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Environmental
Quality Index
Overview Report
*>EPA
United States
Environmental Protection
Agency
EPA/600/R-14/305 September 2014 www.epa.gov/ord
Office of
Research and Development
National Health and
Environmental Effects
Research Laboratory
Environmental Public Health Division

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AEP/K
EPA/600/R-14/305 | September 2014 | www.epa.gov/ord
United States
Environmental Protection
Agency
ENVIRONMENTAL QUALITY INDEX
Overview Report
Office of Research and Development
National Exposure Research Laboratory

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Project Personnel
Dane lie T. Lobdell. U.S. Environmental Protection Agency (EPA), Office of Research
and Development (ORD), National Health and Environmental Effects Research
Laboratory (NHEERL)
Jyotsna Jagai. University of Illinois at Chicago. Oak Ridge Institute for Science and
Education (ORISE) Faculty Grantee
Lynnc C. Messer, Portland State University, Support Contractor
Kristen Rappazzo. University of North Carolina (UNC), Department of Epidemiology.
ORISE Grantee
Shannon Grabich. UNC, Department of Epidemiology. ORISE Grantee
Christine L. Gray. UNC, Department of Epidemiology. ORISE Grantee
Kyle Messier, Student Services Contractor
Gence Smith, Student Services Contractor
Suzanne Pierson, Innovate!. Inc.. Geographic Information Systems (GIS) Contractor
Support
Barbara Rosenbaum. Innovate!. Inc.. GIS Contractor Support
Mark Murphy, Innovate!. Inc.. GIS Contractor Support
Acknowledgments
External Peer Reviewers
Angel Hsu. Yale University, School of Forestry and Environmental Studies
Paul D. Juarez, University of Tennessee Health Science Center, Department of
Preventive Medicine
Peter H. Langlois. Texas Department of State Health Services. Birth Defects
Epidemiology and Surveillance Branch
Internal Peer Reviewers
Jane Gallagher, U.S. EPA, ORD. NHEERL
Thomas Brady, U.S. EPA, Region 5
Lisa Smith, U.S. EPA, ORD. NHEERL
This document has been reviewed by the U.S. Environmental Protection Agency. Office
of Research and Development, and approved for publication. Mention of trade names
or commercial products docs not constitute endorsement or recommendation for use.
iv

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Table of Contents
1.0 Introduction	1
Background	1
Purpose	2
Uses of Environmental Quality Index	2
2.0 Construction of the Environmental Quality Index	3
Domain Identification	3
Approach	3
Summary of Activities	3
Data Source Identification and Review	3
Approach	3
Summary of Activities	3
Variable Construction	6
Approach	6
Summary of Activities	10
Data Reduction and Index Construction	10
Approach	10
Results	12
3.0 Discussion	13
Strengths and Limitations	13
Other Environmental Indices	13
Conclusions	13
4.0 References	15
Appendix I: County Maps of Environmental Quality Index	A-1
Appendix II: Quality Assurance	B-l
v

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List of Figures
Figure 1. Conceptual environmental quality—hazardous and beneficial aspects	1
Figure 2. Principal component analysis for the Environmental Quality Index (EQ1). All counties
included with four rural-urban continuum codes (RUCCs)	10
Figure 3. Rural-urban continuum codes (RUCCs) for all counties in the United States	11
Figure 4. Map of the Environmental Quality Index by rural-urban continuum codes (RUCCs)	11
vi

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List of Maps
Map 1. Environmental Quality Index by County, 2000-2005 NOTE: EQ1 valus suggest worse
environmental quality, and lower EQ1 values suggest better environmental quality	A-1
Map 2. Air Domain Index by County, 2000-2005 	A-2
Map 3. Water Domain Index by County, 2000-2005 	A-2
Map 4. Land Domain Index by County, 2000-2005	A-3
Map 5. Built Domain Index by County, 2000-2005 	A-3
Map 6. Sociodemographi c Domain Index by County, 2000-2005 	A-4
Map 7. Environmental Quality Index Stratified by Rural-Urban Continuum Codes by County,
2000-2005	A-5
Map 8. Air Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005	A-5
Map 9. Water Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 .... A-6
Map 10. Land Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 .... A-6
Map 11. Built Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005 .... A-7
Map 12. Soci odemographi c Domain Index Stratified by Rural-Urban Continuum Codes by County,
2000-2005	A-7
vii

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List of Tables
Table 1. Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographi c Domains for
Use in the Environmental Quality Index	4
Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Soci odemographi c
Domains for Use in the Environmental Quality Index 	5
Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Soci odemographi c
Domains for Use in the Environmental Quality Index 	6
Table 2. List of Variables by Domain Included in the Environmental Quality Index	7
Table 2. (continued) List of Variables by Domain Included in the Environmental Quality Index	8
Table 2. (continued) List of Variables by Domain Included in the Environmental Quality Index	9
Table 3. Weights for Each Domain's Contribution to the Environmental Quality Index for 3 141
U.S. Counties (2000-2005) and for the Counties Stratified by Their
Rural-Urban Status (RUCC code)	12
viii

