EJScreen

Environmental Justice Mapping and Screening Tool
EJScreen Technical Documentation

October 2022

U.S. Environmental Protection Agency

Office of Environmental Justice and External Civil Rights

Washington, D.C. 20460

Suggested citation:

U.S. Environmental Protection Agency (EPA), 2022. EJScreen Technical Documentation.

For more information:
www.epa.gov/EJScreen

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Contents

EJScreen	

1

1	INTRODUCTION	

Geographic Framework	

2	OVERVIEW OF DATA IN EJSCREEN	

Socioeconomic Indicators in EJScreen	

Summary Overview of Socioeconomic Indicators Featured in EJScreen	

Detailed Descriptions of Socioeconomic Indicators	

Environmental Indicators in EJScreen	

Summary Overview of Environmental Indicators Featured in EJScreen	

Detailed Descriptions of Environmental Indicators	

Considerations for Selection of Environmental Indicators in EJScreen

3	OVERVIEW OF INDEXES IN EJSCREEN	

DEMOGRAPHIC INDEXES INCLUDED IN EJSCREEN	

Demographic Index	

Supplemental Demographic Index	

Indexes Included in EJScreen	

EJ Indexes	

Supplemental Indexes	

4	TECHNICAL DETAILS ON PERCENTILES	

What a Percentile Means	

Color-coded High Percentile Bins	

How Percentiles are Calculated	

5	THRESHOLDS	

Indexes Threshold Map Widget	

Initial Filter Approach for Screening	

6	BUFFER REPORTS	

7	TECHNICAL DETAILS ON PROXIMITY INDICATORS	

Calculating Proximity to Facilities	

Calculating Proximity to Traffic	

Calculating Proximity to Toxic Weighted Wastewater Dischargers	

Calculating Proximity-Additional Details	

Extremely Small d,j Values	

Accommodating to Computational Intensity-Combine a Distance Limit with a Nearest Facility Approach ....
Data and Computational Scheme..

Caveats and Observations	

8	DETAILS ON U.S. TERRITORIES

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Data Sources	46

Processing Steps	46

The EJ Index Availability for the Territories	46

Notes about Puerto Rico	47

9 OTHER DATA ELEMENT DESCRIPTIONS	48

Health Disparities Data	48

Wildfire and Flood Risk Data from First Street	48

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1 Introduction

EJScreen is EPA's environmental justice (EJ) screening and mapping tool that utilizes standard and
nationally-consistent data to highlight places that may have higher environmental burdens and
vulnerable populations. The tool offers EJ indexes by combining environmental and demographic
indicators in basic geographic units of Census block groups. The tool also provides a variety of powerful
data and mapping capabilities that enable users to access environmental and demographic information
across the entire country, at high geographic resolution, displayed in color-coded maps and standard
data reports.

This technical document provides details on the data and methods used to select the indicators and
create the indexes in EJScreen. EPA annually updates EJScreen with the newest datasets available and
improvements to the interface. For more information on the updates to EJScreen over time, visit the
EJScreen Change Log.

Geographic Framework

•	The geographic framework for EJScreen was built from 2020 Census TIGER/Line data for all 50
states, the District of Columbia, and Puerto Rico.

•	The socioeconomic data source is U.S. Census Bureau's American Community Survey (ACS)
2016-2020 5-Year Estimates (ACS 2020).

•	There are a total 242,335 block groups.

•	The application defines the spatial reference as "WGS 1984 Web Mercator (Auxiliary Sphere)."

•	The U.S. Territories of American Samoa, Commonwealth of the Northern Mariana Islands
(CNMI), Guam, Puerto Rico, and the U.S. Virgin Islands are included in the application. However,
Puerto Rico is included with all 50 states and the District of Columbia. The term "territories" in
this document refers to the four territories excluding Puerto Rico.

•	Puerto Rico uses ACS 2020. American Samoa, CNMI, and Guam use 2013 Census Place
boundaries. The U.S. Virgin Islands use 2013 Census Estate boundaries. The territories data for
American Samoa, CNMI, Guam, and the U.S. Virgin Islands provide additional 605 records.

•	The Census Place boundaries and Census Estate boundaries for the territories are appended to
the block groups for all 50 states and the District of Columbia and Puerto Rico (see the Details
on U.S. Territories section for details).

•	Census block centroids with population from 2020 Decennial Census P.L. 94-171 Redistricting
data are used to provide population-weights for the application.

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2 Overview of Data In EJScreen

This section describes the environmental and socioeconomic indicators featured in the application,
describes why they are included, and the data used to derive them.

Socloe	:ators in EJScreen

In the current release:

•	All demographic indicators are from Census Bureau's ACS 2016-2020 5-year Summary.

•	All territories' socioeconomic data are from the Census 2010 Demographic Profile Summary File
for each territory, using the Place summary level for American Samoa, CNMI, and Guam, and
Estates summary level for U.S. Virgin Islands. The Demographic Profiles were published in 2014
by the U.S. Census Bureau.

Summary Overview of Socloecc	tors Featured in EJScreen

EJScreen uses socioeconomics indicators as very general indicators of a community's potential
susceptibility to the types of environmental factors included in EJScreen. There are seven socioeconomic
indicators featured in EJScreen. These indicators form the basis for both the demographic index and the
supplemental demographic index:

1.	People of color:

o The 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, not multiracial.

2.	Low-income:

o The percent of a block group's population in households where the household income is
less than or equal to twice the federal "poverty level."

3.	Unemployment rate:

o The percent of a block group's population that did not have a job at all during the
reporting period, made at least one specific active effort to find a job during the prior
four weeks, and were available for work (unless temporarily ill).

4.	Limited English speaking household:

o A "limited English speaking household" is one in which no member 14 years old and
over (1) speaks only English or (2) speaks a non-English language and speaks English
"very well." In other words, all members 14 years old and over have at least some
difficulty with English.

5.	Less than high school education:

o Percent of people age 25 or older in a block group whose education is short of a high
school diploma.

6.	Under age 5:

o Percent of people in a block group under the age of 5.

7.	Over age 64:

o Percent of people in a block group over the age of 64.

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scr	ioeco 1„ " . 1 1

People of Color

People of color are individuals 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, not multiracial.

The U.S. Census Bureau classifies races based on the Office of Management and Budget (OMB)
standards on race and ethnicity and is determined based on individuals' self-identification with one or
more groups. These groups are:

•	White

•	Black or African American

•	American Indian or Alaska Native

•	Asian

•	Native Hawaiian or Other Pacific Islander

Furthermore, ethnicity is defined as to whether an individual is of Hispanic origin or not. Individuals of
any race can report as Hispanic.

How does EJScreen determine percent people of color?

• The ACS information on people of color is captured in the table Hispanic or Latino Origin by Race
(ACS Table ID: B03002). The ACS divides race and Hispanic status into 21 categories, four of which
are shown in Table 1.

Table 1: ACS Hispanic or Latino Origin by Race Table Element Reference

Table Element

Hispanic Status/Race Population

B03002.001

Total Population: All races/ethnicities

B03002.002

Total Population: Non-Hispanic

B03002.003

Total Population: Non-Hispanic, White Alone

B03002.012

Total Population: Hispanic



Note: The data can be downloaded from the US Census Bureau's FTP Server.

To calculate percent people of color, two elements from Table 1 are used in the following equation:

603002.001 — 603002.003

% People of Color =

603002.001

In EJScreen, the raw values for the people of color indicator range from 0% to 100%. In order to
make the indicator more comparable, statistical percentiles are used. The percentiles for the people
of color indicator range from 0 to 100% with a median at the 50th percentile, which corresponds to
the raw value of 31%. If a raw value is higher than 31%, it would be placed above the 50th
percentile; if the value is lower, it would be placed below the 50th percentile. For example, if the

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people of color indicator raw value for a block group is 74%, which is higher than 31%, that block
group will be placed in the 80th percentile (which is higher than the 50th percentile).

Low-Income

Low-income is defined as a household whose income is less than or equal to twice the poverty level. For
example, a household of four with a reported $40,000 total annual income is lower than twice the
poverty threshold of $52,992 ($26,496 is the poverty threshold defined by the U.S. Census Bureau for
2020). This household will fall into the category of "low income" in EJScreen. The poverty level is
updated by the U.S. Census Bureau annually and varies by family size and composition.

The poverty level is a national number and the same across all geographic regions. To accommodate
differences in the varying costs of living across the United States and other factors, EJScreen uses twice
the poverty level to capture low income households especially in high cost areas. The rationale for using
twice the poverty threshold rather than just the poverty threshold includes the following considerations:

•	The effects of income on baseline health and probably on other aspects of susceptibility are not
limited to those below the poverty thresholds.

•	Many studies in various fields use 2x poverty.

•	When using twice the poverty threshold, the number or percent low income happens to roughly
equal the number or percent people of color in the United States.

How does EJScreen determine percent low income?

•	The ACS low income information is captured in the table Ratio of Income to Poverty Level in the Past
12 Months (ACS Table ID: C17002). The ACS divides the ratio of income to poverty into seven
categories, as shown in Table 2.

Table 2. ACS Income/Poverty Level Table Element Reference

Table Element

Income/Poverty Level

C17002.001

Total Population Whose Poverty Status is Known

C17002.002

People with Ratio of Income to Poverty under .50

C17002.003

People with Ratio of Income to Poverty from .50 to .99

C17002.004

People with Ratio of Income to Poverty from 1.00 to 1.24

C17002.005

People with Ratio of Income to Poverty from 1.25 to 1.49

C17002.006

People with Ratio of Income to Poverty from 1.50 to 1.84

C17002.007

People with Ratio of Income to Poverty from 1.85 to 1.99

C17002.008

People with Ratio of Income to Poverty from 2.00 and over

Note: The data can be downloaded from the US Census Bureau's FTP Server.

• To calculate percent low income, two elements from Table 2 are used in the following equation:

C17002.001 - C17002.008

% Low Income =

C17002.001

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• In EJScreen, the raw values for the low income indicator range from 0% to 100%. In order to
make the indicator more comparable, statistical percentiles are used. The percentiles for the
low income indicator range from 0 to 100% with a median at the 50th percentile, which
corresponds to the raw value of 27%. If a raw value is higher than 27%, it would be placed above
the 50th percentile; if the value is lower, it would be placed below the 50th percentile. For
example, if the low income indicator raw value for a block group is 49%, which is higher than
27%, that block group will be placed in the 80th percentile (which is higher than the 50th
percentile).

Unemployment

Unemployment refers all those who did not have a job at all during the reporting period, made at least
one specific active effort to find a job during the prior four weeks, and were available for work (unless
temporarily ill). The U.S. Census Bureau determines unemployment through a series of questions in the
ACS that establish several labor force elements.

The first parameter is whether or not an individual is age 16 or older. This is necessary because, under
federal law, individuals under the age of 16 cannot fully participate in the labor force.

The second parameter is labor force participation. Individuals over the age of 16 who are not classified
as members of the labor force include students, stay-at-home parents, retired workers, and others who
are not actively searching for employment. An individual can only be considered unemployed if they are
part of the labor force.

