposure .......................................................... 8

2.4 Screening for potential acute impacts of concern........................................................ 8

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

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...................................................... 17

2.7 Risk characterization ................................................................................................. 20

2.7.1 General ............................................................................................................... 20

2.7.2 Mixtures............................................................................................................. 22

2.7.3 Facility-wide risks.............................................................................................. 22

3 Risk results for the Secondary Aluminum production source category....................... 23

3.1 Source category description and results.................................................................... 23

3.2 Baseline risk characterization.................................................................................... 25

4 General discussion of uncertainties and how they have been addressed ......................... 28

4.1 Exposure modeling uncertainties .............................................................................. 28

4.2 Uncertainties in the dose-response relationships....................................................... 29

5 References........................................................................................................................ 38

Index of Tables

Table 2.2-1 AERMOD version 09292 model options for RTR modeling................................ 6

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens.... 14

Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens............. 15

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

.................................................................................................................................................. 16

Table 2.6-2 Dose-Response Values for Acute Exposure........................................................ 19

Table 3.1-1 Summary of Emissions from the Major Secondary Aluminum Production Source 

Category Facilities and Availability of Dose-Response Values.............................................. 23

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Major Secondary

Aluminum Production Sources................................................................................................ 27

Appendices

Appendix 1 Technical Support Document for HEM-3 Modeling

Appendix 2 Meteorological Data for HEM-3 Modeling

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

rates

Appendix 4 Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation
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Appendix 5 Draft Detailed Risk Modeling Results

Appendix 6 Dispersion Model Receptor Revisions and Additions

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

CTE Central Tendency Estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standards

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NPRM Notice of Proposed Rulemaking

PB-HAP Persistent and Bioaccumulative - HAP

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

RME Reasonable Maximum Exposure

RTR Risk and Technology

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
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1 Introduction

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

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

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

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

and paper mills, etc.) [1]. EPA has largely completed the initial Maximum Achievable 

Control Technology (MACT) standards as required under this provision. Under section 

112(d)(6), EPA must review each of these technology-based standards at least every eight 

years and revise a standard, as necessary, “taking into account developments in practices, 

processes and control technologies.” In the second stage, EPA is required under section 

112(f)(2) to assess the health and environmental risks that remain after implementation of the 

MACT standards. If additional risk reductions are necessary to protect public health with an 

ample margin of safety or to prevent an adverse environmental effect, EPA must develop 

standards to address these remaining risks. This second stage of the regulatory process is 

known as the residual risk stage. For each source category for which EPA issued MACT 

standards, the residual risk stage must be completed within eight years of promulgation of the 

initial technology-based standard.

In December of 2006 we consulted with a panel from the EPA's Science Advisory Board 

(SAB) on the “Risk and Technology Review (RTR) Assessment Plan,” and in June of 2007, 

we received a letter with the results of that consultation. Subsequent to the consultation, in 

June of 2009, a meeting was held with an SAB panel for a formal peer review of the “Risk 

and Technology Review (RTR) Assessment Methodologies” [2]. We received the final SAB 

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

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

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

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

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

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

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

for the Secondary Aluminum Production source category. The methods discussion includes 

descriptions of the methods used to screen for acute health risks, chronic non-inhalation 

health risks, and adverse environmental effects, as well as descriptions of the methods used to 

develop refined estimates of chronic inhalation exposures and human health risks for cancer 

and noncancer endpoints.

2 Methods

2.1 Emissions and source data

Although there are 53 major sources and 108 area sources covered by the subpart RRR 

MACT standards, 52 major sources and 103 area sources were modeled due to the other 

sources’ lack of equipment with applicable emission standards. Notably, while all HAP are 

covered under MACT standards for major sources, only dioxins and furans are regulated 

under the MACT standards for area sources. Thus, the risk assessment presents two sets of 
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results. For major sources, we present risk results associated with all HAP emitted, and for 

area sources, we present risk results associated with dioxins and furans only. The preliminary

dataset for both major and area sources was based on information in an Information 

Collection Request (ICR) sent to all facilities. The dataset for major sources was also based 

on a testing ICR sent to nine companies; the data from this testing ICR were used to develop 

speciation profiles for HAP metals and HAP organics. These ICR data were then used to 

develop the emissions dataset for this source category. We performed an engineering review 

of the information to identify readily apparent limitations and issues with the emissions data. 

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

found in the emissions memo: Development of the RTR Risk Modeling Dataset for the 

Secondary Aluminum Production Source Category, available in the docket for the proposed 

rulemaking. In addition, Section 3 below provides a brief summary of the emissions. 

2.2 Dispersion modeling for inhalation exposure assessment

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

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

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

dispersion modeling system (HEM-3). The approach used in applying this modeling system 

is outlined below, and further details are provided in Appendix 1. HEM-3 performs three 

main operations: atmospheric dispersion modeling, estimation of individual human exposures 

and health risks, and estimation of population risks. This section focuses on the dispersion 

modeling component. The exposure and risk characterization components are discussed in 

other subsections of Sections 2 and 3.

The dispersion model in the HEM-3 system 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-3 modeling system is to predict ambient 

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

summarized in Table 2.2-1 and are discussed further below.
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Table 2.2-1 AERMOD version 11103 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point and area sources

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids)

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site (over 200

stations)

In HEM-3, meteorological data are ordinarily selected from over 200 National Weather 

Service (NWS) surface observation stations across the continental United States, Alaska, 

Hawaii, and Puerto Rico. In most cases the nearest station is selected as representative of the 

conditions at the subject facility. Ideally, when considering off-site meteorological data most 

site specific dispersion modeling efforts will employ up to five years of data to capture 

variability in weather patterns from year to year. However, to minimize data processing time 

and resources for this assessment, we modeled only a single year, typically 1991. While the 

selection of a single year may result in under-prediction of long-term ambient levels at some 

locations, likewise it may result in over-prediction at others. For each facility identified by its 

characteristic latitude and longitude coordinates, the closest meteorological station was used 

in the dispersion modeling. The average distance between a modeled facility and the 

applicable meteorological station was 40 miles (72 km). Appendix 2 (Meteorological Data 

Processing Using AERMET for HEM-3) provides a complete listing of stations and 

assumptions along with further details used in processing the data through AERMET. The 

sensitivity of model results to the selection of the nearest weather station and the use of one 

year of meteorological data is discussed in “Risk and Technology Review (RTR) Risk 

Assessment Methodologies” [2].

The HEM-3 system estimates ambient concentrations at the geographic centroids of census 

blocks (using the 2000 Census) and at other receptor locations that can be specified by the 

user. The model accounts for the impacts of multiple facilities when estimating concentration 

impacts at each block centroid. Typically we combined only the impacts of facilities within 

the same source category, and assessed chronic exposure and risk only for census blocks with 

at least one resident (i.e., locations where people may reasonably be assumed to reside rather 

than receptor points at the fenceline of a facility). Chronic ambient concentrations were 

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

block centroid. Possible future residential use of currently uninhabited areas was not 
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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 emissions data for this source category were based on annual emission totals, we 

typically applied 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 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 heavilyindustrialized 4-county area (Harris, Galveston, Chambers, and Brazoria Counties, TX) over 

an eleven-month time period in 2001. We obtained the dataset and performed our own 

analysis, focusing 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, the highest was a factor of 74 times the annual average, and the 99th percentile ratio 

of peak hourly emission rate to the annual hourly emission rate was 9. Based on these results, 

we chose the factor of ten for all initial screening; it is intended to cover all possible hourly

peaks associated with routinely variable 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. We have no data 

relating specifically to peak short-term emissions of particulate HAP. In the absence of 

source category-specific data, we use this same default approach for particulate emissions as 

well.1. 

