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Draft Residual Risk Assessment for the Chromic Acid Anodizing, 

Decorative Chromium Electroplating, and Hard Chromium 

Electroplating Source Categories

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

January 2012
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Table of Contents

1 Introduction........................................................................................................................ 3

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

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

2.2 Dispersion modeling for inhalation exposure assessment........................................... 4

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

2.4 Acute Risk Screening and Refined Assessments ........................................................ 7

2.5 Multipathway and environmental risk screening ........................................................ 8

2.6 Dose-Response Assessment ........................................................................................ 8

2.6.1 Sources of chronic dose-response information .................................................... 8

2.7 Risk Characterization ................................................................................................ 10

2.7.1 General ............................................................................................................... 10

2.7.2 Mixtures............................................................................................................. 12

2.7.3 Facility-Wide Risks............................................................................................ 13

2.7.4 MACT-Allowable Risks .................................................................................... 13

3 Risk Results for the Chromium Anodizing, Decorative Chromium Electroplating, and 

Hard Chromium Electroplating Source Categories.............................................................. 13

3.1 Source Category Descriptions and Results................................................................ 13

3.2 Risk Characterization ................................................................................................ 16

4 General Discussion of Uncertainties and How They Have Been Addressed................... 22

4.1 Exposure Modeling Uncertainties............................................................................. 22

4.2 Uncertainties in the Dose-Response Relationships................................................... 23

5 References........................................................................................................................ 30

Index of Tables

Table 2.2-1 AERMOD Version 11103 Model Options for RTR Modeling ............................. 5

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposures.................................. 10

Table 3.1-1 Summary of Emissions from the Hard Chromium Electroplating Source Category

.................................................................................................................................................. 15

Table 3.1-2 Summary of Emissions from the Decorative Chromium Electroplating Source 

Category................................................................................................................................... 16

Table 3.1-3 Summary of Emissions from the Chromium Anodizing Source Category ......... 16

Table 3.2-1 Summary of Source Category Level Risks for Hard Chromium Electroplating . 17

Table 3.2-2 Source Category Contribution to Facility-Wide Cancer Risks – Hard Chromium 

Electroplating ........................................................................................................................... 18

Table 3.2-3 Summary of Source Category Level Risks for Decorative Chromium 

Electroplating ........................................................................................................................... 18

Table 3.2-4 Source Category Contribution to Facility-Wide Cancer Risks – Decorative 

Chromium Electroplating......................................................................................................... 20

Table 3.2-5 Summary of Source Category Level Risks for Chromium Anodizing................ 20

Table 3.2-6 Source Category Contribution to Facility-Wide Cancer Risks – Chromium 

Anodizing................................................................................................................................. 22
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Appendices

Appendix 1 Technical Support Document for HEM-AERMOD Modeling

Appendix 2 Meteorological Data for HEM-AERMOD Modeling

Appendix 3 Analysis of Data on Short-Term Emission Rates Relative to Long-Term 

Emission Rates

Appendix 4 Detailed Risk Modeling Results

Index of Acronyms

AERMOD American Meteorological Society/EPA Regulatory Model

AEGL Acute exposure guideline level

ASTDR US Agency for Toxic Substances and Disease Registry

CalEPA California Environmental Agency

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutant

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NATA National Air Toxics Assessment

NED National Elevation Dataset

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

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 

July 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 the memo entitled, ―EPA’s Actions in Response 

to Key Recommendations from the SAB Review of RTR Risk Assessment Methodologies‖ 

[4].

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

implementation of the respective MACT standards) performed for the chromium anodizing, 

decorative chromium electroplating, and hard chromium electroplating source categories. The 

methods discussion includes descriptions of the methods used to develop refined estimates of 

chronic inhalation exposures and human health risks for cancer and non-cancer endpoints, as 

well as descriptions of the methods used to screen chronic non-inhalation health risks and 

adverse environmental effects. Since none of the screening assessments indicated any 

significant potential for chronic non-inhalation health effects or environmental impacts,

including effects to threatened and endangered species, no further refinement of these 

assessments was performed. Because there were no HAPs with an acute dose-response 

benchmark value, we did not perform a quantitative risk assessment for acute exposures to 

these pollutants. 
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2 Methods

2.1 Emissions and source data

We contacted 28 State and local air pollution control agencies to request information on the 

industry. The requested information included facility data (name, location, number of 

employees), process type, tank design and operating parameters, annual hours of operation, 

emission control technology, control device operating parameters, emission test data, and 

other available supporting documents, such as emission inventory reports and operating 

permits. Agencies were asked to provide data on the 5 to 10 facilities that represent the worst 

case, in terms of emissions and risk, and any additional facilities for which the data were 

readily available. We received data from a total of 24 agencies. We supplemented these data

with additional data from operating permits and other information downloaded from State 

websites. To assess nationwide residual risk, it was necessary to estimate emissions for much 

of the industry. We used a Monte Carlo procedure to simulate actual emissions for those 

facilities for which actual emissions data were not available. The simulation model used the 

pool of available data on actual emissions concentrations, exhaust flow rates, and annual 

operating hours for each process type (hard chromium electroplating, decorative chromium 

electroplating, and chromium anodizing). 

