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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 

July 2010 
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Table of Contents 

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

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

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

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

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

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

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

2.6 Dose-Response Assessment....................................................................................... 9 

2.6.1 Sources of chronic dose-response information .................................................. 9 

2.7 Risk Characterization............................................................................................... 11 

2.7.1 General ............................................................................................................. 11 

2.7.2 Mixtures ........................................................................................................... 13 

2.7.3 Facility-Wide Risk ........................................................................................... 14 

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

Hard Chromium Electroplating Source Categories.................................................................. 14 

3.1 Source Category Descriptions and Results .............................................................. 14 

3.2 Risk Characterization............................................................................................... 21 

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........................................................................................................................ 29 

Index of Tables 

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

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposures.................................. 11 

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

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

Table 3.1-2 Summary of Source Category Level Risks for Hard Chromium Electroplating. 17 

Table 3.1-3 Summary of Emissions from the Decorative Chromium Electroplating Source 

Category ................................................................................................................................... 18 

Table 3.1- 4 Summary of Source Category Level Risks for Decorative Chromium 

Electroplating ........................................................................................................................... 19 

Table 3.1-5 Summary of Emissions from the Chromic Acid Anodizing Source Category.... 20 

Table 3.1-6 Summary of Source Category Level Risks for Chromic Acid Anodizing .......... 20 

Appendices 

Appendix 1 Emissions Inventory Support Memorandum 
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Appendix 2 Technical Support Document for HEM-AERMOD Modeling 

Appendix 3 Meteorological Data for HEM-AERMOD Modeling 

Appendix 4 Analysis of Data on Short-Term Emission Rates Relative to Long-Term 

Emission Rates 

Appendix 5 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 

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)(2) requires the Environmental Protection Agency (EPA, or the Agency) 

to develop technology-based standards for categories of industrial 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) Risk Assessment Methodologies” [2]. We received the final SAB 

report on this review in May of 2010. 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. 

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

the implementation of the respective MACT standards) performed for the chromic acid 

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 noncancer 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 are no HAP with an acute dose-response 

benchmark value, no acute hazard quotients were calculated for these source categories. 
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2 Methods 

2.1 Emissions and source data 

The 2005 National-Scale Air Toxics Assessment (NATA) National Emissions Inventory 

(NEI) served as the starting point for this assessment. The 2005 NEI purportedly contains 

information on actual emissions during the entire 2005 base year. Using the process MACT 

codes1

, we developed a subset of this inventory that contains emissions and facility data for 

the source categories addressed. Next, we performed an engineering review of the 

information using EPA engineers who were directly involved in the development of the 

MACT standards for the source category, and/or who have extensive knowledge of the 

characteristics of this industry. NEI data were updated with available industry supplied data. 

The goal of the engineering review was to identify limitations and issues with the emissions 

data (particularly those that would greatly influence risk estimates) and to make changes to 

the data where possible to address these issues and decrease the uncertainties associated with 

the assessment. 

 

A review of the NEI data indicated that not all chromium electroplating facilities were 

included in the data set. To address this situation, an additional data set was developed based 

on 1629 facilities with known addresses. Emissions for each type of plant were estimated 

based on the model plants developed for the MACT standards, with hard chromium model 

plants having the highest emissions, followed by decorative chromium electroplating, and 

then chromic acid anodizing. 

Details on the development of the emissions and source data for these source categories are 

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

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

2.2 Dispersion modeling for inhalation exposure assessment 

Both long- and short-term inhalation exposure concentrations and associated health 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). The approach used in applying this modeling 

system is outlined below, and further details are provided in Appendix 2. HEM-AERMOD 

performs three main operations: atmospheric dispersion modeling, estimation of individual 

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

the dispersion modeling component. The exposure and risk characterization components are 

discussed in other subsections of Sections 2 and 3. 