 

Census block elevations for HEM-3 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 

 

1

In situations where we have information on hourly emissions, we will take this into account when evaluating 

acute risks.
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an elevation is not provided for the emission source, the model uses the average elevation of 

all sectors within the innermost model ring.

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

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

on how hill heights were estimated and used in the AERMOD modeling see Appendix 1. 

2.3 Estimating chronic human inhalation exposure

We used the annual average ambient air concentration of each HAP at each census block 

centroid as a surrogate for the lifetime inhalation exposure concentration of all the people who 

reside in the census block. That is, the risk analysis did not consider either the short-term or 

long-term behavior (mobility) of the exposed populations and its potential influence on their 

exposure.

 

We did not address short-term human activity for two reasons. First, our experience with 

NATA assessments (which modeled daily activity using EPA’s HAPEM model) suggests 

that, given our current understanding of microenvironment concentrations and daily activities, 

modeling short-term activity would, on average, reduce risk estimates about 25% for 

particulate HAPs; it will also reduce risk estimates for gaseous HAPs, but typically by much 

less. Second, basing exposure estimates on average ambient concentrations at census block 

centroids may underestimate or overestimate actual exposure concentrations at some 

residences. Further reducing exposure estimates for the most highly exposed residents by 

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

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

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

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

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

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

that the MIR can be estimated for each source as well as for the entire source category); 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 MIR nor does it affect 

the estimate of cancer incidence since the total population number remains the same. It does, 

however, affect the shape of the distribution of individual risks across the affected population, 

shifting it toward higher estimated individual risks at the upper end and reducing the number 

of people estimated to be at lower risks, thereby increasing the estimated number of people at 

specific risk levels.

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

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

potential acute exposure concentration for any individual.

2.4 Screening for potential acute impacts of concern

In establishing a scientifically defensible approach for the assessment of potential health risks 

due to acute exposures to HAP, we followed the same general approach that has been used for 

developing chronic health risk assessments for other rules under the residual risk program. 
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That is, we developed a tiered, iterative approach. This approach to risk assessment was

endorsed by the National Academy of Sciences in its 1993 publication “Science and Judgment 

in Risk Assessment” and subsequently was adopted in the EPA’s “Residual Risk Report to 

Congress” in 1999. 

With respect to screening for potential acute health risks, the assessment methodology is 

designed to eliminate from further consideration those facilities for which we have confidence 

that no acute adverse health effects of concern will occur. To do so, we use what is called a 

tiered, iterative approach to the assessment. This means that we began with a screening 

assessment, which relies on readily available data and uses conservative assumptions that in 

combination approximate a worst-case exposure. The result of this screening process is that

either the facility being assessed poses no potential acute health risks (i.e., it “screens out”), or 

that it requires further, more refined assessment with respect to potential acute exposures from

emitted HAP. A more refined acute assessment could use industry- or site-specific data on 

the temporal pattern of emissions, the layout of emission points at the facility, the boundaries 

of the facility, and/or the local meteorology. In some cases, all of these site-specific data are 

used to refine the assessment; in others, lesser amounts of site-specific data can be used to 

determine that acute exposures are not a concern, and significant additional data collection is

not necessary. 

Acute health risk screening was performed for each facility as the first step. We used 

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

the following worst-case assumptions in our screening approach:

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

rates. 

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

occur at all emission points at the same time.

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

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

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

hour. 

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

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

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

impact during this same worst-case 1-hour period, but no nearer to the source than 100 

meters. As a result, the maximum modeled 1-hour off-site concentration equals the 

worst-case acute exposure concentration.

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

HAP being assessed to determine whether a possible acute health risk might exist. 

These benchmarks are described in section 2.6 of this report.

As mentioned above, when we identify acute impacts which exceed their relevant 

benchmarks, we pursue refining our acute screening estimates to the extent possible. For 

example, this may include the use of a facility-specific emissions multiplier to estimate the 
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peak hourly emission rates from the average rates (rather than using the default factor of 10)

and/or determining the actual physical layout and boundaries of a facility to more accurately

gauge where people might reasonably be exposed for an hour.

2.5 Multipathway human health and environmental risk screening

The potential for significant human health risks and adverse environmental impacts due to 

exposures via routes other than inhalation (i.e., multipathway exposures) was screened by first 

determining whether any sources emitted any hazardous air pollutants known to be persistent 

and bioaccumulative in the environment (PB-HAP). The PB-HAP compounds or compound 

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

[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. 

With respect to PB-HAP other than lead, emissions were evaluated for potential noninhalation risks and adverse environmental impacts using our recently-developed screening 

scenario which was developed for use with the TRIM.FaTE2 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.3 In

the development of the screening scenario a sensitivity analysis was conducted to ensure that 

its key design parameters were established such that environmental media concentrations 

were not underestimated, and to also minimize the occurrence of false positives for human 

health endpoints. See Appendix 4 for a complete discussion of the development and testing 

of the screening scenario, as well as for the values of facility-level screening emission rates 

developed for screening potentially significant multi-pathway impacts. In general, for the 

purpose of developing 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 multi-pathway risks from emissions of lead compounds, rather than 

developing a screening emission rate, we generally compare maximum estimated chronic (3-

month average) atmospheric concentrations with the current primary National Ambient Air 

Quality Standard (NAAQS) for lead. Values below the NAAQS are considered to have a low 

potential for multi-pathway risks. Notably, for this source category, dioxins and furans, 

POM, and cadmium were the PB-HAP identified in the emissions inventory.

 

We also evaluated the potential for significant direct environmental exposures to HAP by

 

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

3 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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evaluating the potential for exceedances of chronic human health inhalation reference values 

in the ambient air near these facilities. Human health dose-response reference values are 

generally derived from studies conducted on laboratory animals (such as rodents) and 

developed with the inclusions of uncertainty factors that could be as high as 3000. As a 

result, these human reference values are often significantly lower than the level expected to 

cause an adverse effect in an exposed rodent. It should be noted that there is a scarcity of data 

on the direct atmospheric impact of these HAP on other receptors, such as plants, birds, and 

wildlife. Thus, if the maximum inhalation hazard in an ecosystem is below the level of 

concern for humans (i.e., is below the human health reference value which is itself a lower 

reference value than would be expected for an ecologic effect), we have generally concluded 

that mammalian receptors should be at no risk of adverse effects due to inhalation exposures 

from non-PB-HAP, and have assurance that other ecological receptors are also not at any

significant risk from direct atmospheric impact. In some isolated cases where we have data 

indicating potential adverse impacts on plants, birds, or other wildlife due to the direct 

atmospheric impacts of specific HAP, we note that as an uncertainty and, where possible, 

refine our analysis by comparing our modeled impacts to available reference values from the 

scientific literature.

2.6 Dose-response assessment

2.6.1 Sources of chronic dose-response information 

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

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

Aluminum Production sources were based on the EPA Office of Air Quality Planning and 

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

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

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

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

available science with respect to dose-response information. The recommendations are based 

on the following sources, in order of priority: 

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

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

assessments typically provide a qualitative statement regarding the strength of scientific 

data and specify a reference concentration (RfC, for inhalation) or reference dose (RfD, 

for ingestion) to protect against effects other than cancer and/or a unit risk estimate (URE, 

for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing 

cancer. The RfC is defined as an “estimate (with uncertainty spanning perhaps an order of 

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

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime.” Similarly, the RfD is “an estimate (with uncertainty spanning perhaps 

an order of magnitude) of a daily oral exposure to the human population (including 

sensitive subgroups) that is likely to be without an appreciable risk of deleterious effects 

during a lifetime.” The URE is defined as “the upper-bound excess cancer risk estimated 

to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m3in 
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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. New 

IRIS values are developed and old IRIS values are updated as new health effects data 

become available. Refer to the “IRIS Track” website for detailed information on status 

and scheduling of current individual IRIS assessments and updates 

(http://cfpub.epa.gov/ncea/iristrac/index.cfm). EPA’s science policy approach, under the 

current carcinogen guidelines, is to use linear low-dose extrapolation as a default option 

for carcinogens for which the mode of action (MOA) has not been identified. We expect 

future EPA dose-response assessments to identify nonlinear MOAs where appropriate, 

and we will use those analyses (once they are peer reviewed) in our risk assessments. At 

this time, however, there are no available carcinogen dose-response assessments for 

inhalation exposure that are based on a nonlinear MOA.