The last parameter is employment status. An individual is considered unemployed if they were neither
"at work" nor "with a job but not at work" during the reference week (the week the survey was
conducted), were actively looking for work during the last four weeks, and were available to accept a
job. This also includes individuals who did not work at all during the reference week, were waiting to be
called back to a job from which they had been laid off, and were available for work except for temporary
illness.

How does EJScreen determine unemployment?

• The ACS unemployment information is captured in the table Employment Status for the Population
16 Years and Over (Table ID: B23025). The ACS divides the population age 16 or older into seven
categories, which are shown in Table 3. EJScreen uses the Civilian Labor Force numbers.

Table 3: ACS Employment Status Table Element Reference

Table Element

Employment Status

B23025.001

Total Population Age 16 Years and Over

B23025.002

Total Labor Force

B23025.003

Total Civilian Labor Force

B23025.004

Civilian Labor Force: Employed

B23025.005

Civilian Labor Force: Unemployed

B23025.006

In Armed Forces

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B23025.007

Total Not In Labor Force

Note: These data can be downloaded from the US Census Bureau's FTP Server.

•	To calculate the percentage of unemployment in the civilian labor force, two elements from Table 3
are used in the following equation:

623025.005

% Unemployment =			

F 7	623025.003

•	In EJScreen, the raw values for the unemployment indicator range from 0% to 100%. In order to
make the indicator more comparable, statistical percentiles are used. The percentiles for the
unemployment indicator range from 0 to 100% with a median at the 50th percentile, which
corresponds to the raw value of 4.2%. If a raw value is higher than 4.2%, it would be placed above
the 50th percentile; if the value is lower, it would be placed below the 50th percentile. For example,
if the unemployment indicator raw value for a block group is 10%, which is higher than 4.2%, that
block group will be placed in the 85th percentile (which is higher than the 50th percentile).

Limited English Speaking Household

A limited English speaking household is defined as a household in which no one age 14 and over speaks
only English, or speaks a non-English language and speaks English "very well" as reported in the U.S.
Census Bureau's ACS. The percent of limited English speaking households is used instead of the actual
number of limited English speaking households because percentages account for possible differences
among population sizes and make block groups comparable.

How does EJScreen determine Limited English Speaking Households?

•	The ACS limited English speaking household information is captured in the table Household
Language by Household Limited English Speaking Status (ACS Table ID: C16002). The ACS divides
limited English speaking households into four language groups as shown in Table 4.

Table 4. ACS Limited English Speaking Table Element Reference

Table Element

Language & Ability Status

C16002.001

Total Households

C16002.004

Limited English Speaking - Spanish

C16002.007

Limited English Speaking - Other Indo-European Languages

C16002.010

Limited English Speaking - Asian/Pacific Island Languages

C16002.013

Limited English Speaking - Other Languages

Note: The data can be downloaded from the US Census Bureau's FTP Server.

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• To calculate percent limited English speaking, the elements from Table 4 are used in the following
equation:

C16002.004 + C16002.007 + C16002.010 + C16002.013

% Limited Enalish Speakina = 	

w F w	C16002.001

•	In EJScreen, the raw values for the limited English speaking household indicator range from 0% to
100%. In order to make the indicator more comparable, statistical percentiles are used. The
percentiles for the limited English speaking household indicator range from 0 to 100% with a median
at the 50th percentile, which corresponds to the raw value of 1.1%. If a raw value is higher than
1.1%, it would be placed above the 50th percentile; if the value is lower, it would be placed below
the 50th percentile. For example, if the limited English speaking household indicator raw value for a
block group is 8%, which is higher than 1.1%, that block group will be placed in the 80th percentile
(which is higher than the 50th percentile).

Less than High School Education

Percent of people age 25 or older in a block group whose education is short of a high school diploma.

How does EJScreen determine less than high school education?

•	The ACS education information is captured in the table Sex by Educational Attainment for the
Population 25 Years and Over (ACS Table ID: B15002). The ACS divides the education information by
grade as shown in Table 5.

Table 5. ACS Educational Attainment Table Element Reference

Table Element

Sex

Educational Attainment

B15002.001



Total Population Age > 25

B15002.003



No Schooling Completed

B15002.004



Nursery - 4th Grade

B15002.005



5th & 6th Grade

B15002.006

Male

7th & 8th Grade

B15002.007



9th Grade

B15002.008



10th Grade

B15002.009



11th Grade

B15002.010



12th Grade, No Diploma

B15002.020



No Schooling Completed

B15002.021



Nursery - 4th Grade

B15002.022



5th & 6th Grade

B15002.023



7th & 8th Grade

B15002.024

Female

9th Grade

B15002.025



10th Grade

B15002.026



11th Grade

B15002.027



12th Grade, No Diploma

Note: These data can be downloaded from the US Census Bureau's FTP Server.

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•	To calculate the percentage of the 25 and older population with less than high school education, the
elements from Table 5 are used in the following equation:

_		 . ....	S15002.003+ .004+.005+.006+.007+.008+.009+.010+.020+.021+.022+.023+.024+.025+.026

% Less Than High School Education =	

S15002.001

•	In EJScreen, the raw values for the less than high school education indicator range from 0% to 100%.
In order to make the indicator more comparable, statistical percentiles are used. The percentiles for
the less than high school education indicator range from 0 to 100% with a median at the 50th
percentile, which corresponds to the raw value of 9%. If a raw value is higher than 9%, it would be
placed above the 50th percentile; if the value is lower, it would be placed below the 50th percentile.
For example, if the less than high school education indicator raw value for a block group is 20%,
which is higher than 9%, that block group will be placed in the 80th percentile (which is higher than
the 50th percentile).

Individuals under Age 5

Percent of people in a block group under the age of 5.

How does EJScreen determine individuals under age 5?

•	The ACS information on individuals under age 5 is captured in the table Sex by Age (ACS Table ID:
B01001). The ACS divides the population into 23 age groups for each sex. The elements that were
used are shown in Table 6.

Table 6. ACS Age Table Element Reference

Table Element

Age

B01001.001

Total Population

B01001.003

Male, Under 5 Years

B01001.027

Female, Under 5 Years

Note: The data can be downloaded from the US Census Bureau's FTP Server.

•	To calculate the percentage individuals under age of 5 for a block group, the elements from Table 6
are used in the following equation:

B01001.003 + B01001.027

% Individuals Under Aqe 5 = 	

w	501001.001

•	In EJScreen, the raw values for the individuals under age 5 indicator range from 0% to 50%. In order
to make the indicator more comparable, statistical percentiles are used. The percentiles for the
individuals under age 5 indicator range from 0 to 100% with a median at the 50th percentile, which
corresponds to the raw value of 5.6%. If a raw value is higher than 5.6%, it would be placed above
the 50th percentile; if the value is lower, it would be placed below the 50th percentile. For example,
if the individuals under age 5 indicator raw value for a block group is 9%, which is higher than 5.6%,
that block group will be placed in the 80th percentile (which is higher than the 50th percentile).

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Individuals over Age 64

Percent of people in a block group over the age of 64.

How does EJScreen determine individuals over age 64?

• The ACS information on individuals over age 64 is captured in the table Sex by Age (ACS Table ID:
B01001). The ACS divides the population into 23 age groups for each sex. The elements that were
used are shown in Table 7.

Table 7. ACS Age Table Element Reference

Table Element

Sex

Age

B01001.001



Total Population

B01001.020

Male

65 and 66 years

B01001.021

67 to 69 years

B01001.022

70 to 74 years

B01001.023

75 to 79 years

B01001.024

80 to 84 years

B01001.025

85 years and over

B01001.044

Female

65 and 66 years

B01001.045

67 to 69 years

B01001.046

70 to 74 years

B01001.047

75 to 79 years

B01001.048

80 to 84 years

B01001.049

85 years and over

Note: The data can be downloaded from the US Census Bureau's FTP Server.

•	To calculate the percentage individuals over the age of 64 for a block group, the elements from
Table 7 are used in the following equation:

B01001.020+.021+.022 + .023+.024+.025+.044+.045+.046+.047+.048+.049

% Individuals over Age 64 =	

°	B01001.001

•	In EJScreen, the raw values for the individuals over age 64 indicator range from 0% to 100%. In order
to make the indicator more comparable, statistical percentiles are used. The percentiles for the
individuals over age 64 indicator range from 0 to 100% with a median at the 50th percentile, which
corresponds to the raw value of 14.2%. If a raw value is higher than 14.2%, it would be placed above
the 50th percentile; if the value is lower, it would be placed below the 50th percentile. For example,
if the individuals over age 64 indicator raw value for a block group is 22%, which is higher than
14.2%, that block group will be placed in the 80th percentile (which is higher than the 50th
percentile).

Environmental Indicators in EJScreen

In the current release:

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•	Particulate Matter (PM) 2.5 and Ozone values are derived from 2018 source data from EPA's Office
of Air Quality Planning and Standards (OAQPS), Non-attainment areas (NAA).

•	Air Toxics data (Diesel PM, Cancer Risk, and Respiratory HI) are derived from 2017 source data from
EPA's OAQPS.

•	Traffic proximity source is derived from the 2019 Highway Performance Monitoring System (HPMS).

•	Lead paint has been upgraded to Census Bureau's ACS 2016-2020 5-year Summary.

•	Superfund proximity source is derived from Superfund Enterprise Management System (SEMS)
database on April 26, 2022.

•	Risk Management Plan (RMP) facility proximity source is derived from EPA's Facility Registry Service
(FRS) by selecting facilities included in the RMP National Program System on April 26, 2022.

•	Hazardous waste proximity sources are derived from operating Treatment, Storage, and Disposal
Facilities (TSDFs) from RCRAInfo and Large Quantity Generators (LQGs) from the 2019 Biennial
Reports (BR) on April 26, 2022.

•	Underground Storage Tanks source provided by EPA's Office of Underground Storage Tanks on July
7, 2022.

•	Wastewater discharge source provided by EPA's Office of Pollution Prevention and Toxics (OPPT) on
March 15, 2021 from 2019 Risk-Screening Environmental Indicators (RSEI) modeled results.

Summary Over	tors Featured in EJScreen

This section describes the environmental indicator data used in EJScreen. Some of these environmental
indicators quantify proximity to and the numbers of certain types of potential sources of exposure to
environmental pollutants, such as nearby hazardous waste sites or traffic. The lead paint indicator
indicates the presence of older housing, which often, but not always, indicates the presence of lead
paint, and therefore the possibility of exposure. In some cases, the term "exposure" is used very broadly
here to refer to the potential for exposure. Other indicators in EJScreen are estimates of ambient levels
of air pollutants, such as PM 2.5, ozone, and diesel PM. Still others are actual estimates of air toxics-
related cancer risk or a hazard index (HI), which summarizes the ratios of ambient air toxics levels to
health-based reference concentrations. In other words, these environmental indicators vary widely in
what they indicate. EJScreen contains these 12 environmental indicators:

1.	PM 2.5

o PM 2.5 levels in air measured using an annual average.

