Appendix 1

Technical Support Document for HEM-AERMOD Modeling

Modeling for the Residual Risk and Technology Review 

Using the Human Exposure Model 3 – AERMOD Version

Updated 01/08/2014

Technical Support Document

Prepared for: 

U.S. Environmental Protection Agency

Office of Air and Radiation 

Office of Air Quality Planning and Standards

Health and Environmental Impacts Division 

Air Toxics Assessment Group

Research Triangle Park, NC 27711

Prepared by:

EC/R Incorporated 

501 Eastowne Drive, Suite 250

Chapel Hill, North Carolina 27514
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Disclaimer

The research described in this document has been funded by the United States 

Environmental Protection Agency contracts 68-D-01-076, EP-D-06-119, and 

currently EP-W-12-011 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.
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Contents

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

2. Overview of the HEM-3 – 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 HEM-3 ........................................................... 6

2.2.2 Use of Dilution Factors................................................................................................... 7

2.3 Postprocessing of AERMOD Results in HEM-3 .................................................................. 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.............................................................................................................. 11

2.4 Data Libraries Used in HEM-3 ........................................................................................... 12

2.4.1 Chemical Health Effects Information........................................................................... 12

2.4.2 Census Block Locations and Elevation Data................................................................ 13

2.4.3 Meteorological Data ..................................................................................................... 14

2.4.4 Gaseous Deposition Parameters ................................................................................... 14

3. Modeling for the Residual Risk Technology Review............................................................... 15

3.1 Emission Source Inputs....................................................................................................... 15

3.2 Pollutant Cross-Referencing ............................................................................................... 15

3.3 Meteorological Data............................................................................................................ 16

3.4 Model Options Selected ...................................................................................................... 19

3.4.1 Urban or Rural Dispersion Characteristics................................................................... 19
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3.4.2 Deposition and Plume Depletion.................................................................................. 19

3.4.3 Cutoff Distance for Modeling of Individual Blocks..................................................... 20

3.4.4 Facility Boundary Assumptions................................................................................... 20

3.5 Modeling of Multiple Facilities .......................................................................................... 22

4. Quality Assurance..................................................................................................................... 23

4.1 Engineering Review ............................................................................................................ 23

4.2 Geographic Pre-Modeling Checks...................................................................................... 23

4.3 Geographic Post-Modeling Checks..................................................................................... 24

5. Uncertainties............................................................................................................................. 26

6. References................................................................................................................................. 32

Figures

Figure 3-1. AERMOD Meteorological Stations........................................................................... 18

Tables

Table 2-1. Parameters Used to Delineate the Modeling Domain in HEM-3 .................................. 4

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and Technology 

Review Assessments..................................................................................................................... 21

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review 

Risk Assessments.......................................................................................................................... 28
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1. Introduction

This document describes the general modeling approach used to estimate the risks to 

human populations in support of the Residual Risk and Technology Review (RTR) currently 

being carried out by the U.S. Environmental Protection Agency (EPA). It is important to note 

that risk characterizations of individual source categories under the RTR program may not 

follow every item/approach noted in this document. The reader is referred to the main body of 

the risk assessment document for more details on source category specific approaches that may 

have been included in the analysis. 

The model used in these risk assessments is the Human Exposure Model, Version 3 

(HEM-3). HEM-3 incorporates 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 HEM-3-AERMOD 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 HEM-3 – AERMOD System

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

exposure, and estimation of human health risks. The state-of-the-art American Meteorological 

Society (AMS) EPA Regulatory Model (AERMOD)1,2 is used for dispersion modeling.

AERMOD can handle a wide range of different source types which may be associated with an 

industrial source complex, including stack (point) sources, area and polygon sources, and volume 

sources. 

To prepare dispersion modeling inputs and carry out risk calculations, HEM-3 draws on 

four data libraries, which are provided with the model. The first is a library of meteorological 

data for over 824 stations, which are used for dispersion calculations. A second library of Census 

block (“centroid”) internal point locations and populations provides the basis of human exposure 

calculations. The Census library also includes the elevations of every Census block, which are 

used in the dispersion calculations for the RTR assessments. 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. The fourth data library, which 

provides deposition parameters for gaseous pollutants, is used only when the user opts to 

compute deposition and plume depletion (not computed for the RTR assessments to date). 

HEM-3 has been implemented in two versions: a single facility version, and a multiple 

facility version (“Multi HEM-3”). Multi HEM-3 is used in the RTR risk assessment modeling. 

Both versions operate under the same general principles. In essence, Multi HEM-3 provides a 

platform for running the single facility 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. Single HEM-3 includes a graphical user interface (GUI) for 

the selection of various dispersion modeling options. In Multi HEM-3, 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 (or “centroids”), at concentric rings 

surrounding the facility center, 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, HEM-3 estimates the total incremental cancer risks for people living within 

different distances of the modeled emission sources. 

Potential noncancer health effects due to chronic exposures are quantified using hazard 

quotients and hazard indices for various target organs. The “hazard quotient” (HQ) for a given 

chemical and receptor site is the ratio of the ambient concentration of the chemical to the 

reference concentration. The “hazard index” (HI) for a given organ is the sum of hazard 

quotients for substances that affect that organ. HEM-3 computes target-organ-specific hazard

indices (TOSHI) for HAPs and other toxic air pollutants, and estimates the numbers of people 
3

exposed to different hazard index levels. In addition, maximum short term (“acute”) 

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 HEM-3–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 HEM-3 to calculate risks 

and exposures, and Section 2.4 details the sources and methods used to produce HEM-3’s data 

libraries. The HEM-3 User’s Manuals – for single HEM-3 and Multi HEM-3 – provide 

additional details on the input data and algorithms used in the model.3 Specific model options

used in the RTR assessments are discussed in Chapter 3.

