United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/600/R-12/518
May 2012
r/EPA
Methods to Select
Metropolitan Areas of
Epidemiologic Interest for
Enhanced Air Quality
Monitoring
Air Quality Monitoring by
the EPA Speciation Trends
Network, 2001-2005
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May 2012
Method to Select Metropolitan Areas of
Epidemiologic Interest for
Enhanced Air Quality Monitoring
Air Quality Monitoring by the
EPA Speciation Trends Network,
2001-2005
By
Lisa Baxter, Stephen Teet, and Lucas M. Neas
National Exposure Research Laboratory
National Health and Environmental Effects Laboratory
Research Triangle Park, NC, 27711
Project Officer
Lucas M. Neas, ScD.
Epidemiology Branch/EPHD/NHEERL/ORD
U.S. EPA MD-58A
Research Triangle Park, NC, 27711
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Notice
The information in this document has been subjected to review by the U.S. Environmental Protection
Agency, Office of Research and Development, and has been approved for publication. Approval does not
signify that the contents reflect the views of the Agency, not does mention of trade names or commercial
products constitute endorsement or recommendation for use.
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Executive Summary
The U.S. Environmental Protection Agency's current Speciation Trends Network (STN) covers most major
U.S. metropolitan areas and a wide range of paniculate matter (PM) constituents and gaseous co-pollutants.
However, using filter-based methods, most PM constituents are not measured daily and the lack of daily air
quality data complicates epidemiologic analyses of the potential adverse health effects of these PM
constituents.
Possible criteria for the identification of metropolitan areas with the greatest epidemiologic value for
enhanced monitoring are population, mean levels and variation of criteria air pollutants and PM
constituents, correlations among these pollutants, and the relationship of these correlations to the
coefficient of variation.
Using a review of air quality measurements from 49 STN monitors for 2001-2005 as an illustration of this
criteria, we selected metropolitan areas that had the appropriate population size, sufficient PM2.5
concentration levels, variability for most pollutants, and appropriate correlations between pollutants.
Once these criteria had been met, the geographical distribution of the selected cities was further examined.
Due to an over-representation on Northeastern cities and an under-respresentation of Western cities, the
final list was adjusted to include western sites
Thus, as an example the list of candidatemetropolitan areas of greatest epidemiologic interest for enhanced
air quality monitoring area,:
Sacramento, CA
San Diego, CA
Atlanta, GA
Baltimore, MD
Boston, MA
Newark, NJ
Cleveland, OH
Pittsburgh, PA
Providence, RI
Salt Lake City, UT
Milwaukee, WI
Using the presented methodology daily monitoring of the widest range of paniculate matter (PM)
constituents and gaseous co-pollutants at these locations would be of great advantage for future
epidemiologic time senes studies.
in
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Contents
Notice ii
Executive Summary iii
Contents iv
Acronyms xiii
Acknowledgements xiv
Chapter 1 Introduction 1
Background 1
Design Criteria 2
Chapter 2 Methods 3
Creation of Database 3
Data Analysis 5
Interpretation of Figures 5
Chapter 3 Existing Speciation Trends Network (STN) Monitors, 2001-2005 6
Location of Current Monitors 6
Number of Monitoring Days 6
Chapter 4 Metropolitan Characteristics 8
Population 9
Mortality 11
Chapter 5 Mean Concentrations of Total PM Mass and Selected PM2 5 Species 13
Relative to National Ambient Air Quality Standards 13
Overall Mean Concentrations 15
Regional and Seasonal Patterns 17
Conclusions Regarding Mean Concentrations 22
iv
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Contents (continued)
Chapter 6 Variability in Concentrations of Criteria Pollutants and Selected PM2 5
Species 23
Indicators of Monitor-Specific Variability 23
Variability Relative to the Mean 24
Seasonal Differences in Coefficient of Variation 25
Conclusions Regarding Coefficients of Variation 26
Chapter 7 Correlations with Total Fine Particle Mass (PM25) 27
Fossil Fuel Combustion 28
Mobile Sources 29
Residual-Oil Fly Ash 30
Biomass Combustion 31
Crustal Materials 32
Metallic Elements 33
Sea Spray/Road Salt 34
Conclusions Regarding Correlations with PM25 35
Chapter 8 Inter-Correlations Between Selected PM Constituents 36
Within-Source Category 36
Fossil Fuel Combustion 38
Mobile Sources 38
Residual-Oil Fly Ash 38
Biomass Combustion 38
Crustal Materials 38
Metallic Elements 38
Sea Spray/Road Salt 38
Between-Source Categories 39
Midwest 39
Northeast 40
South 41
West 42
Conclusions Regarding Interconstituent Correlations 43
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Contents (continued)
Chapter 9 Relationships Between Coefficients of Variation and Correlations 44
Low Correlations with PM25 44
Low Interconstituent Correlations 44
Conclusions Regarding Relationships Between Coefficients of
Variations and Correlations 45
Chapter 10 Potential Monitors for Increased Frequency 46
Summary of Results 46
Selection of Cities 47
Criterion 1: Adequate Population Levels 47
Criterion 2: Mean Levels of PM25 47
Criterion 3: Adequate Level of Variability 47
Criterion 4: Correlation Among Criteria Air Pollutants and
Particulate Matter Constituents 48
Criterion 5: Relationship of Correlations to the Coefficient of
Variation 48
Cities Meeting Criteria 1-5 48
Geographical Distribution 49
Conclusion 49
References 50
Appendix A. Tables A-l
VI
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Tables
2-1 Distance in Miles from the STN Monitor to the Nearest PM10 and Gaseous
Monitor and to the Nearest Weather Station: 49 STN Monitors, 2001-2005 4
3-1 Percent of Days with Missing Values: 49 STN Monitors, 2001-2005 7
4-1 Population on April 1, 2000 of Core Based Statistical Areas (CBSA) with
an STN Monitor: 49 STN Monitors, 2001-2005 10
4-2 Average Daily Deaths by Cause and Age Groups in CBSA with an STN
Monitor: 49 STN Monitors, 2001-2005 12
5-1 STN Monitors with the Highest Pollutant Concentrations Overall and by
Season: 49 STN Monitors, 2001-2005 15
9-1 Cities with PM2 5 Correlations in the Lowest 10th Percentile and Coefficients
of Variations Less Than 50 Percent for Either PM2s or the Other Pollutant 44
9-2 Cities with Between Pollutant Correlations in the Lowest 10th Percentile and
Coefficients of Variations Less Than 50 Percent for Either PM2 5
Constituent 44
10-1 Selection Criteria for Identifying Metropolitan Areas for Enhanced PM
Speciation Monitoring, 49 STN Monitors, 2001-2005 48
vn
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Figures
3-1 Map of selected Speciation Trends Network (STN) monitor sites: 49 STN
monitors, 2001-2005 6
4-1 Probabilities of specified event counts as a function of average daily deaths
for Poisson distributed counts 8
4-2 Population on April 1, 2000 of Core Based Statistical Areas (CBSA) with
an STN monitor and the percentage of the CBSA population in the monitor
county: 49 STN monitors, 2001-2005 9
4-3 Average daily deaths by cause and age groups in CBSA with an STN
monitor: 49 STN monitors, 2001-2005 11
5-1 Distribution of monitor-specific 24-hour PM2 5 concentrations compared to
the daily NAAQS (35 |Jg/m3): 49 STN monitors, 2001-2005 13
5-2 Percent of the annual NAAQS (15 |Jg/m3) for each monitor-specific annual
mean PM25 concentration: 49 STN monitors, 2001-2005. (Circles = single
year average) 14
5-3 Distribution of monitor-specific mean pollutant concentrations as percent of
the pollutant-specific overall STN network grand mean concentration: 49
STN monitors, 2001-2005 16
5-4 Mean fine particulate matter (PM25) concentrations as proportions of 25
|lg/m3: 49 STN monitors, 2001-2005 17
5-5 Mean winter and summer fine particulate matter (PM2 5) concentrations as a
proportion of 35 |lg/m3: 49 STN monitors, 2001-2005 17
5-6 Distributions of monitor-specific ratios of mean concentrations in the
summer (June - August) and the winter (December - February): 49 STN
monitors, 2001-2005 18
5-7 Mean sulfate (SO4) concentrations as a proportion of 10 |ig/m3: 49 STN
monitors, 2001-2005 19
5-8 Mean selenium (Se) concentrations as a proportion of 3 ng/m3: 49 STN
monitors, 2001-2005 19
5-9 Mean nitrate (NO3) concentrations as a proportion of 10 |lg/m3: 49 STN
monitors, 2001-2005 19
5-10 Mean silicon (Si) concentrations as a proportion of 5 |lg/m3: 49 STN
monitors, 2001-2005 19
5-11 Mean iron (Fe) concentrations as a proportion of 300 ng/m3: 49 STN
monitors, 2001-2005 20
5-12 Mean zinc (Zn) concentrations as a proportion of 120 ng/m3: 49 STN
monitors, 2001-2005 20
Vlll
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Figures (continued)
5-13 Mean elemental carbon concentrations as a proportion of 2 |lg/m3: 49 STN
monitors, 2001-2005 20
5-14 Mean organic carbon concentrations as a proportion of 10 |lg/m3: 49 STN
monitors, 2001-2005 20
5-15 Mean vanadium (V) concentrations as a proportion of 10 ng/m3: 49 STN
monitors, 2001-2005 21
5-16 Mean nickel (Ni) concentrations as a proportion of 25 ng/m3: 49 STN
monitors, 2001-2005 21
6-1 Distributions of monitor-specific interquartile range and coefficients of
variation by monitor-specific means for PM25: 49 STN monitors,
2001-2005 23
6-2 Distributions of monitor-specific coefficients of variation for various
pollutants over the entire year: 49 STN monitors, 2001-2005 24
6-3 Distributions of monitor-specific coefficients of variation for various
pollutants for winter and for summer days: 49 STN monitors, 2001-2005 25
6-4 Monitor-specific ratios of summer/winter coefficients of variation for
various pollutants: 49 STN monitors, 2001-2005 26
7-1 Distributions of the monitor-specific percentage of the daily variation in
PM2 5 determined by the variation PMi0 and O3 for all days, winter days,
and summer days: 49 STN monitors, 2001-2005 27
7-2 Distributions of the monitor-specific percentage of the daily variation in
PM25 determined by the variation in selected markers of coal combustion
for all days, winter days, and summer days: 49 STN monitors, 2001-2005 28
7-3 Monitor-specific correlations between PM2 5 and sulfate: 49 STN monitors,
2001-2005 28
7-4 Distributions of the monitor-specific percentage of the daily variation in
total PM2 5 mass determined by the variation in selected markers of mobile
sources for all days, winter days, and summer days: 49 STN monitors,
2001-2005 29
7-5 Monitor-specific correlations between total PM2 5 mass and organic carbon:
49 STN monitors, 2001-2005 29
7-6 Distributions of the monitor-specific percentage of the daily variation in
total PM2 5 mass determined by the variation in selected markers of
residual-oil fly ash for all days, winter days, and summer days: 49 STN
monitors, 2001-2005 30
IX
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Figures (continued)
7-7 Monitor-specific correlations between total PM2 5 mass and vanadium
during the winter months (December - February): 49 STN monitors,
2001-2005 30
7-8 Distributions of the monitor-specific percentage of the daily variation in
total PM2 5 mass determined by the variation in selected markers of biomass
combustion for all days, winter days, and summer days: 49 STN monitors,
2001-2005 31
7-9 Monitor-specific correlations between total PM2 5 mass and potassium (K)
during winter: 49 STN monitors, 2001-2005 31
7-10 Distributions of the monitor-specific percentage of the daily variation in
total PM2 5 mass determined by the variation in selected markers of crustal
elements for all days, winter days, and summer days: 49 STN monitors,
2001-2005 32
7-11 Monitor-specific correlations between total PM2 5 mass and silicon (Si)
during summer: 49 STN monitors, 2001-2005 32
7-12 Distributions of the monitor-specific percentage of the daily variation in
total PM2 5 mass determined by the variation in selected metals for all days,
winter days, and summer days: 49 STN monitors, 2001-2005 33
7-13 Monitor-specific correlations between total PM2 5 mass and iron (Fe): 49
STN monitors, 2001-2005 33
7-14 Distributions of the monitor-specific percentage of the daily variation in
PM2 5 determined by the variation in selected markers of sea spray/road salt
for all days, winter days, and summer days: 49 STN monitors, 2001-2005 34
7-15 Monitor-specific correlations between total PM2 5 mass and chlorine (Cl)
during winter: 49 STN monitors, 2001-2005 34
8-1 Distributions of monitor-specific of coefficients of determination for
constituents within-source category for all days: 49 STN monitors,
2001-2005 36
8-2 Distributions of monitor-specific of coefficients of determination for
constituents within-source category for winter days: 49 STN monitors,
2001-2005 37
8-3 Distributions of monitor-specific of coefficients of determination for
constituents within-source category for summer days: 49 STN monitors,
2001-2005 37
8-4 Distributions of monitor-specific of coefficients of determination for
constituents between-source categories in Midwestern cities for all days: 49
STN monitors, 2001-2005 39
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Figures (continued)
8-5 Distributions of monitor-specific of coefficients of determination for
constituents between-source categories in Northeastern cities for all days:
49 STN monitors, 2001-2005 40
8-6 Distributions of monitor-specific of coefficients of determination for
constituents between-source categories in Southern cities for all days: 49
STN monitors, 2001-2005 41
8-7 Distributions of monitor-specific of coefficients of determination for
constituents between-source categories in Western cities for all days: 49
STN monitors, 2001-2005 42
XI
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List of Appendix Tables
A-l Identification Numbers for STN Monitors, Nearest Co-Pollutant Monitors,
and Weather Bureau Network (WBN) Stations: 49 STN Monitors, 2001-2005
A-2 Core-Based Statistical Areas (CBSA) with a Speciation Trends Network
(STN) Monitor Along with Population on April 1, 2000 and Federal
Information Processing Standard (FIPS) Codes for Constituent Counties: 49
STN Monitors, 2001-2005
A-3 Mean Concentrations of Criteria Air Pollutants for Annual, Winter, and
Summer: 49 STN Monitors, 2001-2005 5
A-4 Coefficients of Variation for Criteria Air Pollutants for Annual, Winter, and
Summer: 49 STN Monitors, 2001-2005 7
A-5 Coefficients of Determination for Criteria Air Pollutants with PM2 5 for
Annual, Winter, and Summer: 49 STN Monitors, 2001-2005 9
A-6 Mean Concentrations of Selected PM2 5 Constituents for Annual, Winter,
and Summer: 49 STN Monitors, 2001-2005 11
A-7 Coefficients of Variation for Selected PM2 5 Constituents for Annual,
Winter, and Summer: 49 STN Monitors, 2001-2005 17
A-8 Coefficients of Determination for Selected PM25 Constituents with PM25
for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005 23
A-9 Coefficients of Determination for Within-Source PM2 5 Constituents for All
Days: 49 STN Monitors, 2001-2005 29
A-10 Coefficients of Determination for Within-Source PM25 Constituents for
Winter Days: 49 STN Monitors, 2001-2005 31
A-l 1 Coefficients of Determination for Within-Source PM25 Constituents for
Summer Days: 49 STN Monitors, 2001-2005 33
A-12 Coefficients of Determination for Elemental Carbon and Selected
Pollutants: 49 STN Monitors, 2001-2005 35
A-13 Coefficients of Determination for Organic Carbon and Selected Pollutants:
49 STN Monitors, 2001-2005 37
A-14 Coefficients of Determination for Copper and Potassium, and Selected
Pollutants: 49 STN Monitors, 2001-2005 39
A-15 Coefficients of Determination for Zinc and Titanium, and Selected
Pollutants: 49 STN Monitors, 2001-2005 41
A-16 Coefficients of Determination for Aluminum, Chlorine, and Nitrate, and
Selected Pollutants: 49 STN Monitors, 2001-2005 43
A-17 Coefficients of Determination for Sulfate, Iron, Nickel, Silicon, and
Vanadium: 49 STN Monitors, 2001-2005 45
xn
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Acronyms
Al
Br
Ca
CBSA
Cl
CO
Cu
EC
EPA
Fe
K
Mn
Na
NAAQS
Ni
NO2
NO3
03
OC
PM
PM2.5
PM10
Se
Si
SO2
SO4
STN
Ti
Zn
Aluminum
Bromine
Calcium
Core Based Statistical Areas
Chlorine
Carbon Monoxide
Copper
Elemental carbon
U.S. Environmental Protection Agency
Iron
Potassium
Manganese
Sodium
National Ambient Air Quality Standards
Nickel
Nitrogen dioxide
Nitrate
Ozone
Organic carbon
Particulate matter
Particulate matter less than 2.5 microns
Particulate matter less than 10 microns
Selenium
Silicon
Sulfur dioxide
Sulfate
Speciation Trends Network
Titanium
Zinc
Xlll
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Acknowledgements
The authors of this report wish to thank Kyle Messier for assistance with additional maps required for the
final publication. We also wish to acknowledge the general contribution of the panelists of the April 16-17,
2008 workshop on Ambient Air Quality Monitoring and Health Research. In addition, we wish to thank
Tim Watkins of the Air, Climate, and Energy National Program Office, Mel Peffers of the Office of
Research and Development, Beth Hassett-Sipple, Joann Rice, Neil Frank, and Tim Hanly of the Office of
Air and Radiation for their technical guidance.
xiv
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Chapter 1
Introduction
Background
Epidemiologic studies of daily mortality and hospitalization
(time series studies) have previously demonstrated adverse
human health effects of paniculate matter (PM), both in the
fine fraction (PM2.5) and in the coarse fraction (PM10_2.5),
and gaseous criteria air pollutants, including carbon
monoxide (CO), nitrogen dioxide (N02), sulfur dioxide
(S02), and ozone (03) (United States Environmental
Protection Agency 2009c). Although the epidemiology has
been supported by recent toxicological studies in both
humans and animals, the identification of the specific
etiologic agents responsible for the observed health effects
remains a major uncertainty in the science underlying the
current National Ambient Air Quality Standards (NAAQS).
Furthermore, the specific constituents of PM vary among
the different major air pollution sources with consequences
for control strategies (United States Environmental
Protection Agency 2009c). The Environmental Protection
Agency's (EPA's) Multi-Year Plan for Clean Air included a
commitment to the investigation of multipollutant health
effects (United States Environmental Protection Agency
2008b). The EPA's current Strategic Research Action Plan
for Air, Climate and Energy continues this commitment to
the investigation of multipollutant health effects.
The evaluation of the health effects of specific PM
constituents has been the focus of many recent
epidemiologic and clinical studies. Using single pollutant
models, several studies have observed associations between
various PM constituents and mortality (Mar et al. 2000;
Burnett et al. 2000; Ostro et al. 2007). An alternative to the
constituent-by-constituent approach is the consideration of
major sources as characterized by multiple constituents. For
example, previous time-series analyses indicate that, of the
sources of PM, motor vehicle exhaust usually has the
strongest associations with all non-accidental or
cardiovascular mortality (Mar et al. 2000; Laden et al.
2000; Janssen et al. 2002).
Time series studies have also shown that certain chemical
species significantly modify the association between PM2.5
and mortality (Franklin and Schwartz 2008), while others
have examined the role of gaseous pollutants such as 03 as
either potential confounders or surrogates to PM25
exposures (Sarnat et al. 2001). The degree of spatial and
temporal variability can differ by pollutant, having
implications for epidemiologic research. Heterogeneity in
city-specific and seasonal-specific estimates have been
demonstrated in PM10 (Peng et al. 2005; Dominici et al.
2003), PM25 (Franklin et al. 2007), and ozone (Bell et al.
2005; Bell et al. 2004; Levy et al. 2005; Ito et al. 2005) as
well as with the effects of PM25 on hospital admissions
(Bell et al. 2006).
The various constituents of PM probably exert their adverse
effects through different modes of action and over different
time scales (National Research Council 2004). Long-term
exposures to spatial gradients, primarily driven by regional-
scale air pollutants, have been associated with decreased
survival in well-defined cohorts (Puett et al. 2009; Laden et
al. 2006) and decreases in city-specific life expectancy
(Pope et al. 2009). Short-term exposures to temporal
gradients, driven by more local-scale air pollutants, have
been associated with increased hospitalization and mortality
both at very prompt effects (Zanobetti and Schwartz 2009;
Bell et al. 2006; Franklin et al. 2008) and over more
extended periods (Zanobetti et al. 2003). Using biological
indicators of early adverse effects, investigators have
recently reported very immediate adverse effects on cardiac
and endothelial function (Schneider et al. 2008) and
delayed inflammatory effects (Mann et al. 2010).
The current air quality monitoring network covers most
major U.S. metropolitan areas and a wide range of PM
constituents and gaseous co-pollutants. However, using
filter-based methods, most PM constituents are measured
not daily, but only 1 in 3 days or 1 in 6 days. The lack of
daily monitoring of PM constituents complicates time-
series analyses (Franklin et al. 2008). At a 2008 EPA
workshop, epidemiologists raised the lack of daily
monitoring data as a major impediment to future studies of
PM constituents and requested enhanced monitoring at
selected metropolitan areas (United States Environmental
Protection Agency 2008a). Because monitoring resources
are constrained, identifying the most important locations for
daily monitoring is critical.
This report focuses on ambient air quality monitoring in
support of epidemiologic time series studies. Other
longitudinal study designs, such as repeated clinical
measures in a small closed cohort, have made substantial
contributions to our understanding of PM health effects.
Some of the design criteria, such as population of the
metropolitan area, are simply not relevant to the selection of
a study location for a clinical panel, which is generally
limited by the clinic location.
1
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Design Criteria
To maximize the research potential, the enhanced
monitoring frequency should be conducted in major
metropolitan areas with short-term variations in criteria air
pollutants in the vicinity of the current NAAQS. An ability
to distinguish between various PM constituents would be
enhanced by independent variation in these constituents as
characterized by low pair-wise correlations. However, low
correlations between pairs of constituents may be due to a
variety of factors, including a lack of variation of one or
both constituents.
The five criteria for the identification of metropolitan areas
with the greatest epidemiologic value for enhanced
monitoring frequency are:
1. Population of the metropolitan area (Chapter 4);
2. Mean levels of criteria air pollutants (Chapter 5);
3. Variation in levels of criteria air pollutants and
paniculate matter constituents (Chapter 6);
4. Correlation among criteria air pollutants and paniculate
matter constituents (Chapters 7 and 8); and
5. Relationship of correlations to the coefficient of
variation (Chapter 9).
The population of the metropolitan area largely determines
the daily number of clinical events, such as mortality and
hospitalizations, and thus the statistical power to detect
potential adverse health effects of air pollutants, as reflected
in the confidence intervals around their effect estimates.
Small cities with relatively few daily events will have more
uncertainty for their city-specific effect estimates and less
statistical power to detect potential adverse health effects of
air pollutants. Metropolitan areas of more than 100,000
people with an average of more than three clinical events
per day will have smaller confidence intervals and more
power to detect potential adverse effects. Since statistical
power increases only as the square root of sample size,
further increases in population provide much less
improvement in the confidence intervals. Geographically
extensive metropolitan areas of more than 1,000,000 people
may also enter the summary analysis of effects across
multiple areas with extreme influence and may be less well
characterized by a single, central-site monitor. Thus, the
most populated metropolitan areas are not necessarily the
most critical areas for daily speciation monitoring.
Through the standard setting process, EPA has already
determined that air pollutant levels in excess of the current
NAAQS are hazardous to human health. Further studies at
levels far above the NAAQS cannot address issues of
uncertainty in the science underlying the current standards.
One such uncertainty in the city-to-city variation in PM2.5
health effect estimates. Therefore, ambient concentrations
in the vicinity of the current standard provide more relevant
information. Ideally, the metropolitan areas identified for
daily speciation monitoring would experience a range of air
pollution levels at or below the current NAAQS.
In the vicinity of the current NAAQS, greater temporal
variation in the measured levels of criteria air pollutants
enhance the statistical power to detect potential adverse
health effects of criteria air pollutants and PM constituents.
Metropolitan areas with greater variation will be more
informative than areas with little variation in ambient
concentrations.
Since recent and future epidemiologic and clinical studies
are focused on resolving the relative independent and joint
effects of PM constituents and co-pollutants in a
multipollutant context, the ability to detect potential adverse
health effects will be enhanced by low correlations among
the various pollutants. Observational studies cannot resolve
the independent effects of highly correlated air pollutants.
So, low correlations among criteria air pollutants and PM
constituents are an important factor in the identification of
metropolitan areas for daily speciation monitoring.
However, a low correlation between a pair of air pollutants
(criteria 4) may also indicate a lack of temporal variability
in one or both pollutants (criteria 3). So, a final criterion
concerns the relationship pair-wise correlations of air
pollutants to the temporal variation in each pollutant. The
ideal city for daily speciation monitoring would have both
low pair-wise correlations and high temporal variation in air
pollutants.
This report analyzes existing air quality monitoring data
from a selection of major U.S. metropolitan areas for these
five major criteria with the goal of identifying a few areas
for daily speciation monitoring from these metropolitan
areas. This report also examined the spatial and temporal
patterns of selected air pollutants as a guide to the
interpretation of results from recent and future
epidemiologic and clinical studies.
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Chapter 2
Methods
Creation of Database
Ambient concentrations of the criteria pollutants are
measured at more than 4,000 air quality monitoring stations
operated by state, local, and tribal environmental agencies
and compiled by the EPA's Aerometric Information
Retrieval Service (AIRS) Database (United States
Environmental Protection Agency 2009b). Since 2001, the
EPA's Speciation Trends Network (STN) has been
measuring fine particle components at 174 sites: 54
permanent sites intended to capture long-term trends and
120 additional locations identified as "supplemental
speciation."
These STN monitors are a subset of the Chemical
Speciation Network (CSN) that also includes State and
Local Monitoring Stations (SLAMS). As of January 1,
2011, the National Core (NCore) Multipollutant Monitoring
Network became operational consisting of 80 sites; 63
urban sites and 17 rural sites. NCore integrates several
advanced measurement systems for particles, pollutant
gases and meteorology. Most of the STN monitors (26)
described in this report have been included as part of the
NCore network design. For an additional 16 STN sites, the
additional platform requirements of NCore necessitated a
shift of the monitor location within the same county. Only
7 STN monitors were not included in the NCore network.
For this analysis, the focus is on metropolitan areas with
STN monitors during 2001-2005. Four monitors that were
not operational for the entire 2001-2005 time period were
excluded: New Haven County, CT (New Haven); Monroe
County, NY (Rochester); Henrico County, VA (Richmond);
and Kanawha County, WV (Charleston). Monitors located
in Cass County, ND (background site) and Guaynabo
County, Puerto Rico (outside the continental United States)
were also excluded. Following common practice, the urban
speciation site in Washington, DC that is part of the
EPA/National Park Service Visibility (IMPROVE) program
was included as an STN-equivalent monitor. Thus, 49 STN
monitors (or STN-equivalent) were included in this
analysis.
The 49 STN monitors selected for this analysis were those
that had data from 2001-2005. The purpose of this report is
to illustrate the selection criteria. The metropolitan areas
chosen for such as future epidemiologic study may differ
slightly depending on the STN monitoring area analyzed.
All of the STN monitors were collocated with
measurements of total PM2.5 mass. Where available,
measurements of PM10 and the gaseous co-pollutants were
obtained from collocated monitors, otherwise the next
nearest non-STN monitor was used (Table 2-1). For
comparability with the 24-hour filter-based measurements
of PM constituents, hourly measurements of gaseous
pollutants (CO, N02, 03, and S02) were combined into 24-
hour averages.
Meteorologic data were generally obtained from National
Weather Service stations, except in two cases where a U.S.
Air Force weather station was much closer than the nearest
National Weather Service Station. Data from monitors
located more than 20 miles (32 kilometers) from the STN
monitors were excluded from this analysis. Site identifiers
for the monitors used in this report are provided in
Appendix A, Table A-l.
The precise locations of these selected STN monitors were
verified using aerial photography and are available as a
keyhole markup language (kml) file (United States
Environmental Protection Agency 2010).
The definitions for the 49 multi-county metropolitan areas
in this report are based on the Core-Based Statistical Areas
(CBSA) of the White House Office of Management and
Budget (Appendix A). While the division of metropolitan
areas across multiple county-level jurisdictions is largely
unrelated to air quality considerations, the segregation of
the population into urban core and suburban ring for some
metropolitan areas may introduce an air quality
relationship. Sacrificing precision to readability, CBSAs in
this report will be referenced by the name of the largest city
in the CBS A (shortened name underlined in Appendix A,
Table A-2).
Mortality data for 2001 through 2005 at the county level
was obtained from the National Center for Health Statistics
under a restricted data use agreement.
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Table 2-1.
City
Distance in Miles from the STN Monitor to the Nearest PM10 and Gaseous Monitor and to the Nearest Weather Station:
49 STN Monitors, 2001-2005
CO
NO,
SO,
PMi,
Weather
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Measurements that were
b __
1
-
_
_
-
-
-
13
-
1
2
<1
6
-
1
7
3
-
-
-
6
-
2
68 b
-
-
2
4
_
36 b
-
2
-
-
5
4
6
5
1
<1
4
-
-
-
-
-
-
-
collocated with STN
>100b
-
_
_
-
-
-
-
-
1
-
<1
15
-
>100b
-
3
-
-
-
-
11
16
33 b
-
3
>100b
>100b
_
-
-
-
-
-
-
-
6
-
4
6
6
-
-
-
-
-
-
-
monitor are indicated
3
-
_
_
-
-
-
-
-
1
-
5
15
-
17
-
3
-
-
-
-
-
13
-
-
-
>100b
3
_
-
-
5
-
-
5
-
6
-
4
14
3
-
-
-
-
-
-
-
by a hyphen.
