United States Office of Pollution Prevention 

Environmental Protection and Toxics 

Agency Washington, DC 20460 June 2010 

EPA’s Risk-Screening 

Environmental Indicators 

(RSEI) Methodology 

RSEI Version 2.3.0
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Acknowledgments

Many people have contributed to the RSEI project over the years. We would especially like to 

acknowledge the contributions of Nicolaas Bouwes and Steven Hassur who were the originators 

of the model and instrumental to its ongoing development and improvement. 

Current Members of the RSEI team are: 

Lynne Blake-Hedges 

Richard Engler 

Kristen Loughery 

Cody Rice 

Development support provided by Abt Associates Inc., 55 Wheeler Street, Cambridge, 

Massachusetts. 
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Table of Contents 

Executive Summary ...................................................................................................1

ES-1. Introduction............................................................................................................. 1 

ES-2. General Description of the RSEI Model................................................................. 2 

ES-2.1 Geographic Basis of the Model................................................................ 2 

ES-2.2 RSEI Results ............................................................................................ 6 

ES-2.3 Adjusting RSEI Results for Changes in TRI Reporting .......................... 8 

ES-2.4 Model Implementation............................................................................. 8 

ES-2.5 How the RSEI Chronic Human Health Toxicity Weightings Differ from 

EPCRA Section 313 Statutory Criteria.................................................... 9 

ES-3. Important Caveats Regarding the RSEI Model .................................................... 10 

ES-4. New Features in Version 2.3.0.............................................................................. 13 

1. Introduction....................................................................................................14

1.1 Background........................................................................................................... 14 

1.2 Model Implementation.......................................................................................... 15 

1.3 Organization of this Document............................................................................. 16 

2. General Description of the RSEI Model .......................................................18

2.1 General Description .............................................................................................. 18 

2.2 Summary of the Strengths and Limitations of the RSEI Model ........................... 21 

2.2.1 Strengths ................................................................................................ 21 

2.2.2 Limitations ............................................................................................. 22 

3. TRI Emissions Data.......................................................................................23

4. Methods for Calculating Toxicity Weights ...................................................24

4.1 Toxicity Weighting Scheme for Non-carcinogens and Carcinogens.................... 25 

4.1.1 Qualitative Data ..................................................................................... 25 

4.1.2 Quantitative Data ................................................................................... 27 

4.1.3 Algorithm for Calculating Toxicity Weight .......................................... 28 

4.2 Selecting the Final Chronic Human Health Toxicity Weight for a Chemical ...... 29 

4.3 Chemical Groups .................................................................................................. 30 

4.4 Sources of Data..................................................................................................... 30 

4.5 How Indicator Toxicity Weightings Differ from EPCRA Section 313 Criteria... 33 

5. Exposure and Population Modeling ..............................................................35

5.1 Geographic Basis of the RSEI Model................................................................... 36 

5.1.1 The Model Grid Cell System ................................................................. 36 

5.1.2 Locating Facilities on the Grid .............................................................. 38 

5.1.3 Locating People on the Grid .................................................................. 39 

5.2 Pathway-specific Methods to Evaluate Chronic Human Exposure Potential....... 41 

5.3 Modeling Air Releases.......................................................................................... 42 

5.3.1 Stack Air Emissions: Method ............................................................... 42 
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5.3.2 Fugitive Air Releases: Method ............................................................. 46 

5.3.3 Calculating Surrogate Dose for Air Releases ........................................ 46 

5.3.4 Estimating Population Size for Air Releases......................................... 47 

5.3.5 Calculating an Indicator Element for Air Releases................................ 47 

5.3.6 Stack and Fugitive Air Releases: Data................................................... 49 

5.4 Modeling Surface Water Releases........................................................................ 54 

5.4.1 Surface Water Releases: Methods ........................................................ 54 

5.4.2 Calculating the Indicator Element for Surface Water............................ 59 

5.4.3 Surface Water Releases: Data............................................................... 61 

5.5 Modeling Transfers to POTWs............................................................................. 66 

5.5.1 Transfers to POTWs: Method................................................................ 67 

5.5.2 Transfers to POTWs: Data.................................................................... 70 

5.6 Modeling Other Off-site Transfers ....................................................................... 71 

5.6.1 Off-site Transfers: Method ................................................................... 71 

5.6.2 Estimating Population for Off-Site Transfers........................................ 72 

5.6.3 Off-site Transfers: Data ......................................................................... 73 

5.7 Modeling On-site Land Releases.......................................................................... 73 

6. Calculating Results ........................................................................................74

6.1 Combining Indicator Elements ............................................................................. 76 

6.2 Accounting for Changes in TRI Reporting........................................................... 76 

7. Current Implementation of the RSEI Method ...............................................78

7.1 RSEI Model .......................................................................................................... 78 

7.2 Conclusion ............................................................................................................ 79 

8. References......................................................................................................80
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Executive Summary 

ES-1. Introduction 

EPA’s Risk-Screening Environmental Indicators (RSEI) is a screening-level tool that assesses 

the potential impact of industrial chemical releases from pounds-based, hazard-based, and riskrelated perspectives. RSEI uses risk concepts to quickly and easily screen large amounts of 

Toxics Release Inventory (TRI) data, saving time and resources. RSEI is particularly useful for 

examining trends to measure change, ranking and prioritizing chemicals and industry sectors for 

strategic planning, conducting risk-related targeting, and supporting community-based projects. 

Using estimates of pounds of chemical releases to investigate potential health and environmental 

impacts is limited by the assumptions that all chemicals are equally toxic and all people are 

equally exposed. Formal risk assessments are more accurate, but are complicated and time 

consuming to prepare, requiring detailed data that is not always available, and the results are 

often limited in scope and geographic area. The RSEI approach augments estimates of pounds 

released with toxicity and exposure considerations, but does not address all of the potential 

factors that a full risk-assessment would include. 

RSEI considers the following information: the amount of chemical released, the toxicity of the 

chemical, its fate and transport through the environment, the route and extent of human 

exposure, and the number of people affected. This information is used to create numerical 

values that can be added and compared in limitless ways to assess the relative risk of chemicals, 

facilities, regions, industries, or many other factors. The values are for comparative purposes 

and only meaningful when compared to other values produced by RSEI. It should be 

emphasized that the result is not a detailed or quantitative risk assessment, but offers a screeninglevel, risk-related perspective for relative comparisons of chemical releases. 

The RSEI approach is very flexible and can be implemented in various ways. The use of the 

model is not limited to any specific set of chemicals; in principle, the adaptable method can 

model any chemical if toxicity characteristics, physicochemical properties, release levels, and 

release location are known or can be estimated. 

As an indication of improvements in environmental quality over time, RSEI provides a valuable 

tool to measure general trends based upon relative risk-related impacts of TRI chemicals. 

Although RSEI results do not capture all environmental releases of concern, they generally relate 

changes in releases to relative changes in chronic human health impacts from a large number of 

toxic chemicals of concern to the Agency. Importantly, RSEI provides an ability to analyze the 

relative contribution of chemicals and industrial sectors to human health impacts, and RSEI 

results serve as an analytical basis for setting priorities for further risk analysis, pollution 

prevention, regulatory initiatives, enforcement targeting, and chemical testing requirements. 
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ES-2. General Description of the RSEI Model 

The RSEI model calculates values that reflect the risk-related impacts on chronic human health 

of modeled TRI chemical releases and transfers. These values do not provide absolute measures 

of risk and can only be interpreted as relative measures to be compared with other such values 

(reflecting the direction and the general magnitude of changes at different points in time when 

analyzing trends, or identifying the relative contribution of variables in a comparative analysis). 

The model uses the reported quantities of TRI releases and transfers of chemicals to estimate the 

risk-related impacts associated with each type of air and water release or transfer by every TRI 

facility. The risk-related impacts potentially posed by a chemical are a function of chemical 

toxicity, the fate and transport of the chemical in the environment after it is released, the pathway 

of human exposure, and the number of people exposed.1

The RSEI model starts with TRI releases. For each exposure pathway associated with each 

chemical release, the model generates an “Indicator Element”. For instance, a release of the 

chemical benzene to air via a stack from the “ABC” Facility in 1999 is an “Indicator Element”. 

Each Indicator Element is associated with a set of results, including pounds-based, hazard-based, 

and risk-related results, also called scores. The risk-related score is a unitless value proportional 

to the potential risk-related impact of each element. 

Each Indicator Element can be combined and compared with other Indicator Elements. There 

are countless ways that Indicator Elements can be summed together to assess chronic human 

health impacts. For example, all of the RSEI results can be aggregated for each year to allow an 

assessment of trends in estimated impacts, or results can be grouped to allow users to compare 

results for facilities, regions, chemicals, and any combinations of these and other variables. 

RSEI does not perform a detailed or quantitative risk assessment, but offers a screening-level, 

risk-related perspective for relative comparisons of chemical releases. The model does not 

estimate actual risk to individuals. RSEI results are only meaningful when compared to other 

results produced by RSEI. 

The current version of the model calculates risk-related results for the air and surface water 

pathways only. For other pathways, and in instances where information needed to model a 

release is not available, only pounds-based and hazard-based perspectives are available. In cases 

where toxicity weights are not available, only pounds-based results can be viewed. 

ES-2.1 Geographic Basis of the Model 

The model relies on the ability to locate facilities and people geographically, and to attribute 

characteristics of the physical environment, such as meteorology, to areas surrounding the 

facilities once they are located. To locate the facilities and the attribute data to those facilities, 

the model describes the U.S. and its territories2

 as an 810 m by 810 m grid system. For each cell 

 

1

 The method is focused on general populations; individuals, particularly highly exposed individuals, are not the 

focus of the model. 

2

 The model also includes Puerto Rico, the U.S. Virgin Islands, Guam, American Samoa, and the Northern Mariana 
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in the grid, a location “address” in terms of (x,y) coordinates is assigned based on latitude and 

longitude (lat/long). 

In order to estimate potential exposure, TRI facilities and the U.S. population must be 

geographically located on the model grid. TRI facilities are located using the facilities’ lat/long 

coordinates. To locate population, the model uses U.S. Decennial Census data for 1990 and 

2000 at the block level. These data3

 are used to create detailed age-sex-defined population 

groups for each of the census blocks in the US for 1990 and for 2000. The following population 

groups are used in the model:4

• Males Aged 0 through 9 years 

• Males Aged 10 through 17 years 

• Males Aged 18 through 44 years 

• Males Aged 45 through 64 years 

• Males Aged 65 Years and Older 

• Females Aged 0 through 9 years 

• Females Aged 10 through 17 years 

• Females Aged 18 through 44 years 

• Females Aged 45 through 64 years 

• Females Aged 65 Years and Older 

Because the Census block boundaries have changed between 1990 and 2000, each set of Census 

block level data is first transposed onto the model grid, which is unchanging, using an areaweighted method. Once populations for 1990 and 2000 are placed on the grid system, the model 

uses a linear interpolation in each grid cell to create annual estimates of the population sizes for 

each year between 1990 and 2000. The straight-line plot is extrapolated to estimate population 

for 1988-89 and for 2001-2007. 

Once facilities and people are located on the model’s grid system, three main components are 

used to compute risk-related impacts in the model. These components are: 

• The quantity of chemicals released or transferred, 

• Adjustments for chronic human health toxicity, and 

• Adjustments for exposure potential and population size. 

 Islands. U.S. Census data were provided by GeoLytics, Inc., East Brunswick, NJ. 

3

 For 1990, not all of the variables were available at the block level for the Continental U.S, Alaska and Hawaii. 

For those variables that were only available at the block group level, block group ratios were calculated and applied 

to the data at the block level. For 2000, all of the required data were available at the block level. For the U.S. 

Virgin Islands and the territories, data from larger geographic units (block groups or county-equivalents) were used. 

 For Puerto Rico, block group data was used for 1990 and block-level data for 2000. 

4

 Not all of the population groups listed are used in viewing results. Model results can only be viewed for the 

following groups: Children Under 10, Children 10 through 17, Males 18 through 44, Females 18 through 44, and 

Adults 65 years and Older. 
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These components and the method used to combine them are described in the following sections. 

Chemical Releases and Transfers. The model uses information on facilities’ chemical releases 

and transfers from these facilities to off-site facilities (such as sewage treatment plants and 

incinerators) to model risk-related impacts. These releases are reported by facilities to the TRI, 

as mandated by the Emergency Planning and Community Right-to-Know Act. As of the 2007 

reporting year, there are 611 TRI chemicals and chemical categories listed. Users can view 

pounds of chemicals released per year (pounds-based results) for any combination of variables 

included in the model. 

Adjustments for Chronic Human Health Toxicity. The model is based on current EPA 

methodologies for assessing toxicity. The method EPA has chosen for assigning toxicity weights 

to chemicals is clear and reproducible, based upon easily accessible and publicly available 

information, and uses expert EPA-wide judgments to the greatest extent possible. RSEI reflects 

the toxicities of chemicals relative to one another using a continuous system of numerical 

weights. Toxicity weights for chemicals increase as the toxicological potential to cause chronic 

human health effects increases. Toxicity-adjusted releases are called “hazard-based results” and 

provide an alternative perspective to pounds-based or full risk-related results, and are especially 

valuable when necessary data for risk-related modeling are not available. 

Values developed by EPA experts are used to differentiate the degrees and types of toxicity of 

chemicals, and rank them in a consistent manner. Values called Oral Slope Factors and 

Inhalation Unit Risks5

 provide information pertaining to toxicity for chemicals that may cause 

cancer. Reference Doses (RfDs) and Reference Concentrations (RfCs) provide toxicity 

information related to noncancer effects.6 Where these values are not available from EPA, other 

data sources may be used. 

The following data sources are used, in the order of preference: 

• EPA’s Integrated Risk Information System (IRIS); 

• EPA Office of Pesticide Programs’ Acute and Chronic Reference Doses Table, 

List of Chemicals Evaluated for Carcinogenic Potential, and Pesticide 

Reregistration Eligibility Documents (OPP); 

• Final, published Minimum Risk Levels (MRLs) from the Agency for Toxic 

Substances and Disease Registry (ATSDR); 

• Final published toxicity values from California Environmental Protection 

Agency’s Office of Environmental Health Hazard and Assessment (CalEPA); 

 

5

 The Oral Slope Factor represents the upper-bound (approximating a 95% confidence limit) estimate of the slope of 

the dose-response curve in the low-dose region for carcinogens. The units of the slope factor are usually expressed 

as (mg/kg-day) -1. The Inhalation Unit Risk is the upper-bound excess lifetime cancer risk estimated to result from 

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

 in air. 

6

 RfDs and RfCs are estimates (with uncertainty spanning perhaps an order of magnitude) of daily exposure [RfD], 

or continuous inhalation exposure [RfC], to the human population (including sensitive subgroups) that is likely to be 

without an appreciable risk of deleterious noncancer effects during a lifetime. 
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• EPA’s Provisional Peer Reviewed Toxicity Values (PPRTVs), which include 

toxicity values that have been developed by EPA’s Office of Research and 

Development/National Center for Environmental Assessment/Superfund Health 

Risk Technical Support Center (STSC); 

• EPA’s Health Effects Assessment Summary Tables (HEAST); and 

• Final Derived/Interim Derived Toxicity Weights estimated by EPA’s Office of 

Pollution Prevention and Toxics (Derived). 

The toxicity scoring method separately evaluates exposure routes (inhalation and oral) and 

classes of effects (cancer and noncancer). For each exposure route, chemicals are scored based 

on their single most sensitive adverse effect; if a chemical exhibits both cancer and noncancer 

effects, the higher of the two weights is assigned as the final weight for that route. The 

algorithms used to assign toxicity weights are shown below in Exhibit ES.1. 

Exhibit ES.1 

Algorithms for Assigning Toxicity Weights 

 Oral Pathway Inhalation Pathway 

Non-Carcinogens: 1 / RfD (mg/kg-day) 3.5 / RfC (mg/m3) 

Carcinogens

(WOE categories 

A and B): 

Oral Slope Factor (risk per 

mg/kg-day)/ 1.0 x 10-6 

Inhalation Unit Risk (risk 

per mg/m3)/ 2.8 x 10-7

Carcinogens (WOE 

category C): 

Oral Slope Factor (risk per 

mg/kg-day)/ (1.0 x 10-6 * 

10) 

Inhalation Unit Risk (risk 

per mg/m3)/ (2.8 x 10-7 * 

10) 

The distribution of toxicity values for TRI chemicals corresponds to a range of toxicity weights 

of approximately 0.02 to 500,000,000. However, toxicity weights are not bounded. Continuous 

toxicity weights are expressed as values with two significant figures. 

There are 611 chemicals and chemical categories on the 2007 TRI Chemical List. Toxicity 

weights are available for 433 of these chemicals and chemical categories. Chemicals with 

toxicity weights account for over 99% of the reported pounds for all on-site releases in 2007. 

Adjustments for Exposure Potential and Population Size. Quantitatively, exposure potential 

is estimated using a “surrogate” dose. To estimate the surrogate dose, a separate exposure 

evaluation is conducted for each pathway-specific chemical emission. The exposure evaluations 

use models that incorporate data on pathway-specific chemical releases and transfers, 

physicochemical properties and, where available, site characteristics, to estimate the ambient 

chemical concentration in the medium into which the chemical is released or transferred. The 

ambient concentrations are combined with human exposure assumptions and estimates of 

exposed population size specific to age and sex. 
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The algorithms for calculating surrogate doses rely on the ability to locate facilities and people 

geographically on the 810 m by 810 m grid cell system described earlier. While this method 

uses the EPA exposure assessment paradigm to evaluate exposure potential, the results should 

not be construed as an actual numerical estimate of dose resulting from TRI releases, because 

limited facility-specific data and the use of models that rely on default values for many 

parameters prevent the calculation of an actual dose. Instead, the purpose of the methodology is 

to generate as accurate a surrogate dose as possible without conducting an in-depth risk 

assessment. The estimates of surrogate doses from releases of TRI chemicals are relative to the 

surrogate doses resulting from other releases included in the model. Please note that not all 

pathways are currently modeled. 

ES-2.2 RSEI Results 

Because of the multi-functional nature of the model, a variety of results can be created. All 

RSEI results are based on the Indicator Element, which is a unique combination of facility, 

chemical, release pathway, exposure pathway, and year.7

 Each Indicator Element has a set of 

associated results: 

Exhibit ES.2 

Description of RSEI Results 

Risk-related results Surrogate Dose x Toxicity Weight x Population 

Hazard-based results Pounds x Toxicity Weight 

Pounds-based results TRI Pounds released 

Risk-related results. The pathway-specific toxicity weight, surrogate dose, and population 

components are multiplied to obtain a risk score for the Indicator Element. The surrogate dose is 

determined through pathway-specific modeling of the fate and transport of the chemical through 

the environment, combined with subpopulation-specific exposure factors. The score is a unitless 

measure that is not independently meaningful, but is a risk-related estimate that can be compared 

to other estimates calculated using the same methods. If the Indicator Element cannot be 

modeled, because of a lack of data needed for modeling or because the release pathway is not 

currently modeled, then the risk-related score is zero. The model calculates risk-related results 

for the entire population and also for the following subpopulations: children under 10, children 

aged 10 to 17, males aged 18 to 44, females aged 18 to 44, and adults aged 65 and older. In 

addition, the model also calculates “Modeled Pounds,” which is simply the number of pounds 

that can be modeled for risk-related scores. Modeled pounds are the pounds to which fate and 

transport modeling and exposure assumptions have been applied. 

 

7

 Several related Indicator Elements may be associated with certain release and exposure pathways (e.g., direct water 

releases may be associated with exposure from drinking water intakes, as well as fish ingestion from recreational 

fishing and from subsistence fishing). 
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Hazard-based results. Each Indicator Element is also associated with a hazard-based result 

(“Hazard”), calculated by multiplying the pounds released by the appropriate chemical-specific 

toxicity weight (the toxicity weight also depends on the exposure pathway). The inhalation 

toxicity weight is used for releases or transfers to fugitive air, stack air, off-site incineration, and 

off-site incineration-no fuel value. The oral toxicity weight is used for releases to direct water 

and transfers POTWs. For releases that are not modeled (because the pathway is not modeled or 

because other necessary data, such as physicochemical properties, are lacking), the higher 

toxicity weight is used. For these results, no exposure modeling or population estimates are 

involved. If there is no toxicity weight available for the chemical, then the hazard score is zero. 

The model also calculates “Modeled Hazard,” which is the chemical- and pathway-specific 

toxicity weights multiplied by the Modeled Pounds (as described above), and “Modeled Hazard 

* Pop,” which multiplies modeled hazard by the potentially exposed population, but without the 

fate and transport modeling (and application of exposure assumptions) that would be found in 

risk-related results. 

Pounds-based results. These results (“TRI Pounds”) reflect only the number of pounds released 

or transferred that are reported to TRI, and are available for virtually all Indicator Elements. The 

model also provides “TRI Pounds with Toxicity Weights,” which simply sums the pounds for 

chemicals that have toxicity weights in RSEI. 

Once results are calculated for each Indicator Element, they can be combined in many different 

ways. All of the results are additive, so a result for a specific set of variables is calculated by 

summing all the relevant individual Indicator Element results, as follows: 

R IE = ∑ ∑ ∑ cf p , , (Eq. ES.1) 

where: 

R = RSEI result, and 

IEc,f,p = chemical-facility-pathway-specific Indicator Element result. 

This method is very flexible, allowing for countless variation in the creation of results. For 

example, results can be calculated for various subsets of variables (e.g., chemical, facility, 

release pathway) and compared to each other to assess the relative contribution of each subset to 

the total potential impact. Or, results for the same subset of variables for different years can be 

calculated, to assess the general trend in pounds-based, hazard-based, or risk-related impacts 

over time. 

It must be reiterated that while changes in results over the years would imply that there have 

been changes in hazard- or risk-related environmental impacts, the actual magnitude of any 

specific change or the reason may not be obvious. Although the value itself may be useful in 

identifying facilities or chemicals with the highest potential for hazard or risk, the score does not 

represent a quantitative estimate or provide an exact indication of the magnitude of individual 

hazard or risk associated with that facility or chemical. 
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ES-2.3 Adjusting RSEI Results for Changes in TRI Reporting 

When a change occurs in the number of, or reporting requirements for, chemicals and facilities 

represented in TRI, the numerical value of RSEI results will be altered if no adjustments are 

made to the method of calculation to account for the changes respective to trend analyses. 

However, such changes would not necessarily represent a large change in actual environmental 

impact, but would reflect a broader understanding of the impacts that may have always existed. 

To maintain comparability in the weights over time, the results must be adjusted in some manner 

when such changes in TRI reporting occur. 

A change in the number of chemicals and facilities in the TRI database can occur through several 

mechanisms. The addition to or deletion of chemicals from the TRI chemical list will occur as 

EPA responds to petitions or initiates its own action through the chemical listing or delisting 

process. The largest revision to the list occurred in November 1994, when the Agency added 

245 chemicals and chemical categories to the existing TRI chemical list, effective for the 

reporting year 1995. Other revisions have occurred since. To allow for meaningful trend 

analysis, the model maintains a list of “core” chemicals which have been reported since 1987, 

and for which reporting requirements have not changed. 

Compliance with TRI reporting has changed over time, which has led to more facilities 

reporting. Increases in the number of reporting facilities may also occur as a result of changes in 

reporting requirements. For instance, in the first two years of reporting, facilities that 

manufactured or processed more than 50,000 pounds were required to report their releases. 

However, EPCRA lowered this threshold to 25,000 pounds in 1989. And for reporting year 

2000, thresholds and other reporting requirements for 18 Persistent Bioaccumulative Chemicals 

(PBTs) have been changed. Effective for reporting year 1998, TRI has enlarged the set of 

facilities required to report to include electric utilities, mining facilities, commercial hazardous 

waste facilities, solvent recovery facilities, and wholesale chemical and petroleum terminal 

facilities. All of these modifications can act to alter the total emissions reported on the TRI and 

the model’s estimate of the associated relative risk-based impacts. 

When deletions from the chemical list of TRI chemicals occur, RSEI’s chemical database is 

modified to remove all results from previous reporting years. Also, the yearly TRI data for a 

given chemical list of chemicals and facilities are the subject of ongoing quality control review 

and correction by both EPA and reporting facilities. As a result, yearly comparisons could be 

flawed if such revisions in reported data were not included in each previous year’s results. 

Therefore, the Indicator Elements are recomputed for all years on an annual basis in order to 

incorporate chemical deletions and revisions to the reporting data. 

ES-2.4 Model Implementation 

The RSEI model is currently implemented in a Microsoft Windows-based computer program. 

The program allows users to calculate RSEI results for TRI reporting years 1996-2007 (earlier 

years are available upon request) and to present the results in various GIS, graphical, and tabular 

formats, as well as to save selected data to spreadsheet and database formats (e.g., Microsoft 
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Excel and databases such as Access). The program includes on-line help for all of the program 

functions, as well as a User’s Manual in Adobe Acrobat format. 

Users of the model can perform, usually in a matter of minutes, a variety of screening-level 

analyses. Previously, such activities would have taken days, weeks, or even months to organize 

the relevant information, evaluate that information, and perform the complex and sophisticated 

analyses that are necessary to provide a risk-related perspective. Results can be used for 

screening-level ranking and prioritization for strategic planning purposes, risk-related targeting, 

and trends analyses. Considerable resources can be saved by conducting preliminary analyses 

with the model to identify risk-related situations of high potential concern, which warrant further 

evaluation. 

As noted above, users can evaluate releases using a number of variables, such as chemical, 

medium, geographic area or industry. For instance, the following types of questions can be 

investigated: 

• How do industry sectors compare to one another from a risk-related perspective? 

• What is the relative contribution of chemicals within a given industry sector? 

• What release pathway for a particular chemical poses the greatest risk-related 

impacts? 

Users can view pounds-based, hazard-based, and other results, to investigate the relative 

influence of toxicity and population components on the risk-related results, which also 

incorporate exposure modeling. 

Information regarding the RSEI project is available on OPPT’s RSEI web site at 

http://www.epa.gov/oppt/rsei/. Complete documentation, frequently asked questions, and 

contact information are all posted on the site. Periodic updates and troubleshooting information 

are also available for users. 

ES-2.5 How the RSEI Chronic Human Health Toxicity Weightings Differ from 

EPCRA Section 313 Statutory Criteria 

As described above, the RSEI model uses TRI chemical reporting data. However, it is important 

that the public not confuse the use of the model as a screening-level tool for investigating relative 

risk-based impacts related to the releases and transfers of TRI chemicals, with the very different 

and separate activity of listing/delisting chemicals on the TRI using statutory criteria. 

The goal of RSEI is to use data reported to the Agency to investigate the relative risk-based 

impacts of the releases and transfers of these chemicals on the general, non-worker population. 

The model differentiates the relative toxicity of listed chemicals and ranks them in a consistent 

manner. The ranking of each chemical reflects its toxicity only relative to other chemicals that 

are included in the model. Toxicity is not compared to some benchmark or absolute value as is 

required when adding or removing a chemical from the TRI. Furthermore, the model addresses 

only the single, most sensitive toxicity endpoint for chronic human health. 
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In contrast, the EPCRA statutory criteria used for listing and delisting chemicals consider acute 

and chronic human toxicity, as well as environmental toxicity, and consider multiple effects and 

the severity of those effects. The criteria also address the “absolute” chronic toxicity of 

chemicals on the TRI relative to a benchmark value. 

Because of these differences, the toxicity weightings in the model cannot be used as a scoring 

system for evaluating listing/delisting decisions. RSEI does not attempt to reflect the statutory 

criteria for these chemicals. 

ES-3. Important Caveats Regarding the RSEI Model 

The RSEI model is a screening tool that provides a risk-related perspective in assessing the 

relative impacts of releases of toxic chemicals. Risk-related results are available for releases and 

transfers to air and water, and pounds- and hazard-based results are available for all media. 

RSEI combines estimates of toxicity, exposure level, and the exposed population to provide 

risk-related comparisons. It does not provide a detailed or quantitative assessment of risk, and is 

not designed as a substitute for more comprehensive, site-specific risk assessments. There are a 

number of important considerations associated with each component of the model, as described 

in the following sections. 

Release Component. The following caveat should be considered regarding the release 

component of the model: 

• RSEI uses facility-reported TRI data, which has been known to contain some 

reporting errors. Since facility management must certify reports to be accurate, 

the TRI program does not change any reported data until the reporting facility 

submits an official correction. Therefore, there are some releases in the TRI data 

that are thought to be erroneous but are still included because facilities have not 

submitted corrected reporting forms by the time of the annual public data release 

that RSEI uses. Some of these releases are associated with large risk-related 

impacts. One erroneous release warrants special note: a 2002 fugitive air release 

of 184,770 pounds of nickel in Johnstown, PA, probably overstates the release 

amount and may be assigned to the wrong media. 

Toxicity Component. The following caveats should be considered regarding the toxicity 

component of the model: 

• Toxicity weights are not designed to (and may not) correlate with statutory 

criteria used for listing and delisting chemicals in TRI. RSEI risk-related model 

results account for estimated exposure and may not correlate with listing/delisting decisions. 

• The RSEI model only addresses chronic human toxicity (cancer and noncancer 

effects, such as developmental toxicity, reproductive toxicity, neurotoxicity, etc.) 

associated with long-term exposure and does not address concerns for either acute 

human toxicity or environmental toxicity. 
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• Toxicity weights are based upon the single, most sensitive chronic human health 

endpoint for inhalation or oral exposure pathways, and do not reflect severity of 

effects or multiple health effects. 

• Estimated Reference Doses and Reference Concentrations for noncancer effects 

incorporate uncertainty factors which are reflected in toxicity weights that are 

based upon these values. 

• Toxicity weights for chemicals that are reported to TRI as groups are based on the 

toxicity for the most toxic member of the group. One exception to this is 

polycyclic aromatic compounds (PACs), the toxicity for which is assumed to be 

18% of the toxicity for benzo(a)pyrene, its most toxic member. This is based on 

speciation information and follows the method used by EPA’s National-Scale Air 

Toxics Assessment (NATA) evaluation for polycyclic organic matter.8 

• Several significant assumptions are made regarding metals and metal compounds, 

because important data regarding these chemicals are not subject to TRI reporting. 

Metals and metal compounds are assumed to have the same toxicity weight, 

although the chronic toxicity of some metal compounds may be higher. Metals 

and metal compounds are assumed to be released in the valence (or oxidation 

state) associated with the highest chronic toxicity. There are two exceptions to 

this: (1) For chromium and chromium compounds, it is assumed that facilities 

may release some combination of hexavalent chromium and trivalent chromium. 

SIC-code specific estimates from the 2002 National Emissions Inventory are used 

to estimate the fraction of each type.9

 As trivalent chromium has a very low 

toxicity, only the hexavalent fraction is modeled, using a toxicity weight 

specifically for that valence state; and (2) For mercury and mercury compounds, 

toxicity for the oral pathway is based on methyl mercury, and toxicity for the 

inhalation pathway is based on elemental mercury. 

• While the physical form of released metals or metal compounds can affect 

toxicity, a reasonable assumption is made regarding the likely form of most 

releases (e.g., the non-cancer toxicity weight for chromic acid mists and dissolved 

hexavalent chromium aerosols is much higher than for hexavalent chromium 

particulates, but releases of these chemicals as acid aerosols are not expected to be 

typical so the toxicity weight for cancer based on the inhalation of particulates is 

used). Analysts need to consider these assumptions, and whether the gathering of 

additional data is warranted, when examining model results for metals and metal 

compounds. 

 

8

 Additional information is available in the NATA documentation (http://www.epa.gov/ttn/atw/sab/appendix-h.pdf). 

RSEI assumes that PAC emissions reported to TRI are most like NATA’s “7-PAH” category. 

9

 Available from http://www.epa.gov/ttn/chief/net/2002inventory.html 
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Exposure Component. The following caveats should be considered regarding the exposure 

component of the model: 

• Like other exposure models, RSEI estimates exposure levels. It does not yield 

actual exposures. The model provides estimated air concentrations in each grid 

cell. 

• The model uses some generic assumptions, e.g., default median stack heights, 

diameters, and exit gas velocities related to 2- or 3-digit Standard Industrial 

Classification (SIC) codes, or a nationwide median, where facility-specific 

median stack height, diameter, and exit gas velocity data are unavailable. For 

large facilities with multiple stacks, the median height for all stacks is used as the 

stack height for the entire facility. 

• In the current version of the model, only air and direct surface water exposures 

are fully modeled. 

• The model does not account for population activity patterns. 

• The model has greater uncertainty when examining disaggregated results at the 

local or facility level. The model does not account for indirect exposure, air 

deposition of pollutants to other media, or absorption of pollutants through the 

skin. 

Population Component. The following caveats should be considered regarding the population 

component of the model: 

• Population values for non-decennial years are estimated based on linear 

interpolations at the block level between the 1990 and 2000 U.S. Census dates, 

and on extrapolations back to 1988 and forward to 2007. 

• Drinking water populations are estimated by using the total drinking water 

populations associated with individual downstream drinking water intakes. 

Estimated populations for the fish ingestion pathway are based upon U.S. Fish 

and Wildlife Service surveys. 

• Because RSEI results reflect changing population size at the local level, a 

facility’s relative contribution could increase or decrease even without changes in 

its releases over time. While the model is designed to reflect the overall riskrelated impacts on the local population, such population changes should be 

considered when examining a facility’s environmental management practices. 
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ES-4. New Features in Version 2.3.0 

• Includes TRI Reporting Years 1988-2007 (data for years 1996-2007 are available 

in the public release; data for 1988-1995 are available upon request). 

• Toxicity weights have been updated. The equivalence between cancer and 

noncancer toxicity weights has been changed from 2.5 x 10-4 to 1.0 x 10-6. This 

has the effect of increasing the relative weight given to cancer endpoints. 

• Information on speciation has been used for the chemical category polycyclic 

aromatic compounds (PACs). The overall toxicity for the chemical group is now 

assumed to be 18% of the toxicity for benzo(a)pyrene. 

• The network of streams used to model water discharges from TRI facilities and 

Publicly-Owned Treatment Works (POTWs) has been updated from EPA's 

Enhanced Reach File 1 (E-RF1) to the more detailed National Hydrography 

Dataset (NHD). 

• The grid that underlies the model has increased detail (the grid cell is now 810 m 

square rather than 1 km square) and now uses a standard GIS projection, to make 

use with GIS systems easier.
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1. Introduction 

EPA’s Risk-Screening Environmental Indicators (RSEI) is a screening-level tool that assesses 

the potential impact of industrial releases from pounds-based, hazard-based, and risk-related 

perspectives. RSEI uses risk concepts to quickly and easily screen large amounts of TRI data, 

saving time and resources. RSEI is particularly useful for examining trends to measure change, 

ranking and prioritizing chemicals and industry sectors for strategic planning, conducting riskrelated targeting, and supporting community-based projects. 

Using estimates of pounds of chemical releases to investigate potential health and environmental 

impacts is limited by the assumptions that all chemicals are equally toxic and all people are 

equally exposed. Formal risk assessments are more accurate, but are complicated and time 

consuming to prepare, requiring detailed data that is not always available, and the results are 

often limited in scope and geographic area. The RSEI approach augments estimates of pounds 

released with toxicity and exposure considerations, but does not address all of the potential 

factors that a full risk-assessment would include. 

RSEI considers the following information: the amount of chemical released, the toxicity of the 

chemical, its fate and transport through the environment, the route and extent of human 

exposure, and the number of people affected. This information is used to create numerical 

values that can be added and compared in limitless ways to assess the relative risk of chemicals, 

facilities, regions, industries, or many other factors. The values are for comparative purposes 

and only meaningful when compared to other values produced by RSEI. It should be 

emphasized that the result is not a detailed or quantitative risk assessment, but offers a screeninglevel, risk-related perspective for relative comparisons of chemical releases. 

The RSEI approach is very flexible and can be implemented in various ways. The use of the 

model is not limited to TRI chemicals; in principle, the adaptable method can model any 

chemical if toxicity characteristics, physicochemical properties, release levels, and release 

location are known or can be estimated. 

1.1 Background 

In 1989, EPA outlined the goals for establishing strategic planning processes at the Agency 

(EPA, 1990c). Underlying this approach was the Agency’s desire to set priorities and direct 

resources to areas with the greatest opportunity to achieve health and environmental risk 

reductions. As part of this initiative, the Administrator set forth a plan to develop indicators to 

track changes in environmental health impacts over time. Tracking these changes would allow 

the Agency to measure its progress in implementing environmental protection and pollution 

prevention programs. In addition, comparing the relative contribution of particular chemicals, 

industries and geographic regions through the indicators would allow the Agency (and other 

users) to establish priorities for improving human health and the environment. 

To efficiently track changes in human health and environmental impacts over time, the Agency 

should take advantage of existing data sources that reflect multimedia trends in environmental 
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contaminant releases. The TRI is one of the Agency’s most relevant source of continuous data 

for developing indicators of change in environmental impacts over time. The TRI is mandated by 

the Emergency Planning and Community Right-to-Know Act (EPCRA) Title III Section 313 and 

requires that U.S. manufacturing facilities file annual reports documenting multimedia 

environmental releases and off-site transfers for more than 600 chemicals and chemical 

categories which are of concern to the Agency. 

In response to the need for environmental indicators, and to take advantage of the rich data 

source offered by the TRI, the Office of Pollution Prevention and Toxics (OPPT) convened a 

workgroup to explore the development of an indicator or indicators based on the TRI that could 

track changes in human health and environmental impacts. Specifically, the approach would 

integrate toxicity, exposure and population considerations into the risk-related evaluation of 

releases. The RSEI model was developed in response to this initiative. 

When evaluating impacts of chemicals, it is important to not only consider the number of pounds 

of a chemical released to the environment, but also the toxicity of the chemical, its exposure 

potential, and the size of the receptor population. RSEI integrates these factors and provides a 

relative risk-based perspective of chemical releases and transfers. To the extent possible, the 

RSEI model is based on existing EPA approaches, data, and models, to minimize duplication of 

effort and to maximize consistency with other Agency efforts to evaluate human health impacts. 

The current version of the model tracks changes in chronic human health impacts. Ultimately, 

the model may be expanded to track acute human health and chronic and acute ecological 

impacts. 

This document explains how the RSEI model is constructed, and describes the conceptual 

method, data sources, and the computational approach. The aim is to explain the model to a 

variety of agencies and groups that may wish to use or adapt the model, or the RSEI 

methodology in general, to their own needs. In addition, it describes the advantages of the RSEI 

approach in terms of flexibility, power, and usefulness as an analytical and strategic policyplanning tool. 

1.2 Model Implementation 

The RSEI model is currently implemented in a Microsoft Windows-based computer program. 

Version 2.3.0 of the model contains TRI data from 1996-2007, and allows users to calculate 

RSEI results for these years of reporting, and to present the results in various GIS, graphical, and 

tabular formats, as well as to save selected data to spreadsheet and database formats (e.g., 

Microsoft Excel and databases such as Access). Data for the previous years of TRI reporting 

(1988 though 1995) are updated every year, and available upon request. The program includes 

on-line help for all of the program functions, as well as a set of introductory tutorials for firsttime users. A User’s Manual is also available. 

Users of the RSEI model can perform a variety of screening-level analyses, usually in a matter of 

minutes. Previously, such activities would have taken days, weeks, or even months to organize 

the relevant information, evaluate that information, and perform the complex and sophisticated 
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analyses that are necessary to provide a risk-related perspective. Results can be used for 

screening-level ranking and prioritization for strategic planning purposes, risk-related targeting, 

and trends analyses. Considerable resources can be saved by conducting preliminary analyses 

with the RSEI model to identify risk-related situations of high potential concern, and which 

warrant further evaluation. 

Users can evaluate releases using a number of variables, such as chemical, medium, geographic 

area or industry. For instance, users can investigate the following types of questions: How do 

industry sectors compare to one another from a risk-related perspective? What is the relative 

contribution of chemicals within a given industry sector? What release pathway for a particular 

chemical poses the greatest risk-related impacts? Users can view pounds-based, hazard-based, 

and other results, to investigate the relative influence of toxicity and population on the riskrelated results, which also incorporate exposure modeling. 

Information regarding the RSEI project is available on OPPT’s RSEI web site, at 

www.epa.gov/oppt/rsei/. 

1.3 Organization of this Document 

Chapter 2 of this document gives a brief description of the RSEI method and model, as well as a 

discussion of their overall strengths and limitations. Chapter 3 describes the TRI emissions data 

used in the model. Chapter 4 describes the methods used to adjust the emissions data for 

chemical toxicity, and Chapter 5 provides a discussion of the geographic basis of the model, as 

well as pathway-specific descriptions of adjustments made for exposure potential and population 

size. Chapter 6 presents the equations for calculating RSEI results, and Chapter 7 describes 

issues pertinent to the current implementation of the RSEI method. 

There are also six Technical Appendices that accompany this methodology document and 

provide additional information on the data used in the model. The Appendices are as follows: 

Technical Appendix A - Listing of All Toxicity Weights for TRI Chemicals and Chemical 

Categories 

Technical Appendix B - Physicochemical Properties for TRI Chemicals and Chemical 

Categories 

Technical Appendix C - Derivation of Model Exposure Parameters 

Technical Appendix D - Locational Data for TRI Reporting Facilities and Off-site Facilities 

Technical Appendix E - Derivation of Stack Parameter Data 

Technical Appendix F - Summary of Differences Between RSEI Data and the TRI Public Data 

Release 

In addition, two documents containing background and supporting information are available on 

the project web site. Analyses Performed for the Risk-Screening Environmental Indicators

contains three parts: Part A describes the result of a ground-truthing analyses performed to 

determine the accuracy of the air pathway modeling; Part B contains additional analyses 

performed on the air pathway to determine optimal modeling parameters; and Part C describes 

the results of an analysis of SIC code-based stack parameter data. Developing the Risk-
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Screening Environmental Indicators describes the development of the model, and outlines 

options that were considered for several important aspects of the method. These background 

documents are available on the RSEI web site at www.epa.gov/oppt/rsei. The RSEI web site 

also contains complete methodological information, a document archive, the RSEI User’s 

Manual, RSEI Tutorials, answers to frequently asked questions, contact information, and a 

glossary. 
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2. General Description of the RSEI Model 

2.1 General Description 

The RSEI model is a screening-level tool that assesses the potential impact of TRI releases from 

pounds-based, hazard-based, and risk-related perspectives. A basic outline of the modeling 

approach is illustrated in Exhibit 2.1. 

Three main components are used in the model to calculate results:10

• The TRI, maintained by EPA, provides the data on the quantity of chemicals

released to air, water, on-site disposal facilities, and transferred to off-site 

facilities for the more than 600 toxic chemicals that are listed on the TRI. 

Reporting by facilities to the TRI began in 1987, and has continued each year 

since then (RSEI uses TRI reporting data beginning in 1988). RSEI Version 2.3.0 

contains TRI data for reporting years 1996 through 2007. Data for reporting years 

1988 through 1995 are updated every year and are available upon request. 

Releases are reported in pounds per year. 

• Toxicity weights are assigned to each chemical for which adequate data are 

available. These weights are assigned using quantitative toxicity values 

developed by EPA scientists and additional qualitative assessments, as described 

below. 

• Exposure and population modeling are performed for the air and surface water 

pathways to model the movement of each chemical release through the 

environment to the exposed population. A surrogate dose, the amount of 

chemical that a human contacts, is estimated. The estimation of a surrogate dose 

allows comparisons across pathways. Then the population exposed to each 

release is estimated using U.S. Decennial Census data. 

The RSEI model starts with TRI releases. For each exposure pathway associated with each 

chemical release, the model generates an “Indicator Element.” For instance, a release of the 

chemical benzene to air via a stack from the “ABC” Facility in 1999 is an “Indicator Element.” 

Each Indicator Element is associated with a set of results, including pounds-based, hazard-based, 

and risk-related results, or scores. The risk-related score is a unitless value proportional to the 

potential risk-related impact of each element. 

 

10 The method focuses on general populations: individuals, particularly highly exposed individuals, are not the focus 

of the model. Furthermore, worker exposures are not addressed. 
Version 2.3.0 1988-2007 TRI data 

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Exhibit 2.1 

RSEI Modeling Approach 

Stack

air emissions(2)

(1) indicates media code 1: Fugitive Air Release(2) indicates media code 2: Stack Air Release(750) indicates media code 750: Offsite Incineration/ Thermal Treatment Release(754) indicates media code 754: Offsite Incineration (no fuel value) Release(6) indicates media code 6: POTW Transfer(3) indicates media code 3: Direct Water Release

Fugitive

air emissions(1)

Non-POTW off-site

transfers(750 & 754)

POTW off-site

transfers(6)

Incineration Volatilization POTW effluent

Water M

(see Exh

Air Modeling

(see Exhibit 5.3)
Chapter 2: General Description of the Model 

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Exhibit 2.2 

Description of RSEI Results 

Risk-related results Surrogate Dose x Toxicity Weight x Population 

Hazard-based results Pounds x Toxicity Weight 

Pounds-based results TRI Pounds Released 

Risk-related results. The toxicity, surrogate dose, and population components are multiplied to 

obtain a risk score for the Indicator Element. The surrogate dose is determined through pathwayspecific modeling of the fate and transport of the chemical through the environment, combined 

with subpopulation-specific exposure factors. The score is a unitless measure that is not

independently meaningful, but is a risk-related estimate that can be compared to other estimates 

calculated using the same methods. If the Indicator Element cannot be modeled, because of the 

lack of data needed for modeling or because the release pathway is not currently modeled, then 

the risk-related score is zero. The model calculates risk-related results for the entire population 

and also for the following subpopulations: children under 10, children aged 10 to 17, males aged 

18 to 44, females aged 18 to 44, and adults aged 65 and older. In addition the model also 

calculates “Modeled Pounds,” which is simply the number of pounds that can be modeled for 

risk-related scores. Modeled pounds are the pounds to which fate and transport modeling and 

exposure assumptions have been applied. 

Hazard-based results. Each Indicator Element also is associated with a hazard-based result 

(“Hazard”), calculated by multiplying the pounds released by the appropriate chemical-specific 

toxicity weight (the toxicity weight also depends on the exposure pathway). The inhalation 

toxicity weight is used for releases or transfers to fugitive air, stack air, off-site incineration, and 

off-site incineration - no fuel value. The oral toxicity weight is used for releases or transfers to 

direct water and POTWs. For releases that are not modeled (because the pathway is not modeled 

or because other necessary data, such as physicochemical properties, are lacking), the higher 

toxicity weight is used. For these results, no exposure modeling or population estimates are 

involved. If there is no toxicity weight available for the chemical, then the hazard score is zero. 

The model also calculates “Modeled Hazard,” which is the chemical- and pathway-specific 

toxicity weights multiplied by the Modeled Pounds (as described above), and “Modeled Hazard 

* Pop,” which multiplies modeled hazard by the potentially exposed population, but without the 

fate and transport modeling (and application of exposure assumptions) that would be found in 

risk-related results. 

Pounds-based results. These results (“TRI Pounds”) reflect only the number of pounds released 

or transferred that are reported to TRI, and are available for virtually all Indicator Elements. The 

model also provides “TRI Pounds with Toxicity Weights,” which simply sums the pounds for 

chemicals that have toxicity weights in RSEI. 
Chapter 2: General Description of the Model 

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Once results are calculated for each Indicator Element, they can be combined in many different 

ways. All of the results are additive, so a result for a specific set of variables is calculated by 

summing all the relevant individual Indicator Element results, as follows: 

R IE = ∑ ∑ ∑ cf p , ,

 (Eq. 2.1) 

where: 

R = RSEI result, and 

IEc,f,p = chemical-facility-pathway-specific Indicator Element result. 

This method is very flexible, allowing for countless variation in the creation of results. For 

example, results can be calculated for various subsets of variables (e.g., chemical, facility, 

release pathway) and compared to each other to assess the relative contribution of each subset to 

the total potential impact. Or, results for the same subset of variables for different years can be 

calculated, to assess the general trend in pounds-based, hazard-based, or risk-related impacts 

over time. 

It must be reiterated that while changes in results over the years would imply that there have 

been changes in hazard- or risk-related environmental impacts, the actual magnitude of any 

specific change or the reason may not be obvious. Although the value itself may be useful in 

identifying facilities or chemicals with the highest potential for hazard or risk, the score does not 

represent a quantitative estimate or provide an exact indication of the magnitude of individual 

hazard or risk associated with that facility or chemical. 

2.2 Summary of the Strengths and Limitations of the RSEI Model 

2.2.1 Strengths 

The following are strengths of the model: 

• The model provides important hazard-based and risk-related perspectives 

regarding the impacts of TRI releases on chronic human health. 

• The model quickly organizes and evaluates complex data. For example, the air 

exposure model is combined with U.S. Census data to directly estimate the size of 

exposed populations and subpopulations and the magnitude of their exposure, 

rather than assuming that all individuals surrounding a facility are equally 

exposed. 

• The model allows for greatly increased speed in performing screening analyses, 

thereby conserving resources for conducting more precise, site-specific risk 

evaluations. In addition, its use as a priority-setting tool allows resources to be 

focused in areas that will provide the greatest potential risk reduction. 

• The model can perform single- and multi-media analyses. 

• Custom-designed selections can be based upon a wide range of variables. 
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• This adaptable method can model any chemical if toxicity characteristics, 

appropriate physicochemical properties, release levels and release location are 

known or can be estimated. 

• The model considers both cancer and non-cancer chronic human health endpoints. 

• The RSEI method has been subject to repeated expert peer review. 

• The model’s methodology and assumptions are transparent. Complete and 

detailed documentation of the RSEI model is available. 

2.2.2 Limitations 

The following are limitations of the model: 

• RSEI results do not provide users with quantitative risk estimates (e.g., excess 

cases of cancer). 

• RSEI results do not evaluate individual risk. 

• The model does not account for all sources of TRI chemicals; it only accounts for 

those sources that are required to report to TRI. It also does not provide scores for 

all TRI chemicals, although chemicals without toxicity weights account for a very 

small percentage of total releases and of total risk-related impacts. 

• TRI does not account for all toxic chemicals. 

• The model assumes that air concentrations of TRI chemicals are the same for 

indoor and outdoor exposures, and that populations are continuously exposed. 

• Dermal and food ingestion pathways (other than fish consumption), and some 

other indirect exposure pathways are not evaluated. 

• Acute health effects associated with short-term, periodic exposures to higher 

levels of these same chemicals are not addressed. 

• Ecological effects are not addressed. 
Chapter 3: TRI Emissions Data 

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3. TRI Emissions Data 

The RSEI model uses information on chemical releases and transfers collected by the TRI. The 

TRI is a publicly available EPA database that contains information on toxic chemical releases 

and other waste management activities reported annually by federal facilities and facilities in 

certain industry groups. TRI operates under the Emergency Planning and Community Right-toKnow Act of 1986 (EPCRA). EPCRA’s primary purpose is to inform communities and citizens 

of chemical hazards in their areas. Sections 311 and 312 of EPCRA require businesses to report 

the locations and quantities of chemicals stored on-site in order to help communities prepare to 

respond to chemical spills and similar emergencies. EPCRA Section 313 requires EPA and the 

States to annually collect data on releases and transfers of certain toxic chemicals from industrial 

facilities, and to make the data available to the public in the TRI. In 1990 Congress passed the 

Pollution Prevention Act which required that additional data on waste management and source 

reduction activities be reported under TRI. The goal of TRI is to empower citizens, through 

information, to hold companies and local governments accountable in terms of how toxic 

chemicals are managed. EPA compiles the TRI data each year and makes it available through 

several data access tools, including the TRI Explorer and Envirofacts.11

The TRI release and transfer data reported each year are the initial source of quantitative data on 

potential chronic human exposure used in RSEI. However, the EPA has an open revision policy 

that allows facilities reporting to the TRI to submit changes and corrections to their TRI data at 

any time. To avoid the effects of these fluctuations on RSEI results, the model extracts release 

and transfer data during the two week period each year when EPA “freezes” the data, that is, 

when no changes are allowed. To ensure that each RSEI model update is current on all revisions 

to the TRI data, data for all years are extracted once a year during the “data freeze” period, and 

added to the model, replacing the previous data. The same data freeze is used in preparing 

EPA’s TRI Public Data Release (PDR). It should be noted that the “frozen” data set is not 

necessarily the same as the TRI database publicly accessible through the Internet: EPA’s TRI 

Explorer or Envirofacts are “live” databases that are regularly updated. Despite EPA’s open 

revision policy, errors do still happen, and some errors remain in the system for more than one 

year. It is therefore important to perform additional inquiries and analyses to support and verify 

results from RSEI, which should only be used as a screening tool. 

Even though the TRI PDR and RSEI both use the annual data freeze, there still are some 

important differences between the two data sets. RSEI performs considerable processing on the 

set of on-site and off-site facilities, including quality-assuring their locations, and identifying 

duplicate records of off-site facilities. The TRI PDR adjusts its data to account for double 

counting of releases from RCRA-regulated chemical treatment, storage, and disposal (TSD) 

facilities, while RSEI does not make that adjustment. Additionally, each year there may be 

several corrections to individual facility releases that may be made in one database but not the 

other. For more detail on these differences and any year-specific differences, please see 

Technical Appendix F, “Summary of Differences Between RSEI Data and the TRI Public Data 

Release.” 

 

11 This program description is from the TRI web site, http://www.epa.gov/tri/. The web site provides additional 

information, including reporting requirements for facilities. 
Chapter 4: Methods for Calculating Toxicity Weights 

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4. Methods for Calculating Toxicity Weights 

The EPCRA Section 313 criteria list several human toxicity parameters that EPA must consider 

when evaluating a chemical for addition to TRI, including acute toxicity, cancer or teratogenic 

effects, serious or irreversible reproductive dysfunctions, neurological disorders, heritable 

genetic mutations, or other chronic health effects. EPCRA also considers environmental 

toxicity. Some chemicals have toxicity data for only one effect, while others have evidence of 

effects within several of these toxicity categories. The definitions of types of toxicity as given in 

Section 313 are presented in Exhibit 4.1. 

The RSEI model focuses on carcinogens and other types of chronic toxic effects that are 

typically associated with chronic exposures.12 The method relies heavily on current EPA 

methodologies for assessing toxicity, and will be continually updated to reflect any changes in 

these methods. 

Exhibit 4.1 

Toxicity Endpoints 

Endpoint Definition

Carcinogenicity The ability of a chemical to produce cancer in animals or humans. 

Heritable Genetic and 

Chromosomal Mutation 

The failure to transmit genetic information. This can involve at least 

three separate modes of action: the gain or loss of whole chromosomes 

(aneuploidization), rearrangement of parts of chromosomes 

(clastogenesis), and addition or deletion of a small number of base pairs 

(mutagenesis). 

Developmental Toxicity Any detrimental effect produced by exposures to developing organisms 

during embryonic stages of development, resulting in: prenatal or early 

postnatal death, structural abnormalities, altered growth, and functional 

deficits (reduced immunological competence, learning disorders, etc.). 

Reproductive Toxicity Interference with the development of normal reproductive capacity. 

Chemicals can affect gonadal function, the estrous cycle, mating 

behavior, conception, parturition, lactation, and weaning. 

Acute Toxicity The potential for a short-term exposure (typically hours or days) by 

inhalation, oral, or dermal routes to cause acute health effect or death. 

Chronic Toxicity The potential for any adverse effects other than cancer observed in 

humans or animals resulting from long-term exposure (typically months 

or years) to a chemical. 

Neurotoxicity Changes to the central and/or peripheral nervous system, which may be 

morphological (biochemical changes in the system or neurological 

diseases) or functional (behavioral, electrophysiological, or 

neurochemical effects). 

 

12 Chronic effects are those that generally persist over a long period of time whether or not they occur immediately 

after exposure or are delayed. Chronic exposure refers to multiple exposures occurring over an extended period of 

time, or a significant fraction of an individual’s lifetime. 
Chapter 4: Methods for Calculating Toxicity Weights 

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4.1 Toxicity Weighting Scheme for Non-carcinogens and 

Carcinogens 

The RSEI method uses a proportional system of numerical weights that reflect the toxicities of 

chemicals relative to one another. The toxicity weights of chemicals increase as the 

toxicological potential to cause chronic human health effects increases. The method EPA has 

chosen for assigning toxicity weights to chemicals is clear and reproducible, based upon easily 

accessible and publicly available information, and uses expert EPA-wide judgments to the 

greatest extent possible. 

Factors that could be used to weight a chemical’s toxicity include: the number of these effects 

that it causes; the relative severity of the effects it causes; the potency of the chemical for one or 

more of these effects; and the uncertainty inherent in characterizing effects. The RSEI method 

focuses on the latter two factors (potency and uncertainty inherent in characterizing effects), and 

thus considers both quantitative and qualitative elements to judge the relative toxicity of 

chemicals. The types of data required and the method used to combine these data into toxicity 

weights are described below. 

4.1.1 Qualitative Data 

Uncertainty reflecting the quality and adequacy of the data is incorporated into the toxicity 

weights or in their underlying toxicity values. The approach is intended to differentiate the 

relative toxicity of these chemicals in a uniform manner. 

When evaluating the potential toxicity of a chemical to humans, risk assessors use a variety of 

data, including epidemiological data, data from acute and chronic animal studies, and in vitro 

toxicity tests. Together, these data form a body of evidence regarding the potential for toxic 

chemicals to cause a particular health effect in humans. The risk assessor can judge qualitatively 

the strengths of this body of evidence when determining the probability of the occurrence of the 

effect in humans. Based on this judgment, the chemical is assigned a weight-of-evidence (WOE) 

classification. Weight-of-evidence schemes can be designed to indicate whether a chemical 

either causes a specific health effect in general, or specifically in humans. 

For cancer effects, the WOE system presented in this method relies on categorical definitions 

from the 1986 EPA Guidelines for Carcinogenic Risk Assessment (EPA, 1986a), which are 

related to the potential for a chemical to be carcinogenic to humans.13 The Cancer Guidelines 

define the six WOE categories shown in Exhibit 4.2. In the RSEI model, weight-of-evidence 

categories A, B1, and B2 (known and probable carcinogens) are combined. The combination of 

 

13 It should be noted that EPA’s Cancer Guidelines were updated in 2005 (EPA, 2005). The 2005 EPA WOE categories 

are not grouped by letter as are the EPA’s 1986 WOE categories. The new categories are translated into 1986 

designations in the following way: 

• Carcinogenic to humans: A

• Likely to be carcinogenic to humans: B

• Suggestive evidence of carcinogenic potential: C

• Inadequate information to assess carcinogenic potential: D

• Not likely to be carcinogenic to humans: E
Chapter 4: Methods for Calculating Toxicity Weights 

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the A and B categories represents a modification of the Hazard Ranking System (HRS), which is 

used by EPA’s Office of Superfund Remediation and Technology Innovation to rank hazardous 

waste sites for inclusion in the National Priorities List. Under the HRS scheme, A, B, and C 

categories are each considered separately. This revision reduces the dichotomy between the A 

and B categories, a dichotomy that may be inappropriate in the context of assigning toxicity 

weights. Also, combining A and B categories stabilizes the model results against changes 

induced by chemicals switching between the A and B designations. Class C chemicals (possible 

carcinogens) are assigned weights by dividing the calculated toxicity weights by a factor of 10 

(see Section 4.1.3), because evidence that they cause cancer in humans is less certain. The 

choice of applying a factor is based on the advice of peer review and the HRS; an order of 

magnitude difference is an arbitrary uncertainty factor. Categories D and E are not considered in 

this weighting scheme (i.e., no toxicity weight is assigned). 

Exhibit 4.2 

Weight-of-Evidence Categories for Carcinogenicity 

Category Weight-of-Evidence

A Sufficient evidence from epidemiological studies to support a causal relationship 

between exposure to the agent and cancer. 

B1 Limited evidence from epidemiological studies and sufficient animal data. 

B2 Sufficient evidence from animal studies but inadequate or no evidence or no data from 

epidemiological studies. 

C Limited evidence of carcinogenicity in animals and an absence of evidence or data in 

humans. 

D Inadequate human and animal evidence for carcinogenicity or no data. 

E 

No evidence for carcinogenicity in at least two adequate animal tests in different 

species or in both adequate epidemiological and animal studies, coupled with no 

evidence or data in epidemiological studies. 

Source: 51 FR 33996 

For noncancer effects, weight-of-evidence is considered qualitatively in the hazard 

identification step of determining an RfD or an RfC. The WOE evaluation for noncancer effects 

is different from that for carcinogenic effects. The WOE judgment for noncancer effects focuses 

on the dose where chemical exposure would be relevant to humans (Dourson, 1993). That is, the 

focus of the WOE evaluation and the expression of the level of confidence in the RfD is a 

judgment of the accuracy with which the dose relevant to humans has been estimated. The WOE 

evaluation is included qualitatively in the RfD, but does not affect its numerical calculation. 

Since weight-of-evidence has been considered in developing RfDs, RSEI does not consider 

WOE separately for noncancer effects. 
Chapter 4: Methods for Calculating Toxicity Weights 

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4.1.2 Quantitative Data 

Quantitative data on the relative potencies of chemicals are needed for toxicity weighting. 

These data generally result from analyses done during the third stage of risk assessment, the 

dose-response assessment. This stage involves describing the quantitative relationship between 

the amount of exposure to a chemical and the extent of toxic injury or disease observed. Risk 

posed by exposure to a chemical cannot be described without quantitative dose-response data. 

Dose-response data are derived from animal studies or, less frequently, from studies in exposed 

human populations. There may be many different dose-response relationships for a chemical if it 

produces different toxic effects under different conditions of exposure. 

For cancer risk assessment, EPA has developed standard methods for predicting the 

incremental lifetime risk of cancer per dose of a chemical. EPA quantitatively models the doseresponse function of a potential carcinogen and typically provides estimates of Oral Slope 

Factors (OSFs) or Inhalation Unit Risks (IURs). The OSF represents the upper-bound estimate 

of the slope of the dose-response curve in the low-dose region for carcinogens, and is a measure 

of cancer potency. The units of the slope factor are usually expressed as (mg/kg-day)-1. The 

IUR is the upper-bound excess lifetime cancer risk estimated to result from continuous exposure 

to an agent at a concentration of 1 μg/m3 in air (RSEI toxicity weights are based on this value 

when expressed as risk per mg/m3). Although the level of conservatism inherent in the OSFs and 

Unit Risks varies by chemical, OSFs and IURs nonetheless are the best readily available values 

that allow a comparison of the relative cancer potency of chemicals. 

RSEI’s oral cancer toxicity weight (OTWc) represents how toxic a substance is relative to a 

substance that produces a 1 in 1 million risk14 (above background, over a lifetime) at an average 

lifetime daily dose of 1 mg/kg-day. If the OSF is greater than this arbitrary slope factor (i.e. the 

substance is more toxic than the arbitrary slope factor), the OTWc is greater than 1. 

kg day mg

OSF kg day mg OTWc

10 /

/

6 −

− = − (Eq. 4.1) 

For noncancer risk assessment, data on dose-response are typically (though not always) more 

limited; generally, a risk assessor evaluates dose compared to a Reference Dose (RfD) or 

Inhalation Reference Concentration (RfC). Both the RfD and RfC are defined as “an estimate 

(with uncertainty spanning perhaps an order of magnitude) of a daily exposure [or continuous 

inhalation exposure the RfC] to the human population (including sensitive subgroups) that is 

likely to be without an appreciable risk of deleterious [noncancer] effects during a lifetime” 

(EPA, 1988a; EPA, 1990b). The units of RfD are mg/kg-day, while the units of the RfC are 

mg/m3

. A chemical’s RfD or RfC is typically based on a No Observable Adverse Effect Level 

(NOAEL) or Lowest Observable Adverse Effect Level (LOAEL), combined with appropriate 

uncertainty factors to account for intraspecies variability in sensitivity, interspecies extrapolation, 

 

14 EPA programs commonly use a risk management range corresponding to an excess individual lifetime risk of 

cancer of 1 in 10-6 to 10-4 (EPA, 1999a). 
Chapter 4: Methods for Calculating Toxicity Weights 

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extrapolation from LOAELs to NOAELs, and extrapolation from subchronic to chronic data. In 

addition, a modifying factor can be applied to reflect EPA’s best professional judgment on the 

quality of the entire toxicity database for the chemical. By definition, exposures below the 

RfD/RfC are unlikely to produce an adverse effect; above this value, an exposed individual may 

be at risk for the effect. Empirical evidence generally shows that as the dosage of a toxicant 

increases, the severity and/or incidence of effect increases (EPA, 1988a), but for a given dose 

above the RfD/RfC, the specific probability or severity of an effect is not known. For purposes 

of the RSEI model, it is assumed that noncancer risk varies as the ratio of the estimated dose to 

the RfD/RfC. 

RSEI’s oral non-cancer toxicity weight (OTWnc) represents how toxic a substance is relative to 

an arbitrary dose of 1 mg/kg-day. If the RfD is greater than this arbitrary dose (i.e. the substance 

is less toxic than the arbitrary dose), the OTWnc is less than 1. 

 

RfD mg kg day

mg kg day OTWnc −

− = /

1 / (Eq. 4.2) 

4.1.3 Algorithm for Calculating Toxicity Weight 

The RSEI method uses several different algorithms to assign toxicity weights. RSEI toxicity 

weights are designed to be proportional to a substance’s toxicity. The more toxic a substance is, 

the higher the toxicity weight. 

Toxicity values for the inhalation pathway are typically expressed in units of exposure, that is, 

mg of chemical per m3

 of air. The toxicity weighting methodology, therefore, uses standard 

adult human exposure factors for inhalation rate (20 m3

/day) and body weight (70 kg) to modify 

toxicity values expressed in units of exposure. This adjustment means that different constants are 

used to calculate the toxicity weights when inhalation toxicity values are used rather than oral 

toxicity values (3.5 versus 1.0 for non-carcinogens, and 2.8*10-7 versus 1*10-6 for carcinogens). 

All RSEI toxicity weights are expressed as reciprocal units of mg/kg-day. 

As these calculations show, noncancer toxicity weights are proportional to the reciprocal of the 

RfD or RfC for the oral and inhalation exposure pathways, respectively. Cancer toxicity weights 

are proportional to the OSF or IUR, for the oral and inhalation exposure pathways, respectively. 

When multiplied by the surrogate dose estimated by RSEI, the risk-related scores calculated by 

the model are unitless, and should be used only for comparative purposes within RSEI. 

Exhibit 4.3 below shows the algorithms used to calculate RSEI toxicity weights. Chemicals 

classified as WOE category C are assigned the same algorithm as those in WOE categories A 

and B, but the OSF or IUR is divided by an additional factor of 10 because the evidence of 

cancer causation in humans is uncertain. 
Chapter 4: Methods for Calculating Toxicity Weights 

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Exhibit 4.3 

Algorithms for Assigning Toxicity Weights 

 Oral Pathway Inhalation Pathway 

Non-Carcinogens: 1 / RfD (mg/kg-day) 3.5 / RfC (mg/m3) 

Carcinogens

(WOE categories 

A and B): 

Oral Slope Factor (risk per 

mg/kg-day)/ 1.0 x 10-6 

Inhalation Unit Risk (risk 

per mg/m3)/ 2.8 x 10-7

Carcinogens

(WOE category C): 

Oral Slope Factor (risk per 

mg/kg-day)/ (1.0 x 10-6 * 10) 

Inhalation Unit Risk (risk 

per mg/m3)/ (2.8 x 10-7 * 10) 

4.2 Selecting the Final Chronic Human Health Toxicity Weight for a 

Chemical 

Inhalation and oral toxicity weights are developed separately. If values are available for each 

route, then separate toxicity weights are assigned to each route. If data are available for only one 

route, the same toxicity weight is applied for both routes, provided there is no evidence the 

effects are route-specific or limited to the “portal of entry” into the body. In rare instances, 

toxicity studies are available to show that a given chemical causes no effects via one route; in 

these instances, a toxicity weight is assigned only to the route that results in chronic human 

health effects. Although assigning the same weight to both routes is not an ideal method, it is 

appropriate for a screening-level tool like the RSEI model. 

Although chemicals can cause several types of toxic effects, the model assigns a toxicity weight 

to a chemical based on the single most sensitive adverse effect for the given exposure route (oral 

or inhalation). If a chemical exhibits both carcinogenic and noncarcinogenic effects, the higher 

of the associated cancer and noncancer weights is assigned as the final weight for the chemical 

for the given route. 

The approach of weighting based on the most sensitive adverse effect does not consider 

differences in the type, number or target of the effects posed by the chemicals. For example, 

liver toxicity is weighted in the same way that neurotoxicity is weighted; in principle, chemicals 

causing a certain type of effect could be assigned additional weight if special concern existed for 

that type of effect. However, applying such additional weights would require a subjective 

evaluation of the relative severity of the health effects. Also, chemicals with a broad range of 

adverse health effects are weighted the same as a chemical causing only one effect. This 

approach may appear to under-estimate the risk of chemicals with a broad spectrum of effects 

relative to chemicals with one or few effects. However, a chemical may appear to demonstrate 

just one adverse effect only because there are no data on other effects; thus, applying an 

additional weight based on the number of endpoints may undervalue some poorly-studied but 

still hazardous chemicals. For these reasons, the options for applying additional weights based 

on number and relative severity of endpoints were not adopted. 
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4.3 Chemical Groups 

TRI collects information for some chemicals as combined groups, such as glycol ethers, 

polycyclic aromatic compounds, and metal compounds. For metal compounds, RSEI combines 

the elemental form of the metal with its compounds category. This is done to reflect the 

uncertainty of the chemical identity of the substance released. For example, TRI has separate 

reporting for ‘nickel’ and ‘nickel compounds’. Both reflect the pounds of the parent metal nickel 

that is released and, in some cases, the two can be combined as a single report for nickel 

compounds. RSEI combines the pounds into one entry listed as ‘nickel and nickel compounds’. 

For all chemical groups, data for the most toxic member of the group is used to represent the 

toxicity of the entire group. There are two exceptions. The first exception is chromium and 

chromium compounds, for which it is assumed that facilities may release some combination of 

hexavalent chromium and trivalent chromium. SIC-code specific estimates from the 2002 

National Emissions Inventory are used to estimate the fraction of each type.15 As trivalent 

chromium has a very low toxicity, only the hexavalent fraction is modeled, using a toxicity 

weight specifically for that valence state. 

The second exception is for polycyclic aromatic compounds (PACs), for which it is assumed that 

the overall toxicity for the category is 18% of the toxicity for benzo(a)pyrene, the most toxic 

member of the group. This figure is based on the polycyclic organic matter (POM) methodology 

used by EPA’s National-Scale Air Toxics Assessment (NATA) model, which is based on 

emissions factors for representative processes used in industries that emit large amounts of POM. 

RSEI assumes that the TRI PACs category is most like the 7-PAH category used in NATA.16

Toxicity weights for individual chemicals and chemical groups are presented in Technical 

Appendix A. 

4.4 Sources of Data 

Information regarding the human health effects data on the TRI chemicals is compiled from the 

following sources: 

IRIS. The primary (and most preferred) source of these data is EPA’s Integrated Risk 

Information System (IRIS). IRIS is available on the internet (at http://www.epa.gov/iris/), and 

includes information on EPA evaluations of chemical toxicity for both cancer and noncancer 

effects of chemicals. IRIS provides both background information on the studies used to develop 

the toxicity evaluations and the numerical toxicity values used by EPA to characterize risks from 

these chemicals. These values include upper-bound OSF or IUR values for chemicals with 

carcinogenic effects as well as RfDs or RfCs for chemicals with noncancer effects. Data 

contained in IRIS have been peer-reviewed and represent Agency-wide expert judgments. The 

 

15 Available from http://www.epa.gov/ttn/chief/net/2002inventory.html 

16 The documentation on modeling polycyclic organic matter (POM) from EPA’s NATA model can be found at 

http://www.epa.gov/ttn/atw/nata2002/methods.html#pom. 
Chapter 4: Methods for Calculating Toxicity Weights 

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peer-review process involves literature review and evaluation of a chemical by individual EPA 

program offices and intra-Agency work groups before inclusion in IRIS. 

OPP. EPA’s Office of Pesticide Programs (OPP) Acute Chronic and Reference Doses Table 

lists OPP’s evaluations of the noncarcinogenic potential of chemicals that are of interest to OPP. 

OPP also publishes the List of Chemicals Evaluated for Carcinogenic Potential, which examines 

carcinogens. Both of these lists are updated periodically. Individual Pesticide Regregistration 

Eligibility Documents (REDs) are also used. 

ATSDR. The Agency for Toxic Substances and Disease Registry (ATSDR) is an agency of the 

U.S. Department of Health and Human Services, which deals with the effect on public health of 

hazardous substances in the environment. ATSDR develops Minimum Risk Levels (MRLs) for 

chemicals on the CERCLA National Priorities List. An MRL is an estimate of the daily human 

exposure to a hazardous substance that is likely to be without appreciable risk of adverse 

noncancer health effects over a specified duration of exposure. RSEI uses data from MRLs 

developed for chronic exposure only. MRLs are intended to serve as screening levels only, and 

are useful in identifying contaminants and potential health effects that may be of concern at 

hazardous waste sites. See http://www.atsdr.cdc.gov/mrls/index.html for more information on 

MRLs and specific values. 

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

Health Hazard and Assessment (OEHHA) is responsible for developing and distributing 

toxicological and medical information needed to protect public health. RSEI uses final toxicity 

values published by CalEPA in the Consolidated Table of OEHHA & California’s Air Resources 

Board (ARB) Approved Risk Assessment Health Values. The table is continuously updated and 

can be found on the internet at http://www.arb.ca.gov/toxics/healthval/healthval.htm. 

PPRTVs. EPA's Provisional Peer Reviewed Toxicity Values (PPRTVs) include toxicity values 

developed by the Office of Research and Development/National Center for Environmental 

Assessment/Superfund Health Risk Technical Support Center (STSC). 

HEAST. EPA’s Health Effects Assessment Tables (HEAST) are constructed for use in the 

Superfund and RCRA programs but do not represent Agency-wide expert judgments. These 

tables are publicly available from the Superfund program. The tables include OSFs, IURs, and 

WOE categorizations for chemicals with cancer effects, and RfDs and RfCs for noncancer 

effects. 

Derived Values. For a prioritized group of chemicals for which sufficient data was not found in 

the above sources, a group of EPA expert health scientists reviewed other available data to derive 

appropriate toxicity weights. Although individual literature searches for toxicological and 

epidemiological data for each chemical were beyond the scope of this project, sources such as 

the Hazardous Substances Data Base (HSDB), as well as various EPA and ATSDR summary 

documents, provided succinct summaries of toxic effects and quantitative data, toxicological and 

epidemiological studies, and, in some cases, regulatory status data. When the available data on 

chronic human toxicity were sufficient to derive values, a toxicity weighting summary was 

developed summarizing the information used to develop each of these values. The summaries 

are available in Technical Appendix A. The EPA scientists use a technical approach analogous 
Chapter 4: Methods for Calculating Toxicity Weights 

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to the Agency’s method for deriving RfD values, RfC values, cancer risk estimates, and weightof-evidence (WOE) determinations. However, it must be emphasized that these derived values 

are not the equivalent of the more rigorous and resource-intensive IRIS process and are only 

useful for screening-level purposes. 

Data from these sources are categorized in three-tiered, hierarchical fashion to give preference to 

EPA and consensus data sources, where possible. Data are gathered separately for individual 

endpoints; a chemical’s RfD may be from IRIS, while its OSF may be from HEAST. 

The hierarchy used in toxicity weighting is as follows: 

Tier 1. The most recent data from IRIS and OPP is used for each chronic health endpoint. If the 

dates are comparable, preference is given to IRIS, since IRIS reflects Agency-wide judgments. 

Tier 2. In the absence of data from the above sources for an individual chronic health endpoint, 

toxicity data from the most recent entry in ATSDR and CalEPA are used. 

Tier 3. In the absence of data from the above sources for an individual chronic health endpoint, 

the following data sources, in this order, are used: 1) PPRTVs; 2) HEAST; 3) Derived; and 4) 

IRIS values previously used in toxicity weighting, that were withdrawn pending revision. 

For chemicals with carcinogenicity risk values, weight-of-evidence (WOE) values are obtained 

using the same data source hierarchy. Therefore, preference is given to WOE’s from IRIS or 

OPP. As a general rule, chemicals with cancer potency factors from IRIS or OPP will also have 

WOEs. CalEPA, however, references either EPA or the International Agency for Research on 

Cancer (IARC) for WOE designations. Therefore, in the absence of an EPA consensus WOE, 

WOE’s are obtained from IARC. However, due to the differences in WOE definition, it is not 

always possible to translate IARC WOE’s into EPA WOE’s without examining the toxicity data. 

 WOE’s are matched in the following way: 

• IARC Group 1 = EPA Group A (Human Carcinogen) 

• IARC Group 2A = EPA Group B (Probable Human Carcinogen) 

• IARC Group 2B = EPA Group B or EPA Group C (Possible Carcinogen) 

• IARC Group 3 = EPA Group D (Not Classifiable as to Human Carcinogenicity) 

• IARC Group 4 = EPA Group E (Evidence of Non-Carcinogenicity) 

The IARC 2B designation is not easily translated to the EPA designation, because its definition 

spans EPA Groups B and C. This is a particularly important distinction because the use of a B2 

or C designation will affect the calculation of the toxicity weight. Therefore, for the chemicals 

with IARC 2B designations, summaries of the toxicity data used to generate the OSF or IUR are 

evaluated to derive WOEs. To date, this approach has been used for chemicals with data from 

CalEPA; therefore, the CalEPA “Technical Support Document for Describing Available Cancer 

Potency Factors” was used for the background information. 

Currently, using all of the available data sources described above, toxicity weights are available 

for 430 of 611 chemicals and chemical categories on the 2007 TRI Chemical List. Chemicals 
Chapter 4: Methods for Calculating Toxicity Weights 

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with toxicity weights account for over 99% of the reported pounds for all on-site releases in 

2007. The Indicator Elements are recomputed for all years in the TRI database on an annual 

basis in order to incorporate revisions to the reporting data. However, only reporting years 1996 

through 2007 are available in RSEI Version 2.3.0. Data for reporting years 1988 through 1995 

are available upon request. 

Toxicity weights for individual chemicals and chemical groups are presented in Technical 

Appendix A. 

4.5 How Indicator Toxicity Weightings Differ from EPCRA Section 313 

Criteria 

As noted above, the model uses TRI chemical reporting data. All TRI chemicals included in the 

model are listed on the TRI because they meet one or more statutory criteria regarding acute or 

chronic human toxicity, or environmental toxicity. The goal of the RSEI model is to use these 

data reported to the Agency to investigate the relative risk-based impacts of the releases and 

transfers of these chemicals on the general, non-worker population. To achieve this goal, the 

model differentiates the relative toxicity of listed chemicals and ranks them in a consistent 

manner. The ranking of each chemical reflects its toxicity only relative to other chemicals that 

are included in the model. Toxicity is not compared to some benchmark or absolute value as is 

required when adding or removing a chemical from the TRI. Furthermore, the model addresses 

only the single, most sensitive chronic human health toxicity endpoint. 

It is important that the public not confuse the use of the RSEI model as a screening-level tool for 

investigating relative risk-based impacts related to the releases and transfers of TRI chemicals, 

with the very different and separate activity of listing/delisting chemicals on the TRI using 

statutory criteria. A description of the listing/delisting criteria and process is described below. 

The Emergency Planning and Community Right-to-Know Act of 1986 (EPCRA) section 

313(d)(2) sets out criteria for adding chemicals to the list of chemicals subject to reporting under 

EPCRA section 313(a). The statutory criteria used for listing and delisting chemicals addresses 

the “absolute” chronic toxicity of chemicals on the TRI (e.g., multiple effects or the severity of 

effects). For a chemical (or category of chemicals) to be added to the EPCRA section 313(c) list 

of toxic chemicals, the Administrator must judge whether there is sufficient evidence to establish 

any one of the following: 

Acute Human Toxicity §313(d)(2)(A) - The chemical is known to cause or can 

reasonably be anticipated to cause significant adverse acute human health effects at 

concentration levels that are reasonably likely to exist beyond facility site boundaries as a 

result of continuous, or frequently recurring, releases. 

Chronic Human Toxicity §313(d)(2)(B) - The chemical is known to cause or can 

reasonably be anticipated to cause in humans– 

 (i) cancer or teratogenic effects, or 

 (ii) serious or irreversible– 

 (I) reproductive dysfunctions, 
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 (II) neurological disorders, 

 (III) heritable genetic mutations, or 

 (IV) other chronic health effects. 

Environmental Toxicity §313(d)(2)(C) - The chemical is known to cause or can 

reasonably be anticipated to cause, because of – 

 (i) its toxicity, 

 (ii) its toxicity and persistence in the environment, or 

(iii) its toxicity and tendency to bioaccumulate in the environment, significant 

adverse effect on the environment of sufficient seriousness, in the judgment of the 

Administrator, to warrant reporting under this section. 

To remove a chemical from the section 313(c) list, the Administrator must determine that there is 

not sufficient evidence to establish any of the criteria described above as required by EPCRA 

section 313(d)(3). 

The EPA examines all of the studies available for a chemical to decide if the chemical is capable 

of causing any of the adverse health effects or environmental toxicity in the criteria. Agency 

guidelines describe when a study shows such effects as cancer (EPA, 1986a), developmental 

toxicity (teratogenic effects) (EPA, 1991b), or heritable genetic mutations (EPA, 1986b). The 

review makes a qualitative judgment regarding the potential of each chemical to meet at least 

one of the criteria and the chemical is added to the list if this judgment is positive. If a chemical 

is on the list and it is not possible to make a positive judgment regarding any of the criteria, then 

the chemical can be removed. 

There is no correlation between the toxicity criteria and methodology used to make listing 

decisions under EPCRA section 313 and the methodology used to assign toxicity weights to 

chemicals for the RSEI model. Therefore, these toxicity weights cannot be used as a scoring 

system for evaluating listing/delisting decisions. RSEI also does not attempt to reflect the 

statutory criteria for these chemicals. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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5. Exposure and Population Modeling 

To estimate the magnitude of exposure potential from TRI releases, a separate exposure 

evaluation is conducted for each chemical release pathway. The following pathways are 

evaluated: 

• Air: stack and fugitive pathways; 

• Surface Water: drinking water intake and fish ingestion pathways; 

• Publicly-Owned Treatment Works (POTWs): fugitive air, groundwater (not 

currently modeled), drinking water intake and fish ingestion pathways; 

• Land: groundwater pathway (not currently modeled), volatilization to air 

included in fugitive air emission pathway for on-site releases; and 

• Off-site transfers: groundwater (not currently modeled), volatilization (not 

currently modeled) and stack air (from incineration) pathways. 

The ideal derivation of a dose would involve a site-specific exposure assessment for each release 

and exposure pathway. However, such an effort is well beyond the scope of this project; further, 

reporting of extensive site-specific information relevant for exposure modeling is not part of a 

TRI data submission. For example, the EPA Form R (Toxic Release Inventory Reporting Form) 

does not require submission of data on groundwater flow, soil conditions, and other factors that 

affect groundwater contamination from land releases. Although some site-specific data are used 

in the model, it is not the intent of this project to gather extensive site-specific data or 

measurements that would be needed to perform site-specific calculations. The need to accurately 

reflect exposure characteristics in the RSEI model must be balanced by the need for simple and 

understandable results that are easily communicated to the public and that are based on currently 

available data. 

Therefore, in this method, the exposure evaluations combine data on pathway and media-specific 

emission volumes, physicochemical properties and, where available, site characteristics, with 

models to determine an estimate of the ambient concentration of contaminant in the medium into 

which the chemical is released. The ambient media concentrations are then combined with 

human exposure assumptions to estimate a “surrogate dose”. The term surrogate dose is used 

because limited facility-specific data and the use of models that rely on default values for many 

parameters preclude the calculation of an actual dose estimate. Instead, the purpose of the 

method is to generate as accurate a surrogate dose as possible without conducting an in-depth 

risk assessment. The estimates of surrogate doses from releases of TRI chemicals are 

comparable only to the surrogate doses resulting from other releases included in the model. 

Estimates of the surrogate dose for each potentially exposed person are combined with estimates 

of the number of people potentially exposed. Potential exposure is determined by the geographic 

location of the population, as identified by the U.S. Decennial Census for 1990 and for 2000. 

The size of the exposed population is calculated separately for each pathway. The model 

assumes continuous exposure, and does not account for the activity patterns of the people 

potentially exposed. However, population estimates do consider changing demographic patterns 

(total population, as well as subpopulations by age and sex). 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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The methods used to model each type of release are specific to that type of release and depend on 

data available to evaluate that pathway. In some cases, models are combined with some sitespecific data to estimate exposure; in other cases, generic reasonable worst-case models may be 

used in the absence of any site-specific data. The physicochemical property data used for the 

exposure evaluation are found in Technical Appendix B. It should be noted, however, that 

products of decay are not modeled. Exclusion of these decay products from the model may 

underestimate or overestimate the risk impact of releases, since the decay product may be more 

or less toxic than the parent compound. 

Specific pathway calculations are discussed in the sections below. First, Section 5.1 discusses 

the geographic basis of the model, and describes the grid cell system underlying the model and 

how facilities and people are located on it. This discussion describes how annual grid cell-level 

general population data sets are created. From these general population data sets, the model 

generates estimates of populations exposed through particular pathways. The next sections 

describe the modeling for each pathway, including the estimation of surrogate doses and exposed 

population for that pathway. 

5.1 Geographic Basis of the RSEI Model 

Underlying the RSEI method is the ability to locate facilities geographically, and to attribute 

characteristics of the physical environment, such as meteorology, to areas surrounding the 

facilities, once they are located. To accomplish the location of facilities and the attribution of 

data to those facilities, the model describes the U.S. and its territories as an 810 m by 810 m grid 

system. For each cell in the grid system, a location “address” in terms of grid and (x,y) 

coordinates is assigned based on latitude and longitude (lat/long). 

5.1.1 The Model Grid Cell System 

RSEI uses a standard Albers Equal-Area projection17 to create each of the grids that is used in the 

model. The grid system is split into six individual grids which cover the continental U.S., 

Alaska, Hawaii, and the territories. Each unique cell address is composed of (1) the grid number, 

and (2) the (x,y) address of the cell in that grid. Exhibit 5.1 below provides the grid number 

(used in the model to identify each grid), the grid characteristics that can be used to recreate the 

grid in a GIS-based system, and the bounding coordinates for each. 

 

17 Versions 2.2.0 and earlier used a non-standard grid developed specifically for RSEI. The use of a standard 

projection will make it easier for users to import RSEI data into GIS applications. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 37 

Exhibit 5.1 

RSEI Grid Characteristics 

 Grid Reference Characteristics 

Grid 

Code 

Grid Latitude 

of Origin 

Central 

Meridian 

Standard 

Parallel 1

Standard 

Parallel 2

Lower Left 

x Coord. 

(m) 

Lower Left 

y Coord. 

(m) 

Columns Rows 

14 Conterminous 

US 23°N 96°W 29.5°N 45.5°N -2,365,605 251,505 5,724 3,618 

24 Alaska 50°N 154°W 55°N 65°N -1,046,115 564,975 3,291 2,505 

34 Hawaii 20.5625°N 157.5625°W 19.4375°N 21.2375°N -287,955 -185,895 739 480 

44 Puerto Rico/ 

Virgin Islands 18°N 66.25°W 17.875°N 18.5°N -185,895 -40,095 462 129 

54 Guam/ 

Marianas 0° 155°E 12°N 15°N -1,133,595 1,468,935 203 295 

64 American 

Samoa 0° 170°W 12°S 15°S -91,125 -1,578,285 203 36 

Bounding Coordinates for Lower-Left (LL), Upper-Right (UR), 

Lower-Right (LR), and Upper-Left (UL) Corners 

Grid 

Code Grid LL Long LL Lat UR Long UR Lat LR Long LR Lat UL Long UL Lat 

14 Conterminous 

US 118.78°W 22.69°N 65.14°W 48.29°N 74.09°W 22.89°N 128.05°W 48.01°N 

24 Alaska 170.07°W 53.95°N 111.99°W 68.54°N 129.76°W 52.41°N 176.63°W 71.23°N 

34 Hawaii 160.29°W 18.86°N 154.54°W 22.37°N 154.6°W 18.86°N 160.36°W 22.38°N 

44 Puerto Rico/ 

Virgin Islands 68°W 17.63°N 64.46°W 18.58°N 64.47°W 17.63°N 68.01°W 18.58°N 

54 Guam/ 

Marianas 144.54°E 13.18°N 145.98°E 15.4°N 146.06°E 13.24°N 144.44°E 15.34°N 

64 American 

Samoa 170.85°W 14.38°S 169.32°W 14.12°S 169.32°W 14.38°S 170.84°W 14.12°S 

The (x,y) coordinates used in each grid are defined as: 

x = number of cells from the center cell in the East-West direction, and 

y = number of cells from the center cell in the North-South direction. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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They can be calculated as follows: 

 ) (Easting / 810 Adjust x x = INT + (Eq. 5.1) 

( / 810 ) Northing Adjust y y = INT − (Eq. 5.2) 

where: 

Easting = projected Albers distance East of the central meridian for the grid 

(m); 

Northing = projected Albers distance North of the central meridian for the grid 

(m); 

AdjustX = 0.5 if Easting >0, -0.5 if Easting<0; 

AdjustY = 0.5 if Northing>0, -0.5 if Northing<0. 

5.1.2 Locating Facilities on the Grid 

Once the grid system for the U.S. is created, each facility must be located on the grid and 

assigned to a grid cell. Facilities are projected onto each grid using GIS software and the (x,y) 

coordinates of the cell where the facility is mapped are assigned to the facility. Once a grid 

cell’s (x,y) coordinates are assigned, the facility is assumed to be at the cell’s center, for ease of 

modeling. For a complete description of the method used to select lat/long coordinates for both 

reporting facilities and off-site facilities, see Technical Appendix D. 

Reporting Facilities. Because the location of a facility is key to the subsequent exposure 

modeling (e.g, facility location will determine which population is assumed to be exposed to its 

air releases), it is important that the lat/long coordinates are as accurate as possible. RSEI uses 

the best pick coordinates from EPA’s Locational Reference Tables (LRT), which collects 

coordinates and related documentation on location from programs across EPA. The facility 

lat/long coordinates are projected onto the relevant grid, and the (x,y) coordinates of the grid cell 

to which the facility maps is assigned. The facility is then modeled as being located at the center 

of its assigned cell. 

Off-site Facilities. RSEI also models some potential exposures that may result from 

environmental releases of chemicals from “off-site” facilities, that is, facilities that receive 

transfers from TRI-reporting facilities. Note that off-site facilities do not report transfers 

received from other facilities directly to TRI; instead their names and addresses are reported by 

the facilities that transfer chemicals to them.18 Each report of a receiving off-site facility 

becomes a separate record in the TRI, even though each off-site facility often receives transfers 

from more than one TRI-reporting facility. This produces multiple records of the same off-site 

facility; however, because the names and addresses are not standardized, the records are usually 

 

18 Some facilities may be considered both on-site and off-site facilities, if they both receive chemical transfers from 

other facilities (as an off-site facility) and emit reportable chemical releases (as an on-site facility). RSEI does not 

attempt to account for emissions that may be double-counted in this way. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 39 

slightly different, and so cannot easily be matched to each other. EPA has developed an 

approximate text string matching program to identify imperfect matches in order to collapse the 

set of off-site facilities to what are considered to be unique off-site facilities. The program 

matches values without requiring their exact equality. This approach accommodates misspelled 

words and inconsistencies in how a facility might report its identifying information over time. 

For example, “DuPont,” “Du Pont,” and “E.I. DuPont” might all refer to the same facility. A 

possible match is identified based on similarity rather than exact equality in the name field, and 

then the address fields are examined to determine whether the records match. 

For RSEI Version 2.1.3 (RY 2003), all off-site records went through the approximate text 

matching program, and were also geocoded (lat/long coordinates were assigned based on street 

address). For each group of facilities that were determined to be matches, the record whose 

geocoded lat/long was of the highest confidence level was selected. The name, address and 

lat/long for this facility record is selected for the master database, and used in the model to 

represent all of the records in that matched group. For Version 2.1.3, this resulted in a master 

off-site database of approximately 47,000 off-site facilities. 

Beginning with Version 2.1.5 (RY2005), off-site facilities are no longer geocoded. Instead, the 

entire set of all years of off-site records (1988-2007) is matched back to the previous years’ 

master off-site database using an approximate text matching program. Again, this is necessary 

because there are no IDs assigned to off-site records in TRI that would allow for direct matching 

from year to year. Any records that are not matched back to the previous years’ database 

(including any new off-site facilities) are added in to the master database, resulting in a new set 

of approximately 45,000 records.19 Additional data from EPA’s Facility Registry System (FRS) 

are also used where possible to identify lat/longs for off-site facilities. Grid-cell addresses for 

off-site facilities are determined in the same manner as for reporting facilities; the facility 

lat/long coordinates are projected onto the relevant grid, and the (x,y) coordinates of the grid cell 

to which the facility maps is assigned. The off-site facility is assumed to be in the center of its 

assigned grid cell. 

5.1.3 Locating People on the Grid 

In order to estimate potential exposure, the U.S. population must also be geographically located 

on the model grid. To match annual TRI emissions and capture the effect of the changing 

distribution of the population, RSEI uses detailed annual population datasets at the grid cell 

level. The data is based on U.S. Decennial Census data, and includes information on population, 

age and sex. In previous versions of the model, U.S. Census race categories were available for 

viewing on the map (although not for calculating results). However, due to complications arising 

from changes in race categorization for the 2000 Census, race categories are not available for 

viewing in the current version of the model. The following sections describe how the U.S. 

Census data is used to generate annual population estimates, and how the unit of analysis for the 

U.S. Census (the block) is translated into the unit of analysis for the model (the grid cell). 

 

19 The final offsite database has less records than the 2.1.3 master database because additional matches were made 

within the master set during the process of matching the new 2.2.0 data to the master. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 40 

U.S. Census Data. The model uses U.S. Decennial Census data for 1990 and 2000 at the block 

level.20 Census blocks are the smallest geographic area for which decennial census data are 

collected. Blocks are of varying size, formed by streets, roads, railroads, streams and other 

bodies of water, other visible physical and cultural features, and the legal boundaries shown on 

Census Bureau maps. In 1990, there were approximately 7 million census blocks. Due to 

boundary changes and increased resolution for highly populated areas, there were approximately 

9 million blocks in the 2000 Census. Block-level data from the 1990 Census and the 2000 

Census21 are used to create detailed age-sex population groups for each of the census blocks in 

the U.S. for 1990 and for 2000. Because the U.S. Census presents data in slightly different 

format, some data processing was necessary to create the following age-sex population groups 

used in the model: 

• Males Aged 0 through 9 years 

• Males Aged 10 through 17 years 

• Males Aged 18 through 44 years 

• Males Aged 45 through 64 years 

• Males Aged 65 Years and Older 

• Females Aged 0 through 9 years 

• Females Aged 10 through 17 years 

• Females Aged 18 through 44 years 

• Females Aged 45 through 64 years 

• Females Aged 65 Years and Older 

For Puerto Rico, mapping limitations dictated the use of block group data rather than block level 

data for 1990 (shape files were not available at the lower level of resolution). However, block 

level data and shape files were used for 2000. For the U.S. Virgin Islands, American Samoa, 

Guam, and the Northern Mariana Islands, mapping was limited to whole-island areas or county 

equivalents, so the population data is also at this level of detail. Detailed demographic data were 

not available, so Census Bureau estimates of age and sex ratios for 2000 were used instead, and 

applied to actual 1990 and 2000 Census totals. The mapping to grid cells for Puerto Rico and 

island areas is the same as described below, except that the shape files are at various levels of 

geography rather than all at the block level. 

Mapping blocks to grid cells. Because the grid cell is the unit of analysis for the model, Census 

data must be transposed from blocks to the model grid cells. Census provides the geometry for 

each block in the Topologically Integrated Geographic Encoding and Referencing (TIGER) 

geographic database, which was used to create shape files for the 1990 and the 2000 Census 

years. A corresponding set of shape files for grid cells was created, with each grid cell defined 

by its four corner points, calculated from its (x,y) coordinates. The shape files were then 

 

20 U.S. Census data and block shape files were provided by GeoLytics, Inc. 

21 For 1990, not all of the variables were available at the block level. For those variables that were only available at 

the block group level, block group ratios were calculated and applied to the data available at the block level. For 

2000, all of the required variables were available at the block level. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 41 

compared, in essence overlaid, and each block was mapped to the cells in the grid that it 

overlaid, and the percentage of the block’s total area falling within each cell was calculated.22

The process described above was performed separately for 1990 and 2000, as the block 

boundaries had changed between the Censuses. This process resulted in two tables, each with 

four fields: the X coordinate and the Y coordinate which identify the grid cell, the block 

identifier assigned by the Census, and the percent of that block assigned to the grid cell. 

Calculating Populations. For each block assigned to a grid cell, the block populations were 

multiplied by the percent of that block assigned to that grid cell. Those values were then 

summed over each grid cell. This process was performed separately for 1990 and 2000, resulting 

in two grid cell level datasets, each containing the ten age-sex population groups listed above.23 

For 1990, there were 11,083,291 populated grid cells; for 2000, there were 9,399,819. To create 

annual datasets for 1991 through 1999, a straight-line interpolation at the grid cell-level is 

performed within the model between the two data sets. The line is extrapolated backward to 

create annual datasets for 1988 and 1989 and forward to create the datasets for 2001 through 

2007. 

5.2 Pathway-specific Methods to Evaluate Chronic Human Exposure 

Potential 

The following sections describe the algorithms for modeling exposure for each of the following 

exposure pathways: (1) stack and fugitive air releases, (2) direct surface water releases, (3) 

transfers to POTWs, (4) off-site transfers, and (5) on-site land releases. An overview of the 

pathways and methods used to evaluate each pathway is presented in Exhibit 2.1. 

The following discussions of exposure modeling frequently mention concentration and surrogate 

dose. This is not meant to imply that dose can be accurately calculated within this model. The 

exposure algorithms are intended to be simple ways to gauge relative risks from releases to 

different media in a consistent, defensible way, by modeling and estimating exposure. In some 

cases, the modeling is purposely simplified, given the lack of site-specific data. 

When possible, exposures are estimated for relevant subpopulations defined by age, sex, or other 

factors. Exposure for individual subpopulations is modeled using exposure factors (i.e., 

inhalation rates, drinking water intakes, fish ingestion rates, and body weight) and population 

data specific to such subpopulations. For example, ingestion rates specific to recreational and 

subsistence fishers are used to estimate exposures for these fishers and their families. Also, ageand sex-specific inhalation and drinking water ingestion rates are used. The relevant exposure 

assumptions for these subpopulations are also described in the following sections. 

 

22 Due to irregular, invalid block shapes, some of the block percentages did not sum to 100 percent. For these 

blocks, the boundary overlay process was not used; instead, the whole block was assigned to whatever grid cell 

contained the centroid of the block (an approximate center point defined by the Census). 

23 The data processing results in fractional people; populations were rounded to four decimal places for use in 

calculations, but are rounded to the nearest integer for display in the model. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 42 

5.3 Modeling Air Releases 

On-site air releases accounted for approximately 17 percent of TRI releases and transfers by 

weight in 2007. Air releases can either be released through stacks or as fugitive releases. Stack 

(or point) air releases include releases to air through stacks, confined vents, ducts, pipes, or other 

confined air streams, and represent the majority of air releases (87% of on-site air releases). 

Fugitive releases to air include all other on-site air releases, including leaks, evaporation from 

surface impoundments, and releases for building ventilation systems. These are modeled as two 

separate pathways in the model, although the potentially exposed population and human 

exposure assumptions are the same for both. The following sections describe the method and 

data sources for each pathway. 

5.3.1 Stack Air Emissions: Method 

Stack air releases are modeled using the American Meteorological Society/EPA Regulatory 

Model (AERMOD). AERMOD replaced the Industrial Source Complex (ISC) model as EPA’s 

preferred regulatory model in 2005. AERMOD is a steady-state Gaussian plume model used to 

estimate pollutant concentrations downwind of a stack or area source. The pollutant 

concentration is a function of facility-specific parameters, meteorology, and chemical-specific, 

first-order air decay rates. The following sections describe the parameters of the AERMOD 

model used.24

5.3.1.1 AERMOD 

The AERMOD model is specifically designed to support the EPA’s regulatory modeling 

programs, as specified in the Guideline on Air Quality Models (Revised).25 AERMOD is a 

steady-state plume model. In the stable boundary layer (SBL), it assumes the concentration 

distribution to be Gaussian in both the vertical and horizontal. In the convective boundary layer 

(CBL), the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is 

described with a bi-Gaussian probability density function (pdf). Additionally, in the CBL, 

AERMOD treats “plume lofting,” whereby a portion of plume mass, released from a buoyant 

source, rises to and remains near the top of the boundary layer before becoming mixed into the 

CBL. AERMOD also tracks any plume mass that penetrates into the elevated stable layer, and 

then allows it to re-enter the boundary layer when and if appropriate. For sources in both the 

CBL and the SBL, AERMOD treats the enhancement of lateral dispersion resulting from plume 

meander. Unlike existing regulatory models, AERMOD accounts for the vertical inhomogeneity 

of the planetary boundary layer (PBL) in its dispersion calculations. This is accomplished by 

averaging the parameters of the actual PBL into effective parameters of an equivalent 

homogeneous PBL. 

 

24 The following description is based on equations and text provided in the AERMOD manuals and documentation. 

The most recent AERMOD manuals are available from EPA’s SCRAM website at 

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

25 The Guideline on Air Quality Models can be found in 40CFR Part 51, Appendix W or accessed online at: 

http://www.epa.gov/ttn/scram/guidance/guide/appw_05.pdf. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 43 

5.3.1.2 Model Dispersion Options 

AERMOD is used with its regulatory default options26, except for the following: chemicalspecific decay is considered (the TOXICS with SCIM option is used), and flat terrain is assumed. 

 The non-default option of modeling urban areas with increased surface heating is not used. 

Weather data from National Weather Service (NWS) observation stations is used as the 

meteorological input (see Section 5.3.1.4 below). 

5.3.1.3 Source Parameters 

In the RSEI model, the U.S.27 is represented by a grid system composed of 810 m by 810 m 

square grid cells. Facilities are assigned to a particular grid cell in this grid according to their 

latitude and longitude coordinates (see Technical Appendix D for details on the coordinates 

used). To increase modeling efficiency, a facility is then assumed to be located at the center of 

the grid cell, regardless of where its latitude and longitude coordinates place it within the cell 

As a result of this assumption, the actual location of a facility may differ from its modeled 

location by up to 573 meters, the maximum distance between the center and the corner of the 

cell. To simplify the analysis, a facility’s point source emissions are modeled as a single stack 

located at the facility’s geographic center. 

RSEI uses facility-specific stack parameters when available. These include stack height, exit-gas 

velocity, and stack diameter. Stack exit gas temperature is assumed constant for all stacks (432° 

K). For facilities with multiple stacks, the median value for the stack heights and diameters for 

that facility is used. For facilities without stack-specific values, a Standard Industrial 

Classification (SIC) code-based median stack parameter is assigned to the facility. If no valid 

SIC code is available, or no stack data is available for that SIC code, then overall median values 

are used. Stack parameters are discussed further in Section 5.3.6.1 and in Technical Appendix E. 

Annual stack air releases as reported to TRI are converted to an equivalent constant emission rate 

in grams per second according to the following equation:28

31,536,000

453.6 q Q= (Eq. 5.3) 

where: 

Q = pollutant emission rate (g/sec), 

q = TRI annual stack or point air emissions (lb/yr), 

453.6 = constant to convert pounds (lb) to grams (g), and 

 

26 See AERMOD User’s Guide (EPA, 2007b), http://www.epa.gov/scram001/7thconf/aermod/aermod_userguide.zip 

27 Including Puerto Rico, the U.S. Virgin Islands, American Samoa, Guam, and the Northern Mariana Islands. 

28 Although RSEI can model any chemical air emission that is accompanied by the appropriate locational, chemical, 

and toxicity weight information, the model currently uses TRI reporting as the source of chemical release 

information. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 44 

31,536,000 = constant to convert years (yr) to seconds (sec) assuming 365 days 

per year. 

5.3.1.4 Meteorological Input Data 

For a given pollutant source, meteorology around the source affects the dispersion 

characteristics. Meteorological factors such as wind speed and direction, air temperature, 

stability, turbulence and the height of the mixing layer all have a direct effect on the dispersion 

and dilution of air pollution and the resulting magnitude and location of ground level 

concentrations of emitted pollutants.

AERMOD is designed to run with a minimum of observed meteorological parameters, and 

requires only a single surface measurement of wind, wind direction and ambient temperature. 

Like ISC, AERMOD also needs observed cloud cover. However, if cloud cover is not available 

(e.g. from an on-site monitoring program) two vertical measurements of temperature (typically at 

2 and 10 meters), and a measurement of solar radiation can be substituted. A full morning upper 

air sounding is required in order to calculate the convective mixing height throughout the day. 

Surface characteristics (surface roughness, Bowen ratio, and albedo) are also needed in order to 

construct similarity profiles of the relevant PBL parameters. 

5.3.1.5 Calculating Pollutant Concentration 

RSEI calculates air concentrations at hypothetical “receptors” located within a circle with a 

radius of 49 km surrounding each facility. Any cells with centers within the 49 km circle are 

included. The model calculates ground-level concentrations for receptors at 5 kilometer 

increments for distances from 5 to 49 km away from the modeled facility. The concentration 

assigned to a grid cell containing a receptor is determined at the point in that cell which is nearest 

to the facility (see Exhibit 5.2). 

For grid cells between the receptor points where calculations are made, concentrations are 

interpolated using a spatial weighting technique similar to inverse distance-weighted averaging. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 45 

Exhibit 5.2

Assigned concentration at point nearest to facility. 

To estimate the concentration for the 810 m by 810 m center cell containing the facility, the 

model calculates the interpolated concentration based on calculated concentrations at 441 

receptor points within a kilometer of the cell center. Analyses performed during the 

development of RSEI Version 2.0 (which used the ISC model29) indicated that using the 

concentration in a surrounding cell as an estimate for the center cell may either significantly over 

or under-represent chemical concentrations there (See Part B of Analyses Performed for the RiskScreening Environmental Indicators). 

The model estimates concentrations up to 49 km from the facility. To determine the optimal 

distance, EPA modeled air concentrations for the 20 most toxic carcinogens and the 20 most 

toxic non-carcinogens included the model at various stack heights. These analyses indicated that 

extending modeled distances to 50 km30 was necessary to capture potential concentrations of 

concern under certain atmospheric conditions. This distance is expected to capture the majority 

of the potential impacts from the TRI facilities, including electric utilities, which usually have 

taller stacks than other facilities. Details of these analyses can be found in Part B of Analyses 

Performed for the Risk-Screening Environmental Indicators.31 

 

29 Version 3 of the long-term ISC model (ISCLT3) was used to calculate chemical concentrations in air until Version 

2.2.0, the first version in which AERMOD was used. 

30 In the final RSEI modeling, 49 km was used instead of 50km due to modeling constraints. 

31 These analyses were performed using an earlier version of RSEI that incorporated EPA’s Industrial Source 

Complex (ISC) model. RSEI now uses AERMOD, which has replaced ISC as the Agency’s recommended air 

modeling program. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 46 

5.3.2 Fugitive Air Releases: Method 

As for stack air releases, long-term pollutant concentrations downwind of the facility due to TRIreported fugitive air releases are modeled using algorithms from AERMOD. 

5.3.2.1 Model Dispersion Options 

Model dispersion options used in modeling fugitive air releases are the same as those used for 

stack air releases, as described in Section 5.3.1.2. 

5.3.2.2 Source Options 

Fugitive emissions are modeled as an area source which is 10 meters by 10 meters in size, 

located at the center of the cell containing the facility. The model assumes a release height of 3 

meters.

Fugitive emissions are converted from pounds per year to grams per square meter per second 

(g/m2

s) according to the following equation: 

31,536,000* 10

453.6 q Q = 2

a

a (Eq. 5.4) 

where: 

Qa = pollutant area emission rate (g/m2

s), 

qa = TRI annual fugitive air emissions (lb/yr), 

453.6 = constant to convert pounds (lb) to grams (g), 

31,536,000 = constant to convert years (yr) to seconds (sec), and 

102

 = conversion factor necessary to convert annual emissions (g/s) to 

area emission rate (g/m2

s), assuming an area 10 m x 10 m. 

5.3.2.3 Calculating Pollutant Concentration 

Pollutant concentrations fugitive air releases are calculated using AERMOD, as described above 

for stack air releases. 

5.3.3 Calculating Surrogate Dose for Air Releases 

The calculated air concentrations described earlier are combined with assumptions regarding 

inhalation rate and human body weight to arrive at a surrogate dose for a given cell: 

1000

1

BW

C I DOSE = air air

air

*

*

 (Eq. 5.5) 

where: 

DOSEair = surrogate dose of contaminant from air (mg/kg-day), 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 47 

Cair = air concentration in cell (μg/m3

), 

Iair = inhalation rate (m3

/day), 

BW = human body weight (kg), and 

1000 = constant to convert (μg) to (mg). 

5.3.4 Estimating Population Size for Air Releases 

The population potentially exposed to air releases is assumed to be equal to the population 

assigned to the grid cells in the 810 m by 810 m modeled area, as described above in Section 

5.1.3. Exposed population is only considered for grid cells with nonzero pollutant 

concentrations. 

5.3.5 Calculating an Indicator Element for Air Releases 

Exhibit 5.3 provides a graphical overview of the steps for determining the air modeling 

component of the model. First, the pollutant concentration in each cell is calculated using TRI 

emissions data and the AERMOD algorithms. Then, subpopulation-specific exposure factors are 

used to calculate a surrogate dose for each cell. Finally, the surrogate dose is multiplied by the 

number of people in each subpopulation in the cell and by the chemical toxicity weight to obtain 

an Indicator Element for the grid cell. Then the results for all grid cells are summed. The result 

is an Indicator Element for an air release. To calculate the overall Indicator Element for all air 

releases, the same steps are followed for each air release, and the results are summed. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 48 

Exhibit 5.3 

Calculating the Indicator Element for Air Releases 

EPA/AMS Regulatory Model

(AERMOD) algorithm

Subpopulation-specific

exposure factors

Population data

and

toxicity weights

Sum over all 11,289 grid cells

around facility

Chemical concentration

in grid cell X

(μg/m3)

Indicator Element for Air Release

Indicator element for

grid cell X

Surrogate Dose

(mg/kg-day)

Air Release

(lbs/yr)
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 49 

5.3.6 Stack and Fugitive Air Releases: Data 

The air pathways use facility-specific values (stack height, stack diameter and exit gas velocity), 

meteorology, chemical-specific first-order air decay rates, and exposure assumptions (inhalation 

rate and body weight). The values used for these pathways are summarized in Exhibit 5.4. 

Exhibit 5.4 

Air Modeling Parameters 

Parameter Value Source/Comment

Pollution emission 

rate 

Site-specific (lbs/yr) TRI 

Stack height Varies based on the facility and the availability of 

information. If available, the median height of all 

stacks at the facility is used. Otherwise one of the 

following is used in declining order of preference: 

the median stack height for the facility’s 3-digit SIC 

code, the median stack height for the facility’s 2-

digit SIC-code, or the median stack height for all 

TRI-reportable SIC codes.* 

AFS, NET, NEI, 

EPRI (for Electric 

Utilities) and 

databases for CA, 

NY, and WI; these 

data are used in the 

vertical term of the 

model 

Stack diameters Varies based on the facility and the availability of 

information. If available, the median diameter of all 

stacks at the facility is used. Otherwise one of the 

following is used in declining order of preference: 

the median stack diameter for the facility’s 3-digit 

SIC code, the median stack diameter for the 

facility’s 2-digit SIC-code, or the median stack 

diameter for all TRI-reportable SIC codes. 

AFS, NET, NEI, 

EPRI (for Electric 

Utilities) and 

databases for CA, 

NY, and WI 

Exit gas velocity Varies based on the facility and the availability of 

information. If available, the median exit gas 

velocity of all stacks at the facility is used. 

Otherwise one of the following is used in declining 

order of preference: the median exit gas velocity 

for the facility’s 3-digit SIC code, the median exit 

gas velocity for the facility’s 2-digit SIC-code, or 

the median exit gas velocity for all TRI-reportable 

SIC codes. 

AFS, NET, NEI, 

EPRI (for Electric 

Utilities) and 

databases for CA, 

NY, and WI 

Exit gas 

temperature 432E K Based on EPA 

(2004b) 

Meteorological 

data 

Site-specific Processed using 

AERMET as 

contained in the 

HEM-3 data library 

(EPA, 2007) 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 50 

Exhibit 5.4 

Air Modeling Parameters 

Parameter Value Source/Comment

Decay rate Pollutant-specific values account for removal by 

physical and chemical processes (s-1) 

SRC (1994-1999) 

Area source size 10 m2 Based on EPA 

(1992b) 

Area source height 3 m Based on EPA 

(1992b) 

* As of RY2006, TRI requires facilities to report NAICS codes instead of SIC codes. RSEI maintains historically 

reported SIC codes. For new reporters without SIC codes, NAICS codes are matched to their equivalent SIC codes 

where possible, using the Census crosswalk available at http://www.census.gov/epcd/naics02/NAICS02toSIC87.xls. 

5.3.6.1 Stack Height, Stack Diameter, and Exit Gas Velocity 

Stack parameter data (height, diameter, and exit-gas velocity) were obtained from the AIRS 

Facility Subsystem (AFS) within the Aerometric Information Retrieval System (AIRS), the 

National Emission Trends (NET) Database, the National Emissions Inventory (NEI), and 

databases from three individual states (California, New York, and Wisconsin). For each TRI 

facility that had stack parameter data in one or more of these sources, the median parameter of all 

stacks at the facility is used. For the TRI facilities that had no stack parameter data in these 

sources, the median parameter values for all of the facilities in that facility’s Standard Industrial 

Classification (SIC) code is used instead. The SIC code-based stack parameters are estimated 

from data in AFS and the NET Database for facilities in the appropriate 3-digit SIC code, or in 

the 2-digit SIC code if the 3-digit SIC code is unavailable. If no 2-digit SIC code is available, 

the median of all stack parameters with TRI-reportable SIC codes is used. 

The Electric Power Research Institute (EPRI) provided EPA with site-specific data for electric 

utilities (electric utility SIC codes were added to TRI for Reporting Year 1998), transmitted in 

two databases. These data included stack height, stack diameter, and exit-gas velocity. Of the 

approximately 600 TRI facilities classified in one of the three electric utility SIC codes (4911- 

Electric Services; 4931- Electric and Other Services Combined; or 4939- Combination Utilities, 

not elsewhere classified) in RY 1998, almost 70 percent matched a corresponding facility listed 

in one of the EPRI databases; approximately 30 percent of TRI electric utility facilities did not.32

 For the 30 percent that did not match specific facilities, the median parameters taken across all 

of the coal or oil combusting stacks in the EPRI databases were used. 

Statistical analysis of stack heights by SIC code revealed that, for certain SIC codes, no 

significant height differences existed between stacks emitting TRI chemicals and those not 

emitting TRI chemicals. For these SIC codes, the median stack height was based on stack 

 

32 One TRI facility classified in one of the relevant utility SIC codes that could not be matched to a specific facility 

in the EPRI dataset was matched to a specific facility in the AFS database. In this case, the facility-specific 

parameters were taken from AFS. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 51 

heights for all facilities. For SIC codes in which there were significant height differences 

between stacks emitting or not emitting TRI chemicals, only those stacks emitting TRI chemicals 

were used to calculate the median stack height for that SIC code. 

For both stack diameter and exit gas velocity, the model uses the same data sources, criteria, and 

statistical methods described above for stack height data. Specifically, the model uses either the 

median value of all stacks for TRI facilities with this information or an SIC code-based median 

value for facilities without the appropriate stack data. Exit gas velocity data were obtained from 

AFS, NET, NEI, and state-specific databases. Stack diameter data were obtained from AFS, 

NET, NEI, EPRI and databases from three individual states (California, New York, and 

Wisconsin). 

Analyses have been conducted that show air concentrations predicted by the model using a 

combination of generic and site-specific data closely match concentrations estimated by using 

more complete site-specific data.33 See Technical Appendix E for details on the derivation of 

stack data. 

5.3.6.2 Meteorology34

The meteorological data used in RSEI is taken from EPA’s Human Exposure Model, Version 3 

(HEM-3), a model for use in site-specific air toxics risk-assessment. RSEI uses weather data 

included in EPA’s HEM-3 data library, which has been prepared using AERMOD’s 

meteorological processor, AERMET. AERMET requires hourly surface weather observations 

and the full twice-daily upper air soundings (i.e., meteorological variables reported at all levels). 

The surface and upper air stations are paired to produce the data files require for input into 

AERMOD: one file consists of surface scalar parameters, and the other file consists of vertical 

profiles of meteorological data. 

To simplify processing and minimize the amount of quality assurance needed, HEM-3’s 

processing was restricted to meteorological data collected prior to the installation of the 

Automated Surface Observation System (ASOS). The ASOS has previously been found to omit 

the ceiling height for a large percentage of the observations at a number of meteorological 

stations. Installation and operation of ASOS equipment began in 1992; therefore, data for 1991 

were used. Data were retrieved from products available from the National Climatic Data Center 

(NCDC). The surface data for 1991 were retrieved from the Hourly United States Weather 

Observation (HUSWO) CD. Upper air soundings were obtained from the Radiosonde Data of 

North America CDs produced by NCDC and the Forecast Systems Laboratory (FSL). 

Certain surface characteristics must be specified when processing meteorological data using 

AERMET, including the surface roughness length, the Bowen ratio (an indicator of surface 

moisture), and the albedo (an indicator of surface reflectivity). These surface characteristics are 

 

33 These analyses were performed using an earlier version of RSEI that incorporated EPA’s Industrial Source 

Complex (ISC) model. RSEI now uses AERMOD, which has replaced ISC as the Agency’s recommended air 

modeling program. 

34 This description is taken from the HEM-3 User’s Manual (EPA, 2007), available at 

http://www.epa.gov/ttn/fera/data/hem/hem3_users_guide.pdf. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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used by AERMET to calculate the level of shear-induced mechanical turbulence generated by 

flow over the surface and for the energy balance calculations used in the determination of the 

Monin-Obukhov stability parameter and the convective velocity scale. For the HEM-3 

meteorological data, the following surface characteristics were used: 

• Surface roughness length = 0.25 m. At the airport meteorological site, the surface 

roughness includes runways, terminal buildings and other airport structures. In 

addition, off-airport structures often are within 3 kilometers of the measurement 

site. This combination of land covers suggests a value of 0.2 – 0.3 meters is 

appropriate. 

• Bowen ratio = 1.0. Representing an equal partition of the heat fluxes Albedo = 

0.15. Representing conditions for all seasons, including winter without continuous 

snow cover. 

• The file STNS.TXT located on the HUSWO CD was used for the anemometer 

heights required by AERMET. These heights are to the nearest meter and were 

deemed appropriate for use in this application. 

5.3.6.3 First-Order Air Decay Rates 

Pollutants may be removed from the atmosphere by either physical processes or chemical 

transformation. The model uses pollutant-specific air decay rates from SRC’s Atmospheric 

Oxidation Program (AOPWIN), an atmospheric oxidation computer program (SRC, 1994-1998). 

AOPWIN estimates the second-order rate constant for the atmospheric, gas-phase reaction 

between photochemically produced hydroxyl radicals and organic chemicals.35 The daughter 

products of photodegradation are not modeled further, i.e., it is assumed that all chemicals are 

photodegraded into nontoxic compounds. AOPWIN data also contains certain empirically 

derived air decay rates. For the model, a concentration of hydroxyl radicals of 1.5 x 106

molecules/cm3

 is used to convert the second-order rate constant provided in AOPWIN to a firstorder rate constant. Furthermore, the rate is divided by a factor of two to reflect an assumed 

average day length of 12 hours: 

* 1. 10 3600

2

AOPWIN

K = 6

air 5× * (Eq. 5.6)

 

where: 

Kair = air decay rate (hr -1),

AOPWIN = second-order rate constant from AOPWIN, 

1.5×106

 = hydroxyl radical concentration (molecules/cm3), 

3600 = constant to convert molecules/seconds to molecules/hour, and 

2 = constant to reflect assumed day length of 12 hours. 

 

35 For a few chemicals, other sources were used. See Technical Appendix B for the source used for each chemical. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 53 

5.3.6.4 Human Exposure Data and Assumptions 

For the air pathways, sex- and age-specific inhalation rates and body weights are used in the 

model. The primary source for all exposure factors used in the model is EPA’s Exposure Factors 

Handbook (EPA, 1997b, hereafter denoted as EFH), which provides a summary of the available 

statistical data on various factors used in assessing human exposure. These factors include: 

drinking water consumption, soil ingestion, inhalation rates, dermal factors including skin area 

and soil adherence factors, consumption of fruits and vegetables, fish, meats, dairy products, 

homegrown foods, breast milk intake, human activity factors, consumer product use, and 

residential characteristics. In EFH, EPA recommends mean values for the general population 

and also for various segments of the population who may have characteristics different from the 

general population. RSEI uses inhalation rates and body weights derived from the recommended 

factors, except where noted. The Exposure Factors Handbook (EFH) can be found on the 

internet at http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464. 

EFH (EPA, 1997b, Table 5-23, p. 5-24) was used to estimate inhalation rates for the eight RSEI 

age-sex groups (ages 0-17, 18-44, 45-64, 65+).36 The inhalation rates recommended by EFH 

were not categorized into the same age groups used in RSEI. For children, the RSEI age groups 

were broader than the EFH age groups. Therefore, the exposure factor was calculated using a 

weighted average of the inhalation rates for all EFH age groups that overlap the RSEI age group 

as follows: 

N

IR n

EF i

∑ i i ∗

=

( )

 (Eq. 5.7) 

where: 

EF = RSEI exposure factor, 

IRi = intake rate for EFH age group i, 

ni = number of years that EFH age group i overlaps with the RSEI age group, 

and 

N = number of years in RSEI age group. 

For adults, EFH provides only a single recommended inhalation rate for males and a single rate 

for females. However, EFH also provides the underlying data used to develop the summary 

values, which were available for more narrow age ranges (EPA, 1997b, Tables 5-11 to 5-13, p. 

5-13 to 5-15). The RSEI adult inhalation factors are based on weighted averages calculated from 

this data, using Equation 5.15. The RSEI inhalation factors are then divided by age- and sexspecific body weights, averaged to match the RSEI age groups using data provided in EFH 

(EPA, 1997b, Tables 7-2 and 7-3, p. 7-4). Exhibit 5.5 provides the range of data used, and 

Exhibit 5.6 presents the final exposure factors used in the model. More detail on the derivation 

of exposure factors can be found in Technical Appendix C. 

 

36 The RSEI model provides the ability to view risk scores for the 0-9 year old age group, however, there is not a 

separate exposure factor for this age group. Instead, the exposure factor for the 0-17 year old age group is used. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 54 

Exhibit 5.5 

Range of Data Used to Estimate Exposure Factors 

Parameter Value Source/Comment

Inhalation rate 4.5-17 m3

/day 

(Varies by age and sex) EPA (1997b) 

Body weight 33.0 -79.9 kg 

(Varies by age and sex) EPA (1997b) 

Exhibit 5.6 

Inhalation Exposure Factors (m3

/kg-day) 

Model Age 

Group 

Male Female 

0-17 0.341 0.310 

18-44 0.209 0.186 

45-64 0.194 0.165 

>65 0.174 0.153 

5.4 Modeling Surface Water Releases 

In 2007, approximately three percent of TRI releases and transfers by weight were released onsite as direct surface water releases. People may be exposed to chemicals released into surface 

water in one of two ways: by drinking tap water from a public water system whose water intake 

was located in the stream path of a chemical release; or by eating contaminated fish caught in a 

water body in the stream path of a chemical release. The following sections first describe the 

methods used to calculate the initial stream concentration for both pathways, and then the 

different methods used to calculate surrogate dose and population for the drinking water pathway 

and the fish ingestion pathway. The data section presents the data used for both pathways and 

the human exposure assumptions used. 

5.4.1 Surface Water Releases: Methods 

5.4.1.1 Locating the Facility Discharge Reach 

The first step in assessing surface water emissions is to locate the discharging facility on the 

model grid using the lat/long. Facilities are then matched to a receiving stream reach (a linear, 

unbranched section of a water body). 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 55 

The main data source used is EPA’s records of discharge permits for facilities, which in most 

cases specify the discharge reach by name or reach number. Permit records, including permits, 

monitoring data, and locational and descriptive information pertaining to more than 67,000 

regulated facilities were previously maintained in EPA’s Permit Compliance System (PCS), but 

are being transferred into the Integrated Compliance Information System-National Pollutant 

Discharge Elimination System (ICIS-NPDES). 

Facilities without assigned reaches are assumed to discharge to the nearest reach, as long as that 

reach is within four kilometers of the facility and meets minimum criteria for flow and reach 

type, as described below in Section 5.4.3.1. If no assigned reach is available, and no acceptable 

reach is found within four kilometers, the discharge is not modeled. Reach data is not available 

for Alaska, Guam, American Samoa and the Northern Mariana Islands; therefore, no surface 

water releases are modeled for these areas. 

5.4.1.2 Calculating Pollutant Concentrations 

Chemical concentrations in the receiving stream at a distance x from the discharging facility at 

time t are estimated by using a simple first-order decay equation. The facility is assumed to 

release its annual discharge at a constant rate throughout the year. Annual average 

concentrations are then estimated until one of three conditions occurs: (1) the release has traveled 

300 km downstream; (2) the release has been traveling downstream for a week; or (3) the 

concentration reaches 1 x 10-9 mg/L. Within the initial stream reach, the mass of the release is 

assumed to be instantaneously mixed with the flow at the upstream end of that reach. The 

calculated concentration at the downstream end of the reach is then converted back to a mass 

(after any decay) and the process is repeated in the adjoining reach. Reaches are defined by 

intersections with other hydrological features and these “nodes” initiate the next reach segment. 

The chemical-specific decay coefficient is predominantly based on abiotic hydrolysis or 

microbial biodegradation, but may also include photooxidation. The general form of the firstorder decay equation is as follows: 

C =C e

-k t

x 0 water (Eq. 5.8) 

where: 

Cx = concentration at distance x meters from the facility release point (mg/L), 

C0 = initial concentration (mg/L), which equals chemical release (mg/day) 

divided by mean flow (see Eq. 5.16), 

kwater = decay coefficient (sec-1), and 

t = time at which Cx occurs (sec), which equals x/u, where u is the water 

velocity (m/sec). 

For surface water releases, the RSEI model estimates chronic human health exposures for two 

pathways: drinking contaminated water and eating non-commercial contaminated fish. Methods 

used to estimate each of these exposures are described below. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 56 

5.4.1.3 Modeling the Drinking Water Pathway 

Surrogate doses from drinking water are calculated using the chemical concentrations in stream 

reaches where drinking water intakes are located. Drinking water intake locations were obtained 

from the Public Supply Database, a database of drinking water system information developed 

and maintained by the US Geological Survey (USGS), based on information in the Safe Drinking 

Water Information System (SDWIS). Each intake is assumed to be drawing water from the 

stream reach nearest to its plotted location, as long as the reach is within one kilometer.37 For 

this exposure pathway, the chemical concentration in drinking water is assumed to be equal to 

the stream concentration (calculated at the upstream end of the reach; conservatively using the 

highest concentration), up to the level of the Maximum Contaminant Level (MCL),38 where 

applicable. (Seventy-nine TRI chemicals had existing MCLs in effect during one or more years 

for which TRI data are available;39 this number includes chemical categories that are treated as 

their elemental forms). If the stream concentration exceeds the MCL, the drinking water is 

assumed to be treated to the level of the MCL for the year of that release. For each stream reach 

with a drinking water intake, the chemical concentration is combined with standard exposure 

parameters (see Section 5.4.3.6) to yield a surrogate dose: 

BW

C I DOSE = water,reach water

dw

∗

 (Eq. 5.9) 

where: 

DOSEdw = surrogate dose of chemical in drinking water (mg/kg-day), 

Cwater,reach = average annual chemical concentration in the reach of interest, 

calculated at the upstream end of the reach (mg/L), 

Iwater = drinking water ingestion rate (L/day), and 

BW = human body weight (kg). 

5.4.1.4 Estimating Population Size for the Drinking Water Pathway 

To estimate the size of the population exposed to TRI releases through drinking water, the model 

uses estimates of the population served by each drinking water intake from USGS’s Public 

Supply Database, which incorporates population estimates from the Safe Drinking Water 

Information System (SDWIS)40. (More information about SDWIS can be found at 

 

37 One intake, for the Los Angeles Dept. of Water and Power is not modeled, even though there is a reach within one 

kilometer (the L.A. River). Although it is clear that this is not the correct source, the actual reach is not known, so 

the intake is not currently modeled. 

38 Copper and lead have action levels instead of MCLs; however, RSEI models them in the same manner as MCLs. 

This also applies to copper compounds and lead compounds, as metal compounds are modeled like their elemental 

forms. 

39 As MCLs are sometimes revised and new ones have been added since TRI began reporting, RSEI applies MCL 

limits for only the years that the MCLs were in effect. For several chemicals for which MCLS were first instituted 

in 1976 and then revised in 1991, the original MCL values were not readily available, so the revised values were 

also used for the years before the revision. These chemicals are barium, cadmium, chromium, lead, lindane, 

mercury, methoxychlor, nitrate, selenium, and toxaphene. 

40 RSEI uses SDWIS data that is contained in the USGS Public Supply Database (PSDB), see Section 5.4.3.2. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 57 

http://www.epa.gov/enviro/html/sdwis/.) However, this data set only lists the intake location and 

the number of people served by the water system. In many cases, there are multiple water 

intakes per water system. In the absence of other data, it is assumed that the total population of 

the water system is exposed to the full concentration of the released chemical estimated at the 

reach where a water intake is located (calculated at the upstream end of the reach). 

The drinking water intake information from SDWIS contains only the number of people served 

by each drinking water system; it does not provide demographic or locational information for 

those served (the time frame in which this information was collected also varies widely). To 

derive demographic information (that is, age and sex breakdowns) for the population served, 

RSEI uses the percentage of people in each of the ten age-sex categories for the total population 

located in grid cells within an 80-km radius of each reach containing a drinking water intake (this 

information is calculated for the fish ingestion pathway - see Section 5.4.1.4). Then, these 

percentages are applied to the SDWIS intake population (population served), creating the 

subpopulation groups that are used for calculating results. 

5.4.1.5 Modeling the Fish Ingestion Pathway 

A second potential exposure pathway is through consumption of fish contaminated by chemicals 

discharged from TRI reporting facilities. These fish may be consumed by recreational and 

subsistence fishers and their families.41 As in the drinking water pathway, chemical 

concentrations are calculated until one of three conditions occurs: (1) the release has traveled 300 

km downstream; (2) the release has been traveling downstream for a week; or (3) the 

concentration reaches 1 x 10-9 mg/L. The chemical concentration in fish is estimated using the 

following equation: 

C fish,reach=Cwater,reach

* BCF (Eq. 5.10) 

where: 

Cfish, reach = concentration in fish in the specified stream reach (mg/kg), 

Cwater, reach = average annual chemical concentration in the reach of interest 

(mg/L), and 

BCF = bioconcentration factor for chemical (L/kg). 

The chemical concentration in fish in a reach is combined with exposure assumptions to 

determine the surrogate dose from this pathway: 

BW

C I

DOSE = fish,reach fish,pop

f,c

*

 (Eq. 5.11) 

where: 

 

41 Although store-bought fish may also contain pollutants originating from TRI facilities, modeling this exposure 

pathway is not currently possible. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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DOSEf,c = surrogate dose of chemical c from facility f (mg/kg-day), 

Cfish, reach = average annual chemical concentration in fish tissue (mg/kg), 

Ifish, pop = fish ingestion rate for recreational or subsistence fishers (kg/day), 

and 

BW = human body weight (kg). 

5.4.1.6 Estimating Population for the Fish Ingestion Pathway 

The model uses several steps to estimate the population within each grid cell that consumes noncommercial fish. First, a county-level dataset containing the number of fishing or 

hunting/fishing combination licenses was created from state fish and wildlife licensing data for 

1996 (if 1996 was not available, 1997 was used). The number of fishing licenses in a county is 

then divided by the 1990 total population in the county.42 The resulting ratio is multiplied by the 

population in each grid cell to obtain the number of individuals with fishing licenses within that 

cell. To account for family members who also eat fish caught by one member, the model 

multiplies the number of fishers by 2.62, the size of the average U.S. household in 1995 (U.S. 

Census Bureau, 1996). The total population in a grid cell consuming non-commercial fish is 

described by the following equation: 

FamSize

Pop

Licenses FishPop =TotalPop cell cell * (Eq. 5.12) *

where: 

FishPopcell = total fish-eating population in a grid cell, 

TotalPopcell = total resident population in a cell (see Section 5.1.3), 

Licenses = number of fishing licenses in the county or state, 

Pop = total population in the county or state in 1990, and 

FamSize = average family size. 

Next, the population that consumes fish is then apportioned based on whether fish are eaten 

recreationally or for subsistence. Recreational fishers may fish during only certain times of the 

year for recreational purposes or to supplement their diet. In contrast, subsistence fishers may 

fish throughout the year and a major part of their diets may consist of fish they catch. Data are 

lacking on numbers of recreational compared to subsistence fishers; RSEI follows guidance from 

EPA’s Office of Water (Harrigan, 2000). The model assumes that of the population that eats 

non-commercial fish, 95 percent eat fish on a recreational basis, and the remaining 5 percent 

subsist on fish. This apportionment is described by the following relationships: 

RecPop = FishPop 0.95 cell cell

* (Eq. 5.13) 

 

42 If no licensing information for a county was available, all of the grid cells in that county are assigned the ratio of 

total state licenses to total state population. If no information was available for the state in which the grid cell is 

located, ratio for the state closest to that grid cell is assigned. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 59 

SubsistPop = FishPop 0.05 cell cell

* (Eq. 5.14) 

where: 

RecPopcell = recreational fishers (and families) in a cell, and 

SubsistPopcell = subsistence fishers (and families) in a cell. 

The fishing population in each cell is then assigned to specific stream reaches where they are 

presumed to catch fish. This is done in two steps. First, overlapping circles of 80-km radii 

associated with each of the two to seven points that describe individual stream reaches are used 

to define those grid cells that will be modeled for fishing population in the 48 contiguous states 

(i.e., all fishing areas within 80 km of all stream reaches). The distance of 80 kilometers (50 

miles) from the reach is chosen based on a finding reported in the 1991 National Survey of 

Fishing, Hunting, and Wildlife-Associated Recreation that 65 percent of anglers travel less than 

50 miles to fish (U.S. Department of the Interior, 1993). This distance approximates the size of 

many counties and corresponds with the use of county-level fishing license data. 

Second, all reaches within an 80-km radius of the center of each grid cell from the first selection 

are identified. The fish-eating population in the grid cell is apportioned to each surrounding 

stream reach based on the ratio of the length of that reach to the total reach kilometers within 80 

km of the cell. For example, Reach A and B may be located within 80 km of a given cell. If 

Reach A is 15 km in length and Reach B is 5 km in length (and the entire length of each reach is 

completely within 80 km of the cell), then a total of 20 km of stream reaches are located within 

the specified distance. Because Reach A represents three-fourths (15/20) and Reach B represents 

one-fourth (5/20) of total km, the model therefore assumes that three-fourths of the fishing 

population in the cell catches fish from Reach A and one-fourth catches fish from Reach B. Note 

that the model uses only the portion of the reach’s length that is within 80 km of the cell. 

Because of the size of the database created, the fishing population data attributed to individual 

reaches is summed and stored at the reach level. The percentage of people in each of the ten agesex categories for the aggregated total fishing population (reflecting the ratio of the various age 

and gender subpopulations in the neighboring grid cells) is also maintained for each reach. The 

model then matches the chemical concentration in fish in the appropriate reach (Cfish,reach) to the 

correctly-apportioned population. This is done for all reaches that have modeled chemical 

concentrations. 

5.4.2 Calculating the Indicator Element for Surface Water 

The Indicator Elements for drinking water and fish ingestion are calculated by generating for 

each unique combination of chemical release, stream reach, and exposure pathway a surrogate 

dose, then multiplying this dose by the toxicity weight of the chemical released and the estimated 

population. The RSEI risk-related result (score) for a surface water release from a TRI facility is 

calculated by adding the drinking water element and the fish consumption elements (recreational 

and subsistence fishing) for each reach and then summing over all reaches affected by the release 

(up to 200 km downstream from the facility). Exhibit 5.7 shows the approach for calculating the 

three Indicator Elements for surface water. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

June 2010 60 

Exhibit 5.7 

Calculating the Indicator Elements for Surface Water

Pollutant concentration in

surface water Reach X (mg/L)

Water volume and velocity

estimates; decay equation

Standard exposure assumptions

(fish ingestion rate, body weight)

Population Data1

and

Toxicity Weights

Population Data2

and

Toxicity Weights

+

1 Estimated using fish license data, household size, and distance traveled to fish

2 Served by drinking water intakes in Reach X (if any)

Direct Water Release

(lbs/yr)

Surrogate dose

from drinking water

in Reach X (mg/kg-day)

Pollutant concentration in

fish in Reach X (mg/ kg)

Surrogate dose

from fish consumption

in Reach X (mg/kg-day)

Bioconcentration

factor (BCF)

Standard exposure assumptions

(drinking water ingestion

rate, body weight)

Indicator Elements for

Surface Water Release

Sum over all reaches and both pathways

Indicator elements for

recreational and subsistence

fish consumption for Reach X

Indicator element for

drinking water

for Reach X
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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5.4.3 Surface Water Releases: Data 

A variety of data are required to estimate exposure to chemical discharges to surface waters. The 

parameters required for surface water modeling and the data sources are described below and 

listed in Exhibit 5.8 

Exhibit 5.8 

Surface Water Modeling Parameters 

Parameter Value (Units) Source/Comment 

Stream reach location Lat/long in decimal degrees NHDPlus (US EPA/USGS, 

2009) 

Drinking water location and 

population served 

Lat/long in decimal degrees 

Number of persons 

Public Supply Database, 

based on SDWIS (2007) 

Water flow mean flow (million L /day) NHDPlus (US EPA/USGS, 

2009) 

Decay rate of chemical in water chemical-specific (sec-1) SRC (1994-99) 

Chemical concentration in stream (mg/L) calculated 

Bioconcentration factor chemical-specific (L/kg) SRC (1994-99); 

Lyman et al. (1990); 

EPA (1999b) 

Fish tissue concentration calculated (mg/kg) 

Family size 2.62 U.S. Census Bureau (1996) 

5.4.3.1 Stream Reaches 

Each facility is matched to an EPA-assigned discharge reach, or if no assigned discharge reach is 

available, it is assumed to discharge into the nearest stream reach within four kilometers of the 

facility. Certain minimum criteria regarding flow and reach type are applied to the set of 

potential discharge reaches, as explained below. If no acceptable reach is found within four 

kilometers, then the discharge is not modeled. The stream reaches used in the model are linear 

sections of streams, lakes, reservoirs, and estuaries that are linked to form a skeletal structure 

representing the branching patterns of surface water drainage systems. Non-transport reaches 

(i.e., those that do not have an upstream or downstream connection) are excluded from the 

model. 

RSEI has adopted the National Hydrography Dataset (NHD) system for indexing stream reaches, 

which is much more detailed than the Reach File 1 (RF1) system, which was used in RSEI 

Version 2.2.0 and before. The NHD is a feature-based database that interconnects and uniquely 

identifies the stream segments or reaches that comprise the nation's surface water drainage 

system. The NHD provides a national framework for assigning reach addresses to water-related 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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entities such as industrial dischargers, drinking water supplies, fish habitat areas, and wild and 

scenic rivers. Reach addresses establish the locations of these entities relative to one another 

within the NHD surface water drainage network in a manner similar to street addresses. Once 

linked to the NHD by their reach addresses, the upstream/downstream relationships of these 

water-related entities and any associated information about them can be analyzed using software 

tools ranging from spreadsheets to Geographic Information Systems (GIS). EPA’s Watershed 

Assessment, Tracking & Environmental Results (WATERS) system uses the reach codes in 

NHD to link multiple databases containing water quality and programmatic information. EPA 

has created a version of the NHD called NHDPlus43, which contains information on stream 

velocity and flow volume, which are necessary for modeling chemical concentrations in streams 

in the RSEI model. 

It should be noted that the atomic unit of the NHDPlus data is the “flowline” which is a subsection of a reach. RSEI maintains the data at this level; however, the discussion throughout this 

document will mostly be in terms of reaches, since much of the other data (such as discharge 

reach) are at this level. In cases where a reach needs to be matched to a flowline, the most 

upstream flowline in that reach is used. 

Certain criteria were applied to the NHDPlus dataset to select the reaches to be used in the 

model. Specifically, because RSEI calculates the movement of a chemical release downstream 

using flow and velocity data, qualifying reaches must have at least one downstream or upstream 

connecting reach44, and have a non-negative flow and velocity. RSEI will not calculate 

concentrations for certain types of reaches, such as coastlines, treatment reservoirs, and bays; the 

downstream path of any chemical is assumed to stop if one of these types of reach is 

encountered. Additionally, some types of reaches are excluded from the set of fishable reaches, 

such as pipelines, aqueducts, and certain types of reservoirs. NHDPlus does not separate canals 

(presumably fishable) and ditches (presumably not fishable), so RSEI excludes reaches in the 

canal/ditch category if the annual mean flow is less than 5 ft3

/s. This is an arbitrary minimum, 

and is intended primarily to exclude ditches at the point of the facility discharge. For reaches 

designated as not fishable in NHDPlus, the chemical is still assumed to travel downstream to the 

next reach, which may or may not be fishable. 

Because NHDPlus contains a large number of reaches with very small annual mean flows, RSEI 

also excludes the very smallest reaches from assignment as discharge reaches. To determine an 

appropriate minimum, a national set of EPA-assigned discharge reaches from the ICIS-NPDES 

database was matched to the NHDPlus annual mean flow data. For each NHDPlus region, the 

non-zero annual mean flows were ranked, and the fifth percentile flow for each region was 

selected as the minimum annual mean flow. Assigned regional minimum values ranged from 

0.0036 ft3

/s to 1.9 ft3/s. For instance, if a facility in a region whose minimum value was selected 

as 0.0036 ft3

/s had a nearest reach with annual mean flow of 0.0025 ft3/s, this would not be 

 

43 The NHDPlus data was provided by Horizon Systems Corporation, which hosts and maintains the NHDPlus data. 

 Documentation and data are available at http://www.horizon-systems.com/nhdplus/index.php. 

44 In the NHDPlus dataset, topologically connected reaches with known flow are indicated by a “FlowDir” value of 

“With Digitized.” Only reaches with this value for this field were included in RSEI, which includes 2.6 million out 

of 2.9 million flowlines in NHD. 
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selected as the discharge reach. Instead the next closest reach with a flow greater than 0.0036 

ft3

/s would be selected as the discharge reach. 

5.4.3.2 Drinking Water Intakes and Populations 

Drinking water intake locations were obtained from the Public Supply Database, a database of 

drinking water system information developed and maintained by the US Geological Survey 

(USGS), based on information in EPA’s Safe Drinking Water Information System (SDWIS). 

SDWIS is a publicly-accessible database that contains the information EPA uses to monitor 

public water systems. The database contains information on over 172,000 water systems, which 

serve over 90 percent of the U.S. population.45 SDWIS is operated and maintained by EPA’s 

Office of Water. USGS’s Public Supply Database was designed to support USGS analyses of 

the water resources used by public drinking water systems. The Public Supply Database includes 

SDWIS data, and supplements it with additional information about the water bodies from which 

systems draw their water. In addition, multiple quality-assurance checks have been performed 

on the data, including the lat/longs. 

The version of the Public Supply database used contains SDWIS population data from 2007. 

Approximately 6,500 drinking water intakes were included in the database. Several types of 

intakes were excluded from the set used for RSEI: 1) if the drinking water system for the intake 

closed prior to 2002; 2) if the source water for the intake was something that could not 

reasonably be expected to be connected a network of streams (such as an aqueduct or an 

infiltration gallery); or 3) if the intake was emergency, interim (peak) or other (rather than 

permanent or seasonal). Excluding these cases left 4,599 intakes that are modeled in RSEI. 

5.4.3.3 Water Flow and Velocity 

RSEI uses NHDPlus estimates of water flow and velocity based on the unit runoff method,46 

which was developed for the National Water Pollution Control Assessment Model (NWPCAM). 

The unit run-off method calculates average runoff per square kilometer in a watershed (8-digit 

HUC) based on gages in the HydroClimatic Data Network (HCDN). These gages are usually not 

affected by human activities, such as major reservoirs, intakes, and irrigation withdrawals; thus, 

the mean annual flow estimates are most representative of “natural” flow conditions. Based on 

elevation and drainage patterns, each square kilometer in a watershed is assigned to a catchment area, 

from which the runoff flows to a specific flowline. The runoff from each catchment area is summed 

and attributed to its assigned flowline. That flow is assigned to the next downstream flowline, to 

which the downstream’s flowline’s catchment runoff is added, and so on down the stream path. Unit 

runoff estimates are calibrated for areas west of the Mississippi to account for water withdrawals and 

transfers. 

 

45 Statistics are from EPA’s Center for Environmental Information and Statistics (CEIS) review of SDWIS, “Major 

Findings from the CEIS Review of EPA’s SDWIS Database,” based on 1996 data. 

46 NHDPlus also contains estimates developed using the Vogel method, but this is method is considered to have a 

narrow band of applicability. 
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NHDPlus velocities are estimated for mean annual flow conditions (using the unit runoff 

method) based on the work of Jobson (1996). This method uses regression analyses on hydraulic 

variables for over 980 time-of-travel studies, which represent about 90 different rivers in the U.S. 

representing a range of sizes, slopes, and channel geometries. Four principal flowline variables 

are used in the Jobson methods: drainage area, flowline slope, mean annual discharge, and 

discharge at the time of the measurement.47

NHDPlus is based on the medium-resolution NHD dataset, which does not include the Great Lakes. 

Therefore, flow estimates of rivers in the Great Lakes area in the NHDPlus dataset do not include 

discharges from the Great Lakes. To account for this, RSEI includes adjustment factors based on 

flow estimates from either in-stream gages or annual mean flow estimates from the U.S. Army Corps 

of Engineers (USACE)48. The flow adjustment is added to the first reach downstream of each Great 

Lake, and the additional flow is added to each downstream reach, and branched as appropriate. RSEI 

does not model potential risk in the Great Lakes themselves, due to the complicated hydrology. 

5.4.3.4 Water Decay Rates 

Water decay rates are required to model downstream chemical concentrations. The primary 

sources for water decay values were Syracuse Research Corporation’s (SRC’s) ChemFate 

database, a component of SRC’s Environmental Fate Data Base (SRC, 2002a), which contains 

experimental data, and SRC’s Aqueous Hydrolysis Rate Program, HYDROWIN (part of the EPI 

suite of estimation programs (SRC, 1994-1999)), both of which were developed for the 

Environmental Protection Agency. The ChemFate database contains environmental fate and 

physical/chemical property information for commercially important chemical compounds, 

including TRI chemicals. HYDROWIN estimates hydrolysis rate constants for esters, 

carbamates, epoxides, halomethanes, and selected alkyl halides. Values of water decay rates can 

be found in Technical Appendix B. 

5.4.3.5 Bioconcentration Factors 

Bioconcentration factor (BCF) is the term used to describe the equilibrium concentration of 

chemicals in aquatic organisms living in contaminated water. The BCF is defined as the ratio of 

the chemical concentration in the organism (mg/kg) to that in the surrounding water (mg/L). The 

term “bioconcentration” refers to the uptake and retention of a chemical by an aquatic organism 

from the surrounding water only.

49 Experimental BCF values were obtained from SRC’s 

ChemFate database. Other BCFs were estimated from either log(Kow) values using regression 

equations from Lyman et al. (1990), or from the SRC estimation program BCFWIN. See 

 

47 For more information, see the NHDPlus User’s Guide (US EPA/USGS 2009). 

48 USGS’ National Water Information System (NWIS) Mapper shows stream gages (available at 

http://wdr.water.usgs.gov/nwisgmap/), and the USACE website has average annual flow data (available at 

http://www.lre.usace.army.mil/greatlakes/hh/outflows/historic%20connecting%20channel%20outflows/index.cfm) 

49 The BCF can underestimate the accumulation of chemicals that are highly persistent and hydrophobic relative to 

the bioaccumulation factor (BAF), which measures the uptake and retention of a chemical by an aquatic organism 

from all surrounding media (e.g. water, food, sediment). The bioaccumulation factor (BAF) is defined as the ratio 

of the chemical concentration in the organism (mg/kg) to that in the surrounding water (mg/L), in situations where 

both the organism and its food are exposed. Due to data limitations at the present time, only BCFs are used in the 

RSEI model. 
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Technical Appendix B for values and references for the bioconcentration factors used in the 

model. 

5.4.3.6 Human Exposure Assumptions 

Drinking Water. For the drinking water pathway, the model uses estimates for the amount of 

tapwater ingested to estimate exposure. As in the stack and fugitive air pathways, data is 

acquired from EPA’s Exposure Factors Handbook (EFH) (EPA, 1997b). EFH recommends 

mean tapwater intakes from two key studies, Ershow and Cantor (1989) and the Canadian 

Ministry of National Health and Welfare (1981) (as cited in EPA, 1997b, Table 3-30, p. 3-26). 

Because Ershow and Cantor (1989) provide body weight adjusted intakes, EFH recommends 

using this data in preference to data from the Canadian Ministry of National Health and Welfare 

(1981). As a result, the RSEI exposure factors are based on data from Ershow and Cantor 

(1989). The age groups presented for the recommended values are, in some cases, broader than 

those used in RSEI. However, EFH also presents more refined age-specific intakes for the 

preferred study (Table 3-7, p.3-6). Drinking water intake rates per body weight were calculated 

for each of the modeled groups (male/female: ages 0-17, 18-44, 45-64, 65+)50 using the weighted 

average approach presented in Equation 5.7. The final drinking water exposure factors are 

presented in Exhibit 5.9. Since body weight was already incorporated into the intake rates and 

sex-specific intake rates were not presented, the drinking water exposure factors are equivalent 

for males and females. More detail on the derivation of exposure factors can be found in 

Technical Appendix C. 

Exhibit 5.9 

Drinking Water Exposure Factors 

Exposure 

Factors (Male) 

Exposure Factors 

(Female) 

Model Age Group (L/kg-day) 

0-17 0.0298 0.0298 

18-44 0.0184 0.0184 

45-64 0.0220 0.0220 

$65 0.0219 0.0219 

Fish Ingestion. RSEI uses annual estimates of the amount of fish ingested by recreational and 

subsistence fishers and their families. However, there are no national data on fish ingestion rates 

specific to recreational and subsistence fishers. In the absence of such data, RSEI uses fish 

 

50 The RSEI model provides the ability to view risk scores for the 0-9 year old age group, however, there is not a 

separate exposure factor for this age group. Instead, the exposure factor for the 0-17 year old age group is used 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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ingestion rates from the 1994-1996 USDA Continuing Survey of Food Intake by Individuals 

(CSFII). This survey was conducted by the U.S. Department of Agriculture in 50 states and the 

District of Columbia over a three-year period. A total of 15,303 individuals provided two nonconsecutive days of data on dietary intake. Appropriate statistical techniques were used to 

extrapolate to the national population. In a 2000 publication, EPA assigned specific fish species 

to habitats (freshwater, estuarine, and marine) based on the majority of time the species spend in 

those habitats (EPA, 2000). Based on these assignments, EPA estimated a distribution of 

uncooked finfish and shellfish ingestion rates specific to freshwater and estuarine fish.51 As 

recommended by EPA’s Office of Water (Tudor et al., 2000), for environmental assessments the 

90th percentile is used to represent ingestion rates for recreational fishers, and the 99th percentile 

is used for subsistence fishers. The ingestion rates are reported by age group (<15 years, 15-44 

years, 45+ years) and sex (EPA, 2000). These values are roughly similar to ingestion rates 

obtained from regional studies of recreational fishers and subsistence fishers, respectively. Fish 

ingestion values were estimated for the RSEI age groups using Equation 5.7. These values are 

then divided by age- and sex-specific body weights, averaged to match the RSEI age groups 

using data provided in EFH (EPA, 1997b, Tables 7-2 and 7-3, p. 7-4). Exhibit 5.10 presents the 

fish ingestion rates used in the model. More detail on the derivation of exposure factors can be 

found in Technical Appendix C. 

Exhibit 5.10 

Fish Ingestion Exposure Factors 

 Recreational (g/kg-day)* Subsistence (g/kg-day)1

Model Age Group Male Female Male Female 

0-17 0.0756 0.0372 2.83 2.05 

18-44 0.199 0.114 1.92 1.71 

45-64 0.407 0.262 2.08 1.60 

>65 0.434 0.267 2.22 1.63 

1

 Fish ingestion exposure factors are converted to kg/kg-day for the surrogate dose calculation in the model. 

5.5 Modeling Transfers to POTWs 

In 2007, approximately three percent of TRI releases and transfers were transferred to PubliclyOwned Treatment Works (POTWs). These transfers are mostly of facility wastewater through 

underground sewage pipes to a POTW. Each chemical transfer to a POTW is modeled as 

entering as liquid influent. Depending on the chemical’s physical properties, some portion of the 

chemical release in the influent may be discharged into surface water from the POTW, 

potentially resulting in human exposure through drinking water or fish ingestion. The rest of the 

chemical release may be removed by the POTW through one or more of the following processes: 

1) biodegradation, which is not modeled; 2) volatilization, which is modeled like other area air 

 

51 Consumption of marine fish is not included in the ingestion rates, because marine areas are not modeled in RSEI. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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releases (see Section 5.2.3); or 3) landfilling of sludge, which is not modeled. The following 

sections describe the method and data used to model transfers to POTWs. 

5.5.1 Transfers to POTWs: Method 

Modeling exposure from TRI-reported transfers to POTWs requires: (1) location of the POTW to 

which the chemicals are discharged, (2) location of the reach to which the POTW discharges, 

(3) consideration of overall removal efficiencies of POTWs and resulting effluent discharges 

from POTWs (the chemical-specific removal rate), and (4) consideration of residuals 

management at POTWs (partitioning within the POTW). 

5.5.1.1 Locating the POTW 

In order to model releases from POTWs, these facilities must first be located on the model grid. 

Like other off-site facilities, POTW names and addresses are reported to TRI by the facility 

transferring its waste. Latitude and longitude are not reported. In order to derive lat/long 

coordinates, the reported street addresses were geocoded for Version 2.1.3 (coordinates were 

assigned based on street address).52 POTWs (and incinerators) were also matched to EPA’s 

Facility Registry System (FRS), based on name, address, and RCRA identification number, 

where possible. Duplicate entries for the same POTW (in the common instance where two or 

more reporting facilities have transferred to the same POTW) were collapsed to a single entry 

using an approximate string matching program (see Technical Appendix D for details). Once 

latitude and longitude for a facility are assigned (from geocoding, the FRS data, or based on zip 

code centroids), the data are used to map the facility to the grid cell with the same coordinates. 

Substantial data processing was necessary to prepare the set of off-site facilities for use in the 

model; see Technical Appendix D for details on the steps that were taken. 

5.5.1.2 Locating the POTW Discharge Reach 

As with TRI reporting facilities, the POTW’s discharge reach must be identified. The main data 

source used is EPA’s records of discharge permits for POTWs, which in most cases specify the 

discharge reach by name or reach number. Permit records, including permits, monitoring data, 

and locational and descriptive information pertaining to more than 67,000 regulated facilities 

were previously maintained in EPA’s Permit Compliance System (PCS), but are being 

transferred into the Integrated Compliance Information System-National Pollutant Discharge 

Elimination System (ICIS-NPDES). 

POTWs were matched to the FRS system based on name and address to obtain the FRS ID for 

each POTW. THE FRS ID was then used to access PCS/ICIS-NPDES and the assigned 

discharge reach for each POTW. Approximately 2,361 records were matched to a discharge 

reach using this method. 

 

52 Geocoding services were provided by Thomas Computing Services, a commercial firm. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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POTWs not matched to a PCS/ICIS-NPDES discharge reach were assumed to discharge to the 

nearest reach within four kilometers that meets the minimum flow requirements described above 

in Section 5.4.3.1.. 

5.5.1.3 Overall POTW Removal Rate 

POTWs cannot completely remove all of the chemicals that are transferred to the plant from the 

TRI facility. Some of the chemical loading in the influent will be discharged as effluent to 

surface waters. To calculate the fraction of transferred chemical removed by the POTW, the 

typical chemical-specific removal rate is applied to the volume transferred to the POTW from the 

TRI facility. See Technical Appendix B for a listing of removal rates and references for each 

chemical. The remainder is assumed to exit the POTW in water effluent. This effluent is 

modeled for drinking water and fish ingestion using the same methods for surface water releases 

described above. 

5.5.1.4 Partitioning within the POTW 

Chemical loadings may be removed by the POTW treatment processes through biodegradation, 

volatilization, and adsorption to sludge. The amount of the chemical that is removed by each of 

these processes is modeled using average partitioning rates (see Technical Appendix B for the 

listing of partitioning rates and references for each chemical). 

Once the fates of chemicals entering the POTW are estimated, exposures associated with 

chemical loadings to each compartment are estimated. Chemicals discharged in the POTW 

effluent are modeled using the surface water evaluation methods described above. Chemicals 

that biodegrade are assumed to degrade to chemicals that do not pose risk. POTW volatilization 

releases are treated like area-source air releases, as described earlier. 

For chemicals that partition to sludge, the model used to estimate exposure should ideally depend 

on the sludge disposal method employed by the POTW. However, sludge disposal practices at a 

POTW receiving a TRI transfer cannot be determined from the TRI database. Therefore, the 

model algorithm currently assumes all POTW sludge is landfilled at the POTW, a common 

method of sludge disposal. POTWs may in reality use other methods of sludge disposal, such as 

incineration of sludge. If sludge were incinerated by a POTW, for example, this would result in 

different exposure levels (and a different, larger exposed population). RSEI does not currently 

model land releases. A summary of the approach to modeling POTW emissions is found in 

Exhibit 5.11. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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Exhibit 5.11 

POTW Modeling Approach 

POTW residual (fate determined by partitioning rate)

Biodegradation Volatilization

POTW partitioning removal rate

Transfer to POTW

(lbs/yr)

Water Modeling

(see Exhibit 5.7)

POTW effluent

Air Modeling

(see Exhibit 5.3) No risk assumed Currently not modeled

Sludge - assumed

deposition in POTW

on-site landfill
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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5.5.1.5 Estimating Population Size for POTW Transfers 

The population exposed to air releases is assumed to be the population within 49 km around the 

POTW. The method used to estimate the population surrounding the POTW that is exposed to 

surface water effluent discharges up to 200 km downstream from these facilities is described in 

the section on exposed populations for surface water releases (see Sections 5.4.1.2 and 5.4.1.4.) 

5.5.2 Transfers to POTWs: Data 

Exhibit 5.12 presents data used to estimate exposure from releases to POTWs. In addition to the 

parameter data presented here, data from the air release pathway (see Exhibit 5.3), and water 

release pathway (see Exhibit 5.7) are also used. Environmental fate and transport data and 

exposure factors specific to these pathways are described in the relevant sections above. 

Exhibit 5.12 

Data Used to Estimate Exposure from Releases to POTWs 

Parameter Value Source/Comment

Removal efficiencies chemical-specific RREL or STPWIN 

(SRC, 1994-99) 

Partitioning within the 

POTW 

chemical-specific RREL or STPWIN 

(SRC, 1994-99) 

5.5.2.1 POTW Removal Rate Efficiency and Within-POTW Partitioning 

Data specific to this pathway include POTW removal efficiencies and within-POTW partitioning 

rates. These parameters describe the fate of chemicals during treatment at POTWs. The “POTW 

Partition Removal” is the total POTW removal efficiency, or the total percentage of the chemical 

removed by the POTW (influent minus effluent). The within-POTW partition values describe the 

fate of that portion of the chemical removed, that is, whether the chemical may sorb to sludge 

(POTW Partition Sludge), volatilize into the air (POTW Partition Volatile) or be biodegraded by 

microorganisms (POTW Partition Biodeg). The within-POTW partitioning values are expressed 

as percentages of the total POTW removal efficiency; that is, they sum to 100 percent. 

POTW removal efficiencies were obtained from the Treatability Database maintained by EPA’s 

Risk Reduction Engineering Laboratory (RREL). For any given chemical, the RREL 

Treatability Database provides a list of removal efficiencies published in the scientific literature. 

 Each value is characterized by the technology used, the type of influent, and the scale of the 

experiment. For all values associated with activated sediment and full scale experiments, a 

geometric mean was derived and used as the POTW removal efficiency. Within-POTW 

partitioning values were obtained from two sources. For most organic chemicals, values were 

supplied by the EPA’s Exposure Assessment Branch within OPPT. Inorganic chemicals, except 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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for ammonia, were assumed to partition 100% to sludge. For chemicals without data from these 

sources, SRC’s Sewage Treatment Plant Fugacity Model (STPWIN) was used to estimate total 

removal efficiency and within-POTW partitioning values. 

5.6 Modeling Other Off-site Transfers 

In 2007, approximately 48 percent of TRI emissions were transferred to off-site locations other 

than POTWs for storage or disposal. TRI reporters are required to supply the name and address 

of the receiving facility, and the treatment method used. Currently, only transfers that are 

incinerated at the off-site facility are modeled; waste transfers treated by other methods do not 

receive risk-related scores. 

5.6.1 Off-site Transfers: Method 

To assess the exposure potential associated with off-site transfers, it is important to have 

information about the off-site facility location and some of its characteristics. Locations of other 

off-site facilities are determined in the same way as the locations of POTWs. The reported street 

addresses were geocoded for Version 2.1.3 (coordinates were assigned based on street address).53

 Incinerators (and POTWs) were also matched to EPA’s Facility Registry System (FRS), based 

on name, address, and RCRA identification number, where possible. Duplicate entries for the 

same off-site facility (in the common instance where two or more reporting facilities have 

transferred to the same off-site facility) were collapsed to a single entry using an approximate 

string matching program (see Technical Appendix D for details). Once latitude and longitude for 

a facility are assigned (from geocoding, the FRS data, or based on zip code centroids), the data 

are used to map the facility to the grid cell with the same coordinates. See Technical Appendix 

D for detailed information on locating off-site facilities. 

The TRI forms require the reporting facility to indicate the treatment/disposal method used at the 

off-site facility. If this information is not reported (despite the requirement), the transfer is not 

evaluated in the algorithm, but is flagged as a missing value and assigned a zero. 

If the treatment method is incineration, then destruction and removal efficiencies (DREs) are 

applied to the transfer amount. Once DREs have been applied, the releases are modeled using 

the AERMOD-based air modeling algorithms described earlier. 

For off-site landfills, there are two exposure pathways, groundwater and volatilization. 

However, as land releases are not currently modeled, there are no risk-related results available 

for either pathway. Off-site transfers to underground injection wells are also not modeled. 

Exhibit 5.13 summarizes the modeling of off-site transfers. 

 

53 Geocoding services were provided by Thomas Computing Services, a commercial firm. 
Chapter 5: Exposure and Population Modeling 

Version 23.0 1988-2007 TRI data 

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5.6.2 Estimating Population for Off-Site Transfers 

Similar to on-site air releases, the population exposed to air releases from off-site transfers is the 

population within 49 km around the off-site incinerator. 

Exhibit 5.13 

Modeling Approach for Off-site Transfers 

Transfer to non-POTW Off-site Facility

(lbs/yr)

Transfer to waste facility

Destruction and

removal efficiencies

Treatment method as

reported on TRI Form R

Air Modeling

(see Exhibit 5.3)

Currently not

modeled

Currently not

modeled

Air release estimate Volatilization Rate

Incineration Landfilling

Potential

groundwater

contamination

Chemical

partitioning data;

wind speed data
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5.6.3 Off-site Transfers: Data 

5.6.3.1 Destruction and Removal Efficiencies 

For organics, the destruction and removal efficiency (DRE) is assumed to be 99 percent (see 

Technical Appendix B). The exception to the 99 percent removal assumption is PCBs, which are 

assumed to have a DRE of 99.9999 percent, as required by TSCA regulation. For inorganics, 

values are taken from multiple hearth sludge incinerator studies (EPA, 1992a). 

5.7 Modeling On-site Land Releases 

In 2007, approximately 23 percent of TRI emissions were released as on-site land releases. Onsite land releases include releases to landfills, surface impoundments, land treatment units, and 

underground injection wells. For these releases, two major exposure pathways are of interest–

volatilization to air and leaching into groundwater. Volatilization of chemicals from on-site land 

releases is reported to TRI under the fugitive emission estimate for the facility, and is modeled 

by RSEI as part of the facility’s fugitive air release. For more information on RSEI modeling of 

fugitive air releases, see Section 5.3 above. EPA is evaluating screening-level methodologies 

which might be used to assess risk-related exposures pertaining to groundwater exposure from 

on- and off-site land releases and volatilization from off-site land releases, so this version of 

RSEI does not provide risk modeling for reported land releases. However, RSEI does provide 

the capability for users to examine the pounds of releases to land that are reported to TRI, as well 

as viewing these releases from a hazard-based perspective. 

The potential for groundwater contamination from land releases depends on the regulatory status 

of the unit in which the chemical is released. For example, chemicals could be deposited in an 

on-site RCRA-regulated, subtitle C hazardous waste unit, or in an on-site nonhazardous solid 

waste management unit. RCRA standards for hazardous waste units are, by regulation, designed 

to include technical controls to prevent release of contaminants into groundwater. If chemicals 

are placed in such regulated units, EPA assumes that releases to groundwater are negligible so 

RSEI assigns a zero value to the risk-related scores for such releases. If chemicals are placed in 

nonhazardous land disposal units (landfills, etc.), there is a potential for exposure. This exposure 

pathway and volatilization from off-site landfills are currently under review for inclusion in a 

future version of RSEI. 

On- and off-site land releases to underground injection will not be modeled for exposure by 

RSEI. The hydrogeological, spatial, and temporal considerations that are associated with 

exposures to toxic chemicals in underground injection wells are situation- and site-specific, so 

RSEI is only able to provide pounds-based and hazard-based perspectives for this type of land 

release. Note, however, that under well-managed conditions, Class I wells (there are five classes 

of wells) are specifically designed to pose minimal risk to human health or the environment. 
Chapter 6: Calculating Results 

Version 23.0 1988-2007 TRI data 

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6. Calculating Results 

This section summarizes the computation of the principal types of RSEI results. Because of the 

multi-functional nature of the model, a wide variety of results can be created. All of the RSEI 

functionality is based upon the Indicator Element, which is a unique combination of chemical, 

facility, release and exposure pathway, and year. Each Indicator Element has a set of associated 

results: 

Exhibit 6.1 

Description of RSEI Results 

Risk-related results Surrogate Dose x Toxicity Weight x Population 

Hazard-based results Pounds x Toxicity Weight 

Pounds-based results TRI Pounds released 

Risk-related results. The surrogate dose, toxicity, and population components are multiplied to 

obtain a risk-related score for the Indicator Element. The surrogate dose is determined through 

pathway-specific modeling of the fate and transport of the chemical through the environment, 

combined with subpopulation-specific exposure factors. Risk-related scores are unitless, and 

each of the components (toxicity weight, surrogate dose, and population) when multiplied 

provide scores that are relevant only when compared to each other. The unitless Indicator 

Elements are not a physically meaningful measure of quantitative risk associated with the 

facility, but are approximate measures of relative risk-related impacts that are comparable to 

approximate measures for other facilities (or other chemicals, pathways, etc.) calculated using 

the same methods within RSEI. If the Indicator Element cannot be modeled, because of a lack of 

data required for modeling, or because the pathway is not currently modeled, then the riskrelated score is zero. The model calculates risk-related results for the entire population and also 

for the following subpopulations: children under 10, children aged 10 to 17, males aged 18 to 44, 

females aged 18 to 44, and adults aged 65 and older. In addition the model also calculates 

“Modeled Pounds,” which is simply the number of pounds that can be modeled, before fate and 

transport modeling and exposure assumptions have been applied. 

Higher component “weights” are associated with higher relative risk-related values (and lower 

weights are associated with lower relative risks). For chemicals with cancer effects, multiplying 

the weights associated with cancer toxicity and exposure to the chemical seems intuitive, since 

this is similar to the calculation of cancer risk with a slope factor or unit risk value and dose or 

exposure level. For chemicals with noncancer effects, the multiplicative nature of the toxicity 

and exposure weights may not seem intuitive, because in risk assessments, risk is usually 

characterized as the estimated exposure divided by the RfD/RfC. However, because of the 

manner in which the toxicity weights have been constructed, the product of toxicity weight and 

surrogate dose varies in the same direction and degree as the ratio of exposure to RfD/RfC. This 

is because the toxicity weight is inversely related to the magnitude of the RfD/RfC. Thus, for a 

given exposure level, a chemical with a more stringent (i.e., lower) RfD will receive a higher 
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Indicator Element value than a chemical with a less stringent (i.e., higher) RfD, as shown in the 

following example: 

RfD (mg/kgday) Toxicity 

Weight 

Surrogate dose 

(mg/kg-day) 

Exposure 

(i.e., surrogate 

dose) /RfD 

Ratio 

Toxicity 

Weight * 

Surrogate 

Dose 

Scenario 1 0.1 5 1 1/0.1 = 10 5*1 = 5 

Scenario 2 0.01 50 1 1/0.01 = 100 50*1 =50 

Since no adverse effects are expected to occur below the RfD, one could argue that releases that 

result in surrogate doses below the RfD should be excluded. However, this approach was not 

pursued for the following reasons: first, the estimation of surrogate dose is only a screening-level 

approximation for the purposes of comparing one release to another in a relative way, and should 

never be considered an actual calculation of exposure. To exclude releases resulting in surrogate 

doses below the RfD would incorrectly imply that the method could predict precisely when doses 

would occur below the RfD. Second, exposure to the same chemical from multiple facilities, or 

multiple chemicals from one or more facilities affecting the same health endpoint could act 

additively to pose risk, even if each release individually did not exceed the RfD. Finally, if the 

surrogate dose is low, this will be reflected by a correspondingly low score relative to other 

releases for that chemical. 

Hazard-based results. Each Indicator Element also is associated with a hazard-based result 

(“Hazard”), calculated by multiplying the pounds released by the appropriate chemical-specific 

toxicity weight (the toxicity weight also depends on the exposure pathway). The inhalation 

toxicity weight is used for releases or transfers to fugitive air, stack air, off-site incineration, and 

off-site incineration-no fuel value. The oral toxicity weight is used for releases or transfers to 

direct water and POTWs. For releases that are not modeled (because the pathway is not modeled 

or because other necessary data, such as physicochemical properties, are lacking), the higher 

toxicity weight is used. For these results, no exposure modeling or population estimates are 

involved. If there is no toxicity weight available for the chemical, then the hazard score is zero. 

The model also calculates “Modeled Hazard,” which is the chemical- and pathway-specific 

toxicity weights multiplied by the Modeled Pounds (as described above), and “Modeled Hazard 

* Pop,” which multiplies modeled hazard by the potentially exposed population, but without the 

fate and transport modeling (and application of exposure assumptions) that would be found in 

risk-related results. 

Pounds-based results. These results (“TRI Pounds”) reflect only the number of pounds released 

or transferred that are reported to TRI, and are available for virtually all Indicator Elements. 

The model also provides “TRI Pounds with Toxicity Weights,” which simply sums the pounds 

for chemicals that have toxicity weights in RSEI. 
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6.1 Combining Indicator Elements 

Once results are calculated for each Indicator Element, they can be combined in many different 

ways. All of the results are additive, so a result for a specific set of variables is calculated by 

summing all the relevant individual Indicator Element results, as follows: 

= ∑∑∑ c , f ,p R IE (Eq. 6.1) 

where: 

R = RSEI result, and 

IEc,f,p = chemical-facility-pathway-specific Indicator Element result. 

This method is very flexible, allowing for countless variation in the creation of results. For 

example, results can be calculated for various subsets of variables (e.g., chemical, facility, 

pathway) and compared to each other to assess the relative contribution of each subset to the 

total potential impact. Or, results for the same subset of variables for different years can be 

calculated, to assess the general trend in pounds-based, hazard-based, or risk-related impact over 

time. 

It must be reiterated that while changes in results over the years would imply that there have 

been changes in hazard- or risk-related environmental impacts, the actual magnitude of any 

specific change or the reason may not be obvious. Although the value itself may be useful in 

identifying facilities or chemicals with the highest potential for hazard or risk, the score does not 

represent a quantitative estimate or provide an exact indication of the magnitude of individual 

hazard or risk associated with that facility or chemical. 

6.2 Accounting for Changes in TRI Reporting 

When a change occurs in the number of, or reporting requirements for, chemicals and facilities 

represented in TRI, the numerical value of RSEI results will be altered if no adjustments are 

made to the method of calculation to account for the changes respective to trend analyses. 

However, such changes would not necessarily represent a large change in actual environmental 

impact, but would reflect a broader understanding of the impacts that may have always existed. 

A change in the number of chemicals and facilities in the TRI can occur through several 

mechanisms. First, the addition to or deletion of individual chemicals from the TRI chemical list 

will occur as EPA responds to petitions or initiates its own action through the chemical listing or 

delisting process. Several additions and deletions to the data set have already occurred since 

1987 (the first year of TRI reporting). Furthermore, the Agency added 245 chemicals and 

chemical categories to the TRI chemical list in a single year, effective in 1995. The deletion of 

chemicals would presumably have a minor effect since such chemicals would be deleted due to 

their low hazard. Delisted chemicals are removed from RSEI. To account for changes in the 

representation of chemicals in the TRI database, RSEI uses a special identifier called “Core 

Chemical” which denotes chemicals that have been listed on the TRI since the first year (1987) 

of reporting and which have had no change in their reporting requirements. Another identifier, 
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“Mini Core” denotes chemicals that have been listed on the TRI since 1995 and which have had 

no change in their reporting requirements. These identifiers allow users to conduct separate 

analyses for the “Core” or “Mini Core” chemicals, and so exclude chemicals whose emissions 

changes over time are caused by reporting requirement changes. Similarly, the 245 chemicals 

added for the 1995 reporting year can also be analyzed separately. 

Facility-level changes can also affect year-to-year scores generated using RSEI. For instance, 

compliance with TRI reporting has improved over time, which has led to more facilities 

reporting. Increases in the number of reporting facilities may occur as a result of changes in 

reporting requirements. For instance, in the first two years of reporting, facilities that 

manufactured or processed more than 50,000 pounds were required to report their releases. 

However, EPCRA lowered this threshold to 25,000 pounds in 1989. For reporting year 2000, 

thresholds and other reporting requirements for 18 Persistent Bioaccumulative Chemicals (PBTs) 

have been changed. These modifications can act to alter the total emissions reported to the TRI 

and the model’s estimates of associated hazard- and relative risk-based impacts. Also, effective 

in the 1998 reporting year, certain SIC codes were added to TRI, adding to the universe of 

reporting facilities.54 To assist users in separating out the effects of the 1998 expansion, RSEI 

allows for the exclusion of facilities in the newly-required SIC codes when doing trend analyses. 

The yearly TRI reporting data for a given list of chemicals and facilities are the subject of 

ongoing quality control review and revision. As a result, yearly comparisons could be flawed if 

ongoing revisions by individual facilities were not included in each year’s results. Therefore, the 

Indicator Elements are re-computed for all years in the database on an annual basis in order to 

incorporate revisions to the reporting data. This annual calculation is based on the corrections 

incorporated in annual Public Data Releases available from EPA’s TRI program. 

 

54 This facility expansion rule required the affected facilities to report their releases for the 1998 reporting year. The 

added SIC codes are: codes 10 (except 1011, 1081, and 1094), 12 (except 1241), industry codes 4911, 4931 and 

4939 (limited to facilities that combust coal and/or oil for the purpose of generating power for distribution in 

commerce), 4953 (limited to facilities regulated under RCRA), 5169, 5171, and 7389 (limited to facilities engaged 

primarily in solvent recovery services on a contract or fee basis) ( EPA 1997a). 
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7. Current Implementation of the RSEI Method 

7.1 RSEI Model 

The RSEI model is currently implemented in a Microsoft Windows-based computer program. 

The program allows users to calculate RSEI results for reporting years 1996-2007 and to present 

the results in various GIS, graphical, and tabular formats, as well as to save selected data to 

spreadsheet and database formats (e.g., Microsoft Excel and databases such as Access). The 

program includes on-line help for all of the program functions, as well as User’s Manual in 

Adobe Acrobat format. 

Users of the model can perform, usually in a matter of minutes, a variety of screening-level 

analyses. Previously, such activities would have taken days, weeks, or even months to organize 

the relevant information, evaluate that information, and perform the complex and sophisticated 

analyses that are necessary to provide a risk-related perspective. Results can be used for 

screening-level ranking and prioritization for strategic planning purposes, risk-related targeting, 

and trends analyses. Considerable resources can be saved by conducting preliminary analyses 

with the model to identify risk-related situations of high potential concern, and which warrant 

further evaluation. 

As noted above, users can evaluate releases using a number of variables, such as chemical, 

medium, geographic area or industry. For instance, the following types of questions can be 

investigated: 

• How do industry sectors compare to one another from a risk-related perspective? 

• What is the relative contribution of chemicals within a given industry sector? 

• What release pathway for a particular chemical poses the greatest risk-related 

impacts? 

Users can view various pounds- and hazard-based results to investigate the relative influence of 

toxicity and population components on the risk-related results. However, only the risk-related 

results incorporate exposure modeling. 

Users should note that, as implemented for the personal computer, RSEI employs a facility-based 

approach. All modeled impacts are attributed to the facility originally releasing or transferring 

the chemical. For instance, an air release from an off-site incinerator is modeled as exposing the 

population around the off-site facility, but the results (pounds, hazard, score, etc.) are attributed 

to the reporting facility that transferred the chemical to the off-site incinerator. Similarly, while 

impacts may extend beyond geographic boundaries such as zip code, county, or state, the results 

are attributed to the geographic entity in which the facility is located. EPA employs the RSEI 

methodology to create other databases which are geographic-based. These very large datasets 

are operated outside of the user-friendly interface provided by the RSEI model. 

RSEI Version 2.3.0 is available without charge from EPA on an installation disk (EPA No. 740-

F-10-001, June 2010, or it can be downloaded from the RSEI web site at 
Chapter 7: Current Implementation of the RSEI Method 

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http://www.epa.gov/oppt/rsei/pubs/get_rsei.html. The installed RSEI model requires 

approximately 2 GB of hard disk space. It is designed for operation using a 32-bit operating 

system (Windows 95, 98, 2000, XP, and NT4). The program is written using Borland’s Delphi 

(the software is coded in Object Pascal) and uses the Paradox file format. RSEI Version 2.3.0 

contains data for TRI reporting years 1996-2007; data for TRI reporting years 1988-1995 are 

available upon request. 

Information regarding the RSEI project is available on the RSEI web site.55

7.2 Conclusion 

As an indication of improvements in environmental quality over time, RSEI provides EPA and 

the public with a valuable tool to measure general trends based upon the relative risk-related 

impacts of TRI chemicals. Although RSEI results do not capture all environmental releases of 

concern, they generally relate changes in releases to relative changes in chronic human health 

impacts from a large number of toxic chemicals of concern to the Agency. Importantly, RSEI 

provides an ability to analyze the relative contribution of chemicals and industrial sectors to 

human health impacts, and RSEI results serve as an analytical basis for setting priorities for 

pollution prevention, regulatory initiatives, enforcement targeting and chemical testing 

requirements. 

 

55 The RSEI website is available at www.epa.gov/oppt/rsei. 
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8. References 

General References 

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Canadian Ministry of National Health and Welfare. 1981. Tapwater Consumption in Canada. 

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Chow, V.T., Open-channel Hydraulics, McGraw-Hill, New York, 1959. 

Dourson, M. 1993. Environmental Criteria and Assessment Office, U.S. Environmental 

Protection Agency. Personal communication, October 19. 

Ershow, A.G. and K.P. Cantor. 1989. Total Water and Tapwater Intake in the United States: 

Population-Based Estimates of Quantities and Sources. Life Sciences Research Office, 

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Harrigan, P. 2000. Office of Water, U.S. Environmental Protection Agency (EPA). Personal 

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Horn, Marilee. 2008. U.S. Geological Survey (USGS). Written communication and provision 

of data via email. April. 

Jobson, H. E., Prediction of Traveltime and Longitudinal Dispersion in Rivers and Streams, U.S. 

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Geological Survey.

Lyman, W. J., W. F. Reehl, and D. H. Rosenblatt. 1990. Handbook of Chemical Property 

Estimation Methods. American Chemical Society. Washington, D.C. 

Syracuse Research Corporation (SRC). 1994-1999. EPIWin v 3.01-EPA. Syracuse, NY. 

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Accessed online at http://esc.syrres.com/efdb.htm (now at 

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Syracuse Research Corporation (SRC). 2002b. PhysProp Data Base. Accessed online at 

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Tudor, L., H. Jacobs, and P. Harrigan. 2000. Memorandum to EAD, SASD, and HECD. 

Subject: Fish Consumption Rates to Use in Environmental Assessments/Benefits 

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U.S. Census Bureau. 1996. TABLE 1. Projections of Households by Type: 1995 to 2010, Series 

1, 2, and 3. Available at: http://www.census.gov/prod/1/pop/p25-1129.pdf. May. 

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Number 85-527-0100-2140. August. 

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Assessment. 51 Federal Register 33992 (September 24, 1986). 

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U.S. Environmental Protection Agency (EPA). 1987. Graphical Exposure Modeling System 

(GEMS) User’s Guide. Prepared for the Office of Pesticides and Toxic Substances, 

Exposure Evaluation Division by General Sciences Corporation under Contract No. 

68023970. February. 

U.S. Environmental Protection Agency (EPA). 1988a. IRIS Background Document #1. 

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U.S. Environmental Protection Agency (EPA). 1988b. Report to Congress: Solid Waste 

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Health and Environmental Assessment. EPA/600/8-89/043. March. 

U.S. Environmental Protection Agency (EPA). 1990b. Hazard Ranking System: Final Rule. 55 

Federal Register 241. pp. 51532-51667. 

U.S. Environmental Protection Agency (EPA) 1990c. Science Advisory Board, Relative Risk 

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U.S. Environmental Protection Agency (EPA). 1991a. Toxics in the Community. National and 

Local Perspectives. The Office of Pesticides and Toxic Substances. EPA 560/4-91-014. 

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Risk Assessment. 56 Federal Register 63798 (December 5, 1991). 

U.S. Environmental Protection Agency (EPA). 1992a. Human Health Risk Assessment for the 

Use and Disposal of Sewage Sludge: Benefits of Regulation. Prepared for the Office of 

Water. January. 

U.S. Environmental Protection Agency (EPA). 1992b. User’s Guide for the Industrial Source 

Complex (ISC2) Dispersion Models. Volume 2. Description of Model Algorithms. 

Prepared for the Office of Air Quality, Planning and Standards, Technical Support 

Division. March. 

U.S. Environmental Protection Agency (EPA). 1993b. User’s Guide to the Building Profile 

Input Program. Office of the Quality Planning and Standards. October. 

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EPA-453/R-99-001. United States Environmental Protection Agency, Office of Air 

Quality Planning and Standards. March. 

U.S. Environmental Protection Agency (EPA). 1999b. Enhanced Reach File Version 1.2 

Configured for ARC/INFO. http://water.usgs.gov/GIS/metadata/usgswrd/XML/erf1.xml. 

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Sectors. 62 Federal Register 84, pp.23833-23892. 

U.S. Environmental Protection Agency (EPA). 1997b. Exposure Factors Handbook. Office of 

Health and Environmental Assessment. Volume 1. EPA/600/P-95/002Fa. 

U.S. Environmental Protection Agency (EPA). 2000. Estimated Per Capita Fish Consumption 

in the United States: Based on Data Collected by the United States Department of 

Agriculture’s 1994-1996 Continuing Survey of Food Intake by Individuals. Office of 

Water, Office of Science and Technology. March. 

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Formulation. Office of Air Quality, Planning and Standards. September. EPA-454/R03-004. Accessed online at 

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U.S. Environmental Protection Agency (EPA). 2004b. User's Guide for the AMS/EPA 

Regulatory Model – AERMOD. Office of Air Quality, Planning and Standards. 

September. EPA-454/B-03-001. Accessed online at 

http://www.epa.gov/scram001/7thconf/aermod/aermod_userguide.zip 
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U.S. Environmental Protection Agency (EPA). 2005. Guidelines for Carcinogen Risk 

Assessment. Risk Assessment Forum. March. EPA/630/P-03/001B. Accessed online at 

http://www.epa.gov/IRIS/cancer032505.pdf. 

U.S. Environmental Protection Agency (EPA). 2007. The HEM-3 User’s Guide. HEM-3 

Human Exposure Model Version 1.1.0 (AERMOD version). Draft. January. Available 

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RSEI supporting documentation released by EPA 

C these documents can be found on the RSEI website (under the Documents and Documents 

Archive headings) at http://www.epa.gov/oppt/rsei/documents.html. 

User’s Manual for RSEI Version 2.3.0. June 2010. 

RSEI Technical Appendices: 

Technical Appendix A - Listing of All Toxicity Weights for TRI Chemicals and 

Chemical Categories 

Technical Appendix B - Physicochemical Properties for TRI Chemicals and 

Chemical Categories 

Technical Appendix C - Derivation of Model Exposure Parameters 

Technical Appendix D - Locational Data for TRI Reporting Facilities and Off-site 

Facilities 

Technical Appendix E - Derivation of Stack Parameter Data 

Technical Appendix F - Summary of Differences Between RSEI Data and the TRI 

Public Data Release 

TRI Relative Risk-based Environmental Indicators: Summary of Comments Received on the 

Draft 1992 Methodology and Responses to Comments. Prepared for the Office of 

Pollution Prevention and Toxics, Economics, Exposure and Technology Division, 

Regulatory Impacts Branch. May 1997. Prepared by Abt Associates under Contract # 68-

D2-0175. 

TRI Relative Risk-based Environmental Indicators: Methodology. Prepared for the Office of 

Pollution Prevention and Toxics, Economics, Exposure and Technology Division, 

Regulatory Impacts Branch. June 1997. Prepared by Abt Associates under Contract # 

68-D2-0175. 

TRI Relative Risk-based Environmental Indicators: Interim Toxicity Weighting Summary 

Document. Prepared for the Office of Pollution Prevention and Toxics, Economics, 

Exposure and Technology Division, Regulatory Impacts Branch. 1997. Prepared by Abt 

Associates under Contract # 68-D2-0175. 
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Ground-Truthing of the Air Pathway Component of OPPT’s Risk-Screening Environmental 

Indicators Model. December 1998. 

Estimates of Stack Heights and Exit Gas Velocities for TRI Facilities in OPPT’s Risk-Screening 

Environmental Indicators Model. June 1999.