2) US Agency for Toxic Substances and Disease Registry (ATSDR). ATSDR, which is part 

of the US Department of Health and Human Services, develops and publishes Minimum 

Risk Levels (MRLs) [12] for inhalation and oral exposure to many toxic substances. As 

stated on the ATSDR web site: “Following discussions with scientists within the 

Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a 

practice similar to that of the EPA's Reference Dose (RfD) and Reference Concentration 

(RfC) for deriving substance specific health guidance levels for non neoplastic endpoints.” 

The MRL is defined as “an estimate of daily human exposure to a substance that is likely

to be without an appreciable risk of adverse effects (other than cancer) over a specified 

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 assessments4: “The 

guidelines for developing chronic inhalation exposure levels incorporate many

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

 

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

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

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

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)
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includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [15]. CalEPA defines the REL as “the concentration 

level at or below which no health effects are anticipated in the general human population.” 

CalEPA's quantitative dose-response information on carcinogenicity by inhalation 

exposure is expressed in terms of the URE [16], defined similarly to EPA's URE. 

 

In developing chronic risk estimates, we adjusted dose-response values for some HAPs based 

on professional judgment, as follows: 

1) In the case of HAP categories such as glycol ethers and cyanide compounds, the most 

conservative dose-response value of the chemical category is used as a surrogate for other 

compounds in the group for which dose-response values are not available. This is done in 

order to examine, under conservative assumptions, whether these HAPs that lack doseresponse values may pose an unacceptable risk and require further examination, or screen 

out from further assessment. 

2) Where possible for emissions of unspecified mixtures of HAP categories such as POM

(see Tables 2.6-1 (a) to 2.6-1 (c), we apply category-specific chemical speciation profiles 

appropriate to the source category to develop a composite dose-response value for the 

category. 

3) Emissions from Secondary Aluminum Production facilities include POM. Some of the 

POM reported to EPA’s national emission inventory (NEI) are not speciated into 

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

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

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

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

potency, for which we can apply the same URE. 

The emissions inventory for the Secondary Aluminum Production source category includes 

emissions of HAP with available chronic quantitative inhalation dose-response values. These 

HAP, their dose-response values, and the source of the values are listed in Tables 2.6-1 (a) 

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

Carcinogens

URE (unit risk estimate for cancer)5= cancer risk per μg/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of 

Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the 

EPA Office of Air Quality Planning and Standards. EPA ORD = EPA Office of Research and 

Development

Pollutant CAS Number6 URE5

(1/μg/m3)

Source

1,1,1-trichloroethane 79005 0.000016 IRIS

1,1,2-trichloroethane 79005 0.000016 IRIS

1,3-dichloropropene 542756 0.000004 IRIS

2,3,7,8-tetrachlorodibenzo-pdioxin 1746016 33 EPA ORD

Acrylonitrile 107131 0.000068 IRIS

Arsenic Compounds 7440382 0.0043 IRIS

Benzene771432 0.0000078 IRIS

Beryllium Compounds 7440417 0.0024 IRIS

Cadmium Compounds 7440439 0.0018 IRIS

Carbon Tetrachloride 56235 0.000006 IRIS

Chloroprene 126998 0.00048 IRIS

Chromium Compounds

 Chromium (VI) 18540299 0.012 IRIS

Ethylene Dichloride 107062 0.000026 IRIS

Ethylidene Dichloride 107062 0.000026 IRIS

Methylene Chloride 75092 0.00000047 IRIS

Naphthalene 91203 0.000034 CAL

Nickel Compounds 7440020 0.00048 EPA OAQPS8

 

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

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

a plausible upper limit to the true value. 

6 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.

7 The EPA IRIS assessment for benzene provides a range of plausible UREs. This assessment used the highest 

value in that range, 7.8E-06 per ug/m3. The low end of the range is 2.2E-06 per ug/m3.

8 The EPA IRIS assessments for nickel compounds provide a range of plausible UREs. This assessment used the 

highest value in that range which is equal to the URE for nickel subsulfide, 4.8E-04 per ug/m3. The low end of 

the range is equal to 50% of the URE for nickel subsulfide, 2.4E-04 per ug/m3.
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Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)5= cancer risk per μg/m3of average lifetime exposure. 

Sources: IRIS = EPA Integrated Risk Information System, CAL = California EPA Office of 

Environmental Health Hazard Assessment, EPA/OAQPS = interim value recommended by the 

EPA Office of Air Quality Planning and Standards. EPA ORD = EPA Office of Research and 

Development

Pollutant CAS Number6 URE5

(1/μg/m3)

Source

Polycyclic Organic Matter

 Chrysene 218019 0.0000176 CAL

 Benz[a]anthracene 56553 0.000176 CAL

 Benzo[b]fluoranthene 205992 0.000176 CAL

 Benzo[k]fluoranthene 207089 0.000176 CAL

 Benzo[a]pyrene 50328 0.00176 CAL

 Indeno[1,2,3-c,d]pyrene 193395 0.000176 CAL

 Dibenzo[a,h]anthracene 53703 0.0019184 CAL

Propylene Dichloride 78875 0.000019 Conv. Oral

Tetrachloroethene 127184 0.0000059 CAL

Trichloroethylene 79016 0.000002 CAL

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

SF (oral slope factor for cancer) = cancer risk per mg/kg/d of average lifetime exposure. Sources: IRIS 

= EPA Integrated Risk Information System, CAL = California EPA Office of Environmental Health 

Hazard Assessment, EPA/OAQPS = interim value recommended by the EPA Office of Air Quality 

Planning and Standards.

Pollutant CAS Number10

SF

(1/mg/kg/d) Source

2,3,7,8-tetrachlorodibenzo-p-dioxin 1746016 150000 EPA ORD

Polycyclic Organic MatterPOM

 Benz[a]anthracene 56553 1.2 CAL

 Benzo[b]fluoranthene 205992 1.2 CAL

 Benzo[k]fluoranthene 207089 1.2 CAL

 Benzo[a]pyrene 50328 7.3 IRIS
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Table 2.6-1 (c) Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

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

magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) 

that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA 

Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health 

Assessment, ATSDR = US Agency for Toxic Substances Disease Registry. 