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1.0
Introduction
A better way to calculate overall environmental quality
is needed for researchers who study the environment and
its effects on human health. This report is an overview of
how the environmental quality index (EQI) was developed
for all counties in the United States for the period 2000-
2005. The EQI represents five areas (called "domains")
of the environment ([1] air, [2] water, [3] land, [4] built,
and [5] sociodemographic). In addition to the EQI, there is
an index for each of the five domains. The EQI accounts
for environmental differences between urban and rural
areas by grouping counties into one of four rural-urban
continuum codes (RUCCs). ranging from highly urban to
rural-isolated areas.
The EQI was developed in four steps: (1) The five domains
were identified, (2) data for each of the five domains were
located and reviewed, (3) enviromnental variables were
developed from the data sources, and (4) data were combined
in each of the environmental domains; then these domain
indices were used to create the overall EQI. The EQI relied
on data sources that are mostly available to the public. The
approach to creating the EQI is outlined, so others can repeat
the steps for their own unique areas of interest.
This report gives an overview of the EQI. A companion
report. Creating an Overall Enviromnental Quality Index,
Technical Report, provides the detailed methodology and
results. The variables. EQI, domain-specific indices, and
EQI stratified by rural-urban data are available publically
at the U.S. Environmental Protection Agency's (EPA's)
Environmental Dataset Gateway. Also, an interactive map of
the EQI is available at EPA's GeoPlatform.
Background
The assessment of environmental exposures for human
health is changing, and new methods constantly are being
developed. Exposures (both good and bad) that affect
human health happen at the same time, but understanding
their combined impact is difficult. For example, negative
environmental features, such as landfills and industrial plants,
often are located in neighborhoods with a high percentage of
minority and poor residents. [1-7] On the other hand, high-
income neighborhoods often have features that promote
health, such as parks, health clubs, and well-stocked grocery
stores. 18.91 Yet, no single exposure can be held responsible
for good or poor health. It is not just good quality air or high
income that produces health because many other exposures
promote good health as well.
ENVIRONMENTAL QUALITY
Hazardous	Beneficial
Figure 1. Conceptual environmental quality—hazardous
and beneficial aspects.
One limitation to current methods in enviromnental health
research is the focus on single-exposure types. Well-
designed enviromnental health studies face a trade-off:
Either researchers can collect a lot of high-quality data on
only a few participants because collecting detailed exposure
data is expensive and time-consuming, or researchers can
collect less-detailed exposure data on a larger number
of study participants because, the more participants in a
study, the more expensive it is to conduct. This trade-off
makes it impossible to account for many exposures that
study participants might experience in addition to the main
exposures of interest.
An index that summarizes many variables into a single
variable is one approach that could improve statistical
efficiency and still account for many enviromnental
exposures at once. The index then could be used to identify
areas with different levels of enviromnental quality. Clusters
of negative enviromnental exposures could be identified and
linked to health outcomes.
Conceptually, an EQI accounts for the multiple domains
of the environment that encompass an area where humans
interact (see Figure 1). These domains include chemical,
natural, built, and sociodemographic environments that have
both positive and negative influences on health. People move
in and out of these positive and negative influences. Also, the
positive and negative influences may even be co-located. As
a result, the EQI examines both adverse health outcomes and
protective health events.
1
Polluted Air
Home Ownei
Factories