7
-
82 b
>100b
22 b
-
-
13
28 b
1
4
13
9
8
>100b
-
6
-
-
-
6
7
2
31 b
-
2
>100b
4
>100b
-
-
-
-
-
-
4
>100b
3
1
6
8
-
<1
-
6
-
-
-
a
-
_
_
-
-
-
-
-
-
4
-
14
7
1
5
3
3
-
-
13
-
2
31 b
-
-
_
_
_
-
-
-
11
2
-
4
-
2
-
4
4
3
-
-
-
-
-
3
3
7
5
3
8
4
7
3
2
6
5
5
6
8
9
4
5
5
8
4
1
7
5
1
2
10
6
5
3
3
0
3
3
9
10
5
6
6
7
6
9
8
3
5
9
6
3
8
8
-------
Data Analysis
A database of concentrations for 19 PM2.5 components,
PM2.5 mass, PM10, CO, N02, 03, and S02 was developed
based on data obtained from the EPA's Technology
Transfer Network's Air Quality System Data Mart (United
States Environmental Protection Agency 2009d). These
PM25 species were chosen based on their potential to be
markers for a particular source. S04, S02, and Se can be
markers for coal combustion and Mn, Ni, and V can be
markers of residual oil fly ash (Kim and Hopke 2008).
Biomass combustion can consist of K, Br, Ca (Watson et al.
2001; Maykut et al. 2003). Mobile sources can be identified
by concentrations of Cu, Zn, N03, NH4, EC, and OC as
well as CO and N02 (Maykut et al. 2003; Lough et al.
2005) Finally, Al, Fe, Si, and Ti are considered crustal
elements, and Na and Cl can be markers for sea spray and
road salt (United States Environmental Protection Agency
2009b).
The mean and variability of the pollutants were calculated
for each site during the 2001-2005 time period and
seasonally. The variability was represented by the
coefficient of variation, a dimensionless number that is a
normalized measure of dispersion, defined as the ratio of
the standard deviation to the mean pollutant concentration.
The overall and seasonal spearman correlations between
selected pollutants were also calculated.
Seasons were based on 3-month periods, with December
through February as winter season, March through May as
spring, June through August as summer, and September
through November as fall.
For the site selection, epidemiologists are interested in
identifying cities with low bi-pollutant correlations for each
of the two pollutants. However, the low correlations may be
due to a low coefficient of variation for one of the
pollutants. Therefore interest was in cities that had low
correlations between pollutants that still had high
coefficients of variation for those pollutants. To examine
these three dimensions, bubble plots were created to plot
two pollutant coefficients of variations against one another
with the areas of those circles proportional to the
correlations. Although informative, the interpretation of
these numerous bubble plots was tedious and these plots are
not presented in this report.
Interpretation of Figures
For all box plots, the shaded box provides the interquartile
range with a solid line indicating the median, whiskers
indicating the 10th and 90th percentiles, and dots indicating
outliers beyond the tenth and ninetieth percentiles.
For the maps with mean concentrations, the monitor-
specific mean concentrations are expressed by filled bars
representing the proportion of a fixed reference
concentration. Aside from selenium where a single city had
an extreme mean concentration (Pittsburgh, PA at 6.7
ng/m3), the reference concentration was fixed at an integer
value slightly greater than the highest city-specific
concentration.
For maps with correlations, the monitor-specific Pearson
correlations (0-1) are expressed by filled bars with a
completely filled bar representing a perfect correlation.
Aside from winter ozone correlations with PM25 that were
always negative for all STN monitors, all correlations with
PM25 or inter-correlations among PM constituents were
either positive or small, that is less than R2 = 0.1.
For maps with coefficients of determination, the monitor-
specific coefficients (0-1) are expressed by filled bars
representing the proportion of the variation in one pollutant
explained by the variation in the other pollutant.
Limitations
Limitations of these data and analyses include measurement
error and detection limits. In general sampling methods
were improved after 2007 so future work may include
applying these criteria to data collected after 2007. In
addition sulfate, followed by crustal materials, has the
smallest uncertainty associated with its measurement
among the components, while uncertainties for organic
carbon, elemental carbon, and nitrate are larger (United
States Environmental Protection Agency 1996). The
organic carbon data provided by the EPA are not blank-
corrected and thus have a positive bias due to sampling
artifacts. Blank concentrations were not available until 2003
so the organic carbon concentrations presented in this
analysis are not blank-corrected. The EPA did not include a
method detection limit (MDL) with each PM25 species
concentration recorded until July 2003. Therefore, all
reported concentrations were included. Additionally, in
order to interpret these results, each pollutant was assigned
to only one source category ignoring the fact that a
pollutant could be generated from multiple sources. For
example, iron could reflect crustal components of road dust
re-suspended by vehicles, or particles generated during
combustion in engines (Gotschi et al. 2005). Silicon (road
dust) and bromine can also represent traffic emissions
(Martuzevicius et al. 2004). To examine the potential
overlap, the correlation within source categories was
studied and the finding indicates that they differed
depending on the city.
-------
Chapter 3
Existing Speciation Trends Network (STN) Monitors, 2001-2005
Location of Current Monitors
The 49 selected Speciation Trends Network (STN)
monitors are most densely located in the northeastern
metropolitan corridor from Washington, DC through
Rockingham, NH and in California from San Diego through
San Jose (Figure 3-1). In four cities (San Jose, CA; Tampa,
FL; Missoula, MT; and Portland, OR), the location of the
STN monitors were shifted slightly sometime within the
2001-2005 time period.
Number of Monitoring Days
Not all air pollutants are measured daily so the amount of
information on air quality varies by pollutant and location.
By design, the filter-based samples for PM2.5 constituents at
most STN sites were collected on a schedule of either 1 -in-
3 day, with 67 percent missing by design, or l-in-6 day,
with 83 percent missing by design (Table 3-1). Completely
(100%) missing data for a gaseous pollutant and PM10
indicates that the nearest alternative air quality monitor was
more than 20 miles away. In addition, 17 STN sites had
measurements of 03 only during the warmer months, when
03 levels are the highest, resulting in higher percentages of
missing values. Finally, the monitoring frequency may
have changed during the study period. The purpose of
Table 3-1 is to describe the information available for this
report and not to thoroughly describe monitoring frequency.
Figure 3-1. Map of selected Speciation Trends Network (STN) monitor sites: 49 STN monitors, 2001-2005.
-------
Table 3-1. Percent of Days with Missing Values: 49 STN Monitors, 2001-2005
PM2.5 Species
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
PM2.5
2
55
14
9
69
8
49
11
46
65
5
11
5
11
67
69
26
2
7
41
40
11
46
66
68
2
69
10
66
71
71
11
67
8
68
36
40
13
4
9
9
7
6
56
4
3
67
3
52
SO4
67
66
75
72
87
71
70
76
76
74
75
74
68
69
73
22
73
79
76
73
80
69
73
77
76
68
69
80
70
72
73
76
68
73
72
70
69
71
71
86
69
73
70
71
71
68
69
27
68
NO3
67
66
75
72
87
71
70
76
76
74
75
74
68
69
73
17
73
79
76
73
80
69
73
77
76
68
69
80
70
72
73
76
68
73
72
70
69
71
71
86
69
73
70
71
71
68
69
22
68
EC
68
67
75
69
87
71
70
76
76
74
75
74
68
69
73
19
73
78
76
73
77
70
73
77
77
68
69
78
70
73
73
76
68
73
73
70
67
70
72
86
69
74
70
70
71
68
69
11
68
OC
68
69
75
69
87
71
70
76
76
74
75
74
68
70
73
19
73
78
76
73
77
70
73
77
77
68
69
78
70
73
89
76
68
73
73
70
67
70
73
86
69
74
70
70
84
68
69
19
68
Other
67
67
75
69
87
71
69
76
76
74
75
74
68
69
73
73
73
79
76
73
81
69
73
72
77
68
69
78
70
73
73
75
68
73
73
70
67
70
72
86
69
73
70
71
71
68
69
67
68
PM10
0
53
65
82
83
68
7
84
85
3
87
84
20
84
5
84
84
84
53
90
84
84
90
100
90
92
10
83
84
91
85
94
88
85
9
84
74
86
4
85
70
84
84
99
85
8
94
91
88
CO
3
0
3
0
1
1
9
0
12
2
0
2
1
11
1
0
1
1
0
8
1
3
1
100
5
0
53
1
0
100
1
80
1
2
2
2
50
1
5
1
4
3
3
1
2
0
2
1
0
NO2
100
1
2
1
2
2
20
1
10
11
0
5
2
6
100
20
8
0
2
4
4
9
1
100
3
0
100
100
20
49
1
1
6
2
1
16
60
5
0
3
6
23
2
5
5
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19
8
28
SO2
2
4
100
100
100
3
2
0
100
1
2
9
1
1
100
60
18
1
1
3
0
7
2
100
3
0
100
7
100
48
0
2
1
2
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1
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4
1
1
2
0
1
81
3
2
1
3
21
03
3
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2
0
1
2
2
0
10
1
0
3
0
35
47
60
50
0
17
2
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51
27
37
0
41
100
44
0
73
1
80
1
42
43
1
62
1
1
51
2
33
0
1
2
58
1
62
30
-------
Chapter 4
Metropolitan Characteristics
Most previous observational studies of mortality and
hospitalization have examined daily counts of health events
aggregated across single or multiple counties. The statistical
power of such studies is determined, in part, by the average
number of daily events, and thus is proportional to the
population of the study area. A study area with a larger
population will have greater statistical power and a more
precise effect estimate.
For time-series models, the analytic choice between an
entire multi-county metropolitan area and the single county
with the Speciation Trends Network (STN) monitor
involves several competing factors. Restricting the analysis
to the STN-monitored county may improve the assessment
of population exposure by a single, central-site air quality
monitor, but the restriction may dramatically reduce the
average number of daily deaths, decrease the precision of
the subsequent point estimate and alter the relative weight
of that metropolitan area in subsequent pooled analyses.
Small numbers of daily deaths will have a major statistical
impact on any time-series analysis. The daily counts of non-
accidental deaths are expected to follow a Poisson
distribution with the probabilities of discrete event counts
(N) as a function of the average daily number of deaths
(Figure 4-1). For an average daily deaths of three or more
deaths per day, 80 percent or more of the days would be
expected to have two or more deaths. Conversely, for a
mean of one or less death per day, at least 75 percent of the
days would be expected to have a count of one or zero.
Since time-series models evaluate the impact of varying air
quality on the day-to-day variation in mortality, the
relatively modest contribution of air pollution to risk may
be difficult to determine in time-series with a high
proportion of days with one or zero events. While no
decision rule can be formulated regarding a minimum
average number of events per day, the fitting of age-specific
temporal smoothing functions may be very problematic for
metropolitan areas with low populations and average deaths
per day, especially for specific causes of death.
Poisson Distribution of Mortality Counts
w
c
0)
LU
z
"S
.a
TO
J2
O
345
Average Daily Deaths
Figure 4-1. Probabilities of specified event counts (N) as a function of average daily deaths for Poisson distributed counts.
-------
Population
The population in 2000 (U.S. Census Bureau 2010) varied
greatly across these 49 metropolitan areas (Figure 4-2,
Table 4-1). The two most populous Core Based Statistical
Areas (CBSAs), New York City, NY (11,298,122) and
Chicago, IL (7,628,496), were not included in the figure so
that the lower portion of the distribution would be more
legible. While most of the CBSAs with STN monitors had
populations of more than 750,000, populations less than
250,000 were present for three metropolitan areas
(Gulfport, MS; Missoula, MT; and Burlington, VT) and for
the monitored county of one additional metropolitan area
(Kansas City, MO-KS).
As expected, the monitored county within the CBS A was
often much less populous than the CBSA as a whole. Only
seven CBSAs with an STN monitor were comprised of a
single county. For most CBSAs, the monitored county
comprised less than two-thirds of the CBSA population.
The population of the monitored county comprised less than
20 percent of the entire CBSA for five metropolitan areas:
Washington, DC (District of Columbia with 15%); Atlanta,
GA (Fulton County with 19%); Kansas City, MO-KS
(Wyandotte County with 9%); St. Louis, MO-IL (St. Louis
City with 13%); and New York, NY (Bronx Borough with
12%).
60
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o
o
o
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CM
50 -
40 -
r= 30 -
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20 -
1CH
100
CO
CD
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2
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Entire CBSA Monitor County
Core-Based Statistical Areas (CBSA)
so -
60 -
40 -
20 -
t
Monitor County
Figure 4-2. Population on April 1, 2000 of Core Based Statistical Areas (CBSA) with an STN monitor and the percentage of the
CBSA population in the monitor county: 49 STN monitors, 2001-2005 (presented data excludes New York City and
Chicago).
-------
Table 4-1. Population on April 1, 2000 of Core Based Statistical Areas (CBSA) with an STN Monitor: 49 STN Monitors, 2001-2005
CBSA 2000 Population Monitor County 2000 Population
Birmingham-Hoover, AL
Phoenix-Mesa-Scottsdale, AZ
Bakersfield, CA
Fresno-Madera, CA
Oxnard-Thousand Oaks-Ventura, CA
Riverside-San Bernardino-Ontario, CA
Sacramento-Arden-Arcade-Roseville, CA
San Diego-Carlsbad-San Marcos, CA
San Jose-Sunnyvale-Santa Clara, CA
Denver-Aurora, CO
Washington-Arlington-Alexandria, DC-VA-MD-VW
Miami-Miami Beach-Kendall, FL
Tampa-St. Petersburg-Clearwater, FL
Atlanta-Sandy Springs-Marietta, GA
Boise City-Boise City, ID
Chicago-Naperville-Joliet, IL
Indianapolis-Carmel, IN
Baton Rouge, LA
Baltimore-Towson, MD
Boston-Quincy, MA - Metropolitan Division
Springfield, MA
Detroit-Livonia-Dearborn, Ml
Minneapolis-St. Paul-Bloomington, MN-WI
Gulfport-Biloxi-Pascagoula, MS
Kansas City, MO-KS
St. Louis, MO-IL
Missoula, MT
Omaha-Council Bluffs, NE-IA
Reno-Sparks, NV
Rockingham-Strafford, NH Division
Edison-New Brunswick, NJ Division
Newark-Union, NJ-PA Division
New York-White Plains-Wayne, NY-NJ Division
Charlotte-Gastonia-Concord, NC-SC
Cleveland-Elyria-Mentor, OH
Tulsa, OK
Portland-Vancouver-Beaverton, OR-WA
Philadelphia-Camden-Wilmington, PA-NJ-DE-MD
Pittsburgh, PA
Providence-New Bedford-Fall River, RI-MA
Charleston-North Charleston-Summerville, SC
Memphis, TN-MS-AR
Dallas-Plano-lrving, TX
El Paso, TX
Houston-Sugar Land-Baytown, TX
Salt Lake City, UT
Burlington-South Burlington, VT
Seattle-Tacoma-Bellevue, WA
Milwaukee-Wauksha-West Allis, Wl
1 ,051 ,300
3,251 ,888
661 ,649
921 ,930
753,186
3,254,817
1,796,852
2,813,834
1,735,818
2,179,343
3,727,452
2,253,786
2,396,014
4,248,021
464,842
7,628,496
1,525,103
705,962
2,553,022
1,812,937
680,016
2,061,161
2,968,812
246,197
1 ,836,425
2,698,664
95,799
767,144
342,885
389,595
2,173,876
2,097,523
11,298,122
1,330,552
2,148,017
859,533
1,927,883
5,687,158
2,431 ,086
1,582,997
548,974
1,205,196
3,451 ,204
679,622
4,715,417
968,883
198,892
3,043,897
1 ,490,743
Jefferson, AL
Maricopa, AZ
Kern, CA
Fresno, CA
Ventura, CA
Riverside, CA
Sacramento, CA
San Diego, CA
Santa Clara, CA
Denver, CO
District of Columbia
Miami-Dade, DL
Hillsborough, FL
Fulton, GA
Ada, ID
Cook, IL
Marion, IN
East Baton Rouge, LA
Baltimore City, MD
Suffolk, MA
Hampden, MA
Wayne, Ml
Hennepin, MN
Harrison, MS
Wyandotte, KS
St. Louis City
Missoula, MT
Douglas, NE
Washoe, NV
Rockingham, NH
Middlesex, MD
Baltimore, MD
Bronx, NY
Mecklenburg, NC
Cuyahoga, OH
Tulsa, OK
Multnomah, OR
Philadelphia, PA
Allegheny, PA
Providence, Rl
Charleston, SC
Shelby, TN
Dallas, TX
El Paso, TX
Harris, TX
Salt Lake, UT
Chittenden, VT
King, WA
Milwaukee, Wl
662,062
3,072,168
661 ,649
798,821
753,186
1 ,545,374
1 ,223,497
2,813,834
1 ,682,584
553,691
572,055
2,253,786
998,943
815,827
300,906
5,376,837
860,457
412,854
651,154
689,809
456,226
2,061,161
1,116,037
189,606
157,882
348,189
95,799
463,585
339,486
277,357
749,167
792,313
1,332,652
913,639
1 ,393,848
563,302
660,486
1,517,542
1,281,665
621,595
310,099
897,472
2,218,792
679,622
3,400,590
898,412
146,572
1 ,737,047
940,165
10
-------
Mortality
Across all age groups, the average daily counts for total
non-accidental deaths were sufficient (great than 3) for
time-series analyses across all of the CBSA with STN
monitors (Figure 4-3). Only the CBSAs of Missoula, MT
and Burlington, VT have average daily numbers of total
non-accidental deaths for all ages of 1.7 and 3.3,
respectively. Both of these areas were selected for
monitoring to provide measurements of fine particles
characteristics far from local sources.
80
60 -
ro
Q
Many CBSAs have low average counts for age and cause
subgroups, especially for respiratory deaths and deaths
below age 75 years (Figure 4-3, Table 4-2). For
cardiovascular mortality, 36 of the CBSAs have averages of
more than one death per day in all three age groups.
Conversely, for respiratory mortality, only the Chicago, IL
and New York, NY CBSAs have averages of more than one
death per day in all three age groups. For time-series
models of respiratory mortality, the estimation of age-
specific temporal functions for the baseline hazard may be
problematic for all but the two most populous CBSAs due
to the very large proportion of days with only 0 or 1 event.
All < 65 65-74 75+
All Non-Accidental
All < 65 65-74 75+
Cardiovascular
All < 65 65-74 75+
Respiratory
ro
Q
« 2 -
ro
en
ro
TZT
All < 65 65-74 75+
All Non-Accidental
All < 65 65-74 75+
Cardiovascular
All < 65 65-74 75+
Respiratory
Figure 4-3. Average daily deaths by cause and age groups in CBSA with an STN monitor: 49 STN monitors, 2001-2005 (bottom
graph is a blow-up of the lower portion of the top graph).
11
-------
Table 4-2. Average Daily Deaths by Cause and Age Groups in CBSA with an STN Monitor: 49 STN Monitors, 2001-2005
All Non-Accidental Cardiovascular Respiratory
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR-WA
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
<65
7.0
14.1
3.1
4.0
2.2
14.7
8.1
10.4
4.8
8.4
16.1
10.4
13.8
21.1
1.7
35.3
7.6
4.1
14.8
7.1
3.0
12.6
9.8
2.3
8.6
13.7
0.3
3.3
1.8
1.1
7.7
9.3
49.9
6.7
10.8
4.7
8.0
7.9
12.0
6.5
3.1
8.3
14.9
2.6
23.2
3.2
0.7
8.1
6.6
65-74
5.1
10.8
2.3
2.8
1.7
11.2
6.1
8.0
3.6
5.6
9.4
8.0
12.1
12.0
1.3
24.7
5.8
2.5
10.0
5.9
2.4
7.4
7.3
1.9
6.6
11.2
0.3
2.8
1.4
1.0
7.6
6.4
34.9
4.6
9.2
3.6
5.8
7.9
12.1
5.7
2.1
4.9
8.8
2.2
13.8
2.1
0.6
5.9
5.3
75+
14.8
36.3
6.5
9.2
6.9
33.9
20.7
31.0
13.2
19.1
27.5
27.2
45.2
33.2
5.1
83.3
17.7
7.2
31.9
22.4
10.2
22.3
29.4
4.4
22.9
38.2
1.1
8.6
3.8
3.5
30.3
22.9
120.5
13.5
33.8
11.1
22.9
33.7
47.8
25.0
5.7
13.1
25.4
5.8
35.9
7.8
2.0
24.0
20.3
<65
2.1
3.6
0.9
1.0
0.5
4.1
2.1
2.5
1.1
2.0
4.3
3.0
3.7
6.1
0.4
10.7
2.1
1.3
4.2
1.7
0.8
4.5
2.2
0.8
2.3
4.2
0.1
0.9
0.5
0.3
2.0
2.3
14.1
1.9
3.4
1.5
1.8
1.9
3.6
1.8
0.9
2.7
4.4
0.6
6.6
0.7
0.2
2.0
1.8
65-74
1.7
3.5
0.8
1.0
0.6
4.1
2.0
2.7
1.1
1.6
3.2
3.1
4.0
4.1
0.4
8.4
1.8
0.9
3.3
1.8
0.8
3.1
1.8
0.7
2.1
3.9
0.1
0.8
0.5
0.3
2.4
2.1
14.4
1.5
3.3
1.3
1.7
2.3
4.1
1.8
0.7
1.9
3.1
0.7
4.9
0.6
0.2
1.8
1.7
75+
6.3
15.5
3.3
4.6
3.2
16.8
9.6
14.3
6.1
7.8
11.8
13.7
19.0
13.8
2.0
37.3
7.5
3.0
13.8
8.6
4.0
11.3
10.2
2.1
10.1
17.5
0.4
3.6
1.8
1.5
13.9
10.3
67.9
5.4
15.7
5.3
9.6
14.7
21.1
11.3
2.4
6.2
11.2
2.4
16.2
2.9
0.8
10.1
8.8
<65
0.3
0.8
0.2
0.2
0.1
0.8
0.4
0.4
0.2
0.5
0.5
0.3
0.7
0.9
0.1
1.4
0.5
0.2
0.7
0.3
0.2
0.6
0.4
0.1
0.4
0.7
0.0
0.2
0.1
0.0
0.3
0.3
2.2
0.3
0.4
0.3
0.4
0.3
0.5
0.3
0.1
0.4
0.6
0.1
0.9
0.2
0.0
0.4
0.3
65-74
0.5
1.2
0.3
0.3
0.2
1.3
0.7
0.8
0.3
0.7
0.6
0.5
1.2
1.2
0.1
1.8
0.6
0.2
0.8
0.5
0.2
0.5
0.7
0.2
0.7
1.0
0.0
0.3
0.2
0.1
0.6
0.4
2.4
0.5
0.6
0.4
0.6
0.7
1.0
0.5
0.2
0.4
0.8
0.1
1.1
0.3
0.1
0.6
0.4
75+
1.5
4.2
0.9
1.1
0.8
4.2
2.7
3.5
1.6
2.2
2.5
2.4
4.1
3.3
0.6
8.0
2.1
0.7
3.3
2.7
1.1
1.9
2.8
0.4
2.4
4.0
0.2
1.1
0.5
0.3
2.6
2.0
11.5
1.4
2.8
1.1
2.1
3.5
4.6
2.7
0.5
1.3
2.6
0.6
3.2
0.8
0.2
2.5
2.0
12
-------
Chapter 5
Mean Concentrations of Total PM Mass and Selected PM2.s Species
In order to address issues of scientific uncertainty
underlying current standards, observational studies of
ambient concentrations far above current standards will be
less informative than studies of ambient concentrations at or
below current standards. Conversely, observational studies
of very low ambient concentrations may lack sufficient
statistical power to correctly assess adverse health effects.
Relative to National Ambient Air Quality
Standards
The 24-hour average PM2.5 concentrations measured by
STN monitors varied greatly across the 49 Core Based
Statistical Areas (CBSAs) (Figure 5-1). While many
CBS As were well below 17.5 |J,g/m3 (half the 24-hour
National Ambient Air Quality Standard (NAAQS) of 35
|j,g/m3) on most days, three CBSAs in Southern California
had more than 10 percent of days above 35 |J,g/m3:
Bakersfield, Fresno, and Riverside. For the association
between air quality and health, these three CBSAs may not
be as informative regarding the remaining health effects of
PM2.5 concentrations at or below the current 24-hour
NAAQS since the linear concentration-response
relationship may be largely determined by days well above
the current standard. Similarly, the nine CBSAs with 10
percent or fewer days above 17.5 |J,g/m3 may also contribute
less information to the analysis.
O
o
I
4
CM
O>
£
o>
Q.
100%
50%
24-Hour NAAQS for PM9, (35 ug/m
E*
ro <=
.c - c c JS nj ±± o>:= 2i, m~ c ~u 'o , (/f ro ro o p £;
1 | ||||| ||| ||p ||||||||tl|||||
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The annual average PM2.5 concentrations measured by STN
monitors for the 5 years from 2001 through 2005 also
varied greatly across the 49 CBSAs (Figure 5-2). Compared
with the daily concentrations, a greater proportion of the
CBSAs appeared to have at least one annual average above
the NAAQS for PM2.5 of 15 |ig/m3 during 2001-2005. The
three California CBSAs with high 24-hour PM2.5
concentrations also had high annual average concentrations.
At the lower end of the distribution, 13 CBSAs consistently
had average PM2.5 concentrations below 10.5 (ig/m3 (75%
of the annual NAAQS). Five of these CBSAs also had 10
percent or fewer individual days above 17.5 |J,g/m3: Denver,
CO; Miami, FL; Rockingham, NH; El Paso, TX; and
Seattle, WA (Figure 5-1).
w
<
200% -
150% -
100%
° 50% -
0%
Annual NAAQS for PM25 (15 |ig/m
• n >i i
~: '•' "r • :l!-• *• . • ..!, .:
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Iwro2c™g?°l|-|o fg_™5 o^ S.= o .$••=§ "5 ! = I 8 |
|fj||5^J|i|p||£j|p|f^§J£pg
Figure 5-2. Percent of the annual NAAQS (15 ug/m3) for each monitor-specific annual mean PM2.s concentration: 49 STN monitors,
2001-2005. (Circles = single year average).
14
-------
Overall Mean Concentrations
The inter-comparison across all pollutants of the
distributions of monitor-specific mean pollutant
concentrations is complicated by varying units of
measurement and by great differences in the mean
concentrations (Table 5-1). Gaseous pollutants are
measured in parts per hundred million (CO) or parts per
billion (03, N02, S02). Particle mass (PM25, PM10), the
major ions (NH4, N03, S04), and carbonaceous material
(elemental carbon [EC], organic carbon [OC]) are measured
in |J,g/m3. The elemental constituents of PM are measured in
ng/m3. Even for pollutants measured in the same units, the
grand mean concentrations can vary by orders of
magnitude, for example, sulfur at 1011.4 ng/m3 to selenium
at 1.5 ng/m3.
The Speciation Trends Network (STN) monitors in
southern California (Riverside, Fresno, and Bakersfield)
had the highest concentrations of PM2.5 and traffic-related
pollutants (OC, and N03). The Pittsburgh, PA STN monitor
had the highest concentrations of pollutants related to coal
combustion (Se, S04, S, and S02). The Birmingham, AL
STN monitor had the highest concentrations of Zn and Mn.
The STN monitors in the northeastern urban corridor (New
York, NY; Providence, RI) had the highest concentrations
for residual oil fly ash (Ni and V). The highest
concentrations of wood smoke pollutants and motor
vehicles were not consistent in a particular STN monitor or
set of monitors. Many of the crustal elements (Si, Al, Ti,
and Ca) were highest in Phoenix, AZ and El Paso, TX.
