Appendix 1 T echnical Support Document for HE M-AERMOD Modeling

## Modeling for the Residual Risk and Technology Review

Using the Human Exposure Model 3

– AERMOD Version

Updated 11/2 6 /2013

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 27514iii 

### Disclaimer

The research described in this document has been funded by the United States Environmental Protection Agency contracts 68-D-01 -076

, EP -D06 -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.iv 

## 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 ................................................................... 10 2.3.3 Calculation of Population Exposures and Incidence ................................ .............................. 10 2.3.4 Model Outputs ................................................................................................ ........................ 11 2.4 Data Libraries Used in HEM-3

................................ ...................................................................... 13 2.4.1 Chemical Health Effects Information ................................................................ ..................... 13 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 ................................................................................................................. 1 8 3.4.1 Urban or Rural Dispersion Characteristics ............................................................................. 1 8 3.4.2 Deposition and Plume Depletion ............................................................................................ 1 8 3.4.3 Cutoff Distance for Modeling of Individual Blocks ............................................................... 19 3.4.4 Facility Boundary Assumptions .............................................................................................. 19 3.5 Modeling of Multiple Facilities ..................................................................................................... 2 1

4. Quality Assurance

................................................................................................................................ 2 2 4.1 Engineering Review ....................................................................................................................... 2 2 4.2 Geographic Pre-Modeling Checks ................................................................................................. 2 2 4.3 Geographic Post-Modeling Checks ............................................................................................... 2 3

5. Uncertainties ........................................................................................................................................ 2 5

6. References ............................................................................................................................................ 3 2v 

## Figures

Figure 3-1. AERMOD Meteorological Stations. ..........................................................................1 7



## 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 ....................................................................................................2 0

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

Risk Assessments .........................................................................................................

. 2 71 

## 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 (RTR) currently being carried out by the U.S. Environmental Protection Agency (EPA). The model used in these risk assessments is the Human Exposure Model, Vers ion 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 inpu t 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 hazard3

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 (“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. C alculation 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.4

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. Table 2

- 1. Parameters Used to Del ineate 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 blocks 1 3,000 m

Overlap distance

–

where receptors are considered on facility property 1 30 m

Polar receptor network:

Distance to the innermost ring 2 ≥ 100 m Number of concentric rings 13

Number of radial directions 16

1 Measured from each stack at the facility, and from the edges of each area or volume source

2 Generally model-calculated to encompass all emission sources but not less than 100 meters from the facility center5

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 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 an y 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 r eceptor 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 f irst ring dist ance ) for the polar receptor network to be just outside the emission source loc ations, but not less than 100 meters fr om the facility c enter

. However, the user can override the d efault dist ance c alculated by the model to fit the size a nd shape of the f acility p roperties to be modele d. Likewise, the model will also use de fault va lues for the number of concentric r ings to be a nalyzed (13 rings by de fault), and the number of ra dial directions (16 radials by default), although these d efault va lues can also be c hanged by the user to mee t the ne eds of a spe cific modeling stud y. The inner r adius of the polar ne twork shoul d be t he mi nimum distance f rom the f acility center th at is generally outsi de of f acility prop erty

. (Fo r complex f acility shap es, it is sometimes useful to specify a n inner ring that encroaches on facility property in some directions.) HEM-3 will dist ribute the radia l directions evenly a round the facility

. For th e c oncentric r ings, the model will generate a logarithmic progression of distances starting at the inner ring radium and ending at the outer radiu m of the modeling domain.

Elev ations 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, H EM -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 to6

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 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;7

 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.

As noted in Section 2.1, the calculation of deposition or depletion and the consideration of building wake effects require additional user inputs.

The u ser 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

n ot 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.8

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 Ca lifornia EPA reference exposure level [REL] for no adverse effects).

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: CR T =  i, j CR i, j

CR i, j = DF i, j × CF ×  k [E i, k × URE k ]

For TOSHI: TOSHI T = 

###  i, j TOSHI i, j

TOSHI i, j = DF i, j  CF  

###  k 

### [ E i, k  RfC k 

### ] where: CR T = 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) CR i, j = cancer risk at the given receptor for source i and pollutant type j DF i, j = dilution factor [(  g/m 3 ) / (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) E i, k = emis sions of pollutant k from source i and in pollutant type j9

The above equations are equivalent to the following simpler equations:

CR T =  i, k AC i, k × URE k

HI T =  i, k AC i, k  RC k where: AC i, k = ambient concentration (  g/m 3 ) for pollutant k at the given receptor. This is the

same as [E i, k × DF i, 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:
AC T, k = 