2.1 Preparation of Dispersion Modeling Inputs 

HEM-3 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. HEM-3 can analyze point sources, 

area and polygon sources, and volume sources - configurations that are described in AERMOD's 

documentation.1, 2 For stack (point) 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 provide the 

terrain elevation at the base of each source. (The controlling hill height is also used in 

AERMOD’s flow calculations. Calculation of the controlling hill height by HEM-3 is discussed 

in Section 2.4.2.) If the terrain elevations are not provided by the user, HEM-3 will calculate 

elevations and controlling hill heights based on elevations and hill heights provided by the 

Census database for the Census blocks nearest to the facility. 

If particulate deposition and plume depletion are to be considered, then HEM-3 requires a 

third input file to specify the particle size distribution. This input file must include the average 

particle diameter, the mass fraction percentage, and the average particle density for each size 

range emitted. Another optional file can be used to specify building dimensions if building wake 

effects are to be modeled.
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2.1.2 Defining the Modeling Domain and Receptors 

HEM-3 defines a modeling domain for each facility that is analyzed based on parameters 

specified by the model user or calculated by the model. 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). HEM-3 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 

generally set to 3 km for the RTR assessments, although it can be set differently 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 “user receptors” may 

include facility boundary locations, monitoring sites, individual residences, schools, or other 

locations of interest. 

For Census blocks in the outer group, 

beyond this modeling cutoff distance, 

emission 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 facility 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 within AERMOD. The cutoff distance for modeling of 

individual Census blocks may be chosen by the user, but is typically 3000 meters for the RTR 

assessments. 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.

HEM-3 checks Census blocks that are very close to the facility in order to assess whether 

they overlap any point, area or volume emission sources. In addition, the user can specify an 

overlap distance, within which receptors will be considered to be on facility property. The 

default value for the overlap distance is 30 meters, or approximately equal to the width of a 

narrow buffer and a roadway. HEM-3 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 

Table 2-1. Parameters Used to Delineate the 

Modeling Domain in HEM-3

Parameter

Typical 

value

Modeling domain size – maximum 

radial distance to be modeled from 

facility center

50 km

Cutoff distance for modeling of 

individual blocksa 3,000 m

Overlap distance – where receptors 

are considered on facility propertya 30 m

Polar receptor network:

Distance to the innermost ringb ≥100 m

Number of concentric rings 13

Number of radial directions 16 a Measured from each stack at the facility, and from the 

 edges of each area or volume source

b Generally model-calculated to encompass all emission 

 sources but not less than 100 meters from the facility 

 center
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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 user-specified receptor locations.

Polar receptor network 

The polar receptor network used in HEM-3 serves three functions. First, it is used to 

estimate default impacts if one or more Census locations are inside the overlap cutoff distance 

used to represent the facility boundary. Second, it is used to evaluate potential acute effects that 

may occur due to short-term exposures in locations outside the facility 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. 

Generally, the model calculates the inner radius (or first ring distance) for the polar 

receptor network to be just outside the emission source locations, but not less than 100 meters 

from the facility center. However, the user can override the default distance calculated by the 

model to fit the size and shape of the facility properties to be modeled. Likewise, the model will 

also use default values for the number of concentric rings to be analyzed (13 rings by default), 

and the number of radial directions (16 radials by default), although these default values can also 

be changed by the user to meet the needs of a specific modeling study. The inner radius of the 

polar network should be the minimum distance from the facility center that is generally outside 

of facility property. (For complex facility shapes, it is sometimes useful to specify an inner ring 

that encroaches on facility property in some directions.) HEM-3 will distribute the radial 

directions evenly around the facility. For the concentric rings, the model will generate a 

logarithmic progression of distances starting at the inner ring radium and ending at the outer 

radium of the modeling domain. 

Elevations and hill heights for model receptors 

HEM-3 includes terrain elevations by default for the RTR assessments, but the user can 

choose to exclude terrain effects when running AERMOD. If the default terrain option is used, 

HEM-3 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. 

Elevations and controlling hill heights for the polar grid receptors are also estimated 

based on values from the Census library. HEM-3 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.
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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 HEM-3 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, HEM-3 will select the 

closest station to the center of the modeling domain, as is generally done for the RTR 

assessments.

2.2 Running of AERMOD

Based on the user input data and other data described in the previous section, HEM-3 

produces an input file suitable for AERMOD. HEM-3 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 HEM-3.

2.2.1 AERMOD Dispersion Options Used by HEM-3

AERMOD provides a wide array of options for controlling dispersion modeling 

calculations. In general, HEM-3 uses the regulatory default options when running AERMOD.1

These options include the following: 

• Use stack-tip downwash (except for Schulman-Scire downwash);

• Use buoyancy-induced dispersion (except for Schulman-Scire downwash);

• Do not use gradual plume rise (except for building downwash);

• Use the “calms processing” routines;

• Use upper-bound concentration estimates for sources influenced by building downwash

from super-squat buildings;

• Use default wind profile exponents;

• Use low wind speed threshold;

• Use default vertical potential temperature gradients;

• Use of missing-data processing routines; and

• Consider terrain effects.