The development of the emissions and source data for these source categories is discussed in 

Section 3. Additional details on the simulation approach can be found in the emissions 

technical document ―Draft Development of the RTR Emissions Data Set for Chromium 

Electroplating and Chromic Acid Anodizing Source categories,‖ available in the docket for 

the rulemaking for these source categories.

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 Model 

dispersion modeling system (HEM-AERMOD, or HEM3). The approach used in applying 

this modeling system is outlined below, and further details are provided in Appendix 1. The 

HEM3 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 HEM3 system, AERMOD version 11103, is a state-of-thescience Gaussian plume dispersion model that is preferred by EPA for modeling point, area, 

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

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

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

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

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

inventory into the 8,760 hours of the year.
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The first step in the application of the HEM3 modeling system is to predict ambient 

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

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

Table 2.2-1 AERMOD Version 11103 Model Options for RTR Modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly ambient concentration

Source type Point

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids)

Terrain characterization Actual from USGS 1/3 Arc Second NED data

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site

In HEM3, meteorological data are ordinarily selected from a list of approximately 200

National Weather Service (NWS) surface observation stations across the continental United 

States, Alaska, Hawaii, and Puerto Rico. In most cases, the nearest station is selected as 

representative of the conditions at the subject facility. Ideally, when considering off-site 

meteorological data most site-specific dispersion modeling efforts will employ up to five 

years of data to capture variability in weather patterns from year to year. However, because 

we had an insufficient number of appropriately formatted model input files derived from 

available meteorological data, we modeled only a single year, typically 1991. While the 

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

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

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

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

applicable meteorological station was 52 miles (84 km). Appendix 2 (Meteorological Data 

Processing Using AERMET for HEM3) provides a complete listing of stations and 

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

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

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

Assessment Methodologies‖ [2].

The HEM3 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. 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 
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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 collected emissions data contain only annual emission totals, we generally apply the 

assumption to all source categories 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. The choice of a factor of ten for screening was 

originally based on engineering judgment. To develop a more robust peak-to-mean emissions 

factor, and in response to one of the key messages from the SAB consultation on our RTR 

Assessment Plan, we performed an analysis using a short-term emissions dataset from a 

number of sources located in Texas (originally reported 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, TX) over an eleven-month 

time period in 2001. We obtained the dataset and performed our own analysis, focusing that 

analysis on sources which reported emitting high quantities of HAPs over short periods of 

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

emission rates). Most peak emission events were less than twice the annual average, the 

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

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

chose the factor of ten for all initial screening. These factors are intended to cover all possible 

hourly peaks associated with routinely-variable emissions. While there have been some 

documented emission excursions above this level, our analysis of the data from the Texas 

Environmental Research Consortium suggests that this factor should cover more than 99% of 

the short-term peak gaseous or volatile HAP emissions from typical industrial sources. We 

have no data relating specifically to peak short-term emissions of particulate HAPs. In the 

absence of source category-specific data, we use this same default approach for particulate 

emissions as well.

 

Census block elevations for HEM3 modeling were determined nationally from the US 

Geological Service 1/3 Arc Second National Elevation Dataset, which has 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 
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contain any blocks, the model defaults the elevation to that of the nearest block. If an 

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

sectors within the innermost model ring. AERMOD adjusts the plume’s flow if nearby 

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

were estimated and used in the AERMOD modeling, see Appendix 1. 

2.3 Estimating 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, this risk analysis did not consider either the short-term or 

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

exposure.

 

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

National-scale Air Toxics Assessment (NATA) (which models daily activity using EPA’s 

HAPEM model) suggests that, given our current understanding of microenvironment 

concentrations and daily activities, modeling short-term activity would, on average, reduce 

risk estimates about 25% for particulate HAP; it will also reduce risk estimates for gaseous 

HAPs, but typically by much less. Second, basing exposure estimates on average ambient 

concentrations at census block centroids may underestimate or overestimate actual exposure 

concentrations at some residences. Further reducing exposure estimates for the most highly 

exposed residents by modeling their short-term behavior could add a systematic low bias to 

these results

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

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

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

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

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

population risk distribution; and the cancer incidence. The assumption of not considering 

short or long-term population mobility does not bias the estimate of the theoretical MIR nor 

does it affect the estimate of cancer incidence since the total population number remains the 

same. It does, however, affect the shape of the distribution of individual risks across the 

affected population, shifting it toward higher estimated individual risks at the upper end and 

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

number of people at specific risk levels.

2.4 Acute Risk Screening and Refined Assessments

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

due to acute exposures to HAPs, we follow the same general approach that is used for 

developing chronic health risk assessments under the residual risk program. However, for 

these source categories, there are no HAPs with an acute dose-response benchmark value. 