Table 5-1. STN Monitors with the Highest Pollutant Concentrations Overall and by Season: 49 STN Monitors, 2001-2005
Pollutant
Fine participate matter
Coarse participate matter
Ozone
Sulfate
Sulfur dioxide
Selenium
Nitrogen dioxide
Nitrate
Elemental carbon
Organic carbon
Carbon monoxide
Manganese
Nickel
Vanadium
Potassium
Bromine
Silicon
Aluminum
Titanium
Calcium
Iron
Copper
Zinc
Sodium
Chlorine
PM2.5
PM10
03
SO4
SO2
Se
NO2
N03
EC
OC
CO
Mn
Ni
V
K
Br
Si
Al
Ti
Ca
Fe
Cu
Zn
Na
Cl
Mean
Concentration
13.1 Ljg/m3
26.6 Ljg/m3
26.0 ppb
3.1 Ljg/m3
4.0 ppb
1 .5 ng/m3
17.6 ppb
2.0 Ljg/m3
0.7 LJg/m3
4.5 LJg/m3
6.3 pphm
3.3 ng/m3
2.7 ng/m3
2.8 ng/m3
82.4 ng/m3
3.3 ng/m3
133.8 ng/m3
36.7 ng/m3
6.6 ng/m3
70.9 ng/m3
101 .3 ng/m3
5.1 ng/m3
16.2 ng/m3
100.4 ng/m3
42.3 ng/m3
STN Monitor with Highest Concentration
Overall
Riverside
El Paso
Burlington
Pittsburgh
New York
Pittsburgh
Newark
Riverside
Newark
Fresno
Missoula
Birmingham
New York
Providence
Missoula
Riverside
Phoenix
Phoenix
Phoenix
El Paso
Cleveland
St Louis
Birmingham
San Jose
Miami
Winter
Bakersfield
El Paso
Burlington
Pittsburgh
New York
Pittsburgh
Salt Lake City
Bakersfield
Riverside
Fresno
Phoenix
Birmingham
New York
New York
Missoula
Salt Lake City
El Paso
Phoenix
Reno
El Paso
Phoenix
St Louis
Birmingham
Miami
Miami
Summer
Riverside
Riverside
Fresno
Pittsburgh
Pittsburgh
Pittsburgh
Newark
Riverside
Newark
Missoula
Boise City
Birmingham
New York
Newark
Indianapolis
Riverside
Houston
Miami
Miami
Omaha
Cleveland
Bakersfield
Birmingham
San Jose
Miami
15
-------
The inter-comparison across all pollutants of the
distributions of monitor-specific mean pollutant
concentrations is facilitated by scaling each pollutant to the
pollutant-specific overall STN network grand mean
concentration for 2001-2005 (Figure 5-3). Criteria air
pollutants (PM15, PM10, CO, N02, 03, S02), for which EPA
has set a National Ambient Air Quality Standard (NAAQS),
show less monitor-specific variation around the overall
STN grand mean than most PM constituents, which are not
specifically regulated. Certain PM constituents (Se, OC, Br,
and K) also show relatively tighter distributions, while
Nickel (Ni) and chlorine (Cl) had the greatest variation in
monitor-specific means.
o
N
> Z O
Figure 5-3. Distribution of monitor-specific mean pollutant concentrations as percent of the pollutant-specific overall STN
network grand mean concentration: 49 STN monitors, 2001-2005.
16
-------
Regional and Seasonal Patterns
Over this 5-year period, mean fine paniculate matter
(PM2.s) concentrations were higher in the Eastern United
States and in California, and lower in the Northwest, the
inter-mountain West, and the Central United States. (Figure
5-4).
Seasonally, PM2.5 concentrations were higher during the
winter months (December - February) in California's
central valley and in Salt Lake City, UT, but higher during
the summer months (June - August) in southern California
and in the Eastern United States. (Figure 5-5).
Figure 5-4. Mean fine participate matter (PM2.s) concentrations as proportions of 25 ng/m3: 49 STN monitors, 2001-2005.
Winter
Summer
Figure 5-5. Mean winter and summer fine particulate matter (PM2.s) concentrations as a proportion of 35 ng/m3: 49 STN monitors,
2001-2005.
17
-------
PM constituents and gaseous co-pollutants showed various
patterns of seasonal means concentrations (Figure 5-6).
Ozone and oxidation products (sulfate and organic carbon)
were all higher in the summer, while sulfur dioxide, the
precursor to sulfate particles, was higher in the winter.
Elements associated with wind-blown dust (Si, Al, Ti, and
Ca) (United States Environmental Protection Agency
2009c) were also generally higher in the summer.
Conversely, higher winter concentrations were observed for
PM constituents and gaseous co-pollutants associated with
mobile source emissions (N03, CO, N02, and elemental
carbon) and for chlorine and bromine (possibly from road
salt) (Gao et al. 2006). Zinc (possibly an indicator of tire
wear) (Davis et al. 2001) also showed higher winter
concentrations. The remaining PM constituents did not
show strong seasonal ratios, including elements associated
with residual-fuel oil (Mn, V, and Ni) and with coal
combustion (Se).
O
D. o.
Figure 5-6. Distributions of monitor-specific ratios of mean concentrations in the summer (June - August) and the winter
(December- February): 49 STN monitors, 2001-2005.
18
-------
Sulfate (S04) concentrations were higher in the Eastern
United States, reflecting the major contribution of sulfur
oxides from fossil fuel combustion (Figure 5-7). Similarly,
the highest S02 concentrations were also found in the
Northeast.
Figure 5-7. Mean sulfate (SO4) concentrations as a
proportion of 10 ng/m3: 49 STN monitors, 2001-
2005.
Nitrate (N03) concentrations show a different spatial
distribution more reflective of mobile source emissions
with the highest concentrations in California (Figure 5-9).
In the Eastern United States, the north-south gradient of
nitrate concentrations may reflect the temperature
dependence of the partition of nitrate into the gas or particle
phase (Lee and Hopke 2006).
Figure 5-9. Mean nitrate (NO3) concentrations as a
proportion of 10 ng/m3: 49 STN monitors, 2001-
2005.
Selenium (Se) concentrations, probably related to coal
combustion and coking process (Gildemeister et al. 2007),
were highest in CBS As with steel manufacturing
(Pittsburgh, PA at 6.7 ng/m3 (not mapped) and Cleveland,
OH at 2.8 ng/m3) and higher than average in the Midwest
and in the Northeastern United States (Figure 5-8).
Silicon (Si) concentrations are much higher in the arid
Southwestern United States and other locations impacted by
wind-blown sandy soils (Figure 5-10). Since the monitor-
specific means for aluminum and silicon are highly
correlated (r = 0.96), the geographic pattern for aluminum
concentrations was essentially identical to that for silicon.
Figure 5-8. Mean selenium (Se) concentrations as a
proportion of 3 ng/m3: 49 STN monitors, 2001-
2005.
-------
Iron (Fe) concentrations reflect the presence of iron in both
sandy and clay soils and the additional contributions from
manufacturing processes, such as steel production
(Gildemeister et al. 2007), with the highest levels in
Cleveland, OH and Birmingham, AL (Figure 5-11).
Elemental carbon (EC) concentrations show a more
uniform distribution across the United States (Figure 5-13).
This lack of a strong geographic pattern reflects the
contributions of many combustion sources to elemental
carbon concentrations.
tf
rf
V
[JU
> l-diiv-
f \
f
"4
Figure 5-11. Mean iron (Fe) concentrations as a proportion of
300 ng/m3: 49 STN monitors, 2001-2005.
Zinc (Zn) concentrations, in contrast to Fe, are almost
entirely related to manufacturing processes (Kim et al.
2007), such as steel production, with the highest levels in
Birmingham, AL and Cleveland, OH (Figure 5-12).
n-
Figure 5-13. Mean elemental carbon concentrations as a
proportion of 2 ng/m3: 49 STN monitors, 2001-
2005.
Organic carbon (OC) concentrations also show a relatively
uniform distribution across the United States, but with
higher concentrations in California's central valley. (Figure
5-8).
rf
-y IK
v-c, • •"-
---'-VTJ-" " S
T:, i [t/
r- . -s '' \ ^
l\ y-icl
t,,'—^\___,fit x,
\ f
Figure 5-12. Mean zinc (Zn) concentrations as a proportion of
120 ng/m3: 49 STN monitors, 2001-2005.
Figure 5-14. Mean organic carbon concentrations as a
proportion of 10 ng/m3: 49 STN monitors, 2001-
2005.
20
-------
Vanadium (V) concentrations were high in seaport cities
and in the northeastern metropolitan areas reflecting
vanadium in residual fuel oil used for shipping and for
domestic heating (Figure 5-15).
Nickel (Ni) concentrations were similarly high in two major
seaports: New York, NY with 22 ng/m3 and San Jose, CA
with 12 ng/m3. Excluding these two cities, nickel
concentrations were very low throughout the United States
(Figure 5-16).
[JU
I
Figure 5-15. Mean vanadium (V) concentrations as a
proportion of 10 ng/m3: 49 STN monitors, 2001-
2005.
Figure 5-16. Mean nickel (Ni) concentrations as a proportion
of 25 ng/m3: 49 STN monitors, 2001-2005.
21
-------
Conclusions Regarding Mean
Concentrations
Through the NAAQS process, EPA has already determined
unhealthy levels of total fine particle mass (PM2.5): 35
|j,g/m3 for a 24-hour average and 15 |J,g/m3 for an annual
average. Further studies of the adverse health effects of
human exposures to ambient PM2.5 concentrations above
these levels will not address the scientific uncertainties
underlying the current standards. Five CBS As had more
than 10 percent of days above 35 |J,g/m3: three in Southern
California (Bakersfield, Fresno, and Riverside); Edison, NJ;
and Houston, TX (Figure 5-1).
Conversely, some metropolitan areas may have too few
days with air quality near the current standards to address
key scientific uncertainties. Seven CBS As had both 10
percent or fewer individual days above 17.5 |J,g/m3 (half the
current 24-hour NAAQS for PM2.5) and 5-year average
concentrations below 10.5 (ig/m3 (75% of the annual
NAAQS for PM25): Phoenix, AZ; Denver, CO; Miami, FL;
Tampa, FL; Rockingham, NH; El Paso, TX; and Seattle,
WA (Figures 5-1 and 5-2).
PM2 5 and most key constituents of PM2 5 are present within
a four-fold range of concentrations throughout the United
States. (Figure 5-3). Elemental carbon (EC) and organic
carbon (OC) were widely distributed across the U.S. with
little regional pattern (Figures 5-13 and 5-14). Other
constituents showed regional patterns: vanadium (V)
concentrations were highest in the northeastern
metropolitan corridor (Figure 5-15), nitrate (N03)
concentrations were highest in southern California (Figure
5-9), and sulfate (S04) concentrations were highest in the
Eastern United States. (Figure 5-7). For these constituents,
epidemiologic studies would benefit from the selection of a
few characteristic metropolitan areas in each of these
regions.
In the extreme, some PM2 5 constituents have distributions
skewed by extreme levels in only one or two metropolitan
areas (Table 5-1 and Figure 5-3). Nickel (Ni)
concentrations are extraordinarily high in two major
seaports: New York, NY and San Jose, CA (Figure 5-16).
Silicon (Si) concentrations are extraordinarily high in the
desert southwest: El Paso, TX and Phoenix, AZ (Figure 5-
10). Metals, such as iron (Fe) and zinc (Zn), have extreme
concentrations in manufacturing and refining centers:
Cleveland, OH; Phoenix, AZ; and Birmingham, AL
(Figures 5-11 and 5-12). Selenium (Se) concentrations are
extraordinarily high in steel manufacturing centers
(Pittsburgh, PA and Cleveland, OH), but still had
considerable variation across the remaining metropolitan
areas (Figure 5-8). These metropolitan areas would tend to
dominate any epidemiologic analyses of these specific
constituents.
22
-------
Chapter 6
Variability in Concentrations of Criteria
Pollutants and Selected PM2.s Species
Indicators of Monitor-Specific Variability
For time-series and case-crossover studies of short-term
exposures, greater day-to-day variability in ambient air
pollutant concentrations tends to increase the statistical
power of such studies. Locations with greater variability
will be more influential in the determination of
concentration-response relationships. However, not all
variations are equally useful; variations due to infrequent
extreme events are less useful than frequent regular
variation.
For total fine particle mass (PM2.5), the city-specific
interquartile ranges vary closely with the city-specific
means (r = 0.82), while city-specific coefficients of
variation, the monitor-specific variance divided by the
monitor-specific mean expressed as a percentage, are
independently distributed around the grand mean of 62
percent (Figure 6-1).
Extreme variability generally reflects episodic extreme
events. For example, the outlier interquartile range in
Oxnard, CA, reflects the episodic impact of the Los
Angeles area. The three most extreme coefficients of
variation (Salt Lake City, UT; Missoula, MT; and Boise
City, ID) reflect infrequent extreme pollution events in
normally low particle areas.
30
25 -
•= 20 -
CD
CO
I i5i
0)
§ 10 i
cr
S
— 5 -
10 15 20 25
Mean (M9/m3)
120 -
100 -
.52 80 -
CO
"o 60 -
-i--
c
0)
| 40
CD
O
0 20 J
10
15
Mean
20
25
Figure 6-1. Distributions of monitor-specific interquartile range and coefficients of variation by monitor-specific means for PM2.s:
49 STN monitors, 2001-2005.
23
-------
Variability Relative to the Mean
The distributions of coefficients of variation show
considerable differences across the criteria pollutants and
the components of PM (Figure 6-2). Pollutants with
monitor-specific coefficients of variation greater than 100
percent have substantially variability relative to their means
than pollutants with monitor-specific coefficients of
variations less than 100 percent. Pollutants with lower
coefficients of variation generally have relatively stable
source contributions, such as those pollutants related to
local mobile source emissions in these urban areas.
For most pollutants, the monitor-specific coefficients of
variation fell within a tight range from 40 to 100 percent of
the pollutant-specific means. Even pollutants with the
lowest coefficients of variation, ozone and N02, have
shown sufficient variation for epidemiologic studies.
Pollutants with higher coefficients of variation may have
strong seasonal patterns, for example, nitrate (N03), or
episodic source events, for example, chlorine (Cl).
Potassium (K) is associated with seasonal burning of wood
and other plant materials. Other PM constituents (Si, Al,
and Ti) may be related to wind-blown dusts. Since seasonal
patterns in pollutants are generally removed by modeling or
by design, the useful variation for an epidemiologic study is
the within season variation.
1000%
i:
Hfiia]
10%
UO O CO
o
Figure 6-2. Distributions of monitor-specific coefficients of variation for various pollutants over the entire year: 49 STN monitors,
2001-2005.
24
-------
Seasonal Differences in Coefficient of
Variation
Season-specific distributions of the monitor-specific
coefficients of variation provide insights into the sources of
these pollutants (Figure 6-3). For example, chlorine (Cl)
showed higher coefficients of variation in both seasons with
the highest summer values in Tulsa, OK and Salt Lake City,
UT suggestive of wind-blown salt and the highest winter
values in Baltimore, MD; Charleston, SC, and Houston,
TX. Conversely, for sulfate (S04), the high coefficients of
variation disappeared with seasonal adjustment.
Potassium (K) had greater coefficients of variation in the
summer, perhaps related to episodic controlled or
1000%
uncontrolled combustion of plant materials, with high
values in mid-western cities. In the summer, high values
were observed for Indianapolis, IN (470%) and
Minneapolis, MN (386%); while, in the winter, high values
were observed for Miami, FL (622%) and Charleston, SC
(379%).
Aluminum (Al) had greater summer coefficients of
variation in mid-western cities, such as St. Louis, MO
(405%) and Indianapolis, IN (352%), and greater winter
values in the southern cities, such as Miami, FL (289%); El
Paso, TX (272%); and Houston, TX (237%), perhaps
related to episodic dust events involving windborne clay
soils rich in kaolin.
10%
o
-------
The increased summer-time coefficients of variation for K
and Al are particularly evident with monitor-specific ratios
of the summer and winter coefficients of variation
(Figure 6-4).
O
_ — ^J^^"'fH^LLl\J — ^ ^ ^
CL D-
Figure 6-4. Monitor-specific ratios of summer/winter coefficients of variation for various pollutants: 49 STN monitors, 2001-2005.
Conclusions Regarding Coefficients of
Variation
For most metropolitan areas with Speciation Trends
Network (STN) monitors, total fine particle mass (PM2.5)
and most PM2.5 constituents had modest coefficients of
variation between 50 percent and 100 percent of the
monitor specific means reflecting a regular pattern with
small daily variations (Figure 6-2). This general pattern was
present in both the winter and summer months (Figure 6-3).
A few constituents had skewed distributions of coefficients
of variation, indicating a pattern of isolated extreme
concentrations (Figure 6-1). The constituents showing the
most skewed distributions were nickel (Ni), potassium (K),
aluminum (Al), and chlorine (Cl).
26
-------
Chapter 7
Correlations with Total Fine Particle Mass (PM2.s)
Since the association between total fine particle mass
(PM2.5) and mortality has already been well established, the
specific PM2.5 constituents most likely to explain this
association should show strong associations with total PM2.5
mass. The monitor-specific coefficient of determinations
(the square of the correlation coefficient, R2) between PM2.5
and other pollutants vary greatly across pollutants and
seasons (Figures 7-1 to 7-15). Horizontal reference lines
indicate strong correlations (R2 greater than 49%), moderate
correlations (R2 between 25% and 49%), and weak
correlations (R2 less than 10%). City-specific correlations
are presented in Tables A-5 and A-8 in the Appendix.
Paniculate matter that is less than 10(im in aerodynamic
diameter (PM10) was strong-to-moderately correlated with
total PM25 mass in the majority of cities, while moderate
correlations with ozone (03) were observed in the majority
of cities (Figure 7-1). Not fully reflected by the unsigned
coefficient of determination, the overall relationship
between PM25 and 03 varied widely across cities,
exhibiting both negative and positive correlations. Much of
this variation appeared to be due to seasonal patterns. For
every monitor, 03 and PM25 had weak-to-moderate inverse
correlations during the winter. In contrast, during the
summer the correlations were all positive or weak. In the
winter the strongest inverse correlations were observed in
California and in the Northeast while in the summer the
strongest positive correlations were found in the Southeast.
°- 90 -
i so-
N
£ 70 -
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icient of determ
5 8 8 § k
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Coarse PM (PM10)
i i Annual
^M Winter
i / /i Summer
• r1-! r^
• / /
- - 1 )T
-*- T -4-
Ozone (O3)
Figure 7-1. Distributions of the monitor-specific percentage of the daily variation in PM2.s determined by the variation PM10 and O3
for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
27
-------
Fossil Fuel Combustion
Sulfate (S04) from fossil fuel combustion had the highest
R2 with total PM2.5 mass of any single PM2.5 constituent,
with summertime R2 above 70 percent in most Core Based
Statistical Areas (CBSAs) (Figure 7-2). For the entire year,
S04 was highly correlated with total PM2.5 mass for most
CBSAs east of the Mississippi River (Figure 7-3). Selenium
(Se), a more specific marker for coal combustion, had lower
correlations in most cities, but was strongest in Washington,
DC; Chicago, IL; Baton Rouge, LA; Baltimore, MD;
Detroit, MI; and Pittsburgh, PA. The seasonal differences
for S04 and S02 reflect seasonal differences in the
conversion of gaseous S02 into paniculate S04.
D- 90 -
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• ^^ Winter
i / i Summer
t T
•
1 1
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— . LZ
Sulfur Dioxide (SO2)
Sulfate (SO")
Selenium (Se)
Figure 7-2.
Distributions of the monitor-specific percentage of the daily variation in PM2.s determined by the variation in selected
markers of coal combustion for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-3. Monitor-specific correlations between PM2.s and sulfate: 49 STN monitors, 2001-2005.
28
-------
Mobile Sources
Potential markers of mobile sources (N02, N03) EC, OC,
and CO) were moderate-to-strongly correlated with total
PM2.5 mass in many cities, especially in the winter (Figure
7-4). Total PM2.5 mass was moderately or strongly
correlated with organic carbon (OC) throughout the United
States (Figure 7-5). For nitrogen dioxide (N02), the
strongest correlations with total PM25 mass were observed
in the Northeast, while, for nitrate (N03), the strongest
correlations were observed in California and the Pacific
Northwest.
« 10°
f 90:
o 80 -
=£ 70-
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IS
50
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?T?
Nitrogen
Dioxide (NO2)
Nitrate (NO.)
Elemental
Carbon (EC)
Organic
Carbon (OC)
Carbon
Monoxide (CO)
Figure 7-4. Distributions of the monitor-specific percentage of the daily variation in total PM2.s mass determined by the variation
in selected markers of mobile sources for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-5. Monitor-specific correlations between total PM2.s mass and organic carbon: 49 STN monitors, 2001-2005.
29
-------
Residual-Oil Fly Ash
Residual-oil fly ash may be indicated by manganese (Mn),
nickel (Ni) or vanadium (V), depending on the source of the
fuel oil . During the winter, a few cities, especially in the
Northeastern United States, had moderate correlations
between PM2.5 and these three metals (Figure 7-7). During
the summer, only weak correlations were observed between
these pollutants and PM2.5 in the majority of cities with no
real geographic pattern (Figure 7-6).
eg IUU
2
• K) GO J^ C
O O O O C
1 1 1 ! : i
0)
8 o-
i i Annual
^M Winter
i^ / i Summer
1
i—i-a • -i_. __J__^_.
v y~^ "0" y "w i 9 i
Figure 7-6.
Manganese (Mn)
Vanadium (V)
Nickel (Ni)
Distributions of the monitor-specific percentage of the daily variation in total PM2.s mass determined by the variation
in selected markers of residual-oil fly ash for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-7. Monitor-specific correlations between total PM2.s mass and vanadium during the winter months (December •
February): 49 STN monitors, 2001-2005.
30
-------
Biomass Combustion
Potential markers for biomass combustion, potassium (K)
and bromine (Br), had moderate to strong correlations with
total PM2.5 mass in many cities, especially during the winter
(Figure 7-8). During the winter, the strongest correlations
were for Speciation Trends Network (STN) monitors
located in the western part of the United States, especially
in the Northwest, but were still moderate in the
Northeastern United States and California (Figure 7-9).
Tampa, FL is an exception to the national pattern, perhaps
due to local agricultural burning in central Florida.
CM
E go-
's 80 -
SI
D£ 70 -
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•£ 20
-------
Crustal Materials
Elements common in wind-blown crustal materials,
aluminum (Al) and silicon (Si), appeared to only be weakly
correlated with total PM2.5 mass (Figure 7-10). In the
summer, silicon was weakly correlated aside from the
desert southwest and agricultural Midwest (Figure 7-11).
The higher correlations in Seattle, WA and Tampa, FL may
be related to transoceanic transportation of sand during
extreme desert dust events. Overall for the other crustal
elements, calcium (Ca) and titanium (Ti) produced
moderate correlations in only a few cities. For Si and Ti, the
monitor-specific averages were similar regardless of season
or region.
100
o
IS
(D
O
!t=
0)
O
O
90 -
80 -
70 -
60 -
50
40 -
30 -
20 -
10
0 -
Annual
Winter
Summer
Silicon (Si)
Aluminum (Al)
Titanium (Ti)
Calcium (Ca)
Figure 7-10.
Distributions of the monitor-specific percentage of the daily variation in total PM2.s mass determined by the variation
in selected markers of crustal elements for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-11. Monitor-specific correlations between total PM2.s mass and silicon (Si) during summer: 49 STN monitors, 2001-2005.
32
-------
Metallic Elements
Metallic elements represent a complex pattern due to the
wide variety of sources: windblown crustal materials,
emissions from manufacturing processes, and emission
from the use of manufactured products. Iron (Fe) was
moderately correlated with total PM2.5 mass in the majority
of cities, with stronger correlations in the winter (Figure 7-
12). The strongest year-round correlations were observed in
Northern California, the Northwest, and the Northeast
(Figure 7-13).
Iron (Fe)
Zinc (Zn)
Copper (Cu)
Figure 7-12. Distributions of the monitor-specific percentage of the daily variation in total PM2.s mass determined by the variation
in selected metals for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-13. Monitor-specific correlations between total PM2.s mass and iron (Fe): 49 STN monitors, 2001-2005.
33
-------
Sea Spray/Road Salt
Potential markers of sea spray/road salt, sodium (Na) and
chlorine (Cl), had only weak to moderate correlations with
total PM2.5 mass regardless of location or season
(Figure 7-14). During the winter, chlorine (Cl) was
moderately correlated in some cities in the Western and
Northern parts of the country (Figure 7-15).
(M IUU
^- 90 -
o
!X so -
£ 70 -
•2 60-
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& 40 -
4)
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0 0-
i i Annual
^^ Winter
>- / << Summer
.
J T
J L J
T
1 1 /////
•«• -» ^ 'V^ '
Chlorine (Cl)
Figure 7-14. Distributions of the monitor-specific percentage of the daily variation in PM2.s determined by the variation in selected
markers of sea spray/road salt for all days, winter days, and summer days: 49 STN monitors, 2001-2005.
Figure 7-15. Monitor-specific correlations between total PM2.s mass and chlorine (Cl) during winter: 49 STN monitors, 2001-2005.
34
-------
Conclusions Regarding Correlations with
PM2.s
Since previous epidemiologic studies in many metropolitan
areas throughout the world have already well-established
the association between total fine particle mass (PM2.5) and
increased mortality and hospitalizations, the examination of
associations with specific PM constituents should begin
with those constituents that covary with PM2.5. For this
report, the key PM constituents associated with total PM2.5
mass are sulfate, nitrate during the winter, elemental
carbon, and organic carbon. These constituents tend to be
ones that are associated with significant health effects
estimates possibly explaining the association of total PM2 5
mass with mortality and hospitalization. However, previous
studies have observed associations between copper,
potassium, zinc, titanium, aluminum, chlorine, iron, nickel,
silicon, and vanadium and adverse health effects (United
States Environmental Protection Agency 2009a).
Aluminum, chlorine, silicon, and titanium had low
correlations with total PM25 mass while correlations
between PM2 5 mass and the other constituents were source
and therefore city dependent.
35
-------
Chapter 8
Inter-Correlations Between Selected PM Constituents
Recent and future epidemiologic and clinical studies are
focused on resolving the relative independent and joint
effects of PM constituents and co-pollutants in a
multipollutant context. Observational studies cannot resolve
the independent effects of highly correlated air pollutants.
Therefore, the ability to distinguish between various PM
constituents would be enhanced by independent variation in
these constituents as characterized by low pair-wise
correlations.
In this chapter correlation within previously defined source
categories is first examined to determine the effectiveness
of these commonly used groupings. In the second section of
this chapter correlations between selected constituents from
different source categories are presented. The constituents
selected are those shown to be associated with adverse
health effects.
Within-Source Category
The monitor-specific coefficient of determinations (the
square of the correlation coefficient, R2) between PM2.5
constituents vary greatly across pollutants and seasons.
Figures 8-1 through 8-3 present within source categories, as
identified in Chapter 7, annually and in the winter and
summer seasons. Horizontal reference lines indicate strong
correlations (R2 greater than 49%), moderate correlations
(R2 between 25% and 49%), and weak correlations (R2 less
than 10%). City-specific coefficients of determination are
presented in Tables A-9 to A-17 in the Appendix.
100%
IT 90%
c 80%
'la 70%
1 60% -
£ 50%
0)
40% -
Q
M—
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10% -
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ANNUAL
X
Se/S04 N03/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca Alffi Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Interconstituent Correlations
I I Fossil Fuel Combustion \ '• • '• • -\ Residual Oil Fly Ash I / I Crustal Materials I I Sea Spray/Road Salt
Mobile Sources I _ I Biomass Combustion ESSl Metallic Elements
Figure 8-1. Distributions of monitor-specific of coefficients of determination for constituents within-source category for all days:
49 STN monitors, 2001-2005.
36
-------
5-- 90% -
.0 80% -
^ 70% -
| 60% -
fl) cno/
•<-> OU /o
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Se/SO4 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V NiA/ K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Interconstituent Correlations
I I Fossil Fuel Combustion l::::| Residual Oil Fly Ash I / I Crustal Materials I I Sea Spray/Road Salt
Mobile Sources I _ I Biomass Combustion tiiiLJ Metallic Elements
Figure 8-2. Distributions of monitor-specific of coefficients of determination for constituents within-source category for winter
days: 49 STN monitors, 2001-2005.
IUU /O
^ 90% -
c 80% -
'•^ 70% -
c
£ 60% -
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13-91?- : ~fi 5 tf^TT • 1?"?" ' T tf ^T
Se/SO4 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Interconstiutent Correlations
I I Fossil Fuel Combustion l:::: I Residual Oil Fly Ash I/ /I Crustal Materials I I Sea Spray/Road Salt
CZH Mobile Sources I I Biomass Combustion ESS Metallic Elements
Figure 8-3. Distributions of monitor-specific of coefficients of determination for constituents within-source category for summer
days: 49 STN monitors, 2001-2005.
37
-------
Fossil Fuel Combustion
Sulfate and selenium were moderately correlated in only
Baltimore, MD, Cleveland, OH, Detroit, MI, and Chicago,
IL. These correlations are higher in the summer than in the
winter.
Mobile Sources
Correlations between nitrate and elemental carbon, nitrate
and organic carbon, and elemental carbon and organic
carbon were on average higher in the winter than during the
summer. The lower summer correlations possibly reflect the
independent sources of secondary organic aerosols, while
the higher winter correlations reflect the common
combustion sources of both elemental carbon and primary
organic aerosols. In the majority of cities moderate to
strong correaltions were observed between elemental
carbon and organic carbon regardless of season, and
between nitrate and organic carbon during the winter.
During the winter nitrate and elemental carbon had strong
correlations in San Jose, CA and Springfield, MA.
Residual-Oil Fly Ash
Nickel and vanadium had high correlations during the
winter in the Northeastern metropolitan corridor;
Minneapolis, MN; and Seattle, WA; but low in other
metropolitan areas. These correlations also appeared to be
higher in the winter compared with the summer. Similarly,
there were higher correlations between manganese and
nickel and manganese and vanadium during the winter. The
higher winter correlations are possibly due to the higher use
of oil heating during the colder months.
Biomass Combustion
Potassium and bromine exhibited higher winter correlations
with the strongest correlations occurring in Seattle, WA and
Northeastern metropolitan areas.
