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 2.3.0Table of Contents

Version 2.3.0 1988-2007 TRI data June 2010 i 

## Acknowledgments

Many people have contributed to th e 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 it s 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 Associ ates Inc., 55 Wheeler Street, Cambridge, Massachusetts.Table of Contents

Version 2.3.0 1988-2007 TRI data June 2010 ii 

### 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 Hea lth 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. Introduc tion....................................................................................................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 Li mitations 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 We ights ...................................................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 Chroni c 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 EP CRA Section 313 Criteria... 33



### 5. Exposure and Popula tion Mode ling ..............................................................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 ............................................................... 42Table of Contents

Version 2.3.0 1988-2007 TRI data June 2010 iii 5.3.2 Fugitive Air Releases: Method ............................................................. 46 5.3.3 Calculating Surrogate Do se 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 Ai r 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......................................................................................................80Executive Summary

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## Executive Summary

ES-1. Introduction

EPA’s Risk-Screening Environmental Indicators (R SEI) is a screening-level tool that assesses the potential impact of industrial chemical rel eases from pounds-based, hazard-based, and riskrelated perspectives. RSEI uses risk concepts to quickly an d easily screen large amounts of Toxics Release Inventory (TRI) data, saving time a nd resources. RSEI is particularly useful for examining trends to measure change, ranking and prioritizing chemicals a nd industry sectors for strategic planning, conduc ting risk-related target ing, 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 ar e 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 considera tions, but does not address all of the potential factors that a full risk-assessment would include.

RSEI considers the following information: the am ount 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. Th is information is used to create numerical values that can be added and compared in limitle ss 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 othe r values produced by RSEI. It should be emphasized that the result is not a detailed or quantit ative risk assessment, but offers a screeninglevel, risk-related perspective for rela tive 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 chem icals; 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 environmenta l quality over time, RSEI provides a valuable tool to measure general trends based upon rela tive risk-related impacts of TRI chemicals. Although RSEI results do not capture all environmen tal releases of concern, they generally relate changes in releases to relative changes in chro nic human health impacts from a large number of toxic chemicals of concern to the Agency. Impo rtantly, RSEI provides an ability to analyze the relative contribution of chemical s and industrial sector s to human health impacts, and RSEI results serve as an analytical basis for setti ng priorities for further risk analysis, pollution prevention, regulatory initiatives, enforcement ta rgeting, and chemical testing requirements.Executive Summary

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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 me asures 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 th e relative contribution of variables in a comparative analysis).

The model uses the reported quantit ies of TRI releases and transfer s 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 imp acts potentially posed by a chemical are a function of chemical toxicity, the fate and tran sport of the chemical in the environm ent after it is released, the pathway of human exposure, and the number of people exposed. 1

The RSEI model starts with TR I releases. For each exposure pa thway associated with each chemical release, the model generates an “Indicat or Element”. For instance, a release of the chemical benzene to air via a stack from the “A BC” Facility in 1999 is an “Indicator Element”. Each Indicator Element is associated with a se t of results, including pounds-based, hazard-based, and risk-related resu lts, also called scores. The risk-rela ted 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 resu lts can be aggregated for each year to allow an assessment of trends in estimated impacts, or re sults can be grouped to allow users to compare results for facilities, regions, chemicals, and a ny 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 re lative comparisons of chemical releases. The model does not estimate actual risk to individua ls. RSEI results are only meaningful when compared to other results produced by RSEI.

The current version of the mode l calculates risk-related result s for the air and surface water pathways only. For other pathways, and in in stances 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-bas ed results can be viewed.

ES-2.1 Geographic B asis of the Model

The model relies on the ability to locate facilities and people ge ographically, and to attribute characteristics of the physical environment, such as meteorology, to areas surrounding the facilities once they are located. To locate the fac ilities and the attribute data to those facilities, the model describes the U.S. and its territories 2 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 MarianaExecutive Summary

Version 2.3.0 1988-2007 TRI data June 2010 3 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 mode l grid. TRI facilities are locat ed using the facilities’ lat/long coordinates. To locate population, the model us es U.S. Decennial Census data for 1990 and 2000 at the block level. These data 3 are used to create detail ed age-sex-defined population groups for each of the census blocks in th e 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 ch anged 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 lo cated 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 re leased or transferred,

• Adjustments for chronic hu man health toxicity, and

• Adjustments for exposure pot ential 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 requir ed data were available at the block level. For the U.S. Virgin Islands and the territories, data from larger geograp hic 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 throug h 17, Males 18 through 44, Females 18 through 44, and Adults 65 years and Older.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 4 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 f acilities (such as sewage treatment plants and incinerators) to model risk-related impacts. Thes e releases are reported by facilities to the TRI, as mandated by the Emergency Planning and Comm unity Right-to-Know Act. As of the 2007 reporting year, there are 611 TRI chemicals and ch emical categories liste d. Users can view pounds of chemicals released per year (pounds-based re sults) 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 po ssible. RSEI reflects the toxicities of chemicals rela tive to one another using a co ntinuous system of numerical weights. Toxicity weights for chemicals increase as the toxicological pot ential to cause chronic human health effects increases. Toxicity-adjuste d releases are called “hazard-based results” and provide an alternative pe rspective 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 diffe rentiate the degrees and types of toxicity of chemicals, and rank them in a consistent ma nner. Values called Oral Slope Factors and Inhalation Unit Risks 5 provide information pertaining to toxicity for chemicals that may cause cancer. Reference Doses (RfDs) and Referen ce Concentrations (RfC s) 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 Ca rcinogenic 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 Hea lth 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/m 3 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 popul ation (including sensitive subgroups) that is likely to be without an appreciable risk of deleteri ous noncancer effects during a lifetime.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 5 • EPA’s Provisional Peer Reviewed Toxici ty Values (PPRTVs), which include toxicity values that have been deve loped by EPA’s Office of Research and Development/National Center for Envi ronmental 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 T oxics (Derived).

The toxicity scoring method separately evalua tes exposure routes (i nhalation and oral) and classes of effects (cancer and non cancer). For each exposure rout e, 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 we ights is assigned as the final weight for that route. The algorithms used to assign toxicity we ights 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/m 3 ) 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/m 3 )/ 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/m 3 )/ (2.8 x 10 -7 * 10)

The distribution of toxicity valu es for TRI chemicals corresponds to a range of toxicity weights of approximately 0.02 to 500,000,000. However, t oxicity weights are not bounded. Continuous toxicity weights are expressed as values with two significant figures.

There are 611 chemicals and chemical categor ies on the 2007 TRI Chemical List. Toxicity weights are available for 433 of these chemical s and chemical categories. Chemicals with toxicity weights account for over 99% of the repo rted 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 es timate 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, s ite 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 sp ecific to age and sex.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 6 The algorithms for calculating surrogate doses rely on the ability to loca te facilities and people geographically on the 81 0 m by 810 m grid cell system descri bed earlier. While this method uses the EPA exposure assessment paradigm to ev aluate exposure potential, the results should not be construed as an actual numerical estimat e of dose resulting from TRI releases, because limited facility-specific data and the use of m odels 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 mode l, 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 we ight, surrogate dose, and population components are multiplied to obtain a risk score fo r the Indicator Element. The surrogate dose is determined through pathway-specific modeling of th e fate and transport of the chemical through the environment, combined with subpopulation-speci fic 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 calculat es risk-related results for the entire population and also for the follo wing subpopulations: chil dren 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 Po unds,” which is simply the number of pounds that can be modeled for risk-r elated scores. Modeled pounds ar e the pounds to which fate and transport modeling and exposure as sumptions 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 wa ter intakes, as well as fish ingestion from recreational fishing and from subsistence fishing).Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 7 Hazard-based results. Each Indicator Element is also as sociated with a hazard-based result (“Hazard”), calculated by multiplying the pounds re leased 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 we ight is used for releases to direct water and transfers POTWs. For releas es that are not modeled (because the pathway is not modeled or because other necessary data, such as physicoc hemical 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,” wh ich is the chemical- and pathway-specific toxicity weights multiplied by the Modeled Poun ds (as described above), and “Modeled Hazard * Pop,” which multiplies modeled hazard by the pot entially 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 re ported to TRI, and are available for vi rtually all Indicator Elements. The model also provides “TRI Pounds with Toxicity Weights,” which simply sums the pounds for chemicals that have toxi city weights in RSEI.

Once results are calculated for each Indicator Elem ent, they can be combined in many different ways. All of the results are additive, so a resu lt for a specific set of variables is calculated by summing all the relevant individual Indi cator Element results, as follows:



## R IE cf p 

## = 

# ∑ ∑ ∑

, , (Eq. ES.1)

where:

R = RSEI result, and IE c,f,p = chemical-facility-pathway-specific Indicator Element result.

This method is very flexible, allowing for coun tless variation in the cr eation 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 genera l trend in pounds-based, hazard-b ased, or risk-related impacts over time.

It must be reiterated that while changes in re sults over the years would imply that there have been changes in hazard- or ri sk-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 chemical s with the highest potential for ha zard 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.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 8 ES-2.3 Adjusting RSEI Results for Changes in TRI Reporting

When a change occurs in the number of, or re porting 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 fo r the changes respective to trend analyses. However, such changes would not necessarily re present a large change in actual environmental impact, but would reflect a broade r understanding of th e 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 chem icals from the TRI chemical list will occur as EPA responds to petitions or initiates its ow n 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 ex isting 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 “cor e” chemicals which have been reported since 1987, and for which reporting requirements have not changed.

Compliance with TRI reporting ha s changed over time, which ha s led to more facilities reporting. Increases in the number of reporting facili ties 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 repo rt 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 re porting year 1998, TRI has enlarged the set of facilities required to report to include electric utilities, mini ng facilities, commercial hazardous waste facilities, solvent recove ry facilities, and wholesale chemical and petroleum terminal facilities. All of these modificat ions can act to alter the total emissions reported on the TRI and the model’s estimate of the associat ed 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 re porting years. Also, th e 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 a nd 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 reporti ng years 1996-2007 (earlier years are available upon request) an d to present the result s in various GIS, graphical, and tabular formats, as well as to save selected data to spreadsheet and database formats (e.g., MicrosoftExecutive Summary

Version 2.3.0 1988-2007 TRI data June 2010 9 Excel and databases such as Access). The progr am includes on-line help for all of the program functions, as well as a User’s Ma nual in Adobe Acrobat format.

Users of the model can perform, usually in a matter of minutes, a variety of screening-level analyses. Previously, such activit ies would have taken days, weeks, or even months to organize the relevant information, evaluate that inform ation, and perform the complex and sophisticated analyses that are necessary to provide a risk-r elated perspective. Results can be used for screening-level ranking and prioritization for strategic pla nning purposes, risk-related targeting, and trends analyses. Consider able resources can be saved by conducting preliminary analyses with the model to identify risk-related situati ons of high potential concern, which warrant further evaluation.

As noted above, users can evaluate releases us ing a number of variable s, such as chemical, medium, geographic area or industry. For inst ance, the following types of questions can be investigated:

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

• What is the relative contri bution 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 popul ation 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 docu mentation, frequently asked questions, and contact information are all posted on the site. Periodic updates and troub leshooting information are also available for users.

