## Final, November 2014



# EJSCREEN Environmental Justice Mapping and Screening Tool



# EJSCREEN Technical Documentation

May 201 5

# EJSCREEN Environmental Justice Mapping and Screening Tool

EJSCREEN Technical Documentation

May 201 5

U.S. Environmental Prote ction Agency Office of Policy Washington, D.C. 20460

Suggested citation:

U. S. E nvironmental Protection Agency (EPA), 201 5 . EJSCREEN Technical Documentation.

For more information: www.epa.gov/ejscreenACKNOWLEDGEMENTS

This document was prepared by the United States Environmental Protection Agency’s Office of Policy (OP) with d rafting, e diting 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

re viewers 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

E NVIRONMENTAL J USTICE AT EPA

.................................................................................................................... 6
E NVIRONMENTAL J USTICE M APPING AND S CREENING AT EPA

.............................................................................. 7
D EVELOPMENT OF EJSCREEEN

....................................................................................................................... 8
P URPOSES AND U SES OF EJSCREEN

................................................................................................................. 8 C AVEATS AND L IMITATIONS OF EJSCREEN

........................................................................................................ 9
A DVANCES IN EJ S CREENING P ROVIDED BY EJSCREEN

...................................................................................... 11

2 OVERVIEW OF DATA AND METHODS IN EJSCREEN

..................................................................... 13

E NVIRONMENTAL I NDICATORS S ELECTED FOR EJSCREEN

.................................................................................. 13
D EMOGRAPHICS IN EJSCREEN

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

............................................................................... 16 Demographic Indicators Included in EJSCREEN

................................................................................................... 20 Demographic Indexes in EJSCREEN

..................................................................................................................... 21 E NVIRONMENTAL J USTICE I NDEXES IN EJSCREEN

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

................................................................................................ ...................................... 22 Supple mentary EJ Indexes

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

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

........................................................................................................... 26 P ERCENTILES

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

................................................................................................ .................................... 27 Color

- coded High Percentile Bins

........................................................................................................................ 28 B UFFER R EPORTS

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

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

.................................................... 30

3 DETAILS ON THE ENVIR ONMENTAL INDICATORS IN EJSCREEN

.................................................... 33

E NVIRONMENTAL F ACTORS N OT I NCLUDED

..................................................................................................... 33
E NVIRONMENTAL F ACTORS 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 n ew 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 a nd indicators
, and highlight s 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 method s used to create the i ndicators and i ndexes in EJSCREEN

. The Appendi ces 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 commi tment 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, i n 1994, Executive Order 12898 (EO 12898) mandated that each covered federal agency m ake 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 an d 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
A gency to make a difference in overburdened communities , a goal that has be en 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 effort s have been guided by Plan EJ 201 4 , 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 b y environmental pollution by integrating environmental justice into all EPA programs, policies and activities.



## E **nvironmental Justice Mapping and Screening at EPA** 
Mapping tools as well as s creening

- level applications have a substantial history at EPA , and E J is one area in which maps and screening can be useful

. Several EPA Regional o ffices 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) , t he National Environmental Justice Advisory Council ( NEJAC ) provided recommendations to EPA about how to design an EJ screening t ool 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 201 5 wa s EJVIEW, 4 a web

based tool that display ed selected demographic and environmental data, and allow ed 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 include d a commitment to develop a nationally consistent environmental justice screening and mapping tool in order to better meet the Agency’s responsibilit ies 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 screenin g 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 on e hand, and high

quality 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 screenin g 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 outreac h. 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 s creening 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

.

S creening 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 S uperfund 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. Scre ening 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 limitati ons

. 

## Caveats and Limitations of EJSCREEN

EJSCREEN is a pre

- decisional screening tool, and was not designed to be the basis for agency decision

making or determination s regarding the existence or absence of EJ concerns. It also s hould 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 a dditional 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 understan d 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). A ny 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 environmenta l 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 demogr aphic factor that ma y 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, su ch

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 resi dences . Therefore, it is typically very useful to summarize EJSCREEN data for a larger area
, cover ing several block groups, in what is called a “buffer” report, as explained later in this document

. There is a tradeoff between resolution and precision: Deta iled 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 c ombined 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 m eaningful 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 o n 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).

U ncertainties 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 t he environmental indicators are only screening -l evel proxies for actual exposures or health risks . This is particularly true for the proximity indicators, for example

. Even for the indicators that directly estimate risk s or hazards , as with the air toxics cancer risk indicator, estimate s ha ve substantia l uncertainty because emissions, ambient levels in the air, exposure of individuals, and toxicity are uncertain. Section 3 provides technical details on each enviro nmental 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 v intage of envi ro nmental indicators varies and is not the same as the vintage of the demographic data. The NATA air toxics indicators and the PM 2.5 and ozone indicators in particular should be viewed with this in mind, because emissions related to PM 2.5 , ozone, and air toxics generally have decr eased 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 fut ure 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 conditio ns, 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 condition s 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 PM 2.5 and ozone nonattainment a reas (areas not meeting national ambient air quality standards).

There are also some l imitations 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 screenin g 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 i nformation 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 too l 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 wide r 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

- ba sed 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 repor ts. 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 present s multiple “EJ I ndex es ” for each place . An EJ Index is a way of combining , in a quantitative way rather than only visually on a map , the environment al indicator and the demographic information for a location

. A separate EJ I ndex 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 resolut ion 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 other s

. 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 ex perience.

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 unc ertain. 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 cover s several block g roups, 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 summarize s data for all residents within some distance from a selected point, using a circular buffer, or within a user

- defined buffer of any sha pe , 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 **O VERVIEW**  **O F DATA**  **A ND METHODS**  **I N**  **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 201 5 , EJSCREEN contains 12 environmental indicators, which range from estimates of human health risk to proxies for potential exposure such as
prox imity 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. T he 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. S ection 3 provides more detail on each environmental indicator.