The following additional AERMOD options are available to the HEM-3 user: 

• Calculation of wet and dry deposition rates for vapor and particulate matter;

• Consideration of plume depletion (due to deposition) when calculating air concentrations;

• Consideration of building wake effects;

• Calculation of short term (acute) impacts; and 

• Use of the FASTALL option, which conserves model runtime by simplifying the 

AERMOD algorithms used to represent meander of the pollutant plume. 
7

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of 

building wake effects require additional user inputs. 

The user can opt to analyze short term impacts on a number of different time scales (i.e., 

1 hour, 6 hours, 8 hours, or 24 hours) 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. For RTR assessments, acute impacts are generally included in the modeling and the 

default multiplier of 10 is used, unless more source-specific information is available upon which 

to base the acute factor for the source category being modeled.

2.2.2 Use of Dilution Factors 

To save computer run time when analyzing the impacts of multiple pollutants, HEM-3 

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 and plume depletion, the dilution factor does 

not vary from pollutant to pollutant. If deposition and depletion is chosen as a model option, 

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 HEM-3, 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 HEM-3 

HEM-3 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 (MIR) 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.

For acute impacts, HEM-3 calculates the maximum short term concentrations for all 

pollutants emitted by the facility. These maximum short term concentrations are compared with 

various threshold levels for acute health effects (e.g., the California EPA reference exposure 

level [REL] for no adverse effects). 
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At the option of the model user, HEM-3 will also compute the long term and short term 

predicted ambient concentrations of all pollutants emitted by the facility 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, HEM-3 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. HEM-3 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 [(μg/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 type j (particulate or gas)

 Ei,k = emissions of pollutant k from source i and in pollutant type j

 UREk = cancer unit risk factor for pollutant k

 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 = non-cancer health effect reference concentration for pollutant k

 
9

The above equations are equivalent to the following simpler equations: 

CRT = ∑i,k ACi,k × UREk

HIT = ∑i,k ACi,k / RCk

where: 

ACi,k = ambient concentration (μg/m3) for pollutant k at the given receptor. This is the same 

 as [Ei,k × DFi.j × CF]

However, use of these simpler equations would require modeling all pollutants individually in 

AERMOD, and performing separate risk calculations for each pollutant. 

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. 

HEM-3 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 = Ei,k x DFi,j × CF

Short term concentrations: 

ACT = ∑i ACi,k

ACi,k = Ei,k x DFi,j × CF × M

where: 

ACT,k = total estimated ambient concentration for pollutant k at a given receptor

 ∑i = the sum over all sources i (μg/m3)

ACi,k = estimated ambient concentration of pollutant k at the given receptor as a result of 

emissions from source i (μg/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 cutoff distance 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. If terrain elevation is part of the modeling, then an 

elevation is estimated for each polar receptor. HEM-3 estimates elevations and controlling hill 

heights for the polar grid receptors based on values from the census library. HEM-3 divides the 
10

modeling domain into sectors based on the polar grid receptor network, with each census block 

assigned to the sector corresponding to the closest polar grid receptor. 

HEM-3 then assigns each polar grid receptor 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. 

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 = IA1,r + (IA2,r – IA1,r) × (a – A1) / (A2 – A1)

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 receptor

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, HEM-3 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
11

Furthermore, HEM-3 estimates the contributions of different chemicals and emission sources to 

total annual cancer incidence for the overall modeling domain using the following equations:

TCRi,j = ∑m [(∑k Ei,k × UREk) × DFi,j,m × CF / LT]

TCRi,k = TCRi j × Ei,k × UREk / (∑k Ei,k × UREk)

where: 

TCRi,j = the estimated total annual cancer incidence (cancers/year) to the population in the 

modeling domain due to emissions from pollutant type j (1 = particulate, 2 = gas) and 

emission source i

 ∑m = the sum over all Census blocks m within distance the modeling domain

 ∑k = the sum over all pollutant k, within pollutant type j

 Ei,k = emissions of pollutant k from source i (tons/year)

UREk = unit risk factor for pollutant k

DFi,j,m = dilution factor at receptor m, for emissions of pollutant type j (which includes 

pollutant k), from source i

 CF = conversion factor, 0.02877 [(g/sec) / (ton/year)]

TCRi,k = the estimated annual cancer incidence (cancers/year) of the population in the

modeling domain due to emissions of pollutant k (in pollutant type j) from emission 

source i

2.3.4 Model Outputs

The following is a summary of the outputs produced by HEM-3. These are written to a 

collection of files in ExcelTM and dBaseTM format (dbf).

• Maximum long term impacts at populated locations 

o maximum lifetime individual cancer risk (MIR) 

o maximum TOSHI for the following health effects 

- respiratory system effects 

- liver effects 

- 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 

- whole body effects 

o locations of the maximum cancer risk and TOSHI 

o Census block identification codes for the maximum cancer risk and TOSHI, and 

number of people in the Census block 
12

o contributions of different chemicals and emission sources to the maximum risk and 

TOSHI 

• Maximum acute impacts

o maximum short term ambient concentration for each chemical

o threshold levels for acute health effects of each chemical (compared with the 

maximum short term concentrations)

o locations of the maximum impacts for different chemicals (often polar receptors)

o Census block identification codes at the locations of maximum concentration, and 

number of people in the block

o contribution of each emission source at the facility to the maximum short term 

concentration of each chemical

• Outputs for all receptors

o maximum individual cancer risk and TOSHI (all target organs) for each Census 

block and each user-specified discrete receptor (monitoring sites, etc.)