ES-2.5 How the RSEI Chronic Human Heal th Toxicity Weightings Differ from EPCRA Section 313 Stat utory Criteria

As described above, the RSEI model uses TRI chem ical 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 tran sfers of TRI chemicals, with the very different and separate activity of listing/delisting ch emicals on the TRI usi ng 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 tran sfers of these chemicals on the ge neral, 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 compar ed 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.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 10 In contrast, the EPCRA statutory criteria used for listing and de listing chemicals consider acute and chronic human toxicity, as well as environmen tal 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 weigh tings 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 provid es a risk-related perspe ctive in assessing the relative impacts of releases of t oxic chemicals. Risk-related result s 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 quantita tive assessment of risk, and is not designed as a substitute for more comprehens ive, site-specific risk assessments. There are a number of important consideratio ns associated with each compone nt of the model, as described in the following sections.

Release Component. The following caveat should be c onsidered regarding the release component of the model:

• RSEI uses facility-report ed 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 re ported 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 Johnstow n, PA, probably overstates the release amount and may be assigned to the wrong media.

Toxicity Component. The following caveats should be co nsidered regarding the toxicity component of the model:

• Toxicity weights are not designed to (and may not) correlate with statutory criteria used for listing a nd 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 addre sses chronic human toxici ty (cancer and noncancer effects, such as developmental toxicit y, reproductive toxicity, neurotoxicity, etc.) associated with long-term exposure and doe s not address concerns for either acute human toxicity or environmental toxicity.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 11 • Toxicity weights are based upon the single, most sensitive chronic human health endpoint for inhalation or or al exposure pathways, and do not reflect severity of effects or multiple health effects.

• Estimated Reference Doses and Referen ce 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 re ported 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 assump tions are made regarding metals and metal compounds, because important data regarding these ch emicals are not subject to TRI reporting. Metals and metal compounds are assumed to have the same toxicity weight, although the chronic toxicity of some me tal compounds may be higher. Metals and metal compounds are assumed to be re leased in the vale nce (or oxidation state) associated with the highest chroni c toxicity. There are two exceptions to this: (1) For chromium and chromium co mpounds, it is assumed that facilities may release some combination of hexavale nt 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; an d (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 assu mption 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 chemical s as acid aerosols are not expected to be typical so the toxicity wei ght for cancer based on the i nhalation of particulates is used). Analysts need to consider these assumptions, and whether the gathering of additional data is warranted, when examin ing 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.htmlExecutive Summary

Version 2.3.0 1988-2007 TRI data June 2010 12 Exposure Component. The following caveats should be co nsidered regarding the exposure component of the model:

• Like other exposure models, RSEI estimat es exposure levels. It does not yield actual exposures. The model provides estimat ed air concentrations in each grid cell.

• The model uses some generic assumptions , e.g., default median stack heights, diameters, and exit gas velo cities related to 2- or 3- digit Standard Industrial Classification (SIC) codes, or a nation wide median, where facility-specific median stack height, diameter, and exit ga s velocity data ar e unavailable. For large facilities with multiple stacks, the me dian 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 fo r population activity patterns.

• The model has greater uncertainty when examining disaggregated results at the local or facility level. The model doe s 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 c onsidered regarding the population component of the model:

• Population values for non-decennial years are estimated based on linear interpolations at the block level betw een 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 individua l downstream drinki ng water intakes. Estimated populations for the fish inge stion 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 increas e or decrease even without changes inits releases over time. While the model is designed to reflect the overall riskrelated impacts on the local populati on, such population changes should be considered when examining a facility’s environmental management practices.Executive Summary

Version 2.3.0 1988-2007 TRI data June 2010 13 

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

• Includes TRI Reporting Years 1988-2007 (dat a for years 1996-2007 are available in the public release; data fo r 1988-1995 are available upon request).

• Toxicity weights have been updated. The equivalence between cancer and noncancer toxicity weights ha s been changed from 2.5 x 10 -4 to 1.0 x 10 -6 . This has the effect of increasing the rela tive 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 wa ter 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 incr eased 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.Chapter 1: Introduction

Version 2.3.0 1988-2007 TRI data June 2010 14 

## 1. Introduction

EPA’s Risk-Screening Environmental Indicators (R SEI) is a screening-level tool that assesses the potential impact of industrial releases fr om pounds-based, hazard-based, and risk-related perspectives. RSEI uses risk concepts to qui ckly and easily screen la rge amounts of TRI data, saving time and resources. RSEI is particularly useful for examining trends to measure change, ranking and prioritizing chemical s and industry sectors for stra tegic planning, c onducting 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 ar e 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 considera tions, but does not address all of the potential factors that a full risk-ass essment would include.

RSEI considers the following information: the am ount 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. Th is information is used to create numerical values that can be added and compared in limitle ss 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 othe r values produced by RSEI. It should be emphasized that the result is not a detailed or quantit ative risk assessment, but offers a screeninglevel, risk-related perspective for rela tive 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 pr inciple, the adaptable method can model any chemical if toxicity characteristics, physicoc hemical properties, rele ase levels, and release location are known or can be estimated.



### 1.1 Background

In 1989, EPA outlined the goals for establishing strategic planning proc esses at the Agency (EPA, 1990c). Underlying this approach was the Ag ency’s desire to set priorities and direct resources to areas with the gr eatest opportunity to achieve health and environmental risk reductions. As part of this init iative, 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 impl ementing 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 improvi ng 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 th at reflect multimedia trends in environmentalChapter 1: Introduction

Version 2.3.0 1988-2007 TRI data June 2010 15 contaminant releases. The TRI is one of the Ag ency’s most relevant source of continuous data for developing indicators of change in envir onmental 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 facilitie s 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 indica tors, and to take advantage of the rich data source offered by the TRI, the Office of Pollutio n Prevention and Toxics (OPPT) convened a workgroup to explore the development of an indicat or or indicators based on the TRI that could track changes in human health and environmenta l impacts. Specifically, the approach would integrate toxicity, exposure and population considerations into the risk-related evaluation of releases. The RSEI model was devel oped in response to this initiative.

When evaluating impacts of chemicals, it is im portant 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 re ceptor population. RSEI integrates these factors and provides a relative risk-based perspective of chemical releas es 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 Agen cy 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 hum an health and chroni c and acute ecological impacts.

This document explains how the RSEI model is constructed, and desc ribes the conceptual method, data sources, and the computational appr oach. 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 a dvantages of the RSEI approach in terms of flexibility, power, and usef ulness 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 pr esent the results in vari ous 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 ever y year, and available upon re quest. 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 perf orm a variety of screening-level analyses, usually in a matter of minutes. Previously, such activities would have ta ken days, weeks, or even months to organize the relevant information, evaluate that inform ation, and perform the complex and sophisticatedChapter 1: Introduction

Version 2.3.0 1988-2007 TRI data June 2010 16 analyses that are necessary to provide a risk-r elated perspective. Results can be used for screening-level ranking and prioritization for strategic pla nning purposes, risk-related targeting, and trends analyses. Consider able resources can be saved by conducting preliminary analyses with the RSEI model to identify risk-related s ituations of high potential concern, and which warrant further evaluation.

Users can evaluate releases using a number of variables, such as chem ical, medium, geographic area or industry. For instance, users can inve stigate the following type s of questions: How do industry sectors compare to one another from a ri sk-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 imp acts? Users can view pounds-based, hazard-based, and other results, to investigat e the relative influence of t oxicity 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 descript ion of the RSEI method and model, as well as a discussion of their overall streng ths and limitations. Chapter 3 describes the TRI emissions data used in the model. Chapter 4 describes the me thods used to adjust the emissions data for chemical toxicity, and Chapter 5 provides a discu ssion of the geographic ba sis of the model, as well as pathway-specific descrip tions of adjustments made for exposure potential and population size. Chapter 6 presents the equations for cal culating RSEI results, a nd Chapter 7 describes issues pertinent to th e current implementation of the RSEI method.

There are also six Technical Appendices th at 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 Toxic ity Weights for TRI Chemicals and Chemical Categories Technical Appendix B - Physicochemical Pr operties for TRI Chemicals and Chemical Categories Technical Appendix C - Derivation of Model Exposure Parameters Technical Appendix D - Locational Data for TRI Reporting Faciliti es and Off-site Facilities Technical Appendix E - Derivati on of Stack Parameter Data Technical Appendix F - Summary of Differences Between RSEI Data and the TRI Public Data Release

In addition, two documents containing backgrou nd and supporting information are available on the project web site. Analyses Performed for the Risk-S creening Environmental Indicators contains three parts: Part A describes the re sult of a ground-truthing analyses performed to determine the accuracy of the air pathway mode ling; Part B contains additional analyses performed on the air pathway to determine optim al modeling parameters; and Part C describes the results of an analysis of SIC code-based stack parameter data. Developing the Risk-Chapter 1: Introduction

Version 2.3.0 1988-2007 TRI data June 2010 17 Screening Environmental Indicators describes the development of the model, and outlines options that were considered for several im portant 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 inform ation, a document archive, the RSEI User’s Manual, RSEI Tutorials, answers to frequent ly asked questions, contact information, and a glossary.Chapter 2: General Description of the Model

Version 2.3.0 1988-2007 TRI data June 2010 18 

## 2. General Description of the RSEI Model

2.1 General Description

The RSEI model is a screening-le vel tool that assesses the potentia l impact of TRI releases from pounds-based, hazard-based, and risk-related perspectives. A basic outline of the modeling approach is illustra ted in Exhibit 2.1.

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

• The TRI, maintained by EP A, provides the data on the quantity of chemicals released to air, water, on -site disposal facilities, a nd transferred to off-site facilities for the more than 600 toxic ch emicals 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 da ta 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 reporte d in pounds per year.

• Toxicity weights are assigned to each chemical for which adequate data are available. These weights are assigne d 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 TR I releases. For each exposure pa thway associated with each chemical release, the model generates an “Indicat or Element.” For instance, a release of the chemical benzene to air via a stack from the “A BC” Facility in 1999 is an “Indicator Element.” Each Indicator Element is associated with a se t of results, including pounds-based, hazard-based, and risk-related result s, 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, work er exposures are not addressed.Version 2.3.0 1988-2007 TRI data
J

une 2010 19 Chapter 2: General Description of the Mode lExhibit 2.1RSEI Modeling ApproachStackair 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 ReleaseFugitiveair emissions(1)Non-POTW off-sitetransfers(750 & 754)POTW off-sitetransfers(6)Direct waterdischarges(3)IncinerationVolatilizationPOTWeffluentWater Modeling(see Exhibit 5.7)Air Modeling(see Exhibit 5.3)Chapter 2: General Description of the Model

Version 23.0 1988-2007 TRI data June 2010 20 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, a nd population components are multiplied to obtain a risk score for the Indica tor Element. The surrogate dos e 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 es timate that can be compared to other estimates calculated using the same methods. If the Indi cator Element cannot be modeled, because of the lack of data needed for modeli ng or because the release pathway is not currently modeled, then the risk-related score is zero. The model calcul ates risk-related results for the entire population and also for the following subpopulations: childr en 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 a ssociated with a hazard-based result (“Hazard”), calculated by multiplying the pounds re leased 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 valu e. The oral toxicity weight is used for releases or transfers to direct water and POTWs. For releases that ar e not modeled (because the pathway is not modeled or because other necessary data, such as physic ochemical 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,” wh ich is the chemical- and pathway-specific toxicity weights multiplied by the Modeled Poun ds (as described above), and “Modeled Hazard * Pop,” which multiplies modeled hazard by the pot entially 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 re ported to TRI, and are available for vi rtually all Indicator Elements. The model also provides “TRI Pounds with Toxicity Weights,” which simply sums the pounds for chemicals that have toxi city weights in RSEI.Chapter 2: General Description of the Model

Version 23.0 1988-2007 TRI data June 2010 21 Once results are calculated for each Indicator Elem ent, they can be combined in many different ways. All of the results are additive, so a resu lt for a specific set of variables is calculated by summing all the relevant individual Indi cator Element results, as follows:



## R IE cf p 

## = 

# ∑ ∑ ∑

, , (Eq. 2.1)

where:

R = RSEI result, and IE c,f,p = chemical-facility-pathway-specific Indicator Element result.