## Environmental **I ndicators**  **Selected for EJSCREEN**

Some environmental indicators us ed in EJSCREEN quantify proximity to and the number s of certain types of potential sources of exposure to environmental pollutants , such as nearby hazardous waste sites or traffic

. The lead paint indicator i ndicates the presence of older housing, which oft en, 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 estimate s 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 conce ntrations

. In other words, t he se 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: S creening level data are available (or could be readily developed) at the block group level (or at least close to this resolution).

• Coverage: Screening lev el 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, susceptibil ity , 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 expo sure 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 PM 2.5 ambient standards). EPA also reviewed data from the CDC ( Centers for Disease Control and Preventi on, 2011c ) and other sources in the federal government ( Fedstats, 2007 ) , and consulted EPA Regions and
program o ffices that ar e responsible for data collection and analysis under EPA’s key environmental
statutes. The State of California’s work on CalEnviro S creen 5 was also tracked throughout its development. Other internal EPA data tools were also examined , including EJSEAT, EJVIE W, 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 PM 2.5 level in air

.
o Ozone level in air

.
o NATA air toxics :

 D iesel particulate matter level in air

.

 A ir toxics cancer risk

.

 A ir toxics respiratory hazard index

.

 A ir 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 Priorit ies L ist (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 ind icators is explained in detail in s ection 3 , and Appendix D provides summary statistics for the indicators. Again, i t is important to understand that these indicators v a ry 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 en vironmental 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/m 3 )

Potential Exposure Particulate Matter (PM 2.5 ) Annual average (μg/m 3 )

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 k ilometers (k m )

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 distance 6 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 Priorit ies List , Superfund program Integrated Risk Information System NPDES National Pollutant Discharge Elimination System PM 2.5 Particulate matter (PM) composed of RMP Risk Management Plan particles smaller than 2.5 microns TSDF s Hazardous waste Treatment, Storage , and μg/m 3 micrograms of PM 2.5 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 o f 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 o n 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.

A ll 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. E xposures that occur away from the h ome, 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 

## Demographic **s in EJSCREEN**

Th is 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 D**  **emographic s**  **as Proxies for**  **Potential Susceptibility** 

EJSCREEN has been designed in the context of Executive Order 12898 8 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 Fe deral 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 popul ations and low

- income populations 9

EJSCREEN was also designed in the context of EPA’s EJ policies, including EPA’s I nterim G uidance on
C onsidering 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 recogn izing 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

- environmental

justice

9 http://www.archives.gov/federal

- register/executive

- orders/pdf/12898.pdf

16 | Page

## 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 indicator s, 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 communiti es 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. Demog raphics, 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 th e 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 ma y 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 Reg ional 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 vulnerabil ity 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 vario us definitions of vulnerability in the context of natural disasters, or work on vulnerability indexes for developing countries by Briguglio ( 1997 ).

17 | Page

## 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

.

• Susceptibil ity 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
t hreshold 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 PM 2.5 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.

• S usceptibility because of higher baseline risk or rates of pre

- existing disease s. The same percent increase in mortality risk has a larger impact on absolute risk if baseline risk is higher.

• S usceptibility 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, low

income 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 d ifferences 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 :

• A bout two thi rds (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

• R esidents with lower income report fe wer average healthy days than others ( Centers fo r Disease Control and Prevention, 2011a ), and report worse health overall ( Centers for Disease Control and Prevention, 20 10 ).

• 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 elevat ed 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 ).

18 | Page

## 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 dispa rities 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 ar e associated with residential location, and could be considered in EJSCREEN (while other s 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). V arious env ironmental 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 increa sed 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

- Hispan ic blacks than non

- Hispanic whites (U.S. EPA 2011 c).

• S ome but not all studies suggest lead has a greater impact on IQ among low SES than high SES individuals ( U.S. EPA 2011 c).

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 purpose s 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 bett er describe or determine susceptibility but for which nationally consistent data are not available . EJSCREEN screen s geographic

areas for increased potential for exposure and increased potential for susceptibility to exposures. Additional analysis is alway s needed to explore any underlying reasons for differences in susceptibility
, exposure or health

.

S ome studies have begun to quantify the degree of susceptibility to specific pollutants in particular demographic groups, such as the work on educational atta inment as an effect modifier for risks associated with PM 2.5 exposure, but such emerging knowledge is still limited to a handful of pollutants

19 | Page

## 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 b een 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 b lock 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 cate gory – 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 var ious EPA EJ screening tools
, the four other factors most commonly used by EPA Headquarters and Regions for EJ analyses are also included in EJSCREEN . T he 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 non

English language and also speak English less than “very well” (have difficulty wit h 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.htm l . 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 /

20 | Page

## 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 . F or 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 D emographic 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 D emographic 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 d enominators 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 th e total block group population, but some people in each block group are not evaluated for each demographic characteristic. 

## Demographic Index = (% minor ity + % 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

21 | Page

## Overview of Data and Methods

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

.

T he 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 d emographic 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 low

income and minority, for example. In fact, these indicators are co rrelated 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 thes e complexities can be reexamined.

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

. 

## Environmental Justice Ind **ex es in E**  **JSCREEN**

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 demogr aphic 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 3 5 %). 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 portion s 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)

22 | Page

## Overview of Data and Methods

“S usceptible” or “potentially susceptible individuals” are u sed 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
averag e of these counts as “the susceptible individuals” in these examples.