The dispersion model in the HEM-AERMOD system, AERMOD version 09292, is a state-ofthe-science Gaussian plume dispersion model that is preferred by EPA for modeling point, 

 

1

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

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

source data. MACT codes may be assigned either at the process level or at the site level in the point source data 

(e.g., the MACT code for municipal waste combustors (MWCs) is assigned at the site level whereas the MACT 

code for petroleum refinery catalytic cracking is assigned at the process level). 
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area, and volume sources of continuous air emissions from facility applications [3]. Further 

details on AERMOD can be found in the AERMOD Users Guide [4]. The model is used to 

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

dispersion from the emission sources into the surrounding atmosphere. Hourly emission rates 

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

the inventory into the 8,760 hours of the year. 

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

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

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

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

Modeling Option Selected Parameter for chronic exposure 

Type of calculations Hourly ambient concentration 

Source type Point, area represented as pseudo point source 

Receptor orientation Polar (13 rings and 16 radials) 

Discrete (census block centroids) 

Terrain characterization Actual from USGS 1-degree DEM data 

Building downwash Not Included 

Plume deposition/depletion Not Included 

Urban source option No 

Meteorology 1 year representative NWS from nearest site (158 stations) 

Meteorological data for HEM-AERMOD is selected from a list of 158 National Weather 

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

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

conditions at the subject facility. 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 underprediction of long-term ambient levels at some locations, likewise it may result 

in overprediction at others. For each facility identified by its characteristic latitude and 

longitude coordinates, the closest meteorological station was used in the dispersion modeling. 

The average distance between a modeled facility and the applicable meteorological station 

was 40 miles (72 km). Appendix 3 (Meteorological Data Processing Using AERMET for 

HEM-AERMOD) provides a complete listing of stations and assumptions along with further 

details used in processing the data through AERMET. The sensitivity of model results to the 

selection of the nearest weather station and the use of one year of meteorological data is 

discussed in “Risk and Technology Review (RTR) Risk Assessment Methodologies.” 
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The HEM-AERMOD model estimates ambient concentrations at the geographic centroids of 

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

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

concentration impacts at each block centroid. Typically, we combined only the impacts of 

facilities within the same source category, and assessed chronic exposure and risk only for 

census blocks with at least one resident (i.e., locations where people may reasonably be 

assumed to reside rather than receptor points at the fenceline of a facility). Chronic ambient 

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

concentrations at each block centroid. Possible future residential use of currently uninhabited 

areas was not considered. Census blocks, the finest resolution available in the census data, 

are typically comprised of approximately 40 people or about ten households. 

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

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

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

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

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

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

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

since the NEI contains only annual emission totals, we applied the general assumption to all 

source categories that the maximum one-hour emission rate from any source was ten times the 

average annual hourly emission rate for that source. Average hourly emissions rate is defined 

as the total emissions for a year divided by the total number of operating hours in the year. 

This choice of a factor of ten for screening was originally based on engineering judgment. To 

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

messages from the SAB consultation on our RTR Assessment Plan, we recently 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)[5]. 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 HAP over short periods of time (see Appendix 4, 

“Analysis of data on short-term emission rates relative to long-term emission rates”). Most 

peak emission events were less than twice the annual average, and the highest was a factor of 

11 times the annual average. The factor of ten is intended to cover both routinely-variable 

emissions as well as startup, shutdown, and malfunction (SSM) emissions. While there are 

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

Texas Environmental Research Consortium suggests that this factor should cover more than 

99% of the short-term peak gaseous or volatile HAP emissions from typical industrial 

sources. 

 

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

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

spatial resolution of about 90 meters. Polar grid elevations (used in estimating short- and 

long-term ambient concentrations) were evaluated at the highest elevation of any census block 
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in that sector. If a sector does not contain any blocks, the model defaults to the elevation of 

the nearest block. If the elevation is not provided for the emission source, the model takes the 

average elevation of all sectors of the nearest model ring. 

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

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

on how hill heights were estimated and used in the AERMOD modeling, see Appendix 2. 

2.3 Estimating human inhalation exposure 

For this assessment, 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 in this assessment for two reasons. First, our 

experience with the 1996, 1999, 2002 and 2005 NATA assessments [6] (which modeled daily 

activity using EPA’s HAPEM model) suggests that, given our current understanding of 

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

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

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

on average ambient concentrations at census block centroids may underestimate or 

overestimate actual exposure concentrations at some residences. Further reducing exposure 

estimates for the most highly-exposed residents by modeling their short-term behavior could 

add a systematic low bias to these results. 