Therefore, we did not perform a quantitative risk assessment for acute exposures to these 

pollutants.
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2.5 Multipathway and environmental risk 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)1. The PB-HAP compounds or compound classes are identified 

for the screening from the EPA’s Air Toxics Risk Assessment Library [8]. Examples of PBHAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

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

toxaphene, and trifluralin. PB-HAP emissions were not identified from the chromium

anodizing, decorative chromium electroplating, and hard chromium electroplating source 

categories, indicating that exposures due to non-inhalation routes of exposure are not 

significant. The lack of PB-HAP emissions from these source categories indicates a very low 

potential for adverse environmental effects due to exposures resulting from atmospheric 

deposition of PB-HAPs. Thus, even if sensitive species are present in the areas surrounding 

these facilities with no PB-HAP emissions, it is reasonable to assume that there are no adverse 

effects to populations in the areas surrounding these facilities.

Additionally, we evaluated the potential for significant ecological exposures to non PB-HAP 

from exceedances of chronic human health inhalation thresholds in the ambient air near these 

facilities. Human health dose-response threshold values are generally derived from studies 

conducted on laboratory animals (such as rodents) and developed with the inclusions of 

uncertainty factors that could be as high as 3000. As a result, these human threshold values 

are often significantly lower than the level expected to cause an adverse effect in an exposed 

rodent. It should be noted that there is a scarcity of data on the direct atmospheric impact of 

these HAPs on other receptors, such as plants, birds, and wildlife. Thus, if the maximum 

inhalation hazard in an ecosystem is below the level of concern for humans, we have 

generally concluded that mammalian receptors should be at no risk of adverse effects due to 

inhalation exposures from non PB-HAP, and have assurance that other ecological receptors 

are also not at any significant risk from direct atmospheric impact. In some isolated cases 

where we have data indicating potential adverse impacts on plants, birds, or other wildlife due 

to the direct atmospheric impacts of specific HAP, we note that as an uncertainty and, where 

possible, refine our analysis by comparing our modeled impacts to available threshold values 

from the scientific literature.

2.6 Dose-Response Assessment

2.6.1 Sources of chronic dose-response information 

 

1 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).
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Dose-response assessments (carcinogenic and non-carcinogenic) for chronic exposure (either 

by inhalation or ingestion) for the HAPs reported in the emissions inventory for the source 

categories in this document were based on the EPA Office of Air Quality Planning and 

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

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

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

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

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

on the following sources, in order of priority:

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

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

assessments typically provide a qualitative statement regarding the strength of scientific 

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

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

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

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

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

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

during a lifetime.‖ The RfD is ―an estimate (with uncertainty spanning perhaps an order 

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

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

lifetime.‖ The URE is defined as ―the upper-bound excess cancer risk estimated to result 

from continuous lifetime exposure to an agent at a concentration of 1 μg/m3in air.‖ The 

SF is ―an upper bound, approximating a 95% confidence limit, on the increased cancer 

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

proportion (of a population) affected per mg/kg-day...‖ EPA disseminates dose-response 

assessment information in several forms, based on the level of review. The Integrated 

Risk Information System (IRIS) [11] is an EPA database that contains scientific health 

assessment information, including dose-response information. All IRIS assessments since 

1996 have also undergone independent external peer review. The current IRIS process 

includes review by EPA scientists, interagency reviewers from other federal agencies, and 

the public, and peer review by independent scientists external to EPA. EPA’s science 

policy approach, under the current carcinogen guidelines, is to use linear low-dose 

extrapolation as a default option for carcinogens for which the mode of action (MOA) has 

not been identified. We expect future EPA dose-response assessments to identify 

nonlinear MOAs where appropriate, and we will use those analyses (once they are peer 

reviewed) in our risk assessments. At this time, however, there are no available 

carcinogen dose-response assessments for inhalation exposure that are based on a 

nonlinear MOA.

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

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

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

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

Department of Health and Human Services (HHS) and the EPA, ATSDR chose to adopt a 
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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 assessments2: ―The 

guidelines for developing chronic inhalation exposure levels incorporate many 

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

includes available inhalation health risk guidance values expressed as chronic inhalation 

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

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

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

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

 

The emissions inventories for the source categories include emissions of one HAP with 

available quantitative inhalation dose-response values. This HAP, its dose-response values, 

and the source of the values are listed in Table 2.6-1.

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposures

Pollutant

CAS 

Number

RfC

(mg/m3)

URE

(1/μg/m3) Sourcea

Chromium VI Compounds 18540299 0.0001 0.012 IRIS -- L

a

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.

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 

 

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

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

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

February 2000 (http://www.oehha.ca.gov/air/chronic_rels/pdf/relsP32k.pdf)
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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 [17] 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 [18], in 2002 by the Agency’s information quality guidelines [19], and in the 

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis3, and 

are incorporated in these assessments.

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

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

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

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

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

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

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

limitations.

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

individual and population cancer risks are 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 [20], we apply 

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

Carcinogens [21]. 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 [22] 

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

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

to all unspeciated POM mixtures.

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

 

3Memorandum 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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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 is divided by 70 years to obtain an 

estimate of the number of cancer cases per year.