###  i AC i, k

AC i, k = E i, k  DF i, j  CF

Short term concentrations:
AC T = 

###  i AC i, k

AC i, k = E i, k  DF i, j  CF  M where: URE k = cancer unit risk factor for pollutant k TOS HI T = total target

- organ

- specific hazard index at a given receptor TOS HI i, j = target

- organ

- specific hazard index at the given receptor for sou rce i and pollutant

type j R f C k = non

- cancer health effect reference concentration for pollutant k

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

###  i = the sum over all sources i (  g/m 3 )
AC i, k = estimated ambient concentration of pollutant k at the given receptor as a result of emissions from source i (  g/m 3 ) M = ratio between the estimated maximum short term emission rate and the long term average emission rate (dimensionless)10

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 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:

I a, r = I A1, r + (I A2, r –

I A1, r ) × (a

– A1)  (A2

– A1)

I A1, r = exp{ln(I A1,R1 ) + [ln(I A1,R2 )

– ln(I A1,R1 )] × [(ln r)

– ln(R1)]  [ln(R2)

– ln(R1)]} I A2, r = exp{ln(I A2,R1 ) + [ln(I A2,R2 )

– ln(I A2,R1 )] × [(ln r)

– ln(R1)]  [ln(R2)

– ln(R1)]}

where:

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: I a,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 rec eptors 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 receptor11 TCR = 

### 
m 

### [ CR m  P m 

### ]  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 CR m = the total lifetime cancer risk (from all modeled pollutants and emission sources) at Census block m P m = the population at Census block m LT = the average lifetime used to develop the cancer unit risk factor, 70 years

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:

TCR i, j = 

###  m 

## [(  k E i, k × URE k ) × DF i, j, m × CF  LT]

TCR i, k = TCR i, j × E i, k × URE k 

##  (  k E i, k × URE k ) where:

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

" maximum lifetime individual cancer risk (MIR)

" maximum TOSHI for the following health effects - respiratory system effects - liver effects - neurological system effects TCR i, 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
E i, k = emissions of pollutant k from source i (tons/year) URE k = u nit risk factor for pollutant k DF i, 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)] TCR i, k = the estimated annual cancer inciden ce (cancers/year) of the population in the

modeling domain due to emissions of pollutant k (in pollutant type j) from emission source i12 - 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

" 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 (often polar receptors)

" 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 and polar grid receptor (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, and for each pollutant and source combination13



### 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 chro nic 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/m 3 ) in air (e.g., if the URE = 1.5 x 10 -6 per μg/m 3 , 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 internal14

point may not be in the geographic center of the block. Locations and population data for Ce nsus blocks in the 50 states and Puerto Rico were extracted from the LandView® database For the 2000 Census 7 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. To

save r un time and resources, the HEM- 3 census block elevation database i s substituted for the DEM da ta generally used in AERMAP. As noted a bove, the census bloc k elevations were originally de rived from the DEM database. To determine the controlling hil l height for each census block, a cone is projected away f rom the block centroid location, representing a 10% elevati on grade. The c ontrolling hil l height is selected based on the highe st elevation above th at 10% grade (in accordance with the A ERMAP methodology). The dist ance cutoff f or thi s calculation is 100 km. (This corresponds to a n elevation difference at a 10% gra de o f 10,000 m, which considerably exceeds the max imum elevation difference in North America.)

2.4.3 Meteorological Data

HEM-3 includes an extensive li brary 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 discuss es the preparation of meteorological data for the RTR in more detail.

2.4.4 Gaseou s 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 r CL ). 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 all 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.

Currently, emissions data for the RTR assessments are taken from the 2005 National Emissions Inventory (NEI). 15 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 the16

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 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 document 16 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 a re a lso adjusted using a lgorithms developed for the Emissions Modeling S ystem for Ha za rdous Air Pollutants (EMS-HAP) under the Na tional-scale Air Toxics Assessment (NATA). A ma ss adjustment fac tor was applied to the emissions of metal compounds to account for a pa rticular portion (e.g., the lead portion of le ad su lfate) or to partition them among mul tiple pollutant categories (e.g. chromium arsenate into chromium VI c ompounds and arsenic c ompounds ). I n addition, where no specific compound information was available, metals were sp eciated into appropriate oxidation states (e.g. chromium c ompounds int o chrome VI and chr ome III) based on f actors that have be en developed for sp ecific source c ategor ies 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. 17

, 18



### 3.3 M eteorological 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 the preprocessing performed on the meteorological data used by AERMOD and includes a detailed listing of the 824 meteorological station pairs.1 7 Figur e 3-1 . AERMOD Meteorological Stations1 8



### 3.4 M odel 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 assessment s, established defaults and common practices are relied on to make these choices. The choices available to a HEM-3 user and the selec tions 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 note d that although routine emissions are not expected to vary significantly with time, nonroutine ( upset) e missions can be si gnificant relative to routine e missions. Upset emissions occur durin g p eriods of star tup, shutdown, and malfunction. Upset emissions a re not likely f or equipment or storage tanks, but do result f rom malfunctioning control devices and le aks in cooling towe r heat exchangers. There is some limited data on upset emissions a vailable, 21 but no facility-specific a nalyses of these da ta we re pe rformed to characterize s hort-term emissions from these e mission sources, and upse t emissi ons are generally not modeled for the RTR r isk
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
pro vide 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

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

conc entrations in the plume due to wet or dry d eposition, although HEM-3 c an model deposition19

a nd 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 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).2 0

Table 3

- 1

. HEM

- 3 Domain and Set

- Up Options As Used in the Residual Risk and Technology Review Assessments Option Selection Dispersion model AERMO D
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 source type-specific factors are used if available

D ispersion characteristics : u rban 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 fo r 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 site s , 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 km a Overlap distance where receptors are considered to be on facility property

–
m easured from each source 30 m b

Polar receptor network specifications : Distance from the facility center to the innermost ring ≥ 100 m c 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.2 1



### 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.2 2 

## 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

c entroids 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.2 3

Ce nsus 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 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 H EM -3 input files to better represent them. If residential locations c an not 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 addit ional checks include:

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

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

" 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 emissionsources 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, theemission coordinates initially modeled by HEM-3 tend to be correct, as they undergo premodeling scrutiny and QA checks (as discussed in Section 4.2).2 4

More r arely, 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 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 a ssessments 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.2 5 

## 5. Uncertainties

Th e 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 theuncertainty 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

a ssumption 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 th e HEM-3 AERMOD analyse s are re stricted 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.2 6

A number of o ther 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 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, facilityspecific meteorological observations would be used. A single year of meteorological data (2011 ) is currently used for AERMOD’s dispersion modeling. (The 2008 and prior screening-level 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 analyse s:

! 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 assess ments.2 7

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

Parameter Assumption Uncertaint y/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 200 5 NEI, our internal review, and public comments received. The degree to which the data in our inventory represents actual emissions i s likely to vary a cross sources. For the 2008 screening level assessments, n early half of the sources in a given source categor y submitted a review of their emissions and facility characteristics data. Some detailed data, such as property boundary information is not availab le for most facilit ies

. This is an important consideration in determining acute impacts. Unbiased overall, magnitude variable

Multiplier for short

- term emission rates Generally, m aximum short term emission rates are estimated by applying a simple multipli er (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.

O verestimate due to lack of actual information on short

term emission rates. Fate and Transport Modeling

Atmospheric dispersion model choice AERMOD is EPA's recommended dispersion model for assessing pollutant concentrations from industrial facilities Fie ld testing of dispersion models , including AERMOD, have shown results to generally be within a factor of 2 of measured concentrations. Unbiased overallTable 5 -1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

2 8 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates

Building downwash Not included in assessment s 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 dep osition and depl etion Not included in assessment s Igno ring 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.

Meteorology One year of meteorological data from the nearest we ather 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 climat ology. 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. Minim al underestimate or overestimate.

Reactivity Not included in the assessment s 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 gen eral, 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.Table 5 -1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

29 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates

Maximum modeling distance 50 kilometers from center of facility This distan ce 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. Minima l 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.

Effe ct 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/w ork 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.

Long

- term movements of individuals MIR individual is exposed continuously to the highest exposure concentration for a 70

- year lifetime. M aximum individual risk (MIR ) is defined in this way to be a maximum theoretical risk at a point where a person can actually reside. Unbiased f or 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.Table 5 -1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

3 0 Parameter Assumption Uncertaint y/Variability Discussion Potential Direction of Bias on Risk Estimates 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 targe t 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 der ives RfCs using procedures whose goal is to avoid underestim ating risks in light of uncertainty and variability. The greater the

uncer tainties , 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 extrapo lation. Uncertainty in extrapolating the impacts from short

- duration, 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 he alth effects data

. In general, EPA derives URE values using procedures aimed at overestimating risks in light of uncertaint y and variabilit y

.

Toxicity of mixtures Cancer risks and noncancer hazard quotients were calculated for each HAP individually and th en 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.Table 5 -1. Summary of General Uncertainties Associated with Risk and Technology Review Risk Assessments (continued)

3 1 Parameter Assumption Uncertaint y/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

conserv ative 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 dos e

- 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. Overestimate w here most conservative values used. Unbiased where category

specific profiles applied.

There is the potential to underestimate risks for pollutants which are not included in the assessment.3 2 

## 6. R eferences

1 . EPA. 2004. AERMOD: Description of Model Formulation . EPA-454/R-03 -004, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/scram/7thconf/aermod/aermod_mfd.pdf

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

3 . EPA. 2013. The Human Exposure Model (HEM): Single-facility HEM-3 Use r’s Guide; and Multi-facility HEM- 3 and RTR Summary Programs User’s Guide. Prepared for the U.S. Environmental Protection Agency, Research Triangle Park, NC, by EC/R Inc., Chapel Hill, NC. February 2013. http://www.epa.gov/ttn/fera/human_hem.html

4 . EPA. 2012. Dose-Response Assessment for Assessing Health Risks Associated With Exposure to Hazardous Air Pollutants. U.S. Environmental Protection Agency, Research Triangle Park, NC. Available at: http://www.epa.gov/ttn/atw/toxsource/summary.html

5

. EPA. 2011. Glossary of Key Terms . Technology Transfer Network Air Toxics, 2005 National-Scale Air Toxics Assessment. U.S. Environmental Protection Agency. http://www.epa.gov/ttn/atw/natamain/gloss1.html

.

6

. EPA. 2004. Air Toxics Risk Assessment Reference Library, EPA-453 -K- 04 -001A, U.S. Environmental Protection Agency, Research Triangle Park, NC. April 2004. www.epa.gov/ttn/fera/risk_atra_vol1.html

7 . Census. 2000. LandView® 5 on DVD . U.S. Census Bureau, Washington, DC.

8 . Census. 2010. Census Summary File 1

– Un ited States: http://www2.census.gov/census_2010/04-Summary_File_1/ prepared by the U.S. Census Bureau, Washington, DC, 2011. See also Technical Documentation for the 2010 Census Summary File 1.

9 . USGS. 2006. US GeoData Digital Elevation Models

– Fact Sheet 040-00 (April 2000)

. U.S. Department of the Interior - U.S. Geological Survey, Washington, DC. http://data.geocomm.com/sdts/fs04000.pdf

10 . USGS. 2010. USGS Seamless Data Warehouse. U.S. Department of the Interior - U.S. Geological Survey, Washington, DC. http://nationalmap.gov/viewer.html3 3

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. http://www.epa.gov/scram001/dispersion_related.htm#aermap

12

. EPA. 2011. Addendum to the User’s Guide for the AERMOD Terrain Preprocessor (AERMAP) (EPA-454/B-03 -003, October 2004), U.S. Environmental Protection Agency, Research Triangle Park, NC. March 2011. http://www.epa.gov/scram001/dispersion_related.htm#aermap

13

. EPA. 2012. Addendum: User's Guide for the AMS/EPA Regulatory Mode l

– AERMOD ( EPA-454/B-03-001, September 2004) (Section 2.2 related to deposition). U.S. Environmental Protection Agency, Research Triangle Park, NC. December 2012. http:// www.epa.gov/scram001/dispersion_prefrec.htm#aermod

. See also the AERMOD Deposition Algorithms

– Science Document (Revised Draft)” at 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 Document” pdf link under “Model Supporting Documents” section of TTN’s Preferred/Recommended Models webpage at http://www.epa.gov/scram001/dispersion_prefrec.htm#aermod

15 . EPA. 2012. 2005 National Emissions Inventory Data & Documentation . U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/chief/net/2005inventory.html#inventorydata

16

. EPA. 2011. An Overview of Methods for EPA’s National -Scale Air Toxics Assessment. Prep ared for EPA’s Office of Air Quality, Planning, and Standards, Research Triangle Park, NC, by ICF International , Durham, NC. January 2011, p 55. http://www.epa.gov/ttn/atw/nata2005/05pdf/nata_tmd.pdf

.
17

. EPA. 200 4

. EMS

- HAP Modeling System

. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/scram001/dispersion_related.htm#ems-h ap

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

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.pdf3 4

20 . EPA. 2012. Addendum to the User’s Guide for the AERMOD Meteorological 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

21 . Texas Commission on Environmental Quality. 2006. Reports on Air Emission Events. http://www.tceq.texas.gov/field/eventreporting

22 . 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. 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

24 . EPA. 2006. NEI Quality Assurance and Data Augmentation for Point Sources . U.S. Environmental Protection Agency, Emission Inventory Group, RTP, NC. http://ww w.epa.gov/ttn/atw/nata2005/05pdf/2002nei_qa_augmentation_report0206.pdf

25 . 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

26 . EPA, 2001, National-Scale Air Toxics Assessment for 1996; EPA-453/R-01 -003; EPA 454/B-03 -003, U.S. Environmental Protection Agency, Research Triangle Park, NC. http://www.epa.gov/ttn/atw/nata/peer.html