This method is very flexible, allowing for coun tless variation in the cr eation 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 genera l trend in pounds-based, hazard-b ased, or risk-related impacts over time.

It must be reiterated that while changes in re sults over the years would imply that there have been changes in hazard- or ri sk-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 chemical s with the highest potential for ha zard 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 a nd Limitations of the RSEI Model

2.2.1 Strengths The following are strengths of the model:

• The model provides important hazard-bas ed and risk-related perspectives regarding the impacts of TRI re leases on chronic human health.

• The model quickly organizes and evaluate s 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 a nd the magnitude of their exposure, rather than assuming that all individu als surrounding a faci lity are equally exposed.

• The model allows for greatly increased speed in performing screening analyses, thereby conserving resources for conducti ng more precise, site-specific risk evaluations. In addition, its use as a prior ity-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 ba sed upon a wide range of variables.Chapter 2: General Description of the Model

Version 23.0 1988-2007 TRI data June 2010 22 • This adaptable method can model any chem ical if toxicity characteristics, appropriate physicochemical properties, re lease levels and re lease 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 fo r 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 concentrati ons of TRI chemicals are the same for indoor and outdoor exposures, and that populations are co ntinuously exposed.

• Dermal and food ingestion pathways (oth er than fish cons umption), and some other indirect exposure pa thways are not evaluated.

• Acute health effects associated with s hort-term, periodic exposures to higher levels of these same chemicals are not addressed.

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

Version 23.0 1988-2007 TRI data June 2010 23 

## 3. TRI Emissions Data

The RSEI model uses information on chemical re leases and transfers co llected by the TRI. The TRI is a publicly available EPA database that co ntains information on toxic chemical releases and other waste management activities reported a nnually by federal facilities and facilities in certain industry groups. TR I operates under the Emergency Pl anning and Community Right-toKnow Act of 1986 (EPCRA). EPCRA’s primary pur pose 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 emerge ncies. EPCRA Section 313 requires EPA and the States to annually collect data on releases and tr ansfers of certain toxic chemicals from industrial facilities, and to make the data available to the pub lic in the TRI. In 1990 Congress passed the Pollution Prevention Act which required that ad ditional data on waste management and source reduction activities be reported under TRI. The goal of TRI is to em power citizens, through information, to hold companies and local gove rnments accountable in terms of how toxic chemicals are managed. EPA compiles the TRI da ta 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 y ear 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 s ubmit changes and corrections to their TRI data at any time. To avoid the effects of these fluctuat ions on RSEI results, th e model extracts release and transfer data during the two week period e ach 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 shoul d be noted that the “frozen” data set is not necessarily the same as the TRI database publ icly accessible through the Internet: EPA’s TRI Explorer or Envirofacts are “ live” databases that are regular ly updated. Despite EPA’s open revision policy, errors do still happen, and some e rrors remain in the system for more than one year. It is therefore important to perform add itional inquiries and analyses to support and verify results from RSEI, which should onl y be used as a screening tool.

Even though the TRI PDR and RSEI both use th e 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 ad justment. 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 th ese differences and any year-spe cific 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, h ttp://www.epa.gov/tri/. The web site provides additional information, including reporting requirements for facilities.Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 24 

## 4. Methods for Calculating Toxicity Weights

The EPCRA Section 313 criteria li st several human toxicity parame ters that EPA must consider when evaluating a chemical for addition to TRI, including acute toxicit y, cancer or teratogenic effects, serious or irreversib le reproductive dysfunctions, neur ological disorders, heritable genetic mutations, or other chronic health e ffects. EPCRA also considers environmental toxicity. Some chemicals have toxicity data fo r 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 presente d in Exhibit 4.1.

The RSEI model focuses on carcinogens and othe r types of chronic t oxic 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 abnorma lities, altered grow th, 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 adve rse 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 Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 25 

### 4.1 Toxicity Weighting Sche me for Non-carcinogens and Carcinogens

The RSEI method uses a proportiona l system of numerical weights that reflect th e toxicities of chemicals relative to one another. The toxi city weights of chemicals increase as the toxicological potential to cause chronic human health eff ects increases. The method EPA has chosen for assigning toxicity weights to chemi cals is clear and reproduc ible, based upon easily accessible and publicly available information, a nd uses expert EPA-wide judgments to the greatest extent possible.

Factors that could be used to we ight a chemical’s toxicity incl ude: 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 inhere nt in characterizing effects. The RSEI method focuses on the latter two factors (potency and uncer tainty inherent in characterizing effects), and thus considers both quantitative and qualitative elements to j udge the relative toxicity of chemicals. The types of data required and the me thod 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 in corporated into the toxicity weights or in their underlying toxi city values. The approach is intended to differentiate the relative toxicity of these ch emicals in a uniform manner.

When evaluating the potential toxicity of a chemi cal 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 he alth effect in humans. The risk assessor can judge qualitatively the strengths of this body of evidence when dete rmining 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 th is method relies on categorical definitions from the 1986 EPA Guidelines for Carcinogenic Risk Assessment (EPA, 1986a), which are related to the potential for a chemi cal to be carcinogenic to humans. 13 The Cancer Guidelines define the six WOE categories shown in Exhibi t 4.2. In the RSEI model, weight-of-evidence categories A, B1, and B2 (known and probable ca rcinogens) 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 cate gories. 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: EChapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 26 the A and B categories represents a modification of the Hazard Ranking System (HRS), which is used by EPA’s Office of Superfund Remediatio n and Technology Innovation to rank hazardous waste sites for inclusion in the National Priori ties List. Under the HR S 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 ina ppropriate in the contex t of assigning toxicity weights. Also, combining A and B categories stabilizes the model re sults against changes induced by chemicals switching between the A and B designations. Class C chemicals (possible carcinogens) are assigned weights by dividing the calculated toxici ty 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 advi ce of peer review and the HRS; an order of magnitude difference is an arbitrary uncertainty f actor. 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 anim als 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 cons idered 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 no ncancer effects focuses on the dose where chemical exposure would be rele vant to humans (Dours on, 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 rele vant to humans has been estimated. The WOE evaluation is included qualitativel y in the RfD, but does not affe ct its numerical calculation. Since weight-of-evidence has been considered in developing RfDs, RS EI does not consider WOE separately for noncancer effects.Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 27 

### 4.1.2 Quantitative Data Quantitative data on the relative po tencies 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 exte nt of toxic injury or disease observed. Risk posed by exposure to a chemical cannot be descri bed without quantitative dose-response data. Dose-response data are derived from animal studi es 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 Un it Risks (IURs). The OSF repr esents 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 usua lly 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/m 3 in air (RSEI toxicity wei ghts are based on this value when expressed as risk per mg/m 3 ). Although the level of conserva tism inherent in the OSFs and Unit Risks varies by chemical, OSFs and IURs none theless are the best r eadily available values that allow a comparison of the rela tive cancer potency of chemicals.

RSEI’s oral cancer toxicity weig ht (OTWc) represents how toxic a substance is relative to a substance that produces a 1 in 1 million risk 14 (above background, over a lifetime) at an average lifetime daily dose of 1 mg/kg-day. If the OSF is gr eater than this arbitrar y slope factor (i.e. the substance is more toxic than the arbitrary slope factor), the OTWc is greater than 1.

mg day kg mg day kg OSF OTWc / 10 /

6

− −

=

− (Eq. 4.1)

For noncancer risk assessment, data on dose-response are typi cally (though not always) more limited; generally, a risk assessor evaluates dose compared to a Reference Dose (RfD) or Inhalation Reference Concentrati on (RfC). Both the RfD and RfC are defined as “an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily e xposure [or continuous inhalation exposure the RfC] to the human population (including sensitive subgroups) that is likely to be without an appreci able risk of deleterious [non cancer] effects during a lifetime” (EPA, 1988a; EPA, 1990b). The units of RfD are mg/kg-day, while the units of the RfC are mg/m 3 . A chemical’s RfD or RfC is typically ba sed on a No Observable Adverse Effect Level (NOAEL) or Lowest Observable Adverse Effect Level (LOAEL), combined with appropriate uncertainty factors to account for intraspecies vari ability 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 Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 28 extrapolation from LOAELs to NOAELs, and extra polation from subchronic to chronic data. In addition, a modifying f actor can be applied to reflect EP A’s best professional judgment on the quality of the entire toxicity database for th e chemical. By definition, exposures below the RfD/RfC are unlikely to produce an adverse effect; above this va lue, an exposed individual may be at risk for the effect. Em pirical 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 pr obability or severity of an effect is not known. For purposes of the RSEI model, it is assumed that noncancer risk varies as th e ratio of the estimated dose to the RfD/RfC.

RSEI’s oral non-cancer toxicity we ight (OTWnc) represents how toxi c 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 do se), the OTWnc is less than 1.

day kg mg RfD day kg mg OTWnc

− −

= / / 1 (Eq. 4.2)



### 4.1.3 Algorithm for Calculating Toxicity Weight
The RSEI method uses several diffe rent algorithms to assign toxic ity weights. RSEI toxicity weights are designed to be proportional to a substa nce’s toxicity. The more toxic a substance is, the higher the toxicity weight.

Toxicity values for the inhalation pathway are typi cally expressed in units of exposure, that is, mg of chemical per m 3 of air. The toxicity weighting methodology, therefore, uses standard adult human exposure factors for inhalation rate (20 m 3 /day) and body weight (70 kg) to modify toxicity values expressed in units of exposure. This adjustment m eans that different constants are used to calculate the toxicity we ights when inhalation toxicity valu es 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 expresse d as reciprocal uni ts of mg/kg-day.

