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Revised Draft Residual Risk Assessment for the Primary

Aluminum Reduction Plants Source Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

September 2014
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Table of Contents

1 Introduction.........................................................................................................................5

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

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

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

2.3 Estimating chronic human inhalation exposure ...........................................................9

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

2.5 Multipathway human health and environmental risk screening.................................12

2.6 Dose-response assessment..........................................................................................17

2.6.1 Sources of chronic dose-response information...................................................17

2.6.2 Sources of acute dose-response information.......................................................25

2.7 Risk characterization..................................................................................................28

2.7.1 General.....................................................................................................................28

2.7.2 Mixtures..................................................................................................................30

2.7.3 Facility-wide risks...................................................................................................31

2.7.4 MACT-Allowable Risks.........................................................................................31

3 Risk results for the Primary Aluminum Reduction source category ............................31

3.1 Source category emissions and results.......................................................................31

3.2 Baseline risk characterization.....................................................................................35

4 General discussion of uncertainties and how they have been addressed ..........................41

4.1 Exposure modeling uncertainties...............................................................................41

4.2 Uncertainties in the dose-response relationships .......................................................42

5 References.........................................................................................................................50
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Index of Tables

Table 2.2-1 AERMOD version 13350 model options for RTR modeling.................................7

Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to Carcinogens.....21

Table 2.6-1 (b) Dose-Response Values for Chronic Oral Exposure to Carcinogens..............23

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

...................................................................................................................................................24

Table 2.6-1 (d) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens.........25

Table 2.6-3 Dose-Response Values for Acute Exposure.........................................................28

Table 3.1-1 Summary of Emissions from the Primary Aluminum Reduction Source Category 

Used in the Residual Risk Assessment Availability of Dose-Response Values.......................32

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

Reduction Sources ....................................................................................................................36

Table 3.2-2 Summary of the “Tier 1 and Tier 2” Multipathway Screening Analysis for the 

Primary Aluminum Reduction Source Category ......................................................................39

Table 3.2-3 Summary of Environmental Risk Screen Results for the Primary Aluminum 

Reduction Source Category ......................................................................................................40

Appendices

Appendix 1 Draft Development of the Risk and Technology Review (RTR) Emissions 

 Dataset for the Primary Aluminum Reduction Source Category

Appendix 2 Technical Support Document for HEM-3 Modeling

Appendix 3 Meteorological Data for HEM-3 Modeling

Appendix 4 Analysis of data on short-term emission rates relative to long-term emission 

rates

Appendix 5 Technical Support Document for TRIM-Based Multipathway Screening 

Scenario for RTR: Summary of Approach and Evaluation

Appendix 6 Draft Detailed Risk Modeling Results

Appendix 7 Dispersion Model Receptor Revisions and Additions

Appendix 8 Draft Detailed Acute Risk Modeling Results

Appendix 9 Technical Support Document: Human Health Multipathway Residual Risk 

 Screening Assessment for the Primary Aluminum Reduction Source Category
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Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

BDL Below Detection Limit

CalEPA California Environmental Agency

CTE Central Tendency Estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IR Information Request

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 (i.e., after the 

implementation of the MACT standard) performed for the primary aluminum reduction plants 

source category. The methods discussion includes descriptions of the methods used to 

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

and noncancer endpoints, as well as descriptions of the methods used to screen for acute 

health risks, chronic non-inhalation health risks, and adverse environmental effects.

2 Methods

2.1 Emissions and source data

In an effort to gather information on emissions from the primary aluminum reduction plants 

source category, a section 114 information request (IR) was sent by the EPA to ten thenoperating primary aluminum reduction plants in March 2013. The IR requested available 

information regarding process equipment, control devices, point and fugitive emissions, 
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practices used to control fugitive emissions, and other aspects of facility operations, including 

stack parameters and locations. Additionally, all ten of those facilities provided emissions 

tests for certain HAP from specific processes that were considered representative of the 

industry. Pollutants tested included POM’s, arsenic, lead, nickel, chromium (VI), and 

dioxins/furans.. The data from these tests were used to develop speciation profiles and 

emission factors for HAP metals and HAP organics. These IR data were also used to develop 

a model-ready 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, Draft Development of the Risk and Technology 

Review (RTR) Emissions Dataset for the Primary Aluminum Reduction Source Category, 

available in the docket (Docket No. EPA-HQ-OAR-2011-0797), and as Appendix 1 to this 

risk assessment document. Section 3 of this risk assessment document 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 risks 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 

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

modeling system is outlined below, and further details are provided in Appendix 2. The 

HEM-3 performs three main operations: atmospheric dispersion modeling, estimation of 

individual human exposures and health risks, and estimation of population risks. This section 

focuses on the dispersion modeling component. The exposure and risk characterization 

components are discussed in other subsections of Sections 2 and 3.

The dispersion model in the HEM-3 system, AERMOD version 13350, is a state-of-thescience Gaussian plume dispersion model that is preferred by EPA for modeling point, area, 

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

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

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

from the emission sources into the surrounding atmosphere. Hourly emission rates used for 

this simulation are generated by evenly dividing the total annual emission rate from the 

inventory into the 8,760 hours of the year.

The first step in the application of the HEM-3 modeling system is to predict ambient 

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

summarized in Table 2.2-1 and are discussed further below.
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Table 2.2-1 AERMOD version 13350 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) and user supplied receptors

Terrain characterization Actual from USGS 1-degree DEM data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option Included if source in urbanized area

Meteorology 1 year representative NWS from nearest site (over 800

stations)

In HEM-3, meteorological data are ordinarily selected from over 800 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, because we had an insufficient 

number of appropriately formatted model input files derived from available meteorological 

data, we modeled only a single year, typically 2011. 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. 

Appendix 3 (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 AERMOD meteorological data pre-processing program). 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].

Beginning with version 12345, AERMET allows for a minimum wind speed of 0.5 m/s to be 

utilized when processing monitoring stations that are equipped with sonic anemometers and 

for an adjustment to the surface velocity (u*). The public version of AERMET available at 

the time we conducted the modeling for this source category did not include the surface 

friction velocity adjustment. Also at that time, an updated version of AERMET (and 

AERMOD) was under development that incorporated further surface friction velocity 

adjustments to AERMET version 12345. In anticipation of the update, EPA processed 

meteorological data using a pre-public release version of AERMET and incorporated the 

updated surface friction velocity adjustment in the output. It was EPA’s judgment that the 
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pre-public release version of AERMET would generate AERMOD-ready meteorological data 

very close to that generated with the final public release version. 

EPA has posted the AERMET meteorological data used in the primary aluminum analysis on 

the EPA’s FERA (Fate, Exposure, and Risk Analysis) website1under the HEM model page. 

AERMET and AERMOD (version 13350) were released to the public in late December 2013 

on the EPA’s Support Center for Regulatory Atmospheric Modeling (SCRAM) website.2

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

blocks (using the 2010 Census) and at other receptor locations that can be specified by the 

user. See Appendix 7 to this document (Dispersion Model Receptor Revisions and Additions 

for the Primary Aluminum Source Category) for a discussion of user receptors and centroid 

location changes specific to this source category. The model accounts for the effects of 

multiple facilities when estimating concentration impacts at each block centroid. Typically 

we combined only the impacts of facilities within the same source category, and assessed 

chronic exposure and risk only for census blocks with at least one resident (i.e., locations 

where people may reasonably be assumed to reside rather than receptor points at the fenceline 

of a facility). Chronic ambient concentrations were calculated as the annual average of all 

estimated short-term (one-hour) concentrations at each block centroid. Possible future 

residential use of currently uninhabited areas was not considered. Census blocks, the finest 

resolution available in the census data, are typically comprised of approximately 40 people or 

about ten households. 

In contrast to the development of ambient concentrations for evaluating long-term exposures, 

which was performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations were estimated both at the census block centroids and at points nearer the 

facility that represent locations where people may be present for short periods, but generally 

no nearer than 100 meters from the center of the facility (note that for large facilities, this 

100-meter ring could still contain locations inside the facility property). Since short-term 

emission rates were needed to screen for the potential for hazard via acute exposures, and 

since the ICR data contains only annual emission totals, we generally apply the assumption 

that the maximum one-hour emission rate from any source is ten times the average annual 

hourly emission rate for that source. More description of our short-term assessment can be 

found in Section 2.4 of this document - Acute Risk Screening and Refined Assessments. 

 

Census block elevations for HEM-3 modeling were determined nationally from the US 

Geological Service 1/3 Arc Second National Elevation Dataset, which have a spatial 

resolution of about 10 meters. Elevations of polar grid points used in estimating short- and 

long-term ambient concentrations were assumed to be equal to the highest elevation of any 

census block falling within the polar grid sector corresponding to the grid point. If a sector 

does not contain any blocks, the model defaults the elevation to that of the nearest block. If 

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

 

1

 The FERA webpage is http://www2.epa.gov/fera and the HEM3 webpage is http://www2.epa.gov/fera/riskassessment-and-modeling-human-exposure-model-hem . 

2 The SCRAM webpage is http://www.epa.gov/ttn/scram/. 
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all sectors within the innermost model ring. AERMOD adjusts the plume’s flow if nearby 

elevated hills are expected to influence the wind patterns. For details on how hill heights 

were estimated and used in the AERMOD modeling see Appendix 2 of this document, 

Technical Support Document for HEM3 Modeling. 

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 percent 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.

2.4 Screening for potential acute impacts of concern

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

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

developing chronic health risk assessments for other rules under the residual risk program. 

That is, we developed a tiered, iterative approach. This approach to risk assessment was

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

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

Congress” in 1999. 
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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. 

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. When we 

identify acute impacts which exceed their relevant benchmarks, we pursue refining our acute 

screening estimates to the extent possible using available data. In some cases, this may 

include the use of a facility-specific emissions multiplier to estimate the peak hourly emission 

rates from the average rates (rather than the default factor of 10). In other cases, this may 

entail determining the actual physical layout and boundaries of a facility and surrounding 

communities to more accurately gauge where people might reasonably be exposed for an 

hour.

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. These hourly concentrations are 

based on hourly emissions, as described above. Since information is not usually available on 

short-term emission rates, we generally apply the assumption that the maximum one-hour 

emission rate from any source is 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 acute 

screening was originally based on engineering judgment. 

To develop a more robust peak-to-mean emissions factor, and in response to one of the key 

messages from the SAB consultation on our RTR Assessment Plan, we performed an analysis 

using a short-term emissions dataset from a number of sources located in Texas (originally 

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

Consortium Project compared hourly and annual emissions data for volatile organic 

compounds for all facilities in a heavily-industrialized 4-county area (Harris, Galveston, 

Chambers, and Brazoria Counties) 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 4 of this document, Analysis of 

data on short-term emission rates relative to long-term emission rates). Most peak emission 

events were less than twice the annual average, and the highest was a factor of 74 times the 

annual average, and the 99th percentile ratio of peak hourly emission rate to the annual hourly 

emission rate was 9. Based on these results, we generally chose the factor of ten for all initial 
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screening. This factor is intended to cover all possible hourly peaks associated with routinelyvariable 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 percent 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. 

For the primary aluminum source category, however, we did not use the default multiplier of 

10 for all emission points. See Appendix 1 to this document, Revised Development of the 

Risk and Technology Review (RTR) Emissions Dataset for the Primary Aluminum Source 

Category, for the detailed description of how the hourly multipliers were developed for the 

primary aluminum source category. In general, based on ICR data and engineering 

knowledge of the category, we note that fugitive emissions from the potline roof vents are not 

subject to significant hourly fluctuations in emissions over the year so were assigned an acute 

multiplier of 1.5 (i.e., maximum hourly emissions equal annual average hourly emissions). 

Other processes do have emissions cycles, or peak emissions (e.g., anode bake furnaces) and 

these processes received factors of 2 (i.e., maximum hourly emissions were estimated to be 2

times greater than annual average hourly emissions). 

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 the 

primary aluminum source category, we conducted a review of the layout of the polar receptors 

around the facilities compared to the facility boundaries to determine the maximum off-site 

acute impact for the facilities that did not screen out during the initial run. We also examined 

the locations and concentrations at residential receptors, the temporal variability in exposure 

concentrations, and a range of dose-response values. Appendix 6 to this document (Draft 

Detailed Risk Modeling Results) contains the initial results for the source category, while 

Appendix 8 to this document (Acute Impacts Refined Analysis) contains the refined acute 

results.

In summary, we used conservative assumptions for emission rates, meteorology, and exposure 

location, and refined data where available. We used the following assumptions in our acute 

assessment approach:

 Peak 1-hour emissions were obtained from the emissions memo and based on the 

operating characteristics of specific emission sources varying from 1.5 to 2 times the 

average 1-hour emission rates, as described in Appendix 1. 

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

occur at all emission points at the same time.

 We assumed that the peak emissions occur at all emission points at the same time.

 For facilities with multiple emission points emitting the same pollutant, 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. 
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 Worst-case meteorology (from one year of local meteorology) was assumed to occur 

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

dispersion model, AERMOD, is used for simulating atmospheric dispersion.

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

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

meters.

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

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

benchmarks are described in section 2.6 of this report.

2.5 Multipathway human health and environmental risk screening

Multipathway Human Health Screening

The potential for significant human health risks 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)3. The PB-HAP compounds or compound classes are identified 

for the screening from the EPA’s Air Toxics Risk Assessment Library [8]. Examples of 

PB-HAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

compounds, methoxychlor, polychlorinated biphenyls (PCB), polycyclic organic matter

(POM), toxaphene, and trifluralin. Emissions of cadmium compounds, chlorinated 

dibenzodioxins and furans, lead compounds, mercury compounds and POM (of which PAH 

are a subset) were identified in the emissions inventory for the primary aluminum source 

category. 

With respect to PB-HAP emissions other than lead, emissions were evaluated for potential 

non-inhalation risks using a tiered screening approach which was developed for use with the 

EPA’s peer-reviewed Total Risk Integrated Methodology – Fate, Transport, and Ecological 

Exposure (TRIM.FaTE4) model. With this approach, we first determine whether the facilityspecific emission rates of each of the emitted PB-HAP are large enough to create the potential 

for significant non-inhalation human health risks under reasonable worst-case conditions. To 

facilitate this step, we developed emission rate levels for each PB-HAP using a hypothetical 

upper-end screening exposure scenario developed for use in conjunction with TRIM.FaTE. 

The exposure scenario includes a generic farming/fishing exposure scenario that simulates a 

subsistence environment. We conducted a sensitivity analysis on the screening scenario to 

ensure that its key design parameters would represent the upper end of the range of possible 

values, such that it would represent a conservative but not impossible scenario. The PB-HAP 

emissions from each facility in the source category were compared to the emission rate levels

 

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

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

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

document). 

4 EPA’s Total Risk Integrated Methodology (General Information) http://www2.epa.gov/fera/total-riskintegrated-methodology-trim-general
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for each of the PB-HAP identified to assess the potential for significant human health risks via 

non-inhalation pathways. For the purpose of developing emission rates for the multipathway 

screen, we derived emission levels for each PB-HAP (other than lead) 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. We call this application of the TRIM.FaTE model the Tier 1 Screen. 

See Appendix 5 of this document, (Technical Support Document for TRIM-Based 

Multipathway Screening Scenario for RTR: Summary of Approach and Evaluation) for a 

complete discussion of the development and testing of the screening scenario, as well as for 

the values of facility-level emission rates developed for screening potentially significant 

multipathway impacts. 

If the PB-HAP emissions for a facility exceed the Tier screening emission rate, we conduct a 

Tier 2 multipathway screen. In the Tier 2 screen, the location of each facility that exceeded 

the Tier 1 emission level is used to refine some of the assumptions associated with the 

environmental scenario while maintaining the exposure scenario assumptions. We then adjust 

the risk-based Tier 1 screening level for each PB-HAP for each facility based on an 

understanding of how exposure concentrations estimated for the screening scenario change 

with meteorology and environmental assumptions. The screening scenarios developed 

include 2 separate receptors; 1) subsistence fisher and 2) subsistence farmer. This step creates 

a facility-specific emission level for each PB-HAP (i.e., the level for cadmium could be 

different for every facility), unlike the Tier 1 emission level which was constant for the same 

PB-HAP at different facilities (i.e., the level for cadmium was the same for every facility). 

The subsistence fisher scenario assumes a high-end fish consumption rate of 373 grams/day.

5

Also, the assessment uses the assumption that the biological productivity limitation of each 

lake is 1 gram of fish per acre of water Thus, in order to fulfill the adult ingestion rate, we 

may need to model more than one lake, refer to Appendix 5 of this document (Technical 

Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of 

Approach and Evaluation) for a complete discussion of the bioassay studies used to support 

this assumption. 

If we need to include more than one lake to achieve the 373 g/day ingestion rate, we begin 

with the lake with highest chemical concentration of a given PB-HAP group, and “fish” up to 

the lake’s biological productivity. We then systematically proceed to other lakes based on 

concentration, until the 373 g/day limit is met. A maximum travel radius of 50 km relative to 

the facility is used to maintain a realistic scenario. The final Tier 2 screening result for the 

fisher can be expressed as the sum of the risk from each lake that is fished (which is based on 

the amount of fish ingested for each lake multiplied by the fish concentration). If the highestconcentration lake is at least 373 acres, the ingestion rate is not altered (i.e., remains 373 g/d 

for adults). If the cumulative size of multiple visited lakes exceeds 373 acres, the model 

includes from the final lake only the amount of fish necessary to satisfy the ingestion rate (i.e., 

to reach the 373 g/d). If there are not 373 acres of lake, the risk reflects a reduced ingestion 

rate based on the lake acreage. The farmer scenario (same approach for all Tiers) involves an 

individual living for a 70-year lifetime on a farm homestead in the vicinity of the source and 

 

5 Burger, J. 2002. Daily consumption of wild fish and game: Exposures of high end 

recreationalists. Environmental Health Research. 12(4):343–354
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consuming produce grown on, and meat and animal products raised on, the farm. The 

ingestion rate used for these food groups are set at the 90th percentile of EPA’s “Exposure 

Factors Handbook: 2011 Edition”, and is considered an upper-bound level. [9 ]

Facility emissions of PB-HAP that do not exceed these new Tier 2 screening levels are 

considered to pose no significant risks. When facilities exceed the Tier 2 screening levels, it 

does not mean that multipathway impacts are significant, only that we cannot rule out that 

possibility based on the results of the screen. See Appendix 5 of this document (Technical 

Support Document for TRIM-Based Multipathway Screening Scenario for RTR: Summary of 

Approach and Evaluation) for a complete discussion of the Tier 2 screen. 

If the PB-HAP emissions for a facility exceed the Tier 2 screening emissions rate, and data 

are available, we may decide to conduct a more refined multipathway assessment. This next 

iteration of assessment could be an extensive refined assessment that replaces as many of the 

assumptions made in the Tier 2 screen with site-specific data as possible or it could be 

focused on a few parameters such as adequacy of the lake, and/or the effective plume height. 

We refer to the latter as the Tier 3 screen. In the Tier 3 screen, the lakes included in the 

screen are scrutinized for accessibility (i.e., private or public) and appropriateness (i.e., 

fishable lake v. industrial waste pond). In addition, a Tier 3 screen may include an 

examination of the site-specific meteorological mixing height relative to the effective plume 

rise. As with the Tier 2 screen, the Tier 3 step creates a facility-specific emission rate level

for each PB-HAP (i.e., the level for cadmium could be different for every facility). Also as 

with the Tier 2 screen, if facilities exceed the Tier 3 screening levels, it does not mean that 

multipathway impacts are significant, only that we cannot rule out that possibility based on 

the results of the screen. See Appendix 5 of this document (Technical Support Document for 

TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and 

Evaluation) for a complete discussion of the Tier 3 screen. For the primary aluminum source 

category we did conduct a Tier 3 assessment for the facility with the highest Tier 2 excess 

cancer results. 

In evaluating the potential multipathway risks from emissions of lead compounds, rather than 

developing a screening emission rate for them, we compared maximum estimated chronic 

atmospheric concentrations with the current National Ambient Air Quality Standard 

(NAAQS) for lead. Values below the NAAQS were considered to have a low potential for 

multipathway risks.

The NAAQS value, a public health policy judgment, incorporated the Agency’s most recent 

health evaluation of air effects of lead exposure for the purposes of setting a national ambient 

air quality standard. In setting this value, the Administrator promulgated a standard that was 

requisite to protect public health with an adequate margin of safety. We consider values 

below the level of the primary NAAQS to protect against multipathway risks because as 

mentioned above, the primary NAAQS is set as to protect public health with an adequate 

margin of safety. However, ambient air lead concentrations above the NAAQS are 

considered to pose the potential for increased risk to public health. We consider this NAAQS 

assessment to be a refined analysis given: 1) the numerous health studies, detailed risk and 

exposure analyses, and level of external peer and public review that went into the 

development of the primary NAAQS for lead, combined with: 2) the site-specific dispersion 
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modeling used in this assessment to estimated ambient lead concentrations due to primary 

aluminum emissions. It should be noted, however, that this comparison does not account for 

possible population exposures to lead from sources other than the one being modeled; for 

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

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

adequate margin of safety, the health of children and other at-risk populations against an array 

of adverse health effects, most notably including neurological effects, particularly 

neurobehavioral and neurocognitive effects, in children (73 FR 67007). The Administrator, in 

setting the standard, also recognized that no evidence-or risk based bright line indicated a 

single appropriate level. Instead a collection of scientific evidence and other information was 

used to select the standard from a range of reasonable values (73 FR 67006).

We further note that comparing ambient lead concentrations to the NAAQS for lead, 

considering the level, averaging time, form and indicator, also informs whether there is the 

potential for adverse environmental effects. This is because the primary lead NAAQS, set to 

protect against adverse welfare effects (including adverse environmental effects), has the 

same averaging time, form, and level as the primary standard. Thus, ambient lead 

concentrations above the NAAQS for lead also indicate the potential for adverse 

environmental effects.

Environmental Risk Screening

The EPA has developed a screening approach to examine the potential for adverse 

environmental effects as required under section 112(f)(2)(A) of the CAA. The environmental 

screen focuses on the following seven environmental HAP:

 Five persistent bioaccumulative HAP (PB-HAP) – cadmium, dioxins/furans, POM, 

mercury (both inorganic mercury and methyl mercury), and lead,

 Two acid gases – hydrogen chloride (HCl) and hydrogen fluoride (HF).

HAP that persist and bioaccumulate are of particular environmental concern because they 

accumulate in the soil, sediment, and water. The acid gases – HCl and HF – were included 

due to their well-documented potential to cause direct damage to terrestrial plants.

For the environmental risk screening analysis, EPA first determined whether any facilities in 

the source category emitted any of the seven environmental HAP. If one or more of the seven 

environmental HAP evaluated are emitted by at least one facility in the source category we 

proceed to the second step of the evaluation.

For cadmium, mercury, POM, and dioxins/furans, the environmental screening analysis 

consists of two tiers. In the first tier, TRIM.FaTE modeling was conducted under worst-case 

environmental conditions to determine whether the facility-specific off-site emission rates of 

each of the emitted environmental HAP exceeded ecological benchmarks represented as 

emissions levels. If off-site emissions from a facility do not exceed the Tier 1 levels the 

facility “passes” the screen, and therefore, is not evaluated further under the screening 
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approach. If off-site emissions from a facility exceed the Tier 1 levels, we evaluate the 

facility further in Tier 2.

In Tier 2 of the environmental screening analysis, the screening emission levels are adjusted 

to account for local meteorology and the actual location of lakes in the vicinity of facilities 

that did not pass the Tier 1 screen. If off-site emissions from a facility do not exceed the Tier 

2 levels, the facility passes the screen, and is typically not evaluated further. If off-site 

emissions from a facility exceed the Tier 2 levels, the facility does not pass the screen, and, 

therefore, may have the potential to cause adverse environmental effects. Such facilities are 

evaluated further to investigate factors such as the magnitude and characteristics of the area of 

exceedance.

For acid gases, the environmental screening analysis evaluates the potential phytotoxicity and 

reduced productivity of plants due to chronic exposure to acid gases. The environmental risk 

screening methodology for acid gases is a single-tier screen that compares the average off-site 

ambient air concentration over the modeling domain to ecological benchmarks for each of the 

acid gases. For purposes of ecological risk screening, EPA identifies a potential for adverse 

environmental effects to plant communities from exposure to acid gases when the average 

concentration of the HAP around a facility exceeds the LOAEL ecological benchmark. In 

such cases, we further investigate factors such as the magnitude and characteristics of the area 

of exceedance (e.g., land use of exceedance area, size of exceedance area) to determine if 

there is an adverse environmental effect.

For lead compounds, we currently do not have the ability to calculate media concentrations 

using the TRIM.FaTE model. However, air concentrations of lead are already calculated as 

part of the human health exposure and risk analysis using the HEM3-AERMOD air dispersion 

model. To evaluate the potential for adverse environmental effects from lead, we compare the 

average modeled air emission concentrations of lead from each facility in the source category 

emissions to the level of the secondary National Ambient Air Quality Standard (NAAQS) for 

lead. The secondary lead NAAQS is a reasonable means of evaluating environmental risk 

because it is set to provide substantial protection against adverse welfare effects which can 

include “effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, 

weather, visibility and climate, damage to and deterioration of property, and hazards to 

transportation, as well as effects on economic values and on personal comfort and wellbeing.” 6

 

 

6 A secondary standard, as defined in Section 109(b)(2), must “specify a level of air quality the attainment and 

maintenance of which, in the judgment of the Administrator, based on criteria, is requisite to protect the public 

welfare from any known or anticipated adverse effects associated with the presence of [the] pollutant in the 

ambient air.” Welfare effects as defined in section 302(h) (42 U.S.C. 7602(h)) include, but are not limited to, 

“effects on soils, water, crops, vegetation, man-made materials, animals, wildlife, weather, visibility and climate, 

damage to and deterioration of property, and hazards to transportation, as well as effects on economic values and 

on personal comfort and well-being.”
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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 primary

aluminum reduction plant source category were based on the EPA Office of Air Quality 

Planning and Standards’ (OAQPS) existing recommendations for HAP [10], which were also 

used for NATA [11]. 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 

air.” The SF is “an upper bound, approximating a 95 percent confidence limit, on the 

increased cancer risk from a lifetime exposure to an agent. This estimate, [is] usually 

expressed in units of proportion (of a population) affected per mg/kg-day...” EPA 

disseminates dose-response assessment information in several forms, based on the level of 

review. The Integrated Risk Information System (IRIS) [12] 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.
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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) [13] 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 assessments7: “The 

guidelines for developing chronic inhalation exposure levels incorporate many 

recommendations of the U.S. EPA [14] and NAS [15].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [16]. 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 [17], defined similarly to EPA's URE. 

 

As mentioned above, in order to characterize non-cancer chronic effects, and in response to key 

recommendations from the SAB, the EPA selects dose-response values that reflect the best 

available science for all HAP included in RTR risk assessments8. More specifically, for a given 

HAP, EPA examines the availability of inhalation reference values from the sources included in 

our tiered approach (e.g., IRIS first, ATSDR second, Cal EPA third) and determines which 

inhalation reference value represents the best available science. Thus, as new inhalation reference 

values become available, EPA will typically evaluate them and determine whether they should be 

given preference over those currently being used in RTR risk assessments. In developing 

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

professional judgment, as follows: 

 

7 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)

8 The SAB peer review of RTR Risk Assessment Methodologies is available at: 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/EPA-SAB-10-007-

unsigned.pdf.
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1) There is an existing IRIS RfC for manganese (Mn) published in 19939. This value was in the 

RTR risk assessment supporting the 2011 Ferroalloys Notice of Proposed Rulemaking. 