We did not address long-term migration in this assessment 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, three metrics are generally estimated, 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. This assumption of 

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

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

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

risks across the affected population, shifting it toward higher estimated individual risks at the 

upper end and reducing the number of people estimated to be at lower risks, thereby biasing 

the risk estimates high. 

2.4 Acute Risk Screening and Refined Assessments 

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

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

developing chronic health risk assessments under the residual risk program. However, for 
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these source categories, there are no HAP with an acute dose-response benchmark value. 

Therefore, no acute hazard quotients were calculated for the source categories. 

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). The PB-HAP compounds or compound classes are identified for 

the screening from the EPA’s Air Toxics Risk Assessment Library [7]. Examples of PB-HAP 

are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

compounds, methoxychlor, polychlorinated biphenyls, polycyclic organic matter, toxaphene, 

and trifluralin. PB-HAP emissions were not identified from the chromic acid anodizing, 

decorative chromium electroplating, and hard chromium electroplating source categories, 

indicating that exposures due to non-inhalation routes of exposure were 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 PBHAPs. Thus, even if sensitive species are present in the areas surrounding these facilities with 

no PB-HAP emissions, it is still 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 HAPs, we note that as an uncertainty and, where 

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

from the scientific literature. 

2.6 Dose-Response Assessment 

2.6.1 Sources of chronic dose-response information 

Dose-response assessment information (carcinogenic and non-carcinogenic) for chronic 

exposure (either by inhalation or ingestion) for the HAPs reported in the emissions inventory 
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is based on the EPA Office of Air Quality Planning and Standards’ existing recommendations 

for HAPs, also used for NATA 1999, 2002, and 2005 [8]. 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 doseresponse 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 URE is defined as “the upper-bound excess cancer risk estimated 

to result from continuous lifetime exposure to an agent at a concentration of 1 μg/m3

 in 

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

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

units of proportion (of a population) affected per mg/kg-day...” EPA disseminates doseresponse assessment information in several forms, based on the level of review. The 

Integrated Risk Information System (IRIS) [9] 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) [10] for inhalation and oral exposure to many toxic substances. As 

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

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

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

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

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

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

duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be 
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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 [11] and NAS [12].” The non-cancer information 

includes available inhalation health risk guidance values expressed as chronic inhalation 

reference exposure levels (RELs) [13]. 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 [14], defined similarly to EPA's URE. 

 

The emissions inventory for the source categories include emissions of three HAP with 

available quantitative inhalation dose-response values. These HAP, their 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 III Compounds 16065831 

Chromium VI Compounds 18540299 0.0001 0.012 IRIS -- L 

Chromium Trioxide (VI), chromic acid 

mist 11115745 0.000008 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 

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 [15] specifies that a risk characterization “be prepared 

 

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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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 [16], in 2002 by the Agency’s information quality guidelines [17], 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. In our 

assessments we provide summaries of risk metrics (including maximum individual cancer 

risks and non-cancer 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 [18], we apply 

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

Carcinogens [19]. 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 [20] 

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 

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. 

 

3

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

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

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

Office of Science and Technology Policy (http://www.whitehouse.gov/omb/memoranda/fy2007/m07-24.pdf) 
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In the case of benzene, the high end of the reported cancer URE range is used in our 

assessments to provide a conservative estimate of potential cancer risks. Use of the high end 

of the range provides risk estimates that are approximately 3.5 times higher than use of the 

equally-plausible low end value. If the estimated benzene–associated risks exceed 1 in a 

million, we evaluate the impact of using the low end of the URE range on our risk results. 

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

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

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

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

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

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

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

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

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

this on possible health interpretations. 

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

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

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

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

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

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

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’ 

[21,22] 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 
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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 

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 the 2005 NATA. For the facilities in these source categories, we extracted 

(based on facility ID) the NATA 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 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 5. 