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

not expressed as a probability of an adverse occurrence. Instead, ―risk‖ for non-cancer effects 

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

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

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

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

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

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

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

this on possible health interpretations.

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

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

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

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

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

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

2.7.2 Mixtures

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

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

exposures from a particular source category combined.

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

[23,24] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

In assessing non-cancer hazard from chronic exposures, in cases where different pollutants 

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

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

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

To conform to these guidelines, we aggregated non-cancer HQs of HAPs that act by similar 

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

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

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

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

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

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

TOSHI, this is 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 
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included in the TOSHI calculations, some TOSHIs may be 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.

2.7.3 Facility-Wide Risks

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

the results of a risk assessment based on the 2005 National Emissions Inventory (NEI) that 

was used in the 2005 NATA. For the facilities in these source categories, we extracted (based 

on facility ID) the estimates of inhalation cancer and chronic non-cancer risks associated with 

all HAP emissions sources at the facilities, including emissions sources that are not part of the

source categories but that are located within a contiguous area and are under common control. 

We replaced the inhalation cancer and chronic non-cancer risks from chromium anodizing 

and chromium electroplating with the estimates from the current risk assessment to provide a 

facility-wide assessment as current as possible. We analyzed risks due to the inhalation of 

HAP for the populations residing within 50 kilometers of each facility. The results of this 

facility-wide assessment are provided for the highest-risk facility below in the Risk Results 

section of this document. The complete results of the facility-wide assessment are provided in 

Appendix 4.

2.7.4 MACT-Allowable Risks

The emissions data in the data sets for these source categories are estimates of actual 

emissions on an annual basis, and the results presented in the following sections focus on the 

risks based on these actual emissions. However, we also estimated MACT-allowable 

emissions and their associated risks. To estimate allowable emissions for facilities for which 

actual emissions concentration data were available, we calculated the allowable emissions by 

assuming the emissions concentration is equal to the emissions limit. For other facilities, we 

used the same approach used to estimate actual emissions described previously (i.e., a 

simulation model of the pool of available data on emissions concentrations, exhaust flow 

rates, and annual operating hours for each process type). However, instead of using the actual 

emissions concentration data in the simulation model, we used the corresponding emissions 

limit. Thus, we calculated the allowable emissions by using the pool of available data on 

exhaust flow rates and annual operating hours for each process type and assumed each 

sources had emissions concentrations equal to the emissions limit (i.e., we assumed they were 

emitting at the maximum level allowed by the MACT standard). As was used for calculating 

actual emissions estimates, 5,000 simulations were performed for each facility, and the 

average of simulated values was used to represent allowable emissions for the facility.

3 Risk Results for the Chromium Anodizing, Decorative Chromium 

Electroplating, and Hard Chromium Electroplating Source Categories

3.1 Source Category Descriptions and Results
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The hard chromium electroplating source category consists of facilities that plate base metals 

with a relatively thick layer of chromium using an electrolytic process. Hard chromium 

electroplating provides a finish that is resistant to wear, abrasion, heat, and corrosion. These 

facilities plate large cylinders and industrial rolls used in construction equipment and printing 

presses, hydraulic cylinders and rods, zinc die castings, plastic molds, engine components, 

and marine hardware.

The decorative chromium electroplating source category consists of facilities that plate base 

materials such as brass, steel, aluminum, or plastic with layers of copper and nickel, followed 

by a relatively thin layer of chromium to provide a bright, tarnish- and wear-resistant surface. 

Decorative chromium electroplating is used for items such as automotive trim, metal 

furniture, bicycles, hand tools, and plumbing fixtures.

 

The chromium anodizing source category consists of facilities that use chromic acid to form 

an oxide layer on aluminum to provide resistance to corrosion. The chromium anodizing 

process is used to coat aircraft parts (such as wings and landing gears) as well as architectural 

structures that are subject to high stress and corrosive conditions. 

 

The HAP emission sources subject to the chromium electroplating MACT standards are the 

tanks in which the chromium deposition takes place. For hard chromium and decorative 

chromium electroplating facilities, the emission sources are electroplating tanks. For the 

chromium anodizing source category, the emission sources are anodizing tanks. 

The primary emission controls used by the facilities in these source categories include packed 

bed scrubbers, mesh pad mist eliminators, composite mesh pad (CMP) systems, high 

efficiency particulate air (HEPA) filters, and wetting agent/fume suppressants (WAFS). Most 

decorative chromium electroplating facilities comply with the MACT standards by using 

WAFS in the tank bath to control surface tension, which in turn reduces emissions. Some 

facilities use a combination of WAFS and add-on control to meet the MACT emission limits. 

If a facility controls emissions using an add-on control device, the tank is generally equipped 

with a hood and duct work to exhaust emissions through the control device and out the stack. 

However, when WAFS are used as the only means of emission control, the tanks often are not 

equipped with exhaust hoods. In such cases, emissions from the tank are fugitive and are 

exhausted to the outside using wall-mounted exhaust fans.