Crustal Materials
Correlations between all of the crustal materials were
higher in the summer than during the winter. Silicon and
aluminum, the major elemental constituents of sandy and
clay soils (United States Environmental Protection Agency
2009c), are highly correlated in the summer throughout the
Eastern United States, but in the winter the monitor-specific
correlations are high only in the desert Southwest. The
influence of wind-blown dust arising from arid conditions
and agricultural operations is apparent from these seasonal
correlations. Silicon is also highly correlated with calcium
in the majority of cities, and with titanium during the
summer. In the winter high correlations between titanium
and silicon were only observed in the Western United
States. Aluminum was poorly correlated with both titanium
and calcium in the majority of cities regardless of season.
The exceptions were a few cities in California; Denver, CO;
and Phoenix, AZ.
Metallic Elements
High correlations in the winter for zinc and iron, especially
in northern metropolitan areas, may reflect a common
source. Although metropolitan areas with steel
manufacturing do show moderate winter-time correlations,
the common source for zinc and iron appears to be present
in less industrialized metropolitan such as Portland, OR,
Chicago, IL, and New York City, NY. One possible
common source might be dusts from mobile sources, such
as tire and engine wear (Majestic et al. 2009; Sanders et al.
2003). Zinc and copper had low seasonal correlations in
most metropolitan areas, but rose to moderate correlations
in the densely populated, and high traffic, mid-Atlantic
areas during the winter months. Similarly, iron and copper
had higher correlations during the winter; however, there
did not appear to be a spatial pattern to these correlations.
Sea Spray/Road Salt
Sodium and chlorine were not well correlated in the
majority of metropolitan areas. The exceptions were those
cities along bodies of water such as San Jose, CA,
Providence, RI, Miami, FL, and Gulfport, MS.
38
-------
Between-Source Categories
In order to summarize the results, cities were grouped into
four regions (Northeast, South, Midwest, and West) as
defined by the U.S. Census Bureau. In Figures 8-4 through
8-7 horizontal reference lines indicate strong correlations
(R2 greater than 49%), moderate correlations (R2 between
25% and 49%), and weak correlations (R2 less than 10%).
The constituents were selected based on their significant
associations with morbidity and mortality as well as their
significant modification of these health effects. The selected
elements include elemental carbon, organic carbon, copper,
potassium, zinc, titanium, aluminum, chlorine, nitrate,
sulfate, iron, nickel, silicon, and vanadium (United States
Environmental Protection Agency 2009a). Inter-constituent
correlations presented in the previous section (i.e., within-
source category) will not be discussed in this section. City-
specific correlations are presented in Tables A-9 to A-17 in
the Appendix.
Midwest
Elemental carbon was moderately correlated with
potassium, zinc, and iron. Average correlations between
these constituents were mainly driven by Indianapolis, IN,
and Milwaukee, WI. Organic carbon was moderately
correlated with potassium in all Midwestern cities. Overall
organic carbon was moderately correlated with iron except
in Detroit, MI, Omaha, NE, and St. Louis, MO. Moderate
correlations were observed between potassium and iron in
all Midwestern cities except St. Louis, MO. Potassium was
moderately correlated with silicon except in Indianapolis,
IN, Kansas City, MO, and Minneapolis, MN.
Seasonal differences were noted in the above correlations.
Moderate correlations between elemental carbon and
potassium were observed in the winter but not the summer.
Some correlations were not significant overall but were
significant during certain seasons. During the winter
organic carbon was moderately correlated with zinc and
nitrate, potassium was moderately correlated with zinc, and
nitrate was moderately correlated with sulfate. Nitrate was
also moderated correlated with sulfate during the summer
months. Moderate correlations were observed between
organic carbon and sulfate, zinc, and nitrate; and iron and
titanium in the summer. Only weak correlations were
observed for the other inter-constituent correlation
combinations
1 UU /O -
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§ 70% -
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c 60% -
E
CD c;no/
(15
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0)
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0% -
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r
MIDWEST
I
I
EC/K EC/Zn EC/Fe OC/K OC/Fe K/Fe
Interconstituent Correlations
K/Si
Figure 8-4.
Distributions of monitor-specific of coefficients of determination for constituents between-source categories in
Midwestern cities for all days: 49 STN monitors, 2001-2005.
39
-------
Northeast
Moderate correlations between elemental carbon and zinc,
organic carbon and iron, and organic carbon and potassium
were observed in all Northeastern cities. Elemental carbon
was also moderately correlated with iron except in
Burlington, VT. Over this region, elemental carbon was
moderately correlated with nickel; however, the average
correlation was mainly driven by New York City, NY and
Philadelphia, PA. Moderate correlations with iron were also
observed with titanium, sulfate, nickel, silicon, and zinc.
Iron was moderately correlated with titanium in all
Northeastern cities except Burlington, VT whereas the
moderate correlations observed between iron and sulfate
were driven by New York City, NY, Rockingham, NH, and
Springfield, MA. Iron and silicon were moderately
correlated in most cities in this region with the exception of
Edison, NJ and Springfield, MA.
The moderate correlations observed between nickel and
zinc, and organic carbon and sulfate were heavily
influenced by the strong correlations in New York City, NY
(R2 = .59). Overall, nitrate had moderate correlations with
zinc and sulfate, but was poorly correlated in Edison, NJ,
Philadelphia, PA, and Pittsburgh, PA. Potassium was
moderately correlated with zinc in all Northeastern cities
except Boston, MA. Seasonally, correlations of elemental
carbon, iron, and zinc with nickel were only observed in the
winter. The overall moderate correlations observed between
potassium and zinc also were driven by the winter months.
Significant correlations between organic carbon and iron,
organic carbon and sulfate, and nitrate and zinc were seen
during the summer but not during the winter. Only weak
correlations were observed for the other inter-constituent
correlation combinations.
1 UU /O -
90% -
^ 80% -
0^
o 70% -
c 60% -
E
m ^0% -
0)
2 40% -
o
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0) •3U/0
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«*D zu/o
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-------
South
Moderate correlations were observed between elemental
carbon and iron and titanium. Strong correlations between
these constituents were observed in Atlanta, GA. Iron was
also moderately correlated with silicon except in Baltimore,
MD and Memphis, TN. The overall moderate correlations
observed between iron and zinc were driven by the strong
correlation seen in Atlanta, GA, Baltimore, MD,
Birmingham, AL, and Washington, DC.
For the region zinc was moderately correlated with organic
carbon, with strong correlations in Houston, TX. Moderate
correlations between elemental carbon and iron and iron
and potassium were observed during the winter but not
during the summer. In contrast, significant correlations
between iron and silicon were seen during the summer and
not the winter. Only weak correlations were observed for
the other inter-constituent correlation combinations.
90% -
^ 80% -
0^
g 70% -
"CD
c 60% -
^
m c;n% -
0)
Q
^ 40% -
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0 "
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t=
SOUTH
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EC/Zn EC/Fe Fe/Ti Fe/Si Fe/K
Interconstituent Correlations
OC/Zn
Figure 8-6. Distributions of monitor-specific of coefficients of determination for constituents between-source categories in
Southern cities for all days: 49 STN monitors, 2001-2005.
41
-------
West
As with the other regions, elemental carbon was moderately
correlated with iron and zinc. The overall moderate
correlations between elemental carbon and iron were
heavily influenced by the strong correlations observed in
Denver, CO, Riverside, CA, and Seattle, WA. Strong-to-
moderate correlations between elemental carbon and zinc
were observed in all western cities as well as between iron
and silicon, and iron and titanium. Iron was also moderately
correlated with aluminum and organic carbon. Overall iron
was moderately correlated with potassium, with strong
correlations in Boise City, ID, Denver, CO, and Phoenix,
AZ.
Moderate correlations were observed between potassium
and organic carbon, with the strongest correlation seen
along the west coast cities. The overall moderate
correlations between potassium and zinc were mainly
driven by a few cities: Fresno, CA; Missoula, MT; Portland,
OR; and Sacramento, CA. As with the other regions the
correlation between elemental carbon and zinc were driven
by the winter months. Stronger correlations between iron
and aluminum, organic carbon and iron, and potassium and
zinc were observed in the winter compared to the summer.
Only weak correlations were observed for the other inter-
constituent correlation combinations.
1 UU /O -
90% -
^ 80% -
O^
§ 70% -
CO
c 60% -
m ^no/.
Q .no/
4_ 40% -
O
-1-.
c 30% -
Q)
O
it 20% -
0)
o
O 1 no/.
0% -
WEST
•
" £ T
• •
—i— •
T
I
. I
• T -r-
m I
i
I —
IT • . b
• i
±
•
EC/Zn EC/Fe Fe/AI Fe/OC Fe/K Fe/Si Fe/Ti OC/K K/Zn
Interconstituent Correlations
Figure 8-7. Distributions of monitor-specific of coefficients of determination for constituents between-source categories in
Western cities for all days: 49 STN monitors, 2001-2005.
42
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Conclusions Regarding Interconstituent
Correlations
A common approach for estimating the total health effect of
multiple pollutant exposures is to use the indicator approach
where the concentration of one pollutant is used to
represent the combined exposure to several pollutants or to
an emissions source (Dominici et al. 2010). In the first
section of this chapter, the strength of the intercorrelations
among constituents assigned to the same source category
was examined. Selenium, a constituent of coal combustion,
is not well correlated with the other constituents typically
associated with fossil fuel combustion. For mobile sources
and crustal materials, elemental carbon and silicon could
serve as indicators, respectively. Potential indicators of
residual oil fly ash, biomass combustion, and sea spray/road
salt appear to be geographically and seasonal dependent.
The correlations between the metallic elements appear to be
source dependent, varying based on either manufacturing or
dusts from mobile sources.
The second section of this chapter focused on between-
source categories. Previous studies have identified
associations between several PM2.5 constituents and adverse
health effects. In metropolitan areas where these
constituents are highly correlated, observational studies will
have difficulties in determining the causal constituent. More
significant interconstituent correlations were seen in
Northeastern cities compared to the other regions. Across
all regions strong-to-moderate correlations were observed
between elemental carbon and zinc, and elemental carbon
and iron. Significant correlations were seen between
organic carbon and potassium in the Midwest, Northeast,
and West. Iron and potassium had strong-to-moderate
correlations in all regions except the Northeast. In
Midwestern and Northeastern cities, iron was strongly-to-
moderately correlated with organic carbon. Iron was also
strongly-to-moderately correlated with both titanium and
silicon on all regions except the Midwest.
43
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Chapter 9
Relationships Between Coefficients of Variation and Correlations
Low correlation between a pair of air pollutants may be
caused by a lack of temporal variability in one or both
pollutants rather than simply an absence of a relationship.
The purpose of this chapter is to determine whether low
correlations between pairs of pollutants were due to low
coefficient of variations of either pollutant. Only a few
pollutants had monitor-specific coefficients of variations
less than 50 percent. These include PM10> ozone, carbon
monoxide, nitrogen dioxide, sulfur dioxide, selenium,
sulfate, elemental carbon, and organic carbon.
Table 9-1 lists those cities with both correlations between
the pollutant and PM2.5 in lowest 10th percentile and
coefficient of variations less than 50 percent for either
pollutant. PM10 was strong-to-moderately correlated with
PM2.5 in all of the cities therefore it was not included. For
the majority of these cities the low correlations between the
pollutants and PM2.5 may have been caused by the low
coefficients of variation of that pollutant. However, in
Baton Rouge, LA, Charlotte, NC, and Dallas, TX low
coefficients of variation for PM2 5 were also observed.
Low Correlations with PM2.s
Table 9-1. Cities with PM25 Correlations in the Lowest 10 Percentile and Coefficients of Variations Less Than 50 Percent for
Either PM2 5 or the Other Pollutant
O3 CO NO2 SO2 Se SO4 EC OC
Portland, OR Omaha, NE Charlotte, NC San Jose, CA San Diego, CA Phoenix, AZ Baton Rouge, LA
Houston, TX Dallas, TX Miami, FL Reno, NV Charlotte, NC
Oxnard, CA
Kansas City, MO
Omaha, NE
Low Interconstituent Correlations
Those pollutants with high interconstituent correlations in
all cities were not further examined since these
relationships are unlikely to have been greatly affected by
low coefficient of variations. In the previous section those
cities were presented in which both the correlations
between the pollutants were in the lowest 10th percentile
and the coefficients of variation were less than 50 percent
for either pollutant. Elemental carbon was poorly correlated
with organic carbon, potassium, and zinc in Baton Rouge,
LA possibly due to low coefficients of variation of
elemental carbon. Other poor correlations potentially due to
low coefficients of variation of elemental carbon include
correlation between elemental carbon and zinc in Detroit,
MI and between elemental carbon and vanadium in Tulsa,
OK. In Phoenix, AZ, we observed low coefficients of
variation for sulfate possibly leading to weak correlations
between sulfate and vanadium and sulfate and organic
carbon. For other correlations with organic carbon, Table 9-
2 list those cities with both correlations between the
pollutants lowest 10th percentile and coefficient of
variations less than 50 percent for either pollutant. In all
cities weak correlations between organic carbon and the
other constituent may have been caused by a low
coefficient of variation of organic carbon.
Table 9-2.
Cu
Cities with Between Pollutant Correlations in the Lowest 10th Percentile and Coefficients of Variations Less Than 50
Percent for Either PM25 Constituent
K
Zn
Cl
N03
Fe
Ni
Si
V
Omaha, NE Miami, FL Omaha, NE St. Louis, MO
Tulsa, OK Tampa, FL Charleston, SC Omaha, NE
Memphis, TN Memphis, TN
Indianapolis, ID Tampa, FL Miami, FL
Kansas City, MO Charleston, SC St Louis, MO
St. Louis, MO Kansas City, MO
Tulsa, OK Memphis, TN
Memphis, TN
Miami, FL Tampa, FL
Tampa, FL Indianapolis, IN
Minneapolis, MN
Tulsa, OK
44
-------
Conclusions Regarding Relationships
Between Coefficients of Variations and
Correlations
Only PMio, ozone, carbon monoxide, nitrogen dioxide,
sulfur dioxide, selenium, sulfate, elemental carbon and
organic carbon had monitor-specific coefficients of
variations less than 50 percent. Low correlations with PM2.5
due to low coefficients of variations were only possible in a
handful of cities. Many of these cities were also identified
when examining interconstituent correlations. Cities that
were listed for multiple pollutants include Omaha, NE,
Miami, FL, Tampa, FL, Charlotte, NC, Charleston, SC,
Tulsa, OK, Memphis, TN, St Louis, MO; Kansas City, MO,
and Indianapolis, IN.
45
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Chapter 10
Potential Monitors for Increased Frequency
Summary of Results
This analysis illustrates that criteria pollutants and PM2.5
species concentrations, variability, and relationships with
PM2.5 can differ both temporally and spatially. For those
pollutants exhibiting a geographic pattern, the highest
concentrations were observed in the Northeast (S, S04, and
S02), California (N03), or both (PM25 and V). Examining
all of the selected sites, PM10, 03, Ca, K, Zn, V, Al, Si, Ti,
Na, and S04 were significantly higher in the summer
compared to the winter, whereas the opposite was true for
CO, N02, S02, Br, N03, EC, OC, Mn, Ni, and Cl. A few of
the pollutants (PM2.5, PM10, OC, V, and N03) also exhibited
seasonal patterns that differed regionally.
In comparison with the PM25 NAAQS for all cities, the
majority of days were below the daily standard of 35 (ig/m3
while a few cities had annual concentrations either well
above or well below the annual standard of 15 (ig/m3.
Additionally, in approximately half of the cities,
coefficients of variation of less than 49 percent for PM10,
Se, CO, N02, and OC were observed whereas only a few
are below 49 percent for PM2.5, EC, N03, S04, and S02.
Regardless of season or region PM10 and S04 were strong-
to-moderately correlated with PM2 5 while Se showed weak
correlations and S02 exhibited both seasonal and regional
patterns. The correlations between PM25 and 03 were
negative in the winter and positive in the summer, and
showed some regional differences. Strong correlations with
markers of wood smoke (K, Br, and Ca) were seen in some
cities, usually in the winter in the Western part of the
United States. Elements associated with mobile sources
were well correlated with PM25 in most cities, with the
exception of Cu, which exhibited different geographic and
seasonal patterns. Of the crustal elements (Al, Fe, Si, and
Ti) only Fe was moderately correlated with PM25 in the
majority of cities, with the strongest correlations observed
in the winter. Finally, markers of residual-oil fly ash and
sea spray were at best moderately correlated with PM2 5 in a
small percentage of cities.
Next, correlations within and between selected source
groups were examined. Although used as a marker of coal
combustion, selenium was not well correlated with the
other fossil fuel constituents (i.e., sulfur and sulfate). For
mobile sources, elemental carbon and organic were
moderately correlated and correlations with nitrate were
stronger in the winter. Correlations between nickel and
vanadium were only significant in a few cities with the
strongest correlations observed in the winter. Similarly,
potassium and bromine (markers for biomass combustion)
were also more highly correlated in the winter. Correlations
between all of the crustal materials were higher in the
summer than during the winter. Silicon was significantly
correlated with other crustal materials in the majority of the
cities whereas correlations between aluminum and the other
crustal elements were generally weak. Elements used as
markers for seas spray/road salt were not well correlated
and correlations between the metallic elements appear to be
source dependent, varying based on either manufacturing or
dusts from mobile sources.
To examine between source groups, elements that have
previously been associated with adverse health effects were
selected. Strong-to-moderate correlations were observed
across all regions between elemental carbon and zinc, and
elemental carbon and iron. Iron was also correlated with
elements associated with mobile sources, crustal materials,
and biomass combustion in all regions. Correlations such as
those between elemental carbon and zinc in the Northeast
were mainly driven by a few cities (e.g., New York City,
NY) while others such as iron and silicon were present in
all cities across a region.
Some of the observed low correlations between pollutants
in certain cities may have been due to a low coefficient of
variation for either one or both pollutants. Cities that
appeared frequently were Omaha, NE; Miami, FL; Tampa,
FL; Charlotte, NC; Charleston, SC; Tulsa, OK; Memphis,
TN; St Louis, MO; Kansas City, MO; and Indianapolis, IN.
46
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Selection of Cities
An increasing number of studies are examining the
associations between gaseous pollutants and PM species
and health effects, as well as their role as potential
confounders in the PM-health effects associations. A meta-
analysis of PM and gaseous pollutants showed that PM,
N02, CO, and S02, all showed a positive and significant
mortality risk estimate (Stieb et al. 2002). Among the
pollutants from the Speciation Trends Network (STN), the
strongest associations with mortality have been observed
for PM2.5 mass, EC, N03, Cl, Cu, Fe, K, Ti, V, and Zn
(Ostro et al. 2007). Studies have also shown that certain
chemical species significantly modify the association
between PM2.5 and mortality (Franklin and Schwartz 2008),
while others have examined the role of gaseous pollutants
such as 03 as either potential confounders or surrogates to
PM25 exposures (Sarnat et al. 2001).
Although these studies have identified serious health risks,
there is still uncertainty as to which components are the
most harmful. Furthermore, the pollutant itself may not be
the causal agent but rather a surrogate for a particular
source. For example, previous time-series analyses indicate
that, of the sources of PM2 5, motor vehicle exhaust usually
has the strongest associations with all non-accidental
mortality and with cardiovascular mortality (Mar et al.
2000; Laden et al. 2000; Janssen et al. 2002).
Various studies have documented significant heterogeneity
among community-specific health effect estimates of PM10
on mortality (Peng et al. 2005; Dominici et al. 2003), PM25
and mortality (Franklin et al. 2007), and PM25 on hospital
admissions (Bell et al. 2006). Heterogeneity in city-specific
and seasonal-specific estimates have also been
demonstrated in ozone mortality studies (Bell et al. 2005;
Bell et al. 2004; Levy et al. 2005; Ito et al. 2005).
Differences in city-specific health effect estimates may
reflect variations in the air pollution mixture and its
sources. These variations may be city-specific, adding to
the complexity of focusing on a particular pollutant.
Daily monitoring data would aid an epidemiologist in
determining the health effect of a particular pollutant. The
criteria for selecting cities for daily speciation monitoring
includes sufficient population size, sufficient concentration
levels and variability, and low correlations between
pollutants independent of low variability.
Using the analyses of air quality measurements from 49
STN monitors for 2001-2005 of the previous chapters, daily
monitoring would be of greatest epidemiologic interest for
metropolitan areas that meet the following five criteria:
1 . Population of the metropolitan area;
2. Mean levels of criteria air pollutants;
3. Variation in levels of criteria air pollutants and
paniculate matter constituents;
4. Correlation among criteria air pollutants and paniculate
matter constituents; and
5. Relationship of correlations to the coefficient of
variation.
In Table 10-1, those cities that do not meet each criteria are
indicated by a bar shaded in gray.
Criterion 1: Adequate Population Levels
In Table 10-1, those cities with populations less than
500,000 and greater than 5,000,000 are highlighted. Cities
with smaller populations may not have enough daily deaths
to perform a time-series analysis. Conversely, very large
cities will have an adequate number of daily deaths but the
monitor may not represent well the population's exposure.
City-specific populations can be found on Table 4-1 .
Criterion 2: Mean Levels
The city-specific daily and/or annual PM25 in comparison
to NAAQS was examined. Concentrations were deemed too
high if more than 10 percent of days were above the daily
standard (35(ig/m3) and/or the 5-year annual average was
more than 10 percent above the annual standard (15(ig/m3).
Cities were identified as having too low PM25
concentrations when 10 percent or fewer days above 17.5
|j,g/m3 (50% of the daily standard) and/or 5-year annual
average concentrations below 10(ig/m3 (75% of the annual
standard). These concentrations are presented in Figures 5-1
and 5-2.
Criterion 3: Adequate Level of Variability
Only 10 pollutants (PM25, PM10, CO, N02, 03, S02, Se,
S04, EC, and OC) had coefficients of variation less than 50
percent in any of the cities. N02, 03, and Se had
coefficients of variations less than 50 percent in most cities
so it would be difficult to eliminate a city based on one low
coefficient of variation. Therefore, those cities with
coefficients of variation less than 50 percent for five or
more pollutants were identified as having inadequate levels
of variation. City- and pollutant-specific coefficients of
variation are presented in Tables A-4 and A-7 of the
Appendix.
47
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Criterion 4: Correlation Among Criteria
Air Pollutants and Particulate Matter
Constituents
Ideally in a selected city correlations between PM2.5 and
constituents and the other criteria pollutants would be
strong. In contrast, correlations among the paniculate
matter constituents should be low. Some of pollutants (e.g.,
selenium and chlorine) will have low correlations with
PM2.5 in the majority of cities or be highly correlated (e.g.,
elemental carbon and organic carbon) in the majority of
cities. Therefore the following exclusion criteria were
developed: Strong correlations (R2 > 0.49) with PM2.5 must
be present with over 50 percent of the other pollutants; and
R2 > 0.49 can only occur between less than 10 percent of
the potential constituent combinations described in Chapter
8. These coefficients of determination are shown in Tables
A-8 through A-17 of the Appendix.
Criterion 5: Relationship of Correlations
to the Coefficient of Variation
As described in Chapter 9, low correlations between
pollutants may be caused by low coefficients of variation of
either pollutant. In Table 10-1, those cities where low
correlations could be due to low variability are indicated by
bars shaded in gray.
Cities Meeting Criteria 1-5
According to Table 10-1, the 11 metropolitan areas meeting
the above criteria are:
Atlanta, GA
Boston, MA
Edison, NJ
Cleveland, OH
Providence, RI
Milwaukee, WI
Baltimore, MD
Springfield, MA
Newark, NJ
Pittsburgh, PA
Salt Lake City, UT
Table 10-1.
Selection Criteria for Identifying Metropolitan Areas for Enhanced PM Speciation Monitoring, 49 STN Monitors, 2001-
2005
City
Too High/Low
Population Size
Too High/Low
PM2.5 Levels
Coefficient of
Variation < 50%
R < 0.49 between
PM2.5 and other
pollutants
R2> 0.49 between
constituents
Low R due to low
coefficient of
variation
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Detroit,
Minneapolis, MN
Gulfport, MS
High
Low
High
High
High
Low
Low
Low
Low
48
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Table 10-1. Continued
City
Population Size
Too High/Low
PM2.5 Levels
Coefficient of
Variation < 50%
R2 < 0.49 between
PM2.5 and other
pollutants
R > 0.49 between
constituents
Low R2 due to low
coefficient of
variation
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
I Edison"NJ
| Newark, NJ
New York, NY
Charlotte, NC
| Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Low
Low
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Low
Low
Low
Low
Geographical Distribution
An additional consideration not previously discussed is the
geographical distribution of selected cities. The above list
of cities does not represent well the Western part of the
United States while Northeastern cities appear to be overly
represented. In the West, San Diego, CA and Sacramento,
CA met four out of five criteria and was excluded only by
the low correlation between PM2.5 and selenium possibly
due to the minor contribution of coal combustion. In the
Northeast, Springfield, MA and Edison, NJ may be dropped
in preference to their larger neighbors.