3 Risk Results for the Chromic Acid Anodizing, Decorative Chromium 

Electroplating, and Hard Chromium Electroplating Source Categories 

3.1 Source Category Descriptions and Results 

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 chromic acid anodizing source category consists of facilities that use chromic acid to 

form an oxide layer on aluminum to provide resistance to corrosion. The chromic acid 

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. 
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The only HAP emission sources at chromium electroplating facilities 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 chromic acid 

anodizing source category, the emission sources are anodizing tanks. 

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

scrubbers, mesh pad mist eliminators, composite mesh pad systems, high efficiency 

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

chromium electroplating plants comply with the MACT standards by using WAFS in the tank 

bath to control surface tension, which in turn reduces emissions. Some plants use a 

combination of WAFS and add-on control to meet the 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. 

There are an estimated 1,795 plants currently subject to the MACT standards. Of these, there 

are an estimated 800 hard chromium electroplating plants, 750 decorative chromium 

electroplating plants, and 245 chromic acid anodizing plants. Some facilities perform more 

than one type of chromium electroplating or anodizing; in those cases, the facility is classified 

as hard chromium, decorative chromium, or chromic acid anodizing based on the primary 

type of electroplating operation performed at the facility. In addition, many chromium 

electroplating plants also perform other types of electroplating, such as nickel or cadmium 

electroplating. 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 co-located with the printing and publishing processes. 

For those facilities for which the source category is unknown, we assigned them to the hard 

chromium electroplating source category for the purposes of assessment because those 

sources have the highest estimated emissions. Consequently, we modeled more sources as 

hard chromium electroplating sources than the 800 we estimate are in the source category, and 

we modeled fewer than the 750 and 245 sources in the decorative chromium and chromic acid 

anodizing source categories, respectively. 

For the chromium electroplating source categories, we compiled preliminary data using the 

2005 NEI, which identified 122 chromium electroplating facilities. We reviewed the 

emissions data in the NEI and made revisions based on our knowledge of the facilities, the 

processes involved, typical emission rates, and information contained in facility permits or 

permit applications. We revised emission estimates for eight hard chromium electroplating 

plants and six decorative chromium electroplating plants. Data were available for 44 

additional facilities through responses to a CAA section 114 information request that was sent 

to facilities for the Plating and Polishing Area Source rule. The data for these facilities were 

added to the NEI data set, resulting in a data set for 166 facilities, which are approximately 9 

percent of the total estimated facilities covered by the MACT standards. 
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To address the possibility that the 166 facilities may not be representative of the source 

categories, an additional data set was developed based on facilities with known addresses 

(excluding the 166 facilities) using model plant emissions data. This data set includes a total 

of 1,629 facilities, and data on emissions and release characteristics were based on model 

plants used in the MACT standards. The annual emissions for each type of plant were 

estimated based on the emission rates for uncontrolled model plants. Post-MACT emissions 

were then estimated by applying the control efficiencies developed for the MACT standards. 

Using this approach, the resulting emissions resemble allowable emissions. More information 

about the development of the model plants can be found in “Model Plant Data Used to 

Estimate Risk from Chromium Electroplating Sources” [23] 

In both the NEI-based and the model plant data sets, chromium compounds account for all the 

HAP emissions. For the hard chromium electroplating source category, in the NEI-based data 

set, chromium VI compounds account for 98 percent of the emissions, with chromium III and 

chromium trioxide compounds comprising the remaining HAP. In the model plant data set, 

we made the conservative assumption that 100 percent of the emissions are chromium VI 

compounds. For the decorative chromium electroplating source category, in the NEI-based 

data set, chromium VI compounds account for 94 percent of the emissions, with chromium III 

and chromium trioxide compounds comprising the remaining HAP. In the model plant data 

set, we made the conservative assumption that 100 percent of the emissions are chromium VI 

compounds. For the chromic acid anodizing source category, in the NEI-based data set, 

chromium VI compounds account for 99 percent of the emissions with chromium III 

compounds comprising the remaining HAP. In the model plant data set, we made the 

conservative assumption that 100 percent of the emissions are chromium VI compounds. 