We estimate there are 1455 facilities currently subject to the chromium electroplating MACT 

standards. Of these, we estimate there are 699 hard chromium electroplating facilities, 577

decorative chromium electroplating facilities, and 179 chromium anodizing facilities. Some 

facilities perform more than one type of chromium electroplating or anodizing. For purposes 

of our estimates, we classified facilities as hard chromium, decorative chromium, or 

chromium anodizing based on the primary type of electroplating operation performed at the 

facility. Some chromium electroplating facilities electroplate items that are used internally in 

the manufacturing process at the same facility or within the same company. For example, 

some large printing facilities electroplate their printing rollers in house, and the chromium 

electroplating processes are located at the same site as the printing and publishing processes. 
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The data collected include information on 346 facilities and actual emissions data for 

approximately 270 facilities. After eliminating redundancies in the data and deleting data for 

facilities that were no longer in operation or no longer performing chromium electroplating, 

the data set included annual emissions for 197 facilities currently in operation. To assess 

nationwide residual risk, it was necessary to estimate emissions for much of the industry. We 

used a Monte Carlo procedure to simulate actual emissions for those facilities for which 

actual emissions data were not available. The simulation model used the pool of available 

data on actual emissions concentrations, exhaust flow rates, and annual operating hours for 

each process type (hard chromium electroplating, decorative chromium electroplating, and 

chromium anodizing). Actual emissions data (lbs/yr) were fitted to a Weibull distribution and 

emissions were simulated using the actual data for each facility type. A total of 5,000 

simulations were performed for each facility, and the average value for the simulated 

emissions was used to represent actual emissions for each facility.

In all the data sets, chromium compounds account for all the HAP emissions from the 

chromium electroplating and chromium anodizing source categories. It was assumed that 

emissions from all chromium electroplating sources consisted of 98 percent hexavalent 

chromium and 2 percent trivalent chromium. The actual speciation of chromium in exhaust 

streams may vary slightly from source to source. However, historical data indicate that 

emissions from chromium electroplating sources are almost entirely comprised of hexavalent 

chromium, and the 98%/2% assumed speciation was believed to be representative of sources 

on average.

The emissions for the hard chromium electroplating source category are summarized in Table 

3.1-1, the emissions for the decorative chromium electroplating source category are 

summarized in Table 3.1-2, and the emissions for the chromium anodizing source category 

are summarized in Table 3.1-3.

Table 3.1-1 Summary of Emissions from the Hard Chromium Electroplating Source 

Category

HAP

NPRM 

Emissions 

(tpy)

Number of 

Facilities in 

the dataset

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSa

PBHAP

?

Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Non-cancer?

Health 

Benchmark 

Values for 

Acute Noncancer?

Chromium III 

Compounds 0.008 699

Chromium VI 

Compounds 0.4 699 Y

a

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

at http://www.epa.gov/ttn/atw/toxsource/summary.html and are available in Appendix 5 of the Development of Preliminary 

Risk Estimates for Source Categories Previously Subjected to Technology-based Standards document.
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Table 3.1-2 Summary of Emissions from the Decorative Chromium Electroplating 

Source Category

HAP

NPRM 

Emissions 

(tpy)

Number of 

Facilities in 

the data set

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSa

PBHAP

?

Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Non-cancer?

Health 

Benchmark 

Values for 

Acute Noncancer?

Chromium III 

Compounds 0.003 577

Chromium VI 

Compounds 0.1 577 Y

a

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

at http://www.epa.gov/ttn/atw/toxsource/summary.html and are available in Appendix 5 of the Development of Preliminary 

Risk Estimates for Source Categories Previously Subjected to Technology-based Standards document.

Table 3.1-3 Summary of Emissions from the Chromium Anodizing Source Category

HAP

NPRM 

Emissions 

(tpy)

Number of 

Facilities in 

the data set

Prioritized Inhalation Dose-Response Value 

Identified by OAQPSa

PBHAP

?

Unit Risk 

Estimate for 

Cancer?

Reference 

Concentration 

for Chronic 

Non-cancer?

Health 

Benchmark 

Values for 

Acute Noncancer?

Chromium III 

Compounds 0.0007 179

Chromium VI 

Compounds 0.03 179 Y

a

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

at http://www.epa.gov/ttn/atw/toxsource/summary.html and are available in Appendix 5 of the Development of Preliminary 

Risk Estimates for Source Categories Previously Subjected to Technology-based Standards document.

3.2 Risk Characterization

This section presents the results of the risk assessment for the source categories. The basic 

chronic inhalation risk estimates presented here are the maximum individual lifetime cancer 

risk, the maximum chronic hazard index, and the cancer incidence. Also presented are the 

HAP ―drivers,‖ which are the HAPs that collectively contribute 90 percent of the maximum 

cancer risk or maximum hazard index at the highest exposure location, as well as a summary 

of the results of our facility-wide assessments and 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 4.

Table 3.2-1 summarizes the chronic inhalation risk results for the hard chromium 

electroplating source category. The results indicate that maximum lifetime individual cancer 

risks could be as high as 20 in a million, with chromium VI as the sole contributor to the risk. 