Conclusion
In this report we developed a methodology for selecting
cities for daily speciation monitoring. As an illustration of
this methodology we applied the 5 design criteria to the
ambient air quality data from 2001-2005 of 49
meteropolitan areas. Based on these results and in
combination with consideration to geographical
distribution we generated the following list of potential
candidatemetropolitan areas for enhanced air quality
monitoring;
San Diego, CA
Baltimore, MD
Newark, NJ
Pittsburgh, PA
Salt Lake City, UT
Atlanta, GA
Boston, MA
Cleveland, OH
Providence, RI
Milwaukee, WI
49
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52
-------
Metropolitan Areas of Epidemiologic
Interest for Enhanced Air Quality
Monitoring
Appendix A
A-l
-------
(This page intentionally blank)
A-2
-------
Table A-1. Identification Numbers for STN Monitors, Nearest Co-Pollutant Monitors, and Weather Bureau Network
(WBN) Stations: 49 STN Monitors, 2001-2005
EPA Monitor Identification Number
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Boston, MA
Springfield, MA
Baltimore, MD
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
STN
01 0730023
0401 39997
06029001 4
0601 90008
061112002
060658001
060670006
060730003
060850005
080010006
110010043
120861016
1 20571 075
130890002
160270004
170310076
180970078
220330009
250250042
2501 30008
240052001
261630001
270530963
280470008
202090021
2951 00085
300630024
310550019
320310016
3301 5001 4
340230006
340390004
360050083
371190041
390350060
401431127
410510080
421 01 0004
420030008
440070022
4501 90049
471570047
481130069
481410044
482011039
490353006
50007001 2
530330080
550790026
CO
01 0736004
(1)
(1)
(1)
(1)
(1)
(1)
060730001
(1)
080130001
110010023
1 20864002
120571070
(1)
160270006
170314002
180970073
(1)
(1)
2501 3001 6
(1)
261 630001
270530954
220710012
(1)
(1)
300630005
310550035
(1)
330110020
(1)
340390003
(1)
(1)
390350048
401 4301 91
(1)
(1)
420030038
440071009
450190005
471 570036
(1)
(1)
(1)
(1)
(1)
(1)
(D
NO2
132230003
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
080130001
110010041
120864002
120571065
(1)
490570002
(1)
180970073
(1)
(1)
(1)
(1)
261630016
270370423
280450001
(1)
2951 00086
300870762
191530058
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
4501 90003
471570024
(1)
(1)
(1)
(1)
(1)
(1)
(D
03
01 736002
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
080130001
(1)
120860027
120571035
(1)
160010030
(1)
180970073
(1)
(1)
(1)
(1)
(1)
270031 002
(1)
(1)
(1)
300298001
31 0550028
(1)
(1)
(1)
3401 70006
(1)
(1)
390350034
(1)
(1)
(1)
(1)
440071 01 0
4501 50002
471570021
(1)
(1)
(1)
(1)
(1)
(1)
(D
SO2
010731003
(1)
060831025
0601 31 002
060371 002
(1)
(1)
060730001
060870003
080130001
110010041
120860019
120570095
131210055
1 60050004
(1)
1 80970057
(1)
(1)
250130016
(1)
261630015
270530954
280470007
(1)
2951 00086
301 1 1 0066
310550053
060670002
(1)
(1)
(1)
(1)
(1)
(1)
401430501
(1)
(1)
42003001 0
440071 009
4501 90003
471570046
(1)
481410037
(1)
49035001 2
(1)
(1)
(D
PM10
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
110010041
(1)
1 20571 070
131150005
160270002
1 7031 3301
180970083
221210001
(1)
2501 54002
(1)
(1)
270530966
280590006
(1)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
3604701 22
371190003
(1)
401 4301 91
(1)
(1)
420030031
(1)
450190047
471570016
481130057
(1)
(1)
(1)
(1)
(1)
550790064
NWS
Coop
Station
010831
026481
040442
043257
048261
047470
049781
042706
047821
052220
448906
087205
085663
090451
102444
1 1 1 577
124286
1 60549
1 8571 8
190770
1 4703(2)
202103
21 5435
223666
234359
237455
245745
256255
266779
1 72602
286055
286026
30581 1
311690
331657
348992
356751
366886
362574
376698
381 544
405954
412244
412797
414307
427598
431081
457473
475479
(1) Collocated with STN monitor
(2) Weather Bureau/Army/Navy (WBAN) station identification number
A-1
-------
Table A-2. Core-Based Statistical Areas (CBSA) with a Speciation Trends Network (STN) Monitor Along with Population on April 1, 2000 and Federal Information Processing
Standard (FIPS) Codes for Constituent Counties: 49 STN Monitors, 2001-2005
STN
01 -073-0023
04-013-9997
06-019-0008
06-029-0014
06-065-8001
06-067-0006
06-073-0003
06-085-0004
06-1 1 1 -2002
08-001 -0006
1 1 -001 -0043
12-057-3002
12-086-1016
13-089-0002
CBSA
13820
38060
260
12540
40140
40900
41740
41940
37100
19740
47894
45300
33124
12060
Core Based Statistical Area
Name
Birmingham-Hoover. AL
Phoenix-Mesa-Scott sdale. AZ
Fresno-Madera. CA
Bakersfield. CA
Riverside-San Bernardino-Ontario, CA
Sacramento-Arden-Arcade-Roseville. CA
San Diego-Carlsbad-San Marcos, CA
San Jose-Sunnvvale-Santa Clara, CA
Oxnard-Thousand Oaks-Ventura, CA
Denver-Aurora. CO
Washington-Arlington-Alexandria. DC-VA-MD-VW
Tampa-St. Petersburg-Clearwater, FL
Miami-Miami Beach-Kendall, FL
(one county with two FIPS codes)
Atlanta-Sandy Springs-Marietta, GA
2000 Pop
1 ,051 ,300
3,251 ,888
921 ,930
661 ,649
3,254,817
1 ,796,852
2,813,834
1,735,818
753,186
2,179,343
3,727,452
2,396,014
2,253,786
4,248,021
Core County
FIPS Name 2000 Pop %
01073 Jefferson 662,062 63%
04013 Maricopa 3,072,168 94%
06019 Fresno 798,821 87%
06029 Kern 661,649 100%
06065 Riverside 1,545,374 47%
06067 Sacramento 1,223,497 68%
06073 San Diego 2,813,834 100%
06085 Santa Clara 1,682,584 97%
06111 Ventura 753,186 100%
08031 Denver 553,691 25%
1 1 001 District of Columbia 572,055 1 5%
12057 Hillsborough 998,943 42%
12086 Miami-Dade 2,253,786 100%
13121 Fulton 815,827 19%
Other Counties in CBSA
01007
01127
04021
06039
06071
06017
06069
08001
08039
24009
51059
51179
51630
12053
12025
13013
13063
13097
13149
13217
13255
01009
06061
08005
08047
24017
51061
51187
51683
12101
13015
13067
13133
13151
13223
13297
FIPS
01021
06113
08014
08059
24033
51107
51510
51685
12103
13035
13077
13117
13159
13227
01115
08019
08093
51013
51153
51600
54037
13045
13085
13135
13171
13231
01117
08035
51043
51177
51610
13057
13089
13143
13199
13247
A-2
-------
Table A-2. Continued
Core Based Statistical Area
Core County
Other Counties in CBSA
STN
1 6-027-0004
17-031-0076
1 8-097-0078
20-209-0021
22-033-0009
24-005-3001
25-013-0008
25-025-0042
26-1 63-0001
27-053-0963
28-047-0008
29-510-0085
30-063-0024
31-055-0019
32-031-0016
33-015-0014
34-023-0006
34-039-0004
CBSA
14260
16974
26900
28140
12940
12580
44140
14484
19804
33460
274
41180
33540
36540
39900
40484
20764
35084
Name
Boise Citv-Nampa. ID
Chicago-Nape rville-Joliet. IL
Indianapolis-Carmel. IN
Kansas City. MO-KS
Baton Rouge. LA
Baltimore-Towson. MD
Springfield. MA
Boston-Quincv. MA
Detroit-Livonia-Dearborn. Ml
Minneapolis-St. Paul-Bloomington, MN-WI
Gulfport-Biloxi-Pascagoula. MS
St. Louis. MO-IL
Missoula. MT
Omaha-Council Bluffs, NE-IA
Reno-Sparks. NV
Rockingham-Stafford. NH Division of Boston-Cambridge-Quincy,
Edison-New Brunswick, NJ Division of New York, NY
Newark-Union. NJ-PA Division of New York, NY
2000 Pop
464,842
7,628,496
1,525,103
1 ,836,425
705,962
2,553,022
680,016
1,812,937
2,061,161
2,968,812
246,197
2,698,664
95,799
767,144
342,885
MA-NH 389,595
2,173,876
2,097,523
FIPS
16001
17031
18097
20209
22033
24005
25013
25025
26163
27053
28047
29510
30063
31055
32031
33015
34023
34013
Name
Ada
Cook
Marion
Wyandotte
East Baton Rouge
Baltimore City
Hampden
Suffolk
Wayne
Hennepin
Harrison
St. Louis City
Missoula
Douglas
Washoe
Rockingham
Middlesex
Essex
2000 Pop
300,906
5,376,837
860,457
157,879
412,854
651,154
456,226
689,809
2,061,161
1,116,037
189,606
348,189
95,799
463,585
339,486
277,357
750,167
792,313
%
65%
70%
56%
9%
58%
26%
67%
38%
100%
38%
77%
13%
100%
60%
99%
71%
35%
38%
16015
17037
17111
18011
18081
20059
29013
29095
22005
22091
24003
24510
25011
25021
27003
27123
55093
28039
17005
17119
29099
19085
31155
32029
33017
34025
34019
16027
17043
17197
18013
18109
20091
29025
29107
22037
22121
24013
25015
25023
27019
27139
55109
28045
17013
17133
29113
19129
31177
34029
34027
FIPS
16045
17063
18057
18133
20103
29037
29165
22047
22125
24025
27025
27141
28059
17027
17163
29183
19155
34035
34037
16073
17089 17093
18059 18063
18145
20107 20121
29047 29049
29177
22063 22077
24027 24035
27037 27027
27163 27171
28131
17083 17117
29055 29017
29189 29219
31025 31153
34039 42103
A-3
-------
Table A-2. Continued
Core Based Statistical Area
Core County
Other Counties in CBSA
STN
CBSA
Name
2000 Pop FIPS
Name
2000 Pop %
FIPS
36-005-0083 35644 New York-White Plains-Wayne, NY-NJ Division of New York, NY
37-119-0041 16740 Charlotte-Gastonia-Concord. NC-SC
39-035-0060 17460 Cleveland-Elvria-Mentor. OH
40-143-112746140 Tulsa. OK
41-051-0080 38900 Portland-Vancouver-Beaverton. OR-WA
42-003-0008 38300 Pittsburgh. PA
42-101-0004 37964 Philadelphia-Camden-Wilmington. PA-NJ-DE-MD
44-007-0022 39300 Providence-New Bedford-Fall River, RI-MA
45-019-0049 16700 Charleston-North Charleston-Summerville. SC
47-157-002432820 Memphis. TN-MS-AR
11,298,122 36005 Bronx 1,332,65212%
1,330,552 37119 Mecklenburg 913,639 69%
2,148,017 39035 Cuyahoga 1,393,84865%
859,533 40143 Tulsa 563,302 66%
1,927,883 41051 Multnomah 660,486 34%
2,431,086 42003 Allegheny 1,281,665 53%
5,687,158 42101 Philadelphia 1,517,54227%
1,582,997 44007 Providence 621,595 39%
548,974 45019 Charleston 310,099 56%
1,205,196 47157 Shelby 897,472 74%
34003
36079
37007
39055
40037
40145
41005
53059
42005
42129
42017
25005
45015
05035
47047
34017 34031 36047 36061
36081 36085 36087 36119
37025 37071 37179 45091
39085 39093 39103
40111 40113 40117 40131
41009 41067 41071 53011
42007 42019 42051 42125
42029 42045 42091
44001 44003 44005 44009
45035
28033 28093 28137 28143
47167
48-113-0069 19124 Dallas-Plano-lrving. TX
48-141-0044 21340 El Paso. TX
48-201-1039 26420 Houston-Sugar Land-Baytown, TX
49-035-3006 41620 Salt Lake City. UT
50-007-0012 15540 Burlington-South Burlington. VT
53-033-0080 42644 Seattle-Tacoma-Bellevue. WA
55-079-0026 33340 Milwaukee-Waukesha-West Allis. Wl
3,451,204 48113 Dallas
679,622 48141 El Paso
4,715,417 48201 Harris
968,883 49035 Salt Lake
198,892 50007 Chittenden
3,043,897 53033 King
1,500,743 55079 Milwaukee
2,218,79264% 48085 48119481214813948231
48257 48397
679,622 100%
3,400,59072% 48015 48039480714815748167
48291 48339 48407 48473
898,412 93% 49043 49045
146,572 74% 50011 50013
1,737,047 57% 53061
940,165 63% 55089 55131 55133
A-4
-------
Table A-3. Mean Concentrations of Criteria Air Pollutants for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
PM2.5 (
PM10 (ng/m3)
03 (ppb)
CO (pphm)
N02 (ppb)
S02 (ppb)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
18.2
10.1
19.6
18.3
13.4
25.3
14.4
14.2
14.4
10.3
15.0
9.5
10.6
15.7
9.2
15.5
15.6
13.2
15.2
11.9
10.6
15.7
10.4
12.1
13.4
14.8
10.8
10.3
8.7
Winter
14.8
14.4
31.6
30.9
10.7
21.2
20.4
17.3
17.7
13.0
13.4
10.2
11.2
13.4
15.9
17.5
15.4
12.2
15.5
12.4
11.3
16.8
12.2
10.9
14.9
14.7
16.8
11.7
12.5
Summer
21.4
7.9
12.6
10.7
15.7
25.5
7.8
12.6
8.7
9.6
20.2
9.4
10.2
18.9
6.6
16.4
18.1
13.0
19.7
14.4
13.2
17.4
10.0
12.0
13.7
17.3
9.6
10.9
8.0
Annual
35.2
30.6
43.6
35.9
28.7
57.1
20.6
32.8
24.3
37.1
28.7
25.7
27.9
25.5
29.1
28.9
20.7
31.1
22.2
20.2
11.8
23.6
25.1
NA
28.6
27.6
20.1
40.6
29.9
Winter
25.2
29.7
38.2
37.1
20.0
43.2
17.6
33.9
24.9
40.2
25.2
26.6
25.4
17.5
21.2
28.2
18.3
28.4
21.8
19.4
9.2
20.8
27.4
NA
25.2
24.1
18.9
33.8
35.1
Summer
39.5
31.2
44.5
30.6
34.6
66.5
21.3
30.6
20.8
37.8
33.2
27.9
29.1
32.0
36.8
31.3
24.7
31.7
25.4
22.9
17.2
29.7
25.9
NA
31.0
31.2
23.0
48.5
30.6
Annual
21.2
22.9
29.2
30.6
30.6
28.2
29.2
24.1
18.4
19.4
23.1
30.7
24.4
23.7
30.4
21.4
32.7
22.9
25.4
19.7
28.7
26.4
25.8
33.9
24.0
28.9
NA
30.3
25.5
Winter
13.0
11.6
11.5
11.6
20.7
15.2
17.7
13.9
11.4
11.0
12.8
31.0
20.0
NA
NA
10.8
NA
17.0
12.0
12.0
21.3
NA
18.0
NA
14.2
NA
NA
NA
13.2
Summer
25.5
32.8
45.6
48.3
38.6
40.1
38.8
28.4
22.7
27.4
34.7
22.1
21.2
25.9
34.0
31.2
34.3
23.9
36.1
27.1
34.3
28.2
29.9
31.2
34.0
34.7
NA
33.2
37.7
Annual
9.27
10.02
5.08
4.99
7.46
7.18
5.41
6.97
7.40
5.57
9.19
7.28
10.39
5.84
10.88
7.44
4.87
6.22
5.38
3.99
5.57
3.48
4.30
NA
5.89
4.67
NA
3.98
5.51
Winter
10.50
14.80
7.25
7.76
11.07
9.01
6.66
9.85
10.69
7.70
9.70
8.31
11.45
5.82
11.55
8.09
5.67
6.31
7.22
4.95
7.84
3.53
4.75
NA
7.03
5.11
13.41
4.31
8.36
Summer
7.28
6.13
3.29
2.68
4.86
5.25
3.82
4.38
4.20
4.23
8.31
7.63
9.93
5.60
9.98
6.47
4.55
6.23
3.48
3.38
4.16
3.38
4.19
NA
5.14
3.91
NA
3.63
3.82
Annual
NA
23.2
19.8
18.7
15.9
21.5
13.6
19.9
21.1
21.8
22.6
13.9
9.7
15.1
NA
21.1
16.3
16.4
17.1
21.6
12.3
21.0
7.8
NA
17.0
18.5
NA
NA
12.0
Winter
NA
28.8
22.3
22.8
17.2
23.1
16.0
25.6
26.4
26.9
26.4
17.7
12.7
20.6
NA
23.9
18.6
19.9
20.9
25.9
16.6
23.6
10.7
NA
18.2
21.2
NA
NA
14.6
Summer
NA
17.6
15.8
12.7
14.9
18.7
9.3
15.8
14.2
19.4
19.9
12.1
8.2
10.8
NA
18.6
14.3
13.5
14.4
18.0
10.0
18.7
5.6
NA
15.9
15.4
NA
NA
10.4
Annual
4.1
1.7
NA
NA
2.0
2.5
1.5
3.3
1.3
2.6
6.6
1.4
2.8
3.0
NA
5.1
5.3
3.7
6.6
4.9
5.7
6.5
2.9
1.8
3.1
4.7
NA
2.0
NA
Winter
5.01
1.91
NA
NA
1.73
1.49
1.31
3.64
1.24
2.75
111
1.55
2.57
4.04
NA
6.31
6.19
5.16
111
7.30
9.46
5.71
3.50
2.04
3.67
5.21
NA
2.17
NA
Summer
3.53
1.57
NA
NA
2.12
3.99
1.81
3.18
1.21
2.55
6.67
1.30
3.06
2.53
NA
4.40
4.63
2.26
6.67
3.62
3.33
6.59
2.31
1.43
2.90
4.49
NA
1.50
NA
A-5
-------
Table A-3. Continued
PM2.5 (
PM1
03 (ppb)
CO (pphm)
N02 (ppb)
S02 (ppb)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
9.5
12.4
15.4
13.7
14.5
17.6
11.5
8.7
14.8
15.7
11.2
11.5
13.9
12.4
9.9
8.7
12.0
9.6
8.3
12.5
Winter
9.7
11.2
15.2
14.1
12.5
16.4
10.5
9.8
15.3
13.7
12.0
11.4
12.6
11.6
10.4
7.1
23.5
10.3
9.4
14.4
Summer
12.7
17.7
19.2
17.4
17.9
20.4
13.6
6.2
18.8
21.6
13.4
12.2
17.1
13.7
9.7
10.1
7.8
12.0
7.4
13.2
Annual
16.7
0.0
31.3
19.2
25.1
31.8
23.4
15.5
23.7
23.5
18.7
19.6
26.3
30.8
63.7
16.3
27.7
14.0
14.4
23.6
Winter
20.2
0.0
27.2
15.0
21.0
23.6
18.6
14.9
21.8
18.8
18.0
16.4
21.1
29.0
63.7
12.3
39.3
12.8
14.1
21.8
Summer
20.0
0.0
37.2
26.0
27.4
38.9
27.1
14.0
29.9
30.1
22.9
20.7
30.8
32.8
NA
20.3
24.5
18.7
16.0
25.7
Annual
31.4
25.4
21.4
17.4
32.1
26.3
26.0
19.1
16.8
20.9
33.1
25.3
29.0
24.8
25.8
26.5
34.1
35.1
19.2
26.9
Winter
NA
15.5
11.1
9.1
NA
NA
17.3
NA
8.0
11.6
NA
21.1
NA
14.1
15.9
19.7
NA
32.8
NA
14.8
Summer
32.0
37.4
33.1
26.7
35.6
31.2
33.7
19.8
26.8
32.0
35.6
24.8
31.4
32.8
34.3
27.2
36.5
35.7
19.3
33.3
Annual
NA
6.17
10.44
6.37
5.30
6.54
6.14
6.06
4.60
5.08
7.06
3.60
5.37
3.73
6.72
3.36
8.45
4.31
4.63
3.56
Winter
NA
7.58
11.61
6.58
6.47
7.36
6.60
5.86
6.41
5.71
8.45
4.16
5.50
4.36
9.13
3.63
13.28
5.19
5.94
3.93
Summer
NA
5.35
9.39
6.59
4.22
6.28
6.05
NA
3.24
4.20
5.58
3.31
5.18
3.11
4.72
3.12
4.83
3.60
3.73
3.11
Annual
7.6
17.5
34.9
27.1
15.4
22.3
9.5
12.0
23.7
18.8
18.6
10.2
20.5
17.8
17.9
11.4
25.3
12.6
18.6
16.4
Winter
11.2
23.6
34.2
29.6
20.5
21.0
11.2
N.A
28.8
22.3
23.9
13.8
20.4
20.6
21.7
15.1
36.6
16.0
21.0
19.6
Summer
5.8
12.6
35.2
24.9
11.9
23.0
8.4
10.7
19.7
16.5
13.6
7.0
19.5
14.9
14.3
8.0
18.1
10.1
16.4
14.2
Annual
3.5
4.5
8.0
9.1
3.3
6.6
5.4
NA
4.9
8.7
4.8
2.7
3.8
1.4
1.4
5.4
4.0
1.4
3.1
3.3
Winter
5.61
7.06
10.23
16.03
4.55
7.50
6.04
NA
7.83
10.43
8.03
4.05
4.29
1.55
1.74
6.25
4.30
1.86
3.08
3.66
Summer
2.47
2.99
6.70
4.72
2.94
5.98
4.99
NA
3.80
8.75
2.78
1.73
3.55
1.24
1.09
5.14
3.92
1.18
3.27
2.86
A-6
-------
Table A-4. Coefficients of Variation for Criteria Air Pollutants for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
PM2.5(%) PM10(%) 03(%) C0(%)
N02
S02
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
52
56
82
51
85
63
89
78
68
50
56
42
49
46
100
55
53
45
58
61
70
60
62
51
50
53
97
60
79
Winter
48
56
66
62
79
67
73
53
79
56
49
46
46
43
88
55
49
43
54
52
62
61
59
44
47
51
78
58
78
Summer
48
34
44
51
37
44
44
32
42
32
51
42
55
44
52
50
52
45
55
60
75
57
61
54
47
49
125
53
80
Annual
63
45
52
49
57
46
47
51
55
49
49
37
32
61
63
42
47
47
52
47
70
52
48
NA
39
44
71
61
51
Winter
62
51
77
73
60
52
54
44
65
50
45
32
33
44
68
42
43
68
52
33
49
54
53
NA
38
42
58
69
50
Summer
51
46
25
30
30
28
33
26
45
41
45
45
34
51
45
39
43
37
49
45
65
47
41
NA
39
46
93
52
56
Annual
45
49
53
41
55
47
43
48
36
52
55
34
41
37
30
56
29
44
51
56
42
39
43
31
50
41
NA
37
51
Winter
45
51
56
55
42
52
50
47
59
61
58
27
33
NA
NA
55
NA
37
55
57
47
NA
45
NA
52
NA
NA
NA
65
Summer
35
25
16
18
21
24
26
24
28
29
31
28
41
35
22
33
27
43
30
43
33
40
32
33
29
31
NA
32
21
Annual
137
52
54
52
76
55
68
64
65
55
35
44
35
50
47
42
45
46
54
51
60
46
50
NA
59
54
39
48
74
Winter
145
39
47
61
57
49
62
42
53
51
39
42
36
61
53
40
45
55
48
48
53
58
50
NA
52
50
36
48
64
Summer
106
31
28
16
37
44
58
31
40
30
28
41
31
36
42
43
43
32
33
47
46
38
47
NA
51
39
NA
36
62
Annual
NA
36
40
46
45
47
53
45
44
48
35
48
55
54
NA
40
38
41
42
35
64
40
52
NA
39
39
NA
NA
55
Winter
NA
27
32
29
47
41
37
35
33
40
34
40
53
42
NA
35
37
38
34
31
61
38
44
NA
39
35
NA
NA
54
Summer
NA
40
37
37
35
45
50
36
41
42
34
44
45
49
NA
43
35
35
41
31
49
41
41
NA
35
37
NA
NA
42
Annual
103
54
NA
37
NA
95
73
46
67
69
61
47
100
86
NA
55
81
83
61
72
74
116
182
99
68
67
NA
161
NA
Winter
113
50
NA
NA
74
53
67
39
41
68
54
42
91
82
NA
56
76
67
54
61
53
102
230
98
61
70
NA
160
NA
Summer
84
50
NA
NA
45
82
67
44
41
64
61
45
92
79
NA
46
84
64
61
59
66
108
120
48
85
62
NA
71
NA
A-7
-------
Table A-4. Continued
PM2.
PM1
CO (%)
N02
S02
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
67
59
70
62
47
54
54
65
62
56
63
50
51
49
69
57
105
76
59
66
Winter
61
56
55
49
42
54
53
66
53
46
55
42
46
49
62
60
82
60
59
60
Summer
67
64
56
63
45
51
48
46
60
50
67
59
48
45
43
65
43
83
45
67
Annual
52
53
66
55
42
56
42
52
55
59
53
39
42
44
48
44
72
69
42
55
Winter
48
56
48
48
39
52
47
63
56
58
39
40