2.	Ozone

o Ozone summer seasonal average of daily maximum 8-hour concentration in air.

3.	Diesel PM

o Diesel PM level in air.

4.	Air toxics cancer risk

o Lifetime cancer risk from inhalation of air toxics.

5.	Air toxics respiratory HI

o Ratio of exposure concentration to health-based reference concentration.

6.	Traffic proximity and volume

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o Count of vehicles (annual average daily traffic [AADT]) at major roads within 500 meters,
divided by distance in meters (not kilometers [km]).

7.	Lead paint

o Percent of housing units built before 1960.

8.	Superfund proximity

o Count of proposed and listed NPL sites within 5 km (or nearest one beyond 5 km), each
divided by distance in km. Count excludes deleted sites.

9.	RMP facility proximity

o Count of RMP (potential chemical accident management plan) facilities within 5 km (or
nearest one beyond 5 km), each divided by distance in km.

10.	Hazardous waste proximity

o Count of hazardous waste management facilities (TSDFs and LQGs) within 5 km (or
nearest one beyond 5 km), each divided by distance in km.

11.	Underground storage tanks (UST) and leaking UST (LUST)

o Count of LUSTs (multiplied by a factor of 7.7) and the number of USTs within a 1,500-
foot buffered block group.

12.	Wastewater discharge

o RSEI modeled Toxic Concentrations at stream segments within 500 meters, divided by
distance in km.

Detailed Descriptions of Environmental Indicators

Environmental Indicator—PM 2.5
What is the PM 2.5 indicator?

The PM 2.5 indicator is a measure of potential exposure to inhalable particles that are 2.5 micrometers
or smaller. This is measured in terms of annual average concentration in air measured in micrograms per
cubic meter. PM 2.5 information included in EJScreen highlights areas across the U.S. that are not
meeting the national ambient air quality standard for PM 2.5. In other words, the levels of PM 2.5
present in these areas are deemed harmful to human health.

The PM 2.5 indicator in EJScreen is a measure of potential exposure but not a measure of risk. The raw
PM 2.5 data is compiled by census tract which is supplied for use in the tool by EPA's OAQPS. For air
toxics risk measures (as opposed to exposure) users can turn to EJScreen's other three indicators: cancer
risk, respiratory HI, and diesel PM.

How does EJScreen determine the PM 2.5 indicator?

The PM 2.5 indicator data was provided by EPA's OAQPS using a fusion of monitor data and Community
Multiscale Air Quality (CMAQ) air quality modeling. For more information about the methods used, see
EPA Report EPA-454/S-15-001. This is provided to EJScreen as a spreadsheet compiled by Census tracts.
The tract values are re-assigned to each block group, so all block groups within each tract have the same
PM 2.5 value as for the tract.

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In EJScreen, the raw values for the PM 2.5 indicator range from 3.93 to 17.75. In order to make the
indicator more comparable, statistical percentiles are used. The percentiles for the PM 2.5 indicator
range from 0 to 100% with a median at the 50th percentile, which corresponds to the raw value of 8.6. If
a raw value is higher than 8.6, it would be placed above the 50th percentile; if the value is lower, it
would be placed below the 50th percentile. For example, if the PM 2.5 indicator raw value for a block
group is 9.78, which is higher than 8.6, that block group will be placed in the 80th percentile (which is
higher than the 50th percentile).

Where to find more information on PM 2.5?

For more information about PM, please visit EPA's website on PM. For real-time and forecasted
information about all standard EPA air quality criteria, please visit EPA's AirNow website-

Environmental Indicator—Ozone

What is the ozone indicator?

The ozone indicator in EJScreen reflects potential ozone exposure measured in terms of summer
seasonal daily average maximum concentrations in an 8-hour period measured in parts per billion.

Ozone information included in EJScreen highlights areas across the U.S. that are not meeting the
national ambient air quality standard for ozone. In other words, the levels of ozone present in these
areas are deemed harmful to human health.

The ozone indicator in EJScreen is a measure of potential exposure but not a measure of risk. The raw
ozone data is compiled by census tract, which is supplied for use in the tool by EPA's OAQPS. For air
toxics risk measures (as opposed to exposure) users can turn to EJScreen's other three indicators: cancer
risk, respiratory HI, and diesel PM.

How does EJScreen determine the ozone indicator?

The ozone indicator data was provided as a spreadsheet by EPA's OAQPS using a fusion of monitor data
and CMAQ air quality modeling. For more information about the methods used, see EPA Report EPA-
454/S-15-001. This is provided to EJScreen as a spreadsheet compiled by Census tracts. The tract values
are assigned to each block group, so all block groups within each tract have the same ozone value as for
the tract.

In EJScreen, the raw values for the ozone indicator range from 24.6 to 74.4. In order to make the
indicator more comparable, statistical percentiles are used. The percentiles for the ozone indicator
range from 0 to 100% with a median at the 50th percentile, which corresponds to the raw value of 42.2.
If a raw value is higher than 42.2, it would be placed above the 50th percentile; if the value is lower, it
would be placed below the 50th percentile. For example, if the ozone indicator raw value for a block
group is 43.6, which is higher than 42.2, that block group will be placed in the 64th percentile (which is
higher than the 50th percentile).

Where to find more information on ozone?

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To learn more, visit EPA's website on ozone. For real-time and forecasted information about all standard
EPA air quality criteria, please visit EPA's AirNow website-

Environmental Indicator—Diesel PM

What is the Diesel PM indicator?

The Diesel PM indicator is the estimated concentration of Diesel PM as provided by the 2017 Air Toxics
update. The value of the indicator is in ng /m3, and reported at the Census tract level. Block group level
values are assigned by repeating each parent tract level value.

How does EJScreen determine the Diesel PM indicator?

Diesel PM concentrations are provided by a spreadsheet from EPA OAQPS. The source data is compiled
by Census tract. The tract values are repeated for each Census block group.

In EJScreen, the raw values for the Diesel PM indicator range from 2.213 X10"6 to 1.927. In order to make
the indicator more comparable, statistical percentiles are used. The percentiles for the Diesel PM
indicator range from 0 to 100% with a median at the 50th percentile, which corresponds to the raw
value of 0.242. If a raw value is higher than 0.242, it would be placed above the 50th percentile; if the
value is lower, it would be placed below the 50th percentile. For example, if the Diesel PM indicator raw
value for a block group is 0.306, which is higher than 0.242, that block group will be placed in the 64th
percentile (which is higher than the 50th percentile).

Where to find more information on Diesel PM?

Diesel PM comes from EPA's Air Toxics Data Update. The Air Toxics Data Update is the Agency's ongoing,
comprehensive evaluation of air toxics in the United States. This effort aims to prioritize air toxics,
emission sources, and locations of interest for further study. It is important to remember that the air
toxics data presented here provide broad estimates of health risks over geographic areas of the country,
not definitive risks to specific individuals or locations. More information on the Air Toxics Data Update
can be found at: https://www.epa.gov/haps.

Environmental Indicator—Air Toxics Cancer Risk
What is the air toxics cancer risk indicator?

The air toxics cancer risk indicator is the estimated lifetime inhalation cancer risk from the analyzed
carcinogens in ambient outdoor air, as provided by the 2017 Air Toxics data Update. The value of the
indicator is persons per million lifetime. The data is reported at the Census tract level. Block group level
values are assigned by repeating each parent tract level value.

How does EJScreen determine the air toxics cancer risk indicator?

•	The indicator data for air toxics cancer risk per million are provided by a spreadsheet from EPA
OAQPS. The source data is compiled by Census tract.

•	The tract values are repeated for each Census block group.

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•	In EJScreen, the raw values for the air toxics cancer risk indicator range from 8.0 to 2000.0. In
order to make the indicator more comparable, statistical percentiles are used. The percentiles
for the air toxics cancer risk indicator range from 0 to 100% with a median at the 50th
percentile, which corresponds to the raw value of 23.46. If a raw value is higher than 23.46, it
would be placed above the 50th percentile; if the value is lower, it would be placed below the
50th percentile. For example, if the air toxics cancer risk indicator raw value for a block group is
26.18, which is higher than 23.46, that block group will be placed in the 64th percentile (which is
higher than the 50th percentile).

•	To estimate air toxics cancer risks, the results of cancer dose-response assessments for a given
chemical were converted to a unit risk estimate (URE). That URE was then multiplied by the
estimated inhalation exposure concentration to obtain an estimate of individual lifetime cancer
risk.

Where to find more information on air toxics cancer risk?

Air toxics cancer risk comes from EPA's Air Toxics Data Update. The Air Toxics Data Update is the
Agency's ongoing, comprehensive evaluation of air toxics in the United States. This effort aims to
prioritize air toxics, emission sources, and locations of interest for further study. It is important to
remember that the air toxics data presented here provide broad estimates of health risks over
geographic areas of the country, not definitive risks to specific individuals or locations. More
information on the Air Toxics Data Update can be found at: https://www.epa.gov/haps.

Environmental Indicator—Air Toxics Respiratory HI
What is the air toxics respiratory HI indicator?

The air toxics respiratory HI indicator is the respiratory HI from the analyzed carcinogens in ambient
outdoor air, as provided by the 2017 Air Toxics Data Update. The data is reported at the Census tract
level. Block group level values are assigned by repeating each parent tract level value.

How does EJScreen determine the air toxics respiratory HI indicator?

•	The indicator data for air toxics respiratory HI are provided by a spreadsheet from EPA OAQPS.
The source data is compiled by Census tract.

•	The tract values are repeated for each Census block group.

•	In EJScreen, the raw values for the air toxics respiratory HI indicator range from 0.0 to 5.0. In
order to make the indicator more comparable, statistical percentiles are used. The percentiles
for the air toxics respiratory HI indicator range from 0 to 100% with a median at the 50th
percentile, which corresponds to the raw value of 0.307. If a raw value is higher than 0.307, it
would be placed above the 50th percentile; if the value is lower, it would be placed below the
50th percentile. For example, if the air toxics respiratory HI indicator raw value for a block group
is 0.348, which is higher than 0.307, that block group will be placed in the 64th percentile (which
is higher than the 50th percentile).

•	Air toxics estimated chronic noncancer hazards for multiple air toxics by summing chronic
noncancer hazard quotients (HQs) for individual air toxics that cause similar adverse health

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effects. The result is a HI. Aggregation in this way produces a target-organ-specific HI, defined as
a sum of HQs for individual air toxics that affect the same organ or organ system.

Where to find more information on the air toxics respiratory HI indicator?

Air toxics respiratory HI comes from EPA's Air Toxics Data Update. The Air Toxics Data Update is the
Agency's ongoing, comprehensive evaluation of air toxics in the United States. This effort aims to
prioritize air toxics, emission sources, and locations of interest for further study. It is important to
remember that the air toxics data presented here provide broad estimates of health risks over
geographic areas of the country, not definitive risks to specific individuals or locations. More
information on the Air Toxics Data Update can be found at: https://www.epa.gov/haps.

Environmental Indicator—Traffic Proximity

What is the traffic proximity indicator?