The emissions estimates in the model plant data set are considered to be allowable emissions 

because they are based on the MACT level of control. The emissions included in the NEIbased data set represent actual emissions levels. Most of these facilities have low emissions, 

which are generally less than 2 pounds per year. For hard chromium electroplating and 

decorative chromium electroplating, stack emission test data indicate that, for the control 

systems in use, there is considerable variability in actual emission rates. Even for the best 

controls used to comply with the MACT standards, there is significant variability in 

emissions; some sources with properly operating controls have emissions near the emission 

limit, while other sources with the same type of control device operate well below the 

emission limit. These variations are characteristic of the industry and the types of emission 

controls used. There is little emissions data available for decorative chromium electroplating 

facilities and chromic acid anodizing facilities. We conclude these facilities operate near the 

surface tension limit so that the actual emissions would essentially be equal to the allowable 

emissions. Details about the test data and ratios of allowable to actual emissions can be found 

in “Actual vs. Allowable Emissions for Chromium Electroplating Plants - Emission Standards 

Technical Support and Post Promulgation Activities” [24] 
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Tables 3.1-1 through 3.1-6 summarize the estimated emissions and health risks for each 

source category. 

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

Categorya

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb 

HAP

NPRM 

Emissions 

(tpy) 

Number of 

Facilities in 

the dataset 

Unit Risk 

Estimate 

for 

Cancer? 

Reference 

Concentrati

on for 

Chronic 

Non-cancer? 

Health 

Benchmark 

Values for 

Acute Noncancer? 

PBHAP

? 

Chromium III 

Compounds 0.0008 48 

Chromium VI 

Compounds 5.7 1,282 Y Υ 

Chromium Trioxide 

(VI), chromic acid mist 0.0003 1 Y 

a This summary combines emissions data based on site-specific data and model plant data.

b

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

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

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

Table 3.1-2 Summary of Source Category Level Risks for Hard Chromium 

Electroplatinga

Result HAP “Drivers” 

Facilities in Source Category 

Number of Major Facilities Estimated to be 

Subject to MACT in Source Category 800 n/a 

Number of Facilities Identified Modeled in 

NPRM Risk Assessment 1,282b

 n/a 

Cancer Risks 

Maximum Individual Lifetime Cancer Risk 

(in 1 million) from any Facility in the 

Category 

70 Chromium 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 342 Chromium compounds 

 Greater than or equal to 1 in 1 

million 1175 Chromium compounds 

Chronic Non-cancer Risks 

Maximum Respiratory Hazard Index 0.06 Chromium compounds 

Number of Facilities with Maximum Respiratory Hazard Index:

 Greater than 1 0 n/a 
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Table 3.1-2 Summary of Source Category Level Risks for Hard Chromium 

Electroplatinga

Result HAP “Drivers” 

Acute Non-cancer Screening Results 

Maximum Acute Hazard Quotient c n/a 

Number of Facilities With Potential for 

Acute Effects 

c n/a 

Population Exposure 

Number of People Living Within 50 

Kilometers of Facilities Modeled 224,000,000 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 71,000 Chromium compounds 

 Greater than or equal to 1 in 1 

million 14,200,000 Chromium compounds 

Number of People Exposed to Non-cancer Respiratory Hazard Index: 

 Greater than 1 0 n/a 

Estimated Cancer Incidence (excess cancer 

cases per year) 0.8 Chromium compounds 

Contribution of HAP to Cancer Incidence

 Chromium compounds 100% a This summary combines the results of the assessments based on site-specific data and model plant data. 

b

 For those facilities for which the source category is unknown, we assigned them to the hard chromium electroplating source 

category for the purposes of assessment because those sources have the highest estimated emissions. Consequently, we 

modeled more hard chromium electroplating sources than the 800 we estimate are in the source category. 

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

Table 3.1-3 Summary of Emissions from the Decorative Chromium Electroplating 

Source Categorya

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb 

HAP

NPRM 

Emissions 

(tpy) 

Number of 

Facilities in 

the data set 

Unit Risk 

Estimate 

for 

Cancer? 

Reference 

Concentrati

on for 

Chronic 

Non-cancer? 

Health 

Benchmark 

Values for 

Acute Noncancer? 