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Purpose
A better estimate of overall environmental quality is needed.
It will improve the understanding of the relationship between
environmental conditions and human health. Thus, an EQI
was developed for all counties in the United States. The EQI
uses indicators from the chemical, natural, built, and social
environment. The EQI is composed of five environmental
domains: (1) air. (2) water, (3) land. (4) built, and
(5) sociodemographic.
Uses of EQI
The EQI was designed to be used in two main ways: (1) to
represent "environmental quality" in research designed to
assess the relationship between environmental quality and
human health outcomes and (2) as a variable to account for
surrounding conditions for researchers interested in a specific
environmental exposure (e.g., exposure to pesticides) and
human health outcomes (e.g., cancer). However, other uses
of the data arc expected by different end users, such as local,
county. State and Federal governments, nongovernmental
organizations, and academic institutions.
The EQI holds promise for improving environmental
estimation in public health because it describes the
surrounding county-level conditions to which residents arc
exposed. Use of the EQI will help public health researchers
investigate the cumulative impact of many diverse
environmental domains. The EQI was developed to help
understand which domains (air, water, etc.) contribute the
most to the overall environment. It also may be important
for policymakers and environmental health workers to have
information specific to the domains. Thus, domain-specific
indices also were created. Each domain-specific index can
be helpful to understand which domain is making the biggest
contribution to the total environment in that particular county.
This also can be expanded to understanding environmental
differences by urban or rural status. In addition, researchers
can use the EQI to control for environmental quality in their
studies of specific exposures on health outcomes, adding
environmental context to isolated exposures.
Another potential use of the EQI is for the comparison of
county environmental quality across the United States. The
EQI can be used to identify counties having a greater burden
of poor health because of poor environmental quality and
to see the important environmental domains contributing to
an individual county's environmental quality. With the EQI
currently at county level, environmental injustice may be
difficult to tease out; however, the methods applied may be
used to make local EQIs for smaller geographical areas.
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2.0
Construction of the EQI
Domain Identification
Approach
Three sources were used to identify EQI domains:
1.	EPA's Report on the Environment (ROE),[10]
2.	an environmental health literature review (searches
for published papers reporting on "environment" and
"infant mortality"), and
3.	expert consultation.
The ROE served as the starting point for the EQI. The media
chapters from the ROE were used to identify environmental
domains, data sources, and variables. Three domains were
identified: (1) air, (2) water, and (3) land.
After reviewing the ROE. studies of environmental effects on
infant mortality were reviewed. This enabled exploration of
environmental domains using an indicator of national health
and well-being. To be thorough, publications that came up in
many searches were used to find more references. A broader
definition of "environment" emerged.
Based on the literature search, the built and
sociodemographic environments were explored. Negative
environmental exposures have been associated with social
exposures. A social epidemiologist and other experts
were consulted to help create a broader definition of
"environment" for the EQI.
Summary of Activities
Based on the three sources. (1) the ROE. (2) literature review,
and (3) experts, five environmental domains were identified
and developed for the EQI: (1) air. (2) water, (3) land. (4)
built, and (5) sociodemographic.
Data Source Identification and Review
Approach
Predetermined categories were identified to represent each
domain. Based on these categories, data were gathered for
each domain (air, water, land, built, and sociodemographic)
for all 3141 counties in the United States. The process
included the following steps:
•	find EPA and non-EPA environmental data sources;
summarize the data sources in terms of availability,
data quality, spatial and temporal coverage, storage
requirements, and how to access the data;
•	decide the most appropriate data sources for each
domain; and
•	obtain the identified datasets.
Possible data sources for each of the five domains were
found using Web-based search engines (e.g., Google),
sitc-spccific search engines (e.g., Federal and State data
sites), scientific data sources (e.g., PubMed, Science Direct.
TOXNET), and personal communication from data owners.
Data available for all U.S. counties for the years 2000-2005
was wanted. An inventory of all the found data sources
was created.
Several criteria were used to assess data sources. Three key
criteria included (1) data representing the predetermined
category. (2) data quality, and (3) data coverage (available
across the United States, including Hawaii and Alaska).
Other factors were the ability to aggregate data at the county
level and having data within the 2000-2005 time period.
Ideally, data would be available every year from 2000
to 2005.
Summary of Activities
The overall data inventory is available at EPAs
Environmental Dataset Gateway. Table 1 lists and describes
the data sources that were used to make the EQI. An
overview of the number of data sources kept for each domain
is presented below.
Air Domain
Three data categories were considered: (1) monitoring data,
(2) emissions data, and (3) modeled estimates representing
concentrations of either criteria air pollutants or hazardous
air pollutants (toxics). Twelve data sources were identified,
and seven were considered for the EQI. Two were used
for the air domain of the EQI because they were the
most complete.
Water Domain
Five broad data categories within the water domain were
identified: (1) modeled, (2) monitoring, (3) reported. (4)
surveyed/studied and (5) miscellaneous data. Eighty data
sources were identified. Five were used for the water domain
of the EQI.
Land Domain
Land domain data sources were grouped into four categories:
(1) agriculture. (2) industrial facilities, (3) geology/mining,
and (4) land cover. Eighty sources were identified. Eleven
were kept and used in the land domain of the EQI: two from
agriculture, seven from facilities, and two from geology/
mining.
Sociodemographic Domain
The sociodemographic domain is represented by crime and
socioeconomic data. Only two data sources were kept for the
sociodemographic domain of the EQI.
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Table 1. Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use in the
Environmental Quality Index
Air Domain



Source of Data
Description
Strengths
Limitations
Air Quality System[11]
National-Scale Air
Toxics Assessment^ 2]
Water Domain
Source of Data
Watershed
Assessment, Tracking
and Environmental
Results Program
Database/Reach
Address Database[13]
National Contaminant
Occurrence
Database[14]
Estimates of Water
Use in the United
States[15]
Drought Monitor
Data[16]
National Atmospheric
Deposition
Program[17]
Repository of ambient air quality
data, including both criteria and
hazardous air pollutants (HAPs)
Estimates of hazardous air
pollutant concentrations using
emissions information from the
National Emissions Inventory
and meteorological data input
into the Assessment System
for Population Exposure
Nationwide model
Description
Collection of EPA water
assessments programs,
including impairment, water
quality standards, pollutant
discharge permits and beach
violations
Samples both regulated and
unregulated contaminants
in public water supplies;
maintained by EPA to satisfy
statutory requirements for Safe
Drinking Water Act
County-level estimates of
water withdrawals for domestic,
agricultural, and industrial
use calculated by the U.S.
Geological Survey
Geographic information systems
raster files reporting weekly
modeled drought conditions.
A collaboration that includes
the National Atmospheric and
Oceanic Administration, the
U.S. Department of Agriculture,
and academic partners.
Measures deposition ofvarious
pollutants, such as calcium,
sodium, potassium, and sulfate,
from rainfall
Measured values; network of
criteria air pollutant monitors
is substantial; measurement
occurs regularly and is
synchronized; data are audited
for accuracy and precision.
Validated models; coverage for
all U.S. counties; majority of
HAPs included.
Strengths
Only database maintaining
information on EPA Clean Water
Act regulations
Provides measures for several
chemicals and pathogens that
are not measured elsewhere
County-level estimates
Weekly coverage for the entire
country
Weekly coverage for the entire
country
The HAP network is sparse;
some counties have no
monitors, necessitating
interpolation of concentrations
for unmonitored locations.
Data are available at 3-year
intervals; may underestimate
concentrations; uses simplifying
assumptions when information
is missing or of poor quality;
changes in methodology may
result in different estimates
between years.
Limitations
Data maintained and provided
by States and, therefore, difficult
to compare across States and
not consistently reported with
respect to temporal reporting
and type of data reported
across States.
Data provided by public water
supplies; therefore, need to
use spatial aggregation to get
county-level estimates
Estimated based on various
data sources
Modeled data; raster data,
therefore, required spatial
aggregation.
Data not at the county level and
required spatial interpolation.
4