The traffic proximity indicator is based on AADT count divided by distance in meters from the Census
block centroid. The proximity score is based on the traffic within a search radius of 500 meters (or
further if none is found in that radius). This distance was selected to be large enough to capture the
great majority of road segments (with traffic data) that could have a significant impact on the local
residents, balanced against the need to limit the scope due to computational constraints. The closest
traffic is given more weight, and the distant traffic is given less weight, through inverse distance
weighting. For example, traffic 500 meters away is given only one tenth as much weight as traffic 50
meters away.

Why is traffic proximity an indicator in EJScreen?

•	Proximity to roads can provide access to jobs, health care, food, recreational opportunities, and
other benefits. However, in EJScreen, the indicator is designed to screen for the negative
aspects of very close proximity to very high volumes of traffic, which include asthma and
cardiovascular and heart disease, among others.

•	Residential proximity to traffic has been associated with various health impacts, particularly
asthma exacerbation and possibly onset of asthma, as well as mortality rates. Proximity to traffic
has also been associated with subclinical atherosclerosis (a key pathology underlying
cardiovascular disease [CVD]), prevalence of CVD and coronary heart disease (CHD), incidence of
myocardial infarction, and CVD mortality.

How does EJScreen determine the traffic proximity indicator?

•	Highway segments are from the HPMS lines and AADT counts are from the 2019 HPMS release,
Federal Highway Administration, U.S. Department of Transportation (DOT). Proximity scores are
calculated by assigning inverse distance weighted scores to Census blocks (distance between
block centroids and highway segments). Note that blocks outside a 3-km cutoff are set to null
due to computational constraints. The weighted scores are then multiplied by AADT to produce
the final block scores. The results are aggregated to the parent block group using the population
weight for each block within the block group.

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•	Information about HPMS can be found on this FHWA website. The 2019 spatial HPMS data is
available by state from this DOT-hosted ArcGIS server: https://geo.dot.gov/server/services.
EJScreen processing uses a subset of highways that include:

o Interstates

o Principal Arterial—Other Freeways and Expressways
o Principal Arterial—Other
o Minor Arterial in urban areas

•	In EJScreen, the raw values for traffic proximity range from 0.00006 to 56,126. In order to make
the indicator more comparable, statistical percentiles are used. The percentiles for traffic
proximity range from 0 to 100% with a median at the 50th percentile, which corresponds to the
raw value of 222.0. If a raw value is higher than 222.0, it would be placed above the 50th
percentile; if the value is lower, it would be placed below the 50th percentile. For example, if
the traffic proximity raw value for a block group is 406.0, which is higher than 222.0, that block
group will be placed in the 64th percentile (which is higher than the 50th percentile).

Environmental Indicator—Lead Paint Indicator
What is the lead paint indicator?

The lead paint indicator is the percentage of occupied housing units built before 1960, calculated from
the U.S. Census Bureau's ACS 5-year summary estimates on age of housing stock. EJScreen uses age of
housing stock as a surrogate for potential lead exposure as regulations banning lead-based residential
paint in 1978 led to the reduction and finally an end to the use of such paint in housing. The percentage
of older housing units is a proxy for potential exposure to lead paint and lead-containing dust that
accumulates indoors, in homes, or in other buildings where lead paint was used. EJScreen uses housing
units built before 1960.

How does EJScreen determine the lead paint indicator?

•	The data are derived from the ACS Summary, block group-level estimates.

•	The ACS information on lead paint is captured in the table Year Structure Built (ACS Table ID:
B25034). The elements that were used are shown in Table 8.

Table 8. ACS Year Structure Built Table

Table Element

Year Structure Built

B25034.001

Total Housing Units

B25034.002

Housing Units Built 2014 or later

B25034.003

Housing Units Built 2010 to 2013

B25034.004

Housing Units Built 2000 to 2009

B25034.005

Housing Units Built 1990 to 1999

B25034.006

Housing Units Built 1980 to 1989

B25034.007

Housing Units Built 1970 to 1979

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B25034.008

Housing Units Built 1960 to 1969

B25034.009

Housing Units Built 1950 to 1959

B25034.010

Housing Units Built 1940 to 1949

B25034.011

Housing Units 1939 or earlier

Note: The data can be downloaded from the US Census Bureau's FTP Server.

• To calculate the lead paint indicator, which is an indicator for percent pre-1960 housing units,
four elements from Table 8 are used in the following equation:

(625034.009 + £25034.010 + £25034.011)

% Lead Paint =	

£25034.001

•	In EJScreen, the raw values for the lead paint indicator range from 0 to 100%. In order to make
the indicator more comparable, statistical percentiles are used. The percentiles for the lead
paint indicator range from 0 to 100% with a median at the 50th percentile, which corresponds
to the raw value of 17%. If a raw value is higher than 17%, it would be placed above the 50th
percentile; if the value is lower, it would be placed below the 50th percentile. For example, if
the lead paint indicator raw value for a block group is 30%, which is higher than 17%, that block
group will be placed in the 64th percentile (which is higher than the 50th percentile).

Environmental Indicator—Superfund Proximity
What is the Superfund proximity indicator?

The Superfund proximity indicator is reflective of the total count of sites proposed and listed (final) on
the National Priorities List (NPL) in each block group 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.

How does EJScreen determine the Superfund proximity indicator?

•	Final and proposed NPL sites are downloaded from the SEMS website. Proximity scores are
calculated by assigning distance-weighted scores to 2010 Census blocks (distance between block
centroids and facilities). The results are aggregated to the parent block group using the
population weight for each block within the block group.

•	In EJScreen, the raw values for the Superfund proximity indicator range from 0 to 8.988. In order
to make the indicator more comparable, statistical percentiles are used. The percentiles for the
Superfund proximity indicator range from 0 to 100% with a median at the 50th percentile, which
corresponds to the raw value of 0.061. If a raw value is higher than 0.061, it would be placed
above the 50th percentile; if the value is lower, it would be placed below the 50th percentile.
For example, if the Superfund proximity indicator raw value for a block group is 0.092, which is
higher than 0.061, that block group will be placed in the 64th percentile (which is higher than
the 50th percentile).

•	More information about NPL sites can be found on this SEMS website.

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Environmental Indicator—RMP Facility Proximity

What is the RMP facility proximity indicator?

The RMP facility proximity is reflective of the total count of RMP facilities in each block group 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.

How does EJScreen determine the RMP facility proximity indicator?

•	RMP facilities are pulled from EPA's FRS by selecting facilities included in the RMP National
Program System.

•	Proximity scores are calculated by assigning distance-weighted scores to Census blocks (distance
between block centroids and facilities). The results are assigned to block groups through
population-weighted block to block group assignments.

•	RMP facilities are queried by using the FRS Query website and selecting RMP in the program
system list.

•	In EJScreen, the raw values for the RMP facility proximity indicator range is from 0 to 18.45. In
order to make the indicator more comparable, statistical percentiles are used. The percentiles
for the RMP facility proximity indicator range from 0 to 100% with a median at the 50th
percentile, which corresponds to the raw value of 0.314. If a raw value is higher than 0.314, it
would be placed above the 50th percentile; if the value is lower, it would be placed below the
50th percentile. For example, if the RMP facility proximity indicator raw value for a block group
is 0.573, which is higher than 0.314, that block group will be placed in the 64th percentile (which
is higher than the 50th percentile).

Environmental Indicator—Hazardous Waste Proximity
What is the hazardous waste proximity indicator?

The hazardous waste proximity indicator is reflective of the total count of hazardous waste facilities in
each block group 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. Hazardous waste facilities are defined as
Resource Conservation and Recovery Act (RCRA) handlers that are either operating TSDFs from RCRA or
reporting LQGs from the 2019 BR.

How does EJScreen determine the hazardous waste proximity?

•	Proximity scores are calculated by assigning distance weighted scores to Census blocks (distance
between block centroids and facilities). The results are assigned to block groups through
population-weighted block to block group assignments.

•	TSDFs are collected by using the RCRAInfo Search website and selecting TSDF Handler Universe.

•	2019 BR LQGs are collected by using the BR Search website.

•	In EJScreen, the raw values for the hazardous waste proximity indicator range is from 0 to 61.57.
In order to make the indicator more comparable, statistical percentiles are used. The percentiles
for the hazardous waste proximity indicator range from 0 to 100% with a median at the 50th

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percentile, which corresponds to the raw value of 0.702. If a raw value is higher than 0.702, it
would be placed above the 50th percentile; if the value is lower, it would be placed below the
50th percentile. For example, if the hazardous waste proximity indicator raw value for a block
group is 1.440, which is higher than 0.702, that block group will be placed in the 64th percentile
(which is higher than the 50th percentile).

Environmental Indicator—Underground Storage Tanks
What is the UST indicator?

The UST indicator quantifies the relative risk of being affected by a LUST for a block group. The indicator
is derived by the weighted sum of active LUSTs and sum of active and temporarily out of service USTs
within a certain distance from a block group.

How does EJScreen determine the UST indicator?

•	The UST indicator scores are provided by the EPA Office of Underground Storage Tanks.

•	EJScreen's UST indicator is calculated using the sum of LUSTs (multiplied by a factor of 7.7) and
the number of USTs within a 1,500-foot buffered block group. That number is then divided by
the area of the buffered block group in km2. The 7.7 multiplier is derived from the average
number of active USTs divided by the average number of LUSTs in the U.S. backlog (cleanups
remaining) from 2011-2020. A 1,500-foot buffer is used as a radius of influence for the Benzene
plume migration to encompass USTs/LUSTs near block groups that could potentially be affected
by a release. See EPA's website on underground and leaking underground storage tanks for
more information.

•	In EJScreen, the raw values for the UST indicator range from 0 to 174.15. In order to make the
indicator more comparable, statistical percentiles are used. The percentiles for the UST indicator
range from 0 to 100% with a median at the 50th percentile, which corresponds to the raw value
of 1.25. If a raw value is higher than 1.25, it would be placed above the 50th percentile; if the
value is lower, it would be placed below the 50th percentile. For example, if the UST indicator
raw value for a block group is 5.6, which is higher than 1.25, that block group will be placed in
the 80th percentile (which is higher than the 50th percentile).

•	To calculate the UST indicator for a block group, the following equation is used:

(# of LUSTs x 7.7) + (# of Active USTs)

UST iTidiccLtov — 	

Area of bufferd block group1S00ft_bUffer

Environmental Indicator—Wastewater Discharge

What is the wastewater discharge indicator?

The wastewater discharge indicator quantifies a block group's relative risk of exposure to pollutants in
downstream water bodies. This is achieved using toxicity-weighted concentrations in stream reach
segments within 500 meters of a block centroid, divided by distance in meters, presented as the
population-weighted average of blocks in each block group.

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How does EJScreen determine the wastewater discharge indicator?

•	The wastewater discharge indicator utilizes pollutant loadings from the Discharge Monitoring
Report (DMR) Loading Tool along with the RSEI model to estimate concentrations of pollutants
in downstream water bodies and derive a toxicity-weighted concentration.