Th e number of potentially susceptible individuals (Demog raphic Index times population count) of course is typically less than t he actual number who are minority, low

- income, or both . Th e demographic breakdown is not reported by block group –t he 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 . Th ere might be 2 00 (20%) who are low

- income but not minority, and 200 (20%) who are minority but not low

- income. In that case, t here 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 grou p. 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 I ndex u ses the concept of “ excess r isk” b y l ooking a t how far a bove the national a verage the block gr oup demographics are

. F or exa mple, assum e a b lock gr oup with 1 000 p eople i n i t. In t hat b lock
group, o ne w ould e xpect 3 5 0 potentially susceptible i ndiv iduals ( 1000 people h ere x US average o f 35 %). However, if the Demographic I ndex f or that b lock g roup is 75 %, well above t he U S av erage , th en th ere

are the equivalent o f

7 50 potentially susceptible p eople i n t hat b lock gr oup , o r 400 more t han expected for a block g roup with a population o f 1 000. The EJ I ndex would b e 400 ti mes th e environmental indicator in this c ase.

T h is formula for the EJ Index is useful because for each environmental indicator it finds the block groups that contribute the most towa rd 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 low

income residents. The per cent 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 cross

tabulations 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.

23 | Page

## Overview of Data and Methods

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 i n 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 re st 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 thir d 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 positiv e number occurs whe re 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 low

income) 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 on e 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 conclud ed 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 Index es 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. T his index simply omits the demographic index

24 | Page

## 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:

Th e 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.

T he EJ SCREEN mapping appl ication 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.

D etails on all the available indexes are also included in Appendix E

.

**EJ Indexes, Population Density, and Rural Areas**

It is very important to u nderstand 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 be tween 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 ha d a population between 500 and 2500 in 2008

- 2012, and only about 1% had a population over
4000

.



### Supplement ary 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)

25 | Page

## 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. D ifferences in environmental indicator values (and to some extent percentage demog raphics) 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 PM 2.5 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 demogr aphic 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 ar e 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 su mmarize 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 ha s been covered extensively elsewhere 17 , but a very brief explanation may be useful here.

First, a so

- called “equal weighting” does not exist, because it would j ust be an artifact of the units (scaling) and aggregation method one chose, which would carry imp licit 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 le vels, as with a neurological hazard index well below 1) and much higher risks (e.g., PM 2.5 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 wo uld be a mistake to assume the 80 th 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 rankin gs 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 inde x. 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.

26 | Page

## Overview of Data and Methods

with the highest PM 2.5 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 PM 2.5 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 singl e 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 indicato r 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 80 th percentile nationwide. For a place at the 80 th percentile nationwide, that means 20% of the US population has a higher value.

A percentile in EJSCREEN tells us roughly w hat 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 indicator s

are missing for only about one twentieth of 1% of the US population in the 2015 version of EJSC REEN. 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 percentile s, 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.

27 | Page

## Overview of Data and Methods

**Color**

**- coded High Percentile Bins** 

Locations at least at the 80 th percentile but less than the 90 th are shown in yellow on EJSCREEN maps, while those at th e 90 th percentile but less than 95 th percentile are orange on the maps, and those at the
95 th percentile or above are shown in red on maps and reports. These colors call attentio n 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 attenti on.

Percentiles at or above the 95 th percentile are shown in red on the EJSCREEN standard report. T his 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 th e 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). Fo r 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 PM 2.5 and lead paint indicators, which don’t vary as much across block groups. In general, though, indicator or index values above the 95 th percentile represent much higher dem ographic, environmental, or EJ I ndex values than those at lower percentiles.

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

based 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 expos ures 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 cal culated 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 geo graphic 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 perc entile 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., 80 th 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 th e 80 th 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, t his may be useful information because a n 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 compar ed 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

n ormally be considered fairly high nationwide , such as the 90 th percentile in the US, would not be considered very unusual within that state , so the state percentile would be lower, and might be only
78 th 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 Re gion 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 m aps 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 Rep orts** 

**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 su mmary 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 th e 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 rep resent the absolute total amount the buffer contributes to overall disparity in indicator scores nationwide or statewide. E ven 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, a s 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, rathe r 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

I t is important not to confuse two different distances:

1) the dist ance 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 sit es, or 500 meters for traffic).

The se 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 gr oups ) 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 (o r 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 t o 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 NP L 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 ma y 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 radi us) 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 wi th 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 t o 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 erro r 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.

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## 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 fact ors were identified in the review, which were not ultimately included in EJSCREEN due to various limitations . The se 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 EJSCREE N, 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.

F actors not currently used in EJSCREEN i nclude:

• 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 s urface 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/rwjf

datahub/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 change 24

• Radon gas exposure 25 or i ndoor air pollutants other than radon 26

• Criteria air pollutants other than PM 2.5 and ozone (Pb, CO, SO x and NO x ) 27 (Not included due to resource constraints, and more limited modeling/coverage, frequency of nonattainment, or health impact than for PM 2.5 and ozone).

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

• E xposures 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 fracking 29

• Mining (e.g., uranium mining, etc.) 30

• Coal ash ponds

• Combined animal feeding operations (CAFOs) 31

• Leaking underground storage tanks or other conta minated 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 p esticide exposures from residential and other non

- agricultural uses 33

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 vario us 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://wa ter.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

- techno logy/pesticides

- science

34 | Page

## Details on Environmental Indicators

• Noise pollution and odors not already accounted for in other data 34

• 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 services 35

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 acro ss 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 da ta. 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 t he following sections descr ibe each environmental indicator in more detail. The section s 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/ K ey M edium Indicator Details Source Data Year

Air NATA air toxics cancer risk L ifetime 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, μ g/m 3 EPA NATA, retrieved 20XX http://www.epa.gov/ttn/atw/nat amain/index.html 20xx

35 | Page

## 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 PM 2.5 levels in air, μ g/m 3 annual avg. ( 2011 ) EPA, OAR ( fusion of model and monitor data ) . For methods, see http://www.epa.gov/esd/land

sci/lcb/lcb_faqsd.html 2011

Air Ozone O zone summer seasonal avg. of daily max imum 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/land

sci/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/ind ex.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 , r etrieved 201 4 http://www2.census.gov/acs201 2_5yr/summaryfile/ 2008

