Creating an Overall
Environmental
Quality Index
Technical Report
vvEPA
United States
Environmental Protection
Agency
EPA/600/R-14/304 September 2014 www.epa.gov/ord
b*
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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-14/304 | September 2014 | www.epa. gov/ord
United States
Environmental Protection
Agency
CREATING AN OVERALL
ENVIRONMENTAL QUALITY INDEX
TechnicalReport
Environmental Public Health Division
Epidemiology Branch
Chapel Hill, North Carolina
Office of Research and Development
National Exposure Research Laboratory
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Project Personnel
Dancllc 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
Gencc 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
Acknowledgements
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 Gallaghcr.U.S. EPA. ORD. NHEERL
Thomas Brody, 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.
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Table of Contents
1.0 Overview of Report 1
Background 1
Purpose 2
Conceptual Framework 2
2.0 Domain Identification 3
Approach 3
Summary of Activities 4
3.0 Data Source Identification and Review 5
Approach 5
Data Selection 5
Data Source Search 5
Data Quality and Coverage Assessment 5
Summary of Activities 6
Air Domain 6
Water Domain 6
Land Domain 7
Sociodemographic Domain 7
Built-Environment Domain 7
4.0 Variable Construction 11
Approach 11
Summary of Activities 12
Domain-Specific Variable Descriptions 12
Air Domain 12
Water Domain 13
Land Domain 14
Sociodemographic Domain 15
Built-Environment Domain 15
5.0 Data Reduction and Index Construction 17
Overall Approach 17
Principal Component Analysis 17
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Results 18
Description of Variables Comprising EQI Domains 18
Air Domain 18
Water Domain 26
Land Domain 30
Sociodemographic Domain 32
Built Environment Domain 32
Variable Loadings on EQI Domains 34
Air Domain 34
Water Domain 36
Land Domain 38
Sociodemographic Domain 39
Built-Environment Domain 40
Domain-Specific Index Description and Loadings on Overall EQI 40
Description of Overall EQI 40
6.0 Discussion 43
Uses of EQI 43
Strengths and Limitations 43
Other Environmental Indices 45
Conclusions 46
7.0 References 47
Appendix I Modified Data Inventory A-1
Appendix II Identified Variables by Source for Each Domain B-l
Appendix 111 Table of Highly Correlated Variables for Each Domain C-l
Appendix IV County Maps of Environmental Quality Index D-l
Appendix V Quality Assurance E-l
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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 8
Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Sociodemographic
Domains for Use in the Environmental Quality Index 9
Table 1. (continued) Sources of Data for Air, Water, Land, Built-Environment, and Soci odem ogra ph i c
Domains for Use in the Environmental Quality Index 10
Table 2. Selected Hazardous Air Pollutants from the National-Scale Air Toxics Assessment (1999, 2002,
and 2005) Used in the Environmental Quality Index 12
Table 3. Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified 19
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 20
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 21
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 22
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 23
Table 3. (continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 24
Table 3. (continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 25
Table 4. Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-
Urban Continuum Codes (RUCCs) Stratified 26
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 27
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 28
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 29
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Table 5. Land Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-
Urban Continuum Codes (RUCCs) Stratified 30
Table 5. (continued) Land Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and
Rural-Urban Continuum Codes (RUCCs) Stratified 31
Table 6. Sociodemographic Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall
and Rural-Urban Continuum Codes (RUCCs) Stratified 32
Table 7. Built-Environment Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall
and Rural-Urban Continuum Codes (RUCCs) Stratified 33
Table 8. Variable Loadings—Air Domain 34
Table 8. (continued) Variable Loadings—Air Domain 35
Table 9. Variable Loadings—Water Domain 36
Table 9. (continued) Variable Loadings—Water Domain 37
Table 10. Variable Loadings—Land Domain 38
Table 11. Variable Loadings—Sociodemographic Domain 39
Table 12. Variable Loadings—Buil t-En vi ronm ent Dom ai n 39
Table 13. Description of the Domain Indices Contributing to the Overall and Rural-Urban Continuum
Codes (RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005) 40
Table 13. (continued) Description of the Domain Indices Contributing to the Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties
(2000-2005) 41
Table 14. Loadings of the Domain Indices Contributing to the Overall and Rural-Urban Continuum Codes
(RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005) 41
Table 14. (continued) Loadings of the Domain Indices Contributing to the Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties
(2000-2005) 42
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List of Figures
Figure 1. Conceptual environmental quality—hazardous and beneficial aspects 1
Figure 2. Rural-urban continuum code (RUCC) stratification for all counties in the United States 18
Figure 3. Principal component analysis concept for Environmental Quality Index. Performed for all
counties and each of the four strata of the rural-urban continuum (RUCC) codes 19
Figure 4. Distribution of overall EQI scores across rural-urban continuum code (RUCC) categories.. . . 42
List of Maps
Map 1. Overall Environmental Quality Index by County, 2000-2005 D-l
Map 2. Air Domain Index by County, 2000-2005 D-2
Map 3. Water Domain Index by County, 2000-2005* D-2
Map 4. Land Domain Index by County, 2000-2005 D-l
Map 5. Sociodemographic Domain Index by County, 2000-2005* D-l
Map 6. Built Domain Index by County, 2000-2005 D-2
Map 7. Overall Environmental Quality Index Stratified by Rural Urban Continuum Codes by County,
2000-2005 D-3
Map 8. Air Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005* D-3
Map 9. Water Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005 .... D-4
Map 10. Land Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005* . . . D-4
Map 11 Sociodemographic Domain Index Stratified by Rural Urban Continuum Codes by County,
2000-2005 D-5
Map 12. Built Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005* . . . D-5
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List
of Acronyms
ACRES Assessment, Cleanup, and Redevelopment Exchange
AQS Air Quality System
ccc Concordance correlation coefficients
CI Confidence interval
CO Carbon monoxide
CWA Clean Water Act
EPA U.S. Environmental Protection Agency
EPI Environmental Performance Index
EQI Environmental Quality Index
ESI Environmental Sustainability Index
EVI Environmental Vulnerability Index
FBI UCR Federal Bureau of Investigation Uniform Crime Report
CIS Geographic information systems
HAP Hazardous air pollutant
NATA National-Scale Air Toxics Assessment
NCOD National Contaminant Occurrence Database
NGS National Gcochemical Survey
NPDES National Pollutant Discharge Elimination System
NPUD National Pesticide Use Database
PC A Principal component analysis
PM Particulate matter
PM2 5 Particulate matter below 2.5 nin in aerodynamic diameter
PM10 Particulate matter below 10 jim in aerodynamic diameter
R AD RE ACH Address Database
RCR A Resource Conservation and Recovery Act
ROE Report on the Environment
RUCC Rural-urban continuum code
SD Standard deviation
TIGER Topological^ Integrated Geographic Encoding and Referencing
WATERS Watershed Assessment. Tracking, and Environmental Results
WQS Water quality standards
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1.0
Overview of Report
A better estimate of overall environmental quality is
needed to improve the understanding of the relationship
between environmental conditions and human health. This
report describes the effort to construct an environmental
quality index representing multiple domains of the
ambient environment including air, water, land, built, and
sociodemographic, for all counties in the United States
for the 2000-2005 period. The Environmental Quality
Index (EQI) was created for two main purposes: (1) as an
indicator of ambient conditions/exposure in environmental
health modeling and (2) as a covariate to adjust for ambient
conditions in environmental models. However, as detailed in
the discussion of this report, the EQI can be adapted and used
for other objectives. The EQI was developed in four parts:
(1) domain identification. (2) data source identification and
review, (3) variable construction, and (4) data reduction. Each
of these four areas represents a chapter in the report, where
detailed information is provided on the development of the
EQI. The methods applied provide a reproducible approach
that capitalizes almost exclusively on publicly available
data sources.
This report is written for audiences interested in the
construction of the EQI and is technical in nature. The created
variables, EQI, domain-specific indices, and EQI stratified by
rural-urban continuum codes (RUCCs) are available publicly
at the U.S. Enviromnental Protection Agency's (EPA's)
Environmental Dataset Gateway. Also, an interactive map of
the EQI is available at EPA's GeoPlatfonn.
Background
The assessment of environmental exposures for human
health is an advancing field, characterized by multiple new
methodologic and analytic approaches. The difficulties in
examining the many broad-based factors impacting human
health outcomes are increasingly recognized, with exposures
to harmful and benign substances occurring simultaneously.
For instance, it is understood that enviromnental exposures
tend to cluster; environmental disamenities, such as landfills
or industrial plants, often are located in neighborhoods
with a high percentage of minority and poor residents.17
Conversely, high-income neighborhoods frequently contain
amenities conducive to promoting and maintaining optimal
health, such as parks, health clubs, and well-stocked grocery
stores.8 9 Yet, it is unlikely that any single exposure alone is
responsible for good or poor health. Each exposure estimated
in epidemiologic models accounts for a relatively small
proportion of observed variance in health outcomes. Clearly,
it is not just good-quality air or high income that produces
health but, rather, the combination of these and other various
exposures or health-related variables.
ENVIRONMENTAL QUALITY
Hazardous Beneficial
Figure 1. Conceptual environmental quality—hazardous
and beneficial aspects.
One limitation to current approaches in enviromnental
research is their focus on single exposure categories. For
instance, an environmental scientist investigating the effects
of pesticides on women's reproductive health may control for
individual-level education or income but exclude information
related to the water and air quality to which the women are
exposed simultaneously. These other enviromnental factors
are not excluded because the data are unav ailable but out
of disciplinary boundaries and a lack of statistical power
to include multiple covariates in most statistical models.
Well-designed enviromnental studies need to balance
collecting a sufficient depth of high-quality and expensive
data against the breadth, or number, of people on whom data
can be collected. This trade-off frequently results in studies
comprising a relatively small number of participants for
whom a few high-quality exposure measures are taken. Under
these circumstances, it is statistically impossible to include
additional variables representing the host of exposures that
study participants might experience, in addition to the main
exposures of interest.
A scale or index produced through data reduction
approaches could be used to help improve statistical
efficiency, while simultaneously summarizing information
on the wider environment to w hich humans are exposed.
The resulting scale or index, hereafter referred to as the
"Enviromnental Quality Index" or "EQI", could be used
to identify geographies characterized by varying quantities
of enviromnental disamenities. The clustering of adverse
enviromnental exposures into unhealthy places could be
identified and associated with health outcomes. The EQI,
Polluted Air
Home 0\a1i
Factories
Parks
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which would constitute a single item, could also be included
as a covariate in statistical models assessing the effects
of a specific exposure (e.g., water quality) on a specific
health outcome.
Purpose
A better estimate of overall environmental quality is needed
to improve the understanding of the relationship between
environmental conditions and human health. The EQI
was developed for all counties in the United States using
indicators from the chemical, natural, built, and social
environments. Included were five environmental domains:
air. water, land, built and sociodemographic. The EQI
is anticipated to be used in two primary ways: (1) as an
indicator of ambient conditions/exposure in environmental
health modeling and (2) as a covariate to adjust for ambient
conditions in environmental models. 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.
Conceptual Framework
The underlying purpose of the EQI is to quantify overall
environmental quality that encompasses where humans
interact and may impact human health (see Figure 1). To
achieve that, a conceptual framework was developed to
explicitly represent traditional environmental domains
(air, water, and land), as well as the socially structured
environment, such as neighborhood poverty or fast-food
restaurants that arc known to be important to human health.
Consultation w ith health experts, as well as literature
review, ultimately resulted in the grouping of these social-
environmental constructs into two separate domains: (1) the
built and (2) sociodemographic environments.
Conceptually, the explicit identification of these five specific
domains, each based on evidenced associations w ith human
health, served two purposes: (1) It provided a guiding
framework for a more complete definition of the environment
as it relates to human health, and (2) it enabled a targeted
search for specific variables that represented each domain,
w hich could be used in the estimation of environmental
quality. These domains, comprising chemical, natural, built,
and sociodemographic environments, would include both
positive- and negative-health influences. As a result, the EQI
could be used to examine both adverse health outcomes and
protective health events. More detailed methods for selecting
each domain arc described in Part 1.
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2.0
Domain Identification
Approach
Review of Report on the Environment
The EPA Report on the Environment (ROE)10 served as
the starting point for the EQI. Recognizing that specific
geographies were exposed simultaneously to poor
environmental conditions, the media chapters (on air, water,
and land) from the ROE were consulted to identify domains,
data sources, and variables for inclusion in the EQI.
Literature Review
Following the ROE consultation, the team undertook a more
extensive review of environmental associations with human
health. Searching for all possible human health outcomes
was not feasible. To facilitate a targeted search that would
help identify broad-based key environmental constructs,
the team included a search with infant mortality a.s an
indicator of national health and well-being. Infant mortality
has several unique features that make it a helpful outcome
to represent human health: Relative to other outcomes, it
is very consistently measured, well-studied, and broadly
considered a primary indicator of population human health.
The literature review was conducted in PubMed, a service
of the U .S. National Library of Medicine and the National
Institutes of Health. Searching began with the broad term
of "environment and infant mortality." followed by more
focused searches using the domains identified through the
ROE review and further searching on specific environmental
indicators revealed through the literature review search
process. For instance, after searching under "air environment
and infant mortality." one would find literature assessing
carbon monoxide (CO), particulate matter (PM), ozone,
etc. These references were explored from the main papers,
defined as "those that come up repeatedly in the various
searches," to make sure seminal papers, environmental
indicators, or subdomains or domains had not been missed.
Expert Consultation
Adverse environmental exposures have been associated
with social exposures. To ensure the social environment
was part of the environment considered by the EQI. expert
consultation was sought from a social epidemiologist.
Other experts were approached, both one on one (e.g.,
face-to-face meetings, telephone conversations, etc.), as
well as at professional meetings and environmental justice
workshops. Initially, the built-cnvironnicnt variables were
considered part of the sociodemographic domain. However,
after consulting with scientists and community members at
the EPA-sponsored symposium on Environmental Justice
in March 2010,11 the built environment was developed as a
separate domain. These interactions, in addition to studies
observed in the literature review, supported a broader EQI
definition of "environment."
Overview of the Five Domains
Based on the above approach, five environmental domains
were identified: (1) air, (2) water, (3) land. (4) built, and (5)
sociodemographic. The air domain represents the ambient air
environment. Two traditional air pollutant constructs were
considered: (1) criteria air pollutants and (2) hazardous air
pollutants (H APs). Health effects linked to air pollutants
include death, cancer, heart disease, respiratory disease, birth
outcomes, and neurologic disorders.12"19
The water domain represents the overall water environment.
Seven constructs were considered to represent water quality:
(1) overall water quality, (2) general water contamination. (3)
recreational water quality, (4) domestic use. (5) atmospheric
deposition. (6) drought, and (7) chemical contamination.
Several studies have demonstrated the association between
particular water contaminants and pathogens and health
outcomes. The range of health outcomes associated with
water quality include gastrointestinal infection to cancer0-1
In addition, water contamination with mercury and other
chemicals has been associated with adverse birth outcomes-"
The land domain represents the physical environment not
covered by air or water. Five constructs were considered
to represent land environmental quality: (1) agricultural
environment, (2) pesticides, (3) facilities. (4) soil
contaminants, and (5) radon potential. Health effects linked to
land constructs include cancer, birth outcomes, birth defects,
and asthma.23-25
The built-cnvironnicnt domain considered five constructs:
(1) traffic-related environment. (2) transit participation
and access. (3) pedestrian safety. (4) the various business
environments (such as the food, recreation, health care, and
educational environments), and (5) public housing. Each
of these constructs has both direct and indirect influences
on health that have been documented in prior research. For
instance, neighborhoods with an abundance of fast-food
restaurants or liquor stores have been associated with poorer
health, whereas those with an abundance of protective factors
like educational or physical activity resources have been
associated with better health.-"27 Traffic and pedestrian safety
arc important for health, and the presence of public housing
has been associated with poor mental health.-"
The literature review and expert consultation identified key
constructs that were grouped into the sociodemographic
domain: socioeconomic and crime. The association between
sociodemographic factors and human health has been well-
established over the past 40 years of research. For example,
educational attainment and markers of poverty (income and
neighborhood median income) have been linked to prevalent
chronic diseases, such as obesity and cardio-vascular
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disease, as well as acute conditions like sexually transmitted
infections and influenza.28"30 High-wealth neighborhoods,
considered a protective factor, have been associated with
good health. Similarly, neighborhood crime rates have
been associated with mental health, increased human-
imnnnrodcficicncy-virus-transmission and poor reproductive
health outcomes."
Summary of Activities
Three environmental domains were initially identified from
the media chapters of the 2008 EPA ROE: (1) air. (2) water,
and (3) land). All three were validated through an extensive
literature search and. thus, were kept as domains for the EQI.
The literature search also identified sociodemographic factors
to be important. Thus, the sociodemographic domain was
considered for inclusion.
To better assess the need for the sociodemographic domain,
consultants were contacted and verified the need to include
the sociodemographic domain in the EQI. Initially, the
built-environment variables were considered part of the
sociodemographic domain. However, after consultation with
scientists and community members, the built environment
was developed as a separate domain.
Thus, five environmental domains were identified and
developed for the EQI: (1) Air. (2) Water, (3) Land, (4) Built,
and (5) Sociodemographic.
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3.0
Data Source Identification and Review
Approach
Data Selection
An index that comprehensively captures the total
environment relating to human health requires numerous
variables representing the full range of health-influencing
exposures. From w ithin each domain identified in the
conceptual model, specific constructs or major areas were
identified. For example, HAPs were a construct of the air
domain. Once essential constructs were identified, data
sources needed to be located that contained variables
representing those constructs. For instance, data sources with
variables estimating H APs were sought to represent the H AP
construct within the air domain. Similarly, neighborhood
poverty was identified as a construct contained within the
sociodemographic domain; therefore data sources containing
the variable "percentage of persons living below the poverty
line" was sought to represent this construct for this domain.
The majority of variables were identified a priori. In a
few cases, the data source search for one variable led to
the discovery of another variable that was not identified
explicitly before searching for data sources but was known
to be associated with human health. For instance, in locating
the percentage of roads within a county that were highways
(roadways construct of the built domain), it became apparent
that pedestrian fatalities was another important construct
representing the built domain with obvious implications for
human health. Therefore, the pedestrian safety construct was
included in the built domain, and data sources to represent
pedestrian safety were sought.
Data Source Search
Once the desired constructs were identified, the research team
conducted an extensive search for potential sources with
those data. In general, a broad approach to searching for data
sources was undertaken to
identify EPA and non-EPA domain-specific
environmental data sources for all counties in the 50
States of the United States;
summarize environmental data source availability,
quality, spatial and temporal coverage, storage
requirements, and acquisition steps; and
• obtain the identified data.
Possible data sources were identified using Web-based
search engines (e.g., Google), site specific search engines
(e.g., Federal and State data sites), literature-reported data
sources (e.g., Pub Med, ScienceDircct. TOXNET), and
personal communications from data owners. Data that were
available at—or had the potential to be aggregated to—the
United States county level were sought. Data were restricted
to the years 2000-2005 to coincide with the available
sociodemographic and health data to be used for initial testing
of the EQI. For each data source identified, the following
information (when available) was collected: data title, source
URL (uniform resource locator), data description, data
ownership, data provider, data format, secondary data format,
data geometry, geographic coverage, smallest geographic unit
represented, data resolution, record start and end years, date
data published, data refresh frequency, metadata availability,
metadata link/location, method to obtain data, point of
contact information, data constraints, and data limitations.
Within each domain, a database containing information on
each identified dataset was compiled. A sample version of
the data inventory (reflecting all sources identified but not all
information on the source) is located in Appendix I of this
report and the full data inventory can be downloaded from
EPA's Environmental Dataset Gateway.
Data Quality and Coverage Assessment
Once potential data sources were identified, several criteria
were used in the assessment of sources for inclusion in
the EQI. First, constructs representing the domain were
identified. Data sources were evaluated as to whether or not
variables could be developed to be included to represent
the construct. If a data source could provide variables for a
construct in the domain, then the next two critical criteria
used to evaluate data sources for use in the EQI were (1)
data quality and (2) coverage. Data sources of the highest
quality were sought. Quality was assessed by the data source
managers, in data reports and internal documentation; project
investigators; and the larger field of environmental research
through use and critique of the various data sources. Data
quality concerns were weighted against data coverage.
Often, it was relatively straightforward to achieve high-
quality data on a few individuals or a small geographic area.
How ever, the extent of the data coverage w as also very
important, as all counties (N=3141) in all 50 States were
required to be represented. The best data sources would
have had spatial data available across the entire United
States, including Hawaii and Alaska. The ability for the data
to be aggregated at the county level was also a factor for
inclusion (e.g., average of point measures or census tract
values). Temporally, ideal sources would have had data
at least annually for the 2000-2005 period. At minimum,
at least some data must have fallen w ithin the 2000-2005
period. In theory, a "perfect" data source would have variable
measurements at high temporal and spatial resolutions.
In practice, data often met one but not both criteria, and
evaluation of trade-oil values was required, along with
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consideration of data quality. Redundant data sources
that were determined to meet the criteria for inclusion but
were not selected for inclusion were retained for use in
sensitivity analyses.
Summary of Activities
Table 1 identifies the data sources that were acquired and
used for the construction of the EQI. and it includes a
description of the data source and provides strengths and
limitations. Domain-specific information about the number
of data sources was identified, and reasons for exclusions arc
summarized below.
All evaluated datasets arc listed in the data inventory in
Appendix I. The inventory includes an indicator for whether
the dataset was included in the EQI or not. If a dataset was
not included in the EQI, a reason for exclusion is provided
in the inventory listing. If a data source was utilized for
sensitivity analysis, that also is indicated.
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 toxics or
HAPs. Twelve data sources were identified, and seven were
considered for inclusion. Ultimately, the two identified as the
most complete were chosen for use in the EQI air domain.
The Air Quality System (AQS)33 is a repository for criteria
ambient air pollution data collected by Federal. State, local,
and tribal agencies from thousands of monitors for the EPA's
ambient air monitoring program across the United States.
Monitored pollutants include all criteria air pollutants, PM
species, and approximately 60 o/one precursors. Major
strengths of the AQS arc that data arc measured, rather than
modeled, and these measurements arc synchronized across
the country. Monitors in the network and the reported data
arc audited regularly for accuracy and precision. However,
most of the ambient air monitors are located in or near urban
areas, leaving many U.S. counties without reported data. In
addition, the AQS provides sparse and limited data collection
for H APs.
The National-Scale Air Toxics Assessment (NATA)
database34 uses data from the National Emissions Inventory
to construct air dispersion models for estimating ambient
concentrations of H APs at the county and census-tract
levels. Beginning in 1996, the National Emissions Inventory
data arc constructed every 3 years, providing annual
estimates. The NATA databases contain estimated ambient
concentrations for 177 to 180 of the 187 H APs and use
validated models that take meteorology and chemical
dispersion into account. The methodology for estimating
concentrations may change between assessments, but these
modifications arc well-documented and justified. Although
the ambient concentrations may be comparable over time,
some differences between estimates arc attributable to these
minor methodological modifications. The temporal resolution
of the assessments is adequate for the intended EQI. but.
because of the 3-ycar release schedule, there arc gaps
in temporal coverage.
Water Domain
Five broad data categories within the water domain were
identified: (1) modeled. (2) monitoring. (3) reported. (4)
surveyed/studied. (5) and miscellaneous data. Eighty data
sources were identified. Five, which met selection criteria and
provided data to represent the water domain, were selected
for use in the EQI. and three, which provided duplicate data,
were selected as part of sensitivity analysis.
The Watershed Assessment. Tracking, and Environmental
Results (WATERS) Program35 database represents the
surface water assessment programs under the Clean Water
Act (CWA). A limitation of this data source is that data arc
maintained at the State level and reported to the Federal
system. Although all States report county-level data, there is
little consistency in the temporal reporting and type of data
reported across States. These data were first gcocodcd to a
specific stream length in the National Hydrography Dataset
via the RE ACH Address database (R AD). The gcocodcd
WATERS Program data were used to calculate hunian-
c.xposurc-rclatcd variables, such as percentage of stream
length impaired for recreational use. This dataset is the only
database maintaining information on EPA CWA regulations,
which is a strength.
The National Contaminant Occurrence Database (NCOD)36 is
a surveillance database maintained to satisfy the requirements
of the Safe Drinking Water Act. This database includes
information on contaminants in public water supplies tliat
arc not measured elsewhere. The survey is conducted every
6 years, and data arc provided by public water suppliers.
The data arc limited as they arc provided by public water
suppliers, and. therefore, spatial aggregation was needed to
get county-level estimates. Estimated Use of Water in the
United States,37 which is modeled by the U.S. Geological
Surv ey, provided county-level estimates of water withdrawals
(an indication of water stress in a county) for domestic,
irrigation, livestock, and industrial use. This dataset already
is provided at the county level, which is a strength. However,
it is limited as the estimates are based on several different
data sources.
Two data sources provided information on meteorological
impacts on water quality. The Drought Monitor Data38 arc
modeled weekly drought conditions. Weekly coverage for
the entire country is a strength of this dataset; however, it is
limited, as the data arc modeled raster data, which required
spatial aggregation to achieve county-level estimates.
The National Atniosplieric Deposition Program (NADP)39
provided weekly measures and national coverage of the
deposition of various pollutants from rainfall using monitors
around the country. Again, this database provided weekly
information for the entire country; however, it was reported
by monitors and required spatial aggregation to achieve
county-level estimates.
Two data sources were used for sensitivity analyses. These
two data sources arc repositories maintained for compliance
with Federal regulations. They were catcgori/cd as
"miscellaneous" because they include monitored, reported,
and surveyed/studied data. The National Water Information
6
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System,40 a repository maintained by the U.S. Geological
Survey, includes monitoring data from streams. The Safe
Drinking Water Inforniation System41 contains information
from public water systems and violations of EPA's drinking
water regulations. The number and type of violations
reported by each water supply were calculated using this
database. Both repositories include several measures of water
quality; however, few have the spatial and temporal coverage
required for the EQI. Additionally, the data maintained
in these repositories arc represented in other datasets.
Therefore, selected data from both repositories were used for
sensitivity analysis.
The Nutrient Loss Database for Agricultural Fields42 provided
information on agricultural impacts on water quality. This
database only provides information on nutrients in areas
with dense agriculture and docs not provide national-level
coverage. These data may be considered in the future for
regional-level versions of the EQI or for sensitivity analyses;
however, they were not useable for the national-level
index development.
Land Domain
Land domain data sources were grouped into four categories:
(1) agriculture. (2) industrial facilities. (3) geology and
mining, and (4) land cover. Eighty sources were identified, of
which 11 were retained: 2 from agriculture. 7 from facilities,
and 2 from geology/mining. An additional source from
agriculture was identified for sensitivity analysis. None of the
land cover data sources were retained. Future versions of the
EQI will explore these data sources.
The three agricultural data sources considered for inclusion
in the EQI were (1) the National Pesticide Use Database
(NPUD) 2002,43 (2) the 2002 Census of Agriculture Full
Report." and (3) the Dun and Bradstrcet Agriculture Data.45
The NPUD provides State-level rates of pesticide use. A
significant limitation of the NPUD involved the resolution
of data, as this database has State-level rates of pesticide
use and is only available for contiguous States. The Census
of Agriculture data provided mostly farm-related summary
cliaracteristics and did not offer direct pesticide measures or
probable exposure information. As a strictly environmental
indicator, the Census of Agriculture was useful, but its ability
to link to human health was somewhat limited. Because no
single database provided complete coverage or information,
the NPUD and 2002 Census of Agriculture were used to
estimate county-level pesticide use. Crop acres for corn,
oats, potatoes, soybeans, and wheat were multiplied by
individual pesticide State application rates (pounds per acre)
for specific crop type and then summed by pesticide class
to estimate pounds of pesticide class applied per county.
The Dun and Bradstrcet agricultural data arc similar to the
Census of Agriculture data, with many of the same strengths
and limitations. Therefore, tliese data will be used for
sensitivity analyses.
The industrial facilities data sources included the EPA
Geospatial Data Download Service;46 the Superfund National
Priorities List sites;'" the Resource Conservation and
Recovery Act (RCRA) Treatment, Storage, and Disposal
and Corrective Action Facilities;48 the RCRA Large Quantity
Generators;49 Toxic Release Inventory sites;50 Assessment.
Cleanup, and Redevelopment Exchange (ACRES)
Brownficld sites;51 and the Section Seven Tracking System
Pesticide Producing site locations.52 All facilities-related
data were retained for inclusion in the EQI with extensive
information on each facility for the years 2000-2005.
The two geology/mining data sources were National
Gcochcinical Survey (NGS)53 and the Map of Radon Zones.'1
The NGS data provided the mean and standard deviations
for multiple soil chemicals. However, these values were
calculated from multiple surveys of soil samples collected
over several years based on local agencies interests and
resources and. tliereforc. were combining many varying
sources of data. No particular sampling strategy was
employed in the collection of tliese data. The radon map
assigned a radon potential level to each county in the United
States. As the data source provided radon potential, not actual
measurement, these data were limited. The three-level radon
categorization masked important radon-level heterogeneity
across the United States. Despite tliese limitations, both
of these data sources provided land-related data not
available elsewhere.
Sociodemographic Domain
Few sociodemographic data sources were available at the
county level. Only two data sources were identified and
retained for sociodemographic data: (1) the U.S. Census
Bureau" and (2) the Federal Bureau of Investigation
Uniform Crime Reports (FBI UCRs).56 The U.S. Census
reports county-level population and housing characteristics,
including population density, race, spatial distribution,
socioeconomic characteristics, home and neighborhood
features, and land use. One strength of this data source is its
national coverage and consistency of data collection with
standard methods. One weakness of this data source is its
decennial collection. The FBI UCR provides annual violent
and property crime counts and rates for reporting areas. These
data are a valuable source of crime exposure, but reporting
is not mandatory and may vary by jurisdiction. Each of
these data sources represents critical aspects of the human
sociodemographic environment, is updated regularly, and is
available at the county-level for the entire country.
Jiuilt-En vironment Domain
Built-cnvironnicnt data sources were grouped by topic:
traffic-related, transit access, pedestrian safety, access to
various business environments (such as food, recreation,
health care, and educational environments) and household
health measures. Twelve data sources were identified, and
four were retained: one each for traffic-related, pedestrian-
safety. use in various business environments (physical
activity, food, health care, and educational), and urban/
rural residence. Because of the nonconiparablc county-level
data quality, none of the transit access or household health
measures were retained.
7
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For the traffic-related data source, Topologically Integrated
Geographic Encoding and Referencing (TIGER)57 was
retained. The TIGER files provide relatively uniform and
nationwide coverage. From these files, county-specific
proportions were characterized for various road types.
Unfortunately, considerable heterogeneity may be lost; for
instance, a tertiary road in Maryland may not be qualitatively
equivalent to one located in Wyoming.
The Fatality Annual Reporting System of the National
Highway Safety Commission58 was retained as part of
pedestrian safety because of its national coverage. The data
are regularly updated and available from the Web site. A
limitation of these data is that pedestrian fatalities result from
diverse types of events (e.g., from crossing busy intersections
or deserted highways), but this diversity is not well-captured.
North American Industry Classification System codes
through Dun and Bradstreet45 were used as the data source to
estimate three different topics: (1) physical activity, (2) food,
and (3) educational enviromnents. These data are available
as geocoded business addresses. Although these data have
sometimes been criticized for inadequate spatial resolution
(e.g., inaccurate geocoding to small units of aggregation
like census tracts),59 they should be sufficient as a construct
for county-level food, physical activity, and educational
enviromnents.
The Housing and Urban Development database60 includes
data on Section 8 and low-income housing. These housing
units are a unique feature of built enviromnents associated
with known and suspected health risks and disamenities.
Summary
After consideration and evaluation of the available databases,
datasets for derivation were limited to the following.
• Two data sources for the air domain representing
criteria pollutants and HAPs
• Five data sources for the water domain representing
overall water quality, general water contamination,
recreational water quality, domestic use, atmospheric
deposition, drought, and chemical contamination
• Eleven land data sources representing agricultural
exposures, pesticides, soil contamination, large
industrial facilities (for which the seven original data
sources were combined into one data source), and
elevated indoor radon
• Two data sources to represent the sociodemographic
domain and the crime and socioeconomic environments
• Four data sources in the built domain representing
the housing enviromnent, traffic safety, public
transportation usage, road properties, and the business
and service enviromnents
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[33]
Repository of ambient air quality data,
including both criteria and hazardous air
pollutants (HAPs)
Measured values; network of criteria
air pollutant monitors is substantial;
measurement occurs regularly and
is synchronized; data are audited for
accuracy and precision.
The HAP network is sparse; some
counties have no monitors, necessitating
interpolation of concentrations for
unmonitored locations.
National-Scale Air Toxics Estimates of HAP concentrations using Validated models; coverage for all U.S. Data are available at 3-year intervals;
Assessment[34] emissions information from the National counties; majority of HAPs included. may underestimate concentrations; uses
Emissions Inventory and meteorological simplifying assumptions when information
data input into the Assessment System for is missing or of poor quality; changes
Population Exposure Nationwide model in methodology may result in different
estimates between years.
8
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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
Water Domain
Source of Data
Description
Strengths
Limitations
Watershed Assessment, Tracking
and Environmental Results
Program Database/REACH Address
Database[61]
Collection of EPA water assessments
programs, including impairment, water
quality standards, pollutant discharge
permits and beach violations
Only database maintaining information on
EPA Clean Water Act regulations
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.
National Contaminant Occurrence
Database[36]
Samples both regulated and unregulated
contaminants in public water supplies;
maintained by EPA to satisfy statutory
requirements for Safe Drinking Water Act
Provides measures for several chemicals
and pathogens that are not measured
elsewhere
Data provided by public water supplies;
therefore, need to use spatial aggregation
to get county-level estimates.
Estimates of Water Use in the United
States[37]
County-level estimates of water
withdrawals for domestic, agricultural,
and industrial use calculated by the U.S.
Geological Survey
County-level estimates
Estimated based on various data sources
Drought Monitor Data[38]
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.
Weekly coverage for the entire country
Modeled data; raster data, therefore,
required spatial aggregation.
National Atmospheric Deposition
Program[39]
Measures deposition of various pollutants,
such as calcium, sodium, potassium, and
sulfate, from rainfall
Weekly coverage for the entire country
Data not at the county level and required
spatial interpolation.
Land Domain
Source of Data
Description
Strengths
Limitations
National Pesticide Use Database:
2002[43]
Delineates State-level pesticide usage
rates for cropland applications; contains
estimates for active ingredients, of which
68 are insecticides, and 22 are other
pesticides.
Provides a measure of pesticide usage
Pesticide rates only available at the State
level for contiguous States; noncropland
uses are not included.
2002 Census of Agriculture Full
Report[44]
Summary of agricultural activity, including
number of farms by size and type,
inventory and values for crops and
livestock, and operator characteristics
Can be used to approximate land- and
water-related agricultural outputs (e.g.,
potential pesticide burden per acre,
potential exposure to cattle, dust, etc.)
Not direct measures of pesticides or
probable exposures
EPA Geospatial Data Download
Service[46]
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.
Indicators for major facilities (e.g.,
Superfund sites;[47] Large Quantity
Generators;[49] Toxics Release
lnventory;[50] Resources Conservation
and Recovery Act Treatment, Storage,
and Disposal Facilities and Corrective
Action Facilities;[48] Assessment,
Cleanup, and Redevelopment Exchange
Brownfield sites;[51] and Section Seven
Tracking System pesticide producing site
locations[52]) are available.
Contains much more information than
just the facilities, type, and location; for
example, Standard Industrial Classification
System and Dun and Bradstreet North
American Industry Classification System
codes, Native American jurisdictions,
interest type, etc.
National Geochemical Survey[53]
Geochemical data (arsenic, selenium,
mercury, lead, zinc, magnesium,
manganese, iron, etc.) for the United
States based on stream sediment samples
Provides county-level means and standard
deviations for each element; sampled
data interpolated over nonsampled space
results in variance estimates.
Includes data from several surveys;
therefore, sampling locations and number
of samples available vary by location.
Map of Radon Zones[54]
Identifies areas of the United States with
the potential for elevated indoor radon
levels; maintained by EPA
Each U.S. county is assigned to one
of three radon zones based on radon
potential.
Data are not actual measurements of
radon, and only three levels of radon
potential reduce possible county-level
variability.
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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
Sociodemographic Domain
Source of Data
Description
Strengths
Limitations
U.S. Census[55]
County-level population and housing
characteristics, including density
race, spatial distribution, education,
socioeconomics, home and neighborhood
features, and land use
Uniformly collected and constructed
across the United States and can be used
for construction of a variety of different
variables
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
Uniform Crime Reports[56]
County-level reports of violent crime
General estimate of public safety
exposure
Reporting may differ across geography
Built-Environment Domain
Source of Data
Description
Strengths
Limitations
Dun and Bradstreet North American
Industry Classification System
codes[45]
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 Referencing[57]
Road type and length per county
National coverage
Different road types may not be equivalent
across U.S. geography; confer different
exposure risks.
Fatality Annual Reporting System[58]
Annual pedestrian-related fatality per
100,000 population; maintained by
National Highway Safety Commission
County-level reports and annual updates
Pedestrian fatalities result from diverse
types of events and are not well captured
in the database.
Housing and Urban Development
Data[60]
Housing authority profiles provide general
housing details (low-rent and subsidized/
Section 8 housing); information updated
by individual public housing agencies.
Complete data source for unique element
of the urban built environment
Not all counties contain housing authority
properties; when the value for housing
authority = 0, no housing authority
property is present.
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4.0
Variable Construction
Approach
After data source evaluation and selection, individual
variables representing the five environmental domains
were developed. It was necessary to develop variables from
the data sources because the raw data were not always
appropriate for the chosen statistical analysis. (Note:
Principal component analysis [PCA], which was used to
create EQI. will be explained in more detail in the next
chapter.) Many of the variables needed to be standardized, as
a proportion of geographical space (e.g., road proportions) or
as a rate per population (e.g., violent crimes per capita) for
use in the EQI. Additionally, sonic data were not available
for all counties but required spatial kriging to provide
national coverage. Kriging is a geospatial technique that
uses known data points to interpolate data at locations with
unknown measurements.62
The process for variable development was as follows:
• identify and develop relevant variables within each
domain for each available year (2000-2005),
• assess collinearity among the variables within each
domain and eliminate redundant variables,
• assess missing data and variability of each variable; and
• assess normality of variables and transform as
necessary.
Appendix 11 lists all the variables considered for inclusion in
the EQI for each of the five domains, which variables were
retained, and reasons for exclusion. The created variables arc
available publicly at EPA's Environmental Dataset Gateway.
Identification and Construction of Variables from
Data Sources
For each domain, constructs of interest were identified, and
variables were created from selected data sources to represent
these constructs (e.g., the air domain has constructs for
criteria air pollutants and H APs). Variables were developed
in a variety of manners, including kriging and standardization
by area or population. Each domain section below provides
the details of variable construction.
Assessing Variables
The data reduction method was based on the variability
betw een variables; therefore, collinearity of variables
was assessed. This assessment was done by developing
correlation matrices for each domain. Variables with any
correlation coefficient >0.70 were examined; representative
variables were chosen for each pair or group of highly
correlated variables ( Appendix III).
Ideally, developed variables would have measured or
estimated values for each county of the United States. When
this criterion was not met, or when a majority of values were
zero, the proportion of missing data and zero values were
evaluated for variable inclusion. If a particular variable had
information missing for many counties, the nature of the
missing data was evaluated. When it was determined that
the missing data could be interpreted as meaningful zeros
(i.e., no measures were taken because that condition did not
occur in that county), the missing values were set to zero. For
instance, the counties with no reported public housing were
set to zero because public housing is truly absent from some
counties. When counties were missing data because reporting
areas were centralized, but the data could not be assumed
to be truly missing, the data were spatially kriged. when
possible. For instance, crime was only reported for specific
counties, even though it likely occurred in counties other than
those in which it was reported as well. Therefore, crime rates
were averaged spatially over adjacent counties to create an
estimate for a county with no official reported crime. If the
missing data could not be determined to be legitimate zeros,
and the data could not be reasonably kriged or averaged over
geography, and the number of counties with missing data was
too high (more than 50% of counties), the variable was not
used in the EQI.
In some instances, there may have been more than one data
source that could represent a particular domain construct
(e.g., o/one could be measured as in the AQS or modeled as
in the Community Multiscale Air Quality Modeling System).
In that case, the data source deemed to have better data
quality and coverage was utilized.
Finally, normality of variables was evaluated. Using PCA.
the chosen data reduction technique, a key assumption is that
variables are distributed normally. If data were nonnornial.
transformations were applied (typically log-transformation)
to increase normality. For those variables with zero values,
half of the nonzero minimum value was added to all
observations before log-transformation.
Variable consistency (mean and standard deviation) was
compared across each year of the 6-year period (2000-2005).
Additionally. proto-EQIs were constructed using data from
1 year (2002) and from the average of all 6 years. When
data were kriged spatially, the team compared county-level
values before and after kriging. Because these county-level
values were temporally consistent, the EQI was constructed
based on county-level averages for the 6-year period for each
variable in each domain.
11
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Summary of Activities
Domain-Specific Variable Descriptions
Variables were created for each environmental domain to
various constructs within that domain.
• Air domain variables were created to represent two
constructs: (1) criteria air pollutants and (2) HAPs.
• Water domain had eight constructs: (1) overall
water quality, (2) general water contamination, (3)
recreational water quality, (4) domestic use, (5)
atmospheric deposition, (6) drought, and (7) chemical
contamination.
• Land domain included variables representing five
constructs: (1) agriculture, (2) pesticide use, (3) soil
contaminants, (4) large facilities, and (5) radon zones.
• Built-enviromnent domain was represented by five
constructs: (1) the housing enviromnent, (2) traffic
safety, (3) public transportation usage, (4) road
properties, and (5) the business enviromnents.
Sociodemographic enviromnent domain was
represented by two constructs: (1) socioeconomic and
(2) crime.
Air Domain
The air domain consists of two data sources, (1) the AQS"
and (2) the NATA,34 representing criteria air pollutants and
HAPs.
Criteria Air Pollutants
Daily concentration data from the EPA's AQS monitors
(point scale) were downloaded for ozone, CO, sulfur dioxide,
nitrogen dioxide, PM under 10 |im in aerodynamic diameter
(PM10), and PM under 2.5 |im in aerodynamic diameter
(PM2 5). Annual averages were calculated for each pollutant
at each monitor with data. These averages were then used in
a kriging procedure to estimate annual concentration at each
county's center point for each year from 2000 to 2005.
For the EQI spanning 2000 to 2005, a single average
concentration was calculated for each county from the kriged
estimates. When indicated (i.e., log-normal distribution) half
of the minimum nonzero value was added, and variables were
log transformed.
HAPs
County-level concentrations estimates from NATA were
used for all HAPs included in the EQI. HAPs were selected
for inclusion from the full NATA pollutant list. Using data
from 2002, variables were evaluated for collinearity and
Table 2. Selected Hazardous Air Pollutants from the National-Scale Air Toxics Assessment (1999,2002, and 2005) Used
in the Environmental Quality Index
All Years
2002,2005 Only
All Years
2002,2005 Only
1,1,2,2-tetrachloroethane
2-chloroacetophenone
Ethylene oxide
Isophorone
1,1,2-trichloroethane
2-nitropropane
Glycol ethers
Methanol
1,2-dibromo-3-chloropropane
4-nitrophenol
Hexachlorobenzene
Methyl isobutyl ketone
2,4-toluene diisocyanate
Acetophenone
Hydrazine
Methyl methacrylate
Acetonitrile
Biphenyl
Hydrochloric acid
Methylhydrazine
Acrolein
Bromoform
Lead compounds
Methyl tert-butyl ether
Acrylic acid
Carbon disulfide
Manganese compounds
Nitrobenzene
Acrylonitrile
Carbon sulfide
Mercury compounds
N,N-dimethylaniline
Antimony compounds
Chlorobenzene
Methyl chloride
Pentachlorophenol
Benzidine
Chromium compounds
o-toluidine
Phosphine
Benzyl chloride
Cresol/cresylic acid
Polycyclic aromatic hydrocarbon
Phosphorus
Beryllium compounds
Cumene
compounds/polycyclic organic mattei
£>/'s-2-ethylhexyl phthalate
Dibutylphthalate
Polychlorinated biphenyls
Selenium compounds
Cadmium compounds
Diesel engine emissions
Propylene dichloride
Styrene
Carbon tetrachloride
Dimethyl phthalates
Propylene oxide
Tetrachloroethylene
Chlorine
Dimethyl sulfate
Quinoline
Trichloroethylene
Chloroform
Epichlorohydrin
Toluene
Triethylamine
Chloroprene
Ethyl chloride
Vinyl chloride
Vinyl acetate
Cyanide compounds
Ethylene glycol
Vinylidene chloride
Dimethyl formamide
Ethylidene dichloride
Ethyl acrylate
Hexachlorobutadiene
Ethylene dibromide
Hexachlorocyclopentadiene
Ethylene dichloride
Hexane
12
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variability. Variables with any correlation coefficient >0.70
were examined, and representative variables were chosen
for each pair or group of highly correlated variables. Of the
remaining variables, all missing values were set to zero,
with the assumption that lack of measurement for an area
indicated low concern for contamination with a particular
H AP, and the number of zero values was evaluated for each
variable. Pollutants with more than 50% zero values were
dropped. This process left 81 HAPs included in the EQI. For
the EQI spanning 2000 to 2005, concentration estimates for
the included pollutants were taken from NATA for 1999 (only
41 available). 2002, and 2005 and averaged across years
(Table 2). Correlations between individual years and averages
were very high (>0.9). When indicated (i.e., log-normal
distribution), half of the minimum nonzero value was added
and variables were log transformed.
The air domain includes 87 variables representing criteria
and H APs.
Water Domain
The water domain included five data sources: (1) the
WATERS program database." (2) Estimates of Water Use
in the United States.'" (3) the NADP,39 (4) the Drought
Monitor Network,38 and (5) the NCOD."" Using these five
data sources, variables were created to represent seven
constructs that describe the overall water environment. The
seven constructs were (1) overall water quality. (2) general
water contamination. (3) recreational water quality. (4)
domestic use. (5) atmospheric deposition. (6) drought, and
(7) chemical contamination.
Overall Water Quality
Impairment and water quality standards (WQS) data were
obtained for the most recent State reported data that were
collected under Sections 303(d) and 305(b) of the CWA.63
The CWA is administered at the State level, and data arc
voluntarily reported from the States to the Federal level.
The dates of the reported data ranged from 2004 to 2010 as
the Federal reporting system maintains only the most recent
data reported by each State. Under Section 305(b) of the
CWA, States establish WQS for each hydrological feature
based on the expected use (or uses) of these waters. Under
Section 303(d) of the CWA, States assess whether waters arc
impaired (do not meet the standards) for the uses established
in the WQS. This assessment is conducted biennially, and the
States voluntarily report these data to the Federal level.
County-level impaired stream length was estimated for
the contiguous United States using impairment and WQS
data (from the WATERS database). With the designated
uses listed for each State, the WQS was classified into
five broad categories of water use: (1) agriculture. (2)
drinking water, (3) recreation. (4) wildlife, and (5) industry.
Using geographic information systems (CIS), county-
level percentages of impairment were calculated. WQS
and impairment datasets were joined to the map layer of
hydrologic features in EPA's RAD.64 RAD is a replicate
of the National Hydrography Dataset Plus65 augmented
for reporting water quality data. The defined broad water
use categories were joined to the WQS data, and a table
summarizing hydrologic features with multiple uses was
created. WQS and impairment tables were assigned to
features in the R AD using CIS Network and Event tools.
These tools link tabular database information with linear or
polygon features. Stream lengths were clipped by county
boundaries to calculate percent impairment by county. Only
linear water features were included in each category. Polygon
features, such as lakes, were excluded because of the lack
of well-defined county and State boundaries across water
bodies. Next, county and State designations were linked
with linear features in R AD. Once all data were associated
to linear hydrologic features, lengths were calculated for
water features impaired for any use, drinking water use. or
recreational use and for all stream lengths within a county. Of
the 11 variables developed, only 1, the cumulative measure
of percent of water impaired for any use. was used; the others
were not used because of missing data ( Appendix II).
General Water Contamination
Water contamination can be caused by several sources.
Unfortunately. EPA only has consistent data on the point
sources of contamination in the form of the number of
National Pollutant Discharge Elimination System (NPDES)66
permits. Therefore, the number of permits in a county was
used as a proxy for general water contamination. Using
permit information in the WATERS database. 13 variables
were calculated for the number of discharge permits in a
county. Permits that were current during the period 2000-
2005 were selected. The 10 variables that were calculated
based on individual permit types had too many missing
data; therefore, three composite variables were created for
inclusion in the EQI. A composite variable was developed
for the number of sewage permits per 1000 km of stream
length in a county. The number of animal feeding operations
and concentrated animal feeding operations NPDES permits,
combined sewer overflow NPDES permits, and NPDES
permits for sludge in each county were summed and divided
by the total stream length in the county. Similarly, composite
variables were calculated for industrial permits (combining
the total of prctrcatnient NPDES permits, general facilities
NPDES permits, and individual facilities NPDES permits)
and stornivvater permits (combining the total of general
stormwater NPDES permits, industrial stormwater NPDES
permits) by county per 1000 km of stream length. These three
variables were not collinear.
Recreational Water Quality
The WATERS database includes annual information on the
number of days of beach closures. Three variables were
created for (1) the total number of days of beach closure for
any event in a county. (2) the total number of days of beach
closure for contamination events in a county, and (3) the
total number of days of beach closure for rain events in a
county for the entire period 2000-2005. The three variables
constructed from these data were not collinear.
Domestic Use
Data from the Estimates of Water Use in the United States
database were used as a proxy for domestic water quality. If
water is being withdrawn for competing uses (agriculture,
13
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industry, etc.), it will put stress on water supplies, which,
in turn, will affect water quality. This database includes
county-level estimates of water withdrawals for domestic,
agricultural, and industrial use. Initially. 15 variables of water
withdrawals for domestic, agricultural, and industrial use
were developed. These data are estimated every 5 years and
were included in the EQI as averaged data for 2000 and 2005.
Two variables were included in the EQI after evaluation for
collincaritv (four variables removed) and missing data (nine
variables rcinovcdl.Tlic two variables were (1) the percent
of population on self-supplied water supplies and (2) the
percent of those on public water supplies that are on surface
waters. For these variables, higher values are not necessarily
a marker for poor water quality. The data were provided
at the county level and normally distributed; therefore, no
additional transformation was required.
Atmospheric Deposition
The atmospheric deposition of chemicals can affect water
quality. The NADP dataset provides measures for the
concentration of nine chemicals in precipitation: (1) calcium.
(2) magnesium, (3) potassium, (4) sodium, (5) ammonium,
(6) nitrate. (7) chloride. (8) sulfate, and (9) mercury. Annual
summary data from each monitoring site for each year 2000-
2005 were kriged spatially to achieve national coverage
and county-level estimates. The annual estimates for each
pollutant then were averaged over the 6-year study period.
The data for all pollutants, except sulfate, were skewed and.
therefore, were natural log transformed to achieve normal
distributions. No variables were removed for collincaritv or
missing data.
Drought
Drought affects the concentration of pathogens and chemicals
in water bodies and. therefore, can affect water quality. The
Drought Monitor dataset provides raster data on six possible
drought status conditions for the entire United States on
a weekly basis. The data were aggregated spatially to the
county level to estimate the percentage of the county in each
drought status condition. The weekly data were averaged to
achieve annual estimates for 2000-2005 and. then, averaged
to create a composite for the entire period. From this data,
the percentage of the county in extreme or exceptional
drought (intensity levels D3 andD4, respectively) was
used in the EQI. The remaining five drought status
conditions were removed, as all of the drought statuses were
highly correlated.
Chemical Contamination
Chemical contamination of water supplies can directly
affect human health. The NCOD dataset provides data on 69
contaminants provided by public water supplies throughout
the country for the period from 1998-2005. Data for all
samples in a county for each contaminant were averaged over
the entire period of the dataset. 1998-2005. The data were
also natural log transformed to achieve normal distributions.
Missing values were set to zero, with the assumption that
lack of measurement for an area indicated low concern
for contamination with that particular contaminant. Eight
contaminants. (1) asbestos. (2) diquat. (3) endothall. (4)
glyphosate. (5) dioxin. (6) radium, (7) beta particles, and (8)
uranium, did not include data for enough counties (missing
data) to be included in the EQI construction. No variables
were deleted for collincarity.
Land Domain
The land domain consisted of eleven data sources,
representing five constructs: (1) agriculture. (2) pesticide use,
(3) soil contaminants. (4) facilities, and (5) radon zone.
Agriculture
Information on nonpesticidc chemicals used in fanning,
animal units, harvested acreage, irrigated acreage, and
proportion of farms was taken from the 2002 Census of
Agriculture" Final acreage for each item then was divided
by total acreage for each county to return a percentage (e.g.,
percentage of irrigated acres out of total acres in a county). In
some cases, county-level acreage for items was suppressed.
In these, case estimates were imputed based on unaccounted
for and total State-level acreage. Known acreage was
subtracted from total State acreage, leaving an "unassigned"
total acreage for each State. This total number was divided by
the total number of farms in counties with suppressed acreage
to return an average acreage for each farm. This average
acreage then was multiplied by the number of farms in each
county with suppressed acreage to estimate acreage. Animal
units were estimated by multiplying the number of livestock
(cows, hogs, and poultry) by the animals per animal unit
statistic67 and then adding together all livestock categories for
each county. Seven variables representing agriculture were
included in the EQI.
Pesticide Use
Pesticide use for each county was estimated using crop data
from the 2002 Census of Agriculture and Statc-pesticidc-use
data from the 2002 National Pesticide Use Dataset." Where
available, county-level acreage for oats, potatoes, soybeans,
and wheat crops was used for estimation of pesticide use.
When county-level data were not available, it was imputed
based on unaccounted for and total State-level acreage, as
was described in the agriculture construct above. These crops
were used as they had the most complete spatial coverage
in the United States, and pesticide use information was
available for them. County-level acreage was multiplied by
State-level pesticide use rates (tons per acre) to estimate tons
of herbicides, fungicides, and insecticides applied in each
county. These three pesticide categories were included in the
EQI. Pesticide variables were evaluated for normality and
log transformed.
Soil Contaminants
Extracted from the NGS," soil contaminant concentrations
were collected from States and combined over multiple
years (ranging variably from 1998-2007). These data,
collected for stream sediments, soils, and other media, were
combined at the county level to estimate the mean values of
13 geoclieinical contaminants. Contaminant variables were
evaluated for normality and log transformed.
14
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Facilities
Large facilities have the capacity to affect land quality. The
facilities included in the land domain arc those represented
on the EPA Gcospatial Data Download Service46. Because
many counties had at least one. but no counties had all six of
the facility types present, a composite facilities data variable
was constructed by summing the count of any one of the
six facilities types (Brownfield sites (n=1226),51 Supcrfund
sites (n=721),47 Toxic Release Inventory sites (n=2670),50
pesticidc-producing-location sites (n=2095),52 large-quantity
generator sites (n= 1926),49 and treatment, storage, and
disposal sites (n=874)48) across the counties. Facilities were
included in the count if they were identified during the
2000-2005 period. The count of facilities was divided by
the county population, which produced a facilities rate. The
facilities rate variable was assessed for normality and log
transformed.
Radon Zone
The potential for elevated indoor radon levels was
represented using the county score from the EPA Radon
Zone map,54 which was available for 3126 counties. The
EPA Radon Zone map identified areas of the U nited States
with the potential for elevated indoor radon levels. Each U.S.
county was assigned to one of three zones based on radon-
level elevation potential.
Sociodemographic Domain
This domain was constructed to explore the
sociodemographic features of counties in the U nited States.
These features were used to approximate the social-stress
associated with residing in more deprived (low education,
high unemployment, high violent crime, high poverty,
etc.) or more affluent (high professional occupations, low
property crime, high proportion of college graduates, etc.)
counties. This domain includes variables from the 2000 U.S.
Census" and the FBI UCRs.56 Because the sociodemographic
domain is related to population density, by virtue of the
data collection and reporting, variables were developed as
population rates (denominator: count of persons per county),
rather than area-based rates (denominator: square miles per
county).
Eleven variables were obtained from the 2000 U.S. Census.
The 11 variables were (1) percent renter-occupied housing.
(2) percent vacant housing units. (3) median household
value. (4) median household income. (5) percent persons
living below the Federal poverty line. (6) percent no English-
speaking. (7) percent earning more than a high school
education. (8) percent unemployed. (9) percent working
outside the county of residence, (10) median number of
rooms in the housing unit, and (11) percent of housing with
more than 10 units. The sociodemographic domain contains
a mix of positive and negative features; therefore, when
the sociodemographic domain was constructed, positive
variables were reverse-coded to ensure that a higher amount
of the sociodemographic domain will represent adverse
environmental conditions.
The area-level crime environment was represented using
the FBI UCRs.56 The first step in constructing crime data
was to assign each jurisdiction or place to a county using
county Federal Information Processing Standards"* code.
In cases when a jurisdiction covered more than one county,
the reported crime was assigned to both counties. Although
this double assignment results in a slight inflation of crime
reports for a State, there was no way to determine which
county should receive the crime report. Further, if police
or municipal jurisdictions crossed county lines, it is likely
residents of both counties were "exposed" to the crime
environment. Crime data attributed to more than one county
occurred in approximately 15 counties. Second, because
crime was reported for less than half the U.S. counties, crime
data were kriged spatially and temporally to estimate values
for counties with no reported crime. The decision was made
to krige these data because data reporting was voluntary, and
it seemed unlikely that no crime occurred in the nonrcported
areas. Because zeros could not be reasonably assigned to
the missing counties, the data were interpolated spatially
and temporally instead. The kriged values for violent and
property crime (two variables), constructed for all counties,
were considered for inclusion in the sociodemographic
domain of the EQI. The correlation between the property and
violent crime rates was very high (0.96); therefore, only log
violent crime was included in the EQI.
Buili-Environment Domain
Five data sources were included in the built domain,
representing (1) the housing environment. (2) traffic safety.
(3) public transportation usage (commuting behaviors). (4)
road properties (road type and density), and (5) the business
and service environments (e.g., food, recreation).
Housing Environment
The subsidized housing environment was represented by
the Housing and Urban Development data.60 These data
provide a count of the low-rent and Section 8 housing in
each housing authority data area. The housing authority areas
correspond to cities, which were assigned county codes. Data
were collected in 2010, but. because low-rent and Section 8
housing docs not cliange substantially over time, tliese data
were considered representative of the 2000-2005 period. The
variables were summed to result in the count of any low-rent
or Section 8 housing in each county. The rate of subsidized
housing was constructed by dividing the count of subsidized
housing units per county by the county population. The data
were log transformed prior to inclusion in the EQI.
Traffic Safety
Traffic fatalities, an important feature and consequence of the
built environment, were estimated using the FARS data. The
FARS is a national census providing the National Highway
Traffic Safety administration yearly reports of fatal injuries
suffered in motor vehicle crashes. Rates for the 2000-
2005 counts of fatal crashes per county were constructed
by dividing the count of county-level fatal crashes by the
county-level population. Many counties had no fatal craslies.
15
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To accommodate the large number of meaningful zeros in the
data, the log of this rate variable was used in the built domain
of the EQI.
Public Transportation Usage
The percent of county residents who use public transportation
was estimated using the 2000 U.S. Census55 variable in the
EQI. For many counties, the percent of the population who
reports using public transportation is near zero. Therefore,
this variable was log transformed prior to its use in the built
domain of the EQI.
Road Properties
For the built-cnvironmcnt domain, characterizing the relative
proportions of each county that was served by highways,
secondary roads, and primary roads were of interest, as these
types of roads confer different risks (related to speed and
safety) and benefits (related to neighborhood walking or ease
of transit). Road type for the year 2003 was approximated
using TIGER data,57 which arc available at multiple units of
geography. Three proportion variables were constructed by
dividing the mileage of each road type (e.g., secondary roads)
by the total road mileage in each county. The proportions
of all roadways that were highways or primary roads
were included.
Business and Service Environments
Businesses represent an important component of the built
environment and can contribute to the risk and amenity
landscape. Variables representing various built-cnvironincntal
features were constructed using 2002 Dun and Bradstrcct
data,45 which include commercial information on businesses,
data on more than 195 million records, and arc proprietary.
Nine rate variables were constructed by dividing the
county-level count of a business type by the county-level
population count. The nine variables that were constructed
included the (1) positive food environment. (2) negative
food environment. (3) vice environment (alcohol, pawn,
and gaming), (4) entertainment environment, (5) health
care business environment. (6) recreation environment. (7)
education environment. (8) social-service environment, and
(9) transportation-related environment. Note: Positive food
environments included those that sold healthier foods, like
grocery stores, sit-down restaurants, and organic shops,
whereas the negative food environment included businesses
like fast-food restaurants, convenience stores, and pretzel
trucks. Although related, these two food environments
comprise different businesses and arc not 100% inversely
correlated. Nonnormally distributed variables were log
transformed and all nine were included in the EQI.
16
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5.0
Data Reduction and Index Construction
Overall Approach
After variable development, all the variables were combined
into an index representing the overall environmental quality.
The specific tasks required for index construction were
as follows:
• included all the variables from one domain in a
PC A to empirically summarize that domain-specific
environmental context (retaining the first component as
the domain index);
• did this for each of the five domains;
• combined each of the five domain-specific indices in
another PC A to empirically summarize the overall
environmental context into one index of environmental
quality and retained the initial component as the overall
EQI; and
• repeated the three previous steps for each of the four
RUCC strata (e.g., RUCC stratum 1 air domain; RUCC
stratum 2 air domain, etc.), such that each RUCC had
its own set of domain-specific indices, as well as its
own overall index.
The EQI. domain-specific indices, and EQI stratified by rural-
urban data are available publicly at EPA's Environmental
Dataset Gateway. Also, an interactive map of the EQI is
available at EPA's GeoPlatform.
PCA
PC A is a data reduction technique frequently used to
create sociodemographic scales or indices for inclusion in
statistical models."" PC A analyzes total variance and the
loading represents the correlation between the variable and
the component. PCA assumes no underlying latent variable
structure but. rather, seeks to empirically summarize multiple
possible domains. Three major goals of PC A are to
1. summarize the patterns of correlations among observed
or measured variables,
2. provide an operational definition—in this case, a
regression equation—for underlying processes by using
observed or measured variables, and
3. reduce a large number of observed variables into a
smaller number of factors or a single component.
PC A was chosen for data reduction for several reasons.
Production of an empirical summary of the various
constituent components of the EQI was desired. Various data
sources measured on multiple scales needed to be combined.
PC A standardized these measures prior to combining.
Therefore, the differing scales were less problematic. To
assess variables influences on the index, variables cannot
simply be added together. To do so would mean knowledge
for most of the variables would not be available to indicate
if any one variable would prove to be more "influential" for
environmental quality than another. PC A enabled variable
loadings to vary by their relative importance to the total
component. This feature enabled exploration of variable
loading differences for interpretation purposes.
The PCA steps included
• selecting the set of variables to be used.
• preparing the correlation matrices.
• extracting the set of components from the correlation
matrix.
• determining the number of components observed, and
• interpreting the findings.
PC A analyzes the total variance. Therefore, in the PCA
correlation matrix. "1" is in the positive diagonal. To
construct the EQI. variables from each domain were
entered into domain-specific PC As. PC A produced variable
loadings, which were roughly equivalent to the "weight"
or contribution that each variable made toward explaining
the total variance. The weights, however, need not sum to
1.0 because the loadings were for the total variance, not
just the shared variance. The loading associated with each
variable then was multiplied by its mean value for the given
geography (county, for the EQI). and these weighted mean
values were summed.
Rural-Urban Continuum
Both the domain-specific indices and the overall EQI were
created for each county in the U nited States. Recognizing
that environments differ dramatically across the rural-urban
continuum," the decision was made that the EQI would be
most useful if it accommodated rural-urban environmental
differences. The EQI was stratified by RUCCs. The RUCC is
a nine-item categorization code of proximity to or influence
of major metropolitan areas."1 The nine-item categories were
condensed into four, where RUCC1 represents metropolitan-
urbani/ed = codes 1+2+3; RUCC2 nonmetropolitan-
urbani/ed = 4+5; RUCC3 less urbanized = 6+7; and
RUCC4 thinly populated (rural) = 8+9 (see Figure 2).72-75
RUCC-stratified EQIs and an overall EQI were constructed.
Loadings on the stratified and nonstratified sets of indices
were assessed to determine loading heterogeneity across
counties. Because these loadings differed meaningfully by
RUCC level, RUCC-stratified EQIs were constructed for
each county.
17
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Rural-urban continuum code (RUCC)
Metropolitan urbanized
J Non-metro urbanized
1 Less urbanized
| Thinly populated
Figure 2. Rural-urban continuum code (RUCC) stratification for all counties in the United States.
Although it was possible to form as many independent
linear combinations as there were variables in PCA, only the
first principal component was retained. The first principal
component was the unique linear combination that accounted
for the largest possible proportion of the total variability in
the component measures. Therefore, the first component
from each of these domain-specific indices was retained
(e.g., air index, water index). Domain-specific indices were
then entered into another PCA, where the first component
was retained as the EQI (Figure 3). This process was
undertaken separately for each of the four RUCC strata.
Within each RUCC strata, domain-specific variable loadings
were evaluated based on the value of variable loading
and the variable's hypothesized relevance to health. For
instance, although arsenic may occur in low frequency
in a lot of counties and, therefore, may have a relatively
small component loading, it is an important health hazard
when present. Based on variable loading magnitude
alone, dropping arsenic from an EQI may be a reasonable
conclusion. However, it was retained for the EQI based on its
relevance to human health.
The first principal component, titled the domain-specific EQI
(e.g., air domain EQI), was then standardized to have a mean
of 0 and standard deviation (SD) of 1 by dividing the index
by the square of its eigenvalue.76 Each domain-specific index
was then included in a second PCA procedure (Figure 3) to
result in the overall EQI for each strata of RUCC.
Results
Description of Variables Comprising EQI Domains
Air Domain
Variables included in the air domain generally showed
moderate-to-high variability between rural and urban strata,
with higher averages in the most urban stratum decreasing to
the most rural stratum (Table 3). For example, CO had mean
values of 705, 598,472, and 343 ppm for each stratum from
most urban to most rural. This pattern held true for most of
the HAPs as well, although some pollutants showed higher
means in the nonmetropolitan-urbanized or less urbanized
strata (e.g., chlorine, dimethyl sulfate). Others, like carbon
tetrachloride, are similar across rural-urban strata.
18
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Principal component
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.
RUCC1 = metropolitan-urbanized
RUCC2 =nonmetropolitan-urbanized
RUCC3 =less urbanized
RUCC4 =thinfy populated
OVERALL
Air
variables
Water
variables
Air
Indices
Water
Indices
Indices
R
R
R
R
O
U
U
U
U
V
C
C
C
C
E
C
c
C
C
R
1
2
3
4
A
L
L
\k
fill
R
R
R
R
0
U
U
U
U
V
C
C
C
C
E
C
C
C
C
R
1
2
3
4
A
L
L
EQI
Built
variables
Built
Indices
R R
U U
Socio-
demographic
variables
Socio-
demographic
Indices
Figure 3. Principal component analysis concept for Environmental Quality Index. Performed for all counties and each of
the four strata of the rural-urban continuum (RUCC) codes."
Table 3. Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban Continuum
Codes (RUCCs) Stratified
Variable
Air Domain
Units
Metropolitan-
Urbanized
(RUCC1 = 1089)
Mean (SD)
[Range]
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Mean (SD)
[Range]
Less Urbanized
(RUCC3 = 1059)
Mean (SD)
[Range]
Thinly Populated
(RUCC4 = 670)
Mean (SD)
[Range]
OVERALL
(n=3141)
Mean (SD)
[Range]
Construct: Criteria Air Poliutants
Nitrogen dioxide
Carbon monoxide
Sulfur dioxide
PPb
ppm
PPb
7.95E+02 (7.05E+02)
[1.01E+00, 8.65E+03]
7.05E+02 (1.06E+03)
[1.26E+00, 2.48E+04]
2.95E+02 (5.56E+02)
[1.00E+00,1.22E+04]
4.97E+02 (4.13E+02)
[1.29E+00,2.59E+03]
5.98E+02 (5.55E+02)
[1.11E+00, 3.25E+03]
2.09E+02 (2.99E+02)
[1.00E+00, 3.71 E+03]
4.21 E+02 (3.95E+02)
[1.00E+00, 8.66E+03]
4.72E+02 (4.95E+02)
[1.00E+00, 4.61 E+03]
1.63E+02 (2.09E+02)
[1.00E+00, 2.32E+03]
3.53E+02 (3.36E+02)
[1.00E+00, 3.42E+03]
3.43E+02 (4.55E+02)
[1.05E+00, 4.45E+03]
1.39E+02 (2.01 E+02)
[1.00E+00,1.73E+03]
5.44E+02
(5.49E+02)
flOOE+OO,
8.66E+03]
5.38E+02
(7.52E+02)
[1.00E+00,
2.48E+04]
2.08E+02
(3.79E+02)
[1.00E+00,
1.22E+04]
For orientation to the results, low index scores (EQI and domain-specific)
indicate higher environmental quality, and higher index scores (EQI and
domain-specific) mean lower environmental quality.
19
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Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Variable
Ozone
PMlr
PM,
Units
pptn
|jg/m-
|jg/m-
Metropolitan-
Urbanized
(RUCC1 = 1089)
Mean (SD)
[Range]
6.02E+03 (5.07E+03)
[1.72E+00, 8.03E+04]
14.199 (5.193)
[1.777,39.554]
10.621 (2.205)
[3.443,16.912]
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Mean (SD)
[Range]
5.06E+03 (4.06E+03)
[7.80E+00, 4.20E+04]
11.258 (4.533)
[2.370, 27.095]
9.586 (2.351)
[2.162,14.397]
Less Urbanized
(RUCC3 = 1059)
Mean (SD)
[Range]
Thinly Populated
(RUCC4 = 670)
Mean (SD)
[Range]
4.30E+03 (3.50E+03) 3.75E+03 (2.54E+03)
[1.24E+00, 5.81 E+04] [1.33E+00, 2.05E+04]
9.852 (3.924)
[1.000,34.625]
9.379 (2.466)
[1.029,14.451]
8.446 (3.596)
[1.011,21.404]
8.265 (2.745)
[1.138,13.437]
OVERALL
(n=3141)
Mean (SD)
[Range]
4.86E+03
(4.11E+03)
[1.24E+00,
8.03E+04]
11.204 (4.974)
[1.000,39.554]
9.593 (2.582)
[1.029,16.912]
Construct: Hazardous Air Pollutants
1,1,2,2-
Tetrachloroethane
1,1,2-T richloroethane
1,2-Dibromo-3-
chloropropane
2,4-Toluene diisocyanate
2-Chloroacetophenone
2-Nitropropane
4-Nitrophenol
Acetonitrile
Acetophenone
Acrolein
Acrylic acid
Acrylonitrile
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
Tons
emitted
0.014(0.008)
[0.002, 0.048]
0.001 (0.014)
[0, 0.426]
3.91E-06 (1.17E-05)
[7.80E-07, 2.74E-04]
Tons 1.05E-03 (1.61E-03)
emitted [4.05E-07,1.32E-02]
7.58E-07 (1.63E-05)
[0, 5.38E-04]
3.82E-05 (4.64E-04)
[0, 9.10E-03]
4.32E-05 (6.16E-05)
[2.62E-07, 6.44E-04]
0.001 (0.008)
[0,0.150]
1.98E-04 (8.85E-04)
[1.74E-07,1.69E-02]
0.045 (0.039)
[0.002, 0.372]
2.17E-04 (1.25E-03)
[9.57E-12, 2.70E-02]
4.33E-03 (6.72E-03)
[3.87E-04,1.66E-01]
0.012 (0.003)
[0.003,0.019]
5.16E-04 (4.01E-03)
[0, 4.38E-02]
3.04E-06 (5.61E-06)
[8.27E-07, 5.46E-05]
7.57E-04 (2.05E-03)
[2.66E-06, 3.02E-02]
9.70E-08 (1.90E-07)
[0,1.26E-06]
7.48E-06 (5.99E-05)
[0, 8.76E-04]
2.25E-05 (1.46E-05)
[2.26E-06,1.29E-04]
5.75E-04 (2.18E-03)
[0, 2.46E-02]
1.71E-04 (7.63E-04)
[1.59E-06,1.09E-02]
0.025 (0.022)
[0.003,0.165]
8.63E-05 (5.61E-04)
[2.38E-11, 6.81 E-03]
3.06E-03 (3.49E-03)
[3.83E-04, 3.84E-02]
0.005 (0.003)
[0.002,0.016]
2.72E-04 (4.47E-03)
[0,1.33E-01]
2.81E-06 (4.37E-06)
[7.22E-07, 3.63E-05]
3.44E-04 (1.25E-03)
[9.78E-09, 2.00E-02]
1.08E-07 (6.82E-07)
[0,1.75E-05]
5.15E-06 (1.04E-04)
[0, 3.26E-03]
7.80E-06 (7.66E-06)
[0, 9.69E-05]
5.34E-04 (2.58E-03)
[0, 4.57E-02]
1.40E-04 (1.84E-03)
[0, 4.42E-02]
0.018 (0.023)
[0.001,0.245]
1.09E-04 (1.51 E-03)
[8.45E-13, 3.20E-02]
2.47E-03 (2.60E-03)
[3.67E-04, 2.00E-02]
2.02E-03 (5.76E-04)
[1.37E-03, 6.83E-03]
6.82E-05 (1.04E-03)
[0, 2.43E-02]
1.90E-06 (2.80E-06)
[7.22E-07, 2.50E-05]
5.69E-05 (2.06E-04)
[1.74E-09, 4.52E-03]
6.30E-08 (3.56E-07)
[0, 7.32E-06]
6.99E-07 (1.09E-05)
[0, 2.21E-04]
1.65E-06 (2.20E-06)
[0,1.73E-05]
3.75E-04 (2.23E-03)
[0, 3.65E-02]
2.41 E-05 (1.92E-04)
[0, 2.94E-03]
0.012 (0.038)
[0.001,0.920]
1.30E-05 (7.92E-05)
[1.43E-13,1.02E-03]
1.75E-03 (2.19E-03)
[3.67E-04,1.76E-02]
0.008 (0.007)
[0.001,0.048]
4.86E-04
(8.75E-03)
[0, 4.26E-01]
3.02E-06
(7.70E-06)
[7.22E-07,
2.74E-04]
5.68E-04
(1.42E-03)
[1.74E-09,
3.02E-02]
3.23E-07
(9.63E-06)
[0, 5.38E-04]
1.59E-05
(2.81E-04)
[0, 9.10E-03]
2.03E-05
(4.09E-05)
[0, 6.44E-04]
7.75E-04
(4.90E-03)
[0,1.50E-01]
1.39E-04
(1.22E-03)
[0, 4.42E-02]
0.027 (0.036)
[0.001,0.920]
1.23E-04
(1.16E-03)
[1.43E-13,
3.20E-02]
3.02E-03
(4.60E-03)
[3.67E-04,
1.66E-01]
20
-------
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL
(n=3141)
Variable
Units
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Antimony compounds
Tons
emitted
1.60E-04 (1.36E-03)
[1.31E-10, 3.96E-02]
1.92E-04 (2.19E-03)
[2.16E-10, 3.91 E-02]
3.98E-05 (2.78E-04)
[0, 5.86E-03]
1.10E-05 (9.61 E-05)
[0, 1.72E-03]
9.09E-05
(1.08E-03)
[0, 3.96E-02]
Benzidine
Tons
emitted
3.69E-07 (3.63E-06)
[5.94E-09,1.07E-04]
2.43E-07 (1.23E-06)
[6.30E-09, 2.12E-05]
2.39E-07 (6.65E-07)
[5.50E-09,1.20E-05]
1.61E-07 (3.95E-07)
[5.50E-09, 5.31 E-06]
2.68E-07
(2.21 E-06)
[5.50E-09,
1.07E-04]
Benzyl chloride
Tons
emitted
4.32E-05 (2.78E-04)
[2.51 E-09, 7.58E-03]
1.08E-05 (2.62E-05)
[0, 2.20E-04]
7.76E-06 (2.37E-05)
[0, 3.54E-04]
3.87E-06 (1.24E-05)
[0, 1.43E-04]
1.95E-05
(1.65E-04)
[0, 7.58E-03]
Beryllium compounds
Tons
emitted
4.44E-05 (1.54E-04)
[1.01E-05, 3.60E-03]
2.79E-05 (2.90E-05)
[1.02E-05, 3.73E-04]
2.89E-05 (1.87E-04)
[9.40E-06, 5.93E-03]
1.63E-05 (2.28E-05)
[9.27E-06, 4.52E-04]
3.15E-05
(1.42E-04)
[9.27E-06,
5.93E-03]
Biphenyl
Tons
emitted
4.87E-04 (2.69E-03)
[3.43E-07, 5.87E-02]
3.02E-04 (1.04E-03)
[1.34E-06,1.15E-02]
1.70E-04 (1.53E-03)
[3.53E-08, 3.79E-02]
4.51 E-05 (5.16E-04)
[2.94E-08,1.30E-02]
2.67E-04
(1.87E-03)
[2.94E-08,
5.87E-02]
bis-2-Ethylhexyl phthalate
Tons
emitted
5.37E-01 (1.57E-03)
[5.36E-01, 5.68E-01]
0.537 (0.001)
[0.537, 0.550]
0.537 (0.001)
[0.536, 0.555]
5.37E-01 (2.48E-04)
[5.36E-01, 5.39E-01]
0.537 (0.001)
[0.536, 0.568]
Bromoform
Tons
emitted
2.01E-06 (8.74E-06)
[0, 1.48E-04]
6.48E-07 (1.86E-06)
[0, 2.69E-05]
3.12E-06 (8.24E-05)
[0, 2.68E-03]
3.78E-07 (2.80E-06)
[0, 6.99E-05]
1.90E-06
(4.81 E-05)
[0, 2.68E-03]
Cadmium compounds
Tons
emitted
1.10E-04 (1.96E-04)
[2.48E-05, 3.30E-03]
8.19E-05 (2.21E-04)
[2.44E-05, 3.20E-03]
5.91 E-05 (2.71E-04)
[2.34E-05, 8.26E-03]
3.31 E-05 (2.87E-05)
[2.08E-05, 6.31 E-04]
7.37E-05
(2.10E-04)
[2.08E-05,
8.26E-03]
Carbon disulfide
Tons
emitted
9.05E-03 (1.05E-01)
[5.50E-07, 2.30E+00]
5.94E-03 (6.12E-02)
[1.91E-06,1.02E+00]
2.07E-03 (2.48E-02)
[1.34E-07, 5.66E-01]
7.92E-04 (1.13E-02)
[5.88E-09, 2.62E-01]
4.61 E-03
(6.66E-02)
[5.88E-09,
2.30E+00]
Carbon tetrachloride
Tons
emitted
0.497 (0.006)
[0.429, 0.558]
0.497 (0.003)
[0.468,0.511]
0.497 (0.005)
[0.429, 0.568]
0.496 (0.008)
[0.395, 0.509]
0.497 (0.006)
[0.395, 0.568]
Carbon sulfide
Tons
emitted
2.29E-03 (1.22E-02)
[1.05E-07, 2.50E-01]
5.12E-03 (7.86E-02)
[1.02E-11,1.41E+00]
0.002 (0.019)
[0,0.410]
5.07E-04 (4.68E-03)
[0, 8.43E-02]
0.002 (0.028)
[0,1.411]
Chlorine
Tons
1.39E-02 (2.40E-01)
0.004 (0.011)
0.003 (0.024)
5.85E-04 (3.97E-03)
0.006 (0.142)
emitted
[6.44E-11, 7.90E+00]
[0, 0.089]
[0, 0.594]
[0, 9.25E-02]
[0,7.901]
Chlorobenzene
Tons
emitted
6.80E-03 (1.81E-02)
[2.78E-07, 2.25E-01]
3.18E-03 (4.98E-03)
[1.37E-06, 2.47E-02]
1.12E-03 (2.24E-03)
[2.26E-08,1.18E-02]
2.43E-04 (6.13E-04)
[2.88E-09, 5.47E-03]
3.11 E-03 (1.12E-02)
[2.88E-09,
2.25E-01]
Chloroform
Tons
emitted
0.074 (0.037)
[0.048,0.616]
0.062 (0.012)
[0.050,0.158]
0.055 (0.017)
[0.043, 0.420]
0.050 (0.005)
[0.039,0.140]
0.062 (0.026)
[0.039, 0.616]
Chloroprene
Tons
emitted
0.001 (0.017)
[0, 0.434]
2.05E-05 (2.80E-04)
[0, 5.03E-03]
4.01 E-05 (1.03E-03)
[0, 3.30E-02]
2.28E-05 (5.58E-04)
[0, 1.44E-02]
4.18E-04
(1.00E-02)
[0, 4.34E-01]
Chromium compounds
Tons
emitted
6.72E-04 (1.00E-03)
[4.15E-05,1.66E-02]
3.49E-04 (9.03E-04)
[4.24E-05,1.03E-02]
2.50E-04 (1.91E-03)
[4.08E-05, 5.88E-02]
7.97E-05 (1.61 E-04)
[3.42E-05, 2.40E-03]
3.70E-04
(1.31 E-03)
[3.42E-05,
5.88E-02]
-------
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL
(n=3141)
Variable
Units
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Cresol/cresylic acid
Tons
emitted
5.84E-03 (1.30E-02)
[8.63E-05,1.06E-01]
3.17E-03 (1.02E-02)
[7.49E-05, 8.76E-02]
1.43E-03 (4.29E-03)
[1.01E-05, 5.27E-02]
7.67E-04 (3.15E-03)
[8.92E-06, 4.52E-02]
3.00E-03
(9.06E-03)
[8.92E-06,
1.06E-01]
Cumene
Tons
emitted
1.94E-03 (9.17E-03)
[1.26E-05, 2.65E-01]
9.86E-04 (2.31E-03)
[7.40E-05, 2.54E-02]
3.99E-04 (1.21E-03)
[1.34E-06, 2.98E-02]
1.34E-04 (4.90E-04)
[1.79E-07, 9.55E-03]
9.37E-04
(5.55E-03)
[1.79E-07,
2.65E-01]
Cyanide compounds
Tons
emitted
3.88E-02 (5.55E-02)
[4.91 E-04, 1.35E+00]
0.028 (0.037)
[0.003, 0.635]
1.29E-02 (3.04E-02)
[1.15E-04, 9.50E-01]
4.05E-03 (5.00E-03)
[3.08E-05, 6.45E-02]
2.16E-02
(4.16E-02)
[3.08E-05,
1.35E+00]
Dibutylphthalate
Tons
emitted
6.75E-03 (6.17E-02)
[3.91 E-07, 1.71E+00]
3.42E-03 (1.34E-02)
[2.26E-06,1.35E-01]
6.87E-04 (4.64E-03)
[2.87E-08,1.18E-01]
7.58E-05 (6.61 E-04)
[2.61 E-09,1.23E-02]
2.94E-03
(3.68E-02)
[2.61 E-09,
1.71E+00]
Diesel engine emissions
Tons
emitted
0.607 (0.516)
[0.034,8.815]
0.352 (0.188)
[0.035,1.791]
0.235 (0.129)
[0.001,0.991]
1.53E-01 (9.87E-02)
[1.59E-04, 5.42E-01]
3.59E-01
(3.73E-01)
[1.59E-04,
8.82E+00]
Dimethyl formamide
Tons
emitted
1.89E-03 (4.79E-03)
[3.69E-06,1.09E-01]
1.51E-03 (8.82E-03)
[3.86E-05,1.59E-01]
6.16E-04 (7.65E-03)
[2.56E-06, 2.46E-01]
7.33E-05 (2.41 E-04)
[5.32E-07, 5.54E-03]
1.03E-03
(6.01 E-03)
[5.32E-07,
2.46E-01]
Dimethyl phthalates
Tons
emitted
2.40E-04 (1.63E-03)
[7.75E-08, 3.76E-02]
2.01 E-04 (1.43E-03)
[9.54E-08,1.84E-02]
1.44E-04 (1.30E-03)
[0, 2.76E-02]
4.62E-05 (5.58E-04)
[1.30E-09,1.28E-02]
1.62E-04
(1.33E-03)
[0, 3.76E-02]
Dimethyl sulfate
Tons
emitted
3.14E-06 (1.55E-05)
[0, 3.35E-04]
7.71E-06 (9.77E-05)
[0,1.67E-03]
1.01E-06 (7.34E-06)
[0,1.80E-04]
3.44E-07 (1.21E-06)
[0,1.73E-05]
2.30E-06
(3.29E-05)
[0,1.67E-03]
Epichlorohydrin
Tons
emitted
1.53E-04 (1.91E-03)
[0,5.71E-02]
1.24E-05 (8.63E-05)
[0,1.40E-03]
1.23E-05 (1.30E-04)
[0, 2.85E-03]
7.01 E-06 (1.31 E-04)
[0, 3.31 E-03]
5.99E-05
(1.13E-03)
[0, 5.71 E-02]
Ethyl acrylate
Tons
emitted
1.10E-04 (6.10E-04)
[0,1.10E-02]
3.41E-05 (2.25E-04)
[0, 3.26E-03]
1.13E-05 (1.07E-04)
[0, 2.59E-03]
1.09E-05 (1.17E-04)
[0, 2.32E-03]
4.76E-05
(3.78E-04)
[0,1.10E-02]
Ethyl chloride
Tons
emitted
3.25E-03 (1.94E-02)
[1.94E-06, 5.22E-01]
1.34E-03 (1.47E-03)
[1.27E-06, 2.08E-02]
4.96E-04 (9.94E-04)
[0, 2.94E-02]
1.43E-04 (3.75E-04)
[6.74E-07, 8.29E-03]
0.001 (0.012)
[0, 0.522]
Ethylene dibromide
Tons
emitted
4.59E-03 (3.64E-03)
[8.26E-05,1.97E-02]
3.26E-03 (1.43E-03)
[8.66E-05, 6.87E-03]
7.39E-04 (8.23E-04)
[7.16E-05, 4.83E-03]
2.51 E-04 (1.66E-04)
[7.18E-05,1.05E-03]
2.23E-03
(2.94E-03)
[7.16E-05,
1.97E-02]
Ethylene dichloride
Tons
emitted
0.011 (0.007)
[0.001,0.088]
0.010 (0.003)
[0.003, 0.045]
0.005 (0.003)
[0.001,0.050]
0.002 (0.002)
[0.001,0.020]
0.007 (0.006)
[0.001,0.088]
Ethylene glycol
Tons
emitted
1.31E-01 (1.74E-01)
[4.27E-04, 1.43E+00]
0.090 (0.214)
[0.002, 3.089]
2.70E-02 (5.91E-02)
[3.72E-05, 8.75E-01]
5.35E-03 (1.10E-02)
[6.49E-06,1.63E-01]
6.49E-02
(1.38E-01)
[6.49E-06,
3.09E+00]
-------
Table 3.(continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL
(n=3141)
Variable
Units
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Ethylene oxide
Tons
emitted
4.87E-03 (5.12E-03)
[3.41 E-04, 6.56E-02]
3.53E-03 (3.43E-03)
[3.31 E-04, 2.76E-02]
2.42E-03 (2.53E-03)
[3.07E-04, 2.99E-02]
1.46E-03 (1.76E-03)
[2.67E-04,1.57E-02]
3.18E-03
(3.87E-03)
[2.67E-04,
6.56E-02]
Ethylidene dichloride
Tons
emitted
3.73E-04 (1.20E-03)
[6.21E-09,1.53E-02]
1.60E-04 (3.09E-04)
[0, 3.84E-03]
9.69E-05 (2.91 E-04)
[0, 6.08E-03]
3.94E-05 (1.70E-04)
[0, 3.11 E-03]
1.87E-04
(7.53E-04)
[0, 1.53E-02]
Glycol ethers
Tons
emitted
3.14E-02 (5.43E-02)
[1.34E-04, 7.83E-01]
2.04E-02 (2.91E-02)
[3.44E-04, 2.04E-01]
1.00E-02 (2.16E-02)
[1.71E-05, 3.66E-01]
4.59E-03 (1.63E-02)
[5.93E-06, 3.13E-01 ]
1.73E-02
(3.80E-02)
[5.93E-06,
7.83E-01]
Hexachlorobenzene
Tons
emitted
3.16E-06 (3.42E-05)
[1.13E-10, 9.59E-04]
8.63E-07 (3.25E-06)
[0, 5.59E-05]
5.69E-07 (2.52E-06)
[0, 6.57E-05]
1.82E-07 (4.93E-07)
[8.76E-10, 5.96E-06]
1.41E-06
(2.03E-05)
[0, 9.59E-04]
Hexachlorobutadiene
Tons
emitted
1.62E-06 (1.67E-05)
[0, 3.76E-04]
6.20E-07 (5.70E-06)
[0, 8.08E-05]
1.44E-07 (1.18E-06)
[0, 2.09E-05]
2.29E-06 (5.60E-05)
[0, 1.45E-03]
1.16E-06
(2.77E-05)
[0, 1.45E-03]
Hexachlorocyclopentadiene
Tons
emitted
6.11E-05 (1.88E-03)
[0, 6.20E-02]
6.79E-05 (1.11 E-03)
[0, 1.99E-02]
6.47E-07 (7.44E-06)
[0, 1.60E-04]
3.42E-06 (6.71 E-05)
[0, 1.69E-03]
2.91 E-05
(1.16E-03)
[0, 6.20E-02]
Hexane
Tons
emitted
0.245 (0.345)
[0.005, 4.984]
0.148 (0.156)
[0.018,1.461]
0.093 (0.540)
[0.001,14.485]
3.43E-02 (3.05E-01)
[1.49E-04, 7.89E+00]
1.39E-01 (4.11E-01)
[1.49E-04,
1.45E+01]
Hydrazine
Tons
emitted
1.61E-05 (2.35E-04)
[8.37E-08, 6.81 E-03]
6.53E-06 (4.10E-05)
[8.31 E-08, 6.24E-04]
4.07E-06 (4.78E-05)
[7.22E-08,1.44E-03]
2.02E-06 (1.58E-05)
[7.22E-08, 3.78E-04]
8.06E-06
(1.42E-04)
[7.22E-08,
6.81 E-03]
Hydrochloric acid
Tons
emitted
5.13E-01 (1.42E+00)
[3.36E-04,1.68E+01]
0.259 (1.178)
[0.002,15.970]
1.74E-01 (8.97E-01)
[2.77E-05,1.66E+01]
4.53E-02 (1.51E-01)
[1.62E-05, 2.04E+00]
2.73E-01
(1.08E+00)
[1.62E-05,
1.68E+01]
Isophorone
Tons
emitted
1.96E-04 (9.98E-04)
[0, 2.26E-02]
9.07E-05 (1.95E-04)
[0, 1.75E-03]
6.37E-05 (2.41 E-04)
[0, 4.32E-03]
2.94E-05 (2.15E-04)
[0, 5.39E-03]
1.05E-04
(6.19E-04)
[0, 2.26E-02]
Lead compounds
Tons
emitted
2.23E-03 (3.84E-03)
[3.41 E-04, 8.17E-02]
1.43E-03 (1.65E-03)
[3.51 E-04,1.45E-02]
9.34E-04 (2.21 E-03)
[2.98E-04, 4.74E-02]
6.01 E-04 (1.48E-03)
[2.74E-04, 2.35E-02]
1.36E-03
(2.82E-03)
[2.74E-04,
8.17E-02]
Manganese compounds
Tons
emitted
3.24E-03 (1.79E-02)
[3.87E-04, 3.78E-01]
2.18E-03 (6.52E-03)
[3.68E-04, 9.12E-02]
1.49E-03 (5.71 E-03)
[3.52E-04,1.12E-01]
7.26E-04 (1.67E-03)
[2.92E-04, 2.75E-02]
2.01 E-03
(1.13E-02)
[2.92E-04,
3.78E-01]
Mercury compounds
Tons
emitted
5.61 E-04 (1.52E-04)
[5.00E-04, 4.26E-03]
5.34E-04 (8.45E-05)
[5.00E-04,1.62E-03]
5.19E-04 (1.34E-04)
[5.00E-04, 4.36E-03]
5.07E-04 (3.35E-05)
[5.00E-04,1.04E-03]
5.33E-04
(1.25E-04)
[5.00E-04,
4.36E-03]
Methanol
Tons
emitted
0.424(1.309)
[0.002, 39.557]
0.280 (0.454)
[0.016,6.764]
1.05E-01 (2.08E-01)
[4.82E-04, 4.24E+00]
2.96E-02 (6.00E-02)
[7.19E-05, 7.59E-01]
2.17E-01 (8.11E-01)
[7.19E-05,
3.96E+01]
-------
Table 3. (continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Variable
Methyl isobutyl ketone
Methyl methacrylate
Units
Tons
emitted
Tons
emitted
Metropolitan-
Urbanized
(RUCC1 = 1089)
Mean (SD)
[Range]
1.71E-01 (2.20E-01)
[2.57E-04, 2.30E+00]
1.53E-03 (5.28E-03)
[3.45E-07, 6.05E-02]
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Mean (SD)
[Range]
0.121 (0.188)
[0.002, 2.004]
8.16E-04 (3.80E-03)
[8.35E-06, 5.37E-02]
Less Urbanized
(RUCC3 = 1059)
Mean (SD)
[Range]
5.06E-02 (1.59E-01)
[1.79E-05, 4.30E+00]
7.63E-04 (5.53E-03)
[1.10E-07,1.04E-01]
Thinly Populated
(RUCC4 = 670)
Mean (SD)
[Range]
9.35E-03 (1.98E-02)
[7.04E-07, 2.53E-01]
2.17E-04 (2.49E-03)
[2.22E-08, 5.26E-02]
OVERALL
(n=3141)
Mean (SD)
[Range]
9.06E-02
(1.82E-01)
[7.04E-07,
4.30E+00]
9.18E-04
(4.80E-03)
[2.22E-08,
1.04E-01]
Methyl chloride
Tons
emitted
0.890 (0.094)
[0.827, 2.398]
0.863 (0.076)
[0.834, 2.023]
0.851 (0.067)
[0.827,2.312]
0.842 (0.036)
[0.818,1.572]
0.864 (0.076)
[0.818,2.398]
Methylhydrazine
Tons
emitted
3.60E-06 (4.47E-06)
[0, 4.27E-05]
2.65E-06 (3.87E-06)
[0, 2.88E-05]
1.96E-06 (3.28E-06)
[0, 3.05E-05]
1.61 E-06 (2.76E-06)
[0, 2.37E-05]
2.53E-06
(3.80E-06)
[0, 4.27E-05]
Methyl tert-butyl ether
Tons
emitted
2.17E-01 (4.65E-01)
[1.34E-04, 5.11E+00]
5.33E-02 (6.67E-02)
[2.70E-04, 5.61E-01]
2.56E-02 (3.83E-02)
[8.88E-06, 5.74E-01]
8.11E-03 (1.69E-02)
[2.71E-06, 2.73E-01]
9.12E-02
(2.91E-01)
[2.71 E-06,
5.11E+00]
Nitrobenzene
Tons
emitted
1.15E-05 (1.22E-04)
[0, 3.49E-03]
1.56E-06 (6.15E-06)
[0, 7.15E-05]
9.29E-07 (7.20E-06)
[0,1.41E-04]
3.07E-07 (3.00E-06)
[0, 5.12E-05]
4.52E-06
(7.24E-05)
[0, 3.49E-03]
N,N-dimethylaniline
Tons
emitted
6.74E-05 (1.18E-04)
[0, 2.04E-03]
3.67E-05 (2.55E-05)
[0,1.62E-04]
1.54E-05 (4.39E-05)
[0,1.24E-03]
3.47E-06 (4.68E-06)
[0, 3.43E-05]
3.31 E-05
(7.90E-05)
[0, 2.04E-03]
o-toluidine
Tons
emitted
3.09E-06 (2.20E-05)
[0, 3.78E-04]
5.36E-06 (8.12E-05)
[0,1.46E-03]
1.39E-06 (3.58E-05)
[0,1.16E-03]
7.23E-08 (7.09E-07)
[0,1.56E-05]
2.10E-06
(3.58E-05)
[0,1.46E-03]
Polycyclic aromatic
hydrocarbon compounds/
polycyclic organic matter
Tons
emitted
1.62E-02 (3.00E-02)
[1.79E-04, 4.45E-01]
0.014(0.021)
[0.001,0.139]
7.66E-03 (1.58E-02)
[2.64E-05, 3.36E-01]
3.20E-03 (7.15E-03)
[4.44E-05,1.25E-01]
1.03E-02
(2.19E-02)
[2.64E-05,
4.45E-01]
Pentachlorophenol
Tons
emitted
2.21 E-06 (1.34E-05)
[0, 2.68E-04]
1.37E-06 (4.02E-06)
[0, 6.14E-05]
2.59E-06 (3.92E-05)
[0,1.06E-03]
3.90E-07 (1.82E-06)
[0, 3.75E-05]
1.86E-06
(2.41 E-05)
[0,1.06E-03]
Phosphine
Tons
emitted
4.33E-05 (9.63E-05)
[0,1.47E-03]
4.14E-05 (7.15E-05)
[0, 7.66E-04]
4.20E-05 (5.58E-05)
[0, 5.70E-04]
3.75E-05 (6.22E-05)
[0, 8.28E-04]
4.14E-05
(7.49E-05)
[0,1.47E-03]
Phosphorus
Tons
emitted
9.41 E-05 (9.30E-04)
[0, 2.61E-02]
4.27E-05 (3.17E-04)
[0, 4.89E-03]
4.90E-05 (8.38E-04)
[0, 2.59E-02]
1.84E-05 (1.41E-04)
[0, 2.20E-03]
5.75E-05
(7.43E-04)
[0, 2.61E-02]
Polychlorinated biphenyls
Tons
emitted
1.71E-04 (4.16E-05)
[1.27E-04, 5.94E-04]
1.84E-04 (3.62E-05)
[1.27E-04, 3.47E-04]
1.69E-04 (2.06E-04)
[1.27E-04, 6.27E-03]
1.43E-04 (6.60E-05)
[1.27E-04,1.59E-03]
1.66E-04
(1.27E-04)
[1.27E-04,
6.27E-03]
Propylene dichloride
Tons
emitted
5.35E-03 (3.39E-03)
[3.09E-04, 3.60E-02]
4.51E-03 (1.19E-03)
[1.12E-03, 9.46E-03]
2.14E-03 (1.70E-03)
[3.07E-04, 2.85E-02]
5.96E-04 (6.36E-04)
[2.71 E-04, 5.08E-03]
3.17E-03
(2.97E-03)
[2.71 E-04,
3.60E-02]
24
-------
Table 3. (continued) Air Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL
(n=3141)
Variable
Units
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Propylene oxide
Tons
emitted
5.90E-04 (2.01E-03)
[2.90E-06, 4.27E-02]
2.28E-04 (5.80E-04)
[6.33E-06, 8.58E-03]
2.05E-04 (1.74E-03)
[4.34E-07, 4.46E-02]
3.28E-05 (7.97E-05)
[1.46E-07,1.00E-03]
3.04E-04
(1.58E-03)
[1.46E-07,
4.46E-02]
Quinoline
Tons
emitted
1.22E-04 (3.46E-04)
[5.59E-07, 8.65E-03]
1.12E-04 (2.22E-04)
[5.69E-07, 9.12E-04]
5.81 E-05 (1.40E-04)
[5.13E-07, 8.52E-04]
2.68E-05 (7.97E-05)
[4.89E-07, 6.88E-04]
7.90E-05
(2.37E-04)
[4.89E-07,
8.65E-03]
Selenium compounds
Tons
emitted
1.54E-04 (3.63E-04)
[2.60E-07, 4.96E-03]
6.78E-05 (1.10E-04)
[8.80E-07,1.39E-03]
4.38E-05 (1.57E-04)
[6.22E-09, 3.40E-03]
1.50E-05 (4.10E-05)
[5.72E-10, 5.36E-04]
7.82E-05
(2.43E-04)
[5.72E-10,
4.96E-03]
Styrene
Tons
emitted
0.046 (0.076)
[0.001,1.345]
0.043 (0.092)
[0.002, 0.952]
3.08E-02 (1.11E-01)
[8.54E-05, 2.15E+00]
7.82E-03 (3.40E-02)
[3.59E-05, 6.78E-01]
3.24E-02
(8.62E-02)
[3.59E-05,
2.15E+00]
Tetrachloroethylene
Tons
emitted
0.104 (0.121)
[0.022,1.534]
0.060 (0.043)
[0.034,0.414]
0.037 (0.019)
[0.020,0.199]
0.024 (0.005)
[0.017,0.111]
0.060 (0.081)
[0.017,1.534]
Toluene
Tons
1.756 (1.333)
1.123 (0.465)
0.561 (0.457)
0.191 (0.177)
0.954(1.057)
emitted
[0.052,14.898]
[0.401,4.857]
[0.037,10.023]
[0.030,1.236]
[0.030,14.898]
Trichloroethylene
Tons
emitted
0.063 (0.060)
[0.005,1.079]
0.049 (0.048)
[0.005, 0.440]
0.035 (0.036)
[0.005, 0.495]
0.023 (0.025)
[0.005,0.198]
0.043 (0.048)
[0.005,1.079]
Triethylamine
Tons
emitted
9.42E-04 (6.80E-03)
[5.43E-07,1.79E-01]
4.97E-04 (2.28E-03)
[2.74E-06, 2.97E-02]
2.63E-04 (2.36E-03)
[4.40E-08, 6.24E-02]
3.06E-05 (1.20E-04)
[7.82E-09,1.56E-03]
4.73E-04
(4.31E-03)
[7.82E-09,
1.79E-01]
Vinyl acetate
Tons
emitted
1.68E-03 (9.99E-03)
[8.63E-07,1.64E-01]
1.49E-03 (1.43E-02)
[6.33E-06, 2.40E-01]
3.33E-04 (2.48E-03)
[1.16E-07, 6.16E-02]
1.85E-04 (2.08E-03)
[2.11E-08, 4.95E-02]
8.86E-04
(7.68E-03)
[2.11E-08,
2.40E-01]
Vinyl chloride
Tons
emitted
1.10E-02 (1.09E-02)
[5.00E-06,1.05E-01]
6.93E-03 (3.54E-03)
[1.27E-06,1.97E-02]
1.33E-03 (3.75E-03)
[4.37E-09, 9.41 E-02]
1 60E-04 (1.09E-03)
[6.99E-10, 2.56E-02]
4.99E-03
(8.36E-03)
[6.99E-10,
1.05E-01]
Vinylidene chloride
Tons
emitted
1.54E-04 (4.95E-04)
[3.32E-08,1.34E-02]
7.72E-05 (2.01E-04)
[0, 3.60E-03]
3.42E-05 (1.39E-04)
[5.19E-09, 4.03E-03]
1.78E-05 (2.22E-04)
[0, 5.74E-03]
7.67E-05
(3.31E-04)
[0,1.34E-02]
NOTE: Calculated with nontransformed data
-------
Water Domain
The variables included in the water domain demonstrated
moderate variability across the rural/urban strata. The
metropolitan-urbanized and nomnetropolitan-urbanized
strata both had higher overall impaired stream length
(14.00% and 14.20%, respectively), compared with the less
urbanized and thinly populated strata (8.79% and 6.54%,
respectively) (Table 4). The urban stratum also demonstrated
a higher number of discharge permits per stream length
than did the rural stratum. The thinly populated stratum
had the highest percentage of population on self-supplied
sources (35.61%) and the lowest percentage of population
on surface water sources (21.94%). Although most chemical
contaminants demonstrated similar concentrations across
the rural/urban strata, there were a few differences. Fluoride
and di(2-ethylhexyl) adipate were present in higher
concentrations on the metropolitan-urbanized stratum.
There was little variability across urban/rural strata for
atmospheric deposition of chemicals and percent of land in
extreme drought.
Table 4. Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban Continuum
Codes (RUCCs) Stratified
Metropolitan-
Less
Thinly
Urbanized
Nonmetropolitan-Urbanized
Urbanized
Populated
OVERALL
(RUCC1 = 089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Water Domain
Construct: Overall Water Quality
Percent of stream length
0/
14.004(16.528)
14.203 (20.403)
8.791 (12.682)
6.537 (9.470)
10.674(14.853)
impaired in county
/O
[0, 92.570]
[0, 94.450]
[0, 95.740]
[0, 98.500]
[0, 98.500]
Construct: General Water Contamination
Sewage permits
permits/
2.227 (7.217)
2.176 (5.565)
1.116(6.136)
0.323 (1.342)
1.441 (5.905)
1000 km
[0,111.570]
[0, 65.970]
[0,131.620]
[0,15.400]
[0,131.620]
Industrial permits
permits/
51.139 (96.466)
27.241 (31.332)
18.423 (38.732)
10.080 (22.009)
28.893 (64.947)
1000 km
[0,1195.680]
[0, 280.860]
[0,674.150]
[0, 337.300]
[0,1195.680]
Stormwater permits
permits/
39.080(157.506)
9.865 (32.657)
4.737(13.711)
1.731 (5.524)
16.530 (95.130)
1000 km
[0, 2253.860]
[0, 325.640]
[0,183.790]
[0, 65.200]
[0, 2253.860]
Construct: Recreational Water Quality
Number of days of beach
days
3.229(19.625)
1.421 (9.859)
0.143 (1.945)
0.022 (0.508)
1.318(12.118)
closure
[0, 365.000]
[0,116.000]
[0, 55.000]
[0,13.000]
[0, 365.000]
Number of days beach closure
for contamination advisory
events
days
2.795(13.074)
[0,157.000]
3.254 (22.401)
[0, 364.000]
0.325 (4.025)
[0,110.000]
0.216(2.915)
[0, 64.000]
1.459 (10.939)
[0, 364.000]
Number of days beach closure
days
0.144(1.630)
0.037 (0.423)
0.002 (0.061)
0.009 (0.232)
0.056 (0.987)
for rain advisory events
[0, 34.000]
[0, 7.000]
[0, 2.000]
[0, 6.000]
[0, 34.000]
Construct: Domestic Use
Percent of population on self
%
22.337 (21.251)
22.067 (15.447)
28.292 (19.944)
35.608 (23.612)
27.148 (21.451)
supply
[0, 98.250]
[0, 75.680]
[0, 99.028]
[0,100.000]
[0,100.000]
Percent of public supply
population that is on surface
water
%
46.863 (41.736)
[0,100.000]
41.922 (41.306)
[0,100.000]
33.775 (40.841)
[0,100.000]
21.942 (36.526)
[0,100.000]
36.627 (41.386)
[0,100.000]
Construct: Atmospheric Deposition
Calcium precipitation weighted
mg/L
0.192 (0.120)
0.217(0.130)
0.255 (0.144)
0.279 (0.144)
0.231 (0.139)
mean
[0.040, 0.594]
[0.043, 0.634]
[0.042,1.183]
[0.047, 0.806]
[0.040, 1.183]
Magnesium (precipitation
weighted mean
mg/L
0.058 (0.046)
[0.011,0.787]
0.065 (0.075)
[0.011,1.064]
0.071 (0.100)
[0.008,1.288]
0.089 (0.132)
[0.011,1.284]
0.070 (0.092)
[0.008, 1.288]
Potassium precipitation
weighted mean
mg/L
0.124 (0.063)
[0.025, 0.752]
0.133 (0.084)
[0.027,1.001]
0.151 (0.106)
[0.037,1.192]
0.180 (0.132)
[0.031,1.189]
0.146 (0.100)
[0.025, 1.192]
Sodium precipitation weighted
mg/L
0.156 (0.149)
0.148 (0.168)
0.127 (0.142)
0.109 (0.145)
0.135 (0.149)
mean
[0.021,1.221]
[0.023,1.228]
[0.019,1.730]
[0.020,1.543]
[0.019, 1.730]
Ammonium precipitation
weighted mean
mg/L
0.230 (0.103)
[0.005, 0.588]
0.243 (0.120)
[0, 0.758]
0.260 (0.129)
[0.003, 0.707]
0.312(0.160)
[0.012,0.625]
0.259 (0.131)
[0, 0.758]
Nitrate precipitation weighted
mg/L
0.944 (0.303)
0.952 (0.343)
0.938 (0.291)
0.959 (0.285)
0.946 (0.300)
mean
[0.029,1.598]
[0.021,1.608]
[0.002,1.600]
[0.012,1.588]
[0,1.608]
26
-------
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
Nonmetropolitan-Urbanized
Less
Urbanized
Thinly
Populated
OVERALL
(RUCC1 = 089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Chloride precipitation weighted
mg/L
0.273 (0.248)
0.250 (0.267)
0.203 (0.199)
0.163 (0.177)
0.224 (0.225)
mean
[0.040,1.886]
[0.036, 1.726]
[0.039, 2.326]
[0.040,1.863]
[0.036, 2.326]
Sulfate precipitation weighted
mg/L
1.263 (0.456)
1.217 (0.496)
1.132 (0.419)
1.011 (0.364)
1.160 (0.440)
mean
[0.144,2.267]
[0.152, 2.338]
[0.013,2.388]
[0.047, 2.208]
[0.013,2.388]
Total Mercury deposition
ng/m-
4.784 (1.249)
[1.101,9.219]
4.635 (1.364)
[1.100, 7.950]
4.780 (1.397)
[1.109,8.473]
4.520 (1.405)
[1.103,8.458]
4.711 (1.350)
[1.100,9.219]
Construct: Drought
Percent of county drought-
0/
3.160 (5.273)
3.522 (6.215)
3.908 (6.931)
5.030 (8.577)
3.848 (6.777)
extreme (D3-D4)
/O
[0, 46.900]
[0, 42.430]
[0, 40.400]
[0, 48.800]
[0, 48.800]
Construct: Chemical Contamination
Arsenic
mg/L
2.46E-03 (5.22E-03)
[0,1.33E-01]
2.59E-03 (3.47E-03)
[0, 3.80E-02]
2.70E-03
(5.09E-03)
[0, 7.10E-02]
1.72E-03
(3.22E-03)
[0, 3.00E-02]
2.46E-03
(4.67E-03)
[0,1.33E-01]
Barium
mg/L
0.074(0.406)
0.074 (0.232)
0.055 (0.093)
0.041 (0.085)
0.060 (0.260)
[0,13.100]
[0, 3.970]
[0, 1.020]
[0, 1.010]
[0, 13.100]
Cadmium
mg/L
7.30E-04 (8.64E-04)
[0, 5.00E-03]
6.50E-04 (7.95E-04)
[0, 6.00E-03]
6.48E-04
(7.68E-04)
[0, 7.00E-03]
4.63E-04
(6.92E-04)
[0, 6.00E-03]
6.37E-04
(7.96E-04)
[0, 7.00E-03]
Chromium
mg/L
5.29E-03 (7.37E-03)
[0,1.45E-01]
5.15E-03 (5.68E-03)
[0, 3.50E-02]
5.10E-03
(7.29E-03)
[0, 5.50E-02]
3.40E-03
(6.42E-03)
[0,1.00E-01]
4.81E-03
(7.03E-03)
[0,1.45E-01]
Cyanide
mg/L
0.014(0.028)
0.016 (0.027)
0.015 (0.032)
0.014(0.041)
0.014(0.033)
[0, 0.266]
[0,0.100]
[0, 0.338]
[0, 0.815]
[0, 0.82]
Fluoride
mg/L
1.195 (8.080)
[0,150.310]
0.381 (0.408)
[0, 2.630]
0.462 (0.644)
[0, 8.690]
0.344 (0.661)
[0,11.360]
0.683 (4.797)
[0,150.310]
Mercury (inorganic)
mg/L
1 25E-04 (4.95E-04)
[0,1.00E-02]
9.91E-05 (3.09E-04)
[0, 2.00E-03]
1.13E-04
(1.11E-03)
[0, 3.50E-02]
7.61 E-05
(3.40E-04)
[0, 6.00E-03]
1.08E-04
(7.33E-04)
[0, 3.50E-02]
Nitrate
mg/L
0.801 (1.650)
0.660 (1.191)
0.733 (2.786)
0.606 (1.952)
0.722 (2.127)
[0, 20.000]
[0,14.610]
[0, 81.000]
[0, 32.830]
[0,81.000]
Nitrite
mg/L
0.061 (0.182)
0.055 (0.139)
0.045 (0.113)
0.043 (0.169)
0.051 (0.155)
[0, 3.590]
[0,1.890]
[0, 1.530]
[0, 3.400]
[0, 3.590]
Selenium
mg/L
3.13E-03 (4.73E-03)
[0, 5.00E-02]
2.93E-03 (3.65E-03)
[0, 3.00E-02]
3.00E-03
(5.12E-03)
[0, 9.40E-02]
2.25E-03
(4.45E-03)
[0, 4.70E-02]
4.72E-03
(2.88E-03)
[0, 9.40E-02]
Antimony
mg/L
1.55E-03 (1.79E-03)
[0,1.90E-02]
1.46E-03 (1.57E-03)
[0, 6.00E-03]
1.45E-03
(1.62E-03)
[0, 6.00E-03]
1.05E-03
(1.46E-03)
[0, 6.00E-03]
1.40E-03
(1.65E-03)
[0,1.90E-02]
Beryllium
mg/L
9.18E-06 (3.03E-04)
[0,1.00E-02]
0(0)
[0,0]
0.015 (0.492)
[0,16.000]
2.99E-05
(5.46E-04)
[0,1.00E-02]
5.11E-03
(2.85E-01)
[0, 1.60E+01]
Thallium
mg/L
6.52E-04 (6.57E-04)
[0, 4.00E-03]
6.16E-04 (5.96E-04)
[0, 2.00E-03]
6.03E-04
(6.34E-04)
[0, 4.00E-03]
4.57E-04
(6.09E-04)
[0, 6.00E-03]
5.90E-04
(6.37E-04)
[0, 6.00E-03]
Endrin
mg/L
0.067 (0.195)
0.063 (0.181)
0.073 (0.213)
0.047 (0.172)
0.064 (0.195)
[0,1.000]
[0,1.000]
[0, 1.000]
[0, 1.000]
[0,1.000]
Lindane
mg/L
0.074(0.406)
0.074 (0.232)
0.055 (0.093)
0.041 (0.085)
0.060 (0.260)
[0,13.100]
[0, 3.970]
[0,1.020]
[0,1.010]
[0,13.100]
-------
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Less
Thinly
Urbanized
Nonmetropolitan-Urbanized
Urbanized
Populated
OVERALL
(RUCC1 = 089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Methoxychlor
pg/L
0.666 (1.958)
[0,10.000]
0.605 (1.706)
[0,10.000]
0.420 (1.307)
[0, 9.640]
0.150 (0.594)
[0, 8.000]
0.467 (1.521)
[0,10.000]
Toxaphene
pg/L
0.395 (0.495)
[0, 5.000]
0.464 (0.499)
[0, 3.000]
0.385 (0.516)
[0, 5.000]
0.288 (0.449)
[0,1.000]
0.375 (0.496)
[0, 5.000]
Dalapon
pg/L
7.120 (22.565)
9.045 (25.358)
8.085 (24.497)
7.903 (25.207)
7.808 (24.086)
[0,100.000]
[0,100.000]
[0,100.000]
[0,100.000]
[0, 100.000]
di(2-Ethylhexyl) adipate
pg/L
11.680 (303.985)
[0, 10030.000]
3.620(10.814)
[0, 50.000]
2.947(17.476)
[0, 501.000]
1.243 (5.627)
[0, 50.000]
5.679(179.303)
[0,10030.000]
Oxamyl (Vydate)
pg/L
0.774 (0.947)
[0, 3.000]
0.890 (0.968)
[0, 2.000]
0.771 (0.944)
[0, 3.500]
0.534 (0.861)
[0, 2.000]
0.723 (0.936)
[0, 3.500]
Simazine
pg/L
0.178 (0.319)
0.174 (0.253)
0.171 (0.311)
0.108 (0.231)
0.160 (0.294)
[0, 4.840]
[0,1.010]
[0, 5.000]
[0,1.000]
[0, 5.000]
di(2-Ethylhexyl) phthalate
pg/L
0.767 (1.257)
[0, 9.330]
0.834(1.226)
[0, 6.000]
0.704(1.294)
[0, 15.830]
0.416 (0.898)
[0, 9.070]
0.677 (1.207)
[0,15.830]
Picloram
pg/L
2.263 (9.736)
3.878(12.409)
2.597(10.458)
1.136 (6.237)
2.301 (9.712)
[0, 50.000]
[0, 50.000]
[0,100.000]
[0, 50.000]
[0, 100.000]
Dinoseb
pg/L
0.416(0.211)
0.254 (0.439)
0.212 (0.470)
0.172 (0.382)
0.208 (0.431)
[0, 3.000]
[0, 2.000]
[0, 9.000]
[0, 2.000]
[0, 9.000]
Hexachlorocyclopentadiene
pg/L
0.045 (0.050)
[0, 0.295]
0.052 (0.050)
[0, 0.200]
0.047 (0.050)
[0, 0.900]
0.035 (0.048)
[0,0.117]
0.044 (0.049)
[0, 0.295]
Carbofuran
pg/L
0.372 (0.441)
[0, 0.900]
0.416 (0.444)
[0, 0.900]
0.352 (0.436)
[0, 0.900]
0.257 (0.404)
[0, 0.900]
0.345 (0.435)
[0, 0.900]
Atrazine
pg/L
0.179 (0.319)
0.176 (0.260)
0.245 (2.333)
0.104 (0.225)
0.185 (1.374)
[0, 2.500]
[0, 2.000]
[0, 75.230]
[0, 2.000]
[0, 75.230]
Alachlor
pg/L
0.176 (0.303)
0.164 (0.237)
0.160 (0.288)
0.116 (0.228)
0.156 (0.278)
[0, 2.000]
[0, 2.000]
[0, 2.500]
[0, 2.000]
[0, 2.500]
Heptachlor
pg/L
0.018 (0.020)
0.020 (0.020)
0.019 (0.020)
0.014(0.019)
0.018 (0.020)
[0, 0.040]
[0, 0.040]
[0, 0.040]
[0, 0.040]
[0, 0.040]
Heptachlor epoxide
pg/L
9.33E-03 (9.95E-03)
[0, 4.00E-02]
1.07E-02 (9.90E-03)
[0, 2.00E-02]
9.38E-03
(9.95E-03)
[0, 2.00E-02]
7.10E-03
(9.95E-03)
[0, 2.00E-02]
8.99E-03
(9.92E-03)
[0, 4.00E-02]
2,4-Dichlorophenoxyacetic acid
pg/L
0.047 (0.084)
[0, 2.030]
0.061 (0.144)
[0, 2.420]
0.051 (0.224)
[0,7.100]
0.033 (0.054)
[0, 0.720]
0.047 (0.149)
[0, 7.100]
Silvex
pg/L
0.384(1.020)
0.579 (1.836)
0.384(1.087)
0.184 (0.633)
0.362 (1.096)
[0, 5.000]
[0, 25.250]
[0, 12.500]
[0, 5.000]
[0, 25.250]
Hexachlorobenzene
pg/L
0.045 (0.049)
[0,0.100]
0.050 (0.049)
[0,0.100]
0.047 (0.058)
[0,1.050]
0.035 (0.047)
[0,0.100]
0.044 (0.052)
[0, 1.050]
Benzo[a]pyrene
pg/L
0.038 (0.054)
[0, 0.337]
0.040 (0.057)
[0, 0.324]
0.043 (0.060)
[0, 0.300]
0.029 (0.050)
[0, 0.200]
0.038 (0.056)
[0, 0.337]
Pentachlorophenol
pg/L
0.068 (0.163)
[0,1.700]
0.076 (0.169)
[0,1.000]
0.079 (0.179)
[0,1.000]
0.054 (0.138)
[0,1.000]
0.069 (0.165)
[0, 1.700]
1,2,4-Trichlorobenzene
pg/L
0.370 (0.426)
[0,11.880]
0.379 (0.363)
[0,5.170]
0.367 (0.225)
[0,1.200]
0.315 (0.252)
[0, 2.070]
0.358 (0.328)
[0,11.880]
Polychlorinated biphenyls
pg/L
0.108 (1.232)
[0, 40.360]
0.057 (0.127)
[0,1.000]
0.052 (0.186)
[0, 4.250]
0.024 (0.075)
[0,1.000]
0.066 (0.735)
[0, 40.360]
1,2-Dibromo-3-chloropropane
pg/L
0.012 (0.020)
[0, 0.535]
0.010(0.010)
[0, 0.052]
0.010(0.010)
[0, 0.035]
0.009 (0.010)
[0, 0.020]
0.011 (0.014)
[0, 0.535]
-------
Table 4. (continued) Water Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Metropolitan-
Less
Thinly
Urbanized
Nonmetropolitan-Urbanized
Urbanized
Populated
OVERALL
(RUCC1 = 089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Ethylene dibromide
pg/L
0.071 (0.158)
0.057 (0.135)
0.062 (0.145)
0.074 (0.160)
0.067 (0.152)
[0,1.160]
[0, 0.500]
[0, 0.860]
[0, 0.500]
[0,1.160]
Xylenes
pg/L
0.751 (6.233)
0.791 (3.373)
1.854 (43.030)
1.861 (38.631)
1.363 (30.927)
[0, 200.330]
[0,50.710]
[0,1400.250]
[0,1000.240]
[0, 1400.250]
Chlordane
pg/L
0.088 (0.100)
0.099 (0.096)
0.090 (0.097)
0.068 (0.094)
0.086 (0.098)
[0, 0.950]
[0, 0.273]
[0, 0.267]
[0, 0.200]
[0, 0.950]
Dichloromethane (Methylene
chloride)
pg/L
0.503 (0.407)
[0,10.240]
0.380 (0.271)
[0,1.880]
0.390 (0.309)
[0, 3.950]
0.335 (0.504)
[0,11.670]
0.383 (0.427)
[0,11.670]
1,2-Dichlorobenzene
pg/L
0.349 (0.288)
0.346 (0.231)
0.352 (0.226)
0.296 (0.242)
0.338 (0.254)
(o-Dichlorobenzene)
[0,6.130]
[0,1.030]
[0,1.000]
[0,1.000]
[0,6.130]
1,4-Dichlorobenzene
pg/L
0.505 (5.310)
0.342 (0.244)
0.337 (0.256)
0.297 (0.352)
0.387 (3.135)
(p-Dichlorobenzene)
[0, 175.380]
[0,1.520]
[0, 2.750]
[0, 6.000]
[0,175.380]
Vinyl chloride
pg/L
0.264(0.350)
0.351 (0.224)
0.361 (0.244)
0.305 (0.236)
0.344 (0.248)
[0, 4.850]
[0, 0.740]
[0, 3.830]
[0, 0.500]
[0, 4.850]
1,1 -Dichloroethylene
pg/L
0.362 (0.261)
[0, 2.220]
0.362 (0.259)
[0,2.130]
0.363 (0.239)
[0, 2.750]
0.304 (0.242)
[0,1.000]
0.350 (0.251)
[0, 2.750]
trans-1,2-Dichloroethylene
pg/L
0.340 (0.261)
[0, 4.550]
0.336 (0.263)
[0,1.300]
0.346 (0.235)
[0, 2.750]
0.290 (0.239)
[0, 0.750]
0.331 (0.246)
[0, 4.550]
1,2-Dichloroethane (Ethylene
pg/L
0.370 (0.426)
0.379 (0.363)
0.367 (0.225)
0.315 (0.252)
0.358 (0.328)
dichloride)
[0,11.880]
[0,5.170]
[0,1.200]
[0, 2.070]
[0,11.880]
1,1,1 -T richloroethane
pg/L
0.697 (10.637)
[0, 351.230]
0.748 (6.965)
[0,125.380]
0.383 (0.935)
[0, 30.250]
0.297 (0.249)
[0,2.150]
0.511 (6.672)
[0,351.230]
Carbon tetrachloride
pg/L
0.370 (0.289)
[0, 4.100]
0.383 (0.384)
[0, 5.400]
0.379 (0.256)
[0, 2.600]
0.320 (0.285)
[0, 4.130]
0.364 (0.290)
[0, 5.400]
1,2-Dichloropropane
pg/L
0.360 (0.232)
[0,1.270]
0.364 (0.243)
[0,1.900]
0.368 (0.217)
[0, 0.560]
0.313 (0.240)
[0, 0.620]
0.353 (0.231)
[0,1.900]
Trichloroethylene
pg/L
0.428 (0.501)
[0, 6.480]
0.378 (0.274)
[0,2.010]
0.374 (0.270)
[0, 3.730]
0.314(0.252)
[0,1.190]
0.380 (0.367)
[0, 6.480]
1,1,2-T richloroethane
pg/L
0.358 (0.248)
[0, 3.050]
0.357 (0.224)
[0, 0.500]
0.375 (0.351)
[0, 9.330]
0.312 (0.240)
[0, 0.500]
0.354 (0.284)
[0, 9.330]
Tetrachloroethylene
pg/L
0.460 (0.584)
[0, 8.000]
0.397 (0.376)
[0,5.110]
0.407 (0.771)
[0, 23.750]
0.325 (0.300)
[0, 4.330]
0.407 (0.595)
[0, 23.750]
Monochlorobenzene
pg/L
0.353 (0.273)
0.345 (0.240)
0.357 (0.225)
0.299 (0.238)
0.342 (0.248)
(Chlorobenzene)
[0, 4.130]
[0,1.440]
[0,1.190]
[0, 0.750]
[0, 4.130]
Benzene
pg/L
0.382 (0.324)
0.3888 (0.342)
0.378 (0.249)
0.321 (0.259)
0.368 (0.290)
[0, 4.130]
[0, 3.480]
[0,3.130]
[0, 2.200]
[0, 4.130]
Toluene
pg/L
0.721 (6.435)
2.404 (22.078)
0.640 (6.240)
0.896 (13.580)
0.904(10.816)
[0, 200.550]
[0, 333.670]
[0, 200.380]
[0, 350.250]
[0, 350.250]
Ethylbenzene
pg/L
0.394(0.355)
[0, 3.900]
0.426 (0.666)
[0,9.150]
0.393 (0.364)
[0, 4.550]
0.303 (0.246)
[0,1.100]
0.377 (0.385)
[0,9.150]
Styrene
pg/L
0.479 (2.455)
0.384 (0.343)
0.387 (0.310)
0.320 (0.246)
0.404(1.467)
[0, 78.540]
[0, 3.480]
[0, 4.900]
[0,1.100]
[0, 78.540]
cis-1,2-Dichloroethylene
pg/L
0.370 (0.426)
[0,11.880]
0.379 (0.363)
[0,11.390]
0.367 (0.225)
[0,1.200]
0.315 (0.252)
[0, 2.070]
0.358 (0.328)
[0,11.880]
Alpha particles
pCi/L
1.034 (2.333)
[0, 35.800]
1.113(1.851)
[0,11.390]
1.364 (3.517)
[0, 51.450]
0.781 (2.053)
[0,18.100]
1.099 (2.711)
[0,51.450]
NOTE: Calculated with nontransformed data
-------
Land Domain
In the land domain, the metropolitan-urbanized counties had
higher averages of soil contaminants, more facilities, and
lower agricultural-related variables (percent harvested and
percent irrigated) than did nomnetropolitan-urbanized, less
urban and thinly populated counties (Table 5). Pesticides
and animal units showed no clear pattern in variation across
the strata. For example, average pounds of herbicides applied
were 68,500, 108,000, 95,600, and 68,100 for most urban to
most rural strata, respectively. There was little variation in the
distribution of radon zones or agricultural chemicals applied
across the urban/rural strata.
Table 5. Land Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban Continuum
Codes (RUCCs) Stratified
Metropolitan-
Nonmetropolitan-
Less
Thinly
Urbanized
Urbanized
Urbanized
Populated
OVERALL
(RUCC1 =1089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Land Domain
Construct: Agriculture
Harvested acreage
Acres harvested per
county acres
0.183 (0.208)
[0, 0.920]
0.240 (0.244)
[0, 0.895]
0.240 (0.253)
[0,1.221]
0.201 (0.218)
[0, 0.946]
0.212(0.231)
[0,1.221]
Irrigated acreage
Acres irrigated per
county acres
0.062 (0.111)
[0, 0.863]
0.094 (0.165)
[0, 0.879]
0.105 (0.175)
[0,1.017]
0.111 (0.181)
[0, 0.940]
0.090 (0.158)
[0,1.017]
Farms per acre
Number of farms
per county acres
2.04E-04 (3.52E-04)
[0, 5.04E-03]
1.52E-04 (2.01 E-04)
[0,1.24E-03]
1.26E-04 (2.03E-04)
[0, 2.06E-03]
8.37E-05 (1.39E-04)
[0,1.30E-03]
1.47E-04
(2.59E-04)
[0, 5.04E-03]
Manure
Acres applied per
0.016 (0.024)
0.021 (0.029)
0.019 (0.027)
0.011 (0.019)
0.016 (0.025)
county acres
[0, 0.269]
[0,0.176]
[0,0.231]
[0,0.169]
[0, 0.269]
Chemicals used to control
Acres applied per
0.004 (0.010)
0.005 (0.012)
0.005 (0.012)
0.003 (0.009)
0.004 (0.011)
nematodes
county acres
[0,0.111]
[0, 0.080]
[0,0.104]
[0,0.132]
[0,0.132]
Chemicals used to control disease
Acres applied per
county acres
0.008 (0.019)
[0,0.195]
0.008 (0.022)
[0, 0.235]
0.006 (0.016)
[0,0.217]
0.005 (0.015)
[0,0.198]
0.007 (0.018)
[0, 0.235]
Chemicals used to defoliate/control
Acres applied per
0.006 (0.020)
0.010 (0.035)
0.009 (0.029)
0.005 (0.022)
0.007 (0.025)
growth/thin fruit
county acres
[0,0.213]
[0, 0.336]
[0,0.311]
[0, 0.429]
[0, 0.429]
Animal units
Animal units per
county acres
0.151 (0.727)
[0, 20.984]
0.079 (0.116)
[0,1.235]
0.174(1.639)
[0, 46.941]
0.103 (0.143)
[0,1.481]
0.141 (1.047)
[0, 46.941]
Construct: Pesticides
6.85E+04
1.08E+05
9.56E+04
6.81 E+04
8.16E+04
Herbicides
Pounds applied
(1.28E+05)
(1.66E+05)
(1.52E+05)
(1.08E+05)
(1.38E+05)
[0,1.18E+06]
[0,1.08E+06]
[0,1.15E+06]
[0, 7.16E+05]
[0,1.18E+06]
1.82E+03
4.03E+03
2.74E+03
2.14E+03
2.43E+03
Fungicides
Pounds applied
(1.50E+04)
(2.88E+04)
(2.35E+04)
(1.40E+04)
(1.98E+04)
[0, 3.55E+05]
[0, 3.91 E+05]
[0,5.21 E+05]
[0, 2.67E+05]
[0,5.21 E+05]
3.69E+03
6.14E+03
5.11E+03
2.75E+03
4.22E+03
Insecticides
Pounds applied
(8.89E+03)
(1.36E+04)
(1.05E+04)
(5.33E+03)
(9.52E+03)
[0,1.72E+05]
[0,1.89E+05]
[0,1.53E+05]
[0, 4.87E+04]
[0,1.89E+05]
Construct: Contaminants
Arsenic
ppm
6.530 (5.445)
[0,91.333]
6.850 (8.171)
[0,131.369]
6.435 (5.139)
[0, 98.893]
6.342 (4.304)
[0, 43.595]
6.491 (5.476)
[0,131.369]
Selenium
ppm
0.316 (0.240)
[0, 5.095]
0.314(0.196)
[0,1.306]
0.336 (0.253)
[0,2.981]
0.359 (0.321)
[0, 5.322]
0.332 (0.260)
[0, 5.322]
Mercury
ppm
0.093 (0.281)
0.046 (0.085)
0.042 (0.077)
0.041 (0.100)
0.060 (0.181)
[0, 5.438]
[0,1.052]
[0,1.159]
[0,1.648]
[0, 5.438]
Lead
ppm
29.901 (45.950)
[0,1007.300]
22.049 (17.221)
[0,196.867]
21.219 (24.976)
[0,691.838]
22.812 (51.740)
[0,1123.110]
24.654 (39.465)
[0,1123.110]
30
-------
Table 5. (continued) Land Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Variable
Zinc
Copper
Sodium
Magnesium
Titanium
Calcium
Iron
Aluminum
Phosphorus
Construct: Facilities
Facilities
Units
ppm
ppm
% weight
% weight
% weight
% weight
% weight
% weight
% weight
Metropolitan-
Urbanized
(RUCC1 = 1089)
Mean (SD)
[Range]
63.139 (47.279)
[0,560.912]
14.753 (10.938)
[0,105.580]
0.606 (0.500)
[0, 2.473]
0.603 (0.574)
[0, 4.554]
0.404 (0.228)
[0, 2.118]
1.744 (2.321)
[0, 22.244]
2.531 (1.480)
[0,13.731]
4.041 (1.812)
[0, 9.409]
0.067 (0.139)
[0, 2.203]
Facilities per county 3.94E-04 (2.77E-04)
population [0j 2.30E-03]
Construct: Radon
Radon zone Radon zone
NOTE: Calculated with nontransformed data
2.010(0.815)
[0, 3.000]
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Mean (SD)
[Range]
57.856 (33.804)
[0, 365.070]
14.592 (11.736)
[0, 96.644]
0.650 (0.465)
[0, 2.198]
0.631 (0.579)
[0, 4.091]
0.369 (0.185)
[0, 1.405]
1.623 (1.849)
[0,15.590]
2.372 (1.282)
[0, 8.440]
4.199 (1.871)
[0, 9.171]
0.056 (0.095)
[0, 1.296]
4.95E-04 (3.08E-04)
[4.23E-05,
2.19E-03]
2.000 (0.856)
[0, 3.000]
Less
Urbanized
(RUCC3 = 1059)
Mean (SD)
[Range]
53.581 (34.096)
[0, 432.142]
13.211 (10.985)
[0,180.806]
0.589 (0.459)
[0, 2.412]
0.556 (0.510)
[0, 4.995]
0.327 (0.165)
[0, 2.109]
1.597 (1.936)
[0,17.371]
2.152 (1.186)
[0, 9.461]
3.943 (1.814)
[0, 9.914]
0.051 (0.059)
[0, 1.025]
Thinly
Populated
(RUCC4 = 670)
Mean (SD)
[Range]
59.663 (70.332)
[0,1500.990]
14.423 (20.393)
[0, 436.832]
0.679 (0.439)
[0, 2.192]
0.613 (0.465)
[0, 3.624]
0.318(0.185)
[0, 1.941]
1.758 (1.872)
[0,18.709]
2.154(1.054)
[0, 7.165]
4.256 (1.778)
[0, 9.506]
0.053 (0.040)
[0, 0.509]
5.59E-04 (4.60E-04) 7.77E-04 (2.33E-03)
[0, 7.55E-03] [0, 5.42E-02]
2.022 (0.834)
[0, 3.000]
1.849 (0.809)
[0, 3.000]
OVERALL
(n=3141)
Mean (SD)
[Range]
58.631 (48.511)
[0,1500.990]
14.146(13.613)
[0, 436.832]
0.620 (0.471)
[0, 2.473]
0.592 (0.532)
[0, 4.995]
0.356 (0.198)
[0, 2.118]
1.685 (2.057)
[0, 22.244]
2.307 (1.292)
[0,13.731]
4.070 (1.815)
[0, 9.914]
0.057 (0.096)
[0, 2.203]
5.42E-04
(1.13E-03)
[0, 5.42E-02]
1.979 (0.827)
[0, 3.000]
31
-------
Sociodemographic Domain
Socioeconomic variables included in the sociodemographic
domain indicated that rural counties generally were more
deprived than were more urban counties (Table 6), with both
the lowest household income ($30,300) and highest percent
of persons in poverty (16.1%). From the crime perspective,
however, rural areas were at an advantage compared with
more urban areas; the mean violent crime rate for rural
counties was 352.5 compared with 390.9 for the most urban
and 397.1 for the nomnetropolitan-urbanized counties.
Table 6. Sociodemographic Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-Urban
Continuum Codes (RUCCs) Stratified
Built Environment Domain
The most rural counties had the smallest proportion of
highways and a significantly higher rate of traffic fatalities
compared with more urban areas (Table 7). Urban counties
also had fewer education-related businesses, positive food
establishments, recreation-related resources, and subsidized
housing units compared with more rural counties.
Metropolitan-
Nonmetropolitan-
Less
Thinly
Urbanized
Urbanized
Urbanized
Populated
OVERALL
(RUCC1 =1089)
(RUCC2 = 323)
(RUCC3 = 1059)
(RUCC4 = 670)
(n=3141)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Mean (SD)
Variable
Units
[Range]
[Range]
[Range]
[Range]
[Range]
Sociodemographic Domain
Construct: Socioeconomic
Percent renter occupied
%
27.734(9.557)
[10.561,80.458]
29.307 (6.499)
[13.562,52.731]
25.338 (5.588)
[13.545,72.205]
22.947 (6.814)
[10.464,100]
26.067 (7.791)
[10.464, 100]
Percent vacant units
%
9.146(5.810)
[1.539,53.707]
12.026 (7.190)
[3.457,58.416]
15.324 (8.392)
[4.336, 62.316]
21.980 (11.880)
[4.183,77.014]
14.263 (9.668)
[1.539,77.014]
1.10E+05
8.86E+04
7.25E+04
6.09E+04
(3.06E+04)
[0, 3.58E+05]
8.46E+04
(4.77E+04)
[0,1.00E+06]
Median household value
Dollar value
(5.51 E+04)
[3.46E+04,
1.00E+06]
(3.48E+04)
[3.78E+04,
3.69E+05]
(3.90E+04)
[2.26E+04,
7.50E+05]
4.17E+04
3.53E+04
3.21 E+04
3.03E+04
3.54E+04
Median household income
Dollar value
(9.84E+03)
[1.98E+04,
(6.39E+03)
[1.65E+04,
(6.03E+03)
[1.63E+04,
(5.59E+03)
[9.33E+03,
(8.92E+03)
[9.33E+03,
8.29E+04]
6.27E+04]
7.90E+04]
5.37E+04]
8.29E+04]
Percent persons less than
%
11.567 (5.307)
14.187 (6.275)
15.601 (6.565)
16.147 (7.107)
14.173 (6.554)
poverty level
[2.100,35.900]
[4.500, 50.900]
[2.900, 52.300]
[0, 56.900]
[0, 56.900]
Percent no English
%
9.490 (10.534)
[1.000,91.900]
9.257 (12.094)
[1.900,92.100]
8.451 (12.103)
[0.700, 84.800]
6.791 (9.492)
[0.400, 85.400]
8.540 (11.092)
[0.400,92.100]
Percent greater than high school
%
80.181 (7.546)
78.708 (7.814)
74.877 (8.813)
76.139 (9.478)
77.379 (8.755)
education
[50.500, 97.000]
[34.700, 93.800]
[43.400, 96.300]
[39.500, 94.400]
[34.700, 97.000]
Percent unemployed
5.293 (2.301)
6.433 (2.468)
6.298 (2.757)
5.631 (3.745)
5.821 (2.868)
/O
[1.700, 41.700]
[2.500, 20.900]
[1.400, 33.000]
[0,41.400]
[0, 41.700]
Percent work outside county
%
40.137 (20.673)
[1.100,90.800]
21.479 (12.061)
[1.300,60.800]
28.042 (13.447)
[0.600, 77.200]
32.952 (16.543)
[0, 76.400]
32.608 (17.936)
[0, 90.800]
Median number rooms per house
Count
5.522 (0.459)
[3.100,7.300]
5.372 (0.345)
[4.000, 6.600]
5.361 (0.360)
[3.300, 6.400]
5.368 (0.485)
[2.000, 6.500]
5.420 (0.430)
[2.000, 7.300]
Percent of housing with more
6.856 (7.239)
4.845 (3.542)
2.689 (2.717)
1.475 (2.146)
4.096 (5.268)
than 10 units
/O
[0, 90.100]
[0.600,35.100]
[0, 42.100]
[0,31.400]
[0,90.100]
Construct: Crime
Mean number of violent crimes
Crime rate
per capita
NOTE: Calculated with nontransformed data
390.884(322.135) 397.099 (374.610) 366.303 (233.612) 352.528 (158.067) 375.054(272.635)
[0,2481.800] [0,1955.500] [0,1897.300] [0,783.402] [0,2481.800]
32
-------
Table 7. Built-Environment Domain Variable Means, Standard Deviations (SDs), and Ranges—Overall and Rural-
Urban Continuum Codes (RUCCs) Stratified
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less
Urbanized
(RUCC3 = 1059)
Thinly
Populated
(RUCC4 = 670)
OVERALL
(n=3141)
Variable
Units
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Mean (SD)
[Range]
Built-Environment
Domain
Construct: Roads
Proportion of roads
that are highway
Mile proportion
0.045 (0.026)
[0, 0.156]
0.045 (0.025)
[0,0.158]
0.039 (0.029)
[0,0.210]
0.029 (0.031)
[0,0.291]
0.039 (0.029)
[0, 0.291]
Proportion of roads
that are primary streets
Mile proportion
0.171 (0.059)
[0.009, 0.536]
0.148 (0.067)
[0.015,0.438]
0.136 (0.063)
[0, 0.406]
0.119 (0.063)
[0,0.371]
0.146 (0.065)
[0, 0.536]
Construct: Highway/Road Safety
Traffic fatality rate
Fatality count per
county population
4.72E-04 (3.92E-04)
[0, 5.04E-03]
5.14E-04 (2.63E-04)
[0,1.60E-03]
6.94E-04 (5.48E-04)
[0, 6.29E-03]
9.45E-04 (1.33E-03)
[0, 1.10E-02]
6.52E-04 (7.57E-04)
[0,1.10E-02]
Construct: Public Transit Behavior
Percent of population
using public transport
%
1.699 (4.542)
[0, 59.600]
0.714(1.033)
[0, 8.800]
0.447 (0.780)
[0, 10.600]
0.393 (0.603)
[0, 6.900]
0.897 (2.809)
[0, 59.600]
Construct: Business Environment
Vice-related
businesses
Count/county
population
3.56E-04 (2.10E-04)
[1.66E-05,1.96E-03]
4.48E-04 (2.29E-04)
[3.05E-05,1.39E-03]
4.71 E-04 (3.32E-04)
[2.47E-05, 2.06E-03]
7.25E-04 (6.56E-04)
[3.71 E-05, 4.66E-03]
4.76E-04 (3.98E-04)
[1.66E-05, 4.66E-03]
Entertainment-related
businesses
Count/county
population
4.06E-04 (2.40E-04)
[3.80E-05, 2.51 E-03]
4.43E-04 (2.26E-04)
[6.72E-05,1.63E-03]
3.99E-04 (2.98E-04)
[2.82E-05, 2.97E-03]
5.24E-04 (6.01 E-04)
[5.15E-05, 6.80E-03]
4.28E-04 (3.51 E-04)
[2.82E-05, 6.80E-03]
Education-related
businesses
Count/county
population
5.80E-04 (3.19E-04)
[7.30E-05, 3.25E-03]
6.09E-04 (3.98E-04)
[1.01E-04, 3.33E-03]
6.11 E-04 (4.37E-04)
[4.73E-05, 3.92E-03]
6.06E-04 (4.50E-04)
[6.10E-05, 3.26E-03]
5.99E-04 (3.97E-04)
[4.73E-05, 3.92E-03]
Negative food-related
businesses
Count/county
population
7.67E-04 (2.15E-04)
[9.44E-05, 2.26E-03]
8.67E-04 (2.12E-04)
[2.01 E-04,1.82E-03]
8.85E-04 (2.89E-04)
[1.35E-04, 2.82E-03]
8.27E-04 (5.03E-04)
[6.18E-05, 5.38E-03]
8.30E-04 (3.20E-04)
[6.18E-05, 5.38E-03]
Positive food-related
businesses
Count/county
population
1.70E-03 (5.98E-04)
[3.88E-04,1.04E-02]
1.84E-03 (4.70E-04)
[6.28E-04, 4.63E-03]
1.85E-03 (6.51 E-04)
[3.82E-04, 7.87E-03]
1.98E-03 (1.11 E-03)
[1.92E-04,1.49E-02]
1.82E-03 (7.50E-04)
[1.92E-04,1.49E-02]
Health-care-related
businesses
Count/county
population
2.69E-03 (1.39E-03)
[1.94E-04, 2.47E-02]
2.96E-03 (8.53E-04)
[7.79E-04, 8.89E-03]
2.56E-03 (9.80E-04)
[1.42E-04,1.13E-02]
2.15E-03 (1.03E-03)
[1.42E-04, 7.66E-03]
2.56E-03 (1.16E-03)
[1.42E-04, 2.47E-02]
Recreation-related
businesses
Count/county
population
2.49E-04 (1.24E-04)
[3.38E-05,1.16E-03]
3.11 E-04 (1.47E-04)
[2.91 E-05,1.13E-03]
3.32E-04 (2.41 E-04)
[3.03E-05, 2.00E-03]
5.13E-04 (6.47E-04)
[5.57E-05,1.08E-02]
3.30E-04 (3.29E-04)
[2.91 E-05,1.08E-02]
Transportation-related
businesses
Count/county
population
1.15E-04 (7.92E-05)
[1.16E-05,1.25E-03]
1.14E-04 (5.67E-05)
[2.17E-05, 3.60E-04]
1.27E-04 (1.09E-04)
[2.39E-05,1.79E-03]
2.29E-04 (3.60E-04)
[2.97E-05, 4.95E-03]
1.31 E-04 (1 47E-04)
[1.16E-05, 4.95E-03]
Social-service-related
businesses
Count/county
population
8.64E-05 (5.72E-05)
[9.77E-06, 7.06E-04]
9.18E-05 (5.12E-05)
[6.79E-06, 2.76E-04]
1.17E-04 (8.79E-05)
[1.44E-05, 8.37E-04]
2.03E-04 (1.54E-04)
[2.97E-05,1.06E-03]
1.10E-04 (9.03E-05)
[6.79E-06,1.06E-03]
Construct: Subsidized Housing Environment
Total subsidized units
Count/county
population
1.11E-02 (2.91E-02)
[0, 6.46E-01]
1.27E-02 (1.82E-02)
[0,1.77E-01]
0.010(0.019)
[0,0.416]
0.009 (0.042)
[0, 0.834]
1.03E-02 (2.86E-02)
[0, 8.34E-01]
NOTE: Calculated with nontransformed data
-------
Variable Loadings on EQI Domains
Air Domain
The loadings for the variables that comprise the air domain
varied by RUCC stratum, although not extensively (Table 8).
Direction of loadings was similar across the urban/rural
strata. Criteria air pollutants were less influential in the
metropolitan-urbanized stratum compared with the other
strata, whereas influence of HAPs varied.
Table 8. Variable Loadings—Air Domain
Metropolitan-Urbanized
(RUCC1 = 1089)
Air Domain
Construct: Criteria Air Pollutants
Nitrogen dioxide
Carbon monoxide
Sulfur dioxide
Ozone
PM|0
PM25
Construct: Hazardous Air Pollutants
1,1,2,2-Tetrachloroethane
1,1,2-T richloroethane
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
Dibutylphthalate
Diesel engine emissions
Dimethyl formamide
0.0613
0.0308
0.0436
0.048
0.0845
0.0701
0.1525
0.0765
0.0113
0.1131
0.0317
0.0738
0.1628
0.0934
0.1229
0.1371
0.0964
0.0778
0.1032
-0.0232
0.0952
0.0916
0.1251
0.0392
0.0464
0.1072
0.1169
0.0259
0.0731
0.082
0.1093
0.1274
0.1189
0.1344
0.1267
0.1506
0.1655
0.098
0.1545
0.1548
Nonmetropolitan-Urbanized
(RUCC2 = 323)
0.1091
0.0938
0.1227
0.1232
0.0677
0.1513
0.1087
0.0879
0.0352
0.111
0.0932
0.1075
0.1219
0.0977
0.0914
0.0684
0.1305
0.1022
0.1149
0.0186
0.1275
0.1123
0.1024
0.0516
0.0975
0.0776
0.114
0.0281
0.0469
0.0977
0.0594
0.0897
0.1366
0.0986
0.1033
0.1179
0.1674
0.0859
0.1441
0.132
Less Urbanized
(RUCC3 = 1059)
0.1014
0.0754
0.1121
0.0982
0.0627
0.1281
0.1157
0.0945
0.0212
0.1307
0.082
0.099
0.1214
0.0737
0.1206
0.0762
0.124
0.1112
0.1126
0.002
0.1191
0.0664
0.1219
0.0366
0.082
0.0676
0.1172
0.0186
0.0494
0.1054
0.0982
0.0655
0.1302
0.0893
0.1147
0.1476
0.168
0.1055
0.1431
0.1443
Thinly Populated
(RUCC4 = 670)
0.0911
0.0913
0.1053
0.0751
0.0937
0.1354
0.0644
0.1145
0.0508
0.1301
0.0829
0.1018
0.1117
0.0834
0.1234
0.0932
0.1261
0.1158
0.1146
0.0231
0.1183
0.0745
0.1327
0.0904
0.0825
0.0764
0.1261
-0.0028
0.0757
0.1174
0.1065
0.0797
0.124
0.0875
0.1251
0.1446
0.1497
0.1251
0.1163
0.1418
OVERALL(n=3141)
0.0848
0.0738
0.0822
0.0711
0.0897
0.1036
0.1236
0.1045
0.0332
0.128
0.076
0.0929
0.1309
0.0972
0.1265
0.1164
0.1208
0.0963
0.1153
0.0051
0.12
0.0857
0.1264
0.0373
0.0808
0.0905
0.1242
0.018
0.0829
0.1066
0.1076
0.0985
0.1259
0.1098
0.1248
0.1414
0.1477
0.1163
0.1321
0.1404
34
-------
Table 8. (continued) Variable Loadings—Air Domain
Air Domain
Metropolitan-Urbanized
(RUCC1 = 1089)
Nonmetropolitan-Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL(n=3141)
Dimethyl phthalates
0.0968
0.0962
0.1105
0.1254
0.1183
Dimethyl sulfate
0.0472
0.1201
0.1024
0.1072
0.0942
Epichlorohydrin
0.0867
0.1248
0.1004
0.0995
0.0986
Ethyl acrylate
0.1008
0.1341
0.1251
0.1206
0.1175
Ethyl chloride
0.1032
0.0788
0.0971
0.1192
0.1132
Ethylene dibromide
0.1534
0.1185
0.123
0.1212
0.1272
Ethylene dichloride
0.1525
0.1214
0.1385
0.108
0.133
Ethylene glycol
0.1628
0.1427
0.1604
0.15
0.1464
Ethylene oxide
0.1106
0.1225
0.1218
0.1226
0.117
Ethylidene dichloride
0.0807
0.0212
0.0476
0.0796
0.0874
Glycol ethers
0.1338
0.1229
0.1234
0.0979
0.1228
Hexachlorobenzene
0.0276
0.1141
0.1396
0.1365
0.0991
Hexachlorobutadiene
0.0701
0.1053
0.0866
0.0874
0.0764
Hexachlorocyclopentadiene
0.0612
0.1013
0.0819
0.0792
0.0704
Hexane
0.1556
0.1247
0.144
0.1418
0.1412
Hydrazine
0.0619
0.0916
0.0707
0.0781
0.0644
Hydrochloric acid
0.0901
0.1209
0.1242
0.1347
0.1231
Isophorone
0.0537
0.0832
0.0694
0.0696
0.0676
Lead compounds
0.1366
0.0778
0.069
0.0581
0.1045
Manganese compounds
0.0791
0.0771
0.0724
0.0786
0.0759
Mercury compounds
0.083
0.0611
0.0336
0.0491
0.0605
Methanol
0.1559
0.1421
0.1545
0.1457
0.1434
Methyl isobutyl ketone
0.1556
0.157
0.155
0.144
0.1424
Methyl methacrylate
0.1229
0.1079
0.1222
0.1275
0.1277
Methyl chloride
0.0883
0.0287
0.0174
0.0073
0.0562
Methylhydrazine
0.0272
0.0613
0.0469
0.049
0.0527
Methyl tert-butyl ether
0.1226
0.0937
0.1376
0.1397
0.1313
Nitrobenzene
0.0751
0.1147
0.0954
0.091
0.0868
N,N-dimethylaniline
0.0655
0.1157
0.1003
0.0955
0.0877
o-toluidine
0.1203
0.1279
0.1289
0.1257
0.1255
Polycyclic aromatic hydrocarbon
compounds/polycyclic organic matter
Pentachlorophenol
0.1143
0.0822
0.1172
0.1192
0.1199
0.002
0.1202
0.0899
0.087
0.0485
Phosphine
-0.0272
0.0129
0.0145
0.0066
-0.0015
Phosphorus
0.0174
-0.0073
-0.0022
0.0173
0.012
Polychlorinated biphenyls
0.0284
0.13
0.0954
0.0779
0.0729
Propylene dichloride
0.1524
0.1202
0.132
0.0882
0.1302
Propylene oxide
0.1471
0.1537
0.1415
0.1417
0.1395
Quinoline
0.064
0.1238
0.1092
0.1088
0.0848
Selenium compounds
0.1198
0.1102
0.1268
0.1245
0.127
Styrene
0.1375
0.1309
0.1358
0.1372
0.1334
Tetrachloroethylene
0.1433
0.0865
0.0852
0.0788
0.1115
Toluene
0.1673
0.1541
0.1313
0.1298
0.1404
Trichloroethylene
0.1147
0.1441
0.1327
0.1279
0.1163
Triethylamine
0.1281
0.1066
0.1349
0.1391
0.1332
Vinyl acetate
0.125
0.1005
0.1249
0.1353
0.1308
Vinyl chloride
0.1489
0.0997
0.1008
0.097
0.1257
Vinylidene chloride
0.1464
0.1121
0.1172
0.124
0.1314
-------
Water Domain
The loadings for the variables that comprise the water domain
varied by RUCC and also by construct, suggesting that some
constructs were more influential in urban areas and others
in rural areas (Table 9). Variables representing overall water
quality loaded positively in the two urban RUCC strata
and negatively in the rural RUCC stratum. The loadings
for variables representing general water contamination and
recreational water quality varied by RUCC, although they
were, overall, quite low. Loadings for variables representing
domestic water quality and drought varied by RUCC,
although they were all positive. The loadings for variables
representing the atmospheric deposition construct varied by
RUCC and did not demonstrate any clear patterns. Variables
in the chemical contamination construct demonstrated little
variability by RUCC, with loadings of similar values for all
variables across all RUCCs.
Water Domain
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL (n=3141)
Construct: Overall Water Quality
Percent of stream length impaired in
county
0.0078
0.0063
-0.0067
-0.0172
0.0031
Construct: General Water Contamination
Sewage permits
0.0004
0.0017
0.009
0.0104
0.0059
Industrial permits
-0.0214
-0.0394
-0.0078
0.0084
-0.0114
Stormwater permits
-0.0353
-0.0243
-0.0158
0.021
-0.0209
Construct: Recreational Water Quality
Number of days of beach closure
-0.0019
-0.0072
0.0085
0.0092
0
Number of days beach closure for
contamination advisory events
0.0035
-0.0067
0.0015
0.0075
0.0019
Number of days beach closure for rain
advisory events
0.014
0.0009
0.0051
0.001
0.0091
Construct: Domestic Use
Percent of population on self supply
0.0068
0.0166
0.0349
0.0185
0.0139
Percent of public supply population on
surface water
0.019
0.0175
0.0098
0.0346
0.022
Construct: Atmospheric Deposition
Calcium precipitation weighted mean
0.0325
0.0179
0.0231
-0.0055
0.0154
Magnesium precipitation weighted
mean
0.0028
0.0064
-0.0074
-0.0241
-0.0089
Potassium precipitation weighted
mean
-0.0066
-0.0043
-0.0131
-0.0253
-0.0163
Sodium precipitation weighted mean
-0.0358
-0.0229
-0.0375
-0.0133
-0.0262
Ammonium precipitation weighted
mean
0.044
0.0211
0.0123
-0.025
0.0076
Nitrate precipitation weighted mean
0.0262
0.0034
0.0167
-0.0002
0.0144
Chloride precipitation weighted mean
-0.0392
-0.0303
-0.0408
-0.0065
-0.0259
Sulfate precipitation weighted mean
0.0002
-0.0144
0.0015
0.0167
0.0051
Total mercury deposition -0.0413
Construct: Drought
Percent of county drouqht—extreme „ „„„
(D3-D4) 00035
Construct: Chemical Contamination
Arsenic 0.119
Barium 0.1265
Cadmium 0.1045
Chromium 0.1147
Cyanide 0.085
Fluoride 0.1234
Mercury (inorganic) 0.0905
Nitrate 0.1159
Nitrite 0.0985
Selenium 0.1123
Antimony 0.1086
Beryllium 0.107
Thallium 0.1099
-0.0359
0.0337
0.1187
0.1297
0.1019
0.1183
0.0965
0.1277
0.0899
0.1133
0.0937
0.1196
0.1027
0.1126
0.1117
-0.0293
0.0234
0.1043
0.1173
0.0967
0.102
0.0749
0.1175
0.0763
0.0883
0.0816
0.1044
0.0972
0.0859
0.1059
0.0072
0.0242
0.0996
0.1199
0.1085
0.1069
0.0797
0.116
0.091
0.0867
0.0822
0.1071
0.1038
0.1105
0.1077
-0.0228
0.0164
0.1112
0.1234
0.1036
0.1103
0.0825
0.1209
0.0868
0.1016
0.0902
0.1103
0.1041
0.1005
0.1091
36
-------
Table 9. (continued) Variable Loadings—Water Domain
Water Domain
Metropolitan-
Urbanized
(RUCC1 = 1089)
Nonmetropolitan-
Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL (n=3141)
Endrin
0.1188
0.1195
0.1205
0.1249
0.121
Lindane
0.1265
0.1297
0.1173
0.1199
0.1234
Methoxychlor
0.1282
0.1282
0.1313
0.1351
0.131
Toxaphene
0.1212
0.1232
0.1177
0.1191
0.1203
Dalapon
0.1161
0.1121
0.112
0.1165
0.1148
di(2-ethylhexyl) adipate
0.1241
0.1204
0.1227
0.122
0.1234
Oxamyl (Vydate)
0.1215
0.1186
0.1189
0.122
0.1209
Simazine
0.134
0.1356
0.1348
0.1369
0.1354
di(2-ethylhexyl) phthalate
0.1202
0.1163
0.1171
0.116
0.1187
Picloram
0.1165
0.1111
0.1134
0.1189
0.1156
Dinoseb
0.1235
0.1223
0.1211
0.1264
0.1235
Hexachlorocyclopentadiene
0.1305
0.1335
0.1349
0.1356
0.1336
Carbofuran
0.1209
0.1185
0.1184
0.122
0.1206
Atrazine
0.1356
0.1362
0.1348
0.138
0.1362
Alachlor
0.1373
0.1403
0.1384
0.1393
0.1387
Heptachlor
0.1306
0.1325
0.1341
0.1356
0.1332
Heptachlor Epoxide
0.1305
0.1336
0.1335
0.1349
0.133
2,4-Dichlorophenoxyacetic acid
0.1243
0.1244
0.121
0.1265
0.1241
Silvex
0.1217
0.1209
0.1204
0.127
0.1226
Hexachlorobenzene
0.1309
0.1323
0.1345
0.1358
0.1335
Benzo[a]pyrene
0.1217
0.119
0.1198
0.1191
0.1207
Pentachlorophenol
0.1286
0.1268
0.1314
0.1338
0.1305
1,2,4-T richlorobenzene
0.1445
0.1445
0.1507
0.1455
0.1467
Polychlorinated biphenyls
0.0966
0.0857
0.0857
0.0922
0.0918
1,2-Dibromo-3-chloropropane
0.1199
0.1142
0.1221
0.1219
0.1206
Ethylene dibromide
0.0899
0.0975
0.0996
0.098
0.0952
Xylenes
0.1343
0.1313
0.1466
0.1446
0.1402
Chlordane
0.1314
0.1344
0.1343
0.1346
0.1336
Dichloromethane (Methylene chloride)
0.1434
0.1446
0.1499
0.1451
0.1461
1,2-Dichlorobenzene
(o-Dichlorobenzene)
0.144
0.1443
0.1497
0.1459
0.1463
1,4-Dichlorobenzene
(p-Dichlorobenzene)
0.1391
0.1401
0.1377
0.1392
0.139
Vinyl chloride
0.1447
0.1448
0.1502
0.1457
0.1467
1,1-Dichloroethylene
0.1439
0.1449
0.1505
0.1461
0.1467
trans-1,2-Dichloroethylene
0.1435
0.1412
0.1489
0.1454
0.1455
1,2-Dichloroethane (Ethylene
dichloride)
0.1445
0.1445
0.1507
0.1455
0.1467
1,1,1 -T richloroethane
0.1441
0.1437
0.1498
0.1457
0.1464
Carbon tetrachloride
0.145
0.1441
0.1509
0.1462
0.1471
1,2-Dichloropropane
0.1451
0.1452
0.1512
0.1461
0.1473
Trichloroethylene
0.1426
0.1446
0.1492
0.1454
0.1457
1,1,2-T richloroethane
0.1451
0.145
0.1511
0.1462
0.1472
Tetrachloroethylene
0.1442
0.1438
0.1495
0.1458
0.1463
Monochlorobenzene(Chlorobenzene)
0.1441
0.1421
0.1503
0.1459
0.1463
Benzene
0.145
0.1439
0.1511
0.1458
0.1471
Toluene
0.1439
0.1424
0.1505
0.1447
0.1462
Ethylbenzene
0.144
0.1431
0.1494
0.1453
0.1461
Styrene
0.1443
0.1451
0.151
0.146
0.147
cis-1,2-Dichloroethylene
0.1445
0.1445
0.1507
0.1455
0.1467
Alpha particles
0.062
0.0794
0.0703
0.0704
0.0691
-------
Land Domain
The loadings for variables in the land domain varied
considerably (Table 10). For mercury, lead, and titanium,
loading magnitudes were much lower in the most urban
stratum, whereas the loadings across all other strata were
comparable. Some variables had the highest loading in the
most urban and most rural strata (e.g., herbicides), whereas
others remained stable across strata (e.g., arsenic, iron,
harvested acreage). Direction of loadings was consistent
across strata.
Table 10. Variable Loadings—Land Domain
Land Domain
Metropolitan-Urbanized
(RUCC1 =1089)
Nonmetropolitan-
Urbanized (RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 = 670)
OVERALL (n=3141)
Construct: Agriculture
Harvested acreage
0.1572
0.1395
0.1398
0.1373
0.14
Irrigated acreage
0.0876
0.0318
0.064
0.0954
0.0773
Farms per acre
0.1182
0.0304
0.0738
0.1138
0.0965
Manure
0.1546
0.1141
0.1053
0.137
0.1265
Chemicals used to control
nematodes
0.0626
0.033
0.0272
0.0416
0.0435
Chemicals used to control
disease
0.0819
0.0074
0.0336
0.0592
0.0547
Chemicals used to defoliate/
control growth/thin fruit
0.0316
-0.0219
-0.0162
0.0351
0.0127
Animal units
-0.0358
-0.0133
0.0019
0.0202
-0.0034
Construct: Pesticides
Herbicides
0.1701
0.1393
0.135
0.1762
0.1524
Fungicides
0.1447
0.1026
0.0657
0.0956
0.1005
Insecticides
0.1407
0.1049
0.0874
0.1047
0.1072
Construct: Contaminants
Arsenic
0.2617
0.2774
0.2722
0.2626
0.2685
Selenium
0.2186
0.2267
0.2339
0.2428
0.2338
Mercury
0.1264
0.1763
0.1863
0.1883
0.1682
Lead
0.1731
0.23
0.2395
0.2386
0.2228
Zinc
0.3142
0.3242
0.3199
0.2976
0.3186
Copper
0.3032
0.2879
0.3062
0.2897
0.3043
Sodium
0.2934
0.2985
0.2906
0.278
0.2916
Magnesium
0.3195
0.31
0.3037
0.2915
0.3085
Titanium
0.1012
0.1965
0.1701
0.202
0.1682
Calcium
0.2624
0.2577
0.2656
0.2686
0.2677
Iron
0.3099
0.3218
0.3139
0.2948
0.3144
Aluminum
0.2795
0.2695
0.2746
0.2544
0.2712
Phosphorus
0.1011
0.0858
0.1428
0.1775
0.1053
Construct: Facilities
Facilities
0.1169
0.1164
0.0604
0.0732
0.0779
Construct: Radon
Radon zone
-0.1703
-0.1877
-0.1909
-0.1606
-0.1753
38
-------
Sociodemographic Domain
The loadings for the variables that comprise the
sociodemographic domain varied by RUCC (Table 11),
indicating some variables were more influential on the
domain score in urban counties, whereas others exerted more
of an effect in rural counties. The patterns of association
within the socioeconomic construct were fairly consistent,
meaning the variables that loaded negatively in the urban
counties also loaded negatively in the least urban counties.
For instance, renter occupation and vacant units were
Table 11. Variable Loadings—Sociodemographic Domain
Sociodemographic Domain
Metropolitan-Urbanized
(RUCC1 =1089)
Nonmetropolitan-Urbanized
(RUCC2 = 323)
Less Urbanized
(RUCC3 = 1059)
Thinly Populated
(RUCC4 =670)
OVERALL
(n=3141)
Socioeconomic Construct
Percent renter occupied
0.2344
-0.1665
-0.0246
-0.1235
-0.0374
Percent vacant units
0.1757
-0.0586
-0.0209
0.0142
-0.1968
Median household value
-0.1762
0.2484
0.2604
0.216
0.2907
Median household income
-0.4096
0.419
0.4399
0.4545
0.449
Percent persons < poverty
0.4535
-0.4568
-0.4728
-0.5169
-0.4557
Percent no English
0.1562
-0.2656
-0.1923
-0.1847
-0.1252
Percent > high school
-0.3328
0.3673
0.4345
0.4559
0.3925
Percent unemployed
0.3718
-0.4053
-0.3429
-0.3322
-0.325
Percent work outside county
-0.1967
0.1228
-0.0892
-0.0663
0.0996
Median number of rooms
-0.4091
0.3314
0.3077
0.2878
0.3501
Percent housing > 10 units
0.0205
0.1325
0.2289
0.0733
0.2017
Crime Construct
Log violent crime
0.1728
-0.1039
-0.1251
-0.1385
-0.1325
associated negatively with median household value and
median household income across rural-urban status. The
one socioeconomic variable for which this was not the case
was for the percentage of persons who worked outside the
county. For this variable, working outside the county in
less urbanized and thinly populated strata was associated
inversely with more than a high school education but
was associated positively in metropolitan-urbanized and
nomnetropolitan-urbanized counties.
Table 12. Variable Loadings—Built-Environment Domain
Metropolitan-Urbanized Nonmetropolitan-Urbanized Less Urbanized Thinly Populated OVERALL
Built-Environment Domain (RUCC1 = 1089) (RUCC2 = 323) (RUCC3 = 1059) (RUCC4 = 670) (n=3141)
Roads Construct
Highway proportion 0.1249 -0.0209 0.1275 -0.0106 0.132
Primary streets proportion 0.0857 -0.0744 -0.1143 -0.1103 0.0578
Highway/Road Safety
Log traffic fatalities -0.1507 -0.1938 0.0097 0.0272 0.0018
Public Transit Behavior
Proportion using public transport 0.2794 0.0635 -0.0212 0.074 0.2058
Business Environment
Log vice-related environment 0.2547 0.2157 0.321 0.3536 0.2687
Log entertainment environment 0.347 0.4422 0.3822 0.3721 0.3585
Log education environment 0.2405 0.2355 0.2866 0.3713 0.3242
Log negative food environment 0.2147 0.2372 0.2536 0.2514 0.2162
Log positive food environment 0.3666 0.4004 0.4241 0.3127 0.2995
Log health care environment 0.4245 0.4497 0.4653 0.4055 0.4241
Log recreation environment 0.212 0.3901 0.3309 0.3434 0.2888
Log transportation environment 0.2998 0.1979 0.1985 0.2752 0.3207
Log civic environment 0.2865 0.2114 0.1692 0.2209 0.2912
Subsidized Housing Environment
Log total subsidized units -0.2448 0.0518 0.1440 0.2024 0.2566
39
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most thinly populated counties, the air and water domains
were characterized by the lowest loadings (0.03 and 0.13,
respectively), whereas the sociodemographic and land
domains were the most influential (loadings of 0.63 and
0.58, respectively).
The built and the sociodemographic domains loaded
approximately equally on the overall EQI. The air domain
also had a somewhat similar loading (0.49). The water
domain appeared to contribute least to the overall EQI in the
most metropolitan-urbanized areas.
Description of Overall EQI
The distribution of the RUCC-stratified overall EQI scores
is displayed in Figure 4. For these scores, higher values
tended toward poorer enviromnents, whereas negative
values were associated with healthier (positive domain
attributes) environments. The bulk of the EQI scores across
all RUCC strata was at the negative end of the distribution,
indicating more counties could be characterized by
healthier enviromnents, compared with unhealthy (positive)
enviromnents. Although more numerous than other RUCC
strata, the less urbanized counties (RUCC3) demonstrated the
greatest heterogeneity and range of EQI scores (-6.25, 2.34).
The thinly populated counties had the smallest range of EQI
score (-4.34, 2.06).
Appendix IV contains county mapping of overall EQI,
domain-specific indices, RUCC-stratified overall EQI, and
RUCC-stratified domain-specific indices.
Table 13. Description of the Domain Indices Contributing to the Overall and Rural-Urban Continuum Codes (RUCCs)
Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005)
Metropolitan-Urbanized Areas RUCC1 (n=1089)
Mean
Standard Deviation
Minimum
Maximum
Air domain index
0.756
0.662
-1.780
2.790
Water domain index
0.052
1.019
-1.641
1.478
Land domain index
0.089
0.909
-5.136
2.095
Sociodemographic domain index
0.594
0.955
-3.027
3.979
Built-environment domain index
-0.213
0.878
-4.109
3.884
Nonmetropolitan-Urbanized Areas RUCC2 (n=323)
Mean
Standard Deviation
Minimum
Maximum
Air domain index
0.484
0.474
-1.553
1.517
Water domain index
0.111
1.033
-1.570
1.306
Land domain index
0.089
0.909
-5.019
1.479
Sociodemographic domain index
0.023
0.858
-4.810
2.165
Built-environment domain index
-0.563
0.485
-1.043
2.165
Less Urbanized Areas RUCC3 (n=1059)
Mean
Standard Deviation
Minimum
Maximum
Air domain index
-0.199
0.654
-2.731
1.204
Water domain index
0.066
0.955
-1.565
1.301
Land domain index
-0.069
1.007
-5.139
1.408
Sociodemographic domain index
-0.316
0.854
-4.620
3.127
Built-Environment Domain
The variables that comprised the built domain loaded much
less consistently across the rural-urban categories (Table
12). In general, there were more inverse or negative variable
loadings in the most urban counties compared with the less
urbanized counties, and the most rural counties had fairly
consistent positive variable loadings.
Domain-Specific Index Description and Loadings on
Overall EQI
The means, standard deviations, and ranges for each domain-
specific index are presented in Table 13. In general, higher
values of the air and sociodemographic indices were found in
the more metropolitan areas, and the most thinly populated
areas had the lowest values of each of the indices. Mean
values for the land domain index did not vary substantially by
RUCC strata. As expected, the index loadings on the overall
EQI index were mean (0) and standard deviation (1).
The pattern of association for the domain-specific loadings
differed by rural-urban status (Table 14). In the most urban
areas, RUCC1, the built-enviromnent domain was most
influential, as indicated by its highest loading value (0.52),
followed by the air domain (0.51). For the nonmetropolitan-
urbanized areas (RUCC2), the sociodemographic and land
domains loaded similarly on the overall EQI (0.60 and 0.55,
respectively), followed by the built-enviromnent domain. For
this particular grouping of counties, the water domain was
least influential, based on its low PC A coefficient (0.30). The
air domain was the least influential for the less urbanized
counties (0.16), followed by the water domain (0.30). In the
40
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Table 13. (continued) Description of the Domain Indices Contributing to the Overall and Rural-Urban Continuum
Codes (RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005)
Built-environment domain index
-0.096
0.792
-6.086
3.127
Thinly Populated Areas RUCC4 (n=670)
Mean
Standard Deviation
Minimum
Maximum
Air domain index
-1.1141
0.879
-3.258
0.7300
Water domain index
-0.241
0.987
-1.555
1.732
Land domain index
-0.072
1.122
-5.210
1.732
Sociodemographic domain index
-0.477
0.860
-4.332
1.263
Built-environment domain index
-0.770
1.225
-5.530
2.787
OVERALL (n=3141)
Mean
Standard Deviation
Minimum
Maximum
Air domain index
7.17E-10
1
-3.258
2.790
Water domain index
-3.40E-10
1
-1.641
1.478
Land domain index
2.38E-10
1
-5.210
2.095
Sociodemographic domain index
1.71E-09
1
-6.086
3.884
Built-environment domain index
9.72E-11
1
-4.810
3.980
Table 14. Loadings of the Domain Indices Contributing to the Overall and Rural-Urban Continuum Codes (RUCCs)
Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005)
Metropolitan-Urbanized Areas RUCC1 (n=1089)
Coefficient/Loading
95% CI
Air domain index
0.5063
0.4379, 0.5747
Water domain index
0.2757
0.1828,0.3686
Land domain index
0.4379
0.36552,0.5107
Sociodemographic domain index
0.4538
0.3945,0.5131
Built-environment domain index
0.5196
0.4565, 0.5827
Nonmetropolltan-Urbanlzed Areas; RUCC2 (n=323)
Coefficient/Loading
95% CI
Air domain index
0.3343
0.0.80, 0.5705
Water domain index
0.2958
0.0738,0.5178
Land domain index
0.5506
0.4168,0.6845
Sociodemographic domain index
0.5963
0.4913,0.7012
Built-environment domain index
0.3769
0.1719,0.5819
Less Urbanized Areas RUCC3 (n=1059)
Coefficient/Loading
95% CI
Air domain index
0.1609
0.0477, 0.2740
Water domain index
0.2981
0.1976,0.3987
Land domain index
0.5503
0.4905, 0.6058
Sociodemographic domain index
0.5675
0.5112,0.6238
Built-environment domain index
0.5102
0.4479, 0.5726
Thinly Populated Areas RUCC4 (n=670)
Coefficient/Loading
95% CI
Air domain index
0.0285
-0.1507,0.2076
Water domain index
0.1347
-0.0444,0.3138
41
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Table 14. (continued) Loadings of the Domain Indices Contributing to the Overall and Rural-Urban Continuum Codes
(RUCCs) Stratified Environmental Quality Index for 3141 U.S. Counties (2000-2005)
Land domain index
0.5785
0.4920, 0.6649
Sociodemographic domain index
0.6263
0.5555, 0.6972
Built-environment domain index
0.5041
0.3980,0.6103
OVERALL (n=3141)
Coefficient/Loading
95% CI
Air domain index
0.4867
(0.4543,0.5192)
Water domain index
0.2618
(0.2161,0.3074)
Land domain index
0.3887
(0.3493,0.4281)
Sociodemographic domain index
0.5345
(0.5090,0.5601)
Built-environment domain index
0.5077
(0.4795, 0.5359)
3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
-4.00
-5.00
-6.00
-7.00
2.50
2.28
2.34 o
\
f 1 \
f 1 \
r i \
f
-4.43
-4.34
-5.47
-6.25
RUCC1 Metropolitan-
Urbanized Areas
RUCC2
Nonmetropolitan
Urbanized Areas
RUCC3 Less
Urbanized Areas
RUCC4 Thinly
Populated Areas
Figure 4. Distribution of overall EQI scores across rural-urban continuum code (RUCC) categories.
42
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6.0
Discussion
Uses of EQI
An EQI was developed for all counties (N=3141) in the
United States, incorporating data for five environmental
domains: (1) air. (2) water, (3) land. (4) built, and (5)
sociodemographic. For each environmental domain,
variables were developed from various datasets. domain-
specific indices were created, and the EQI was developed by
stratifying by four RUCCs. The loadings varied by domain
and RUCC, suggesting that environmental quality is driven
by different domains in rural and urban areas. The majority
of counties demonstrated values at the negative end of the
distribution, suggesting that more counties demonstrated
good overall environmental quality rather than poor
environmental quality.
The EQI holds promise for improving the environmental
estimation in public health. The EQI describes the ambient
county-level conditions to which residents arc exposed,
whether they arc at home, at school, or at work, provided
these multiple human activity spaces occur in the same
county. Use of the EQI will help public health researchers
investigate cumulative impact of various diverse constructs
that typically arc viewed in isolation. Each of the domain-
specific pieces of information, which contribute to the EQI.
is also informative. Because most environmental health
practice occurs on a domain-specific basis, this domain-
specific information may be important to policymakers
and environmental health activists. The domain-specific
loadings to the EQI indicate which of the environmental
domains accounts for the largest portion of the variability in
the EQI; in essence, these loadings answer the question as
to which domain is making the biggest contribution to the
total environment. In addition, the variable loadings on each
of the domains arc also informative for the same reason. In
the land environment, for instance, it might be important
to know whether pesticides or Superfund sites seem to be
contributing the largest share of variability to the land index.
This information has obvious implications for public health
intervention. The RUCC-stratificd domains and EQI indices
also will make an important public health contribution.
Urban-rural arcas arc known to differ in important ways;
these RUCC-stratificd indices help disentangle what domains
may be driving some of the observed urban-rural differences
in public health outcomes.
The EQI offers a comprehensive measure of environmental
quality for all counties in the Unitcd States and is comprised
of many of the best environmental measures currently
available. The EQI can be used as a general health
exposure metric to help identify environmental health
issues for communities. It provides information on overall
environmental exposures faced in a community. In addition,
because collection of data was relevant to the entire United
States, the EQI is comparable across communities to help
identify arcas of better and worse overall environmental
quality. The development of domain-specific indices enables
communities to assess the drivers of poor environmental
quality in their community. Additionally, because it is
comparable across counties, arcas that arc burdened most
by poor environmental quality can be identified. Finally,
the EQI can be used in a variety of environmental health
research activities as a control variable to adjust for
overall environmental exposure, while trying to isolate a
specific effect. Such a control variable will provide better
estimates of effects without confounding by co-occurring
environmental factors.
The EQI is a national-level index that potentially can provide
a better understanding into how multiple environmental
conditions affect U.S. counties. At its current county-level
scale, the EQI may not reveal environmental injustices seen
at the local community level. However, it docs highlight
those counties experiencing an increased burden of
environmental impacts. Further, the EQI can contribute to
environmental justice endeavors by describing
• the process by which EQI data were obtained.
• how the EQI was constructed, and
• the Web sites containing available data tliat can be used
to construct indices at different levels of aggregation.
The EQI can be a tool for interested investigators to consider
constructing local EQIs and adding relevant, local-level data
for more focused comparisons.
Strengths and Limitations
Data
Data sources evaluated represented each of the five
environmental domains. Each data source was reasonably
well documented. Despite finding a considerable number
of data sources applicable to each environmental domain,
significant data gaps exist.
The data used to create the index balanced quality
measurement with geographic breadth of coverage.
Therefore, the index docs a solid job estimating the ambient
environment but may be less useful for estimating specific
environments (e.g., in a particular location in the United
States |not county | at a specific time). Not all relevant
environmental exposures were necessarily included in the
index. Data inclusion was dependent on data collection
43
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and coverage; if relevant data were not being collected,
the information was not captured in the EQI. Rclatcdly. in
areas where little data collection occurs, the data may be
overrepresenting the environmental profile of those areas.
For example, a county that contains a National Park without
data collected and a town with data collection will be solely
represented by the town area, although that may be inaccurate
for the entire county. Conversely, environments with a wealth
of environmental measurements, like urban areas, will be
better estimated by the EQI.
Environmental data sources often arc plagued by inadequate
spatial and temporal coverage. Most of the data sources
obtained for the EQI required spatial interpolation to achieve
county-level estimates. For example, even with extensive
air monitoring networks, the measured spatial coverage of
the United States was incomplete, particularly in rural areas.
Some types of measures were located disproportionately
in urban areas (e.g., PM air pollution), whereas others arc
found in rural areas (e.g., industrial livestock operations).
The nonrandoni distribution of environmental risk meant tliat
virtually all interpolated data were inaccurate, impairing the
assessment of how pollutants differentially impacted urban
and rural areas.
From a human health perspective, probably the biggest
limitation to existing environmental data sources is that data
arc collected with little thought given to potential health
impacts. For instance, monitoring sites may collect relevant
air pollutant data, but their location (e.g., air monitors
located on top of buildings) is inappropriate for assessing the
street-level values to which humans are exposed. Pesticide
data, from the land domain, usually reports pesticide sales
in relation to crops and livestock, not application, handling,
or disbursement. Even the US Census, which is widely used
in health research, primarily is collected for tax and political
districting purposes. Some of the data sources identified
have not been used in human health research and. as such,
arc a limitation. Regularly collected, high-quality data that
considers probable human health impacts would make the
task of assessing differential exposures considerably easier.
Environmental data also were collected rarely with adequate
temporal frequency. Although data on some parameters were
collected on a consistent and frequent basis, the majority
were not. Water data, for instance, were collected only
sporadically in response to a particular query or based on
regulatory statute. Within the sociodcniographic domain,
the complete U.S. Census was collected decennially, which
limits investigators' capacity to explore temporal changes.
Characteristics of places can change rapidly, but. under
current data collection schedules, these changes cannot
be assessed. Initially, the EQI looked to estimate yearly
measures. However, ultimately, only a 6-year (2000-2005)
measure was created because of the lack of yearly data for
some of the variables.
Many environmental parameters were compiled at a smaller
unit of aggregation (e.g., for a municipality or city), and
most were not maintained in a single source, such as a
data repository. Although national repositories for some
domains exist (e.g., water, air), often in response to Federal
regulations, no built-cnvironnicnt repository exists (for
transit, walkability/physical activity, street connectivity,
presence of sidewalks, or pedestrian lighting measures).
Localities with limited funds may not be motivated—or
able—to collect these data.
PCA Methodology
The use of PCA was not without limitations. Normality
is an important assumption for PCA, and not all the data
were normally distributed in their raw form. Many of the
iionnornial variables were those with a substantial number
of meaningful zeros (e.g., there were no public housing
units contained within these counties). This "absence" of
attribute is important information to convey, and, yet, it was
problematic from a score-construction perspective. Although
transforming the data improved their distribution, it reduced
each variable's intcrprctability. A PC A-dcrivcd score also
can be challenging to interpret. Outliers in the data also can
be a limitation. However, with 3141 counties and normality
checks, this is less problematic in the EQI.
Although limited, the use of PCA was also an important
strength of this project. PC A provided a means to overcome
one of the significant limitations in the field of environmental
health and combine multiple environmental domains into
one index of ambient environmental quality; the whole
endeavor would not have been possible without this data
reduction strategy. The resulting scale is standardized, which
will facilitate its comparison to other scales constructed
in di lie re nt countries or at different units of aggregation.
Further, it is the approach that has been used in other scale or
score construction activities." 78
Application
Use of the EQI as a measure of exposure assumes exposure
to "environment" is consistent for all individuals, but the
extent of environmental exposure was not assessable. The
EQI was focused solely on the outside environment, which
may not be the most relevant exposure in relation to human
health and disease. Finally, population-level analyses offer
little predictive utility for individual-level risk. Therefore,
although the index may be useful at identifying less healthy
environments, it will not be useful for predicting individual-
level adverse outcomes.
The EQI was developed for research purposes and is not
meant to be a diagnostic tool. The EQI would be useful to
identify potential areas of concern for counties to target
future research, but it should not be used to target regulatory
purposes.
Sensitivity Analyses
Different types of sensitivity analyses arc planned or have
been completed for the EQI. The only domain with a
completed sensitivity analysis is the water domain. For the
other four domains, planned activities arc presented.
Three alternate air domain constructions arc planned for
forthcoming sensitivity analyses. To assess the influence of
H APs on the air domain, two alternate datascts were created.
44
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One contains only criteria air pollutant variables, and the
other reduces H AP variables to a count variable. This variable
reduction was accomplished by assigning a value of 1 to each
value above the 25th percentile for that H AP. then summing
across HAPs for each county. This method incorporates all
estimated H APs from the 2002 NATA, rather than a subset.
Because of differences in variable elimination chronology,
the H APs were excluded before log-transformation occurred.
A dataset was constructed by log transforming all H APs
before correlation exclusion criteria were applied, examining
the variables with correlations above 0.7, and selecting a
single variable to represent highly correlated pairs or groups.
Sensitivity analysis was conducted on the variables included
in the water domain. The primary water domain index was
developed using data from five data sources and representing
eight constructs. Variables were created using data from
two additional data sources for variables in the atmospheric
deposition and chemical contamination constructs. The
variables were created from the National Water Information
System database and the Safe Drinking Water Information
System database. Alternative indices were constructed
either by exchanging a single variable or by exchanging
all variables for which alternate variables were available.
Concordance correlation coefficients (ccc's) were used
to assess similarity to the primary index. The sensitivity
analysis demonstrated strong correlations when comparing
alternative indices to the primary index. The lowest
correlation was seen for the full alternate index (all available
variables replaced) compared with the primary index (ccc:
0.999018 [95% CI: 0.998949, 0.999082]). The greatest
differences between the indices were seen in the southeastern
and northwestern United States. Therefore, the sensitivity
analysis demonstrated differing sources of variables did not
alter county ranking, which indicated the water domain index
is robust with choice of measures.
Forthcoming sensitivity analyses for the land domain will
involve constructing alternative agriculture variables using
Dun and Bradstreet agriculture data. Variables that arc
comparable to those employed in the EQI will be used.
Alternative types of variables will be used for EQI sensitivity
analyses. Constructing a different type of facilities variable
also will be explored, in which a count of facilities per square
mile is constructed. The current facilities variable estimates
count of facilities per county population; the sensitivity
analysis facilities variable will enable consideration of a
facilities variable with a land, versus population, impact.
For the sociodemographic domain, sensitivity analyses
will involve the use of alternative crime and census data.
Many di lie rent types of data can be used to represent
county deprivation/affluence, and the sensitivity analyses
will employ a different set of data to assess how robust the
loadings arc to the variables chosen to represent the domain.
Similarly for the built domain, an extremely inclusive
strategy was employed for constructing the various business-
related environments. Most businesses that plausibly could
be related to education, for instance, were included in the
education-related business variable. The sensitivity analysis
for the built domain will revisit some of those inclusion
criteria and construct less inclusive environmental variables
for use in the built-cnvironnicnt domain.
Other Environmental Indices
Although well-established environmental indices exist,
the EQI makes a unique contribution to the environmental
health literature. The Yale Center for Environmental Law
and Policy and the Center for Earth Information Science
Information Network at Columbia University developed the
Environmental Sustainability Index (ESI) in 2000.77 The ESI,
the predecessor to the Environmental Performance Index
(EPI), was launched as a complement to the Millennium
Development Goals. Both the ESI and EPI arc country-
level indices. The ESI included 76 elements, but the more
current EPI was pared down to 22 performance indicators
for which countries can be held accountable. Important
similarities between the EPI and EQI exist. Both indices rely
on similar data sources (official statistics, monitoring data,
modeled data, and spatial data), prepare data similarly for
variable construction (e.g., use of population denominators
to construct standardized weights), employ weighting and
aggregation in construction, and use child mortality as
an indicator of environmental health. The comparability
between the EQI and the EPI is a strength of this research.
The EPI differs from the EQI in important ways, however.
The EPI includes a narrower set of environmental domains
(environmental health, water effects on human health, air
pollution effects on human health, air pollution |ecosystem
effects|. water resources |ecosystems effects], biodiversity
and habitat, forests, fisheries, agriculture, climate cliange. and
energy). It also is constructed using target-based indicators
for assessing country-level performance for specific
environmental health indicators. Its international focus also
requires a much larger unit of aggregation—the country—
than was intended for the EQI.
Another index, which explored natural environment
vulnerability, was developed by the South Pacific Applied
Gcoscicnce Commission, the United Nations Environment
Programme, and their partners. The Environmental
Vulnerability Index (EVI)79 was developed through
collaboration with countries, institutions, and experts across
the globe and was designed for use with other economic
and social vulnerability indices to provide insights in the
processes that can negatively influence the sustainable
development of countries. EVI is based on 50 indicators
for estimating country-level environmental vulnerability.
Unlike the EQI, it is constructed by averaging the various
measures. One limitation of the EVI is that it docs not
reflect environments dominated by human systems
(e.g., cities, farms).
Most other environmental quality indices focus on one
environmental domain (e.g., Air Quality Index80) or a specific
type of activity (e.g., Pedestrian Environmental Quality
Index81) or vulnerability (e.g., Cumulative Environmental
Vulnerability Assessment,82 heat vulnerability index83).
45
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Statc-spccific indices also exist, (e.g., CalEnviro Screen
1.0,84 Virginia Environmental Quality Index85), but their
comparability across States is limited by their respective data
sources and construction.
Conclusions
The EQI was constructed for all counties (N=3141) in the
United States, incorporating data for five environmental
domains. (1) air, (2) water, (3) land. (4) built, and (5)
sociodemographic. and stratified by RUCCs. The techniques
used were based on a reproducible approach that almost
exclusively accesses publicly available data sources. This
effort was a first step in the direction of assessing multiple
environmental exposures. The EQI will be used as a
measure in environmental health research. This broad-based
effort acknowledges the many factors that together impact
environmental quality and. more generally, recognizes that
these factors work together to impact public health Updates
to the EQI for 2006-2010 arc planned, as well as exploration
of other finer spatial aggregations.
46
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7.0
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-------
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49
-------
-------
Appendix I
Modified Data Inventory
A listing of the different databases found through the data
inventory. For complete information about the data source
go to https://edg.epa.gov/metadata/catalog/main/liome.page.
where the full data inventory can be downloaded.
Air Domain
ID
BOUNDARY DATA
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£ o
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E "E
Air
Air
California Air Basins
California Air Districts
Air
Air
State 03, N02, S02, PB, VRP
- California
State PM,, - California
The California Air Basins layer is
a polygon shapefile representing
the 15 California air basins,
as defined in state statute and
regulation.
The California Air Districts layer is
a polygon shapefile representing
the California air pollution control
and air quality management
districts, as defined in federal and
state law.
The State 03, N02, S02, PB,
VRP designations layer is a
polygon shapefile showing
area designations as required
under Health and Safety Code
section 39608 for ozone, nitrogen
dioxide, sulfur dioxide, lead, and
visibility reducing particles.
The State PM,5 designations
layer is a polygon shapefile
showing area designations as
required under Health and Safety
Code section 39608.
CEPA
CEPA
CEPA CA
CEPA CA
air basin
air district
CEPA
CEPA CA
CEPA
CEPA CA
air district
not
applicable
not
applicable
No Non-informative
No Non-informative
air district no\. .. No Non-informative
applicable
not
applicable
No Non-informative
Air
State PM,n - California
The State PM10 designations
layer is a polygon shapefile
showing area designations as
required under Health and Safety
Code section 39608.
CEPA
CEPA CA
air district
not
applicable
No Non-informative
Air
Air
State Carbon Monoxide
- California
State Sulfates - California
The State Carbon Monoxide
designations layer is a polygon
shapefile showing area
designations as required under
Health and Safety Code section
39608.
The State Sulfates designations
layer is a polygon shapefile
showing area designations as
required under Health and Safety
Code section 39608.
CEPA
CEPA
CEPA CA air district no* No Non-informative
applicable
CEPA CA air district n0*, ,, No Non-informative
applicable
Air
State Hydrogen Sulfide
- California
The State Hydrogen Sulfide
designations layer is a polygon
shapefile showing area
designations as required under
Health and Safety Code section
39608.
CEPA
CEPA CA
air district no* ,, No Non-informative
applicable
57
Air
Nonattainment Boundaries -
8-Hour Ozone
A GIS file of counties that were
nonattainment for the 8-hr ozone
(1997 standard) as of June 2005.
EPA
EPA conterminous US county county No Non-informative
A-l
-------
Air Domain
58
Air
Nonattainment Boundaries
-pm25
A GIS file of counties that were
nonattainment for the PM25 (1997
standard) as of June 2005.
EPA
EPA
conterminous US
county
county
No
Non-informative
59
Air
Nonattainment Boundaries -
Carbon Monoxide
A list of nonattainment counties
for CO (1997 standard).
EPA
EPA
conterminous US
not applicable
county
No
Non-informative
60
Air
Nonattainment Boundaries -
Nitrogen Dioxide
A list of nonattainment counties
for N02 (1997 standard).
EPA
EPA
conterminous US
not applicable
county
No
Non-informative
61
Air
Nonattainment Boundaries -
Sulfur Dioxide
A list of nonattainment counties
for S02 (1997 standard).
EPA
EPA
conterminous US,
AK and Guam
not applicable
county
No
Non-informative
62
Air
Nonattainment Boundaries
-PM10
A list of nonattainment counties
for PM10 (1997 standard).
EPA
EPA
conterminous US,
AK and Puerto
Rico
not applicable
county
No
Non-informative
63
Air
Nonattainment Boundaries
-Lead
A list of nonattainment counties
for PB (1997 standard).
EPA
EPA
conterminous
US, AK
not applicable
county
No
Non-informative
CONCENTRATION DATA
14
Air
Air Toxics - Indiana
Air toxic monitoring data from
Indiana.
IDEM
IDEM
urban Indiana
point
not
applicable
No
Spatial coverage
64
Air
AQS - Hourly Ozone Data
Hourly ozone data from AQS in
.csv format at sites throughout
US.
EPA
EPA
conterminous US
not applicable
not
applicable
Yes
N/A
65
Air
AQS - Daily Ozone Data
Daily 1- and 8-hour maximum
ozone values from AQS in .csv
format at sites throughout US.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico, Virgin
Islands and some
Mexico
not applicable
not
applicable
No
Represented
elsewhere
66
Air
AQS - Annual Ozone Data
Annual 1- and 8-hour maximum
value ozone data from AQS in
.csv format for sites throughout
US.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico, Virgin
Islands and some
Mexico
not applicable
not
applicable
No
Represented
elsewhere
67
Air
AQS - Sulfur Dioxide Data -
5-minute values
Average S02 concentration on
5-minute intervals.
EPA
EPA
southeastern NC
not applicable
not
applicable
No
Represented
elsewhere
68
Air
AQS - S02 Data - Hourly Max
5-minute values
Maximum S02 concentration on
5-minute intervals.
EPA
EPA
regional - see
areal estimate
not applicable
not
applicable
No
Represented
elsewhere
69
Air
Ambient concentration monitoring
AQS - Arsenic Total Suspended data for arsenic total suspended
Particulate Matter particulate matter made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
70
Air
AQS - Benzene (including
benzene from gasoline)
Ambient concentration monitoring
data for benzene made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
71
Air
AQS - Benzyl Chloride
Ambient concentration monitoring
data for benzyl chloride made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
72
Air
AQS - Beryllium (PMJ
Ambient concentration monitoring
data for beryllium (PMJ made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
73
Air
AQS - Beryllium (PM25)
Ambient concentration monitoring
data for beryllium (PM25) made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
74
Air
AQS - Beryllium Total
Suspended Particulate Matter
Ambient concentration monitoring
data for beryllium total suspended
particulate matter made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
75
Air
AQS - Biphenyl
Ambient concentration monitoring
data for biphenyl made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
76
Air
AQS - Bromoform
Ambient concentration monitoring
data for bromoform made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-2
-------
Air Domain
Q M-
ID
77
78
Air AQS -1,3-Butadiene
Air
AQS - Carbon Monoxide
Concentration
Ambient concentration monitoring
data for 1,3-butadiene made
available by AQS.
Hourly ambient CO
concentrations at site locations
across the country, provided as
an annual file.
O Q.
-c
CO
5 o
c ^
§ O
ro
EPA
EPA
9- o
2 E
o o
o >
o o
CD O
conterminous US,
O o
®
E "E
07 3
EPA
EPA
not
AK, HI, Puerto site - monitor .. .. No
Rico apP"Cable
conterminous US,
not
elsewhere
AK, HI, Puerto site - monitor r .. Yes N/A
Rico apP"Cable
79
Air AQS - Lead - PB - daily
Daily ambient lead concentrations
at site locations across the
country, provided as an annual
file.
EPA
conterminous US,
EPA
not
AK, HI, Puerto site - monitor .. ,. No N/A
Rico apP"Cable
82
Air AQS - Nitrogen Dioxide
Air AQS - Ozone Hourly
Air AQS - Oxides of Nitrogen
Hourly ambient nitrogen dioxide
concentrations at site locations
across the country, provided as
an annual file.
Hourly ambient ozone
concentrations at site locations
across the country, provided as
an annual file.
Hourly ambient NOX
concentrations at site locations
across the country, provided as
an annual file.
EPA
EPA
EPA
conterminous US,
EPA AK, HI, Puerto site - monitor
Rico
not
applicable
Yes N/A
EPA
EPA
conterminous US, .
AK, Hi, Puerto site - monitor no .. ,. Yes N/A
Rico apP"Cable
conterminous US, . n +
AK, Hi, Puerto site - monitor no .. ,. No ePresen e'
applicable elsewhere
no .. AQS-Volatile Organic
Compounds (VOC)-PAMS
^ AQS - PM^ Daily - local
conditions
gg AQS - PM Fine Speciation
- weekly
.. AQS - PM Fine Speciation
Blanks
Weekly average ambient
VOC concentrations from
photochemical assessment
monitoring stations (PAMS),
provided as an annual file.
Daily average ambient PM^
concentrations at site locations
across the country, provided as
an annual file.
Weekly average ambient PM
fine speciation concentrations at
site locations across the country,
provided as an annual file.
Weekly average ambient PM fine
speciation blanks concentrations
at site locations across the
country, provided as an annual
file. See additional info in Notes
section below.
EPA
EPA
EPA
EPA
conterminous US,
EPA AK, HI, Puerto site - monitor
Rico
not
No
applicable elsewhere
conterminous US, .
EPA AK, HI, Puerto site - monitor no .. ,. Yes
Rico apP"Cable
N/A
conterminous US, . n +
EPA AK, HI, Puerto site - monitor no .. ,, No ePresen e'
applicable elsewhere
conterminous US,
not
EPA AK, HI, Puerto site - monitor .. ,. No . ,
applicable elsewhere
87
Air AQS - PM Fine - IMPROVE
Air AQS - PM,n
3-day average ambient PM fine
concentrations at site locations
across the country, provided
as an annual file. These are
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) data provided in
AQS format to simplify analysis
with other AQS data.
Weekly average ambient PM10
concentrations at site locations
across the country, provided as
an annual file.
EPA
EPA
conterminous US,
not
EPA AK, HI, Puerto site - monitor .. ,. No . ,
applicable elsewhere
conterminous US,
not
EPA AK, HI, Puerto site - monitor .. ,. Yes N/A
Rico appllCable
^ AQS - PM,5 Non Reference
- hourly
90
Air
AQS - Reactive Oxides of
Nitrogen - NOY
Hourly ambient PM25
concentrations at site locations
across the country, provided as
an annual file. Non reference
data are less accurate than other
PM data provided by AQS.
Hourly ambient oxides of nitrogen
concentrations at site locations
across the country, provided as
an annual file.
EPA
EPA
conterminous US,
not
EPA AK, HI, Puerto site - monitor .. ,. No . ,
applicable elsewhere
conterminous US,
not
EPA AK, HI, Puerto site - monitor r .. No . .
applicable elsewhere
A-3
-------
Air Domain
ID
91 Air
AQS - Sulfur Dioxide - hourly
o
ro
"ra
Q
Hourly ambient sulfur dioxide
concentrations at site locations
across the country, provided as
an annual file.
O Q.
-c IE
CO
5 o
c ^
S o
ro
EPA
>
o
Q_
EPA
9- o
2 E
o Q
o >
o o
CD O
conterminous US,
AK, HI, Puerto
Rico
O o
®
O o
E "E
to 3
site - monitor
a:
re
"ra
O
not
applicable
Yes N/A
92
Air
AQS -Acetaldehyde
Ambient concentration monitoring
data for acetaldehyde made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
93
Air
AQS -Acetonitrile
Ambient concentration monitoring
data for acetonitrile made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
94
Air
AQS - Acrolein
Ambient concentration monitoring
data for acrolein made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
95
Air
AQS -Acrylonitrile
Ambient concentration monitoring
data for acrylonitrile made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
96
Air
AQS -Allyl Chloride
Ambient concentration monitoring
data for allyl chloride made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
97
Air
AQS - Antimony Total
Suspended Particulate Matter
Ambient concentration monitoring
data for antimony made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
98
Air
AQS - Arsenic (PM10)
Ambient concentration monitoring
data for arsenic (PM10) made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
99
Air
AQS - Arsenic (PM^)
Ambient concentration monitoring
data for arsenic (PM25) made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
100
Air
AQS - Cadmium (PM10)
Ambient concentration monitoring
data for cadmium (PMJ made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
101
Air
AQS - Cadmium (PM25)
Ambient concentration monitoring
data for cadmium (PM25) made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
102
Air
AQS - Cadmium Total
Suspended Particulate Matter
Ambient concentration
monitoring data for cadmium total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
103
Air
AQS - Carbon Disulfide
Ambient concentration monitoring
data for carbon disulfide made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
104
Air
AQS - Carbon Tetrachloride
Ambient concentration monitoring
data for carbon tetrachloride
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
105
Air
AQS - Chlorobenzene
Ambient concentration monitoring
data for chlorobenzene made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
106
Air
AQS - Chloroform
Ambient concentration monitoring
data for chloroform made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
165
Air
AQS -Phenol
Ambient concentration monitoring
data for phenol made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
166
Air
AQS -Phosphorous
Ambient concentration monitoring
data for phosphorous made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
167
Air
AQS -Polycyclic Organic Matter
Ambient concentration monitoring
" data for polycyclic organic matter
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
107
Air
AQS - Chloroprene
Ambient concentration monitoring
data for chloroprene made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-4
-------
Air Domain
Q M-
ID
108
Air AQS - Chromium (PM
Ambient concentration monitoring
data for chromium (PMJ made
available by AQS.
Q Q.
sz Ic
co
5 o
E
S O
05
EPA
>
0
01
Q. o)
ra ro
O Q
o >
o o
O O
O
O a>
If
ro -t->
E 'E
co 3
£
¦O
a>
c/>
o
c
o
a>
q;
conterminous US,
EPA AK, HI, Puerto site - monitor
Rico
not
applicable
Kepresentf
No i u
elsewhere
109
Air
AQS - Chromium (PM^)
Ambient concentration monitoring
data for chromium (PM25) made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
110
Air
AQS - Chromium Total
Suspended Particulate Matter
Ambient concentration monitoring
data for chromium total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
111
Air
AQS - Chromium VI Total
Suspended Particulate Matter
Ambient concentration monitoring
data for chromium VI total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
112
Air
AQS - Cobalt Total Suspended
Particulate Matter
Ambient concentration monitoring
data for cobalt total suspended
particulate matter made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
113
Air
AQS - o-Cresol
Ambient concentration monitoring
data for o-cresol made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
114
Air
AQS - p-Cresol
Ambient concentration monitoring
data for p-cresol made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
115
Air
AQS - Cumene
Ambient concentration monitoring
data for cumene made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
116
Air
AQS - Dibenzofurans
Ambient concentration monitoring
data for dibenzofurans made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
117
Air
AQS - 1,4-Dichlorobenzene(p)
Ambient concentration monitoring
data for 1,4-dichlorobenzene(p)
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
118
Air
AQS - 3,3'-Dichlorobenzidene
Ambient concentration monitoring
data for 3,3'-Dichlorobenzidene
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
119
Air
AQS - Dichloroethyl ether
(Bi s [2-ch loroethy l]ether)
Ambient concentration monitoring
data for dichloroethyl ether (Bis[2-
chloroethyl]ether) made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
120
Air
AQS - 1,3-Dichloropropene
Ambient concentration monitoring
data for 1,3-dichloropropene
made available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
121
Air
AQS -1,4-Dioxane
Ambient concentration monitoring
data for 1,4-dioxane made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
122
Air
AQS - Ethyl Chloride
(Chloroethane)
Ambient concentration
monitoring data for ethyl chloride
(chloroethane) made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
123
Air
AQS - Ethylbenzene
Ambient concentration monitoring
data for ethylbenzene made
available by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
124
Air
AQS - Ethylene Dibromide
(Dibromoethane)
Ambient concentration monitoring
data for ethylene dibromide
(Dibromoethane) made available
by AQS.
EPA
EPA
conterminous US,
AK, HI, Puerto
Rico
site - monitor
not
applicable
No
Represented
elsewhere
125
Air
AQS - Ethylene Dichbride
(1,2-Dichloroethane)
Ambient concentration
monitoring data for ethylene
dichloride (1,2-Dichloroethane)
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-5
-------
Air Domain
Q M-
ID
126
Air
AQS - Ethylene I mine
(Aziridine)
Ambient concentration
monitoring data for ethylene
imine (Aziridine) made available
by AQS.
Q Q_
-c
co
5 o
E
§ °
EPA
o O
ll
ro +-
E "E
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor no*.. ,,
applicable
Hj Represented
elsewhere
127
Air
AQS - Ethylene Dichbride
(1,1 -Dichloroethane)
Ambient concentration
monitoring data for ethylene
dichloride (1,1-Dichloroethane)
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
128
Air
AQS - Formaldehyde
Ambient concentration
monitoring data for formaldehyde
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
129
Air
AQS - Hexachlorobenzene
Ambient concentration
monitoring data for
hexachlorobenzene made
available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
130
Air
AQS - Hexachlorobutadiene
Ambient concentration
monitoring data for
hexachlorobutadiene made
available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
131
Air
AQS
- Hexachlorocyclopentadiene
Ambient concentration
monitoring data for
hexach lorocyclopentadiene
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
132
Air
AQS - Hexachloroethane
Ambient concentration
monitoring data for
hexachloroethane made
available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
133
Air
AQS - Hexane
Ambient concentration
monitoring data for hexane
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
134
Air
AQS - Hydrochloric Acid
Ambient concentration
monitoring data for hydrochloric
acid made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
135
Air
AQS - Hydrogen Fluoride
(Hydrofluoric Acid)
Ambient concentration
monitoring data for hydrogen
fluoride (Hydrofluoric acid) made
available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
136
Air
AQS - Isophorone
Ambient concentration
monitoring data for isophorone
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
137
Air
AQS-Lead(PMJ
Ambient concentration
monitoring data for lead (PM10)
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
138
Air
AQS-Lead(PM25)
Ambient concentration
monitoring data for lead (PM25)
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
139
Air
AQS - Lead Total Suspended
Particulate Matter
Ambient concentration
monitoring data for lead total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
140
Air
AQS - Manganese (PM10)
Ambient concentration
monitoring data for manganese
(PM10) made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
141
Air
AQS - Manganese (PM^)
Ambient concentration
monitoring data for manganese
(PMJ made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
142
Air
AQS - Manganese Total
Suspended Particulate Matter
Ambient concentration
monitoring data for manganese
total suspended particulate
matter made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
143
Air
AQS - Mercury (PM10)
Ambient concentration
monitoring data for mercury
(PM10) made available by AQS.
EPA
EPA
conterminous
US. AK. HI.
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-6
-------
Air Domain
Q M-
ID
144
Air AQS - Mercury (PM2e
Ambient concentration
monitoring data for mercury
(PM25) made available by AQS.
O Q.
-c IE
CO
5 o
c ^
5 o
ro
EPA
>
o
Q_
EPA
2 2
o o
o >
O o
O O
conterminous
US, AK, HI,
Puerto Rico
O o
®
O o
E "E
07 3
site-monitor no\. .. No . .
applicable elsewhere
145
Air
AQS - Mercury Total
Suspended Particulate Matter
Ambient concentration
monitoring data for mercury total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
146
Air
AQS - Mercury Compounds
Ambient concentration
monitoring data for mercury
compounds made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
147
Air
AQS - Methanol
Ambient concentration
monitoring data for methanol
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
148
Air
AQS - Methoxychlor
Ambient concentration
monitoring data for methoxychlor
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
149
Air
AQS - Methyl Bromide
(Bromomethane)
Ambient concentration
monitoring data for methyl
bromide (bromomethane) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
150
Air
AQS - Methyl Chloride
(Chloromethane)
Ambient concentration
monitoring data for methyl
chloride (chloromethane) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
151
Air
AQS - Methyl Chloroform
(1,1,1 -Trich loroeth ane)
Ambient concentration
monitoring data for
methyl chloroform
(1,1,1 -trich loroethane) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
152
Air
AQS - Methyl Ethyl Ketone
(2-Butanone)
Ambient concentration
monitoring data for methyl ethyl
ketone (2-butanone) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
153
Air
AQS - Methyl Iodide
(lodomethane)
Ambient concentration
monitoring data for methyl iodide
(iodomethane) made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
154
Air
AQS - Methyl Isobutyl ketone
(Hexone)
Ambient concentration
monitoring data for methyl
isobutyl ketone (hexone) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
155
Air
AQS - Methyl Methacrylate
Ambient concentration
monitoring data for methyl
methacrylate made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
156
Air
AQS - Methyl Tert-Butyl Ether
Ambient concentration
monitoring data for methyl
tert-butyl ether made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
157
Air
AQS - Methylene Chloride
(Dichloromethane)
Ambient concentration
monitoring data for methylene
chloride (dichloromethane) made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
158
Air
AQS - Naphthalene
Ambient concentration
monitoring data for naphthalene
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
159
Air
AQS - Nickel (PMJ
Ambient concentration
monitoring data for nickel (PMJ
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
160
Air
AQS - Nickel (PM25)
Ambient concentration
monitoring data for nickel (PM25)
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-7
-------
Air Domain
161
Air
AQS - Nickel Total Suspended
Particulate Matter
Ambient concentration
monitoring data for nickel total
suspended particulate matter
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
162
Air
AQS - N-Nitrosodimethylamine
Ambient concentration
monitoring data for
n-nitrosodimethylamine made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
163
Air
AQS -Pentachloronitrobenzene
(Quintobenzene)
Ambient concentration
monitoring data for
pentach loronitrobenzene
(quintobenzene) made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
164
Air
AQS -Pentachlorophenol
Ambient concentration
monitoring data for
pentachlorophenol made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
168
Air
AQS -Propionaldehyde
Ambient concentration
monitoring data for
propionaldehyde made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
169
Air
AQS -Propylene Dichbride
(1,2-Dichloropropane)
Ambient concentration
monitoring data for propylene
dichloride (1,2-dichloropropane)
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
170
Air
AQS -Selenium Total
Suspended Particulate Matter
Ambient concentration
monitoring data for selenium
total suspended particulate
matter made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
171
Air
AQS -Styrene
Ambient concentration
monitoring data for styrene made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
172
Air
AQS
-2,3,7,8-Tetrach lorodibenzo-
p-dioxin
Ambient concentration
monitoring data for
2,3,7,8-tetrachlorodibenzo-p-
dioxin made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
173
Air
AQS
-1,1,2,2-Tetrach loroethane
Ambient concentration
monitoring data for
1,1,2,2-tetrachloroethane made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
174
Air
AQS -Tetrachloroethylene
(Perch loroethylene)
Ambient concentration
monitoring data for
tetrachloroethylene
(perch loroethy lene)made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
175
Air
AQS -Toluene
Ambient concentration
monitoring data for toluene made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
176
Air
AQS -o-Toluidine
Ambient concentration
monitoring data for o-toluidine
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
177
Air
AQS - 1,2,4-Trichlorobenzene
Ambient concentration
monitoring data for
1,2,4-trichlorobenzene made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
178
Air
AQS -1,1,2-Trich loroethane
Ambient concentration
monitoring data for
1,1,2-trichloroethane made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
179
Air
AQS - Trich loroethylene
Ambient concentration
monitoring data for
trich loroethylene made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site - monitor
not
applicable
No
Represented
elsewhere
A-8
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180
Air
AQS - 2,2,4-Trimethylpentane
Ambient concentration
monitoring data for
2,2,4-tri methy Ipentane made
available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
181
Air
AQS - Vinyl Acetate
Ambient concentration
monitoring data for vinyl acetate
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
182
Air
AQS - Vinyl Chloride
Ambient concentration
monitoring data for vinyl chloride
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
183
Air
AQS - Vinylidene Chloride
(1,1-Dichloroethylene)
Ambient concentration
monitoring data for vinylidene
ch bride (1,1-Dich loroethy lene)
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
184
Air
AQS - m-Xylene
Ambient concentration
monitoring data for m-xylene
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
185
Air
AQS - o-Xylene
Ambient concentration
monitoring data for o-xylene
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
186
Air
AQS - p-Xylene
Ambient concentration
monitoring data for p-xylene
made available by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
187
Air
AQS - Xylenes (mixed
isomers)
Ambient concentration
monitoring data for xylenes
(mixed isomers) made available
by AQS.
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico
site -
monitor
not
applicable
No
Represented
elsewhere
329
Air
IMPROVE -Fine Ammonium
Sulfate (calculated)
Fine ammonium sulfate
concentration from the
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
330
Air
IMPROVE -Fine Ammonium
Sulfate Extinction (calculated)
Fine ammonium sulfate
extinction concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
331
Air
IMPROVE -Fine Elemental
Carbon Extinction (calculated)
Fine elemental carbon
extinction: concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
332
Air
IMPROVE -Fine Ammonium
Nitrate (calculated)
Fine ammonium nitrate
concentration from the
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
333
Air
IMPROVE -Fine Ammonium
Nitrate Extinction (calculated)
Fine ammonium nitrate
extinction concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
334
Air
IMPROVE -Fine Organic
carbon Extinction (calculated)
Fine organic carbon extinction
concentration from the
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
335
Air
IMPROVE -Fine Soil
Concentration (calculated)
Fine soil concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
INTERAGENCY
Colorado
State
conterminous
US, AK and HI
point
not
applicable
No
Represented
elsewhere
A-9
-------
Air Domain
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337
338
339
340
341
342
343
Air
Air
Air
Air
Air
Air
Air
IMPROVE - Fine Aluminum
Concentration
IMPROVE -Fine Arsenic
Concentration
IMPROVE -Fine Bromine
Concentration
IMPROVE -Fine Calcium
Concentration
IMPROVE -Fine Chloride
Concentration
IMPROVE -Fine Chlorine
Concentration
IMPROVE -Fine Chromium
Concentration
Fine aluminum concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Fine arsenic concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine bromine concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine calcium concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine chloride concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine chlorine concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine chromium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Q Q_
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INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
Q. o
ra ra
o o
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
O Q3
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It
re •*->
E "E
C/5 ID
point
point
point
point
point
point
not
applicable
not
applicable
not
applicable
not
applicable
not
applicable
not
applicable
No
Represented
elsewhere
Hj Represented
elsewhere
Hj Represented
elsewhere
Hj Represented
elsewhere
Hj Represented
elsewhere
Hj Represented
elsewhere
Colorado conterminous . ,
NTERAGENCY c. . MC Al/ . Ul point
State US.AKandH r
not Represented
applicable elsewhere
344
345
346
Air
Air
Air
IMPROVE -Fine Copper
Concentration
IMPROVE -Fine Iron
Concentration
IMPROVE -Fine Hydrogen
Concentration
Fine copper concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine iron concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine hydrogen concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Colorado conterminous . ,
NTERAGENCY c. . MC Al/ . Ul point
State US.AKandH r
INTERAGENCY C°J°r1ado point
State US, AK and HI
Colorado conterminous . ,
NTERAGENCY c. . MC A)/ .Ul point
State US.AKandH r
not
applicable
No
not
applicable
Represented
elsewhere
not hj Represented
applicable elsewhere
No
Represented
elsewhere
347
Air
IMPROVE -Fine Potassium
Concentration
Fine potassium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Colorado conterminous . ,
NTERAGENCY c. . MC A)/ .Ul point
State US.AKandH r
not
applicable
No
Represented
elsewhere
348
Air
IMPROVE -Fine Magnesium
Concentration
Fine magnesium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Coorado conterminous ,
INTERAGENCY c, , ,,c Al/ JUi Point
State US.AKandH r
not hj Represented
applicable elsewhere
349
Air
IMPROVE -Fine Manganese
Concentration
Fine manganese concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Coorado conterminous . .
INTERAGENCY c, , ,,c Al/ JUi Point
State US.AKandH r
not hj Represented
applicable elsewhere
350
Air
IMPROVE -Fine Molybdenum
Concentration
Fine molybdenum concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Coorado conterminous ,
INTERAGENCY c, , ,,c Al/ JUi Point
State US.AKandH r
not hj Represented
applicable elsewhere
A-10
-------
Air Domain
ID
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ro
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07 3
351
352
353
354
355
356
357
Air
Air
Air
Air
Air
Air
Air
IMPROVE-Fine Nitrite
Concentration
IMPROVE -Fine Sodium
Concentration
IMPROVE -Fine Ammonium
Ion Concentration
IMPROVE -Fine Nickel
Concentration
IMPROVE-Fine Nitrate
Concentration
IMPROVE -Fine Organic
Carbon Concentration
IMPROVE -Fine Phosphorus
Concentration
Fine nitrite concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine sodium concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine ammonium ion
concentration from the
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine nickel concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine nitrate concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine organic carbon
concentration from the
Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine phosphorus concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
INTERAGENCY
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
point
point
point
point
point
point
not
No
Colorado conterminous . ,
INTERAGENCY c, , ,,c Al/ , Ul point
State US, AK and H r
applicable elsewhere
not
applicable
not
No
No
elsewhere
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
358
359
360
361
Air
Air
Air
Air
IMPROVE -Fine Lead
Concentration
IMPROVE -Fine Rubidium
Concentration
IMPROVE -Fine Sulfur
Concentration
IMPROVE -Fine Selenium
Concentration
Fine lead concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine rubidium concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine sulfur concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine selenium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Coorado conterminous ,
INTERAGENCY c, , ,,c Al/ ^ Ui Point
State US, AK and H r
INTERAGENCY point
State US, AK and HI
INTERAGENCY point
State US, AK and HI
INTERAGENCY 0c^D ™nt™u, point
State US, AK and HI
not
No
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
362
363
364
Air
Air
Air
IMPROVE-Fine Silicon
Concentration
IMPROVE -Fine Sulfate
Concentration
IMPROVE -Fine Strontium
Concentration
Fine silicon concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine sulfate concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine strontium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
Colorado conterminous . ,
INTERAGENCY c, , ,,c Al/ ^ Ui Point
State US, AK and H r
INTERAGENCY ™nt™u, point
State US, AK and HI
Colorado conterminous . ,
NTERAGENCY c. . MC A)/ . Ul point
State US, AK and H r
not
No
applicable elsewhere
not
No
applicable elsewhere
not
No
applicable elsewhere
365
Air
IMPROVE -Fine Titanium
Concentration
Fine titanium concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Colorado conterminous . ,
INTERAGENCY c, , ,,c Al/ ^ Ui Point
State US, AK and H r
not
No
applicable elsewhere
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-------
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366 Air
IMPROVE -Fine Vanadium
Concentration
Fine vanadium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
INTERAGENCY
Colorado conterminous
State US, AK and HI
point
not
applicable
Hj Represented
elsewhere
367 Air
368 Air
IMPROVE -Fine Zinc
Concentration
IMPROVE -Fine Zirconium
Concentration
Fine zinc concentration from
the Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network.
Fine zirconium concentration
from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE)
network.
INTERAGENCY
INTERAGENCY
Colorado conterminous
State US, AK and HI
Colorado conterminous
State US, AK and HI
point
point
not
applicable
not
applicable
Hj Represented
elsewhere
Hj Represented
elsewhere
188 Air
CASTNET - Hourly Ozone
Data 1-hr
Hourly ozone data from the
CASTNET network in .csv
format.
EPA
EPA
conterminous
US
not applicable
not
applicable
Hj Represented
elsewhere
189 Air
190 Air
191 Air
193 Air
194 Air
195 Air
196 Air
197 Air
CASTNET- Weekly Ambient
Concentrations
CASTNET- Summary Weekly
Ambient Concentrations
CASTNET- Hourly Gas
Values 2008 and 2009
Weekly ambient concentrations
of S02 and HN03 gases, and
S04, N03, NH4 and base cation
concentrations for particles as
measured by open-face filter
packs.
Weekly summaries of ambient
concentrations of S02 and
HN03 gases, andS04,
N03, NH4 and base cation
concentrations for particles as
measured by open-face filter
packs.
Hourly S02, NOy and CO values
as measured by gas analyzers.
EPA
EPA
conterminous
US
not applicable
not
applicable
Hj Represented
elsewhere
EPA
EPA
cnA conterminous , r u not Kl Represented
EPA . lo not applicable .... No . r ,
rr applicable elsewhere
US
r-n« conterminous . r .. not M Represente'
EPA . lo not applicable .... No . r ,
US rr applicable elsewhere
Multi Layer Model (MLM) output
providing hourly estimates for
192
Air
CASTNET - Deposition and
concentration, dry deposition
EPA
nnfl conterminous . .... not
EPA MO not applicable ..
US rr applicable
Hj Represented
elsewhere
Concentration Model Output
velocity, and dry deposition flux
for 03, S02, NH03, PM,S04,
no3, nh4, CA, MG, NA, K, CL.
CASTNET - Daily Maximum
Ozone 8-hour
CASTNET - Total Deposition
Daily maximum of rolling 8-hour
average ambient concentration,
calculated according to 40CFR
Part58, per station.
Total annual deposition
at CASTNET sites using
MODEL_OUTPUT_ANNUAL as
the source of dry deposition and
WETDEP (interpolated NADP/
NTN) as the source of wet
deposition.
Chemical speciation
concentration data from aerosol
CASTNET - Chemical filter packs at local conditions
Speciation Concentration Data from October 1993 to December
2001. This table is no longer
updated.
Nephelometer data (conforms
to Interagency Monitoring of
Protected Visual Environments
(IMPROVE) network standards)
from October 1993 to June
2002. This table is no longer
updated.
8-hr maximum ozone data from
the CASTNET network in .csv
format.
EPA EPA
conterminous
US
.... not k ¦ Represented
not applicable .... No , ,
applicable elsewhere
EPA EPA »"ter™°us not applicable not,. .. No RsP^Bd
applicable elsewhere
US
EPA EPA not applicable not.. No RePresented
rr applicable elsewhere
conterminous
US
CASTNET - Nephelometer
CASTNET - Hourly Ozone
Values 8-hour
EPA
EPA
cnA conterminous + r l,i not M Represented
EPA . lo not applicable .... No . r ,
rr applicable elsewhere
US
cnA conterminous , r u not M Represented
EPA . lo not applicable .... No . r ,
rr applicable elsewhere
US
A-12
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1Qft .. CASTNET- Daily ozone c
Ir -1-and 8-hr max
daily 1- and 8-hr maximum
ozone values from CASTNET
network in .csv format.
EPA
EPA
conterminous
US
O o
®
E "E
J> ID
not applicable
not
applicable
No
elsewhere
199 Air CASTNET-Weekly Wet
Deposition Concentrations
369 Air NADP-Total Deposition
- monthly
07rt ». NADP - Total Deposition
370 Air . r
- seasonal
NADP-Total Deposition
-annual
372 Air NADP-Total Deposition
- weekly
EMISSIONS DATA
Weekly wet deposition
concentrations and sampling
information from January 1989 -
February 1999. This table is no
longer updated.
Total atmospheric deposition (kg/
Ha) and precipitation-weighted
mean concentrations of the
following species as monitored
by the National Atmospheric
Deposition Program - Ca, Mg, K,
Na, NH4, N03, Inorganic N, CL,
S04, H-h (lab), H+(field).
Total atmospheric deposition (kg/
Ha) and precipitation-weighted
mean concentrations of the
following species as monitored
by the National Atmospheric
Deposition Program - Ca, Mg, K,
Na, NH4, N03, Inorganic N, CL,
S04, H-h (lab), H+(field).
Total atmospheric deposition (kg/
Ha) and precipitation-weighted
mean concentrations of the
following species as monitored
by the National Atmospheric
Deposition Program - Ca, Mg, K,
Na, NH4, N03, Inorganic N, CL,
S04, H-h (lab), H+(field).
Total atmospheric deposition (kg/
Ha) and precipitation-weighted
mean concentrations of the
following species as monitored
by the National Atmospheric
Deposition Program - Ca, Mg, K,
Na, NH4, N03, Inorganic N, CL,
S04, H+ (lab), H+(field).
EPA
EPA
NADP
NADP
NADP
NADP
NADP
NADP
NADP
NADP
conterminous
US
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and American
Samoa
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and American
Samoa
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and American
Samoa
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and American
Samoa
not applicable
station - point
station - point
station - point
station - point
not
applicable
not
applicable
No
elsewhere
No
elsewhere
not
applicable
No
elsewhere
not
applicable
No
elsewhere
not
applicable
No
elsewhere
1 Air Smoke Emissions
16 Ajr Daily C02, S02, and NOX
Emissions (annual)
17 Ajr Daily C02, S02, and NOX
Emissions (quarterly)
18 Air
Hourly C02, S02, and NOX
Emissions
19 Ajr Hourly C02, S02, and NOX
Emissions (quarterly)
373 Air NATA1996 Emissions
Emissions data from portable
smoke detectors.
Daily C02, S02, and NOX
emissions data for multiple
states provided by state as an
annual file.
Daily C02, S02, and NOX
emissions data for multiple
states provided by state as
quarterly files.
Hourly C02, S02, and NOX
emissions data for multiple
states provided by state as
monthly files.
Hourly C02, S02, and NOX
emissions data for multiple
states provided by state as
quarterly files.
1996 National Air Toxics
Assessment emissions of 32
toxics plus diesel PM data listed
by county.
USFS, USFWS,
NPS, BLM
EPA
EPA
EPA
EPA
EPA
USFS,
USFWS,
NPS,
BLM
EPA
EPA
EPA
EPA
EPA
some western
and southern
states: CA, ID,
MT,AZ, CO, TX,
MS, FL
statewide for
not applicable
not
applicable
statewide for
statewide for
statewide for
not sure if it's not
mu Itiple states point or state applicable
not sure if it's not
mu Itiple states point or state applicable
not sure if it's not
mu Itiple states point or state applicable
not sure if it's not
mu Itiple states point or state applicable
conterminous
US and Puerto
Rico
county
county
No Non-informative
No Spatial coverage
No Spatial coverage
No Spatial coverage
No Spatial coverage
No Temporal coverage
A-13
-------
Air Domain
Q M-
ID
376 Air
386 Air
387 Air
NATA1999 Air Toxics
Emissions
NEI - Facility Summary
NEI - Point SCC Summary for
CAP and HAP
1999 National Air Toxics
Assessment emissions of 177
toxics plus diesel PM data listed
by county.
Facility-level summary of all
point sources, both CAPs and
HAPs, as found in the 2002 NEI
Final v3 file.
Source classification code
summary for both criteria air
pollutants (CAP) and hazardous
air pollutants (HAP) at county,
state and national level.
Q Q.
-E Ic
co
5 o
E
S O
ro
EPA
EPA
EPA
EPA
EPA
EPA
o o
conterminous
US, AK, HI and
Puerto Rico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
O (/>
O o
E e
county
not applicable
not applicable
county
not
No
Represented
elsewhere
applicable
not
applicable
No Non-informative
No Non-informative
388 Air
389 Air
390 Air
Emissions data summarized by
NEI - Hazardous Air Pollutants point stack for hazardous air
pollutants (HAP).
NEI - Criteria Air Pollutants
NEI - Nonpoint Summary
Emissions data summarized
by point stack for criteria air
pollutants (CAP).
Nonpoint source data for
criteria air pollutants (CAP) and
hazardous air pollutants (HAP)
at national, state and county
level.
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
not applicable
not applicable
not
applicable
not
applicable
not
not applicable r ..
applicable
No Non-informative
No Non-informative
No Non-informative
391 Air
392 Air
393 Air
394 Air
NEI - Nonroad County
NEI - Onroad County
NEI - Tier Summaries
Mobile source emissions data
for trains, aircraft and marine
vessels aggregated to county
level. Also available at state and
national scale.
Mobile source emissions data
for automobiles aggregated to
county level. Also available at
state and national scale.
Emissions data for criteria air
pollutants (CAP) from NEI
summarized by county, state and
national scale.
Emissions data for criteria air
pollutants (CAP) and hazardous
NEI - 42 Category Summaries air pollutants (HAP) from NEI
summarized by county, state and
national scale.
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
not applicable
not applicable
not applicable
not applicable
not
applicable
not
applicable
not
applicable
not
applicable
No Non-informative
No Non-informative
No Non-informative
No Non-informative
395 Air
396 Air
NEI - Biogenic Sector Data
2002/2005
NEI - 1970 - 2008 Average
Annual Emissions
This spreadsheet contains
county-total estimates of 2002
and 2005 biogenic emissions
based on the BEIS3.12 model.
National-scale, yearly emissions
totals for all criteria pollutants
provided by National Emissions
Inventory.
EPA
EPA
EPA
EPA
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
conterminous
US, AK, HI,
Puerto Rico,
Virgin Islands
and Gulf of
Mexico
not applicable
US
not
applicable
not
applicable
No Non-informative
Hj Represented
elsewhere
A-14
-------
Air Domain
397
Air
NEI - PM25 Filterable and PM1(
Filterable Emissions Trends
PM25 Filterable and PM10
Filterable emissions trends for
Electric Generating Utilities for
1970 to 2005.
EPA
EPA
conterminous
JS.AK. H
Duerto Rico
virgin Islands
and Guf of
Mexico
US
not
applicable
No Non-informative
MISCELLANEOUS
Air
Ambient Monitoring Data
Analysis System (AMDAS)
This tool can be downloaded to
access air quality data. It was
designed to work with EPA's
AQS.
not available
not
not
.. ,, not applicable not applicable .. ,,
available rr rr applicable
No Non-informative
3
Air
Air Quality Images
Web-cam air quality imagery
from select locations in the US.
USFS
USFS
conterminous
US
not applicable
not
applicable
No
Non-informative
12
Air
2001-2003 Air Monitoring
Stations - California
All California active air
monitoring stations from 2001
-2003.
CEPA
CEPA
CA
point
not
applicable
No
Non-informative
13
Air
2002-2004 Air Monitoring
Stations
All California active air
monitoring stations from 2002
-2004.
CEPA
CEPA
CA
point
not
applicable
No
Non-informative
One of a series of Excel
15
Air
Site-Level Data for Various
NAAQS Metrics - lead
spreadsheets that contains
site level statistics for National
Ambient Air Quality Standards
(NAAQS) metrics.
EPA
EPA
conterminous
US
not applicable
not
applicable
No
Non-informative
incomplete
national
20
Air
Acid Rain Averaging Plan
Acid Rain NOx Program Rate-
Based Compliance for Averaging
Plans.
EPA
EPA
coverage - AL,
FL, GA, IA, IL,
IN, KY, MD, MN,
MO, MS, NY,
OH, PA, TN,
WI.WV
point
not
applicable
No
Non-informative
incomplete
national
coverage - AL,
FL, GA, IA, IL,
21
Air
Acid Rain Compliance
Acid Rain NOx Program Rate-
Based Compliance data.
EPA
EPA
IN, KS, KY, MA,
MD, Ml, MN,
MO, MS, NH,
NJ, NY, OH,
PA, TN, UT, Wl,
WV, WY
point
not
applicable
No
Non-informative
incomplete
national
coverage - AL,
22
Air
Acid Rain NOx Compliance
Acid Rain NOx Program Rate-
Based Compliance data.
EPA
EPA
FL, GA, IA, IL,
IN, KS, KY, MD,
Ml, MN, MO,
MS, NY OH,
PA, TN, UT, Wl,
WV, WY
not applicable
not
applicable
No
Non-informative
23
Air
National Budget Trading
Program
National Budget Trading
Program data.
EPA
EPA
eastern US -
States included
are: AL, CT, DC,
DE, IL, IN, KY
MA, MD, Ml, NC,
NJ, NY, OH,
PA, Rl, SC. TN,
VA, WV
point
not
applicable
No Non-informative
24
Air
Compliance Ozone Transport OTC NOx Budget Program
Commission (OTC) Annual Reconciliation data.
EPA
EPA
some
northeastern
states - CT, DC,
DE, MA, MD,
NH, NJ, NY
PA, Rl
point
not
applicable
No Non-informative
A-15
-------
Air Domain
Q M-
ID
54
Air
Ctonsolidated Human Activity
Database (CHAD)
55
Air
Human Exposure Database
System (HEDS)
CHAD is a master database
providing access to other
human activity databases using
a consistent format. CHAD
facilitates access and retrieval
of activity/and questionnaire
information from those
databases that EPA currently
has access to-and-uses-in its
various regulatory analyses
undertaken by program offices.
Human Exposure database
System (HEDS) is a
web-enabled data repository
for human exposure studies. Its
mission is to provide data sets,
documents, and metadata for
human exposure studies that
can be easily accessed and
understood by a diverse set
of users.
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EPA
EPA
few cities and
schools around not applicable
the country
not
applicable
No Non-informative
not available
not
available
not available not applicable
not
applicable
No Non-informative
200
Air
CMAQ - Acetaldehyde
(ALD2)
201
Air
CMAQ - Aldehyde
Dehydrogenase (ALDX)
202
Air
CMAQ - Carbon Monoxide
(CO)
203 Air
CMAQ - Ethene (ETH)
204 Air
CMAQ - Ethane (ETHA)
205
Air
CMAQ - Formaldehyde
(FORM)
EPA
EPA
MODELED DATA
36km gridded, predicted
acetaldehyde data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ). Available as hourly,
daily average and monthly
average.
36km gridded, predicted
acetaldehyde dehydrogenase
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted carbon
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted ethene
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted ethane
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted
formaldehyde data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
conterminous
US
36km cell
36km
Hj Represented
elsewhere
conterminous
US
36km cell
36km
No
Represented
elsewhere
conterminous
US
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
36km cell
36km
No
Represented
elsewhere
36km cell
36km
Hj Represented
elsewhere
A-16
-------
Air Domain
ID
206
Air
CMAQ - Hydrogen Peroxide
(H202)
207 Air CMAQ - Nitrous Acid (H ON 0)
208
Air
209
Air
CMAQ - Odd Hydrogen
(HOX)
CMAQ - Internal Olefins
(IOLE)
210 Air CMAQ - Isoprene (ISOP)
211
Air
CMAQ - Dinitrogen Pentoxide
(N205)
212 Air CMAQ - Ammonia (NH3)
213
Air
214
Air
CMAQ - Ammonia (NH3)
pg/m3
CMAQ - Total Ammonium
(NHX)[jg/m3
36km gridded, predicted
hydrogen peroxide data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
nitrous acid data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted odd
hydrogen data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
internal olefins data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
isoprene data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
dinitrogen pentoxide data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
ammonia pentoxide data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
ammonia data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted total
ammonium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
Q M-
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CO
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§ °
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
Q. o
2 2
O Q
o >
o o
CD O
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
O o
®
E "E
to id
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
A-17
-------
CMAQ - Nitric Oxide (NO)
CMAQ - Total Reactive
Nitrogen (NOY)
CMAQ - Nitrogen (NTR)
CMAQ - Coarse Mode Nitrate
CMAQ - Fine Particulate
Organic Carbon
CMAQ - Anthropogenic
Aerosol (fine)
CMAQ - Biogenic Aerosol
(fine)
CMAQ - Cloud Aerosol (fine)
CMAQ - Primary
Anthropogenic Aerosol (fine)
36km gridded, predicted
nitric oxide data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted total
reactive nitrogen data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted nitrogen
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted coarse
mode nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate organic carbon data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
anthropogenic aerosol data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
biogenic aerosol data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
cloud aerosol data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted primary
anthropogenic aerosol data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
36km cell
36km
No
Represented
elsewhere
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
36km cell
36km
No
36km cell
36km
No
Represented
elsewhere
Represented
elsewhere
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
-------
Air Domain
ID
224 Air CMAQ - Other Aerosol (fine)
225
Air
226
Air
227
Air
228
Air
CMAQ - Fine Particulate
Sulfate
CMAQ - Coarse Mode
Sulfate
CMAQ - Total Fine Particulate
Mass
CMAQ - Fine Particulate
Chloride
229
Air
CMAQ - Fine Particulate
Elemental Carbon
230
Air
231
Air
232
Air
CMAQ - Fine Particulate
Sodium
CMAQ - Fine Particulate
Ammonium
CMAQ - Fine Particulate
Nitrate
36km gridded, predicted
other aerosol data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted coarse
mode sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted total
fine particulate mass data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
fine particulate chloride data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate elemental carbon
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed
in units of pg/m3. Available
as hourly, daily average and
monthly average.
36km gridded, predicted
fine particulate sodium data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate ammonium data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted Fine
particulate nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
Q M-
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
Q. o
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CD O
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
O o
®
E
CO
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
EPA
EPA
conterminous
US
36km cell
36km
No
elsewhere
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
A-19
-------
Air Domain
Q M-
ID
233
Air
234
Air
235
Air
236
Air
CMAQ - Fine Particulate
Organic Carbon
CMAQ - Other Fine
Particulate Mass
CMAQ - Fine Particulate
Sulfate
CMAQ - Total Fine Particulate
Mass
237
Air
CMAQ - Course Chloride
238 Air
CMAQ - Course Sodium
239
Air
CMAQ - Course Ammonium
240
Air
CMAQ - Course Nitrate
241
Air
CMAQ - Other Course Mass
36km gridded, predicted fine
particulate organic carbon data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted other
fine particulate mass data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3 Available as hourly, daily
average and monthly average.
36km gridded, predicted total
fine particulate mass data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
course chloride data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3 Available as hourly, daily
average and monthly average.
36km gridded, predicted
course sodium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted course
ammonium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
course nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted other
course mass data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
36km cell
36km
No
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
A-20
-------
Air Domain
ID
242
Air
CMAQ - Course Sulfate
243
Air
CMAQ - Total Course mass
244
Air
CMAQ - Total Nitrate (nitrate
+ nitric acid)
245 Air CMAQ - Nitric Acid (HNO ]
246
Air
CMAQ - Nitric Acid (jg/m3
(HNOs)
247 Air CMAQ - Ozone (03)
248 Air
CMAQ - Olefins (OLE)
249 Air
CMAQ - Paraffin (PAR)
250
Air
CMAQ - Peroxyacetyl Nitrate
(PAN)
36km gridded, predicted
course sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted total
course mass data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
total nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted
nitric acid data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb.. Available as hourly, daily
average and monthly average.
36km gridded, predicted
nitric acid data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted ozone
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted olefins
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted paraffin
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted
peroxyacetyl nitrate data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
A-21
-------
CMAQ - C3 and Peroxyacetyl
Nitrate (PANX)
CMAQ - Sulfur Dioxide (S02)
CMAQ - Sulfur Dioxide
pg/m3 (SO)
CMAQ - Sulfur (SULF)
CMAQ - Terpene (TERP)
CMAQ - Toluene (TOL)
CMAQ - Volatile Organic
Compounds (VOC)
CMAQ - Xylene (XYL)
CMAQ - Fine Particulate
Chloride (ACLIJ)
36km gridded, predicted c3
and peroxyacetyl nitrate data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
sulfur dioxide data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
36km gridded, predicted
sulfur dioxide data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted sulfur
dafa produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted terpene
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted toluene
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted
volatile organic compound
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted xylene
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
36km gridded, predicted
fine particulate chloride data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
conterminous
US
36km cell
36km
No
Represented
elsewhere
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
36km cell
36km
No
Represented
elsewhere
36km cell
36km
36km cell
36km
Hj Represented
elsewhere
Hj Represented
elsewhere
EPA
EPA
conterminous
US
36km cell
36km
No
Represented
elsewhere
conterminous
US
conterminous
US
36km cell
36km
Hj Represented
elsewhere
36km cell
36km
No
Represented
elsewhere
-------
Air Domain
ID
260
Air
CMAQ - Total Coarse Mode
Mass
261
Air
CMAQ - Fine Particulate
Elemental Carbon
262
Air
263
Air
264
Air
265
Air
CMAQ - Fine Particulate
Sodium
CMAQ - Fine Particulate
Nitrate
CMAQ - Fine Particulate
Ammonium
CMAQ - Acetaldehyde
(ALD2)
266
Air
CMAQ - aldehyde
Dehydrogenase (ALDX)
267
Air
CMAQ - Carbon Monoxide
(CO)
268 Air
CMAQ - Ethene(ETH)
36km gridded, predicted
total coarse mode mass data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate elemental Carbon
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed
in units of pg/m3 Available
as hourly, daily average and
monthly average.
36km gridded, predicted
fine particulate sodium data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
36km gridded, predicted fine
particulate ammonium data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
acetaldehyde data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ). Available as hourly,
daily average and monthly
average.
12km gridded, predicted
acetaldehyde dehydrogenase
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted carbon
monoxide data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted ethene
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
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EPA
EPA
EPA
EPA
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EPA
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conterminous
US
EPA
conterminous
US
EPA
EPA
EPA
EPA
EPA
EPA
EPA
O o
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to id
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
conterminous
US
conterminous
US
conterminous
US
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
36km cell
36km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
A-23
-------
269
Air CMAQ - Ethane (ETHA)
270
Air
271
Air
CMAQ - Formaldehyde
(FORM)
CMAQ - Flydrogen Peroxide
(H202)
272 Air CMAQ - Nitric Acid (HNQ3)
273
Air
CMAQ - Nitric Acid (jg/m
(FIN 03)
274 Air CMAQ - Nitrous Acid (HONO)
275
Air
276
Air
CMAQ - Odd Flydrogen
(HOX)
CMAQ - Internal Olefins
(I OLE)
277 Air CMAQ - Isoprene (ISOP)
12km gridded, predicted ethane
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in EPA EPA
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
formaldehyde data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
hydrogen peroxide data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
nitric acid data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb . Available as hourly, daily
average and monthly average.
12km gridded, predicted
nitric acid data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
nitrous acid data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted odd
hydrogen data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
internal olefins data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
isoprene data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
12km cell
12km
No
12km cell
12km
No
Represented
elsewhere
Represented
elsewhere
12km cell
12km
12km cell
12km
Hj Represented
elsewhere
Hj Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
Hj Represented
elsewhere
12km cell
12km
No Non-informative
12km cell
12km
No Non-informative
12km cell
12km
No
Represented
elsewhere
A-24
-------
Air Domain
ID
278
Air
CMAQ - Dinitrogen Pentoxide
(N205)
279 Air
CMAQ - Ammonia (NH3)
280
Air
281
Air
CMAQ - Ammonia (NH3)
pg/m3
CMAQ - Total Ammonium
(NHX)[jg/m3
282 Air CMAQ - Nitric Oxide (NO)
283
Air
CMAQ - Total Reactive
Nitrogen (NOY)
284 Air
CMAQ - Nitrogen (NTR)
285 Air
CMAQ - Ozone (03)
286 Air
CMAQ - Olefins (OLE)
12km gridded, predicted
dinitrogen pentoxide data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
ammonia pentoxide data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
ammonia data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted total
ammonium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
nitric oxide data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted total
reactive nitrogen data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted nitrogen
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted ozone
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted olefins
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
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O O
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
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12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No Non-informative
A-25
-------
Air Domain
ID
287 Air CMAQ - Paraffin (PAR)
Air
289
Air
CMAQ - Peroxyacetyl Nitrate
(PAN)
CMAQ - C3 and Peroxyacetyl
Nitrate (PANX)
290
Air CMAQ - Sulfur Dioxide (S02)
291
Air
292
Air
CMAQ - Total Nitrate (nitrate
+ nitric acid)
CMAQ - Sulfur Dioxide (jg/
m3 (S02)
293 Air
CMAQ - Sulfur (SULF)
294 Air CMAQ - Terpene (TERP)
295 Air CMAQ - Toluene (TOL)
12km gridded, predicted paraffin
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
peroxyacetyl nitrate data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted C3
and peroxyacetyl nitrate data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
sulfur dioxide data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
ppb. Available as hourly, daily
average and monthly average.
12km gridded, predicted
total nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3 Available as hourly, daily
average and monthly average.
12km gridded, predicted
sulfur dioxide data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted sulfur
data produced by Community
Multi-Scale Atmospheric Quality
Model (CMAQ), expressed in
units of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
terpene data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units
of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
toluene data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units
of ppb. Available as hourly,
daily average and monthly
average.
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EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
12km cell
12km
No Non-informative
12km cell
12km
No Non-informative
12km cell
12km
No Non-informative
12km cell
12km
12km cell
12km
Hj Represented
elsewhere
Hj Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
Hj Represented
elsewhere
12km cell
12km No Non-informative
12km cell
12km
No
Represented
elsewhere
A-26
-------
Air Domain
ID
296
Air
CMAQ - Volatile Organic
Compounds (VOC)
297
Air CMAQ - Xylene (XYL)
298
Air
CMAQ - Fine Particulate
Cchloride (ACLIJ)
299
Air
CMAQ - Total Coarse
Mode Mass
300
Air
CMAQ - Fine Particulate
Elemental Carbon
301
Air
CMAQ - Fine Particulate
Sodium
12km gridded, predicted
volatile organic compounds
data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units
of ppb. Available as hourly
daily average and monthly
average.
12km gridded, predicted
xylene data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units
of ppb. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
fine particulate chloride data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
total coarse mode
mass data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
fine particulate elemental
Carbon data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
fine particulate sodium data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
S
o
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
o o
o >
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
C3 £
E
CO
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
A-27
-------
Air Domain
ID
302
Air
CMAQ
Nitrate
Fine Particulate
303
Air
CMAQ - Fine Particulate
Ammonium
304
Air
CMAQ - Coarse Mode
Nitrate
305
Air
CMAQ - Fine Particulate
Organic Carbon
306
Air
CMAQ - Anthropogenic
Aerosol (fine)
307
Air
CMAQ - Biogenic
Aerosol (fine)
12km gridded, predicted
fine particulate nitrate data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
fine particulate ammonium
data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
coarse mode nitrate data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
fine particulate organic
carbon data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
anthropogenic aerosol data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
biogenic aerosol data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
« S2
S
o
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
E E
O) Q>
o >
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
O flj
®
o £
E 'E
co id
12km cell 12km No RePreshented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell 12km No RePreshented
elsewhere
12km cell 12km No RePreshented
elsewhere
12km cell 12km No RePreshented
elsewhere
A-28
-------
Air Domain
ID
308
Air
CMAQ - Cloud Aerosol
(fine)
309
Air
CMAQ - Primary
AnthropoqenicAerosol
(fine)
310
Air
CMAQ - Other Aerosol
(fine)
311
Air
CMAQ - Fine Particulate
Sulfate
312
Air
CMAQ - Coarse Mode
Sulfate
313
Air
CMAQ - Total Fine
Particulate Mass
12km gridded, predicted
cloud aerosol data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly
daily average and monthly
average.
12km gridded, predicted
primary anthropogenic
aerosol data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
other aerosol data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
fine particulate sulfate data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
coarse mode sulfate data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ),
expressed in units of (jg/
m;:. Available as hourly,
daily average and monthly
average.
12km gridded, predicted
total fine particulate
mass data produced by
Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in
units of |jg/m;:. Available as
hourly, daily average and
monthly average.
S2
s
o
EPA
EPA
EPA
EPA
EPA
EPA
M—
o
ro
Q
<
o
IE
0)
CL
V)
2
E
O)
o
>
o
o
O
O
EPA
EPA
EPA
EPA
EPA
EPA
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
O flj
®
o £
E
CO
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
A-29
-------
CMAQ - Fine Particulate
Chloride
CMAQ - Fine Particulate
Elemental Carbon
CMAQ - Fine Particulate
Sodium
CMAQ - Fine Particulate
Ammonium
CMAQ - Fine Particulate
Nitrate
CMAQ - Fine Particulate
Organic Carbon
CMAQ - Other Fine
Particulate Mass
CMAQ - Fine Particulate
Sulfate
12km g ridded, predicted
fine particulate chloride data
produced by Community
Multi-Scale Atmospheric
Quality Model (CMAQ), EPA EPA
expressed in units of (jg/
m3. Available as hourly,
daily average and monthly
average.
12km g ridded, predicted
fine particulate elemental
carbon data produced by
Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in
units of [jg/m3. Available as
hourly, daily average and
monthly average.
12km gridded, predicted
fine particulate sodium data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted fine
particulate ammonium data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted Fine
particulate nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted fine
particulate organic carbon data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted other
fine particulate mass data
produced by Community Multi-
Scale Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted fine
particulate sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model EPA EPA
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern US -
from roughly
central TX
(-100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
Represented
elsewhere
12km cell
12km
No
12km cell
12km
No
12km cell
12km
No
12km cell
12km
No
12km cell
12km
No
12km cell
12km
No
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
Represented
elsewhere
-------
Air Domain
Q M-
ID
322
Air
CMAQ - Total Fine Particulate
Mass
323
Air CMAQ - Course Chloride
324 Air CMAQ - Course Sodium
325 Air CMAQ - Course Ammonium
326 Air CMAQ - Course Nitrate
327 Air
CMAQ - Other Course Mass
328 Air
CMAQ - Course Sulfate
336 Air
374 Air
375 Air
CMAQ - Total Course Mass
NATA1996 Ambient
Concentrations
NATA 1996 Exposure
Concentrations
12km gridded, predicted total
fine particulate mass data
produced by Community Multi-
Scale Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
course chloride data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
course sodium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted course
ammonium data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
course nitrate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted other
course mass data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted
course sulfate data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
12km gridded, predicted total
course mass data produced
by Community Multi-Scale
Atmospheric Quality Model
(CMAQ), expressed in units of
pg/m3. Available as hourly, daily
average and monthly average.
1996 National Air Toxics
Assessment ambient
concentrations (pg/m3) of 32
toxics plus diesel PM data listed
by county.
1996 National Air Toxics
Assessment exposure
concentration distributions of 32
toxics plus diesel PM data listed
by county.
Q Q.
-E Ic
co
5 o
E
S O
ro
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
ra re
o o
O O
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
eastern
US - from
roughly central
TX(~100W
longitude)
eastward
conterminous
US and Puerto
Rico
conterminous
US and Puerto
Rico
? =
O Q3
O (/>
O £
If
re •*->
E 'E
co id
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
12km
No
elsewhere
12km cell
county
county
12km
county
county
No
elsewhere
No Temporal coverage
No Temporal coverage
A-31
-------
Air Domain
Q M-
ID
377
Air
1999 National Air Toxics
Assessment ambient
NATA1999 Air Toxics Modeled concentrations of 177 toxics plus
Ambient Concentrations diesel PM data listed by county.
Estimates created using ASPEN
model.
1 12
> a>
5
5 o
EPA
EPA
re ro
conterminous
US, AK, HI and
Puerto Rico
re ^ re
E 'c re
county
county
Yes N/A
NATA 2002 Air Toxics Modeled
Ambient Concentrations
Yes
N/A
NATA 2005 Air Toxics Modeled
Ambient Concentrations
Yes
N/A
378
379
380
Air
Air
Air
381
Air
382
Air
NATA 1999 County Level
Cancer Risk
NATA 1999 County Level
Neurological Risk
NATA 1999 County Level
Respiratory Risk
NATA 1999 County Level
Pollutant-Specific Cancer/Non
Cancer Risk
NATA 2002 State-specific
Emission by County
383
Air
NATA 2002 US Neurological
Risks County
384
Air
NATA 2002 US Respiratory
Risks County
1999 National Air Toxics
Assessment percentile
distribution of risk across census
tracts, and contribution by
source sector to the average risk
for each county in the U.S.
1999 National Air Toxics
Assessment percentile
distribution of risk across census
tracts, and contribution by
source sector to the average risk
for each county in the U.S.
1999 National Air Toxics
Assessment percentile
distribution of risk across census
tracts, and contribution by
source sector to the average risk
for each county in the U.S.
1999 National Air Toxics
Assessment percentile
distribution of risk across census
tracts, and contribution by
source sector to the average risk
for each county in the U.S. with
the pollutant-specific contribution
to the total risk.
Nationwide, pollutant-specific
tons/year emissions for the year
2002 for every state (including
Puerto Rico, the Virgin Islands,
and the District of Columbia) and
for every county in each state.
EPA's 2002 national-scale
assessment estimates across
the United States plus Puerto
Rico, the Virgin Islands, and
the District of Columbia using
2002 national air toxics emission
inventory as input to the air
dispersion models ASPEN, and
an inhalation exposure model
(HAPEM5).
EPA's 2002 national-scale
assessment estimates across
the United States plus Puerto
Rico, the Virgin Islands, and
the District of Columbia using
2002 national air toxics emission
inventory as input to the air
dispersion models ASPEN, and
an inhalation exposure model
(HAPEM5).
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
conterminous
US, AK, HI and
Puerto Rico
conterminous
US, AK, HI and
Puerto Rico
conterminous
US, AK, HI and
Puerto Rico
conterminous
US, AK, HI and
Puerto Rico
conterminous
US, AK, HI,
Puerto Rico and
Virgin Islands
conterminous
US, AK, HI,
Puerto Rico and
Virgin Islands
county
county
county
county
county
county
No Non-informative
No Non-informative
No Non-informative
county
county
No Non-informative
county
county
No
Represented
elsewhere
county
county
No Non-informative
EPA
conterminous
US, AK, HI,
Puerto Rico and
Virgin Islands
county
county
No Non-informative
A-32
-------
Air Domain
Q M-
5
O
ID
385
Air
NATA 2002 US Cancer Risks
County
REMOTELY SENSED DATA
25
Air
Total Ozone - Earth Probe/
TOMS
EPA's 2002 national-scale
assessment estimates across
the United States plus Puerto
Rico, the Virgin Islands, and
the District of Columbia using
2002 national air toxics emission
inventory as input to the air
dispersion models ASPEN, and
an inhalation exposure model
(HAPEM5).
Total ozone at ground pixel
resolution from the Earth Probe/
TOMS satellite system.
EPA
EPA
o o
o >
conterminous
US, AK, HI,
Puerto Rico and
Virgin Islands
O o
®
E
CO
county
county
No Non-informative
NASA
NASA global
13x24 km
13 x 24km
at nadir
No
elsewhere
26
Air
Total Ozone -Aura/OMI
Total ozone at ground pixel
resolution from the Aura/OMI
satellite system.
NASA
NASA global
13 x 24km
13 x 24km
at nadir
No
Represented
elsewhere
27
Air
Total Ozone - Nimbus-7/TOMS
Total ozone at ground pixel
resolution from the Nimbus-7/
TOMS satellite system.
NASA
NASA global
13 x 24km
13 x 24km
at nadir
No
Represented
elsewhere
28
Air
Total Ozone -Aqua/AIRS
Total ozone at ground pixel
resolution from the Aqua/AIRS
satellite system.
NASA
NASA global
13 x 24km
13 x 24km
at nadir
No
Represented
elsewhere
29
Air
Total Ozone Daily Average
-Aura/OMI
Total ozone daily average at
ground pixel resolution from the
Aura/OMI satellite system.
NASA
NASA global
1 degree
1 degree
No
Represented
elsewhere
30
Air
Total Ozone - Aura/OMI
Total ozone at ground pixel
resolution from the Aura/OMI
satellite system.
NASA
NASA global
.25 degree
.25 degree
No
Represented
elsewhere
31
Air
Total Ozone daily averaged,
globally gridded
Daily average, globally gridded
ozone data from Nimbus-7/
TOMS, EarthProbe/TOMS, and
Meteor-3/TOMS
NASA
NASA global
1x1.25
degrees
1x1.25
degrees
No
Represented
elsewhere
32
Air
Total Ozone daily/weekly/
monthly average
Daily/weekly/monthly average,
globally gridded ozone data from
Aqua/AIRS satellite system.
NASA
NASA global
1x1.25
degrees
1x1.25
degrees
No
Represented
elsewhere
33
Air
Nitric Oxide (NO) Profiles
Nitric oxide (NO) profiles (mixing
ratios at different pressure
levels) from UARS/HALOE
platform, at pixel resolution.
NASA
NASA global
4 degrees
4 degrees
No
Represented
elsewhere
34
Air
Nitrous 0xide(N20) Profiles
Nitrous oxide (NO) profiles
(mixing ratios at different
pressure levels) from Aura/MLS
or Aura/HIRDLES platform
at pixel
solution
NASA
NASA
global
unsure
could not k ¦
locate No
Represented
elsewhere
35
Air
Nitrogen Dioxide (N02)
Profiles
Nitrogen dioxide (N02) profiles
(mixing ratios at different
pressure levels) from Aura/
HIRDLES (High Resolution
Dynamics Limb Sounder)
and UARS/HALOE (Upper
Atmospheric Research
Sate I lite)/( H a logen Occupation
Experiment) platform, at pixel
resolution.
NASA
NASA global
5 degrees
(500km)
5 degrees
(500km)
No
Represented
elsewhere
36
Air
Dinitrogen Pentoxide, N205
Profiles
Dinitrogen pentoxide(N205)
profiles at ground pixel resolution
from the Aura/HIRDLS (High
Resolution Dynamics Limb
Sounder) satellite platform, at
pixel resolution.
NASA
NASA global
5 degrees
(500km)
5 degrees
(500km)
No
Represented
elsewhere
A-33
-------
Air Domain
ID
O Q.
-c IE
co
5 o
c ^
5 o
Q M_
ra pa
O
-------
Air Domain
Q M-
ID
49
Air
50
Air
52
53
Air
Air
Air
Profiles of hydroxy I radical
(OH) (mixing ratios at different
Hydroxy I Radical (OH) Profiles pressure levels); pixel resolution
from the AURA/MLS (Microwave
Limb Sounder) satellite platform.
Profile of carbon monoxide
(CO), mixing ratios at different
pressure levels (ground pixel
resolution) from AQUA/AIRS
(Atmospheric Infrared Sounder)
and AURA/MLS (Microwave
Limb Sounder) satellite
platforms.
Methyl cyanide (CH3CN)
profiles, mixing ratios at different
pressure levels from AURA/
MLS (Microwave Limb Sounder)
satellite platform.
Hydrogen cyanide (HCN)
profiles (mixing ratios at different
pressure levels), pixel resolution
from AURA/MLS (Microwave
Limb Sounder) satellite platform.
Vertical and slant column
abundance of formaldehyde
in molecules/cm2 from the
O Q.
-c
co
5 o
c ^
§ °
NASA
ra ro
O
-------
Water Domain
O)
E
11
'£ "S
ID w m
BASE DATA
172 Water
173 Water
o "J
CD TO
< Q
National
Hydrography
Dataset (NHD)
National dataset of hydrography from different scales of data (Medium -
1:100,000; High - 1:24,000; Local - 1:5,000).
Improved attributes to NHD database. Point-in-time extract of NHD dataset to
NHDPIus - National generate new attributes and improved navigation tools. Improvements to the
176 Water
Hydrography
Dataset Plus
Aquifers - Principal
Aquifers
143 Water
National Wetlands
Inventory - State
download page
126 Water
National Flood
Hazard Layer
127 Water
USGS
EPA
USGS
O) o
O O
conterminous US, AK,
HI, and Puerto Rico,
Virgin Islands, and US
territories
USFWS USFWS
142 Water
167 Water
Coastal Barriers
Resource Systems
Bureau of
Reclamation
(BOR) Projects and
Facilities Database
CorpsMap -
National Inventory
of Dams
database will be maintained only in NHDPIus, whereas some information on
flow wi II be passed back to NHD for integration. Future extractions of NHD are
planned - but not on a regular basis.
The Principal Aquifers of the 48 Conterminous United States, Hawaii, Puerto
Rico, and the U.S. Virgin Islands map layer shows those aquifers that supply
ground water. For each geographic area, the aquifer shown is generally the
uppermost principal aquifer. Each principal aquifer is classified as one of six USGS USGS
types of permeable geologic material: unconsolidated deposits of sand and
gravel, semi consolidated sand, sandstone, carbonate rocks, interbedded
sandstone and carbonate rocks, or basalt and other types of volcanic rock.
GIS database providing information on the extent and status of the Nation's
wetlands. Each State data download is available as either a compressed file
Geodatabase or a Shapefile. Both files are compressed by using the .zip
format. The data is also available as a web mapping service (http://www.fws.
gov/wetlands/Data/WebMap Services. html)
Not all of the United States and U.S. Territories have been digitally mapped
by the U.S. Fish and Wildlife Service. Please refer to the Wetlands Mapper
Wetlands Data Availability layer to view where wetlands have been mapped.
Each download also includes a 'Public_Metadata' data layer that identifies
where and when wetlands were mapped within the state.
NOTE: Due to the variation in use and analysis of these data by the end user,
each of states wetlands data extends beyond the state boundary. Each state
includes wetlands data that intersect the 1:24,000 quadrangles that contain
part of that state (1:2,000,000 source data). This allows the user to clip the data
to their specific analysis datasets. Beware that two adjacent states will contain
some of the same data along their borders.
National Flood Hazard Layer (NFHL) dataset is a compilation of effective Digital
Flood Insurance Rate Map (DFIRM) databases (a collection of the digital data
that are used in GIS systems for creating new Flood Insurance Rate Maps)
and Letters of Map Change (Letters of Map Amendment and Letters of Map
Revision only) that create a seamless GIS data layer for a State or Territory.
It is updated on a quarterly basis. Note: Currently, not all areas of a State or
Territory have effective DFIRM data. As a result, users may need to refer to
the effective Flood Insurance Rate Map for effective flood hazard information.
Web mapping service is available at: https://hazards.fema.gov/femaportal/wps/
porta l/NFHLW MS
The John H. Chafee Coastal Barrier Resources System (CBRS) is a collection
of specific units of land and associated aquatic habitats that serve as barriers
protecting the Atlantic, Gulf, and Great Lakes coasts. Undeveloped coastal
barriers were mapped by the Department of the Interior using specific criteria,
and were then enacted by Congress as units of the CBRS. The affected areas
are delineated on maps enacted by Congress and entitled "John H. Chafee USFWS USFWS
Coastal Barrier Resources System." The CBRS currently includes 585 System
units, which comprise nearly 1.3 million acres of land and associated aquatic
habitat. There are also 271 "otherwise protected areas," a category of coastal
barriers already held for conservation purposes that include an additional 1.8
million acres of land and associated aquatic habitat.
The Projects and Facilities Database includes information on the major
Reclamation dams, as well as the Reclamation powerplants and projects. It
does not include information on some of the smaller diversion dams. Some USDOI USDOI
recreational areas at Reclamation facilities are managed by the National Park - BOR - BOR
Service. Additional recreational information can be found at
www, recreation, gov.
CorpsMap is the USACE nationwide enterprise GIS implementation. CorpsMap
is the single authoritative source for USACE national geospatial data assets.
CorpsMap consists of an operational geospatial database, an open interface,
and a web portal. CorpsMap supports data analysis and visualization using USACE USACE
a Web Browser, Google Earth, ESRI ArcGIS, C/JMTK, and other off-the-shelf
software. National Inventor of Dams is available through this webpage with
login.
FEMA/DHS FEMA/DHS
S 2 «
=
feature
No non-informative
Horizon
Systems Inc.
conterminous US, AK,
HI, and Puerto Rico
8 digit HUC No non-informative
conterminous US,
HI, Puerto Rico, and
Virgin Islands
region
No non-informative
conterminous US,
AK, HI, Puerto Rico,
Virgin Islands, and US
territories
quadrangle No non-informative
conterminous US,
AK, HI, Puerto Rico, ..
Virgin Islands, and US Cl^'coun^
territories
No non-informative
AL, CT, DE, FL, GA,
LA, ME, MD, MA, Ml,
MS, NJ, NY, NC, OH,
PR, Rl, SC, TX, VI,
VA, WI
not applicable No non-informative
western US
dam
No non-informative
conterminous US,
AK, HI, Puerto Rico, . .. ,. KI
¦ i . . I IC not applicable No
Virgin Islands, and US
territories
non-informative
A-36
-------
Water Domain
I I
"> 3?
ID
MISCELLANEOUS
Links to National
121 Water Geospatial
Datasets - NRCS
144 Water
156 Water
168 Water
171 Water
WATSTORE-
National Water
Data Storage and
Retrieval System
National Listing of
Fish Advisories
Army Geospatial
Center - Water
Resource
Information and
Links
Center for Human
Health Risk
(CHHR)-links to
project data
174 Water
WATERS
GeoServices
175 Water
WATERS Web
Mapping Services
Links to national datasets and data servers - common datasets used or
available to the public through NRCS including the Watershed Boundaries USDA
Data (WBD)
The US Geological Survey (USGS) National Water Data Storage and Retrieval
System (WATSTORE) consists of several files in which water data are grouped
and stored by common characteristics and data-collection frequencies. Files
are maintained for the storage of (1) surface-water, quality-of-water, and
ground-water data measured daily or more frequently, (2) annual peak values
and peaks above a base flow for stream flow stations, (3) chemical analyses USGS
for surface- and ground-water sites, (4) geologic and inventory data for
ground- water sites, and (5) water use summary data. In addition, an index file
station header file of sites for which data are stored in the system is maintained
in WATSTORE. This dataset has been integrated into other USGS on-line
datasets at the NWiS site.
The database includes all available information describing state-, tribal-, and
federally-issued fish consumption advisories in the United States for the 50
States, the District of Columbia, and four U.S. Territories, and in Canada for EPA
the 12 provinces and territories. The database contains information provided to
EPA by the states, tribes, territories and Canada.
Common Background Map (CMB) provides digital map and image data to
the Warfighter. CMB utilizes a comprehensive digital data library and custom
ArcGIS toolset designed to dramatically reduce the time and expense required
to acquire, manage and distribute geospatial data. CMB tools for ArcGIS
allow the generation of custom datasets to the user, ensuring that each users
mission requirements are met. Data is disseminated on CD, DVD or hard drive.
NOAA's National Center for Coastal Ocean Science (NCCOS's) Center for
Human Health Risk (CHHR) conducts research to understand and forecast
relationships between coastal ocean ecosystems and human health and make NOAA
information and tools available to managers and public health officials. Specific
projects may beuseful.
The WATERS services provide application friendly interfaces to complex
analyses. These services make extensive use of the NHD and indexed
program data in the RAD, and also integrate other WATERS program data in
selected services.
Web and Database Services
The WATERS Web and Database services provide open interfaces to complex
analyses. These services make extensive use of the NHD and referenced
program data in the RAD, and also integrate other WATERS program
data. Designed as modular units, the services are developed in a common
architecture
o "J
CD TO
< o
8 2 8
=
USDA
not available
not applicable No non-informative
USGS
EPA
USACE USACE
NOAA
conterminous US, AK,
and Hawaii
conterminous US,
AK, HI, Puerto Rico,
Virgin Islands, and US
territories
not applicable No
elsewhere
state
No
not available
not applicable No
coastal conterminous
US, AK, HI, Puerto
Rico
not applicable No
spatial
coverage
spatial
coverage
spatial
coverage
The WATERS Web Services use the Web Services Description Language
(WSDL) to describe the functions available in each service. The services also
utilize the Simple Object Access Protocol (SOAP) protocol to exchange XML
messages between client applications and the services.
The WATERS Database Services require users to be on the EPA network
and to have a TSMSS ID and schema password. Database Services provide
users with the advantage of deploying applications within EPA that can take
advantage of direct database call functionality
For services with more complex interfaces, a simplified interface has been
provided for use in testing and evaluation.
The EPA Office of Water has made available several Web Mapping Services
that contain nearly all of the NHD-based WATERS Program data, plus other
WATERS-related layers. The services are available for use from the EPA's
intranet and internet environments. The purpose of this help page is to describe
the contents and purpose of each mapping service.
Each service is provided in ESRI proprietary formats and Open Geospatial
Consortium open standards formats to enable the display and query of spatial
WATERS data. Non-developers can utilize these services using a variety
of mapping and GIS applications. Using such tools the WATERS mapping
services can be used as a data source, transparently integrated with other data
sources such as shapefiles or geodatabase layers.
EPA
EPA
not available
not applicable No
spatial
coverage
EPA
EPA
conterminous US not applicable No non-informative
A-37
-------
Water Domain
I I
"> 3?
177 Water
136 Water
125 Water
148 Water
150 Water
Agricultural
Research Service
Water Database
National Water
Information System
(NWIS) Water
Quality Web
Services
Reach Address
Environmental
Conservation
System Online
(ECOS)
National
Contaminant
Occurrence
Database (NCOD)
o =£- < £
The ARS Water Database is a collection of precipitation and stream flow data
from small agricultural watersheds in the United States. This national archive
of variable time-series readings for precipitation and runoff contains sufficient
detail to reconstruct storm hydrographs and hyetographs. There are currently
about 16,600 station years of data stored in the data base. Watersheds used
as study areas range from .2 hectare (0.5 acres) to 12,400 square kilometers
(4,786 square miles). USDA
Raingauge networks range from one station per watershed to over 200
stations. The period of record for individual watersheds vary from 1 to 50 years.
Some watersheds have been in continuous operation since the mid 1930's.
Various types of ancillary data are also maintained with the precipitation and
stream flow. These include air temperature, land management practices,
topography and soils information
USGS and EPA are working together to provide scientists and policy-makers
an easier way to integrate access to their large water-quality databases.
A common suite of web services allow for the automated sharing of water USGS
monitoring data via a common format and terminology. Initial web services are
now available.
Collection of EPA Water assessment programs (303d, 305b, TMDL, NPDES,
CWNS, CWSRF) associated with a single point-in-time extraction of the EPA
NHDPIus database. Extraction was completed in July of 2008.
The Environmental Conservation Online System (ECOS) is a gateway web
site that provides access to data systems in the Endangered Species and
Fisheries and Habitat Conservation program areas, as well as other FWS and
Government data sources. ECOS provides a central point of access to assist
FWS personnel in managing data and information as well as provide general
public access to information from numerous FWS databases.
EPA developed the NCOD to satisfy the statutory requirements set by
Congress in the 1996 amendments to the Safe Drinking Water Act (SDWA)
to maintain a national drinking water contaminant occurrence database using
samples data for both regulated and unregulated contaminants in public water
systems.
This site describes water sample analytical data that EPA is currently using
and has used in the past for analysis, rulemaking, and rule evaluation. EPA
The data have been checked for data quality and analyzed for national
representativeness.
NCOD data include the following:
Unregulated Contaminant Occurrence Data (listed separately in this database)
Six Year Review of National Drinking Water Regulations Ambient/Source Water
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8 2 8
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USDA
conterminous US not applicable No
spatial
coverage
USGS
EPA
conterminous US, AK, . .. ,, N/
HI, and Puerto Rico applicable Yes
conterminous US, AK,
HI, Puerto Rico, and not applicable No non-informative
Virgin Islands
USFWS USFWS
conterminous US, AK,
and Hawaii
not applicable No non-informative
EPA
conterminous US, AK,
and Hawaii
state
Yes
162 Water
Oceanographic
(Coastal) data
access at National
Oceanographic
Data Center
(NODC)
MODELED DATA
117 Water
122 Water
134 Water
The NODC archives and distributes global oceanographic data and
information. The data is used to preserve a historical record of the Earth's
changing environment for ocean climate research, and for operational
applications. NODC provides data products and services to scientists,
engineers, resource managers, policy makers, outdoor adventurers and
hobbyists, as well as other users in the United States and around the world. Of NOAA
interest is data from the National Coastal Data Development Center: National
Coastal Data Development Center - Pilot Project; Coastal Habitat; Coastal
Ocean Observing Systems (COOS); Coastal Risk Atlas;, Coastal Studies,
Information, & Data for the Ecosystem (C-SIDE); Gulf of Mexico Hypoxia
Watch; Harmful Algal Blooms Observing System (HABSOS)
NOAA
world, coastal US not applicable No non-informative
PRISM
- Parameter-
elevation
Regressions on
Independent Slope
Model
Drought Monitor
Data
Shallow ground
water and drinking-
water wells to
nitrate
Climate data (precipitation. Temperature, dewpoint, PPT%) - modeled using
station values
PRISM
Climate
Group
Downloadable shape files of weekly drought conditions and "impacts"
(Agriculture, water activities)
Nolan and Hitt (2006) developed these two national models to predict
contamination of ground water by nonpoint sources of nitrate. The nonlinear
approach to national-scale Ground-WAter Vulnerability Assessment
(GWAVA) uses components representing nitrogen (N) sources, transport, and
attenuation. Users should consult the individual metadata file for each data set
for details.
NOAA
USGS
PRISM
Climate
Group
NOAA
USGS
conterminous US not applicable No non-informative
conterminous US, AK,
HI, and Puerto Rico
not applicable Yes
conterminous US 1000m No ^emPora'
coverage
A-38
-------
Water Domain
I I
"> ^
138 Water
Estimate Use of
Water in the US
178 Water
Nutrient Loss
database for
Agricultural Fields
in the US
MONITORING DATA
101 Water
102 Water
103 Water
104 Water
105 Water
106 Water
107 Water
>. M
The U.S. Geological Survey's National Water-Use Information Program is
responsible for compiling and disseminating the nation's water-use data. The
USGS works in cooperation with local, State, and Federal environmental
agencies to collect water-use information. USGS compiles these data to
produce water-use information aggregated at the county, state, and national
levels. Every five years, data at the county level are compiled into a national
water-use data system and state-level data are published in a national circular.
The primary objective of this effort was to compile measured annual nitrogen
(N) and phosphorus (P)load and concentration data representing field-scale
transport from agricultural land uses.
The resulting publicly available data base provides:
1) measured nutrient load and concentration data and corresponding
watershed characteristics from numerous field-scale studies,
2) readily accessible, easily queried information to support water quality
management, modeling, and future research design, and
3) a platform allowing user input of additional project-specific data.
USGS
USGS
USDA
USDA
conterminous US
National Coastal
Beach Monitoring
web access page
Beach Monitoring
and Notification
data access page
Iowa Watershed
Monitoring
Assessment
USGS Real-Time
Surface Water Data
USGS Surface
Water Data - Daily
EPA
EPA
Monitoring of (ocean/great lakes) coastal beaches - data collected at the
national level and maintained and presented through this web page.
Data submitted to EPA for beach monitoring program by state - stored in EPA
PRAWN database (PRAWN is the PRogram tracking, beach Advisories, Water
quality standards, and Nutrients database. PRAWN stores beach advisory and
closing data.)
Web site listing numerous water monitoring programs within the state stored in
EPA STORET database format. Information no beach monitoring is available IA
for the state of Iowa and is located on this page.
Daily Stream flow Conditions from National Water Information System (NWIS). USGS
Stream flow Conditions from National Water Information System (NWIS).
USGS
Daily Stream flow Conditions from National Water Information System (NWIS) - ygQg
statistics calcu lated for dai ly/month ly/annual
USGS Surface
Water Data
- Statistics
USGS Surface
Water Data
- Peak-flow
USGS Surface
Water Data - Field Periodic manual measurements used to supplement or verify the time-series
measurements from National Water Information System (NWIS)
Peak-flow (annual maximum) Conditions from National Water Information
System (NWIS)
USGS
IA
USGS
USGS
USGS
USGS
™ o a
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Water Domain
E
11
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I « S
= &r
118 Water
SNOTEL-
Snowpack
Telemetry
119 Water Snow Course
USDA
USDA
123 Water
Active Groundwater
Level Network
USGS
145 Water
146 Water
170 Water
National
Atmospheric
Deposition
Program - National
Trends Network
(NTN)
National
Atmospheric
Deposition
Program - Mercury
Deposition Network
(MDN)
Phytoplankton
Monitoring Network
¦a ~ w >
ro ± cn >
Q < O
The Natural Resources Conservation Service (NRCS) installs, operates, and
maintains an extensive, automated system to collect snowpack and related
climatic data in the Western United States called SNOTEL (for SNOwpack
TELemetry). The system evolved from NRCS's Congressional mandate in
the mid-1930's "to measure snowpack in the mountains of the West and
forecast the water supply." The programs began with manual measurements
of snow courses; since 1980, SNOTEL has reliably and efficiently collected the
data needed to produce water supply forecasts and to support the resource
management activities of NRCS and others.
Permanent sites where manual measurements of snow depth and snow water
equivalents are taken. Reports on snowpack at the state and basin level are
created from these data. Part of the National Water and Climate Center of the
Natural Resources Conservation Service (NRCS).
Ground water level and well information for 20,000 wells- current condition
only. The Active Groundwater Level Network contains water levels and well
information from more than 20,000 wells that have been measured by the
USGS or USGS cooperators at least once within the past 365 days. This
network includes all of these wells, regardless of measurement frequency,
aquifer monitored, or the monitoring objective.
The U.S. Geological Survey has a database/archive of about 850,000 wells
across the Nation. Information about these wells is available to the world
via NWISWeb. Through various groundwater programs, the USGS actively
measures water levels in about 20,000 of these wells each year. These wells
are measured for a variety of disparate purposes, such as statewide monitoring
programs, or more local effects like monitoring well drawdown, hydrologic
research, aquifer tests, or even earthquake effects on water levels.
The National Atmospheric Deposition Program/National Trends Network
(NADP/NTN) is a nationwide network of precipitation monitoring sites. The
network is a cooperative effort between many different groups, including
the State Agricultural Experiment Stations, U.S. Geological Survey, U.S.
Department of Agriculture, and numerous other governmental and private
entities. For a full list of contributors, see the collaborating agencies page. The
NADP/NTN has grown from 22 stations at the end of 1978, our first year, to
over 250 sites spanning the continental United States, Alaska, and Puerto Rico,
and the Virgin Islands.
The objective of the MDN is to develop a national database of weekly
concentrations of total mercury in precipitation and the seasonal and annual
flux of total mercury in wet deposition. The data will be used to develop NADP
information on spatial and seasonal trends in mercury deposited to surface
waters, forested watersheds, and other sensitive receptors.
Phytoplankton Monitoring Network (PMN) is an outreach program with the
ultimate goal of linking laboratory scientists to the general public. Traditionally
scientists rarely interacted with the public they serve. The link PMN provides,
fosters a more informed public while providing qualitative data to scientists.
O Q_ 1-
USDA
USDA
western US
western US
not applicable No
not applicable No
spatial
coverage
spatial
coverage
USGS
conterminous US, AK,
HI, and Puerto Rico
not applicable No
spatial
coverage
NADP
NADP
conterminous US not applicable Yes
NOAA
REPORTED DATA
120 Water
Water Supply
Forecasting
Service - multiple
1) Water Supply Forecasting, 2) Reservoir reporting, 3) Surface Water Supply
Index - Modelled data.
USDA
NADP
NOAA
USDA
conterminous US, AK not applicable Yes
coastal conterminous . .
US,AK, HI, Puerto not applicable No temPoral
rICq coverage
western US
basins No
spatial
coverage
124 Water
147 Water
159 Water
160 Water
Safe Drinking
Water Information
System
Drinking water information - public health risks. The Safe Drinking Water
Information System (SDWIS) federal and state databases contain information
submitted by states, EPA regions, and public water systems in conformance
with reporting requirements established by the Safe Drinking Water Act
(SDWA) and related regulations and guidance. EPA
EPA
Fish Passage
Decision Support
System
COAST
(Coastal Ocean
Assessments,
Status, and Trends)
Mussel Watch
Contaminant
Monitoring Program
COAST
(Coastal Ocean
Assessments,
Status, and
Trends) Bioeffects
Assesment Project
States supervise the drinking water systems within their jurisdictions to ensure
that each public water system meets state and EPA standards for safe drinking
water. SDWA requires states to report drinking water information periodically to
EPA; this information is maintained in the federal database, SDWIS/FED
The dataset was created to identify barrier locations across the United States
and to support modeling/report activities on the Fish Passage Support System USFWS USFWS
website (http://ecos.tws.gov/fpdss).
conterminous US, AK,
HI, Puerto Rico, and not applicable Yes
Virgin Islands
conterminous US barrier site No non-informative
Mussel Watch represents the longest running continuous contaminant
monitoring program in U.S. coastal and Great Lakes waters. The project was j^qaa
developed to analyze chemical and biological contaminant trends in sediments
and bivalve tissues collected at over 300 coastal sites from 1986 to present.
The Bioeffects program is a nationwide program of environmental assessment
and related research designed to describe the current status of environmental
quality in our Nation's estuarine and coastal areas. Over thirty multidisciplinary NOAA
project studies have been carried out since 1991 in close cooperation or in
partnership with coastal states or regional organizations.
NOAA
NOAA
coastal conterminous
US, AK, HI, Puerto site
Rico
coastal conterminous
US, AK, HI, Puerto site
Rico
No non-informative
No non-informative
A-40
-------
Water Domain
I I
">
161 Water
|«S
= & r
C31 ?
< o
O Q- i-
165 Water
Fisheries
Information System
(FIS) - National
Marine Fisheries
Service
National Outbreak
Reporting System
NOAA
166 Water
Centers for
Disease Control
and Prevention
(CDC)-Water-
related Health Data
National Pollutant
149 Water
Elimination System
(NPDES)
154 Water
303(d) Listed
Impaired Waters -
WATERS Database
The Fisheries Information System (FIS) provides a context for the design,
development, and implementation of data collection and data management for
fishery-dependent statistics nationwide to improve the timeliness and accuracy
of data. FIS is a portal that identifies the existing federal and state fisheries
information systems or databases (data collections) and provides integrated
business solutions for effective information sharing.
The National Outbreak Reporting System (NORS) is a web-based platform
designed to support reporting of waterborne, foodborne, enteric person-to-
person, and animal contact-associated disease outbreaks to CDC by state
and territorial public health agencies. NORS launched in 2009 following a four
year commitment by CDC to the planning, development, and launch phases of
the project. CDC developed NORS for waterborne disease outbreak reporting
in collaboration with the Council for State and Territorial Epidemiologists
(CSTE) and the Environmental Protection Agency (EPA) to improve the quality,
quantity, and availability of data submitted to the Waterborne Disease and
Outbreak Reporting System (WBDOSS).
This list provides links to various health related data (surveillance summaries,
outbreak reports, and surveillance systems) for potentially waterborne or
water-related diseases and injuries. Data on all nationally notifiable diseases
in the U.S. are summarized annually in the MMWR (Measles Mumps, Rubella,
Varicella Vaccine Safety) and can be accessed at http://www.cdc.gov/ncphi/ CDC
disss/nndss/annsum/index.htm. Surveillance system and data links have
been chosen because the information is electronically accessible. Outbreaks
cited are those documented in CDC publications (MMWR, Emerging Infectious
Diseases (EID)) which are not copyrighted.
I CIS - Integrated Compliance. The Integrated Compliance Information System
(ICIS) provides a database that, when complete, will contain integrated
enforcement and compliance information across most of EPA's programs.
The vision for I CIS is to replace EPA's independent databases that contain
Enforcement data with a single repository for that information. Currently, ICIS
contains all Federal Administrative and Judicial enforcement actions. A future
release of ICIS will replace the Permit Compliance System (PCS) which
supports the National Pollutant Discharge Elimination System (NPDES) and
will integrate that information with Federal actions already in the system. This
file contains the subset of NPDES Majors that have thus far been integrated
into ICIS. The site contains technical and regulatory information about the
NPDES permit program. The NPDES Permits Program consists of a number
of programs and initiatives. Links to each of these programs and initiatives are
located on the left navigational bar.
For information on specific facilities with NPDES permits, you can visit EPA's
Envirofacts Warehouse. Simple searches can be done by clicking on "Water"
and entering your zip code. You can also view a map with NPDES facilities
by clicking on "Maps" and choosing "EnviroMapper." Finally, advanced search
capabilities can be found under "Queries" and selecting "PCS."
For information on the compliance and enforcement status of facilities with
NPDES permits, you can visit EPA's Enforcement and Compliance History
Online (ECHO)
River segments, lakes, and estuaries designated under Section 303(d) of
the Clean Water Act. Each State will establish Total Maximum Daily Loads
(TMDLs) for these waters. 303(d) Waterbodies are coded onto route.rch
(Transport and Coastline Reach) feature of NHD to create Linear and Point
Events. Point events are attached to a reach in NHD to represent a TMDL
for many reasons: to represent an estuary, to represent a shellfish area (if
state preferred to represent the TMDL in this manner) - refer to NOAA's
shellfish areas for a more accurate representation (http://state-of-coast.noaa.
gov/bu I leti n s/htm l/sgw_04/sgw. htm I). Point events represent point source
NOAA
not available
not applicable No
spatial
coverage
CDC
not available
not applicable No non-informative
CDC
not available
not applicable No non-informative
EPA
EPA
conterminous US,
AK, HI, Puerto Rico,
Virgin Islands, and US
territories
site
Yes
EPA
EPA
conterminous US, AK,
and Hawaii
feature
Yes
dischargers, or, if there is no reach in NHD, they are used to represent the
TMDL. 303(d) Waterbodies are coded onto NHD Waterbody reaches (region,
rch) to create Waterbody Shapefiles. In addition to NHD reach indexed data
there may also be custom shapefiles (point, line, or polygon) that are not
associated with NHD and are in an EPA standard format that is compatible
with EPA's Reach Address Database. These custom shapefiles are used to
represent locations of 303(d) waterbodies that are not represented well in NHD.
A-41
-------
Water Domain
ep.tr o
2=i- Q
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ID LU ^ Q Q
State (also includes DC, tribes, and territories; i.e., "jurisdictions") Water
Quality Standards' Designated Uses for river segments, lakes, and estuaries.
The Water Quality Standards' Designated Uses are able to be linked to tables
of water quality criteria which are located in EPA's Water Quality Standards
Database. Water Quality Standards coded onto route.rch (Transport and
Coastline Reach) feature of the National Hydrography Dataset (NHD) to create
^ w t Linear an^ P°'nt Events. Point events are attached to a reach in NHD for
w t n i t <^t H H manVreasons: to represent an estuary; represent a drinking water intake point;
3 eP watfr^ n" +a[-jS or represent a designated use when there is no reach in NHD to represent
a a ase ^ reac^ y\/a^er Quality Standards coded onto the National Hydrography
Dataset (NHD) Waterbody Reaches (region.rch) to create Waterbody
Shapefiles. In addition to NHD reach indexed data there may also be custom
shapefiles (point, line, or polygon) that are not associated with NHD and
are in an EPA standard format that is compatible with EPA's Reach Address
Database. These custom shapefiles are used to represent Water Quality
Standards that are not represented well in NHD.
SURVEY/STUDY DATA
140 Water
141 Water
139 Water
163 Water
164 Water
179 Water
180 Water
EMAP National
Coastal
Assessment (NCA)
- Estuaries data
Mid-Atlantic
Integrated
Assessment
(MAIA)
Wadeable Streams
Assessment
National Lake Fish
Tissue Study
National Aquatic
Resource Surveys
EMAP West
- Ecological
Assessment of
Western Streams
and Rivers
Environmental
Monitoring and
Assessment
Program (EMAP) /
Aquatic Resource
Monitoring (ARM)
Program
w >
< O
O TO
CD TO
< O
CD O
O O
EPA
EPA
conterminous US, AK,
and Hawaii
feature
Yes
To answer broad-scale questions on environmental conditions, EMAP and
its partners have collected estuarine and coastal data from thousands of
stations along the coasts of the continental United States. EMAP's National
Coastal Assessment comprises all the estuarine and coastal sampling done
by EMAP beginning in 1990. This coverage includes the sampling done in the
biogeographic provinces as well as data from the Regional EMAP (REMAP)
studies done by EPA Regional Offices. These data can be retrieved and
stations mapped from applications under NCA Data.
Data on Estuaries, Surface Waters, Landscape Ecology, Forests, and Agro-
ecosystems. See documentation for all variables surveyed.
The Wadeable Streams Assessment: A Collaborative Survey of the Nation's
Streams (WSA) is a first-ever statistically-valid study of the biological condition
of small streams throughout the U.S. It establishes a national baseline we
can use to compare to results from future studies. This information will help
us evaluate the successes of our national efforts to protect and restore water
quality.
EPA conducted a national freshwater fish contamination survey to estimate the
national distribution of selected persistent, bioaccumulative and toxic chemical
residues in fish tissue from lakes and reservoirs in the lower 48 states. This
four-year study provides the first national estimates of mean concentrations
for 268 chemicals in lake fish, defines a national fish contamination baseline
to track progress of pollution control activities, and identifies areas where
contaminant levels are high enough to warrant further investigation.
The U.S. Environmental Protection Agency, states, and tribes are conducting a
series of national aquatic resource surveys. Often referred to as probability-
based surveys, these studies report on core indicators of water condition
using standardized field and lab methods. The surveys include a national
quality assurance program and are designed to yield unbiased, statistically-
representative estimates of the condition of the whole water resource (such as
rivers and streams, lakes, ponds, and reservoirs, wetlands, etc).
This statistical summary reports data from the Environmental Monitoring and
Assessment Program (EMAP) Western Pilot (EMAP-W). EMAP-W was a
sample survey (or probability survey, often simply called 'random') of streams
and rivers in 12 states of the western U.S. (Arizona, California, Colorado,
Idaho, Montana, Nevada, North Dakota, Oregon, South Dakota, Utah,
Washington and Wyoming), comprising the conterminous portions of EPA
Regions 8, 9 and 10. The eventual objective of EMAP-W is to assess the
ecological condition of, and relative importance of, stressors in streams and
rivers of the West at multiple scales.
The Environmental Monitoring and Assessment Program (EMAP) is a
research program to develop the tools necessary to monitor and assess the
status and trends of national ecological resources (see EMAP Research
Strategy). EMAP's goal is to develop the scientific understanding for translating
environmental monitoring data from multiple spatial and temporal scales into
assessments of ecological condition and forecasts of the future risks to the
sustainability of our natural resources. EMAP's research supports the National
Environmental Monitoring Initiative of the Committee on Environment and
Natural Resources (CENR).
EPA
EPA
EPA
EPA
EPA
EPA
EPA
EPA
coastal conterminous . .. ,, M temporal
MO not applicable No r
US rr coverage
Mid-Atlantic - EPA . .. M spatial
n • 0 not applicable No r
Region 3 coverage
conterminous US state
No
spatial
coverage
conterminous US feature No non-informative
EPA
EPA
conterminous US state
No
temporal
coverage
EPA
EPA
western US
state
No
spatial
coverage
EPA
EPA
western US
state
No
spatial
coverage
A-42
-------
Water Domain
E
S E
129 Water
130 Water
131 Water
132 Water
133 Water
135 Water
137 Water
151 Water
152 Water
153 Water
National Water-
Quality Assessment
(NAWQA)
Program - Quality
of Domestic Wells
Study
National Water-
Quality Assessment
(NAWQA) Program
- Source Water-
Quality Assessment
Program (SWQA)
National Water-
Quality Assessment
(NAWQA) Program
- Health Based
Screening Levels
National Water-
Quality Assessment
(NAWQA) Program
- Pesticide National
Synthesis Project
National Water-
Quality Assessment
(NAWQA) Program
- Nutrients National
Synthesis Project
NAWQA-
Volatile Organic
Compounds
in the Nation's
Groundwater and
Drinking-Water
Supply Wells
National Water-
Quality Assessment
(NAWQA) Program
- Trace Element
National Synthesis
Project
Unregulated
Contaminant
Monitoring Data
- States Round 1
and 2
Unregulated
Contaminant
Monitoring Rule 1
(UCMR 1)
Unregulated
Contaminant
Monitoring Rule2
(UCMR 2)
w >
< O
O TO
CD TO
< o
= 2
® o a
E o
woo;
This study from the National Water-Quality Assessment (NAWQA) Program
of the U.S. Geological Survey (USGS) assesses water-quality conditions
for about 2,100 domestic wells across the United States. As many as 219
properties and contaminants, including pH, major ions, nutrients, trace
elements, radon, pesticides, and volatile organic compounds, were measured.
Fecal indicator bacteria and additional radionuclides were analyzed for a
smaller number of wells. The large number of contaminants assessed and the
broad geographic coverage of the present study provides a foundation for an
improved understanding of the quality of water from the major aquifers tapped
by domestic supply wells in the United States.
The primary objective of SWQAs is to determine the occurrence of about
280 primarily unregulated anthrop ogenicorganic compounds in source water
used by community water systems. Source water is the raw (ambient) water
collected at a supply well or surface-water intake prior to water treatment used
to produce finished water. A secondary objective is to understand occurrence
patterns in source water and determine if these patterns also occur in finished
water prior to distribution. The NAWQA Program is planning as many as 30
surface-water and 30 ground-water assessments through 2013. The findings
are not intended to comprehensively portray the quality of our Nation's source
waters owing to the relatively small number of water supplies studied. They are,
however, intended to improve understanding of ambient resource conditions in
a drinking-water-supply context.
This is a tool to extract information on contaminant levels detected in water
resources. To supplement existing Federal drinking-water standards and
guidelines, USGS began a collaborative project with the U.S. Environmental
Protection Agency (USEPA), New Jersey Department of Environmental
Protection (NJDEP), and Oregon Health & Science University (OHSU) to
calculate Health-Based Screening Levels (HBSLs). HBSLs were calculated for
contaminants that do not have USEPA Maximum Contaminant Levels.
A decadal assessment by the National Water-Quality Assessment (NAWQA)
Program of the United States Geological Survey (USGS) provides the most
comprehensive national-scale analysis to date of pesticide occurrence and
concentrations in streams and ground water, based on results from studies
completed during 1992-2001. Among the major findings are that pesticides are
frequently present in streams and ground water, are seldom at concentrations
likely to affect humans, but occur in many streams at concentrations that may
have effects on aquatic life or fish-eating wildlife.
Nutrient studies portion of NAWQA decade long research. Some studies
include: Nutrient trends, Nitrate change in ground water, Nutrients in streams,
Vulnerability to nitrates, Drinking-water nitrate and health. This webpage is
a link to these research studies. Data can be retrieved through NWIS or by
contacting the authors.
Volatile organic compounds VOCs are produced in large volumes and are
of concern in water resources because of their potential toxicity to humans,
in part via exposure from drinking water. The general purpose of the VOC
National Synthesis of the NAWQA Program is to expand knowledge about
the occurrence of this group of organic compounds in the environment with
emphasis on ground water, surface water, and water withdrawn for supply of
drinking water. The synthesis is unique in that its focus is on ambient water
resources rather than extensively contaminated release sites. Furthermore, the
synthesis fills important voids of information about VOCs for the Nation's major
aquifers and urban streams.
Arsenic concentration data for 20,043 ground-water samples. Data compiled
using the same methods and criteria as for USGS Publication: WRI 99-4279,
but extending the sampling dates until March 30, 2000
EPA uses the Unregulated Contaminant Monitoring Regulation (UCMR) to
collect data for contaminants suspected to be present in drinking water, but
do not have health-based standards set under the Safe Drinking Water Act
(SDWA). The data assist the Administrator in determining whether or not to
regulate those contaminants
The regulation for the first cycle of the Unregulated Contaminant Monitoring
Rule (UCMR 1), covering the period 2001 - 2005, was published in the Federal
Register September 17,1999 for a list of 26 contaminants. UCMR1 was a
redesign of the original UCM Program, and incorporated a tiered monitoring
approach along with EPA implementation. UCMR1 had Assessment Monitoring
(List 1) and Screening Survey (List 2) components.
The Unregulated Contaminant Monitoring Regulation supporting the second
cycle (UCMR2) of monitoring, conducted under EPA oversight, was published
in the Federal Register on January 4, 2007. The UCMR2 requires monitoring
for 25 contaminants using five analytical methods.
USGS
USGS
well locations
conterminous US, AK given at
county level
No
spatial
coverage
USGS
USGS
conterminous US not applicable No
spatial
coverage
USGS
USGS
not available not applicable No non-informative
USGS
USGS
USGS
USGS
conterminous US not applicable No
spatial
coverage
conterminous US, AK, . .. ,, M temporal
. u not applicable No r
and Hawaii coverage
USGS
USGS
conterminous US, AK,
and Hawaii
site
No
spatial
coverage
USGS
EPA
EPA
EPA
USGS
EPA
EPA
EPA
conterminous US, AK, . .. ,, M spatial
lii rt n ^ n- not applicable No r
HI, and Puerto Rico coverage
conterminous US state
conterminous US state
No
No
spatial
coverage
spatial
coverage
conterminous US, AK,
and Hawaii
state
No temporal
coverage
A-43
-------
Water Domain
I I
"> ^3
^ Ic
>> M
CD o
O >
o o
e> o
O Q. ^
169 Water
National Estuarine
Eutrophication
Assessment
(NEEA)
National Stream
The National Estuarine Eutrophication Assessment (NEEA) is a joint initiative
between the NOAA National Centers for Coastal Ocean Science (NCCOS)
and the Integration and Application Network (IAN). The NEEA Update is part
of the NEEA Program (Program Guidance Document) and uses the ASSETS
(Assessment of Estuarine Trophic Status) methodology which is a collaborative NOAA
effort between NOAA and the Portuguese Institute of Marine Research (I MAR).
The ASSETS methodology is designed improve upon the assessment of the
1999 U.S. National Estuarine Eutrophication Assessment (NEEA) to make it
more accurate and more broadly applicable to different types of systems.
The objectives and scope of the NASQAN program have changed several
times since its beginnings in 1973 to reflect changes in funding, technology,
and societal priorities and needs. The latest design for NASQAN was
implemented in October 2007. Under this design, the major objective of the
NASQAN program is to report on the concentrations and loads of selected
U of
Maryland
Integration
and
Application
Network
141 coastal US
estuaries
site
No non-informative
-no xa/ + Quality Accounting constituents delivered by major rivers to the coastal waters of the United States . ,cnc ,ICnc conterminous US, AK, . r .. M f
128 Water M . , . ¦ . . ¦ ¦ . . . ¦¦1 ¦ ¦ ¦, ¦ . . . . . ... USGS USGS ... not applicable No non-informative
Motuiniv anH colorton inlann ciih.hacinc in nnnrin/ riwor hacinc tn notormino tho cm imoc and Hawaii
Network and selected inland sub-basins in priority river basins to determine the sources
(NASQAN) and relative yields of constituents within these basins. These priority basins
have significant management interest in reducing delivery of constituents that
contribute to adverse conditions in receiving waters. Other objectives include
monitoring for climate change and describing long-term trends in the loads and
concentrations of select constituents at key locations.
The CWNS is conducted in response to Sections 205(a) and 516 of the Clean
Water Act. The CWNS is a comprehensive assessment of the capital needs to
meet the water quality goals set in the Clean Water Act. Every four years, the
states and EPA collect information about:
Clean Watersheds Publicly owned wastewater collection and treatment facilities
158 Water Needs Survey EPA
(CWNS) Stormwater and combined sewer overflows (CSOs) control facilities
Nonpoint source (NPS) pollution control projects
Decentralized wastewater management
Estuary management projects
Nutrient pollution, especially from nitrogen and phosphorus, has consistently
Water Quality ranked as one of the top causes of degradation in some U.S. waters for more
Criteria for Nitrogen than a decade. Excess nitrogen and phosphorus lead to significant water
and Phosphorus quality problems including harmful algal blooms, hypoxia and declines in
Pollution wildlife and wildlife habitat. Excesses have also been linked to higher amounts
of chemicals that make people sick.
EPA
conterminous US, AK,
HI, Puerto Rico, and not applicable No non-informative
Virgin Islands
157 Water
EPA
EPA
conterminous US, AK,
HI, Puerto Rico, and state
Virgin Islands
No
temporal
coverage
A-44
-------
Land Domain
ID m s
AGRICULTURE
159
Land
160 Land
161 Land
162 Land
185 Land
186 Land
187 Land
191 Land
192 Land
193 Land
i 5
O
1997 County
Pesticide Use
Estimates for 220
Compounds
1992 County
Pesticide Use
Estimates for 200
Compounds
Grids of Agricultural
Pesticide Use in the
Conterminous US
1997
Grids of Agricultural
Pesticide Use in the
Conterminous US
1992
Potential Priority
Watersheds for
Protection of Water
Quality from Nonpoint
Sources Related to
Agriculture
Potential Priority
Watersheds for
Protection of
Water Quality from
Contamination by
Manure Nutrients
Manure Nutrients
Relative to the
Capacity of Cropland
and Pastureland to
Assimilate Nutrients:
Spatial Trends for
the US
2007 Census of
Agriculture Full
Report
2002 Census of
Agriculture Full
Report
Agriculture Census
of the United States
-2002
USGS
USGS
NRCS
This dataset includes information for 220 pesticides
on the average amount (pounds) applied to 87
agricultural crops and the acres of crops treated
for counties within the conterminous United States.
These data were derived by combining published
state pesticide use coefficients published by the
National Center for Food and Agricultural Policy
(NCFAP) and county harvested crop acres available
from the 1997 Census of Agriculture.
This dataset includes information for 200 pesticides
on the average amount (pounds) applied to 87
agricultural crops and the acres of crops treated
for counties within the conterminous United States.
These data were derived by combining published
state pesticide use coefficients published by the
National Center for Food and Agricultural Policy
(NCFAP) and county harvested crop acres available
from the 1992 Census of Agriculture.
This spatial dataset consists of 219 1-kilometer (km)
resolution grids depicting estimated agricultural use
of 219 pesticides in 1997 for the conterminous United
States.
This spatial dataset consists of 199 1-kilometer (km)
resolution grids depicting estimated agricultural use USGS
of 199 pesticides in 1992 for the conterminous United
States.
National maps were developed to assist decision-
makers in identifying priority watersheds for water
quality protection from nonpoint sources related
to agriculture. The purpose of these maps is to
systematically identify where the greatest potential
exists for water pollution based on factors known to
be important influences on soil and chemical loss
from farm fields, such as climate, soil characteristics,
and pesticide and nitrogen loadings from agricultural
sources. The basis for the analysis is 2,105 8-digit
hydrologic units, or watersheds, in the 48 States
(910,000 acres average size).
National maps were developed to assist decision-
makers in identifying priority watersheds for water
quality protection from contamination by manure
nutrients. Manure applied to the land is susceptible to
leaching and runoff, and can be a significant source of
contamination of groundwater and surface water.
Data from the Census of Agriculture were used
to estimate livestock populations, quantities of
manure produced, and land available for manure
application for 1982,1987,1992, and 1997. Livestock
include beef cattle, dairy cattle, swine, and poultry.
A descriptive analysis is presented of the temporal
and spatial changes in the number, size, and kind of
livestock operations, and the changes in animal units,
quantity of manure nutrients produced, land available
for manure application, and excess manure nutrients
at both the farm level and the county level.
A comprehensive summary of agricultural activity
is provided for the United States and each of the
50 states. This report includes number of farms by
size and type, inventory and values for crops and
livestock, operator characteristics and much more.
A comprehensive summary of agricultural activity
is provided for the United States and each of the
50 states. This report includes number of farms by
size and type, inventory and values for crops and
livestock, operator characteristics and much more.
The census provides comprehensive, uniform data
about America's farms and farmers. American
agriculture is counted, measured, priced, analyzed,
and reported to provide the facts needed by farmers
and ranchers, agribusiness, policymakers, farm
organizations, and state and local agencies.
USGS
conterminous US
ro o
o c
O O)
a u)
0 £
1 i
ra ¦+--
E "E
CO ID
county
not applicable NO
Temporal
coverage
USGS USGS conterminous US county
not applicable NO Temporal
coverage
USGS
USGS
conterminous US 1km cell
conterminous US 1km cell
1km
1km
NO
NO
Temporal
coverage
Temporal
coverage
NRCS
conterminous US ,
hydrologic unit
not applicable NO Non-informative
NRCS
NRCS
conterminous US
8-digit
hydrologic unit
not applicable NO Non-informative
NRCS
NRCS
conterminous US,
AK, HI, and US county
territories
not applicable NO , ,
^ elsewhere
USDA
USDA
USDA
USDA
USDA
USGS
conterminous US,
AK, and HI
conterminous US,
AK, and HI
conterminous US,
AK, and HI
county
county
not applicable NO Temporal
coverage
not applicable YES N/A
county not applicable NO .K ,
3 rr elsewhere
A-45
-------
Land Domain
ID
194
195
153
118
119
120
121
122
123
124
125
126
127
128
129
130
131
re
c
CD
E
° i
>|
lu nn
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
^ In
>> w
Z7) j»
< O
a> is
U) re
< Q
National Pesticide
Use Database - 2002
Dun and Bradstreet
Agriculture Data
California Pesticide
Use Reporting (PUR)
Database
The National Pesticide Use Database (2002),
compiled by the Crop Protection Research Institute of
the CropLife Foundation, contains quantitative data
on the use of fungicides, herbicides, insecticides, and
other pesticides in US crop production.
This data layer is made up of agricultural data
licensed from Dun & Bradstreet for the entire United
States. These data include information about both
crops and livestock.
Pesticide use data for California.
CropLife CropLife
Foundation Foundation
9- CT)
ra re
o Q
o >
a> o
O O
conterminous US,
AK, and HI
-
O O
O (/>
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= £*
re •*->
E "E
state
not applicable YES
N/A
Cropland Data Layer
(CDL) - Arkansas
Cropland Data Layer
(CDL) - California
Cropland Data Layer
(CDL) - Colorado
Cropland Data Layer
(CDL) - Connecticut
Cropland Data Layer
(CDL) - Delaware
Cropland Data Layer
(CDL)-Florida
Cropland Data Layer
(CDL)-Idaho
Cropland Data Layer
(CDL)-Illinois
Cropland Data Layer
(CDL) - Indiana
Cropland Data Layer
(CDL) - Iowa
Cropland Data Layer
(CDL) - Kansas
Cropland Data Layer
(CDL) - Kentucky
Cropland Data Layer
(CDL) - Louisiana
Cropland Data Layer
(CDL) - Maryland
132 Land
Cropland Data Layer
(CDL) - Michigan
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
EPA
EPA
conterminous US,
AK, and HI
point not applicable NO Represented
r ^ elsewhere
California California
Department Department
of Pesticide of Pesticide
Regulation Regulation
USDA USDA
CA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
AR
CA
CO
CT
DE
FL
KS
KY
LA
MD
individual land
parcels
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
not applicable NO Spatial coverage
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
A-46
-------
Land Domain
> -o
ID
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Land
Cropland Data Layer
(CDL) - Minnesota
Cropland Data Layer
(CDL) - Mississippi
Cropland Data Layer
(CDL) - Missouri
Cropland Data Layer
(CDL) - Montana
Cropland Data Layer
(CDL)-Nebraska
Cropland Data Layer
(CDL) - New Jersey
Cropland Data Layer
(CDL)-New York
Cropland Data
Layer (CDL) - North
Carolina
Cropland Data Layer
(CDL) - North Dakota
Cropland Data Layer
(CDL)-Ohio
Cropland Data Layer
(CDL) - Oklahoma
Cropland Data Layer
(CDL) - Oregon
Cropland Data Layer
(CDL) - Pennsylvania
Cropland Data Layer
(CDL)-Rhode Island
Cropland Data Layer
(CDL) - South Dakota
Cropland Data Layer
(CDL) - Texas
Cropland Data Layer
(CDL) - Virginia
Cropland Data Layer
(CDL) - West Virginia
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
The USDA, NASS (National Agricultural Statistics
Service) Cropland Data Layer (CDL) is a raster,
geo-referenced, crop-specific land cover data layer
with a ground resolution of 56 meters.
jd 3 U) "ra
< O < Q
USDA USDA
O o
o >
a> o
o o
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
USDA
MS
MO
MT
NE
NJ
NY
NC
ND
OH
USDA USDA OK
USDA USDA OR
USDA USDA PA
USDA USDA Rl
USDA USDA SD
USDA USDA TX
USDA USDA VA
USDA USDA WV
o o
O (/>
O 2
= £*
re •*->
E "E
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m cell
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
60m
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
NO Spatial coverage
A-47
-------
Land Domain
ra
"c
a>
E
c
o
¦> IS
ID
Q
1C;1 , . Cropland Data Layer
(CDL) - Washjngton
1
Q
o
O
O
WA
Wl
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
conterminous US,
AK, HI, and US
territories
ro 5
5? =
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E "E
60m cell
60m cell
point
point
point
point
point
point
point
60m
60m
NO Spatial coverage
NO Spatial coverage
not applicable YES
not applicable YES
not applicable YES
not applicable YES
not applicable YES
not applicable YES
not applicable YES
N/A
N/A
N/A
N/A
N/A
N/A
N/A
US National
Library of
Medicine
conterminous US,
AK, and HI
point
not applicable YES N/A
101
102
108
109
... . R Mineral Resources Data System (MRDS) describes
Land n "I61? +eS°/I?nne\ metallic and nonmetallic mineral resources throughout USGS
Data System (MRDS) ^ a
USGS world, mostly US
Land
Land
Land
National
Geochemical Survey
(NGS)
Active Mines and
Mineral Processing
Plants 2003
Mine Claim Activity
on Federal Lands
Geochemical data (arsenic, selenium, mercury,
lead, zinc, copper, aluminum, sodium, magnesium,
phosphorus, calcium, titanium, manganese, and iron)
for US based primarily on stream sediments.
Mineral and metal operations in the US 2003.
Represents commodities monitored by the USGS
Minerals Information Team (MIT) and considered
active in 2003.
USGS
USGS
Public Land Survey section polygons with mining
claims recorded in US Bureau of Land Management's BLM
LR2000 database as of Dec 12, 2005.
a oo i t Coa Fie ds of the Areas that contain significant coa deposits in A aska . ,cnc
182 Land ,c, , , , * K USGS
United States and conterminous US.
USGS
USGS
USGS
USGS
conterminous US
US
AZ, AK, AR, CA,
CO, FL, ID, MT,
NE, NM, NV,
ND, OR, SD, UT,
WA, WY
conterminous US
andAK
point
county
point
not applicable NO Missing data
not applicable YES N/A
not applicable NO RePreshented
^ elsewhere
not available not applicable NO Non-informative
not available not applicable NO Spatial coverage
A-48
-------
Land Domain
ID
183
184
Land
Land
196
Land
Oregon and
Washington
Abandoned Mine
Lands
EPA Uranium
Location Database
(ULD) Compilation
Geo Communicator
National Integrated
Land System (NILS)
This data set contains known abandoned hardrock
mines on or affecting lands administered by BLM in
Oregon and Washington.
EPA compilation of mine location information from
federal, state, and Tribal agencies into a single
database as part of its investigation into the potential
environmental hazards of wastes from abandoned
uranium mines in the western United States.
Map feature service (http:/www.geocommunicator.
gov) which can be used to display and obtain
abandoned mine locations, BLM sites including
recreation, administrative, campgrounds,
and buildings, BLM issued land and mineral
authorizations, mining claims, land and mineral title
records, roads, surface management agency data,
impaired watersheds, and Section 303 listed waters
as well as many other reference themes and base
maps.
u) 5
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EPA
BLM
EPA
197
199
Land
Land
Generalized
Geologic Map of the
Conterminous US
103
104
105
106
107
Land
Land
Land
Land
Land
Database (NLCD
2001)-Land Cover
National Land Cover
Database (NLCD
2001)- Impervious
Surface
National Land Cover
Database (NLCD
2001) - Canopy
Density
Broad-based inventory of soils and non-soil areas
US General Soil Map that occur in a repeatable pattern on the landscape.
(STATSG02) National Resources Conservation Service (NRCS)
- USDA.
National Land Cover Database - canopy density
based on multi-season Landsat 5 and 7 imagery.
Soil Survey
n + u Resources Conservation Service (NRCS) - USDA.
(SSURGO) Database v '
Most detailed level of soil mapping done by National
110
111
Land
Land
National Resource
Inventory (NRI) 1997
National Resource
113
114
115
116
Land
Land
Land
Land
Multi-institutional cooperative effort to map and
GAP Analysis Project assess biodiversity for a five-state region (AZ, CO,
- Southwest NV, NM, UT) comprising approximately 560,000
square miles in the southwestern US.
GAP Analysis Project
- Southeast
Provisional land cover data
GAP land cover mapping - 2008 for California
GAP - California
Land Cover
GAP Land Cover GAP landcover data that are not available through
Data - various states GAP regional websites.
BLM
BLM
This data set contains boundaries and tags for major
geologic units in the conterminous United States.
The EPA Radon Zone map identifies areas of the US
Map of Radon Zones with the potential for elevated indoor radon levels.
(EPA) Each US county (3141) is assigned to one of three
zones based on radon potential.
LAND COVER/LAND USE/VEGETATION/SOILS
National Land Cover
National Land Cover Database - land cover based on
multi-season Landsat 5 and 7 imagery.
National Land Cover Database - impervious surface
based on multi-season Landsat 5 and 7 imagery.
Natural resource conditions and trends on nonfederal
US lands. Provides data on landuse, soil erosion,
water quality, and wetlands.
The National Resources Inventory (NRI) is a
statistical survey of natural resource conditions and
trends on non-Federal land in the United States —
Inventory (NRI) 2003 non-Federal land includes privately owned lands,
tribal and trust lands, and lands controlled by state
and local governments.
112 Land ^^na'^f's Northwest Land Cover Data
- Northwest
EPA
USGS
USGS
USGS
USDA
USDA
NRCS
NRCS
USGS
USGS
USGS
USGS
USGS
EPA
USGS
USGS
USGS
USDA
USDA
NRCS
NRCS
USGS
USGS
USGS
USGS
USGS
ORandWA
conterminous US
andAK
conterminous US
andAK
point
point
not applicable NO Spatial coverage
not applicable NO Spatial coverage
point
not applicable NO , ,
elsewhere
USGS USGS conterminous US not available not applicable NO Non-informative
conterminous US,
AK, and HI
conterminous
US, AK, HI, and
Puerto Rico
conterminous
US, AK, HI, and
Puerto Rico
conterminous
US, AK, HI, and
Puerto Rico
conterminous US,
AK, HI, Puerto
Rico, and Virgin
Islands
conterminous US,
AK, HI, Puerto
Rico, and Virgin
Islands
US
county
30m cell
30m cell
30m cell
grouped soil
study units
not applicable YES N/A
30m NO Non-informative
30m NO Non-informative
30m NO Non-informative
not applicable NO Non-informative
US
soil study units not applicable NO Non-informative
point not applicable NO Non-informative
point not applicable NO Non-informative
by state (OR, WA,
ID, MT, WY) or by
mapzone
by state (AZ, CO,
NV, NM, UT)
by state (AL, FL,
GA, KY MS, NC,
SC, TN, VA)
mapzone or entire
state (CA)
by state - see
notes.
not available not applicable NO Spatial coverage
not available not applicable NO Spatial coverage
not available
not available
not applicable NO Spatial coverage
not applicable NO Spatial coverage
not available not applicable NO Spatial coverage
A-49
-------
Land Domain
ID
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158
USGS
USGS
155
Omernik (EPA)
, , Ecological Regions of
an North America Levels
1, 2, and 3
EPA
EPA
Omernik (EPA)
156 Land Ecoregions of the
Conterminous US
Level III
EPA
EPA
This data set depicts land use and land cover from
the 1970s and 1980s and has been previously
Enhanced Historical published by the U.S. Geological Survey (USGS) in
Land Use and Land other file formats. This version has been reformatted
Land Cover Data Sets of to other file formats and includes minor edits applied
the US Geological by the U.S. Environmental Protection Agency
Survey (USEPA) and USGS scientists. This data set was
developed to meet the needs of the USGS National
Water-Quality Assessment (NAWQA) Program.
.or. ¦ I r -r Contains the broad distribution of various tree types Moro ,,o/-vo
180 Land Forest Cover Types f .. ,, . n ^ n- USFS USGS
}r found in the United States and Puerto Rico.
REGIONS/WATERSHEDS
Level I: North America has been divided into 15
broad, level I ecological regions. Level II: The 50
level II ecological regions that have been delineated
are intended to provide a more detailed description
of the large ecological areas nested within the level
I regions. For example, the Tropical Wet Forests of
level I is the region covering coastal portions of the
United States and Mexico, and is composed of six
level II regions. Level III: Level III mapping describes
smaller ecological areas nested within level II regions.
At level III, the continent currently contains 182
ecological regions.
Ecoregion boundaries were determined by examining
patterns of vegetation, animal life, geology, soils,
water quality, climate, and human land use, as well
as other living and non-living ecosystem components.
Designed to serve as a spatial framework for
environmental resource management, ecoregions
denote areas within which ecosystems (and the type,
quality, and quantity of environmental resources) are
generally similar. The most immediate needs are to
develop regional biological criteria and water quality
standards and to set management goals for nonpoint
source pollution.
Ecoregion boundaries were determined by examining
patterns of vegetation, animal life, geology, soils,
water quality, climate, and human land use, as well
as other living and non-living ecosystem components.
Ecoregions denote areas of general similarity in
ecosystems and in the type, quality, and quantity of
environmental resources. They are designed to serve
as a spatial framework for the research, assessment,
management, and monitoring of ecosystems and
ecosystem components. By recognizing the spatial
differences in the capacities and potentials of
ecosystems, ecoregions stratify the environment
purpose regions are critical for structuring and
implementing ecosystem management strategies
across federal agencies, state agencies, and
nongovernment same geographical areas (Omernik
and others, 2000).
Watershed boundaries define the aerial extent of
surface water drainage to a point. The intent of
defining hydrologic units (HU) for the Watershed
Boundary Dataset is to establish a base-line drainage
boundary framework, accounting for all land and
surface areas.
NHDPIus catchments form the basis of the NHDPIus
> i .¦ - m . . data model. Based on USGS 1:100,000 quad maps,
a rana y rograp y |sj|_|[)p|us catchments are highly complex polygons
Dataset (NHD) Plus ... , , • , K, yc ,a
Catch ments (not Posin9 a challenge for analysis or display. See map
.. .x at: http ://www. epa.gov/waters/doc/auxiliary/
' hvdroregions.html for comparison to smoothed
NHDPIus catchments.
conterminous US,
AK, and HI
conterminous
US, AK, HI, and
Puerto Rico
ra 5
5? =
= £*
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raster version
unknown
30m
unknown
NO
Temporal
coverage
NO Non-informative
North America
ecoregion
not applicable NO Non-informative
conterminous US ecoregion
not applicable NO Non-informative
Omernik (EPA)
157 Land Ecoregions of the
Conterminous US
Level IV
EPA
EPA
conterminous US ecoregion
not applicable NO Non-informative
a-,c . . Watershed Boundary
176 Land n , .m/Dnx
Dataset (WBD)
177
Land
NRCS
EPA
NRCS
EPA
conterminous US,
AK, and HI
conterminous US
and HI
watershed
catchment
not applicable NO Non-informative
not applicable NO Represented
elsewhere
A-50
-------
Land Domain
ID
178
Land
179
181
117
198
Land
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EPA
NHDPIus catchments form the basis of the NHDPIus
data model. Based on USGS 1:100,000 quad
maps, Smoothed catchments and hydroregions
are a generalized version of the original NHDPIus
catchment data, with roughly 85% fewer vertices.
National Hydrography Useful for display purposes or for determining the
Dataset (NHD) NHDPIus hydrologic region of a feature. Note that
Plus Catchments the generalization process may result in inaccurate
(smoothed) analysis results along the smoothed feature borders.
Users should weigh the performance gain verses
accuracy loss when utilizing smoothed NHDPIus
layers. See map at: http://www.epa.gov/waters/
doc/ auxiliary/hy dror eg ions. html for comparison to
non-smoothed NHDPIus catchments.
Bailey's (USDA)
Ecoregions and Ecoregions defined by common climatic and
Subregions of the vegetation characteristics, shown as domains, USDA
US, Puerto Rico, and divisions, provinces and sections,
the US Virgin Islands
EPA
Land Federal Lands
Lands owned or administered by the Federal
government.
National database of federal and state conservation
lands.
USGS
Protected Areas
Land Database of the US uulu"uov'Wl m °l"l° Wl ,ov",UUWI1 USGS
(PAD-US)
Ecological This data set includes polygons for ecological
Subregions: Sections sections and subsections within subregions within the
Land and Subsections for conterminous United States. This data set contains USDA
the Conterminous US regional geographic delineations for analysis of
ecological relationships across ecological units.
(ECOMAP)
URBAN / RURAL DESIGNATIONS
189
190
Land
Land
Rural-Urban
Commuting Area
Codes by Census
Tractor ZIP Code
Census 2000
Urbanized Area
and Urban Cluster
Boundaries
The Rural-Urban commuting area (RUCA) codes,
a detailed and flexible scheme for delineating
sub-county components of the US settlement
system, have been updated using data from the 2000
decennial census.
Urbanized Areas and Urban Clusters (2000 Census)
used by the Census Bureau to tabulate data for urban
and rural populations.
USDA
Census
Bureau
conterminous US
and HI
catchment not applicable NO , ,
rr elsewhere
USGS
USGS
USGS
USDA
USDA
Census
Bureau
conterminous US,
AK, HI, and US ecoregion
territories
conterminous US,
AK, HI, and US not available
territories
not applicable NO Non-informative
not applicable NO Spatial coverage
US
not available not applicable NO Spatial coverage
. . IC ecological
conterminous US , a..
subsection
not applicable NO Non-informative
conterminous US,
AK, and HI
conterminous US,
AK, and HI
census tract not applicable NO , .
^ elsewhere
urban area not applicable NO
elsewhere
A-51
-------
Socio-demographic Domain
ID
Environmental
Medium
Data Title
Data Description
(brief)
Agency with Data
Ownership
Agency Providing
Data
Geographic Area of
Coverage
Smallest Geographical
Unit Represented
Data Resolution
Used for EQI
Reason for non-use
CENSUS DATA
211
Social Environment
Residential
Density;
Population
Density
population and residential
density at various geographies
USCB
USCB
nationwide
block
not applicable
YES
N/A
212
Social Environment
Total Working
Population
employed population (>age 16)/
total population (>age 16)
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
213
Social Environment
Non-white
population
non-white population
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
214
Social Environment
Immigrant
Concentration
% pop Hispanic, foreign-born
and with limited English
USCB
USCB
nationwide
block
not applicable
YES
N/A
215
Social Environment
Segregation
isolation and dissimilarity indices
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
216
Social Environment
Mean Number of
Units
average number of units in
the tract
USCB
USCB
nationwide
tract
not applicable
NO
Represented
elsewhere
217
Social Environment
Education
proportion of population with
college + education
USCB
USCB
nationwide
block
not applicable
YES
N/A
218
Social Environment
Economic
Advantage
% total pop in poverty, % pop
65+ in poverty; % hh on public
assistance; unemployment
rate; % housing units without a
vehicle; % black population
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
219
Social Environment
Economic
Disadvantage
low income, low education,
high unemployment, unskilled
occupations
USCB
USCB
nationwide
block
not applicable
YES
N/A
220
Social Environment
Economic
Disadvantage
Sampson index (FHH, poverty,
PA, unemp)
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
221
Social Environment
Income / Wealth
income to poverty level (<
185%)
USCB
USCB
nationwide
block
not applicable
YES
N/A
222
Social Environment
Residential
Stability
percent living in same house
since 1995; median years in
residence
USCB
USCB
nationwide
block
not applicable
NO
Non-informative
223
Social Environment
Home Occupancy owner occupied, renter
Status occupied, vacant
USCB
USCB
nationwide
block
not applicable
YES
N/A
224
Social Environment
Home Age
year structure built
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
225
Social Environment
Pre - 1950s
Housing
median home built
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
226
Social Environment
1950- 1969
Housing
median home built
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
227
Social Environment
Post 1969
Housing
median home built
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
228
Social Environment
Commuting
Method
percent labor force (16+) reports
driving alone to work
USCB
USCB
nationwide
block
not applicable
YES
N/A
229
Social Environment
Proportion
Working in County
of Residence
number reporting working in
county of residence (< age
16) / total county population (>
age 16)
USCB
USCB
nationwide
block
not applicable
YES
N/A
230
Social Environment
Aggregate Travel
Time to Work
total number of minutes spent in
travel to work
USCB
USCB
nationwide
block
not applicable
YES
N/A
231
Social Environment
Median household total number of minutes spent in
value travel to work
USCB
USCB
nationwide
block
not applicable
YES
N/A
232
Social Environment
Median number of total number of minutes spent in
rooms travel to work
USCB
USCB
nationwide
block
not applicable
YES
N/A
233
Social Environment
> 10 housing units
total number of minutes spent in
travel to work
USCB
USCB
nationwide
block
not applicable
YES
N/A
CRIME DATA
234
Social Environment
Crime
Uniform Crime Reports available
through FBI
FBI
FBI
nationwide
county
not applicable
NO
Represented
elsewhere
235
Social Environment
Violent Crime
number violent + prop per
1k pop
FBI
FBI
nationwide
county
not applicable
YES
N/A
236
Social Environment
Serious Crime
serious crime arrests per 100k
FBI
FBI
nationwide
county
not applicable
NO
Represented
elsewhere
DISCRIMINATION DATA
237
Social Environment
Discriminatory
lending practices
home mortgage lending activity
FFIEC
HMDA
nationwide
county
not applicable
NO
Spatial coverags
A-52
-------
Built Domain
i i
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c/3 O 3
q;
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ro
o
TRAFFIC DATA
181
Built Environment
Vehicle Miles
Traveled Per Day
Geospatial layer of vehicle
miles traveled per day
state highway
systems
not available
not applicable
not applicable
NO
Non-informative
182
Built Environment
Traffic Speed
geospatial layer of posted
speed limits
not available
not applicable
not available
not applicable
not applicable
NO
Represented
elsewhere
count of traffic volume
183
Built Environment
Traffic Count
at over 1,000,000
intersections throughout
TrafficMetrix
TrafficMetrix
nationwide
msa or county
not applicable
NO
Non-informative
the US.
184
Built Environment
Street type
Topographically Integrated
Geographic Encoding
and Referencing data;
including road type,
proportion, length.
USCB
UCSB
nationwide
block
not applicable
YES
N/A
185
Built Environment
Distance to Public
Transportation
not available
not available
not available
not applicable
not applicable
NO
Missing data
186
Built Environment
Pedestrian Fatalities
annual pedestrian fatality
per 100k population
NHTSC
FARS
assumed
nationwide
coverage
county
county-level
YES
N/A
AMENITIES AND DISAMENITIES DATA
187
Built Environment
Counts of YMCA/
YWCA
count of YMCA/YWCA per
unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
188
Built Environment
Recreation Density
beaches, pools, tennis
courts, recreation centers
per unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
189
Built Environment
Counts of Parks
parks, recreation areas
per unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
190
Built Environment
Park Density
park count per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
191
Built Environment
Physical Fitness
Facilities, Bicycle
Rental Public Golf
Courses
physical fitness facilities,
bicycle rental public
golf courses per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
192
Built Environment
Dance Studios,
Basketball, Martial
Arts Instruction
dance studios, basketball,
martial arts instruction per
unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
193
Built Environment
Outdoor Recreation
outdoor activities; sport,
recreational camps per
unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
athletic club and
194
Built Environment
Gym Memberships
gymnasiums, tennis,
basketball per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
195
Built Environment
Fast Food Density
fast food density per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
198
Built Environment
School Density
school count per block
group per unit aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
199
Built Environment
Low Income /
Subsidized Housing
counts of low income and
subsidized housing units
per unit aggregation
HUD
HUD
nationwide
housing authority
reporting area
housing
authority
reporting area
YES
N/A
202
Built Environment
Restaurant Density
number of restaurants
+/ 10k pop per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
203
Built Environment
Grovery Store
Density
number of groceries,
sm/ 10k pop per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
204
Built Environment
Convenience Store
Density
number of convenience
stores/10k pop per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
205
Built Environment
Specialty Food
Density
number of specialty food
markets/ 10k pop per unit
aggregation
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
206
Built Environment
Vice-related Density
count of vice-related
businesses / 10k
population per unit
Dun
& Bradstreet
Dun'
& Bradstreet
nationwide
block group
block group
YES
N/A
A-53
-------
Built Domain
2 i
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ID
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< Q
O
CT)
II
< CL
<
g)
— ra
S- o
2 >
ro o
o O
CD
O o
"S Q. a.
« 2 $
= O)
E o
co O
q;
ro
«
Q
count of entertainment-
207
Built Environment
Entertainment-
related Density
related businesses /
10k population per unit
aggregation
Dun
& Bradstreet
Dun
& Bradstreet
nationwide
block group
block group
YES
N/A
count of health care-
208
Built Environment
Health-care-related
Density
related businesses /
10k population per unit
aggregation
Dun
& Bradstreet
Dun
& Bradstreet
nationwide
block group
block group
YES
N/A
count of social service-
209
Built Environment
Social service-
related Density
related businesses /
10k population per unit
aggregation
Dun
& Bradstreet
Dun
& Bradstreet
nationwide
block group
block group
YES
N/A
count of trasnportation-
210
Built Environment
Transportation-
related density
related businesses /
10k population per unit
aggregation
Dun
& Bradstreet
Dun
& Bradstreet
nationwide
block group
block group
YES
N/A
LAND USE DATA
200
Built Environment
Land Use Mix
tertiles of number of land
uses per acre
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
201
Built Environment
Mixed Land Use
land uses per acre
USCB
USCB
nationwide
block
not applicable
NO
Represented
elsewhere
OTHER DATA
196
Built Environment
Lead Poisoning
- Children
percent children tested
positive for lead poisoning
DHFS
DHFS
not available
not available
couny
NO
Represented
elsewhere
197
Built Environment
Radon Levels
radon levels (EPA)
DHFS
DHFS
not available
not available
couny
NO
Represented
elsewhere
A-54
-------
Appendix II
Identified Variables by Source for Each Domain
Variables of Interest (Air Domain)
Air quality system (AQS)
Variable
Variable Name
Counties/Monitors
Variable Notes
EQI?
Notes
Particulate Matter <10 microns in
aerodynamic diameter (PM10)
A_PM10_mean_ln
3141/303
pg/m3, log-transformed
YES
Particulate Matter <2.5 microns in
aerodynamic diameter (PMJ
A_PM25_mean
3141/1146
pg/m3
YES
Nitrate (NO2)
A_N02_mean_ln
3141/442
ppb, log-transformed
YES
Sulfate (SO2)
A_S02_mean_ln
3141/575
ppb, log-transformed
YES
Ozone (O3)
A_03_mean_ln
3141/1187
ppm, log-transformed
YES
Carbon Monoxide (CO) A_CO_mean_ln 3141 /499 ppm, log-transformed YES
Notes: Raw data is from monitoring stations across the country, daily and hourly values downloaded and averaged to yearly (2000-2005) for each monitoring station/pollutant.
Averaged data were then kriged to get a value for each county centroid.
Variables of Interest (Air Domain)
National Air Toxics Assessment (NATA)
Variable
Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
1,1,1 -T rich loroethane
3141 (0/0)
No
High Correlation
1,1,2,2-Tetrach loroethane
A_TeCA_ln
3141 (0/0)
Yes
1,1,2-Trich loroethane
A_112T CAJn
3141 (0/142)
Yes
1,1 -Di methy Ihydrazine
AJJDMHJn
1014(2127/985)
No
Zeros
1,2,3,4,5,6-Hexachlorocyclyhexane
A_HCCH_ln
182(2201/742)
No
Zeros
1,2,4-Trichlorobenzene
A_124TCIB_ln
3141 (0/119)
No
High Correlation
1,2-Dibromo-3-chloropropane
A_DBCP_ln
3141 (0/0)
Yes
1,2-Diphenyhydrazine
92(3049/77)
No
Zeros
1,2-Epoxybutane
2373 (768/1996)
No
Zeros
1,2-Propyleneimine
885(2256/806)
No
Zeros
1,3-Butadiene
3141 (0/0)
No
High Correlation
1,3-Dichloropropene
3062(79/1297)
No
High Correlation
1,4-Dichlorobenzene
3141 (0/0)
No
High Correlation
1,4-Dioxane
A_dioxane_ln
3038 (180/2065)
No
Zeros
2,2,4-Tri methy Ipentane
3141 (0/0)
No
High Correlation
2,4,5-T rich loropheno I
A_245TCP_ln
1256(1885/1119)
No
Zeros
2,4,6-Trichlorophenol
A_246TCP_ln
2222 (919/1730)
No
Zeros
2,4-D salts and esters
2141 (1000/1192)
No
Zeros
2,4-Dinitrophenol
A_DNP_ln
2763 (378/1934)
No
Zeros
2,4-D i n itroto luene
3141 (0/125)
No
High Correlation
2,4-Toluene diamine
1223(1918/1118)
No
Zeros
2,4-Toluene diisocyanate
A_TDI_ln
3141 (0/0)
Yes
2-Ch loroacetophenone
A_2Clacephen_ln
3094(47/1155)
Yes
2-Nitropropane
A_2NP_ln
3141 (0/144)
Yes
3,3-Dichlorobenzidine
3062(79/1297)
No
Zeros
3,3-Demthoxybenzidine
317(2824/300)
No
Zeros
3,3-Di methy benzidine
660(2481/605)
No
Zeros
4,4-Mthylene bi s-2-ch loroan i li ne
1285(1856/1196)
No
Zeros
4,4-Methylenedianiline
1964(1177/1668)
No
Zeros
4,4-Methylenediphenyl diisocyanante
3141 (0/0)
No
High Correlation
4-Aminobiphenyl
444(2697/415)
No
Zeros
4-Dimethylaminoazobenzene
344(2797/320)
No
Zeros
4-Nitrobiphenyl
627(2514/576)
No
Zeros
4-Nitrophenol
A_PNP_ln
3140 (1/34)
Yes
Acetaldehyde
3141 (0/0)
No
High Correlation
Acetamide
A_Acetamide_ln
2451 (690/1054)
No
Zeros
Notes: When dafa is missing/not recorded, zero values were deemed appropriate. Most variables kept for EQI have been log-transformed. EQI 2000 = NATA1999; EQI 2001, 2002,2003 = NATA 2002; EQI
2004, 2005 = NATA 2005. All variables reported in tons emitted per year. Unless otherwise noted, all variables are log-transformed. Variables were dropped due to insufficient data (high numbers of missing or
zero observations) or due to high correlation with other variables.
-------
Variables of Interest (Air Domain)
National Air Toxics Assessment (NATA)
Variable
Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
Acetonitrile
A_CH3CN_ln
3141 (0/106)
Yes
Acetophenone
A_Acetophenone_ln
3140 (1/3)
Yes
Acrolein
A_Aroclein_ln
3141 (0/0)
Yes
Acrylamide
A_Acrylamide_ln
2789(352/2226)
No
Zeros
Acrylic acid
A_Acrylic_acid_ln
3108 (1165)
Yes
Acrylonitrile
A_C3H3N_ln
3141 (0/0)
Yes
Ally I chloride
3141 (0/215)
No
High Correlation
Aniline
A_Aniline_ln
2846(295/2073)
No
Zeros
Anisidine
899 (2242/856)
No
Zeros
Antimony compounds
A_Sb_ln
3136(5/199)
Yes
Arsenic compounds
3141 (0/0)
No
High Correlation
Asbestos
A_Asbestos_ln
1349(1792/1168)
No
Zeros
Benzene
3141 (0/0)
No
High Correlation
Benzidine
A_Benzidine_ln
3141 (0/0)
Yes
Benzotrich bride
647 (2494/591)
No
Zeros
Benzyl chloride
A_Benzyl_CI_ln
3141 (0/98)
Yes
Beryllium compounds
A_Be_ln
3141 (0/0)
Yes
fc-Propiolactone
128(3013/118)
No
Zeros
Biphenyl
A_biphenyl_ln
3141 (0/0)
Yes
bis-2-Ethylhexyl phthalate
A.DEHPJn
3141 (0/0)
Yes
bis-ChloromethyI ether
701 (2440/643)
No
Zeros
Bromoform
A_Bromoform_ln
3094(47/1108)
Yes
Cadmium compounds
A_Cd_ln
3141 (0/0)
Yes
Calcium cyanamide
315 (2826/279)
No
Zeros
Captan
1503 (1638/1018)
No
Zeros
Carbaryl
1203(1938/797)
No
Zeros
Carbon disulfide
A_CS2_ln
3141 (0/0)
Yes
Carbon tetrachloride
A_CCI4
3141 (0/0)
Not log-transformed
Yes
Carbon sulfide
A_CS_ln
3141 (0/8)
Yes
Catechol
2056(1085/1711)
No
Zeros
Chlordane
1075(2066/1001)
No
Zeros
Chlorine
A_CI_ln
3114(27/520)
Yes
Chloroacetic acid
1815(1326/1594)
No
Zeros
Chlorobenzene
A_C6H5CI_ln
3141 (0/0)
Yes
Chlorobenzilate
483 (2658/447)
No
Zeros
Chloroform
A_chloroform_ln
3141 (0/0)
Yes
Chloromethyl methyl ether
1279(1862/1157)
No
Zeros
Chloroprene
A_Chloroprene_ln
3141(220)
Yes
Chromium compounds
A_Cr_ln
3141 (0/0)
Yes
Cobalt compounds
3136(5/135)
No
High Correlation
Coke oven emissions
985 (2156/842)
No
Zeros
Cresol/cresylic acid
A_Cresol_ln
3141 (0/0)
Yes
Cumene
A_Cumene_ln
3141 (0/0)
Yes
Cyanide compounds
A_CN_ln
3141 (0/5)
Yes
DDE
276 (2865/265)
No
Zeros
Diazomethane
43 (3098/37)
No
Zeros
Dibenzofuran
A_Dibenzofuran_ln
2543(598/1906)
No
Zeros
Dibutylphtha late
A_DBP_ln
3140 (1/0)
Yes
Dichloroethyl ether
1466(1675/1045)
No
Zeros
Dichlorvos
342(2799/296)
No
Zeros
Diesel engine emissions
A_Diesel_ln
3141 (0/0)
Yes
Diethanolamine
A_DEA_ln
3063 (78/1651)
No
Zeros
Diethyl sulfate
A_Et2S04_ln
1418(1723/1240)
No
Zeros
Dimethyl formamide
A_DMF_ln
3140 (1/228)
Yes
Dimethyl phthalates
A_Me2_phatalte_ln
3140 (1/0)
Yes
Dimethyl sulfate
A_Me2S04_ln
3141 (0/136)
Yes
Dimethylcarbamoyl chloride
528 (2613/505)
No
Zeros
Epich lorohydrin
A_ECH_ln
3141 (0/183)
Yes
Notes: When data is missing/not recorded, zero values were deemed appropriate. Most variables kept for EQI have been log-transformed. EQI 2000 = NATA1999; EQI 2001, 2002, 2003 = NATA2002; EQI
2004, 2005 = NATA 2005. All variables reported in tons emitted per year. Unless otherwise noted, all variables are log-transformed. Variables were dropped due to insufficient data (high numbers of missing or
zero observations) or due to high correlation with other variables.
-------
Variables of Interest (Air Domain)
National Air Toxics Assessment (NATA)
Variable
Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
Ethyl acrylate
A_Etacrylate_ln
3141 (0/160)
Yes
Ethyl carbamate chloride
1121 (2020/1053)
No
Zeros
Ethyl chloride
A_EtCI_ln
3140 (1/0)
Yes
Ethylbenzene
3141 (0/0)
No
High Correlation
Ethylene dibromide
A_EDB_ln
3141 (0/0)
Yes
Ethylene dich bride
A_EDC_ln
3141 (0/0)
Yes
Ethylene glycol
A_EGLY_ln
3141 (0/0)
Yes
Ethylene oxide
A_EOx_ln
3141 (0/0)
Yes
Ethylene thiourea
877 (2264/735)
No
Zeros
Ethyleneimine
671 (2470/591)
No
Zeros
Ethylidene dich bride
A_EdCI2_ln
3140(1/27)
Yes
Fine mineral fibers
744 (2397/658)
No
Zeros
Formaldehyde
3141 (0/0)
No
High Correlation
Glycol ethers
A_Glycol_ethers_ln
3138(3/616)
Yes
Heptachlor
812 (2329/732)
No
Zeros
Hexachlorobenzene
A_HCB_ln
3141 (0/148)
Yes
Hexach lorobutadiene
AJHCBDJn
3141 (0/426)
Yes
Hexach lorocyclopentadiene
A_HCCPD_ln
3141 (0/426)
Yes
Hexach loroethane
A_PCA_ln
1768 (1373/1496)
No
Zeros
Hexamethylene diisocyanate
A.HDIJn
2927(214/1826)
No
Zeros
Hexamethylphosphoramide
32 (3109/28)
No
Zeros
Hexane
A_Hexane_ln
3141 (0/0)
Yes
Hydrazine
A_N2H2_ln
3141 (0/0)
Yes
Hydrochloric acid
A_HCI_ln
3141 (0/0)
Yes
Hydrogen fluoride (hydrofluoric acid)
3141 (0/0)
No
High Correlation
Hydroquinone
A_quinol_ln
3137(4/1929)
No
Zeros
Isophorone
A_lsophorone_ln
3135 (6/287)
Yes
Lead compounds
A_Pb_ln
3141 (0/0)
Yes
Maleic anhydride
A_Maleic_anhyd_ln
2825(316/1917)
No
Zeros
Manganese compounds
A_Mn_ln
3141 (0/0)
Yes
Mercury compounds
A_Hg_ln
3141 (0/0)
Yes
Methanol
A_MeOH_ln
3141 (0/0)
Yes
Methoxychlor
1023(2118/924)
No
Zeros
Methyl bromide
3141 (0/0)
No
High Correlation
Methyl chloride
3141 (0/0)
No
High Correlation
Methyl iodide
A_Mel_ln
2522(619/1805)
No
Zeros
Methyl isobutyl ketone
A.MIBKJn
3141 (0/0)
Yes
Methyl isocyanate
1084(2057/988)
No
Zeros
Methyl methacrylate
A_MMA_ln
3141 (0/0)
Yes
Methyl chloride
A_MeCI_ln
3141 (0/0)
Yes
Methylhydrazine
A_Mehydrazine_ln
3094(47/1151)
Yes
MTBE
A_MTBE_ln
3141 (0/0)
Yes
Napthalene
3141 (0/0)
No
High Correlation
Nickel compounds
3141 (0/0)
No
High Correlation
Nitrobenzene
A_nitrobenzene_ln
3141(0/211)
Yes
N,N-Dimethylaniline
A_DMA_ln
3141 (0/209)
Yes
N-Nitrosodimethylamine
A_NDMA_ln
653 (2488/571)
No
Zeros
N-Nitrosomorpholine
920 (2221/840)
No
Zeros
o-Toluidine
A_otoluidine_ln
3141 (0/176)
Yes
PAH/POM
A_PAHPOM_ln
3141 (0/0)
Yes
Parathion
249 (2892/234)
No
Zeros
Pentachlorophenol
A_PCP_ln
3141 (0/171)
Yes
Phenol
3141 (0/0)
No
High Correlation
Phosgene
1668 (1473/1328)
No
Zeros
Phosphine
A_PH3_ln
3049(92/380
Yes
Phosphorus
A_P_ln
3052 (89/1255)
Yes
Phthalic anhydride
2957(184/2082)
No
Zeros
Polychlorinated biphenyls
A_PCBs_ln
3141 (0/98)
Yes
Notes: When data is missing/not recorded, zero values were deemed appropriate. Most variables kept for EQI have been log-transformed. EQI 2000 = NATA1999; EQI 2001, 2002, 2003 = NATA 2002; EQI
2004, 2005 = NATA 2005. All variables reported in tons emitted per year. Unless otherwise noted, all variables are log-transformed. Variables were dropped due to insufficient data (high numbers of missing or
zero observations) or due to high correlation with other variables.
-------
Variables of Interest (Air Domain)
National Air Toxics Assessment (NATA)
Variable
Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
p-phenylenediamine
1264(1877/1115)
No
Zeros
Propionaldehyde
3141 (0/0)
No
High Correlation
Propoxur
706(2435/648)
No
Zeros
Propylene dich bride
A_ProCI2_ln
3141 (0/0)
Yes
Propylene oxide
A_ProO_ln
3141 (0/86)
Yes
Quinoline
A_Quinolin_ln
3141 (0/0)
Yes
Quinone
77(3064/59)
No
Zeros
Radionuclides
131 (3010/88)
No
Zeros
Selenium compounds
A_Se_ln
3141 (0/0)
Yes
Styrene
A_Styrene_ln
3141 (0/0)
Yes
Styrene oxide
696(2445/660)
No
Zeros
Tetrachloroethylene
A_CI4C2_ln
3141 (0/0)
Yes
Titanium tetrachloride
1656(1485/1440)
No
Zeros
Toluene
A_Toluene_ln
3141 (0/0)
Yes
Toxaphene
A_Toxaphene_ln
814(2327/762)
No
Zeros
Trichloroethylene
A_C2HCI3_ln
3141 (0/0)
Yes
Triethylamine
A_Et3N_ln
3141 (0/0)
Yes
Trifluralin
A_Trif I u ra li n _ln
2529 (612/1628)
No
Zeros
Vinyl acetate
A_VyAc_ln
3141 (0/0)
Yes
Vinyl bromide
392(2749/377)
No
Zeros
Vinyl chloride
A_VyCI_ln
3411 (0/4)
Yes
Vinylidene chloride
A_11DCE_ln
3411 (0/4)
Yes
Xylenes
3141 (0/0)
No
High Correlation
Pentacholoronitrobenzene (PCNB)
687(2454/637)
No
Zeros
1,3-Propoane sultone 637(2504/599) No Zeros
Notes: When data is missing/not recorded, zero values were deemed appropriate. Most variables kept for EQI have been log-transformed. EQI2000 = NATA1999; EQI2001, 2002, 2003 = NATA 2002; EQI
2004,2005 = NATA 2005. All variables reported in tons emitted per year. Unless otherwise noted, all variables are log-transformed. Variables were dropped due to insufficient data (high numbers of missing or
zero observations) or due to high correlation with other variables.
B-4
-------
Variables of Interest (Water Domain)
WATERS Program Database/REACH Address Database
Variable
Variable Name
Counties
(Missing/Zeros)
Variable Notes
EQI?
Notes
Percent of stream length impaired in county
D303_Percent
2513(628/0)
Calculated with REACH database
3 information
Yes
Sewage permits per 1000 km of stream in county
SEWAGENPDESperKM
3141 (0/0)
Total of AFO/CAFO NPDES permits, CSO NPDES
permits, sludge NPDES permits in county
Yes
Industrial permits per 1000 km of stream in county
INDNPDESperKM
3141 (0/0)
Total of pretreatment NPDES permits, general
facilities NPDES permits, individual facilities NPDES
permits, unpermitted by county
Yes
Stormwater permits per 1000 km of stream in
county
STORMNPDESperKM
3141 (0/0)
Total of general stormwater NPDES permits,
industrial stormwater NPDES permits by county
Yes
Number of days closed per event in county 2002
nu mDays_Close_Activity_2002
54 (0/3087)
Count of days of beach closure in county
Yes
Number of days per contamination-advisory event
in county 2002
nu mDays_Cont_Activity_2002
66 (0/3075)
Count of days of beach closure for contamination
events
Yes
Number of days per rain advisory event in county
2002
numDays_Rain_Activity_2002
5(0/3136)
Count of days of beach closure for rain events
Yes
Percent of stream length in county assigned with
Water Quality Standards (WQS)
WQS_Percent
2022 (1119/0)
Calculated with REACH database
3 information
No
Noninformative
Percent of stream length impaired for agriculture
D303_AG_Percent
1168(1973/0)
Calculated with REACH databasf
3 information
No
Zeros
Percent of stream length impaired for drinking water
D303_DW_Percent
274(2867/0)
Calculated with REACH databasf
3 information
No
Zeros
Percent of stream length impaired for recreation
D303_REC_Percent
1374 (1767/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length impaired for wildlife
D303_WILD_Percent
1607 (1534/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length impaired for industrial use
D303_IND_Percent
613(2528/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length with assigned WQS
impaired for agriculture
D303_WQS_AG_Percent
1166(1975/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length with assigned WQS
impaired for drinking water
D303_WQS_DW_Percent
274(2867/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length with assigned WQS
impaired for recreational water
D303_WQS_REC_Percent
1373 (1767/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length with assigned WQS
impaired for wildlife water
D303_W QS_WILD_Percent
1605 (1536/0)
Calculated with REACH database
3 information
No
Zeros
Percent of stream length with assigned WQS
impaired for industrial water
D303_WQS_IND_Percent
606(2535/0)
Calculated with REACH database
3 information
No
Zeros
Number of concentrated animal feeding operations
(AFO/CAFOs) National Pollutant Discharge
Elimination System (NPDES) permits in county
NPDES_AFOCAFO
257(2884/0)
Simple count of permits by county
No
Zeros
Nu mber of combined sewer overflows (CSO)
NPDES permits in county
NPDES_CSO
427(2714/0)
Simple count of permits by county
No
Zeros
Number of pretreatment NPDES permits in county
NPDES_PRETREATER
559(2582/0)
Simple count of permits by county
No
Zeros
Number of sludge NPDES permits in county
NPDES_SLUDGE
260(2881/0)
Simple count of permits by county
No
Zeros
Number of general facilities NPDES permits in
county
NPDES_GENERALFACILITIES
2053 (1088/0)
Simple count of permits by county
No
Zeros
Number of individual facilities NPDES permits in
county
NPDESJNDIVIDUAL
2962(179/0)
Simple count of permits by county
No
Zeros
Number of general stormwater NPDES permits
in county
NPDES_STORMWATERGENERAL
1164(1977/0)
Simple count of permits by county
No
Zeros
Number of industrial stormwater NPDES permits
in county
NPDES_STORMWATERIND
260(2881/0)
Simple count of permits by county
No
Zeros
Number of unpermitted NPDES facilities
NPDESJJN PERMITTED
1285( 1856/0)
Simple count of permits by county
No
Zeros
Number of total NPDES permits in county
NPDES_TOTAL
3072 (69/0)
Simple count of permits by county
No
Zeros
Notes: These measures were computed; because of a lot of missing data, several variables cannot be used. Variables were calculated using the REACH stream-length database.
B-5
-------
Variables of Interest (Water Domain)
Estimate Use of Water in the United States
Variable
Variable Name
Counties
Missing/Zeros)
Variable Notes
EQI?
Notes
Percent of population on self supply, 2000
Per_TotPopSS_2000
3141 (0/0)
Estimate provided at county level.
Yes
Percent of public supply Population that is on surface water, 2000
Per_PSWithSW_2000
3067 (74/0)
Estimate provided at county level.
Yes
Percent of population on Public water supply, 2000
Per_TotPopPS_2000
3141 (0/0)
Estimate provided at county level.
No
High Correlation
Percent of public supply Population that is on groundwater, 2000
Per_PS With GW_2000
3067 (74/0)
Estimate provided at county level.
No
High Correlation
Percent of self supply Withdrawls from groundwater sources, 2000
Per_SSWith GW_2000
2307(834/0)
Estimate provided at county level.
No
High Correlation
Percent of self supply Withdrawls from surface water sources, 2000
Per_SSWithSW_2000
2307(834/0)
Estimate provided at county level.
No
High Correlation
Percent of domestic use from public water supply, 2000
Per_DOPS_2000
3098 (43/0)
Estimate provided at county level.
No
High Correlation
Percent of domestic use from self supply, 2000
Per_DOSS_2000
3098 (43/0)
Estimate provided at county level.
No
High Correlation
Percent of total withdrawals for domestic use, 2000
Per_TotWithD 0_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for industrial use, 2000
Per_TotWithlN_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for irrigation use, 2000
Per_TotWithlT_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for livestock use, 2000
Per_TotWithLS_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for aquaculture use, 2000
Per_TotWithLA_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for mining use, 2000
Per_TotWithMI_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Percent of total withdrawals for thermoelectric use, 2000
Per_TotWith PT_2000
2444(697/0)
Estimate provided at county level.
No
Zeros
Notes: These measures were computed for 2000 and 2005 data and averaged. The U.S. Geological Survey provides estimates at county level, so no additional manipulation required.
Variables of Interest (Water Domain)
National Atmospheric Deposition Program
Counties (Missing/
Variable Variable Name Zeros) Variable Notes EQI? Notes
Calcium precipitation weighted mean (mg/L)
Cajn
3141
0/0)
Kriged and log transformed
Yes
Magnesium precipitation weighted mean (mg/L)
Mgjn
3141
0/0)
Kriged and log transformed
Yes
Potassium precipitation weighted mean (mg/L)
KJn
3141
0/0)
Kriged and log transformed
Yes
Sodium precipitation weighted mean (mg/L)
Najn
3141
0/0)
Kriged and log transformed
Yes
Ammonium precipitation weighted mean (mg/L)
NH4_mean
3141
0/0)
Kriged - transformation not needed
Yes
Nitrate precipitation weighted mean (mg/L)
N03_mean
3141
0/0)
Kriged - transformation not needed
Yes
Chloride deposition
Cljn
3141
0/0)
Kriged and log transformed
Yes
Sulfate deposition
S04_mean
3141
0/0)
Kriged - transformation not needed
Yes
Total mercury deposition (ng/m2) - Kriged and log transformed
Use only values with A or B quality rating v ' » »
Notes: Measures provided at various monitoring stations. Values for 2000-2005 were kriged to national level coverage. Data for all years were averaged together.
Yes
Variables of Interest (Water Domain)
Drought Monitor Data
Variable
lf ... .. Counties (Missing/
Variable Name _ *
Zeros)
Variable
Notes
Notes
Percent of county drought - extreme (D3-D4)
D3Condition
3141 (0/0)
Yes
Percent of county without drought
NoDroughtCondition
3141 (0/0)
No
High Correlation
Percent of county abnormally dry (D0-D4)
DOCondition
3141 (0/0)
No
High Correlation
Percent of county drought - moderate (D1-D4)
D1 Condition
3141 (0/0)
No
High Correlation
Percent of county drought - severe (D2-D4)
D2Condition
3141 (0/0)
No
High Correlation
Percent of county drought - exceptional (D4)
D4Condition
3141 (0/0)
No
High Correlation
Notes: Raster data aggregated to the county level. Data for all years 2000-2005 was averaged together.
B-6
-------
Variables of Interest (Water Domain)
National Contaminant Occurrence Database
Variable Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
Arsenic- average
W_As_ln (mg/L)
2017(1124/0)
Average for a
I samples in county
og transformed
Yes
Barium - average
W_Ba_ln (mg/L)
1990 (1151/0)
Average for a
I samples in county
og transformed
Yes
Cadmium - average
W_Cd_ln (mg/L)
1991 (1150/0)
Average for a
I samples in county
og transformed
Yes
Chromium (total) - average
W_Cr_ln (mg/L)
1989 (1152/0)
Average for a
I samples in county
og transformed
Yes
Cyanide - average
W_CN_ln (mg/L)
1385(1765/0)
Average for a
I samples in county
og transformed
Yes
Fluoride - average
W_FL_ln (mg/L)
2138(958/0)
Average for a
I samples in county
og transformed
Yes
Mercury (inorganic) - average
W_HG_ln (mg/L)
2056(1085/0)
Average for a
I samples in county
og transformed
Yes
Nitrate (as N) - average
W_N03_ln (mg/L)
1988 (1153/0)
Average for a
I samples in county
og transformed
Yes
Nitrite (as N) - average
W_N02_ln (mg/L)
1583(1558/0)
Average for a
I samples in county
og transformed
Yes
Selenium - average
W_SE_ln (mg/L)
1986 (1155/0)
Average for a
I samples in county
og transformed
Yes
Antimony - average
W_Sb_ln (mg/L)
1994 (1147/0)
Average for a
I samples in county
og transformed
Yes
Beryllium - average
W_Be_ln (mg/L)
1932(1209/0)
Average for a
I samples in county
og transformed
Yes
Thallium - average
W_TI_ln (mg/L)
1996 (1145/0)
Average for a
I samples in county
og transformed
Yes
Endrin - average
W_Endrin_ln (pg/L)
1509(1632/0)
Average for a
I samples in county
og transformed
Yes
Lindane - average
W_Lindane_ln (mg/L)
1990 (1151/0)
Average for a
I samples in county
og transformed
Yes
Methoxychlor - average
W_methoxychlor_ln ((jg/L)
1512(1629/0)
Average for a
I samples in county
og transformed
Yes
Toxaphene - average
W_Toxaphene_ln ((jg/L)
1273(1868/0)
Average for a
I samples in county
og transformed
Yes
Dalapon - average
W_Dalapon_ln (pg/L)
1292(1849/0)
Average for a
I samples in county
og transformed
Yes
di(2-Ethylhexyl) adipate (DEHA) - average
W_DEHA_ln (pg/L)
1456(1685/0)
Average for a
I samples in county
og transformed
Yes
Oxamyl (Vydate) - average
W_Oxamyl_ln (pg/L)
1254(1887/0)
Average for a
I samples in county
og transformed
Yes
Simazine - average
W_Simazine_ln ((jg/L)
1669(1472/0)
Average for a
I samples in county
og transformed
Yes
di(2-Ethylhexyl) phthalate (DEHP) - average
W_DEHP_ln (pg/L)
1449(1692/0)
Average for a
I samples in county
og transformed
Yes
Picloram - average
W_Picloram_ln ((jg/L)
1352(1789/0)
Average for a
I samples in county
og transformed
Yes
Dinoseb - average
W_Dinoseb_ln (pg/L)
1347(1794/0)
Average for a
I samples in county
og transformed
Yes
Hexachlorocyclopenta-diene - average
W_HCCPD_ln (pg/L)
1518(1623/0)
Average for a
I samples in county
og transformed
Yes
Carbofuran - average
W_Carbofuran_ln ((jg/L)
1262(1879/0)
Average for a
I samples in county
og transformed
Yes
Atrazine - average
W_atrazine_ln ((jg/L)
1726(1415/0)
Average for a
I samples in county
og transformed
Yes
Alachlor - average
W_Alachlor_ln (pg/L)
1662(1479/0)
Average for a
I samples in county
og transformed
Yes
Heptachlor - average
W_Heptachlor_ln ((jg/L)
1509(1632/0)
Average for a
I samples in county
og transformed
Yes
Heptachlor epoxide - average
W_Heptachlor_epox_ln ((jg/L)
1508(1633/0)
Average for a
I samples in county
og transformed
Yes
2,4-D (2,4-Dichlorophenoxyacetic acid) - average
W_24D_ln (pg/L)
1360(1781/0)
Average for a
I samples in county
og transformed
Yes
2,4,5-TP (Silvex) - average
W_silvex_ln (pg/L)
1348(1793/0)
Average for a
I samples in county
og transformed
Yes
Hexachlorobenzene - average
W_HCB_ln (pg/L)
1520(1621/0)
Average for a
I samples in county
og transformed
Yes
Benzo[a]pyrene - average
W_BenzoAP_ln ((jg/L)
1430 (1711/0)
Average for a
I samples in county
og transformed
Yes
Pentachlorophenol - average
W_PCP_ln (pg/L)
1547(1594/0)
Average for a
I samples in county
og transformed
Yes
1,2,4-Trichlorobenzene - average
W_124TCIB_ln ((jg/L)
2239(902/0)
Average for a
I samples in county
og transformed
Yes
Polychlorinated biphenyls (PCBs) - average
W_PCB_ln (pg/L)
848(2293/0)
Average for a
I samples in county
og transformed
Yes
1,2-Dibromo-3-chloropropane (DBCP) - average
W_DBCP_ln (pg/L)
1652(1489/0)
Average for a
I samples in county
og transformed
Yes
Ethylene dibromide (EDB) - average
W_EBD_ln (pg/L)
1630 (1511/0)
Average for a
I samples in county
og transformed
Yes
Xylenes (total) - average
W_xylenes_ln (pg/L)
2203(938/0)
Average for a
I samples in county
og transformed
Yes
Chlordane - average
W_Chlordane_ln (pg/L)
1498(1652/0)
Average for a
I samples in county
og transformed
Yes
Dichloromethane (Methylene chloride) - average
W_DCM_ln (pg/L)
2245(896/0)
Average for a
I samples in county
og transformed
Yes
Notes: Will use 6-Year Review 2 (data collected between 1998 and2005). Calculate the following variables for each chemical for each county (aggregating all public water supply in county) for all years
combined, missing for those counties without any data; did not keep detects.