•	To place higher emphasis on stream reaches with higher toxicity-weighted pollutant
concentrations, the toxicity-weighted value for all stream reaches within 500 meters of a census
block centroid is divided by the distance in meters to the census block centroid to create a
weighted proximity value indicating a block's risk of exposure to pollutants in the stream
reaches.

•	The results are aggregated to the parent block group using the population weight for each block
within the block group. The population weights come from the 2010 Census. Minor adjustments
are needed to crosswalk Census 2010 blocks and 2019 blocks.

•	The data was provided by EPA as a polyline feature class in a file geodatabase.

•	In EJScreen, the raw values for the wastewater discharge indicator range from 0 to 63,257.7. In
order to make the indicator more comparable, statistical percentiles are used. The percentiles
for the wastewater discharge indicator range from 0 to 100% with a median at the 50th
percentile, which corresponds to the raw value of 0.00104. If a raw value is higher than 0.00104,
it would be placed above the 50th percentile; if the value is lower, it would be placed below the
50th percentile. For example, if the wastewater discharge indicator raw value for a block group
is 0.045, which is higher than 0.00104, that block group will be placed in the 80th percentile
(which is higher than the 50th percentile).

Con :	,	11	creen

A variety of considerations has informed the selection of these environmental indicators; in general, the
selected indicators exhibit the following characteristics:

•	Resolution: Screening level data are available (or could be readily developed) at the block
group level (or at least close to this resolution).

•	Coverage: Screening level data are available (or could be readily developed) for the entire
United States (or with nearly complete coverage).

•	Relevance to EJ: Pollutants or impacts are relevant to EJ (e.g., differences between groups
have been indicated in exposures, susceptibility, or health endpoints associated with the
exposures).

•	Public health significance: Pollutants or impacts are potentially important in the United
States (e.g., notable impacts estimated or significant concerns have been expressed, at least
locally, or exposure has been linked to health endpoints with substantial impacts
nationwide).

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3 Overview of Indexes in EJScreen

This section describes the environmental and socioeconomic indexes featured in the tool, why they are
included, and how they are derived.

Demographic Indexes Included In EJScreen

EJScreen includes two demographic indexes based on different variations of the socioeconomic
indicators. The two demographic indexes featured in EJScreen are:

•	Demographic Index is based on the average of two demographic indicators: percent low-income
and percent people of color.

•	Supplemental Demographic Index is based on the average of five socioeconomic indicators:
percent low-income, percent limited English speaking, percent less than high school education,
percent unemployed, and low life expectancy.

Demographic Index

What is the Demographic Index in EJScreen?

The Demographic Index in EJScreen is a combination of percent low-income and percent people of color.
These are the two demographic factors explicitly named in Executive Order 12898 on Environmental
Justice. For each Census block group, these two numbers are simply averaged together. The formula is
as follows:

% Low Income + % People of Color
Demographic Index = 			

For example, if a Census block group has a low income indicator value of 25% and a people of color
indicator value of 75%, the Demographic Index value would be 50%.

How does EJScreen determine the Demographic Index?

EJScreen uses these two demographic indicators:

1. The ACS low income information is captured in the table Ratio of Income to Poverty Level in the Past
12 Months (ACS Table ID: C17002). The ACS divides the ratio of income to poverty into seven
categories, as shown in Table 9.

Table 9. ACS Income/Poverty Level Table Element Reference

Table Element

Income/Poverty Level

C17002.001

Total Population Whose Poverty Status is Known

C17002.002

People with Ratio of Income to Poverty under .50

C17002.003

People with Ratio of Income to Poverty from .50 to .99

C17002.004

People with Ratio of Income to Poverty from 1.00 to 1.24

C17002.005

People with Ratio of Income to Poverty from 1.25 to 1.49

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C17002.006

People with Ratio of Income to Poverty from 1.50 to 1.84

C17002.007

People with Ratio of Income to Poverty from 1.85 to 1.99

C17002.008

People with Ratio of Income to Poverty from 2.00 and over

Note: The data can be downloaded from the US Census Bureau's FTP Server.

• To calculate percent low income, two elements from Table 9 are used in the following equation:

C17002.001 - C17002.008

% Low Income =

C17002.001

2. The ACS people of color information is captured in the table Hispanic or Latino Origin by Race (ACS
Table ID: B03002). The ACS divides race and Hispanic status into 21 categories, four of which are
shown in Table 10.

Table 10. ACS Hispanic or Latino Origin by Race Table Element Reference

Table Element

Hispanic Status/Race Population

B03002.001

Total Population: All races/ethnicities

B03002.002

Total Population: Non-Hispanic

B03002.003

Total Population: Non-Hispanic, White Alone

B03002.012

Total Population: Hispanic



Note: The data can be downloaded from the US Census Bureau's FTP Server.

To calculate percent people of color, two elements from Table 10 are used in the following
equation:

603002.001 — 603002.003
% People of Color = —

603002.001



To calculate the EJScreen Demographic Index, the results from the two previous calculations are
averaged as follows:

% Low Income + % People of Color
Demographic Index = 			

In EJScreen, the raw values for the Demographic Index range from 0% to 100%. In order to make the
indicator more comparable, statistical percentiles are used. The percentiles for the Demographic Index
range from 0 to 100% with a median at the 50th percentile, which corresponds to the raw value of 30%.
If a raw value is higher than 30%, it would be placed above the 50th percentile; if the value is lower, it
would be placed below the 50th percentile. For example, if the Demographic Index raw value for a block
group is 50%, which is higher than 30%, that block group will be placed in the 74th percentile (which is
higher than the 50th percentile).

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Why is the Demographic Index in EJScreen?

•	These are the two demographic factors explicitly named in Executive Order 12898 on
Environmental Justice.

•	Low income and people of color populations often experience greater exposure to
environmental burdens than the general population as a whole.

•	Many studies have established that sources of environmental hazards are often located and
concentrated in areas that are dominated by low income and people of color populations.

Supplemental Demographic Index

What is the Supplemental Demographic Index in EJScreen?

The Supplemental Demographic Index uses the same updated methodology and calculation as the EJ
Indexes but replaces the current Demographic Index (the average percent low-income and percent
people of color) with a supplemental five-factor demographic index. The five socioeconomic indicators
considered are percent low life expectancy, percent low-income, percent unemployed, percent limited
English speaking, and percent less than high school education.

How does EJScreen determine the Supplemental Demographic Index?

The Supplemental Demographic Index is the average of the following five indicators:

1.	Low Life Expectancy—To highlight areas where the life expectancy is lower than National norms,
EJScreen uses an inverse measure showing higher values for lower years and lower scores for higher
years. Low Life Expectancy is an inverse of the normalized life expectancy (as defined below) derived
from the Life Expectancy at Birth from Centers for Disease Control and Prevention (CDC), National
Center for Health Statistics (NCHS):

% Low Life Expectancy is defined as "1 - (Life Expectancy / Max Life Expectancy)"

Note: This is derived from the CDC life expectancy at birth data using the formula above.

2.	The ACS low income information is captured in the table Ratio of Income to Poverty Level in the Past
12 Months (ACS Table ID: C17002). The ACS divides the ratio of income to poverty into seven
categories, as shown in Table 11.

Table 11. ACS Income/Poverty Level Table Element Reference

Table Element

Income/Poverty Level

C17002.001

Total Population Whose Poverty Status is Known

C17002.002

People with Ratio of Income to Poverty under .50

C17002.003

People with Ratio of Income to Poverty from .50 to .99

C17002.004

People with Ratio of Income to Poverty from 1.00 to 1.24

C17002.005

People with Ratio of Income to Poverty from 1.25 to 1.49

C17002.006

People with Ratio of Income to Poverty from 1.50 to 1.84

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Table Element

Income/Poverty Level

C17002.007

People with Ratio of Income to Poverty from 1.85 to 1.99

C17002.008

People with Ratio of Income to Poverty from 2.00 and over

Note: The data can be downloaded from the US Census Bureau's FTP Server.

o To calculate percent low income, two elements from Table 11 are used in the following
equation:

C17002.001 - C17002.008
% Low Income =	

C17002.001

The ACS unemployment information is captured in the table Employment Status for the Population
16 Years and Over (Table ID: B23025). The ACS divides the population age 16 or older into seven
categories, which are shown in Table 12. EJScreen uses the Civilian Labor Force numbers.

Table 12: ACS Employment Status Table Element Reference

Table Element

Employment Status

B23025.001

Total Population Age 16 Years and Over

B23025.002

Total Labor Force

B23025.003

Total Civilian Labor Force

B23025.004

Civilian Labor Force: Employed

B23025.005

Civilian Labor Force: Unemployed

B23025.006

In Armed Forces

B23025.007

Total Not In Labor Force

Note: These data can be downloaded from the US Census Bureau's FTP Server.

o To calculate the percentage of unemployment in the civilian labor force, two elements from
Table 12 are used in the following equation:

623025.005

% Unemployment =			

F 7	623025.003

The ACS limited English speaking household information is captured in the table Household
Language by Household Limited English Speaking Status (ACS Table ID: C16002). The ACS divides
limited English speaking households into four language groups as shown in Table 13.

Table 13. ACS Limited English Speaking Table Element Reference

Table Element

Language & Ability Status

C16002.001

Total Households

C16002.004

Limited English Speaking - Spanish

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Table Element

Language & Ability Status

C16002.007

Limited English Speaking - Other Indo-European Languages

C16002.010

Limited English Speaking - Asian/Pacific Island Languages

C16002.013

Limited English Speaking - Other Languages

Note: The data can be downloaded from the US Census Bureau's FTP Server.

5. The ACS education information is captured in the table Sex by Educational Attainment for the

Population 25 Years and Over (ACS Table ID: B15002). The ACS divides the education information by
grade as shown in Table 14.

Table 14. ACS Educational Attainment Table Element Reference

Table Element

Sex

Educational Attainment

B15002.001



Total Population Age > 25

B15002.003



No Schooling Completed

B15002.004



Nursery - 4th Grade

B15002.005



5th & 6th Grade

B15002.006

Male

7th & 8th Grade

B15002.007



9th Grade

B15002.008



10th Grade

B15002.009



11th Grade

B15002.010



12th Grade, No Diploma

B15002.020



No Schooling Completed

B15002.021



Nursery - 4th Grade

B15002.022



5th & 6th Grade

B15002.023



7th & 8th Grade

B15002.024

Female

9th Grade

B15002.025



10th Grade

B15002.026



11th Grade

B15002.027



12th Grade, No Diploma

Note: These data can be downloaded from the US Census Bureau's FTP Server.

Here is the formula for computing the Supplemental Demographic Index:

Supplemental Demographic Index = (% Low Life Expectancy + % Low Income + % Unemployment Rate
+ % Limited English Speaking + % Less Than High School Education) / 5

Note that the CDC Life Expectancy data are available for about 90% of the country. For the areas where
the Life Expectancy data are not available, the Supplemental Demographic Index becomes the average
of four indicators instead of five, which is calculated as "(% Low Income + % Unemployment Rate + %
Limited English Speaking + % Less Than High School Education) / 4".