- 2012

Waste / air/
water P roximity to RMP sites C ount of RMP (potential chemical accident management plan) facilities within 5 km (or nearest one beyond 5 km)
, each divided by distance in kilometers C alculated from EPA RMP database
, retrieved 11 /201 3 201 3

Waste / air/
water P roximity to TSDFs Count of TSDF s (hazardous waste management facilities ) within 5 km (or nearest beyond 5 km), each divided by dist ance in kilometers C alculated from EPA RCRAInfo database, retrieved 11/2013 http://www.epa.gov/enviro/fact s/rcrainfo/search.html 201 3

Waste / air/
water P roximity to NPL sites Count of proposed and listed NPL sites 36 within 5 km (or nearest one beyond 5 km)
, each divided by distance in kilometers C alculated from EPA CERCLIS database
, retrieved 11/12 /201 3 http://cumulis.epa.gov/supercpa d/cursites/srchsite s.cfm 201 3

Water Proximity to major direct
water dischargers C ount of NPDES major direct water discharger facilities within 5 km (or nearest one beyond 5 km) , each divided by distance in kilometers C alculated from EPA PCS/ICIS database , retrieved 12 /201 3 http://www.epa.gov/enviro/fact s/pcs

- icis/search.html 201 3

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 200 8- 2012 ACS 5

- year summary file data, which is based on 2010 Census boundaries.

36 | Page

## 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 EJSCRE EN:

• 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 Integra ted 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 poten tial for incurring adverse effects from the portion that stays in the body.

**More Information** More information is available at the a i r 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 ( 200 6), 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 poll utants, along with many other sources of air pollutants , such as motor vehicles.

Note that the publicly

- available NATA , PM 2.5 , 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 tr acts.

**Data Version** The 2015 version of EJSCREEN uses 20xx NATA data, which is based on NEI emissions estimates for 20xx ( U.S. EPA, 20 15b )

. 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).

D ata from recent year s 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 interven ing 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 de lay 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 b e 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
t he raw values of the indicators

.

38 | Page

## Details on Environmental Indicators

**Discussion** EPA’s NATA website has extensive documentation of all of the data and methods

use d in developing the NATA indicators, as well as discussions of uncertainty, caveats, and limitations in the NATA estimates. That information is not repeated here, b ut it is important that anyone using NATA data understand these issues, so anyone using EJSCREEN should consult the NATA documentation ( ww w.epa.gov/nata ).

Very briefly, t he air pollutants in NATA include likely or kn own 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 haza rd 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 ind ex es 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 inven tories.

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## Details on Environmental Indicators


# Particulate Matter (PM **2.5** 

# )

PM 2.5 is particulate matter that is 2.5 microns or less in diameter. Common sources of PM 2.5 emissions include power plants and industrial facilities. Secondary PM 2.5 can form from gases, such as oxides of nitrogen (NO x ) or sulfur dioxide (SO 2 ), reacting in the atmosphere. EPA set the first PM 2.5 National Ambient Air Quality Standards (NAAQS) in 1997
, and revised the standard s in 2006 and 2012

.

**Indicator** Annu al average PM 2.5 concentration in micrograms per cubic meter (μg/m 3 ).

**Rationale for I**  **nclusion** EPA’s work associated with the PM NAAQS has documented the health effects associated with exposure to PM 2.5 , including elevated risk of premature mortality from cardiovascular diseases or lung cancer, and increased health problems such as asthma attacks ( U.S. EPA, 2009b ).

PM 2.5 concentrations at different levels are found in all parts of the United States, so residents ar e exposed via inhalation to varying degrees. A 2012 EPA report found that the majority of the U.S. population live d 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).

PM 2.5 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 s tudies focused on disparities in exposure to PM 2.5 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 NO X , a precursor to PM 2.5 . (Clark, Millet
, & Marshall , 2014)

.

**Data Source** EJSCREEN’s PM 2.5 data are estimated from a combination of monitoring d ata and air quality modeling

. Ambient PM 2.5 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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## Details on Environmental Indicators

of three published journal articles ( Berrocal, Gelfand, & Holland, 2010a
, 2010b
, 2011 ). 37

PM 2.5 and ozone estimates were not available for Alaska or Hawaii for use in the 2015 version of EJSCREEN, due t o 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 PM 2.5 data that are based on 2011 monitoring and modeling estimates (U.S. EPA, 2015a)

.

D ata from several years ago may no longer be as representative of current conditions as they were at the time the data was collected. The PM 2.5 and ozone indicators in particular should be viewed with this in mind, because emissions related to PM 2.5 and ozone generally have decre ased 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 m uch 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 drama tically, 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 PM 2.5 and ozone nonattainment areas (areas not meeting national ambient air quality standards).

**Resolution** High

- resolution estimates of PM 2.5 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.

41 | Page 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/land

sci/lcb/pdf/DSMetadataAir_0612.pdf

.

## Details on Environmental Indicators

Some small areas have used the high

- resolution AERMOD model to estimate PM 2.5 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 PM 2.5 concentration in each cell.

In a 2010 study, satellite data have been used to estimate PM 2.5 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 f or NO x but not PM 2.5 .
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 PM 2.5 (daily average) and O 3 (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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## Details on Environmental Indicators

each tract, and then assigned the same tract value to every block group within the given tract

. D aily estimates from the downscaling method were averaged for the whole year in the case of PM 2.5 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 o r 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 PM 2.5 NAAQS have used estimates of PM 2.5 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 PM 2.5 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 interpolatio n 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 suitabl e PM 2.5 data are available at fewer than 900 monitors in the United States. While urban areas tend to have PM 2.5 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 estimat es 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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## Details on Environmental Indicators



# Ozone

Ozone (O 3 ) is not usually emitted directly into the air, but is created at ground level by a chemical reaction between oxides of nitrogen ( NO x ) 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 na tural 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 I**  **nclusion** 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 wa s 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 pre existing pulmonary disease and those with higher exposure levels such as outdoor workers ( U.S. EPA, 2006b ). A recent review of studies id entifying 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 live d 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 web site ( http://www.epa.gov/air/ozonepollution/ ).