PBHAP

? 

Chromium III 

Compounds 0.002 38 

Chromium VI 

Compounds 0.43 433 Y Υ 

Chromium Trioxide 

(VI), chromic acid mist 0.00002 1 Y 

a This summary combines emissions data based on site-specific data and model plant data.

b

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

at http://www.epa.gov/ttn/atw/toxsource/summary.html 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 3.1-4 Summary of Source Category Level Risks for Decorative Chromium 

Electroplatinga

Result HAP “Drivers” 

Facilities in Source Category 

Number of Major Facilities Estimated to be 

Subject to MACT in Source Category 750 n/a 

Number of Facilities Identified Modeled in 

NPRM Risk Assessment 433b

 n/a 

Cancer Risks 

Maximum Individual Lifetime Cancer Risk 

(in 1 million) from any Facility in the 

Category 

70 Chromium 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 58 Chromium compounds 

 Greater than or equal to 1 in 1 

million 301 Chromium compounds 

Chronic Non-cancer Risks 

Maximum Respiratory Hazard Index 0.06 Chromium compounds 

Number of Facilities with Maximum Respiratory Hazard Index:

 Greater than 1 0 n/a 

Acute Non-cancer Screening Results 

Maximum Acute Hazard Quotient c n/a 

Number of Facilities With Potential for 

Acute Effects 

c n/a 

Population Exposure 

Number of People Living Within 50 

Kilometers of Facilities Modeled 166,000,000 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 4,300 Chromium compounds 

 Greater than or equal to 1 in 1 

million 390,000 Chromium compounds 

Number of People Exposed to Non-cancer Respiratory Hazard Index: 

 Greater than 1 0 n/a 

Estimated Cancer Incidence (excess cancer 

cases per year) 0.08 Chromium compounds 

Contribution of HAP to Cancer Incidence

 Chromium compounds 100% a This summary combines the results of the assessments based on site-specific data and model plant data. 

b

 For those facilities for which the source category is unknown, we assigned them to the hard chromium electroplating source 

category for the purposes of assessment because those sources have the highest estimated emissions. Consequently, we 

modeled fewer than the 750 sources estimated to be in the decorative chromium electroplating source category. c Because there are no HAP with an acute dose-response benchmark value, no acute hazard quotients were calculated.
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Table 3.1-5 Summary of Emissions from the Chromic Acid Anodizing Source Categorya

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSb 

HAP

NPRM 

Emissions 

(tpy) 

Number of 

Facilities in 

the data set 

Unit Risk 

Estimate 

for 

Cancer? 

Reference 

Concentrati

on for 

Chronic 

Non-cancer? 

Health 

Benchmark 

Values for 

Acute Noncancer? 

PBHAP

? 

Chromium III 

Compounds 0.0002 4 

Chromium VI 

Compounds 0.04 80 Y Υ 

a This summary combines emissions data based on site-specific data and model plant data.

b

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

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

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

Table 3.1-6 Summary of Source Category Level Risks for Chromic Acid Anodizinga

Result HAP “Drivers” 

Facilities in Source Category 

Number of Major Facilities Estimated to be 

Subject to MACT in Source Category 245 n/a 

Number of Facilities Identified Modeled in 

NPRM Risk Assessment 80b

 n/a 

Cancer Risks 

Maximum Individual Lifetime Cancer Risk 

(in 1 million) from any Facility in the 

Category 

5 Chromium 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 Chromium compounds 

 Greater than or equal to 1 in 1 

million 28 Chromium compounds 

Chronic Non-cancer Risks 

Maximum Respiratory Hazard Index 0.004 Chromium compounds 

Number of Facilities with Maximum Respiratory Hazard Index:

 Greater than 1 0 n/a 

Acute Non-cancer Screening Results 

Maximum Acute Hazard Quotient c n/a 

Number of Facilities With Potential for 

Acute Effects 

c n/a 

Population Exposure 

Number of People Living Within 50 

Kilometers of Facilities Modeled 108,000,000 n/a 

Number of People Exposed to Cancer Risk: 
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Table 3.1-6 Summary of Source Category Level Risks for Chromic Acid Anodizinga