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Why is the Supplemental Demographic Index in EJScreen?

The Supplemental Demographic Index offers a different perspective on community-level vulnerability
across the country. The Supplemental Demographic Index also provides additional functionality for use
in decision-making consideration, such as the allocation of government resources when awarding
grants. For questions on the appropriate use of the EJ and Supplemental Demographic Indexes, please
contact your servicing legal office.

1 ixes Incl 11 1 ,	een

EJScreen features two sets of indexes—12 EJ Indexes and 12 Supplemental EJ Indexes, which are
described in detail below. The indexes are a combination of environmental indicators described above
and the Demographic Index, or the Supplemental Demographic Index described above.

ij Indexes

The EJ Indexes are a combination of environmental indicators described above and the Demographic
Index. EJScreen features a single EJ Index for each of the 12 environmental indicators.

EJ Index Calculations:

To calculate a single EJ Index, EJScreen combines a single environmental indicator with the Demographic
Index.

The equation for EJ Index calculations is as follows:

EJ Index = Demographic Index x Normalized Environmental Indicator
where Normalized Environmental Indicator is Percentile of Environmental Indicator Source Data

Percentiles Methodology Notes:

•	Percentiles calculations are unweighted.

•	Percentile ties use a floor method. This produces the lowest value, for example, for an
element with more than 50 percent zeros, like "Limited English Speaking", 0 will yield 0
Percentile.

EJ Index Example:

For block group (BG) = 410510068022

% people of color for BG = 0.24898694281855
% low income for BG = 0.153534443944169

National Percentile for Environmental Indicator for Superfund Proximity = 65

Demographic Index for BG = (% people of color + % low income) / 2
Demographic Index for BG = ( 0.24898694281855 + 0.153534443944169 ) / 2
Demographic Index for BG = 0.2012606933813595

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EJ Index for Superfund Proximity = National Percentile for Environmental Indicator * Demographic Index
for BG

EJ Index for Superfund Proximity = 65 * 0.2012606933813595
EJ Index for Superfund Proximity = 13.082

EJ Index Percentile in USA = 54

Supplemental Indexes

The supplemental indexes are a combination of environmental indicators described above and the
Supplemental Demographic Index. Similar to the EJ Indexes, EJScreen features a single supplemental
index for each of the 12 environmental indicators.

Supplemental Index Calculations:

To calculate a single supplemental index, EJScreen combines a single environmental indicator with the
Supplemental Demographic Index.

Supplemental Index = Supplemental Demographic Index x Normalized Environmental Indicator

where Normalized Environmental Indicator is Percentile of Environmental Indicator Source Data

Supplemental Index Example:

For block group (BG) = 410510068022

% low life expectancy for BG = 0.152820512820513
% low income for BG = 0.153534443944169
% unemployment rate for BG = 0.016653449643140
% limited English speaking for BG = 0.011891891891892
% less than high school education for BG = 0.067593177511055

National Percentile for Environmental Indicator for Superfund Proximity = 65

Supplemental Demographic Index for BG = [% low life expectancy + % low income + % unemployment
rate + % limited English speaking + % less than high school education) / 5
Supplemental Demographic Index for BG = (0.152820512820513 + 0.153534443944169 +
0.016653449643140 + 0.011891891891892 + 0.067593177511055) / 5
Supplemental Demographic Index for BG = 0.0804986951621538

Supplemental Index for Superfund Proximity = National Percentile for Environmental Indicator *
Supplemental Demographic Index for BG

Supplemental Index for Superfund Proximity = 65 * 0.0804986951621538
Supplemental Index for Superfund Proximity = 5.232415185539997

Supplemental Index Percentile in USA = 50

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4 Technical Details on Percentiles

What a Perc	ns

EJScreen puts each indicator or index value in perspective by reporting the value as a percentile. For
example, an area may show 60% of housing was built prior to 1960. It may not be obvious whether this
is a relatively high or low value, compared to the rest of the nation or in the state. Therefore, EJScreen
also reports that 60% pre-1960 puts this area at the 80th percentile nationwide. For a place at the 80th
percentile nationwide, that means 20% of the U.S. population has a higher value.

A percentile in EJScreen tells us roughly what percent of the U.S. 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 U.S. 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.

Note that when there are ties, a "floor" method is used to make the assignment. For example, if an
indicator value of 0 is assigned percentiles from 0 to 10, the reported percentile will be 0 instead of 10.

More precisely, the exact percentile for a given raw indicator value is calculated as the number of U.S.
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 U.S. population,
but for some indicators some locations do not have an indicator value. For example, the Air Toxics
indicators are missing for only about one twentieth of 1% of the U.S. population in EJScreen. The
calculated percentile would change by much, much less than 1 percentile point if calculated as a fraction
of the total population instead of as a fraction of those with valid indicator values.

Color-coded High Perc«

Locations at least at the 80th percentile but less than the 90th are shown in yellow on EJScreen maps,
while those at the 90th percentile but less than 95th percentile are orange on the maps, and those at
the 95th percentile or above are shown in red on maps and reports. These colors call attention to
certain locations as a very simple way to communicate relative screening results. There is no official
policy significance assigned to each individual color on the maps, but the choice of these categories or
"bins" is noteworthy because it signifies that certain ranges of percentiles may merit closer attention.

Percentiles at or above the 95th percentile are shown in red on the EJScreen standard report. This is a
way to call particular attention to those cases where the value is in the top 5% of the nation (or region
or state). Indicator or index values in the top 5% tend to be much higher than those in the next 5-10%,
so they may merit close attention. This is especially true for the indicators with highly skewed
distributions, such as the traffic proximity indicator. For example, block groups in the top 5% (shown in
red on maps and reports) have traffic, NPL, and TSDF proximity indicators on average that are about
three times as high as in the next 5% (shown in orange on the maps). These differences are far less


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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 95th percentile represent much higher
demographic, environmental, or EJ Index values than those at lower percentiles.

The maps also identify areas in the 90th to 95th percentiles as orange, and those at the 80th to 90th as
yellow. These additional categories highlight larger groups of locations that have indicator or index
values well above the national mean or median for the given indicator or index. The actual values are
lower than those in the top 5%, typically much lower, but they are still in the top 10 to 20% of values for
the U.S. population overall.

A relatively high percentile means the value is relatively uncommon. However, a high percentile is not
necessarily a real concern from a health or legal perspective. To understand the actual health or other
implications of any screening results requires looking at the actual data 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 health-
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 exposures to the relevant environmental
pollutants.

In maps, EJScreen focuses on the U.S. percentiles as a way to visualize all results in common units.

The U.S. percentile uses the U.S. population as the basis of comparison. The state percentile was
calculated based on the population in a given state (or District of Columbia or Puerto Rico). The national
or state mean value was calculated as the population weighted average of the block groups with data
for that indicator, within the respective geographic scope.

Note that the U.S. 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 U.S. or state percentile is used. The difference between state and U.S. percentiles
becomes apparent mainly in two situations: when comparing places across states, or when comparing
results to some pre-determined, specific reference percentile (e.g., 80th percentile).

The advantage of U.S. 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 U.S. percentile indicates how uncommon it is to
have such a high level for an indicator or index.

State percentiles cannot be compared across states as easily. If two places A and B, in two different
states, happen to both be at the 80th percentile for the traffic proximity 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

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that state as B's is in B's state. However, this may be useful information because an EJScreen user may
want to know how high the indicator is relative to the rest of that state.

The state and U.S. percentiles will be very similar if the state and U.S. average indicator values are very
similar. However, if the state average is very low compared to the U.S., the state percentile shown will
be higher than U.S. percentile shown, for a given raw value of an indicator. If the state average is much
higher than the U.S. average, for an indicator like the traffic proximity indicator, then a traffic score that
would normally be considered fairly high nationwide, such as the 90th percentile in the U.S., would not
be considered very unusual within that state, so the state percentile would be lower, and might be only
78th percentile, for example. The state percentile being lower than the U.S. percentile does not mean
the indicator value is lower in the given place, it just means the state average is higher than the U.S.
average.

v Percentiles are Calculated

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

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5

ixes Threshold Map Widget

The Indexes Threshold Map Widget allows users to investigate a potential hotspot with user-specified
criteria based on all 12 EJ Indexes. It works with EJScreen Indexes and Supplemental Indexes.

It provides users with a new capability of specifying custom areas of concern based on Percentile bins
tabulated for the 12 EJ Indexes.

•	The Indexes Threshold Map Widget is a tool for users to produce custom threshold maps. The
tool allows the user to select:

o Data type—EJ Index or Supplemental Index
o Data source-U.S. or State Percentiles
o Index Percentile Range
o All indexes or user-selected subset of indexes

•	The tool uses data built from these EJScreen Index datasets:

1.	National Percentiles built based on the two-factor Demographic Index

2.	State Percentiles built based on the two-factor Demographic Index

3.	National Percentiles built based on the Supplemental Demographic Index

4.	State Percentiles built based on the Supplemental Demographic Index

•	Datasets include these elements:

1.	12 Calculated EJ Index Percentiles

2.	101 Percentile bin counters (0 to 100)

Initial Filter Approa - Screening

In past screening experience, EPA has found it helpful to establish a suggested Agency starting point for
the purpose of identifying geographic areas that may warrant further consideration, analysis, or
outreach. The use of an initial filter promotes consistency and provides a pragmatic first step for EPA
programs and regions when interpreting screening results. For early applications of EJScreen, EPA
identified the 80th percentile filter as that initial starting point. In other words, an area with any of the
12 EJ Indexes at or above the 80th percentile nationally should be considered as a potential candidate
for further review. Further review may include considering other factors and other sources of
information such as health-based information, local knowledge, proximity and exposure to
environmental hazards, susceptible populations, unique exposure pathways, and other federal, regional,
state, and local data. This filter is simply a starting point, and program offices and regions should
perform additional analysis before making any decisions about potential environmental justice issues. As
EPA gains further experience and insight into the performance of the tool and its applicability for
different uses, program offices and regions may opt to designate starting points that are more inclusive
or specifically tailored to meet programmatic needs more effectively.

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The 80th percentile filter in EJScreen is not intended to designate an area as an "EJ community."
EJScreen provides screening level indicators, not a determination of the existence or absence of EJ
concerns. Nor does the use of the 80th percentile filter suggest that all of the 12 environmental
indicators are equal in terms of their impact on human health and the environment. Instead, the 80th
percentile filter encourages programs to consider environmental indicators outside of their areas of
concentration. The Agency may revise this approach in the future based on experience. This 80th
percentile filter is for internal EPA use and is not intended to apply to States or other organizations.

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L5 U ff(31" O I*t

EJScreen allows a user to define a buffer, such as the circle that includes everything within 1 mile of a
specific point. Non-circular, user-defined shapes also can be defined to represent buffers of any shape. A
report summarizes the demographics of residents within this buffer, as well as the environmental
indicators and EJ Index values within the buffer.