**Relevant Studies** S ome examples of s tudies 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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## Details on Environmental Indicators

**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 PM 2.5 , 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.

PM 2.5 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 e stimates (U.S. EPA, 2015a)

. Tract estimates were assigned to block groups as described for the PM 2.5 indicator.

**Resolution** See “Resolution” section under the previous environmental indicator,
PM 2.5 .

**Discussion** See “Discussion” section under the previous environmental indicator,
PM 2.5 .

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## Details on Environmental Indicators



# 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

. E levated short

- term lead dust loadings have also been observed following demolition of old buildings ( U.S. EPA, 2011c ). Lead

- based paint w as 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 . L ead paint and contaminated dust and soil are co nsidered the leading cause of high lead levels in U.S. children ( Levin et al., 2008 ).

**Indicator** The percent age of occupied housing units built before 1960 was selected as an indicator of the likelihood of having significa nt lead

- based paint hazards in the home.

**Rationale for I**  **nclusion** 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, bl ood lead’s association with cardiovascular outcomes appears to be stronger among Mexican Americans and non

- Hispanic blacks than non

- Hispanic whites (U.S. EPA 2011 c). Also, some but not all studies suggest lead has a greater impact on IQ among those of low socioeconomic status ( U.S. EPA 2011 c).

Despite significant reductions in ambient levels of lead from the phase

out 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 Preventio n (ACCLPP) for defining elevated blood

- lead levels (BLLs) among children. The ACCLPP recommended that CDC use a childhood BLL reference value based on the 97.5 th 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 97.5 th 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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## Details on Environmental Indicators

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 throug h 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 μ g/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. observe d that 15.1% ha d blood lead levels above 5 μ g/dL. For chil dren 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 le ad website ( http://www2.epa.gov/lead ).

**Relevant Studies** Several examples of EJ studies of exposure from lead paint exist, includ ing 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 200 8

– 201 2

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## Details on Environmental Indicators

data in EJSCREEN indicate the average person in the US lives in a block group where about 3 0 % of the housing was built before 1960.

**Discussion** Lead paint was used e xtensively 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., 200 9 ; Jacobs et al., 2002 )

. In 2002, Jacobs et al. reported that approximately 40 million homes in the U nited S tates still had lead

- based paint hazards, based on a nationally represen tative survey conducted in
1998

– 2000. The likelihood of such hazards was found to have changed dramati cally 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 wit h 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 assoc iated 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 pri or 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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## Details on Environmental Indicators

Table 3 . Likelihood of Lead

- Based Paint Hazards by Housing Construction Date

38 The de minimis levels for paint deterioration are ≤ 20 ft 2 (exterior) or ≤ 2 ft 2 (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 H ousing 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 vehicle

miles travel ed 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** T he 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. A djustments are made so that the minimum distance used is reasonable when very small

.

**Rationale for I**  **nclusion** 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 ri sk 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 exacerba tion 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 un derlying 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
PM 2.5 , lead and other metals
, and mobile source air toxics such as benzene, nitrogen oxides (NO x ), hydrocarbons and carbon monoxide (CO)

— are believed to contribute to these health effects. Vehicles also emit precursors that add to ambient ozone and PM 2.5 . Additionally, EPA’s 2005 NATA estimate d that mobile emissions
account ed 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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## Details on Environmental Indicators

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 t raffic 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 h ome, 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 Tr ansportation ( 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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## Details on Environmental Indicators

**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 16,0 00 AADT at
4 00 meters distance w ould result in a score of 16,0 00/ 4 00= 40 , which is close to the median person’s block group traffic proxim ity 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 t raffic volumes vary widely across roads and communities. The most traveled highway section in the United St ates, the I

- 405 in the Los Angeles

Long 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 o f 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 wei ght, 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

– 30 0 meters , or perhaps up to 50 0 meters , from traffic sh ould 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. C ollector 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 Inf ormation, US DOT, 2008 Highway performance monitoring system (HPMS) July 27, 2010.

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## Details on Environmental Indicators

associated with health impacts in near

- roadway epidemiology. Epidemiologic studies of the impacts of proximity to traffic often utiliz e distances of 50

– 1,5 00 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 grea ter than 200 meters as the reference group and found adjusted odds ratios of 1.08 for residences within 100

– 200 m eters , 1.71 for 50

– 100 m eters and 1.55 within 50 meters of a major road. Only 15% of participants lived within
200 m eters of a major road, and only 3% within 50 m eters 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 concer n, although impacts may be largely limited to just 100 meters from roads for ultrafine particles and PM 2.5 mass from mobile sources alone.

A critical review of literature on traffic

- related air pollution in 20 1 0 “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 PM 2.5 levels in Southern California found that traffic within 300 meters of a monitor was the most informative predictor of monitored PM 2.5 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 firs t 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 particula r 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 l evels (generally 2

– 5 times higher) on the road than near the road, for PM 2.5 mass, CO, NO, NO x , 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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## Details on Environmental Indicators

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 NO x , PM 2.5 mass (μg/m 3 ), black carbon or ultrafine PM (as particle n umber concentration) nearby (50

– 50 0
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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## Details on Environmental Indicators



# Proximity to Major Direct **Water Dischargers** 

The Clean Water Act regulates f acilities 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 ( NPDE S ) permits issued by EPA or states authorized to administer the NPDES program

. There are tens of thousands of dischargers with such permits, many releas ing only limited quantities of pollutants

. Several thousand facilities, however, are “major direct disch argers” as defined by law, including industrial direct dischargers ( facilities that discharge pollutants directly into water bodies) and P ublicly O wned
T reatment W orks (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 I**  **nclusion** 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 industr y categor y can be found at http://water.epa.gov/scitech/w astetech/guide/industry.cfm

.