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Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use
in the Environmental Quality Index
Land Domain



Source of Data
Description
Strengths
Limitations
National Pesticide Use
Database: 2002[18]
2002 Census of
Agriculture Full
Report[19]
EPAGeospatial Data
Download Service[20]
National Geochemical
Survey[27]
Map of Radon
Zones[28]
Delineates State-level pesticide
usage rates for cropland
applications; contains estimates
for active ingredients, of which
68 are insecticides, and 22 are
other pesticides.
Summary of agricultural activity,
including number of farms by
size and type, inventory and
values for crops and livestock,
and operator characteristics
Maintained by EPA and provides
locations of and information on
facilities throughout the United
States; different datasets within
this database are updated at
different intervals, but most
are updated monthly; no set
spatial scale across datasets.
Some provide addresses, some
geocoded addresses, etc.
Geochemical data (arsenic,
selenium, mercury, lead, zinc,
magnesium, manganese, iron,
etc.) for the United States based
on stream sediment samples
Identifies areas of the United
States with the potential for
elevated indoor radon levels;
maintained by EPA
Provides a measure of pesticide
usage
Can be used to approximate
land- and water-related
agricultural outputs (e.g.,
potential pesticide burden per
acre, potential exposure to
cattle, dust, etc.)
Indicators for major facilities
(e.g., Superfund sites;[21]
Large Quantity Generators;[22]
Toxics Release lnventory;[23]
Resources Conservation
and Recovery Act Treatment,
Storage, and Disposal
Facilities and Corrective Action
Facilities;[24] Assessment,
Cleanup, and Redevelopment
Exchange Brownfield sites;[25]
and Section Seven Tracking
System pesticide producing site
locations[26]) are available.
Provides county-level means
and standard deviations for
each element; sampled data
interpolated over nonsampled
space results in variance
estimates.
Each U.S. county is assigned to
one of three radon zones based
on radon potential.
Pesticide rates only available at
the State level for contiguous
states; noncropland uses are
not included.
Not direct measures of
pesticides or probable
exposures
Contains much more
information than just the
facilities, type, and location; for
example, Standard Industrial
Classification System and North
American Industry Classification
System codes, Native American
jurisdictions, interest type, etc.
Includes data from several
surveys; therefore, sampling
locations and number of
samples available vary by
location.
Data are not actual
measurements of radon, and
only three levels of radon
potential reduce possible
county-level variability.
Sociodemographic Domain
Source of Data Description
Strengths
Limitations
U.S. Census[29]
Uniform Crime
Reports[30]
County-level population
and housing characteristics,
including density, race,
spatial distribution, education,
socioeconomics, home and
neighborhood features, and
land use
County-level reports of violent
crime
Uniformly collected and
constructed across the United
States and can be used for
construction of a variety of
different variables
General estimate of public
safety exposure
Decennial census available
every 10 years; sample data
are available at more frequent
(e.g., 1-, 3-, and 5-year)
intervals; may underestimate
concentrations; uses simplifying
assumptions when information
is missing or of poor quality
Reporting may differ across
geography
5

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Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic Domains for Use
in the Environmental Quality Index
Built-Environment Domain


Source ot Data
Description
Strengths
Limitations
Dun and Bradstreet
North American
Industry Classification
System codes[31]
Description of physical activity
environment (recreation
facilities, parks, physical-
fitness-related businesses)
food environment (fast-
food restaurants, groceries,
convenience stores) education
environment (schools,
daycares, universities) per
county
Detailed, thorough data;
geocoding to county level
is likely accurate; ongoing
updates.
Proprietary data; not publicly
available
Topologically
Integrated Geographic
Encoding and
Road type and length per
county
National coverage
Different road types may not
be equivalent across U.S.
geography; confer different
Referencing[32]
Fatality Annual
Reporting System[33]
Housing and Urban
Development Data[34]
Annual pedestrian-related
fatality per 100,000 population;
maintained by National Highway
Safety Commission
Housing authority profiles
provide general housing details
(low-rent and subsidized/section
8 housing); information updated
by individual public housing
agencies.
County-level reports and annual
updates
Complete data source for
unique element of the urban
built environment
exposure risks.
Pedestrian fatalities result from
diverse types of events and
are not well captured in the
database.
Not all counties contain housing
authority properties; when the
value for housing authority = 0,
no housing authority property is
present.
Built-Environment Domain
Built-environment data sources were grouped by categories:
traffic-related, transit access, pedestrian safety, access to
various business environments (such as the food, recreation,
health care, and educational environments), and the presence
of subsidized housing. Twelve data sources were identified,
and four were kept for the built-environment domain of the
EQI: (1) one traffic-related, (2) one for pedestrian-safety, (3)
one for use in the various business enviromnents (physical
activity, food, health care, and educational), and (4) one for
subsidized housing.
Variable Construction
Approach
After researching and choosing data sources, variables
were created to represent each of the five domains ([1] air,
[2] water, [3] land, [4] sociodemographic, and [5] built
environment]. New variables were created because raw data
sources were not always appropriate for statistical analysis.
For example, a data source might provide the count of
Superfund sites in a county, but that raw count is not terribly
informative for enviromnental health research because counts
likely vary by the number of people who live in a county.
Therefore, a population-adjusted count or rate variable is
created, where the count of Superfund sites in a county is
adjusted for the number of people who live in that county.
The process for creating variables was to
•	make variables for each domain for each available year
of data (2000-2005),
•	look for pairs or groups of variables that are giving the
same information statistically and decide which of the
variables best represents the enviromnental domain
(and remove the extra variables),
•	look for missing data,
•	look at the distribution and statistical properties of
each variable and decide how it should be scaled for
analysis, and
•	average variables from 2000-2005 for each county.
Table 2 provides a listing of variables for each domain.
Appendix II in Creating an Overall Enviromnental Quality
Index, Technical Report lists all the variables considered
for the EQI. It also lists which variables were kept and why
others were not kept.
6