Pollutant CAS Number6 RfC 

(mg/m3)

Source9

1,1,1-trichloroethane 71556 5 IRIS - M

1,1,2-trichloroethane 79005 0.4 CAL

1,3-dichloropropene 542756 0.02 IRIS - H

Acrylonitrile 107131 0.002 IRIS - M

Antimony Compounds 7440360 0.0002 IRIS - L

Arsenic Compounds 7440382 0.000015 CAL

Benzene 71432 0.03 IRIS - M

Beryllium Compounds 7440417 0.00002 IRIS - M

Cadmium Compounds 7440439 0.00001 ATSDR

Carbon Disulfide 75150 0.7 IRIS - M

Carbon Tetrachloride 56235 0.1 IRIS - M

Chloroform 67663 0.098 ATSDR

Chloroprene 126998 0.02 IRIS – M/H

Chromium (VI) Compounds 18540299 0.0001 IRIS - M

Cobalt Compounds 7440484 0.0001 ATSDR

Dioxins/furans 51207319 0.0000004 CAL

Ethylene Dichloride 107062 2.4 ATSDR

Ethylidene Dichloride 107062 2.4 ATSDR

Hydrochloric Acid 7647010 0.02 IRIS - L

Lead Compounds 7439921 0.00015 EPA OAQPS

Manganese Compounds 7439965 0.00005 IRIS - M

Mercury Compounds 184 0.0003 IRIS - M

 Mercuric Chloride 7487947 0.0003 IRIS - M

 Mercury (elemental) 7439976 0.0003 IRIS - M

Methylene Chloride 75092 1 ATSDR

Naphthalene 91203 0.003 IRIS - M

Nickel Compounds 7440020 0.00009 ATSDR

Propylene Dichloride 75569 0.03 IRIS - M

Selenium Compounds 7782492 0.02 CAL

Tetrachloroethene 127184 0.27 ATSDR

Toluene 108883 5 IRIS - H

 

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

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

magnitude) of a continuous inhalation exposure to the human population (including sensitive subgroups) 

that is likely to be without an appreciable risk of deleterious effects during a lifetime. Sources: IRIS = EPA 

Integrated Risk Information System, CAL = California EPA Office of Environmental Human Health 

Assessment, ATSDR = US Agency for Toxic Substances Disease Registry. 

Pollutant CAS Number6 RfC 

(mg/m3)

Source9

Trichloroethylene 79016 0.6 CAL

Vinylidene Chloride 75354 0.2 IRIS – H/M

2.6.2 Sources of acute dose-response information 

Hazard identification and dose-response assessment information for preliminary acute 

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

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

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

Reference Exposure Levels (RELs), as well as emergency response levels, such as Acute 

Exposure Guideline Levels (AEGLs) and Emergency Response Planning Guidelines 

(ERPGs), with the recognition that the ultimate interpretation of any potential risks associated 

with an estimated exceedance of a particular reference level depends on the definition of that 

level and any limitations expressed therein. Comparisons among different available 

inhalation health effect reference values (both acute and chronic) for selected HAPs can be 

found in an EPA document.

10

California Acute Reference Exposure Levels (RELs). The California Environmental 

Protection Agency (CalEPA) has developed acute dose-response reference values for many 

substances, expressing the results as Acute inhalation Reference Exposure Levels (RELs). 

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

“the concentration level at or below which no adverse health effects are anticipated for a 

specified exposure duration. [19]. RELs are based on the most sensitive, relevant, 

adverse health effect reported in the medical and toxicological literature. RELs are 

designed to protect the most sensitive individuals in the population by the inclusion of

margins of safety. Since margins of safety are incorporated to address data gaps and

uncertainties, exceeding the REL does not automatically indicate an adverse health 

impact.” Acute RELs are developed for 1-hour (and 8-hour) exposures. The values 

incorporate uncertainty factors similar to those used in deriving EPA’s Inhalation 

Reference Concentrations (RfCs) for chronic exposures (and, in fact, California also has 

developed chronic RELs).

Acute Exposure Guideline Levels (AEGLs). AEGLs are developed by the National Advisory

 

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

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

2009. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003.)
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Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous 

Substances, and then reviewed and published by the National Research Council. As described 

in the Committee’s “Standing Operating Procedures (SOP)”

(http://www.epa.gov/opptintr/aegl/pubs/sop.pdf), AEGLs “represent threshold exposure limits 

for the general public and are applicable to emergency exposures ranging from 10 min to 8 h.” 

Their intended application is “for conducting risk assessments to aid in the development of 

emergency preparedness and prevention plans, as well as real time emergency response 

actions, for accidental chemical releases at fixed facilities and from transport carriers.” The 

document states: “the primary purpose of the AEGL program and the NAC/AEGL Committee 

is to develop guideline levels for once-in-a-lifetime, short-term exposures to airborne 

concentrations of acutely toxic, high-priority chemicals.” In detailing the intended application 

of AEGL values, the document states that, “It is anticipated that the AEGL values will be 

used for regulatory and nonregulatory purposes by U.S. Federal and State agencies, and 

possibly the international community in conjunction with chemical emergency response, 

planning, and prevention programs. More specifically, the AEGL values will be used for 

conducting various risk assessments to aid in the development of emergency preparedness and 

prevention plans, as well as real-time emergency response actions, for accidental chemical 

releases at fixed facilities and from transport carriers.” The NAC/AEGL defines AEGL-1 and 

AEGL-2 as:

“AEGL-1 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

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

experience notable discomfort, irritation, or certain asymptomatic nonsensory effects. 

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

exposure.”

“AEGL-2 is the airborne concentration (expressed as ppm or mg/m3) of a substance above 

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

experience irreversible or other serious, long-lasting adverse health effects or an impaired 

ability to escape.”

“Airborne concentrations below AEGL-1 represent exposure levels that can produce mild 

and progressively increasing but transient and nondisabling odor, taste, and sensory

irritation or certain asymptomatic, nonsensory effects. With increasing airborne 

concentrations above each AEGL, there is a progressive increase in the likelihood of 

occurrence and the severity of effects described for each corresponding AEGL. Although 

the AEGL values represent threshold levels for the general public, including susceptible 

subpopulations, such as infants, children, the elderly, persons with asthma, and those with 

other illnesses, it is recognized that individuals, subject to unique or idiosyncratic 

responses, could experience the effects described at concentrations below the 

corresponding AEGL.”

Emergency Response Planning Guidelines (ERPGs). The American Industrial Hygiene 

Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [20] 

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

concentrations for exposure of the general population (but not particularly sensitive persons) 
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for up to 1 hour associated with effects expected to be mild or transient (ERPG-1), 

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

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

described in their supporting documentation as follows: “Emergency Response Planning 

Guidelines (ERPGs) were developed for emergency planning and are intended as health based 

guideline concentrations for single exposures to chemicals. These guidelines (i.e., the ERPG 

Documents and ERPG values) are intended for use as planning tools for assessing the 

adequacy of accident prevention and emergency response plans, including transportation 

emergency planning and for developing community emergency response plans. The emphasis 

is on ERPGs as planning values: When an actual chemical emergency occurs there is seldom 

time to measure airborne concentrations and then to take action.” ERPG-1 and ERPG-2

values are defined by AIHA as follows:

“ERPG-1 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing other than mild 

transient adverse health effects or without perceiving a clearly defined, objectionable 

odor.”

“ERPG-2 is the maximum airborne concentration below which it is believed that nearly all 

individuals could be exposed for up to 1 hour without experiencing or developing 

irreversible or other serious health effects or symptoms which could impair an individual's 

ability to take protective action.”

The emissions inventory for the Secondary Aluminum Production source category includes 

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

values. These HAPs, their acute threshold values, and the source of the value are listed below 

in Table 2.6-2.

Table 2.6-2 Dose-Response Values for Acute Exposure

Pollutant CAS Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG-1

(mg/m3)

ERPG-2

(mg/m3) REL 

1,1,1-Trichloroethane 

(methyl chloroform) 71556 1300 3300 1900 3800 68

Acrylonitrile 107131 10 130 22 77

Benzene 71432 170 2600 160 480 1.3

Beryllium compounds 7440417 0.025

Carbon disulfide 75150 40 500 3.1 160 6.2

Carbon tetrachloride 56235 280 1200 130 630 1.9

Chloroform 67663 310 240 0.15

Ethylene dichloride 107062 200 810

Hydrochloric acid 7647010 2.7 33 4.5 30 2.1

Mercury (elemental) 7439976 1.7 2 0.0006

Methylene chloride 75092 690 1900 1000 2600 14
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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 

Nickel compounds 7440020 0.006

Tetrachloroethene 127184 240 1600 680 1400 20

Trichloroethylene 79016 700 2400 540 2700

Toluene 108883 750 4500 190 1100 37

2.7 Risk characterization

2.7.1 General

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

from the previous steps is integrated and an overall conclusion about risk is synthesized that is 

complete, informative, and useful for decision makers. In general, the nature of this risk 

characterization depends on the information available, the application of the risk information 

and the resources available. In all cases, major issues associated with determining the nature 

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

1995 Policy for Risk Characterization [21] specifies that a risk characterization “be prepared 

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

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

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

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

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis11, and 

are incorporated in these assessments.