The total estimated cancer incidence from the source category is 0.05 excess cancer cases per 

year, or one case in every 20 years. Approximately 1,100 people are estimated to have cancer 

risks at or above 10 in a million, and approximately 130,000 people are estimated to have 
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cancer risks at or above 1 in a million as a result of the emissions from 196 facilities. The 

maximum chronic noncancer TOSHI value for the source category could be as high as 0.02

from emissions of chromium VI.

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

allowable under the MACT standards for hard chromium electroplating were also calculated. 

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

(available in Appendix 4) indicate that the MIR could be as high as 50 in a million, and that 

the maximum chronic noncancer TOSHI value could be as high as 0.04.

Table 3.2-1 Summary of Source Category Level Risks for Hard Chromium 

Electroplating

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 699 n/a

Number of Facilities Identified Modeled in 

Preliminary Risk Assessment 699 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 20 chromium (VI) compounds

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 13 chromium (VI) compounds

Greater than or equal to 1 in 1 million 196 chromium (VI) compounds

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.02 chromium (VI) compounds

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient a n/a

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 226,000,000 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 1,100 n/a

Greater than or equal to 1 in 1 million 130,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.05 n/a

Contribution of HAP to Cancer Incidence

chromium (VI) compounds 100% n/a

a

Because there are no HAPs with an acute dose-response benchmark value, no acute hazard quotients were calculated.
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Facility-wide MIR and TOSHI values (available in Appendix 4) are based on actual emissions 

from all sources at the identified facilities. The results of the facility-wide assessment for 

facilities with hard chromium electroplating operations are summarized in Table 3.2-2. The 

results indicate that 206 facilities have a facility-wide cancer MIR greater than or equal to 1 in 

a million. The maximum facility-wide MIR is 70 in a million, driven by emissions of nickel 

compounds from unspecified sources. The source category contributes approximately 20

percent to the facility-wide MIR. The maximum facility-wide TOSHI value is 2, also driven 

by emissions of nickel compounds from unspecified sources. The source category contributes 

less than 1 percent to the facility-wide TOSHI value.

Table 3.2-2 Source Category Contribution to Facility-Wide Cancer Risks – Hard 

Chromium Electroplating

Hard Chromium Electroplating

Number of Facilities Binned by Facility-Wide MIR 

(in 1 million)

Source Category MIR 

Contribution to Facility-Wide MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 468 180 12 0 660

50-90% 8 2 1 0 11

10-50% 9 3 1 0 13

< 10% 8 6 1 0 15

Total 493 191 15 0 699

Table 3.2-3 summarizes the chronic inhalation risk results for the decorative chromium 

electroplating source category. The results indicate that maximum lifetime individual cancer 

risks could be as high as 10 in a million, with chromium VI as the sole contributor to the risk. 

The total estimated cancer incidence from the source category is 0.02 excess cancer cases per 

year, or one case in every 50 years. Approximately 100 people are estimated to have cancer 

risks at or above 10 in a million, and approximately 43,000 people are estimated to have 

cancer risks at or above 1 in a million as a result of the emissions from 100 facilities. The 

maximum chronic noncancer TOSHI value for the source category could be as high as 0.008

from emissions of chromium VI.

Table 3.2-3 Summary of Source Category Level Risks for Decorative Chromium 

Electroplating

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 577 n/a

Number of Facilities Identified Modeled in 

Preliminary Risk Assessment 577 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 10 chromium (VI) compounds

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 1 chromium (VI) compounds
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Table 3.2-3 Summary of Source Category Level Risks for Decorative Chromium 

Electroplating

Result HAP “Drivers”

Greater than or equal to 1 in 1 million 100 chromium (VI) compounds

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.008 chromium (VI) compounds

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient a n/a

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 219,000,000 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 140 n/a

Greater than or equal to 1 in 1 million 43,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.02 n/a

Contribution of HAP to Cancer Incidence

chromium (VI) compounds 100% n/a

a Because there are no HAPs with an acute dose-response benchmark value, no acute hazard quotients were calculated.

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

allowable under the MACT standards for decorative chromium electroplating were also 

calculated. Risk results from the inhalation risk assessment using the MACT-allowable

emissions (available in Appendix 4) indicate that the MIR could be as high as 70 in a million, 

and that the maximum chronic noncancer TOSHI value could be as high as 0.06.

The results of the facility-wide assessment for facilities with decorative chromium 

electroplating operations are summarized in Table 3.2-4. The results indicate that 121

facilities have a facility-wide cancer MIR greater than or equal to 1 in a million. The 

maximum facility-wide MIR is 80 in a million, driven by emissions of trichloroethylene from 

solvent degreasing. The source category contributes approximately 1 percent to the facilitywide MIR. The maximum facility-wide TOSHI value for the source category is 7, driven by 

emissions of nickel and manganese compounds from unspecified sources. The source 

category contributes less than 1 percent to the facility-wide TOSHI value.
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Table 3.2-4 Source Category Contribution to Facility-Wide Cancer Risks – Decorative 

Chromium Electroplating

Decorative Chromium 

Electroplating

Number of Facilities Binned by Facility-Wide MIR 

(in 1 million)