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Table 2. List of Variables by Domain Included in the Environmental Quality Index
Domain Variable Definition
Air
Particulate matter under 10 |jm in aerodynamic
diameter
Particulate matter under 2.5 |jm in aerodynamic
diameter
Nitrogen dioxide
Sulfur dioxide
Ozone
Carbon monoxide
1,1,2,2-tetrachloroethane
1,1,2-trichloroethane
1,2-dibromo-3-chloropropane
2,4-toluene diisocyanate
2-chloroacetophenone
2-nitropropane
4-nitrophenol
Acetonitrile
Acetophenone
Acrolein
Acrylic acid
Acrylonitrile
Antimony compounds
Benzidine
Benzyl chloride
Beryllium compounds
Biphenyl
bis-2-ethylhexyl phthalate
Bromoform
Cadmium compounds
Carbon disulfide
Carbon tetrachloride
Carbon sulfide
Chlorine
Chlorobenzene
Chloroform
Chloroprene
Chromium compounds
Cresol/cresylic acid
Cumene
Cyanide compounds
Domain Variable Definition
Air
Dibutylphthalate
Diesel engine emissions
Dimethyl formamide
Dimethyl phthalates
Dimethyl sulfate
Epichlorohydrin
Ethyl acrylate
Ethyl chloride
Ethylene dibromide
Ethylene dichloride
Ethylene glycol
Ethylene oxide
Ethylidene dichloride
Glycol ethers
Hexachlorobenzene
Hexachlorobutadiene
Hexachlorocyclopentadiene
Hexane
Hydrazine
Hydrochloric acid
Isophorone
Lead compounds
Manganese compounds
Mercury compounds
Methanol
Methyl isobutyl ketone
Methyl methacrylate
Methyl chloride
Methylhydrazine
Methyl tert-butyl ether
Nitrobenzene
N,N-dimethylaniline
o-toluidine
Polycyclic organic matter/polycyclic aromatic
hydrocarbons
Pentachlorophenol
Phosphine
Phosphorus
Polychlorinated biphenyls
7

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Table 2. (continued) List of Variables by Domain Included in the Environmental Quality Index
Domain Variable Definition
Air
Propylene dichloride
Propylene oxide
Quinoline
Selenium compounds
Styrene
Tetrachloroethylene
Toluene
Trichloroethylene
Triethylamine
Vinyl acetate
Vinyl chloride
Vinylidene chloride
Domain Variable Definition
Water
Percent of stream length impaired in county
Sewage permits per 1000 km of stream in
county
Industrial permits per 1000 km of stream in
county
Stormwater permits per 1000 km of stream in
county
Number of days closed per event in county,
2000-2005
Number of days per contamination advisory
event in county, 2000-2005
Number of days per rain advisory event in
county, 2000-2005
Percent of population on self supply, average
2000 and 2005
Percent of public supply population that is on
surface water, average 2000 and 2005
Calcium precipitation weighted mean
Magnesium precipitation weighted mean
Potassium precipitation weighted mean
Sodium precipitation weighted mean
Ammonium precipitation weighted mean
Nitrate precipitation weighted mean
Chloride precipitation weighted mean
Sulfate precipitation weighted mean
Total mercury deposition
Percent of county in extreme or exceptional
drought (intensity levels D3 and D4,
respectively)
Arsenic
Barium
Cadmium
Chromium
Cyanide
Fluoride
Mercury (inorganic)
Nitrate
Nitrite
Selenium
Antimony
Beryllium
Thallium
Endrin
Lindane
Methoxychlor
Toxaphene
Dalapon
di(2-ethylhexyl) adipate
Oxamyl (Vydate)
Simazine
di(2-ethylhexyl) phthalate
Picloram
Dinoseb
Hexachlorocyclopentadiene
Carbofuran
Atrazine
Alachlor
Heptachlor
Heptachlor epoxide
2,4-Dichlorophenoxyacetic acid
Hexachlorobenzene
Benzo[a]pyrene
Pentachlorophenol
1,2,4-Trichlorobenzene
Polychlorinated biphenyls
1,2-Dibromo-3-chloropropane
Ethylene dibromide
Xylenes
Chlordane
Dichloromethane (Methylene chloride)
1,2-Dichlorobenzene (o-Dichlorobenzene)
1,4-Dichlorobenzene (p-Dichlorobenzene)
8