B-7
-------
Variables of Interest (Water Domain)
National Contaminant Occurrence Database
Variable Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
1,2-Dichlorobenzene (o-Dichlorobenzene)
- average
W_ODCB_ln (pg/L)
2236(905/0)
Average for all samples in county, log transformed
Yes
1,4-Dichlorobenzene (p-Dichlorobenzene)
- average
W_PDCB_ln (pg/L)
2165(976/0)
Average for all samples in county, log transformed
Yes
Vinyl chloride - average
W_VCM_ln (pg/L)
2235(906/0)
Average for all samples in county, log transformed
Yes
1,1 -Dichlonoethylene - average
W_11DCE_ln (pg/L)
2238(903/0)
Average for all samples in county, log transformed
Yes
trans-1,2-Dichloroethylene - average
W_t12DCE_ln (pg/L)
2231 (910/0)
Average for all samples in county, log transformed
Yes
1,2-Dichloroethane (Ethylene dich bride)
- average
W_EDC_ln (pg/L)
2238(903/0)
Average for all samples in county, log transformed
Yes
1,1,1 -Trichloroethane - average
W_ 111 trich lorane_ln ((jg/L)
2238(903/0)
Average for all samples in county, log transformed
Yes
Carbon tetrachloride - average
W_CCI4_ln (pg/L)
2237(904/0)
Average for all samples in county, log transformed
Yes
1,2-Dichloropropane - average
W_PDC_ln (pg/L)
2239(902/0)
Average for all samples in county, log transformed
Yes
Trichloroethylene - average
W_Trichlorene_ln ((jg/L)
2250(891/0)
Average for all samples in county, log transformed
Yes
1,1,2-Trich loroethane - average
W_112TCA_ln (pg/L)
2235(906/0)
Average for all samples in county, log transformed
Yes
Tetrachloroethylene - average
W_C2CI4_ln (pg/L)
2249(892/0)
Average for all samples in county, log transformed
Yes
Monochlorobenzene (Chlorobenzene) - average
W_benzene_ln ((jg/L)
2239(902/0)
Average for all samples in county, log transformed
Yes
Benzene - average
W_CI1benz_ln (pg/L)
2231 (910/0)
Average for all samples in county, log transformed
Yes
Toluene - average
W_Toluene_ln (pg/L)
2245(896/0)
Average for all samples in county, log transformed
Yes
Ethyibenzene - average
W_ethylbenz_ln (pg/L)
2241 (900/0)
Average for all samples in county, log transformed
Yes
Styrene - average
W_styrene_ln (pg/L)
2235(906/0)
Average for all samples in county, log transformed
Yes
cis-1,2-Dichloroethylene - average
W_DCE_ln (pg/L)
2238(903/0)
Average for all samples in county, log transformed
Yes
Alpha particles (gross alpha, excluding radon and
uranium) - average
W_alpha (pCi/L)
1243(1898/0)
Average for all samples in county
Arsenic - detects
W_As_detect (mg/L)
2017
Average for all detects in county
No
Noninformative
Barium - detects
W_Ba_dectect (mg/L)
1990
Average for all detects in county
No
Noninformative
Cadmium - detects
W_Cd_detect (mg/L)
1991
Average for all detects in county
No
Noninformative
Chromium (total) - detects
W_Cr_detect (mg/L)
1989
Average for all detects in county
No
Noninformative
Cyanide - detects
W_CN_detect (mg/L)
1385
Average for all detects in county
No
Noninformative
Fluoride - detects
W_FL_detect (mg/L)
2138
Average for all detects in county
No
Noninformative
Mercury (inorganic) - detect
W_HG_detect (mg/L)
2056
Average for all detects in county
No
Noninformative
Nitrate (as N) - detect
W_N03_detect (mg/L)
1988
Average for all detects in county
No
Noninformative
Nitrite (as N) - detect
W_N02_detect (mg/L)
1583
Average for all detects in county
No
Noninformative
Selenium - detect
W_SE_detect (mg/L)
1986
Average for all detects in county
No
Noninformative
Antimony - detect
W_Sb_detect (mg/L)
1994
Average for all detects in county
No
Noninformative
Beryllium - detect
W_Be_detect (mg/L)
1932
Average for all detects in county
No
Noninformative
Thallium - detect
W_TI_detect (mg/L)
1996
Average for all detects in county
No
Noninformative
Asbestos - average
W_asbestos (mg/L)
457
Average for all samples in county, log transformed
No
Zeros
Asbestos - detect
W_asbestos_detect (mg/L)
457
Average for all detects in county
No
Noninformative
Endrin - detect
W_Endrin_detect ((jg/L)
1509
Average for all detects in county
No
Noninformative
Lindane - detect
W_Lindane_detects (mg/L)
1990
Average for all detects in county
No
Noninformative
Methoxychlor - detect
W_methoxychlor_detect ((jg/L)
1512
Average for all detects in county
No
Noninformative
Toxaphene - detect
W_Toxaphene_detect ((jg/L)
1273
Average for all detects in county
No
Noninformative
Dalapon - detect
W_Dalapon_detect ((jg/L)
1292
Average for all detects in county
No
Noninformative
Diquat - average
W_Diquat (pg/L)
722
Average for all samples in county
No
Noninformative
Notes: Will use 6-Year Review 2 (data collected between 1998 and2005). Calculate the following variables for each chemical for each county (aggregating all public water supply in county) for all years
combined, missing for those counties without any data; did not keep detects.
B-8
-------
Variables of Interest (Water Domain)
National Contaminant Occurrence Database
Variable
Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
Diquat - detect
W_Diquat_detect ((jg/L)
722
Average for all detects in county
No
Noninformative
Endothall - average
W_Endothall (pg/L)
702
Average for all samples in county
No
Zeros
Endothall - detect
W_Endothall_detect ((jg/L)
702
Average for all detects in county
No
Noninformative
Glyphosate - average
W_Glyphosate ((jg/L)
772
Average for all samples in county
No
Zeros
Glyphosate - detect
W_Glyphosate_detect ((jg/L)
772
Average for all detects in county
No
Noninformative
di(2-Ethylhexyl) adipate (DEHA) - detect
W_DEHA_detect((jg/L)
1456
Average for all detects in county
No
Noninformative
Oxamyl (Vydate) - detect
W_Oxamyl_detect ((jg/L)
1254
Average for all detects in county
No
Noninformative
Simazine - detect
W_Simazine_detect ((jg/L)
1669
Average for all detects in county
No
Noninformative
di(2-Ethylhexyl) phthalate (DEHP) - detect
W_DEHP_detect((jg/L)
1449
Average for all detects in county
No
Noninformative
Picloram - detect
W_Picloram_detect ((jg/L)
1352
Average for all detects in county
No
Noninformative
Dinoseb - detect
W_Dinoseb_detect ((jg/L)
1347
Average for all detects in county
No
Noninformative
Hexachlorocyclopenta-diene - detect
W_HCCPD_detect((jg/L)
1518
Average for all detects in county
No
Noninformative
Carbofuran - detect
W_Carbofuran_detect ((jg/L)
1262
Average for all detects in county
No
Noninformative
Atrazine - detect
W_atrazine_detect ((jg/L)
1726
Average for all detects in county
No
Noninformative
Alachlor - detect
W_Alachlor_detect ((jg/L)
1662
Average for all detects in county
No
Noninformative
2,3,7,8-TCDD (Dioxin) - average
W_dioxin (pg/L)
123
Average for all samples in county
No
Zeros
2,3,7,8-TCDD (Dioxin) - detects
W_dioxin_detect ((jg/L)
123
Average for all detects in county
No
Noninformative
Heptachlor - detects
W_Heptachlor_detect ((jg/L)
1509
Average for all detects in county
No
Noninformative
Heptachlor epoxide - detects
W_Heptach lor_epox_detect
(MQ/L)
1508
Average for all detects in county
No
Noninformative
2,4-D (2,4-Dichlorophenoxyacetic acid) - detect
W_24D_detect (fjg/L)
1360
Average for all detects in county
No
Noninformative
2,4,5-TP (Silvex) - detect
W_silvex_detect ((jg/L)
1348
Average for all detects in county
No
Noninformative
Hexachlorobenzene - detect
W_HCB_detect((jg/L)
1520
Average for all detects in county
No
Noninformative
Benzo[a]pyrene - detect
W_BenzoAP_detect ((jg/L)
1430
Average for all detects in county
No
Noninformative
Pentachlorophenol - detect
W_PCP_detect (fjg/L)
1547
Average for all detects in county
No
Noninformative
1,2,4-Trichlorobenzene - detect
W_124TCIB_detect((jg/L)
2239
Average for all detects in county
No
Noninformative
Polychlorinated biphenyls (PCBs) - detect
W_PCBs_detect ((jg/L)
848
Average for all detects in county
No
Noninformative
1,2-Dibromo-3-chloropropane (DBCP) - detect
W_DBCP_detect (fjg/L)
1652
Average for all detects in county
No
Noninformative
Ethylene dibromide (EDB) - detect
W_EBD_detect (fjg/L)
1630
Average for all detects in county
No
Noninformative
Dichloromethane (Methylene chloride) - detect
W_DCM_detect ((jg/L)
2245
Average for all detects in county
No
Noninformative
1,2-Dichlorobenzene (o-Dichlorobenzene)
- detect
W_ODCB_detect ((jg/L)
2236
Average for all detects in county
No
Noninformative
1,4-Dichlorobenzene (p-Dichlorobenzene)
- detect
W_PDCB_detect (fjg/L)
2165
Average for all detects in county
No
Noninformative
Vinyl chloride - detect
W_VCM_detect (fjg/L)
2235
Average for all detects in county
No
Noninformative
1,1 -Dichloroethylene - detect
W_11DCE_detect((jg/L)
2238
Average for all detects in county
No
Noninformative
trans- 1,2-Dichloroethylene - detect
W_t12DCE_detect((jg/L)
2231
Average for all detects in county
No
Noninformative
1,2-Dichloroethane (Ethylene dichbride) - detect
W_EDC_detect((jg/L)
2238
Average for all detects in county
No
Noninformative
1,1,1 -Trichloroethane - detect
W_111 trich lorane_detect
(MQ/L)
2238
Average for all detects in county
No
Noninformative
Carbon tetrachloride - detect
W_CCI4_detect(|jg/L)
2237
Average for all detects in county
No
Noninformative
1,2-Dichloropropane - detect
W_PDC_detects (fjg/L)
2239
Average for all detects in county
No
Noninformative
Trichloroethylene - detect
W_Trichlorene_detect ((jg/L)
2250
Average for all detects in county
No
Noninformative
1,1,2-Trichloroethane - detect
W_112TCA_detect((jg/L)
2235
Average for all detects in county
No
Noninformative
Notes: Will use 6-Year Review 2 (data collected between 1998 and2005). Calculate the following variables for each chemical for each county (aggregating all public water supply in county) for all years
combined, missing for those counties without any data; did not keep detects.
B-9
-------
Variables of Interest (Water Domain)
National Contaminant Occurrence Database
Variable Variable Name
Counties (Missing/Zeros)
Variable Notes
EQI?
Notes
Tetrachloroethylene - detect
W_C2CI4_detect((jg/L)
2249
Average for all detects in county
No
Noninformative
Monochlorobenzene (Chlorobenzene) - detect
W_CI1benz_detect ((jg/L)
2231
Average for all detects in county
No
Noninformative
Benzene - detect
W_benzene_detect ((jg/L)
2239
Average for all detects in county
No
Noninformative
Toluene - detect
W_Toluene_detect ((jg/L)
2245
Average for all detects in county
No
Noninformative
Ethylbenzene - detect
W_ethylbenz_detect ((jg/L)
2241
Average for all detects in county
No
Noninformative
Styrene - detect
W_styrene_detect ((jg /L)
2235
Average for all detects in county
No
Noninformative
Alpha particles (gross alpha, excluding radon and
uranium) - detect
W_alpha_detect (pCi/L)
1251
Average for all detects in county
No
Noninformative
Combined radium-226/228 - average
W_Ra (pCi/L)
558
Average for all samples in county
No
Zeros
Combined radiu m-226/228 - detect
W_Ra_detect (pCi/L)
558
Average for all detects in county
No
Noninformative
Beta particles (gross beta) - average
W_beta (pCi/L)
845
Average for all samples in county
No
Noninformative
Beta particles (gross beta) - detect
W_beta_detect (pCi/L)
845
Average for all detects in county
No
Noninformative
Uranium - average
W_U (pg/L)
265
Average for all samples in county
No
Zeros
Uranium - detect
W_U_detect ((jg/L)
265
Average for all detects in county
No
Noninformative
cis-1,2-Dichloroethylene - average
W_DCE_detect ((jg/L)
2238
Average for all detects in county
No
Noninformative
Notes: Will use 6-Year Review 2 (data collected between 1998 and2005). Calculate the following variables for each chemical for each county (aggregating all public water supply in county) for all years
combined, missing for those counties without any data; did not keep detects.
Variables of Interest (Water Domain)
National Water Information System
EPA Storet Database—Not currently using
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
Maximum ammonia
ln_NWIS_NH4 (mg/L)
1843
Log-transformed
Sensitivity Analysis
Maximum arsenic
ln_NWIS_Arsenic (pg/L)
1401
Log-transformed
Sensitivity Analysis
Maximum nitrate
ln_NWIS_Nitrate (mg/L)
1630
Log-transformed
Sensitivity Analysis
Maximum nitrate-nitrite
ln_NWIS_N_N (mg/L)
1909
Log-transformed
Sensitivity Analysis
Maximum nitrite
ln_NWIS_Nitrite (mg/L)
1618
Log-transformed
Sensitivity Analysis
Maximum nitrogen (mixed forms)
ln_NWIS_Mixed_N (mg/L)
1716
Log-transformed
Sensitivity Analysis
Maximum organic nitrogen
ln_NWIS_Organic_N (mg/L)
1622
Log-transformed
Sensitivity Analysis
Maximum phosphate
ln_NWIS_Phosphate (mg/L)
1681
Log-transformed
Sensitivity Analysis
Maximum phosphorus
ln_NWIS_Phosporus (mg/L)
1634
Log-transformed
Sensitivity Analysis
Average Turbidity
In Access Database
1348
Measured in different units and not comparable
No
Not Comparable
Turbidity category
In Access Database
690
No
Zeros
Maximum discharge
In Access Database
78
No
Zeros
Maximum acifluorfen
In Access Database
728
No
Zeros
Maximum chlorophyll
In Access Database
30
No
Zeros
Maximum chlorophyll a
In Access Database
1183
No
Zeros
Maximum chlorophyll b
In Access Database
224
No
Zeros
Maximum chlorophyll c
In Access Database
9
No
Zeros
Maximum nitrogen
In Access Database
633
No
Zeros
Maximum nitrogen (15/14 ratio)
In Access Database
132
No
Zeros
Maximum phosphate-phosphorus
In Access Database
400
No
Zeros
Maximum phosphoric acid
In Access Database
260
No
Zeros
Sediment
In Access Database
1005
No
Zeros
Notes: Average of all values for a measure in the county. Not enough data for annual measure; therefore, used all years combined, 2000-2005. Use as replacements for sensitivity analysis, but all measures
are in other datasets. In sensitivity analysis, all have very low loadings.
B-10
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Variables of Interest (Water Domain)
Safe Drinking Water Information System
Variables
Variable Name
Counties
Variable Notes
EQI? Notes
Total arsenic violations
SDWIS_Arsenic
575
Count variable
Sensitivity Analysis
Total Total Coliform Rule violations
SDWIS_Coliform
2368
Count variable
Sensitivity Analysis
Total other inorganic chemicals violations
SDWISJOC
830
Count variable
Sensitivity Analysis
Total Lead and Copper Rule violations
SDWIS_LCR
895
Count variable
Sensitivity Analysis
Total nitrates violations
SDWIS_Nitrates
1309
Count variable
Sensitivity Analysis
Total radionuclides violations
SDWIS_Radionuclides
259
Count variable
Sensitivity Analysis
Total synthetic organic chemicals violations
SDWIS_SOC
554
Count variable
Sensitivity Analysis
Total Surface Water Treatment Rule violations
SDWIS_SWTR
507
Count variable
Sensitivity Analysis
Total volatile organic chemicals violations
SDWIS_VOC
641
Count variable
Sensitivity Analysis
Total pathogen violations
SDWIS_Pathogens
4
Zeros
Total Stage 1 Disinfectants By-Product Rule violations
SDWIS_DBP
32
Zeros
Total turbidity violations
S D WI S_T u rbi dity
25
Zeros
Notes: Cumulative count of violations for all public water suppliers in county for the year. Data are available annually. Data complied for 2002. Use as replacements for sensitivity analysis, but all measures are
in other datasets.
Variables of Interest (Land Domain)
2002 Census of Agriculture
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
Commercial fertilizer, lime, and
soil conditioners
pct_lime_acres
3065
Yes
Manure
pct_manure_acres_ln
2975
Yes
Chemicals used to control
insects
pct_insecticide_acres
3141
Yes
Chemicals used to control
weeds, grass, or brush
pct_weed_acres
3061
Yes
Chemicals used to control
nematodes
pct_nematode_acres_ln
1933
Yes
Chemicals used to control
diseases in crops and orchards
pct_disease_acres_ln
2530
Yes
Chemicals used to control
growth, thin fruit, or defoliate
pct_defoliate_acres_ln
1980
Yes
Barley for grain (bushels)
pct_barley_acres
1252
No
Deleted; too many missing counties
Buckwheat (bushels)
pct_buckwheat_acres
233
No
Deleted; too many missing counties
Corn for grain (bushels)
pct_corn_acres
2588
Yes
Cotton, all (bales)
pct_cotton_acres
663
No
Deleted; too many missing counties
Dry southern peas (cowpeas)
(bushels)
pct_cowpeas_acres
186
No
Deleted; too many missing counties
Durum wheat for grain (bushels)
pct_duru m_wheat_acres
154
No
Deleted; too many missing counties
Dry edible beans, excluding
limas (cwt)
pct_edible_beans_acres
583
No
Deleted; too many missing counties
Flaxseed (bushels)
pct_flaxseed_acres
117
No
Deleted; too many missing counties
Lentils (cwt)
pct_lentils_acres
48
No
Deleted; too many missing counties
Dry lima beans (cwt)
pctji ma_beans_acres
48
No
Deleted; too many missing counties
Proso millet (bushels)
pct_millet_acres
178
No
Deleted; too many missing counties
Mustard seed (pounds)
pct_mustard_acres
92
No
Deleted; too many missing counties
Oats for grain (bushels)
pct_oats_acres
2221
Deleted; too many missing counties
Other spring wheat for grain
(bushels)
pct_oth_wheat_acres
520
No
Deleted; too many missing counties
Peanuts for nuts (pounds)
pct_peanuts_acres
406
No
Deleted; too many missing counties
Notes: Acres of crop or treatment were divided by total county acres to get percentage of item per county. Some counties had suppressed acreage due to identifiablity issues. For these, the unaccounted for
acreage for each State was calculated (total State acreage—listed county acreage) .The acreage was divided equally among the farms in counties with suppressed information. Data for Hawaii and Alaska are
not available. These data are refreshed every 5 years. The next available data is for 2007.
B-ll
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Variables of Interest (Land Domain)
2002 Census of Agriculture
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
Dry edible peas (cwt)
pct_peas_acres
222
No
Deleted; too many missing counties
Pima cotton (bales)
Pct_pi ma_cotton_acres
21
No
Deleted; too many missing counties
Popcorn (pounds, shelled)
Pct_popcorn_acres
426
No
Deleted; too many missing counties
Potatoes (cwt)
Pct_potato_acres
1565
Yes
Rice (cwt)
Pct_rice_acres
150
No
Deleted; too many missing counties
Rye for grain (bushels)
pct_rye_acres
1211
No
Deleted; too many missing counties
Safflower (pounds)
pct_safflower_acres
113
No
Deleted; too many missing counties
Sorghum for grain (bushels)
pct_sorgum_acres
1318
No
Deleted; too many missing counties
Soybeans for beans (bushels)
pct_soybean_acres
2082
Yes
Emmer and spelt (bushels)
pct_spelt_acres
181
No
Deleted; too many missing counties
Sugarbeets for sugar (tons)
pct_sugarbeet_acres
158
No
Deleted; too many missing counties
Sunflower seed, all (pounds)
pct_sunflower_acres
779
No
Deleted; too many missing counties
Sweet potatoes (cwt)
pct_sweet_potatoes_acres
600
No
Deleted; too many missing counties
Tobacco(pounds)
pct_tobacco_acres
565
No
Deleted; too many missing counties
Triticale (bushels)
pct_triticale_acres
180
No
Deleted; too many missing counties
Upland cotton (bales)
pct_upland_cotton_acres
663
No
Deleted; too many missing counties
Wheat for grain, all (bushels)
pct_wheat_acres
2520
Yes
Wild rice (cwt)
pct_wild_rice_acres
20
No
Deleted; too many missing counties
Winter wheat for grain (bushels)
pct_winter_wheat_acres
2472
No
Under wheat
Animal units
pct_au_ln
3078
1 animal unit is equal to 0.94 cattle and calves, 5.88
hogs and pigs, 250 egg-laying chickens, and 455
broiler chickens.
Yes
Number of farms
farms_per_acre_ln
3039
Yes
Irrigated acres
pctj rri gated_acres_l n
2815
Yes
Harvested acres pct_harvest_acres 2755 Yes
Notes: Acres of crop or treatment were divided by total county acres to get percentage of item per county. Some counties had suppressed acreage due to identifiable issues. For these, the unaccounted for
acreage for each State was calculated (total State acreage—listed county acreage) .The acreage was divided equally among the farms in counties with suppressed information. Data for Hawaii and Alaska are
not available. These data are refreshed every 5 years. The next available data is for 2007.
Variables of Interest (Land Domain)
National Pesticide Use Dataset and 2002 Census of Agriculture
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
Insecticides
insecticidesjn
2761
Yes
Herbicides
herbicidesjn
2907
Yes
Fungicides
fungicidesjn
2256
Yes
Other pesticides
oth_pesticides
820
No
Deleted; too many missing counties
Notes: Pesticide concentrations were estimated by multiplying the State rate of use by crop by the acres for each crop. Pesticides then were grouped by class and added together to get class level estimates
of pesticide application. These data are refreshed every 5 years. The next
B-12
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Variables of Interest (Land Domain)
National Geochemical Survey
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
Mean level of arsenic from sampled county sources
Mean_as_ln
3083
See notes above
Yes
Mean level of selenium from sampled county sources
Mean_se_ln
3082
See notes above
Yes
Mean level of mercury from sampled county sources
Mean_hg_ln
3082
See notes above
Yes
Mean level of lead from sampled county sources
Mean_pb_ln
3083
See notes above
Yes
Mean level of zinc from sampled county sources
Mean_zn_ln
3083
See notes above
Yes
Mean level of copper from sampled county sources
Mean_cu_ln
3083
See notes above
Yes
Mean level of aluminum from sampled county sources
Mean_al_pct
3083
See notes above
Yes
Mean level of sodium from sampled county sources
Mean_na_pct
3083
See notes above
Yes
Mean level of magnesium from sampled county sources
Mean_mg_pct_ln
3083
See notes above
Yes
Mean level of titanium from sampled county sources
Mean_ti_pct_ln
3083
See notes above
Yes
Mean level of calcium from sampled county sources
Mean_ca_pct_ln
3083
See notes above
Yes
Mean level of manganese from sampled county sources
Mean_mn
3083
See notes above
Yes
Mean level of iron from sampled county sources
Mean_fe_pct_ln
3083
See notes above
Yes
Mean level of phosphorus from sampled county sources
mean_al_pct
3083
See notes above
Yes
Notes: The U.S. National Geochemical Survey (NGS) geochemistry by county database (for the United States) was published on September 30, 2008, but the actual data were collected over many years
across the 50 States (ranging from 1998-2007) (http://tin.er. usgs.gov/geochem/doc/averages/countv data, html To create the county database, "All data in the NGS database were pooled for the purpose
of display in the gridded maps. Data for stream sediments, soils, and other media were combined" (from http://tin.er.usgs.gov/geochem/doc/mapdoc.htmV "The current (2008) NGS database (http://
tin.er.usgs.gov/geochem/doc/home.htm) contains analyses of 74,498 samples and 2714 standards"(from http://tin.er.usgs.gov/geochem/doc/status.htm). According to NGS, "The samples and data
that comprise the NGS database come from a wide variety of sources." See http://tin.er.usgs.gov/geochem/doc/groups-cats.htm for a description of the sources, along with their associated date ranges.
Refresh dates are not available.
Variables of Interest (Land Domain)
U.S. Environmental Protection Agency (EPA) Radon Zones Map
Variables Variable Name
Counties
Variable Notes
EQI?
Notes
Radon zones Radon_zone 3126 Three-level variable Yes
Notes: The EPA Map of Radon Zones identifies areas of the United States with the potential for elevated indoor radon levels. Each U.S. county (3141) is assigned to one of three zones based on radon
potential. Data years are unavailable. Presumably, radon is a stable feature, and the map is not variable, but refresh dates are not available. No other information is available in data documentation.
Variables of Interest (Land Domain)
Superfund National Priorities List Sites
Variables Variable Name
Counties
Variable Notes
EQI?
Notes
^ . x r . Mni x 4, ^ x x x/ Included as part of composite count
Count of superfund N PL sites per county sf_county_count 721 Yes variable
Notes: Superfund National Priorities List (NPL) site locations are available through the EPA Geospatial Data Access Project. Sites were included in the counts if they were identified in 2000 through 2005.
Published July 2, 2009. Start and end dates are not available. Data are refreshed monthly.
Variables of Interest (Land Domain)
Resource Conservation and Recovery Act Treatment, Storage, and Disposal Facilities and RCRA Corrective Action Facilities
Variables Variable Name Counties Variable Notes EQI? Notes
Count of RCRA TSD and corrective action . , . , ,. 0-,. N/ Included as part of composite count
t ..... , rcra tsd count by fips 874 Yes r . ,, r
facilities per county ~ ~ ~ J~ r vanable
Notes: Resource Conservation and Recovery Act (RCRA) treatment, storage, and disposal (TSD) and corrective action facilities site locations available through the EPA Geospatial Data Access Project. Sites
were included in the counts if they were identified in 2000 through 2005. Published July 2, 2009. Start and end dates not available. Data refreshed monthly.
Variables of Interest (Land Domain)
Resource Conservation and Recovery Act Large-Quantity Generators
Variables Variable Name
Counties
Variable Notes
EQI?
Notes
Count of RCRA LQG facilities per . . N/ Included as part of composite count
, r rcralqg count 1926 Yes r . ,,
county variable
Notes: Resource Conservation and Recovery Act (RCRA) large-quantity generator (LQG) site locations through the EPA Geospatial Data Access Project. Sites were included in the counts if they were
identified in 2000 through 2005. Published July 2,2009. Start and end dates not available. Data refreshed monthly.
B-13
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Variables of Interest (Land Domain)
Toxic Release Inventory Sites
Variables
Variable Name
Counties
Variable Notes
EQI?
Notes
~ , (xn, , x xx oc7A w Included as part of composite count
Count of TRI sites per county tn county count 2670 Yes r . .. r
r variable
Notes: Toxic Release Inventory (TRI) sites available through the EPA Geospatial Data Access Project. Sites were included in the counts if they were identified in 2000 through 2005. Published July 2, 2009.
Start and end dates not available. Data refreshed monthly.
Variables of Interest (Land Domain)
Assessment, Cleanup and Redevelopment Exchange (ACRES) Brownfield Sites
Variables Variable Name Counties
Variable Notes
EQI?
Notes
^ . i a r>nirc + + v Included as part of composite count
Count of ACRES sites per county acres county count 1226 Yes r . .. r
r variable
Notes: Brownfield site locations available through the EPA Geospatial Data Access Project. Sites were included in the counts if they were identified in 2000 through 2005. Published July 2, 2009. Start and end
dates not available. Data refreshed monthly.
Variables of Interest (Land Domain)
Section Seven Tracking System Pesticide-Producing-Site
Locations
Variable
Variable Name
Counties
Variable Notes
EQI?
Notes
Count of SSTS sites per county ssts county count 2095 Yes Included as part of composite
r ' -j— nni int wariah p
Notes: Section Seven Tracking System (SSTS) pesticide-producing-site locations available through the EPA Geospatial Data Access Project. Sites were included in the counts if they were identified in 2000
through 2005. Published July 2, 2009. Start and end dates not available. Data refreshed but not annually.
Variables of Interest (Sociodemographic Domain)
U.S. Census Summary Files
Variable
Variable Name
Counties
Variable Notes
EQI?
No, Notes
Percent tenter-occupied units
pct_rent_occ
3141
Yes
Percent vacant units
pct_vac_units
3141
Yes
Median household value
med_hh_value
3141
Yes
Median household income
med_hh_inc
3141
Yes
Percent of persons less than poverty level
pct_pers_lt_pov
3141
Yes
Percent of persons who do not speak English
pct_no_eng
3141
Yes
Percent of persons with more than high school education
pct_hs_more
3141
Yes
Percent of persons who work outside their county of residence
work_out_co
3141
Yes
Median number of rooms in residence
med_rooms
3141
Yes
Percent of residences with more than 10 units
pct_mt_10units_log
3141
Yes
Notes: Many, many more variables are available from the U.S. Census than will be described here. The variables identified here are those that will be used in the EQI and not the plethora of variables that
could be constructed. Data are available for multiple units of geographic aggregation, including the county-level. Full population data are collected decennially; sample data are collected more frequently. The
percent of residences with more than 10 units variable was log-transformed to enable it to approximate normality. Data are available for download from the U.S. Census Bureau Web site.
B-14
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Variables of Interest (Sociodemographic Domain)
Federal Bureau of Investigation Uniform Crime Reports
Variable Variable Name
Counties
Variable Notes
EQI?
No, Notes
Violent crime rate
Property crime rate
vio lent_rate_log
1055
Variable kriged to estimate values for counties
with no reported violent crime data.
Yes
Murder-manslaughter crime rate
murder_manslaughter_rate
1062
Variable kriged to estimate values for counties
with no reported violent crime data.
No
Constituent of violent crime rate
Rape crime rate
rape_rate
1055
Variable kriged to estimate values for counties
with no reported violent crime data.
No
Constituent of violent crime rate
Robbery crime rate
rob_rate
1062
Variable kriged to estimate values for counties
with no reported violent crime data
No
Constituent of violent crime rate
Aggravated assault crime rate
agg_assault_rate
1062
Variable kriged to estimate values for counties
with no reported violent crime data.
No
Constituent of violent crime rate
prop_rate
1062
Variable kriged to estimate values for counties
with no reported property crime data.
Highly correlated with violent crime;
No violent crime shown more consistent
association in literature.
Dix u + Variable kriged to estimate values for counties M ~ ... . f , .
Burglary crime rate burg rate 1062 ... ° . . . . . No Constituent of property crime rate
3 3 with no reported property crime data. r r 3
. . . . Variable kriged to estimate values for counties M ~ ... . , , .
Larceny cnme rate larc rate 1062 ... ° . . . . . No Constituent of property crime rate
3 - with no reported property crime data. r r 3
,. - ., u . sr.™ Variable kriged to estimate values for counties M ~ ... . t
Vehicle theft rate vehicle theft rate 1062 ... u . . .. No Constituent of property crime rate
with no reported property crime data. r r 3
Notes: Federal Bureau of Investigation (FBI) Uniform Crime Reports data were downloaded for each county in each State from the Web site (http ://www.ucrdatatool.gov/). Data are available by year and
by crime type (violent = murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault; property = burglary, larceny/theft, and motor vehicle theft). Data from 2000-2005 were kriged
temporally and spatially for use in the EQI. Data reporting is voluntary. Data are available at the city and county levels, but many counties do not report these data. Data are for law enforcement agencies
serving city jurisdictions with populations of 10,000 or more and county agencies of 25,000 or more. Therefore, data may not be available for each jurisdiction each year. Data are available from 1960 to
current year. Rates were obtained from the FBI. The violent crime rate data were transformed (log) to account for the large number of zeros and to result in nearly normally distributed data.
Variables of Interest (Sociodemographic Domain)
Home Mortgage Disclosure Act Data - Not used.
Notes: These data are available by year and by State from this Web site (http://www.ffiec.gov/hmdaadwebreport/AggWelcome.aspxV Unfortunately, only a few counties per state report these data.
While this data source would be a good data source for larger Metropolitan Statistical Areas or municipalities, it was not appropriate for a county-level analysis.
Variables of Interest (Built Domain)
Housing and Urban Development (HUD) Data
Variable
Variable Name Counties
Variable Notes
EQI?
Notes
Rate of low-rent HUD units in county
low rent rate
2080
Variable transformed (log) to enable it to
approximate normal distribution.
No
Constituent of
total unit rate
Rate of Section 8 units in county
l sec 8 rate
2080
Variable transformed (log) to enable it to
approximate normal distribution.
No
Constituent of
total unit rate
Rate of low-rent plus Section 8 units in county
to unit rate I
3141
Variable transformed (log) to enable it to
approximate normal distribution.
Yes
Zeros considered
meaningful zeros
(lack of public housing)
Notes: These data provide a count of the low-rent and Section 8 housing in each housing authority area. These housing authority areas correspond to cities, which then are assigned FIPS codes. Counties
without housing authority cities are given a count of zero for low-rent and/or Section 8 housing. These data were transformed (log) to account for the large number of zeros and to result in nearly normally
distributed data. Data are refreshed frequently (e.g., updates on Alaska data were April 2012 and August 2012), but update frequency was not provided. Historic data do not appear to be available from
Web site. Data were collected in 2010, but, because low-rent and Section 8 housing do not change substantially overtime, these data are considered representative of the 2000-2005 period. Rates for each
variable constructed by dividing count by county population
Variables of Interest (Built Domain)
Fatality Analysis Reporting System Data
Variable
Variable Name Counties
Variable Notes
EQI?
Notes
n . tt, . , . f . I . I o.,,,. Variable transformed (log) to enable it to M
Rate of fatal car crashes per county fatal rate log 3141 . x ' .., .. No
r 3 - - » approximate normal distribution.
Notes: The Fatality Analysis Reporting System (FARS) is a nationwide census providing the National Highway Traffic Safety Administration yearly data regarding fatal injuries suffered in motor vehicle traffic
crashes. FARS data are available from 1975 (http://www.nhtsa.gov/FARS/V Rates for the count of fatal crashes per county for 2000-2005 were constructed by dividing count by county population. These
data were transformed (log) to account for the large number of zeros and to result in nearly normally distributed data. These data can be updated annually.
B-15
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Variables of Interest (Built Domain)
2000 U.S. Census Summary Files
Variable
Variable Name
Counties
Variable Notes
EQI?
Notes
Percent of county residents who report using public . ... . . . Variable transformed (log) to enable it to x/
. . pet public transport log 3141 , v Yes
transportation r -r ~ r ~ ° approximate normal distribution.
Notes: Many, many more variables are available from the U.S. Census than will be described here. The variables identified here are those that will be used in the EQI and not the plethora of variables that
could be constructed. Data are available for multiple units of geographic aggregation, including the county-level. Full population data are collected decennially; sample data are collected more frequently.
These data were transformed (log) to account for the large number of zeros and to result in nearly normally distributed data. Data are available for download from the U.S. Census Bureau Web site.
Variables of Interest (Built Domain)
TIGER Files
Variable
Variable Name
Counties
Variable Notes
EQI? Notes
Proportion of all roads that are highways
hwyprop
3141
Yes
Proportion of all roads that are secondary roads
ryprpo
3141
Yes
Proportion of all roads that are streets
strprop
3141
jsjo Remaining roads are streets; therefore, having two
of three types sufficient.
Notes: Topological^ Integrated Geographic Encoding and Referencing (TIGER) products provide maps and road layers worldwide and for the United States. These data are updated regularly but do not
change substantially overtime. The data used in the EQI are from 2003. Data are available at Census geography. For the street types, the highway, and secondary and local roads (tertiary roads) per county
per state were downloaded. Proportion of each road type was constructed by dividing the distance of each road type by the total amount of each road.
Variables of Interest (Built Domain)
Dun and Bradstreet
Variable
Variable Name
Counties
Variable Notes
EQI? Notes
Rate of positive food environment businesses
per county
rate_food_env_pos_log
3134
Yes
Rate of negative food environment businesses
per county
rate_food_env_neg
2770
Yes
Rate of alcohol, pawn, gaming businesses per
county
rate al pn bm env log
2942
Yes
Rate of entertainment businesses per county
rate_ent_env_log
2877
Yes
Rate of health-care-related businesses per
county
rate_hc_env_log
3108
Yes
Rate of recreation-related businesses per county
rate_rec_env_log
2951
Yes
Rate of education-related businesses per county
rate_ed_env_log
1283
Yes
Rate of social-service-related businesses per
county
rate_ss_env_log
3097
Yes
Rate of transportation-related businesses per
county
rate_trans_env_log
2628
Yes
Notes: Dun and Bradstreet collect commercial information on businesses. Its database contains more than 195 million records and is proprietary. The data are put through an extensive quality assurance
process, which includes over 2000 separate automated checks, plus several manual checks. Data are updated daily. Rates of each type of business in 2002 were calculated by dividing the counts of each
variable by the county population. These data were transformed (log) to account for the large number of zeros and to result in nearly normally distributed data.
Variables of Interest (Built Domain)
Rural-Urban Commuting Area—Not used
Notes: These data are available from the U.S. Census and are constructed at the census-tract level. Because they were constructed for a smaller unit of aggregation (tract) and would have to be aggregated
to a larger unit of geography (county), they were not used in the EQI. Full population data are collected decennially; sample data are collected more frequently. Data are available for download from the U.S.
Census Bureau Web site.
Variables of Interest (Built Domain)
Urban-Influence Code—Not used
Notes: The 2003 urban influence codes form a classification scheme that distinguishes metropolitan counties by size and nonmetropolitan counties by size of the largest city or town and proximity to
metropolitan and micropolitan areas. The standard Office of Management and Budget (OMB) metropolitan and non metropolitan categories have been subdivided into two metropolitan and 10 non metropolitan
categories, resulting in a 12-part county codification. This scheme was originally developed in 1993. This scheme enables researchers to break county data into finer residential groups, beyond metropolitan
and non metropolitan, particularly for the analysis of trends in non metropolitan areas that are related to population density and metropolitan influence.. This data source was not used because it is very similar
to the rural-urban continuum codes, which have been used more widely in the literature.
B-16
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Appendix III
Table of Highly Correlated Variables
for Each Domain
Air Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
Trichlorobenzene124
Methanol (.85), Phenol (.98)
No
Methanol
Butadinene13
Trimethylpentane224 (.83) Acetaldehyde (.89)
Ethylbenzene (.81) Formaldehyde (.93) Propionaldehyde
(.75) Toluene (.9)
Xylenes (.81)
Benzene (.88)
No
Toluene
Dichloropropene13
Chlorobenzene (.82) Methyl_bromide (.87)
No
Chlorobenzene
Dichlorobenzene14
Tetrachloroethylene (.76)
No
Tetrachloroethylene
Butadiene 13 (.83)
Di n itroto Iuene24 (.72)
Acetaldehyde (.82)
Ethylbenzene (.94)
Trimethylpentane224
Formaldehyde (.74)
Naphthalene (.70)
Propionaldehyde (.78)
Toluene (.93)
Xylenes (.91)
Benzene (.85)
No
Toulene
Trimethylpentane224 (.72)
Chloroform (.72)
Di n itroto Iuene24
Ethylbenzene (.76)
Naphthalene (.70)
Xylenes (.74)
Benzene (.79)
No
Toluene
Nitropropane2
MDI_44 (.77)
Yes
Butadiene 13 (.89)
Trimethylpentane224 (.82)
Ethylbenzene (.79)
Acetaldehyde
Formaldehyde (.92)
Propionaldehyde (.84)
Toluene (.90)
Xylenes (.78)
Benzene (.86)
No
Toluene
Ally l_ch bride
Epich lorohydri n (.93)
No
Epich lorohydri n
Carbonjetrach bride
Methyl_ch bride (.7)
Yes
Chlorobenzene
Dichloropropene13 (.82) Methyl_bromide (.86)
Naphthalene (.73)
Yes
Chloroform
Di n itroto Iuene24 (.72)
Xylenes (.71)
Yes
Chromium_compounds
Cobalt_compounds (.88) Nickel_compounds (.8)
Yes
Cobalt_compounds
Nickel_compounds (.8) Chromium_compounds (.88)
No
Chromium_compounds
Epich lorohydri n
Ally l_ch bride (.93)
Yes
Ethylbenzene
Butadinene13 (.81) Trimethylpentane224 (.94)
Di n itroto Iuene24 (.76) Acetaldehyde (.79) Formaldehyde
(.73) Naphthalene (.74) Propionaldehyde (.77)
Tetrachloroethylene (.71)
Toluene (.94)
Xylenes (.98)
Benzene (.86)
No
Toluene
Formaldehyde
Butadinene13 (.93) Trimethylpentane224 (.74)
Acetaldehyde (.92) Ethylbenzene (.73) Propionaldehyde
(.76)
Toluene (.84)
Xylenes (.73)
Benzene (.83)
No
Toluene
c-i
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Air Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
Hydrochloric_acid
Hydrof I uoric_aci d (1) Arsenic_cmpds (.8)
Yes
Hydrof 1 uoric_aci d
Hydrochloric_acid (1) Arsenic_cmpds (.8)
No
Hydrochloric_acid
Methanol
Trichlorobenzene124 (.85) Phenol (.86)
Yes
MethyLbromide
Dichloropropene13 (.87) Chlorobenzene (.86)
Naphthalene (.72)
No
Chlorobenzene
Methyl_ch bride
Carbonjetrach bride (.7)
No
Carbonjetrach bride
Naphthalene
Chlorobenzene (.73) MethyLbromide (.72)
Trimethylpentane224 (.7) D i n itroto Iuene24 (.7)
Ethylbenzene (.74) Tetrachloroethylene (.74) Toluene
(.75)
Xylenes (.78)
No
Toluene
Nickel_compounds
Cobalt_compounds (.8) Chromium_compounds (.8)
No
Chromium_compounds
Phenol
Trichlorobenzene124 (.98) Methanol (.86)
No
Methanol
Propionaldehyde
Butadinene13 (.75) Trimethylpentane224 (.78)
Acetaldehyde (.84) Ethylbenzene (.77) Formaldehyde
(.76)
Toluene (.81)
Xylenes (.76)
Benzene (.74)
No
Toluene
Tetrachloroethylene
Dichlorobenzene14 (.76)
Yes
Toluene
Butadinene13 (.9) Trimethylpentane224 (.94)
D i n itroto Iuene24 (.74) Acetaldehyde (.90) Ethylbenzene
(.94) Formaldehyde (.84) Naphthalene (.75)
Propionaldehyde (.81) Tetrachloroethylene (.74) Xylenes
(.93)
Benzene (.91)
Yes
Butadinene13 (.81) Trimethylpentane224 (.91)
D i n itroto Iuene24 (.79) Acetaldehyde (.78)
Chloroform (.71)
Xylenes
Ethylbenzene (.98) Formaldehyde (.73) Naphthalene
(.78) Propionaldehyde (.76) Tetrachloroethylene (.73)
Toluene (.93)
Benzene (.85)
No
Toluene
MDI_44
Nitropropane2 (.76)
No
Nitropropane 2
Arsenic_compounds
Hydrochloric_acid (1) Hydrof I uoric_aci d (1)
No
Hydrochloric_acid
Benzene
Butadinene13 (.88) Trimethylpentane224
(.85) Acetaldehyde (.86) Ethylbenzene (.85)
Formaldehyde (.83) Propionaldehyde (.74)
Toluene (.91)
Xylenes (.85)
No
Toluene
Water Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
NPDES_GENERALFACILITIES
INDNPDESperKM (.75)
No
INDNPDESperKM
NPDESJNDIVIDUAL
INDNPDESperKM (.73)
No
INDNPDESperKM
NPDES_TOTAL
INDNPDESperKM (.80)
No
INDNPDESperKM
INDNPDESperKM
NPDES_GENERALFACILITIES (.75), NPDES_
INDIVIDUAL (.73), NPDES_TOTAL (.80)
Yes
Per_TotPopPS_2000
Percent SS (.80)
No
Per_PSWithSW_ave
Per_PSWith GW_2000
Percent PS with SW Supply (1.0)
No
Per_PSWithSW_ave
Per_DOPS_2000
Percent PS with SW Supply (.73)
No
Per_PSWithSW_ave
Per_DOSS_2000
Percent SS (.92)
No
Per_TotPopSS_ave
AvgOfNothing_ave
AvgofDO AvgOfDO (1.00), AvgofD 1 AvgOfD 1 (.94), AvgofD2 No
AvgOfD2 (.86), AvgofD3 AvgOfD3 (.71)
AvgOfD3_ave
AvgOfDO_ave
AvgOfNothing (.94), AvgofD 1 AvgOfD 1 (.94), AvgofD2
AvgOfD2 (.86), AvgofD3 AvgOfD3 (.71)
No
AvgOfD3_ave
Avg0fD1_ave
AvgOfNothing (.94), AvgofDO AvgOfDO (.94), AvgofD2
AvgOfD2 (.86), AvgofD3 AvgOfD3 (.71)
No
AvgOfD3_ave
Avg0fD2_ave
AvgOfNothing (.86), AvgofDO AvgOfDO (.86), AvgofD 1
AvgOfD 1 (.94), AvgofD3 AvgOfD3 (.71)
No
AvgOfD3_ave
C-2
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Water Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
AvgOfD4_ave
AvgofDI AvgOfDI (.94), AvgofD2 Avg0fD2 (.86), AvgofD3
Avg0fD3 (.80)
No
AvgOfD3_ave
Land Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
Mean iron percent
Mean manganese (0.90)
Yes
Mean manganese
Mean iron percent (0.90)
No
Mean iron percent
Percent weed acres
Percent harvested acres (0.96), percent lime acres (0.95)
No
Percent harvested acres
Percent lime acres
Percent harvested acres (0.97), percent weed acres (0.95)
No
Percent harvested acres
Percent harvested acres
Percent weed acres (0.96), percent lime acres (0.97)
Yes
Sociodemographic Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
Property crime rate
Violent crime rate (0.91)
No
Violent crime rate
Violent crime rate
Property crime rate (0.91)
Yes
Built Domain
Variable
Correlated Variables
Used in EQI?
If Not, Variable Used To Represent Group
Secondary road proportion
Street proportion (-0.94)
No
Street proportion
Street proportion
Secondary road proportion
(-0.94)
Yes
C-3
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Appendix IV
County Maps of Environmental Quality Index
Percentile
Map 1. Overall Environmental Quality Index by County, 2000-2005"
* For orientation to the maps, low index scores (EQi and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
-------
¦ ¦¦ _«M
Percentile
Map 2. Air Domain Index by County, 2000-2005*
Percentile
Map 3. Water Domain Index by County, 2000-2005*
* For orientation to the maps, low index scores (EQI and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
D-2
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Percentile
Map 4. Land Domain Index by County, 2000-2005"
Percentile
Map 5. Sociodemographic Domain Index by County, 2000-2005"
* For orientation to the maps, low index scores (EQi and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
D-l
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Percentile
Map 6. Built Domain Index by County, 2000-2005"
* For orientation to the maps, low index scores (EQI and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
D-2
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J I ] I I 0 - 5th Percentile
~ n n n 5* - 20th Percentile
1 I [ 1 I | | 20th - 40th Percentile
I [¦ 40th - 60th Percentile
^H| Bi 60th - 80th Percentile
[ 1 r II 80th - 95th Percentile
95th- 100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 = Thinly populated
Map 7. Overall 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"
* For orientation to the maps, low index scores (EQI and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
D-3
J [ i 0 - 5th Percentile
~ ~~~ 5th _ 20th Percentile
I I [m I I 20th - 40th Percentile
H I: I ¦ I I 40th - 60th Percentile
HI 6Qth" 80lh Percentile
80th - 95th Percentile
1 ll 95th - 100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 = Thinly populated
-------
I I I 1 I I 0 - 5th Percentile
¦ BEH 5th _ 20th Percentile
¦I ~ ¦ {¦' 20th - 40'h Percentile
[ ¦ 1 1 40th - 60th Percentile
I I I I 60th - 80th Percentile
I 80th - 95th Percentile
B ¦ ¦ 95"1- "lOO* 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*
0 - 5®1 Percentile
I 1 | ] I I I I 5th - 20lh Percentile
[ ] i | ] ] | 20"1 - 40lh Percentile
¦ H IHI 40th - 60th Percentile
I I !¦] I 60th - 80th Percentile
¦ [ J 80th - 95th Percentile
H HI ¦ 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*
* For orientation to the maps, low index scores (EQI and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
D-4
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I I I I I I I 0 - 5th Percentile
I I \ZZ ~ ZD 5th - 20th Percentile
I I |~~l 20th - 40th Percentile
I ( | I I 40th - 60"! Percentile
|H I I 60th - 80th Percentile
I I 80th - 95th Percentile
|^B 95th- 100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
Map ll.Sociodemographic Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005"
[] 0 - 5th Percentile
~ ~ 5th- 20th Percentile
20th - 40th Percentile
H [3 ¦ ¦ 40th
H ¦ ¦ ¦ 60th
¦ ¦ ¦ ¦ 80th
¦ 95th
60th Percentile
80th Percentile
95th Percentile
100th Percentile
RUCC1 = Metropolitan urbanized
RUCC2 = Non-metro urbanized
RUCC3 = Less urbanized
RUCC4 = Thinly populated
Map 12. Built Domain Index Stratified by Rural Urban Continuum Codes by County, 2000-2005*
* For orientation to the maps, low index scores (EQI and domain-specific) indicate higher environmental quality, and higher index scores (EQI and domain-specific) mean lower environmental quality.
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Appendix V
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 Brody. 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 this report in
Part II: Data Source Identification and Review. Additional
information about the sources can be found in Appendix I and
Appendix II. Table 1 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 under Part V: Discussion.
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