Result HAP “Drivers” 

 Greater than or equal to 100 in 1 

million 0 n/a 

 Greater than or equal to 10 in 1 

million 0 Chromium compounds 

 Greater than or equal to 1 in 1 

million 2,800 Chromium compounds 

Number of People Exposed to Non-cancer Respiratory Hazard Index: 

 Greater than 1 0 n/a 

Estimated Cancer Incidence (excess cancer 

cases per year) 0.003 Chromium compounds 

Contribution of HAP to Cancer Incidence

 Chromium compounds 100% a This summary combines the results of the assessments based on site-specific data and model plant data. 

b

 For those facilities for which the source category is unknown, we assigned them to the hard chromium electroplating source 

category for the purposes of assessment because those sources have the highest estimated emissions. Consequently, we 

modeled fewer than the 245 sources estimated to be in the chromic acid anodizing source category. c Because there are no HAP with an acute dose-response benchmark value, no acute hazard quotients were calculated.

3.2 Risk Characterization 

The modeling results for the hard chromium electroplating, decorative chromium 

electroplating, and chromic acid anodizing source categories indicate that maximum lifetime 

individual cancer risks could be as high as 70-in-1 million, 70-in-1 million, and 5-in-1 

million, respectively. Also, the maximum chronic non-cancer TOSHI values for the hard 

chromium electroplating, decorative chromium electroplating, and chromic acid anodizing 

source categories could be as high as 0.06, 0.06, and 0.004, respectively. The total estimated 

cancer incidence from hard chromium electroplating facilities is 0.8 excess cancer cases per 

year, or one case in every 1.25 years. The total estimated cancer incidence from decorative 

chromium electroplating facilities is 0.08 excess cancer cases per year, or one case in every 

12.5 years. The total estimated cancer incidence from chromic acid anodizing facilities is 

0.003 excess cancer cases per year, or one case in every 333 years. 

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

acute hazard quotients were calculated, and the potential for any acute impacts of concern is 

considered negligible. Also, no PB-HAP emissions were identified from these source 

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

significant. Regarding the “facility-wide” assessment, there are no facilities in any of the 

source categories with a source category TOSHI value greater than one, and there is only one 

facility in the three source categories with a facility-wide TOSHI value greater than 1. This 

facility has a facility-wide TOSHI value of 2 (nervous system), and the source category does 

not contribute to it at all because the respiratory system is the target organ for the chromium 

compounds. The highest source category MIR for the hard chromium electroplating source 

category is 70-in-1 million, which is also the facility-wide MIR for the same facility. The 

highest facility-wide MIR for the hard chromium electroplating source category is 90-in-1 
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million, for which the source category contributes 1 percent. The highest source category 

MIR for the decorative chromium electroplating source category is 70-in-1 million, but we do 

not have a facility-wide MIR estimate for the same facility. The highest facility-wide MIR 

for the decorative chromium electroplating source category is 90-in-1 million, for which the 

source category contributes 7 percent. The highest source category MIR for the chromic acid 

anodizing source category is 5-in-1 million, which occurs at the same facility that has the 

highest facility-wide MIR of 20-in-1 million. 

Our analyses of potential differences between actual emission levels and emissions allowable 

under the MACT standards are based on emissions test data from specific facilities. A 

comparison of these test results to allowable emissions at these facilities indicates that the 

ratio of MACT-allowable to actual emissions varies considerably from facility to facility. As 

a result, a uniform factor was not available to apply to all facilities. However, for the hard 

chromium electroplating source category, we did evaluate the facility that was modeled as 

having the highest maximum individual lifetime cancer risk (70-in-1 million) based on actual 

emissions. Our analysis indicates that this facility, if operated at the allowable emissions 

limit, could have a maximum individual lifetime cancer risk as high as 90-in-1 million. 

Furthermore, we have concluded that no other hard chromium electroplating facility would 

have a cancer risk that high if operated at the allowable emissions limit. 