As these calculations show, noncancer toxicity we ights are proportional to the reciprocal of the RfD or RfC for the oral and inha lation exposure pathways, respectivel y. Cancer toxicity weights are proportional to the OSF or IU R, 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 a dditional factor of 10 b ecause the evidence of cancer causation in humans is uncertain.Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 29 Exhibit 4.3 Algorithms for Assigning Toxicity Weights Oral Pathway Inhalation Pathway Non-Carcinogens : 1 / RfD (mg/kg-day) 3.5 / RfC (mg/m 3 ) 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/m 3 )/ 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/m 3 )/ (2.8 x 10 -7 * 10)



### 4.2 Selecting the Final Chronic Huma n Health Toxicity Weight for a Chemical

Inhalation and oral toxicity weig hts 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 fo r both routes, provided there is no evidence the effects are route-specific or limited to the “por tal of entry” into the body. In rare instances, toxicity studies are available to show that a gi ven chemical causes no effects via one route; in these instances, a toxicity weight is assigned onl y to the route that results in chronic human health effects. Although assigni ng 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 fo r the given exposure route (oral or inhalation). If a chemical exhibits both carcino genic 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 th e 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 additi onal 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-es timate the risk of chemicals w ith 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 ar e no data on other effects; thus, applying an additional weight based on the number of endpoi nts 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.Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 30 

### 4.3 Chemical Groups
TRI collects information for some chemicals as combined groups, such as glycol ethers, polycyclic aromatic compounds, and metal comp ounds. For metal compounds, RSEI combines the elemental form of the meta l with its compounds category. This is done to reflect the uncertainty of the chemical iden tity of the substan ce 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 en try 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 releas e 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 hexa valent 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 th e 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 representati ve 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 individua l 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 chem ical toxicity for both cancer and noncancer effects of chemicals. IRIS provides both bac kground information on the st udies used to develop the toxicity evaluations and the num erical toxicity values used by EPA to characterize risks from these chemicals. These values include upper-b ound OSF or IUR values for chemicals with carcinogenic effects as well as Rf Ds or RfCs for chemicals w ith noncancer effects. Data contained in IRIS have been peer-reviewed and represent Agen cy-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/na ta2002/methods.html#pom

.Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 31 peer-review process involves literature review and evaluation of a chem ical 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 potentia l of chemicals that are of interest to OPP. OPP also publishes the List of Chemicals Evalua ted for Carcinogenic Potential, which examines carcinogens. Both of these lists are updated peri odically. Individual Pesticide Regregistration Eligibility Documents (REDs) are also used.

ATSDR. The Agency for Toxic Substances and Dis ease Registry (ATSDR) is an agency of the U.S. Department of Health and Human Services, which deals with the eff ect on public health of hazardous substances in the environment. ATSD R 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 sp ecified duration of exposure. RSEI uses data from MRLs developed for chronic exposure only. MRLs are in tended to serve as scr eening levels only, and are useful in identifying contam inants and potential health eff ects that may be of concern at hazardous waste sites. See h ttp://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 pr otect public health. RSEI uses final toxicity values published by CalEPA in the Consolidated Table of OEHHA & California’s Air Resources Board (ARB) Approved Risk Assess ment 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 De velopment/National Center for Environmental Assessment/Superfund Health Risk T echnical Support Center (STSC).

HEAST. EPA’s Health Effects Assessment Tables (HEAST) are constructed for use in the Superfund and RCRA programs but do not represen t Agency-wide expert judgments. These tables are publicly available from the Superfund program. The tables include OSFs, IURs, and WOE categorizations for chemicals with cance r effects, and RfDs and RfCs for noncancer effects.

Derived Values. For a prioritized group of chemicals fo r which sufficient data was not found in the above sources, a group of EPA e xpert health scientists reviewed other availa ble data to derive appropriate toxicity weights. Although individual literature searches for toxicological and epidemiological data for each chemical were beyo nd 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 e ffects and quantitative da ta, toxicological and epidemiological studies, and, in some cases, regul atory status data. When the available data on chronic human toxicity were sufficient to deri ve values, a toxicity weighting summary was developed summarizing the inform ation used to develop each of these values. The summaries are available in Technical Appendi x A. The EPA scientists us e a technical approach analogousChapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 32 to the Agency’s method for deriving RfD values, RfC values, cancer risk estimates, and weightof-evidence (WOE) determinations. However, it mu st 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 categor ized 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 IR IS, since IRIS reflects Agency-wide judgments.

Tier 2 . In the absence of data from the above sour ces 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 sour ces 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 wei ghting, that were withdr awn pending revision.

For chemicals with carcinogenicity risk values , weight-of-evidence (WOE) values are obtained using the same data source hierarchy. Therefor e, preference is given to WOE’s from IRIS or OPP. As a general rule, chemi cals with cancer potency factors fr om IRIS or OPP will also have WOEs. CalEPA, however, references either EPA or the International Agency for Research on Cancer (IARC) for WOE designati ons. 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 EP A WOE’s without examini ng 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 EP A Group C (Possible Carcinogen)

• IARC Group 3 = EPA Group D (Not Classifiable as to Human Carcinogenicity)

• IARC Group 4 = EPA Group E (Evi dence 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 toxic ity weight. Therefore, for the chemicals with IARC 2B designations, summaries of the toxici ty data used to generate the OSF or IUR are evaluated to derive WOEs. To date, this approa ch has been used for chemicals with data from CalEPA; therefore, the CalEPA “Technical Supp ort 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 categorie s on the 2007 TRI Chemical List. ChemicalsChapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 33 with toxicity weights account for over 99% of the reported pounds for all on-site releases in 2007. The Indicator Elements are recomputed fo r all years in the TRI database on an annual basis in order to incorporate revisions to th e reporting data. Howeve r, 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 individua l chemicals and chemical groups are presented in Technical Appendix A.



### 4.5 How Indicator Toxicity Weighti ngs Differ from EPCRA Section 313 Criteria

As noted above, the model uses TRI chemical re porting 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 envir onmental 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 compar ed 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 chroni c 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 descripti on of the listing/delisting criteria and process is described below.

The Emergency Planning and Community Ri ght-to-Know Act of 1986 (EPCRA) section 313(d)(2) sets out criteria for addi ng chemicals to the list of chem icals subject to reporting under EPCRA section 313(a). Th e statutory criteria used for listin g and delisting chemicals addresses the “absolute” chronic toxicity of chemicals on the TRI (e.g., multiple effect s or the severity of effects). For a chemical (or category of chemical s) to be added to the EPCRA section 313(c) list of toxic chemicals, the Administra tor 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 signifi cant adverse acute human health effects at concentration levels that are reasonably likely to exist beyond facility site boundaries as a result of continuous, or fre quently recurring, releases.

Chronic Human Toxicity §313(d)(2)(B) - The chemical is known to cause or can reasonably be anticipate d to cause in humans– (i) cancer or teratogenic effects, or (ii) serious or irreversible– (I) reproductive dysfunctions,Chapter 4: Methods for Ca lculating Toxicity Weights

Version 23.0 1988-2007 TRI data June 2010 34 (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 bioa ccumulate in the environment, significant adverse effect on the environm ent 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 estab lish any of the criteria descri bed 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 hea lth effects or environmental toxi city in the criteria. Agency guidelines describe when a study shows such effects as cancer (EPA , 1986a), developmental toxicity (teratogenic effects) (EPA, 1991b), or heritable gene tic 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 pos itive judgment regarding any of the criteria, then the chemical can be removed.

There is no correlation between the toxicity criteria and me thodology used to make listing decisions under EPCRA section 313 and the met hodology used to assign toxicity weights to chemicals for the RSEI model. Therefore, thes e 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 June 2010 35 

## 5. Exposure and Population Modeling

To estimate the magnitude of exposure potentia l from TRI releases, a separate exposure evaluation is conducted for each chemical rel ease 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 in take 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), volati lization (not currently modeled) and stack ai r (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 re levant for exposure modeling is not part of a TRI data submission. For example, the EPA Fo rm 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 proj ect to gather extensive site-specific data or measurements that would be need ed to perform site-specific calcu lations. The need to accurately reflect exposure characteristics in the RSEI mode l 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 evalua tions combine data on pa thway and media-specific emission volumes, physicochemical properties and, wh ere available, site ch aracteristics, with models to determine an estimate of the ambient c oncentration 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 “surroga te dose”. The term surrogate dose is used because limited facility-specific da ta and the use of models that rely on default values for many parameters preclude the calculation of an actua l 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 potentia lly exposed person are combined with estimates of the number of people potentially exposed. Po tential exposure is dete rmined 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 calculate d separately for each pathway. The model assumes continuous exposure, and does not ac count for the activity patterns of the people potentially exposed. However, population estimate s 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 June 2010 36 The methods used to model each type of release ar e 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 exposur e; in other cases, generic reas onable 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 s hould 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 de cay 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 describe s 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 ar e created. From these genera l population data sets, the model generates estimates of populations exposed through particular pathways. The next sections describe the modeling for each pathway, includin g 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 f acilities geographically, and to attribute characteristics of the physical environment, such as meteorology, to areas surrounding the facilities, once they are located. To accomplish th e location of facilities and the attribution of data to those facilities, the mode l describes the U.S. and its terr itories 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 lat itude and longitude (lat/long).



### 5.1.1 The Model Grid Cell System RSEI uses a standard Albers Equal-Area projection 17 to create each of the grid s that is used in the model. The grid system is split into six i ndividual grids which cover the continental U.S., Alaska, Hawaii, and the territories. Each unique cell address is co mposed of (1) the grid number, and (2) the ( x,y ) address of the cell in that grid. E xhibit 5.1 below provides the grid number (used in the model to identify each grid), the grid characteristics th at 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 de veloped 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 Up per-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 June 2010 38 They can be calculated as follows:

) 810 / (

x Adjust Easting INT x +

= (Eq. 5.1)

) 810 / ( y Adjust Northing INT y

−

= (Eq. 5.2)

where:

Easting = projected Albers distance East of the central meridian for the grid (m); Northing = projected Albers di stance North of the central meridian for the grid (m); Adjust X

= 0.5 if Easting >0, -0.5 if Easting<0; Adjust Y = 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 lo cated on the grid and assigned to a grid cell. Facil ities are projected onto each grid using GIS software and the ( x,y ) coordinates of the cell where the facility is mappe d 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 met hod used to select lat/lo ng coordinates for both reporting facilities and off-site f acilities, 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 wh ich 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 Locati onal Reference Tables (LRT), which collects coordinates and related documentation on locati on 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 facil ity 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-s ite” facilities, that is, facilities that receive transfers from TRI-reporting facilities. Note th at 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 tran sfer chemicals to them. 18 Each report of a receiving off-site facility becomes a separate record in the TRI, even thou gh 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 addresse s are not standardized, the records are usually

18 Some facilities may be considered both on-site and off-site facilities, if they both recei ve chemical transfers from other facilities (as an off-site facility) and emit reportable ch emical 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 ma tched to each other. EPA has developed an approximate text string matching program to iden tify imperfect matches in order to collapse the set of off-site facilities to wh at are considered to be unique o ff-site facilities. The program matches values without requiring their exact equality. This a pproach accommodates misspelled words and inconsistencies in how a facility mi ght report its identifying information over time. For example, “DuPont,” “Du Pont,” and “E.I. Du Pont” might all refer to the same facility. A possible match is identified based on similarity rather than exact e quality 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 hi ghest confidence level was sel ected. 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 approxima tely 47,000 off-site facilities.

Beginning with Version 2.1.5 (RY 2005), off-site facilities are no longer geocoded. Instead, the entire set of all years of off-site records (1988-2007) is ma tched back to the previous years’ master off-site database using an approximate te xt 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 prev ious years’ database (including any new off-site facilitie s) 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 id entify lat/longs for off-site fac ilities. Grid-cell addresses for off-site facilities are determined in the same manner as for reporting fa cilities; 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-s ite 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 emissi ons and capture the effect of the changing distribution of the population, RS EI uses detailed annual populat ion datasets at the grid cell level. The data is based on U.S. Decennial Ce nsus 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 populati on estimates, and how the un it of analysis for the U.S. Census (the block) is transl ated into the unit of analysis fo r 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 Ce nsus 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 cultu ral features, and the le gal boundaries shown on Census Bureau maps. In 1990, there were a pproximately 7 million census blocks. Due to boundary changes and increased re solution for highly populated areas, there were approximately 9 million blocks in the 2000 Census. Block-level data from the 1990 Census and the 2000 Census 21 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 us e 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 Marian a 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 Cens us totals. The mapping to grid cells for Puerto Rico and island areas is the same as descri bed 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 crea te 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 f iles were provided by GeoLytics, Inc.

21 For 1990, not all of the variables were available at the bloc k level. For those variables that were only available at the block group level, block group ratios were calculated a nd 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 result ed 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 percen t of that block assigne d 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-se x 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 inte rpolation at the gr id cell-level is performed within the mode l between the two data sets. The line is extrapolated backward to create annual datasets for 1988 and 1989 and forw ard 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 re leases, (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 ea ch pathway is presented in Exhibit 2.1.

The following discussions of exposure modeling fr equently mention concen tration and surrogate dose. This is not meant to imply that dose can be accurately calculated wi thin this model. The exposure algorithms are intended to be simple wa ys to gauge relative ri sks from releases to different media in a consistent, defensible way, by modeling and estimating exposure. In some cases, the modeling is purpos ely simplified, given the l ack of site-specific data.

When possible, exposures are estimated for releva nt subpopulations defined by age, sex, or other factors. Exposure for individual subpopulati ons is modeled using exposure factors (i.e., inhalation rates, drinki ng water intakes, fish ingestion rates, and body weight) and population data specific to such subpopulations. For exampl e, ingestion rates specif ic to recreational and subsistence fishers are used to estimate exposures for these fishers and their families. Also, ageand sex-specific inhalation and drinking water in gestion rates are used. The relevant exposure assumptions for these subpopulations are al so described in th e 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; inst ead, 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; popula tions 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 rele ased through stacks or as fugitive releases. Stack (or point) air releases include releases to air thro ugh 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 vent ilation 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 de scribe the method and data sources for each pathway.



### 5.3.1 Stack Air Emissions: Method Stack air releases are modeled using the Amer ican Meteorological Society/EPA Regulatory Model (AERMOD). AERMOD replaced the Indus trial Source Complex (ISC) model as EPA’s preferred regulatory model in 2005. AERMOD is a steady-state Ga ussian 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 sect ions describe the parameters of the AERMOD model used. 24

5.3.1.1 AERMOD The AERMOD model is specifi cally designed to support th e EPA’s regulatory modeling programs, as specified in the Guide line on Air Quality Models (Revised). 25 AERMOD is a steady-state plume model. In the stable bounda ry layer (SBL), it assumes the concentration distribution to be Gaussian in both the vertical and horizontal. In the c onvective boundary layer (CBL), the horizontal distribution is also assumed to be Gaussian, but the vertical distribution is described with a bi-Gaussian probability densit y 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 th e boundary layer before becoming mixed into the CBL. AERMOD also tracks any plume mass that penetr ates into the elevated stable layer, and then allows it to re-enter the boundary layer wh en and if appropriate. Fo r 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 ve rtical inhomogeneity of the planetary boundary layer (PBL) in its di spersion calculations. This is accomplished by averaging the parameters of the actual PBL in to effective parameters of an equivalent homogeneous PBL.

24 The following description is based on equations and te xt provided in the AERMOD manuals and documentation. The most recent AERMOD manuals are av ailable from EPA’s SCRAM website at http://www.epa.gov/scram001/dis persion_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 options 26 , 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 h eating is not used. Weather data from National Weather Service (NWS) observation stations is used as the meteorological input (see S ection 5.3.1.4 below).

5.3.1.3 Source Parameters In the RSEI model, the U.S. 27 is represented by a grid syst em composed of 810 m by 810 m square grid cells. Facilities are assigned to a particular grid ce ll in this grid according to their latitude and longitude coordina tes (see Technical Appendix D fo r 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 lo cation of a facility may differ from its modeled location by up to 573 meters, the maximum distance between the cen ter 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. St ack exit gas temperature is assume d 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 Standa rd Industrial Classification (SIC) code-based median stack para meter is assigned to the facility. If no valid SIC code is available, or no stack data is availa ble 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 report ed to TRI are converted to an equivalent c onstant emission rate in grams per second according to the following equation: 28

31,536,000 q 453.6

= Q (Eq. 5.3)

where:

Q = pollutant emission rate (g/sec),
q = TRI annual stack or po int 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, Am erican 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 (y r) to seconds (sec) assuming 365 days
per year.

5.3.1.4 Meteorological Input Data For a given pollutant source, meteorology around the source a ffects 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 po llution and the resulting magnit ude and location of ground level concentrations of emitted pollutants.

AERMOD is designed to run with a minimum of observed meteorologi cal parameters, and requires only a single surface measurement of wi nd, wind direction and ambient temperature. Like ISC, AERMOD also needs obs erved cloud cover. However, if cloud cover is not available (e.g. from an on-site monitoring program) two vert ical measurements of temperature (typically at 2 and 10 meters), and a measurement of solar ra diation can be substituted. A full morning upper air sounding is required in or der to calculate the convective mixing height throughout the day. Surface characteristics (sur face roughness, Bowen ratio, and albe do) are also needed in order to construct similarity profiles of the relevant PBL parameters.

5.3.1.5 Calculating Pollutant Concentration
RSEI calculates air concentrati ons at hypothetical “receptors” lo cated within a circle with a radius of 49 km surrounding each facility. Any cell s with centers within the 49 km circle are included. The model calculates ground-level co ncentrations 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 wh ere calculations are made, concentrations are interpolated using a spat ial weighting technique similar to inve rse 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 th e interpolated concentration based on calculated conc entrations at 441 receptor points within a kilome ter of the cell center. Analyses performed during the development of RSEI Version 2. 0 (which used the ISC model 29 ) indicated that using the concentration in a surrounding cell as an estimate for the center cell may either significantly over or under-represent chemical concen trations 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 conc entrations for the 20 most t oxic carcinogens and the 20 most toxic non-carcinogens include d the model at various st ack heights. These analyses indicated that extending modeled distances to 50 km 30 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 facil ities. Details of these analys es 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 versi on of RSEI that incorporated EPA’s Industrial Source Complex (ISC) model. RSEI now uses AERMOD, whic h 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-te rm pollutant concentrations downw ind 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 desc ribed in Section 5.3.1.2.

5.3.2.2 Source Options Fugitive emissions are modeled as an area sour ce 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/m 2 s) according to the following equation:

10 * 31,536,000 q 453.6

= Q 2 a

a (Eq. 5.4)

where:

Q a = pollutant area emission rate (g/m 2 s),
q a = TRI annual fugitive air emissions (lb/yr), 453.6 = constant to convert pounds (lb) to grams (g), 31,536,000 = constant to convert ye ars (yr) to seco nds (sec), and 10 2 = conversion factor necessary to convert annual em issions (g/s) to area emission rate (g/m 2 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
B W I C

= DOSE air air air * * (Eq. 5.5)

where:

DOSE air

= surrogate dose of contam inant from air (mg/kg-day),Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 47 C

air

= air concentration in cell (μg/m 3 ), I

air

= inhalation rate (m 3 /day), BW = human body weight (kg), and 1000 = constant to convert (μg) to (mg).



### 5.3.4 Estimating Population Si ze for Air Releases The population potentially exposed to air releas es 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 consider ed 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 subpopula tion in the cell and by the chemi cal toxicity weight to obtain an Indicator Element for the grid cell. Then the results for all gr id cells are summed. The result is an Indicator Element for an ai r release. To calcula te the overall Indicator Element for all air releases, the same steps are followed for each air release, and th e 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

( μ g/m 3 )

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 (st ack height, stack diameter and exit gas velocity), meteorology, chemical-specific first-order air d ecay rates, and exposure assumptions (inhalation rate and body weight). The values used for th ese 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 432 E 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 m 2 Based on EPA (1992b)
Area source height 3 m Based on EPA (1992b)

* As of RY2006, TRI requires facilities to report NAICS code s instead of SIC codes. RSEI maintains historically reported SIC codes. For new reporters without SIC codes, NAICS codes are ma tched to their equivalent SIC codes where possible, using the Census cr osswalk available at http://www.censu s.gov/epcd/naics02/NAICS02toSIC87.xls.

5.3.6.1 Stack Height, Stack Diameter, and Exit Gas Velocity Stack parameter data (height, diameter, and ex it-gas velocity) were obt ained from the AIRS Facility Subsystem (AFS) within the Aerometric Information Retrieval System (AIRS), the National Emission Trends (NET) Database, th e National Emissions Inventory (NEI), and databases from three individual states (Califor nia, 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 th at facility’s St andard Industrial Classification (SIC) code is used instead. The SIC code-based stack parameters are estimated from data in AFS and the NET Database for facilit ies in the appropriate 3- digit SIC code, or in the 2-digit SIC code if the 3-digit SIC code is un available. 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) provid ed EPA with site-specific data for electric utilities (electric utility SIC codes were adde d to TRI for Reporting Year 1998), transmitted in two databases. These data incl uded 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 Servi ces 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 pe rcent of TRI electric utility facilities did not. 32 For the 30 percent that did not match specific f acilities, 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 c ode 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 signifi cant 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 ga s velocity, the model uses the sa me 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 informati on or an SIC code-based median value for facilities without the appr opriate stack data. Exit gas velocity data were obtained from AFS, NET, NEI, and state-specific databases. Stack diameter data we re obtained from AFS, NET, NEI, EPRI and databases from three i ndividual states (Calif ornia, 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 cl osely 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 Meteorology 34 The meteorological data used in RSEI is take n 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 da ta 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 vari ables 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 surf ace scalar parameters, and the ot her file consists of vertical profiles of meteorological data.

To simplify processing and minimize the am ount of quality assurance needed, HEM-3’s processing was restricted to me teorological 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 pe rcentage of the observations at a number of meteorological stations. Installation and operation of ASOS e quipment began in 1992; th erefore, data for 1991 were used. Data were retrieve d from products available from th e National Climatic Data Center (NCDC). The surface data for 1991 were retrieved from the H ourly 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 mu st be specified when proces sing meteorological data using AERMET, including the surface roughness length, the Bowen ratio (an indicator of surface moisture), and the albedo (an indicator of surf ace reflectivity). These surface characteristics are

33 These analyses were performed using an earlier versi on of RSEI that incorporated EPA’s Industrial Source Complex (ISC) model. RSEI now uses AERMOD, whic h 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 June 2010 52 used by AERMET to calculate the level of sh ear-induced mechanical turbulence generated by flow over the surface and for the energy balance cal culations used in the determination of the Monin-Obukhov stability parameter and the c onvective velocity scale. For the HEM-3 meteorological data, the following su rface characteristics were used:

• Surface roughness length = 0.25 m. At the airport meteorological site, the surface roughness includes runways, terminal buildi ngs and other airport structures. In addition, off-airport structur es often are within 3 kilo meters of the measurement site. This combination of land covers s uggests a value of 0.2 – 0.3 meters is appropriate.

• Bowen ratio = 1.0. Representing an equa l 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 height s 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 pr ocesses or chemical transformation. The model uses pollutant-spec ific air decay rates from SRC’s Atmospheric Oxidation Program (AOPWIN), an atmospheri c oxidation computer pr ogram (SRC, 1994-1998). AOPWIN estimates the second-order rate consta nt for the atmospheric, gas-phase reaction between photochemically produced hydr oxyl 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 10 6 molecules/cm 3 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:

3600 10 1. * 2 AOPWIN

= K 6

air * 5 × (Eq. 5.6)

where:

K air

= air decay rate (hr -1),
AOPWIN = second-order rate constant from AOPWIN, 1.5 × 10 6 = hydroxyl radical con centration (molecules/cm 3 ), 3600 = constant to convert molecu les/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 T echnical 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 in halation rates and body wei ghts are used in the model. The primary source for all exposure factor s 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 fact ors used in assessing human e xposure. These factors include: drinking water consumption, soil in gestion, inhalation rates, derm al factors including skin area and soil adherence factors, consumption of fruits and vegetables, fish, meats, dairy products, homegrown foods, breast milk in take, human activity factors, consumer product use, and residential characteristics. In EFH, EPA reco mmends mean values for the general population and also for various segments of the population who may have char acteristics different from the general population. RSEI uses inhalation ra tes and body weights derived from the recommended factors, except where noted. The Exposur e Factors Handbook (EFH) can be found on the internet at http://cf pub.epa.gov/ncea/cfm/recordisplay.cfm?deid=12464.

EFH (EPA, 1997b, Table 5-23, p. 5-24) was used to estimate inhalation ra tes 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 us ed in RSEI. For children, the RSEI age groups were broader than the EFH age groups. Therefor e, the exposure factor was calculated using a weighted average of the inhala tion rates for all EFH age groups that overlap the RSEI age group as follows:

N n IR
EF i i i 

## ∑ ∗

= ) ( (Eq. 5.7)

where:

EF = RSEI exposure factor, IR i = intake rate for EFH age group i
, n i = 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 si ngle recommended inhalation rate for males and a single rate for females. However, EFH also provides th e 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 ba sed on weighted averages calculated from this data, using Equation 5.15. The RSEI inha lation 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). Exhi bit 5.5 provides the ra nge of data used, and Exhibit 5.6 presents the final e xposure 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 m 3 /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 (m 3 /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 rele ases and transfers by we ight were released onsite as direct surface water rel eases. 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 re lease; or by eating contam inated fish caught in a water body in the stream path of a chemical releas e. The following sections first describe the methods used to calculate the initial stream concentration fo r both pathways, and then the different methods used to calcu late surrogate dose and population for the drinking water pathway and the fish ingestion pathway. The data secti on 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 discha rging facility on the model grid using the lat/long. F acilities are then matched to a r eceiving 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 pe rtaining 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 mini mum 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 kilomete rs, the discharge is not modeled. Reach data is not available for Alaska, Guam, American Samoa and the Nort hern 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 occu rs: (1) the releas e has traveled 300 km downstream; (2) the release has been tr aveling 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 fl ow 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 gene ral form of the firstorder decay equation is as follows:



### e C = C t k

- 0 x water (Eq. 5.8)

where:

C x = concentration at distance x meters from the facility release point (mg/L), C

0 = initial concentration (mg/L), which equals chemical release (mg/day) divided by mean flow (see Eq. 5.16), k

water = decay coefficient (sec -1 ), and
t = time at which C x occurs (sec), which equals x/u , where u is the water velocity (m/sec).

For surface water releases, the RSEI model esti mates chronic human health exposures for two pathways: drinking contaminated water and eatin g 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 (USG S), 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 (calcula ted 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 ch emicals had existing MCLs in e ffect 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 th e stream concentration exceeds th e 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, th e chemical concentration is co mbined with standard exposure parameters (see Section 5.4.3.6) to yield a surrogate dose:

BW I C

= DOSE water reach water, dw ∗ (Eq. 5.9)

where:

DOSE dw = surrogate dose of chemical in drinking water (mg/kg-day), C

water,reach = average annual chemical concen tration in the reach of interest, calculated at the upstream end of the reach (mg/L), I

water = drinking water inges tion rate (L/day), and BW = human body weight (kg).

5.4.1.4 Estimating Population Size fo r the Drinking Water Pathway To estimate the size of the populat ion exposed to TRI releases th rough drinking water, the model uses estimates of the population served by each drinking water intake from USGS’s Public Supply Database, which incorporates populati on 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 b een 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 bari um, 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, th is data set only lists the intake location and the number of people served by the water syst em. In many cases, there are multiple water intakes per water system. In the absence of other data, it is assu med that the tota l 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 upst ream end of the reach).

The drinking water intake information from SD WIS contains only the number of people served by each drinking water system; it does not provid e 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 ingesti on 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 path way is through consumption of fi sh contaminated by chemicals discharged from TRI reporting facilities. Th ese fish may be consumed by recreational and subsistence fishers and their families. 41 As in the drinking water pathway, chemical concentrations are calculated unt il one of three conditions occurs: (1) the release has traveled 300 km downstream; (2) the release has been tr aveling downstream for a week; or (3) the concentration reaches 1 x 10 -9 mg/L. The chemical concentrati on in fish is estimated using the following equation:



### BCF C = C reach water, reach fish, 

### * (Eq. 5.10)

where:

C fish, reach = concentration in fish in the specified stream reach (mg/kg), C

water , reach = average annual chemical concen tration 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 I C

= DOSE pop fish, reach fish,

c f, * (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 June 2010 58 DOSE f,c = surrogate dose of chemical c from facility f (mg/kg-day), C

fish, reach = average annual chemical concentration in fish tissue (mg/kg), I fish, pop = fish ingestion rate for recreati onal 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 data set containing the numb er of fishing or hunting/fishing combination licenses was created from state fish a nd 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 to tal population in the county. 42 The resulting ratio is multiplied by the population in each grid cell to obtai n the number of individuals with fishing licenses within that cell. To account for family members who al so eat fish caught by one member, the model multiplies the number of fishers by 2.62, the si ze 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 TotalPop = FishPop cell cell * * (Eq. 5.12)

where:

FishPop cell = total fish-eating population in a grid cell, TotalPop cell = 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 th en 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 c ontrast, subsistence fishers may fish throughout the year and a majo r part of their diets may consist of fish they catch. Data are lacking on numbers of recreational compared to subsistence fisher s; 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 re creational basis, and the remaining 5 percent subsist on fish. This apportionment is described by the fo llowing relationships:



### 0.95 FishPop = RecPop 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 informat ion 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 

### 0.05 FishPop = SubsistPop cell cell 

### * (Eq. 5.14)

where:

RecPop cell = recreational fishers (and families) in a cell, and SubsistPop cell = 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 st eps. First, overlapping circles of 80-km radii associated with each of the two to seven points that describe indi vidual stream reaches are used to define those grid cells that will be modeled for fishing populat ion in the 48 contiguous states (i.e., all fishing areas within 80 km of all str eam 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 Wild life-Associated Recreation that 65 percent of a nglers 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 th e center of each grid cell from the first selection are identified. The fish-eating population in the grid cell is apportio ned to each surrounding stream reach based on the ratio of the length of th at reach to the total re ach 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 cel l), then a total of 20 km of st ream 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 theref ore assumes that three-fourths of the fishing population in the cell catches fish from Reach A and one-fourth catche s fish from Reach B. Note that the model uses only the portion of the reach ’s length that is with in 80 km of the cell.

Because of the size of the database created, the fishing population data at tributed to individual reaches is summed and stored at the reach level. The percentage of peopl e in each of the ten agesex categories for the aggregated total fishing po pulation (reflecting the ra tio of the various age and gender subpopulations in the neighboring grid cel ls) is also maintained for each reach. The model then matches the chemical concentra tion in fish in the appropriate reach (C fish,reach ) to the correctly-apportioned popul ation. This is done for all reac hes that have modeled chemical concentrations.



### 5.4.2 Calculating the Indicator Element for Surface Water The Indicator Elements for drinking water and fi sh ingestion are calcul ated by generating for each unique combination of chemical release, stream reach, and exposure pathway a surrogate dose, then multiplying this dose by the toxicity wei ght of the chemical released and the estimated population. The RSEI risk-related result (score) for a surface water re lease from a TRI facility is calculated by adding the drinking water element a nd 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 Data 1 and Toxicity Weights Population Data 2 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 XChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 61 

### 5.4.3 Surface Water Releases: Data A variety of data are required to estimate exposure to chemical di scharges to surface waters. The parameters required for surface water modeling a nd 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 d ecimal 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 ch emical-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 disc harge reach, or if no assi gned discharge reach is available, it is assumed to discharge into the near est stream reach within four kilometers of the facility. Certain minimum crite ria regarding flow and reach t ype 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 estuar ies that are linked to fo rm a skeletal structure representing the branching patte rns of surface water drainage sy stems. 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 Datase t (NHD) system for indexing stream reaches, which is much more detailed than the Reach F ile 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-relatedChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 62 entities such as industrial discha rgers, drinking water supplies, fish habita t areas, and wild and scenic rivers. Reach addresses establish the loca tions of these entities re lative to one another within the NHD surface water draina ge network in a manner simila r to street addresses. Once linked to the NHD by their reach addresses, th e upstream/downstream relationships of these water-related entities and any asso ciated 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 NHDPlus 43 , which contains information on stream velocity and flow volume, which are necessary for modeling chemi cal concentrations in streams in the RSEI model.

It should be noted that the atomic unit of th e 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, sinc e much of the other data (such as discharge reach) are at this level. In cases where a reac h needs to be matched to a flowline, the most upstream flowline in that reach is used.

Certain criteria were applied to the NHDPlus data set to select the reaches to be used in the model. Specifically, because RSEI calculates th e movement of a chemical release downstream using flow and velocity data, qua lifying reaches must have at l east one downstream or upstream connecting reach 44 , and have a non-negative flow and ve locity. 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 thes e types of reach is encountered. Additionally, some types of reaches are excluded from the set of fishable reaches, such as pipelines, aqueducts, a nd 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 annua l mean flow is less than 5 ft 3 /s. This is an arbitrary minimum, and is intended primarily to exclude ditches at th e 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 reach es with very small annual mean flows, RSEI also excludes the very smallest reaches from assi gnment as discharge reaches. To determine an appropriate minimum, a nationa l set of EPA-assigned discharg e 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 th e fifth percentile flow for each region was selected as the minimum annual mean flow. Assigned regional minimum values ranged from 0.0036 ft 3 /s to 1.9 ft 3 /s. For instance, if a facility in a region whose minimum value was selected as 0.0036 ft 3 /s had a nearest reach with annual mean flow of 0.0025 ft 3 /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 ht tp://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.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 63 selected as the discharge reach. Instead the next closest reach with a flow greater than 0.0036 ft 3 /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 Dr inking Water Information System (SDWIS). SDWIS is a publicly-accessible database that co ntains 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 Pub lic 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, includi ng the lat/longs.

The version of the Public Suppl y database used contains SD WIS 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 netw ork 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 kilo meter 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 reser voirs, intakes, and irriga tion 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 flowlin e. The runoff from each catchment area is summed and attributed to its assigned flowline. That fl ow 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 th e 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 Voge l method, but this is method is considered to have a narrow band of applicability.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 64 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, sl opes, and channel geometries. F our 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 fo r this, RSEI includes adjustment factors based on flow estimates from either in-stream gages or annua l 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 Lake s 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 A queous 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 co mmercially important chemical compounds, including TRI chemicals. HYDROWIN estimat es hydrolysis rate constants for esters, carbamates, epoxides, halomethanes , and selected alkyl halides. Va lues 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 th e equilibrium concentration of chemicals in aquatic organisms living in contaminat ed water. The BCF is defined as the ratio of the chemical concentration in the organism (mg/ kg) to that in the surr ounding 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( K ow ) values using regression equations from Lyman et al. (1990), or from the SRC estim ation 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/g reatlakes/hh/outflows/hist oric%20connecti ng%20channel%20outfl ows/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 uptak e 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 li mitations at the present time, only BCFs are used in the RSEI model.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 65 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 Exposur e 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 adjust ed intakes, EFH recommends using this data in preference to data from the Canadian Ministry of Na tional Health and Welfare (1981). As a result, the RSEI exposure factors are based on da ta from Ershow and Cantor (1989). The age groups presented for the recomme nded 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 rate s 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. Sin ce body weight was alrea dy 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 de rivation 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 am ount of fish ingested by recreational and subsistence fishers and their families. However, there are no national data on fish ingestion rates specific to recreational a nd 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 usedChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 66 ingestion rates from the 1994-1996 USDA Conti nuing Survey of Food Intake by Individuals (CSFII). This survey was conducted by the U.S. De partment of Agriculture in 50 states and the District of Columbia over a three-year period. A total of 15,303 indi viduals provided two nonconsecutive days of data on diet ary intake. Appropriate statisti cal techniques were used to extrapolate to the national popula tion. In a 2000 publication, EP A assigned specific fish species to habitats (freshwater, estuarine, and marine) ba sed on the majority of time the species spend in those habitats (EPA, 2000). Based on these as signments, 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 90 th percentile is used to represent ingesti on rates for recreational fishers, and the 99 th percentile is used for subsistence fishers. The inges tion rates are reported by age group (<15 years, 15-44 years, 45+ years) and sex (EPA, 2000). These va lues are roughly simila r 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 wei ghts, 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 releas es and transfers were tr ansferred to PubliclyOwned Treatment Works (POTWs). These transfer s are mostly of facility wastewater through underground sewage pipes to a POTW. Each ch emical transfer to a POTW is modeled as entering as liquid influent . Depending on the chemical’s physic al properties, some portion of the chemical release in the influent may be di scharged into surface water from the POTW, potentially resulting in human e xposure through drinking wate r or fish ingestion. The rest of the chemical release may be removed by the POTW th rough one or more of the following processes: 1) biodegradation, which is not modeled; 2) volatilization, whic h is modeled like other area air

51 Consumption of marine fish is not in cluded 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 June 2010 67 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 resulti ng effluent discharges from POTWs (the chemical-specific removal rate), and (4) consid eration of residuals management at POTWs (partitioning within the POTW).

5.5.1.1 Locating the POTW
In order to model re leases 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 stre et addresses were geocoded for Version 2.1.3 (coordinates were assigned based on st reet address). 52 POTWs (and incinerators) we re also matched to EPA’s Facility Registry System (FRS), based on na me, address, and RCRA identification number, where possible. Duplicate entries for the sa me 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 ma tching program (see Technical Appendix D for details). Once latitude and longitude for a facility are assigne d (from geocoding, the FRS data, or based on zip code centroids), the data are used to map the fac ility to the grid cell with the same coordinates. Substantial data processing was nece ssary to prepare the set of off- site facilities for use in the model; see Technical Appendix D for deta ils 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, wh ich in most cases specify the discharge reach by name or reach number. Pe rmit records, including pe rmits, monitoring data, and locational and descriptive in formation pertaining to more th an 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 Thom as Computing Services, a commercial firm.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 68 POTWs not matched to a PCS/ICIS -NPDES discharge reach were assumed to discharge to the nearest reach within four kilometers that meets the mini mum 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 chemi cals that are transferred to the plant from the TRI facility. Some of the chemical loading in th e influent will be discharged as effluent to surface waters. To calculate th e fraction of transferred chem ical 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 lis ting 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 us ing 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 part itioning rates (see Techni cal Appendix B for the listing of partitioning rates and re ferences for each chemical).

Once the fates of chemicals en tering 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 chemi cals 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 exposur e should ideally depend on the sludge disposal method employed by the POTW . However, sludge dis posal practices at a POTW receiving a TRI transfer cannot be determin ed 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 ex ample, this would result in different exposure levels (and a different, larger exposed populat ion). RSEI does not currently model land releases. A summary of the appro ach to modeling POTW emissions is found in Exhibit 5.11.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 69 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 landfillChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 70

5.5.1.5 Estimating Population Size for POTW Transfers The population exposed to air releases is assume d to be the population within 49 km around the POTW. The method used to estimate the populati on surrounding the POTW that is exposed to surface water effluent discharges up to 200 km down stream from these fac ilities is described in the section on exposed populations for surface wate r 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 expos ure 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 ar e described in the rele vant 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 re moval 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 effi ciency, or the total per centage 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 (P OTW Partition Volatile) or be biodegraded by microorganisms (POTW Partition Biodeg). The w ithin-POTW partitioning values are expressed as percentages of the total POTW removal e fficiency; 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 effi ciencies 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 act ivated sediment and full scale experiments, a geometric mean was derived and used as th e 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. Inorga nic chemicals, exceptChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 71 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 with in-POTW partitioning values.



### 5.6 Modeling Other Off-site Transfers

In 2007, approximately 48 percent of TRI emissions were transferred to of f-site locations other than POTWs for storage or disposal. TRI reporte rs are required to supply the name and address of the receiving facility, and the treatment method used. Cu rrently, only transfers that are incinerated at the off-site facility are modele d; waste transfers treate d by other methods do not receive risk-related scores.



### 5.6.1 Off-site Transfers: Method To assess the exposure potential a ssociated with off-site transf ers, it is important to have information about the off-site faci lity location and some of its char acteristics. Locations of other off-site facilities are determined in the same wa y as the locations of POTW s. The reported street addresses were geocoded for Version 2.1.3 (coor dinates 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 instan ce where two or more re porting 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 fo r details). Once latitude and longitude for a facility are assigned (from geoc oding, 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 repo rted (despite the requirement), the transfer is not evaluated in the algorithm, but is fla gged as a missing value and assigned a zero.

If the treatment method is incine ration, then destruction and re moval 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 exposur e 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 unde rground injection wells ar e also not modeled. Exhibit 5.13 summarizes the mode ling of off-site transfers.

53 Geocoding services were provided by Thom as Computing Services, a commercial firm.Chapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 72 

### 5.6.2 Estimating Population for Off-Site Transfers Similar to on-site air releases, the population expose d to air releases from off-site transfers is the population within 49 km around th e 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 Volatilization Rate Air release estimate Incineration Landfilling

Potential groundwater contamination Chemical partitioning data; wind speed dataChapter 5: Exposure and Population Modeling

Version 23.0 1988-2007 TRI data June 2010 73 

### 5.6.3 Off-site Transfers: Data

5.6.3.1 Destruction and Removal Efficiencies For organics, the destruction a nd 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 exposur e 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 em ission 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. EP A is evaluating screening-level methodologies which might be used to assess risk-related e xposures pertaining to groundwater exposure from on- and off-site land releases a nd volatilization from off-site land releases, so this version of RSEI does not provide risk modeling for reporte d land releases. However, RSEI does provide the capability for users to examine the pounds of rel eases to land that are re ported to TRI, as well as viewing these releases from a hazard-based perspective.

The potential for groundwater cont amination from land releases de pends 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 ha zardous 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 th at releases to groundwat er 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.), th ere is a potential for exposure. This exposure pathway and volatilization from of f-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, spatia l, and temporal considerations that are associated with exposures to toxic chemicals in underground injection wells are situ ation- and site-specific, so RSEI is only able to provide pounds-based and h azard-based perspectives for this type of land release. Note, however, that unde r well-managed conditions, Class I wells (there are five classes of wells) are specifically designe d to pose minimal risk to huma n health or the environment.Chapter 6: Calculating Results

Version 23.0 1988-2007 TRI data June 2010 74 

## 6. Calculating Results

This section summarizes the comput ation 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 th e Indicator Element. The surrogate dose is determined through pathway-specific modeling of the fate and transp ort of the chemical through the environment, combined with subpopulation-specific exposure fact ors. Risk-related scor es are unitless, and each of the components (toxicity weight, su rrogate 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 meas ures of relative risk-related im pacts that are comparable to approximate measures for other facilities (or othe r 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 path way 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, ch ildren 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 assu mptions have been applied.

Higher component “weights” are as sociated 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 wi th a slope factor or unit risk value and dose or exposure level. For chemicals with noncancer eff ects, the multiplicative na ture of the toxicity and exposure weights may not seem intuitive, be cause in risk assessments, risk is usually characterized as the estimated exposure divide d 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 de gree 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 higherChapter 6: Calculating Results

Version 23.0 1988-2007 TRI data June 2010 75 Indicator Element value than a ch emical 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 belo w 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 estim ation of surrogate dose is only a screening-level approximation for the purposes of comparing one re lease to another in a relative way, and should never be considered an actual cal culation of exposure. To exclude releases re sulting 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 indi vidually 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 a ssociated with a hazard-based result (“Hazard”), calculated by multiplying the pounds re leased by the appropriate chemical-specific toxicity weight (the toxicity weight also depends on the expo sure 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 us ed for releases or transfers to direct water and POTWs. For releases that ar e not modeled (because the pathway is not modeled or because other necessary data, such as physic ochemical 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,” wh ich is the chemical- and pathway-specific toxicity weights multiplied by the Modeled Poun ds (as described above), and “Modeled Hazard * Pop,” which multiplies modeled hazard by the poten tially exposed populat ion, 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 P ounds”) reflect only the numb er 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 Toxi city Weights,” which simply sums the pounds for chemicals that have toxicity weights in RSEI.Chapter 6: Calculating Results

Version 23.0 1988-2007 TRI data June 2010 76 

### 6.1 Combining Indicator Elements

Once results are calculated for each Indicator Elem ent, they can be combined in many different ways. All of the results are additive, so a resu lt for a specific set of variables is calculated by summing all the relevant individual Indi cator Element results, as follows: 

## ∑ ∑ ∑
= p , f

, c IE R (Eq. 6.1)

where:

R = RSEI result, and IE c,f,p = chemical-facility-pathway-specific Indicator Element result.

This method is very flexible, allowing for coun tless variation in the cr eation 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 re sults over the years would imply that there have been changes in hazard- or ri sk-related environmental impacts, the actual magnitude of any specific change or the reason ma y not be obvious. Although the va lue itself may be useful in identifying facilities or chemical s with the highest potential for h azard 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 re porting 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 fo r the changes respective to trend analyses. However, such changes would not necessarily re present a large change in actual environmental impact, but would reflect a broade r understanding of the impacts th at may have always existed.

A change in the number of chemicals and fac ilities 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 initia tes its own action through the chemical listing or delisting process. Several additi ons and deletions to the data se t have already occurred since 1987 (the first year of TRI reporting). Furt hermore, the Agency added 245 chemicals and chemical categories to th e 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 rem oved 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 chemical s that have been listed on the TRI since the first year (1987) of reporting and which have had no change in thei r reporting requirements. Another identifier,Chapter 6: Calculating Results

Version 23.0 1988-2007 TRI data June 2010 77 “Mini Core” denotes chemicals that have been listed on the TRI since 1995 and which have had no change in their reporting requirements. Th ese identifiers allow us ers to conduct separate analyses for the “Core” or “Mini Core” chemical s, and so exclude chemicals whose emissions changes over time are caused by re porting 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 gene rated 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 f acilities may occur as a result of changes in reporting requirements. For instance, in the fi rst two years of reporting, facilities that manufactured or processed more than 50,000 pounds were required to repo rt 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- a nd relative risk-based imp acts. 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 th e 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 fo r a given list of chemicals a nd facilities are the subject of ongoing quality control review and revision. As a re sult, yearly comparisons could be flawed if ongoing revisions by individual facilities were not in cluded in each year’s re sults. Therefore, the Indicator Elements are re-computed for all years in the database on an annual basis in order to incorporate revisions to the repo rting data. This a nnual calculation is ba sed on the corrections incorporated in annual Public Data Rel eases available from EPA’s TRI program.

54 This facility expansion rule required the affected facilitie s 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 R CRA), 5169, 5171, and 7389 (limited to facilities engaged primarily in solvent recovery services on a contract or fee basis) ( EPA 1997a).Chapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 78 

## 7. Current Implementation of the RSEI Method

7.1 RSEI Model

The RSEI model is currently implemented in a Microsoft Wi ndows-based computer program. The program allows users to calculate RSEI re sults for reporting year s 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., Microsof t Excel and databases such as Access). The program includes on-line help for all of the progr am 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 activit ies would have taken days, weeks, or even months to organize the relevant information, evaluate that inform ation, and perform the complex and sophisticated analyses that are necessary to provide a risk-r elated perspective. Results can be used for screening-level rank ing and prioritization for strategic planning purposes, risk-related targeting, and trends analyses. Consider able resources can be saved by conducting preliminary analyses with the model to identify risk-related situa tions of high potential conc ern, and which warrant further evaluation.

As noted above, users can evaluate releases us ing a number of variables, such as chemical, medium, geographic area or industry. For inst ance, the following types of questions can be investigated:

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

• What is the relative contri bution 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 re sults to investigate th e 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 pe rsonal computer, RSEI em ploys a facility-based approach. All modeled impacts are attributed to the facility orig inally 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 transf erred 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 ge ographic-based. These very large datasets are operated outside of the user-friendly interface provi ded by the RSEI model.

RSEI Version 2.3.0 is availabl e without charge from EPA on an installation disk (EPA No. 740- F-10-001, June 2010, or it can be downl oaded from the RSEI web site atChapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 79 http://www.epa.gov/oppt/rsei/pubs/g et_rsei.html. The instal led RSEI model requires approximately 2 GB of hard disk space. It is designed for operati on using a 32-bit operating system (Windows 95, 98, 2000, XP, and NT4). Th e program is written using Borland’s Delphi (the software is coded in Obj ect Pascal) and uses the Paradox f ile 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 environmen tal quality over time, RSEI provides EPA and the public with a valuable tool to measure ge neral trends based upon the relative risk-related impacts of TRI chemicals. Alt hough RSEI results do not capture all environmental releases of concern, they generally relate ch anges 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 chem icals and industrial sectors to human health impacts, and RSEI results serve as an analytical basis for setting priorities for pollution prevention, regulatory initiatives, en forcement targeting and chemical testing requirements.

55 The RSEI website is availa ble at www.epa.gov/oppt/rsei.Chapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 80 

## 8. References

General References Bodek, I., W.J. Lyman, W.F. Reehl, and D.H. Rosenblatt. 1988. Environmental Inorganic Chemistry. Pergamon Press. New York.

Boubel, R.W., et al. 1994. Fundamentals of Air Pollution. Academic Press. New York.

Canadian Ministry of Nationa l Health and Welfare. 1981. Tapwater Consumption in Canada. Doc # 82-EHD-80. Public Affairs Director ate, Department of National Health and Welfare, Ottawa, Canada.

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 In take in the United States: Population-Based Estimates of Quantities and Sources. Life Sciences Research Office, Federation of American Societie s for Experimental Biology.

Harrigan, P. 2000. Office of Water, U.S. E nvironmental Protection Agency (EPA). Personal communication. March.

Horn, Marilee. 2008. U.S. Geological Surv ey (USGS). Written communication and provision of data via email. April.

Jobson, H. E., Prediction of Traveltime and Longit udinal Dispersion in Rivers and Streams , U.S. Geological Survey Water Resources Inve stigations Report 96-4013 (1996), U.S. Geological Survey.

Lyman, W. J., W. F. Reehl, and D. H. Rosenblatt. 1990. Handbook of Chemical Property Estimation Methods. American Chemi cal Society. Washington, D.C.

Syracuse Research Corporation (SRC). 1994- 1999. EPIWin v 3.01-EPA. Syracuse, NY.

Syracuse Research Corporation (SRC). 2002a. Chemfate/Environmental Fate Data Base. Accessed online at http://esc.s yrres.com/efdb.htm (now at http://www.syrres.com/esc/efdb.htm).

Syracuse Research Corporation (SRC). 2002b. PhysProp Data Base. Accessed online at http://esc.syrres.com/inter kow/physdemo.htm (now at http://www.syrres.com/esc/chems3.htm).

Tudor, L., H. Jacobs, and P. Harrigan. 2000. Memorandum to EAD, SASD, and HECD. Subject: Fish Consumption Rates to Us e in Environmental Assessments/Benefits Analyses. May 10.Chapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 81 U.S. Census Bureau. 1996. TABLE 1. Projectio ns of Households by Type: 1995 to 2010, Series 1, 2, and 3. Available at: http:/ /www.census.gov/prod/1/po p/p25-1129.pdf. May.

U.S. Department of the Interior, Fish and Wildlife Service (DOI, FWS). 1993. 1991 National Survey of Fishing, Hunting, and Wildlife Associated Recreation . U.S. Department of Commerce, Bureau of the Census. U.S. G overnment Printing Offi ce, Washington D.C.

U.S. Environmental Protection Agency (EPA). 1985. Exposure to Airborne Contaminants Released from Land Disposal Facilities -- A Proposed Me thodology. Prepared for the Office of Solid Waste by Environmental Scie nce and Engineering, Inc. ESE Document Number 85-527-0100-2140. August.

U.S. Environmental Protection Agency (EPA). 1986a. Guidelines for Carcinogen Risk Assessment . 51 Federal Register 33992 (September 24, 1986).

U.S. Environmental Protection Agency (EPA). 1986b. Guidelines for Mutagenicity Risk Assessment. 51 Federal Register 34006 (September 24, 1986).

U.S. Environmental Protection Agency (EPA). 1987. Graphical Exposure Modeling System (GEMS) User’s Guide . Prepared for the Office of Pe sticides and Toxic Substances, Exposure Evaluation Division by General Sc iences Corporation under Contract No. 68023970. February.

U.S. Environmental Protection Agency (EPA ). 1988a. IRIS Background Document #1. Reference Dose (RfD): Description and Use in Health Risk Assessments. Integrated Risk Information System (IRIS). Online. Ma intained by Environmental Criteria and Assessment Office, Cincinnati, OH.

U.S. Environmental Protection Agency (EPA). 1988b. Report to Congress: Solid Waste Disposal in the United States . Volume 2. April.

U.S. Environmental Protection Agency (EPA). 1988c. National Survey of Solid Waste (Municipal) Landfill Facilities. Office of Solid Waste EPA/530-SW88-034. September.

U.S. Environmental Protection Agency (EPA). 1988d. Industrial Subtitle D Risk Screening Analysis Results. Prepared for the Office of Solid Waste by ICF, Inc. December 30.

U.S. Environmental Protection Agency (EPA). 1990a. Exposure Factors Handbook . Office of Health and Environmental Assess ment. 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 Reduction Project. Reducing Risk. EPA SAB-EC-90-021. September.Chapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 82 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. September.

U.S. Environmental Protection Agency (EPA). 1991b. Guidelines for Developmental Toxicity 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 Sl udge: 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. Descript ion 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 Pla nning and Standards. October.

U.S. Environmental Protection Agency (EPA). 1999a. “Residual Risk Report to Congress”. EPA-453/R-99-001. United Stat es Environmental Protecti on 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.

U.S. Environmental Protection Agency (EPA). 1997a. Addition of Facilities in Certain Industry 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 Collect ed by the United States Department of Agriculture’s 1994-1996 Continuing Sur vey of Food Intake by Individuals. Office of Water, Office of Science and Technology. March.

U.S. Environmental Protection Agency (EPA). 2004a. AERMOD: Description of Model Formulation. Office of Air Quality, Planning and Standards. September. EPA-454/R03-004. Accessed online at http://www.epa.gov/scram001/7t hconf/aermod/aermod_mfd.pdf.

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/scram001/7thcon f/aermod/aermod_userguide.zipChapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 83 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
at: http://www.epa.gov/ttn/fera/d ata/hem/hem3_users_guide.pdf

. U.S. Environmental Protection Agency (EPA ) and U.S Geological Survey (USGS). 2009. NHDPlus User’s Guide. January 20. Available at ftp://ftp.horizonsystems.com/NHDPlus/documentation/NHDPLUS_UserGuide.pdf

.

RSEI supporting documentation released by EPA

Cthese documents can be found on the RSEI website (under the Docu ments 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 - Lis ting of All Toxicity Wei ghts 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 - Locati onal Data for TRI Reporting Facilities and Off-site Facilities Technical Appendix E - Derivati on of Stack Parameter Data Technical Appendix F - Summ ary 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 Co mments. Prepared for the Office of Pollution Prevention and Toxics, Economi cs, Exposure and Technology Division, Regulatory Impacts Branch. May 1997. Prepared by Abt A ssociates under Contract # 68- D2-0175.

TRI Relative Risk-based Environm ental Indicators: Methodology. Prepared for the Office of Pollution Prevention and Toxics, Economi cs, Exposure and Technology Division, Regulatory Impacts Branch. J une 1997. Prepared by Abt A ssociates under Contract # 68-D2-0175.

TRI Relative Risk-based Environmental Indica tors: Interim Toxicity Weighting Summary Document. Prepared for the Office of Pollution Prevention and Toxics, Economics, Exposure and Technology Divisi on, Regulatory Impacts Branch . 1997. Prepared by Abt Associates under Cont ract # 68-D2-0175.Chapter 7: Current Implem entation of the RSEI Method

Version 23.0 1988-2007 TRI data June 2010 84 Ground-Truthing of the Air Path way Component of OPPT’s Ri sk-Screening Environmental Indicators Model. December 1998.

Estimates of Stack Heights and Ex it Gas Velocities for TRI Facili ties in OPPT’s Risk-Screening Environmental Indicators Model. June 1999.