o maximum individual cancer risk and TOSHI (all target organs) for each polar grid 

receptor

o estimated deposition (optional)

o predicted ambient concentration resulting from each emission source at each 

Census block and polar grid receptor (optional)

• Population exposures and total cancer risk, or incidence

o estimated numbers of people exposed to different levels of lifetime individual 

cancer risk (1 in a million, 1 in 100,000, etc.)

o estimated numbers of people exposed to different levels of TOSHI (1, 2, 10, etc.)

o total cancer risk, or incidence, in estimated cancer deaths per year, over the entire 

modeling domain, and for each pollutant and source combination

2.4 Data Libraries Used in HEM-3 

2.4.1 Chemical Health Effects Information

HEM-3 includes a library of available health effects data for HAPs. For each pollutant, 

the library includes the following parameters, where available:

• unit risk estimate (URE) for cancer

• reference concentration (RfC) for chronic noncancer health effects

• reference concentrations for acute health effects 

• target organs affected by the chemical for chronic noncancer health effects

Unit risk estimates and reference concentrations included in the HEM-3 chemical library have 

been taken from EPA’s database of recommended dose-response factors for HAPs, which is 

updated periodically, consistent with continued research on these parameters.4 The URE

represents the upper-bound excess lifetime cancer risk estimated to result from continuous 

exposure to an agent at a concentration of 1 microgram per cubic meter (μg/m3) in air (e.g., if the
13

URE = 1.5 x 10-6 per μg/m3, 1.5 excess tumors are expected to develop per 1 million people if all

1 million people were exposed daily for a lifetime to 1 microgram of the chemical in 1 cubic 

meter of air).5

The RfC is a concentration estimate of a continuous inhalation exposure to the human 

population that is likely to be without an appreciable “risk” of deleterious non-cancer health 

effects during a lifetime. No adverse effects are expected as a result of exposure if the ratio of the 

potential exposure concentration to the RfC, defined as the hazard quotient (HQ), is less than 1.5

The reference benchmark concentration for acute health effects, similar to the chronic 

RfC, is the concentration below which no adverse health effects are anticipated when an 

individual is exposed to the benchmark concentration for 1 hour (or 8 hours, depending on the 

specific acute benchmark used and the formulation of that benchmark). Target organs are those 

organs (e.g., kidney) or organ systems (e.g., respiratory) which may be impacted with chronic 

non-cancer health effects by exposure to the chemical in question. A more in-depth discussion of 

the development and use of these parameters for estimating cancer risk and non-cancer hazard 

may be found in the EPA’s Air Toxics Risk Assessment Library.6

The model user can add pollutants and associated health effects to HEM-3’s chemical 

health effects (dose-response) library, as needed.

2.4.2 Census Block Locations and Elevation Data 

The HEM-3 Census library includes Census block identification codes, locations, 

populations, elevations, and controlling hill heights for all of the over 6 million Census blocks 

identified in the 2010 Census and the over 5 million Census blocks identified in the 2000 

Census. The model user may choose to use either Census database according to their modeling 

needs. The location coordinates reflect the internal “centroid” 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 For the 

2000 Census7 and from the U.S. Census Bureau website for the 2010 Census.8 Locations and

populations for blocks in the Virgin Islands were obtained from the U.S. Census Bureau website. 

U.S. Geological Survey data was used to estimate the elevation of each census block in 

the continental U.S. and Hawaii. The data used for the 2000 Census elevations have a resolution 

of 3 arc seconds, or about 90 meters.9 The data used for the 2010 Census elevations have a

resolution of 1/3 of an arc second, or about 10 meters.10 Using analysis tools (ArcGIS® 9.1

software application for the 2000 Census, and ArcGIS® 10 for the 2010 Census), elevation was 

estimated for each census block in Alaska and the U.S. Virgin Islands. The point locations of the 

census blocks in Alaska and the U.S. Virgin Islands were overlaid with a raster layer of North 

American Digital Elevation Model (DEM) elevations (in meters).9 An elevation value was

assigned to each census block point based on the closest point in the ArcGIS elevation raster file. 

An algorithm used in AERMAP, the AERMOD terrain processor, is used to determine 

controlling hill heights.11,12 These values are used for flow calculations within AERMOD. 
14

To save run time and resources, the HEM-3 census block elevation database is 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 is projected away from the block centroid location, representing a 10% 

elevation grade. The controlling hill height is selected based on the highest elevation above that 

10% grade (in accordance with the AERMAP methodology). The distance cutoff for this 

calculation is 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

HEM-3 includes an extensive library of meteorological data to support the AERMOD 

dispersion model. Currently 824 meteorological stations have been preprocessed for AERMOD 

as part of the RTR effort. Section 3.3 includes a depiction of these meteorological stations and 

Appendix 2 discusses the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseous Deposition Parameters

HEM-3 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 

the deposition and depletion option 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).13 HEM-3

includes a library of these parameters for most gaseous HAPs. This library is based on a 

compendium of gaseous deposition parameters developed by Argonne National Laboratories.14 It 

should be noted, however, that the deposition and depletion option of HEM-3 and AERMOD 

have not been used to date for the RTR assessments.
15

3. Modeling for the Residual Risk Technology Review

This section discusses the general approach used to implement the HEM-3 AERMOD 

system for the RTR modeling analyses. Separate reports have been prepared for each of the 

emission source categories analyzed to date. These reports provide information on the emissions 

inputs and results for specific emission categories.