43
51
44
40
64
48
54
53
Summer
48
65
57
60
37
50
36
36
51
49
58
36
38
42
NA
49
41
76
36
53
Annual
31
58
53
61
36
44
41
37
63
55
36
38
33
49
45
42
25
31
38
46
Winter
NA
52
71
61
NA
NA
42
NA
67
56
NA
34
NA
47
45
37
NA
22
NA
52
Summer
30
33
31
39
31
39
25
34
36
28
37
41
28
36
21
49
20
33
32
35
Annual
NA
45
40
36
48
44
53
54
56
60
44
42
58
45
36
63
61
50
49
43
Winter
NA
40
53
39
50
39
52
56
49
67
42
49
59
49
58
44
43
57
46
43
Summer
NA
32
41
26
29
47
56
NA
27
44
40
28
49
26
40
24
32
31
40
32
Annual
65
33
50
34
44
37
57
40
37
41
40
49
40
43
57
48
46
46
35
43
Winter
54
43
32
25
33
34
52
NA
29
35
31
37
35
38
43
45
35
44
29
34
Summer
44
39
34
32
40
36
60
37
31
39
31
45
44
39
41
53
31
35
42
42
Annual
84
52
71
76
73
67
97
NA
88
55
76
79
74
45
68
56
62
70
65
77
Winter
71
57
49
48
60
71
98
NA
72
53
56
67
99
53
52
69
67
78
63
81
Summer
69
53
43
58
86
66
82
NA
78
49
53
65
49
25
18
59
48
42
62
53
-------
Table A-5. Coefficients of Determination for Criteria Air Pollutants with PM2.5 for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
PM10 (%) O3 (%) CO (%) NO2 (%) SO2
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
79.2
41.7
10.0
43.6
61.0
46.8
25.2
53.0
79.2
34.2
27.7
51.9
25.1
42.9
21.4
39.4
80.2
29.5
69.5
61.6
50.4
53.1
43.4
N/A
25.7
54.9
46.3
19.0
60.4
Winter
72.4
61.4
45.4
91.9
57.5
52.6
75.1
63.2
89.6
21.6
17.6
43.2
18.4
37.1
45.5
43.5
83.6
31.3
62.5
29.2
51.0
40.6
35.9
N/A
13.6
51.7
49.5
31.9
61.4
Summer
79.3
53.3
48.5
75.7
32.5
16.0
32.9
32.9
76.6
48.2
45.3
74.4
33.2
38.3
47.9
39.1
93.5
55.5
85.8
83.9
77.0
77.8
55.0
NA
64.1
66.9
74.1
37.7
65.9
Annual
9.3
29.2
24.1
28.3
0.9
0.4
32.9
7.1
46.2
18.5
0.0
0.4
7.5
15.8
0.4
0.5
19.8
13.2
2.5
0.2
0.6
22.4
2.2
20.6
0.0
17.4
N/A
21.7
20.3
Winter
14.8
44.5
27.7
25.7
46.7
35.9
28.6
32.5
61.1
35.9
41.1
3.6
1.1
N/A
N/A
32.2
N/A
5.9
39.9
47.9
48.1
N/A
6.1
N/A
15.1
N/A
N/A
N/A
77.4
Summer
44.7
1.8
17.2
37.9
19.9
15.1
36.4
0.3
5.4
0.1
0.0
1.4
23.5
34.4
5.7
29.6
26.5
54.7
30.9
33.4
39.9
36.2
25.5
42.1
29.2
32.9
NA
32.1
6.7
Annual
1.6
39.7
40.1
61.5
3.0
16.2
43.0
13.9
53.5
29.8
3.1
6.1
3.0
31.4
14.6
5.6
7.5
5.9
6.7
6.6
9.6
12.1
4.2
N/A
2.4
5.0
32.0
0.0
32.7
Winter
4.5
57.1
24.5
41.5
26.2
51.6
60.3
39.4
63.9
25.1
10.9
13.1
8.8
31.4
27.6
15.1
5.6
9.7
39.5
24.6
30.0
22.0
0.1
N/A
2.1
10.6
44.5
2.2
54.2
Summer
9.2
6.8
9.1
29.9
2.5
4.4
0.8
6.1
2.3
20.6
6.0
8.4
1.6
35.4
1.0
0.2
4.5
1.0
2.8
3.0
4.5
3.5
6.4
N/A
4.5
2.4
N/A
0.5
1.1
Annual
N/A
37.9
39.2
58.8
16.8
13.5
45.0
32.6
54.2
22.1
11.9
22.7
7.9
4.8
N/A
15.1
14.0
11.4
20.8
17.0
31.7
34.7
6.5
N/A
17.5
11.2
N/A
N/A
19.5
Winter
N/A
38.4
18.5
35.1
52.0
35.6
50.2
37.8
57.1
18.2
46.6
46.7
18.7
30.2
N/A
28.1
36.4
28.9
43.9
29.8
59.6
49.7
10.2
N/A
30.9
24.9
N/A
N/A
33.6
Summer
N/A
5.1
36.4
47.2
0.4
0.2
14.5
17.8
50.9
6.5
6.7
8.3
3.1
22.9
N/A
10.9
10.5
10.8
18.4
10.2
19.9
27.8
0.6
N/A
12.7
6.3
N/A
N/A
6.0
Annual
3.8
12.8
N/A
N/A
0.5
0.1
0.4
5.4
0.2
8.8
10.7
0.2
5.9
0.4
N/A
17.3
30.6
2.0
15.2
11.5
13.2
22.0
3.0
4.7
1.0
20.7
N/A
1.0
N/A
Winter
3.2
13.9
N/A
N/A
0.3
0.3
11.0
4.7
1.4
6.8
12.3
5.9
10.6
1.8
N/A
42.6
29.4
5.1
18.9
36.9
60.7
26.9
4.8
0.5
1.0
21.4
N/A
3.6
N/A
Summer
11.3
0.0
N/A
N/A
1.5
6.4
0.3
10.4
0.0
4.0
8.6
0.3
6.3
6.1
N/A
3.4
35.3
3.3
9.7
3.0
10.7
21.4
0.6
10.6
0.9
16.3
N/A
0.2
N/A
A-9
-------
Table A-5. Continued
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
44.2
55.9
55.9
54.4
67.0
50.7
40.6
64.4
71.4
60.9
69.5
58.0
50.2
41.9
100.0
36.3
56.5
78.2
73.1
62.3
PM10 (%)
Winter
23.0
56.7
43.4
50.5
60.0
20.9
9.9
77.5
65.8
42.4
57.5
59.7
45.3
13.4
100.0
29.4
85.1
78.0
87.3
61.1
Summer
65.3
83.4
87.0
85.8
90.0
75.3
57.4
46.8
91.7
70.6
81.7
75.1
57.6
65.2
N/A
49.4
38.0
90.0
67.9
69.1
Annual
12.6
1.5
0.6
0.9
23.0
16.0
6.6
0.1
0.1
2.0
23.4
5.0
8.5
4.0
3.6
8.3
6.9
10.8
7.0
4.2
03 (%)
Winter
N/A
44.2
22.7
51.3
N/A
N/A
14.2
N/A
45.1
40.0
N/A
3.5
N/A
15.0
30.6
1.0
N/A
5.3
N/A
39.9
Summer
33.3
36.2
22.2
22.0
37.0
33.7
25.3
10.5
30.0
21.0
39.3
38.5
38.5
27.2
0.2
12.6
9.1
53.0
2.2
49.5
Annual
N/A
13.5
8.2
37.9
7.4
17.4
0.8
42.6
19.0
13.2
9.0
2.6
4.9
4.8
27.1
2.3
33.4
16.1
32.5
13.4
CO (%)
Winter
N/A
36.2
23.9
42.1
25.9
19.9
0.2
41.7
46.3
25.9
28.4
7.5
10.6
6.4
66.8
6.1
52.1
25.9
53.0
20.5
Summer
N/A
15.7
1.0
29.4
16.8
17.6
3.8
N/A
15.1
12.8
3.2
1.0
2.6
7.1
6.5
3.1
1.7
11.4
19.4
8.6
Annual
16.1
7.5
32.0
50.8
5.5
33.4
7.8
38.7
31.6
21.2
17.3
6.5
7.1
6.2
19.6
0.5
46.4
18.0
50.5
30.4
N02 (%)
Winter
52.6
47.5
47.6
60.0
25.6
29.7
18.5
N/A
55.1
45.8
55.3
25.9
4.9
9.2
38.9
7.5
76.6
44.9
48.0
46.3
Summer
15.4
0.2
17.1
42.5
26.1
24.4
8.9
32.0
28.1
25.9
9.2
8.5
9.6
13.5
9.7
3.6
7.6
8.8
56.9
16.7
Annual
2.0
10.2
34.9
20.2
2.4
8.2
8.7
N/A
37.9
18.0
15.9
8.2
0.0
7.7
16.0
2.3
8.7
22.8
42.8
26.9
S02 (%)
Winter
4.6
47.4
62.2
67.4
7.8
10.0
4.3
N/A
47.6
29.6
53.5
17.7
0.8
8.3
36.7
3.5
19.1
23.7
38.7
24.5
Summer
0.0
7.0
19.4
39.4
13.8
8.6
2.7
N/A
40.9
22.9
9.5
14.8
0.5
12.5
2.7
1.9
1.9
18.1
45.6
24.7
A-10
-------
Table A-6. Mean Concentrations of Selected PM25 Constituents for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
CBSA
Se (ng/m3)
S04 (ng/m3)
N03 (^g/
EC (ng/m3)
OC (ng/m3)
Br (ng/m3)
Annual Winter Summer Annual Winter Summer Annual Winter Summer Annual Winter Summer Annual Winter Summer Annual Winter Summer
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
1.6
0.9
1.4
1.7
1.4
1.3
1.3
1.3
1.5
1.4
2.0
1.3
1.4
1.3
1.3
1.2
2.0
0.7
1.7
1.1
1.2
1.9
1.4
1.3
1.4
1.6
1.3
1.4
1.3
1.6
1.0
1.5
1.8
1.2
1.3
1.3
1.2
1.6
1.4
2.3
1.4
1.4
1.4
1.3
1.2
2.2
0.8
1.8
1.2
1.3
1.7
1.4
1.3
1.3
1.5
1.3
1.3
1.3
1.6
0.8
1.3
1.6
1.6
1.3
1.3
1.3
1.4
1.4
2.4
1.3
1.3
1.4
1.3
1.2
1.9
0.7
2.0
1.1
1.2
1.9
1.4
1.4
1.4
1.7
1.3
1.4
1.3
4.9
1.2
1.9
1.6
2.5
3.0
1.2
2.8
1.4
1.4
5.2
2.5
3.4
4.7
1.0
3.0
4.9
4.2
5.2
3.1
2.9
4.0
2.3
4.0
2.8
4.2
0.9
2.5
0.7
3.2
0.8
1.8
1.4
0.9
1.1
0.8
1.1
1.1
1.4
3.0
2.6
2.8
2.6
1.5
2.4
3.0
3.2
3.3
2.5
2.3
2.8
2.6
2.9
2.2
2.8
1.2
1.9
0.5
6.7
1.5
2.1
1.8
4.4
4.7
1.6
4.3
1.8
1.5
8.4
1.9
3.3
6.9
0.7
3.8
7.0
4.7
8.1
4.4
4.6
5.6
2.3
4.4
3.4
6.0
0.7
3.1
0.8
1.1
1.2
6.5
6.0
3.4
9.9
2.5
3.6
2.8
1.7
1.7
0.7
0.7
0.8
2.2
1.9
2.7
0.7
2.0
1.2
0.9
3.0
2.3
0.6
2.2
2.5
1.2
2.1
1.0
1.8
2.7
12.2
10.2
3.0
6.7
5.4
3.8
4.2
3.0
2.8
1.0
0.9
1.6
5.3
3.1
4.8
1.2
3.2
1.8
1.4
4.9
4.5
1.0
4.3
4.6
2.9
4.4
2.7
0.6
0.4
1.4
1.4
4.0
11.5
0.9
3.1
1.8
0.9
0.8
0.5
0.5
0.4
0.5
1.0
1.1
0.4
0.9
0.7
0.5
1.5
0.6
0.4
0.6
0.9
0.4
0.7
0.3
1.37
0.95
1.00
0.92
0.69
1.33
0.81
0.83
0.94
1.18
0.69
1.50
0.54
1.01
0.48
0.40
0.65
0.63
0.75
0.79
0.37
0.78
0.49
0.42
0.66
0.84
0.61
0.44
0.93
1.27
1.71
1.37
1.40
0.77
1.72
1.33
1.37
1.49
1.63
0.73
1.65
0.73
1.04
0.56
0.33
0.58
0.63
0.98
0.83
0.47
0.70
0.48
0.56
0.70
0.77
0.88
0.54
1.36
1.26
0.50
0.71
0.52
0.62
0.99
0.38
0.52
0.51
0.96
0.62
1.37
0.43
0.93
0.38
0.47
0.66
0.61
0.64
0.71
0.33
0.89
0.47
0.34
0.68
0.85
0.49
0.38
0.69
6.5
5.5
6.7
7.8
4.7
6.7
6.5
5.3
5.6
4.5
4.4
3.4
3.8
5.1
4.4
2.3
4.4
3.3
5.0
4.1
3.0
4.2
3.8
3.8
5.7
4.6
6.5
3.4
5.4
6.1
7.9
8.4
12.6
4.3
6.7
10.2
7.9
7.8
5.8
3.9
4.3
4.6
5.1
4.5
1.8
3.9
3.2
5.6
3.8
3.2
4.0
3.7
4.0
6.0
4.3
7.4
2.9
6.2
6.6
4.2
5.9
5.4
5.2
7.0
4.3
4.1
4.0
4.6
5.3
3.0
3.2
5.3
4.9
3.0
5.2
3.1
5.7
5.4
3.9
5.1
4.5
3.7
6.1
5.3
7.7
3.9
6.2
4.62
3.58
5.43
4.06
3.97
5.63
2.74
4.04
3.31
2.28
4.21
3.04
3.40
3.25
1.90
3.20
3.14
3.38
4.46
2.52
2.26
3.20
2.22
2.76
3.51
3.84
1.49
2.02
1.84
4.83
3.37
5.68
4.61
2.81
4.37
2.95
4.19
3.69
2.62
4.94
4.15
4.47
3.72
2.34
3.56
3.2
4.18
5.64
2.88
2.79
3.11
2.37
3.15
4.27
4.31
1.56
1.95
2.13
3.07
3.66
5.13
3.75
4.99
6.66
2.36
3.75
2.63
2.37
3.49
2.06
2.07
2.36
1.85
2.94
2.64
2.25
3.85
2.21
1.95
3.05
1.95
1.99
3.67
2.80
1.52
2.05
1.77
A-ll
-------
Table A-6. Continued
Se (ng/m3)
SO4 (ng/m3)
N03 (ng/m3)
EC (ng/m3)
OC (ng/m3)
Br (ng/m3)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
0.8
1.6
1.8
1.7
1.8
2.8
1.8
1.9
1.8
6.8
0.9
1.5
0.9
0.9
0.5
0.8
1.6
1.4
0.9
1.5
Winter
1.0
1.9
1.9
1.9
2.2
3.1
1.7
1.6
1.8
5.6
1.0
1.7
0.9
1.0
0.5
0.8
2.6
1.4
0.7
1.5
Summer
0.8
1.6
1.9
1.6
1.7
2.5
2.0
1.4
1.8
7.2
0.8
1.4
1.0
0.9
0.5
0.7
1.3
1.4
0.9
1.5
Annual
2.6
4.1
4.4
4.0
4.9
4.9
3.2
1.4
4.3
5.9
3.3
4.0
4.1
3.5
1.3
3.6
1.2
2.5
1.6
3.0
Winter
2.2
2.9
3.2
3.3
2.8
3.3
2.2
1.3
3.0
3.4
2.8
3.0
2.8
2.4
0.9
2.9
2.1
1.9
1.2
2.5
Summer
3.7
6.7
6.5
6.0
6.9
6.7
4.1
1.4
7.6
9.4
4.8
4.5
5.9
4.0
1.6
3.6
1.1
3.7
2.0
3.8
Annual
0.7
1.6
2.1
2.0
1.0
2.9
1.4
1.1
2.3
1.6
1.1
0.7
1.5
1.2
0.5
0.8
3.0
1.3
0.9
2.9
Winter
1.1
2.8
3.3
3.0
1.9
4.7
2.9
1.5
3.8
3.0
1.9
1.1
2.8
2.4
1.2
1.4
8.7
2.4
1.1
5.1
Summer
0.5
0.8
1.3
1.1
0.5
1.5
0.5
0.6
1.2
0.8
0.6
0.4
0.6
0.5
0.2
0.5
0.3
0.5
0.6
1.1
Annual
0.46
0.63
1.75
1.27
0.61
1.07
0.49
0.74
0.85
0.83
0.58
0.43
0.97
0.58
0.76
0.38
0.81
0.37
0.57
0.55
Winter
0.63
0.72
1.55
1.67
0.79
0.76
0.54
0.94
0.95
0.73
0.68
0.59
0.86
0.65
1.24
0.41
1.26
0.39
0.69
0.51
Summer
0.40
0.55
1.89
1.02
0.46
1.27
0.42
0.40
0.77
0.86
0.48
0.30
1.04
0.47
0.48
0.33
0.55
0.35
0.49
0.56
Annual
3.5
3.5
5.5
4.4
5.0
4.5
4.3
4.6
4.6
4.5
3.8
3.8
4.8
3.3
2.9
2.4
4.7
3.7
2.3
3.9
Winter
3.7
3.3
5.6
4.3
5.2
3.9
3.8
5.2
4.5
4.1
3.4
4.5
4.1
3.2
3.9
2.3
6.5
3.4
2.5
3.6
Summer
4.5
4.2
6.3
5.5
5.4
5.4
4.8
3.5
5.9
5.5
5.1
3.5
5.6
3.4
2.7
2.2
4.7
4.9
2.2
4.7
Annual
2.55
3.08
3.71
3.28
3.62
4.58
2.54
1.80
4.30
4.49
2.63
3.29
3.13
3.34
3.37
3.31
4.94
2.08
2.27
3.54
Winter
2.85
3.55
4.71
4.01
4.57
4.87
2.61
1.79
4.72
5.22
2.95
4.22
3.42
3.22
4.82
3.63
9.07
2.36
2.20
2.57
Summer
2.52
2.95
3.05
3.06
2.75
3.97
1.97
1.59
3.51
3.65
2.27
2.50
2.62
2.49
2.66
2.10
4.39
1.86
2.05
5.81
A-12
-------
Table A-6. Continued
Si (ng/m3)
Al (ng/m3)
Fe (ng/m3)
Ti (ng/m3)
Ca (ng/m3)
K (ng/m3)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
235
431
243
176
102
201
115
112
128
271
82
183
156
107
168
99
92
136
77
72
41
94
101
128
107
114
119
184
176
Winter
131
280
96
61
66
164
47
103
96
185
50
42
64
52
58
70
55
66
57
52
41
59
57
48
70
61
69
70
133
Summer
316
459
311
206
99
194
117
79
105
330
106
507
339
164
272
131
125
228
109
94
39
128
145
238
179
187
146
240
176
Annual
49
132
65
51
25
58
36
33
33
73
18
73
47
28
41
25
27
31
17
19
15
24
26
42
31
32
33
44
52
Winter
22
104
25
14
21
48
19
33
29
39
11
21
14
12
17
12
15
10
15
14
12
14
18
14
19
17
19
18
33
Summer
85
131
85
58
27
58
37
24
23
90
20
209
121
42
62
36
38
59
21
25
11
34
36
87
56
57
39
59
46
Annual
207
216
145
111
84
174
84
103
131
139
93
148
64
83
61
96
78
58
105
86
37
119
64
62
90
183
60
89
105
Winter
156
233
115
86
75
177
73
122
153
143
79
117
43
76
32
80
59
43
104
76
36
97
50
40
92
169
54
66
100
Summer
231
171
147
106
84
154
72
79
90
140
90
244
113
87
86
111
94
81
110
96
39
145
80
103
104
221
61
102
98
Annual
8.5
15.5
10.3
7.9
6.0
11.7
5.9
7.2
8.0
9.4
5.2
10.3
6.8
5.7
5.8
4.1
5.6
4.8
6.7
4.9
2.8
5.7
5.0
7.2
6.1
6.1
4.6
7.0
14.2
Winter
5.6
15.2
6.2
4.6
4.6
10.0
4.3
7.6
8.4
7.7
3.8
7.5
4.0
4.1
3.0
3.3
3.4
3.7
6.1
4.1
2.7
4.5
3.5
5.0
5.1
4.2
3.8
3.8
16.0
Summer
11.9
13.8
13.2
9.0
6.2
12.9
6.7
6.2
6.5
11.0
6.4
20.9
13.5
7.5
9.7
5.3
9.2
7.4
9.2
5.8
3.1
7.9
7.3
11.8
9.3
9.3
5.7
9.5
11.2
Annual
145
165
85
51
49
164
39
54
78
115
42
111
59
33
58
62
55
85
48
41
18
62
71
36
109
105
49
181
57
Winter
96
121
43
30
31
175
24
49
65
86
33
117
53
26
21
56
37
59
44
34
21
44
48
24
89
72
60
85
46
Summer
164
170
110
58
51
126
48
45
73
137
44
128
72
34
94
69
64
73
57
47
16
82
97
44
134
141
48
245
57
Annual
110
131
142
136
60
117
108
79
89
73
67
116
94
61
92
73
106
74
110
46
47
69
76
67
130
82
193
77
53
Winter
99
150
138
197
55
106
155
123
117
62
64
255
103
70
60
52
62
98
156
44
64
53
49
80
117
60
265
47
58
Summer
124
144
208
116
68
178
104
65
77
105
94
91
143
56
159
124
215
69
125
65
46
106
135
63
180
133
189
119
55
A-13
-------
Table A-6. Continued
Si (ng/m3)
Al (ng/m3)
Fe (ng/m3)
Ti (ng/m3)
Ca (ng/m3)
K (ng/m3)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
58
63
80
77
85
137
141
68
75
141
70
104
115
153
384
219
155
55
54
75
Winter
47
46
62
65
49
89
71
38
55
67
48
47
59
61
292
86
90
37
37
56
Summer
73
76
101
82
113
173
249
89
91
255
92
189
192
327
415
526
167
67
67
96
Annual
14
20
25
22
26
26
41
23
22
30
15
33
26
39
92
63
43
18
17
21
Winter
10
14
20
19
15
16
23
13
16
18
9
15
11
9
69
16
27
17
11
14
Summer
18
26
24
23
30
35
81
28
27
37
17
65
53
102
112
181
37
19
23
31
Annual
37
76
135
107
69
281
90
76
94
132
60
60
104
79
123
76
101
38
63
91
Winter
36
74
106
101
63
177
81
61
87
106
50
46
82
63
123
46
116
29
65
81
Summer
43
78
150
113
69
306
111
79
92
143
61
81
118
116
114
156
82
46
67
99
Annual
2.9
4.7
6.2
5.9
5.6
8.4
6.3
5.5
5.3
6.5
3.8
5.6
6.1
5.7
9.9
7.2
7.4
4.0
3.3
4.4
Winter
2.7
3.9
4.7
5.4
4.2
5.5
4.1
4.1
4.3
4.7
2.9
3.6
4.0
3.0
7.8
3.0
6.1
3.1
3.1
3.5
Summer
3.9
5.7
7.9
6.7
7.2
10.1
10.5
7.1
6.4
8.1
4.5
9.4
9.4
11.0
10.4
17.4
8.8
4.6
4.0
5.8
Annual
41
28
45
65
30
110
80
33
41
48
29
54
68
72
241
60
93
33
31
46
Winter
61
27
40
78
22
57
64
21
31
35
24
34
55
58
218
46
55
27
26
39
Summer
30
27
50
52
34
138
91
40
40
57
32
54
73
87
224
89
117
38
36
53
Annual
54
46
49
48
69
119
83
79
56
75
51
64
105
67
82
73
75
45
54
66
Winter
63
46
50
50
81
90
56
70
52
69
46
106
88
55
85
60
62
53
53
52
Summer
76
59
63
54
78
131
130
93
79
93
68
56
143
74
85
90
137
48
67
105
A-14
-------
Table A-6. Continued
Cu (ng/m3)
Mn (ng/m3)
Zn (ng/m3)
V (ng/m3)
Ni (ng/m3)
Cl (ng/m3)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
8.2
6.4
12.6
5.8
4.6
7.4
6.6
6.3
5.2
4.8
3.9
4.8
4.1
4.6
2.7
3.8
6.4
2.7
4.6
5.8
2.1
6.5
3.7
2.6
8.5
14.3
2.5
3.2
3.4
Winter
6.8
10.2
12.0
6.1
4.8
7.1
7.1
8.0
7.1
5.0
3.5
8.3
5.1
4.1
2.8
3.4
5.2
3.4
7.5
6.0
2.3
4.0
3.7
2.8
10.6
13.3
3.3
3.4
4.6
Summer
8.6
3.4
19.8
7.1
4.3
9.6
6.6
5.2
4.8
5.7
4.9
4.5
5.2
5.3
4.3
5.2
10.1
2.5
4.7
7.0
2.1
9.5
5.6
3.2
7.6
16.6
2.5
4.1
3.1
Annual
14.9
4.0
2.7
2.3
1.7
3.8
2.1
2.7
2.4
3.1
1.8
2.4
1.8
1.5
1.8
2.5
2.8
1.4
2.9
1.5
1.5
3.8
2.3
2.1
3.7
9.2
3.5
3.4
2.9
Winter
12.1
4.1
2.2
1.9
1.6
3.8
1.8
2.5
2.7
3.2
1.8
2.2
1.5
1.2
1.4
2.4
2.4
1.4
3.3
1.5
1.8
3.5
1.8
1.9
4.7
8.7
5.3
3.1
2.5
Summer
13.7
3.8
2.7
2.2
1.6
3.2
1.9
2.5
1.8
2.9
1.7
3.3
2.3
1.6
1.9
2.8
3.1
1.6
2.9
1.5
1.4
4.2
2.7
2.5
2.8
11.3
2.8
3.0
2.5
Annual
111
11
11
12
4
27
6
7
8
24
11
8
5
9
4
25
13
9
22
19
9
23
9
5
14
32
14
13
6
Winter
95
16
19
21
5
44
10
10
12
38
13
11
6
12
6
23
15
12
34
26
15
21
12
8
19
33
32
14
11
Summer
107
7
7
5
3
15
3
6
3
19
10
6
4
7
2
29
12
9
17
18
5
25
8
4
12
27
5
12
4
Annual
2.05
2.46
1.67
1.65
4.26
5.23
1.77
4.16
2.19
1.60
2.35
5.46
3.14
1.05
1.55
1.04
1.57
3.23
3.28
4.45
2.76
1.98
2.72
2.37
1.65
1.82
1.65
1.55
1.92
Winter
2.21
3.12
1.73
1.40
2.74
3.06
1.55
3.42
2.19
1.57
2.52
5.88
2.98
1.08
1.49
0.88
1.54
2.68
3.71
5.55
4.05
1.60
6.19
2.54
1.74
1.66
2.18
1.51
2.56
Summer
2.16
2.06
1.84
1.85
6.35
7.12
2.10
4.92
2.45
1.69
2.35
6.21
3.70
1.08
1.83
1.45
1.88
3.78
3.18
3.75
2.46
2.21
1.77
2.58
1.84
2.21
1.65
1.66
1.51
Annual
1.5
1.3
1.4
2.9
1.8
2.1
5.7
2.0
11.0
1.1
1.1
2.5
4.0
0.4
1.0
0.5
1.6
1.4
1.7
3.6
2.3
1.5
1.9
1.7
1.3
1.4
1.3
4.6
1.4
Winter
1.4
1.1
1.9
1.8
1.4
1.4
7.2
1.8
13.1
1.2
1.4
2.5
9.5
0.4
1.1
0.6
1.4
1.4
2.1
5.4
3.3
1.5
3.7
1.5
1.4
1.5
1.3
2.2
1.5
Summer
1.6
1.0
1.3
2.8
2.4
2.8
9.4
2.2
9.2
1.2
1.2
2.9
2.5
0.4
1.0
0.5
1.8
1.4
1.7
2.2
2.3
1.6
1.5
2.7
1.4
1.2
1.1
5.4
1.4
Annual
38
32
37
50
28
74
47
78
217
27
27
268
47
5
15
46
15
13
35
64
12
20
15
44
15
38
31
11
9
Winter
62
21
81
126
32
91
50
95
167
66
58
259
59
8
24
138
26
9
86
114
21
46
30
28
29
88
95
20
19
Summer
13
62
26
21
18
41
66
34
215
13
7
305
27
4
18
10
14
7
7
19
6
8
14
32
12
8
9
11
6
A-15
-------
Table A-6. Continued
Cu (ng/m3)
Mn (ng/m3)
Zn (ng/m3)
V (ng/m3)
Ni (ng/m3)
Cl (ng/m3)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
5.8
4.9
6.4
4.6
2.9
7.5
3.3
7.2
5.5
5.5
2.5
2.4
3.3
3.1
6.8
3.6
5.0
2.4
3.5
4.4
Winter
6.5
6.0
5.3
5.4
3.1
7.9
3.8
6.0
5.2
6.5
2.1
3.4
2.9
3.4
9.0
3.0
7.5
2.8
4.1
3.8
Summer
4.3
4.7
7.4
4.7
3.6
7.6
4.0
8.7
6.3
5.8
2.9
2.2
3.5
2.9
5.8
3.8
5.2
3.0
3.9
5.5
Annual
1.1
3.1
2.9
2.7
2.0
8.6
2.7
7.5
2.5
7.6
1.2
1.9
2.1
1.5
3.7
1.5
3.0
1.8
3.8
7.5
Winter
1.0
3.0
2.7
3.1
1.9
6.1
2.3
7.0
2.7
5.9
1.2
1.6
1.8
1.6
5.0
1.3
4.3
1.8
4.2
7.2
Summer
1.1
2.9
3.0
2.4
2.0
8.3
2.9
4.9
2.3
7.2
1.1
1.9
2.1
1.5
3.0
2.2
2.3
1.8
4.2
8.0
Annual
15
14
20
33
8
55
10
12
15
29
10
11
13
9
16
9
9
8
10
17
Winter
27
16
21
49
12
46
12
13
19
32
12
13
15
10
18
11
20
11
13
18
Summer
5
11
15
20
6
46
8
7
11
24
7
8
11
7
15
6
4
5
8
16
Annual
3.37
2.97
7.30
6.51
2.01
2.07
1.72
2.96
4.59
1.50
7.75
3.90
1.22
1.20
1.55
4.16
1.59
1.93
4.44
1.43
Winter
5.07
3.64
4.99
9.30
2.50
1.86
1.70
2.87
4.98
1.58
8.48
4.34
1.29
1.06
1.44
4.62
1.54
2.60
2.81
1.48
Summer
3.02
2.72
7.93
4.80
1.93
2.24
1.86
2.96
4.44
1.51
4.84
3.42
1.30
1.51
1.86
4.00
1.94
1.80
7.02
1.49
Annual
1.6
3.2
5.7
21.7
1.4
2.5
1.5
2.4
6.8
2.0
4.9
2.1
0.4
0.6
0.4
1.8
1.2
1.8
2.1
1.9
Winter
2.6
4.6
6.7
35.4
1.7
2.8
2.2
2.4
8.3
2.0
6.0
2.1
0.5
0.6
0.5
2.2
1.6
2.5
1.4
2.0
Summer
1.3
3.1
5.5
12.9
1.3
2.4
1.2
2.0
7.9
2.1
2.7
2.2
0.4
0.4
0.4
1.6
1.0
2.0
3.1
2.2
Annual
59
20
46
29
9
86
12
50
30
16
43
68
9
7
43
77
45
13
42
17
Winter
81
39
87
54
11
169
11
38
64
35
72
45
11
6
97
79
134
30
57
40
Summer
23
9
12
10
8
19
21
43
12
7
17
52
5
7
21
60
25
7
17
7
A-16
-------
Table A-7. Coefficients of Variation for Selected PM2.5 Constituents for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
Se(%) S04(%) N03(%) EC(%) OC
Br (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
53
42
42
67
41
36
30
32
46
44
104
36
41
70
34
80
63
63
70
69
44
82
42
53
36
67
30
42
32
Winter
50
53
44
80
25
32
23
27
56
60
70
34
45
67
39
87
64
66
56
61
48
85
40
49
24
85
26
36
30
Summer
46
33
32
61
49
31
30
37
31
44
138
35
38
73
34
69
55
61
78
67
41
74
46
38
39
55
30
44
28
Annual
65
47
53
52
82
74
60
71
66
62
78
65
61
67
124
79
84
59
80
85
98
92
78
67
71
82
65
77
59
Winter
48
44
72
68
83
88
61
92
98
85
48
73
51
50
118
61
56
53
51
49
52
65
59
48
54
63
71
68
94
Summer
56
34
40
38
40
36
45
38
45
28
66
67
64
54
59
76
73
57
70
93
98
88
84
63
67
73
39
72
37
Annual
83
133
128
118
105
83
139
84
106
118
102
69
49
91
164
89
90
100
95
95
106
98
121
85
115
105
151
116
174
Winter
67
78
78
81
124
100
87
93
103
92
71
71
46
65
103
67
56
84
69
70
82
77
80
79
65
64
100
71
115
Summer
45
41
55
65
66
53
55
46
63
94
70
35
41
37
65
87
70
52
77
70
84
100
81
64
51
88
137
65
87
Annual
78
81
66
80
55
64
99
70
89
69
57
56
59
71
68
73
55
49
66
53
69
50
60
60
68
55
83
101
67
Winter
89
60
65
60
62
58
79
55
74
62
57
56
63
74
62
78
56
59
62
47
75
62
53
62
60
58
62
164
59
Summer
69
43
47
53
34
44
53
40
79
49
45
59
45
68
60
66
51
38
48
46
49
41
47
46
96
41
128
44
51
Annual
52
51
47
84
41
43
87
54
65
49
47
49
49
41
53
46
45
52
66
51
66
45
41
54
49
41
111
43
59
Winter
63
49
45
77
54
52
79
47
63
50
51
49
54
49
59
52
50
57
52
36
53
51
45
56
54
46
57
40
54
Summer
40
27
38
44
23
26
43
32
33
39
42
53
43
35
40
32
37
45
75
59
75
38
36
50
34
33
159
33
69
Annual
116
55
60
64
65
54
86
57
71
68
63
68
67
55
85
60
67
64
77
69
73
65
69
70
197
97
65
60
71
Winter
87
54
68
67
92
69
79
59
79
81
69
58
64
56
91
56
67
66
86
60
73
69
72
75
230
90
77
60
88
Summer
99
58
47
57
49
35
64
51
61
57
47
77
61
45
61
64
63
52
55
63
68
61
56
55
240
64
62
50
63
A-17
-------
Table A-7. Continued
Se (%)
S04
N03
EC (%)
OC (%)
Br (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
80
52
63
66
62
75
96
241
71
174
133
46
70
67
30
51
156
40
168
53
Winter
91
58
67
70
67
78
81
89
60
108
71
58
80
73
36
57
189
36
158
53
Summer
67
44
58
65
59
61
86
53
65
99
62
39
63
61
24
44
31
44
144
42
Annual
90
89
83
82
65
79
68
58
88
83
89
63
76
71
64
64
106
112
66
99
Winter
53
50
48
44
51
54
62
67
53
44
53
45
64
70
62
57
115
60
81
97
Summer
101
80
76
80
52
74
59
47
72
70
91
72
66
62
49
66
46
118
47
98
Annual
98
96
93
94
96
93
129
87
92
88
88
71
106
117
138
94
181
129
75
112
Winter
81
67
66
69
69
73
80
85
68
55
56
58
71
82
97
82
94
83
66
77
Summer
64
72
75
75
62
83
51
49
67
61
78
49
66
43
43
44
43
97
59
87
Annual
69
62
60
53
58
53
50
64
68
52
61
63
54
60
86
58
68
52
84
56
Winter
76
71
72
45
53
52
50
58
63
59
51
62
63
56
75
59
55
51
71
53
Summer
52
41
54
43
49
44
41
45
41
42
52
56
42
46
49
50
44
50
85
50
Annual
55
46
55
59
39
44
44
54
65
45
71
47
40
45
56
64
52
57
55
42
Winter
51
49
47
47
41
52
40
55
53
52
35
48
41
40
64
61
52
48
55
38
Summer
55
40
66
69
34
36
33
37
78
38
87
48
35
44
35
73
30
60
43
35
Annual
69
66
78
74
70
62
78
69
180
63
79
68
63
63
96
64
138
74
59
131
Winter
69
57
65
75
69
62
80
76
73
55
66
67
74
54
104
52
135
74
62
71
Summer
68
62
67
66
86
60
56
60
61
56
67
69
52
50
60
61
59
68
62
138
A-18
-------
Table A-7. Continued
Si (%)
Al (%)
Fe (%)
Ti (%)
Ca (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
96
83
83
90
86
78
189
104
126
77
103
184
162
112
138
122
136
108
130
113
93
89
112
154
145
179
88
291
73
Winter
92
90
92
78
112
75
137
96
103
74
66
72
68
52
90
53
74
60
106
72
85
49
103
74
72
58
87
90
84
Summer
81
66
44
51
67
55
101
65
105
66
111
100
122
97
115
155
161
95
124
112
95
99
101
119
165
198
68
106
44
Annual
175
89
111
124
120
112
220
182
166
108
194
217
257
177
142
267
275
218
216
181
125
174
194
207
275
374
117
248
113
Winter
99
106
185
89
112
104
164
124
151
115
115
289
108
102
110
107
84
166
124
90
91
87
104
89
85
164
103
94
107
Summer
168
83
87
86
123
106
160
141
121
93
276
118
176
154
113
321
352
183
254
203
81
201
237
154
301
405
98
164
84
Annual
86
60
64
63
53
64
112
66
86
66
62
76
111
68
109
79
76
77
79
52
78
63
70
117
102
90
96
158
70
Winter
92
67
77
60
59
62
89
67
78
63
75
62
57
77
92
81
61
68
93
52
84
82
82
69
130
91
72
82
74
Summer
67
49
39
45
41
47
90
47
68
55
52
65
103
62
82
72
90
75
65
50
74
55
67
107
88
89
51
82
42
Annual
88
63
84
78
65
83
115
80
85
83
100
137
148
81
140
98
235
142
124
75
65
100
130
104
113
126
76
206
150
Winter
74
76
70
68
72
66
115
92
76
69
84
206
89
73
71
55
66
274
142
59
68
158
77
115
84
78
89
73
189
Summer
77
56
87
76
49
106
95
60
82
78
127
90
125
77
130
126
269
100
117
86
55
95
155
80
126
143
73
109
112
Annual
93
66
73
67
118
97
125
157
89
80
67
59
71
66
134
71
82
109
77
73
68
63
104
78
77
84
81
201
65
Winter
96
84
93
77
70
82
134
97
88
72
66
78
73
51
94
77
65
99
76
56
69
53
137
58
62
61
99
91
74
Summer
81
46
40
41
43
57
122
44
68
70
52
48
74
57
100
77
72
74
71
61
56
61
84
73
61
91
53
169
40
Annual
134
104
235
135
68
269
189
77
129
144
204
687
308
86
237
241
490
167
235
118
79
140
357
95
106
293
106
252
73
Winter
153
127
85
90
70
91
110
61
97
64
108
622
175
119
89
63
53
222
232
44
57
49
51
117
75
51
96
50
72
Summer
176
107
298
238
76
337
327
94
245
182
261
191
382
84
261
262
470
179
258
155
108
165
386
115
126
359
113
268
84
A-19
-------
Table A-7. Continued
Si (%)
Al (%)
Fe (%)
Ti (%)
Ca (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
103
114
100
102
128
95
131
114
118
541
137
166
106
179
147
178
114
100
99
123
Winter
75
71
78
60
63
70
64
81
68
49
78
74
53
70
210
78
77
71
85
76
Summer
117
132
118
110
100
114
120
100
132
538
86
126
106
146
95
125
64
99
66
159
Annual
168
161
220
154
180
178
226
197
177
213
250
225
174
314
188
286
151
150
212
218
Winter
108
70
107
177
88
73
180
91
111
79
95
124
105
181
272
237
110
133
130
58
Summer
207
201
222
183
145
232
205
157
204
271
129
177
154
217
122
177
106
159
240
272
Annual
77
77
68
62
72
83
85
94
71
72
86
84
58
96
96
141
62
86
77
80
Winter
81
74
70
84
63
85
120
129
86
77
53
70
66
59
108
78
59
70
88
84
Summer
73
104
73
44
51
71
78
75
54
61
48
85
52
108
72
113
39
95
70
77
Annual
101
79
77
75
95
90
106
155
80
84
100
114
109
153
137
176
130
101
90
94
Winter
87
60
68
75
70
99
83
79
71
72
68
106
90
78
196
85
68
63
103
62
Summer
113
87
78
71
86
86
100
115
85
86
75
98
119
138
88
121
183
70
98
108
Annual
97
78
80
72
109
91
93
76
79
77
91
125
58
69
93
87
88
83
63
79
Winter
98
77
97
73
56
81
65
74
68
59
45
67
67
63
106
95
74
65
68
100
Summer
61
67
80
53
129
72
105
66
55
74
76
62
55
75
72
87
53
75
65
78
Annual
216
129
134
92
152
87
316
215
135
116
142
297
185
91
111
82
359
77
202
252
Winter
139
81
69
51
125
63
52
94
72
54
45
379
59
52
118
66
69
62
84
56
Summer
289
172
180
133
224
112
387
352
180
155
185
87
270
108
105
90
388
102
312
303
A-20
-------
Table A-7. Continued
Cu (%)
Mn (%)
Zn (%)
Ni (%)
Cl (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
122
88
136
108
68
164
170
86
113
84
129
425
173
100
212
118
173
130
188
158
99
112
238
129
146
114
137
149
120
Winter
99
78
177
75
75
70
119
79
88
68
89
488
131
90
112
90
102
173
170
157
99
86
132
89
112
117
102
107
103
Summer
73
64
100
131
63
235
251
72
174
94
171
112
209
94
246
133
193
102
208
142
95
91
283
154
113
106
127
181
116
Annual
141
66
75
79
62
78
103
107
103
83
81
97
77
82
89
102
78
82
108
76
93
76
75
92
206
145
101
181
103
Winter
140
76
88
65
65
78
77
80
101
87
96
93
56
73
60
101
73
81
111
81
95
88
64
76
222
136
99
112
97
Summer
146
62
58
66
51
64
77
76
78
78
86
107
90
92
66
92
84
80
127
72
65
71
74
90
106
151
68
81
74
Annual
163
82
155
138
97
135
151
85
114
165
67
98
100
76
106
99
65
65
135
129
102
90
84
114
226
100
151
143
106
Winter
150
77
154
107
103
122
148
71
95
99
74
92
85
63
96
83
67
59
144
131
80
85
78
95
90
96
107
108
81
Summer
143
61
131
127
79
86
101
78
78
177
50
93
96
65
102
99
64
69
91
136
75
91
86
152
293
83
92
181
117
Annual
127
65
81
101
78
74
83
72
89
86
94
98
96
82
79
180
127
102
90
78
82
86
167
116
80
111
127
74
123
Winter
201
74
75
67
92
86
83
83
86
59
110
111
92
93
51
62
72
101
97
64
74
90
130
142
90
87
161
67
136
Summer
89
50
109
133
52
50
97
66
109
73
92
91
87
94
96
237
186
95
87
67
75
91
107
132
75
137
93
80
75
Annual
168
457
164
248
75
74
569
93
252
92
107
166
815
64
85
114
191
114
97
84
102
118
161
236
86
173
267
425
307
Winter
169
109
223
267
79
78
503
84
339
78
130
139
697
57
102
114
87
100
93
67
92
114
122
96
88
130
121
132
151
Summer
166
167
78
261
57
55
549
109
164
89
91
239
154
63
73
135
230
71
107
72
104
135
220
280
90
133
58
334
303
Annual
280
165
236
260
206
163
286
213
166
241
336
144
222
166
366
376
321
397
488
260
277
314
338
269
228
302
387
270
183
Winter
275
111
153
173
223
155
187
153
187
161
255
208
209
154
277
226
207
209
370
171
236
243
257
225
174
198
248
181
181
Summer
259
126
358
345
154
147
321
160
165
193
150
97
217
134
510
578
529
144
228
280
107
117
491
227
339
140
280
405
56
A-21
-------
Table A-7. Continued
Cu (%)
Mn (%)
Zn (%)
Ni (%)
Cl (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
332
148
95
91
116
88
178
120
90
100
92
141
148
78
93
148
127
105
120
145
Winter
278
208
94
107
100
96
135
122
93
143
86
165
121
70
95
92
84
108
105
105
Summer
430
113
92
72
135
59
240
141
98
68
102
108
168
84
90
97
187
95
161
125
Annual
78
121
89
88
71
88
82
178
75
102
89
80
71
90
152
99
86
63
126
125
Winter
82
116
83
97
63
100
87
217
76
114
71
71
85
89
155
81
81
63
148
125
Summer
79
120
85
83
74
77
72
119
77
73
77
77
75
95
147
102
70
66
121
118
Annual
157
140
373
68
84
108
118
105
86
81
122
136
63
60
123
117
118
97
76
106
Winter
97
107
89
49
61
116
111
124
83
94
60
115
63
56
105
100
75
73
78
103
Summer
71
111
72
51
68
97
90
75
79
67
60
164
59
62
110
98
99
116
55
91
Annual
92
87
107
69
101
78
80
104
107
58
183
89
102
83
96
70
132
76
131
57
Winter
80
97
94
55
113
62
71
110
111
64
136
105
98
84
98
83
77
78
147
56
Summer
103
68
95
67
122
86
86
119
77
58
88
76
110
88
92
60
195
66
98
60
Annual
97
152
127
90
93
111
277
114
248
110
188
92
88
140
109
141
101
110
137
212
Winter
86
115
112
42
94
82
386
113
160
81
118
96
74
82
169
155
98
91
144
150
Summer
87
227
165
168
85
155
73
129
386
96
85
86
71
66
60
141
74
134
118
304
Annual
287
312
262
300
104
253
692
235
287
257
275
295
273
186
231
312
347
251
283
295
Winter
259
254
183
226
119
184
129
271
235
213
224
316
143
122
185
382
182
198
281
237
Summer
211
95
130
121
85
164
781
284
142
134
246
192
125
135
140
245
658
100
390
91
A-22
-------
Table A-8. Coefficients of Determination for Selected PM2.5 Constituents with PM2.5 for Annual, Winter, and Summer: 49 STN Monitors, 2001-2005
Se(%) S04(%) N03(%) EC(%) OC (%)
Br (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
14.4
0.3
4.7
5.9
0.2
2.6
0.8
0.8
1.6
1.2
31.2
0.1
0.0
12.3
0.5
43.3
9.8
20.1
27.9
11.8
0.2
28.4
0.0
0.0
0.0
9.6
1.3
0.4
0.5
Winter
15.7
0.6
3.5
11.4
1.0
2.1
1.1
5.3
4.1
3.8
50.1
0.2
0.1
14.1
0.3
42.7
8.6
15.8
25.1
12.0
3.2
29.3
1.5
0.8
0.6
11.7
4.1
5.9
0.0
Summer
17.1
0.0
0.5
1.4
11.1
0.3
0.8
0.9
2.2
7.0
22.8
0.5
0.0
25.4
0.4
42.8
12.4
18.4
23.0
18.9
0.1
26.3
0.0
0.6
0.0
10.0
4.4
0.6
1.5
Annual
57.3
1.0
23.2
12.6
60.0
49.3
0.9
15.4
21.5
21.8
68.9
48.8
46.2
61.5
49.6
69.8
70.1
69.5
61.4
60.6
59.8
68.2
54.0
61.6
47.4
67.7
42.4
57.2
11.5
Winter
35.3
12.0
55.6
44.1
59.7
58.4
27.0
27.4
51.3
26.2
50.6
53.7
25.2
24.4
76.6
61.5
45.3
44.8
48.0
52.1
69.3
49.3
35.9
33.9
43.1
61.2
69.1
39.6
51.9
Summer
78.9
20.8
44.6
60.0
60.0
56.3
22.1
51.4
47.2
7.0
77.6
39.4
51.3
81.4
39.7
82.8
91.1
89.3
63.0
61.4
66.1
85.2
60.5
78.2
56.1
88.0
32.3
79.2
26.4
Annual
1.5
31.4
62.3
76.7
72.7
87.5
46.7
62.1
77.6
19.0
6.7
43.5
20.4
0.3
50.6
31.0
8.8
5.5
13.5
38.0
39.0
37.9
34.5
2.4
15.7
15.5
50.8
21.1
43.0
Winter
15.1
52.0
89.4
80.1
77.4
88.8
52.6
77.9
82.9
53.7
66.6
69.6
32.3
15.3
87.7
85.1
79.2
39.9
55.0
55.6
67.7
75.8
75.0
31.6
60.8
74.8
78.5
86.3
89.9
Summer
10.6
29.0
31.2
43.7
59.1
85.0
17.9
31.0
73.4
0.4
8.1
35.9
24.0
6.7
24.6
29.1
17.7
5.2
16.3
24.1
41.8
41.3
22.4
0.4
15.6
26.3
39.9
15.6
21.6
Annual
44.2
46.3
56.1
73.7
20.3
15.2
55.0
37.3
60.9
34.7
30.8
18.5
14.8
29.9
51.9
24.5
29.8
11.0
27.1
21.6
33.7
18.3
15.1
13.2
19.7
26.2
58.3
9.7
53.1
Winter
54.9
70.0
39.9
61.7
62.4
48.4
62.3
47.4
79.8
32.1
60.0
44.0
30.2
37.0
44.3
40.5
35.4
27.5
53.3
30.7
51.6
27.2
12.2
36.2
29.2
33.4
71.6
7.1
72.8
Summer
25.1
14.5
41.5
60.1
4.9
0.4
17.9
20.1
37.1
27.5
22.0
8.7
14.9
25.6
39.7
15.1
36.0
1.3
24.6
12.3
19.8
16.9
11.1
6.2
12.6
31.2
38.1
10.6
32.7
Annual
66.6
51.5
66.4
78.6
54.1
51.5
71.0
40.3
71.3
48.7
63.1
43.8
31.6
58.4
60.1
43.6
43.5
62.4
58.9
47.1
49.4
44.7
29.2
43.0
37.6
38.4
78.4
23.7
71.3
Winter
69.6
71.9
58.0
65.2
69.9
77.1
73.0
64.5
85.1
41.2
73.2
77.9
43.4
50.9
52.1
56.6
53.2
41.6
71.0
48.8
55.2
49.4
20.6
38.8
42.1
37.2
87.2
16.6
81.3
Summer
67.5
22.8
53.6
69.9
24.9
6.3
43.2
23.4
54.9
50.1
61.4
21.2
19.6
58.5
68.4
52.9
49.7
78.3
52.7
47.9
51.4
49.6
38.8
61.2
38.9
54.2
80.8
45.3
64.9
Annual
28.3
28.1
49.7
44.2
53.4
63.0
36.5
34.9
49.6
15.2
44.7
26.2
19.6
24.0
27.2
51.1
25.2
20.5
45.1
34.7
30.8
40.4
9.0
18.1
19.3
33.7
10.6
16.9
20.8
Winter
56.2
47.5
65.9
52.8
45.0
76.8
41.2
47.9
66.2
10.0
69.2
36.1
21.9
53.4
32.5
47.6
22.8
44.3
62.6
25.7
48.0
30.2
1.2
31.3
23.6
39.4
25.3
15.3
31.3
Summer
37.1
32.7
60.6
52.0
41.2
26.9
22.4
17.5
27.0
19.6
53.1
16.9
10.2
58.3
22.2
56.9
26.4
27.0
35.6
45.6
26.0
40.0
3.7
15.6
12.0
27.1
16.3
12.9
23.6
A-23
-------
Table A-8. Continued
Se (%)
S04 (
N03
EC (%)
OC (%)
Br (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
16.0
6.2
6.1
7.1
3.8
18.2
0.5
0.6
8.5
23.7
7.4
4.6
12.8
18.0
3.3
10.5
1.6
0.5
6.3
3.8
Winter
14.2
7.0
14.7
16.7
14.4
4.0
4.2
2.9
14.7
24.6
18.2
7.8
9.5
28.5
1.6
16.3
8.8
1.3
19.3
6.3
Summer
22.9
11.7
3.9
8.0
8.4
34.4
1.0
0.0
9.4
23.8
6.6
2.5
16.7
19.7
2.6
10.0
0.8
2.2
3.6
3.1
Annual
71.6
73.9
73.7
79.9
68.6
66.9
65.7
43.1
70.2
77.9
73.7
70.6
61.3
60.4
7.3
33.0
35.8
70.4
54.6
68.1
Winter
61.5
64.2
69.3
80.3
45.9
52.3
46.2
62.9
69.4
56.1
69.3
36.5
38.1
41.3
18.0
33.3
69.1
67.1
75.5
62.2
Summer
80.9
82.6
77.6
81.5
89.2
84.5
83.1
56.0
83.3
89.5
77.3
90.4
85.8
69.1
25.5
32.3
20.9
79.0
60.2
82.8
Annual
39.6
27.9
42.4
48.3
2.8
25.5
7.8
59.5
40.9
1.7
39.3
16.4
1.9
8.1
10.5
6.7
33.1
35.9
63.0
51.7
Winter
61.6
68.7
74.8
85.3
60.8
71.5
70.1
60.6
78.0
58.7
71.1
52.6
49.1
67.5
44.7
27.2
91.8
88.0
73.4
87.1
Summer
30.3
30.7
40.8
36.9
3.7
30.3
4.0
39.9
31.1
13.3
31.4
18.9
2.9
8.6
1.4
9.2
6.0
28.2
51.8
48.2
Annual
32.0
17.7
38.8
34.4
11.2
29.0
11.4
49.6
35.8
43.6
31.9
13.3
21.9
9.4
21.5
8.4
42.2
31.7
53.5
32.5
Winter
44.1
41.3
51.1
37.4
26.4
32.4
11.8
55.4
54.1
57.6
47.2
46.2
13.3
14.3
56.8
14.6
58.9
44.1
60.8
29.1
Summer
29.2
9.0
18.5
44.6
31.2
14.7
9.2
15.0
26.5
42.8
18.5
4.5
18.8
10.0
4.7
7.0
13.3
18.4
46.8
35.5
Annual
57.7
52.2
64.1
64.9
52.0
60.8
46.2
70.7
55.9
62.0
54.2
50.6
45.4
48.1
42.9
19.0
53.0
48.8
75.7
39.0
Winter
64.8
47.2
75.3
77.9
44.2
60.4
21.7
64.0
70.5
69.9
59.7
62.4
23.7
48.0
69.4
17.6
78.3
66.5
78.8
43.1
Summer
60.0
39.5
46.0
53.8
64.9
60.8
55.6
62.7
44.1
53.1
62.1
62.2
58.8
48.6
29.4
17.0
50.8
55.6
69.3
45.3
Annual
42.3
40.1
39.4
43.5
24.1
51.5
20.3
19.2
39.5
27.3
42.3
27.2
25.9
36.6
47.8
15.1
28.0
20.2
62.6
31.1
Winter
34.9
38.9
57.7
47.9
42.4
44.7
21.7
27.9
57.6
66.1
28.9
41.7
29.7
51.7
45.2
30.5
61.3
13.0
61.5
28.9
Summer
62.9
52.1
46.0
40.4
39.3
55.7
13.7
14.7
29.9
41.1
58.7
28.8
34.2
25.3
36.2
20.8
31.1
26.8
71.1
34.7
A-24
-------
Table A-8. Continued
Si (%)
Al (%)
Fe (%)
Ti (%)
Ca (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
39.8
8.9
2.9
0.3
3.5
1.7
0.2
14.0
17.7
7.3
15.1
6.2
2.3
19.0
2.9
18.7
10.7
7.5
16.7
11.7
12.8
20.9
4.9
3.6
3.4
7.7
6.9
0.4
15.5
Winter
41.3
18.1
4.0
28.5
8.4
4.2
10.1
17.5
27.8
4.0
17.7
24.7
3.8
16.0
4.1
13.4
14.2
26.3
26.5
1.7
20.7
18.9
3.7
6.9
7.5
4.9
9.6
3.4
27.5
Summer
4.8
39.7
12.8
36.7
5.3
9.6
10.4
16.6
14.7
28.1
9.7
32.2
9.4
2.6
35.6
33.4
16.1
0.4
24.4
10.4
4.0
32.3
23.8
2.0
12.4
12.0
38.8
4.5
23.4
Annual
6.4
8.5
0.8
0.6
0.3
0.0
0.0
2.5
5.9
2.9
0.4
3.0
0.2
3.0
1.9
0.2
1.5
0.2
0.8
1.0
0.1
0.8
0.2
0.2
0.0
0.5
3.9
0.4
7.1
Winter
5.6
13.1
0.2
2.2
0.1
0.0
0.0
2.9
5.7
0.8
3.6
2.2
0.0
19.8
0.4
0.5
0.5
0.9
0.2
4.2
1.1
1.7
0.4
0.4
0.1
0.2
0.2
5.4
11.4
Summer
0.6
15.6
5.1
17.6
1.5
2.6
4.3
0.5
5.7
11.1
0.6
27.9
3.5
2.2
18.1
3.4
3.8
1.7
1.2
0.1
5.8
0.0
1.5
7.4
0.3
0.0
19.1
1.4
10.9
Annual
37.6
35.9
4.5
20.6
16.8
9.3
18.3
28.4
48.4
21.8
35.8
17.4
10.1
28.8
10.8
22.8
35.9
7.9
36.1
21.2
30.8
12.8
6.0
7.4
10.6
7.3
31.2
0.0
34.2
Winter
46.4
36.4
14.1
49.0
32.5
26.2
31.1
28.1
58.0
16.3
59.4
30.2
14.2
33.7
18.0
28.3
36.9
31.0
52.6
16.0
43.1
18.4
5.0
19.8
13.1
8.6
40.6
0.0
48.2
Summer
12.1
37.6
19.0
50.2
1.7
8.4
12.7
15.1
30.0
40.7
24.3
49.3
12.6
13.2
34.6
24.5
46.6
0.6
30.3
11.1
35.8
10.0
13.8
0.3
20.5
13.9
50.4
7.6
22.1
Annual
28.3
27.0
0.0
1.6
11.8
7.7
2.6
15.6
21.6
7.3
20.4
13.1
2.4
19.5
1.9
8.4
9.5
1.3
18.7
7.1
6.0
10.3
5.5
2.0
6.2
9.6
2.3
2.3
17.3
Winter
21.3
28.0
2.8
11.6
19.3
16.8
19.0
14.1
26.3
5.6
25.8
21.7
1.6
25.2
1.2
13.1
3.2
7.3
25.2
4.1
6.7
14.0
0.5
7.6
1.6
10.7
4.3
1.1
41.6
Summer
2.5
40.7
20.2
25.2
5.7
1.0
7.5
8.1
15.0
22.1
9.3
35.7
8.9
3.4
18.4
12.8
9.8
1.5
11.9
9.7
1.7
12.3
18.2
0.5
24.7
13.0
11.5
20.6
8.8
Annual
38.4
12.0
1.7
0.4
4.6
1.1
0.2
10.1
17.4
11.6
25.0
27.8
7.5
16.9
3.2
10.0
16.1
9.1
27.0
16.4
28.8
15.5
4.3
2.3
6.7
12.1
40.8
0.1
16.6
Winter
35.5
21.1
0.7
22.4
11.4
5.4
10.0
17.8
21.7
8.0
37.0
30.4
7.9
16.6
12.5
8.3
9.8
13.4
48.7
13.3
37.8
19.3
1.3
9.4
3.9
4.4
53.9
0.6
32.2
Summer
18.6
44.6
12.9
30.9
6.2
12.9
8.6
19.5
38.8
33.6
21.5
55.6
15.2
12.8
31.1
19.2
24.2
18.9
25.7
5.5
29.2
17.5
21.3
0.0
30.0
25.6
45.8
6.2
13.2
Annual
41.6
46.8
29.4
59.0
21.7
15.7
61.0
24.3
64.6
25.4
41.2
38.7
21.2
24.2
48.1
39.0
28.2
21.4
32.5
37.0
37.5
33.0
18.5
26.4
18.3
29.4
58.1
9.7
53.4
Winter
59.8
67.2
31.4
58.6
61.3
46.7
68.2
55.8
73.0
18.5
69.7
52.7
31.4
63.4
53.4
47.1
42.5
45.0
57.5
34.5
67.7
49.8
10.3
42.1
29.3
34.9
65.2
21.1
61.8
Summer
19.4
53.1
19.9
41.8
0.4
0.1
29.7
22.2
72.6
49.6
25.2
53.3
25.6
13.4
55.0
30.4
21.2
13.7
24.7
24.3
21.3
12.7
19.0
10.0
15.1
21.2
54.5
11.0
41.7
A-25