Source Category MIR 

Contribution to Facility-Wide MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 400 96 1 0 497

50-90% 19 1 0 0 20

10-50% 8 5 3 0 16

< 10% 29 9 6 0 44

Total 456 111 10 0 577

Table 3.2-5 summarizes the chronic inhalation risk results for the chromium anodizing source 

category. The results indicate that maximum lifetime individual cancer risks could be as high 

as 5 in a million, with chromium VI as the sole contributor to the risk. The total estimated 

cancer incidence from the source category is 0.003 excess cancer cases per year, or one case 

in every 300 years. No people are estimated to have cancer risks at or above 10 in a million, 

and approximately 5,000 people are estimated to have cancer risks at or above 1 in a million 

as a result of the emissions from 32 facilities. The maximum chronic noncancer TOSHI value 

for the source category could be as high as 0.004 from emissions of chromium VI.

Table 3.2-5 Summary of Source Category Level Risks for Chromium Anodizing

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 179 n/a

Number of Facilities Modeled in Preliminary 

Risk Assessment 179 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 5 chromium (VI) compounds

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 32 chromium (VI) compounds

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.004 chromium (VI) compounds

Number of Facilities with Maximum Respiratory Hazard Index:

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient a n/a

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure
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Table 3.2-5 Summary of Source Category Level Risks for Chromium Anodizing

Result HAP “Drivers”

Number of People Living Within 50 Kilometers 

of Facilities Modeled 158,000,000 n/a

Number of People Exposed to Cancer Risk:

Greater than or equal to 100 in 1 million 0 n/a

Greater than or equal to 10 in 1 million 0 n/a

Greater than or equal to 1 in 1 million 5,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer 

cases per year) 0.003 n/a

Contribution of HAP to Cancer Incidence

chromium (VI) compounds 100% n/a

a Because there are no HAPs with an acute dose-response benchmark value, no acute hazard quotients were calculated.

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

allowable under the MACT standards for chromium anodizing were also calculated. Risk 

results from the inhalation risk assessment using the MACT-allowable emissions (available in 

Appendix 4) indicate that the MIR could be as high as 60 in a million, and that the maximum 

chronic noncancer TOSHI value could be as high as 0.05.

The results of the facility-wide assessment for facilities with chromium anodizing operations 

are summarized in Table 3.2-6. The results indicate that 35 facilities have a facility-wide 

cancer MIR greater than or equal to 1 in a million. The maximum facility-wide MIR is 10 in 

a million, driven by emissions of trichloroethylene from solvent degreasing. The source 

category contributes approximately 1 percent to the facility-wide MIR. The maximum 

facility-wide TOSHI value for the source category is 0.1. The source category contributes 

less than 1 percent to the facility-wide TOSHI.

As discussed above, there are no HAPs with an acute dose-response benchmark value emitted 

from the sources in these source categories. Therefore, we did not perform a quantitative risk 

assessment for acute exposures to these pollutants. Also, no PB-HAP emissions were 

identified from these source categories, indicating that exposures due to non-inhalation routes 

of exposure are not significant.
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Table 3.2-6 Source Category Contribution to Facility-Wide Cancer Risks – Chromium 

Anodizing

Chromium Anodizing

Number of Facilities Binned by Facility-Wide MIR 

(in 1 million)

Source Category MIR 

Contribution to Facility-Wide MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 137 31 0 0 168

50-90% 1 0 0 0 1

10-50% 3 2 0 0 5

< 10% 3 1 1 0 5

Total 144 34 1 0 179

4 General Discussion of Uncertainties and How They Have Been 

Addressed

While some of the preceding sections discussed specific uncertainties unique to the source

categories, the following includes a general discussion of uncertainties associated with RTR 

assessments.

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 HAPs, 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% below the estimated value to as much as 84% higher than the 

estimated value. This variability translates directly to the estimated exposures and risks in our 
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assessment, indicating that the actual risks could vary from 17% lower to 84% higher than the 

estimated values.

4.2 Uncertainties in the Dose-Response Relationships

In the sections that follow, separate discussions are provided on uncertainty associated with 

cancer potency factors and noncancer reference values. Cancer potency values are derived for 

chronic (lifetime) exposures. Noncancer reference values are generally derived for chronic 

exposures (up to a lifetime), but may also be derived for acute (<24 hours), short-term (>24 

hours up to 30 days), and subchronic (>30 days up to 10% of lifetime) exposure durations, all 

of which are derived based on an assumption of continuous exposure throughout the duration 

specified. For the purposes of assessing all potential health risks associated with the 

emissions included in an assessment, we rely on both chronic (cancer and noncancer) and 

acute (noncancer) benchmarks, which are described in more detail below.

Although every effort is made to identify peer-reviewed dose-response values for all HAPs

emitted by the sources included in an assessment, some HAPs have no peer-reviewed cancer 

potency values or reference values for chronic non-cancer or acute effects (inhalation or 

ingestion). Since exposures to these pollutants cannot be included in a quantitative risk 

estimate, an understatement of risk for these pollutants at environmental exposure levels is 

possible.