However, since the 2011 proposal ATSDR has published an assessment of Mn toxicity (2012) 

which includes a chronic inhalation reference value (i.e., an ATSDR Minimal Risk Level or 

MRL)10. Both the 1993 IRIS RfC and the 2012 ATSDR MRL were based on the same study 

(Roels et al., 1993). In developing their assessment, ATSDR used updated dose-response 

modeling methodology (benchmark dose approach) and considered recent pharmacokinetic 

findings to support their MRL derivation. In the interest of using the best available science, 

the EPA has chosen to rely on the ATSDR MRL for Mn in the current secondary aluminum 

proposal. 

2) In the case of nickel compounds, to provide a health-protective estimate of potential 

cancer risks, we used the IRIS URE value for nickel subsulfide in this risk assessment. 

Based on past scientific and technical considerations, the determination of the percent of 

nickel subsulfide was considered a major factor for estimating the extent and magnitude of 

the risks of cancer due to nickel-containing emissions. Nickel speciation information for 

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

and others) suggested that at least 35 percent of total nickel emissions may be soluble 

compounds and that the URE for the mixture of inhaled nickel compounds (based on 

nickel subsulfide, and representative of pure insoluble crystalline nickel) could be derived 

to reflect the assumption that 65 percent of the total mass of nickel may be carcinogenic. 

Based on consistent views of major scientific bodies (i.e., National Toxicology Program 

(NTP) in their 12th Report of the Carcinogens (ROC)11, International Agency for Research 

on Cancer (IARC)12, and other international agencies)13 that consider all nickel 

compounds to be carcinogenic, we currently consider all nickel compounds to have the 

potential of being carcinogenic to humans. The 12th Report of the Carcinogens states that 

the “combined results of epidemiological studies, mechanistic studies, and carcinogenic 

studies in rodents support the concept that nickel compounds generate nickel ions in target 

cells at sites critical for carcinogenesis, thus allowing consideration and evaluation of 

these compounds as a single group.” Although the precise nickel compound (or 

compounds) responsible for the carcinogenic effects in humans is not always clear, studies 

indicate that nickel sulfate and the combinations of nickel sulfides and oxides encountered 

in the nickel refining industries cause cancer in humans (these studies are summarized in a 

 

9 USEPA Integrated Risk Information System Review of Manganese (1993) available at 

http://www.epa.gov/iris/subst/0373.htm.

10 Agency for Toxic Substances & Disease Registry Toxicological Profile for Manganese (2012) available at 

http://www.atsdr.cdc.gov/toxprofiles/tp.asp?id=102&tid=23.

11

 National Toxicology Program (NTP), 2011. Report on carcinogens. 12th ed. Research Triangle Park, NC: 

US Department of Health and Human Services (DHHS), Public Health Service. Available online at 

http://ntp.niehs.nih.gov/ntp/roc/twelfth/roc12.pdf

12

 International Agency for Research on Cancer (IARC), 1990. IARC monographs on the evaluation of 

carcinogenic risks to humans. Chromium, nickel, and welding. Vol. 49. Lyons, France: International Agency 

for Research on Cancer, World Health Organization Vol. 49:256.

13 World Health Organization (WHO, 1991) and the European Union’s Scientific Committee on Health and 

Environmental Risks (SCHER, 2006).
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review by Grimsrud et al., 201014). The major scientific bodies mentioned above have 

also recognized that there are differences in toxicity and/or carcinogenic potential across 

the different nickel compounds. 

Therefore, in the inhalation risk assessment for this supplemental proposal, to take a 

conservative approach, we considered all nickel compounds to be as carcinogenic as 

nickel subsulfide and applied the IRIS URE for nickel subsulfide without a factor to 

reflect the assumption that 100 percent of the total mass of nickel may be as carcinogenic 

as pure nickel subsulfide. However, given that there are two additional URE values15

derived for exposure to mixtures of nickel compounds, as a group, that are 2-3 fold lower 

than the IRIS URE for nickel subsulfide, the EPA also considers it reasonable to use a 

value that is 50 percent of the IRIS URE for nickel subsulfide for providing an estimate of

the lower end of the plausible range of cancer potency values for different mixtures of 

nickel compounds. 

3) Where possible for emissions of unspecified mixtures of HAP categories such as metal 

compounds and POM, we apply category-specific chemical speciation profiles appropriate 

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

4) Where POM emissions were not speciated into individual compounds, we applied the 

same simplifying assumptions to assessments that are used in NATA [18]. 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 primary aluminum source category includes emissions of 

HAP with available chronic quantitative inhalation dose-response values. Of these, 23 are

classified as known, probably, or possible carcinogens, with quantitative cancer dose-response 

values available. These 23 HAP, their quantitative inhalation chronic cancer dose-response 

values, and the source of each value are listed in Table 2.6-1(a). The dioxin and POM 

compounds with chronic oral cancer dose-response values available (for which multipathway 

screening assessments were performed) are listed in Table 2.6-1(b). Sixteen HAP have 

quantitative inhalation chronic noncancer threshold values available; two of these sixteen

HAP (cadmium and mercury), for which a multipathway screen was performed, also have 

quantitative oral chronic noncancer threshold values available. These two HAP, their 

threshold values, and the source of the value are listed in Table 2.6-2(a) and Table 2.6-2(b). 

 

14

 Grimsrud TK and Andersen A. Evidence of carcinogenicity in humans of water-soluble nickel salts. J Occup 

Med Toxicol 2010, 5:1-7. Available online at http://www.occup-med.com/content/5/1/7.

15

 Two UREs (other than the current IRIS values) have been derived for nickel compounds as a group: one 

developed by the California Department of Health Services 

(http://www.arb.ca.gov/toxics/id/summary/nickel_tech_b.pdf) and the other by the Texas Commission on 

Environmental Quality (http://www.epa.gov/ttn/atw/nata1999/99pdfs/healtheffectsinfo.pdf).
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Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)16 = cancer risk per μg/m3 of average lifetime exposure. 

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

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

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

Development

Pollutant CAS Number17 URE5

(1/μg/m3)

Source

Dioxins/Furans18

1,2,3,4,6,7,8,9-

Octachlorodibenzo-p-dioxin 3268879 0.0099 EPA ORD

1,2,3,4,6,7,8,9-

Octochlorodibenzofuran 39001020 0.0099 EPA ORD

 1,2,3,4,6,7,8-

Heptachlorodibenzo-p-dioxin 35822469 0.33 EPA ORD

1,2,3,4,6,7,8-

Heptachlorodibenzofuran 67562394 0.33 EPA ORD

1,2,3,4,7,8,9-

Heptachlorodibenzofuran 55673897 0.33 EPA ORD

1,2,3,4,7,8-

Hexachlorodibenzo-p-dioxin 39227286 3.3 EPA ORD

1,2,3,4,7,8-

Hexachlorodibenzofuran 70648269 3.3 EPA ORD

1,2,3,6,7,8-

Hexachlorodibenzo-p-dioxin 57653857 3.3 EPA ORD

1,2,3,6,7,8-

Hexachlorodibenzofuran 57117449 3.3 EPA ORD

1,2,3,7,8,9-

Hexachlorodibenzo-p-dioxin 19408743 3.3 EPA ORD

1,2,3,7,8,9-

Hexachlorodibenzofuran 72918219 3.3 EPA ORD

1,2,3,7,8-Pentachlorodibenzop-dioxin 40321764 33 EPA ORD

1,2,3,7,8-

Pentachlorodibenzofuran 57117416 0.99 EPA ORD

2,3,4,6,7,8-

Hexachlorodibenzofuran 60851345 3.3 EPA ORD

 

16 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. 

17 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.

18 Dioxins/Furans URE based on 2005 WHO evaluation of TEF 

(http://www.who.int/foodsafety/chem/tef_update/en/); URE=Group URE x TEF; RfC = 2,3,7,8-TCDD 

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

Carcinogens

URE (unit risk estimate for cancer)16 = cancer risk per μg/m3 of average lifetime exposure. 

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

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

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

Development

Pollutant CAS Number17 URE5

(1/μg/m3)

Source

2,3,4,7,8-

Pentachlorodibenzofuran 57117314 9.9 EPA ORD

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

2,3,7,8-

Tetrachlorodibenzofuran 51207319 3.3 EPA ORD

Arsenic compounds 7440382 4.3E-03 IRIS

Beryllium compounds 7440417 2.4E-03 IRIS

Cadmium compounds 7440439 1.8E-03 IRIS

Chromium compounds

 Chromium (VI) 18540299 1.2E-02 IRIS

Naphthalene 91203 3.4E-05 CAL

Nickel compounds19 7440020 4.8E-04 EPA OAQPS

Polychlorinated biphenyls 1336363 1.0E-04 IRIS

Polycyclic Organic Matter20,

21

 2-Methylnaphthalene 91576 8.8E-05 CAL

Acenaphthene 83329 8.8E-05

CAL

Acenaphthylene 206968 8.8E-05

CAL

Benz[a]anthracene 56553 1.8E-04 CAL

 Benzo[a]pyrene 50328 1.8E-03 CAL

 

19 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.

20 All POM act by a mutagenic mode-of-action, therefore the UREs have been adjusted by a factor of 1.6 (see 

section 2.7.1 for more details).

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

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

http://www.epa.gov/ttn/atw/nata2005/methods.html#pom
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Table 2.6-1 (a) Dose-Response Values for Chronic Inhalation Exposure to 

Carcinogens

URE (unit risk estimate for cancer)16 = cancer risk per μg/m3 of average lifetime exposure. 

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

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

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

Development

Pollutant CAS Number17 URE5

(1/μg/m3)

Source

 Benzo[e]pyrene 192972 8.8E-05 CAL

 Benzo[b]fluoranthene 205992 1.8E-04 CAL

Benzo[g,h,i]perylene 191242 8.8E-05

CAL

Benzo[k]fluoranthene 207089 1.8E-04 CAL

 Chrysene 218019 1.8E-05 CAL

 Dibenzo[a,h]anthracene 53703 1.9E-03 CAL

 Fluoranthene 206440 8.8E-05

CAL

Fluorene 86737 8.8E-05

CAL

Indeno[1,2,3-c,d]pyrene 193395 1.8E-04 CAL

Perylene 198550 8.8E-05 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 Number19

SF

(1/mg/kg/d) Source

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

Polychlorinated Biphenyls 1336363 2 IRIS

Polycyclic Organic Matter POM 246

 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

 Chrysene 218019 0.12 CAL

 Dibenzo[a,h]anthracene 53703 4.1 CAL

Indeno[1,2,3-c,d]Pyrene 193395 1.2 CAL
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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 Number 19 RfC 

(mg/m3)

Source22

Antimony compounds 7440360 0.0002 IRIS - L

Arsenic compounds 7440382 0.000015 CAL

Beryllium compounds 7440417 0.00002 IRIS – M

Cadmium compounds 7440439 0.00001 ATSDR

Carbonyl sulfide 463581 0.163 EPA ORD23

Chromium (VI) compounds 18540299 0.0001 IRIS – M

Cobalt compounds 7440484 0.0001 ATSDR

Dioxins/furans

1,2,3,4,6,7,8,9-Octochlorodibenzofuran 39001020 0.00013 CAL

1,2,3,4,6,7,8,9-Octochlorodibenzo-pdioxin 3268879 0.00013 CAL

1,2,3,4,6,7,8-Heptachlorodibenzofuran 67562394 0.000004 CAL

1,2,3,4,6,7,8-Heptachlorodibenzo-pdioxin 35822469 0.000004 CAL

1,2,3,4,7,8,9-Heptachlorodibenzofuran 55673897 0.000004 CAL

1,2,3,4,7,8-Hexachlorodibenzofuran 70648269 0.0000004 CAL

1,2,3,4,7,8-Hexachlorodibenzo-p-dioxin 39227286 0.0000004 CAL

1,2,3,6,7,8-Hexachlorodibenzofuran 57117449 0.0000004 CAL

1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin 57653857 0.0000004 CAL

1,2,3,7,8,9-Hexachlorodibenzofuran 72918219 0.0000004 CAL

1,2,3,7,8,9-Hexachlorodibenzo-p-dioxin 19408743 0.0000004 CAL

1,2,3,7,8-Pentachlorodibenzo-p-dioxin 40321764 0.00000004 CAL

1,2,3,7,8-Pentachlorodibenzofuran 57117416 0.0000013 CAL

2,3,4,6,7,8-Hexachlorodibenzofuran 60851345 0.0000004 CAL

2,3,4,7,8-Pentachlorodibenzofuran 57117314 0.00000013 CAL

2,3,7,8-Tetrachlorodibenzo-p-dioxin 1746016 0.00000004 CAL

2,3,7,8-Tetrachlorodibenzofuran 51207319 0.0000004 CAL

Hydrofluoric acid 7664393 0.014 CAL

Lead Compounds 7439921 0.00015 EPA OAQPS

Manganese Compounds 7439965 0.0003 ATSDR

 

22 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.

23 A chronic screening level of 0.163 mg/m3 was developed for carbonyl sulfide by EPA ORD from a No 

Observed Adverse Effects Level of 200 ppm based on brain lesions and neurophysiological alteration in rodents. 

see Mineral Wool Report, Section 2.6.1, item 5 for report language on carbonyl sulfide
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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 Number 19 RfC 

(mg/m3)

Source22

Mercury Compounds 184 0.0003 IRIS – M

 Mercuric Chloride 7487947 0.0003 IRIS – M

 Mercury (elemental) 7439976 0.0003 IRIS – M

Naphthalene 91203 0.003 IRIS – M

Nickel Compounds 7440020 0.00009 ATSDR

Selenium Compounds 7782492 0.02 CAL

Table 2.6-1 (d) Dose-Response Values for Chronic Oral Exposure to Noncarcinogens

RfD (reference dose) = an estimate (with uncertainty spanning perhaps an order of magnitude) of a 

continuous oral 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

Pollutant CAS Number

RfD

(mg/kg/d) Source 24

Cadmium compounds 7440439 0.0005 IRIS - H

Mercuric chloride25 7439976 0.0003 IRIS - H

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 [19]. 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 

 

24 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.

25 The multipathway exposure assessment for mercury included fate and transport analysis, that included 

separate oral exposure estimates for divalent mercury and methylmercury.
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inhalation health effect reference values (both acute and chronic) for selected HAPs can be 

found in an EPA document of graphical arrays.

26

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. [20]. RELs are based on the most sensitive, relevant, 

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

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

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

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

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

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

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

developed chronic RELs).

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

Committee (NAC) on Acute Exposure Guideline Levels (NAC/AEGL) for Hazardous 

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

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

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

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

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

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

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

document states: “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. 

 

26 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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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) [21] 

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

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

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

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

ERPG values https://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx) 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 
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ability to take protective action.”

In assessing the potential risks associated with acute exposures to HAP, we do not follow a 

prioritization scheme and therefore we consider available dose-response values from multiple 

authoritative sources. 

The emissions inventory for the primary aluminum reduction source category includes 

emissions of six 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 2.6-3.

Table 2.6-3 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 

(mg/m3)

Arsenic compounds 7440382 0.0002

Beryllium compounds 7440417 0.025

Carbonyl sulfide 463581 140

Hydrofluoric acid 7664393 0.82 20 1.6 16 0.24

Mercury (elemental) 7439976 1.7 2 0.0006

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 [22] 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 [23], in 2002 by the Agency’s information quality guidelines [24], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis27, 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 

 

27Memorandum for the Heads of Executive Departments and Agencies - Updated Principles for Risk Analysis

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

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

Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf)
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the limitations of available time and resources. We provide summaries of risk metrics 

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

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

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

limitations.

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

individual and population cancer risks were calculated by multiplying the corresponding 

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

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

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

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

zero.

For EPA’s list of carcinogenic HAPs that act by a mutagenic mode-of-action [25], we applied 

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

Carcinogens [26]. 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 [27] 

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

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

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

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

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

Screening for potentially significant acute inhalation exposures also followed the HQ 

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

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

thresholds. In general, when none of these HQ values are greater than one, there is no 

potential for acute risk. In those cases where HQ values above one are seen, additional 

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

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’ 

[28,29] 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.
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2.7.3 Facility-wide risks

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

2005 NEI data and modeling as described in Section 2.2. For the facilities in this source 

category, we estimated the maximum inhalation cancer and chronic noncancer risks 

associated with all HAP emissions sources at the facility, including emissions sources that are 

not part of the primary aluminum source category but are located within a contiguous area and 

are under common control. We analyzed risks due to the inhalation of HAP for the 

populations residing within 50 kilometers of each facility. The results of these facility-wide 

assessments are summarized below in the Risk Characterization section (Section 3.2) of this 

document. The complete results of the facility-wide assessments are provided in Appendix 6

of this document (Draft Detailed Risk Modeling Results). 

2.7.4 MACT-Allowable Risks

The emissions data in the dataset for the primary aluminum source category are estimates of 

actual emissions on an annual basis. The risk results presented in the following sections are 

based on these actual emissions. For the primary aluminum reduction source category, 

estimates of MACT-allowable emissions were also estimated, and the risk results based on 

those emissions are presented below as well. For more detail, please refer to the emissions 

memo in Appendix 1 to this document, Draft Development of the Risk and Technology 

Review (RTR) Emissions Dataset for the Primary Aluminum Reduction Source Category. 

3 Risk results for the Primary Aluminum Reduction source category

3.1 Source category emissions and results

The primary aluminum reduction source category consists of one facility that does not 

produce aluminum directly but produces anodes for use in the aluminum producing facilities 

and thirteen major source facilities that produce aluminum from refined ore (alumina) using a 

reduction process whereby the alumina is dissolved in an electrolytic bath in reduction cells 

which are connected electrically in series to form “potlines” in the presence of fluoride. The 

main raw materials in this process are alumina (Al2O3), coke, pitch, and fluoride salts. The 

process of aluminum reduction includes passing a high electrical direct current from a carbon 

anode to a carbon cathode which results in purified molten aluminum sinking to the bottom of 

the reduction cell, beneath the electrolytic bath. The HAP emission sources at primary 

aluminum reduction facilities include anode bake furnaces, potlines, and pitch storage tanks, 

The HAP expected to be emitted from these facilities include: hydrofluoric acid, HAP metals, 

PAH’s, and dioxin/furans

There are fourteen facilities subject to the primary aluminum NESHAP with operations in the 

U.S. One facility is undergoing bankruptcy procedures and it is believed will no longer 

operate. Emissions data, required for modeling, is available from the remaining thirteen 

facilities and are summarized in Table 3.1-1. Further, current as well as future operations at 

two other facilities is uncertain. Complete details about the status of these facilities can be 

found in the emissions memo available in Appendix 1 of this document, “ Draft Development 

of the Risk and Technology Review (RTR) Emissions Dataset for the Primary Aluminum 

Reduction Source Category.
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The actual emissions for the primary aluminum reduction source category (of 13 sources) are 

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

approximately 4,500 tons per year. Based on these data, the HAP emitted in the largest 

quantities are carbonyl sulfide, hydrofluoric acid, and POM,. Emissions of these three HAP 

make up more than 90 percent of the total emissions by mass for the source category. 

Persistent and bioaccumulative HAP (PB-HAP) 28 reported as emissions from these facilities 

include lead compounds, mercury compounds, PCBs, dioxins/furans, cadmium compounds, 

and polycyclic organic matter. The following environmental HAP are emitted from the 

primary aluminum reduction source category and are included in the environmental risk 

screen: cadmium compounds, dioxins/furans, lead compounds, mercury compounds, PAHs, 

and hydrogen fluoride. Stack test data of dioxins/furans, mercury, and PCB’s indicate “these 

emissions are very low and may be non-existent” but, to be conservative, we estimate below 

detection limit (BDL) sample results as 1⁄2 the detection limit and include them in the emission 

estimates that are used in the risk model file. 

Table 3.1-1 Summary of Emissions from the Primary Aluminum Reduction Source Category Used in 

the Residual Risk Assessment Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(13 facilities 

in 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?

Carbonyl sulfide 3400 13  

Hydrofluoric acid 1000 13  

Polycyclic Organic Matter  

 Fluoranthene 7.0 13  

 Phenanthrene 6.1 13  

 Acenaphthene 5.0 13  

 2-Methylnaphthalene 2.2 13  

 Benzo[b]fluoranthene 1.6 13  

 Chrysene 1.4 13  

 Fluorene 1.0 13  

 Pyrene 0.87 13  

 Benzo(e)pyrene 0.73 13  

 Acenaphtylene 0.58 13    

 Benzo[k]fluoranthene 0.51 13    

 Benz[a]anthracene 0.37 13    

 Anthracene 0.35 13  

 Benzo[g,h,i]perylene 028 13  

 

28 Persistent and bioaccumulative HAP are defined in the EPA’s Air Toxics Risk Assessment Library, Volume 1, 

EPA-453K-04-001A, provided on the EPA’s Technology Transfer Network website for Fate, Exposure, and 

Risk Assessment at http://www2.epa.gov/fera/risk-assessment-and-modeling-air-toxics-risk-assessmentreference-library
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Table 3.1-1 Summary of Emissions from the Primary Aluminum Reduction Source Category Used in 

the Residual Risk Assessment Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(13 facilities 

in 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?

 Indeno[1,2,3-c,d]

 pyrene 0.23 13  

 Benzo[a]pyrene 0.18 13  

 Dibenzo[a,h]anthracene 0.07 13    

 Perylene 0.05 13    

Nickel compounds 16.6 13   

Lead compounds 10.6 13  c  

Naphthalene 9.5 13   

Manganese compounds 6.9 13   

Arsenic compounds 5.2 13      

 Chromium (III) 

 compounds 4 13   

 Chromium (VI) 

 compounds 0.24 13     

Antimony compounds 3.2 13  

Selenium compounds 2.8 13  

Cobalt compounds 1.0 13 

Cadmium compounds 0.52 13   

Beryllium compounds 0.22   

Mercury compounds   

 Mercury (elemental) 0.06 13    

 Gaseous Divalent Hg 0.007 13   

 Particulate Divalent Hg 0.007 13   

Polychlorinated biphenyls 0.0006 13   

Dioxin/furans (TEQ)    

1,2,3,4,6,7,8-

Heptachlorodibenzo-

 p-dioxin 4.88E-07 13    

1,2,3,4,6,7,8- 

Heptachlorodibenzofuran 2.05E-06 13     

1,2,3,4,7,8,9-

 

Heptachlorodibenzofuran 3.30E-7 13     

1,2,3,4,7,8-

Hexachlorodibenzo-p-

 dioxin 1.67E-07 13    

1,2,3,4,7,8-

Hexachlorodibenzofuran 6.55E-07 13    
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Table 3.1-1 Summary of Emissions from the Primary Aluminum Reduction Source Category Used in 

the Residual Risk Assessment Availability of Dose-Response Values

HAPa

Emission

Estimates

(tpy)

Number of 

Facilities 

where HAP 

Reported or 

Estimated 

(13 facilities 

in 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?

1,2,3,6,7,8-

Hexachlorodibenzo-p-

 dioxin 2.47E-07 13    

1,2,3,6,7,8-

Hexachlorodibenzofuran 5.89E-07 13    

1,2,3,7,8,9-

Hexachlorodibenzo-p-

 dioxin 2.63E-07 13    

1,2,3,7,8,9-

Hexachlorodibenzofuran 1.74E-07 13    

1,2,3,7,8-

Pentachlorodibenzo-p-

 dioxin 9.83E-07 13    

1,2,3,7,8-

Pentachlorodibenzofuran 5.1E-07 13    

2,3,4,6,7,8-

Hexachlorodibenzofuran 5.58E-07 13    

2,3,4,7,8-

Pentachlorodibenzofuran 1.19E-06 13    

2,3,7,8-

Tetrachlorodibenzo-p-

 dioxin 1.09E-06 13    

2,3,7,8-

Tetrachlorodibenzofuran 9.68E-07 13    

1,2,3,7,8,9-

Hexachlorodibenzo-pdioxin 2.63E-07 13    

a Notes for how HAP were speciated for risk assessment:

 For most metals, emissions reported as the elemental metal are combined with metal compound emissions (e.g., 

“cadmium” emissions modeled as “cadmium & compounds”). In the absence of speciation information, we assume the 

reported mass is 100 percent metal.

 Chromium emissions were speciated based on test data. See Appendix 1 of this document (Revised Development of the 

Risk and Technology Review (RTR) Emissions Dataset for the Primary Aluminum Reduction Source Category) for more 

information. 

 For emissions reported generically as “mercury” or “mercury & compounds,” emissions are speciated for this category 

as 80 percent “mercury (elemental),” 10 percent “gaseous divalent mercury,” and 10 percent “particulate divalent 

mercury.” Mercury speciation profiles can be found on the EPA’s Technology Transfer Network website for emissions 

inventories at http://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata. 

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

 toxics at http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associated-exposure-hazardous-airpollutants .

C There is no reference concentration for lead. In considering noncancer hazards for lead in this assessment, we compared 
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 maximum annual ambient exposure estimates to the National Ambient Air Quality Standards (NAAQS) for lead

 (0.15 μg/m3). These NAAQS for lead were reviewed with revisions adopted in October 2008 

 http://www.epa.gov/air/lead/actions.html. The primary (health-based) standard is a maximum or not-to-be-exceeded, 

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

 identical to the primary standard.

3.2 Baseline risk characterization

This section presents the results of the risk assessment for the primary 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 HAP emissions 

summarized in the previous section. The basic chronic inhalation risk estimates presented 

here are the maximum individual lifetime cancer risk, the maximum chronic hazard index, 

and the cancer incidence. We also present results from our acute inhalation impact screening 

in the form of maximum hazard quotients, as well as the results of our preliminary 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 index at the highest exposure location, as well as our analysis of risks associated with 

the maximum allowed emissions under the current MACT standards. A detailed summary of 

the facility-specific risk assessment results is available in Appendix 6 of this document, Draft 

Detailed Risk Modeling Results. 

Baseline Inhalation Risk Assessment Results Based on Actual Emissions

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 estimate that the 

maximum lifetime individual cancer risk posed by the active operating plants could be as 

high as 70 in a million, with emissions of arsenic compounds, nickel compounds, and 

chromium compounds from the potline roof vents as the major contributors to the risk. The 

total estimated cancer incidence from this source category is 0.06 excess cancer cases per 

year, or about one excess case in every 17 years. Approximately 65,000 people were 

estimated to have cancer risks above 10 in a million and approximately 880,000 people were 

estimated to have cancer risks above 1 in a million. The maximum chronic noncancer 

TOSHI value for the source category could be up to 1 driven by nickel and cadmium 

emissions from the potline roof vents. 

Worst-case acute hazard quotients (HQs) were calculated for every emitted HAP that has an 

acute benchmark, as shown in Table 3.1-1. For cases where the screening HQ was greater 

than 1, we further refined the estimates by determining the highest HQ value that might occur 

outside facility boundaries and at residential locations. Based on actual, baseline emissions, 

the highest off-site acute screening value is 30 and 10 at a residential location, (based on the 

acute REL for arsenic compounds) there were no AEGL or ERPG values for comparison refer 

to Table 3.2-1. Further refinement of the arsenic acute assessment was done to characterize 

the exposure for this source category, we had 8 active facilities exceeding the REL at a 

residential location, with the potential for 8,500 people being exposed above an HQ of 1. 

From these 8 facilities we had 3 facilities with an HQ of 10 with 160 people being potentially 

exposed. The population impact estimates are based upon worst case meteorology for a single 

hour for each facility. Also, the population effected are based upon an acute modeling 
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domain of 3 kilometers, refer to Appendix 8, Draft Detailed Acute Risk Modeling Results for 

more information.

There are also screening HQs greater than 1 for hydrofluoric acid (REL)). The highest offsite acute screening value for hydrofluoric acid (REL) is 3 and 2 for a residential location. 

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

Aluminum Reduction Sources

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in 

Source Category 14 n/a

Number of Facilities Identified in NEI and 

Modeled in Preliminary Risk Assessment 13 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk 

(in 1 million) 70 arsenic compounds, nickelcompounds, PAH

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 12 n/a

Greater than or equal to 1 in 1 million 12 n/a

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 1 nickel compounds

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Refined Screening 

Results

Maximum Acute Hazard Quotient29

(Residential)

10

2

0.6

arsenic compounds (REL)

hydrofluoric acid (REL)

hydrofluoric acid (AEGL-1)

Number of Facilities With Potential for Acute 

Effects 8

arsenic compounds, 

hydrofluoric acid

Population Exposure

Number of People Living Within 50 

Kilometers of Facilities Modeled 3,000,000 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 65,000 n/a

Greater than or equal to 1 in 1 million 881,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

 

29 As shown in Table 2.6-3, there are no AEGL or ERPG values for nickel or arsenic. Additionally, there are no 

short-term occupational values for these metals to compare to the REL values. 
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Table 3.2-1 Summary of Source Category Level Inhalation Risks for Primary

Aluminum Reduction Sources

Result HAP “Drivers”

Estimated Cancer Incidence (excess cancer 

cases per year) 0.06 n/a

Contribution of HAP to Cancer Incidence

Arsenic compounds 64%n/a

Nickel compounds 21% n/a

Chromium compounds 8% n/a

Baseline Inhalation Assessment Results Based on Allowable Emissions

Analysis of potential differences between actual emissions levels and the maximum emissions 

allowable under the MACT standards (i.e., MACT-allowable emissions) were also calculated 

for the source category. (See Draft Development of the Risk and Technology Review (RTR) 

Emissions Dataset for the Primary Aluminum Reduction Plants Source Category in Appendix 

1 of this document for a discussion of the development of the allowable emissions dataset.) 

Risk results from the inhalation risk assessment using the MACT-allowable emissions 

indicate that the maximum lifetime individual cancer risk could be as high as 300 in a million, 

and that the maximum chronic noncancer TOSHI value could be as high as 2 at the MACTallowable emissions level. The MACT-allowable cancer risks are driven by emissions of

arsenic compounds, nickel compounds, and PAHs, while the MACT-allowable TOSHI is 

driven by emissions of arsenic compounds and nickel compounds. Approximately 200 people 

were estimated to have cancer risks above 100 in a million, approximately 76,000 people 

were estimated to have cancer risks above 10 in a million and approximately 950,000 people 

were estimated to have cancer risks above 1 in a million resulting from MACT-allowable 

emissions. It was also estimated that approximately 30 people maybe exposed to a noncancer 

TOSHI value greater than 1. The facility with the highest MACT-allowable risks (Columbia 

Falls Aluminum, NEI12468) has not been operating over the last 5 years but they still retain 

their operating permits for the site. If this facility was not included in the dataset, the MIR

based on MACT-allowable emissions is estimated to be 70 in a million, while the TOSHI is 

estimated to be 1.

Baseline Multipathway Assessment Results Based on Actual Emissions

To identify potential multipathway health risks from PB-HAP other than lead, we first 

performed a screening analysis (Tier 1) that compared emissions of PB-HAP emitted from the 

primary aluminum source category (based on actual emissions) to screening emission rates 

(see section 2.5 or Appendix 5 of this document for a more detailed discussion of the 

screening emission rates). The PB-HAP emitted by facilities in this category include 

cadmium compounds, chlorinated dibenzodioxins and furans (as 2,3,7,8-TCDD toxicity 

equivalents, or TEQ), mercury compounds, polychlorinated biphenyls (PCBs), and POM (as 

benzo(a)pyrene TEQ). PCBs are PB-HAP but do not currently have multipathway screening 

values and so were not evaluated for potential non-inhalation risks. As shown in Table 3.1-1, 

this PB-HAP is not emitted in appreciable quantities. 
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All thirteen facilities emitted each of the PB-HAP listed above. Emissions of POM and 

dioxins at all thirteen facilities were greater than the Tier 1 screening levels for these 

compounds. The maximum exceedance for an active facility of the Tier 1 screening level for 

POM was 50 times, while the maximum exceedance of the Tier 1 screening level for dioxins 

was 200 times. Six facilities emitted cadmium compounds at rates exceeding the Tier 1

screening level, with the maximum being 30 times the level. And, nine facilities emitted 

mercury compounds at rates exceeding the Tier 1 screening level, with the maximum being 10

times the level. Due to the theoretical construct of the screening model, these factors are not 

directly translatable into estimates of risk or hazard quotients for these facilities. Table 3.2-2

summarizes the results of this Tier 1 screen. 

For the PB-HAP and facilities that did not screen out during our initial Tier 1 multipathway 

screening analysis, we improved our analysis with some additional site-specific information 

to develop a Tier 2 screen. (See Appendix 5 of this document, Technical Support Document 

for TRIM-Based Multipathway Screening Scenario for RTR: Summary of Approach and 

Evaluation, for more information about the Tier 2 screen.) The additional site-specific 

information included the location of fishable lakes and local wind direction and speed. The 

result of this analysis was the development of site-specific emission screening levels for each 

of the four PB-HAP for the farmer and fisher scenarios. 

Emissions of both dioxins and POM exceeded the Tier 2 Screen for cancer for all operating 

plants. The highest cancer exceedance for dioxins was 40 times and 7 times for POM for the 

subsistence fisherman scenario (total cancer screen value of 50 for the MIR site) from the 

Alcoa facility in Newburgh, Indiana. For the subsistence farmer scenario, the highest cancer 

exceedance for dioxin was 10 times and POM was 4 times (total cancer screen value of 20 for 

the MIR site), refer to Table 3.2-2. Based on the Tier 2 non-cancer screening analysis for 

operating plants, emissions of mercury and cadmium also exceeded the site-specific levels for 

those PB-HAPs by a factor of 2 from 2 different facilities. The facilities that exceeded are 

Alumax from Goose Creek, South Carolina for mercury emissions and Alcoa Primary Metals 

for their cadmium emissions, refer to Appendix 6, Table 4 for a summary of the screened 

results from Tier 1 and 2. 

Similar to the results from the Tier 1 screen, an exceedance of a screening level in Tier 2

cannot be equated with a risk value or a hazard quotient (or hazard index). Rather, it 

represents a high-end estimate of what the risk or hazard may be. For example, an 

exceedance of 2 for a non-carcinogen can be interpreted to mean that we have high 

confidence that the hazard would be lower than 2. Similarly, an exceedance of 30 for a 

carcinogen means that we have high confidence that the risk is lower than 30 in a million. 

Our confidence comes from the conservative, or health-protective, assumptions that are in the 

screens: we choose inputs from the upper end of the range of possible values for the 

influential parameters used in the screens; and we assume that the exposed individual exhibits 

ingestion behavior that would lead to a high total exposure. If exceedances above an 

acceptable level are observed from the Tier 2 screen we may proceed to the Tier 3 screen to 

further refine this assessment. 
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Table 3.2-2 Summary of the “Tier 1 and Tier 2” Multipathway Screening Analysis for the 

Primary Aluminum Reduction Source Category

a. – Dioxin and POM screen values were based upon the emissions normalized to 2,3,7,8 – TCDD and BaP 

 respectively for oral toxicity and Tier 1 modeled environmental fate and transport

b. -- Maximum Screening Values for Tier 1 and Tier 2 Fisher and Farmer Scenario reflect the maximum screening 

 value for primary aluminum plants that are currently operating based upon actual emissions

A Tier 3 screen was conducted for 3 facilities, including a facility that is no longer operating, 

this plant had the highest exceedance of Tier 2 cancer screening levels for POM and dioxins

at 70 times the screening level (Reynolds Metals, NEI46970, in Massena, New York). Since 

this site is no longer operating their Soderberg process it was not included as part of the MIR 

assessment for the source category. 

In the Tier 3 screens, we examined the set of lakes from which the fisher ingested fish to 

determine if they were fishable and also analyzed plume rise data in relation to the time-series 

meteorology to calculate effective stack release heights. The results of the Tier 3 screen for 

the 3 facilities based upon the fisher scenario are as follows: PAH’s exceeded the cancer 

screening level by 60 times and for dioxins 10 times, resulting in a total exceedance for 

carcinogens of 70 times resulting in no change from the Tier 2 cancer screen value; mercury

and cadmium non-cancer screen values dropped to a value of 1 due to loss of mass associated 

with plume-rise, refer to Appendix 6, Table 4 for a summary of the Tier Screen 1 – 3 results. 

For the subsistence farmer scenario, the highest cancer exceedance for dioxin was 20 times 

and POM was 100 times (total cancer screen value of 200 after rounding), for further 

discussion on the methods and results for each of the three Tiers see Appendix 9 of this 

document (Technical Support Document: Human Health Multipathway Residual Risk 

Screening Assessment for the Primary Aluminum Reduction Source Category). 

The multipathway screens includes some hypothetical elements, namely the hypothetical 

farmer and fisher scenarios. We also included children in different age ranges and adults. It 

is important to note that even though the multipathway assessment has been conducted, no 

data exist to verify the existence of either the farmer or fisher scenario outlined above. The 

farmer scenario involves an individual living for a 70-year lifetime on a farm homestead in 

the vicinity of the source and consuming produce grown on, and meat and animal products 

raised on, the farm. The individual also incidentally ingests surface soil at the location of the 

farm homestead. The fisher scenario involves an individual who regularly consumes fish 

caught in freshwater lakes in the vicinity of the source of interest over the course of a 70-year 

lifetime. In addition, exposure estimates and risks for infants consuming contaminated breast 
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milk were evaluated in the case of dioxins, with the assumption that the nursing mother was 

exposed to chemicals via one of the two scenarios described above. 

In evaluating the potential for multipathway effects from emissions of lead, modeled 

maximum annual lead concentrations were compared to the secondary NAAQS for lead

(0.15 μg/m3). Results of this analysis estimate that the NAAQS for lead would not be 

exceeded at any off-site locations. 

Baseline Environmental Risk Screening Based on Actual Emissions

We conducted a screening-level evaluation of the potential adverse environmental risks 

associated with emissions of the following environmental HAP from the primary aluminum 

reduction source category: cadmium, PAH’s, dioxins/furans, hydrogen fluoride, lead, and 

mercury. The results of the environmental screening analysis are summarized in Table 3.2-3. 

Table 3.2-3 Summary of Environmental Risk Screen Results for the Primary Aluminum 

Reduction Source Category 

Environmental HAP

Number of Facilities In Category 

Exceeding

Percent of 

Modeled Area in 

Category 

Exceeding2

Tier 1 Screen Tier 2 Screen1

NOAEL LOAEL NOAEL LOAEL NOAEL LOAEL

PBHAP

Dioxins/Furans None None - - 0.40% 0% 

Cd 1 1 None None 0% 0% 

MeHg None None - -- 0% 0%

PAH 1 1 1 None NA40%

Acid 

Gases HF3 NA None - - NA 0.2%

NA – Not Applicable. 

1- Tier 2 screen is performed for PB-HAP when there are exceedances of the Tier 1 screen. The acid gas screen 

is a one tier screen.

2 - A value of 0% indicates that none of the modeled data points exceeded the benchmark. For PB-HAP the 

percent area is based on the Tier 2 results, if a Tier 2 analysis is performed. Otherwise, the percent area is based 

on the Tier 1 results. 

3 – For HF, we evaluated two benchmarks, one from Canada and the other from the State of Washington. 

Although, they are both considered to be LOELs - the level between a NOAEL and a LOAEL, we have listed the 

results under the LOAEL column for the Canadian benchmark, which is the more protective of the two. 

4 – One facility had a Tier 2 exceedance for the sediment NOAEL benchmark at one lake. For PB-HAP the 

percent area is calculated for soil benchmarks only.

In our Tier 1 analysis, emissions of dioxins and methyl mercury did not exceed the threshold 

emission rates for any of the ecological benchmarks for any facility in the source category. In 

our Tier 1 analysis, emissions of cadmium and PAHs exceeded some ecological benchmarks 

for one facility, therefore, a Tier 2 analysis was performed. In the Tier 2 analysis, emissions 
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of cadmium did not exceed the threshold emission rates for any of the ecological benchmarks 

for any facility in the source category. In the Tier 2 analysis, emissions of PAHs exceeded the

NOAEL sediment benchmark for one lake by 2 times, but did not exceed the threshold effect 

level. For hydrogen fluoride, the average modeled concentration around each facility (i.e., the 

average concentration of all off-site data points in the modeling domain) did not exceed the 

ecological benchmarks. For lead, we did not estimate any exceedances of the secondary lead 

NAAQS.

Facility-wide Inhalation Assessment Results

The facility-wide chronic MIR and TOSHI, available in Appendix 6, are based on actual 

emissions from all sources at the identified facilities. Considering facility-wide emissions, the 

MIR is estimated to be 70 in a million driven by emissions of arsenic compounds, nickel

compounds, and chromium compounds and the chronic non-cancer TOHSI value is calculated 

to be 1 driven by emissions of cadmium compounds. In both cases, the source of these 

emissions are from pot line roof vents from the primary aluminum source category. 

4 General discussion of uncertainties and how they have been 

addressed

4.1 Exposure modeling uncertainties

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

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

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

modeling estimates. The chronic exposure modeling uncertainties are considered relatively 

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

exposure estimates are biased high by not accounting for short- or long-term population 

mobility and by neglecting processes like deposition, plume depletion, and atmospheric 

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

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

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

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

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

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

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

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 percent below the predicted value to as much as 84 percent 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 percent lower to 84

percent higher than the predicted values.
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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 percent 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.

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 [30] (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 
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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).30 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.31 

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.32 EPA also uses 

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

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

benefits analysis).

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

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

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

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

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

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

 

30 IRIS glossary

(http://ofmpub.epa.gov/sor_internet/registry/termreg/searchandretrieve/glossariesandkeywordlists/search.do?deta

ils=&glossaryName=IRIS%20Glossary).

31 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.

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

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

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

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

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

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

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

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

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

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

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.
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characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [31]. 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.33 However, unless scientific support is available to show 

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

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

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

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

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

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

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

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

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

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

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

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

lead to either over or underestimation of risk.

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

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

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

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

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

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

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

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

test data and the correctness of scaled estimates. 

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

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

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

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

benchmark dose (BMD) approach is generally addressed though use of the 95 percent 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).

 

33 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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(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 

human populations. Another National Research Council (NRC) report [32] 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 
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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) [33]. It is important to note that 

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

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

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

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

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

model.

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

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

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

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

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

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

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

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

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

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

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

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

of uncertainty and variability.

Chronic noncancer assessment

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

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

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

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values34 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 

 

34

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

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

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

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

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

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

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

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

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

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

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf. 
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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.35 To derive values that are intended to be “without 

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

[34],[35] 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 [36]. 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 

 

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

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

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

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

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

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

among humans.

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

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

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

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

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

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

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

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

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

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

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

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

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

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

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

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

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 

(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to 

account for expected species differences and associated uncertainty in extrapolating from 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

often applied. 
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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 

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

characterization as potential uncertainties. 
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5 References

 

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

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

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

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

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

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

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

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

A-SAB-10-007-unsigned.pdf

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

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

Assessment Methodologies.

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

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

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

6. US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf.

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

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

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

Consortium Project H-13. 

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

8. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K-04- 

001A. http://www2.epa.gov/fera/dose-response-assessment-assessing-health-risks-associatedexposure-hazardous-air-pollutants

9. US EPA, September 2011. Exposure Factors Handbook: 2011 Edition; 

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

Air Quality Planning and Standards; http://www2.epa.gov/fera/dose-response-assessmentassessing-health-risks-associated-exposure-hazardous-air-pollutants. 

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

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

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

13. US Agency for Toxic Substances and Disease Registry. 2006. Minimum Risk Levels (MRLs) 

for Hazardous Substances. http://www.atsdr.cdc.gov/mrls/index.html.
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14. US EPA, 1994. US Environmental Protection Agency. Methods for Derivation of 

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 

EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

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

Washington, DC: National Academy Press.

16. CA Office of Environmental Health Hazard Assessment, 2014. Chronic Reference 

Exposure Levels Adopted by OEHHA as of January 2014. 

http://www.oehha.ca.gov/air/allrels.html. 

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

for Describing Available Cancer Potency Factors, May 2005. 

http://www.oehha.ca.gov/air/hot_spots/pdf/may2005Hotspots.pdf

18. US EPA, 2006. Approach for modeling POM. Technical support information for the 1999 

National Air Toxics Assessment. 

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

19. US EPA, 2014. Table 2. Acute Dose-Response Values for Screening Risk Assessments 

(5/09/2014). Office of Air Quality Planning and Standards; http://www2.epa.gov/fera/doseresponse-assessment-assessing-health-risks-associated-exposure-hazardous-air-pollutants

20. CA Office of Environmental Health Hazard Assessment, 2014. All Acute Reference 

Exposure Levels developed by OEHHA as of January 2014. 

http://www.oehha.ca.gov/air/allrels.html

21. American Industrial Hygiene Association , 2013. Current AIHA ERPG Values. 

https://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.a

spx

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

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

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

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

Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. 

EPA Office of Environmental Information. EPA/260R-02-008. 

http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pdf

25. US EPA, 2006. Performing risk assessments that include carcinogens described in the 

Supplemental Guidance as having a mutagenic mode of action. Science Policy Council Cancer 

Guidelines Implementation Workgroup Communication II: Memo from W.H. Farland dated 

14 June 2006. http://www.epa.gov/osa/spc/pdfs/CGIWGCommunication_II.pdf

26. US EPA, 2005. Supplemental guidance for assessing early-life exposure to carcinogens. 

EPA/630/R-03003F. http://www.epa.gov/ttn/atw/childrens_supplement_final.pdf.
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27. US EPA, 2005. Science Policy Council Cancer Guidelines Implementation Workgroup 

Communication I: Memo from W.H. Farland dated 4 October 2005 to Science Policy 

Council. http://www.epa.gov/osa/spc/pdfs/canguid1.pdf

28. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-

R-98-002. http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=22567

29. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of 

Chemical Mixtures. EPA-630/R-00-002. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20533

30. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental 

Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. 

http://www.epa.gov/cancerguidelines/

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

32. NRC (National Research Council) 2006. Assessing the Human Health Risks of 

Trichloroethylene. National Academies Press, Washington DC.

33. R.P. Subramaniam, P. White and V.J. Cogliano. 2006. Comparison of cancer slope factors 

using different statistical approaches, Risk Anal. Vol 26, p. 825–830.

34. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. 

http://www.epa.gov/iris/rfd.htm

35. US EPA. 1994. Methods for Derivation of Inhalation Reference Concentrations and 

Application of Inhalation Dosimetry. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=71993

36. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes. 

http://www.epa.gov/raf/publications/pdfs/rfd-final.pdf