The summary within a buffer is designed to represent the average resident within the buffer, and also
provides an estimate of the total population residing in the buffer. For example, the traffic proximity
indicator for a buffer is the population-weighted average of all the traffic indicator values in the buffer.
Similarly, the percent people of color would be a weighted average, which is the same as the overall
percent people of color for all residents in the buffer.

Some block groups will be partly inside and partly outside a buffer, and any buffer analysis must
estimate how much of each block group's population is inside the buffer. Areal apportionment of block
groups is one standard method, but it assumes that population is evenly spread throughout a block
group, which may be far from the actual distribution of residents. Areal apportionment of blocks would
be even more accurate but extremely computationally intensive.

To provide the most accurate counts that are currently feasible for a screening tool, EJScreen uses an
approach based on decennial 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 the
decennial Census. These blocks provide data about where residents are at a higher resolution than block
groups. Each block has an internal point defined by the Census Bureau, and the entire block population
is counted as inside or outside the buffer depending on whether the block internal point is inside or
outside. This assumption typically introduces relatively little error because blocks are so small relative to
a typical buffer, so a small fraction of the total buffer population is in blocks that span an edge of the
buffer. Also, any blocks along the edge of a buffer whose populations are close to 0 or 100% inside the
buffer will be well represented by this assumption.

As long as users draw buffers much larger than a local block group, this method should represent the
average person inside the buffer reasonably well.

The calculation of a value for the buffer is essentially the population-weighted average of the indicator
values in the blocks included in the buffer, where each block uses the indicator values of the block group
containing it. A block group is weighted based on the fraction of the current ACS block group population
that is considered in the buffer. That fraction is estimated as the decennial Census block population
divided by the decennial Census block group population. The formula below is used to estimate the
population average of a raw indicator value in a buffer. This formula is simply a population-weighted
average - it sums the population-weighted raw values, and then divides that sum by the total
population in the buffer.

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Z^BGPo*^ * BGACSPop * BG_RawValue

			BlkPov	

VBlk, Blk n A Yxmk,BiknA BGPop * BGACSPoP

"BlockPop" refers to the decennial Census 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 current ACS, which is often different than the decennial Census 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 decennial Census is a full count at one point in time.

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7 Technical Details on Proximity Indicators

Several of EJScreen's environmental indicators are direct or indirect estimates of potential exposure or
health risks, such as the Air Toxics 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" (LULUs) is in some cases founded on the potential for routine or episodic releases
of pollutants to the air, land, or water, and the potential for such releases to cause human health or
environmental adverse effects or other societal disamenities.

The purpose of the proximity measures in EJScreen is to systematically and consistently quantify
different degrees of potential for these effects. We have developed a method to calculate a score that
represents the relative magnitude of the proximity of the population within a block group to facilities,
waste sites, or traffic surrounding it. A block group with more facilities closer to the block group's
residential population will have a higher score than a block group where facilities are further away. We
have applied this method to these facility or site types:

•	NPL sites (a key subset of "Superfund," sites).

•	Hazardous waste TSDFs, subject to regulations under the RCRA.

•	RMP facilities, which are facilities that maintain greater than certain quantities of
extremely hazardous substances, and are required to take certain actions, including
filing risk management plans, under section 112 (r) of the Clean Air Act.

We have developed a similar approach to represent proximity to traffic volume on nearby highways and
proximity to toxic concentrations on nearby water segments.

In the sections below, we will describe the general approach, in terms of facility proximity. We will then
describe how it differs for traffic and wastewater proximity. Then we will discuss certain adjustments we
have made, mostly to make the approach computationally efficient, and summarize the data sources
and computational routine that we applied to implement this approach. We conclude with caveats and
other observations.

Calculating Proximity to Facilities

Each of the 242,335 block groups for the U.S. states, District of Columbia, and Puerto Rico 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 from a given point within the block group, such as a facility, as explained at
the end of this section. About 20-25% of block groups covered an area smaller than a circle of radius

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300-350 meters (almost one quarter of a mile). Also, a very small number of block groups are extremely
large in area, in very rural locations.

All of a block group's blocks may have residential population estimated by the 5-year ACS, or only some,
and some block groups have no residents at all. Blocks and block groups vary greatly in geographic area,
and in population. The approach used here works first at the block level, based on measures of
proximity to the facilities in or near the blocks. The block-level measures are then aggregated among all
the blocks within a block group, weighted by the number of people in the different blocks.

Thus, while population is considered in aggregating the block scores, the measure does not increase or
decrease for block groups with higher or lower populations. The measure is, rather, a characteristic of
the residents of the block group, in the same way that cancer risk from Air Toxics or ozone
concentration are estimated measures of the conditions of those places.

Let

i represent a particular facility
j represent a block within a block group
k represent a block group

dij is the distance, in km, from block j's centroid to the given location of facility i
popjk is the estimated population of block j within block group k
popk is the total estimated population of block group k

f(dij) is a function representing the proximity of facility i to block j, a declining function of the
distance, dy

BlockScorejk is the aggregation of the proximity influences of all facilities affecting block jk
BlockGroupScorek is the population-weighted aggregation of the block group's component
blocks

We have chosen to define the proximity function as
f(dij) = 1 / dij

That is, a facility 1 km from a block's population contributes twice the score as a facility 2 km from the
same block. We note that we have made a choice in using inverse distance for this function. Air
dispersion modeling for pollutants following Gaussian plume assumptions would show a generally
greater drop-off in concentration, roughly with the second power to 2.5 power of one over distance. But
actual concentrations around individual plants follow often-complex patterns that depend on the
particular mix of stack versus fugitive emissions, characteristics of stack height, exit velocity and
temperature, the presence of buildings or other land surface characteristics and meteorology. Some
substances react readily with other substances in the atmosphere, or precipitate out readily. It is not
uncommon for concentrations to rise for some distance from the emitting source, and then to fall from
that peak concentration. The Gaussian plume model applies to gases, and emissions of particulates can
drop off more quickly than gases.

Releases to land may follow extremely complex patterns of dispersion. Added to that are the very site-
specific characteristics of potential human exposure via drinking water, vapor intrusion, or contact with
contaminated soils, etc. For water pollution, similar complexities exist, most notably that an effluent is

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carried away downstream of a running body of water, dilution can be complicated by the presence of
other water entering stream segments, by volatilization, by biological and chemical interactions, and by
deposition to sediments, and finally by the treatment and removal of a water pollutant sent to a
publicly-owned treatment works.

We also note that researchers and others have taken varied approaches to representing the proximity of
facilities to populations. The Environmental Justice Screening Method (EJSM) model of environmental
justice concerns, developed for the state of California, scored facility proximity in concentric rings
around a population centroid (Pastor Jr., Morello-Frosch, & Sadd, 2010; Sadd, Pastor, Morello-Frosch,
Scoggins, & Jesdale, 2011). All facilities within 1 mile received a score of 3. All within the 1- to 3-mile
band received a score of 2, and those between 3 and 5 miles received a score of 1. Anything beyond 5
miles received a score of zero. This step-wise scoring represents the judgment of the model developers,
influenced by interactions with various stakeholders.

Finally, we note that EJScreen's measure of proximity is intended to represent more than simply real or
potential human health adverse effects coming from exposure. Some parts of the environmental justice
literature reflect semi-quantitative factors, such as increased psychological stress, fear, and other
reactions to the presence of LULUs. This is not the forum for sorting through those factors.

However, we have made a judgment call: For the purposes of this EJScreen tool, we represent a facility's
measure of proximity by the inverse of its distance from the estimated location of the average person. A
block's proximity score is the sum of the inverse distances of all the facilities of a particular type.

Note that for the minority of block groups in the United States with no residential population, we take a
straight average of the block scores.

The units for these measures are facilities per km. A block group could have a score of 1.0 if all residents
were an average of 1 km 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 5 km from the residents.

Cak	ifflc

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 2. These segments differ from
a facility database in that they are lines on a geographic area, rather than points that represent the
facilities. In our approach, we find the distance from the block centroids to the nearest part of each
surrounding highway segment. The nearest point, dy, could be an end of the highway segment or some
point between the ends.

We also multiplied each dy by the AADT estimate that is associated with each highway segment. This is
meant to reflect the traffic intensity, and this differs from the facility approach, where we have taken

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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 km.

:ic Weighted Wastewater Dischargers

The proximity to wastewater dischargers was calculated using the same methodology as the proximity
to traffic—an inverse distance-weighted sum of water reach segments surrounding each block, and a
population-weighted sum of the individual blocks' contributions to the block group. As described above
in the Environmental Indicators in EJScreen section, the source data is RSEI modeled output of toxics
concentrations mapped to water segments. So essentially EPA replaced highways with National
Hydrography Dataset (NHD) reach water segments, and AADT counts with toxic concentrations.

The Wastewater Discharge map layer (as it is currently referred to in EJScreen) was adjusted to display
all National Pollutant Discharge Elimination System (NPDES) facility latitude/longitude with pollutant
loadings greater than zero. In addition, EPA provided more information in the popup box associated
with each mapped facility, including a link to the detailed facility information in the DMR Pollutant
Loading Tool, so that users can query the specific pollutants the facility may be discharging.

All relevant stream parameters were already included in the RSEI modeling context and were upgraded
to NHD Version 2.0, which contained improved flow estimates and better connectivity.

Currently the DMR Loading Tool includes 672 reported chemicals from DMRs, of which 201 are also
reported to TRI. The ideal set of loadings would be the union of the DMR and TRI datasets. For the
overlap, that is, facility/chemical combinations reported to both DMR and TRI, DMR reported quantities
were prioritized, as they are not subject to some of the constraints of TRI reporting and are usually more
accurate, being based on actual monitoring data.

The concentrations of the modeled chemicals were weighted by their relevant toxicity weight. Only
toxicity weights from RSEI were used. EPA excluded chemicals without RSEI toxicity weights or which
cannot be readily extrapolated for an existing chemical. RSEI toxicity weights are based solely on chronic
human health effects, and therefore, may be more suitable for an application like EJScreen that is
geared toward screening for human health concerns. For water releases, the RSEI toxicity weight is
calculated as the reciprocal of the reference dose (RfD) for noncarcinogens or cancer potency
factor/lxlO"6 for carcinogens. Crosswalks between chemical sets (DMR and TRI) allowed EPA to apply
toxicity weights and decay rates of TRI chemicals to matching DMR chemicals, so that DMR data could
be incorporated into the RSEI model.

The new indicator approach ran the standard RSEI modeling data processing procedures. EPA extracted
DMR and TRI data from the DMR Pollutant Loading Tool and structured the data to closely mimic the
standard RSEI input tables. EPA aimed to minimize the adjustments needed to accommodate DMR data
in the RSEI modeling procedures.

The new water indicator calculates proximity as the distance between the centroid of the Census block
to the midpoint of any stream reaches within a 500-meter radius of the block centroid. If no reaches are

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found within 500 meters, the nearest neighbor within 3 km is used. The indicator uses the output from
RSEI, to give more emphasis to stream reaches with higher toxicity-weighted pollutant concentrations.

uij

Where

W is the weighting factor (described below) and W > 0;

dij is the distance, in km, from the Census block centroid, j, to the midpoint of the stream reach,
/'; and,

f(dipW) is the weighted proximity function.

The resulting block values were aggregated to Census block groups using a population-weighted method
to produce block group level results. This is the same aggregation method used for the other EJScreen
proximity indicators.

Calculating Proximity - Additional Details

Proximity Calculations—block group proximity scores for Traffic, Superfund, RMP, Hazardous Waste, and
Wastewater Discharges are calculated with a new source. Census block centroids with population-
weights were updated to new 2020 version. These block points and associated populations and block
group weights were derived from 2020 Decennial Census P.L. 94-171 Redisricting data.

We address two modifications to the general method described above. The first deals with instances
where a facility or highway segment location is very close to the centroid of the block. The second is an
accommodation to the computational intensity of the general method.

Extremely Small dy Values

Our intention is to represent the proximity of facilities or highway segments to the population within
each block. All facilities and each part of all highway segments fall within one block. By chance, some
portion of those points fall very close to the block centroids.

We do not know how the population is geographically distributed within any block, but we assume that
people are more likely to be distributed across the blocks' expanses than to be concentrated at one
point, such as the centroid. In fact, for rural, suburban, and many non-high rise urban areas, people's
residences are more likely to be closer to the blocks' peripheries (bounded by roads) than clustered at
the centroids. Thus, when a facility location happens to be very close to the block centroid, it would
result in an artificially high contribution to the block's score. This is not a hypothetical problem: We have
observed dy values well below 100 meters, and some below 10 meters.

In looking for solutions to the problem, we conducted analyses and arrived at the approach we have
adopted. Blocks vary widely in their total area and in their shapes. Both can be found in the Census

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Bureau's Tiger shape files. Dealing explicitly with the individual block shapes would be computationally
very intensive because there are over 11 million blocks. Since we cannot easily find out how the
residents are actually distributed in those areas, we made two simplifying assumptions:

•	residents are evenly distributed across the surface area of each block, and

•	each block can be represented by a circle whose radius is [Block area / Pi] 1/2 .

We call this latter value the Block Area Equivalent Radius.

Our investigations indicate that for any dy less than the Block Area Equivalent Radius, 0.9 times that
value is a reasonable representation of the average distance from the facility for all residents in the
block. We call this the dy corrected.

Our computational scheme determines the dy values as described above, tests for the comparison with
Block Area Equivalent Radius, and substitutes dy corrected values. We found that we needed to make
that correction for less than 1% of all facility/block combinations in an early testing dataset that used
2005-2009 ACS data.

Accommodating to Computational Intensity - Combine a Distance Limit with a Nearest
Facility Approach

Our task is to compute a proximity score for each of the facility or site types and highway segments for
each of the more than 235,000 block groups, composed of over 8 million blocks. The number of facilities
nation-wide varies from hundreds of TSDFs to many thousands of RMP facilities. Computing all the
combinations would require more computational time and resources than were available.

In addition, doing so would be wasteful and perhaps irrelevant. The one over distance function we have
chosen to represent concerns about facilities and highways drops off greatly for most facilities beyond
the nearest ones. The miniscule contribution of a facility 100 km or more from a block is not only small,
compared with those that may be within 5 to 10 km, 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 km of a block's centroid, and within 500 meters for highway segments. Depending on the facility or
site type, we find that 30-40% of block groups have at least one facility (RMP or TSDF) within the 5-km
limit, and almost 10% have one or more NPL sites within 5 km, in EJScreen.

Of course, every block and block group has one nearest facility, even though it may be beyond the 5-km
horizon, and some of those may be fairly close to that limit. We have also calculated the distance to the
facility nearest to each of the blocks. For those blocks lacking anything within the 5 km, we represented
the facility proximity by one over the distance to that single nearest facility.

This added computational complexity to the approach, but at far less cost than computing the full matrix
of millions of blocks times thousands of facilities and sites.

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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. Counting
only the single nearest facility beyond 5 km has the effect of shifting scores under 0.2 to the left, to
lower scores than if all were counted, but the graphs show no major discontinuities, suggesting this
limitation (counting only the nearest one) has little impact overall.

mtational Scheme

Using the Census block centroids, the distance to all facilities within 5 km 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.

The djj values were compared to the Block Area Equivalent Radius and corrected values were used when
necessary, before computing 1 / djj. The 1 / djj values were summed for each block to compute the
BlockScorejk- These were then rolled up to the block group level, applying the population weighting
described above, for the final BlockGroupScorek-

Caveats and Observations

Several aspects of the proximity analysis approach have been mentioned above, but deserve summary
here.

•	We recognize that our selection of the inverse of distance is a design choice that represents our
judgment of a balance among competing factors.

•	We recognize that one could potentially attempt to distinguish among facilities within each
facility category by quantitative or qualitative measures of importance. These could include total
pounds released or toxicity-weighted releases for NPDES facilities; the number of accidental
releases and/or their apparent severity for RMP facilities; some classification of the likelihood of
releases for NPL sites or TSDFs; and general indications of scale for all of them. We note that
CalEnviroscreen has addressed this issue to some extent, and that the RSEI tool based on TRI
data may be relevant to future work on this issue. At this point, we have chosen not to develop
any such potential scaling adjustments.

•	We recognize that all location data are subject to potential error. While we have high
confidence in the block centroid locations, we know that the facility or site or roadway location
data may contain larger or smaller errors, and that for large facilities or sites, one point may not
be an entirely adequate representation of the location of its releases or of neighbors'
perceptions.

•	We recognize that the computational accommodation we describe above results in a hybrid of
measures: For some block groups, all blocks have one or more facilities within 5 km and the
score is the summation of all those potentially multiple facility/block combinations; for other
block groups, none of the blocks have a facility within 5 km and the score is the contribution of

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the single facility closest to each block; and for some block groups, we have a mix of those
situations. We believe that this is a reasonable compromise.

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8 Details on U.S. Territories

EJScreen features select environmental and socioeconomic indicators for the following U.S. Territories:
American Samoa, CNMI, Guam, Puerto Rico, and the U.S. Virgin Islands. American Samoa, CNMI, and
Guam use 2013 Census Place boundaries. The U.S. Virgin Islands use 2013 Census Estate boundaries.
However, Puerto Rico is included with all 50 states and the District of Columbia. The term "territories" in
this document refers to the four territories excluding Puerto Rico. The territories data for American
Samoa, CNMI, Guam, and the U.S. Virgin Islands provides an additional 605 records. These territories
have State percentiles data and do not have National percentiles.

Data Sources

•	Census boundaries (Places, Estates, and Counties) are from Cartographic Boundary files, 2013,
1:500K scale.

•	Demographic data—2010 Demographic Profiles of Island Areas published in 2014.

¦ ces	ps

•	The block population weight table was generated based on distributing 2010 population for
Estate or Place boundaries

•	Build demographic indicator tables by extracting the Place/Estate summary level data from the
Demographic Profiles. Indicators include: % people of color; % low income; % limited English
speaking; % less than high school education; % under age 5; and % over age 64

•	Calculate Demographic Index: (% people of color + % low income) / 2

•	Collect all the available environmental data sources

•	Calculate EJ Indexes.

c Availability for the Territories

I	....	I

PM 2.5

No

No

No

No

Ozone

No

No

No

No

Diesel PM

No

No

No

No

Air Toxics Cancer Risk

No

No

No

No

Air Toxics Respiratory HI

No

No

No

No

Traffic Proximity

No

No

No

No

Lead Paint

Yes

Yes

Yes

Yes

Superfund Proximity

No

No

Yes

Yes

RMP Facility Proximity

Yes

No

Yes

Yes

Hazardous Waste Proximity

No

No

Yes

Yes

Underground Storage Tanks

Yes

Yes

Yes

Yes

Wastewater Discharge

No

No

No

No

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ss abi ' 1 to Rico

•	While Puerto Rico is a U.S. Territory, it is included in the Census ACS 5-year Summary datasets,
so EJScreen treats it like a state.

•	It has both National and State-level percentiles and indexes.

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9 Other Data Element Descriptions

This section describes additional datasets now available in EJScreen. They include Health Disparities
(Low Life Expectancy, Heart Disease, and Asthma); Climate Change-related (Wildfire Risk and Flood
Risk); Sensitive Communities (Colonias from U.S. Department of Housing and Urban Development
[HUD], Texas, and New Mexico); and Critical Service Gaps (broadband service gaps, food deserts, and
medically underserved areas).

1th Disparities Data

•	Low Life Expectancy—Average life expectancy data developed as a collaboration between
NCHS, the National Association for Public Health Statistics and Information Systems (NAPHSIS),
and the Robert Wood Johnson Foundation. This data is available at the tract level. Source: U.S.
Small-area Life Expectancy Estimates Project (USALEEP)

It is derived from Life Expectancy at Birth from CDC. National Center for Health Statistics using
the formula of % Low Life Expectancy is defined as "1 - (Life Expectancy / Max Life Expectancy)".
The following processing steps were used to bring the data into EJScreen:

o The source is Census 2015 Tract-level data, so it was first converted to 2010 tracts using

the Census 2010 to 2015 relationship table,
o 2010 tracts were then converted to 2020 tracts using the Census 2010 to 2020
relationship table.

o Low Life Expectancy values were assigned to each child 2020 block group within the
same Census tract.

•	Heart Disease—Heart disease prevalence among adults aged 18 years or older. The term "heart
disease" refers to several types of heart conditions. This data is available at the tract level.
Source: CDC Places Data

•	Asthma—Asthma prevalence among adults aged 18 or older. This data is available at the tract
level. Source: CDC Places Data

¦ 11	" • d Risk	•	et

The First Street Foundation is partnering with EPA to provide climate risk data to EJScreen. First Street
provided Census block group-level data, including percent of properties at risk of being affected by
wildfires or by flooding based on current climate conditions as well as conditions projected to exist in 30
years. EJScreen merged the data with current EJScreen spatial data and generated national and state
percentiles. EJScreen includes:

•	Wildfire Risk—The household risk of wildfire exposure under 2022 weather conditions as
modeled by the First Street Foundation. Source: https://firststreet.org/

•	Flood Risk—The household risk of flooding under 2022 weather conditions as modeled by the
First Street Foundation. Source: https://firststreet.org/

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The First Street Foundation-Wildfire Model (FSF-WFM) is a 30-meter resolution model representing the
wildfire exposure for any specific location in the contiguous U.S., today and with the future climate
change. The risk of wildfire is derived from a series of inputs associated with fire fuels, weather, human
influence, and fire movement. Bringing all of these inputs together, at a national scale, in a high-
resolution, climate-adjusted model represents a first-of-its-kind property-level wildfire risk model.

The First Street Foundation Flood Model is a nationwide probabilistic flood model that shows the risk of
flooding at any location in all 50 states and Puerto Rico due to rainfall (pluvial), riverine flooding (fluvial),
and coastal surge flooding. While other hydraulic and hydrologic models show refined risks of flooding in
certain areas, this model provides complete coverage across the United States at 3-meter resolution.

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