Estimates of health risk are presented in the context of uncertainties and limitations in the data 

and methodology. Through our tiered, iterative analytical approach, we have attempted to 

reduce both uncertainty and bias to the greatest degree possible in these assessments, within 

the limitations of available time and resources. We provide summaries of risk metrics 

(including maximum individual cancer risks and noncancer hazards, as well as cancer 

incidence estimates) along with a discussion of the major uncertainties associated with their 

derivation to provide decision makers with the fullest picture of the assessment and its 

limitations.

For each carcinogenic HAP included in an assessment that has a potency estimate available, 

individual and population cancer risks were calculated by multiplying the corresponding 

lifetime average exposure estimate by the appropriate URE. This calculated cancer risk is 

defined as the upper-bound probability of developing cancer over a 70-yr period (i.e., the 

 

11Memorandum 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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assumed human lifespan) at that exposure. Because UREs for most HAPs are upper-bound 

estimates, actual risks at a given exposure level may be lower than predicted, and could be 

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode-of-action [24], we applied 

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

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

children aged 0-1), 3 (for children aged 2-15), or 1.6 (for 70 years of exposure beginning at 

birth), as needed in risk assessments. In this case, this has the effect of increasing the 

estimated life time risks for these pollutants by a factor of 1.6. In addition, although only a 

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

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

equivalence, based on evidence that carcinogenic POM have the same mutagenic mechanism 

of action as does benzo[a]pyrene. For this reason, EPA implementation policy [26] 

recommends applying the Supplemental Guidance to all carcinogenic PAHs for which risk 

estimates are based on relative potency. Accordingly, we applied the Supplemental Guidance 

to all unspeciated POM mixtures.

Increased cancer incidence for the entire receptor population within the area of analysis was 

estimated by multiplying the estimated lifetime cancer risk for each census block by the 

number of people residing in that block, then summing the results for the entire modeled 

domain. This lifetime population incidence estimate was divided by 70 years to obtain an 

estimate of the number of cancer cases per year.

Unlike linear dose-response assessments for cancer, noncancer health hazards generally are 

not expressed as a probability of an adverse occurrence. Instead, “risk” for noncancer effects 

is expressed by comparing an exposure to a reference level as a ratio. The “hazard quotient” 

(HQ) is the estimated exposure divided by a reference level (e.g., the RfC). For a given HAP, 

exposures at or below the reference level (HQ≤1) are not likely to cause adverse health 

effects. As exposures increase above the reference level (HQs increasingly greater than 1), 

the potential for adverse effects increases. For exposures predicted to be above the RfC, the 

risk characterization includes the degree of confidence ascribed to the RfC values for the 

compound(s) of concern (i.e., high, medium, or low confidence) and discusses the impact of 

this on possible health interpretations. 

The risk characterization for chronic effects other than cancer is expressed in terms of the HQ 

for inhalation, calculated for each HAP at each census block centroid. As discussed above, 

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

extrapolations, such that an HQ greater than one does not necessarily suggest the onset of 

adverse effects. The HQ cannot be translated to a probability that adverse effects will occur, 

and is unlikely to be proportional to adverse health effect outcomes in a population.

Screening for potentially significant acute inhalation exposures also followed the HQ 

approach. We divided the maximum estimated acute exposure by each available short-term 

threshold value to develop an array of HQ values relative to the various acute endpoints and 

thresholds. In general, when none of these HQ values are greater than one, there is no 
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potential for acute risk. In those cases where HQ values above one are seen, additional 

information is used to determine whether there is a potential for significant acute risks.

2.7.2 Mixtures

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

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

exposures from a particular source category combined.

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

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

the independence formula in the mixtures guidelines.

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

cause adverse health effects via completely different modes of action, it may be inappropriate 

to aggregate HQs. In consideration of these mode-of-action differences, the mixtures 

guidelines support aggregating effects of different substances in specific and limited ways. 

Consistent with these guidelines, we aggregated non-cancer HQs of HAPs that act by similar 

toxic modes of action, or (where this information is absent) that affect the same target organ. 

This process creates, for each target organ, a target-organ-specific hazard index (TOSHI), 

defined as the sum of hazard quotients for individual HAPs that affect the same organ or 

organ system. All TOSHI calculations presented here were based exclusively on effects 

occurring at the “critical dose” (i.e., the lowest dose that produces adverse health effects). 

Although HQs associated with some pollutants have been aggregated into more than one 

TOSHI, this has been done only in cases where the critical dose affects more than one target 

organ. Because impacts on organs or systems that occur above the critical dose have not been 

included in the TOSHI calculations, some TOSHIs may have been underestimated. As with 

the HQ, the TOSHI should not be interpreted as a probability of adverse effects, or as strict 

delineation of “safe” and “unsafe” levels. Rather, the TOSHI is another measure of the 

potential for adverse health outcomes associated with pollutant exposure, and needs to be 

interpreted carefully by health scientists and risk managers.

Because of the conservative nature of the acute inhalation screening and the variable nature of 

emissions and potential exposures, acute impacts were screened on an individual pollutant 

basis, not using the TOSHI approach.

2.7.3 Facility-wide risks

To help place the source category risks in context, we typically examine “facility-wide” risks 

using facility-wide and source category data files developed by EPA through emission data 

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

category, a facility-wide assessment was performed for major sources. 
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3 Risk results for the Secondary Aluminum production source category

3.1 Source category description and results

The Secondary Aluminum Production source category consists of facilities that process scrap 

aluminum, including size reduction and removal of oils, coatings, and other contaminants. 

Furnace operations at these facilities include melting, holding, fluxing and tapping of molten 

aluminum.

There are approximately 160 facilities subject to the Secondary Aluminum NESHAP 

operating in the U.S. (including both major and area sources). We performed risk modeling 

for 155 of these sources (52 major and 103 area sources). The sources that were not modeled 

consisted of area sources that did not have equipment that were subject to the area source 

dioxins/furans limits, and a single major source with only Group 2 furnaces (which are not 

subject to emission limits under the secondary aluminum NESHAP). The emissions in the 

Secondary Aluminum Production source category NPRM data set for major sources are 

summarized in Table 3.1-1. The total HAP emissions for the major source facilities in the 

source category are approximately 900 tons per year. Air emissions from Secondary

Aluminum Production major sources include HAP such as hydrochloric acid, polycyclic 

organic matter (POM), benzene, dioxin, and naphthalene. With respect to the 103 area 

sources in the secondary aluminum production source category, we note that a total of 

0.000007 tons per year of dioxin/furans (TEQ) is emitted.

Table 3.1-1 Summary of Emissions from the Major Secondary Aluminum Production Source 

Category Facilities and Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(52 facilities 

in NPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Hydrochloric Acid 892 50  

Methylene Chloride 21 19   

Polycyclic Organic Matter

 Chrysene 0.04 20  

 Benz[a]anthracene 0.03 20  

 Benzo[b]fluoranthene 0.0006 20  

 Benzo[k]fluoranthene 0.00009 7  

 Benzo[a]pyrene 0.00002 7  

 Indeno[1,2,3-

c,d]pyrene 0.00001 7  

 

Dibenzo[a,h]anthracene 0.00001 7  

Benzene 1 19   
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Table 3.1-1 Summary of Emissions from the Major Secondary Aluminum Production Source 

Category Facilities and Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(52 facilities 

in NPRM 

data set)

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSb

PBHAP? Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Noncancer?