For the decorative chromium electroplating source category, we performed a similar analysis 

of the available data and concluded that the maximum individual lifetime cancer risk would 

not exceed 70-in-1 million for any facility that operated at the allowable emissions limit. As 

stated earlier, because most chromic acid anodizing facilities use WAFS, we believe actual 

emissions are essentially the same as allowable emissions. Thus, we believe that the MIR 

based on allowable emissions would be the same as that based on actual emissions, i.e., 5-ina-million. 

4 General Discussion of Uncertainties and How They Have Been 

Addressed 

While each 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 

Emission inventory uncertainties were previously discussed in sections 2.1 and 3.1. We have 

used model plant emissions for most of the sources in the categories because of the lack of 

site-specific emissions data, and we have purposely biased the emission estimates high in all 

of the model plants in order to ensure that we do not underestimate potential cancer risks. The 

chronic exposure modeling uncertainties are considered relatively small since we are using 

EPA’s refined local dispersion model with reasonably representative meteorology. It is 

possible that population exposure estimates are also biased slightly higher by not accounting 

for short- or long-term population mobility, and by neglecting processes like deposition, 

plume depletion, and atmospheric degradation. 
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4.2 Uncertainties in the Dose-Response Relationships 

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

cancer potency factors and for non-cancer reference values. Cancer potency values are 

derived for chronic (lifetime) exposures. Non-cancer reference values are generally derived 

for chronic exposures (up to a lifetime), but may also be derived for acute (<24 hours), shortterm (>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 non-cancer) and 

acute (non-cancer) benchmarks, which are described in more detail below. 

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

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

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

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

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

possible. As discussed above, there are no HAPs with an acute dose-response benchmark 

value in these source categories. Therefore, no acute hazard quotients were calculated. 

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

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

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

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

significantly mischaracterize human health risk. 

Cancer assessment

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

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

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

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

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

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

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

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

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

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

described in the Cancer Guidelines. 

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

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

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

being evaluated, EPA relies on both animal bioassay and epidemiological studies to 
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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 is a source of 

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

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

 

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 predictions. For example, the measure of dose is commonly expressed 

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

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

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

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

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

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

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

lead to either over or underestimation of risk. 

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

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

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

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

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

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

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

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

test data and the correctness of scaled estimates. 

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

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

there is a specified excess risk of cancer (a POD). Statistical uncertainty in developing a POD 

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

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

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

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

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

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

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

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

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

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

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

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

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

 

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

(MOA) 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. A recent National Research Council (NRC) report [27] suggests that 

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

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

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

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

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

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

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

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

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

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

processes. 

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

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

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

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

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

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

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

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

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

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

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

model. 
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(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 non-cancer assessment

Chronic non-cancer reference values represent chronic exposure levels that are intended to be 

health-protective. That is, EPA and other organizations which develop non-cancer 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 values8 e.g., factors of 10 or 3, 

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

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

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

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

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

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

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

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

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

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

 

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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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. 

5 References 
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1. US EPA, 2006. Emission Standards for Hazardous Air Pollutants. 

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

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

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

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

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

3. US EPA, 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

4. 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. 

5. 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

6. US EPA, 1996, 1999, 2002, 2005. National-Scale Air Toxics Assessments (NATA), 

Technology Transfer Network Air Toxics web site: http://www.epa.gov/airtoxics/natamain/. 

7. 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. 

8. 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

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

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

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

11. 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.

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

Washington, DC: National Academy Press. 

13. 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. 

14. 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. 
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15. US EPA, 1995. Guidance for Risk Characterization. Science Policy Council. 

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

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

17. 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

18. 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

19. 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. 

20. 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 

21. 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

22. 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. 

23 RTI International, 2010. Memorandum from R. Marinshaw, RTI International, to P. Mulrine, 

US EPA. Model Plant Data Used to Estimate Risk from Chromium Electroplating Sources 

Emission Standards Technical Support and Post Promulgation Activities. 

24 RTI International, 2010. Memorandum from R. Marinshaw, RTI International, to P. Mulrine, 

US EPA. Actual vs. Allowable Emissions for Chromium Electroplating Plants - Emission 

Standards Technical Support and Post Promulgation Activities. 

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. 

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. 
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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.