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Table 2. (continued) List of Variables by Domain Included in the Environmental Quality Index
Vinyl chloride
1.1-Dichloroethylene
trans-1,2-Dichloroethylene
1.2-Dichloroethane	(Ethylene dichloride)
1.1.1-Trichloroethane
Carbon tetrachloride
1,2-Dichloropropane
Trichloroethylene
1.1.2-Trichloroethane
Tetrachloroethylene
Benzene
Monochlorobenzene (Chlorobenzene)
Toluene
Ethylbenzene
Styrene
Alpha particles
cis-1,2-Dichloroethylene
Silvex
Domain Variable Definition
Land
Harvested acreage
Irrigated acreage
Farms per acre
Manure applied
Chemicals used to control nematodes
Chemicals used to control disease
Chemicals used to defoliate/control growth/thin
fruit
Animal units
Herbicides
Fungicides
Insecticides
Arsenic
Selenium
Mercury
Lead
Zinc
Copper
Sodium
Magnesium
Titanium
Calcium
Iron
Aluminum
Phosphorus
Facilities per county population
Radon zone
Domain Variable Definition
Sociodemog raph ic
Percent renter occupied
Percent vacant units
Median household value
Median household income
Percent persons with income below the poverty
level
Percent who do not report speaking English
Percent earning greater than high school
education
Percent unemployed
Percent work outside county
Median number rooms per house
Percent of housing with more than 10 units
Mean number of violent crimes per capita
Domain Variable Definition
Built Environment
Proportion of roads that are highways
Proportion of roads that are primary streets
Traffic fatality rate
Percent of population using public transport
Vice-related businesses
Entertainment-related businesses
Education-related businesses
Negative-food-related businesses
Positive-food-related businesses
Health-care-related businesses
Recreation-related businesses
Transportation-related businesses
Civic-related businesses
Total subsidized housing units

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Principal components
analysis (PCA) reduced
multiple variables into
domain-specific indices
for each RUCC strata and
overall
Domain-specific indices
combined using PCA to
create EQI for each
RUCC strata and overall
Air
variables
Socio-
demographic
variables
Water
variables
Built
variables
Land
variables
EQI
Built
Indices
Air
Indices
Land
Indices
Water
Indices
OVERALL
RUCC1 - metropolitan-urbanized
RUCC2 =nonmetropolitan-urbamzed
RUCC3 -less urbanized
RUCC4 =thinty populated
Figure 2. Principal component analysis for the Environmental Quality Index (EQI). All counties included with four
rural-urban continuum codes (RUCCs).
Summary of Activities
New variables were created for each domain. These variables
were created using data relevant to that domain. The variable
characteristics were checked to make sure they were created
in a way that would make sense statistically and would work
with the chosen variable reduction method.
Data Reduction and Index Construction
Approach
After variables were created, they were combined into a
single index (the EQI) using statistical methods. Each domain
has its own index (air domain index, water domain index,
etc.). Next, each of the domain-specific indices was used
to create the overall EQI. The statistical process used to
add these variables together is called principal component
analysis (PCA). Figure 2 shows the steps that include
•	use PCA on the variables in each domain to keep the
most important piece of information for each domain
index,
•	use PCA on the domain indices to keep the most
important information for the overall EQI, and
•	group counties by their RUCC and repeat the two steps
above for each RUCC group
PCA
PCA is a statistical method that combines information from
many variables into one summary variable, called an index.
This "reduction" of many variables into one is useful because
the one variable can be used in a statistical analysis of health
outcomes, instead of trying to include hundreds of separate
variables at the same time.
PCA was chosen to turn many variables into one index for a
few reasons. It puts different variables into the same format
(it "standardizes" them), so they can be added together. It
provides each variable a measure of relative importance, or
"weight", in its relationship to all the other variables included
in the PCA. This weight is important for understanding which
variables seem the most important for explaining the index.
It takes into account how much of a variable is present, or its
prevalence, in the overall environment. PCA then creates a
single variable that can be used in other models. Researchers
also can use the PCA values for each variable to understand
differences in variables.
The domain-specific indices and the EQI were created for
each county in the United States. The four RUCC groups
were used to account for differences in rural versus urban
areas. There were originally nine RUCC codes. Those
nine were combined to make four RUCCs for the EQI: (1)
RUCC1 represents metropolitan-urbanized = codes 1+2+3;
(2) RUCC2 nomnetropolitan-urbanized = 4+5; (3) RUCC3
less urbanized = 6+7; and (4) RUCC4 thinly populated

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Rural-urban continuum code (RUCC)
I I Metropolitan urbanized
Non-metro urbanized
| Less urbanized
| Thinly populated
Figure 3. Rural-urban continuum codes (RUCCs) for all counties in the United States.
I I I I 1 I 0 - S"1 Percentile
j 5th - ZO"1 Percentile
II. ~l | | 20th - 40th Percentile
H Hi 40th - 60th Percentile
W	B0'h - 80m Percentile
¦ H H 80th - 95th Percentile
¦ 95lh - 100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 - Non-metro urbanized
RUCC3 - Less urbanized
RUCC4 = Thinly populated
Figure 4. Map of the Environmental Quality Index by rural-urban continuum codes (RUCCs).
11

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Table 3. Weights for Each Domain's Contribution to the Environmental Quality Index for 3141 U.S. Counties
(2000-2005) and for the Counties Stratified by Their Rural-Urban Status (RUCC code)
Thinly
Metropolitan-	Nonmetropolitan- Less Urbanized Populated
Urbanized (RUCC1) Urbanized (RUCC2) (RUCC3)	(RUCC4) OVERALL
Number of Counties	1089	323	1059 670	3141
Air Domain Index	0.5063	0.3343	0.1609	0.0285	0.4867
Water Domain Index	0.2757	0.2958	0.2981	0.1347	0.2618
Land Domain Index	0.4379	0.5506	0.5503	0.5785	0.3887
Sociodemographic Domain Index	0.4538	0.5963	0.5675	0.6263	0.5077
Built-Environment Domain Index	0.5196	0.3769	0.5102	0.5041	0.5345
Because PCA analyzes total, not shared, variance, the weights need not total 1.0.
(rural) =8+9 (see Figure 3).[35-38] The index-creation
process was repeated for those four RUCC groups, leading
to an overall EQI and five domain-specific indices for each
RUCC group.
Results
For detailed results, consult Creating an Overall
Environmental Quality Index, Technical Report.
Description of EQI
For EQI scores in RUCC groups, higher values suggest
worse enviromnental quality, and lower values suggest better
enviromnental quality. Figure 4 provides a map of the EQI by
RUCC divided into percentiles, where the lower percentiles
represent better enviromnental quality, and the higher
percentiles represent worse enviromnental quality. The bulk
of counties had EQI scores in the better range.
Additionally, Appendix I contains county maps for the
nonstratified EQI and domain-specific indices, RUCC-
stratified EQI, and RUCC-stratified domain-specific-indices.
All indices were grouped into percentiles.
Domain-Specific Index Description
The way in which the domain-specific indices contributed
to the EQI differed depending on how rural or urban the
county was (Table 2). In the most urban areas (RUCC1),
the built-enviromnent domain had the most influence
(0.5196, the weight associated with the built enviromnent,
is the largest number for the RUCC1 column from Table
2.). For the nomnetropolitan-urbanized areas (RUCC2),
the sociodemographic and land domains had the most
influence, and the water domain had the least influence. The
air domain was the least influential for the less urbanized
counties (RUCC3). In the most thinly populated counties
(RUCC4), the sociodemographic and land domains were the
most influential.
For the nonstratified EQI, the built and the sociodemographic
domains had the most influence (0.5345 and 0.5077,
respectively). The air domain also had a fair amount of
influence, and the water domain had the least.
12

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3.0
Discussion
An EQI was developed for all counties (N=3141) in the
United States. This EQI includes five environmental
domains: (1) air. (2) water, (3) land. (4) built, and (5)
sociodeniographic. For each domain, variables were created
from many data sources. Then, domain-specific indices and
an EQI were created using PCA. The EQI also is divided into
four RUCC groups to account for rural-urban differences.
The PCA shows that environmental quality is driven by
different domains in rural versus urban areas.
Strengths and Limitations
Data
Data sources represented each of the five environmental
domains. Documentation for each data source was good.
Even though many data sources were found, gaps in the
data remain.
The EQI is useful for representing the overall surrounding
environment. It is not as useful for describing specific
environments. If there were no data available for an
important part of the environment, then the EQI was unable
to capture that part. Areas, either counties or domains, with
little data were not represented as well as areas with a lot
of data.
It is difficult to find environmental data sources that fully
cover all areas at all time intervals. Most data were not
collected often enough. This is why an EQI covering 6 years
was developed. If more data were collected more often, t lie re
would be an EQI for each year.
When counties had data values that were missing,
information on those variables had to be estimated. This
makes it harder to understand how pollutants a fleet urban and
rural areas differently. Although many of the environmental
data points were collected in smaller areas than counties
(e.g., for a municipality or city), most arc not maintained in
a single source, such as a State or county data repository.
National repositories for some domains exist (e.g., water, air),
but no built-cnvironnient repository (fortransit, walkability/
physical activity, presence of sidewalks, or pedestrian
lighting) is available. Cities or towns with less money may
not be able to collect these data. Thus, data were available at
different levels across the United States.
PCA Methodology
Using PC A had limitations. Normality is an important
statistical assumption for PCA. Some data had to be scaled
to be made normal. Scores from a PC A also can be hard
to interpret. Outliers in the data also can be a limitation.
However, with 3141 counties and proper statistical checks,
this is not a big problem for the EQI.
Using PC A was also a strength of this project. PC A enabled
a lot of variables to be combined into a single index. The
EQI is standardized. This means it can be compared to other
EQIs created in other countries or at different levels (e.g.,
city instead of county). Another strength is that PCA has been
used to make other indices.[39, 40]
Application
The EQI was focused solely on the outdoor environment.
This may not be the most relevant exposure in relation to
human health and disease. The EQI is at the county level, not
the individual level. This means it can be used to see which
counties arc less healthy environments. It will not be good at
predicting which people arc likely to have certain diseases.
Other Environmental Indices
The EQI is unique. Most other EQIs focus on one
environmental domain (e.g., Air Quality lndc.\|411) or a
specific type of activity (e.g., Pedestrian Environmental
Quality lndc.\|42|) or vulnerability (e.g., Cumulative
Environmental Vulnerability Assessment,[43] heat
vulnerability inde\[44|). State-specific indices also exist,
(e.g., CalEnviroScrccn 1.0,[45] Virginia Environmental
Quality lndc.\|46|), but they often cannot be compared to
other States because the data arc different.
Other indices are at a larger spatial resolution, usually
at the country level. Country-level indices include
the Environmental Sustainability Index[39] and the
Environmental Vulnerability Index.[47]
Conclusions
The EQI was constructed for all 3141 counties in the United
States. The EQI has five environmental domains: (1) air,
(2) water, (3) land. (4) built, and (5) sociodeniographic. It
is divided into four rural-urban groups. The methods can be
repeated by others, and the data arc available to the public.
The EQI is a first step for looking at many environmental
exposures at once. The EQI can be used as a measure in
environmental health research. This broad effort uses many
factors tliat work together to impact environmental quality
and public health. Updates to the EQI for 2006-2010 are
planned. Looking at smaller geographic areas also is planned.
13

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4.0
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16

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Appendix I:
County Maps of Environmental Quality Index
Percentile
Map 1. Environmental Quality Index by County, 2000-2005,
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality

-------
Percentile
Map 2. Air Domain Index by County, 2000-2005"
Percentile
¦	o-s*1
¦I 5«i- 20th
¦	20m - 40th
r~1 40lh - 60tt>
I 1 60"1 - BO"1
IB 80th -95th
¦I 95^- 100th
Map 3. Water Domain Index by County, 2000-2005*
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality
A-2

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Percentile
Map 4. Land Domain Index by County, 2000-2005*
Percentile
Map 5. Built Domain Index by County, 2000-2005*
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality

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Percentile
Map 6. Sociodemographic Domain Index by County, 2000-2005"
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality
A-4

-------
	J	0 - 5th Percentile
~ u ~ rj	5th - 20th Percentile
II I	20th - 40,h Percentile
IB 1" tl HI	40th - 60th Percentile
j^H |^|	60th - 80th Percentile
H	SO"1 - 95th Percentile
HI EH m |^|	95th- 100lh Percentile
RUCC1 = Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 - Thinly populated
Map 7. Environmental Quality Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005*
Map 8. Air Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005*
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality
_j 0 - 5th Percentile
5(1,20m Percentile
; 20lh - 40"' Percentile
IB l--'l ¦ IB 40th - 60th Percentile
H H H Bi 60th - 80th Percentile
H H Hi 80th - 95lh Percentile
Hi H Hi Hi 95*| - 100th Percentile
RUCC1 - Metropolitan urbanized
RUCC2 - Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 - Thinly populated

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I 0 - 5th Percentile
	 1	 5th . 20lh Percentile
I I f I J 201h - 40"1 Percentile
HI I B t 1 40th - 60"1 Percentile
IB 60th - 80th Percentile
¦ HI 80th ¦ 95th Percentile
95th- 100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 - Non-metro urbanized
RUCC3 - Less urbanized
RUCC4 - Thinly populated
Map 9. Water Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005*
9H I I	I I I I 0 - 5th Percentile
	I		 |	| 5th • 20lh Percentile
I I	H IB 20th • 40th Percentile
¦ H	¦ H 40"> - 60th Percentile
¦I H Hi H 60lh - 80th Percentile
IB H	IH 80th ¦ 95th Percentile
H	IB ¦ 95th - 100th Percentile
RUCC1 - Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 - Thinly populated
Map 10. Land Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005*
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality
A-6

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- 5th Percentile
5«i. 20th Percentile
]_	I I 20th 40"1 Percentile
Hi 1^1 Hi ¦ 40th -60th Percentile
Hi H H H 60th- SO41 Percenlile
H H 80th - 95"* Percentile
H HI H H 95th - 10(Jlh Percentile
RUCC1 = /Wefropo/rfari urfjamzeal
RUCC2 - Non-metro urbanized
RUCC3 - Less urbanized
RUCC4 = Thinly populated
Map 11. Built Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005"
Map 12. Sociodemographic Domain Index Stratified by Rural-Urban Continuum Codes by County, 2000-2005*
* Higher EQI values suggest worse environmental quality, and lower EQI values suggest better environmental quality
A-7
	 I I I 0 • 5*1 Percentile
~ ~ ~ 5th . 2o»i Percentile
I I" J HI H3 20th ¦ 40th Percentile
1^1 ¦ 40th - 60th Percentile
| E	60"' - 80th Percentile
| H |H Hi BO"1 - 95th Percentile
¦ 95"1 • 100th Percentile
RUCC1 - Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 - Less urbanized
RUCC4 = Thinly populated

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Appendix II:
Quality Assurance
The approved National Health and Environmental Effects
Research Laboratory (NHEERL) Environmental Public
Health Division (EPHD) Intramural Research Protocol for
this project is "Creating an Overall Environmental Quality
Index," with Document Control Number IRP-NHEERL/
HSD/EBB/DL/2008-01rl. An internal EPA review of
this report was conducted in August 2003 by Lisa Smith.
NHEERL Gulf Ecology Division; Jane Gallagher. NHEERL
EPHD), and Tom Brady (Region 5). An external peer review
was conducted in July 2014 by Angel Hsu. Yale University,
School of Forestry and Environmental Studies; Paul D.
Juarez, University of Tennessee Health Science Center.
Department of Preventive Medicine; and Peter H. Langlois.
Texas Department of State Health Services. Birth Defects
Epidemiology and Surveillance Branch.
The data sources used to create the EQI and the criteria
used to select the data sources arc mentioned in Creating
an Overall Environmental Quality Index, Technical Report
(Technical Document), in Part II: Data Source Identification
and Review. Additional information about the sources can
be found in the Technical Document in Appendix I and
Appendix II. Table 1 in this report provides the strengths and
limitations of the sources used in the EQI.
Information about uses of the EQI, as well as strengths
and limitations of the EQI, is located in the Discussion
of this report.
B-l

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