Health 

Benchmark 

Values for Acute 

Noncancer?

Tetrachloroethene 1 19   

Naphthalene 1 20  

Carbon Disulfide 1 19  

Acrylonitrile 0.4 19   

Manganese Compounds 0.3 51 

Ethylene Dichloride 0.2 19   

Trichloroethylene 0.1 19   

Lead Compounds 0.1 51 c 

Chloroform 0.08 19  

Chromium Compounds  

 Chromium (III) 0.05 51

 Chromium (VI) 0.0002 51  

Toluene 0.03 19  

Nickel Compounds 0.02 51   

Antimony Compounds 0.02 51 

Cadmium Compounds 0.01 51   

Carbon Tetrachloride 0.01 19   

Selenium Compounds 0.004 51 

Cobalt Compounds 0.002 51 

Arsenic Compounds 0.002 51  

1,3-dichloropropene 0.002 6  

Beryllium Compounds 0.0007 51   

Mercury Compounds

 Mercury (elemental) 0.0004 51   

 Mercuric Chloride 0.0001 51  

Vinylidene Chloride 0.0002 6 

Chloroprene 0.0001 6  

1,1,1-trichloroethane 0.00008 6  

Propylene Dichloride 0.00008 6  

1,1,2-trichloroethane 0.00008 6  

Ethylidene Dichloride 0.00006 6  

dioxin/furans (TEQ)c0.00002 46   

a Notes for how HAP were speciated for risk assessment:

 There were 15 total facilities, but actual emission estimates were not provided for the Columbia Falls facility. 

 For emissions of any chemicals or chemical groups classified as polycyclic organic matter (POM), emissions were 

grouped into POM subgroups as found on EPA’s Technology Transfer Network website for the 2002 National-Scale Air 
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Toxics Assessment at http://www.epa.gov/nata2002/methods.html#pom. (Approach to Modeling POM). For this source 

category, all POMs are PAHs.

b

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

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

C

 Chlorinated dibenzodioxins and furans were modeled and analyzed as 2,3,7,8-tetrachlorodibenzo-p-dioxin equivalent.

3.2 Baseline risk characterization

This section presents the results of the risk assessment for the Secondary Aluminum

Production source category based on the modeling methods described in the previous 

sections. All baseline risk results are developed using the best estimates of actual and 

MACT-allowable HAP emissions. The basic chronic inhalation risk estimates presented here 

are the maximum individual lifetime cancer risk, the maximum target organ specific chronic 

hazard index, and the cancer incidence. We also present results from our acute inhalation 

impact screening assessments in the form of maximum hazard quotients, as well as the results 

of our screen for potential non-inhalation risks from PB HAP. Also presented are the HAP 

“drivers,” which are the HAP that collectively contribute 90 percent of the maximum cancer 

risk or maximum hazard at the highest exposure location. A detailed summary of the facilityspecific risk assessment results is available in Appendix 5.

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

source category. The results of the chronic inhalation cancer risk assessment for major 

sources indicates that the maximum lifetime individual cancer risk, considering actual 

emissions, could be up to 1 in a million, driven by primarily by dioxin emissions from group 

1 furnaces handling other than clean charge . The maximum cancer risks for this source 

category exceeded a cancer risk of 1 in a million at 1 of 52 facilities. The total estimated 

cancer incidence from this source category based on actual emission levels is 0.0006 excess 

cancer cases per year, or one excess case in every 1,666 years. No people were estimated to 

have cancer risks above 10 in a million and approximately 2 people were estimated to have 

cancer risks above 1 in a million considering all major source facilities in this source 

category. Based on MACT-allowable emissions for the major sources in this category, the 

MIR could be up to 20 in a million for the facilities presented in Table 3.2-1- driven mostly

by dioxins from group 1 furnaces handling other than clean charge and POM emissions from 

scrap dryers. 

With respect to chronic inhalation noncancer risk from major sources, we estimate a 

maximum TOSHI value of 0.05 for the Secondary Aluminum source category, primarily from 

hydrochloric acid (HCL) from Group 1 furnaces (handling clean charge only). Considering 

MACT-allowable emissions, this maximum TOSHI value is estimated to be 1 driven by HCL

from Group 1 furnaces (handling clean charge only) and scrap dryers. 

Considering facility-wide emissions at the 52 major sources, the MIR is estimated to be 20 in 

a million driven by POM emissions and the chronic non-cancer TOHSI value is calculated to 

be 0.4 driven by emissions of chlorine.

In addition to the facilities presented in Table 3.2-1, we also estimated risks associated with 

dioxin emissions at the 103 area sources in the Secondary Aluminum Production Source 

category. The results of the cancer assessment indicate that cancer risk could be as high as 
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0.4 in a million from Group 1 furnaces (handling other than clean charge). Considering 

MACT-allowable emissions, the MIR could be up to 6 in a million from Group 1 furnaces 

(handling other than clean charge). With respect to chronic inhalation noncancer risk from 

dioxin emissions, we estimate a maximum TOSHI value of 0.0003 from Group 1 furnaces 

(handling other than clean charge ) . Considering MACT-allowable emissions for area 

sources, this maximum TOSHI value is estimated to be 0.005 from Group 1 furnaces 

(handling other than clean charge). 
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Major Secondary 

Aluminum Production Sources Based on Actual Emissions

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 52 n/a

Number of Facilities Identified in NEI and 

Modeled in Preliminary Risk Assessment 52 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 

1 million) 1 Dioxins/furans

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 

million 0 n/a

Greater than or equal to 1 in 1 million 1 n/a

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.05 hydrochloric acid

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Results

Maximum Acute Hazard Quotient 0.7 hydrochloric acid

Number of Facilities With Potential for Acute 

Effects 0

Population Exposure

Number of People Living Within 50 

Kilometers of Facilities Modeled 29,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 

million 0 n/a

Greater than or equal to 10 in 1 

million 0 n/a

Greater than or equal to 1 in 1 million 2 n/a

Number of People Exposed to Noncancer Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.0006 n/a

Contribution of HAP to Cancer Incidence

Dioxins/Furans

POM

Napthalene

63%

15%

5%

n/a

Our worst-case screening-level acute analysis indicated that no major sources had acute HQ 

values greater than 1 (highest HQ value was 0.7 based on the REL for HCL). With respect to 
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area sources, there are no acute health benchmark values for dioxins, and thus, we did not 

calculate HQ values for any area sources.

 

In addition to the analyses presented above, to screen for potential multipathway effects from 

emissions of cadmium, mercury, and PAHs we compared actual emission rates from major 

source facilities in this source category to the screening value for cadmium, mercury, and 

PAHs described in section 2.5. Similarly, to screen for potential multipathway effects from 

emissions of dioxins, we compared actual emission rates from both major and area sources to 

the screening value for dioxins described in section 2.5. (Also see Appendix 4 for a more 

detailed discussion of screening emission rates). Results of this worst- case screen estimate 

that actual PAH emissions from 10 of the 53 major source facilities exceed the PAH screening

emission rate by as much as approximately 80 times. With respect to dioxins/furans, we note 

that of the 46 major sources that emitted dioxins, 39 exceeded our screening emission rate by 

as much as approximately 2,500 times. Similarly, 76 out of 103 area sources exceeded our 

screeing rate by as much as approximately 1000 times. With respect to these exceedances of 

the worst-case multipathway screening level for PAHs and dioxins/furans, we note that this 

only indicates the potential for multipathway-related cancer risks of concern from PAHs and 

dioxins. That is, while in general, emission rates below the worst-case multipathway

screening level indicate no significant potential for multipathway related health effects,

emission levels above this worst-case screening level only indicate the potential for 

multipathway-related health risks of concern based on a worst-case scenario. Due to sitespecific data limitations, we were not able to refine our multi-pathway analysis beyond the 

worst-case screening assessment. Thus, we note that these results are biased high for 

purposes of screening and are therefore subject to significant uncertainties. As a result, we 

cannot say whether multi-pathway risks may be a concern; we can only say that we cannot 

rule them out.

