Residual Risk Assessment for the Acrylic and Modacrylic Fibers 

Production Source Category

by

EPA’s Office of Air Quality Planning and Standards

Office of Air and Radiation

September 2014
i

Table of Contents

1 Introduction........................................................................................................................ 1

2 Methods.............................................................................................................................. 2

2.1 Emissions and Source Data ......................................................................................... 2

2.2 Dispersion Modeling for Inhalation Exposure Assessment ........................................ 2

2.3 Estimating Human Inhalation Exposure...................................................................... 4

2.4 Acute Risk Screening and Refined Assessments ........................................................ 5

2.5 Multipathway and Environmental Risk Screening...................................................... 6

2.6 Dose-Response Assessment ........................................................................................ 7

2.6.1 Sources of chronic dose-response information .................................................... 7

2.6.2 Sources of acute dose-response information...................................................... 10

2.7 Risk Characterization ................................................................................................ 12

2.7.1 General ............................................................................................................... 12

2.7.2 Mixtures............................................................................................................. 14

2.7.3 Facility-wide risks.............................................................................................. 15

2.7.4 MACT-allowable risk ........................................................................................ 15

3 Risk Results for the Acrylic and Modacrylic Fibers Production Source Category.......... 15

3.1 Source Category Description and Results................................................................. 15

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

4 General Discussion of Uncertainties and How They Have Been Addressed................... 19

4.1 Exposure Modeling Uncertainties............................................................................. 19

4.2 Uncertainties in the Dose-Response Relationships................................................... 19

5 References........................................................................................................................ 28

Index of Tables

Table 2.2-1 AERMOD version 12060 model options for RTR modeling................................ 3

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens ............ 9

Table 2.6-2 Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens .... 10

Table 2.6-3 Dose-Response Values for Acute Exposure ........................................................ 12

Table 3.1-1 Summary of Emissions from the Acrylic and Modacrylic Fibers Production 

Source Category Used in the Residual Risk Assessment and Availability of Dose-Response 

Values....................................................................................................................................... 16

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Acrylic and Modacrylic 

Fibers Production ..................................................................................................................... 17

Table 3.2-2 Source Category Contribution to Facility-Wide Cancer Risks............................ 18

Appendices

Appendix 1 Technical Support Document for HEM3 Modeling

Appendix 2 Meteorological Data for HEM3 Modeling

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

rates

Appendix 4 Detailed Risk Modeling Results
ii

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 Protection Agency

CTE Central tendency estimate

ERPG Emergency Response Planning Guideline

HAP Hazardous Air Pollutants

HEM Human Exposure Model

HI Hazard index

HQ Hazard quotient

IRIS Integrated Risk Information System

MACT Maximum Achievable Control Technology

MIR Maximum Individual Risk

MOA Mode of action

NAC National Advisory Committee

NAAQS National Ambient Air Quality Standard

NATA National Air Toxics Assessment

NEI National Emissions Inventory

NPRM Notice of proposed rulemaking

PB-HAP Persistent and bioaccumulative - HAP

POM Polycyclic organic matter

REL Reference exposure level

RfC Reference concentration

RfD Reference dose

RME Reasonable maximum exposure

RTR Risk and Technology Review

TOSHI Target-organ-specific hazard index

URE Unit risk estimate
1

1 Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

initial technology-based standard.

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

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

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

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

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

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

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

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

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

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

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

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

the implementation of the respective MACT standards) performed for the acrylic and 

modacrylic fibers (AMF) production source category. The methods discussion includes 

descriptions of the methods used to develop refined estimates of chronic inhalation exposures 

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

methods used to screen for acute health risks, chronic non-inhalation health risks, and adverse 

environmental effects. Since the screening assessments indicated low potential for acute 

health risks, or chronic non-inhalation health effects or environmental impacts, no further 

refinement of these assessments was performed.
2

2 Methods

2.1 Emissions and Source Data

We currently estimate that there is one AMF production facility operating in the U.S. We 

developed a data set based on information submitted to the EPA by the one operating facility 

in the source category. On February 23, 2012, the EPA visited this facility, Cytec Carbon 

Fibers, LLC, located in Piedmont, South Carolina. The purpose of this visit was to better 

understand the acrylic fiber production processes, the controls in place to reduce HAP 

emissions, and the characteristics of the emission points at this facility. As part of this visit, 

the EPA requested that facility personnel examine the 2008 NEI HAP inventory data the EPA 

had for the facility, which the EPA provided to the facility prior to the site visit, and, where 

differences exist, correct or update the data. After review of the data, the facility submitted 

updated information, and the updated data formed the basis for the data set used for modeling. 

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

discussed in a memorandum entitled, Emissions Data and Acute Risk Factor Used in Residual 

Risk Modeling: Acrylic and Modacrylic Fibers, Amino/Phenolic Resins, and Polycarbonate 

Production, available in the docket for this rule making. 

2.2 Dispersion Modeling for Inhalation Exposure Assessment

Both long- and short-term inhalation exposure concentrations and associated health risks were 

estimated using the Human Exposure Model (HEM3), which incorporates the American 

Meteorological Society/EPA Regulatory Model (AERMOD) dispersion modeling system. 

The approach used in applying this modeling system is outlined below, and further details are 

provided in Appendix 1. The HEM3 performs three main operations: atmospheric dispersion 

modeling, estimation of individual human exposures and health risks, and estimation of 

population risks. This section focuses on the dispersion modeling component. The exposure 

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

The dispersion model in the HEM3 system, AERMOD version 120601, is a state-of-thescience Gaussian plume dispersion model that is preferred by EPA for modeling point, area, 

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

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

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

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

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

inventory into the 8,760 hours of the year.

The first step in the application of the HEM3 modeling system is to predict ambient 

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

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

 

1 A newer version of AERMOD (version 14134) has been released since the modeling of this source category 

was performed. Because the model inputs did not change as a result of public comment, the source category was 

not remodeled using the newer version of AERMOD.
3

Table 2.2-1 AERMOD version 12060 model options for RTR modeling

Modeling Option Selected Parameter for chronic exposure

Type of calculations Hourly Ambient Concentration

Source type Point

Receptor orientation Polar (13 rings and 16 radials)

Discrete (census block centroids)

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

Building downwash Not Included

Plume deposition/depletion Not Included

Urban source option No

Meteorology 1 year representative NWS from nearest site 

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

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

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

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

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

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

dispersion model runtimes using five years of meteorological data would be too long for RTR 

source categories with many sources, we modeled only a single year, 2011. While the 

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

locations, likewise it may result in over-prediction at others. The single facility in this source 

category was identified by its characteristic latitude and longitude coordinates, and the closest 

meteorological station was used in the dispersion modeling. The distance between the

modeled facility and the applicable meteorological station was 15 miles (24 km). Appendix 2

(Meteorological Data Processing Using AERMET for HEM3) provides a complete listing of 

stations and assumptions along with further details used in processing the data through 

AERMET, the AERMOD meteorological data processing program. The sensitivity of model 

results to the selection of the nearest weather station and the use of one year of meteorological 

data is discussed in “Risk and Technology Review (RTR) Risk Assessment Methodologies” 

[2].

The HEM3 estimates ambient concentrations at the geographic centroids of census blocks 

(using the 2010 Census), and at other receptor locations that can be specified by the user. The 

model accounts for the effects of multiple facilities when estimating concentration impacts at 

each block centroid. When evaluating the risks associated with a particular source category,

we combine only the impacts of facilities within the same source category, and assess 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 are calculated as the annual average of all estimated 

short-term (one-hour) concentrations at each block centroid. Possible future residential use of 
4

currently uninhabited areas is 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 is performed only for occupied census blocks, worst-case short-term (one-hour) 

concentrations are 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, the first 

several rings could still contain locations inside the facility property). Since short-term 

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

the emissions data typically contain only annual emission totals, we generally apply the 

assumption to all source categories that the maximum or peak one-hour emission rate from 

any source is ten times the average annual hourly emission rate for that source.

The average hourly emissions rate used for chronic risk modeling is defined as the total 

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

operated 8,760 hours per year this emissions rate would be appropriate for predicting potential 

acute impacts. Appendix 3, Analysis of data on short-term emission rates relative to longterm emission rates, discusses the analysis used to determine the generic factor of ten that is 

applied to average hourly emissions to estimate peak hourly emissions. While hourly 

emissions data for each source are not available for the AMF source category, we have 

information that indicates peak hourly emissions from equipment leaks would be no higher 

than two times the average hourly emissions. For other sources, we applied the factor of ten. 

 

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

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

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

ambient concentrations are assumed to be equal to the highest elevation of any census block 

falling within the polar grid sector corresponding to the grid point. If a sector does not 

contain any blocks, the model defaults the elevation to that of the nearest block. If an 

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

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

nearby elevated hills are expected to influence the plume. For details on how hill heights 

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

2.3 Estimating Human Inhalation Exposure

We use the estimated annual average ambient air concentration of each HAP at each census 

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

people who reside in the census block. That is, the risk analysis does not consider either the 

short-term or long-term behavior (mobility) of the exposed populations and its potential 

influence on their exposure.

 

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

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

given our current understanding of microenvironment concentrations and daily activities, 

modeling short-term activity would, on average, reduce risk estimates about 25 percent for 
5

particulate HAP; it would also reduce risk estimates for gaseous HAP, 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.

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

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

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

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

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

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

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

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

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

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

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

number of people at specific risk levels. 

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

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

potential acute exposure concentration for any individual.

2.4 Acute Risk Screening and Refined Assessments

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

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

developing chronic health risk assessments under the residual risk program. That is, we 

developed a tiered, iterative approach. This approach to risk assessment was endorsed by the 

National Academy of Sciences in its 1993 publication “Science and Judgment in Risk 

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

in 1999. 

The assessment methodology is designed to eliminate from further consideration those 

facilities for which we have confidence that no acute adverse health effects of concern will 

occur. To do so, we use what is called a tiered, iterative approach to the assessment. This 

means that we begin with a screening assessment, which relies on readily available data and 

uses conservative assumptions that in combination approximate a worst-case exposure. The 

result of this screening process is that either the facility being assessed poses no potential 

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

A refined assessment could use industry- or site-specific data on the temporal pattern of 

emissions, the layout of emission points at the facility, the boundaries of the facility, and/or

the local meteorology. In some cases, all of these site-specific data would be needed to refine 

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

acute exposures are not a concern, and significant additional data collection is not necessary. 
6

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

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

worst-case assumptions in our screening approach:

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

1-hour emission rates.

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

occur at all emission points at the same time.

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

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

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

hour. 

 Worst-case meteorology (from one year of local meteorology) was assumed to occur 

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

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

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

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

meters.

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

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

benchmarks are described in section 2.6 of this report.

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

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

cases, this includes the use of a refined emissions multiplier to estimate the peak hourly 

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

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

gauge where people might reasonably be exposed for an hour. For the AMF production

source category, the default emissions multiplier of 10 was used for all emission sources 

except for equipment leaks, for which a factor of two was used. The memorandum entitled, 

Emissions Data and Acute Risk Factor Used in Residual Risk Modeling: Acrylic and 

Modacrylic Fibers, Amino/Phenolic Resins, and Polycarbonate Production, includes a

detailed description of how the maximum hourly emissions were developed for this source 

category and can be found in the docket for this rulemaking. The single AMF production 

facility screened out, so no refined acute assessment was performed for this source category. 

Refer to Appendix 4 for the detailed results for the facility.

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

 

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

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

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

document).
7

for the screening from the EPA’s Air Toxics Risk Assessment Library [7]. Examples of PBHAP are cadmium compounds, chlordane, chlorinated dibenzodioxins and furans, DDE, 

heptachlor, hexachlorobenzene, hexachlorocyclohexane, lead compounds, mercury 

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

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

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

significant.

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

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

screen focuses on the following seven environmental HAP: 

• Five PB-HAP – cadmium, dioxins/furans, polycyclic organic matter (POM), mercury 

(both inorganic mercury and methyl mercury) and lead.

• Two acid gases - hydrogen chloride (HCl), and hydrogen fluoride (HF). 

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

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

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

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

this source category emitted any of the seven environmental HAP, which it does not. If one 

or more of the seven environmental HAP evaluated were emitted, we would proceed to the 

second step of the evaluation, which is to determine whether the facility-specific off-site 

emission rates of each of the emitted environmental HAP exceeded ecological benchmarks 

represented as emissions threshold values.

2.6 Dose-Response Assessment

2.6.1 Sources of chronic dose-response information 

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

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

production source category were based on the EPA Office of Air Quality Planning and 

Standards’ existing recommendations for HAP [8], also used for NATA [9]. This information 

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

consistency with EPA risk assessment guidelines and (2) level of peer review received. The 

prioritization process was aimed at incorporating into our assessments the best available 

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

following sources, in order of priority: 

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

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

assessments typically provide a qualitative statement regarding the strength of scientific 

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

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

for inhalation) or slope factor (SF, for ingestion) to estimate the probability of developing 
8

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

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

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

during a lifetime.” The RfD is “an estimate (with uncertainty spanning perhaps an order 

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

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

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

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

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

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

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

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

Risk Information System (IRIS) [10] is an EPA database that contains scientific health 

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

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

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

the public, and peer review by independent scientists external to EPA. New IRIS values 

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

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

scheduling of current individual IRIS assessments and updates 

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

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

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

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

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

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

inhalation exposure that are based on a nonlinear MOA.

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

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

Risk Levels (MRLs) [11] for inhalation and oral exposure to many toxic substances. As 

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

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

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

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

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

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

duration of exposure.” ATSDR describes MRLs as substance-specific estimates to be 

used by health assessors to select environmental contaminants for further evaluation. 

Exposures above an MRL do not necessarily represent a threat, and MRLs are therefore 

not intended for use as predictors of adverse health effects or for setting cleanup levels.

3) California Environmental Protection Agency (CalEPA). The CalEPA Office of 

Environmental Health Hazard Assessment has developed dose-response assessments for 

many substances, based both on carcinogenicity and health effects other than cancer. The 

process for developing these assessments is similar to that used by EPA to develop IRIS 
9

values and incorporates significant external scientific peer review. As cited in the 

CalEPA Technical Support Document for developing their chronic assessments [12]: “The 

guidelines for developing chronic inhalation exposure levels incorporate many 

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

includes available inhalation health risk guidance values expressed as chronic inhalation 

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

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

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

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

 

The emissions inventory for the AMF production source category includes emissions of one

HAP (acrylonitrile) with available chronic quantitative inhalation dose-response values. The 

other HAP emitted from this source category (hydroquinone) has no available chronic 

quantitative inhalation dose-response values. Acrylonitrile is classified as a probable 

carcinogen, with a quantitative cancer dose-response value available. This HAP, its

quantitative inhalation chronic cancer dose-response value, and the source of the value are 

listed in Table 2.6-1. There is also a quantitative inhalation chronic noncancer threshold 

value available for acrylonitrile, and this value and its source are listed in Table 2.6-2.

Table 2.6-1 Dose-Response Values for Chronic Inhalation Exposure to Carcinogens

URE (unit risk estimate for cancer)3 = cancer risk per μg/m3of average lifetime exposure. 

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

Research & Development, EPA OAQPS = EPA Office of Air Quality Planning & 

Standards, CAL = California EPA Office of Environmental Health Hazard Assessment.

HEAST = EPA Health Effects Assessment Tables.

Pollutant CAS 

Number4

URE

(1/μg/m3)

Source

Acrylonitrile 107131 0.000068 IRIS

 

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

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

a plausible upper limit to the true value. 

4 Chemical Abstract Services identification number. For groups of compounds that lack a CAS number we have 

used a surrogate 3-digit identifier corresponding to the group’s position on the CAA list of HAP.
10

Table 2.6-2 Dose-Response Values for Chronic Inhalation Exposure to Noncarcinogens

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

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

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

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

Environmental Human Health Assessment, ATSDR = US Agency for Toxic Substances Disease 

Registry, HEAST = EPA Health Effects Assessment Tables, EPA OAQPS = EPA Office of Air 

Quality Planning & Standards, EPA ORD = EPA Office of Research and Development

Pollutant CAS Number4 RfC 

(mg/m3)

Source5

Acrylonitrile 107131 0.002 IRIS - M

2.6.2 Sources of acute dose-response information 

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

assessments are based on the existing recommendations of OAQPS for HAP [17]. Depending 

on availability, the results from screening acute assessments are compared to both “no 

effects” reference levels for the general public, such as the California Reference Exposure 

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

Levels (AEGLs) and Emergency Response Planning Guidelines (ERPGs), with the 

recognition that the ultimate interpretation of any potential risks associated with an estimated 

exceedance of a particular reference level depends on the definition of that level and any 

limitations expressed therein. Comparisons among different available inhalation health effect 

reference values (both acute and chronic) for selected HAP can be found in an EPA document

[18]. 

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

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

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

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

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

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

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

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

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

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

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

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

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

developed chronic RELs).

 

5 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 IRIS.
11

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

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

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

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

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

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

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

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

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

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

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

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

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

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

and possibly the international community in conjunction with chemical emergency response, 

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

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

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

releases at fixed facilities and from transport carriers.” 

The NAC/AEGL defines AEGL-1 and AEGL-2 as:

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

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

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

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

exposure.”

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

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

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

ability to escape.”

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

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

irritation or certain asymptomatic, nonsensory effects. With increasing airborne 

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

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

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

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

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

responses, could experience the effects described at concentrations below the 

corresponding AEGL.”

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

Association (AIHA) has developed Emergency Response Planning Guidelines (ERPGs) [20] 
12

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

concentrations for once in a lifetime exposure of the general population (but not particularly 

sensitive persons) for up to 1 hour associated with effects expected to be mild or transient 

(ERPG-1), irreversible or serious (ERPG-2), and potentially life-threatening (ERPG-3). 

ERPG values are described in their supporting documentation [21] as follows: “ERPGs are air 

concentration guidelines for single exposures to agents and are intended for use as tools to 

assess the adequacy of accident prevention and emergency response plans, including 

transportation emergency planning, community emergency response plans and incident 

prevention and mitigation.” 

ERPG-1 and ERPG-2 values are defined by AIHA as follows:

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

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

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

odor.”

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

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

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

ability to take protective action.”

The emissions inventory for the AMF production source category includes emissions of one 

HAP with relevant and available quantitative acute dose-response threshold values. This

HAP, its acute threshold values, and the source of the values are listed below in Table 2.6-3.

Table 2.6-3 Dose-Response Values for Acute Exposure

Pollutant

CAS 

Number

AEGL-1

(1-hr)

(mg/m3)

AEGL-2

(1-hr)

(mg/m3)

ERPG1

(mg/m3

)

ERPG-2

(mg/m3) REL 

Acrylonitrile 107131 10 130 22 77

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 
13

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

1995 Policy for Risk Characterization [22] specifies that a risk characterization “be prepared 

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

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

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

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

OMB/OSTP September 2007 Memorandum on Updated Principles for Risk Analysis [25], 

and are incorporated in these assessments.

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

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

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

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

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

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

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

limitations.

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

individual and population cancer risks were calculated by multiplying the corresponding 

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

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

assumed human lifespan) at that exposure. Because UREs for most HAP 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 HAP that act by a mutagenic mode-of-action [26], we applied 

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

Carcinogens [27]. 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 lifetime 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 [28] 

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

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

to all unspeciated POM mixtures.

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

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

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

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

estimate of the number of cancer cases per year.
14

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

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

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

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

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

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

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

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

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

this on possible health interpretations. 

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

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

RfCs incorporate generally conservative uncertainty factors in the face of uncertain 

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

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

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

Screening for potentially significant acute inhalation exposures also followed the HQ 

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

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

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

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

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

2.7.2 Mixtures

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

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

exposures from a particular source category combined.

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

[29,30] default assumption of additivity of effects, and combine risks by summing them using 

the independence formula in the mixtures guidelines.

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

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

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

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

To conform to these guidelines, we aggregated noncancer HQs of HAP 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 HAP that affect the same organ or organ 

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

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

HQs associated with some pollutants have been aggregated into more than one TOSHI, this 
15

has been done only in cases where the critical dose affects more than one target organ. 

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

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

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

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

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

interpreted carefully by health scientists and risk managers.

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

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

basis, not using the TOSHI approach.

2.7.3 Facility-wide risks

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

data and modeling as described in Section 2.2. For the single facility in the AMF production

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

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

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

common control. We analyzed risks due to the inhalation of HAP for the populations residing 

within 50 kilometers of the facility. The results of the facility-wide assessment are 

summarized below in the Risk characterization section of this document. The complete 

results of the facility-wide assessment are provided in Appendix 4.

2.7.4 MACT-allowable risk

The emissions data in the data set for the AMF production source category are estimates of 

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

based on these actual emissions. We estimate that the actual emissions level is representative 

of the MACT-allowable level (i.e., the maximum emission levels allowed if in compliance 

with the MACT standards) for all emissions sources, as we are not aware of any situations in 

which the facility is conducting additional work practices or operating a control device such 

that it achieves a higher emission reduction than required. The memorandum entitled, MACT 

Allowable Emissions and Risks for the Acrylic and Modacrylic Fibers, Amino/Phenolic 

Resins, and Polycarbonate Production Source Categories includes more information on

MACT-allowable emissions (EPA-HQ-OAR-2012-0133).

3 Risk Results for the Acrylic and Modacrylic Fibers 

Production Source Category

3.1 Source Category Description and Results

Acrylic and modacrylic fibers are synthetic fibers produced from acrylonitrile. The source 

category that covers the production of these fibers includes any operations that produce either: 

(1) acrylic fiber, in which the fiber-forming substance is any long-chain synthetic polymer 
16

composed of at least 85 percent by weight of acrylonitrile units; or (2) modacrylic fiber, in 

which the fiber-forming substance is any long-chain synthetic polymer composed of at least 

35 percent but less than 85 percent by weight of acrylonitrile units. Emission sources from 

acrylic and modacrylic fiber production include storage vessels, process vents, spinning lines, 

equipment leaks, and wastewater treatment systems. The primary hazardous air pollutant 

(HAP) emitted during acrylic and modacrylic fiber production is acrylonitrile. 

Emissions estimates for the AMF production source category (one facility) are summarized in 

Table 3.1-1. The total HAP emissions for the source category are approximately 32 tons per 

year. Based on these data, the HAP emitted in the largest quantity from this facility is 

acrylonitrile. This chemical accounts for 99 percent of the total emissions by mass. There are 

no reported emissions of persistent and bioaccumulative HAP (PB-HAP) from this facility. 

Table 3.1-1 Summary of Emissions from the Acrylic and Modacrylic Fibers Production Source 

Category Used in the Residual Risk Assessment and Availability of Dose-Response Values

HAP Emissions 

(tpy)

Number of 

Facilities 

Reporting 

HAP (4

facilities in 

data set)

Prioritized Inhalation Dose-Response 

Value Identified by OAQPSa

PBHAP?

Unit 

Risk 

Estimate 

for 

Cancer?

Reference 

Concentration 

for 

Noncancer?

Health 

Benchmark 

Values for 

Acute 

Noncancer?

Acrylonitrile 32 1   

Hydroquinone 0.002 1   

a

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

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

3.2 Risk Characterization

This section presents the results of the risk assessment for the AMF production source 

category. The basic chronic inhalation risk estimates presented here are the maximum 

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

We also present results from our acute inhalation impact screening assessment in the form of 

maximum hazard quotients. Also presented are the HAP “drivers,” which are the HAP that 

collectively contribute 90 percent of the maximum cancer risk or maximum hazard at the 

highest exposure location, as well as a summary of the results of our facility-wide assessments 

and our analysis of risks associated with the maximum allowed emissions under the current 

MACT standards. A detailed summary of the facility-specific risk assessment results is 

available in Appendix 4. 

Table 3.2-1 summarizes the chronic and acute inhalation risk results for the AMF production

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

up to 20 in a million, driven by acrylonitrile emissions from process vents. Approximately 

81,000 people were estimated to have cancer risks above 1 in a million, and 850 people were 
17

estimated to have cancer risks above 10 in a million. The total estimated cancer incidence 

from this source category is 0.006 excess cancer cases per year, or one excess case in every 

170 years. The estimated maximum chronic noncancer target organ specific hazard index 

(TOSHI) value for the source category is 0.1 associated with emissions of acrylonitrile, 

indicating low potential for chronic noncancer impacts.

Because we estimate that the actual emissions level is representative of the MACT-allowable 

level, the risk results for MACT-allowable emissions are the same as those for actual 

emissions. For more detail about MACT-allowable emission levels (and associated risks and 

impacts), see the memorandum, MACT Allowable Emissions and Risks for the Acrylic and 

Modacrylic Fibers, Amino/Phenolic Resins, and Polycarbonate Production Source 

Categories, in the docket for this rulemaking.

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Acrylic and Modacrylic 

Fibers Production

Result HAP “Drivers”

Facilities in Source Category

Number of Facilities Estimated to be in Source 

Category 1 n/a

Number of Facilities Identified in the NEI and 

Modeled in Preliminary Risk Assessment 1 n/a

Cancer Risks

Maximum Individual Lifetime Cancer Risk (in 1 

million) 20 acrylonitrile

Number of Facilities with Maximum Individual Lifetime Cancer Risk:

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

Greater than or equal to 10 in 1 million 1 acrylonitrile

Greater than or equal to 1 in 1 million 1 acrylonitrile

Chronic Noncancer Risks

Maximum Respiratory Hazard Index 0.1 acrylonitrile

Number of Facilities with Maximum Respiratory Hazard Index:

 Greater than 100 0 n/a

 Greater than 10 0 n/a

Greater than 1 0 n/a

Acute Noncancer Screening Results

Maximum Acute Hazard Quotient

0.08

0.04

0.01

0.006

acrylonitrile (AEGL-1)

acrylonitrile (ERPG-1)

acrylonitrile (ERPG-2)

acrylonitrile (AERGL-2)

Number of Facilities With Potential for Acute 

Effects 0 n/a

Population Exposure

Number of People Living Within 50 Kilometers 

of Facilities Modeled 1,020,000 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 850 n/a
18

Table 3.2-1 Summary of Source Category Level Inhalation Risks for Acrylic and Modacrylic 

Fibers Production

Result HAP “Drivers”

Greater than or equal to 1 in 1 million 81,000 n/a

Number of People Exposed to Noncancer Respiratory Hazard Index:

 Greater than 100 0 n/a

 Greater than 10 0 n/a

Greater than 1 0 n/a

Estimated Cancer Incidence (excess cancer cases 

per year) 0.006 n/a

Contribution of HAP to Cancer Incidence:

 acrylonitrile 100% n/a

The highest estimated worst-case acute HQ value is 0.08 (based on the acute AEGL-1 for 

acrylonitrile), indicating low potential for acute affects. See the memorandum entitled, Inputs 

to the Acrylic and Modacrylic Fibers Production Industry October 2011 Residual Risk 

Modeling for detailed description of how the maximum hourly emissions were developed for 

this source category (found in the docket for this rulemaking).

The facility-wide MIR and TOSHI are available in Appendix 4, and the results are 

summarized in Table 3.2-2. The results indicate that the facility-wide cancer MIR is 20 in a 

million, driven by AMF production. The facility-wide TOSHI is also driven by AMF 

production.

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

AMF Production

Number of Facilities Binned by Facility-Wide 

MIR (in 1 million)

Source Category MIR 

Contribution to Facility-Wide 

MIR

<1 1≤ MIR<10 10≤ MIR<100 > 100 Total

> 90% 0 0 1 0 1

50-90% 0 0 0 0 0

10-50% 0 0 0 0 0

< 10% 0 0 0 0 0

Total 0 0 1 0 1
19

4 General Discussion of Uncertainties and How They Have Been 

Addressed

4.1 Exposure Modeling Uncertainties

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

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

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

analysis. The chronic ambient modeling uncertainties are considered relatively small in 

comparison, since we are using EPA’s refined local dispersion model with site-specific 

parameters and reasonably representative meteorology. If anything, the population exposure 

estimates are biased high by not accounting for short- or long-term population mobility, and 

by neglecting processes like deposition, plume depletion, and atmospheric degradation. 

Additionally, estimates of the maximum individual risk (MIR) contain uncertainty, because 

they are derived at census block centroid locations rather than actual residences. This 

uncertainty is known to create potential underestimates and overestimates of the actual MIR 

values for individual facilities, but, overall, it is not thought to have a significant impact on 

the estimated MIR for a source category. Finally, we did not factor in the possibility of a 

source closure occurring during the 70-year chronic exposure period, leading to a potential 

upward bias in both the MIR and population risk estimates; nor did we factor in the possibility 

of population growth during the 70-year chronic exposure period, leading to a potential 

downward bias in both the MIR and population risk estimates.

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

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

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

than the predicted value. This variability translates directly to the predicted exposures and 

risks in our assessment, indicating that the actual risks could vary from 17percent lower to 

84percent higher than the predicted values.

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

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

human activity patterns. Furthermore, in cases where multiple acute threshold values are 

considered scientifically acceptable we have chosen the most conservative of these threshold 

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

cases where these results indicated the potential for exceeding short-term health thresholds, 

we have refined our assessment by developing a better understanding of the geography of the 

facility relative to potential exposure locations and the true variability of short-term emission 

rates. 

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 noncancer reference values. Cancer potency values are derived 

for chronic (lifetime) exposures. Noncancer reference values are generally derived for 
20

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

(>24 hours up to 30 days), and subchronic (>30 days up to 10 percent of lifetime) exposure 

durations, all of which are derived based on an assumption of continuous exposure throughout 

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

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

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

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

emitted by the source category included in an assessment, some HAP have no peer-reviewed 

cancer potency values or reference values for chronic noncancer or acute effects (inhalation or 

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

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

possible.

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

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

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

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

significantly mischaracterize human health risk

Cancer assessment

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

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

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

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

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

we provide a quote from the EPA’s Guidelines for Carcinogen Risk Assessment [31] (herein 

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

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

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

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

described in the Cancer Guidelines.

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

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

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

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

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

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

when other human cancer risk data are unavailable. 

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

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

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

recommendations for methodologies for cancer risk assessment. EPA believes that cancer 
21

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).6 In some circumstances, the true risk 

could be as low as zero; however, in other circumstances the risk could also be greater.7 

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.8 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 [32]. Some of the major sources of uncertainty and 

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

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

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

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

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

tumors would occur in humans.9 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. 

 

6

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

7 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.

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.

9 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.”
22

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

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

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

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

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

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

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

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

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

lead to either over or underestimation of risk.

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

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

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

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

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

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

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

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

test data and the correctness of scaled estimates. 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

consider a linear dose-response to be appropriate for pollutants that interact with DNA and 
23

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

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

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

approach. 

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

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

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

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

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

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

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

models) in representing dose response relationships due to the variability in response within 

human populations. Another National Research Council (NRC) report [33] 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) [34]. It is important to note that 

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

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

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

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

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

model.

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

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

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

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

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

stage differences in the quantitative potency of carcinogens and, with the exception of the 
24

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

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

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

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

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

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

of uncertainty and variability.

Chronic noncancer assessment

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

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

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

approach that is intended not to underestimate risk in the face of uncertainty and variability. 

When there are gaps in the available information, uncertainty factors (UFs) are applied to 

derive reference values that are intended to be protective against appreciable risk of 

deleterious effects. Uncertainty factors are commonly default values10 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 noncancer 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 

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

deleterious effects during a lifetime.11 To derive values that are intended to be “without 

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

[35],[36] which includes consideration of both uncertainty and variability.

 

 

10

 According to the NRC report Science and Judgment in Risk Assessment (NRC, 1994) “[Default] options are 

generic approaches, based on general scientific knowledge and policy judgment, that are applied to various 

elements of the risk-assessment process when the correct scientific model is unknown or uncertain.” The 1983 

NRC report, Risk Assessment in the Federal Government: Managing the Process defined default option as “the 

option chosen on the basis of risk assessment policy that appears to be the best choice in the absence of data to 

the contrary” (NRC, 1983a, p. 63). Therefore, default options are not rules that bind the agency; rather, the 

agency may depart from them in evaluating the risks posed by a specific substance when it believes this to be 

appropriate. In keeping with EPA’s goal of protecting public health and the environment, default assumptions 

are used to ensure that risk to chemicals is not underestimated (although defaults are not intended to overtly 

overestimate risk). See EPA 2004 An examination of EPA Risk Assessment Principles and Practices,

EPA/100/B-04/001 available at: http://www.epa.gov/osa/pdfs/ratf-final.pdf.

11 See IRIS glossary
25

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 [37]. While collectively termed “UFs”, these factors account for a number 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

adjustment steps are described more fully below.

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

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

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

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

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

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

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

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

among humans.

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

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

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

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

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

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

approach is to use the most relevant endpoint from the most sensitive species and the most 
26

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.

There is no RfD or other comparable chronic health benchmark value for lead compounds. 

Thus, to address multipathway human health and environmental risks associated with 

emissions of lead from these facilities, ambient lead concentrations were compared to the 

NAAQS for lead. In developing the NAAQS for lead, EPA considered human health 

evidence reporting adverse health effects associated with lead exposure, as well as an EPA 

conducted multipathway risk assessment that applied models to estimate human exposures to 

air-related lead and the associated risk (73FR at 66979). EPA also explicitly considered the 

uncertainties associated with both the human health evidence and the exposure and risk 

analyses when developing the NAAQS for lead. For example, EPA considered uncertainties 
27

in the relationship between ambient air lead and blood lead levels (73FR at 66974), as well as 

uncertainties between blood lead levels and loss of IQ points in children (73FR at 66981). In 

considering the evidence and risk analyses and their associated uncertainties, the EPA 

Administrator noted his view that there is no evidence- or risk-based bright line that indicates 

a single appropriate level. Instead, he noted, there is a collection of scientific evidence and 

judgments and other information, including information about the uncertainties inherent in 

many relevant factors, which needs to be considered together in making this public health 

policy judgment and in selecting a standard level from a range of reasonable values (73FR at 

66998). In so doing, the Administrator decided that, a level for the primary lead standard of 

0.15 μg/m3, in combination with the specified choice of indicator, averaging time, and form, 

is requisite to protect public health, including the health of sensitive groups, with an adequate 

margin of safety (73FR at 67006). A thorough discussion of the health evidence, risk and 

exposure analyses, and their associated uncertainties can be found in EPA’s final rule revising 

the lead NAAQS (73 FR 66970-66981, November 12, 2008). 

We also note the uncertainties associated with the health-based (i.e., primary) NAAQS are 

likely less than the uncertainties associated with dose-response values developed for many of 

the other HAP, particularly those HAP for which no human health data exist. In 1988, EPA’s 

IRIS program reviewed the health effects data regarding lead and its inorganic compounds 

and determined that it would be inappropriate to develop an RfD for these compounds, 

saying, “A great deal of information on the health effects of lead has been obtained through 

decades of medical observation and scientific research. This information has been assessed in 

the development of air and water quality criteria by the Agency's Office of Health and 

Environmental Assessment (OHEA) in support of regulatory decision-making by the Office 

of Air Quality Planning and Standards (OAQPS) and by the Office of Drinking Water 

(ODW). By comparison to most other environmental toxicants, the degree of uncertainty 

about the health effects of lead is quite low. It appears that some of these effects, particularly 

changes in the levels of certain blood enzymes and in aspects of children's neurobehavioral 

development, may occur at blood lead levels so low as to be essentially without a threshold. 

The Agency's RfD Work Group discussed inorganic lead (and lead compounds) at two 

meetings (07/08/1985 and 07/22/1985) and considered it inappropriate to develop an RfD for 

inorganic lead.” EPA’s IRIS assessment for Lead and compounds (inorganic) (CASRN 7439-

92-1), http://www.epa.gov/iris/subst/0277.htm. 

We note further that because of the multipathway, multi-media impacts of lead, the risk 

assessment supporting the NAAQS considered direct inhalation exposures and indirect airrelated multipathway exposures from industrial sources like primary and secondary lead 

smelting operations. It also considered background lead exposures from other sources (like 

contaminated drinking water and exposure to lead-based paints). In revising the NAAQS for 

lead, we note that the Administrator placed more weight on the evidence-based framework 

and less weight on the results from the risk assessment, although he did find the risk estimates 

to be roughly consistent with and generally supportive of the evidence-based framework 

applied in the NAAQS determination (73FR at 67004). Thus, when revising the NAAQS for 

lead to protect public health with an adequate margin of safety, EPA considered both the 

evidence-based framework and the risk assessment, albeit to different extents.
28

Acute noncancer assessment

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

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

using individual UF values that may be less than 10. UFs are applied based on chemicalspecific or health effect-specific information (e.g., simple irritation effects do not vary 

appreciably between human individuals, hence a value of 3 is typically used), or based on the 

purpose for the reference value (see the following paragraph). The UFs applied in acute 

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

extrapolating from animals to humans; 3) uncertainty in LOAEL to NOAEL adjustments; and 

4) uncertainty in accounting for an incomplete database on toxic effects of potential concern. 

Additional adjustments are often applied to account for uncertainty in extrapolation from 

observations at one exposure duration (e.g., 4 hours) to arrive at a POD for derivation of an 

acute reference value at another exposure duration (e.g., 1 hour). 

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

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

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

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

characterization as potential uncertainties. 

5 References

1. US EPA. National Emission Standards for Hazardous Air Pollutants. 

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

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

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

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

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

3. US EPA, 2010. SAB’s Response to EPA’s RTR Risk Assessment Methodologies. 

http://yosemite.epa.gov/sab/sabproduct.nsf/4AB3966E263D943A8525771F00668381/$File/E

PA-SAB-10-007-unsigned.pdf

4. US EPA, 2010. EPA’s Actions in Response to Key Recommendations of the SAB Review of 

RTR Risk Assessment Methodologies. Document ID: EPA-HQ-OAR-2010-0786-0036. 

http://www.regulations.gov

5. US EPA, 2005. Revision to the Guideline on Air Quality Models: Adoption of a Preferred

General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions; Final 

Rule. 40 CFR Part 51. http://www.epa.gov/EPA-AIR/2005/November/Day-09/a21627.htm

6. US EPA, 2004. Users’ guide for the AMS/EPA regulatory model – AERMOD. EPA-454/B03-001. http://www.epa.gov/scram001/7thconf/aermod/aermodugb.pdf.

7. US EPA, 2004. Air Toxics Risk Assessment Reference Library, Volume 1. EPA-453-K-04- 

 
29

 

001A. http://www.epa.gov/ttn/fera/risk_atra_vol1.html.

8. US EPA, 2012. Table 1. Prioritized Chronic Dose-Response Values (5/21/12). Office of 

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

9. US EPA, 2005. National Air Toxics Risk Assessment. http://www.epa.gov/ttn/atw/nata

10. US EPA, 2008. Integrated Risk Information System. http://www.epa.gov/iris/index.html.

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

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

12. CA Office of Environmental Health Hazard Assessment, 2000. Air Toxics Hot Spots 

Program, Risk Assessment Guidelines, Part III - Technical Support Document for the 

Determination of Non-cancer Chronic Reference Exposure Levels. February 2000

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

Inhalation Reference Concentrations and Application of Inhalation Dosimetry. 

EPA/600/8-90/066F. Office of Research and Development. Washington, DC: U.S.EPA.

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

Washington, DC: National Academy Press.

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

Exposure Levels Adopted by OEHHA as of December 2008. 

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

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

for Describing Available Cancer Potency Factors, May 2005. 

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

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

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

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

18. U.S. EPA, 2009. Graphical Arrays of Chemical-Specific Health Effect Reference Values for 

Inhalation Exposures [Final Report]. EPA/600/R-09/061, 2009. 

http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=211003

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

Exposure Levels developed by OEHHA as of May 2000. 

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

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

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

21. American Industrial Hygiene Association, 2011. Current AIHA ERPG Values. 

http://www.aiha.org/getinvolved/AIHAGuidelineFoundation/EmergencyResponsePlanningGuidelines/Pages/default.

aspx)
30

 

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

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

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

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

Utility, and Integrity of Information Disseminated by the Environmental Protection Agency. 

EPA Office of Environmental Information. EPA/260R-02-008. 

http://www.epa.gov/quality/informationguidelines/documents/EPA_InfoQualityGuidelines.pd

f

25. 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/sites/default/files/omb/assets/omb/memoranda/fy2007/m07-

24.pdf

26. 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

27. 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.

28. 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

29. 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

30. 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.

31. 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

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

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

Trichloroethylene. National Academies Press, Washington DC.

34. 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.
31

 

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

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

36. 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.

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

http://www.epa.gov/ncea/iris/RFD_FINAL1.pdf
Appendix 1 

Technical Support Document for HEM-3 Modeling 
Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 (HEM-3)

Updated 8/7/2008 

Technical Support Document 

Prepared for: 

U. S. Environmental Protection Agency 

Office of Air and Radiation 

Office of Air Quality Planning and Standards 

Emissions Standards Division, Risk and Exposure Group 

Research Triangle Park, NC 27711 

Prepared by: 

EC/R Incorporated 

501 Eastowne Drive, Suite 250 

Chapel Hill, North Carolina 27514 
ii 

Disclaimer

The research described in this document has been funded by the 

United States Environmental Protection Agency contracts 68-D01-076 and 68-D-06-119 to EC/R Incorporated. Although it has 

been subject to the Agency's review, it does not necessarily reflect 

the views of the Agency, and no official endorsement should be 

inferred. 
iii 

Contents

1. Introduction .................................................................................................................................1

2. Overview of the HEM3– AERMOD System..............................................................................2

2.1 Preparation of Dispersion Modeling Inputs ........................................................................3

2.1.1 Compiling Emission Source Data ..............................................................................3

2.1.2 Defining the Modeling Domain and Receptors .........................................................4

Treatment of Nearby Census Blocks and Screening for Overlapping Blocks ...............4

Polar receptor network ...................................................................................................5

Elevations and hill heights for model receptors .............................................................5

2.1.3 Selection of Meteorological Data ..............................................................................6

2.2 Running of AERMOD ........................................................................................................6

2.2.1 AERMOD Dispersion Options Used by HEM3 ........................................................6

2.2.2 Use of Dilution Factors ..............................................................................................7

2.3 Postprocessing of AERMOD Results in HEM3 .................................................................7

2.3.1 Calculation of Impacts at Modeled Receptors ...........................................................8

2.3.2 Interpolation of Impacts at Outer Census Blocks ......................................................9

2.3.3 Calculation of Population Exposures and Incidence ...............................................10

2.3.4 Model Outputs .........................................................................................................10

2.4 Data Libraries Used in HEM3 ..........................................................................................11

2.4.1 Chemical Health Effects Information ......................................................................11

2.4.2 Census Block Locations and Elevation Data ...........................................................12

2.4.3 Meteorological Data .................................................................................................12

2.4.4 Gaseous Deposition Parameters ...............................................................................13

3. Modeling for the Residual Risk Technology Review ..............................................................14

3.1 Emission Source Inputs .....................................................................................................14

3.2 Pollutant Cross-Referencing .............................................................................................15

3.3 Meteorological Data..........................................................................................................15

3.4 Model Options Selected ....................................................................................................18

3.4.1 Urban or Rural Dispersion Characteristics ..............................................................19

3.4.2 Deposition and Plume Depletion .............................................................................19

3.4.3 Cutoff Distance for Modeling of Individual Blocks ................................................20

3.4.4 Plant Boundary Assumptions ...................................................................................20

3.5 Modeling of Multiple Facilities ........................................................................................20

4. Quality Assurance .....................................................................................................................21

4.1 Engineering Review ..........................................................................................................21

4.2 Geographic Checks ...........................................................................................................21

5. Uncertainties .............................................................................................................................24
iv 

6. References .................................................................................................................................31

 

Figures

Figure 3-1. Meteorological data available in the HEM3 library for AERMOD. ..........................18

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on plant 

property. .............................................................................................................................23

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM3 ...................................4

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II Modeling Effort 

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

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in the 

Residual Risk and Technology Review Assessments ........................................................19

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

Risk Assessment ................................................................................................................26
1 

1. Introduction

 This document describes the modeling approach used to estimate the risks to human 

populations in support of the Residual Risk and Technology Review currently being carried out 

by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments 

was the Human Exposure Model, Version 3 (HEM3). HEM3 incorporates the AERMOD, a state 

of the art air dispersion model developed under the direction of the American Meteorological 

Society / Environmental Protection Agency Regulatory Model Improvement Committee 

(AERMIC). 

 Section 2 of this report provides an overview of the HEM3 system; and Section 3 

describes inputs and choices made in implementing the model for the RTR program. Quality 

assurance efforts undertaken in the modeling effort are discussed in Section 4, and uncertainties 

associated with the modeling effort are discussed in Section 5. 
2 

2. Overview of the HEM3– 

AERMOD System

 HEM3 performs three main operations: dispersion modeling, estimation of population 

exposure, and estimation of human health risks. Two options are provided for dispersion 

modeling. The first is the American Meteorological Society (AMS) EPA Regulatory Model 

(AERMOD).1,2 The second option is the Industrial Source Complex - Short Term, Version 3 

(ISCST3) model. Both of these dispersion models handle a wide range of different source types 

which may be associated with an industrial source complex, including stack sources, area 

sources, and volume sources. AERMOD was used for the RTR modeling effort. 

 To prepare dispersion modeling inputs and carry out risk calculations, HEM3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 120 stations, which are used for dispersion calculations. A second library of 

Census block internal point locations and populations provides the basis of human exposure 

calculations (Census, 2000). This Census library also includes the elevation of each Census 

block, which can also be used in dispersion calculations, at the option of the model user. A third 

library of pollutant unit risk estimates and reference concentrations is used to calculate 

population risks. These unit risk estimates and reference concentrations are based on the latest 

values recommended by EPA for hazardous air pollutants (HAP) and other toxic air pollutants 

(EPA, 2007). The fourth data library is used only with AERMOD and only when the user opts to 

compute deposition or plume depletion. This database provides deposition parameters for 

gaseous pollutants. 

 HEM3 has been implemented in two versions: a single-plant version, and a community 

and sector version. The latter was used in this modeling effort. Both versions operate under the 

same general principles. In essence, the community and sector version provides a platform for 

running the single plant version multiple times. In both versions, source location and emissions 

data are input through a set of ExcelTM spreadsheets. The main difference is in the user interface 

for other model inputs. The single plant includes a graphical user interface (GUI) for the 

selection of various dispersion modeling options. In the community and sector version of 

HEM3, a control file replaces many of these GUI inputs. 

 The model estimates cancer risks and noncancer adverse health effects due to inhalation 

exposure at Census block internal point locations, and at other receptor locations that can be 

specified by the user. Cancer risks are computed using EPA’s recommended unit risk estimates 

for Hazardous Air Pollutants (HAP) and other toxic air pollutants. The resulting estimates 

reflect the excess cancer risk for an individual breathing the ambient air at a given receptor site 

24-hours per day over a 70-year lifetime. The model estimates the numbers of people exposed to 

various cancer risk levels. In addition, HEM3 estimates the total incremental cancer risks for 

people living within different distances of the modeled emission sources. 
3 

 Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” for a given organ is the sum of hazard quotients for 

substances that affect that organ. HEM3 computes target-organ-specific hazard indices (TOSHI) 

for HAPs and other toxic air pollutants, and estimates the numbers of people exposed to different 

hazard index levels. In addition, maximum short term concentrations are computed for all 

pollutants, and concentrations are compared with threshold levels for acute health effects. 

 The following sections outline the methodologies used in the HEM3–AERMOD system. 

Section 2.1 describes the preparation of dispersion modeling inputs, Section 2.2 describes the 

running of AERMOD, Section 2.3 describes calculations performed by HEM3 to calculate risks 

and exposures, and Section 2.4 details the sources and methods used to produce HEM3’s data 

libraries. The HEM3 User’s Manual provides additional details on the input data and algorithms 

used in the model.3

 Specific model options used in the RTR are discussed in Chapter 3. 

2.1 Preparation of Dispersion Modeling Inputs

 HEM3 compiles data that will be needed for dispersion modeling, and prepares an input 

file suitable for running AERMOD. The dispersion modeling inputs can be divided into three 

main components: emission source data, information on the modeling domain and receptors for 

which impacts will be computed, and meteorological data. 

2.1.1 Compiling Emission Source Data

 A series of ExcelTM spreadsheet files are used to specify the emissions and configuration 

of the facility to be modeled. At a minimum, two files are needed: a pollutant emission file, and 

an emission location file. The emission file includes an emission source identification code for 

each emission source at the facility, the names of pollutants emitted by each source, and the 

emission rate for each pollutant. In addition, if the model run is to incorporate deposition or 

plume depletion, the emission file must also specify the percentage of each pollutant that is in the 

form of particulate matter. The balance is assumed to be in vapor form. 

 The emission location file includes the coordinates of each source, as well as information 

on the configuration and other characteristics of the source. HEM3 can analyze stack sources, 

area sources, or volume sources.a

 For stack sources, the location file must provide the stack 

height, stack diameter, emission velocity, and emission temperature. The file must also provide 

dimensions for each area or volume source, as well as the height of the source above the ground. 

For area sources, the angle of rotation from north can also be specified. The user can also 

 

I

For definitions of stack, area, and volume sources, please see the AERMOD documentation. AERMOD 

provides the capability to analyze some other emission source configurations, such as storage piles, however these 

configurations are not supported by HEM3.
4 

provide the terrain elevation at the base of each source, and the controlling hill height for each 

source. (The controlling hill height is used in AERMOD’s flow calculations. Calculation of the 

hill height is discussed in Section 2.4.2.) If these are not provided, HEM3 will calculate terrain 

elevations and controlling hill heights based on elevations and hill heights for the Census blocks 

nearest to the facility. 

 If particulate deposition or plume depletion are to be considered, then HEM-3 requires a 

third input file to specify the particle size distribution and scavenging parameters for various 

particle size ranges. Another optional file can be used to specify building dimensions if building 

wake effects are to be modeled. 

2.1.2 Defining the Modeling Domain and Receptors

 HEM3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user. These parameters are summarized in Table 2-1. The modeling 

domain is circular, and is centered on the facility, with a radius specified by the user. For the 

RTR analysis, the radius of the modeling domain is 50 kilometers (km). HEM3 identifies all of 

the Census block locations in the modeling domain from its Census database, and divides the 

blocks into two groups based on their distance from the facility. For the inner group of Census 

blocks (closest to the facility), each block location is modeled as a separate receptor in 

AERMOD. The cutoff distance for modeling individual Census blocks is determined by the 

model user. The model user can also provide an ExcelTM spreadsheet specifying additional 

locations to be included as model receptors in AERMOD. These additional discrete receptors 

may include plant boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

 For Census blocks in the outer group, 

beyond this modeling cutoff distance, 

emissions impacts are interpolated based on 

modeling results for a polar receptor network. 

The user also specifies an “overlap” distance, 

within which Census block coordinates will 

be considered to be on plant property. The 

following paragraphs provide more details on 

the treatment of blocks near the facility, on 

the polar receptor network, and on the 

determination of receptor elevations and 

controlling hill heights to be used in 

AERMOD. 

Treatment of Nearby Census Blocks and 

Screening for Overlapping Blocks

 Census block locations near the 

facility are modeled as separate receptors 

Table 2-1. Parameters Used to Delineate 

the Modeling Domain in HEM3

Parameter 

Typical 

value 

Modeling domain size – maximum 

distance to be modeled a 50 km 

Cutoff distance for modeling of 

individual blocks b

2,000 – 

3,000 m

Overlap distance – where receptors will 

be considered to be on plant property b 30 m 

Polar receptor network 

Distance to the innermost ring a 100 m 

Number of concentric rings 13 

Number of radial directions 16 

 aMeasured from the center of the facility. 

 b

Measured from each stack at the facility, and 

from the edges of each area or volume source. 
5 

within AERMOD. The cutoff distance for modeling of individual Census blocks is chosen by 

the user, and is tpically between 2000 and 3000 meters. This distance is not measured from the 

center of the facility, but is the minimum distance from any source at the facility. Therefore, any 

Census block location that is within the cutoff distance from any emission source is treated as a 

discrete AERMOD receptor. 

 HEM3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any area or volume emission sources. In addition, the user can specify an overlap 

distance, within which receptors will be considered to be on plant property. The default value 

for the overlap distance is 30 meters, or approximately equal to the width of a narrow buffer and 

a roadway. HEM3 tests each nearby receptor to determine whether it is within this distance from 

any stack or from the perimeter of any area or volume source. If a receptor falls within this 

distance, HEM-3 will not calculate risks based on the location of that receptor, but will instead 

assume that the risks associated with the receptor are the same as the highest predicted value for 

any receptor that is not overlapping. The location for calculating the default impact may be 

either another Census block, one of the polar grid receptors, or one of the additional discrete 

receptor locations provided by the model user. 

Polar receptor network

 The polar receptor network used in HEM3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance is 

used to represent the plant boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in unpopulated locations outside the plant boundary. 

Third, the polar receptor network is used to interpolate long-term and short-term impacts at 

Census block locations that are outside the cutoff distance for modeling of individual blocks. 

 The user defines the inner radius for the polar receptor network, the number of concentric 

rings to be analyzed, and the number of radial directions. The inner radius of the polar network 

should the minimum distance from the facility center that is generally outside of plant property. 

(For complex plant shapes, it is sometimes useful to specify an inner ring that encroaches on 

facility property in some directions.) The default, and also the minimum inner radius for the 

polar network is 100 meters (m). A typical run includes 13 concentric rings and 16 radial 

directions. HEM3 will distribute the radial directions evenly around the plant. For the 

concentric rings, the model will generate logarithmic progression of distances starting at the 

inner ring radium and ending at the outer radius of the modeling domain. In the single plant 

version of HEM3, the user can change these ring distances to meet the needs of a specific study. 

Elevations and hill heights for model receptors

 The user can specify whether HEM3 will include the effects of terrain when running 

AERMOD. If the terrain option is selected, HEM3 obtains elevations and controlling hill heights 

for Census block receptors from its internal Census location library. Section 2.4.2 describes the 

derivation of these elevations and hill heights. 
6 

 Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM3 divides the modeling domain into sectors based 

on the polar receptor network, with each Census block assigned to the sector corresponding to 

the closest polar grid receptor. Each polar grid receptor is then assigned an elevation based on 

the highest elevation for any Census block in its sector. The controlling hill height is also set to 

the maximum hill height within the sector. If a sector does not contain any blocks, the model 

defaults to the elevation and controlling hill height of the nearest block outside the sector. 

2.1.3 Selection of Meteorological Data

 In addition to source and receptor information, AERMOD requires surface and upper air 

meteorological observations in a prescribed format. The model user can select a meteorological 

station from the HEM3 meteorological data library, or add new files to the library if site-specific 

data are available. If the user does not specify a meteorological station, HEM3 will select the 

closest station to the center of the modeling domain. 

2.2 Running of AERMOD

 Based on the user input data and other data described in the previous section, HEM3 

produces an input file suitable for AERMOD. HEM3 then runs AERMOD as a compiled 

executable program. No changes have been made from the version of AERMOD released to the 

public by EPA. The following sections give additional information on how AERMOD is used 

within HEM3. 

2.2.1 AERMOD Dispersion Options Used by HEM3

 AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM3 uses the regulatory default options when running AERMOD.1 

These options include the following: 

! consideration of stack-tip downwash (except for Schulman-Scire downwash) 

! use of calm processing routines 

! use of missing-data processing routines 

! consideration of terrain effects 

However, the model user can opt to exclude the effects of terrain when running HEM3. The 

following additional options are available to the HEM3 user: 

! calculation of wet and dry deposition rates for vapor and particulate mater 

! consideration of plume depletion (due to deposition) when calculating air concentrations 

! consideration of building wake effects 

! calculation of short term impacts 

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 
7 

 The user can opt to analyze short term impacts on a number of different time scales (e.g. 

1 hour, 2 hours, etc.), however only one short term time scale can be selected per run. If the user 

chooses to analyze short term impacts, a multiplier must be specified to reflect the ratio between 

the maximum short term emission rate and the long term average emission rate. The default 

multiplier for short term emissions is a factor of 10. This means that in the default case the 

maximum short term emission rate is assumed to be 10 times the long term average emission 

rate. The multiplier can be set to 1.0 if emissions from the facility are known to be constant. 

 HEM3 uses the AERMOD TOXICS run option, which optimizes the area source 

integration routine to reduce model runtimes. The TOXICS Sampled Chronological Input Model 

(SCIM) is not used, since the HEM3 AERMOD meteorological library only includes one year of 

observations for each station. (The SCIM option can be used to skip a portion of the hourly 

observations when meteorological data are available for multiple years.) 

2.2.2 Use of Dilution Factors

 To save computer run time when analyzing the impacts of multiple pollutants, HEM3 

does not model each pollutant separately. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. The dilution factor for a 

particular emission source and receptor is defined as the predicted ambient impact from the given 

source and at the given receptor, divided by the emission rate from the given source. 

 If the user chooses not to analyze deposition or plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If either deposition or depletion is chosen with the 

AERMOD dispersion model option of HEM3, separate dilution coefficients must be computed 

for each gaseous pollutant. In addition, separate dilution factors must be computed for different 

components of particulate matter if the components do not have the same particle size 

distribution. In the current version of HEM3, this can be done by creating a separate emission 

record for each pollutant emitted by from each source. (Common location data and source 

configurations can be used for different pollutant records representing the same emission 

source.) 

2.3 Postprocessing of AERMOD Results in HEM3

 HEM3 estimates total excess cancer risks and potential chronic noncancer health effects 

for all Census block locations in the modeling domain, all user-defined receptors, and all points 

in the polar receptor network. Potential chronic noncancer health effects are expressed in terms 

of TOSHI. Based on the results for Census blocks and other receptors, HEM-3 estimates the 

maximum individual risk and maximum TOSHI for populated receptors, and determines the 

locations of these maximum impacts. The model also determines the concentrations of different 

pollutants at the sites of the maximum risks, and the contributions of different emission sources 

to these maximum estimated risks. It should be noted that the locations may differ for the 

maximum individual cancer risk and for the hazard indices for different target organs. 
8 

 For acute impacts, HEM3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various thresholds for acute health effects. 

 At the option of the model user, HEM3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the plant at all of the receptors in 

the modeling domain. In addition, pollutant contributions from each emission source at the 

facility are computed under this option. 

 Section 2.3.1. describes methods used to calculate cancer risks and hazard indices for 

receptors that are explicitly modeled using AERMOD. Section 2.3.2 describes the interpolation 

approach used to estimate cancer risks and hazard indices at Census blocks that are not explicitly 

modeled. 

2.3.1 Calculation of Impacts at Modeled Receptors

 As noted in Section 2.2.2, HEM3 does not model each pollutant separately unless 

deposition or depletion is being analyzed. Instead, AERMOD is used to compute a series of 

dilution factors, specific to each emission source and receptor. HEM3 also conserves computer 

memory by computing cancer risks and hazard indices directly, without recording the 

concentration of each pollutant at each receptor. The following algorithms are used to compute 

cancer risks and TOSHI for chronic noncancer health effects. 

For cancer risk: 

CRT = i, j CRi, j

CRi, j = DFi, j  CF  k [Ei, k  UREk]

For TOSHI: 

TOSHIT = i, j TOSHIi, j

TOSHIi, j = DFi, j  CF  k [Ei, k  RfCk ]

where: 

 CRT = total cancer risk at a given receptor (probability for one person) 

i, j = the sum over all sources i and pollutant types j (particulate or gas) 

 CRi, j = cancer risk at the given receptor for source i and pollutant type j 

 DFi, j = dilution factor [(g/m3

) / (g/sec)] at the given receptor for source i and pollutant 

type j 

 CF = conversion factor, 0.02877 [(g/sec) / (ton/year)] 

k = sum over all pollutants k within pollutant group j (particulate or gas) 

 Ei, k = emissions of pollutant k from source i and in pollutant type j 

 UREk = cancer unit risk estimate for pollutant k 
9 

 TOSHIT = total target-organ-specific hazard index at a given receptor 

 TOSHIi, j = target-organ-specific hazard index at the given receptor for source i and 

pollutant type j 

 RfCk = noncancer health effect reference concentration for pollutant k 

 If the cancer unit risk estimate is not available for a given chemical, then that chemical is 

not included in the calculation of cancer risk. Likewise, if the noncancer reference concentration 

is not available for a given chemical, that chemical is not included in the calculation of hazard 

indices. Note also that separate reference concentrations are used for acute and chronic hazard 

indices. 

 HEM3 computes short term concentrations and records the highest short term 

concentration for each pollutant. In addition, the user can opt to compute and record the short 

term and long concentrations at each receptor. Concentrations are computed as follows. 

Long term concentrations: 

ACT, k = i ACi, k

ACi, k = DFi, j  CF  Ei, k

Short term concentrations: 

ACT = i ACi, k

ACi, k = DFi, j  CF  Ei, k  M 

where: 

 ACT, k = total estimated ambient concentration for pollutant k at a given receptor 

i = the sum over all sources i (g/m3) 

 ACi, k = estimated ambient concentration of pollutant k at the given receptor as a 

result of emissions from source i (g/m3

) 

 M = ratio between the estimated maximum short term emission rate and the long term 

average emission rate (dimensionless) 

2.3.2 Interpolation of Impacts at Outer Census Blocks

 For Census blocks outside of the distance cutoff for individual block modeling, HEM-3 

estimates cancer risks and hazard indices by interpolation from the polar receptor network. 

Impacts at the polar grid receptors are estimated using AERMOD modeling results and the 

algorithms described in Section 2.3.1. The impacts at each outer Census block are interpolated 

from the four nearest polar grid receptors. The interpolation is linear in the angular direction, 

and logarithmic in the radial direction, as summarized in the following equations: 

Ia, r = I1, r + (I2, r – I1, r)  (a – A1)  (A2 – A1) 
10 

IA1, r = exp{ln(IA1,R1) + [ln(IA1,R2) – ln(IA1,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

IA2, r = exp{ln(IA2,R1) + [ln(IA2,R2) – ln(IA2,R1)]  [ln r) – ln(R1)]  [ln(R2) – ln(R1)]} 

where: 

 Ia, r = the impact (cancer risk, hazard index, or concentration) at an angle, a, from north, 

and radius, r, from the center of the modeling domain 

 a = the angle of the target receptor, from north 

 r = the radius of the target receptor, from the center of the modeling domain 

 A1 = the angle of the polar network receptors immediately counterclockwise from the 

target receptors 

 A2 = the angle of the polar network receptors immediately clockwise from the target 

receptor 

 R1 = the radius of the polar network receptors immediately inside the target receptor 

 R2 = the radius of the polar network receptors immediately outside the target receptor 

2.3.3 Calculation of Population Exposures and Incidence

 Using the predicted impacts for Census blocks, HEM3 estimates the numbers of people 

exposed to various cancer risk levels and TOSHI levels. This is done by adding up the 

populations for receptors that have predicted cancer risks or TOSHI above the given threshold. 

 The model also estimates the total annual excess cancer risk (incidence) for the entire 

modeling region. The following equation is used: 

TCR = m [CRm  Pm ]  LT 

where: 

 TCR = the estimated total annual cancer risk, or incidence, (cancers/year) to the 

population living within the modeling domain 

m = the sum over all Census blocks m within distance the modeling domain 

 CRm = the total lifetime cancer risk (from all modeled pollutants and emission sources) at 

Census block m 

 Pm = the population at Census block m 

 LT = the average lifetime used to develop the cancer unit risk factor, 70 years 

2.3.4 Model Outputs

 The following is a summary of the outputs produced by HEM3. These are written to a 

collection of files in ExcelTM and dBaseTM format. 

! Maximum long term impacts at populated locations 

" maximum lifetime individual cancer risk 

" maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 
11 

- neurological system effects 

- developmental effects 

- reproductive system effects 

- kidney effects 

- ocular system effects 

- endocrine system effects 

- hematological system effects 

- immunological system effects 

- skeletal system effects 

- spleen effects 

- thyroid effects 

" locations of the maximum cancer risk and TOSHI 

" Census block identification codes for the maximum cancer risk and TOSHI, and 

number of people in the Census block 

" contributions of different chemicals and emission sources to the maximum risk 

and TOSHI 

! Maximum acute impacts 

" maximum short term ambient concentration for each chemical 

" threshold levels for acute health effects of each chemical (compared with the 

maximum short term concentrations) 

" locations of the maximum impacts for different chemicals 

" Census block identification codes at the locations of maximum concentration, and 

number of people in the block 

" contribution of each emission source at the facility to the maximum short term 

concentration of each chemical 

! Outputs for all receptors 

" maximum individual cancer risk and TOSHI (all target organs) for each Census 

block and each user-specified discrete receptor (monitoring sites, etc.) 

" maximum individual cancer risk and TOSHI (all target organs) for each polar grid 

receptor 

" estimated deposition (optional) 

" predicted ambient concentration resulting from each emission source at each 

Census block (optional) 

! Population exposures and total cancer risk, or incidence 

" estimated numbers of people exposed to different levels of lifetime individual 

cancer risk (1 in a million, 1 in 100,000, etc.) 

" estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.) 

" total cancer risk, or incidence, in estimated cancer deaths per year, over the entire 

modeling domain 

2.4 Data Libraries Used in HEM3

2.4.1 Chemical Health Effects Information
12 

 HEM3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available: 

! unit risk estimate for cancer 

! reference concentration for chronic noncancer health effects 

! reference concentrations for acute health effects 

! target organs affected by the chemical for chronic health effects 

Unit risk estimates and reference concentrations included in the HEM3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs.4

2.4.2 Census Block Locations and Elevation Data

 The HEM3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 5 million Census blocks 

identified in the 2000 Census. The location coordinates reflect the internal point of the block, 

which is a point selected by the Census to be roughly in the center of the block. For complex 

shapes, the internal point may not be in the geographic center of the block. Locations and 

population data for Census blocks in the 50 states and Puerto Rico were extracted from the 

LandView® database.5

 Locations and populations for blocks in the Virgin Islands were obtained 

from the Census web site. 

 The elevation of each Census block in the continental U.S. and Hawaii was estimated by 

interpolating the elevation at the block’s internal point from the U.S. Geological Service’s 

1:250,000 Digital Elevation Model (DEM) data.6 These data have a resolution of 3 arc seconds, 

or about 90 meters. The estimation of the elevation of each Census block in Alaska and the U.S. 

Virgin Islands was performed with analysis tools in the ArcGis 9.1 software application. The 

point locations of the Census block centroids in Alaska and the Virgin Islands were overlaid with 

a raster layer of North American Digital Elevation Model elevations (in meters). Each Census 

block point was assigned an elevation value based on the closest point in the ArcGis elevation 

raster file. 

 Hill heights were determined with base on the algorithm used in AERMAP, the 

AERMOD terrain processor.7

 In order to save run time and resources, the HEM3 Census block 

elevation database was substituted for the DEM data generally used in AERMAP. As noted 

above, the Census block elevations were originally derived from the DEM database. To 

determine the controlling hill height for each Census block, a cone was projected away from the 

block location, representing a 10% elevation grade. The controlling hill height was selected 

based on the highest elevation above that 10% grade (in accordance with the AERMAP 

methodology). The distance cutoff for this calculation was 100 km. (This corresponds to an 

elevation difference, at a 10% grade, of 10,000 m, which considerably exceeds the maximum 

elevation difference in North America.) 

2.4.3 Meteorological Data
13 

 HEM3 includes an extensive library of meteorological data to support both the 

AERMOD and ISCST3 dispersion models. 150 meteorological stations have been preprocessed 

for AERMOD as part of the RTR effort. In addition, 87 stations have previously been processed 

for ISCST3. Section 3.3 discusses the preparation of meteorological data for the RTR. 

2.4.4 Gaseous Deposition Parameters

 HEM3 provides options to compute the deposition of air pollutants, and to take into 

account the impacts of plume depletion due to deposition of gaseous and particulate pollutants. 

If either of these options is selected by the model user for gaseous pollutants, a number of 

pollutant properties are required by AERMOD. These include the diffusivity of the pollutant in 

air, the diffusivity of the pollutant in water, the Henry’s Law constant, and a parameter reflecting 

the cuticular resistance to uptake of the pollutant by leaves rCL).8

 HEM3 includes a library of 

these parameters for most gaseous HAPs. This library is based on an compendium of gaseous 

deposition parameters developed by Argonne National Laboratories.9

 It should be noted, 

however, that the deposition and depletion options of HEM3 and AERMOD were not used in the 

this component of the RTR analysis. 
14 

3. Modeling for the Residual Risk 

Technology Review 

 This section discusses the general approach used to implement the AERMOD version of 

HEM3 for the RTR modeling analysis. Separate reports have been prepared for all of the emission 

source categories included in the RTR analysis. These reports provide information on the 

emissions inputs and results for specific emission categories. 

3.1 Emission Source Inputs

 HEM3 and AERMOD require detailed data on emissions from each emission source 

included in the modeling analysis. These data include: 

! pollutants emitted 

! emission rate for each pollutant 

! emission source coordinates 

! stack height (or emission height for fugitive sources) 

! stack diameter (or configuration of fugitive sources) 

! emission velocity 

! emission temperature 

 Emissions data for the modeling effort were taken from the 2002 National Emissions 

Inventory (NEI) Final Version 1.10 These data include HAP emission rates, emission source 

coordinates, stack heights, stack diameters, flow rates, and exit temperatures. EPA performed an 

engineering review of the NEI data. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. For each source category, the emissions are summarized in the source category specific 

report. Detailed computer files containing all emission and release characteristics are available on 

the RTR website. 

 As noted in the previous section, industrial emission sources can be characterized in 

AERMOD and other dispersion models as point, area, or volume sources. The NEI includes a 

framework for characterizing area and volume sources; however, these fields are not filled in the 

2002 Final Version 1 of the NEI. Therefore, fugitive emissions are generally characterized as low 

point sources with minimal exit velocities. For some categories, additional information was 

available on the configuration of fugitive emission sources. This information was incorporated 

into the emissions database as part of the engineering review. Thus, fugitive emission sources 

were characterized as area or volume sources when sufficient configuration information was 

available. 
15 

3.2 Pollutant Cross-Referencing

 Because the NEI is developed from a number of different data sources, a single chemical 

may be listed in the inventory under different names (i.e. a “common name” and one or more 

structure-based names). In addition, pollutant groupings such as polycyclic organic matter 

(POM), can be listed in the NEI under the names of individual member compounds, and under 

different synonyms (e.g. polynuclear aromatic hydrocarbons). HEM3 requires an exact match in 

the chemical name in order to link emissions to the appropriate dose-response factors. The model 

will not process any pollutant that is not specifically listed in the chemical library (see Table 2-1). 

Therefore, all of the HAP names used in the NEI were linked to the appropriate chemical names in 

the HEM3 reference file. 

 The individual POM chemicals listed in the NEI were grouped into seven subclasses. 

Table 3-1 shows these groupings, and gives the cancer unit risk estimate used for each subclass. 

Emissions of metal compounds were also adjusted using algorithms developed for the Emissions 

Modeling System for Hazardous Air Pollutants (EMS-HAP) under the National-scale Air Toxics 

Assessment (NATA). A mass adjustment factor was applied to the emissions of metal compounds 

to account for a particular portion (e.g., the lead portion of lead sulfate) or to partition them among 

multiple pollutant categories (e.g. chromium arsenate into chromium VI compounds and arsenic 

compounds). In addition, where no specific compound information was available, metals were 

speciated into appropriate oxidation states (e.g. chromium compounds into chrome VI and chrome 

III) based on factors that have been developed for specific source categories and average factors 

that have been developed for the inventory as a whole. The adjustment factors and speciation 

factors were taken from the HAP Table module of EMS-HAP.11 ,12

3.3 Meteorological Data

 AERMOD meteorological data for the RTR analysis were derived from three sources: (1) 

AERMET processing of 122 National Weather Service (NWS) surface observation stations, (2) 

State agencies, and (3) industry (on-site). The 122 NWS sites were selected to provide thorough 

coverage of the U.S. For the RTR analysis, the average distance between a modeled facility and 

the applicable meteorological station was 72 km. One year of hourly data for these sites were 

processed through the AERMET program, and this is documented in “Meteorological Data 

Processing Using AERMET.”13 Four states provided AERMOD-ready meteorology files. These 

states were: Alabama (5 stations), Florida (4 stations), North Carolina (6 stations), and Wisconsin 

(22 stations). Eleven of the state supplied meteorology sites were co-located with sites in the list 

of 122. The data at the state supplied sites were more recent, so the state data for the 11 co-located 

sites were used. In addition, for the RTR petroleum refinery modeling, two refineries (Fairbanks, 

Alaska and the Virgin Islands) provided on-site AERMOD-ready meteorology files. These on-site 

meteorological data files were used with their respective refinery facilities for the HEM3 refinery 

modeling. Figure 3-1 shows the location of the 150 surface meteorological stations used in the 

RTR analysis. 

 As noted above, 122 NWS stations were processed through the AERMET program. To 

estimate the parameters needed by AERMOD, AERMET requires hourly surface weather 
16 

observations and the full twice-daily upper air soundings (i.e., meteorological variables reported at 

all levels). The surface and upper air stations are paired to produce the required input data for 

AERMOD. To simplify processing and minimize the amount of quality assurance needed, the 

analysis was restricted to meteorological data collected prior to the installation of the Automated 

Surface Observation System (ASOS). The ASOS has previously been found to omit the ceiling 

height for a large percentage of the observations at a number of meteorological stations. 

Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 were 

processed with AERMET (note; the most recent version of AERMET has corrected this issue and 

is now compatible with ASOS data, processing of meteorological data in the future will utilize this 

revised version of AERMET and more recent years of meteorology). Data were retrieved from 

products available from the National 

Table 3-1. Groupings Used for Polycyclic Organic Matter in the RTR Phase II 

Modeling Effort

POM subgroup Names used in the NEI 

Cancer URE 

used for the 

subgroup 

(m3/g) 

POM 71002 16-PAH 0.000055 

 PAH, total 0.000055 

Polycyclic Organic Matter 0.000055 

POM72002 2-Chloronaphthalene 0.000055 

 2-Methylnaphthalene 0.000055 

 Acenaphthene 0.000055 

 Acenaphthylene 0.000055 

 Anthracene 0.000055 

 Benzo(c)phenanthrene 0.000055 

 Benzo[e]Pyrene 0.000055 

 Benzo[g,h,i,]Perylene 0.000055 

 Fluoranthene 0.000055 

 Fluorene 0.000055 

 Perylene 0.000055 

 Phenanthrene 0.000055 

 Pyrene 0.000055 

POM 73002 7,12-Dimethylbenz[a]Anthracene 0.1 

POM 74002 3-Methylcholanthrene 0.01 

POM 75002 5-Methylchrysene 0.001 

 Benzo[a]Pyrene 0.001 

 Dibenzo[a,h]Anthracene 0.001 

POM 76002 B[j]Fluoranthen 0.0001 

 Benz[a]Anthracene 0.0001 

 Benzo[b]Fluoranthene 0.0001 

 Benzo[k]Fluoranthene 0.0001 

 Indeno[1,2,3-c,d]Pyrene 0.0001 
17 

POM 77002 Chrysene 0.00001 

Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the Hourly United 

States Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems Laboratory 

(FSL). 

 The input to and output from AERMET were examined for indications of missing input 

data. The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate the 

convective boundary layer height and several associated parameters. There were many isolated 

days in which the 1200 GMT sounding was missing for many of the stations. However there also 

were several stations for which there were two or more consecutive days of missing 1200 GMT 

soundings. To minimize the impact on the output from AERMET, upper air data from a 

representative upper air station were substituted for those periods for which the 1200 GMT 

sounding was missing for two or more consecutive days. 

 One of the important requirements of applying the AERMOD model is the specification of 

surface characteristics for use in processing the meteorological data using AERMET. Application 

of AERMET requires specification of the surface roughness length, the Bowen ratio (an indicator 

of surface moisture), and the albedo (an indicator of surface reflectivity). These surface 

characteristics are used by AERMET to calculate the level of shear-induced mechanical turbulence 

generated by flow over the surface and for the energy balance calculations used in the 

determination of the Monin-Obukhov stability parameter and the convective velocity scale. For 

this application, the following surface characteristics were used: 

! Surface roughness length = 0.25 m. At the airport meteorological site, the surface 

roughness Includes runways, terminal buildings and other airport structures. In addition, 

off-airport structures often are within 3 kilometers of the measurement site. his 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

! Bowen ratio = 1.0. Representing an equal partition of the heat fluxes 

! Albedo = 0.15. Representing conditions for all seasons, including winter without 

continuous snow cover. 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights required by 

AERMET. These heights are to the nearest meter and were deemed appropriate for use in this 

application. 

 Two problems in the AERMET computer program were fixed before completing this 

application. The first pertained to hourly surface observations. Incorrect missing indicators in the 

code for wind speed and direction were output when the data were extracted from the raw input 

file in Stage 1, which caused the program to stop running in Stage 3. The AERMET program was 

changed to use correct missing indicators. The second problem was associated with the upper air 

soundings. AERMET would not process an entire station’s data if the surface pressure for the first 

sounding was less than 850 millibars (mb). Surface pressures less than 850 mb are not uncommon 

for stations at high elevations. The AERMET program was modified to expand the allowable 

surface pressures. 
18 

Figure 3.1. Locations of RTR AERMOD Meteorological Stations 

 In early 2007, AERMOD was modified to version 07026 which checks the header record 

of the surface meteorological input file to verify that its version number is 06341. As a result of 

this modification, the 122 NWS stations were re-processed through a newer version of AERMET 

(version 06431) to create meteorological data files compatible with AERMOD version 07026. 

The meteorology data received from the States were already at version 06341. 

3.4 Model Options Selected

 HEM3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources were modeled in this assessment, established defaults and common 

practices were relied on to make these choices. The choices available to a HEM3 user and the 

selections that were made in this assessment are presented in Table 3-2. Some of the key 

selections are discussed in more detail in the paragraphs below. 

 Although routine emissions are not expected to vary significantly with time, nonroutine 

(upset) emissions can be significant relative to routine emissions. Upset emissions occur during 

periods of startup, shutdown, and malfunction. Upset emissions are not likely for equipment or 

storage tanks, but do result from malfunctioning control devices and leaks in cooling tower heat 

exchangers. There is some limited data on upset emissions available,14 but no facility-specific 
19 

analyses of these data were performed to characterize short-term emissions from these emission 

sources, and upset emissions were not modeled for the RTR risk assessments. 

Table 3-2. Modeling Domain and Model Set-Up Options in the HEM-3 Model As Used in 

the Residual Risk and Technology Review Assessments

Option Selection

Dispersion model: AERMOD or ISCST3 AERMOD 

Type of analysis: chronic, acute, or both Both 

Averaging time for short term impacts 1-hour 

Multiplier for short term emissions 10 a

Urban or rural dispersion characteristics Rural 

Include terrain impacts Yes 

Include building wake effects No 

Calculate deposition (wet, dry, or both) No 

Include impacts of plume depletion due to deposition No 

User-specified receptor locations (for plant boundary sites or other sites of interest) No 

Modeling domain size – maximum distance to be modeled 50 km 

Cutoff distance for modeling of individual blocks 3 km b

Overlap distance where receptors are considered to be on plant property – measured 

from each source 

30 m c

Polar receptor network specifications 

Distance from the plant center to the innermost ring 100 m c

Number of rings 13 

Number of directions 16 

Meteorology data Closest site 

 a For the secondary aluminum production category, the ratio of maximum short-term emissions to 

long-term average emissions was set to 100, based on the engineering review. 

 b The individual block modeling cutoff was increased for categories and for some facilities to ensure 

that the maximum individual risk values were not interpolated. 

 c

 The overlap distance and inner ring distance were adjusted for some facilities to avoid modeling 

locations that are on plant property (see section 4.2).

3.4.1 Urban or Rural Dispersion Characteristics

 Since 51 source categories were considered in these screening-level RTR assessments, the 

rural option was chosen to be most conservative (i.e., more likely to overestimate risk results). 

3.4.2 Deposition and Plume Depletion

 This modeling analysis did not take into account the depletion of pollutant concentrations 

in the plume due to wet or dry deposition. In addition, reactivity and decay were not considered. 
20 

It is possible that this approach, may overestimate air concentrations and therefore risk. However, 

one of the main metrics used by EPA in the residual risk program is the risk to the individual most 

exposed (maximum risk). Because the maximum risk usually occurs at a receptor very close to 

the emission source, it is unlikely to be influenced by altered plume dispersion characteristics of 

this type. For more refined, multipathway assessments, EPA will consider deposition and 

depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks

 The cutoff distance for modeling individual Census blocks was initially set to 3 km. This 

distance generally ensures that the maximum individual cancer risk and the maximum TOSHI are 

modeled explicitly and not interpolated. Following the modeling run, the results for each facility 

were checked to determine whether the maximum impacts were located inside the modeling cutoff 

distance. If the maximum impacts were outside the cutoff distance any of the impacts were 

significant, then HEM3 was rerun for the facility with an expanded modeling distance cutoff. In 

general, this was done if the cancer risk exceeded 1 in 10 million or any TOSHI exceeded 0.1. 

However, the risks for these facilities were generally very low, since the maximum impacts were 

generally only interpolated when the nearest Census block was more than 3 km from the facility. 

3.4.4 Plant Boundary Assumptions

 The main input mechanisms for incorporating plant boundary information in HEM3 are the 

overlap distance, the distance to the innermost polar receptor ring, and user-specified receptor 

locations. The NEI does not provide information on plant boundaries. Therefore, the userspecified receptor file was not used in this analysis. Conservative default assumptions were used 

for the overlap distance and the innermost polar receptor ring. However, these were adjusted for 

some categories where plant sites are known to be large. In addition, satellite imagery was used to 

check the plant boundary assumptions for facilities with large projected impacts. These checks are 

discussed further in the section on Quality Assurance (Section 4). 

3.5 Modeling of Multiple Facilities

 HEM3 models one facility at a time. However, clusters of nearby facilities may impact the 

same people, resulting in higher risk to those people. To account for this situation, risks were 

summed at each Census block for all facilities affecting the Census block. 

 As described earlier (Section 2.3.4), HEM3 produces detailed output tables containing the 

risk and population for every Census block in the modeling domain. These detailed tables were 

combined for all facilities in a source category and the risk for each Census block was summed. 

Thus, the effect of multiple facilities in the same source category on the same receptor could be 

estimated. The resulting “combined facility” or “cluster-effect” Census block risks were used to 

calculate population exposure to different cancer risk levels and source category incidence. 
21 

4. Quality Assurance

 The National Emissions Inventory is subject to an extensive program of quality assurance 

(QA) and quality control (QC). The QA/QC program for the point source component of the NEI 

is documented in a separate report, available from the NEI website.15 This section describes QA 

activities carried out under the RTR modeling analysis. 

4.1 Engineering Review

 In addition to the standardized QA steps taken for the entire NEI, EPA performed an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review included two main components. The first component addressed the list of 

facilities included in each source category. EPA engineers reviewed independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviewed the list of sources represented as part of each category in the NEI to make sure that the 

facilities actually manufacture products characteristic of the source category. 

 The second component of the engineering review focused on the appropriateness of facility 

emissions. EPA reviewed the list of HAPs emitted by each facility to make sure that the 

pollutants were appropriate to the source category. In addition, EPA engineers review the 

magnitude of those HAP emissions. In cases where new or better data were known to exist for a 

particular source category, that information was integrated into the data used in modeling for that 

category. In these cases, the source category specific documents provide additional details on the 

emissions inputs used. 

4.2 Geographic Checks

 The NEI QA process includes some basic checks on location data for point sources. The 

coordinates for each source are checked to ensure that they are in the county that has been 

specified for the source. If this is not the case, or if no geographic coordinates are available for the 

emission source, then the coordinates are set to a default location based on the nature of the 

emission source category.15 In addition, coordinates for all emission sources at a given facility are 

checked to ensure that they are withing 3 km of one another. 

 As part of the RTR modeling analysis, additional geographical QA checks were made for 

some facilities. Facilities subjected to these additional checks include: 

! cases where the initial estimates of maximum risks were particularly high 

" maximum individual cancer risk of over 1 in 10,000 

" any maximum TOSHI above 10 

! cases where no Census blocks were identified within 3 km of the facility 

 The emission source coordinates were reviewed for each of these facilities, and compared 

with the address reported for the facility. Both the coordinates and the facility address were 
22 

entered into Internet mapping sites which provide the option of viewing aerial or satellite 

photography. These sites include Google Earth® (earth.google.com/), Google Maps® 

(local.google.com/), MapQuest® (www.mapquest.com) and Microsoft Terraserver® 

(www.terraserver.com/). If the address and the coordinates represented the same location, then the 

coordinates were taken to be correct. If the locations were different, then the aerial photographs 

for the address and the coordinate location were reviewed to determine whether either photograph 

included an industrial facility. Generally, where the two locations were different, the facility 

address was found to be correct. In some cases, this comparison could not be made because the 

reported address was a Post Office box or a headquarters address. Where this occurred, the aerial 

photograph for the coordinate location was reviewed to determine whether an industrial facility 

was located at or near the location. If emission source coordinates were found to be incorrect, 

HEM3 was rerun using corrected coordinates. These changes are described in the source category 

documents. 

 For the high-risk facilities, the coordinates used to represent the most impacted Census 

blocks were also reviewed. This review drew on detailed Census block boundary maps and aerial 

photographs. Large industrial facilities will frequently occupy one or more entire Census blocks. 

However, these blocks may also include one or more residences on the periphery of the industrial 

land. Generally, the coordinates listed for a Census block are near the center of the block. In 

these cases of mixed industrial and residential blocks, the coordinates may be on plant property. 

 The potential for overlap between an industrial facility and a Census block coordinate is 

illustrated in Figure 4-1. The figure shows Census blocks divisions around a large industrial 

facility, with the coordinates of emission sources at the facility illustrated by red squares. Census 

block boundaries are shown, and the coordinates given in the Census data base for populated 

blocks are indicated by blue circles. As the figure shows, the facility is spread over five Census 

blocks. A number of populated blocks can be seen to the Southwest of the facility, and most of 

the blocks covering the facility are not populated. However, the block containing most of the 

facility’s emission points also includes at least one residence (the total population given for the 

block is 5). The location of the residence is at the periphery of the block, near the one of the 

adjacent populated blocks. However, the coordinates given for the block are near its center, 

illustrated by the blue star, clearly between various emission sources at the facility. 

 In general, block coordinates were considered to be on plant property if they were located 

between the different emission source locations listed for the facility. In these situations, HEM3 

was rerun with an expanded overlap distance, in order to exclude the Census block coordinates 

that appeared to be located on plant property. The distance to the innermost polar receptor ring 

was also adjusted to ensure that this ring was not on plant property. 
23 

Figure 4-1. Illustration of a quality assurance test for Census block coordinates located on 

plant property. 

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24 

5. Uncertainties

 This analysis is subject to a number of uncertainties. For instance, model verification 

studies for AERMOD showed predicted maximum annual concentrations ranging from 0.3 to 1.6 

times measured values, with an average of 0.9. Predicted maximum short term (1 to 24 hours) 

concentrations were 0.25 to 2.5 times measured values, with an average of 1.0.16

 In addition, a number of simplifying assumptions have been made in this modeling 

analysis. First, the coordinates reported by the Department of Census for Census block “internal 

points” have been used as a surrogate for long-term population exposures. Locations of actual 

residences have not been modeled. In addition, the current version of HEM3 does not take into 

account the movement of people from one Census block to another during the course of their lives, 

or commuting patterns during a given day. Nor does the model take into account the attenuation 

of pollutant from outside emission sources in indoor air. Ideally, risks to individuals would be 

modeled as they move through their communities and undertake different activities. However, 

such modeling is time- and resource-intensive and can only capture a portion of the uncertainty 

associated with the full range of human activities. In general, it is expected that long-term 

exposures will be overstated for high-end estimates (as most individuals will not spend all their 

time at their highly affected residences), but may understate the total population exposed (as some 

individuals living outside the modeled area may regularly commute into the area for work or 

school). 

 When considering long-term or lifetime exposures, it should be noted that relatively few 

people in the United States reside in one place for their entire lives. For the purposes of this 

assessment, cancer risk estimates are based on a lifetime exposure at the 2000 Census-identified 

place of residence. While it is impossible to know how this assumption affects the risk 

experiences by a particular individual (as people can move into higher- or lower-risk areas), it is 

likely that this assumption will overstate the exposure to those most exposed (i.e., people already 

living in high exposure areas are unlikely to move to yet higher exposure areas). However, this 

assumption will also tend to underestimate the total number of people exposed and population risk 

(i.e., incidence) because population levels are generally increasing. 

 In the current analysis, only direct inhalation has been modeled. Other pathways such as 

the deposition of pollutants to drinking water, and to bioaccumulation of deposited pollutants in 

the food supply may be a significant source of exposure for persistent and bioaccumulative 

pollutants. These exposures will be modeled in subsequent analyses. Because this analysis is 

restricted to the inhalation pathway, the impacts of plume depletion due to deposition have not 

been into account. Thus, inhalation impacts may be overestimated for some pollutants, but 

exposures through other pathways would be underestimated. 

 A number of other simplifications have been made in the dispersion modeling 

analysis, as has been noted in Table 3-1. For instance, building wake effects have not been 

considered. Rural dispersion conditions have also been used for all facilities. This is viewed as a 

conservative assumption, since rural conditions result in less mixing close to the emission source. 
25 

In addition, meteorological observations are based on the closest station in the HEM3 

meteorological library (see Figure 2-1). Alternative meteorological stations may be more 

appropriate for some facilities. Ideally, facility-specific meteorological observations would be 

used. A single year of meteorological data (1991) was selected for dispersion modeling. 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 of the large number of facilities in the analysis and the extent of the dispersion modeling 

analysis (national scale), it was not practical to model five years of data. Other national studies 

such as NATA have also considered only a single year of meteorological data. A sensitivity 

analyses performed by the NATA assessment found that variability attributable to the selection of 

the meteorology location/time (both temporal and spatial) resulted in a 17-84% variation in 

predicted concentrations at a given station17. 

 Finally, risk and exposure factors are also subject to uncertainty. Not all individuals 

experience the same degree of exposure or internal dose of a given pollutant due to individualspecific parameters such as weight, age, and gender. While the health benchmarks used in the 

analysis crudely account for sensitive populations, a prototypical human (e.g., body weight, 

ventilation rate) is used to define the benchmark. Because of the variability of these parameters in 

the population, this factor will result in a degree of uncertainty in the resulting risk estimate. 

 Table 5-1 summarizes the general sources of uncertainty for the RTR modeling analysis. 

The table also gives a qualitative indication of the potential direction of bias on risk estimates. 

The sources of uncertainty in Table 5-1 are divided into four categories, based on the major 

components of the analysis: 

! emissions inventory 

! fate and transport modeling 

! exposure assessment 

! toxicity assessment 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which have been prepared for each emission source 

category included in the RTR. 
26 

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Emissions InventoryIndividual HAP emissions rates and facility characteristics (stack parameters, property boundaries) 

Emissions and facility characteristics from the NEI provide an accurate characterization of actual source emissions. 

Our emissions inventory is based on the 2002 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions is likely to vary across sources. Nearly half of the sources in the source category submitted a review of their emissions and facility characteristics data. Some detailed data, such as property boundary information was not available for most facility. This is an important consideration in determining acute impacts. 

Unbiavariab

Multiplier for short-term emission rates 

Maximum short term emission rates are estimated by applying a simple multiplier (a factor of 10) to average annual emissions. 

The ratio between short-term and long-term average emission rates may vary from among the different emission sources at a facility. In addition, the use of a simple multiplier means that impacts of maximum short term emissions are modeled for all meteorological conditions, including the worst-case conditions for population exposure. 

Potentthe facemissioccasiworst-Overeactual term e

Fate and Transport ModelingAtmospheric dispersion model choice 

AERMOD is one of EPA's recommended models for assessing pollutant concentrations from industrial facilities 

Field testing of these models have shown results to generally be within a factor of 2 of measured concentrations. 

Overe
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

27 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Building downwash Not included in assessment 

Use of this algorithm in AERMOD could improve the dispersion calculations at individual facilities. However, data were not readily available to utilize this option. 

PotentmaximNo effout. 

Plume depletion and deposition Not included in assessment 

Ignoring these impacts for pollutants that deposit minimally and whose risks derive predominantly from inhalation should have minimal effect on risk estimates. 

Unbiaoveres

Meteorology 

One year of meteorological data from the nearest weather station (selected from 122 nationwide) is representative of long-term weather conditions at the facility. 

The use of one year of data rather than the five or more adds uncertainty based on whether that year is representative of each location’s climatology. Use of weather station data rather than on-site data can add to uncertainty. Additionally, the use of default surface parameters in the generation of the meteorological datasets imparts uncertainty to the results from any individual facility. 

Minimoveres

Reactivity Not included in the assessment. 

Chemical reactions and transformations of individual HAP into other compounds due to solar radiation and reactions with other chemicals happens in the atmosphere. However, in general, the HAP in this assessment do not react quickly enough for these transformations to be important near the sources, where the highest individual risks are estimated. Further, most of the HAP do not react quickly enough for these transformations to be important to risk estimates in the entire modeled domain (i.e., within 50 km of the source). 

No imestimaon popincide
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

28 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Maximum modeling distance 

50 kilometers from center of facility 

This distance is considered to be the maximum downwind distance for a Gaussian plume model such as AERMOD. This is because, in general, winds cannot be considered to follow straight line trajectories beyond this distance. 

No effindiviundereincide

Exposure AssessmentLocations and shortterm movements of individuals 

Ambient concentration at centroid of each off-site census block is equal to the exposure concentration for all people living in that census block. Effect of human activity patterns on exposures is not included in the assessment. 

People live at different areas within block that may have higher or lower exposures than at the centroid. Individuals also move from outdoors to indoors and from home to school/work to recreation, etc., and this can affect their total exposure from these sources. 

Unbiafor moindivioveresexposfor lea

Long-term movements of individuals 

MIR individual is exposed continuously to the highest exposure concentration for a 70-year lifetime. Population moves into and out of exposure area consistent with national frequency distribution of residency. 

MIR (maximum individual risk) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Individuals may move into or out of areas impacted by the facilities of interest in ways that don’t correspond exactly with national norms or local demographic patterns, and this could lead to localized biases. In general, source categories with larger numbers of facilities should carry less potential for bias. 

Unbiaindivioveresindivilikely least eremainpopulaincrea70 yea

Toxicity Assessment
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

29 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Reference concentrations (RfC) 

Consistent with EPA guidance, RfCs are developed including uncertainty factors to be protective of sensitive subpopulations. Additionally, RfCs are developed based on the level producing an effect in the most sensitive target organ or system. 

While other organ systems may be impacted at concentrations above the RfC, these are not included in the calculation of target organspecific hazard indices. 

In genRfCs uwhose undereof uncvariabuncertpotentrisks. 

Unit Risk Estimate (URE) 

Use of unit risk estimates developed from dose-response models such as linear low-dose extrapolation. 

Uncertainty in extrapolating the impacts from short-duration, high-dose animal or work-related exposures to longer duration, lower-dose environmental impacts. 

Overenonlinfor linsparse generavalues aimed risks iand va

Toxicity of mixtures 

Cancer risks and noncancer hazard quotients were calculated for each HAP individually and then summed into a total risk or hazard index (assumption of additivity). 

Concurrent exposures to multiple chemicals may result in either increased or decreased toxicity due to chemical interactions but the data needed to quantify these effects are generally not available. 

Unbiamixtuundereoverescorrec
Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk 

 (continued) 

30 

Parameter Assumption Uncertainty/Variability Discussion 

Poten

o

Surrogate doseresponse values for HAPs without values 

In the case of groups of HAPs such as glycol ethers, the most conservative dose-response value of the chemical group was used as a surrogate for missing doseresponse values in the group. For others, such as unspeciated metals, we have applied speciation profiles appropriate to the source category to develop a composite dose-response value for the group. For HAP which are not in a group and for which no URE’s or RfC’s are available from credible sources, no assessment of risk is made. 

Rather than neglecting the assessment of risks from some HAPs lacking dose response values, conservative assumptions allow the examination of whether these HAPs may pose an unacceptable risk and require further examination, or whether the conservative level examination with surrogates screens out the HAPs from further assessment. 

OvereconserUnbiaspecifThere underepollutinclud
31 

6. References

 

1. EPA. 2004. AERMOD: Description of Model Formulation. EPA-454/R-03-004, U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA454/B-03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

3. EC/R Incorporated. 2006. The HEM-3 User’s Guide. Prepared for the U.S. 

Environmental Protection Agency, Sector-Based Assessment Group, Research Triangle 

Park, NC. 

4. EPA. 2007. OAQPS Prioritized Chronic Dose-Response Values (6/12/2007). U.S. 

Environmental Protection Agency, Research Triangle Park, NC. Available at: 

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

5. Census. 2000. LandView® 5 on DVD. U.S. Census Bureau, Washington, DC. 

6. USGS. 2003. 1:250,000 Scale Digital Elevation Models (DEM). U.S. Geologic Survey, 

Washington, DC. http://edcsgs9.cr.usgs.gov/glis/hyper/guide/1_dgr_demfig/states.html

7. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#aermap

8. EPA. 2004. Addendum: User's Guide for the AMS/EPA Regulatory Model - AERMOD

(related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, 

NC. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

9. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for 

the Industrial Source Complex (ISC3) Model. Argonne National Laboratory, Argonne, 

Illinois. http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

10. EPA. 2006. 2002 National Emissions Inventory Data & Documentation. U.S. 

Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/chief/net/2002inventory.html

11. EPA. 2002. EMS-HAP Modeling System. Environmental Protection Agency, Research 

Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-hap
32 

 

12. Strum, Madeleine, Richard Mason, and Diane Linderman. 2002. User's Guide for the 

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 2.0. 

EPA-454/B-02-001, Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/dispersion_related.htm#ems-hap

13. MACTEC. 2006. Meteorological Data Processing Using Aermet for HEM-AERMOD. 

Prepared by MACTEC Federal Programs, Inc., Durham, NC for EC/R Incorporated, 

Chapel Hill, NC. 

14. Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. 

http://www.tceq.state.tx.us/compliance/field_ops/eer/

15. EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources. U.S. 

Environmental Protection Agency, Emission Inventory Group, Research Triangle Park, 

NC. 

16. EPA. 2003. AERMOD: Latest Features and Evaluation Results. EPA-454/R-03-003, 

U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/scram001/7thconf/aermod/eval.pdf

17 EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01-003; EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

http://www.epa.gov/ttn/atw/nata/peer.html 
Appendix 2 

Meteorological Data for HEM-AERMOD Modeling 
 

2

DRAFT 

METEOROLOGICAL DATA PROCESSING 

USING AERMET 

FOR 

HEM-AERMOD 

Submitted by: 

MACTEC Federal Programs, Inc. 

5001 S. Miami Blvd., Suite 300 

Durham, NC 27703 

919-941-0333 

July 28, 2006 

Revisions May 14, 2012 
Meteorological Data Processing using AERMET July 28, 2006 

For HEM-AERMOD 

i 

TABLE OF CONTENTS 

PROJECT BACKGROUND ...............................................................................................1 

METEOROLOGICAL DATA PROCESSING ...................................................................1 

MISSING DATA .................................................................................................................5

ANEMOMETER HEIGHTS ...............................................................................................6 

SURFACE CHARACTERISTICS ......................................................................................6 

MODIFICATIONS TO AERMET CODE ..........................................................................7 

RESULTS ............................................................................................................................7 

LIST OF TABLES 

Table

1 SURFACE STATIONS PAIRED WITH UPPER AIR STATION .........................1 

2 UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS 

 AND STATION USED FOR DATA SUBSTITUTION .........................................5 

3 SURFACE CHARACTERISTICS ..........................................................................7 

4 PERCENT CALM AND MISSING BY STATION ...............................................8 

LIST OF FIGURES 

Figure

1 SURFACE AND UPPER AIR STATIONS ............................................................4 
Meteorological Data Processing using AERMET July 28, 2006 

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1 

PROJECT DESCRIPTION AND BACKGROUND 

The AERMOD meteorological processor, AERMET, was used to process one year of 

hourly data from 122 National Weather Service (NWS) surface observation stations 

across the continental United States, Alaska, Hawaii, and Puerto Rico. 

METEOROLOGICAL DATA PROCESSING 

To estimate the boundary layer parameters required by AERMOD, AERMET requires 

hourly surface weather observations and the full (i.e., meteorological variables reported at 

all levels) twice-daily upper air soundings. The surface and upper air stations are paired 

to produce the required input data for AERMOD. 

One of the requirements of the project was to use surface observations prior to the 

introduction of the Automated Surface Observation System (ASOS). Installation and 

operation of ASOS equipment began in 1992; therefore, data for 1991 were processed 

with AERMET. 

A list of 86 previously paired surface and upper air stations was provided to MACTEC. 

On review of these stations, it was determined that data for 1991 for three of the stations 

– Galveston, TX, Houston-Hobby, TX and Boulder, CO – was not available. The 

remaining 83 stations did not provide adequate coverage for the MACT sources to be 

modeled with AERMOD. As a result, an additional 39 surface stations were identified to 

include for processing. These additional surface stations were paired with representative 

upper air stations. A list of the surface and paired upper air stations are in Table 1. 

Data were retrieved from products available from the National Climatic Data Center 

(NCDC). The surface data for 1991 were retrieved from the Hourly United States 

Weather Observation (HUSWO) CD. Upper air soundings were obtained from the 

Radiosonde Data of North America CDs produced by NCDC and the Forecast Systems 

Laboratory (FSL). 

TABLE 1 

SURFACE STATIONS WITH PAIRED UPPER AIR STATION 

SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Paducah, KY 03816 37.07 -88.77 Paducah, KY 03816 37.07 -88.77 

Huntsville, AL 03856 34.65 -86.78 Nashville, TN 13897 36.13 -86.68 

Huntington, WV 03860 38.37 -82.55 Huntington, WV 03860 38.37 -82.55 

Greenville, SC 03870 34.90 -82.22 Athens, GA 13873 33.95 -83.32 

Fort Worth, TX 03927 32.90 -97.02 Stephenville, TX 13901 32.22 -98.18 

Wichita, KS 03928 37.65 -97.45 Topeka, KS 13996 39.07 -95.63 

Lake Charles, LA 03937 30.12 -93.23 Lake Charles, LA 03937 30.12 -93.23 

Jackson, MS 03940 32.32 -90.08 Jackson, MS 03940 32.32 -90.08 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Kansas City, MO 03947 39.30 -94.73 Topeka, KS 13996 39.07 -95.63 

San Juan, PR 11641 18.43 -66.02 San Juan, PR 11641 18.43 -66.00 

Orlando, FL 12815 28.45 -81.32 Tampa, FL 12842 27.70 -82.40 

Gainesville, FL 12816 29.68 -82.27 Waycross, GA 13861 31.25 -82.40 

Tampa, FL 12842 27.97 -82.53 Tampa Bay, FL 12842 27.97 -82.53 

W. Palm Beach, FL 12844 26.68 -80.10 W. Palm Beach, FL 12844 26.68 -80.12 

Victoria, TX 12912 28.85 -96.92 Corpus Christi, TX 12924 29.77 -97.50 

New Orleans, LA 12916 30.00 -90.25 Slidell, LA 53813 30.33 -89.82 

Port Arthur, TX 12917 29.95 -94.02 Lake Charles, LA 03937 30.12 -93.23 

Brownsville, TX 12919 25.90 -97.43 Brownsville, TX 12919 25.90 -97.43 

San Antonio, TX 12921 29.53 -98.47 Del Rio, TX 22010 29.37 -100.92 

Corpus Christi, TX 12924 27.77 -97.52 Corpus Christi, TX 12924 27.77 -97.52 

Houston, TX 12960 29.98 -95.37 Lake Charles, LA 03937 30.12 -93.23 

Raleigh, NC 13722 35.87 -78.78 Greensboro, NC 13723 36.10 -79.95 

Greensboro, NC 13723 36.08 -79.95 Greensboro, NC 13723 36.08 -79.95 

Norfolk, VA 13737 36.90 -76.20 Wallops Island, VA 93739 37.93 -75.47 

Philadelphia, PA 13739 39.87 -75.23 Atlantic City, NJ 93755 39.75 -74.67 

Richmond, VA 13740 37.50 -77.32 Sterling, VA 93734 38.98 -77.47 

Roanoke, VA 13741 37.32 -79.97 Greensboro, NC 13723 36.10 -79.95 

Wilmington, NC 13748 34.27 -77.90 Charleston, SC 13880 32.90 -80.03 

Wilmington, DE 13781 39.67 -75.60 Atlantic City, NJ 93755 39.75 -74.67 

Meridian, MS 13865 32.33 -88.75 Jackson, MS 03940 32.32 -90.08 

Charleston, WV 13866 38.38 -81.58 Huntington, WV 03860 38.37 -82.55 

Atlanta, GA 13874 33.63 -84.45 Athens, GA 13873 33.95 -83.33 

Birmingham, AL 13876 33.57 -86.75 Centerville, AL 03881 32.90 -87.25 

Bristol, TN 13877 36.47 -82.40 Huntington, WV 03860 38.37 -82.55 

Charleston, SC 13880 32.90 -80.03 Charleston, SC 13880 32.90 -80.03 

Charlotte, NC 13881 35.22 -80.93 Greensboro, NC 13723 36.08 -79.95 

Columbia, SC 13883 33.95 -81.12 Athens, GA 13873 33.95 -83.33 

Jacksonville, FL 13889 30.50 -81.70 Waycross, GA 13861 31.25 -82.40 

Memphis, TN 13893 35.05 -89.98 N. Little Rock, AR 03952 34.83 -92.27 

Mobile, AL 13894 30.68 -88.25 Slidell, LA 53813 30.33 -89.82 

Montgomery, AL 13895 32.30 -86.40 Centerville, AL 03881 32.90 -87.25 

Nashville, TN 13897 36.13 -86.68 Nashville, TN 13897 36.13 -86.68 

Shreveport, LA 13957 32.45 -93.82 Longview, TX 03951 32.35 -94.65 

Little Rock, AR 13963 34.73 -92.23 N. Little Rock, AR 03952 34.83 -92.27 

Wichita Falls, TX 13966 33.98 -98.50 Stephenville, TX 13901 32.22 -98.18 

Oklahoma City, OK 13967 35.40 -97.60 Norman, OK 03948 35.23 -97.47 

Baton Rouge, LA 13970 30.53 -91.15 Slidell, LA 53813 30.33 -89.82 

Dodge City, KS 13985 37.77 -99.97 Dodge City, KS 13985 37.77 -99.97 

St. Louis, MO 13994 38.75 -90.37 Peoria, IL 14842 40.67 -89.68 

Springfield, MO 13995 38.82 -92.27 Monet, MO 03946 36.87 -93.97 

Buffalo, NY 14733 42.93 -78.73 Buffalo, NY 14733 42.93 -78.73 

Newark, NJ 14734 41.72 -75.17 Atlantic City, NJ 93755 39.75 -74.67 

Albany, NY 14735 42.75 -73.82 Albany, NY 14735 42.75 -73.82 

Allentown, PA 14737 40.65 -75.45 Albany, NY 14735 42.75 -73.82 

Boston, MA 14739 42.37 -71.02 Chatham, MA 14684 41.67 -69.97 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Hartford, CN 14740 41.93 -72.68 Albany, NY 14735 42.75 -73.80 

Burlington, VT 14742 44.47 -73.15 Albany, NY 14735 42.75 -73.82 

Portland, ME 14764 43.65 -70.30 Portland, ME 14764 43.65 -70.30 

Providence, RI 14765 41.73 -71.43 Chatham, MA 14684 41.67 -69.97 

Syracuse, NY 14771 43.12 -76.12 Buffalo, NY 14733 42.93 -78.73 

Williamsport, PA 14778 41.27 -77.05 Pittsburgh, PA 94823 40.53 -80.23 

Cleveland, OH 14820 41.40 -81.85 Pittsburgh, PA 94823 40.53 -80.23 

Columbus, OH 14821 39.98 -82.88 Dayton, OH 13840 39.83 -84.05 

Fort Wayne, IN 14827 41.00 -85.20 Dayton, OH 13840 39.83 -84.05 

Lansing, MI 14836 42.78 -84.58 Flint, MI 14826 42.97 -83.75 

Madison, WI 14837 43.13 -89.33 Green Bay, WI 14898 44.48 -88.13 

Erie, PA 14860 42.08 -80.18 Buffalo, NY 14733 42.93 -78.73 

Akron, OH 14895 40.92 -81.43 Pittsburgh, PA 94823 40.50 -80.23 

Green Bay, WI 14898 44.50 -88.12 Green Bay, WI 14898 44.50 -88.12 

Duluth, MN 14913 46.83 -92.22 Int'l Falls, MN 14918 48.57 -93.40 

Minneapolis, MN 14922 44.88 -93.23 St. Cloud, MN 14926 45.55 -94.05 

Moline, IL 14923 41.45 -90.52 Peoria, IL 14842 40.67 -89.68 

Rochester, MN 14925 43.92 -92.50 St. Cloud, MN 14926 45.55 -94.08 

Sioux Falls, SD 14944 43.57 -96.73 Huron, SD 14936 44.38 -98.22 

Eau Claire, WI 14991 44.87 -91.48 St. Cloud, MN 14926 45.55 -94.08 

Lihue, HI 22536 21.98 -159.33 Lihue, HI 22536 21.98 -159.33 

Midland, TX 23023 31.95 -102.18 Midland, TX 23023 31.95 -102.18 

El Paso, TX 23044 31.82 -106.38 El Paso, TX 23044 31.82 -106.38 

Amarillo, TX 23047 35.22 -101.70 Amarillo, TX 23047 35.22 -101.70 

Albuquerque, NM 23050 35.03 -106.62 Albuquerque, NM 23050 35.03 -106.62 

Denver, CO 23062 39.77 -104.87 Denver, CO 23062 39.77 -104.88 

Goodland, KS 23065 39.37 -101.70 Dodge City, KS 13985 37.77 -99.97 

Grand Junction, CO 23066 39.12 -108.53 Grand Junction, CO 23066 39.12 -108.53 

Tonopah, NV 23153 38.05 -117.08 Ely, NV 23154 39.30 -114.85 

Tucson, AZ 23160 32.12 -110.93 Tucson, AZ 23160 32.12 -110.93 

Daggett, CA 23161 34.87 -116.78 Desert Rock, NV 03160 36.62 -116.02 

Los Angeles, CA 23174 33.93 -118.38 Miramar, CA 03190 32.87 -117.15 

Prescott, AZ 23184 34.65 -112.43 Tucson, AZ 23160 32.12 -110.93 

San Diego, CA 23188 32.73 -117.17 Miramar, CA 03190 32.87 -117.15 

Sacramento, CA 23232 38.52 -121.50 Oakland, CA 23230 37.75 -122.22 

San Francisco, CA 23234 37.62 -122.40 Oakland, CA 23230 37.72 -122.22 

Bismarck, ND 24011 46.78 -100.75 Bismarck, ND 24011 46.78 -100.75 

Cheyenne, WY 24018 41.15 -104.77 Denver, CO 23062 39.77 -104.88 

Billings, MT 24033 45.80 -108.55 Great Falls, MT 24143 47.47 -111.38 

Casper, WY 24089 42.90 -106.47 Lander, WY 24021 42.82 -108.73 

Rapid City, SD 24090 44.05 -103.07 Rapid City, SD 24090 44.05 -103.07 

Salt Lake City, UT 24127 40.78 -111.97 Salt Lake City, UT 24127 40.78 -111.97 

Great Falls, MT 24143 47.47 -111.38 Great Falls, MT 24143 47.47 -111.38 

Spokane, WA 24157 47.62 -117.53 Spokane, WA 24157 47.62 -117.53 

Medford, OR 24225 42.37 -122.87 Medford, OR 24225 42.37 -122.87 

Portland, OR 24229 45.60 -122.60 Salem, OR 24232 44.90 -123.00 

Salem, OR 24232 44.90 -123.00 Salem, OR 24232 44.90 -123.00 
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SURFACE WBAN LAT LON UPPER AIR WBAN LAT LON 

Seattle, WA 24233 47.45 -122.32 Quillayute, WA 94240 47.93 -124.55 

Juneau, AK 25309 58.37 -134.58 Annette Is., AK 25308 55.03 -131.57 

Cold Bay, AK 25624 55.20 -162.72 Cold Bay, AK 25624 55.20 -162.72 

Fairbanks, AK 26411 64.80 -147.88 Fairbanks, AK 26411 64.80 -147.88 

Valdez, AK 26442 61.13 -146.35 Anchorage, AK 26409 61.22 -149.85 

Anchorage, AK 26451 61.20 -150.00 Anchorage, AK 26409 61.22 -149.85 

Nome, AK 26617 64.50 -165.43 Nome, AK 26617 64.50 -165.43 

Colo. Springs 93037 38.82 -104.72 Denver, CO 23062 39.77 -104.88 

Fresno, CA 93193 36.78 -119.72 Oakland, CA 23230 37.72 -122.22 

Baltimore, MD 93721 39.18 -76.67 Sterling, VA 93734 38.98 -77.47 

Sterling, VA 93738 38.95 -77.45 Sterling, VA 93734 38.98 -77.47 

Evansville, IN 93817 38.05 -87.52 Paducah, KY 03816 37.05 -88.77 

Indianapolis, IN 93819 39.73 -86.28 Dayton, OH 13840 39.83 -84.05 

Lexington, KY 93820 38.03 -84.60 Dayton, OH 13840 39.83 -84.05 

Louisville, KY 93821 38.18 -85.73 Dayton, OH 13840 39.83 -84.05 

Springfield, IL 93822 39.85 -89.68 Peoria, IL 14842 40.67 -89.68 

Pittsburgh, PA 94823 40.53 -80.23 Pittsburgh, PA 94823 40.50 -80.23 

Toledo, OH 94830 41.58 -83.80 Flint, MI 14826 42.97 -83.73 

Chicago, IL 94846 42.00 -87.93 Peoria, IL 14842 40.67 -89.68 

Detroit, MI 94847 42.23 -83.33 Flint, MI 14826 42.97 -83.75 

Figure 1 shows the surface stations (green circles) and upper air stations (red pentagons) 

used in this study. 

FIGURE 1. SURFACE AND UPPER AIR STATIONS 
Meteorological Data Processing using AERMET July 28, 2006 

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Meteorological Data Processing using AERMET July 28, 2006 

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MISSING DATA 

While AERMET will run if input observations are missing, the output will be incomplete, 

with missing data indicators in the output file. The input to and output from AERMET 

were examined for indications of missing input data. 

The 1200 Greenwich Mean Time (GMT) sounding is required for AERMET to calculate 

the convective boundary layer height and several associated parameters. There were 

many isolated days in which the 1200 GMT sounding was missing for many of the 

stations. However there also were several stations for which there were two or more 

consecutive days of missing 1200 GMT soundings. The longest period of consecutive 

missing 1200 GMT soundings was at Dayton, Ohio with seven days of missing 

soundings. To minimize the impact on the output from AERMET and not consume too 

much time and effort, MACTEC substituted upper air data from a representative upper air 

station for those periods for which the 1200 GMT sounding was missing for two or more 

consecutive days. Table 2 shows the station substitutions. 

TABLE 2 

UPPER AIR STATIONS WITH MISSING 1200 GMT SOUNDINGS AND STATION 

USED FOR DATA SUBSTITUTION 

Upper Air Station 

With Missing Data 

WBAN Station Used for Data 

Substitution 

WBAN 

Amarillo, TX 23047 Norman, OK 03948 

Athens, GA 13873 Greensboro, NC 13723 

Brownsville, TX 12929 Corpus Christi, TX 12924 

Centerville, AL 03881 Jackson, MS 03940 

Chatham, MA 14684 Portland, ME 14764 

Dayton, OH 13840 Paducah, KY 03826 

Ely, NV 23154 Winnemucca, NV 24128 

Grand Junction, CO 23066 Salt Lake City, UT 24127 

Lake Charles, LA 03937 Slidell, LA 53813 

Paducah, KY 03826 Nashville, TN 13897 

Peoria, IL 14842 Omaha, NE 94918 

Pittsburgh, PA 94823 Huntington, WV 03860 

Salt Lake City, UT 24127 Ely, NV 23154 

St. Cloud, MN 14926 Huron, SD 14936 
Meteorological Data Processing using AERMET July 28, 2006 

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7 

ANEMOMETER HEIGHTS 

The file STNS.TXT located on the HUSWO CD was used for the anemometer heights 

required by AERMET. These heights are to the nearest meter and were deemed 

appropriate for use in this application. 

SURFACE CHARACTERISTICS 

One of the important requirements of applying the AERMOD model is the specification 

of surface characteristics for use in processing the meteorological data using AERMET. 

Application of AERMET requires specification of the surface roughness length, the 

Bowen ratio (an indicator of surface moisture), and the albedo (an indicator of surface 

reflectivity). These surface characteristics are used by AERMET to calculate the level of 

shear-induced mechanical turbulence generated by flow over the surface and for the 

energy balance calculations used in the determination of the Monin-Obukhov stability 

parameter and the convective velocity scale. 

The surface characteristics can be varied by wind direction sector (upwind) and by month 

or season. Current guidance specifies defining the characteristics for the meteorological 

measurement site and extending outward from the measurement location for three 

kilometers. In an application of this magnitude, it is impractical to evaluate and vary the 

surface characteristics for each of the 122 stations, therefore, MACTEC used annual 

values for one sector, as shown in Table 3. 

The albedo used here is representative of conditions for all seasons which includes winter 

without continuous snow cover. Most stations fall into this category and continuous 

snow coverage for several months even at northern stations in the continental United 

States is unlikely. 

Without a detailed and individual analysis for each of the 122 station locations and for 

this large an area for an entire year, a value representing an equal partition of the fluxes, 

or a Bowen ratio of 1.0, is the best representation to use in this type of analysis. 

The surface roughness not only includes runways and areas between runways, but usually 

terminal buildings and other airport structures. In addition, in urban environments, offairport structures often are within 3 kilometers of the measurement site. This 

combination of land covers suggests a value of 0.2 – 0.3 meters is appropriate. 

MACTEC used 0.25 for the roughness length. 
Meteorological Data Processing using AERMET July 28, 2006 

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TABLE 3 

SURFACE CHARACTERISTICS 

MODIFICATIONS TO AERMET CODE 

Recent use of AERMET identified two ‘bugs’ in the code that required fixing before 

running AERMET. The first had to do with the hourly surface observations. Incorrect 

missing indicators in the code for wind speed and direction were output when the data 

were extracted from the raw input file in Stage 1. When AERMET encountered these 

flags in Stage 3, AERMET simply stopped processing data, resulting in files with less 

data than requested. MACTEC modified the code to use correct missing indicators. 

MACTEC also checked other meteorological variables to be sure the correct missing 

indicators were used. No other missing indicators were in error. 

The second ‘bug’ was associated with the upper air soundings. AERMET would not 

process an entire station’s data if the surface pressure for the first sounding was less than 

850 millibars (mb). Surface pressures less than 850 mb is not uncommon for stations at 

high elevations such as Denver, CO and Albuquerque, NM. MACTEC modified the code 

to expand the allowable surface pressures. 

Both ‘bugs’ were fixed prior to running AERMET with the result that AERMET 

successfully ran to completion for all files. 

RESULTS 

AERMET successfully ran for all 122 stations. While the reports and output from 

AERMET provide an indication of the success of the runs, MACTEC ran AERMOD with 

a single source and single receptor to obtain information on the number of hours with 

calm winds or missing data. Table 4 shows these values by surface station. 

ADDENDUM – May 2012 

Since development of the original meteorological data set (122 stations) discussed above, 

additional sites have been added for Risk and Technology Review (RTR) analyses. 

These include meteorological data provided by state and local agencies, as well as 

additional sites processed by EPA risk staff to better represent dispersion conditions at 

Albedo 0.15 

Bowen Ratio 1.0 

Surface Roughness (m) 0.25 
Meteorological Data Processing using AERMET July 28, 2006 

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9 

specific facility locations. In each case, data were processed using the latest available 

version of AERMET and processing approach outlined on the USEPA’s SCRAM 

website. A listing of these additional sites is provided in Table 5. 
Meteorological Data Processing using AERMET July 28, 2006 

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TABLE 4 

PERCENT CALM AND MISSING BY STATION 

WBAN Station Calms % Calm Missing % Missing Total % Total

03816 Paducah, KY 820 9.4% 66 0.75% 886 10.1%

03856 Huntsville, AL 719 8.2% 0 0.00% 719 8.2%

03860 Huntington, WV 340 3.9% 28 0.32% 368 4.2%

03870 Greenville, SC 810 9.2% 20 0.23% 830 9.5%

03927 Fort Worth, TX 508 5.8% 24 0.27% 532 6.1%

03928 Wichita, KS 215 2.5% 0 0.00% 215 2.5%

03937 Lake Charles, LA 616 7.0% 15 0.17% 631 7.2%

03940 Jackson, MS 1228 14.0% 58 0.66% 1286 14.7%

03947 Kansas City, MO 231 2.6% 0 0.00% 231 2.6%

11641 San Juan, PR 670 7.6% 11 0.13% 681 7.8%

12815 Orlando, FL 144 1.6% 0 0.00% 144 1.6%

12816 Gainesville, FL 952 10.9% 11 0.13% 963 11.0%

12842 Tampa, FL 572 6.5% 0 0.00% 572 6.5%

12844 W. Palm Beach, FL 566 6.5% 8 0.09% 574 6.6%

12912 Victoria, TX 320 3.7% 0 0.00% 320 3.7%

12916 New Orleans, LA 721 8.2% 8 0.09% 729 8.3%

12917 Port Arthur, TX 736 8.4% 10 0.11% 746 8.5%

12919 Brownsville, TX 418 4.8% 36 0.41% 454 5.2%

12921 San Antonio, TX 913 10.4% 11 0.13% 924 10.5%

12924 Corpus Christi, TX 371 4.2% 0 0.00% 371 4.2%

12960 Houston, TX 1097 12.5% 13 0.15% 1110 12.7%

13722 Raleigh, NC 810 9.2% 0 0.00% 810 9.2%

13723 Greensboro, NC 542 6.2% 24 0.27% 566 6.5%

13737 Norfolk, VA 598 6.8% 39 0.45% 637 7.3%

13739 Philadelphia, PA 154 1.8% 2 0.02% 156 1.8%

13740 Richmond, VA 605 6.9% 0 0.00% 605 6.9%

13741 Roanoke, VA 1287 14.7% 1 0.01% 1288 14.7%

13748 Wilmington, NC 976 11.1% 0 0.00% 976 11.1%

13781 Wilmington, DE 837 9.6% 0 0.00% 837 9.6%

13865 Meridian, MS 1567 17.9% 56 0.64% 1623 18.5%

13866 Charleston, WV 1257 14.3% 29 0.33% 1286 14.7%

13874 Atlanta, GA 309 3.5% 21 0.24% 330 3.8%

13876 Birmingham, AL 1228 14.0% 15 0.17% 1243 14.2%

13877 Bristol, TN 2541 29.0% 28 0.32% 2569 29.3%

13880 Charleston, SC 487 5.6% 0 0.00% 487 5.6%

13881 Charlotte, NC 985 11.2% 0 0.00% 985 11.2%

13883 Columbia, SC 1340 15.3% 20 0.23% 1360 15.5%

13889 Jacksonville, FL 1134 12.9% 12 0.14% 1146 13.1%

13893 Memphis, TN 809 9.2% 0 0.00% 809 9.2%

13894 Mobile, AL 527 6.0% 8 0.09% 535 6.1%

13895 Montgomery, AL 1027 11.7% 19 0.22% 1046 11.9%

13897 Nashville, TN 87 1.0% 0 0.00% 87 1.0%

13957 Shreveport, LA 1088 12.4% 11 0.13% 1099 12.5%
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

11 

WBAN Station Calms % Calm Missing % Missing Total % Total

13963 Little Rock, AR 1090 12.4% 0 0.00% 1090 12.4%

13966 Wichita Falls, TX 622 7.1% 23 0.26% 645 7.4%

13967 Oklahoma City, OK 158 1.8% 0 0.00% 158 1.8%

13970 Baton Rouge, LA 787 9.0% 9 0.10% 796 9.1%

13985 Dodge City, KS 30 0.3% 32 0.37% 62 0.7%

13994 St. Louis, MO 416 4.7% 26 0.30% 442 5.0%

13995 Springfield, MO 212 2.4% 39 0.45% 251 2.9%

14733 Buffalo, NY 181 2.1% 0 0.00% 181 2.1%

14734 Newark, NJ 197 2.2% 0 0.00% 197 2.2%

14735 Albany, NY 1217 13.9% 8 0.09% 1225 14.0%

14737 Allentown, PA 465 5.3% 9 0.10% 474 5.4%

14739 Boston, MA 36 0.4% 38 0.43% 74 0.8%

14740 Hartford, CN 204 2.3% 8 0.09% 212 2.4%

14742 Burlington, VT 155 1.8% 9 0.10% 164 1.9%

14764 Portland, ME 381 4.3% 25 0.29% 406 4.6%

14765 Providence, RI 215 2.5% 39 0.45% 254 2.9%

14771 Syracuse, NY 481 5.5% 0 0.00% 481 5.5%

14778 Williamsport, PA 1403 16.0% 24 0.27% 1427 16.3%

14820 Cleveland, OH 271 3.1% 27 0.31% 298 3.4%

14821 Columbus, OH 734 8.4% 0 0.00% 734 8.4%

14827 Fort Wayne, IN 440 5.0% 0 0.00% 440 5.0%

14836 Lansing, MI 632 7.2% 10 0.11% 642 7.3%

14837 Madison, WI 685 7.8% 30 0.34% 715 8.2%

14860 Erie, PA 286 3.3% 4 0.05% 290 3.3%

14895 Akron, OH 232 2.6% 28 0.32% 260 3.0%

14898 Green Bay, WI 414 4.7% 28 0.32% 442 5.0%

14913 Duluth, MN 336 3.8% 0 0.00% 336 3.8%

14922 Minneapolis, MN 311 3.6% 3 0.03% 314 3.6%

14923 Moline, IL 812 9.3% 24 0.27% 836 9.5%

14925 Rochester, MN 10 0.1% 0 0.00% 10 0.1%

14944 Sioux Falls, SD 492 5.6% 0 0.00% 492 5.6%

14991 Eau Claire, WI 1193 13.6% 0 0.00% 1193 13.6%

22536 Lihue, HI 6 0.1% 0 0.00% 6 0.1%

23023 Midland, TX 207 2.4% 19 0.22% 226 2.6%

23044 El Paso, TX 262 3.0% 0 0.00% 262 3.0%

23047 Amarillo, TX 113 1.3% 6 0.07% 119 1.4%

23050 Albuquerque, NM 606 6.9% 0 0.00% 606 6.9%

23062 Denver, CO 530 6.1% 0 0.00% 530 6.1%

23065 Goodland, KS 176 2.0% 41 0.47% 217 2.5%

23066 Grand Junction, CO 295 3.4% 15 0.17% 310 3.5%

23153 Tonopah, NV 341 3.9% 11 0.13% 352 4.0%

23160 Tucson, AZ 154 1.8% 24 0.27% 178 2.0%

23161 Daggett, CA 616 7.0% 11 0.13% 627 7.2%

23174 Los Angeles, CA 944 10.8% 21 0.24% 965 11.0%

23184 Prescott, AZ 376 4.3% 23 0.26% 399 4.6%

23188 San Diego, CA 473 5.4% 21 0.24% 494 5.6%

23232 Sacramento, CA 1324 15.1% 77 0.88% 1401 16.0%
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

12 

WBAN Station Calms % Calm Missing % Missing Total % Total

23234 San Francisco, CA 376 4.3% 77 0.88% 453 5.2%

24011 Bismarck, ND 676 7.7% 22 0.25% 698 8.0%

24018 Cheyenne, WY 110 1.3% 0 0.00% 110 1.3%

24033 Billings, MT 273 3.1% 25 0.29% 298 3.4%

24089 Casper, WY 138 1.6% 37 0.42% 175 2.0%

24090 Rapid City, SD 736 8.4% 33 0.38% 769 8.8%

24127 Salt Lake City, UT 581 6.6% 22 0.25% 603 6.9%

24143 Great Falls, MT 111 1.3% 26 0.30% 137 1.6%

24157 Spokane, WA 537 6.1% 36 0.41% 573 6.5%

24225 Medford, OR 1937 22.1% 0 0.00% 1937 22.1%

24229 Portland, OR 1180 13.5% 0 0.00% 1180 13.5%

24232 Salem, OR 1799 20.5% 0 0.00% 1799 20.5%

24233 Seattle, WA 66 0.8% 14 0.16% 80 0.9%

25309 Juneau, AK 1781 20.3% 6 0.07% 1787 20.4%

25624 Cold Bay, AK 119 1.4% 41 0.47% 160 1.8%

26411 Fairbanks, AK 1393 15.9% 0 0.00% 1393 15.9%

26442 Valdez, AK 661 7.5% 2 0.02% 663 7.6%

26451 Anchorage, AK 724 8.3% 0 0.00% 724 8.3%

26617 Nome, AK 358 4.1% 24 0.27% 382 4.4%

93037 Colo. Springs 195 2.2% 0 0.00% 195 2.2%

93193 Fresno, CA 1578 18.0% 76 0.87% 1654 18.9%

93721 Baltimore, MD 361 4.1% 3 0.03% 364 4.2%

93738 Sterling, VA 995 11.4% 0 0.00% 995 11.4%

93817 Evansville, IN 1434 16.4% 66 0.75% 1500 17.1%

93819 Indianapolis, IN 190 2.2% 0 0.00% 190 2.2%

93820 Lexington, KY 234 2.7% 0 0.00% 234 2.7%

93821 Louisville, KY 385 4.4% 0 0.00% 385 4.4%

93822 Springfield, IL 287 3.3% 25 0.29% 312 3.6%

94823 Pittsburgh, PA 493 5.6% 26 0.30% 519 5.9%

94830 Toledo, OH 898 10.3% 13 0.15% 911 10.4%

94846 Chicago, IL 343 3.9% 38 0.43% 381 4.3%

94847 Detroit, MI 468 5.3% 11 0.13% 479 5.5%

 
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

13 

TABLE 5. 

ADDITIONAL METEOROLOGICAL STATIONS 

ADDED FOR RTR ANALYSES 

City State Year

Fairbanks AK 2001

Anaheim CA 2007

Azusa CA 2007

Banning Airport CA 2007

Burbank CA 2007

Central LA CA 2007

Costa Mesa CA 2007

Crestline CA 2007

Fontana CA 2007

Indio CA 2007

La Habra CA 2007

Lake Elsinore CA 2007

LAX CA 2007

Long Beach CA 2007

Lynnwood CA 2007

Mission Viejo CA 2007

Palm Springs CA 2007

Perris CA 2007

Pico Rivera CA 2007

Pomona CA 2007

Redlands CA 2007

Reseda CA 2007

Riverside CA 2007

San Bernardino CA 2007

Santa Clarita CA 2007

Santa Monica CA 2009

Upland CA 2007

Van Nuys Airport CA 2009

West LA CA 2007

Apalachicola FL 2005

Ft. Myers FL 2005

Gainesville FL 2005

Tallahassee FL 2005

Burlington IA 2004

Cedar Rapids IA 2004

Des Moines IA 2004

Dubuque IA 2004

Mason City IA 2004

Sioux City IA 2004
Meteorological Data Processing using AERMET July 28, 2006 

Rev. 05/12 

For HEM-AERMOD 

14 

City State Year

Waterloo IA 2004

Moline IL 2004

Iron Mountain MI 1999

Duluth MN 1999

Red Wing MN 1999

Herculaneum MO 1998

Omaha NE 2004

Sioux Falls SD 2004

Clintonville WI 1999

Eagle River WI 1999

Eau Claire WI 1999

Green Bay WI 1999

Hayward WI 1999

Juneau WI 1999

La Crosse WI 1999

Madison WI 1999

Manitowoc WI 1999

Marshfield WI 1999

Milwaukee WI 1999

Mineral Point WI 1999

Mosinee WI 1999

Oshkosh WI 1999

Rhinelander WI 1999

Sturgeon Bay WI 1999

Wausau WI 1999

Wisconsin Dells WI 1999

Wisconsin Rapids WI 1999

Parkersburg WV 2011
1

Appendix 3: 

Analysis of data on short-term emission rates 

relative to long-term emission rates 
2

Analysis of data on short-term emission rates 

relative to long-term emission rates 

Ted Palma 

Roy Smith 

EPA/OAQPS/SBAG 

1. Introduction 

1.1. The problem 

The process of listing hazardous air pollutants (HAPs) provided by the Clean Air Act (CAA, 

section 112(b)(2)) explicitly includes acute toxicity as a listing criterion. For this reason, in 

addition to chronic exposures, EPA considers acute exposures in risk-based decision-making for 

the HAP regulatory program. Estimating acute exposures via dispersion modeling requires input 

data on hourly meteorological conditions (available for most areas of the US) and short-term 

emission rates of individual facilities (almost universally absent from the National Emissions 

Inventory (NEI), the Toxic Release Inventory (TRI), and state emission databases). 

Lacking short-term emission rates, we must estimate peak short-term rates based on annual 

average rates, which are available. For Risk and Technology Review (RTR) rulemakings, we 

have assumed that the 1-hour emission rate for each facility could exceed the annual average 

hourly emission rate by as much as tenfold, and further assumed that this tenfold emission spike 

could coincide with worst-case meteorological conditions and the presence of a human receptor 

at the facility boundary, as a means of screening for potentially significant acute exposures. 

In a consultation on the “RTR Assessment Plan”, a panel of the EPA’s Science Advisory Board 

(SAB), several reviewers questioned the appropriateness of the factor of ten; some even 

suggested that this tenfold assumption may underestimate actual maximum short-term emissions 

for some facilities, and thereby also underestimate maximum acute risks. The SAB 

recommended an analysis of available short-term emissions data for HAP to test this assumption. 

This analysis responds to that SAB recommendation and attempts to test the protectiveness of the 

tenfold assumption using a database of “event emissions” collected from facilities in the 

Houston-Galveston area, to compare events representative of HAP releases to long-term release 

rates. We welcome comments from the public on the methods used and the conclusions reached 

by this analysis. 

2. Methods 

2.1. Texas Commission on Environmental Quality event emissions database 

The Texas Commission on Environmental Quality (TCEQ) collects emissions data using online 

reporting required of any facility releasing 100 pounds or more of a listed chemical (primarily 

ozone-forming VOCs) during a non-routine event. The TCEQ data are intended to improve the 

state’s knowledge of how short-term releases affect tropospheric ozone levels in that area. The 

database we utilized in our analysis was a subset of the TCEQ data covering emission events that 
3

occurred in an eight-county area in eastern Texas during a 756-day period between January 31, 

2003 and February 25, 2005. 

The complete emissions event data were obtained in April 2007 from Cynthia Folsom Murphy, a 

research scientists with the University of Texas at Austin (UTA) Center for Energy and 

Environmental Resources. The data were provided in four Excel spreadsheets generated from an 

original MS Access file. We used these Excel files to reconstruct a MS Access database in order 

to facilitate selection of a representative subset of records for this analysis. 

Although some of the released substances were HAPs, this was incidental to the database’s 

primary purpose of enhancing the TCEQ’s knowledge of photochemical activity. Thus, more 

than 80% of the released mass was ethene and propene, neither of which are HAPs. The 

database included release events caused by accidents, equipment failures, maintenance, startup, 

and shutdown. It also contained facility names, information on amounts of individual 

compounds released. To provide a basis for comparing the event releases with “typical” 

emissions, the UTA staff included total VOC emissions data for each facility for calendar year 

2004, obtained from the EPA Toxic Release Inventory (TRI). The database did not contain any 

records for facilities that did not experience any reportable events during this period. 

2.2. Data filtering 

Because the event release data were intended for modeling short-term releases of ozoneproducing VOCs, the database includes releases from accidents (which are regulated under 

section 112(r) of the CAA and are therefore not considered in residual risk assessments) and 

releases of light hydrocarbon compounds that are not HAPs and are much more volatile than 

most HAPs. This intent of this analysis, on the other hand, was to evaluate short-term releases of 

HAPs due to normal process variability or scheduled startups, shutdowns, and maintenance, 

relative to long-term release rates. Because the full emission events database was not 

representative of likely HAP emissions normally considered under the residual risk program, we 

filtered the release data as follows in an attempt to improve its representativeness: 

1. Hydrocarbons of C5 or less were dropped, except that all HAPs (including non-VOCs) 

were retained regardless of molecular structure; 

2. Accidental releases were dropped, but all others (including startup, shutdown, and 

maintenance) were retained; 

3. Only facilities whose long-term VOC releases exceeded 0.068 tons per day (25 tons per 

year) were retained, to approximate the population of facilities likely to be subject to 

residual risk standards (i.e., major facilities); 

4. A few release records had to be dropped because their facility numbers did not link to any 

facility in the database; 

5. A few facilities had to be dropped because the database did not include their 2004 TRI 

VOC release information. 

2.3. Analysis 
4

Annual VOC emissions and emission event release data were both converted to lb/hr. In order to 

conform to our atmospheric dispersion models, which estimate ambient concentrations for 

periods of 1 hour or more, amounts released during events shorter than 1 hour were assigned to 

the whole hour. For example, a release of 100 lb in ten minutes was converted to 100 lb/hr. 

Events longer than 1 hour were converted normally, e.g., a release of 100 lb in 120 minutes was 

converted to 50 lb/hr. The event release rates for individual compounds were summed, yielding 

a total release rate for each event. This total release rate for each event was divided by the 

annual VOC release rate for the facility to derive the ratio of peak-to-mean emission rate for the 

event. 

3. Results and Discussion 

3.1. Database filtering 

The original database contained 505 individual contaminants, including multiple redundancies. 

These redundancies did not affect this analysis, so we did not resolve them. After filtering out 

light, non-HAP, VOCs, 317 contaminants remained (Table 1). 

The database contained release records for 150 unique facilities. Of these, 48 facilities (Table 2) 

were major VOC emitters that reported releases of at least one of the contaminants in Table 1. 

The database contained 3641 individual release events reported by the original 150 facilities. Of 

these, 319 events involved a Table 1 contaminant released by a Table 2 facility during startup, 

shutdown, or maintenance. For evaluating short-term releases for residual risk assessments, 

these 319 events comprise the most representative subset of the full database. 

3.2. Descriptive statistics 

For this subset of emission events, ratios of event release rate to long-term release rate varied 

from 0.00000004 to 74. Distribution statistics appear in Tables 3 and 4. The 99th percentile ratio 

was 9 (i.e., an event release rate nine times the long-term average). Only 3 ratios exceeded our 

default assumption of 10, and of these only one exceeded 11. The full cumulative probability 

density of the ratios is shown in Figure 1. 

Figure 2 shows the relationship between ratio and event duration. As expected, the ratio 

declined as duration increased. Only 18 events lasted less than 2 hours, but these events 

produced the three highest ratios. Figure 3 is a similar ratio vs. duration plot, but with duration 

as a percentage of total time. Only 35 events exceeded 1% of the total period covered by the 

database. Figure 4 shows the relationship between ratio and total amount released, and suggests 

that the highest ratios were produced by facilities whose routine VOC emissions were relatively 

small. Thus, the events themselves also tended to be relatively small in absolute terms. 

3.3. Discussion 

These results suggest that the tenfold ratio assumption for short-term releases is protective, and 

that the facilities for which it may underestimate event releases may tend to be smaller emitters. 

However, this analysis is limited in the following ways by the nature of the database and the 

filtering that we applied: 
5

1. The only long-term release data available from the database were total VOC emissions 

for 2004. Ideally, we would have preferred to have routine release rates for each 

individual contaminant. However, retrieving these data from other sources and linking 

them to this database was not feasible. 

2. Removing VOCs that are not representative of HAPs, and comparing the releases against 

all VOCs, would tend to underestimate the true ratios. This effect could be quantitatively 

large. 

3. Retaining HAPs that are not VOCs (such as toxic metals), and including them in the total 

to be compared against all VOCs, would tend to overestimate the true ratios. The size of 

this effect is not known, but seems likely to be less than for (2) above. 

4. The database contains only facilities that had at least one release event during the 

reporting period. The number of facilities in the statistical population that did not 

experience an event is not known. The lack of data for these facilities (whose ratios in 

this analysis would have been zero) would cause the descriptive statistics to be skewed 

toward an overestimate. The size of this effect is unknown. 

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Methyloctane No 3221-61-2 90008 

2-Methylpentane No 107-83-5 43229 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

2,2,3-Trimethylpentane No 564-02-3

2,2,4-Trimethylpentane Yes 540-84-1 43250 

dimethyl butane No 75-83-2 43291 

2,3-Dimethylbutane No 79-29-8 43276 

2,3,4-Trimethylpentane No 565-75-3 43252 

2,3-Dimethylbutane No 79-29-8 43276 

2,4-Dimethylpentane No 108-08-7 43247 

2-methylheptane No 592-27-8 43296 

2-methylhexane No 591-76-4 43263 

2-Methylpentane No 107-83-5 43229 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 

3-Methylheptane No 589-81-1 43253 

3-Methylhexane No 589-34-4 43295 

3-Methylpentane No 96-14-0 43230 
6

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Acetaldehyde Yes 75-07-0 43503 

Acetic Acid No 64-19-7 43404 

Acetonitrile Yes 75-05-8 70016 

Acetophenone Yes 98-86-2

Acrolein Yes 107-02-8 43505 

Acrylic acid Yes 79-10-7 43407 

Acrylonitrile Yes 107-13-1 43704 

alkylphenol No none

Benzene Yes 71-43-2 45201 

Benzo[a]anthracene Yes 56-55-3 46716 

Benzo[a]pyrene Yes 50-32-8 46719 

Benzo[b]fluoranthene Yes 205-99-2 46717 

Biphenyl Yes 92-52-4 45226 

Butanol No 35296-72-1

Butyl Acrylate No 141-32-2 43440 

t-Butyl Alcohol No 75-65-0 43309 

butylcyclohexane No 1678-93-9 90101 

Butyraldehyde No 123-72-8 43510 

C9 Aromatics No none

Naphthalene Yes 91-20-3 46701 

Nonane No 111-84-2 43235 

C9+ No none

Carbon tetrachloride Yes 56-23-5 43804 

Carbonyl Sulfide Yes 463-58-1 43933 

Chloral No 75-87-6

Trichloromethane Yes 67-66-3 43803 

Chlorothalonil No 1897-45-6

Petroleum No 8002-05-9

Petroleum No 8002-05-9

Cumene Yes 98-82-8 45210 

Cyclohexane No 110-82-7 43248 

Cyclohexanol No 108-93-0 43317 

Cyclohexanone No 108-94-1 43561 

Cyclohexanone No 108-94-1 43561 

Decane No 124-18-5 43238 

Decane No 124-18-5 43238 

1,2-Dichloroethane No 107-06-2 43815 

Diethylbenzene (mixture) No 25340-17-4 45106 

Methyl Ether No 115-10-6 43350 

Dimethylcyclohexane No 27195-67-1 98059 

Dimethylcyclopentane No 28729-52-4 90064 
7

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Dimethylcyclopentane No 28729-52-4 90064 

Dimethyl formamide Yes 68-12-2 43450 

Dimethylhexane No 28777-67-5 90067 

Dimethyl pentane No 38815-29-1 90063 

Epichlorohydrin Yes 106-89-8 43863 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Acrylate Yes 140-88-5 43438 

Ethyl Alcohol No 64-17-5 43302 

Ethyl Benzene Yes 100-41-4 45203 

Ethyl Chloride Yes 75-00-3 43812 

Ethylcyclohexane No 1678-91-7 43288 

ethylacetylene No 107-00-6 43281 

Ethyl Benzene Yes 100-41-4 45203 

Ethylene Oxide Yes 75-21-8 43601 

ethylmethylbenzene No 25550-14-5 45104 

formaldehyde Yes 50-00-0 43502 

Furfural No 98-01-1 45503 

straight-run middle distillate No 64741-44-2

Gasoline No 86290-81-5

Gasoline No 86290-81-5

Heavy Olefins No none

n-Heptane No 142-82-5 43232 

n-Heptane No 142-82-5 43232 

Heptylene No 25339-56-4

hexane Yes 110-54-3 43231 

hexane Yes 110-54-3 43231 

2-Methylpentane No 107-83-5 43229 

hexane Yes 110-54-3 43231 

Hexene No 25264-93-1 43289 

Indeno[1,2,3-cd]pyrene Yes 193-39-5 46720 

Isobutyraldehyde No 78-84-2 43511 

2-Methyl-1-propanol No 78-83-1 43306 

2-Methyl-1-propanol No 78-83-1 43306 

Isobutyraldehyde No 78-84-2 43511 

Isoheptanes (mixture) No 31394-54-4 43106 

2-Methylpentane No 107-83-5 43229 

2,2,4-Trimethylpentane No 540-84-1 43250 

2,2,4-Trimethylpentane No 540-84-1 43250 

Isopar E No 

Isoprene No 78-79-5 43243 

2-Propanol No 67-63-0 43304 
8

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2-Propanol No 67-63-0 43304 

Cumene Yes 98-82-8 45210 

Isopropylcyclohexane No 696-29-7 90128 

Diisopropyl ether No 108-20-3 85005 

Kerosene No 64742-81-0

Methyl ethyl ketone No 78-93-3 43552 

Methyl isobutenyl ketone Yes 141-79-7

Methanol Yes 67-56-1 43301 

Methyl Acetylene No 74-99-7 43209 

Cresol Yes 1319-77-3 45605 

Methyl Chloride Yes 74-87-3 43801 

methyl cyclohexane No 108-87-2 43261 

Methyl ethyl ketone No 78-93-3 43552 

Iodomethane No 74-88-4 86025 

Methyl Mercaptan No 74-93-1 43901 

methyl cyclohexane No 108-87-2 43261 

Methylcyclopentane No 96-37-7 43262 

2-Methyldecane No 6975-98-0 98155 

Methylheptane No 50985-84-7 90045 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

Tert-butyl methyl ether No 1634-04-4 43376 

meta-xylene No 108-38-3 45205 

Nonane No 111-84-2 43235 

Naphtha No 8030-30-6 45101 

Naphthalene Yes 91-20-3 46701 

Naphtha No 8030-30-6 45101 

Naphthalene No 91-20-3 46701 

Butyl acetate No 123-86-4 43435 

Butyraldehyde No 123-72-8 43510 

Nonane No 111-84-2 43235 

Nonane No 111-84-2 43235 

Octadecene No 27070-58-2

n-Octane No 111-65-9 43233 

Octene (mixed isomers) No 25377-83-7

ortho-xylene No 95-47-6 45204 

Parathion Yes 56-38-2

4-Aminohippuric Acid No 61-78-9

Phenol Yes 108-95-2 45300 

Silicone No 63148-62-9

Naphtha No 8030-30-6 45101 
9

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

Naphtha No 8030-30-6 45101 

Polyethylene No 9002-88-4

Poly(Isobutylene) No 9003-27-4

Chloromethyl pivalate No 18997-19-8

Process fuel gas No none

Propionic Acid No 79-09-4 43405 

Propylene oxide No 75-56-9 43602 

para-xylene No 106-42-3 45206 

Styrene Yes 100-42-5 45220 

Sulfolane No 126-33-0

t-Butyl Alcohol No 75-65-0 43309 

t-Butyl Alcohol No 75-65-0 43309 

tert-butyl hydroperoxide No 75-91-2

Toluene Yes 108-88-3 45202 

Aqualyte(TM), LSC cocktail No 25551-13-7 45107 

1,3,4-Trimethylbenzene No 95-63-6 45208 

trimethylcyclopentane No 30498-64-7 98058 

trimethylpentane No 29222-48-8 90092 

Undecane No 1120-21-4 43241 

Vinyl acetate Yes 108-05-4 43453 

Vinyl acetate Yes 108-05-4 43453 

Vinyl chloride Yes 75-01-4 43860 

vinyl resin No none

Vinylcyclohexane No 695-12-5

xylenes Yes 1330-20-7 45102 

xylenes Yes 1330-20-7 45102 

meta-xylene Yes 108-38-3 45205 

ortho-xylene Yes 95-47-6 45204 

para-xylene Yes 106-42-3 45206 

Mineral spirits No 64475-85-0 43118 

Propylene glycol No 57-55-6 43369 

Vinyl chloride Yes 75-01-4 43860 

1-Decene No 872-05-9 90014 

2-Ethyl-1-hexanol No 104-76-7 43318 

2-Pyrrolidone No 616-45-5

Aromatic No none

Decene No 25339-53-1 90014 

2-N,N-Dibutylaminoethanol No 102-81-8 86007 

Diisopropanolamine No 110-97-4 86004 

N,N-Dimethylethanolamine No 108-01-0 84004 

trifluoroethane No 27987-06-0
10

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

2,2'-Oxybisethanol No 111-46-6 43367 

Hydrocarbons No none

Methyl Formate No 107-31-3 43430 

Isopropylamine No 75-31-0 86014 

n-Butanol No 71-36-3 43305 

Polypropylene glycol ether No 

N-Vinyl-2-Pyrrolidinone No 88-12-0

1,1-Di(t-Amylperoxy) 

Cyclohexane No 15667-10-4

1,2,3-Trimethyl-4-ethylbenzene No none

2-Methyldecane No 6975-98-0 98155 

2-methylheptane No 592-27-8 43296 

2-Methyl nonane No 871-83-0 90047 

2,5-Dimethylhexane-2,5-

dihydroperoxide No 3025-88-5

Butyl ether No 142-96-1 43372 

1,2-Dichloroethane Yes 107-06-2 43815 

Hydrindene No 496-11-7 98044 

Methylheptane No 50985-84-7 90045 

methyl methacrylate No 80-62-6 43441 

Naphtha No 8030-30-6 45101 

hexane Yes 110-54-3 43231 

tert-amyl hydroperoxide No 3425-61-4

1,3,4-Trimethylbenzene No 95-63-6 45208 

n-Butanol No 71-36-3 43305 

2-Butoxy ethanol Yes 111-76-2 43308 

hexane Yes 110-54-3 43231 

cycloheptane No 291-64-5 43115 

n-Heptane No 142-82-5 43232 

n-Octane No 111-65-9 43233 

Hexyl Carbitol No 112-59-4

Nonene No 27215-95-8

Silane, ethenyltrimethoxy No 2768-02-7

tetrahydrofuran No 109-99-9 70014 

Vinyl chloride Yes 75-01-4 43860 

Methyl Formate No 107-31-3 43430 

Phenyl ether No 101-84-8

phosgene Yes 75-44-5

1,2-Dichloroethane No 107-06-2 43815 

2-Butoxy ethanol Yes 111-76-2 43308 

Gasoline No 86290-81-5

1-Tridecanol No 112-70-9
11

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2,4-Trichlorobenzene Yes 120-82-1 45208 

2-(2-Butoxyethoxy)ethanol Yes 112-34-5 43312 

2,3,4-trihydroxybenzophenone 

Ester No 1143-72-2

Methyl n-amyl ketone No 110-43-0 43562 

4,4-Cyclohexylidenebis[phenol] No 843-55-0

Anisole No 100-66-3

2-Butoxy ethanol Yes 111-76-2 43308 

Cresol-Formaldehyde novolac 

Resin No proprietary

Decane No 124-18-5 43238 

gamma-Butyrolactone No 96-48-0

Dimethyl pentane No 38815-29-1 90063 

Dodecyl Benzenesulfonic Acid No 27176-87-0

Ethanol Amine No 141-43-5 43777 

ethyl lactate No 687-47-8

Hexamethyldisilazane No 999-97-3

Methyl ethyl ketone No 78-93-3 43552 

Cresol Yes 1319-77-3 45605 

Naphthalene Sulfonic Acid Resin No 

Naphthalene Sulfonic Acid Resin No 

n-Butanol No 71-36-3 43305 

Decane No 124-18-5 43238 

1-Methyl-2-pyrrolidinone No 872-50-4 70008 

Pentyl Ester Acetic Acid No 

Phenol Formaldehyde Resin, 

Novolac No 

Phenol Formaldehyde Resin, 

Novolac No 

Propylene Glycol Monomethyl 

Ether No 107-98-2 70011 

Pyrocatechol No 120-80-9

Carbon Disulfide Yes 75-15-0 43934 

Hexene No 592-41-6 43245 

VOC No none

Methacrylic acid No 79-41-4 84009 

Methyl 3-hydroxybutyrate No 1487-49-6

t-Butyl Alcohol No 75-65-0 43309 

methyl valeraldehyde No 123-15-9

Butyl Methacrylate No 97-88-1 85008 

dipropyl ether No 111-43-3

n-Propanol No 71-23-8 43303 

Propyl propionate No 106-36-5 86052 
12

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

1,2-Epoxybutane Yes 106-88-7

Methylamine No 74-89-5

1,1-Dimethylcyclohexane No 590-66-9

1,1-Dimethylcyclopentane No 1638-26-2

2-Methylpentane No 107-83-5 43229 

dimethyl butane No 75-83-2 43291 

2,3,3-Trimethylpentane No 560-21-4

2,3-Dimethylhexane No 584-94-1

2,3-Dimethylpentane No 565-59-3

2,4-Dimethylhexane No 589-43-5

2,5-Dimethyl-hexane No 592-13-2

2-Butoxy ethanol Yes 111-76-2 43308 

2-mercaptoethanol No 60-24-2

Bisphenol A No 80-05-7

straight-run middle distillate No 64741-44-2

4-Vinylcyclohexene No 100-40-3

straight-run middle distillate No 64741-44-2

Allyl alcohol No 107-18-6

xylenes Yes 1330-20-7 45102 

Naphthalene Yes 91-20-3 46701 

3-Methylethylcyclohexane No 

VOC No none

Gasoline No 86290-81-5

Butyl ether No 142-96-1

dimethyl butane No 75-83-2

Dodecene No 25378-22-7

Styrene Yes 100-42-5 45220 

tetrahydrofuran No 109-99-9 70014 

hexane Yes 110-54-3 43231 

2-Propanol No 67-63-0 43304 

liquified petroleum gas No 68476-85-7

Methyl acetylene propadiene No 

methyl isobutyl ketone Yes 108-10-1

Methyl n-amyl ketone No 110-43-0 43562 

Methylpentane No 43133-95-5

Tert-butyl methyl ether Yes 1634-04-4 43376 

Toluene Yes 108-88-3 45202 

Mineral oil No 8012-95-1

Gasoline No 86290-81-5

2,2-Dimethylpropane No 463-82-1 43222 

n-propylbenzene No 103-65-1
13

Table 1. Event emissions in the Houston-Galveston area. 

Representative contaminants included in the analysis, selected because 

they are either HAPs or VOCs with more than 5 carbon atoms. (These 

data were retrieved directly from the original database, which included 

multiple redundancies that did not affect the analysis and were left 

intact.) 

Contaminant HAP CAS SAROAD 

propylcyclohexane No 1678-92-8

n-Octane No 111-65-9 43233 

ortho-xylene No 95-47-6 45204 

Gasoline No 86290-81-5

propylenimine No 75-55-8

Gasoline No 86290-81-5

Technical White Oil No 

Total Alkylate - non-speciated No 

Trichloroethylene Yes 79-01-6

Di(2-ethylhexyl) 

peroxydicarbonate No 16111-62-9

trimethylcyclopentane No 30498-64-7 98058 

Ultraformate No 

4-Vinylcyclohexene No 100-40-3
14

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

ATOFINA PETROCHEMICALS LA PORTE PLANT 47.88 

BALL METAL BEVERAGE CONTAINER CONROE 

FACILITY 

24.18 

BASF FREEPORT SITE 46.47 

BELVIEU ENVIRONMENTAL FUELS 112.3 

BOC GROUP CLEAR LAKE BOC GASES PLANT 9.52 

BP AMOCO CHEMICAL CHOCOLATE BAYOU PLANT 130.4 

BP AMOCO CHEMICAL PASADENA PLANT 36.92 

BP AMOCO POLYMERS 57.18 

BP PRODUCTS NORTH AMERICA TEXAS CITY 737.4 

BP TEXAS CITY CHEMICAL PLANT B 112.2 

CELANESE BAY CITY PLANT 17.12 

CELANESE CLEAR LAKE PLANT 53.11 

CELANESE PASADENA PLANT 5.934 

CHEVRON PHILLIPS CEDAR BAYOU PLANT 105.3 

CHEVRON PHILLIPS CHEMICAL SWEENY COMPLEX 106.7 

CHEVRON PHILLIPS HOUSTON CHEMICAL COMPLEX 215.7 

CROWN BEVERAGE PACKAGING 18.05 

CROWN CENTRAL PETROLEUM PASADENA PLANT 114.3 

CROWN CORK & SEAL 18.10 

DEER PARK LIQUID STORAGE TERMINAL 124.8 

DOW CHEMICAL LA PORTE SITE 5.902 

DOW TEXAS OPERATIONS FREEPORT 203.2 

E I DUPONT DE NEMOURS AND COMPANY - LA 

PORTE PLANT 

51.30 

EQUISTAR CHEMICALS CHANNELVIEW COMPLEX 275.4 

EQUISTAR CHEMICALS CHOCOLATE BAYOU 

COMPLEX 

84.87 

EQUISTAR CHEMICALS LA PORTE COMPLEX 90.97 

EXXON MOBIL CHEMICAL BAYTOWN OLEFINS PLANT 84.73 

EXXONMOBIL CHEMICAL BAYTOWN CHEMICAL 

PLANT 

313.7 

EXXONMOBIL CHEMICAL MONT BELVIEU PLASTICS 

PLANT 

40.64 

GOODYEAR HOUSTON CHEMICAL PLANT 85.68 

ISP TECHNOLOGIES TEXAS CITY PLANT 22.12 

KANEKA TEXAS CORPORATION 20.55 

KINDER MORGAN LIQUID TERMINALS PASADENA 913.9 

KINDER MORGAN LIQUIDS TERMINALS 132.7 

LBC HOUSTON BAYPORT TERMINAL 12.83 

LYONDELL CHEMICAL BAYPORT PLANT 30.04 

LYONDELL CHEMICAL CHANNELVIEW 74.15 

MARATHON ASHLAND PETROLEUM TEXAS CITY 

REFINERY 

111.8 

MOBIL CHEMICAL HOUSTON OLEFINS PLANT 26.29 

MORGANS POINT PLANT 31.03 

PASADENA PLANT 13.40 
15

Table 2. Event emissions in the Houston-Galveston area. Major emitters 

reporting at least one release event of a representative substance. 

Company Name 

2004 VOC Emission 

Rate (lb/h) 

SHELL OIL DEER PARK 405.2 

SOLUTIA CHOCOLATE BAYOU PLANT 53.09 

STOLTHAVEN HOUSTON TERMINAL 7.347 

SWEENY COMPLEX 157.1 

UNION CARBIDE TEXAS CITY OPERATIONS 174.4 

VALERO REFINING TEXAS CITY 260.1 

WHARTON GAS PLANT 7.552 

Table 3. Frequency distribution for ratio of event 

emission rate to long-term emission rate 

Bin Frequency 

Cumulative 

Frequency 

1.00E-08 0 0 

3.16E-08 0 0 

1.00E-07 2 2 

3.16E-07 1 3 

1.00E-06 0 3 

3.16E-06 2 5 

1.00E-05 1 6 

3.16E-05 2 8 

1.00E-04 5 13 

3.16E-04 9 22 

1.00E-03 15 37 

3.16E-03 28 65 

1.00E-02 33 98 

3.16E-02 41 139 

1.00E-01 59 198 

3.16E-01 38 236 

1.00E+00 33 269 

3.16E+00 31 300 

1.00E+01 16 316 

3.16E+01 2 318 

1.00E+02 1 319 

3.16E+02 0 319 
16

Table 4. Statistics for ratio of event emission 

rate to long-term emission rate 

Statistic for Ratio Value 

Median 0.043923 

75th %ile 0.342655 

90th %ile 2.204754 

95th %ile 3.344422 

96th %ile 3.400832 

97th %ile 3.8126 

98th %ile 4.790098 

99th %ile 8.973897 

Max 74.37138 

Average 0.815352 

Figure 1. Cumulative probability density for ratio of event to routine emission rates. 

Cumulative probability of event ratios

0

50

100

150

200

250

300

350

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04

Ratio of event emission rate to long-term emission rate

n
17

Figure 2. Relationship between ratio of event to duration emission rate and emission 

duration.

Event ratio vs. duration

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1 10 100 1,000 10,000 100,000

Event duration (min)

Ratio -- event emission rate to long-term emission rate

Figure 3. Relationship between ratio of event to duration emission rate and emission duration, as 

percentage of total time. 

Event ratio vs. duration

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

0% 1% 2% 3% 4% 5% 6% 7% 8%

Event duration (as % of total time)

Ratio -- event emission rate to long-term emission rate
18

Figure 4. Relationship between ratio of event to duration emission rate and total amount emitted 

during the event. 

Event ratio vs. 2004 VOC releases -- by event

1.E-08

1.E-07

1.E-06

1.E-05

1.E-04

1.E-03

1.E-02

1.E-01

1.E+00

1.E+01

1.E+02

1 10 100 1,000

Long-term VOC releases (lb/hr)

Ratio -- event emission rate to long-term emission rate
Appendix 4 

Detailed Risk Modeling Results 
Facility NEI ID Facility Name Address City C

NEI41320 CYTEC CARBON FIBERS 7139 AUGUSTA RD PIEDMONT G

Table 1 - Facility Identification Information

1 of 1
Cancer 

MIR

Cancer

 Incidence

Noncancer 

Max HI

Target 

Organ

Cancer 

MIR

Noncancer 

Max HI

Target 

Organ

NEI41320 1.8E-05 6.0E-03 1.3E-01 respiratory 1.8E-05 1.3E-01 respiratory 100%

1 BOLD indicates a cancer risk great than 1 in a million or a noncancer risk greater than 1

Table 2 – Maximum Predicted HEM-3 Chronic Risks

Actual Emissions

Facility NEI ID

 Category Chronic Risk 1 Facility Chronic Risk 1

SC % of 

Facility-wide 

Cancer Risk

1 of 1
REL AEGL1 AEGL2 ERPG1 ERPG2

NEI41320 Acrylonitrile 0.00E+00 7.80E-02 6.00E-03 3.55E-02 1.01E-02

 Note: BOLD indicates acute risks greater than 1

Facility NEI ID Pollutant

Table 3 – Maximum Predicted Acute Risks (HEM-3)

Actual Emissions

1 Some maximum acute impacts may be at onsite locations.

Maximum Hazard Quotient1

 1 of 1