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## Details on Environmental Indicators

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.g ov/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 & T hompson, 2012 ) that tallied the number of facilities of different types within 10 km 2 squares and compared these counts to the demographics of the squares.

**Data Source** Latitudes/Longitudes for m ajor direct discharge r s were taken from EPA’s PCS/ICIS database

.

**Data Version** The 2015 version of EJSCREEN uses l ocational 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 c ases 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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## Details on Environmental Indicators

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 harm onic 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 bl ock 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 affe cted 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

av erage 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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## Details on Environmental Indicators



# P **roximity to**  **NPL Sites** 

Congress enacted the Comprehensive Envir onmental 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 financi al 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 ways 44 :

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. T he 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 anticipa tes 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** T he 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 I**  **nclusion** 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, 199 0 ; 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 km 2 squares in the stu dy 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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## Details on Environmental Indicators

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 inhala tion 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 e nter 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**  S ome examples of s tudies focused on disparities in proximity to NPL sites include Brender et al. ( 2011 ) and those reviewed in Liu ( 2001 )

.

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## Details on Environmental Indicators

**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, th e available data may not provide an accurate representation of proximity to relevant portio ns 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 specifi ed 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 1,3 50 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 o f 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 gi ve 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, wit h 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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## Details on Environmental Indicators



# P **roximity 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 hun dred 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 I**  **nclusion** The substances at TSDF facilities may reach humans in a number of ways. Volatile substances may enter the atmosphere and reach residents vi a 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 inclu ded measures of proximity to all facilities regulated by EPA, which includes TSDFs.

**More Information** More information is available at the hazardous waste web page ( 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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## Details on Environmental Indicators

**Relevant Studies** S ome examples of s tudies 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 a ctive solid waste landfills sites in the tally of pollution points in each of the 10 km 2 squares in the study area, for comparison with demographic variables.

Earlier studies related the presence of TSDFs to population characteristics for different definit ions of the host areas

— Census

- designated areas ( General Accounting Office, 1983 ) , postal ZIP codes ( United Church of Christ, 1987 )
, and Census tr acts ( 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 l ocational 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 distanc eweighted count of TSDF s 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 blo ck ’ 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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## Details on Environmental Indicators

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, s ee Appendix C.

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## Details on Environmental Indicators



# 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 chemica l 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 Regul ated 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 acci dental 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 po llutants (e.g., cancer risk and hazard ind ex es) , 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 c ount 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 I**  **nclusion** 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 rem ove 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 acciden tal 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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## Details on Environmental Indicators

residents, as well as w orkers 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 th e RMP program webpage ( http://www2.epa.gov/rmp ) and the RMP Info database stored in Envirofacts ( http://www.epa.gov/enviro/facts/rcrainfo/search.ht ml )

.

**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 frequ ency and consequences of accidental releases of acutely toxic chemicals or events resulting in fires or explosions ( Binder, 1989, and many other studies ) . After th e 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 fac ilities 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 pr ogram
— 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 d emographic characteristics examined included total population, race, education and income. The study found an association between the presence of l arger and more chemical

- intensive facilities and counties with larger African

American populations
, and in co unties 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 data base

.

**Data Version** The 2015 version of EJSCREEN uses l ocational information retrieved from the RMP data base in May 2012. A total of 12,759 RMP facilities were

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## Details on Environmental Indicators

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 e ven 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 acc ounted 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 EJSCR EEN** 

**Review of Available**  **Data and Other Tool**  **s** Preliminary planning for EJSCREEN began in late 2010 . T he 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 justice 49

• 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 s tate EJ screening tools such as CalEnviroScreen

. 

### Selecting **a n Approach**  **t o 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 f easible 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 pri or 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 78 | Page

## Appendix B

[intentionally blank]

79 | Page

## Appendix B



## APPENDIX B. **TECHNICAL DETAILS ON PERCENTILES, ROUNDIN**  **G, BUFFERING, AND DEMOG**  **RAPHIC DAT A** 

**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 le ss 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 ind icator 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

m aximum 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 n obody 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 we re 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 a s 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 geodata base 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 b e at least 80. Without using the "floor" of the e xact 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 w hether 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 mino rity (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.r

project.org/web/packages/Hmisc/index.html )

. The scripting language R is documented here: http: //cran.r

project.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 t ext 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 EJSCR EEN 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 high est 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 th e 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 communicati ng 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 indicato rs 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 mill ion 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 sig nificant fig ure s). Proximity scores of at least 0.185 but less than 0.195 would be displayed as 0.19 (which shows 2 sig nificant fig ure s). 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 sig nificant fig ure 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 sig nificant fig ure s, a printout may only display it as 0.1 instead of 0.10, but it was still rounded using the 2 sig nificant fig ure s rule.

- Demogr aphic 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 est imates, 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, suc h 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 enviro nmental 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 p roximity 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 bu ffer.

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 block s 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 relat ive 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 thi s 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 o f 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 – i t 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 a**  **nd Geographic Coverage**  In the first decade of this century, the Census Bureau made a fundamental shift in how detailed demographic data are collected. Ra ther 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 20 0,000 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 ce nsus snapshot.

Extensive documentation of the ACS is available . For a general overview, s ee 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 on c e per decade data with the other data items from ACS were worth the problems and ambiguities entailed in such a hybrid approach.

A ll of E JSCREEN’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

- 201 2

. 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 usi ng the 1

- year ACS data.

Each of the nation’s c ounties (or county equivalent s, 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 60 0 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

.

T racts 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 CA 53

. Also see details in the discussion of how NATA data was converted to 2010 boundaries, in Section 3.

There are 11,0 78,297 bloc ks 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.
B lock 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, t he 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.g ov/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 an d 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 geo graphies (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 P uerto R ico )

• ACS tallies, wi th Puerto Rico 55 : 220,333 with PR , 217,739 without P uerto R ico

.

• Census 2010 tallies, by State /PR 56 : 220,334 with P uerto R ico , and 217,740 without P uerto R ico

.



### Table 4 . Tallies of 2008

- 12 ACS Block Groups Used in 2015 Version of E JSCREEN



### Demographic **Variables and Formula s** 
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 n umber, 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 200 8

- 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 Fil e 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 site 57

, 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://factfi nder2.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 th e 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|040000 0US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2 8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38| 0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US4 5|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|040000 0US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2 8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38| 0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US4 5|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|0 40000 0US06|0400000US08|0400000US09|04000 00US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US17|0400000U S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2 8|0400000US29|0400000US30|0400000US31|0400000US 32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38| 0400000US39|0400000US40|0400000US41|0400000US42|0400000US44|0400000US45|0400000US46|0400000US47|0400000US48|0400000US49|04 00000US50|0400000US51|0400000US53|0400000US54|0400000US55|0 400000US56|0400000US72

B16002 http://factfinder2.c ensus.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|0400000U S20|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|0 40000 0 US06|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|040 0000US53|0400000US54|0400000US55|0400000US56|0400000US72

B25034 http://factfinder2.census.gov/bkmk/table/1.0/en/ACS/12_5YR/B25034/0100000US|0400000US01|0400000US02|0400000US04|0400000US05|0 40000 0US06|0400000US08|0400000US09|0400000US10|0400000US11|0400000US12|0400000US13|0400000US15|0400000US16|0400000US1 7|0400000U S18|0400000US19|0400000US20|0400000US21|0400000US22|0400000US23|0400000US24|0400000US25|0400000US26|0400000US27|0400000US2 8|0400000US29|0400000US30|0400000US31|0400000US32|0400000US33|0400000US34|0400000US35|0400000US36|0400000US37|0400000US38|
04 00000US39|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 000 2 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 1 4 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 1 5 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 year s 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 B0 1001 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

a ge10to14m = 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 Blac k 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 B0300 2 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 B03 002 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 = B0300 2.001 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 P rofessional 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 alternativ e 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 = B1500 2.006 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

B160 02 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 0 044 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 I sland l anguages: 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.Numb er Line.Number Field C17002 0049 NA

C1 7002 0049 NA Universe: Population for whom poverty status is determined C17002 0049 1 Total: C17002 0049 2 Under .50 C1 7002 0049 3 .50 to .99 C17002 0049 4 1.00 to 1.24 C17002 0049 5 1.25 to 1.49 C17 002 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 O CCUPIED 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 19 49 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 zer o, 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 t he 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)

95 | Page

## 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 + l ingisoother 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)

96 | Page

## 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 clearl y labels every data item as an “estimate
, ” and accompanies each with an estimate of its margin of error. A nyone 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 environmen tal 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
pe rcent low

- income, for ex ample, 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 sco res 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 o n screening, where appropriate. S ection 1 of this document d iscusses 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 environme ntal indicators

. Technical documentation on methods and challenges in estimating uncertainty for calculated demographic indicators using the ACS is available from the Census Bureau 58 (with challenges described in related technical documents 59 ). ESRI also pro vides 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, howev er, 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

58 http://www.census.gov/acs/www/Do wnloads/data_documentation/Statistical_Testing/2011StatisticalTesting3and5year .pdf

59 http://www.census.gov/acs/www/Downloads/data_documen tation/Accuracy/MultiyearACSAccuracyofData2011.pdf

60 http://www.esri.com/software/american

- community

- survey/understanding

- margin

- error 97 | Page

## 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. Fut ure 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 aspec ts of susceptib ility 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 ).

• M any studies in various fields use 2x poverty, and many others use 1x poverty ( e.g., see Su et al.,
2009 ); t he 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 amou nt 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, t he 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 low

income person affects the suscep tibility 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 level 62 , so it is impossible to know what percentage of a block group is low

- income minorit ies vs. low

- incom e

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. T herefore, t his 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 low

income as defined in EJSCREEN. 98 | Page

## 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 simul t aneously low income and minority . The valu e 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

.

T he 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.

99 | Page

## Appendix C



## APPENDIX C. **TECHNICAL DETA ILS ON PROXIMITY IND**  **ICATORS**

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 PM 2.5 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 h ave 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 Priorit ies List (NP L) sites (a key subset of “ Superfund ,” sites )

.

• Hazardous waste Treatment, Storage or Disposal Facilities (TSDF s ), subject to regulations under the Resource Conservation and Recovery Act (RCRA)

.

• Risk Management Plan (RMP) facilities, which are facilities th at 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 t he 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 eff icient, 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 2 17 ,000 block groups for th e 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 ma y 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, base d 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 ag gregating the block scores, the measure does not increase or decrease for block groups w ith 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 conce ntration 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 d ij is the distance, in kilometers, from block j’s centroid to the given location of facility i

pop jk is the estimated population of block j within block group k

pop k is the total estimated population of block group k f(d ij ) is a function representing the pro ximity of facilty i to block j, a declining function of the distance,
d ij BlockScore jk is the aggrega tion of the proximity influences of all facilities affecting block jk BlockGroupScore k is the population

- weighted aggregation of the block group’s componen t blocks

We have chosen to define the proximity function as

f(d ij ) = 1 / d ij
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
concentrat ions 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 a nd meteorology. Some substances react readily with other substances in the atmosphere, or precipitate out readily. It is not uncommon for concentrations to rise for

101 | Page

## 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 exp osure 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 pres ence 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 t hat 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 aroun d 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 develo pers, 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 purpos es 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 U nited S tates 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 population

weighted 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

102 | Page

## Appendix C

our approach, we find the distance from the block centroids to the nearest part of each surrounding highway segment. The nearest point, d ij , could be an end of the highway segment or some point in between the ends.

We also multiplied each d ij by the annual average daily traffic estimate tha t 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

**– A**  **d ditional Details**  We address two modifications to the general method described above. The first deals with instances where a facility or highway segment lo cation is very close to the centroid of the block. The second is an accommodation to the computational intensity of the general method.

Extremely Small d ij Values
Our intention is to represent the proximity of facilities or highway segments to the populati on 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 blo ck, 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 b e 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 d ij 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 the ir 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 a re 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 va lue the Block Area Equivalent Radius.

Our investigations indicate that for any d ij 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 blo ck. We
call this the d ij corrected

. 103 | Page

## Appendix C

Our computational scheme determines the d ij values as de s cribed above, tests for the comparison with 0.9 * Block Area Equivalent Radius, and substitutes d ij 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 Fac ility 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 hundre ds 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 facilit y (RMP, TSDF, or NPDES) within the 5 kilometers limit , and almost 10% have one or more NPL s ites 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 dist ance to the facilit y 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 approa ch, but at far less cost than computing the full matrix of million s 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 t he 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 d ij values were compared to the 0.9 * Block Area Equivalent Radius and corrected values were used when necessary, before computing 1 / d ij . The 1 / d ij values were summed for each block to compute the

BlockScore jk . These were then rolled up to the block grou p level, applying the population weighting described above, for the final BlockGroupScore k

.
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 me asures 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
N PL 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 ac commodation 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
g roups, 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

105 | Page

## Appendix C

Note : Th e 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/DiskLinePicki ng.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, t he 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= th e facility’s d istance from the center a s a f raction o f the ra dius, a nd the i ntegral o ver a represents di stances o f residences fr om th e center

. We c an s olve t his equation usin g http://WolframAlpha.com
, fo r b =0, 0 .5, o r 1 , r epresenting point s a t t he center, h alfway t o t he edge, a nd a t t he edge of t he circle. F or example, we can use this e quation for b= 0.5 t o find t hat t he a verage p erson, if r andomly lo cated in a cir cle of r adius R, is a d istance o f about 0. 8 R from a f acility t hat is halfway between t he center a nd ed ge of the circle

. For t he distance be tween the a verage pe rson and a r andomly placed facility in t he ci rcle
, we use b =sqrt(0.5) in stead
, a nd t he f ollowing would b e used as t he i nput t o W olframAlpha: Integrate[(1/Pi) Sqrt[a + ( Sqrt( 0.5))^ 2 - 2 * ( Sqrt(0.5)) * Sqrt[a] co s(t)], {a
, 0, 1}, {t, 0, p i}] 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 +

106 | Page

## Appendix D



## APPENDIX D. **SUMMARY STATISTICS F OR INDICATORS** 

This appendix provides basic information and statistics on the environmental and demographic data used in
EJSCREEN.

Table 6 s hows 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 99 th percentile value is only approximately xx . Th e 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, t he P M 2.5 level in the average person’s block group (based on tract estimates)

was 9.7 μg/m 3

. Roughly 7 -8 % of U .S. residents had block group (based on tract) estimates above 12 μg/m 3 in these 2011 estimates . Note that 15 μ g/m 3 wa s the health

- based annual ambient standard as of mid

- 2012 , with a revised standard of 12 μ g/m 3 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 nonatta inment 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 ozon e ambient standard.

The NATA cance r risk m ean was very roughl y xx cases per lifetime per million people, or a lifetime individual cancer risk of x x10 -5 , which is orders of magnitude lower than typical premature mortality risk estimates associated with recent ambient levels of PM 2.5 .
The facility proximity indicators generally have mean scores of 0.0 5 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 kilomete rs distance, for example , and the median is roughly 0.1 4 (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% o f 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 percent s low income and minority were 34% and 36% respectively, and the medians were s omewhat lower. The 80 th 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. 107 | Page

## 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.

Note s : Population percentiles (and means) are shown, not block group percentiles (or means) , so 80%ile means 8 0% 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. S ummary statistics for a given environmental factor exclude block groups where that environment al indicator was not available (missing)

.

108 | Page

## 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 ind ex 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% 1 4 % 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. ind ex 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 i n EJSCREEN reports.

109 | Page

## 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 NATA

derived 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

S ource: 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 % pre

1960 NPL 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

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## Appendix D

Table 9 . Spearman Correlation Coefficients for Demographic Indicators

% minority % low

income % 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 s how s 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, b ut the population distribution is very similar).

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## Appendix D

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

[INTENTIONALLY BLANK]

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## Appendix E



## APPENDIX E. **F ORMULAS FOR DEMOGRAP**  **HICS 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

Th is is the average of percent minority and percen t low income in the block group. Percent low income is defined in Appendix B , and is essentially all residents where household income is b elow t wice 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**

Th is 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

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## Appendix E

**EJ I**  **ndex**

The EJ I ndex 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 comp osition 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 grou p

. 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 B**  **ased 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)

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## Appendix E

**Supplementary EJ Index 1**  **B ased 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 demograph ic index.



### Supplementary **EJ Index 2 B**  **ased on Demographic Index** This EJ index is a combination of a block group environmental factor and the demographic index

.



### Supplementary **EJ Index 2 B**  **ased 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)

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## Appendix F



## APPENDIX F. **QUALITY CONTROL / QU ALITY 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.

Th e EJSCREEN Technical Documentation (this document) has extensive details on the precise sources and exact method s used, to ensure transparency. The transparency of the data inputs is also ensured thro ugh 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 a ccess 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 indicator s 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 do cumented 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 tra ffic 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 s imply 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 separate ly 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 ind icators 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 c onducted 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 doc umentation , 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
qu estions 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 t heir 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 correctin g 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 c reating 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 ration ale in the documentation, and such comments represented about one fourth of all the comments received. They ask ed 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 th is 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 w hether 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 al ready serve d to strength en 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 EJSCRE EN.

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## Appendix H



## APPENDIX H. **INITIAL FILTER APPRO ACH 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 fede ral, 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 percen tile 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

[page intentionally blank]

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