3.1 Emission Source Inputs 

HEM-3 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 and other area sources);

• stack diameter (or configuration of fugitive and other area sources);

• emission velocity; and

• emission temperature.

Emissions data for the RTR assessments are compiled form a variety of data sources (i.e., 

the 2005 National Emissions Inventory (NEI)15 ,information data requests). Each source 

category under the RTR program, in most cases, utilizes the latest best available data. These 

data include HAP emission rates, emission source coordinates, stack heights, stack diameters, 

flow rates, and exit temperatures. EPA performs 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 is 

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 in the docket prepared for the 

specific RTR source category proposed or final rule. 

As noted in the previous section, industrial emission sources can be characterized in 

AERMOD as point, area, polygon, or volume sources. 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.

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). HEM-3 requires an exact match in 
16

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. Therefore, all 

of the HAP names used in the NEI were linked to the appropriate chemical names in the HEM-3 

reference file. 

Pollutant-specific dose response values are used in the HEM-3 modeling whenever 

available, including when modeling POM pollutants. Pollutant groupings, such as POM 

groupings, are used for POMs without a chemical-specific unit URE’s. These POMs are assigned 

a URE associated with various POM compounds having similar characteristics. “An Overview of 

Methods for EPA’s National-Scale Air Toxics Assessment” 2011 document16 provides more

details regarding POM modeling, including: 

[S]ome emissions of POM were reported in [the] NEI as “7-PAH” or “16-PAH,” 

representing subsets of certain POM, or simply as “total PAH” or “polycyclic organic 

matter.” In other cases, individual POM compounds are reported for which no 

quantitative cancer dose-response value has been published in the sources used for 

NATA. As a result, simplifying assumptions that characterize emissions reported as POM 

are applied so that cancer risk can be quantitatively evaluated for these chemicals without 

substantially under- or overestimating risk (which can occur if all reported emissions of 

POM are assigned the same URE). To accomplish this, POM emissions as reported in 

NEI are grouped into categories. EPA assigns dose-response values based on the known 

or estimated toxicity for POM within each group and on information for the POM 

speciation of emission sources, such as wood fires and industrial processes involving 

combustion. 

Emissions of metal compounds are 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 EMSHAP.17,18

3.3 Meteorological Data

Nationwide meteorological data files are accessed by HEM-3 and used for the RTR 

modeling. The current HEM-3 AERMOD Meteorological Library includes over 800 nationwide 

locations, depicted in Figure 3-1. This library contains surface and upper air meteorological data 

from National Weather Service (NWS) observation stations, which are named beginning with the 

state abbreviation for the state in which the station is located. AERMOD requires surface and 

upper air meteorological data that meet specific format requirements.19,20 Appendix 2 discusses 
17

the preprocessing performed on the meteorological data used by AERMOD and includes a 

detailed listing of the 824 meteorological station pairs.
18

Figure 3-1. AERMOD Meteorological Stations
19

3.4 Model Options Selected 

HEM-3 presents a number of options for characterizing the modeling domain and data 

sources. As many sources are generally modeled in RTR assessments, established defaults and 

common practices are relied on to make these choices. The choices available to a HEM-3 user 

and the selections that are made in most RTR assessments are presented in Table 3-1. Some of 

the key selections are discussed in more detail in the paragraphs below. 

It should be noted that 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,21 but no facility-specific analyses of these data were performed to characterize shortterm emissions from these emission sources, and upset emissions are generally not modeled for 

the RTR risk assessments. 

3.4.1 Urban or Rural Dispersion Characteristics 

Current RTR source category assessments which use the 2010 Census are based on either 

urban or rural dispersion characteristics, depending on the land characteristics surrounding each 

modeled facility. The EPA provides guidance on whether to select urban or rural dispersion 

coefficients in its Guideline on Air Quality Models.22 In general, the urban option is used if (1) 

the land use is classified as urban for more than 50% of the land within a 3-kilometer radius of 

the emission source, or (2) the population density within a 3-kilometer radius is greater than 750 

people per square kilometer. Of these two criteria, the land use criterion is more definitive. 

Using the 2010 Census, the HEM-3 model determines, by default, whether to use rural or 

urban dispersion characteristics. HEM-3 will find the nearest census block to the facility center 

and determine whether that census block is in an urban area, as designated by the 2010 Census.23

The population of the designated urban area will be used to specify the population input for 

AERMOD's urban mode. (Alternatively, a user may select the rural or urban option to override 

determination by the model. If a user selects an urban dispersion environment, then the user must 

provide the urban population as well.) 

For the 2008 and prior screening-level RTR assessments of 51 source categories, the rural 

option was chosen to be most conservative (i.e., more likely to overestimate risk results). The 

rural option is also chosen by default by the HEM-3 model whenever the 2000 Census is selected 

by the user. 

3.4.2 Deposition and Plume Depletion 

The RTR modeling analysis to date has not taken into account the depletion of pollutant 

concentrations in the plume due to wet or dry deposition, although HEM-3 can model deposition

and depletion using AERMOD. In addition, reactivity and decay have not been considered. 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 
20

exposed (the maximum individual risk, or MIR). 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 may 

consider deposition and depletion. 

3.4.3 Cutoff Distance for Modeling of Individual Blocks 

The cutoff distance for modeling individual Census blocks is initially set to 3 km by 

default. This distance generally ensures that the maximum individual cancer risk and the 

maximum TOSHI are modeled explicitly and not interpolated. Following a modeling run, the 

results for each facility are checked to determine whether the maximum impacts are located 

inside the modeling cutoff distance. If the maximum impacts are outside the cutoff distance, and 

if any of the impacts are significant, then HEM-3 is rerun for the facility with a cutoff distance 

greater than 3 km. In general, this is done if the cancer risk exceeds 1 in 1 million or any TOSHI 

exceeds 1. However, the risks for such facilities are generally very low, since the maximum 

impacts are in most cases only interpolated when the nearest Census block is more than 3 km 

from the facility (i.e., in sparsely populated areas). 

3.4.4 Facility Boundary Assumptions 

The main input mechanisms for incorporating facility boundary information in HEM-3 

are the overlap distance, the distance to the innermost polar receptor ring, and user-specified 

receptor locations. The NEI does not provide information on facility boundaries. However, 

satellite/aerial images are used to locate residential populations that are closer to a facility than 

the Census block centroid. User-specified receptor locations are used in such assessments to 

avoid underestimating risk. Conservative default assumptions are used for the overlap distance 

and the innermost polar receptor ring. However, these are adjusted for some categories where 

facility sites are known to be large. In addition, satellite imagery is used to check the facility 

boundary assumptions for facilities with large projected impacts. These checks are discussed 

further in the section on Quality Assurance (Section 4).
21

Table 3-1. HEM-3 Domain and Set-Up Options As Used in the Residual Risk and 

Technology Review Assessments

Option Selection

Dispersion model AERMOD

Census database: 2010 or 2000 Based on date of 

analysis

Type of analysis: chronic, acute, or both Both

Averaging time for short term impacts 1-hour

Multiplier for short term emissions 10 generally, although 

multiple source typespecific factors are 

also used if available 

Dispersion characteristics: urban or rural, as determined by model, 

based on closest 2010 Census block to each facility (when using 

2010 Census). Rural by default, when using the 2000 Census.

Urban or Rural based 

on facility location; 

(Rural for 2000

Census)

Include terrain impacts Yes

Include building wake effects No

Calculate deposition (wet, dry, or both) & include impacts of plume 

depletion

No

User-specified receptor locations (for residential population 

locations, facility boundary sites, or other sites of interest)

Yes, for some 

facilities

Modeling domain size – maximum distance to be modeled 50 km

Cutoff distance for modeling of individual blocks 3 kma

Overlap distance where receptors are considered to be on facility 

property – measured from each source measured from each source

30 mb

Polar receptor network specifications:

Distance from the facility center to the innermost ring ≥ 100 mc

Number of rings 13

Number of directions 16

Meteorology data Closest site

 a The individual block modeling cutoff is increased for categories and for some facilities to ensure that the 

 maximum individual risk values are not interpolated.

 b The overlap distance is adjusted for some facilities to avoid modeling locations that are on facility property 

 (see section 4.2).

 c HEM-3 sets the innermost ring distance to be just outside the emission sources but not < 100 m.
22

3.5 Modeling of Multiple Facilities 

HEM-3 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 are 

summed at each Census block for all facilities affecting the Census block. 

As described earlier (Section 2.3.4), HEM-3 produces detailed output tables containing 

the risk and population for every Census block in the modeling domain. These detailed tables are 

combined for all facilities in a source category and the risk for each Census block is summed, 

using the RTR Summary Program add-on module to the Multi HEM-3 model.3 Thus, the effect 

of multiple facilities in the same source category on the same receptor are estimated. The 

resulting “combined facility” or “cluster-effect” Census block risks are used to calculate 

population exposure to different cancer risk levels, noncancer hazard indices, and source 

category incidence.
23

4. Quality Assurance

The National Emissions Inventory (NEI) 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.24 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 performs an 

engineering review of NEI data for the emission source categories included in the RTR analysis. 

This engineering review includes two main components. The first component addresses the list 

of facilities included in each source category. EPA engineers review independent sources of 

information to identify all sources in the category that are included in the NEI. In addition, EPA 

reviews 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 focuses on the appropriateness of 

facility emissions. EPA reviews the list of HAPs reportedly emitted by each facility to make sure 

that the pollutants are appropriate to the source category. In addition, EPA engineers review the 

magnitude of those HAP emissions. In cases where new or better data are known to exist for a 

particular source category, that information is 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 Pre-Modeling 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.25 In addition, coordinates for all emission sources at a given facility 

are checked to ensure that they are within 3 km of one another. These QA checks happen prior to 

HEM-3 modeling and the results of such checks are reflected in the HEM-3 input files. 

Another pre-modeling geographic QA check regards the location of the census block 

receptors. As noted above, to estimate ambient concentrations for evaluating long-term 

exposures, the HEM-3 model uses the census block centroids as dispersion model receptors. The 

census block centroids are often good surrogates for where people live within a census block. A 

census block generally encompasses about 40 people or 10-15 households. However, in cases 

where a block centroid is located on industrial facility property, or where a census block is large 

and the centroid less likely to be representative of the block’s residential locations, the block 

centroid may not be an appropriate surrogate.

Census block centroids that are on facility property can sometimes be identified by their 

proximity to emission sources. In cases where a census block centroid is within 300 meters of 
24

any emission source, aerial images of the facility are reviewed to determine whether the block 

centroid is likely located on facility property. The selection of the 300-meter distance reflects a 

compromise between too few and too many blocks identified as being potentially on facility 

property. Distances smaller than 300 meters would identify only block centroids very near the 

emission sources and could exclude some block centroids that are still within facility boundaries, 

particularly for large facilities. Distances significantly larger than 300 meters would identify 

many block centroids that are outside facility boundaries, particularly for small facilities. Block 

centroids confirmed to be located on facility property are moved to a location that best represents 

the residential locations in the block.

In addition, census block centroids for blocks with large areas may not be representative 

of residential locations. Risk estimates based on such centroids can be understated if there are 

residences nearer to a facility than the centroid, and overstated if the residences are farther from 

the facility than the centroid. To avoid understating the maximum individual risk associated with 

a facility, block centroids are relocated in some cases, or additional user-specified receptors are 

added to a block. Aerial images of all large census blocks within one kilometer of any emission 

source are examined. Experience from previous risks characterizations show that in most cases 

the MIR is generally located within 1 km of the facility boundary. If the block centroid does not 

represent the residential locations, it is relocated in the HEM-3 input files to better represent 

them. If residential locations cannot be represented by a single receptor (that is, the residences 

are spread out over the block), additional user-specified receptors are included in the HEM-3 

input files to represent residences nearer to the facility than the centroid. 

4.3 Geographic Post-Modeling Checks 

As part of the RTR modeling analysis, additional geographical QA checks are made for 

some facilities, after initial HEM-3 modeling results are reviewed. Facilities subjected to these 

additional checks include: 

• cases where the initial estimates of maximum risks are particularly high 

o maximum individual cancer risk of over 1 in 10,000 

o any maximum TOSHI above 10 

• cases where no Census blocks are identified by the model within 3 km of the facility 

HEM-3 produces a detailed Google EarthTM map of the modeled point and area emission 

sources and surrounding receptors (including Census block centroids, polar receptors and userspecified receptors) overlaying Google EarthTM’s satellite imagery. This map allows a QA check

of the specific source locations, as well as an approximate check of the facility boundaries. The 

emission source coordinates are reviewed for each of these facilities and compared with the 

address reported for the facility. If the address and the coordinates represent the same location, 

then the coordinates are taken to be correct. For more recent modeling of source categories, the 

emission coordinates initially modeled by HEM-3 tend to be correct, as they undergo premodeling scrutiny and QA checks (as discussed in Section 4.2).

More rarely, the modeled emission coordinates will be determined post initial modeling 

not to be located on facility property. If the facility and emission coordinate locations are 
25

different, then the satellite imagery for the address and the coordinate location are reviewed to 

determine whether either photograph includes an industrial facility. Generally for the 2008 and 

prior screening-level RTR assessments of 51 source categories, where the two locations were 

different, the facility address was found to be correct (and the emission source coordinates 

required correction). 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 are found to be incorrect, 

HEM-3 is 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 are also reviewed. This review draws on detailed Census block boundary maps and 

satellite imagery. 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 centroid 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 

facility property. 

In general, block coordinates are considered to be on facility property if they are located 

between the different emission source locations listed for the facility. In these situations, HEM-3 

is rerun with an expanded overlap distance, in order to exclude the Census block coordinates that 

appear to be located on facility property. The distance to the innermost polar receptor ring is also 

adjusted to ensure that this ring is not on facility property, but as close to the apparent facility 

boundaries as possible.
26

5. Uncertainties

The RTR risk assessments using HEM-3 and AERMOD are subject to a number of 

uncertainties. For instance, model verification studies for AERMOD show 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 1.0.25

In addition, a number of simplifying assumptions are made in these modeling analyses. 

First, the coordinates reported by the Department of Census for Census block internal points 

(“centroids”) 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 HEM-3 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 longterm 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 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 analyses, only direct inhalation is 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 

hazardous air pollutants (PB HAP). Screening level evaluations of the potential human health 

risks associated with emissions of PB HAP from the modeled facilities are used to determine if 

additional analyses are needed, but these analyses are outside the scope of this document. 

Because the HEM-3 AERMOD analyses are restricted to the inhalation pathway, the impacts of 

plume depletion due to deposition are not taken into account. Thus, inhalation impacts may be 

overestimated for some pollutants, but exposures through other pathways would be 

underestimated.

A number of other simplifications are made in the dispersion modeling analyses, as noted 

in Table 3-1. For instance, building wake effects are not considered. In addition, meteorological 
27

observations are based on the closest station in the HEM-3 meteorological library (see Figure 

3-1). Alternative meteorological stations may be more appropriate for some facilities. Ideally, 

facility-specific meteorological observations would be used. A single year of meteorological data 

(2011) is currently used for AERMOD’s dispersion modeling. (The 2008 and prior screeninglevel RTR assessments of 51 source categories used meteorological data based on the year 

1991.) 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 analyses and the extent of the 

dispersion modeling analysis (national scale), it is not practical to model five years of data. Other 

national studies such as NATA also consider 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 station.26

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 

analyses 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 analyses. 

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 analyses: 

• emissions inventory; 

• fate and transport modeling; 

• exposure assessment; and 

• toxicity assessment. 

It must also be noted that individual source categories may be subject to additional uncertainties. 

These are discussed in separate reports which are prepared for each emission source category 

included in the RTR assessments.
28

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Emissions Inventory

Individual 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 current emissions inventory is based on the 2005 

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. For the 2008 screening level 

assessments, nearly half of the sources in a given 

source category submitted a review of their 

emissions and facility characteristics data. Some 

detailed data, such as property boundary information 

is not available for most facilities. This is an 

important consideration in determining acute 

impacts.

Unbiased overall, magnitude 

variable

Multiplier for short-term 

emission rates

Generally, 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 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.

Potential overestimate due to 

the fact that worst-case 

emissions are assumed to 

occasionally coincide with 

worst-case meteorology.