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

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

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

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

significantly mischaracterize human health risk

Cancer assessment

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

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

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

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

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

we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [25] (herein 

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

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

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

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

described in the Cancer Guidelines.

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

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

developing cancer given long-term exposures to the pollutant. Depending on the pollutant 
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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).4 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.5 

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.6 EPA also uses 

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

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

benefits analysis).

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

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

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

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

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

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

characterizations present estimates demonstrating the impact on the assessment of alternative 

choices, data, models and assumptions [26]. Some of the major sources of uncertainty and 

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

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

experimental animal bioassays and those which would occur in humans are a source of 

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

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

 

4

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

5 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.

6 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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tumors would occur in humans.7 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 estimates. For example, the measure of dose is commonly expressed in 

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

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

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

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

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

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

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

lead to either over or underestimation of risk.

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

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

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

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

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

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

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

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

test data and the correctness of scaled estimates. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

7

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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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 [27] suggests that 

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

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

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

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

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

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

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

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

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

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

processes.

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

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

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

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

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

uncertainty in risk assessment (Subramaniam et. al., 2006) [28]. It is important to note that 

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

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

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

comparisons indicating that slopes based on straight line extrapolation from a POD do not 
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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 values8e.g., factors of 10 or 3, 

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

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

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

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

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

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

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

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

 

8

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

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

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

deleterious effects during a lifetime.9 To derive values that are intended to be ―without 

appreciable risk,‖ EPA’s methodology relies upon an uncertainty factor (UF) approach

[29],[30] 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 [31]. While collectively termed ―UFs‖, these factors account for a number 

of different quantitative considerations when utilizing observed animal (usually rodent) or 

human toxicity data in a risk assessment. The UFs are intended to account for: (1) variation 

in susceptibility among the members of the human population (i.e., inter-individual 

variability); (2) uncertainty in extrapolating from experimental animal data to humans (i.e., 

interspecies differences); (3) uncertainty in extrapolating from data obtained in a study with 

less-than-lifetime exposure (i.e., extrapolating from subchronic to chronic exposure); (4) 

uncertainty in extrapolating from a LOAEL in the absence of a NOAEL; and (5) uncertainty 

when the database is incomplete or there are problems with applicability of available studies.

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

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

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

UF value leading to a downward adjustment of 10-3000 fold from the selected POD. An UF 

of 3 is used when the data do not support the use of a 10-fold factor. If an extrapolation step 

or adjustment is not relevant to an assessment (e.g., if applying human toxicity data and an 

interspecies extrapolation is not required) the associated UF is not used. The major 

adjustment steps are described more fully below.

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

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

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

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

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

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

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

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

among humans.

 

9

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

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

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

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

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

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

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

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

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

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

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

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

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

Pharmacokinetic models are useful to examine species differences in pharmacokinetic 

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

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

toxicodynamic differences between animals and humans, and in many cases a 10-fold UF 

(with separate factors of 3 for toxicokinetic and toxicodynamic components) is used to 

account for expected species differences and associated uncertainty in extrapolating from 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

often applied. 

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

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

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

UF of 10 or 3-fold is typically applied.
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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/E

PA-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://www.epa.gov/ttn/fera/risk_atra_vol1.html. 

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

Quality Planning and Standards. http://www.epa.gov/ttn/atw/toxsource/table1.pdf

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

http://www.epa.gov/ttn/atw/nata2005

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

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

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

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

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 

EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.
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14. NRC, 1994. National Research Council. Science and Judgment in Risk Assessment. 

Washington, DC: National Academy Press.

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

Exposure Levels Adopted by OEHHA as of December 2008. 

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

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

for Describing Available Cancer Potency Factors, May 2005. 

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

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

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

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

19. 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

20. 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

21. 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.

22. 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

23. US EPA, 1986. Guidelines for the Health Risk Assessment of Chemical Mixtures. EPA-630-

R-98-002. http://www.epa.gov/NCEA/raf/pdfs/chem_mix/chemmix_1986.pdf

24. US EPA, 2000. Supplementary Guidance for Conducting Health Risk Assessment of 

Chemical Mixtures. EPA-630/R-00-002. 

http://www.epa.gov/ncea/raf/pdfs/chem_mix/chem_mix_08_2001.pdf.

25. US EPA, 2005. Guidelines for Carcinogen Risk Assessment (2005). U.S. Environmental 

Protection Agency, Washington, DC, EPA/630/P-03/001F, 2005. 

http://cfpub.epa.gov/ncea/raf/recordisplay.cfm?deid=116283

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

27. NRC (National Research Council) 2006. Assessing the Human Health Risks of 

Trichloroethylene. National Academies Press, Washington DC.
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28. 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.

29. US EPA. 1993. Reference Dose (RfD): Description and Use in Health Risk Assessments. 

http://www.epa.gov/iris/rfd.htm.

30. 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.

31. US EPA. 2002. A Review of the Reference Dose and Reference Concentration Processes.

http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf.