Final, November 2014

EJSCREEN Environmental Justice 

Mapping and Screening Tool 

 

EJSCREEN

Technical Documentation

May 2015
EJSCREEN 

Environmental Justice Mapping and Screening Tool

EJSCREEN Technical Documentation

May 2015 

U.S. Environmental Protection Agency

Office of Policy

Washington, D.C. 20460

Suggested citation:

U.S. Environmental Protection Agency (EPA), 2015. EJSCREEN Technical Documentation.

For more information: 

www.epa.gov/ejscreen
ACKNOWLEDGEMENTS

This document was prepared by the United States Environmental Protection Agency’s Office of Policy

(OP) with drafting, editing and technical support from Abt Associates. ESRI and SAIC, working with the 

Office of Environmental Information (OEI), obtained and processed datasets obtained from the Census 

Bureau, US Department of Transportation (DOT), and various EPA offices including the Office of Air

(OAR), Office of Research and Development (ORD), Office of Water (OW), and Office of Solid Waste and 

Emergency Response (OSWER). Ongoing input, review, and outreach have been provided by all of these 

offices as well as EPA’s Regional offices, Office of Chemical Safety and Pollution Prevention (OCSPP), 

Office of International and Tribal Affairs (OITA), Office of Enforcement and Compliance Assurance 

(OECA), Office of Environmental Justice (OEJ), and Office of General Counsel (OGC). External expert peer 

reviewers provided comments on the methods and documentation as well. Specific acknowledgement is 

given to the contributions of Mark Corrales, Bridgid Curry, William Nickerson, and Fred Talcott in the 

design of EJSCREEN and the preparation of this document.
Contents

1 INTRODUCTION..........................................................................................................................6

ENVIRONMENTAL JUSTICE AT EPA.................................................................................................................... 6

ENVIRONMENTAL JUSTICE MAPPING AND SCREENING AT EPA .............................................................................. 7

DEVELOPMENT OF EJSCREEEN....................................................................................................................... 8

PURPOSES AND USES OF EJSCREEN................................................................................................................. 8

CAVEATS AND LIMITATIONS OF EJSCREEN........................................................................................................ 9

ADVANCES IN EJ SCREENING PROVIDED BY EJSCREEN......................................................................................11

2 OVERVIEW OF DATA AND METHODS IN EJSCREEN.....................................................................13

ENVIRONMENTAL INDICATORS SELECTED FOR EJSCREEN ..................................................................................13

DEMOGRAPHICS IN EJSCREEN......................................................................................................................16

Using Demographics as Proxies for Potential Susceptibility ...............................................................................16

Demographic Indicators Included in EJSCREEN...................................................................................................20

Demographic Indexes in EJSCREEN .....................................................................................................................21

ENVIRONMENTAL JUSTICE INDEXES IN EJSCREEN.............................................................................................22

How the EJ Index Works......................................................................................................................................22

Supplementary EJ Indexes...................................................................................................................................24

EJ Indexes, Population Density, and Rural Areas ................................................................................................25

Why the EJ Indexes are Not all Combined...........................................................................................................26

PERCENTILES...............................................................................................................................................27

What a Percentile Means....................................................................................................................................27

Color-coded High Percentile Bins........................................................................................................................28

BUFFER REPORTS.........................................................................................................................................30

What the Buffer Report Calculates .....................................................................................................................30

Choosing a Buffer Size versus Rationale for Distance in Proximity Indicators ....................................................30

3 DETAILS ON THE ENVIRONMENTAL INDICATORS IN EJSCREEN....................................................33

ENVIRONMENTAL FACTORS NOT INCLUDED .....................................................................................................33

ENVIRONMENTAL FACTORS IN EJSCREEN.......................................................................................................35

4 BIBLIOGRAPHY .........................................................................................................................67

Appendices

Appendix A. Development of EJSCREEN............................................................................................................... 78

Appendix B. Technical details on percentiles, rounding, buffering, and demographic data.................................. 80

Appendix C. Technical details on proximity indicators....................................................................................... 100

Appendix D. Summary statistics for indicators................................................................................................... 107

Appendix E. Formulas for demographics and EJ indexes.................................................................................... 115

Appendix F. Quality control / quality assurance ................................................................................................ 118

Appendix G. Peer review ................................................................................................................................... 120

Appendix H. Initial filter approach for screening................................................................................................ 122

iv | Page
Tables 

Table 1. Types of Environmental Indicators Included in EJSCREEN....................................................................... 15

Table 2. Summary Table of Environmental Indicators and Sources...................................................................... 35

Table 3. Likelihood of Lead-Based Paint Hazards by Housing Construction Date.................................................. 49

Table 4. Tallies of 2008-12 ACS Block Groups Used in 2015 Version of EJSCREEN................................................. 85

Table 5. ACS Tables Underlying EJSCREEN Demographic Data and Lead Paint Indicator ...................................... 87

Table 6. Summary Statistics for Environmental Indicators................................................................................. 108

Table 7. Summary Statistics for Demographics.................................................................................................. 109

Table 8. Spearman Correlation Coefficients for Environmental Indicators......................................................... 111

Table 9. Spearman Correlation Coefficients for Demographic Indicators........................................................... 112

Figures

Figure 1. Histograms of Block Group Environmental Indicators as ratio to mean value (log scale shows mean 

value as zero) ............................................................................................................................................ 113

v | Page
Introduction

EJSCREEN Environmental Justice 

Mapping and Screening Tool 

Technical Documentation

1 INTRODUCTION

The United States Environmental Protection Agency (EPA) is charged with protecting human health and 

the environment for all Americans. In order to better meet the Agency’s responsibilities related to the 

protection of public health and the environment, EPA has developed a new environmental justice (EJ) 

screening tool, called EJSCREEN. In some ways, EJSCREEN is similar to prior screening or mapping tools. 

As a new tool, however, it offers improvements such as easy web-based access to powerful mapping 

and data reporting tools, a wide range of updated demographic information, environmental indicators 

addressing more topics, and higher resolution maps covering the entire nation. EJSCREEN also provides 

standard reports that bring together environmental and demographic data in the form of EJ indexes. 

These are summarized as percentiles to put the information in perspective and facilitate comparisons 

between locations.

EJ screening tools may be used to explore one location using a data report, or to look across a wide area 

using maps. EJ tools have been used in a wide variety of circumstances, and EJSCREEN can support a 

similarly broad range of applications. EJSCREEN provides useful data and indicators, and highlights 

places that may be candidates for further review, including additional consideration, analysis or 

outreach.

This document describes EJSCREEN within the context of EPA’s EJ program, and provides details on the 

data and methods used to create the indicators and indexes in EJSCREEN. The Appendices in this 

document provide additional detail on data and methods for interested users.

Environmental Justice at EPA

Since EJ mapping and screening is just one aspect of EPA’s ongoing commitment to environmental 

justice, it is helpful to understand the broad, historical context of EPA’s EJ work. 

EPA has defined “environmental justice” as follows:

Environmental Justice is the fair treatment and meaningful involvement of all people regardless

of race, color, national origin, or income with respect to the development, implementation, and 

enforcement of environmental laws, regulations, and policies. ... Fair treatment means that no 

group of people should bear a disproportionate share of the negative environmental 

6 | Page
Introduction

consequences resulting from industrial, governmental and commercial operations or policies.1

(italics added)

EPA’s efforts to understand EJ concerns date back at least to EPA’s 1992 report on Environmental Equity 

(U.S. EPA, 1992). The 1992 report documented health and exposure disparities associated with 

race/ethnicity and income. To address such disparities, in 1994, Executive Order 12898 (EO 12898) 

mandated that each covered federal agency make achieving environmental justice part of its mission by 

identifying and addressing, as appropriate, any disproportionately high and adverse human health or 

environmental effects of its programs, policies and activities on minority, low-income, tribal and 

indigenous populations.2

These early activities provided a foundation for EPA’s continued commitment to environmental justice, 

which was reaffirmed in January 2010 when EPA Administrator Lisa P. Jackson announced Expanding the 

Conversation on Environmentalism and Working for Environmental Justice as one of the Agency’s top 

seven priorities. EPA has made great progress implementing this priority and has worked across the 

Agency to make a difference in overburdened communities, a goal that has been reiterated by EPA 

Administrator Gina McCarthy. 

EPA has been engaged in a variety of research and analytic efforts related to environmental justice, to 

support both regulatory analysis and screening efforts. Recent efforts have been guided by Plan EJ 2014, 

which was released for public comment in July 2010 and finalized in September 2011.

3 EPA’s Office of 

Environmental Justice (OEJ), in conjunction with the rest of the Agency, works to protect human health 

and the environment in communities overburdened by environmental pollution by integrating 

environmental justice into all EPA programs, policies and activities.

Environmental Justice Mapping and Screening at EPA

Mapping tools as well as screening-level applications have a substantial history at EPA, and EJ is one area 

in which maps and screening can be useful. Several EPA Regional offices have used basic screening tools 

that map demographic information and allow staff to overlay selected environmental data such as 

facility locations. In connection with EJSEAT, a screening tool that was developed by EPA’s Office of 

Enforcement and Compliance Assurance (OECA), the National Environmental Justice Advisory Council 

(NEJAC) provided recommendations to EPA about how to design an EJ screening tool in its May 2010 

report, “Nationally Consistent Environmental Justice Screening Approaches” (NEJAC, 2010). 

These various early screening tools have been used for internal EPA purposes only, and generally were 

not available to the public. EPA’s main publicly available EJ mapping tool until 2015 was EJVIEW,4 a webbased tool that displayed selected demographic and environmental data, and allowed users to overlay 

these data on maps of a community or wider area.

1 http://www.epa.gov/environmentaljustice/basics/index.html, accessed 10/16/2014. 2 http://www.archives.gov/federal-register/executive-orders/pdf/12898.pdf 3 http://www.epa.gov/environmentaljustice/plan-ej/index.html 4 http://www.epa.gov/compliance/ej/mapping.html

7 | Page

 
Introduction

Development of EJSCREEEN

Plan EJ 2014 included a commitment to develop a nationally consistent environmental justice screening

and mapping tool in order to better meet the Agency’s responsibilities related to the protection of 

public health and the environment in a manner that is consistent with EO 12898 and the goals of Plan EJ 

2014. This commitment was the impetus behind the development of EJSCREEN. This new effort 

provided an opportunity to reassess and build upon prior efforts, while considering new data, new 

scientific findings, new analytic methods and a variety of policy considerations.

The goal in developing EJSCREEN has been to take account of this prior progress and learning, and 

provide a new, user-friendly screening tool that addresses policy questions and stakeholder concerns in 

an informative manner. An important part of this effort has been to ensure the screening tool reflects an 

appropriate balance between simple, feasible, screening-level information on the one hand, and highquality data and strong science on the other.

Development of EJSCREEN began in late 2010, and EPA staff began using an early version in 2012, as 

prior tools such as EJSEAT were phased out. EJSCREEN was peer reviewed in early 2014 through a letter 

review (see Appendix G), and updated with newer data and an improved interface in 2014/2015. 

EJSCREEN was released to the public in 2015, replacing EJVIEW as EPA’s public-facing EJ mapping tool.

Purposes and Uses of EJSCREEN

EJ mapping and screening tools combine environmental and demographic indicators in maps and 

reports. This information can help to highlight geographic areas and the extent to which they may be 

candidates for further review, including additional consideration, analysis or outreach. The tools also 

allow users to explore locations at a detailed geographic level, across broad areas or across the entire 

nation. Environmental indicators typically are direct or proxy estimates of risk, pollution levels or 

potential exposure (e.g., due to nearby facilities). Demographic indicators are often used as proxies for a 

community’s health status and potential susceptibility to pollution. Environmental and demographic 

data and indicators may be viewed separately or in combination. 

This type of screening information may be of interest to communities as well as many other 

stakeholders, and also can support a wide range of research and policy goals. In general, EPA’s efforts 

are more effective and efficient if they are informed by an understanding of where the impacts of 

existing pollution may be greatest. Screening tools can also help ensure that such areas are not 

overlooked, and receive appropriate consideration, analysis or outreach. 

Screening tools can be appropriately put toward a wide variety of uses. The public has used EJVIEW in a 

variety of ways, and is likely to use EJSCREEN in many ways as well. EPA has used existing internal EJ 

screening tools in aspects of enforcement, compliance, the Superfund program, permitting, and 

voluntary programs. Screening tools also have been used in developing retrospective reports, and to 

enhance geographically based initiatives. EJSCREEN will be able to support a similarly wide variety of 

uses. 

8 | Page
Introduction

Screening tools should be used for a “screening-level” look. Screening is a useful first step in 

understanding or highlighting locations that may be candidates for further review. However, it is 

essential to remember that screening-level results do not provide a complete assessment of risk, and 

have significant limitations. 

Caveats and Limitations of EJSCREEN

EJSCREEN is a pre-decisional screening tool, and was not designed to be the basis for agency decisionmaking or determinations regarding the existence or absence of EJ concerns. It also should not be used 

to identify or label an area as an “EJ Community.” Instead, EJSCREEN is designed as a starting point, to 

highlight the extent to which certain locations may be candidates for further review or outreach. 

EJSCREEN’s initial results should be supplemented with additional information and local knowledge 

whenever appropriate, for a more complete picture of a location. Additional considerations and data, 

such as national, regional, or local information and concerns, along with appropriate analysis, should 

form the basis for any decisions.

EJSCREEN, as a screening tool, is more limited than a detailed analysis in two key ways. First, it has data 

on only some of the relevant issues, and second, there is uncertainty in the data it does have. It is 

important to understand each of these limitations.

The first limitation arises because a screening tool cannot capture all the relevant issues that should be 

considered (e.g., other local environmental concerns). Any national screening tool must balance a desire 

for data quality and national coverage against the goal of including as many important environmental 

factors as feasible given resource constraints. Many environmental concerns are not yet included in 

comprehensive, nationwide databases. For example, data on environmental factors such as local 

drinking water quality and indoor air quality were not available with adequate quality, coverage and/or 

resolution to be included in this national screening tool. EJSCREEN cannot provide data on every 

environmental impact and demographic factor that may be important to any location.

The second important limitation is that EJSCREEN relies on demographic and environmental estimates 

that involve substantial uncertainty. This is especially true when looking at a small geographic area, such 

as a single Census block group. A single block group is often small and has uncertain estimates. A buffer 

that is roughly the same size as a block group or smaller will introduce additional uncertainty because it 

has to approximate the locations of residences. Therefore, it is typically very useful to summarize 

EJSCREEN data for a larger area, covering several block groups, in what is called a “buffer” report, as 

explained later in this document. There is a tradeoff between resolution and precision: Detailed maps at 

high resolution can suggest the presence of a local “hotpot,” but are uncertain. Estimates based on 

larger areas will provide more confidence and precision, but may overlook local “hotpots” if not 

supplemented with detailed maps.

The demographic uncertainty combined with uncertainty in environmental data means EJ index values 

are often quite uncertain for a single block group. Therefore, modest differences in percentile scores 

between block groups or small buffers should not be interpreted as meaningful because of the 

uncertainties in demographic and environmental data at the block group level. We do not have a high 

9 | Page
Introduction

degree of confidence when comparing or ranking places with only modest differences in estimated 

percentile. For this reason, it is critical that EJSCREEN results be interpreted carefully, particularly for 

individual block groups, and that additional information be used to supplement or follow up on 

screening, where appropriate.

The demographic estimates, such as percent low-income, come from the American Community Survey 

(ACS) from the United States Census Bureau. The ACS is comprised of surveys, not a full census of all 

households. This means the Census Bureau may estimate that a block group is 30% low-income, for 

example, but it might actually be 20% or 40% in some cases (see Appendix B for a discussion of 

uncertainty in demographics).

Uncertainties are also discussed in section 2 (with regard to buffer reports), and Appendix B (in 

discussions of buffering details and demographic data).

Related to the issue of uncertainty is that fact that the environmental indicators are only screening-level 

proxies for actual exposures or health risks. This is particularly true for the proximity indicators, for 

example. Even for the indicators that directly estimate risks or hazards, as with the air toxics cancer risk 

indicator, estimates have substantial uncertainty because emissions, ambient levels in the air, exposure 

of individuals, and toxicity are uncertain. Section 3 provides technical details on each environmental 

indicator.

The inclusion of a dataset in EJSCREEN does not imply it is the newest, best, or primary estimate of 

actual conditions or risks. Estimates are based on historical data and may not reflect current or future 

conditions. The vintage of environmental indicators varies and is not the same as the vintage of the 

demographic data. The NATA air toxics indicators and the PM2.5 and ozone indicators in particular should 

be viewed with this in mind, because emissions related to PM2.5, ozone, and air toxics generally have 

decreased in recent years. This version of EJSCREEN incorporated the most recent data that were 

available at the time of each indicator’s development. Every attempt will be made to use the most 

recent appropriate data available in future updates of EJSCREEN. There is always a delay between the 

release of raw data and their eventual incorporation into any models, tools, or maps. It is also useful to 

note that although the raw numbers for some indicators do not represent current conditions, the 

percentiles are much more likely to be reasonably representative of today’s conditions in most 

locations. This is because even if emissions have been significantly reduced overall, for example, the 

differences between various locations are unlikely to have changed as dramatically, especially when the 

reductions have come from national regulations and other trends affecting entire industries or sectors in 

many locations. For this reason, the percentiles may be more representative of current conditions than 

the raw values of the indicators. Finally, some supplementary maps and local information can 

complement the EJSCREEN indicators to provide more recent information. In particular, EJSCREEN also 

provides updated maps of PM2.5 and ozone nonattainment areas (areas not meeting national ambient 

air quality standards).

There are also some limitations in geographic coverage -- EJSCREEN lacks data in some locations for 

some indicators, such as in Alaska, Hawaii, and Puerto Rico.

10 | Page
Introduction

In short, as with any screening tool, the indicators in EJSCREEN cannot address all the considerations 

that may be relevant to a given situation, they are often only a screening-level proxy for a given issue, 

and in any case, there is significant uncertainty involved, particularly for a single block group. For these 

types of reasons, among others, it generally is not appropriate to rely on any screening tool as the basis 

for a key decision. It is often very useful to obtain information on other issues not included in EJSCREEN, 

updated information when available, as well as local knowledge, data, and concerns.

Advances in EJ Screening Provided by EJSCREEN

EJSCREEN offers a variety of enhancements relative to previous approaches to EJ mapping and screening 

or analysis. For example, the tool includes updated demographic information (from the ACS) rather than 

relying on the Census that is conducted every ten years. It also provides several new environmental 

indicators, covering a wider range of issues such as traffic volume and proximity. EJSCREEN includes a 

suite of EJ indexes that quantify the combination of environmental and demographic indicators. It 

includes high resolution maps, and a new geospatial software and data system that improves access to 

new tools with a simple, browser-based interface and centralized, consistent data. The buffer reports 

are calculated using detailed Census block data for more accurate estimates of where residents are 

located. EJSCREEN provides access to a great deal of data, and presents standardized reports. These 

reports use summary metrics and percentiles to facilitate national, regional or state-level perspectives 

and a better understanding of EJ issues.

EJSCREEN can help explore the environmental, demographic and EJ characteristics of a block group or 

buffer area. It provides numerical estimates for each place, for both environmental and demographic

data, such as the traffic proximity indicator, or the percentage of local residents who are racial/ethnic 

minorities. 

EJSCREEN also presents multiple “EJ Indexes” for each place. An EJ Index is a way of combining, in a 

quantitative way rather than only visually on a map, the environmental indicator and the demographic 

information for a location. A separate EJ Index is provided for each environmental indicator in EJSCREEN, 

for each block group in the US. The EJ Index goes beyond a simple visual overlay of maps of environment 

and demographics, to actually quantify the extent to which these two factors co-occur.

In EJSCREEN, the basic level of geographic resolution is the Census block group. Each block group is 

defined by the U.S. Census Bureau, with a logical and unambiguous numbering scheme, and associated 

digital shape files that permit mapping with modern geographical information system (GIS) software. 

Block group data are widely used by researchers and others. Block groups also provide a relatively stable 

framework; for instance, block groups are not subject to frequent boundary definition changes that 

political jurisdictions and postal ZIP codes may experience. 

Estimates in EJSCREEN are compiled by block group, and that is the most detailed level at which results 

can be viewed. However, demographic estimates for a single block group are often based on a small 

sample of the local population, and are uncertain. Similarly, some environmental indicator estimates 

are derived from lower-resolution data, and all involve uncertainty. Therefore, it is typically very useful 

and advisable to summarize EJSCREEN data within a larger area that covers several block groups, in what 

11 | Page
Introduction

is called a “buffer” report. An EJSCREEN user can specify or draw buffers of custom sizes and shapes as 

needed. For example, a buffer could include all residents within 1 mile of a certain location. When a 

buffer covers several block groups, it provides an estimate that has less uncertainty than a single block 

group or smaller buffer would. EJSCREEN summarizes data for all residents within some distance from a 

selected point, using a circular buffer, or within a user-defined buffer of any shape, using Census blocks

(not just block groups) to refine estimates of how many residents are inside the buffer, as explained in

Appendix B. 

12 | Page
Overview of Data and Methods

2 OVERVIEW OF DATA AND METHODS IN EJSCREEN

This section describes the environmental and demographic data used in the tool, as well as the methods 

used to combine them and produce EJ Indexes. As of 2015, EJSCREEN contains 12 environmental 

indicators, which range from estimates of human health risk to proxies for potential exposure such as 

proximity to hazardous waste sites. The tool also contains six demographic indicators, which are 

combined into two separate demographic indexes (the demographic index uses the average of two 

indicators, and a supplementary demographic index uses the average of all six). A demographic index is 

combined with an environmental indicator to create an associated EJ Index. The environmental,

demographic, and EJ indicators and indexes are all calculated for each block group, and can be 

summarized within a defined buffer area. The sections immediately below summarize the 

environmental data, demographic data, and EJ indexes. Section 3 provides more detail on each 

environmental indicator.

Environmental Indicators Selected for EJSCREEN

Some environmental indicators used in EJSCREEN quantify proximity to and the numbers of certain 

types of potential sources of exposure to environmental pollutants, such as nearby hazardous waste 

sites or traffic. The lead paint indicator indicates the presence of older housing, which often, but not 

always, indicates the presence of lead paint, and therefore the possibility of exposure. In some cases, 

the term “exposure” is used very broadly here to refer to the potential for exposure. Others indicators in 

EJSCREEN are estimates of ambient levels of air pollutants, such as PM2.5, ozone, and diesel particulate 

matter. Still others are actual estimates of air toxics-related cancer risk, or a hazard index, which 

summarizes the ratios of ambient air toxics levels to health-based reference concentrations. In other 

words, these environmental indicators vary widely in what they indicate, as discussed further below.

A variety of considerations has informed the selection of these environmental indicators; in general, the 

selected indicators exhibit the following characteristics: 

• Resolution: Screening level data are available (or could be readily developed) at the block 

group level (or at least close to this resolution).

• Coverage: Screening level data are available (or could be readily developed) for the entire 

United States (or with nearly complete coverage).

• Relevance to EJ: Pollutants or impacts are relevant to EJ (e.g., differences between groups 

have been indicated in exposures, susceptibility, or health endpoints associated with the 

exposures)

• Public health significance: Pollutants or impacts are potentially important in the United 

States (e.g., notable impacts estimated or significant concerns have been expressed, at least 

locally, or exposure has been linked to health endpoints with substantial impacts 

nationwide).

EPA selected environmental indicators after a review of data availability (including the criteria and 

review of data availability for an Environmental Quality Index, now provided by county in Messer, Jagai, 

13 | Page
Overview of Data and Methods

Rappazzo, & Lobdell (2014), and described in Lobdell, Jagai, Rappazzo, & Messer (2011)); health 

disparity information (e.g., CDC’s major 2011 report on health disparities (Centers for Disease Control 

and Prevention, 2011a)); risk ranking studies (e.g., Unfinished Business (U.S. EPA, 1987), Reducing Risk 

(U.S. EPA, 1992), and related reports); and risk estimates from major studies (e.g., related to PM2.5

ambient standards). EPA also reviewed data from the CDC (Centers for Disease Control and Prevention, 

2011c) and other sources in the federal government (Fedstats, 2007), and consulted EPA Regions and 

program offices that are responsible for data collection and analysis under EPA’s key environmental 

statutes. The State of California’s work on CalEnviroScreen5 was also tracked throughout its 

development. Other internal EPA data tools were also examined, including EJSEAT, EJVIEW, C-FERST, and 

tools used in EPA Regional Offices.

After review, EPA selected the following environmental factors for use in the first version of EJSCREEN:

• Air pollution:

o PM2.5 level in air.

o Ozone level in air.

o NATA air toxics:

 Diesel particulate matter level in air.

 Air toxics cancer risk.

 Air toxics respiratory hazard index.

 Air toxics neurological hazard index.

• Traffic proximity and volume: Amount of vehicular traffic nearby, and distance from roads.

• Lead paint indicator: Percentage of housing units built before 1960, as an indicator of 

potential exposure to lead.

• Proximity to waste and hazardous chemical facilities or sites: Number of significant 

industrial facilities and/or hazardous waste sites nearby, and distance from those:

o National Priorities List (NPL) sites.

o Risk Management Plan (RMP) Facilities.

o Hazardous waste Treatment, Storage and Disposal Facilities (TSDFs).

o National Pollutant Discharge Elimination System (NPDES) permitted major direct 

dischargers to water.

Each of these environmental indicators is explained in detail in section 3, and Appendix D provides 

summary statistics for the indicators. Again, it is important to understand that these indicators vary in 

how relevant they are to actual estimated risks to health or welfare, and how significant those impacts 

may be. These indicators represent a spectrum in terms of the quality of information about potential 

impacts, ranging from direct estimates of risk to rough indicators of proximity or exposure to pollution 

or other environmental hazards. Table 1 provides more detail on how closely each environmental 

indicator in EJSCREEN approximates actual estimated risk.

5 http://oehha.ca.gov/ej/ces2.html at http://oehha.ca.gov/ej

14 | Page

 
Overview of Data and Methods

Table 1. Types of Environmental Indicators Included in EJSCREEN

Indicator Place on Exposure–

Risk Continuum

Key 

Medium

NATA Air Toxics Cancer Risk

Lifetime inhalation cancer risk

Risk/Hazard

Air

NATA Respiratory Hazard Index

Ratio of exposure concentration to RfC

NATA Neurological Hazard Index

Ratio of exposure concentration to RfC

NATA Diesel PM (DPM)

(μg/m3)

Potential Exposure

Particulate Matter (PM2.5)

Annual average (μg/m3)

Ozone

Summer seasonal average of daily maximum 8-hour 

concentration in air (ppb)

Lead Paint Indicator 

Percentage of housing units built before 1960

Dust/ Lead 

Paint

Traffic Proximity and Volume

Count of vehicles (average annual daily traffic) at major roads 

within 500 meters, divided by distance in kilometers (km)

Proximity/ Quantity

Air/ Other

Proximity to RMP Sites

Count of facilities within 5 km, divided by distance

Waste/ 

Water/ Air

Proximity to TSDFs

Count of major TSDFs within 5 km, divided by distance

Proximity to NPL Sites

Count of proposed and listed NPL sites within 5 km, divided by

distance6

Proximity to Major Direct Water Dischargers 

Count of NPDES major facilities within 5 km, divided by distance Water

Abbreviations:

NATA National Air Toxics Assessment RfC Reference concentration from EPA’s 

NPL National Priorities List, Superfund program Integrated Risk Information System 

NPDES National Pollutant Discharge Elimination System PM2.5 Particulate matter (PM) composed of

RMP Risk Management Plan particles smaller than 2.5 microns

TSDFs Hazardous waste Treatment, Storage, and μg/m3 micrograms of PM2.5 per cubic meter of air

Disposal Facilities ppb parts per billion, of ozone in air

6 Count of NPL sites excludes deleted sites and sites in U.S. territories.

15 | Page

 
Overview of Data and Methods

It is also important to note that each proximity indicator focuses on one category of facility or site (e.g., 

NPL), but the category’s facilities or sites vary in the degree to which they could actually pose risks. They 

vary in the amount of emissions (if any), the possibility of exposure to any pollutants released, the size 

of the facility or site, and toxicity of the pollutants or severity of the impacts that might occur. As a 

screening tool, EJSCREEN generally does not distinguish based on these factors in proximity indicators 

(although NATA indicators do account for such information). Any closer review of a particular location 

would have to consider these important differences.

All of these indicators are focused on potential impact at residential locations (e.g., proximity of 

residence to traffic), and therefore only address some of the exposures that individuals may face. Data 

are generally insufficient to readily estimate exposures away from the home, particularly in a screening 

tool. Exposures that occur away from the home, such as at work, at school or during a commute, are not 

captured in EJSCREEN unless those exposures are near the home or in other locations that happen to 

have the same level of exposure.

7

Demographics in EJSCREEN

This section describes why demographic indicators are included in EJSCREEN, which specific 

demographic indicators were selected, and what data are used to derive the demographic indicators. 

Using Demographics as Proxies for Potential Susceptibility

EJSCREEN has been designed in the context of Executive Order 128988 which ordered the following:

To the greatest extent practicable and permitted by law, and consistent with the principles set 

forth in the report on the National Performance Review, each Federal agency shall make 

achieving environmental justice part of its mission by identifying and addressing, as appropriate, 

disproportionately high and adverse human health or environmental effects of its programs, 

policies, and activities on minority populations and low-income populations9

EJSCREEN was also designed in the context of EPA’s EJ policies, including EPA’s Interim Guidance on 

Considering Environmental Justice During the Development of an Action (U.S. EPA, 2010). That guidance 

document explained EPA’s focus on demographics as an indicator of potential susceptibility or 

vulnerability to environmental pollution: 

To help achieve EPA’s goal for EJ (i.e., the fair treatment and meaningful involvement of all 

people), EPA places particular emphasis on the public health of and environmental conditions 

affecting minority, low-income, and indigenous populations. In recognizing that these 

populations frequently bear a disproportionate burden of environmental harms and risks ... EPA 

works to protect them from adverse public health and environmental effects of its programs. 

7 A partial exception is the data from NATA, which make some attempt to include some nonresidential exposures, 

as explained in NATA’s technical documentation (http://epa.gov/nata/). 8 http://www2.epa.gov/laws-regulations/summary-executive-order-12898-federal-actions-address-environmentaljustice 9 http://www.archives.gov/federal-register/executive-orders/pdf/12898.pdf 

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Overview of Data and Methods

EPA should pay particular attention to the vulnerabilities of these populations because they have 

historically been exposed to a combination of physical, chemical, biological, social, and cultural 

factors that have imposed greater environmental burdens on them than those imposed on the 

general population. (U.S. EPA, 2010, p. 4) 

EJSCREEN uses demographic indicators as very general indicators of a community’s potential 

susceptibility to the types of environmental exposures included in this screening tool. Impacts of 

pollutants depend on a combination of exposure and susceptibility to those exposures. Demographic 

factors may be related to both of these. Therefore, it is very useful to distinguish between 1) the fact 

that some demographics are associated with higher exposure, and 2) the fact that demographics are 

useful in predicting susceptibility to those exposures. To indicate potential exposures, EJSCREEN uses 

environmental indicators, not demographics. EJSCREEN uses demographics to indicate potential 

susceptibility. EJSCREEN then combines the exposure and susceptibility indicators in the form of an EJ 

Index.

The demographic indicators in EJSCREEN are a way to indicate which communities may be more 

susceptible to a given level of exposure to environmental pollutants. For example, individuals may be 

more susceptible when they are already in poor health, have reduced access to care, lack resources or 

language skills or education that would help them avoid exposures or obtain treatment, or are at 

susceptible life stages. Nationwide direct measures of health status are not available for all block groups 

or even tracts – such data are typically compiled by county in national databases. Demographics, 

however, are available for every block group, and are correlated with health status and these other 

susceptibility factors, making them useful screening-level indicators of potential susceptibility at the 

local level.

Note that this report uses the term susceptibility in a qualitative, general sense, to refer to what various 

authors have called susceptibility and/or vulnerability. Susceptibility in this report means greater 

“impact” for a given environmental indicator value. The terms vulnerability and susceptibility sometimes 

are used interchangeably, although various other reports and programs have made distinctions between 

these terms.10 

The relationships between demographics, exposure, and susceptibility are complex. For example, 

demographics may be associated with susceptibility to pollutants in any of the following ways: 

• Greater personal exposure despite the same ambient level of pollutant. For example, 

children have higher breathing rates or ingest more lead dust than adults (U.S. EPA, 2011a), 

10 For example, EPA’s 2009 National Ambient Air Quality Standards (NAAQS) documents (U.S. EPA, 2009b) and also 

EPA’s Regional Vulnerability Assessment program treat susceptibility and vulnerability as essentially identical 

(http://www.epa.gov/reva/glossary.html). Other EPA definitions have addressed particular contexts, such as in 

EPA glossaries (http://www.epa.gov/OCEPAterms/vterms.html, http://www.epa.gov/OCEPAterms/sterms.html), 

and a report on vulnerability to climate change (U.S. EPA, 2009a). One National Academies report (Science and 

Decisions) distinguished between the two terms (National Research Council, 2009). In other contexts, the terms 

have varied uses – see Villagrán de Léon (2006) for a detailed comparison of various definitions of vulnerability in 

the context of natural disasters, or work on vulnerability indexes for developing countries by Briguglio (1997). 

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Overview of Data and Methods

and certain groups may tend to encounter or be less able to avoid certain exposures due to 

limited resources, language barriers, education, cultural practices, or lack of information. 

• Susceptibility because of a greater percentage increase in health risk for a given exposure, 

e.g., “effect modification” or “multiplicative interaction” may occur. An example would be 

where cumulative previous exposure means a group is more likely to be closer to a 

threshold for adverse effects, or where greater stress/allostatic load increases susceptibility 

through inflammatory or other pathways. Several examples of effect modification relevant 

to EJ and PM2.5 are referenced by Bell & Ebisu, 2012 and in a review of subgroups 

susceptible to ozone (Bell, Zanobetti, & Dominici, 2014). A growing body of research has 

documented interactions of psychosocial stress and environmental exposures.

• Susceptibility because of higher baseline risk or rates of pre-existing diseases. The same 

percent increase in mortality risk has a larger impact on absolute risk if baseline risk is 

higher.

• Susceptibility because of increased overall burden resulting from an initial health risk (e.g., 

because of less ability to recover due to lack of health care or resources). For example, lowincome or minority individuals, or those with less than a high school education, are far less 

likely to have health insurance (Cohen & Martinez, 2011). 

One reason for EJSCREEN to focus on potentially susceptible demographic groups is that a large body of 

research has documented health disparities between demographic groups in the United States, such as 

differences in mortality and morbidity associated with factors that include race/ethnicity, income and 

educational attainment (e.g., Centers for Disease Control and Prevention, 2011a; Galea, Tracy, & 

Hoggatt, 2011). For example: 

• About two thirds (65%) of non-Hispanic white adults reported excellent or very good health 

in 2009. In contrast, less than half (49%) of non-Hispanic black adults and 52% of Hispanic 

adults reported excellent or very good health.11 

• Residents with lower income report fewer average healthy days than others (Centers for 

Disease Control and Prevention, 2011a), and report worse health overall (Centers for 

Disease Control and Prevention, 2010). 

• Both lower income and minority race/ethnicity have independent associations with higher 

asthma rates, particularly among children, and diabetes rates differ greatly by race/ethnicity 

(Centers for Disease Control and Prevention, 2011a). 

• Mortality rates for cancer and heart disease vary somewhat by race/ethnicity (Centers for 

Disease Control and Prevention, 2011b). Coronary heart disease and stroke are elevated 

among black individuals but generally not in other minority subgroups (Centers for Disease 

Control and Prevention, 2011a). 

11 This survey, the National Health Interview Survey, represents the U.S. non-institutionalized civilian population, 

and presents age-adjusted estimates based on household interviews (Centers for Disease Control and Prevention, 

2011d). 

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Overview of Data and Methods

• Infant mortality is higher among non-Hispanic black, American Indian/Alaska Native, and 

Puerto Rican (but not other Hispanic) populations (Centers for Disease Control and 

Prevention, 2011a). 

While some health disparities are due to differences in health care, diet, activities, psychosocial stress or 

even genetics, it is possible that some portion of certain disparities may be related to differences in 

environmental exposures. Some of these differences in exposure are associated with residential 

location, and could be considered in EJSCREEN (while others cannot be considered in EJSCREEN, such as 

those related to use of consumer products or diet, for which high-resolution geographic data are not 

available). Various environmental exposures have been shown to vary by race/ethnicity, income and 

other demographic factors (Liu, 2001; Maantay, Chakraborty, & Brender, 2010; U.S. EPA, 2006a), but 

EJSCREEN is not predicated on an assumption of such a correlation. 

In addition to Executive Order 12898’s call, perhaps the most important reason to focus on key

demographic groups in EJSCREEN is that a growing body of research has shown that demographic 

factors are associated with susceptibility – certain groups are more impacted by a given level of 

exposure to certain pollutants. Various groups have shown increased susceptibility to certain pollutants, 

but further evidence is still emerging in this area and data are limited. Evidence currently available 

includes the following: 

• Certain demographic groups, such as those with lower educational attainment, children, the 

elderly and those with low socio-economic status (SES), appear to be more susceptible to a 

given exposure to particulate matter (U.S. EPA, 2009b). 

• Blood lead’s association with cardiovascular outcomes appears to be stronger among 

Mexican Americans and non-Hispanic blacks than non-Hispanic whites (U.S. EPA 2011c). 

• Some but not all studies suggest lead has a greater impact on IQ among low SES than high 

SES individuals (U.S. EPA 2011c). 

EJSCREEN is not designed to explore the root causes of differences in exposure. The demographic 

factors included in EJSCREEN are not necessarily causes of a given community’s increased exposure or 

risk. This does not limit their usefulness for the limited purposes of the screening tool, however – these 

demographic factors are still useful as indicators of potential susceptibility to the environmental factors 

in EJSCREEN. They may be associated with susceptibility, whether or not they are causal, and can be 

used as proxies for other harder-to-measure factors that would better describe or determine 

susceptibility but for which nationally consistent data are not available. EJSCREEN screens geographic 

areas for increased potential for exposure and increased potential for susceptibility to exposures. 

Additional analysis is always needed to explore any underlying reasons for differences in susceptibility, 

exposure or health. 

Some studies have begun to quantify the degree of susceptibility to specific pollutants in particular 

demographic groups, such as the work on educational attainment as an effect modifier for risks 

associated with PM2.5 exposure, but such emerging knowledge is still limited to a handful of pollutants 

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Overview of Data and Methods

and demographic factors (U.S. EPA, 2009b). EJSCREEN, as a screening tool, does not attempt to use this 

type of emerging quantitative information.

Demographic Indicators Included in EJSCREEN

A wide range of demographic descriptors have been used by researchers and in EJ screening tools to 

represent the “social vulnerability” characteristics of a disadvantaged population (for example, see 

deFur et al., 2007, and Bell & Ebisu, 2008). 

Executive Order (EO) 12898, addressing EJ issues, refers to low-income and minority populations. We 

define these two core factors as:

• Low-Income: The number or percent of a block group’s population in households where the 

household income is less than or equal to twice the federal “poverty level.”12

• Minority: The number or percent of individuals in a block group who list their racial status as 

a race other than white alone and/or list their ethnicity as Hispanic or Latino. That is, all 

people other than non-Hispanic white-alone individuals. The word “alone” in this case 

indicates that the person is of a single race, since multiracial individuals are tabulated in 

another category – a non-Hispanic individual who is half white and half American Indian 

would be counted as a minority by this definition.13 

Based on a review of other factors used in various EPA EJ screening tools, the four other factors most 

commonly used by EPA Headquarters and Regions for EJ analyses are also included in EJSCREEN. The 

other four factors are:

• Less than high school education: The number or percent of people age 25 or older in a block 

group whose education is short of a high school diploma.

• Linguistic isolation: The number or percent of people in a block group living in linguistically 

isolated households. A household in which all members age 14 years and over speak a nonEnglish language and also speak English less than “very well” (have difficulty with English) is 

linguistically isolated.

• Individuals under age 5: The number or percent of people in a block group under the age of 

5.

12 More precisely, percent low-income is calculated as a percentage of those for whom the poverty ratio was 

known, as reported by the Census Bureau, which may be less than the full population in some block groups. More 

information on the federally-defined poverty threshold is available at 

http://www.census.gov/hhes/www/poverty/methods/definitions.html. See Appendix B for details on using twice 

the poverty threshold.

13 Census definitions of race/ethnicity are available at: 

http://www.census.gov/population/www/socdemo/race/index.html and the questions asked about race are 

available at: http://www.census.gov/acs/www/about_the_survey/questions_and_why_we_ask/ 

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Overview of Data and Methods

• Individuals over age 64: The number or percent of people in a block group over the age of 

64.

The source of all demographic data used in EJSCREEN is the American Community Survey (ACS) five-year 

summary file, which the U.S. Census Bureau compiles yearly. 

Appendix D provides summary statistics for the demographic indicators.

The supplementary reports and maps provided by EJSCREEN also include an extensive list of additional 

demographic variables, including statistics on race/ethnicity subgroups (e.g., percent Hispanic or Latino), 

languages spoken (e.g., % speaking Vietnamese), income (% in poverty), and many other factors. This 

supplementary information may be very useful. For example, subgroups within the broad category of 

“minority” can differ greatly in their baseline health, exposures, geographic locations, and other factors.

Demographic Indexes in EJSCREEN

The Demographic Index in EJSCREEN is created using the two demographic indicators that were 

explicitly named in EO 12898, low-income and minority. For each Census block group, these two 

indicators are simply averaged together.

A Supplementary Demographic Index is also available in EJSCREEN, and is the average of all six 

demographic indicators.

14

14 Census evaluates each characteristic for a different population, so the denominators are not the same across 

factors. For example, the denominator for “% less than high school education” is the population in the block group 

25 years and older. The denominators for the factors “% minority” and “% low-income” are both close to the total 

block group population, but some people in each block group are not evaluated for each demographic 

characteristic.

 Demographic Index = (% minority + % low-income) / 2

Supplementary Demographic Index =

(% minority + % low-income + % less than high school education + % linguistic isolation + 

% individuals under age 5 + % individuals over age 64) / 6

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Overview of Data and Methods

Users can also view each demographic indicator separately in EJSCREEN, in reports or in maps. 

The Demographic Indexes count each indicator as adding to overall potential susceptibility of the 

population in a block group, and assumes the demographic indicator have equal and additive impacts. 

The current lack of available data precludes any attempt to disentangle the different influences of the 

individual demographic indicators, or quantify the degree of overlap or potential synergy between them. 

The demographic groups in EJSCREEN overlap to some extent, because some individuals are both lowincome and minority, for example. In fact, these indicators are correlated at the block group level, 

because minorities are more likely to be low-income than non-minorities. Appendix D has information 

on the correlations between these variables. These correlations do not affect the indicator’s ability to 

account for susceptibility, if the assumption of additive effects on susceptibility is appropriate. As more 

data becomes available in the future, some of these complexities can be reexamined.

Additional information about the demographic data used in EJSCREEN is available in Appendix B. 

Environmental Justice Indexes in EJSCREEN

The EJ index is a combination of environmental and demographic information. The environmental 

portion of the EJ index is drawn directly from the environmental indicators described above, and the 

demographic information is also taken from the demographic indicators above.

How the EJ Index Works

To calculate a single EJ Index, EJSCREEN combines a single environmental indicator with demographic

information. It considers the extent to which the local demographics are above the national average. It 

does this by looking at the difference between the demographic composition of the block group, as 

measured by the Demographic Index, and the national average (which is approximately 35%). It also 

considers the population of the block group. 

Mathematically, the EJ Index is constructed as the product of three items, multiplied together as

follows: 

The demographic portions of the EJ Index can be thought of as the additional number of susceptible 

individuals in the block group, beyond what you would expect for a block group with this size total 

population.

EJ Index = 

(Environmental Indicator) 

X (Demographic Index for Block Group –Demographic Index for US) 

X (Population count for Block Group)

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Overview of Data and Methods

“Susceptible” or “potentially susceptible individuals” are used informally in these examples, as a way to 

think of the Demographic Index times the population count in a block group, which is essentially the 

average of the count of minorities and count of low-income individuals.

15 It is easiest to think of the 

average of these counts as “the susceptible individuals” in these examples.

The number of potentially susceptible individuals (Demographic Index times population count) of course 

is typically less than the actual number who are minority, low-income, or both. The demographic 

breakdown is not reported by block group –the ACS does not provide that level of resolution on the 

overlaps.16 For example, suppose that in a certain block group of 1000 people, 350 (35%) are minority 

and 350 (35%) low-income. There might be 200 (20%) who are low-income but not minority, and 200 

(20%) who are minority but not low-income. In that case, there would be 150 (15%) who are both, and 

450 (45%) who are neither. Therefore, there actually would be 550 (55%) who were either minority, 

low-income, or both. The Demographic Index would use 35% in this case, which falls between the 15% 

who were both minority and low-income, and the 55% who were in at least one of these groups. These 

detailed numbers cannot be obtained from the ACS by block group. Therefore, to represent both groups 

in a simple way, the average is used. 

An extreme example shows another situation: Suppose a block group has 1000 people but is 0% 

minority and 100% low-income. The demographic index would be 50%, or the equivalent of 500 

“potentially susceptible individuals” in this case. The same would be true in a block group that was 100% 

minority but 0% low-income – it would treated as having the equivalent of 50% (500) “potentially 

susceptible” for the sake of these examples.

The EJ Index uses the concept of “excess risk” by looking at how far above the national average the

block group demographics are. For example, assume a block group with 1000 people in it. In that block

group, one would expect 350 potentially susceptible individuals (1000 people here x US average of 35%).

However, if the Demographic Index for that block group is 75%, well above the US average, then there 

are the equivalent of 750 potentially susceptible people in that block group, or 400 more than expected 

for a block group with a population of 1000. The EJ Index would be 400 times the environmental 

indicator in this case. 

This formula for the EJ Index is useful because for each environmental indicator it finds the block groups 

that contribute the most toward the national disparity in that environmental indicator. By “disparity” in 

this case we mean the difference between the environmental indicator’s average value among certain 

demographic groups and the average in the US population.

15 To be precise, the percent low-income times population is not always exactly the same as the count of lowincome residents. The percent low-income is calculated as a fraction of those for whom poverty status could be 

determined, which is less than the full population in some block groups. For simplicity, these examples omit that 

detail. 

16 The closest available data would be table B17001 and related tables, which provide tract resolution crosstabulations of race/ethnic groups by poverty status, but this is not available for block groups and does not provide 

the income to poverty ratio data needed to calculated “low-income” as defined in EJSCREEN.

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Minority and low-income individuals live in older housing more often than the rest of the US population, 

for example. The EJ Index for lead paint (pre-1960 housing) tells us how much each block group 

contributes toward this "excess population risk" or "excess number" of people in older housing, for 

potentially susceptible individuals. "Excess" in this context simply means the number of potentially 

susceptible individuals in older housing nationwide is above what it would be if they were in older 

housing at the same rate as the rest of the U.S. population. Locally, it also means the number is above 

what it would be if the block group had the same demographic percentages as the U.S. overall.

Analysis of the EJSCREEN data for minority, or for low income, individuals (roughly one third of the US 

population in either case), shows they have a higher environmental indicator value on average than the 

rest of the U.S. population, for 11 of the 12 environmental indicators (ozone is the exception).

Note that the EJ Index raw value itself is not reported in EJSCREEN reports– it is reported in percentile 

terms, to make the results easier to interpret. If one is calculating the actual raw values using the 

formula, it is clear that the EJ Index value can be a positive or negative number. A positive number 

occurs where the local Demographic Index is above the US average, and this means the location adds to 

any excess in environmental indicator values among the specified populations (minority and lowincome) nationwide. A negative value occurs where the local Demographic Index is below the US 

average, and it means the location offsets the other locations, reducing any excess in nationwide 

average environmental indicator values among minority and low-income populations relative to others. 

Most EJSCREEN users will not work directly with EJ Index raw values, however, and positive raw values 

for an EJ Index will be presented as higher percentiles and negative raw values will appear as lower 

percentiles.

Supplementary EJ Indexes

In addition to this EJ Index formula, two other types of EJ Indexes were tested during EJSCREEN’s 

development, and those supplementary EJ indexes are also available in maps and the database files. The 

three approaches, the basic index and two supplementary approaches, are complementary to one 

another and provide different perspectives on a given area. The difference between the three EJ indexes 

for any single indicator lies in the different ways the demographic portion of the index is developed.

Analyses comparing the three approaches concluded that they differ only moderately, and that the EJ 

Index selected by EPA as the focus of EJSCREEN falls squarely between the other two, in terms of which 

locations it highlighted. In other words, the other two approaches were at either end of a continuum, 

and did not overlap as much, but the EJ Index featured in EJSCREEN overlaps very substantially with 

both of the other two. It also has the advantage of having a clear interpretation, analogous to excess 

population risk or the local contribution to excess risk nationwide.

The Supplementary EJ Indexes also combine an environmental indicator with demographic information. 

In the first Supplementary EJ Index, the demographic information is simply the Demographic Index for 

the block group, times the population of the block group. This index simply omits the demographic index 

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Overview of Data and Methods

for the nation, so it focuses less on the extent to which demographics exceed the national average. The

first Supplementary EJ Index can be expressed mathematically as follows:

The second Supplementary EJ Index can be expressed mathematically as follows:

The second Supplementary EJ Index combines an environmental indicator with the Demographic Index 

for the block group, leaving out demographic index for the nation and also ignoring the block group 

population. This index does not give more weight to block groups with very large numbers of residents. 

An advantage is that it is simple and treats each block group equally regardless of how many people live 

there. A disadvantage is that this effectively gives less weight to a person in a populous block group than 

a person in a less populated block group. 

The EJSCREEN mapping application also allows users to see each of these EJ indexes calculated using the 

Supplementary Demographic Index, which uses all six demographic indicators instead of just two. In 

most locations the results do not differ greatly from what is found using the standard EJ Index.

Details on all the available indexes are also included in Appendix E. 

EJ Indexes, Population Density, and Rural Areas

It is very important to understand that the population count per block group is not the same as 

population density, so the population weighting of the EJ Index has nothing to do with whether a place 

is high density, or rural versus urban. In fact, there is almost no correlation between population count 

per block group and population density (population count per square mile covered by the block group), 

because in low density areas each block group covers a larger area, keeping the population per block 

group fairly consistent. This means population weighting in the EJ Index does not emphasize urban or 

high density locations – it is neutral with regard to population density or urbanization. Furthermore, the 

vast majority of block groups in the US have similar population counts, so the population weighting in 

the EJ Index has a strong influence only in a tiny fraction of locations. For example, about 90% of block 

groups had a population between 500 and 2500 in 2008-2012, and only about 1% had a population over 

4000. 

Supplementary EJ Index 1 = 

(Environmental Indicator) 

X (Demographic Index for Block Group) 

X (Population count for Block Group)

Supplementary EJ Index 2 = 

(Environmental Indicator) 

X (Demographic Index for Block Group) 

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Overview of Data and Methods

It is true that many of the EJ Indexes have higher values in urban, high-density areas, but this is true for 

the supplementary EJ Indexes as well, and is not the result of population weighting. Differences in 

environmental indicator values (and to some extent percentage demographics) are generally the drivers 

of higher EJ Indexes in urban or high-density block groups. NATA indicators and the lead paint indicator,

in particular, are strongly correlated with population density, as are the PM2.5 and traffic indicators to 

some extent. The proximity indicators are also positively (but weakly) correlated with population 

density. In other words, these environmental indicators appear to be lower in rural areas in general, and 

combined with some demographic differences, this tends to make the EJ Indexes lower in those areas. 

Relative to those factors, the population weighting (or choice of EJ Index formula) has a very small 

influence on whether urban or rural areas are highlighted.

Why the EJ Indexes are Not all Combined

For each environmental indicator, one standard EJ index is available in EJSCREEN. At this time, there is 

not a single composite EJ index that combines all the environmental factors. Although it would be useful 

if a simple metric could summarize all of the information in EJSCREEN as a single number, there is no 

widely-accepted, objective way to combine the differing environmental concerns into one number. This 

is because of the value judgments and scientific challenges inherent in deciding how much weight or 

importance should be given to each of the environmental factors. They are very difficult to compare, in 

terms of public health importance, public concerns, and the many other important considerations that 

could be weighed. This topic has been covered extensively elsewhere17, but a very brief explanation may 

be useful here.

First, a so-called “equal weighting” does not exist, because it would just be an artifact of the units 

(scaling) and aggregation method one chose, which would carry implicit value judgments about how to 

weight and combine the factors, even if it seemed simple at first glance. Putting equal weight on each 

percentile, for example, would implicitly equate very low risks (e.g., air toxics well below health based 

reference levels, as with a neurological hazard index well below 1) and much higher risks (e.g., PM2.5

levels well above a health based standard).

Furthermore, while the use of percentiles provides useful perspective by putting the 12 EJ indexes in 

common units, it would be a mistake to assume the 80th percentile, for example, has the same 

“importance” for one index or indicator as for another. If two indexes are at the same percentile, it 

simply means those two scores are equally common (or equally rare) in the United States. It does not 

mean the risks are comparable. It is therefore critical when interpreting EJSCREEN percentiles to also 

look at the actual raw numbers for the environmental and demographic indicators.

The challenge is compounded by the fact that rankings of block groups using a composite environmental 

index would be quite sensitive to the method chosen to combine the environmental indicators, based 

on EPA’s analysis of the data. This is also the case, albeit to a lesser degree, for any composite EJ index. 

This is a result of the environmental indicators not being highly correlated with each other. The locations 

17 See, for example, OECD, 2008, or Finkel & Golding, 1994.

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Overview of Data and Methods

with the highest PM2.5 levels are not usually the same as the ones with the highest NPL proximities, for 

example, as suggested in Appendix D. If the NPL indicator is treated as more important, different block 

groups would be highlighted than if the PM2.5 indicator were given more weight. Again, it is important to 

acknowledge that there is no objective version of “equal weighting.”

For these reasons, the environmental indicators are not combined as a single number, and must be 

understood individually for a complete picture. However, they can be viewed all at the same time in a 

single tabular report, and this facilitates a broad perspective on all the factors at one time. Those using 

EJSCREEN and considering aggregating the data as a single summary metric are strongly urged to 

carefully consider these pitfalls associated with doing so. A thorough understanding of each indicator 

and the ability to view all of them in a report provides a far better picture of the screening results than 

any single number or map is capable of.

Percentiles

What a Percentile Means

EJSCREEN puts each indicator or index value in perspective by reporting the value as a percentile. For 

example, an area may show 60% of housing was built prior to 1960. It may not be obvious whether this 

is a relatively high or low value, compared to the rest of the nation or in the state. Therefore, EJSCREEN 

also reports that 60% pre-1960 puts this area at the 80th percentile nationwide. For a place at the 80th

percentile nationwide, that means 20% of the US population has a higher value.

A percentile in EJSCREEN tells us roughly what percent of the US population lives in a block group that 

has a lower value (or in some cases, a tied value). This means that 100 minus the percentile tells us 

roughly what percent of the US population has a higher value. This is generally a reasonable 

interpretation because for most indicators there are not many exact ties between places and not many 

places with missing data.

More precisely, the exact percentile for a given raw indicator value is calculated as the number of US 

residents of block groups with that value or lower, divided by the total population with known indicator 

values. This is typically the same as or almost exactly the same as dividing by the total US population, 

but for some indicators some locations do not have an indicator value. For example, the NATA indicators

are missing for only about one twentieth of 1% of the US population in the 2015 version of EJSCREEN. 

The calculated percentile would change by much, much less than 1 percentile point if calculated as a 

fraction of the total population instead of as a fraction of those with valid indicator values.

All percentiles in EJSCREEN are population percentiles, meaning they describe the distribution of block 

group indicator scores across the population. Note that a population percentile may be slightly different 

than the unweighted percentile (the percent of block groups, not people, with lower or tied values), 

because not all block groups have the same population size. In practice they are very similar because 

very few block groups diverge very much from the average in population size.

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Color-coded High Percentile Bins

Locations at least at the 80th percentile but less than the 90th are shown in yellow on EJSCREEN maps, 

while those at the 90th percentile but less than 95th percentile are orange on the maps, and those at the 

95th percentile or above are shown in red on maps and reports. These colors call attention to certain 

locations as a very simple way to communicate relative screening results. There is no official policy 

significance assigned to each individual color on the maps, but the choice of these categories or “bins” is 

noteworthy because it signifies that certain ranges of percentiles may merit closer attention.

Percentiles at or above the 95th percentile are shown in red on the EJSCREEN standard report. This is a 

way to call particular attention to those cases where the value is in the top 5% of the nation (or region 

or state). Indicator or index values in the top 5% tend to be much higher than those in the next 5-10%, 

so they may merit close attention. This is especially true for the indicators with highly skewed 

distributions, such as the traffic proximity indicator (see Appendix D, Table 6 and Figure 1). For example, 

block groups in the top 5% (shown in red on maps and reports) have traffic, NPL, and TSDF proximity 

indicators on average that are about three times as high as in the next 5% (shown in orange on the 

maps). These differences are far less extreme in the cases of PM2.5 and lead paint indicators, which don’t 

vary as much across block groups. In general, though, indicator or index values above the 95th percentile 

represent much higher demographic, environmental, or EJ Index values than those at lower percentiles.

The maps also identify areas in the 90th to 95th percentiles as orange, and those at the 80th to 90th as 

yellow. These additional categories highlight larger groups of locations that have indicator or index 

values well above the national mean or median for the given indicator or index. The actual values are 

lower than those in the top 5%, typically much lower, but they are still in the top 10 to 20% of values for 

the US population overall.

A relatively high percentile means the value is relatively uncommon. However, a high percentile is not 

necessarily a real concern from a health or legal perspective. To understand the actual health or other 

implications of any screening results requires looking at the actual data and the indicator represents, 

and also looking at other relevant data if available. Besides the percentile, other important 

considerations in interpreting any screening results include the following:

1. whether and to what extent the environmental data shows values above any relevant healthbased or legal threshold, 

2. the significance of any such thresholds, or the magnitude and severity of the health or other 

impacts of the given environmental concern, nationally or locally, and 

3. the degree of any disparity between various groups, in exposures to the relevant environmental 

pollutants.

In maps, EJSCREEN focuses on the US percentiles, as a way to visualize all results in common units.

28 | Page
Overview of Data and Methods

The US percentile uses the US population as the basis of comparison. The state or regional percentile 

was calculated based on the population in a given state (or DC) or one of EPA’s 10 regions.

18 The 

national or state or regional mean value was calculated as the population weighted average of the block 

groups with data for that indicator, within the respective geographic scope.

Note that the US and state percentiles both will rank block groups in exactly the same rank order within 

the given state. If the goal is just to rank or compare locations within a single state, it does not matter 

whether the US or state percentile is used. The difference between state and US percentiles becomes 

apparent mainly in two situations: when comparing places across states, or when comparing results to 

some pre-determined, specific reference percentile (e.g., 80th percentile).

The advantage of US percentiles for an EJ Index, for example, is that a higher percentile in place A versus 

place B clearly indicates that the combination of the environmental indicator and demographic index is 

greater in place A than place B. In a sense, the US percentile indicates how uncommon it is to have such 

a high level for an indicator or index.

State or regional percentiles cannot be compared across states or regions as easily. If two places A and 

B, in two different states, happen to both be at the 80th percentile for the traffic EJ Index, for example, it 

is not clear which actually has the higher index value. It just means that A’s index is just as uncommon 

within that state as B’s is in B’s state. However, this may be useful information because an EJSCREEN 

user may want to know how high the indicator is relative to the rest of that state.

The state and US percentiles will be very similar if the state and US average indicator values are very 

similar. However, if the state average is very low compared to the US, the state percentile shown will be 

higher than US percentile shown, for a given raw value of an indicator. If the state average is much 

higher than the US average, for an indicator like the traffic indicator, then a traffic score that would 

normally be considered fairly high nationwide, such as the 90th percentile in the US, would not be 

considered very unusual within that state, so the state percentile would be lower, and might be only 

78th percentile, for example. The state percentile being lower than the US percentile does not mean the 

indicator value is lower in the given place, it just means the state average is higher than the US average.

Appendix B provides details on how percentiles are calculated, and rounded when displayed.

18 Regions in the 2014 version of EJSCREEN were defined only by State. A small number of block groups on Tribal 

Lands along the borders of Nevada and Arizona are actually part of EPA Region 9, even though they are within the 

States OR/ID (Region 10), UT/CO (Region 8), or NM (Region 6). EJSCREEN’s percentiles and reports processed those 

block groups as if they were in the Region that corresponds to their State. Regional and Tribal Lands maps should 

be consulted when viewing those locations and reported Regional percentiles should not be used in those 

locations.

29 | Page

 
Overview of Data and Methods

Buffer Reports

What the Buffer Report Calculates

EJSCREEN allows a user to define a buffer, such as the circle that includes everything within 1 mile of a 

specific point. Non-circular, user-defined shapes also can be defined to represent buffers of any shape.

The summary within a buffer represents the average resident within the buffer. A report summarizes 

the demographics of residents within this buffer, as well as the environmental indicators and EJ index 

values within the buffer. It also provides an estimate of the total population residing in the buffer.

Note that this means one cannot compare two buffers of very different population counts without 

understanding what each set of results represents. Each represents the average person in that buffer. It 

does not represent the absolute total amount the buffer contributes to overall disparity in indicator 

scores nationwide or statewide. Even if the two sets of scores are identical other than in population 

counts, the buffer with a larger population will contribute more to any national or overall disparity in 

indicator scores. In general, however, this situation does not tend to arise because most buffers that a 

user creates in practice will be at least roughly similar in size and population. Even if they are not, a user

simply needs to acknowledge that some buffers have larger populations, in which case those percentile 

results represent more people.

Appendix B provides the details of buffer calculations.

Choosing a Buffer Size versus Rationale for Distance in Proximity Indicators

An EJSCREEN user’s choice of distance for a circular buffer is important, and the considerations need to 

be understood.

EJSCREEN is not able to report on buffers that are too large (e.g., ten or more miles in radius) due to 

computational limits. It also is not able to report on buffers that are too small (i.e., they do not intersect 

the internal points of any Census blocks).

In addition to those limits, as a rule of thumb, it is important to know that a buffer that is as small as the 

buffer it centers on will result in estimates with substantially higher uncertainty than one which covers 

several block groups. A buffer covering five or more block groups, for example, will provide much more 

confidence in demographic estimates (because of sampling uncertainty as well as the challenge of 

estimating where residents are located within block groups intersected by the edges of the buffer). 

Examining patterns the size of a block group or smaller requires using maps of block groups and Census 

blocks, rather than attempting to draw buffers in EJSCREEN.

Uncertainties are also discussed in section 1 (as general caveats), and Appendix B (in discussions of 

buffering details and demographic data).

30 | Page
Overview of Data and Methods

It is important not to confuse two different distances: 

1) the distance a user selects for a circular buffer radius (e.g., by default 1 mile, which is 1.6 km), 

and 

2) the distance EJSCREEN used in proximity score calculations (i.e., 5 km for facilities and sites, or 

500 meters for traffic).

These two are very different, as explained here, because proximity scores use a large distance (e.g., 5 

km) and inverse distance weighting, while for circular buffers a user may wish to specify a shorter 

distance (e.g., 1 mile or 1.6 km, but at least as large as one or more local block groups) because a buffer 

report does not use distance weighting.

The buffer analysis provides a summary of the average resident inside the buffer. It gives equal weight 

to each resident, regardless of whether they are closer or further from the center of the buffer. There is 

no distance weighting in a buffer report.

Proximity scores were created very differently than buffer reports are calculated. The proximity scores 

for each block group were calculated for each residential location using distance weighting to give more 

weight to closer facilities, sites, or traffic. Because the proximity score uses distance weighting to focus 

less on the more distant points, it was designed to look at a large area using a large radius, or distance (5 

km for facilities or NPL sites). By distance weighting, the proximity score can examine this large area and 

still provide a useful summary of all the facilities or sites in that wide area.

The proximity score for traffic, for example, looks within a search radius of 500 meters (or further if 

none is found in that radius). This distance, or scope, was selected to be large enough to capture the 

great majority of road segments (with traffic data) that could have a significant impact on the local 

residents, balanced against the need to limit the scope due to computational constraints. Within this 

relatively wide zone, the closest traffic is given more weight, and the distant traffic given less weight, 

through inverse distance weighting. The same approach was used for the facility or NPL site proximity 

scores. A distance of 5 km was chosen to capture the great majority of facilities or sites that could have a 

significant impact on local residents. The fact that impacts may be very small for distances of 4 or 5 km is 

handled by the use of inverse distance weighting in the proximity score formula.

By contrast, a buffer report, again, averages together all residents in the buffer, treating them all equally 

regardless of their distance from the buffer center. Therefore, many EJSCREEN users may wish to define 

a modest buffer distance that focuses on those residents who may be “most affected” and "similarly 

affected" by a single facility or site of interest at the center of the circular buffer.

A very large buffer (e.g., over three miles in radius) could provide misleading results if the goal is to 

describe the "affected" population, because people in a large buffer could be extremely varied in the 

extent of their exposure to some source at the center of such a large buffer. If impacts decline with 

distance, a large buffer would mix many relatively unimpacted, distant residents, with fewer residents 

who are closer and impacted more, giving a diluted result that fails to describe those most impacted.

31 | Page
Overview of Data and Methods

At the other extreme, a very small buffer (e.g., the size of a local block group or smaller) is problematic 

because it could fail to include some significantly affected residents, and also because estimates are 

more uncertain for smaller geographic areas due to sampling error in the ACS and spatial error in 

estimating which residents are inside the buffer. Some EJSCREEN users may wish to define a large buffer 

distance when they know a local facility or site covers a wide area (for example some NPL sites can be 

very large). Some users may wish to run and compare separate buffer reports for two or more choices of 

distance, or define hand-drawn buffers to look at zones at various distances from some point.

32 | Page
Details on Environmental Indicators

3 DETAILS ON THE ENVIRONMENTAL INDICATORS IN 

EJSCREEN

Environmental Factors Not Included

As described above, EJSCREEN contains 12 environmental factors, which were selected after a review of 

available data, other EJ tools and analyses, and the data selection criteria discussed above.

A number of possible factors were identified in the review, which were not ultimately included in 

EJSCREEN due to various limitations. These limitations were almost always a lack of high resolution data 

(e.g., only available at county level), and/or lack of geographic coverage (e.g., only available in selected 

or sampled locations). In some cases a factor was not added because of a high degree of overlap and 

double-counting with existing indicators, or resource constraints and practical considerations. One or 

more of these factors may be included in future versions of EJSCREEN as more data become available.

Other EPA resources also have more information on many of these issues, such as 

• EPA’s website (www.epa.gov)

• Envirofacts (http://www.epa.gov/enviro/)

• A county-level US Environmental Quality Index (EQI)19

• C-FERST (http://www.epa.gov/heasd/c-ferst/)

• EnviroAtlas (http://enviroatlas.epa.gov/enviroatlas/atlas.html)

EJSCREEN also provides the ability to view some of these issues directly within the EJSCREEN maps, such 

as impaired water bodies, criteria air pollutant nonattainment areas, TRI facilities, and others. 

Furthermore, users can import and view, within EJSCREEN, other maps available on the internet, for 

more of these environmental issues. 

Users of EJSCREEN are also encouraged to consider these issues, where appropriate, to the extent they 

have relevant local information.

Factors not currently used in EJSCREEN include: 

• Health data (e.g., overall mortality rate) – Note this is not an environmental factor, but is 

sometimes of interest in this context. Relevant data were found to be available only at 

county level resolution.20

• Drinking water (from private wells or public water supplies) and surface water quality (other 

than through the potential relevance of the NPDES proximity indicator in EJSCREEN)21

19 Messer, Jagai, Rappazzo, & Lobdell (2014) 20 Useful resources include http://www.countyhealthrankings.org, http://www.americashealthrankings.org/, 

http://www.healthindicators.gov/, and http://www.rwjf.org/en/research-publications/research-features/rwjfdatahub/national.html#q/scope/national/ind/31/dist/29/char/119/time/14/viz/map/cmp/brkdwn 21 See http://www2.epa.gov/learn-issues/water-resources and 

http://water.epa.gov/scitech/datait/tools/waters/tools/index.cfm

33 | Page

 
Details on Environmental Indicators

• Contaminated fish/ seafood (other than through the potential relevance of the NPDES 

proximity indicator in EJSCREEN)22

• Beach closures due to pathogens (other than through the potential relevance of the NPDES 

proximity indicator in EJSCREEN)23

• Impaired surface waters (assessed only in certain locations, so lacking complete coverage of 

all locations in the US)

• Sea-level rise or other impacts of global climate change24

• Radon gas exposure25 or indoor air pollutants other than radon26

• Criteria air pollutants other than PM2.5 and ozone (Pb, CO, SOx and NOx)

27 (Not included due 

to resource constraints, and more limited modeling/coverage, frequency of nonattainment, 

or health impact than for PM2.5 and ozone).

• Proximity to other point or area sources not already accounted for by EJSCREEN’s proximity 

indicators, NATA indicators, or other air quality indicators. This can include some TRI 

reporting facilities that do not emit HAPs to air, for example. TRI facilities are a small but 

important fraction of all regulated facilities. Those emitting HAPs to air are already

considered through the NATA indicators, and many others are included in the RMP 

indicator.28

• Exposures to short episodes of elevated releases of air pollutants during startup, shutdown, 

malfunction, etc. (data gaps in coverage and resolution)

• Exposures to undocumented emissions caused by leaks

• Exposures related to oil and gas extraction, such as hydraulic fracking29

• Mining (e.g., uranium mining, etc.)30

• Coal ash ponds

• Combined animal feeding operations (CAFOs)31

• Leaking underground storage tanks or other contaminated sites other than National 

Priorities List (NPL) sites, Risk Management Plan (RMP) facilities, and Treatment, Storage 

and Disposal Facilities (TSDFs)32

• Pesticide exposures from spray drift or other sources, or pesticide exposures from 

residential and other non-agricultural uses33

22 See http://www2.epa.gov/learn-issues/water-resources 23 See http://www2.epa.gov/learn-issues/water-resources 24 See http://www.epa.gov/climatechange/ 25 See http://www.epa.gov/iaq/ia-intro.html 26 See http://www.epa.gov/iaq/ia-intro.html 27 See http://www.epa.gov/air/urbanair/ 28 For examples of other efforts to consider a wide range of facilities, see the various TRI mapping tools such as 

(http://www2.epa.gov/toxics-release-inventory-tri-program/tri-data-and-tools), and see EPA’s RSEI tool: 

http://www.epa.gov/opptintr/rsei/index.html

29 See http://www2.epa.gov/hydraulicfracturing 30 See http://water.epa.gov/polwaste/npdes/Mining.cfm 31 See http://water.epa.gov/polwaste/npdes/afo/index.cfm 32 See http://www.epa.gov/swerust1/overview.htm 33 See http://www2.epa.gov/safepestcontrol and http://www2.epa.gov/science-and-technology/pesticides-science

34 | Page

 
Details on Environmental Indicators

• Noise pollution and odors not already accounted for in other data34

• Occupational exposures

• Exposures related to imported or domestic consumer products, foods and beverages, or 

other sources of exposure where we lack detailed geographic data

• Ecosystem services35

EJSCREEN is designed to be a nationally consistent screening tool, with results calculated and displayed 

at the Census block group level. Data inputs must be from publicly available sources, available and 

consistent across the entire country, and with sufficient spatial resolution. There must be some plausible 

means of quantifying an adverse effect or a proxy for an adverse effect on residential populations. These 

requirements set a high bar for including environmental data. Currently, none of the potential 

environmental factors listed above meet those criteria.

Environmental Factors in EJSCREEN

Each of the 12 environmental indicators included in EJSCREEN can be viewed separately. Each 

environmental indicator is also combined with demographic indexes to form the EJ indexes outlined 

above. Table 2 summarizes the environmental indicators in EJSCREEN, and the following sections 

describe each environmental indicator in more detail. The sections above address criteria for selecting 

which environmental factors to include in this version of EJSCREEN. Appendix D provides summary 

statistics for each indicator, including mean and percentile values.

Table 2. Summary Table of Environmental Indicators and Sources

34 See http://www.epa.gov/air/noise.html 35 See http://enviroatlas.epa.gov/enviroatlas/ 

Key 

Medium

Indicator Details Source Data 

Year

Air NATA air 

toxics 

cancer risk

Lifetime cancer risk from 

inhalation of air toxics

EPA NATA, retrieved 20XX

http://www.epa.gov/ttn/atw/nat

amain/index.html

20xx

Air NATA 

neurological 

hazard 

index

Air toxics neurological hazard 

index (ratio of exposure 

concentration to health-based 

reference concentration)

EPA NATA, retrieved 20XX

http://www.epa.gov/ttn/atw/nat

amain/index.html 

20xx

Air NATA 

respiratory 

hazard 

index

Air toxics respiratory hazard 

index (ratio of exposure 

concentration to health-based 

reference concentration)

EPA NATA, retrieved 20XX

http://www.epa.gov/ttn/atw/nat

amain/index.html 

20xx

Air NATA diesel 

PM

Diesel particulate matter level 

in air, μg/m3

EPA NATA, retrieved 20XX

http://www.epa.gov/ttn/atw/nat

amain/index.html

20xx

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Details on Environmental Indicators

36 Count of NPL sites excludes deleted sites, sites in U.S. territories, and other sites that could not be included.

Air Particulate 

matter

PM2.5 levels in air, μg/m3

annual avg. (2011) 

EPA, OAR (fusion of model and 

monitor data). For methods, see

http://www.epa.gov/esd/landsci/lcb/lcb_faqsd.html

2011

Air Ozone Ozone summer seasonal avg. 

of daily maximum 8-hour 

concentration in air in parts 

per billion (2011)

EPA, OAR (fusion of model and 

monitor data). For methods, see

http://www.epa.gov/esd/landsci/lcb/lcb_faqsd.html

2011

Air/other Traffic

proximity 

and volume

Count of vehicles (AADT, avg.

annual daily traffic) at major 

roads within 500 meters, 

divided by distance in meters

(not km)

Calculated from 2011 U.S. DOT 

traffic data, retrieved 4/2012

http://www.rita.dot.gov/bts/site

s/rita.dot.gov.bts/files/publicatio

ns/national_transportation_atlas

_database/2011/index.html

2011

Dust/ 

lead 

paint

Lead paint 

indicator 

Percent of housing units built 

pre-1960, as indicator of 

potential lead paint exposure 

Calculated based on Census/ACS 

data, retrieved 2014 

http://www2.census.gov/acs201

2_5yr/summaryfile/

2008-

2012

Waste/

air/ 

water

Proximity to 

RMP sites

Count of RMP (potential 

chemical accident 

management plan) facilities 

within 5 km

(or nearest one beyond 5 km), 

each divided by distance in 

kilometers

Calculated from EPA RMP 

database, retrieved 11/2013 

2013

Waste/

air/ 

water

Proximity to 

TSDFs

Count of TSDFs (hazardous 

waste management facilities) 

within 5 km (or nearest 

beyond 5 km), each divided by 

distance in kilometers

Calculated from EPA RCRAInfo 

database, retrieved 11/2013 

http://www.epa.gov/enviro/fact

s/rcrainfo/search.html 

2013

Waste/

air/ 

water

Proximity to 

NPL sites

Count of proposed and listed

NPL sites36 within 5 km (or 

nearest one beyond 5 km), 

each divided by distance in 

kilometers

Calculated from EPA CERCLIS 

database, retrieved 11/12/2013 

http://cumulis.epa.gov/supercpa

d/cursites/srchsites.cfm 

2013

Water Proximity to 

major direct 

water 

dischargers 

Count of NPDES major direct 

water discharger facilities 

within 5 km (or nearest one 

beyond 5 km), each divided by 

distance in kilometers

Calculated from EPA PCS/ICIS 

database, retrieved 12/2013 

http://www.epa.gov/enviro/fact

s/pcs-icis/search.html 

2013

Note: EJSCREEN’s EJ Indexes also include demographic information that is obtained from the U.S. 

Census Bureau’s American Community Survey (ACS). The 2015 version of EJSCREEN includes 2008-

2012 ACS 5-year summary file data, which is based on 2010 Census boundaries.

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Details on Environmental Indicators

NATA Air Toxics and NATA Diesel PM

Air toxics, often referred to as hazardous air pollutants (HAPs), are pollutants that are known or 

suspected to cause cancer or other serious health effects, such as reproductive effects or birth defects, 

or adverse environmental effects. EPA regulates 187 chemicals under its HAP program (U.S. EPA, 2009d). 

Most air toxics originate from transportation and industry, including motor vehicles, industrial facilities 

and power plants. 

Indicator EPA’s National Air Toxics Assessment (NATA) provides the following indicators 

that are used in EJSCREEN:

• Estimated lifetime inhalation cancer risk from the analyzed carcinogens in 

ambient outdoor air. 

• Hazard index for respiratory effects. 

• Hazard index for neurological effects. 

• Diesel particulate matter concentration. 

Rationale for 

Inclusion

A chemical’s listing as a HAP is based on evidence of cancer or other adverse 

health effects or environmental effects associated with exposure to the chemical, 

as determined by EPA and the initial list in the Clean Air Act Amendments of 

1990. EPA’s Integrated Risk Information System (IRIS) program documents the 

health risks associated with these chemicals and serves as a basis for the analysis 

of health implications (U.S. EPA, 2012c). 

Air toxics cancer risk and noncancer impacts have been included in other EPA EJ 

screening tools.

HAPs are emitted from a wide variety of sources and disperse around the sources, 

especially downwind. In some cases, these substances react with other 

constituents in the atmosphere or break down to other chemicals, and most are 

eventually removed through precipitation or other atmospheric processes. People 

are exposed in their daily activities in and around their homes, at school or work, 

and while moving about the area. They inhale the substances, exhale or excrete 

some portion of them, and have the potential for incurring adverse effects from 

the portion that stays in the body. 

More Information More information is available at the air toxics website 

(http://www.epa.gov/air/toxicair), the NATA website (www.epa.gov/nata), and 

the IRIS website (www.epa.gov/iris).

Relevant Studies A comprehensive list of EJ studies using the NATA database can be found in 

Chakraborty et al., (2011). Some examples of EJ studies of chemicals listed as 

HAPs include Morello-Frosch & Jesdale (2006), and other studies reviewed by Liu 

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Details on Environmental Indicators

(2001) and Brender et al., (2011). Diesel particulate matter has also been the 

subject of EJ analysis (Rosenbaum, Hartley, & Holder, 2011). 

Data Source EJSCREEN uses the most recent data from EPA’s National-Scale Air Toxics 

Assessment (NATA). NATA estimates cancer risk or noncancer implications of 

many of the 187 air pollutants classified as HAPs, as well as diesel particulate 

matter. NATA uses emissions estimates from the National Emissions Inventory 

(NEI), which is updated every three years. The NEI includes all of the Toxics 

Release Inventory (TRI) reporting facilities that release hazardous air pollutants, 

along with many other sources of air pollutants, such as motor vehicles.

Note that the publicly-available NATA, PM2.5, and ozone estimates are at tract 

resolution, and tract level is the resolution used for EJSCREEN, unlike with 

proximity indicators, for example. Each block group was assigned the NATA or PM 

or ozone score of the tract containing it. All indicators or statistics then were 

calculated using block group data, whether or not those block group scores had 

been assigned based on tracts.

Data Version The 2015 version of EJSCREEN uses 20xx NATA data, which is based on NEI 

emissions estimates for 20xx (U.S. EPA, 2015b). This version of NATA estimated 

ambient concentrations of 177 HAPs plus DPM, and then estimated health 

implications for 139 of these HAPs (cancer risk for 80 and noncancer results for 

100).

Data from recent years may no longer be as representative of current conditions 

as they were at the time the data was collected. The NATA-based indicators in 

particular should be viewed with this in mind, because emissions of air toxics

generally have decreased in the intervening years. This version of EJSCREEN 

incorporated the most recent data that were available at the time of indicator 

development. Every attempt will be made to use the most recent appropriate 

data available in future updates of EJSCREEN. There is always a delay between the 

release of raw data and their eventual incorporation into any models, tools, or 

maps. It is also useful to note that although the raw numbers for some indicators 

do not represent current conditions, the percentiles are much more likely to be 

reasonably representative of today’s conditions in most locations. This is because 

even if emissions have been significantly reduced overall, for example, the 

differences between various locations are unlikely to have changed as 

dramatically, especially when the reductions have come from national regulations 

and other trends affecting entire industries or sectors in many locations. For this 

reason, the percentiles may be more representative of current conditions than 

the raw values of the indicators. 

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Details on Environmental Indicators

Discussion EPA’s NATA website has extensive documentation of all of the data and methods 

used in developing the NATA indicators, as well as discussions of uncertainty, 

caveats, and limitations in the NATA estimates. That information is not repeated 

here, but it is important that anyone using NATA data understand these issues, so 

anyone using EJSCREEN should consult the NATA documentation

(www.epa.gov/nata). 

Very briefly, the air pollutants in NATA include likely or known carcinogens such 

as formaldehyde, benzene, polycyclic aromatic hydrocarbons and naphthalene, as 

well as important sources of noncancer impact such as acrolein, DPM and 

manganese compounds. The cancer risk in NATA is aggregated as a cumulative 

risk for the combination of all analyzed HAPs, and this total is used in EJSCREEN. 

NATA calculates a hazard quotient, which is the ratio of ambient air concentration 

to a chemical’s health-based RfC. No adverse health effects are expected from 

exposure if the hazard quotient is less than one. A hazard index is the sum of 

hazard quotients for chemicals that cause adverse effects through the same toxic 

mechanism. NATA currently includes hazard indexes for respiratory effects and 

neurological effects, both of which are included as environmental indicators in 

EJSCREEN. Each represents the cumulative impacts of all the relevant air toxics.

The NATA website provides more detailed data than EJSCREEN – Tables and maps

on individual HAPs or specific types of sources (e.g., mobile sources only) can be 

generated by GIS practitioners using data from the NATA website, for those 

requiring more detail than is provided by the data in EJSCREEN.

The reports in EJSCREEN present the environmental indicators from NATA using 

ranges of percentiles such as 90-95 or 95-100 rather than as the numbers 1-100 

(for Regional and US percentiles). This is done in recognition of the uncertainties 

inherent in comparing NATA estimates across States that may have different 

approaches in emissions inventories.

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Details on Environmental Indicators

Particulate Matter (PM2.5) 

PM2.5 is particulate matter that is 2.5 microns or less in diameter. Common sources of PM2.5 emissions 

include power plants and industrial facilities. Secondary PM2.5 can form from gases, such as oxides of 

nitrogen (NOx) or sulfur dioxide (SO2), reacting in the atmosphere. EPA set the first PM2.5 National 

Ambient Air Quality Standards (NAAQS) in 1997, and revised the standards in 2006 and 2012. 

Indicator Annual average PM2.5 concentration in micrograms per cubic meter 

(μg/m3

). 

Rationale for 

Inclusion

EPA’s work associated with the PM NAAQS has documented the health 

effects associated with exposure to PM2.5, including elevated risk of 

premature mortality from cardiovascular diseases or lung cancer, and 

increased health problems such as asthma attacks (U.S. EPA, 2009b). 

PM2.5 concentrations at different levels are found in all parts of the United 

States, so residents are exposed via inhalation to varying degrees. A 2012 

EPA report found that the majority of the U.S. population lived in areas in 

nonattainment of one or more of the NAAQS in effect at that time—a total 

population of 159 million people (based on 2010 Census data for those 

locations) (U.S. EPA, 2012g). 

Several studies relevant to EJ and PM2.5, including those discussing 

susceptible subgroups, are referenced by Bell & Ebisu (2012).

PM2.5 has been included in other EPA EJ screening tools.

More Information More information is available at the PM2.5 website 

(http://www.epa.gov/pm).

Relevant Studies Some examples of studies focused on disparities in exposure to PM2.5 have 

been reviewed in Liu (2001), and more recent studies include Bell & Ebisu 

(2012); Fann et al. (2011); Post, Belova, & Huang (2011); Miranda, 

Edwards, Keating, & Paul (2011); Brochu et al. (2011); and Levy, Wilson, & 

Zwack (2007). A very recent study found disparities in exposure to NOX, a 

precursor to PM2.5. (Clark, Millet, & Marshall, 2014). 

Data Source EJSCREEN’s PM2.5 data are estimated from a combination of monitoring 

data and air quality modeling. Ambient PM2.5 concentration is estimated by 

EPA’s Office of Research and Development using a Bayesian space–time 

downscaling fusion model approach. This approach is described in a series 

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of three published journal articles (Berrocal, Gelfand, & Holland, 2010a, 

2010b, 2011).37

PM2.5 and ozone estimates were not available for Alaska or Hawaii for use 

in the 2015 version of EJSCREEN, due to a lack of CMAQ modeling. EPA 

may be able to include estimates in a future version of EJSCREEN.

Data Version The 2015 version of EJSCREEN uses PM2.5 data that are based on 2011 

monitoring and modeling estimates (U.S. EPA, 2015a). 

Data from several years ago may no longer be as representative of current 

conditions as they were at the time the data was collected. The PM2.5 and 

ozone indicators in particular should be viewed with this in mind, because 

emissions related to PM2.5 and ozone generally have decreased in the 

intervening years. This version of EJSCREEN incorporated the most recent 

data that were available at the time of indicator development. Every 

attempt will be made to use the most recent appropriate data available in 

future updates of EJSCREEN. There is always a delay between the release 

of raw data and their eventual incorporation into any models, tools, or 

maps. It is also useful to note that although the raw numbers for some 

indicators do not represent current conditions, the percentiles are much 

more likely to be reasonably representative of today’s conditions in most 

locations. This is because even if emissions have been significantly reduced 

overall, for example, the differences between various locations are unlikely 

to have changed as dramatically, especially when the reductions have 

come from national regulations and other trends affecting entire industries 

or sectors in many locations. For this reason, the percentiles may be more 

representative of current conditions than the raw values of the indicators. 

Finally, some supplementary maps and local information can complement 

the EJSCREEN indicators to provide more recent information. In particular, 

EJSCREEN also provides updated maps of PM2.5 and ozone nonattainment 

areas (areas not meeting national ambient air quality standards).

Resolution High-resolution estimates of PM2.5 are very difficult to develop for the 

entire United States. Block groups vary widely in geographic area—some 

are larger than 100 square kilometers, but a substantial fraction are 

smaller than 1 sq. km in area. This makes it challenging to develop relevant 

spatial data.

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37 Detailed documentation and GIS metadata describing a tract-level application of the same approach are

provided here: http://www.epa.gov/esd/land-sci/lcb/lcb_faqsd.html and http://www.epa.gov/esd/landsci/lcb/pdf/DSMetadataAir_0612.pdf. 
Details on Environmental Indicators

Some small areas have used the high-resolution AERMOD model to 

estimate PM2.5 levels for EJ analysis (Maroko, 2012), but such modeling is 

not feasible for the entire United States at this time.

In the past (prior to approximately 2007), CMAQ used a grid size of 36x36 

km in the Western United States and 12x12 km in the Eastern United 

States, or 36 km in general, as in a recent EJ analysis of the heavy-duty 

diesel emissions rule finalized in 2001 (Post et al., 2011). After

approximately 2007, CMAQ modeling has divided the nation into a grid of 

cells that are each roughly 12 km by 12 km, and has estimated the PM2.5

concentration in each cell.

In a 2010 study, satellite data have been used to estimate PM2.5 levels with 

a spatial resolution of roughly 10 km by 10 km, showing reasonably good 

agreement with monitoring data (van Donkelaar et al., 2010). 

Land use regression (LUR) has also been proposed, and can provide better 

resolution. Nationwide LUR-based estimates have been developed for NOx

but not PM2.5. 

The downscaler method was selected for EJSCREEN partly because it is 

particularly useful for this application, in that it estimates concentration at 

a specified point, rather than for the average of a large grid cell. The 

downscaler algorithms combine information from nearby monitors and 

CMAQ grid cell estimates. This provides an estimate based on more 

information than models alone or monitors alone could provide. It is 

important to note that the downscaler and indicators here are not

attempting to describe all of the local variations in ambient air 

concentrations. They are merely capturing some additional variation that is 

not seen when relying on models or monitors alone.

Discussion The downscaling fusion model uses both air quality monitoring data from 

NAMS/SLAMS (data collected by EPA, state, local and tribal air pollution 

control agencies at more than 600 hundred monitors nationwide) and 

numerical output from the Models-3/CMAQ model. The CMAQ model is 

used extensively by EPA and has been described in detail elsewhere (Byun 

& Schere, 2006). 

This downscaling approach is designed to provide daily, predictive PM2.5

(daily average) and O3 (daily 8-hour maximum) surfaces for a given year, 

such as 2011, at specified points.

For EJSCREEN, the downscaling method was applied to a point within each 

Census tract. EPA’s Office of Air and Radiation generated an estimate for 

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each tract, and then assigned the same tract value to every block group 

within the given tract. Daily estimates from the downscaling method were 

averaged for the whole year in the case of PM2.5 and for the ozone season 

(May–September) in the case of ozone. 

Again, it is important to note that the downscaler and indicators here are 

not attempting to describe all of the local variations in ambient air 

concentrations. They are merely capturing some additional variation that is 

not seen when relying on models or monitors alone.

Several data sources have been used elsewhere and were considered for 

inclusion in EJSCREEN. For instance, EPA’s regulatory impact analyses 

(RIAs) for recent rules and the PM2.5 NAAQS have used estimates of PM2.5

that combine modeling and monitoring in a different way (U.S. EPA, 

2009b). These estimates also start with CMAQ air quality modeling results, 

but then locally adjust those estimates up or down based on local 

monitoring data using MATS (monitor attainment test software), which 

provides an enhanced Voronoi neighbor averaging interpolation technique. 

Published analyses of PM2.5 health impacts have used similarly fused 

estimates (Fann et al., 2012). Before the downscaler method was 

developed, a different Bayesian modeling approach was also used, as 

described in McMillan, Holland, Morara, & Fang (2010). Other efforts have 

used interpolation between monitors without air quality modeling, such as 

through basic Voronoi neighbor averaging (Fann & Risley, 2011), or simply 

the average of monitors within a county (Bravo, Fuentes, Zhang, Burr, & 

Bell, 2012). Monitors provide reliable estimates where they are located, 

but suitable PM2.5 data are available at fewer than 900 monitors in the 

United States. While urban areas tend to have PM2.5 monitors, more than 

two-thirds of U.S. counties lack any monitoring data, so modeling is an 

important complement to monitoring. Methods based on CMAQ alone, 

monitors alone, CMAQ-MATS and the downscaling approach all provide 

somewhat different estimates.

Note that the EJSCREEN value does not directly indicate nonattainment of 

the NAAQS standard because the indicator in EJSCREEN is based on 

estimates from a combination of modeling and monitoring for a single 

year, while nonattainment is determined for a large area (often a county) 

based on three years of monitoring data. 

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Ozone 

Ozone (O3) is not usually emitted directly into the air, but is created at ground level by a chemical 

reaction between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) in the presence of 

sunlight. These ozone precursors are emitted by motor vehicles, industrial facilities and power plants as 

well as natural sources. Ground-level ozone is the primary constituent of smog.

Indicator The May–September (summer/ ozone season) average of daily-maximum 

8-hour-average ozone concentrations, in parts per billion (ppb). 

Rationale for 

Inclusion

Toxicological and epidemiological studies have established an association 

between exposure to ambient ozone and a variety of health outcomes, 

including reduction in lung function, increased inflammation and increased 

hospital admissions and mortality (U.S. EPA, 2006b). In the 2006 Air Quality 

Criteria Document for Ozone, a comprehensive review of the clinical and 

epidemiological evidence was inconclusive about a possible threshold for 

ozone-induced health effects. EPA concluded that if a population threshold 

level exists, it is near the lower limit of ambient ozone concentrations in 

the United States (U.S. EPA, 2006b). Several subpopulations may 

experience susceptibility to ozone-induced health effects. These 

subpopulations include older adults, children, individuals with preexisting 

pulmonary disease and those with higher exposure levels such as outdoor 

workers (U.S. EPA, 2006b). A recent review of studies identifying 

subgroups susceptible to ozone found the strongest evidence for greater 

sensitivity among the elderly and also the unemployed (Bell, Zanobetti, & 

Dominici, 2014). 

A 2012 EPA report found that the majority of the U.S. population lived in 

areas in nonattainment of one or more of the NAAQS—a total population 

of 159 million people (based on 2010 Census data for those locations) (U.S. 

EPA, 2012g). As standards are updated, nonattainment areas are redefined 

along with the number of people living in redefined nonattainment areas. 

Ozone concentrations at different levels are found in all parts of the United 

States, so residents are exposed via inhalation to varying degrees.

Ozone has been included in other EPA EJ screening tools.

More Information More information is available at the ground-level ozone website 

(http://www.epa.gov/air/ozonepollution/).

Relevant Studies Some examples of studies that have focused on disparities in exposure to 

ozone include Fann et al. (2011), Grineski (2007), Grineski et al. (2007), and 

those reviewed in Liu (2001). 

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Data Source EJSCREEN’s ozone data are estimated by EPA from a combination of 

monitoring data and CMAQ air quality modeling. Ozone was estimated 

with the same approach as PM2.5, and the methodology is described above. 

Ozone faces similar limitations, in that a limited number of U.S. monitors 

have suitable data, so modeling is an important complement to monitoring 

data.

PM2.5 and ozone estimates were not available for Alaska or Hawaii for use 

in the 2015 version of EJSCREEN, due to a lack of CMAQ modeling. EPA 

may be able to include estimates in a future version of EJSCREEN.

Data Version The 2015 version of EJSCREEN uses ozone data that are based on 2011

monitoring and modeling estimates (U.S. EPA, 2015a). Tract estimates 

were assigned to block groups as described for the PM2.5 indicator.

Resolution See “Resolution” section under the previous environmental indicator, 

PM2.5. 

Discussion See “Discussion” section under the previous environmental indicator, 

PM2.5. 

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Lead Paint Indicator 

A key source of exposure to lead for many Americans is through lead paint and lead-containing dust that 

accumulates indoors, in homes or in other buildings where lead paint was used. Exterior structures 

painted with lead-based paint are also a source of ambient lead through chipping exterior paint. 

Elevated short-term lead dust loadings have also been observed following demolition of old buildings 

(U.S. EPA, 2011c). Lead-based paint was banned in the United States by the Consumer Product Safety 

Commission in 1978, but lead-based paint used in housing before the ban remains a significant source of 

exposure to lead for children and adults. Lead paint and contaminated dust and soil are considered the 

leading cause of high lead levels in U.S. children (Levin et al., 2008). 

Indicator The percentage of occupied housing units built before 1960 was selected 

as an indicator of the likelihood of having significant lead-based paint 

hazards in the home. 

Rationale for 

Inclusion

Elevated blood lead levels are a well-documented public health concern 

of particular interest to EJ stakeholders, and represent an important 

environmental health issue (U.S. EPA, 2006a, 2011c). 

Certain demographic groups may be more susceptible to lead exposure. 

For example, blood lead’s association with cardiovascular outcomes 

appears to be stronger among Mexican Americans and non-Hispanic 

blacks than non-Hispanic whites (U.S. EPA 2011c). Also, some but not all 

studies suggest lead has a greater impact on IQ among those of low 

socioeconomic status (U.S. EPA 2011c). 

Despite significant reductions in ambient levels of lead from the phaseout of leaded gasoline and the 1978 ban on lead-based paint, lead 

remains a significant hazard for children. Recent research has 

demonstrated that children can experience neurological damage even at 

low levels of exposure to lead. In May 2012, the Centers for Disease 

Control and Prevention (CDC) agreed to adopt the recommendations of 

the CDC Advisory Committee for Childhood Lead Poisoning Prevention 

(ACCLPP) for defining elevated blood-lead levels (BLLs) among children. 

The ACCLPP recommended that CDC use a childhood BLL reference value 

based on the 97.5th percentile of the population BLL in children under age 

6 to identify children and environments associated with lead-exposure 

hazards (Centers for Disease Control and Prevention, 2012). The 97.5th

percentile value is currently 5 μg/dL. 

Surfaces originally covered with lead-based paint may chip, flake or 

develop a chalky surface. The lead in these pieces or particles may be 

moved about the interior or exterior of the painted structures, and be 

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moved from inside to outside and vice versa. Through direct contact with 

the painted surfaces or through contact with the released particles, lead 

may adhere to hands and other parts of the residents’ bodies, and people 

may ingest some portion of the lead. If the painted surfaces are disturbed 

through renovation or other actions, some lead-based paint particles may 

be temporarily suspended in the air, and particles on surfaces within the 

structures may be re-suspended during the residents’ activities. The 

suspended particles may be inhaled or may fall on food and be ingested. 

Children playing inside or outside and exposed to particles of lead-based 

paint may ingest some of the lead through hand–mouth actions.

An analysis of data collected during the 1999–2004 National Health and 

Nutrition Examination Survey (NHANES) showed that children living in 

older housing stock (built before 1950) were significantly more likely to 

have blood lead levels greater than 5 μg/dL than children living in housing 

built after 1978 (Jones et al., 2009). Jones et al. estimated that 7.4% of 

children under age 6 had blood lead levels greater than 5 μg/dL during 

NHANES 1999–2004. For children under age 6 living in the highest risk 

housing (built before 1950), Jones et al. observed that 15.1% had blood 

lead levels above 5 μg/dL. For children under age 6 living in the lowest risk 

housing (built in 1978 or later), 2.1% had BLLs above 5 μg/dL. 

EPA EJ screening tools in the past generally have not included proxies for 

lead exposure.

More Information More information is available at EPA’s lead website 

(http://www2.epa.gov/lead).

Relevant Studies Several examples of EJ studies of exposure from lead paint exist, including

Gaitens et al., 2009 and others.

Data Source The data were collected at the block group level from the ACS estimates

from the Census Bureau. The indicator was calculated by dividing the 

count of occupied housing units built prior to 1960 by the total number of 

built housing units in the block group (ACS table B25034, see Appendix B 

for details). 

Data Version The block-group level data for the 2015 version of EJSCREEN were 

collected from the 2008-2012 ACS (U.S. Census Bureau, 2011). 

Approximately 40% of occupied, non-institutional housing units in the 

United States were built prior to 1960, as of 1999. The ACS 2008–2012

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data in EJSCREEN indicate the average person in the US lives in a block 

group where about 30% of the housing was built before 1960.

Discussion Lead paint was used extensively in the United States prior to the 1978 ban 

on lead in new residential paint, and a home built prior to 1960 is far 

more likely to have lead hazards than one built more recently (Gaitens et 

al., 2009; Jacobs et al., 2002). In 2002, Jacobs et al. reported that 

approximately 40 million homes in the United States still had lead-based 

paint hazards, based on a nationally representative survey conducted in 

1998–2000. The likelihood of such hazards was found to have changed 

dramatically for housing built in 1960–1977 compared to pre-1960

housing (Table 3).

Based on Jacobs et al. (2002), EPA calculated the following likelihoods of 

significant lead-based paint hazards: 

• Pre-1960 vs. all others: 54% vs. 6% (9 times as likely).

• Pre-1960 vs. all others, among those with children under age 6: 

68% vs. 4% (16 times as likely).

Data and analysis published in 2009 confirmed prior conclusions that 

potential exposure to lead is associated with housing age, providing more 

information on lead concentrations in household dust as a function of 

housing age (Gaitens et al., 2009). Some of the models presented by 

Gaitens et al. (2009) suggest that the largest decreases in lead dust levels 

are seen between housing built prior to 1940 and after 1940, with more 

modest contrasts seen for housing built after 1960 and after 1977. A 

cutoff of 1960, however, is consistent with the data from Jacobs et al.

(2002), and the window sill lead dust models from Gaitens et al. (2009).

It is important to note that older housing alone may not represent any 

actual risk or even exposure.

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Table 3. Likelihood of Lead-Based Paint Hazards by Housing Construction Date

38 The de minimis levels for paint deterioration are ≤ 20 ft2 (exterior) or ≤ 2 ft2 (interior) of lead-based paint on 

large surface area components (walls, doors), or damage to ≤ 10% of the total surface area of interior small surface 

area component types (windowsills, baseboards, trim) (40 C.F.R. § 745.65).

Year Built

Share of Housing with Significant Lead-Based 

Paint Hazards (and 95% Confidence Interval)

Post-1960:

 1978–1998 3% (1–6%)

 1960–1977 8% (6–12%)

Pre-1960:

 1940–1959 43% (32–51%)

 Before 1940 68% (56–75%)

Source: Jacobs et al. (2002). A “significant lead-based paint hazard” is defined as “a lead-based paint hazard above de 

minimis levels as defined in U.S. EPA and U.S. Department of Housing and Urban Development (HUD) regulations.”38

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Traffic Proximity 

A substantial fraction of the U.S. population lives in close proximity to traffic, and the number of vehiclemiles traveled has grown 40% from 1990 to 2010 (U.S. EPA, 2012d). Proximity to motor vehicle traffic is 

associated with increased exposures to ambient noise, toxic gases and particulate matter including 

diesel particulates. Technical details about the methodology used to determine traffic proximity are 

provided in Appendix C. 

Indicator The count of vehicles per day within 500 meters of a block centroid, divided by

distance in meters, presented as the population-weighted average of blocks in each 

block group. Adjustments are made so that the minimum distance used is 

reasonable when very small.

Rationale for 

Inclusion

A 2011 literature review identified several studies that “found that living near 

hazardous waste sites, industrial sites, cropland with pesticide applications, highly 

trafficked roads, nuclear plants, and gas stations or repair shops is related to an 

increased risk of adverse health outcomes” (Brender et al., 2011, p. S37). This 

indicator is the first of five that relate to this category of concern.

Is should first be noted that there are both positive and negative aspects to living 

near major roads. Proximity to roads can provide access to jobs, health care, food, 

recreational opportunities, and other benefits. The indicator of traffic proximity and 

volume is designed to screen for the negative aspects, so it uses distance weighting 

and volume weighting to focus on the extremes of very close proximity to very high 

volumes of traffic, such as living closer than 50-100 meters from a multi-lane 

highway, as explained below.

Residential proximity to traffic has been associated with various health impacts, 

particularly asthma exacerbation and possibly onset of asthma, as well as mortality 

rates (Baumann et al., 2011; Health Effects Institute, 2010). Proximity to traffic has 

also been associated with subclinical atherosclerosis (a key pathology underlying 

cardiovascular disease (CVD)), prevalence of CVD and coronary heart disease (CHD), 

incidence of myocardial infarction, and CVD mortality (Hoffman et al., 2009). 

Vehicle-related emissions of various pollutants—ultrafine and other components of 

PM2.5, lead and other metals, and mobile source air toxics such as benzene, nitrogen 

oxides (NOx), hydrocarbons and carbon monoxide (CO)—are believed to contribute 

to these health effects. Vehicles also emit precursors that add to ambient ozone 

and PM2.5. Additionally, EPA’s 2005 NATA estimated that mobile emissions 

accounted for about 30% of average cancer risk from the pollutants in NATA, mainly 

from benzene (U.S. EPA, 2009c). However, the spatial accuracy of NATA’s mobile 

source impacts is limited, because local estimates are based on countywide total 

mobile source emissions roughly allocated to each part of the county based on 

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presence of major roads. The traffic indicator in EJSCREEN provides a more detailed 

analysis of the volume and location of traffic than was used in NATA. Also, NATA 

captures only some of the impacts associated with traffic, so the traffic indicator is a 

useful complement.

Traffic proximity is also associated with noise, which is a risk factor for various 

health problems. Workplace and transportation-related noise have been associated 

with release of stress hormones; sleep disturbance; hypertension; altered heart 

rate; ischemic heart disease; myocardial infarction; and, among the elderly, risk of 

stroke (Sørensen et al., 2011). In one study, for example, among those older than 

64.5 years of age, the stroke incidence rate ratio was 1.27 per 10 dB more road 

traffic noise (Sørensen et al., 2011).

Whether noise or other factors account for it, local traffic volume is a predictor of 

stress (which itself is associated with significant health risks). In 2010, Yang & 

Matthews concluded that, “[a]t the neighborhood level, the presence of hazardous 

waste sites and traffic volume were determinants of self-rated stress even after 

controlling for other individual characteristics” (2010, p. 803). 

Any indicator of residential proximity addresses exposures relevant to the 

residences within a block group, and would not capture most exposures that occur 

away from the home, such as at work, at school or during a commute.

In the past, EJ screening tools at EPA have not included traffic proximity.

More 

Information

More information is available at the near-roadway website 

(http://www.epa.gov/otaq/nearroadway.htm). 

Relevant 

Studies

Some examples of studies of disparities in proximity to traffic include Tian et al., 

2013; Rowangould, 2013; Brender et al., 2011; Chakraborty (2006); and Liu, 2001.

Data Source Measures of traffic proximity in EJSCREEN are based on average annual daily traffic 

(AADT) estimates in the Highway Performance Monitoring System (HPMS) dataset 

in the Department of Transportation (DOT) National Transportation Atlas Database 

(NTAD). The HPMS highway data is maintained by states and compiled by DOT.

The HPMS data are collected at the state level, and the traffic counting program is 

designed to cover all interstate, principal arterial, other National Highway System 

and HPMS sample sections on a 3-year maximum cycle where at least one-third of 

roads are counted each year. More details on the HPMS are available at 

http://www.fhwa.dot.gov/policyinformation/hpms.cfm. 

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Data Version The 2015 version of EJSCREEN uses 2011 HPMS data (U.S. Department of 

Transportation, 2011)

39. This dataset contains 490,781 road segments, of which 

86.7% contain AADT information, and those have a length of approximately 

548,000 km (340,511 miles). While this is only 8% of total road miles of public road 

in the US, the roads not included (e.g., local streets) tend to have much lower levels 

of traffic, so the roads included appear to account for almost two thirds of all US 

traffic (vehicle-miles travelled).40 For the 2015 version of EJSCREEN, a total of 

11,078,297 Census 2010 blocks were analyzed to find all road segments within 500 

meters of each block’s internal point, or the nearest single segment if none were 

found within 500 meters.

Discussion The traffic proximity indicator is based on average annual daily traffic (AADT)

divided by distance in meters. For example, a single highway with 16,000 AADT at 

400 meters distance would result in a score of 16,000/400=40, which is close to the 

median person’s block group traffic proximity indicator value in the US. About 5% of 

the population has traffic proximity indicator values more than ten times as high as 

the median, because traffic volumes vary widely across roads and communities. The 

most traveled highway section in the United States, the I-405 in the Los AngelesLong Beach-Santa Ana area, had 374,000 vehicles of AADT in 2008.41 About forty 

other highway sections in the US exceeded 250,000 AADT, but about half were in 

just one state – California – and the were rest spread over just a dozen states.

The proximity score is based on the traffic within a search radius of 500 meters (or 

further if none is found in that radius). It is important to understand that this 

distance was selected to be large enough to capture the great majority of road 

segments (with traffic data) that could have a significant impact on the local 

residents, balanced against the need to limit the scope due to computational 

constraints. The closest traffic is given more weight, and the distant traffic given 

less weight, through inverse distance weighting. For example, traffic 500 meter 

away is given only one tenth as much weight as traffic 50 meters away.

Based on the available evidence, a distance of roughly 100–300 meters, or perhaps 

up to 500 meters, from traffic should be considered as most important. This 

distance focuses on the types of exposures typically studied and shown to be 

39

http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/201

1/index.html and also see http://www.fhwa.dot.gov/policyinformation/hpms.cfm 40 The included 340,000 miles is comparable to the road miles covered by all interstate, freeway/expressway, and 

principal arterials, plus 50% of the minor arterial miles in the US, which together carry 64% of VMT. Collector and 

local roads are the balance of public roads. See 

http://www.fhwa.dot.gov/policyinformation/statistics/2011/hm220.cfm and 

http://www.fhwa.dot.gov/policyinformation/statistics/2011/vm202.cfm

41 Office of Highway Policy Information, US DOT, 2008 Highway performance monitoring system (HPMS) July 27, 

2010.

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associated with health impacts in near-roadway epidemiology. Epidemiologic 

studies of the impacts of proximity to traffic often utilize distances of 50–1,500 

meters to define a cutoff between less and more exposed locations (Health Effects 

Institute, 2010). For example, a major study of coronary heart disease prevalence 

used distances greater than 200 meters as the reference group and found adjusted 

odds ratios of 1.08 for residences within 100–200 meters, 1.71 for 50–100 meters

and 1.55 within 50 meters of a major road. Only 15% of participants lived within 

200 meters of a major road, and only 3% within 50 meters in this study of heart 

disease (Hoffman et al., 2009).

Additionally, a distance cutoff of 500 meters captures exposures of concern for 

most definitions of mobile source impact. In a review of numerous prior studies of 

proximity to roads, in combination with a modeling case study, Zhou & Levy (2007)

suggested that a distance of 500 meters should capture exposures of concern, 

although impacts may be largely limited to just 100 meters from roads for ultrafine 

particles and PM2.5 mass from mobile sources alone. 

A critical review of literature on traffic-related air pollution in 2010 “identified an 

exposure zone within a range of up to 300 to 500 meters from a highway or a major 

road as the area most highly affected by traffic emissions... and estimated that 30% 

to 45% of people living in large North American cities live within such zones” 

(Health Effects Institute, 2010, p. 7-5). A 2009 analysis of PM2.5 levels in Southern 

California found that traffic within 300 meters of a monitor was the most 

informative predictor of monitored PM2.5 levels, out of a wide range of factors 

considered such as various distances from roads, population density and the 

presence of industry (Krewski et al., 2009). 

On the other hand, some studies have shown a dramatic drop in at least ultrafine 

levels within the first 100 meters downwind from a freeway, and an even sharper 

(essentially immediate) drop in the upwind direction (Zhu, Hinds, Kim, & Sioutas, 

2002). This pattern has been seen in more recent measurements—levels on 

California highways (measured using monitors on vehicles) were compared to levels 

near those roads (roughly 50–300 meters away), and black carbon levels in 

particular were as much as 10 times higher on the road than near the road, for 1-

hour averages (Fujita, Campbell, Zielinska, Arnott, & Chow, 2011). The same study 

found much higher levels (generally 2–5 times higher) on the road than near the 

road, for PM2.5 mass, CO, NO, NOx, VOCs, benzene, toluene, ethylene, xylene, 

formaldehyde and acetaldehyde. This reinforces the idea that exposures very close 

to a busy highway are most important, and that levels drop rapidly within tens of 

meters, falling to much lower levels within the first 50–300 meters (Spengler et al., 

2011).

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Many studies have analyzed roadway proximity categorically, including only major 

roads, but roads vary in the amount of traffic they carry, so AADT provides a better 

starting point for considering impacts than simply whether a road is a major road. 

Several land use regression studies and other research (Hoek et al., 2011; Hystad et 

al., 2011) have suggested that inverse distance-weighted traffic volume is a 

reasonably good proxy for ambient air concentrations of NOx, PM2.5 mass (μg/m3

), 

black carbon or ultrafine PM (as particle number concentration) nearby (50–500 

meters). Levels are clearly higher downwind of the road, and higher where wind 

speeds are lower, but in the absence of detailed location-specific data, traffic 

volume and distance are useful indicators (Hoek et al., 2011). 

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Proximity to Major Direct Water Dischargers

The Clean Water Act regulates facilities that discharge pollutants from point sources to waters of the 

United States. Discharging facilities are generally prohibited unless authorized by a specific provision of 

the act. Direct discharges may be authorized by National Pollutant Discharge Elimination System

(NPDES) permits issued by EPA or states authorized to administer the NPDES program. There are tens of 

thousands of dischargers with such permits, many releasing only limited quantities of pollutants. Several 

thousand facilities, however, are “major direct dischargers” as defined by law, including industrial direct 

dischargers (facilities that discharge pollutants directly into water bodies) and Publicly Owned 

Treatment Works (POTWs) (which receive and treat domestic and municipal waste and industrial 

wastewater and discharge treated water into water bodies). Major direct dischargers are found in a 

wide variety of industry sectors ranging from cement manufacturing to metal products and machinery 

to petroleum refining. Major direct dischargers may be subject to industry-specific Effluent Limitation 

Guidelines (ELGs),42 which are national technology-based standards for wastewater discharges to 

surface waters (U.S. EPA, 2012b).

Technical details about the methodology used to determine proximity to major direct dischargers to 

water are provided in Appendix C.

Indicator The count of major direct discharger facilities within 5 km, divided by 

distance, presented as population-weighted averages of blocks in each 

block group. Adjustments are made if there are none within 5 km, and so 

that the minimum distance used is reasonable when very small.

Rationale for 

Inclusion

Water pollutants can have human health or adverse ecological effects, 

depending on concentration in the water, exposure to the water, toxicity 

of the particular chemical and other factors. There are approximately 

6,700 major direct dischargers in the United States. These facilities 

discharge around 50 billion pounds of pollutants each year directly into the 

nation’s streams and rivers (including conventional pollutants such as 

nitrogen and phosphorus) (U.S. EPA, 2012a).

People may be exposed to the discharged pollutants either directly or 

through indirect pathways. People swimming in the downstream waters or 

engaging in water-based recreation may be directly exposed dermally, 

orally or through inhalation of volatized substances. If the released 

substances reach a downstream drinking water intake, consumers of the 

finished waters may consume whatever portion of the substances is not 

removed by the drinking water utility. Some portion of the discharged 

materials may enter the groundwater of neighboring areas and reach 

42 A list of Effluent Guidelines by industry category can be found at

http://water.epa.gov/scitech/wastetech/guide/industry.cfm.

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people through drinking water derived from wells that draw upon that 

aquifer.

Some EPA EJ screening tools have included measures of proximity to all 

facilities regulated by EPA, which includes major direct water dischargers.

More Information More information is at the water website (http://www.epa.gov/water) for 

drinking water, rivers, and other categories (http://water.epa.gov/type/), a 

local water quality webpage (http://water.epa.gov/drink/local/index.cfm), 

and the NPDES webpage (http://water.epa.gov/polwaste/npdes/). 

Relevant Studies Some examples of studies of disparities in proximity to water dischargers

include Fitos and Chakraborty (2010); Brender et al., 2011; VanDerslice, 

2011; and a recent study (Deganian & Thompson, 2012) that tallied the 

number of facilities of different types within 10 km2 squares and compared 

these counts to the demographics of the squares. 

Data Source Latitudes/Longitudes for major direct dischargers were taken from EPA’s 

PCS/ICIS database. 

Data Version The 2015 version of EJSCREEN uses locational information retrieved from 

the PCS/ICS database in May 2012 (U.S. EPA, 2012a). A total of 6,981 of the 

major direct dischargers had suitable location information and were 

included. Note that the point data used to show facility locations in the 

“Map Supplementary Layers” menu is updated more often than the 

database with calculated EJSCREEN indicators and indexes, so in some 

small number of cases a facility may be in one data source but not the 

other.

According to these data, almost 50% of the U.S. population lives in block 

groups where at least one block’s centroid is within 5 km of the nearest 

NPDES facility. The population’s mean score was 0.25, which could indicate 

one facility at a distance of 4 km.

Discussion Monitored or modeled data on drinking or surface water quality could not 

be identified with adequate national coverage and resolution. As more 

data become available in the future, such data may be considered for 

inclusion in EJSCREEN. As with all proximity-based indicators, proximity 

alone may not represent any actual risk or even exposure.

Each block group in the United States was assigned a proximity score that 

was the population-weighted sum of block-level proximity scores. 

Appendix C provides more details on calculation of proximity scores. First, 

each block was given a proximity score that was the sum of inverse 

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distance-weighted count of facilities anywhere within 5 km of the block’s 

centroid. This score can be thought of as the number of facilities per 

kilometer of distance from the average person. It is also equal to the 

number of facilities divided by the harmonic mean of their distances. This 

means one facility exactly 2 km away gives a score of 1/2, while three 

facilities exactly 4 km away give a score of 3/4, and five facilities all at 1 km 

away give a score of 5.43 If there is no facility within 5 km of a block 

centroid, 1/d is used, where d is the distance to the single nearest at any 

distance.

Proximity to major NPDES dischargers is only an indirect indicator of 

potential exposure to water pollutants, because residents may not spend 

time in or near the affected water, and the discharges may not impact local 

drinking water supplies, or result in exposure from swimming or fishing.

43 An adjustment was made so that any distance smaller than 90% of the block’s “radius” was set equal to 90% of 

that radius, with radius defined as the square root of (area/pi). This adjustment accounted for the fact that the 

average location (residence) within a circle of radius R is 0.9R away from the average point (facility) that is within 

the circle. For more detail, see Appendix C.

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Proximity to NPL Sites

Congress enacted the Comprehensive Environmental Response, Compensation and Liability Act 

(CERCLA), commonly known as Superfund, in 1980. This law was established to provide broad federal 

authority to respond to uncontrolled abandoned hazardous waste sites. Under CERCLA, EPA’s response 

can involve remedial (long-term) cleanup actions or short-term removal actions.

EPA places sites on the National Priorities List (NPL) (a key subset of all “Superfund” sites) based on a 

defined set of criteria and a public comment process. Inclusion of a site on the NPL does not impose a 

financial obligation on EPA, nor does it assign liability to any party. The NPL serves primarily 

informational purposes, identifying sites that appear to warrant remedial actions, thereby conveying to 

policymakers and the public the size and nature of the nation’s cleanup challenges. 

Sites can be placed on the NPL in one of three ways44:

1. The site receives a score of 28.5 or higher in EPA’s Hazard Ranking System (HRS);

2. States or territories designate a top-priority site; or

3. A site meets these requirements: 

a. The Agency for Toxic Substances and Disease Registry (ATSDR) of the U.S. Public Health 

Service has issued a health advisory that recommends removing people from the site; 

b. EPA determines the site poses a significant threat to public health; and

c. EPA anticipates it will be more cost-effective to use its remedial authority (available only 

at NPL sites) than to use its emergency removal authority to respond to the site.

Technical details about the methodology used to determine proximity to NPL sites are provided in 

Appendix C.

Indicator The count of sites proposed and listed on the National Priorities List (NPL), each 

represented by a point on the map (latitude/ longitude coordinate), within 5 km of 

the average resident in a block group, divided by distance, calculated as the 

population-weighted average of blocks in each block group. Adjustments are made 

if there are no NPL sites within 5 km, and so that the minimum distance used is 

reasonable when very small.

Rationale for 

Inclusion

Soon after the passage of CERCLA and the Superfund Amendments and 

Reauthorization Act, questions started to be raised about the locations, listing 

decisions and pace of cleanup at NPL sites in low-income and minority communities 

(Hird, 1993; Probst, 1990; United Church of Christ, 1987), and such concerns have 

continued to this day (Anderton, Oakes, & Egan, 1997; Baden, Noonan, & Turaga, 

2007; O'Neil, 2007). The study by Deganian & Thompson (2012) included NPL sites 

in the tally of pollution points in each of the 10 km2 squares in the study area, for 

comparison with demographic variables. Earlier studies related the presence of NPL 

sites to population characteristics for different definitions of the host areas—

44 http://www.epa.gov/superfund/programs/npl_hrs/nplon.htm

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counties (Hird, 1993), Census places or minor civil divisions where places are not 

defined (Zimmerman, 1993), and Census tracts (Anderton et al., 1997).

The contaminants in NPL sites may reach humans in a number of ways. Volatile 

contaminants may enter the atmosphere and reach individuals via the inhalation 

route. Particularly in dry climates or seasons, contaminants on the surface of some 

sites can become airborne and reach people directly through inhalation or 

indirectly after being deposited on surfaces that people may contact. Contaminants 

can also enter the food chain if the wind disperses them onto land used for 

agriculture. Some contaminants may migrate into groundwater. People may be 

exposed via drinking water derived from the aquifer, through vapor intrusion into 

their residences or through other routes. 

Some EPA EJ screening tools have included measures of proximity to all facilities or 

other sites regulated by EPA, which include NPL sites.

More 

Information

More information is available at the Superfund website 

(http://www.epa.gov/superfund).

Relevant 

Studies

Some examples of studies focused on disparities in proximity to NPL sites include 

Brender et al. (2011) and those reviewed in Liu (2001).

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Data Source A single point location (latitude/ longitude coordinates) for each proposed and

listed NPL site was obtained from EPA’s CERCLIS database. The database does not 

provide details on the boundaries of each site, so this point data had to serve as a 

way to represent site locations. For residents close to very large sites, the available 

data may not provide an accurate representation of proximity to relevant portions 

of the site. These points are approximations of the locations of sites, and are not 

necessarily at the “center” of a given site. In a few cases a site’s coordinates were 

located in a major body of water according to the database, so EPA manually 

specified new, plausible, nearby coordinates for use in EJSCREEN. 

The count excludes deleted sites and sites in U.S. territories. Sites located in Guam 

and Puerto Rico are not included in the 2015 version of EJSCREEN. 

Data Version The 2015 version of EJSCREEN uses locational information retrieved from the 

CERCLIS database in November 2013. A total of 1,350 proposed and listed NPL sites 

were included in the EJSCREEN indicator. Note that the point data used to show site 

locations in the “Map Supplementary Layers” menu is a different database than the 

database used to calculate the EJSCREEN NPL proximity indicators and indexes. The 

Superfund “Map Supplementary Layer” database includes deleted NPL sites, and 

NPL sites in U.S. territories, excluded from the EJSCREEN NPL proximity indicator 

database.

Discussion Each Census block group in the United States was assigned a proximity score that 

was the population-weighted sum of block-level proximity scores. Appendix C 

provides more details on how proximity scores were calculated. First, each Census 

block was given a proximity score that was the sum of inverse distance-weighted 

count of sites anywhere within 5 km of the block’s internal point. This score can be 

thought of as the number of NPL sites per kilometer of distance from the average 

person. It is also equal to the number of sites divided by the harmonic mean of 

their distances. This means one site 2 km away gives a score of 1/2, while three 

sites each 4 km away give a score of 3/4, and five sites all at 1 km away give a score 

of 5.45 If there is no site within 5 km of a block centroid, 1/d is used, where d is the 

distance to the single nearest at any distance.

As with all proximity-based indicators, proximity alone may not represent any 

actual risk or even exposure.

45 An adjustment was made so that any distance smaller than 90% of the block’s “radius” was set equal to 90% of 

that radius, with radius defined as the square root of (area/pi). This adjustment accounted for the fact that the 

average location (residence) within a circle of radius R is 0.9R away from the average point (site) that is within the 

circle. For more detail, see Appendix C.

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Proximity to TSDFs

The Resource Conservation and Recovery Act (RCRA), an amendment to the Solid Waste Disposal Act, 

was enacted in 1976 to address the growing volumes of municipal and industrial solid waste generated 

nationwide. RCRA was further amended in 1984 with the addition of the Hazardous and Solid Waste 

Amendments. RCRA Subtitle C establishes a federal program to manage hazardous wastes from “cradle 

to grave,” or from generation to disposal, to ensure that hazardous waste is managed in a manner that 

protects human health and the environment. EPA has developed Subtitle C regulations governing

hazardous waste generation, transportation, and the several hundred active treatment, storage or 

disposal facilities (TSDFs).

46

Technical details about the methodology used to determine proximity to TSDF facilities are provided in 

Appendix C.

Indicator The count of all commercial TSDF facilities within 5 km, divided by distance, 

presented as population-weighted averages of blocks in each block group. 

Adjustments are made if there are none within 5 km, and so that the 

minimum distance used is reasonable when very small.

Rationale for 

Inclusion

The substances at TSDF facilities may reach humans in a number of ways. 

Volatile substances may enter the atmosphere and reach residents via the 

inhalation route. Particularly in dry climates or seasons, substances on the 

surface of some sites may be entrained in the atmosphere and reach people 

directly through inhalation or indirectly after being deposited on surfaces 

that people may contact or on arable land. Some substances may migrate 

from the site into groundwater. People may be exposed via drinking water 

derived from the aquifer, through vapor intrusion into their residences or 

through other routes.

Some EPA EJ screening tools have included measures of proximity to all 

facilities regulated by EPA, which includes TSDFs.

More Information More information is available at the hazardous waste webpage

(http://www.epa.gov/epawaste/hazard), the TSD webpage 

(http://www.epa.gov/epawaste/hazard/tsd/), the waste information page 

(http://www.epa.gov/epawaste/inforesources/) and a RCRAInfo page 

(http://www.epa.gov/epawaste/inforesources/data).

46 Basic information and TSDF counts are available here: 

http://www.epa.gov/compliance/data/results/performance/rcra/index.html

More information is available from EPA’s RCRA Orientation Manual, available at 

http://www.epa.gov/osw/inforesources/pubs/orientat/ (U.S. EPA, 2012e).

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Relevant Studies Some examples of studies or reviews that have focused on disparities in 

proximity to TSDFs include Liu (2001) and Brender et al., (2011). Issues 

around environmental justice and TSDFs influenced the early origins of EJ 

work (General Accounting Office, 1983; United Church of Christ, 1987) and 

have been the topic of ongoing research (Been & Gupta, 1997; Boer, Pastor 

Jr., Sadd, & Synder, 1997; Mohai & Saha, 2007; Oakes, Anderton, & 

Anderson, 1996; Pastor Jr., Sadd, & Hipp, 2001; Saha & Mohai, 2005; United 

Church of Christ, 2007). 

The study by Deganian & Thompson (2012) included Hazardous Waste 

Inventory sites, RCRA hazardous waste storage sites and active solid waste 

landfills sites in the tally of pollution points in each of the 10 km2 squares in 

the study area, for comparison with demographic variables.

Earlier studies related the presence of TSDFs to population characteristics for 

different definitions of the host areas—Census-designated areas (General 

Accounting Office, 1983), postal ZIP codes (United Church of Christ, 1987), 

and Census tracts (Anderton, Anderson, Oakes, & Fraser, 1994). 

Data Source Latitudes/Longitudes for all active commercial TSDF sites were obtained 

from the RCRAInfo database. 

Data Version The 2015 version of EJSCREEN uses locational information retrieved from the 

RCRAInfo database in May 2012 (U.S. EPA, 2011e). A total of 586 TSDF 

facilities were included in this version of EJSCREEN. Note that the point data 

used to show facility locations in the “Map Supplementary Layers” menu is 

updated more often than the database with calculated EJSCREEN indicators 

and indexes, so in some small number of cases a facility may be in one data 

source but not the other.

Discussion Each block group in the United States was assigned a proximity score that 

was the population-weighted sum of block-level proximity scores. Appendix 

C provides more details on the calculation of proximity scores. First, each 

block was given a proximity score that was the sum of inverse distanceweighted count of TSDFs anywhere within 5 km of the block’s centroid. This 

score can be thought of as the number of facilities per kilometer of distance 

from the average person. It is also equal to the number of facilities divided 

by the harmonic mean of their distances. This means one facility exactly 2 

km away gives a score of 1/2, while three facilities exactly 4 km away give a 

score of 3/4, and five facilities all at 1 km away give a score of 5.47 If there is 

47 An adjustment was made so that any distance smaller than 90% of the block’s “radius” was set equal to 90% of 

that radius, with radius defined as the square root of (area/pi). This adjustment accounted for the fact that the 

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no facility within 5 km of a block centroid, 1/d is used, where d is the 

distance to the single nearest at any distance.

As with all proximity-based indicators, proximity alone may not represent 

any actual risk or even exposure.

average location (residence) within a circle of radius R is 0.9R away from the average point (facility) that is within 

the circle. For more detail, see Appendix C.

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Proximity to RMP Sites

Accidental releases of toxic substances and incidents involving fires and explosions can result from the 

production, use, or transport of industrial materials. Evacuations, injuries and deaths have resulted in 

some cases. Concern about the risks of chemical accidents led Congress to pass the Emergency Planning 

and Community Right-to-Know Act of 1986 (EPCRA), and amendments to the Clean Air Act (CAA) 

(section 112(r)), which together created reporting and planning obligations for a variety of facility types, 

the EPA, and state and local planning and response organizations.

The facilities discussed here as “RMP facilities” are those facilities required by the CAA to file risk 

management plans. The regulations under CAA section 112(r) establishes a List of Regulated 

Substances—72 substances listed because of their high acute toxicity and 60 because of their flammable 

or explosive potential—along with threshold quantities (TQs) for each. The listed substances are those 

that pose the greatest risk of harm from accidental releases. If a facility maintains a quantity of any such 

chemical above those TQs, it must file an RMP with EPA.

It should be noted that some concerns related to proximity to facilities are already accounted for in 

NATA indicators for ambient air pollutants (e.g., cancer risk and hazard indexes), but NATA is based on 

one year of reported annual releases, which would not account for accidental releases unless they 

occurred that year.

Technical details about the methodology used to determine proximity to RMP facilities are provided in 

Appendix C.

Indicator The count of RMP facilities within 5 km, divided by distance, presented as 

population-weighted averages of blocks in each block group. Adjustments 

are made if there are none within 5 km, and so that the minimum distance 

used is reasonable when very small.

Rationale for 

Inclusion

RMP facilities are diverse in their size, structure, activities and the makeup 

of the regulated substances. As with many types of industrial facilities, 

there may be routine releases to the air and water of the residuals after 

pollution control devices remove what is generally a large fraction of the 

waste stream. Thus, people may be exposed to some substances directly 

through inhalation or indirectly through water routes or via ingestion of 

food. But the primary concerns with RMP facilities are the accidental 

release of substances and fires or explosions. The sudden release of 

relatively large quantities of acutely toxic substances can cause serious 

health effects including death after inhalation or dermal exposure. These 

effects may be prompt or may occur or persist for some time after 

exposure. Fires may affect neighboring areas and the associated smoke 

may expose people to toxic combustion products. Explosions may cause 

material damage and injuries to people in neighboring areas. Local 

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residents, as well as workers and emergency responders, may suffer severe 

adverse effects. 

Some EPA EJ screening tools have included measures of proximity to all 

facilities regulated by EPA, which include RMP facilities.

More Information More information is available at the RMP program webpage 

(http://www2.epa.gov/rmp) and the RMP Info database stored in 

Envirofacts (http://www.epa.gov/enviro/facts/rcrainfo/search.html).

Relevant Studies The EJ literature contains numerous studies that have examined proximity 

to various types of sites, including some relevant to the possibility or 

frequency of chemical accidents. Since the 1980s, many studies have 

examined the frequency and consequences of accidental releases of 

acutely toxic chemicals or events resulting in fires or explosions (Binder, 

1989, and many other studies). After the RMP program was established, 

researchers examined the characteristics of the RMP reporting facilities 

and their reported accident histories for insights into causes, 

consequences, prevention and emergency response (Kleindorfer et al., 

2003).

Fewer studies have focused specifically on the relationship of RMP facilities 

to the demographics of the surrounding populations. Disparity in acute 

exposures to hazardous substances was addressed by Chakraborty (2001). 

The study by Deganian & Thompson (2012) included two categories of 

facilities—facilities having air pollution permits and facilities reporting to 

the Toxics Release Inventory program—which include some overlap with 

RMP facilities, but are not limited to RMP facilities. M. R. Elliott, Wang, 

Lowe, & Kleindorfer (2004) examined the characteristics of RMP-reporting 

facilities and their reported 5-year accident history versus the demographic 

characteristics of the counties in which they are located. The demographic 

characteristics examined included total population, race, education and 

income. The study found an association between the presence of larger 

and more chemical-intensive facilities and counties with larger AfricanAmerican populations, and in counties with high levels of income 

inequality but higher median incomes. Further, the study found a greater 

risk of accidents for facilities in heavily African-American counties.

Data Source Latitudes/Longitudes for RMP facilities were obtained from EPA’s RMP

database.

Data Version The 2015 version of EJSCREEN uses locational information retrieved from 

the RMP database in May 2012. A total of 12,759 RMP facilities were 

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included in the proximity indicators and related EJ indexes in this version of 

EJSCREEN. Note that the point data used to show facility locations in the 

“Map Supplementary Layers” menu is updated more often than the 

database with calculated EJSCREEN indicators and indexes, so in some 

small number of cases a facility may be in one data source but not the 

other.

Discussion Each block group in the United States was assigned a proximity score that 

was the population-weighted sum of block-level proximity scores. 

Appendix C provides more details on the calculation of proximity scores. 

First, each block was given a proximity score that was the sum of inverse 

distance-weighted count of facilities anywhere within 5 km of the block’s 

internal point. This score can be thought of as the number of RMP facilities 

per kilometer of distance from the average person. It is also equal to the 

number of facilities divided by the harmonic mean of their distances. This 

means one facility exactly 2 km away gives a score of 1/2, while three 

facilities exactly 4 km away give a score of 3/4, and five facilities all at 1 km 

away give a score of 5.48 If there is no facility within 5 km of a block 

centroid, 1/d is used, where d is the distance to the single nearest at any 

distance.

As with all proximity-based indicators, proximity alone may not represent 

any actual risk or even exposure.

48 An adjustment was made so that any distance smaller than 90% of the block’s “radius” was set equal to 90% of 

that radius, with radius defined as the square root of (area/pi). This adjustment accounted for the fact that the 

average location (residence) within a circle of radius R is 0.9R away from the average point (facility) that is within 

the circle. For more detail, see Appendix C.

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77 | Page
Appendix A

APPENDIX A. DEVELOPMENT OF EJSCREEN

Review of Available Data and Other Tools

Preliminary planning for EJSCREEN began in late 2010. The first steps involved a review of existing or planned EJ 

screening methods from EPA program and regional offices. EJSCREEN draws upon a great deal of prior research, 

analysis and public involvement in the development of very closely related screening efforts. Early steps also 

included a review of current EJ research on methods and data for EJ analysis. Information was gathered from 

the following sources, among others:

• Stakeholder and expert presentations at EPA’s March 2010 conference on environmental justice.

• EPA’s 2010 expert workshop on economics and environmental justice.

• EPA’s ORD’s C-FERST research program, including a review of data sources for EJ analysis.

• EPA’s ORD’s Environmental Quality Index (EQI) compilation and review of data sources for 

environmental indicators.

• A review of several national reviews of analytic methods including the use of inequality metrics 

(e.g. as presented in the expert workshop).

• Review of EPA guidance documents and related documents on environmental justice49

• Review of the NEJAC report of May 2010 on EJ screening (NEJAC, 2010).

• Review of prior tools including EJSEAT, EJVIEW, various EPA Regional tools, and some state EJ 

screening tools such as CalEnviroScreen.

Selecting an Approach to EJ Screening

A number of important considerations must be balanced when selecting an approach to an EJ screening tool:

• Useful to end-users and other stakeholders.

• Reflects EPA policies and EJ policy goals.

• Reflects sound science.

• Is feasible to develop and maintain, update and upgrade.

Data coverage and quality considerations are also discussed in the chapters describing the environmental 

indicators.

EJSCREEN was developed through an EPA workgroup with participation from a very wide range of program 

offices and Regional offices, and in consultation with management and scientists representing the various 

offices, building upon the public input and scientific information developed in the course of prior screening 

efforts such as EJSEAT and Regional experience with EJ screening. Quality control and peer review of EJSCREEN 

are described in Appendices F and G.

[

49 http://www.epa.gov/environmentaljustice/resources/policy/index.html

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Appendix B

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Appendix B

APPENDIX B. TECHNICAL DETAILS ON PERCENTILES, ROUNDING, 

BUFFERING, AND DEMOGRAPHIC DATA

How Percentiles are Calculated and Displayed

Percentiles, such as "80th percentile," are displayed in EJSCREEN as rounded down to the closest percentile 

that is lower than the exact value. This is called showing the "floor" of the exact percentile (rather than 

rounding off the percentile to the nearest 1 percentile). For example, if the exact percentile is equal to or 

greater than 79 but less than 80, it is displayed as "79th percentile." If the exact percentile is equal to or greater 

than 80 but less than 81, it is displayed as "80th percentile." The reason for this is to ensure that EJSCREEN only 

displays "80th percentile" if the exact percentile truly is as high as 80.50

Ties in indicator values are fairly common, especially for the lead paint indicator and percent linguistic isolation, 

where large shares are tied with values of zero. Ties are assigned a percentile that can be thought of as the 

upper edge of the range of tied values. For example, if 3 percent of the US population were tied at the 

maximum indicator value, they would all be shown as being at the 100th percentile, and the next lower value 

would be assigned the 97th percentile. If 4 percent were all tied for the lowest value, they would all be shown 

as being at the 4th percentile, and nobody would be shown as being at the 0-3 percentiles. The percent 

linguistic isolation was zero in block groups comprising about 45% of the US population in the 2008-2012 ACS 

data, so all those places were shown as tied for the 45th percentile, and none were reported as being at any 

lower percentiles. However, it is worth noting that a group of tied values is usually not shared by more than 1% 

of the population, so once the percentiles are converted to integers 0-100, the tied raw values do not cause 

jumps in percentiles except in a few cases. A jump would be a case where there are no block groups assigned a 

percentile between zero and 45, because around 45% of the population is tied with a value of zero.

Percentiles are assigned to calculated values (such as in buffer reports) by use of national, region-specific, and 

state-specific lookup tables that show the raw value cutoff value that corresponds to each integer percentile 0-

100. To ensure that exact matches are found when looking for the 100th percentile, for example, the cutoff 

values in the lookup tables are all stored with exactly 6 decimal places, and a raw value is rounded to exactly 6 

decimal places before it is looked up in those tables. If a value matches the cutoff, it is assigned that percentile.

If it falls between two cutoffs, it is assigned the lower of the two percentiles, to provide displayed results that 

are consistent with the way percentiles are displayed using the "floor" function described above. The lookup 

tables are stored in a geodatabase used for EJCSREEN.

50 This also ensures that map colors correspond to displayed percentiles. For example, if the exact percentile is 79.99, the 

map will show the place as gray, meaning it is still below the 80th percentile, and the percentile will be shown as "79th 

percentile." If the map is yellow, it indicates the exact percentile is at least 80, and the displayed percentile will also be at 

least 80. Without using the "floor" of the exact percentile, the map colors and displayed percentile would sometimes 

disagree, and a user would not be sure if "80th percentile" actually meant the exact percentile was actually at least 80. 

Using the "floor" instead of rounding ensures clarity about whether a place actually reaches a given percentile.

80 | Page

 
Appendix B

In output tables, percentages are rounded to the nearest percent but percentiles are displayed using a "floor" 

function as described above. Occasionally, this can lead to some potentially confusing situations in some 

tables. For example, a place may be shown as 100% minority but only at the 98th percentile. This is because the 

place may actually be 99.6% minority, which is displayed as 100% minority. But if 1.8% of the US population 

lives where there is an even higher percent minority (e.g., 100%), this place is only at the 98.2 percentile, which 

is displayed as 98th percentile.

The percentiles and lookup tables were calculated using the statistical software called R, using code written by 

EPA, based on wtd.quantile() and wtd.Ecdf() functions in the Hmisc package (http://cran.rproject.org/web/packages/Hmisc/index.html). The scripting language R is documented here: http://cran.rproject.org

How Percentages and Raw Values are Rounded and Displayed

ESJSCREEN displays raw indicator numbers and percentiles in a standard report (on-screen or PDF format), the 

"Explore reports" window, a tabular view (on-screen or downloaded text file), bar graphs, popup windows on 

maps, and in the downloadable raw data files. Several standard rules have been applied to keep these formats 

consistent, clear, and at an appropriate level of detail.

The raw data stored in the database used in EJSCREEN are stored as the "exact" values calculated from 

estimated counts obtained from the Bureau of Census, or from the development of environmental indicators, 

at the highest degree of precision used by the software calculating the indicators and by the GIS database. This 

ensures that all internal calculations use the best estimate of a given number rather than relying on a rounded 

off approximation, and is standard best practice in working with such data. All calculations use the "exact" 

(unrounded) numbers from Census or stored in the GIS database. This includes converting raw Census data into 

a demographic indicator, calculating an EJ index, or estimating the values for a buffer report.

When displaying data, such as in reports, popup windows on maps, or the tabular view, EJSCREEN presents 

formatted numbers that follow certain conventions:

- Raw environmental indicator values are displayed using specified numbers of significant figures (also known 

as significant digits). This is a standard way of communicating precision appropriately. Precision of these 

estimates depends largely on sample size in Census survey data, and the ability of measurements and models 

to estimate environmental conditions. Two significant figures are shown for all environmental indicators other 

than PM2.5, ozone, and diesel PM, which are shown with three significant figures. For example, a cancer risk 

calculated to be 144.44 per million would be displayed as 140 (i.e., using 2 significant figures). A PM2.5 

concentration of 14.44 would be shown as 14.4 (i.e., using 3 significant figures). Proximity scores of at least 

0.185 but less than 0.195 would be displayed as 0.19 (which shows 2 significant figures). This means a proximity 

score displayed as 0.010 came from an exact value of at least 0.0095 but less than 0.015, for example. Note 

that these significant figure rules have been applied in all cases, and if in some case the number is missing a 

trailing zero that should appear, it is simply a limitation of print formatting. For example, if a proximity score is 

81 | Page
Appendix B

shown as 0.1, best practice would be to display it as 0.10 to make explicit the use of 2 significant figures, a 

printout may only display it as 0.1 instead of 0.10, but it was still rounded using the 2 significant figures rule.

- Demographic percentages, such as "34% low-income," are displayed as rounded to the nearest 1%. For 

example, any values equal to or greater than 79.5% but less than 80.5% are displayed as "80%."

It is also important to keep in mind that all of the numbers are estimates, so small differences in raw values or 

percentiles should not be regarded as certain and meaningful, given the uncertainty in the environmental and 

demographic estimates.

Calculations for Buffer Reports

EJSCREEN allows a user to define a buffer, such as the circle that includes everything within 1 mile of a specific 

point. Non-circular, user-defined shapes also can be defined to represent buffers of any shape. A report 

summarizes the demographics of residents within this buffer, as well as the environmental indicators and EJ 

index values within the buffer. 

The summary within a buffer is designed to represent the average resident within the buffer, and also provides 

an estimate of the total population residing in the buffer. For example, the traffic proximity indicator for a 

buffer is the population-weighted average of all the traffic indicator values in the buffer. Similarly, the percent 

minority would be a weighted average, which is the same as the overall percent minority for all residents in the 

buffer.

Some block groups will be partly inside and partly outside a buffer, and any buffer analysis must estimate how 

much of each block group’s population is inside the buffer. Areal apportionment of block groups is one 

standard method, but it assumes that population is evenly spread throughout a block group, which may be far 

from the actual distribution of residents. Areal apportionment of blocks would be even more accurate but 

extremely computationally intensive.

To provide the most accurate counts that are currently feasible for a screening tool, EJSCREEN uses an 

approach based on Census block internal points. EJSCREEN estimates the fraction of the Census block group 

population that is inside the buffer by using block-level population counts from Census 2010. These blocks 

provide data about where residents are at a higher resolution than block groups. Each block has an internal 

point defined by the Census Bureau, and the entire block population is counted as inside or outside the buffer 

depending on whether the block internal point is inside or outside. This assumption typically introduces 

relatively little error because blocks are so small relative to a typical buffer, so a small fraction of the total 

buffer population is in blocks that span an edge of the buffer. Also, any blocks along the edge of a buffer whose 

populations are close to 0 or 100% inside the buffer will be well represented by this assumption. 

As long as users draw buffers much larger than a local block group, this method should represent the average 

person inside the buffer reasonably well.

82 | Page
Appendix B

The calculation of a value for the buffer is essentially the population-weighted average of the indicator values 

in the blocks included in the buffer, where each block uses the indicator values of the block group containing it. 

A block group is weighted based on the fraction of the ACS block group population that is considered in the 

buffer. That fraction is estimated as the Census 2010 block population divided by the Census 2010 block group 

population. The formula below is used to estimate the population average of a raw indicator value in a buffer. 

This formula is simply a population-weighted average – it sums the population-weighted raw values, and then 

divides that sum by the total population in the buffer.

“BlockPop10” refers to the Census 2010 block level population total (used here because the ACS does not 

provide block resolution), and “BG” indicates block group. “BGACSPop” is the block group estimated population 

count from the ACS, which is often different than the Census 2010 total for all blocks in the block group, 

because the ACS data used here is a composite estimate based on survey samples spanning five years, while 

the Census is a full count at one point in time.

Demographic Data and Geographic Coverage

In the first decade of this century, the Census Bureau made a fundamental shift in how detailed demographic 

data are collected. Rather than collecting basic data from everyone, plus more detailed data from a one-in-six 

sample of households once a decade in the decennial census, a mixed approach has been adopted. The basic 

data, required for Congressional redistricting under the U.S. Constitution, are still collected every ten years in 

what is intended to be a 100% census. But that basic information, plus virtually all the more detailed 

demographic data, are also collected throughout each year in a stratified random sample of more than 200,000 

households each month. This is the American Community Survey (ACS). Some of this information is then 

aggregated and displayed in yearly summaries, others in 3-year summaries, and others in 5-year summaries. 

Only the five-year summary files provide block group resolution. The result is a timelier, evolving picture of U.S. 

demographics. For instance, the ACS 2005 to 2009 average data were released in December 2010, while most 

demographics data users were still working with the April 2000 decennial census snapshot. 

Extensive documentation of the ACS is available. For a general overview, see 

http://www.census.gov/acs/www/ and for complete documentation see

http://www.census.gov/acs/www/data_documentation/documentation_main/. For information on the 5-year 

summary file, which is what EJSCREEN uses, see 

http://www.census.gov/acs/www/data_documentation/summary_file/. For information on using the data see 

http://www.census.gov/acs/www/guidance_for_data_users/handbooks/).

Race and ethnicity, the two items that determine minority status in our approach, are available from the 100% 

enumeration from the decennial census or the ACS, while all the other measures are only contained in ACS 

estimates. For the purposes of EJSCREEN, EPA did not believe that the increased precision of the minority 

83 | Page
Appendix B

measures that might be gained from combining those once per decade data with the other data items from 

ACS were worth the problems and ambiguities entailed in such a hybrid approach.

All of EJSCREEN’s demographic data come from the latest annual update of the five-year average ACS 

estimates, with some lag time from publication by Census to inclusion in EJSCREEN. The Census Bureau does 

not recommend making direct comparisons between data from a five-year summary and a prior, overlapping 

five-year period, such as comparing 2007-2011 to 2008-2012.

51 This means attempting to look for trends in 

terms of year-to-year changes is not recommended – changes in ACS estimates at block group resolution can 

be reviewed every five years, but not more often. Yearly changes can be examined at county resolution using 

the 1-year ACS data.

Each of the nation’s counties (or county equivalents, such as Municipios in Puerto Rico) is completely divided 

into Census tracts. Each tract is in turn divided into Census block groups. Census block groups generally have 

between 600 and 3,000 people, with an optimal population of 1,500; however, a few are much more populous 

and a small number have zero residents. A block group consists of one or more Census blocks. In urban areas, a 

block is typically a city block defined by streets. The Census Bureau collects data by household, but block 

groups are the smallest area for which the ACS presents estimates.

Tracts and block groups are defined in ACS exactly as in the Census 2010 other than a few exceptions due to 

updates or corrections.

52 For the 2008-2012 ACS data in EJSCREEN, Census made changes in a few dozen block 

groups and their tracts and FIPS codes in NY, AZ, and CA53. Also see details in the discussion of how NATA data 

was converted to 2010 boundaries, in Section 3.

There are 11,078,297 blocks in the Census 2010 data, not including Puerto Rico and the Island Areas,

54 but 

since then some changes have been made in the FIPS codes that relate blocks to their parent block groups. 

Block to block group relationships are used by EJSCREEN in part of the buffer analysis (as described in Appendix 

C). This required making manual adjustments to reassign some Census 2010 blocks to their updated parent ACS 

block groups for purposes of calculating buffer estimates.

The EJSCREEN dataset based on the ACS 2008-2012 summary file has data for 51 States/equivalents (includes 

DC), 3,143 counties/county equivalents, 73,056 census tracts, and 217,739 block groups. Puerto Rico (2,594 

block groups) is part of the ACS, but could not be included in the 2015 version of the EJSCREEN dataset due to 

environmental data and other constraints, although it may be included in future versions. The Census Bureau 

does not collect ACS data for the Virgin Islands or the other Island Territories. 

Compared to the ACS 2008-2012, the Census 2010 tallies counted one block group more than are included in 

the ACS (or EJSCREEN) – an extra block group in New York State – but they were otherwise identical in block 

51 http://www.census.gov/acs/www/guidance_for_data_users/comparing_data/ 52 http://www.census.gov/acs/www/data_documentation/geography/ 53 See https://www.census.gov/acs/www/data_documentation/2011_geography_release_notes/ on NY changes and 

https://www.census.gov/acs/www/data_documentation/geography_notes/index.php on AZ and CA changes. 54 http://www.census.gov/geo/maps-data/data/tallies/tractblock.html

84 | Page

 
Appendix B

group counts per state. The count of geographies (e.g., number of block groups) covered by the ACS is 

summarized by the Census Bureau for each update:

• ACS tallies, as counted in data downloaded from ACS FTP site, and as used in EJSCREEN (217,739 block 

groups, without Puerto Rico)

• ACS tallies, with Puerto Rico55: 220,333 with PR, 217,739 without Puerto Rico.

• Census 2010 tallies, by State/PR56: 220,334 with Puerto Rico, and 217,740 without Puerto Rico.

Table 4. Tallies of 2008-12 ACS Block Groups Used in 2015 Version of EJSCREEN

Demographic Variables and Formulas

This section provides details on the Census variables and formulas used to calculate demographic indicators for 

each block group. Short variable names used in EJSCREEN internal calculations are shown below, preceded by 

tabular data showing the ACS summary file table number, sequence number, variable number, and name of the 

table or variable. For example, total population, referred to in EJSCREEN calculations as “pop” and called 

“ACSTOTPOP” in the geodatabase, is taken from the Census variable B01001.001, in ACS 5-year summary file 

Table B01001. Field names as used in the EJSCREEN geodatabase differ from the names shown below, and a 

table was used to map between alternative fieldnames.

These details are based on the ACS 2008-2012 summary files.

55 http://www.census.gov/acs/www/data_documentation/areas_published/ 56 http://www.census.gov/geo/maps-data/data/tallies/tractblock.html

Geography Number of Block 

Groups 

In EJSCREEN?

Continental U.S., 48 States plus DC 216,330 Yes

Alaska & Hawaii 534 & 875 Yes

SUBTOTAL: Included in EJSCREEN 217,739 Yes

Puerto Rico 2,594 No

Virgin Islands and other Island Territories 

(American Samoa, Commonwealth of the 

Northern Mariana Islands, Guam)

408 No

Source: U.S. Census Bureau 

http://www.census.gov/acs/www/data_documentation/areas_published/ and 

http://www.census.gov/geo/maps-data/data/tallies/tractblock.html

85 | Page

 
Appendix B

ACS Summary File documentation is here: 

http://www.census.gov/acs/www/data_documentation/summary_file/

86 | Page
Appendix B

ACS Summary File Variables

Tables used from ACS 2008-2012 are shown below. Block group data were obtained from the Census FTP site57, 

and are not available from American Fact Finder at block group resolution.

Note that only selected variables from these tables are in the EJSCREEN geodatabase, for performance reasons. 

Many of the intermediate variables or detailed breakdowns are not in the geodatabase. Documentation 

supplied with the geodatabase download explains the variable names used in the geodatabase.

The URLs that can be used to view and download one ACS 2008-2012 table at a time, for the US total only, are 

as follows:

Table 5. ACS Tables Underlying EJSCREEN Demographic Data and Lead Paint Indicator

ACS 

Table 

ID

URL for US summary table 

via American Fact Finder

Table Title

B01001 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/B01001/0100000US

SEX BY AGE

B03002 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/B03002/0100000US

HISPANIC OR LATINO ORIGIN BY RACE

B15002 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/B15002/0100000US

SEX BY EDUCATIONAL ATTAINMENT FOR THE 

POPULATION 25 YEARS AND OVER

B16002 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/B16002/0100000US

HOUSEHOLD LANGUAGE BY HOUSEHOLDS IN WHICH NO 

ONE 14 AND OVER SPEAKS ENGLISH ONLY OR SPEAKS 

A LANGUAGE OTHER THAN ENGLISH AT HOME AND 

SPEAKS ENGLISH "VERY WELL”

C17002 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/C17002/0100000US

RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 

12 MONTHS

B25034 http://factfinder2.census.gov/bkmk/table

/1.0/en/ACS/12_5YR/B25034/0100000US

YEAR STRUCTURE BUILT

URLs to view and download one ACS 2008-2012 table at a time, for the totals for the US, 

every state plus DC, and Puerto Rico are shown below.

57 http://www.census.gov/acs/www/data_documentation/data_via_ftp/

87 | Page

 
Appendix B

B01001

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B01001/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

B03002

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B03002/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

B15002

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B15002/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

B16002

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B16002/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

C17002

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/C17002/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

B25034

http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B25034/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|040000

0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U

S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2

8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|

0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04

00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0400000US56|0400000US72

88 | Page
Appendix B

TOTAL POPULATION COUNTS AND AGES

SEX BY AGE

Table.ID Sequence.Number Line.Number Field

B01001 0002 NA 

B01001 0002 NA Universe: Total population

B01001 0002 1 Total:

B01001 0002 2 Male:

B01001 0002 3 Under 5 years

B01001 0002 4 5 to 9 years

B01001 0002 5 10 to 14 years

B01001 0002 6 15 to 17 years

B01001 0002 7 18 and 19 years

B01001 0002 8 20 years

B01001 0002 9 21 years

B01001 0002 10 22 to 24 years

B01001 0002 11 25 to 29 years

B01001 0002 12 30 to 34 years

B01001 0002 13 35 to 39 years

B01001 0002 14 40 to 44 years

B01001 0002 15 45 to 49 years

B01001 0002 16 50 to 54 years

B01001 0002 17 55 to 59 years

B01001 0002 18 60 and 61 years

B01001 0002 19 62 to 64 years

B01001 0002 20 65 and 66 years

B01001 0002 21 67 to 69 years

B01001 0002 22 70 to 74 years

B01001 0002 23 75 to 79 years

B01001 0002 24 80 to 84 years

B01001 0002 25 85 years and over

B01001 0002 26 Female:

B01001 0002 27 Under 5 years

B01001 0002 28 5 to 9 years

B01001 0002 29 10 to 14 years

B01001 0002 30 15 to 17 years

B01001 0002 31 18 and 19 years

B01001 0002 32 20 years

B01001 0002 33 21 years

B01001 0002 34 22 to 24 years

B01001 0002 35 25 to 29 years

B01001 0002 36 30 to 34 years

B01001 0002 37 35 to 39 years

B01001 0002 38 40 to 44 years

89 | Page
Appendix B

B01001 0002 39 45 to 49 years

B01001 0002 40 50 to 54 years

B01001 0002 41 55 to 59 years

B01001 0002 42 60 and 61 years

B01001 0002 43 62 to 64 years

B01001 0002 44 65 and 66 years

B01001 0002 45 67 to 69 years

B01001 0002 46 70 to 74 years

B01001 0002 47 75 to 79 years

B01001 0002 48 80 to 84 years

B01001 0002 49 85 years and over

pop = B01001.001 

ageunder5m = B01001.003 

age5to9m = B01001.004 

age10to14m = B01001.005 

age15to17m = B01001.006 

age65to66m = B01001.020 

age6769m = B01001.021 

age7074m = B01001.022 

age7579m = B01001.023 

age8084m = B01001.024 

age85upm = B01001.025 

ageunder5f = B01001.027 

age5to9f = B01001.028 

age10to14f = B01001.029 

age15to17f = B01001.030 

age65to66f = B01001.044 

age6769f = B01001.045 

age7074f = B01001.046 

age7579f = B01001.047 

age8084f = B01001.048 

age85upf = B01001.049

90 | Page
Appendix B

RACE/ETHNICITY

HISPANIC OR LATINO ORIGIN BY RACE

Table.ID Sequence.Number Line.Number Field

B03002 0005 NA

B03002 0005 NA Universe: Total population

B03002 0005 1 Total:

B03002 0005 2 Not Hispanic or Latino:

B03002 0005 3 White alone

B03002 0005 4 Black or African American alone

B03002 0005 5 American Indian and Alaska Native alone

B03002 0005 6 Asian alone

B03002 0005 7 Native Hawaiian and Other Pacific Islander alone

B03002 0005 8 Some other race alone

B03002 0005 9 Two or more races:

B03002 0005 10 Two races including Some other race

B03002 0005 11 Two races excluding Some other race, and three or more 

races

B03002 0005 12 Hispanic or Latino:

B03002 0005 13 White alone

B03002 0005 14 Black or African American alone

B03002 0005 15 American Indian and Alaska Native alone

B03002 0005 16 Asian alone

B03002 0005 17 Native Hawaiian and Other Pacific Islander alone

B03002 0005 18 Some other race alone

B03002 0005 19 Two or more races:

B03002 0005 20 Two races including Some other race

B03002 0005 21 Two races excluding Some other race, and three or more 

races

pop3002 = B03002.001 

nhwa = B03002.003 

EDUCATIONAL ATTAINMENT FOR THOSE AGE 25+

SEX BY EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER 

Table.ID Sequence.Number Line.Number Field

B15002 0043 NA

B15002 0043 NA Universe: Population 25 years and over

B15002 0043 1 Total:

91 | Page
Appendix B

B15002 0043 2 Male:

B15002 0043 3 No schooling completed

B15002 0043 4 Nursery to 4th grade

B15002 0043 5 5th and 6th grade

B15002 0043 6 7th and 8th grade

B15002 0043 7 9th grade

B15002 0043 8 10th grade

B15002 0043 9 11th grade

B15002 0043 10 12th grade, no diploma

B15002 0043 11 High school graduate, GED, or alternative

B15002 0043 12 Some college, less than 1 year

B15002 0043 13 Some college, 1 or more years, no degree

B15002 0043 14 Associate's degree

B15002 0043 15 Bachelor's degree

B15002 0043 16 Master's degree

B15002 0043 17 Professional school degree

B15002 0043 18 Doctorate degree

B15002 0043 19 Female:

B15002 0043 20 No schooling completed

B15002 0043 21 Nursery to 4th grade

B15002 0043 22 5th and 6th grade

B15002 0043 23 7th and 8th grade

B15002 0043 24 9th grade

B15002 0043 25 10th grade

B15002 0043 26 11th grade

B15002 0043 27 12th grade, no diploma

B15002 0043 28 High school graduate, GED, or alternative

B15002 0043 29 Some college, less than 1 year

B15002 0043 30 Some college, 1 or more years, no degree

B15002 0043 31 Associate's degree

B15002 0043 32 Bachelor's degree

B15002 0043 33 Master's degree

B15002 0043 34 Professional school degree

B15002 0043 35 Doctorate degree

age25up = B15002.001 

m0 = B15002.003 (males age 25+ with zero education)

m4 = B15002.004 (males age 25+ with >0 up to 4th grade)

m6 = B15002.005 

m8 = B15002.006 

m9 = B15002.007 

m10 = B15002.008 

m11 = B15002.009 

m12 = B15002.010 (males age 25+ with high school diploma)

92 | Page
Appendix B

f0 = B15002.020 

f4 = B15002.021 

f6 = B15002.022 

f8 = B15002.023 

f9 = B15002.024 

f10 = B15002.025 

f11 = B15002.026 

f12 = B15002.027 

HOUSEHOLDS THAT ARE LINGUISTICALLY ISOLATED

"HOUSEHOLD LANGUAGE BY HOUSEHOLDS IN WHICH NO ONE 14 AND OVER SPEAKS ENGLISH ONLY OR 

SPEAKS A LANGUAGE OTHER THAN ENGLISH AT HOME AND SPEAKS ENGLISH "VERY WELL"

Table.ID Seq.Number Line.Number Field

B16002 0044 NA

B16002 0044 NA "Universe: Households" 

B16002 0044 1 "Total:"

B16002 0044 2 "English only" 

B16002 0044 3 "Spanish:"

B16002 0044 4

No one 14 and over speaks English only or speaks English “very well”

B16002 0044 5

At least one person 14 and over speaks English only or speaks English “very well”

B16002 0044 6 Other Indo-European languages: 

B16002 0044 7

No one 14 and over speaks English only or speaks English “very well”

B16002 0044 8

At least one person 14 and over speaks English only or speaks English “very well”

B16002 0044 9 Asian and Pacific Island languages:

B16002 0044 10

No one 14 and over speaks English only or speaks English “very well”

B16002 0044 11

At least one person 14 and over speaks English only or speaks English “very well”

 

B16002 0044 12 Other languages:

B16002 0044 13

No one 14 and over speaks English only or speaks English “very well”

B16002 0044 14

At least one person 14 and over speaks English only or speaks English “very well”

93 | Page
Appendix B

hhlds = B16002.001 

lingisospanish = B16002.004 

lingisoeuro = B16002.007 

lingisoasian = B16002.010 

lingisoother = B16002.013

INDIVIDUALS BY RATIO OF INCOME TO POVERTY THRESHOLD

RATIO OF INCOME TO POVERTY LEVEL IN THE PAST 12 MONTHS 

Table.ID Sequence.Number Line.Number Field

C17002 0049 NA 

C17002 0049 NA Universe: Population for whom poverty status is determined

C17002 0049 1 Total:

C17002 0049 2 Under .50

C17002 0049 3 .50 to .99

C17002 0049 4 1.00 to 1.24

C17002 0049 5 1.25 to 1.49

C17002 0049 6 1.50 to 1.84

C17002 0049 7 1.85 to 1.99

C17002 0049 8 2.00 and over

povknownratio = C17002.001 

pov50 = C17002.002 (below 0.50 times poverty threshold)

pov99 = C17002.003 (0.5 to 0.99 times poverty threshold)

pov124 = C17002.004 

pov149 = C17002.005 

pov184 = C17002.006 

pov199 = C17002.007 

pov2plus = C17002.008

AGE OF OCCUPIED HOUSING UNITS (CORRELATED WITH LEAD PAINT)

YEAR STRUCTURE BUILT 

Table.ID Sequence.Number Line.Number Field

B25034 0104 NA 

94 | Page
Appendix B

B25034 0104 NA Universe: Housing units

B25034 0104 1 Total:

B25034 0104 2 Built 2010 or later

B25034 0104 3 Built 2000 to 2009

B25034 0104 4 Built 1990 to 1999

B25034 0104 5 Built 1980 to 1989

B25034 0104 6 Built 1970 to 1979

B25034 0104 7 Built 1960 to 1969

B25034 0104 8 Built 1950 to 1959

B25034 0104 9 Built 1940 to 1949

B25034 0104 10 Built 1939 or earlier

builtunits = B25034.001

built1950to1959 = B25034.008

built1940to1949 = B25034.009

builtpre1940 = B25034.010

Calculated Demographic Data Fields

Based on the raw counts from the ACS described above, various demographic variables were calculated for use 

in EJSCREEN. Conditional formulas below are in R syntax, and generally indicate that a value of zero was used in 

cases where the denominator was zero, to avoid division by zero. For example, the formula “pctmin =

ifelse(pop==0,0, as.numeric(mins ) / pop)” indicates that percent minority was calculated as the ratio of 

number of minorities over total population of a block group, but was set to zero if the population was zero.

# RACE/ETHNICITY COMBINED, CALCULATED VARIABLES 

mins = pop - nhwa

pctmin = ifelse(pop==0,0, as.numeric(mins ) / pop)

# POVERTY, LOW-INCOME CALCULATED VARIABLES

# poverty ratios

num2pov = num1pov + pov124 + pov149 + pov184 + pov199

lowinc = num2pov

pct2pov = ifelse( povknownratio==0,0, num2pov/povknownratio)

pctlowinc = pct2pov 

num2pov.alt = povknownratio - pov2plus

pct2pov.alt = ifelse( povknownratio==0,0, num2pov.alt/povknownratio)

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Appendix B

# EDUCATIONAL ATTAINMENT CALCULATED VARIABLES

lths = m0 + m4 + m6 + m8 + m9 + m10 + m11 + m12 +

f0 + f4 + f6 + f8 + f9 + f10 + f11 + f12

pctlths = ifelse(age25up==0,0, as.numeric(lths ) / age25up)

# LINGUISTIC ISOLATION CALCULATED VARIABLES

lingiso = lingisospanish + lingisoeuro + lingisoasian + lingisoother

pctlingiso = ifelse( hhlds==0,0, lingiso / hhlds)

# AGE GROUPS CALCULATED VARIABLES

under5 = ageunder5m + ageunder5f

pctunder5 = ifelse( pop==0,0, under5/pop)

over64 = age65to66m + age6769m + age7074m + age7579m + age8084m + age85upm + 

age65to66f + age6769f + age7074f + age7579f + age8084f + age85upf

pctover64 = ifelse( pop==0,0, over64/pop)

# HOUSING CALCULATED VARIABLES (LEAD PAINT INDICATOR)

pre1960 = builtpre1940 + built1940to1949 + built1950to1959

pctpre1960 = ifelse( builtunits==0,0, pre1960/builtunits)

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Appendix B

Uncertainty and Limitations in the Demographic Data

Uncertainty in demographic data

As with every sample survey, sampling results in unavoidable approximations in every estimate that comes 

from the survey. The Census Bureau clearly labels every data item as an “estimate,” and accompanies each 

with an estimate of its margin of error. Anyone using a screening tool should be aware of those demographic 

uncertainties, together with uncertainties in the environmental measures, in tables, graphical displays and 

descriptive materials. 

Uncertainties are also discussed in section 1 (as general caveats), section 2 (with regard to buffer reports), and 

Appendix B (in discussions of buffering details and demographic data).

Users of EJSCREEN must keep in mind the substantial uncertainty in estimated demographic and environmental 

indicators used in screening tools such as EJSCREEN. Uncertainty is a critical consideration when using 

EJSCREEN because the tool relies on demographic and environmental estimates at block group resolution. As 

the Census Bureau makes clear in documentation of the American Community Survey (ACS), the margin of 

error for an estimate in a given block group is often very large relative to the estimate, so an estimate of 

percent low-income, for example, is often very uncertain for a single block group.

Combined with uncertainty in environmental data, this means EJ index values are often very uncertain at block 

group resolution. Therefore, modest differences in percentile scores between block groups or small buffers 

should not be interpreted as meaningful because of the uncertainties in demographic and environmental data 

at the block group level. We do not have a high degree of confidence when comparing or ranking places with 

only modest differences in estimated percentile. For this reason, it is critical that EJSCREEN results be 

interpreted carefully and that additional information be used to supplement or follow up on screening, where 

appropriate. Section 1 of this document discusses caveats and limitations further.

EPA cannot provide precise confidence intervals on EJ indexes or percentiles due to technical limitations in the 

data made public by the Census Bureau and the challenges of quantifying uncertainty for the environmental 

indicators. Technical documentation on methods and challenges in estimating uncertainty for calculated 

demographic indicators using the ACS is available from the Census Bureau58 (with challenges described in

related technical documents59). ESRI also provides useful discussions of margin of error.

60

It is likely that block group errors in the various data fields reported by Census (e.g., count with income-poverty 

ratio below 0.5, count with ratio 0.5 to 1, etc.) are correlated. Relevant covariances, however, are not provided 

by the Census Bureau. This means simple methods of approximating margin of error for a calculated variable 

(e.g., percent low-income) may not be entirely adequate. In this case, it appears that a custom tabulation by 

58http://www.census.gov/acs/www/Downloads/data_documentation/Statistical_Testing/2011StatisticalTesting3and5year

.pdf

59 http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/MultiyearACSAccuracyofData2011.pdf 60 http://www.esri.com/software/american-community-survey/understanding-margin-error

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Appendix B

the Census Bureau would be the most accurate way to generate reliable estimates of the margin of error for 

variables such as the percent low-income or the demographic indicator, for use in creating confidence intervals 

around an EJ index or the percentile of that index. Future research may be able to produce reasonable 

approximations of confidence intervals around block group or buffer estimates. EJSCREEN users should keep in 

mind that using a buffer larger than the local block groups will produce more reliable estimates than a single 

block group can provide.

Using 2x poverty rate

The rationale for using twice the poverty threshold rather than just the poverty threshold includes the 

following considerations:

• The effects of income on baseline health and probably on other aspects of susceptibility are not 

limited to those below the poverty thresholds — those from 1x to 2x poverty also have worse 

health overall than those with higher incomes (Centers for Disease Control and Prevention, 2010), 

and asthma rates, for example, begin to increase as income falls below twice the poverty threshold 

(Centers for Disease Control and Prevention, 2011a). 

• Many studies in various fields use 2x poverty, and many others use 1x poverty (e.g., see Su et al., 

2009); the same is true for prior EPA screening tools. There is precedent for both. However, a

rationale often mentioned is that today's poverty thresholds are too low to adequately capture the 

populations adversely affected by low income levels, especially in high-cost areas. Some analysts 

have concluded that the amount of income actually required for basic living costs without 

government support is far higher than the current Federal poverty thresholds (Cauthen & Fass, 

2008). 

• When using twice the poverty threshold, the number or percent low income happens to roughly 

equal number or percent minority in the United States. This makes it convenient and simple to use 

the average of the two without applying any other weights to them, and in this way each lowincome person affects the susceptibility indicator about as much as each minority person. 

• The Census Bureau has been developing experimental poverty measures that account for local 

costs of living, but these are not yet in widespread use.61

Interpretation of Demographic Indexes

The demographic indexes are meant to reflect some of the combined impacts of multiple demographic factors. 

The Census Bureau does not provide a tabulation of low-income residents by race/ethnicity at the block group 

level62, so it is impossible to know what percentage of a block group is low-income minorities vs. low-income 

non-minorities, for example. EJSCREEN simply defines the demographic index as the average of the percentage

of people who are low income and the percentage of people who are minorities. Therefore, this demographic 

61 https://www.census.gov/hhes/povmeas/ 62 Table B17001 and related tables provide tract-level cross-tabulations of race-ethnicity and poverty, but not percent lowincome as defined in EJSCREEN.

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Appendix B

index will be (equal to or) smaller than the percentage of people who are in at least one of these groups. In 

other words, it is typically smaller than the share of people who are in one or more of these groups – just low 

income, just minority, or both. The average will also be (equal to or) larger than the percentage of people who 

are simultaneously in all of these groups. It is larger than (or equal to) the share of people who are 

simultaneously low income and minority. The value of the demographic index is almost always larger than the 

number of people who are simultaneously minority and low-income, because usually some people are in only 

one of these demographic groups. Note that one person cannot be under five and over 64, so any one person 

can be in up to five of the six demographic groups used in EJSCREEN. 

The demographic index is also bounded by these two percentages (percent low income and percent minority). 

For example, the actual percentage minority is larger than the value of the demographic index, if the 

percentage low-income is lower than the percentage minority.

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Appendix C

APPENDIX C. TECHNICAL DETAILS ON PROXIMITY INDICATORS

Several of EJSCREEN’s environmental factors are direct or indirect estimates of potential exposure or health 

risks, such as the NATA cancer risk estimates and the ozone and PM2.5 concentration estimates. There are other 

aspects of an individual’s or a community’s environmental concerns that are less readily quantified in terms of 

emissions, concentrations, or risk estimates.

People may be concerned about living near facilities that handle hazardous substances, and other potential

sources of pollution, such as highways or abandoned waste sites. Concern over “locally undesirable land uses”, 

or LULUs, is in some cases founded on the potential for routine or episodic releases of pollutants to the air, land 

or water, and the potential for such releases to cause human health or environmental adverse effects or other 

societal disamenities.

The purpose of the proximity measures in EJSCREEN is to systematically and consistently quantify different 

degrees of potential for these effects. We have developed a method to calculate a score that represents the 

relative magnitude of the proximity of the population within a block group to facilities, waste sites, or traffic

surrounding it. A block group with more facilities closer to the block group’s residential population will have a 

higher score than a block group where facilities are further away. We have applied this method to these facility 

or site types:

• National Priorities List (NPL) sites (a key subset of “Superfund,” sites).

• Hazardous waste Treatment, Storage or Disposal Facilities (TSDFs), subject to regulations under the 

Resource Conservation and Recovery Act (RCRA).

• Risk Management Plan (RMP) facilities, which are facilities that maintain greater than certain 

quantities of extremely hazardous substances, and are required to take certain actions, including 

filing risk management plans, under section 112 (r) of the Clean Air Act.

• Major direct dischargers to water permitted under the National Pollutant Discharge Elimination 

System (NPDES).

We have developed a similar approach to represent proximity to and traffic volume on nearby highways.

In the sections below, we will describe the general approach, in terms of facility proximity. We will then 

describe how it differs for traffic proximity. Then we will discuss certain adjustments we have made, mostly to 

make the approach computationally efficient, and summarize the data sources and computational routine that 

we applied to implement this approach. We conclude with caveats and other observations.

Calculating Proximity to Facilities or NPL Sites

Each of the more than 217,000 block groups for the U.S. states and the District of Columbia is made up of 

between one block and several hundred blocks. Most block groups nationwide are smaller than approximately 

0.5 square miles, an area that if circular would have a radius of about 640 meters. In block groups of this 

median size, the average residence generally would be about 430 to 720 meters (or less than half a mile) away 

100 | Page
Appendix C

from a given point within the block group, such as a facility, as explained at the end of Appendix C. About 20-

25% of block groups covered an area smaller than a circle of radius 300-350 meters (almost one quarter of a 

mile), as of the 2005-2009 geographies. Also, a very small number of block groups are extremely large in area, 

in very rural locations.

All of a block group’s blocks may have residential population estimated by the 5-year ACS, or only some, and 

some block groups have no residents at all. Blocks and block groups vary greatly in geographic area, and in 

population. The approach used here works first at the block level, based on measures of proximity to the 

facilities in or near the blocks. The block-level measures are then aggregated among all the blocks within a 

block group, weighted by the number of people in the different blocks.

Thus, while population is considered in aggregating the block scores, the measure does not increase or 

decrease for block groups with higher or lower populations. The measure is, rather, a characteristic of the 

residents of the block group, in the same way that cancer risk from NATA or ozone concentration are estimated 

measures of the conditions of those places.

Let

i represent a particular facility

j represent a block within a block group

k represent a block group

dij is the distance, in kilometers, from block j’s centroid to the given location of facility i

popjk is the estimated population of block j within block group k

popk is the total estimated population of block group k

f(dij) is a function representing the proximity of facilty i to block j, a declining function of the distance, 

dij

BlockScorejk is the aggregation of the proximity influences of all facilities affecting block jk

BlockGroupScorek is the population-weighted aggregation of the block group’s component blocks

We have chosen to define the proximity function as

f(dij) = 1 / dij

That is, a facility 1 kilometer from a block’s population contributes twice the score as a facility 2 kilometers 

from the same block. We note that we have made a choice in using inverse distance for this function. Air 

dispersion modeling for pollutants following Gaussian plume assumptions would show a generally greater 

drop-off in concentration, roughly with the second power to 2.5 power of one over distance. But actual 

concentrations around individual plants follow often-complex patterns that depend on the particular mix of 

stack vs. fugitive emissions, characteristics of stack height, exit velocity and temperature, the presence of 

buildings or other land surface characteristics and meteorology. Some substances react readily with other 

substances in the atmosphere, or precipitate out readily. It is not uncommon for concentrations to rise for 

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Appendix C

some distance from the emitting source, and then to fall from that peak concentration. The Gaussian plume 

model applies to gases, and emissions of particulates can drop off more quickly than gases.

Releases to land may follow extremely complex patterns of dispersion. Added to that are the very site-specific 

characteristics of potential human exposure via drinking water, vapor intrusion or contact with contaminated 

soils, etc. For water pollution, similar complexities exist, most notably that an effluent is carried away 

downstream of a running body of water, dilution can be complicated by the presence of other water entering 

stream segments, by volatilization, by biological and chemical interactions, and by deposition to sediments, and 

finally by the treatment and removal of a water pollutant sent to a publicly-owned treatment works.

We also note that researchers and others have taken varied approaches to representing the proximity of 

facilities to populations. The EJSM model of environmental justice concerns, developed for the state of 

California, scored facility proximity in concentric rings around a population centroid (Pastor Jr., Morello-Frosch, 

& Sadd, 2010; Sadd, Pastor, Morello-Frosch, Scoggins, & Jesdale, 2011). All facilities within 1 mile received a 

score of 3. All within the 1 to 3 mile band received a score of 2, and those between 3 and 5 miles received a 

score of 1. Anything beyond 5 miles received a score of zero. This step-wise scoring represents the judgment of 

the model developers, influenced by interactions with various stakeholders.

Finally, we note that EJSCREEN’s measure of proximity is intended to represent more than simply real or 

potential human health adverse effects coming from exposure. Some parts of the environmental justice 

literature reflect semi-quantitative factors, such as increased psychological stress, fear and other reactions to 

the presence of LULUs. This is not the forum for sorting through those factors.

However, we have made a judgment call: For the purposes of this EJSCREEN tool, we represent a facility’s 

measure of proximity by the inverse of its distance from the estimated location of the average person. A block’s 

proximity score is the sum of the inverse distances of all the facilities of a particular type. 

Note that for the minority of block groups in the United States with no residential population, we take a 

straight average of the block scores.

The units for these measures are facilities per kilometer. A block group could have a score of 1.0 if all residents 

were an average of one kilometer from a single facility, and all other facilities were so distant (> 5 km) as to 

make no contribution to the score. Another block group could have a score of 1.0 if there were five facilities 

that were all exactly five kilometers from the residents. 

Calculating Proximity to Traffic

We have adopted essentially the same approach described above for representing proximity to highway 

segments – an inverse distance-weighted sum of highway segments surrounding each block, and a populationweighted sum of the individual blocks’ contributions to the block group.

The highway segment database that we have used is described in section 3. These segments differ from a 

facility database in that they are lines on a geographic area, rather than points that represent the facilities. In 

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Appendix C

our approach, we find the distance from the block centroids to the nearest part of each surrounding highway 

segment. The nearest point, dij, could be an end of the highway segment or some point in between the ends.

We also multiplied each dij by the annual average daily traffic estimate that is associated with each highway 

segment. This is meant to reflect the traffic intensity, and this differs from the facility approach, where we have 

taken each facility within each group as having equal importance. Also, for traffic proximity, the search radius is 

500 meters and the score uses distance in meters, not kilometers.

Calculating Proximity – Additional Details

We address two modifications to the general method described above. The first deals with instances where a 

facility or highway segment location is very close to the centroid of the block. The second is an accommodation 

to the computational intensity of the general method.

Extremely Small dij Values

Our intention is to represent the proximity of facilities or highway segments to the population within each 

block. All facilities and each part of all highway segments fall within one block. By chance, some portion of 

those points fall very close to the block centroids.

We do not know how the population is geographically distributed within any block, but we assume that people 

are more likely to be distributed across the blocks’ expanses than to be concentrated at one point, such as the 

centroid. In fact for rural, suburban and many non-high rise urban areas, people’s residences are more likely to 

be closer to the blocks’ peripheries (bounded by roads) than clustered at the centroids. Thus, when a facility 

location happens to be very close to the block centroid, it would result in an artificially high contribution to the 

block’s score. This is not a hypothetical problem: We have observed dij values well below 100 meters, and some 

below 10 meters.

In looking for solutions to the problem, we conducted analyses and arrived at the approach we have adopted. 

Blocks vary widely in their total area and in their shapes. Both can be found in the Census Bureau’s Tiger shape 

files. Dealing explicitly with the individual block shapes would be computationally very intensive because there 

are over 11 million blocks. Since we cannot easily find out how the residents are actually distributed in those 

areas, we made two simplifying assumptions:

• residents are evenly distributed across the surface area of each block, and

• each block can be represented by a circle whose radius is [Block area / Pi]1/2 .

We call this latter value the Block Area Equivalent Radius.

Our investigations indicate that for any dij less than 90% of the Block Area Equivalent Radius, 0.9 times that 

value is a reasonable representation of the average distance from the facility for all residents in the block. We 

call this the dij corrected.

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Appendix C

Our computational scheme determines the dij values as described above, tests for the comparison with 0.9 * 

Block Area Equivalent Radius, and substitutes dij corrected values. We found that we needed to make that 

correction for less than 1% of all facility / block combinations in an early testing data set that used 2005-2009 

ACS data.

Accommodating to Computational Intensity – Combine a Distance Limit with a Nearest Facility 

Approach

Our task is to compute a proximity score for each of the facility or site types and highway segments for each of 

the more than 217,000 block groups, comprised of over 11 million blocks. The number of facilities nation-wide 

varies from hundreds of TSDFs to many thousands of RMP facilities. Computing all the combinations would 

require more computational time and resources than were available.

In addition, doing so would be wasteful and perhaps irrelevant. The one over distance function we have chosen 

to represent concerns about facilities and highways drops off greatly for most facilities beyond the nearest 

ones. The miniscule contribution of a facility 100 kilometers or more from a block is not only small, compared 

with those that may be within 5 to 10 kilometers, but has little common-sense meaning, in our view.

Consequently, we have followed the general approach described above only for facilities or sites within 5 

kilometers of a block’s centroid, and within 500 meters for highway segments. Depending on the facility or site

type, we find that 30-40% of block groups have at least one facility (RMP, TSDF, or NPDES) within the 5 

kilometers limit, and almost 10% have one or more NPL sites within 5 km, in the 2015 version of EJSCREEN.

Of course, every block and block group has one nearest facility, even though it may be beyond the 5 kilometers 

horizon, and some of those may be fairly close to that limit. We have also calculated the distance to the facility

nearest to each of the blocks. For those blocks lacking anything within the 5 kilometers, we represented the 

facility proximity by one over the distance to that single nearest facility.

This added computational complexity to the approach, but at far less cost than computing the full matrix of 

millions of blocks times thousands of facilities and sites.

This hybrid approach results in every block (and thus every block group) having a nonzero, positive proximity 

score. All of the resulting block proximity scores are necessarily less than the score had we computed the full 

matrix, but we judge that this is a reasonable and practical compromise. Figure 1 shows histograms for 

proximity scores. Counting only the single nearest beyond 5 km has the effect of shifting scores under 0.2 to 

the left, to lower scores than if all were counted, but the graphs show no major discontinuities, suggesting this 

limitation (counting only the nearest one) has little impact overall.

Data and Computational Scheme

Using the Census 2010 block boundaries, the distance to all facilities within 5 kilometers of all blocks (not just 

block groups) was determined, and distance to the nearest facility at any distance was determined if none were 

found within 5 km.

104 | Page
Appendix C

The dij values were compared to the 0.9 * Block Area Equivalent Radius and corrected values were used when 

necessary, before computing 1 / dij . The 1 / dij values were summed for each block to compute the 

BlockScorejk. These were then rolled up to the block group level, applying the population weighting described 

above, for the final BlockGroupScorek . 

Caveats and Observations

Several aspects of the proximity analysis approach have been mentioned above, but deserve summary here.

• We recognize that our selection of the inverse of distance is a design choice that represents our 

judgment of a balance among competing factors.

• We recognize that one could potentially attempt to distinguish among facilities within each facility 

category by quantitative or qualitative measures of importance. These could include total pounds 

released or toxicity-weighted releases for NPDES facilities; the number of accidental releases 

and/or their apparent severity for RMP facilities; some classification of the likelihood of releases for 

NPL sites or TSDFs; and general indications of scale for all of them. We note that CalEnviroscreen 

has addressed this issue to some extent, and that the RSEI tool based on TRI data may be relevant 

to future work on this issue. At this point, we have chosen not to develop any such potential scaling 

adjustments. 

• We recognize that all location data are subject to potential error. While we have high confidence in 

the block centroid locations, we know that the facility or site or roadway location data may contain

larger or smaller errors, and that for large facilities or sites, one point may not be an entirely 

adequate representation of the location of its releases or of neighbors’ perceptions.

• We recognize that the computational accommodation we describe above results in a hybrid of 

measures: For some block groups, all blocks have one or more facilities within 5 kilometers and the 

score is the summation of all those potentially multiple facility/block combinations; for other block 

groups, none of the blocks have a facility within 5 kilometers and the score is the contribution of 

the single facility closest to each block; while for some block groups, we have a mix of those 

situations. We believe that this is a reasonable compromise

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Appendix C

Note: The method described in the above section would be the case if homes and facilities were on average uniformly 

distributed within block groups that were roughly circular on average, because the average distance between two random 

points in a circle of radius R is 90% of R (Weisstein, Eric W. "Disk Line Picking." From MathWorld--A Wolfram Web 

Resource. http://mathworld.wolfram.com/DiskLinePicking.html). This means that if a population is randomly spread over a 

roughly circular block, a facility in the block typically would be 0.9R from the average person. Also, the average point in the 

circle is 0.67R from the center, and 1.13R from the edge of the circle. We can describe this relationship using an equation 

that is a portion of the formula for the distance between two random points in a circle of radius=1. The formula is

where b= the facility’s distance from the center as a fraction of the radius, and the integral over a represents distances of 

residences from the center. We can solve this equation using http://WolframAlpha.com, for b=0, 0.5, or 1, representing 

points at the center, halfway to the edge, and at the edge of the circle. For example, we can use this equation for b=0.5 to

find that the average person, if randomly located in a circle of radius R, is a distance of about 0.8 R from a facility that is

halfway between the center and edge of the circle. For the distance between the average person and a randomly placed 

facility in the circle, we use b=sqrt(0.5) instead, and the following would be used as the input to WolframAlpha:

Integrate[(1/Pi) Sqrt[a + (Sqrt(0.5))^2 - 2 * (Sqrt(0.5)) * Sqrt[a] cos(t)], {a, 0, 1}, {t, 0, pi}] or

http://www.wolframalpha.com/input/?i=Integrate%5B%281%2FPi%29+Sqrt%5Ba+%2B+%28Sqrt%280.5%29%29%5E2+-

+2+*+%28Sqrt%280.5%29%29+*+Sqrt%5Ba%5D+cos%28t%29%5D%2C+%7Ba%2C++0%2C++1%7D%2C++%7Bt%2C+0%2C+

pi%7D%5D+ 

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Appendix D

APPENDIX D. SUMMARY STATISTICS FOR INDICATORS

This appendix provides basic information and statistics on the environmental and demographic data used in 

EJSCREEN. 

Table 6 shows summary statistics for the 12 environmental indicators, including the population mean and 

selected population percentiles. The mean score and high percentiles for each indicator provide useful 

perspective on the magnitude of these environmental factors for the average or highly exposed individuals. 

The mean neurological Hazard Index (HI) for example, is only xx, meaning that the estimated mean exposure is 

only x% as high as the health-based Reference Concentration, and even the 99th percentile value is only 

approximately xx. The respiratory HI, however, has a mean above xx, meaning the RfC is typically exceeded by a 

factor of xx.

63 

In the 2015 version of EJSCREEN, the PM2.5 level in the average person’s block group (based on tract estimates) 

was 9.7 μg/m3

. Roughly 7-8% of U.S. residents had block group (based on tract) estimates above 12 μg/m3 in

these 2011 estimates. Note that 15 μg/m3 was the health-based annual ambient standard as of mid-2012, with 

a revised standard of 12 μg/m3 finalized in December 2012. However, it is important to understand that the 

EJSCREEN value does not indicate nonattainment of a standard because it is based on 2011 estimates of each 

block group, from a combination of modeling and monitoring, while nonattainment is determined by individual 

monitors (each intended to represent a relatively large area, often a county), based on three recent years of 

monitoring data. Likewise, the ozone indicator cannot be compared to the ozone ambient standard.

The NATA cancer risk mean was very roughly xx cases per lifetime per million people, or a lifetime individual 

cancer risk of xx10-5, which is orders of magnitude lower than typical premature mortality risk estimates 

associated with recent ambient levels of PM2.5. 

The facility proximity indicators generally have mean scores of 0.05 to 0.31. The NPL’s mean score of 0.10 is 

comparable to the average person having one NPL site 10 kilometers away. The mean RMP score of 0.31 could 

result from one RMP facility at 3.2 kilometers distance, for example, and the median is roughly 0.14 (or about 

1/7), meaning that most of the U.S. population has at least one RMP within about 7 km of their home. About a 

third of the population lives within 5 km of an RMP facility, but less than 7% of the population has any within 1 

km.

Table 7 shows a similar set of statistics for the demographic data used in EJSCREEN. The overall US percents low 

income and minority were 34% and 36% respectively, and the medians were somewhat lower. The 80th

percentiles were 53% and 69%. The top 5% lived in block groups with a Demographic Index above 80%. Note it 

is more common to see block groups close to 100% minority than close to 100% low income.

63 High values for the neurological HI tend to be driven by exposure to acrolein above the Reference Concentration, which 

is designed to protect against the risk of nasal lesions as the health endpoint.

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Appendix D

Table 6. Summary Statistics for Environmental Indicators

Environmental Indicator Missing Minimum 25%ile

50%ile 

(median)

Pop. 

Mean 75%ile 80%ile 90%ile 95%ile 99%ile

PM 2.5 1664 4.3 8.8 9.9 9.8 11 11 12 12 13

Ozone 1664 25 42 46 46 51 51 54 57 66

NATA DPM

NATA cancer risk

NATA respiratory HI

NATA neurological HI

% pre-1960 (lead paint) 0 0.00 0.05 0.21 0.30 0.50 0.59 0.77 0.87 0.96 

Proximity Traffic 253 0.00 11 35 110 100 130 250 430 1,200 

Proximity NPL 0 0.00 0.02 0.05 0.10 0.10 0.12 0.19 0.31 0.88 

Proximity RMP 0 0.00 0.08 0.14 0.31 0.30 0.39 0.76 1.2 2.4 

Proximity TSDF 0 0.00 0.01 0.02 0.05 0.05 0.07 0.11 0.18 0.53 

Proximity NPDES 0 0.00 0.08 0.13 0.25 0.24 0.30 0.55 0.89 1.9 

Source: 2015 version of EJSCREEN. See body of report for sources and definitions of environmental indicators.

Notes: Population percentiles (and means) are shown, not block group percentiles (or means), so 80%ile means 80% of the population has a lower (or 

exactly tied) block group score. Values in table have been rounded to two significant digits, except for PM, ozone, and DPM, which use three significant 

digits. Numbers may differ slightly from those in EJSCREEN reports. Summary statistics for a given environmental factor exclude block groups where that 

environmental indicator was not available (missing). 

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Appendix D

Table 7. Summary Statistics for Demographics

Demographic variable Missing Minimum 25%ile

50%ile 

(median)

Population 

mean 75%ile 80%ile 90%ile 95%ile 99%ile

Demographic index 0 0 17% 29% 35% 50% 57% 71% 80% 90%

% low-income 0 0 17% 30% 34% 48% 53% 65% 74% 87%

% minority 0 0 9% 26% 36% 60% 69% 89% 96% 100%

% less than high school 0 0 5% 11% 14% 21% 24% 33% 42% 60%

% linguistic isolation 0 0 0% 1% 5% 6% 8% 16% 25% 44%

% under 5 0 0 4% 6% 7% 9% 10% 12% 14% 18%

% over 64 0 0 7% 12% 13% 17% 19% 23% 28% 44%

Supplementary demog. 

index 0 0 11% 16% 18% 24% 27% 34% 39% 47%

Source: 2015 version of EJSCREEN. Calculated based on 2008-2012 5-year summary file, American Community Survey (ACS), from the US Census Bureau.

Note: Population percentiles (and means) are shown, not block group percentiles (or means), so 80%ile means 80% of the population has a lower (or 

exactly tied) block group value. Values in table have been rounded to an integer percentile 0-100. Numbers may differ slightly from those in EJSCREEN 

reports.

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Appendix D

Table 8 shows Spearman correlations in block group level scores for all pairs of the 12 environmental indicators

in the 2015 version of EJSCREEN. All correlations are positive except that ozone is weakly, sometimes 

negatively, correlated with the other indicators. The strongest positive correlations were among the NATAderived factors: cancer, neurological HI, respiratory HI, and diesel particulate matter indicators. The TSDF 

proximity indicator was correlated with these four. Similarly, the traffic indicator was correlated with the NATA 

factors. All other coefficients were less than 0.50. 

110 | Page
Appendix D

Table 8. Spearman Correlation Coefficients for Environmental Indicators

Source: 2015 version of EJSCREEN. See body of report for sources of environmental indicators.

PM2.5 ozone DPM

cancer 

risk

resp. 

HI

neuro. 

HI

Traffic 

proximity

% pre1960NPL 

proximity

RMP 

proximity

TSDF 

proximity

NPDES 

proximity

PM2.5 1 0.23 0.22 0.22 0.21 0.30 0.34 0.18

Ozone 0.23 1 -0.03 -0.16 -0.09 0.03 -0.02 -0.06

DPM 1

cancer risk 1

resp. HI 1

neuro. HI 1

Traffic proximity 0.22 -0.03 1 0.17 0.33 0.3 0.35 0.3

% pre-1960 0.22 -0.16 0.17 1 0.18 0.17 0.08 0.17

NPL proximity 0.21 -0.09 0.33 0.18 1 0.2 0.37 0.25

RMP proximity 0.30 0.03 0.3 0.17 0.2 1 0.25 0.35

TSDF proximity 0.34 -0.02 0.35 0.08 0.37 0.25 1 0.17

NPDES proximity 0.18 -0.06 0.3 0.17 0.25 0.35 0.17 1

111 | Page
Appendix D

Table 9. Spearman Correlation Coefficients for Demographic Indicators

% 

minority

% lowincome

% less 

than high 

school

% 

linguistic 

isolation % under 5 % over 64

% minority 1 0.41 0.43 0.51 0.23 -0.34

% low-income 0.41 1 0.67 0.23 0.24 -0.15

% less than 

high school 0.43 0.67 1 0.32 0.19 -0.05

% linguistic 

isolation 0.51 0.23 0.32 1 0.17 -0.2

% under 5 0.23 0.24 0.19 0.17 1 -0.31

% over 64 -0.34 -0.15 -0.05 -0.2 -0.31 1

Source: 2015 version of EJSCREEN. Calculated based on 2008-2012 5-year summary file, American 

Community Survey (ACS), from the US Census Bureau.

Table 9 shows the Spearman correlations in block group level scores for all pairs of the 6 demographic 

factors. All correlations are positive except those between % over 64 and all other factors.

Figure 1 shows histograms or density plots of the environmental indicator data, showing the simple 

distribution across block groups for each of the 12 indicators (i.e., these figures show the distribution of 

block groups, not a population distribution, but the population distribution is very similar). 

112 | Page
Appendix D

Figure 1. Histograms of Block Group Environmental Indicators as ratio to mean value (log scale shows mean value as zero) 

Source: 2015 version of EJSCREEN. Note: Some extreme values are not shown on the x axis (for proximity indicators) and y axis (for ozone). 

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Appendix D

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Appendix E

APPENDIX E. FORMULAS FOR DEMOGRAPHICS AND EJ INDEXES

The EJ indexes rely on demographic indexes combined with environmental indicators. The demographic 

and EJ indexes are calculated as follows:

Demographic Index

This is the average of percent minority and percent low income in the block group. Percent low income 

is defined in Appendix B, and is essentially all residents where household income is below twice the 

federally defined poverty threshold, as a percentage of all those for whom this poverty ratio could be 

determined (typically known for the vast majority of the block group’s population).

Supplementary Demographic Index

This is the average of percent minority, percent low income, percent less than high school education, 

percent linguistically isolated, percent individuals under age 5, and percent individuals over age 64 in the 

block group.

Supplementary Demographic Index =

(% minority + % low-income + % less than high school education + % linguistic isolation + 

% individuals under age 5 + % individuals over age 64) / 6

 Demographic Index = (% minority + % low-income) / 2

115 | Page
Appendix E

EJ Index

The EJ Index measures how much a particular place contributes to overall nationwide differences in 

environmental indicator values between demographic groups. This EJ index is a combination of a block 

group environmental factor, the population of the block group, and the demographic composition of the 

block group. In this index, the demographic composition of the block group is the difference between 

the block group’s composition and the national average, as measured by the demographic index.

EJ Index with Supplementary Demographic Index

This EJ index is a combination of a block group environmental factor, the population of the block group, 

and the demographic composition of the block group. In this index, the demographic composition of the 

block group is the difference between the block group’s composition and the national average, as 

measured by the supplementary demographic index.

Supplementary EJ Index 1 Based on Demographic Index

This EJ index is a combination of a block group environmental factor, the population of the block group, 

and the demographic index. This EJ index measures how much a particular place contributes to the total 

burden faced by the subpopulations highlighted by the demographic index.

Supplementary EJ Index 1 = 

(Environmental Indicator) 

X (Demographic Index for Block Group) 

X (Block Group Population)

EJ Index with supplementary demographics = 

(Environmental Indicator) 

X (Supplementary Demog. Index for Block Group – Supplementary Demographic Index for US) 

X (Block Group Population)

EJ Index = 

(Environmental Indicator) 

X (Demographic Index for Block Group – Demographic Index for US) 

X (Block Group Population)

116 | Page
Appendix E

Supplementary EJ Index 1 Based on Supplementary Demographic Index

This EJ index is a combination of a block group environmental factor, the population of the block group, 

and the supplementary demographic index. This EJ index measures how much a particular place 

contributes to the total burden faced by the subpopulations highlighted by the supplementary

demographic index.

Supplementary EJ Index 2 Based on Demographic Index

This EJ index is a combination of a block group environmental factor and the demographic index. 

Supplementary EJ Index 2 Based on Supplementary Demographic Index

This EJ index is a combination of a block group environmental factor and the supplementary

demographic index.

Supplementary EJ Index 1 

with supplementary demographics = 

(Environmental Indicator) 

X (Supplementary Demographic Index for Block Group) 

X (Block Group Population)

Supplementary EJ Index 2 = 

(Environmental Indicator) 

X (Demographic Index for Block Group) 

Supplementary EJ Index 2 

with supplementary demographics = 

(Environmental Indicator) 

X (Supplementary Demographic Index for Block Group) 

117 | Page
Appendix F

APPENDIX F. QUALITY CONTROL / QUALITY ASSURANCE

EPA’s quality control guidelines emphasize transparency and reproducibility as useful in ensuring the 

quality of data. EPA is providing a very high level of transparency in EJSCREEN by taking several steps 

described here. 

The EJSCREEN Technical Documentation (this document) has extensive details on the precise sources 

and exact methods used, to ensure transparency. The transparency of the data inputs is also ensured 

through references to further technical documentation from the providers of those data inputs, such as 

the PM2.5 and ozone estimates, NATA, the Census demographic data, and the DOT traffic database.

Metadata is linked from the web-based tool, providing quick access to further technical details.

Furthermore, the full raw database of EJSCREEN indicators and indexes, and supplementary material,

will be available to expert users who wish to go beyond the web-based interface and conduct further 

analysis or research. EPA also hopes to make available the Python and R code used to develop all the 

indicators in EJSCREEN, including proximity scores, percentiles, and so on. Access will be provided 

through the data download section of the EJSCREEN website (http://www2.epa.gov/ejscreen).

Extensive quality control/ quality assurance efforts were made in the development of EJSCREEN and a 

very brief summary is provided here. 

The starting point for most of the environmental indicators was information provided by EPA Offices 

(i.e., latitude/ longitude data used to create proximity indicators, and the NATA results). Those sources 

of information had already been subject to QA procedures in the respective offices, and the information 

had already been released to the public. EPA’s Office of Air and Radiation (OAR) provided PM2.5 and 

ozone estimates based on public monitoring data, CMAQ results, and a fusion model to combine them. 

The CMAQ and fusion model have previously been extensively documented in peer-reviewed journal 

articles (Byun & Schere, 2006; Berrocal, Gelfand, & Holland, 2010a, 2010b, 2011). The lead paint 

indicator was calculated from Bureau of Census data by an EPA contractor, and then independently 

replicated by EPA, through separate ACS downloads and calculations. The traffic indicator was calculated 

from publicly available DOT data, as explained in this report.

The calculations of environmental indicators from those inputs was conducted by an EPA contractor for 

the proximity scores and the lead paint score, using their established QA/QC procedures, so EPA did not 

attempt to replicate the proximity calculations. These calculations involved time-consuming proximity 

calculations, and simple calculation of the lead paint indicator. The NATA and PM and ozone indicators 

were simply taken directly from EPA and used in EJSCREEN.

The demographic indicators were calculated by an EPA contractor based on ACS data they obtained 

from the Census Bureau. EPA was able to independently replicate 100% of the resulting indicators by 

separately obtaining the raw ACS data.

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Appendix F

The same is true for the EJ Indexes and all of the percentiles, map bins (for color-coded maps), and 

popup text fields used in EJSCREEN – the entire geodatabase was independently replicated by EPA using 

only the environmental indicators as a starting point, and applying alternative algorithms and code for 

development of percentiles and bins, as well as the rounding procedures defined for popup text.

EPA was also able to conduct some limited manual replication of buffer calculations, although truly 

independently replicating those GIS algorithms is challenging given the need to use data on millions of 

blocks and the challenge of identifying relevant blocks in a fashion that is independent of the 

geoprocessing tool used for buffer analysis in EJSCREEN. Spot checks were conducted on buffer reports 

to ensure raw data and percentile calculations, use of lookup tables, rounding, significant digits, and 

floored percentiles were all handled correctly.

The extensive QA/QC process did uncover numerous complex data challenges early in the process, and 

ultimately lead to a final database that could be fully independently replicated from environmental 

indicators calculated from public information, providing strong assurance of the integrity of the data 

processing and calculations.

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Appendix G

APPENDIX G. PEER REVIEW

EJSCREEN was submitted for peer review in early 2014, through a letter review process conducted by a 

contractor with extensive experience in organizing peer review. Based on pre-defined criteria regarding 

level of expertise in relevant subject areas, four experts were identified.

The reviewers were provided with a draft of this technical documentation, describing EJSCREEN’s 

development, purpose, and use of selected environmental indicators, demographics, and EJ indexes for 

screening and mapping.

Reviewers were also provided a live webinar presentation and demonstration, along with time for 

questions for EPA. One of the four was unable to attend the webinar but contacted EPA with questions 

that EPA responded to in a phone conference call.

The reviews were completed in March of 2014. Each of the four reviewers provided a detailed 

discussion of their technical comments, concerns, and recommendations.

All four agreed that the new environmental justice screening tool will be helpful to its users and is 

generally very well done. Each did point to some weaknesses in the tool, suggesting that correcting 

these shortcomings in the next version could strengthen the tool and help its users.

Of the more than 100 distinct comments from the expert peer reviewers, more than one third were 

positive statements about the quality of EJSCREEN and the documentation. A sampling of direct quotes 

includes the following: 

• “I would like to commend the EPA ... it does represent a major step forward and the EPA should 

be recognized for this achievement”

• “This documentation fairly represents the tremendously difficult task of creating this tool”

• “very impressed by the quality of the work”

All of the reviewers also agreed that the EJSCREEN documentation is generally well-written, clear, and 

easy to follow. Reviewers did ask for editorial changes, clarification, or further rationale in the 

documentation, and such comments represented about one fourth of all the comments received. They 

asked for clarification in some specific sections, such as more discussion of which indicators were chosen 

and why, and which were left out and why. Many of these comments have already been taken into 

consideration in this version of the Technical Document.

About one third of the comments were suggestions or requests for new data (in reports, maps, and data 

files), typically recommending new or improved environmental indicators (e.g., air quality, water quality, 

more facility types, etc.). Some comments made suggestions that would involve adding a new feature to 

the EJSCREEN tool, rather than improving the data layers or the documentation. These will be taken into 

consideration in discussions of possible future updates or upgrades to the tool.

120 | Page
Appendix G

A handful of comments raised policy considerations and inherently challenging issues in screening and 

mapping. These involved basic policy questions such as what is the best spatial resolution for these 

maps, and whether to combine all 12 EJ indexes (Reviewers were divided on this topic). The 2015

version of EJSCREEN continues to use block groups, but recommends an emphasis on buffers as less 

uncertain than a single block group estimate. It continues to use 12 separate indexes, but these issues 

can be a topic of continuing discussions and exploration in the future as the public and others work with 

the new tool.

On the whole, the reviewers’ suggestions have already served to strengthen the tool and its 

documentation, and will continue to inform discussions. By elaborating and clarifying the options and 

choices made, EPA can help the users of EJSCREEN better understand its potential and its limitations. 

Improved data and methods should be considered as well in future versions of EJSCREEN. EPA looks 

forward to working across various offices, with stakeholders across the nation, and academics as well as 

the public, on implementation and future enhancement of EJSCREEN.

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Appendix H

APPENDIX H. INITIAL FILTER APPROACH FOR SCREENING

What is the 80th percentile filter?

In past screening experience, EPA has found it helpful to establish a suggested Agency starting point for 

the purpose of identifying geographic areas that may warrant further consideration, analysis, or 

outreach. The use of an initial filter promotes consistency and provides a pragmatic first step for EPA 

programs and regions when interpreting screening results. For early applications of EJSCREEN, EPA 

identified the 80th percentile filter as that initial starting point. In other words, an area with any of the 

12 EJ indexes at or above the 80th percentile nationally should be considered as a potential candidate 

for further review. Further review may include considering other factors and other sources of 

information such as health based information, local knowledge, proximity and exposure to 

environmental hazards, susceptible populations, unique exposure pathways, and other federal, regional, 

state, and local data. This filter is simply a starting point, and program offices and regions should 

perform additional analysis before making any decisions about potential environmental justice issues. As 

EPA gains further experience and insight into the performance of the tool and its applicability for 

different uses, program offices and regions may opt to designate starting points that are more inclusive 

or specifically tailored to meet programmatic needs more effectively.

The 80th percentile filter in EJSCREEN is not intended to designate an area as an “EJ community.” 

EJSCREEN provides screening level indicators, not a determination of the existence or absence of EJ 

concerns. Nor does the use of the 80th percentile filter suggest that all of the 12 environmental 

indicators are equal in terms of their impact on human health and the environment. Instead, the 80th 

percentile filter encourages programs to consider environmental indicators outside of their areas of 

concentration. The Agency may revise this approach in the future based on experience. This 80th 

percentile filter is for internal EPA use and is not intended to apply to States or other organizations.

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Appendix H

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