Overestimate due to lack of 

actual information on shortterm emission rates.

Fate and Transport Modeling

Atmospheric dispersion 

model choice

AERMOD is EPA's recommended 

dispersion model for assessing 

pollutant concentrations from 

industrial facilities

Field testing of dispersion models, including 

AERMOD, have shown results to generally be within 

a factor of 2 of measured concentrations.

Unbiased overall

Building downwash Not included in assessments

Use of this algorithm in AERMOD could improve 

the dispersion calculations at individual facilities. 

However, data are not readily available to utilize this 

option.

Potential underestimate of 

maximum risks near facility. 

No effect on risks further out.

Plume deposition and 

depletion Not included in assessments

Ignoring these impacts for pollutants that deposit 

minimally, and whose risks derive predominantly 

from inhalation, should have minimal effect on risk 

estimates.

Unbiased or minimal 

overestimate.
29

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Meteorology

One year of meteorological data from 

the nearest weather station (selected 

from 824 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.

Minimal underestimate or 

overestimate.

Reactivity Not included in the assessments

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 impact on maximum risk 

estimates. Minimal impact on 

population risks and incidence.

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 effect on maximum 

individual risks. Minimal 

underestimation of incidence.

Exposure Assessment

Locations and short-term 

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.

Unbiased across population for 

most pollutants and individuals, 

likely overestimate for most 

exposed and underestimate for 

least exposed persons.
30

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Long-term movements of 

individuals

MIR individual is exposed 

continuously to the highest exposure 

concentration for a 70-year lifetime.

Maximum individual risk (MIR) is defined in this 

way to be a maximum theoretical risk at a point 

where a person can actually reside.

Unbiased for most individuals, 

likely overestimate for the 

actual individual most exposed 

and likely underestimate for the 

least exposed. Incidence 

remains unbiased unless 

population around facilities 

increases or decreases over 70 

years.

Toxicity Assessment

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 organ-specific hazard 

indices.

In general, EPA derives RfCs 

using procedures whose goal is 

to avoid underestimating risks 

in light of uncertainty and 

variability. The greater the 

uncertainties, the greater the 

potential for overestimating 

risks.

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 shortduration, high-dose animal or work-related exposures 

to longer duration, lower-dose environmental 

impacts.

Overestimate of risks for 

nonlinear carcinogens and for 

linear carcinogens with sparse 

health effects data. In general, 

EPA derives URE values using 

procedures aimed at 

overestimating risks in light of 

uncertainty and variability.

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.

Unbiased overall. Some 

mixtures may have 

underestimated risks, some 

overestimated, and some 

correctly estimated.
31

Table 5-1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments

(continued)

Parameter Assumption Uncertainty/Variability Discussion

Potential Direction of Bias on 

Risk Estimates

Surrogate dose- response 

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 dose-response values in the 

group. For others, such as unspeciated 

metals, we have applied speciation 

profiles appropriate to the source 

category to develop a composite doseresponse 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.

Overestimate where most 

conservative values used. 

Unbiased where categoryspecific profiles applied.

There is the potential to 

underestimate risks for 

pollutants which are not 

included in the assessment.
32

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2. EPA. 2004. User's Guide for the AMS/EPA Regulatory Model - AERMOD. EPA-454/B03-001, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

www.epa.gov/scram001/dispersion_prefrec.htm#aermod

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U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc., 

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Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html
33

11. EPA. 2004. User's Guide for the AERMOD Terrain Preprocessor (AERMAP). EPA454/B-03-003, U.S. Environmental Protection Agency, Research Triangle Park, NC. 

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http://www.epa.gov/ttn/scram/7thconf/aermod/aer_scid.pdf

14. Wesely, M.L., P.V. Doskey, and J.D. Shannon. 2002. Deposition Parameterizations for 

the Industrial Source Complex (ISC3) Model. ANL/ER/TR–01/003, Argonne National 

Laboratory, Argonne, Illinois 60439. See “AERMOD Deposition Parameterizations 

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Park, NC, by ICF International, Durham, NC. January 2011, p 55. 

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18. Strum, Madeleine, Richard Mason, and James Thurman. 2004. User's Guide for the 

Emissions Modeling System for Hazardous Air Pollutants (EMS-HAP) Version 3.0. 

EPA-454/B-03-006, Environmental Protection Agency, Research Triangle Park, NC. 

August 2004. http://www.epa.gov/ttn/scram/userg/other/emshapv3ug.pdf
34

19. EPA. 2004. User’s Guide for the AERMOD Meteorological Preprocessor (AERMET), 

EPA-454/B-03-002, U.S. Environmental Protection Agency, Research Triangle Park, 

NC. http://www.epa.gov/scram001/7thconf/aermod/aermetugb.pdf

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Preprocessor (AERMET) (EPA-454/B-03-002, November 2004), U.S. Environmental 

Protection Agency, Research Triangle Park, NC. December 2012. 

http://www.epa.gov/ttn/scram/metobsdata_procaccprogs.htm#aermet

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http://www.tceq.texas.gov/field/eventreporting

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General Purpose (Flat and Complex Terrain) Dispersion Model and Other Revisions. 

Appendix W of 40 CFR Part 51. 

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf

23. Federal Register, 2012. Qualifying Urban Areas for the 2010 Census. FR 77:59 (27 

March 2012). p.18652. http://www.gpo.gov/fdsys/pkg/FR-2012-03-27/pdf/2012-6903.pdf

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