With respect to the potential for significant and widespread environmental effects from nonPB-HAP, we note that for both major and area sources. all chronic non-cancer HQ values for 

all pollutants considering actual emissions are well below 1 using human health reference 

values. Thus, we believe that it is unlikely that adverse environmental effects would occur at 

the actual HAP concentrations estimated in our human health risk assessment. 

4 General discussion of uncertainties and how they have been 

addressed

4.1 Exposure modeling uncertainties

Although every effort has been made to identify all the relevant facilities and emission points, 

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

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

modeling estimates. The chronic exposure modeling uncertainties are considered relatively 

small in comparison, since we are using EPA’s refined local dispersion model with sitespecific parameters and reasonably representative meteorology. If anything, the population 

exposure estimates are biased high by not accounting for short- or long-term population 
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mobility and by neglecting processes like deposition, plume depletion, and atmospheric 

degradation. Additionally, estimates of the maximum individual risk (MIR) contain 

uncertainty, because they are derived at census block centroid locations rather than actual 

residences. This uncertainty is known to create potential underestimates and overestimates of 

the actual MIR values for individual facilities, but, overall, it is not thought to have a 

significant impact on the estimated MIR for a source category. Finally, we did not factor in 

the possibility of a source closure occurring during the 70-year chronic exposure period, 

leading to a potential upward bias in both the MIR and population risk estimates; nor did we 

factor in the possibility of population growth during the 70-year chronic exposure period, 

leading to a potential downward bias in both the MIR and population risk estimates.

A sensitivity analysis performed for the 1999 NATA found that the selection of the 

meteorology dataset location could result in a range of chronic ambient concentrations which 

varied from as much as 17% below the predicted value to as much as 84% higher than the 

predicted value. This variability translates directly to the predicted exposures and risks in our 

assessment, indicating that the actual risks could vary from 17% lower to 84% higher than the 

predicted values.

We have purposely biased the acute screening results high, considering that they depend upon 

the joint occurrence of independent factors, such as hourly emissions rates, meteorology and 

human activity patterns. In cases where multiple acute threshold values are considered 

scientifically acceptable we have chosen the most conservative of these threshold values, 

erring on the side of overestimating potential health risks from acute exposures. In the cases 

where these results indicated the potential for exceeding short-term health thresholds postcontrols, we refined our assessment by developing a better understanding of the geography of 

the facility relative to potential exposure locations. 

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 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 sources 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.
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Additionally, chronic dose-response values for certain compounds included in the assessment 

may be under EPA IRIS review and revised assessments may determine that these pollutants 

are more or less potent than currently thought. We will re-evaluate risks if, as a result of these 

reviews, a dose-response metric changes enough to indicate that the risk assessment may

significantly mischaracterize human health risk

Cancer assessment

The discussion of dose-response uncertainties in the estimation of cancer risk below focuses 

on the uncertainties associated with the specific approach currently used by the EPA to 

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

of cancer potency factors by CalEPA, the source of peer-reviewed cancer potency factors 

used where EPA-developed values are not yet available. To place this discussion in context, 

we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [29] (herein 

referred to as Cancer Guidelines). “The primary goal of EPA actions is protection of human 

health; accordingly, as an Agency policy, risk assessment procedures, including default 

options that are used in the absence of scientific data to the contrary, should be health 

protective.” The approach adopted in this document is consistent with this approach as 

described in the Cancer Guidelines.

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

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

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

being evaluated, EPA relies on both animal bioassay and epidemiological studies to 

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

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

when other human cancer risk data are unavailable. 

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

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

and other peer-reviewed methodologies. The EPA Cancer Guidelines describes the Agency’s 

recommendations for methodologies for cancer risk assessment. EPA believes that cancer 

risk estimates developed following the procedures described in the Cancer Guidelines and 

outlined below generally provide an upper bound estimate of risk. That is, EPA’s upper 

bound estimates represent a “plausible upper limit to the true value of a quantity” (although 

this is usually not a true statistical confidence limit).12 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.13 

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.14 EPA also uses 

 

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

13 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.

14 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 
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the upper bound (rather than lower bound or central) estimates in its assessments, although it 

is noted that this approach can have limitations for some uses (e.g. priority setting, expected 

benefits analysis).

Such health risk assessments have associated uncertainties, some which may be considered 

quantitatively, and others which generally are expressed qualitatively. Uncertainties may vary 

substantially among cancer risk assessments associated with exposures to different pollutants, 

since the assessments employ different databases with different strengths and limitations and 

the procedures employed may differ in how well they represent actual biological processes for 

the assessed substance. EPA’s Risk Characterization Handbook also recommends that risk 

characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [30]. Some of the major sources of uncertainty and 

variability in deriving cancer risk values are described more fully below. 

(1) The qualitative similarities or differences between tumor responses observed in 

experimental animal bioassays and those which would occur in humans 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.15 However, unless scientific support is available to show 

otherwise, EPA assumes that tumors in animals are relevant in humans, regardless of target 

organ concordance. For a specific pollutant, qualitative differences in species responses can 

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

(2) Uncertainties regarding the most appropriate dose metric for an assessment can also lead 

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

in units of mg/kg/d ingested or the inhaled concentration of the pollutant. However, data may

support development of a pharmacokinetic model for the absorption, distribution, metabolism 

and excretion of an agent, which may result in improved dose metrics (e.g., average blood 

concentration of the pollutant or the quantity of agent metabolized in the body). Quantitative 

uncertainties result when the appropriate choice of a dose metric is uncertain or when dose 

metric estimates are themselves uncertain (e.g., as can occur when alternative 

pharmacokinetic models are available for a compound). Uncertainty in dose estimates may

lead to either over or underestimation of risk.

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

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

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

 

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.

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

to species size and empirical comparisons of toxicity between experimental animals and 

humans. For a particular pollutant, the quantitative difference in cancer potency between 

experimental animals and humans may be either greater than or less than that estimated by

baseline scientific scaling predictions due to uncertainties associated with limitations in the 

test data and the correctness of scaled estimates. 

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

are generally developed using a benchmark dose (BMD) analysis to estimate a dose at which 

there is a specified excess risk of cancer, which is used as the point of departure (or POD) for 

the remainder of the calculation. Statistical uncertainty in developing a POD using a 

benchmark dose (BMD) approach is generally addressed though use of the 95% lower 

confidence limit on the dose at which the specified excess risk occurs (the BMDL), 

decreasing the likelihood of understating risk. EPA has generally utilized the multistage 

model for estimation of the BMDL using cancer bioassay data (see further discussion below).

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

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

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

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

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

which a pollutant causes cancer). EPA’s Cancer Guidelines express a preference for the use 

of reliable, compound-specific, biologically-based risk models when feasible; however, such 

models are rarely available. The mode of action for a pollutant (i.e., the manner in which a 

pollutant causes cancer) is a key consideration in determining how risks should be estimated 

for low-dose exposure. A reference value is calculated when the available mode of action 

data show the response to be nonlinear (e.g., as in a threshold response). A linear low-dose 

(straight line from POD) approach is used when available mode of action data support a linear 

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

mode of action is unknown. Linear extrapolation can be supported by both pollutant-specific 

data and broader scientific considerations. For example, EPA’s Cancer Guidelines generally

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

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

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

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

approach. 

EPA most frequently utilizes a linear low-dose extrapolation approach as a baseline sciencepolicy choice (a “default”) when available data do not allow a compound-specific 

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

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

as part of EPA’s cancer risk assessment process, provide an upper bound estimate of risk and 

generally provide a health protective approach. Note that another source of uncertainty is the 

characterization of low-dose nonlinear, non-threshold relationships. The National Academy

of Sciences has encouraged the exploration of sigmoidal type functions (e.g., log-probit 

models) in representing dose response relationships due to the variability in response within 
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human populations. Another National Research Council (NRC) report [31] suggests that 

models based on distributions of individual thresholds are likely to lead to sigmoidal-shaped 

dose-response functions for a population. This report notes sources of variability in the 

human population: “One might expect these individual tolerances to vary extensively in 

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

susceptibility factors...” Thus, if a distribution of thresholds approach is considered for a 

carcinogen risk assessment, application would depend on ability of modeling to reflect the 

degree of variability in response in human populations (as opposed to responses in bioassays 

with genetically more uniform rodents). Note also that low dose linearity in risk can arise for 

reasons separate from population variability: due to the nature of a mode of action and 

additivity of a chemical’s effect on top of background chemical exposures and biological 

processes.

As noted above, EPA’s current approach to cancer risk assessment typically utilizes a straight 

line approach from the BMDL. This is equivalent to using an upper confidence limit on the 

slope of the straight line extrapolation. The impact of the choice of the BMDL on bottom line 

risk estimates can be quantified by comparing risk estimates using the BMDL value to central 

estimate BMD values, although these differences are generally not a large contributor to 

uncertainty in risk assessment (Subramaniam et. al., 2006) [32]. It is important to note that 

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

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

dose levels and did not involve the use of a POD. Subramaniam et.,al. (2006) also provide 

comparisons indicating that slopes based on straight line extrapolation from a POD do not 

show large differences from those based on the upper confidence limit of the multistage 

model.

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

in response within the human population — resulting in another source of uncertainty in 

assessments. In the diverse human population, some individuals are likely to be more 

sensitive to the action of a carcinogen than the typical individual, although compound-specific 

data to evaluate this variability are generally not available. There may also be important life 

stage differences in the quantitative potency of carcinogens and, with the exception of the 

recommendations in EPA’s Supplemental Cancer Guidance for carcinogens with a mutagenic 

mode of action, risk assessments do not generally quantitatively address life stage differences. 

However, one approach used commonly in EPA assessments that may help address variability

in response is to extrapolate human response from results observed in the most sensitive 

species and sex tested, resulting typically in the highest URE which can be supported by

reliable data, thus supporting estimates that are designed not to underestimate risk in the face 

of uncertainty and variability.

Chronic noncancer assessment

Chronic noncancer reference values represent chronic exposure levels that are intended to be 

health-protective. That is, EPA and other organizations which develop noncancer reference 

values (e.g., the Agency for Toxic Substances and Disease Registry – ATSDR) utilize an 

approach that is intended not to underestimate risk in the face of uncertainty and variability. 
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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 values16 e.g., factors of 10 or 3, 

used in the absence of compound-specific data; where data are available, uncertainty factors 

may also be developed using compound-specific information. When data are limited, more 

assumptions are needed and more default factors are used. Thus there may be a greater 

tendency to overestimate risk—in the sense that further study might support development of 

reference values that are higher (i.e., less potent) because fewer default assumptions are 

needed. However, for some pollutants it is possible that risks may be underestimated.

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

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

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

of daily oral exposure (RfD) or 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.17 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 (see US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk 

Assessments. http://www.epa.gov/iris/rfd.htm.),and U.S. EPA 1994 (see reference 13 below)

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 (see reference 13 below) and U.S. EPA 2002 (A Review 

of the Reference Dose and Reference Concentration Processes. 

 

16

 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. 

17 See IRIS glossary
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http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf.)). While collectively termed “UFs”, these 

factors account for a number of different quantitative considerations when utilizing observed 

animal (usually rodent) or human toxicity data in a risk assessment. The UFs are intended to 

account for: (1) variation in susceptibility among the members of the human population (i.e., 

inter-individual variability); (2) uncertainty in extrapolating from experimental animal data to 

humans (i.e., interspecies differences); (3) uncertainty in extrapolating from data obtained in a 

study with less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic 

exposure); (4) uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and 

(5) uncertainty when the database is incomplete or there are problems with applicability of 

available studies. When scientifically sound, peer-reviewed assessment-specific data are not 

available, default adjustment values are selected for the individual UFs. For each type of 

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

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

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

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

toxicity data and an interspecies extrapolation is not required) the associated UF is not used. 

The major adjustment steps are described more fully below.

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

Uncertainty related to human variation is considered in extrapolating doses from a subset or 

smaller-sized population, often of one sex or of a narrow range of life stages (typical of 

occupational epidemiologic studies), to a larger, more diverse population. In the absence of 

pollutant-specific data on human variation, a 10-fold UF is used to account for uncertainty

associated with human variation. Human variation may be larger or smaller; however, data to 

examine the potential magnitude of human variability are often unavailable. In some 

situations, a smaller UF of 3 may be applied to reflect a known lack of significant variability

among humans.

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

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

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

extrapolated to estimate the human response. While there is long-standing scientific support 

for the use of animal studies as indicators of potential toxicity to humans, there are 

uncertainties in such extrapolations. In the absence of data to the contrary, the typical 

approach is to use the most relevant endpoint from the most sensitive species and the most 

sensitive sex in assessing risks to the average human. Typically, compound specific data to 

evaluate relative sensitivity in humans versus rodents are lacking, thus leading to uncertainty 

in this extrapolation. Size-related differences (allometric relationships) indicate that typically 

humans are more sensitive than rodents when compared on a mg/kg/day basis. The default 

choice of 10 for the interspecies UF is consistent with these differences. For a specific 

chemical, differences in species responses may be greater or less than this value.

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

processing and associated uncertainties; however, such dosimetric adjustments are not always 

possible. Information may not be available to quantitatively assess toxicokinetic or 

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 
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(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to 

account for expected species differences and associated uncertainty in extrapolating from 

laboratory animals to humans in the derivation of a reference value. If information on one or 

the other of these components is available and accounted for in the cross-species 

extrapolation, a UF of 3 may be used for the remaining component.

3) In the case of reference values for chronic exposures where only data from shorter 

durations are available (e.g., 90-day subchronic studies in rodents) or when such data are 

judged more appropriate for development of an RfC, an additional UF of 3 or 10-fold is 

typically applied unless the available scientific information supports use of a different value.

4) Toxicity data are typically limited as to the dose or exposure levels that have been 

tested in individual studies; in an animal study, for example, treatment groups may differ in 

exposure by up to an order of magnitude. The preferred approach to arrive at a POD is to use 

BMD analysis; however, this approach requires adequate quantitative results for a meaningful 

analysis, which is not always possible. Use of a NOAEL is the next preferred approach after 

BMD analysis in determining a POD for deriving a health effect reference value. However, 

many studies lack a dose or exposure level at which an adverse effect is not observed (i.e., a 

NOAEL is not identified). When using data limited to a LOAEL, a UF of 10 or 3-fold is 

often applied. 

5) The database UF is intended to account for the potential for deriving an 

underprotective RfD/RfC due to a data gap preventing complete characterization of the 

chemical’s toxicity. In the absence of studies for a known or suspected endpoint of concern, a 

UF of 10 or 3-fold is typically applied.

Acute noncancer assessment

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

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

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

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

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. 

Additional adjustments are often applied to account for uncertainty in extrapolation from 

observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an 

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

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

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

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

 

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