-------
Table A-8. Continued
Si (%)
Al (%)
Fe (%)
Ti (%)
Ca (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
18.6
13.0
24.7
19.6
6.4
24.3
9.2
6.7
16.0
27.9
10.7
2.2
4.3
10.0
28.1
12.6
1.6
15.3
38.0
15.1
Winter
20.1
24.1
26.6
30.7
11.0
16.5
5.3
27.9
25.0
24.4
1.6
6.0
3.7
4.0
32.1
4.5
37.5
24.0
55.1
13.8
Summer
13.3
15.4
21.9
15.8
0.5
30.6
2.0
12.3
12.7
24.4
11.5
0.0
0.2
7.2
38.2
13.0
8.3
14.9
52.5
24.1
Annual
3.1
1.8
3.0
1.5
0.8
4.3
1.4
0.6
1.1
0.9
0.7
1.1
0.2
0.0
7.5
1.1
0.9
2.8
9.5
1.3
Winter
3.1
2.7
2.0
0.9
0.0
7.0
0.0
0.2
2.0
5.3
0.0
3.7
3.4
0.5
7.3
0.0
13.0
9.5
8.8
0.7
Summer
0.6
0.0
8.4
3.6
0.1
0.1
0.0
3.5
2.7
0.0
3.8
0.6
3.8
0.4
33.2
0.8
3.0
0.2
19.5
2.1
Annual
38.7
26.1
39.0
43.3
17.6
35.7
9.8
22.3
34.6
35.9
26.1
13.8
19.3
10.1
48.7
13.9
30.0
30.9
49.6
29.1
Winter
41.3
45.1
50.2
56.9
15.4
35.4
3.2
36.3
49.2
42.1
26.8
30.6
15.6
5.2
60.8
7.6
48.7
43.3
56.2
23.2
Summer
36.3
19.5
17.6
44.2
14.4
28.8
9.8
16.1
36.2
28.4
21.1
1.0
12.5
12.0
35.7
12.8
9.2
32.9
47.0
37.5
Annual
23.1
11.7
14.5
13.9
7.7
17.3
7.9
4.1
12.1
24.9
5.9
2.8
12.4
4.0
25.4
5.1
3.9
5.3
30.7
8.8
Winter
17.1
10.1
19.0
26.6
2.9
17.8
0.2
14.0
12.0
18.9
0.1
5.3
15.9
2.2
27.0
1.1
23.8
3.2
42.5
2.9
Summer
22.2
12.8
9.9
18.3
1.7
13.3
3.2
23.0
15.2
25.6
1.7
0.1
0.6
1.0
17.6
6.3
2.7
9.6
33.4
16.5
Annual
13.9
20.8
32.5
18.4
11.8
28.7
8.7
9.7
16.9
34.0
24.4
2.7
12.5
7.5
50.3
12.0
2.2
23.3
31.8
23.4
Winter
15.3
34.9
31.8
17.6
5.1
29.1
2.2
27.9
28.2
39.9
7.2
7.0
9.0
0.2
54.3
6.6
18.1
27.2
30.2
12.9
Summer
14.9
20.8
14.9
37.3
19.3
30.1
10.2
21.9
23.1
38.0
19.5
0.4
13.7
15.8
42.3
17.3
14.7
23.2
61.8
40.9
Annual
54.8
42.5
44.5
46.9
27.3
31.5
37.8
62.3
46.8
35.3
42.2
33.8
7.6
35.4
54.0
23.6
33.4
55.9
68.5
47.4
Winter
61.6
59.9
63.3
52.0
51.8
25.7
28.2
65.4
63.3
65.4
47.0
61.1
9.0
26.6
62.0
20.9
40.8
61.4
78.9
39.9
Summer
47.0
35.3
35.3
45.6
22.4
26.4
22.8
47.4
31.9
18.5
25.0
21.4
5.3
30.3
51.3
25.4
43.1
47.9
51.2
45.9
A-26
-------
Table A-8. Continued
Cu(%) Mn(%) Zn(%) V (%) Ni (%) Cl (%)
CBSA
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Annual
18.0
32.6
1.9
12.7
4.9
8.0
15.4
5.5
27.0
11.0
23.2
3.5
2.4
8.4
0.6
7.9
3.3
3.2
16.5
9.0
8.6
5.6
4.8
2.2
3.0
8.1
5.1
0.3
9.1
Winter
18.6
46.4
8.3
6.3
10.7
30.5
8.4
3.7
36.4
14.7
49.0
11.9
4.6
15.4
0.8
8.4
3.6
14.7
24.1
10.0
16.6
15.4
6.6
2.5
4.6
14.0
14.6
2.3
21.2
Summer
8.9
3.1
3.9
19.8
0.2
2.6
2.5
2.1
8.5
2.8
10.5
1.4
1.0
2.7
0.5
5.1
0.7
0.3
11.4
1.7
4.6
5.0
5.1
4.2
0.5
6.3
0.3
0.1
2.3
Annual
25.9
22.4
2.7
5.0
3.3
6.5
1.1
11.1
9.2
10.6
10.9
3.1
1.9
11.0
4.4
11.9
10.8
4.1
20.8
8.6
5.7
9.3
0.2
1.5
2.6
2.8
28.6
0.3
11.9
Winter
31.9
27.9
9.7
5.0
11.1
6.0
0.4
7.4
9.9
5.3
14.9
6.4
0.7
16.5
0.7
27.2
8.6
10.5
35.8
5.0
16.8
14.8
0.0
3.8
5.8
4.3
52.2
0.4
6.4
Summer
11.5
25.8
5.2
13.4
0.1
0.2
2.0
0.2
1.6
18.2
9.6
14.3
9.9
4.9
9.0
7.9
14.7
2.8
14.1
15.9
5.4
9.8
4.2
0.5
7.1
5.9
17.4
0.9
8.6
Annual
20.4
36.7
44.1
55.2
17.9
11.1
38.8
41.8
42.8
21.9
34.9
27.7
15.8
22.5
31.0
22.6
34.8
21.1
30.0
14.8
33.2
18.8
17.2
19.2
17.0
22.0
51.6
5.1
30.7
Winter
32.0
50.8
49.4
63.2
35.2
20.2
56.0
40.2
56.6
31.6
63.3
49.3
28.9
50.0
45.1
43.2
55.0
46.5
38.9
13.3
67.9
38.9
16.2
32.0
22.2
35.7
80.1
22.6
62.1
Summer
7.2
8.4
13.8
16.2
2.9
0.5
3.5
22.4
22.0
15.1
31.6
10.6
9.7
41.2
6.9
8.8
23.8
22.2
28.7
16.1
36.7
11.7
9.5
19.1
9.0
16.4
37.6
0.0
7.4
Annual
3.3
23.3
0.1
2.6
43.2
38.0
0.0
21.7
12.2
0.2
23.2
4.4
3.7
8.8
0.4
8.5
0.4
0.9
21.0
19.8
24.3
6.4
0.7
0.8
0.7
5.3
1.3
0.0
0.5
Winter
3.2
33.2
0.3
0.1
24.3
42.4
0.4
24.2
16.1
0.0
29.7
4.1
2.1
5.5
4.5
13.7
0.1
0.0
29.7
24.0
43.3
4.8
0.8
0.0
1.4
4.5
3.5
6.3
5.2
Summer
0.1
13.2
1.8
1.8
19.1
3.1
0.7
13.9
0.4
0.1
5.6
5.4
19.0
5.4
0.5
5.3
0.1
0.0
11.2
15.3
18.8
7.1
0.4
0.0
0.0
1.5
0.8
0.6
0.6
Annual
0.1
0.7
0.6
0.1
27.6
27.1
0.1
14.7
3.5
0.3
12.9
0.7
2.2
0.2
0.2
2.6
2.5
1.1
15.2
12.4
22.1
7.0
1.1
0.5
1.4
1.3
0.2
0.2
0.7
Winter
3.8
0.0
6.0
8.1
9.4
16.5
0.6
7.3
2.9
7.5
36.1
1.4
1.6
1.6
0.2
2.0
1.8
1.8
20.1
25.5
40.1
9.8
2.3
0.0
0.5
2.9
0.0
1.8
5.4
Summer
0.0
0.7
0.3
0.6
14.7
2.5
0.7
10.2
1.2
0.2
3.2
0.0
6.7
1.2
1.4
2.0
2.1
0.0
5.8
13.0
27.1
3.9
0.3
3.1
5.5
0.6
0.0
4.2
0.4
Annual
1.5
8.7
14.4
24.3
5.2
19.9
5.7
0.1
0.0
10.1
2.0
18.5
5.5
1.0
8.6
6.5
1.2
0.0
2.1
0.0
4.7
3.8
3.5
3.0
4.1
2.5
11.7
2.6
8.0
Winter
19.6
49.9
19.7
33.8
13.7
45.1
25.1
4.8
0.4
15.2
12.2
34.3
2.2
6.5
10.2
11.9
2.8
2.6
24.2
1.1
25.0
7.1
0.4
5.3
5.5
19.2
41.1
2.0
12.1
Summer
0.6
4.5
0.2
1.3
7.2
22.5
0.1
0.0
16.0
5.1
1.0
0.9
1.1
0.9
1.4
3.4
0.7
4.9
1.6
2.4
2.5
0.9
2.6
13.6
3.7
0.8
2.6
0.2
6.2
A-27
-------
Table A-8. Continued
Cu (%)
Mn (%)
Zn (%)
Ni (%)
Cl (%)
CBSA
Rockingham, NH
Edison, NJ
Newark, NJ
New York, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
Annual
11.8
10.4
16.7
19.1
3.4
13.8
0.6
9.8
10.2
21.8
15.6
1.7
6.5
1.5
14.7
5.6
17.5
3.5
36.2
16.4
Winter
21.0
21.0
28.4
21.6
0.7
10.7
0.8
24.0
20.4
28.6
14.1
10.5
5.8
7.4
27.8
3.3
35.1
5.4
45.5
8.6
Summer
6.4
6.9
3.4
14.0
4.8
9.3
1.9
8.1
1.8
17.9
12.6
0.0
7.8
2.9
2.1
7.0
0.1
2.2
34.9
16.1
Annual
8.8
8.9
14.3
9.1
1.6
34.4
1.9
8.8
8.2
19.9
6.3
3.2
11.2
3.6
4.5
6.9
23.1
4.7
18.2
17.5
Winter
10.6
14.6
20.6
15.8
4.8
38.0
0.6
17.3
11.8
24.9
4.8
6.9
5.9
7.8
15.3
9.4
45.2
1.1
28.1
16.7
Summer
17.4
3.6
10.4
14.2
2.0
29.4
0.7
0.6
7.3
9.6
6.2
0.8
13.4
1.9
1.1
7.7
4.8
12.2
12.9
15.8
Annual
14.8
27.3
46.9
16.5
17.4
33.5
10.8
44.6
31.7
21.9
37.8
13.2
24.8
21.6
22.9
9.7
44.9
40.5
60.3
42.8
Winter
30.1
55.5
67.5
40.7
38.9
34.5
16.0
50.1
54.7
51.3
52.1
18.0
32.7
42.2
30.3
10.7
70.7
70.2
65.7
46.4
Summer
47.3
18.4
26.2
35.3
36.9
30.1
8.4
19.0
24.9
16.3
32.5
19.2
35.4
16.9
5.0
24.3
11.6
38.2
52.1
40.5
Annual
27.8
19.2
22.7
31.2
3.6
1.6
1.1
15.9
37.3
4.1
20.0
5.5
3.0
7.8
7.1
11.7
0.5
12.1
35.5
1.2
Winter
27.6
27.1
20.2
40.3
7.5
0.0
1.5
24.4
42.8
4.4
21.8
3.7
2.0
6.2
23.6
32.5
0.2
22.7
47.0
0.5
Summer
27.0
13.0
5.8
25.5
1.4
4.4
1.1
19.7
20.9
1.8
22.8
2.3
0.6
4.1
0.5
12.3
4.1
9.5
47.0
0.7
Annual
30.3
11.4
21.1
8.1
0.2
3.3
0.4
9.0
26.8
9.9
17.1
4.7
0.0
0.7
0.1
4.4
1.1
12.8
26.6
4.0
Winter
29.8
23.8
26.2
24.2
3.7
0.3
3.0
21.1
31.3
13.6
21.0
9.9
0.2
6.6
3.2
25.7
6.5
21.8
26.3
0.5
Summer
40.6
2.1
3.6
11.6
2.1
10.8
1.2
0.0
16.4
9.1
19.8
2.2
0.7
0.0
1.8
1.8
5.9
9.7
50.2
4.1
Annual
4.8
2.8
4.8
4.7
0.2
4.7
2.3
0.1
2.6
0.8
1.6
11.8
0.4
2.2
23.9
0.6
16.6
1.8
0.0
3.8
Winter
0.3
4.8
10.5
8.2
6.2
23.8
4.5
2.1
11.0
12.0
11.2
0.2
0.1
6.2
41.0
3.7
47.4
0.2
0.4
4.0
Summer
16.4
0.3
0.8
1.5
0.2
1.5
0.3
0.6
2.0
0.1
0.1
24.3
1.3
6.6
3.4
3.2
4.2
7.6
0.1
0.3
A-28
-------
Table A-9. Coefficients of Determination for Within-Source PM25 Constituents for All Days: 49 STN Monitors, 2001-2005
City Se/S04 NOs/EC NOs/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
9.1
0.3
1.1
3.5
0.3
2.2
0.0
0.1
1.0
0.4
17.7
0.2
0.0
14.1
0.1
40.4
8.8
14.1
21.1
11.1
0.5
30.1
0.0
0.5
0.1
10.1
0.0
1.2
0.7
16.8
4.1
2.6
6.7
1.3
21.5
0.7
0.1
3.1
6.1
34.5
37.6
57.9
13.5
6.5
27.2
12.2
34.8
2.8
15.5
4.4
20.2
0.3
37.0
3.9
0.5
7.3
18.7
22.8
40.5
3.4
7.1
13.0
1.4
2.2
45.8
4.7
45.2
24.6
23.4
14.2
34.5
11.4
1.9
6.4
37.3
31.8
7.0
28.0
34.5
53.0
31.9
36.8
25.5
11.3
45.9
3.9
1.5
42.5
20.7
1.6
15.6
0.8
0.1
4.5
11.0
12.0
33.6
9.2
2.2
1.6
0.0
0.1
37.1
0.5
19.0
22.7
8.4
28.1
24.6
5.5
6.4
0.2
41.2
18.7
60.7
68.6
72.8
76.2
57.2
51.9
70.7
68.2
65.6
57.1
42.7
31.2
25.4
50.8
50.7
58.3
50.1
18.8
43.1
29.2
46.7
40.4
36.6
25.7
38.5
38.8
54.2
23.9
41.9
41.2
30.8
37.3
25.3
25.9
38.7
30.4
51.9
43.6
0.8
2.2
0.3
0.8
2.5
2.8
3.4
7.8
10.4
1.1
3.7
0.0
5.3
0.7
0.5
0.3
1.7
0.0
12.7
0.9
11.5
2.0
0.3
3.5
0.1
6.7
0.3
2.8
2.3
7.0
13.9
8.1
4.0
1.1
0.9
1.5
7.8
11.4
0.9
22.6
4.0
0.0
0.1
7.1
4.4
5.1
0.5
2.3
7.1
0.5
0.0
1.4
8.7
6.8
1.4
0.0
16.3
2.8
5.6
2.6
2.6
1.4
0.2
0.2
0.1
0.2
7.9
5.0
9.0
8.3
10.2
0.7
0.7
2.3
10.3
8.4
2.3
0.0
1.0
0.4
24.5
39.1
3.0
18.8
0.0
4.6
40.6
23.0
6.6
0.1
0.3
0.7
2.4
36.4
49.7
52.6
31.5
2.1
0.5
0.7
0.3
1.5
3.3
0.3
2.5
58.8
27.4
50.1
42.1
0.6
1.5
0.0
1.4
38.3
32.4
42.9
37.2
37.9
20.3
30.4
26.2
11.9
37.9
13.5
45.8
19.4
12.0
35.9
18.5
36.0
17.4
21.6
42.6
45.8
33.8
36.0
10.7
18.5
11.2
21.8
11.0
9.6
13.9
42.6
32.3
34.2
36.8
24.0
36.3
16.7
16.0
38.5
20.5
58.3
52.4
53.9
19.8
43.0
26.9
20.4
17.0
50.5
14.6
31.0
20.0
30.1
37.4
8.5
7.3
12.3
2.4
14.5
2.1
13.5
12.4
18.2
9.7
11.8
35.8
31.0
49.0
25.5
1.5
5.9
4.7
10.3
16.8
28.9
6.9
4.6
52.8
69.7
66.3
55.7
22.9
62.9
35.7
40.2
47.0
55.2
16.6
29.4
25.1
35.9
44.6
27.0
16.2
27.4
18.7
28.7
3.9
24.8
25.9
36.9
13.7
25.2
32.6
19.6
35.9
28.5
21.0
17.7
10.3
20.3
17.7
31.9
23.2
18.6
62.0
91.1
84.6
70.6
63.6
72.8
58.0
53.0
68.5
84.4
51.5
22.5
33.0
52.1
83.1
42.1
44.5
26.2
40.2
50.8
26.0
51.5
61.0
47.2
40.6
43.1
35.0
59.5
76.0
21.1
35.3
36.2
8.3
40.1
54.4
54.5
45.2
38.2
17.7
53.0
45.9
41.8
9.2
36.4
19.5
11.3
17.3
36.6
8.5
27.5
13.7
27.8
27.8
10.4
5.4
5.3
6.4
15.7
8.2
11.9
6.8
11.9
2.4
9.2
19.9
7.1
24.8
12.1
3.0
2.6
3.5
6.6
8.6
16.1
7.6
4.3
13.7
53.9
41.6
39.0
17.5
30.9
21.6
16.4
14.6
41.3
13.5
8.4
7.2
19.6
33.2
9.0
8.6
4.6
7.6
12.4
4.5
13.7
15.5
14.3
2.8
7.9
13.6
17.2
36.8
7.3
5.4
4.2
6.7
10.2
18.3
16.3
7.8
5.8
42.7
72.9
61.6
51.3
33.0
54.5
36.0
39.6
45.5
57.1
39.0
24.6
24.0
50.2
46.1
34.8
24.8
12.9
36.4
23.8
12.9
32.6
27.3
28.3
25.9
38.2
18.7
27.3
40.5
21.1
28.6
23.2
18.6
26.0
35.1
33.9
25.4
29.9
62.8
43.1
10.8
10.9
31.3
54.3
20.1
37.6
50.2
25.4
57.2
8.5
3.7
48.3
2.2
56.2
33.5
11.1
52.2
37.8
32.8
57.0
14.0
5.8
19.0
14.3
9.6
7.3
23.5
11.6
40.5
48.3
13.5
26.3
62.9
17.7
41.0
42.9
43.3
39.5
11.5
17.0
20.3
31.2
23.1
13.9
40.2
20.5
49.1
6.8
5.8
15.8
0.3
25.5
9.3
4.7
28.2
14.3
8.4
16.0
15.1
1.9
8.5
16.4
5.6
9.1
7.3
14.4
28.8
36.7
28.7
14.8
19.7
8.8
26.3
25.7
36.6
45.7
1.7
11.7
15.4
24.5
17.3
9.9
37.3
8.1
27.4
11.9
6.6
13.6
2.8
19.5
3.1
2.5
22.5
2.7
11.3
10.9
7.9
4.9
7.4
11.2
7.9
3.3
15.6
8.5
24.6
20.1
9.6
12.5
10.6
5.4
22.6
20.7
1.2
11.2
1.3
0.3
12.4
0.8
7.8
5.6
31.1
7.7
12.1
28.5
12.9
2.7
13.9
0.5
10.1
9.8
7.5
16.7
11.6
1.6
7.8
26.6
8.6
6.9
23.5
11.5
12.8
20.7
17.7
10.3
12.5
9.9
2.7
7.6
14.2
10.3
A-29
-------
Table A-9. Continued
City Se/S04 NO3/EC NOs/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
14.9
6.6
1.4
13.9
17.1
0.0
15.2
1.2
0.2
14.1
3.0
3.6
37.6
28.9
0.0
4.1
25.5
6.2
37.7
21.8
41.1
13.2
1.3
8.0
18.7
0.2
2.8
18.1
2.9
14.2
6.7
47.4
5.6
51.7
24.9
19.8
54.4
42.2
57.8
60.9
45.3
25.7
68.6
37.6
15.8
5.5
3.7
1.2
2.2
0.2
2.4
3.1
4.1
25.2
3.1
0.0
5.8
0.3
0.4
1.1
0.5
3.5
3.1
0.2
14.8
0.1
0.1
76.7
13.1
1.0
0.3
0.3
34.1
3.3
2.9
69.6
2.1
30.6
42.1
24.8
5.6
28.6
34.6
22.3
17.2
20.2
55.0
27.3
7.5
8.4
19.0
30.5
21.0
37.0
23.6
32.2
2.1
23.3
3.4
32.8
16.6
28.1
15.1
37.1
77.1
45.8
35.6
13.4
50.9
14.7
50.8
46.5
28.8
32.5
43.7
76.9
52.3
73.6
46.1
51.5
51.6
2.7
7.9
11.5
14.3
13.4
29.6
14.1
18.4
3.5
17.7
1.4
8.8
5.9
9.6
6.3
9.7
24.6
16.1
21.8
3.8
18.8
6.1
40.6
35.0
17.6
23.3
36.4
61.4
35.2
36.0
19.9
38.5
26.3
51.8
46.5
7.2
23.1
21.8
12.9
7.2
28.7
21.5
57.4
55.8
35.8
36.8
6.0
28.2
9.8
8.8
7.6
15.5
4.9
42.5
25.8
21.1
30.4
7.5
14.3
10.9
13.8
16.9
30.3
2.4
46.2
16.2
4.0
27.5
18.6
12.5
5.4
5.2
21.0
10.6
15.8
6.4
9.5
A-30
-------
Table A-10. Coefficients of Determination for Within-Source PM25 Constituents for Winter Days: 49 STN Monitors, 2001-2005
City Se/S04 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
9.6
8.8
2.6
10.2
1.5
1.5
1.4
2.4
0.7
0.0
29.1
0.3
0.0
27.4
0.0
30.6
14.3
6.6
16.7
17.5
5.1
26.6
1.2
1.4
0.0
12.0
0.4
0.4
1.8
29.0
6.3
5.3
12.4
9.3
17.5
8.1
0.7
9.4
4.3
23.4
27.7
30.9
46.3
29.6
12.1
21.8
56.7
8.8
38.6
17.5
15.8
0.3
20.7
23.8
16.6
12.4
21.7
37.9
51.8
21.9
11.6
29.8
5.9
11.4
46.2
6.8
49.3
37.6
32.6
20.7
21.2
5.6
23.1
0.6
17.8
43.5
11.5
25.6
40.4
30.5
53.3
57.5
16.2
27.6
61.4
23.0
43.5
64.2
23.5
0.0
30.3
48.9
33.6
30.8
29.3
59.6
53.6
45.9
20.4
30.8
5.9
14.8
76.0
12.7
60.5
58.1
34.7
53.7
64.8
12.1
53.3
2.5
31.7
57.0
70.2
83.4
70.8
82.4
82.2
68.5
84.0
82.9
89.4
67.2
73.6
47.2
49.8
62.9
70.5
63.2
61.2
55.4
60.8
54.9
67.7
39.0
36.0
71.6
61.9
51.8
75.8
39.9
79.5
62.7
57.3
53.4
29.5
65.9
37.4
61.6
61.7
64.8
4.8
1.4
1.2
1.7
9.3
0.7
4.8
3.1
5.1
0.1
7.1
0.3
4.4
0.3
2.5
4.9
3.5
0.9
26.4
4.7
21.3
3.2
0.2
0.7
0.0
14.1
0.2
3.6
1.7
6.8
18.7
11.6
1.9
1.1
0.7
2.6
20.7
20.5
0.2
30.4
5.9
6.3
1.7
1.3
16.3
0.1
0.5
3.9
13.0
1.8
0.3
0.0
18.8
8.0
4.3
0.1
30.9
4.0
12.7
2.0
1.5
1.3
1.5
0.2
1.6
0.9
6.5
2.9
12.5
9.5
16.3
0.1
0.4
2.4
10.3
16.0
1.7
2.8
0.9
0.1
8.1
8.6
2.7
19.5
1.1
0.1
33.5
27.2
5.9
0.2
0.5
2.0
0.9
46.1
60.0
71.9
69.2
8.6
48.4
6.4
0.5
2.8
2.6
0.1
0.0
73.0
48.4
46.9
38.2
0.0
6.6
0.1
9.9
52.4
55.7
45.0
48.0
42.3
29.3
46.0
36.2
24.3
53.4
10.3
66.1
28.2
26.8
44.9
19.1
39.0
23.4
43.2
51.7
50.0
39.6
42.6
18.7
25.1
8.4
23.6
24.2
9.3
19.5
47.8
43.8
53.7
42.3
32.2
45.1
21.6
20.4
55.3
2.3
82.7
14.0
4.4
17.5
43.2
0.9
23.7
16.3
25.1
11.9
0.1
0.0
15.7
2.9
1.6
0.2
0.1
1.0
1.2
1.8
7.6
6.1
0.6
2.0
1.1
7.5
6.6
31.0
15.1
0.7
6.2
0.5
0.1
8.1
6.8
0.3
3.0
37.7
79.4
41.8
14.4
15.8
64.2
12.0
58.0
55.6
45.3
10.3
1.7
4.0
22.6
4.6
30.6
3.7
19.8
12.7
30.0
10.3
13.7
4.8
7.5
11.8
14.1
11.8
1.6
46.7
30.7
17.8
30.2
8.3
14.3
12.1
7.1
14.0
15.3
58.7
93.7
53.8
31.3
52.5
75.7
19.0
67.7
72.8
76.1
50.9
25.3
14.2
46.7
37.0
49.4
38.4
38.1
45.2
43.2
33.4
39.0
29.4
42.8
44.5
28.7
15.3
48.9
73.8
17.1
37.5
17.4
0.2
35.0
30.4
50.9
34.4
45.3
2.4
73.5
20.0
4.1
10.2
45.3
6.9
32.0
16.2
28.9
18.9
4.2
1.5
32.8
5.5
4.0
1.3
0.0
1.9
5.4
16.6
2.5
0.2
1.2
4.6
3.7
7.2
0.4
20.9
9.0
11.9
7.0
1.4
0.0
4.1
10.1
0.5
4.5
4.3
81.2
13.6
5.7
14.6
34.8
1.8
17.1
12.9
20.4
17.0
0.0
0.2
17.2
7.7
2.5
3.8
0.0
4.2
3.7
7.1
6.9
7.0
1.2
2.5
0.8
2.6
6.7
28.7
1.6
3.4
1.0
1.4
1.6
15.9
8.0
0.4
9.8
40.9
84.3
40.1
17.3
25.1
56.3
23.4
45.7
51.9
49.5
24.3
7.8
7.4
42.5
15.7
37.4
5.9
17.4
33.7
24.0
23.1
28.4
6.8
14.1
20.0
24.3
13.4
8.1
53.6
23.5
20.7
28.8
17.7
7.9
24.1
19.0
16.3
25.5
74.0
71.2
24.9
47.5
37.7
53.4
42.0
53.6
66.0
55.8
69.3
33.1
15.0
70.5
19.5
58.2
45.5
49.0
61.4
56.2
50.3
50.2
16.3
36.6
47.4
6.2
43.3
19.2
59.8
22.1
52.2
65.5
47.0
48.5
66.2
30.8
53.1
62.2
44.0
59.2
8.1
20.3
16.8
28.5
36.7
8.2
50.1
18.1
70.5
19.5
17.4
23.9
5.8
44.1
10.5
16.0
40.9
14.3
10.8
29.0
14.3
4.7
20.0
20.9
18.4
17.0
33.4
21.5
32.9
48.9
29.2
18.8
25.5
17.0
34.5
31.9
35.3
54.8
11.4
7.3
17.7
26.3
21.2
3.0
49.7
12.2
53.5
15.3
15.3
17.6
2.5
20.3
5.4
11.9
24.8
5.0
19.9
19.4
9.8
1.3
18.6
12.7
15.0
2.3
32.0
12.7
31.3
36.5
15.8
8.6
7.5
12.3
33.2
32.9
2.5
8.0
3.5
0.6
34.4
9.4
0.7
12.6
29.5
12.2
12.4
23.8
15.0
4.5
0.9
0.8
16.7
8.7
4.0
25.8
12.4
5.1
6.8
21.6
14.1
8.0
38.5
15.5
2.7
18.5
15.0
9.5
9.4
10.5
7.1
4.9
23.3
8.1
A-31
-------
Table A-10. Continued
City Se/S04 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
15.7
12.6
0.1
9.1
27.4
8.6
9.1
4.8
0.5
29.5
5.0
15.1
42.4
37.8
4.2
3.8
39.5
19.5
45.4
31.5
33.9
24.3
37.4
56.7
37.4
8.3
25.5
53.3
23.5
70.7
52.9
48.3
38.3
57.4
57.4
58.9
61.9
59.9
87.4
59.6
71.8
48.1
74.6
36.5
15.9
6.9
7.7
1.4
8.0
0.1
3.8
6.4
0.8
40.3
2.1
0.8
9.1
2.7
0.0
0.2
3.5
3.7
2.8
0.0
27.5
0.0
0.1
82.6
24.4
2.9
0.1
0.4
58.7
3.1
18.2
54.4
0.6
49.2
50.0
37.1
5.1
32.7
47.9
40.4
29.5
26.6
57.3
23.2
4.9
0.0
0.3
7.9
7.5
59.6
13.0
5.6
2.3
6.2
0.4
25.8
15.6
7.1
4.1
25.1
69.4
23.3
40.0
8.0
50.8
8.3
33.0
32.1
19.2
30.0
47.6
88.0
37.8
60.6
32.3
44.8
37.5
7.2
6.3
0.9
13.1
2.9
45.8
4.8
14.7
2.6
8.7
2.4
13.2
1.0
0.6
2.1
4.2
48.8
13.1
4.4
0.3
6.2
2.7
26.8
20.5
3.6
13.1
21.4
60.6
19.4
40.8
2.7
35.3
12.3
47.6
62.8
39.0
37.8
42.9
44.0
46.1
72.6
41.0
70.4
45.5
43.6
23.8
16.5
31.6
27.3
28.0
24.5
47.0
8.2
47.9
23.1
23.1
14.3
10.2
11.4
17.3
27.0
28.9
40.0
3.0
53.0
16.6
9.1
28.2
29.2
12.7
8.0
6.6
33.7
4.2
22.7
11.1
6.6
A-32
-------
Table A-11. Coefficients of Determination for Within-Source PM25 Constituents for Summer Days: 49 STN Monitors, 2001-2005
City Se/S04 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
14.3
0.3
0.1
0.6
8.8
0.3
0.5
0.1
1.7
0.1
27.0
2.2
0.3
30.7
3.4
47.4
15.5
19.7
34.1
17.9
3.3
27.7
0.3
0.1
0.1
9.8
0.0
0.4
0.2
20.8
10.1
3.8
11.4
7.5
32.7
0.7
1.9
8.4
19.0
5.7
7.3
31.5
0.1
0.3
1.2
10.9
20.6
3.6
10.1
0.3
13.3
1.6
31.4
13.5
14.2
4.8
15.8
8.0
26.5
12.4
9.1
7.4
10.8
26.1
25.5
7.9
11.7
6.5
12.9
19.3
26.1
1.0
14.9
0.9
13.8
30.1
16.2
8.8
10.1
21.7
2.9
6.3
3.3
9.6
36.3
2.9
3.1
12.2
19.8
15.2
17.8
14.7
4.8
0.5
8.6
0.3
24.6
21.1
7.1
3.9
6.5
8.6
30.2
7.9
11.8
7.1
6.1
13.1
13.7
2.3
17.0
0.8
27.7
8.8
37.7
18.5
62.8
58.2
34.9
37.5
34.6
29.2
35.1
39.9
27.0
30.6
20.7
23.7
39.2
45.5
35.9
1.6
34.4
9.8
33.3
30.7
23.2
7.6
9.2
22.2
38.8
18.1
27.7
35.4
15.5
10.0
34.4
18.9
20.1
11.8
23.7
28.7
0.1
0.3
0.8
3.1
0.7
0.4
4.6
2.5
8.4
6.6
1.2
1.9
5.8
0.1
0.1
1.2
3.9
0.6
5.9
0.6
7.0
7.1
2.2
2.1
3.0
0.5
0.1
1.9
1.7
16.8
11.2
5.9
5.8
0.1
8.3
3.5
0.0
2.2
0.3
17.0
3.8
1.9
0.2
2.3
4.4
5.2
3.0
3.7
8.1
2.2
1.1
2.6
3.4
3.1
0.0
2.0
9.3
2.5
4.1
0.9
0.0
0.5
0.0
0.3
0.0
0.4
19.5
14.1
6.1
5.9
5.1
2.5
2.5
1.2
1.9
2.6
8.0
0.3
8.3
5.2
20.0
15.2
7.4
17.4
2.3
24.0
38.0
22.5
10.5
0.1
0.1
1.1
4.8
19.0
36.6
42.8
19.8
1.5
18.7
0.0
3.6
4.4
2.5
0.9
4.2
33.7
10.8
51.6
21.3
11.5
0.1
7.8
2.9
26.7
22.8
50.6
16.3
22.8
7.4
6.7
7.3
17.7
36.9
19.9
14.7
6.1
1.9
5.4
13.4
32.7
10.5
1.4
23.7
25.4
28.3
27.1
7.6
3.2
10.9
19.1
13.1
7.1
9.1
39.7
17.5
17.8
22.4
19.4
35.3
3.8
2.2
17.4
27.1
44.5
35.1
49.0
25.0
28.3
28.5
0.7
4.1
52.5
2.5
72.2
46.1
22.3
46.8
6.0
3.9
26.7
1.1
12.1
0.1
11.1
20.2
39.6
13.8
9.3
41.5
32.5
48.9
18.6
0.3
6.8
2.3
13.2
9.7
40.8
8.7
5.9
38.7
78.4
40.0
53.0
3.2
43.8
33.2
15.3
35.1
62.4
8.7
85.8
54.9
36.6
37.8
20.8
12.1
54.0
12.5
20.7
6.6
20.7
26.1
52.5
18.3
36.3
36.5
18.9
22.6
21.8
22.0
13.9
14.7
19.0
6.1
50.4
26.7
9.2
43.6
80.8
70.8
68.9
59.2
111
58.7
34.3
55.2
81.1
40.7
64.1
56.2
56.3
87.3
33.5
39.8
25.3
36.0
47.4
13.5
47.5
60.8
66.3
40.2
47.0
57.8
46.4
60.2
28.6
27.4
44.1
18.8
35.8
41.3
57.8
57.7
22.5
31.0
47.3
34.3
39.1
8.3
31.4
20.6
0.0
1.3
44.1
1.7
69.1
43.3
23.8
37.4
14.1
3.8
20.8
9.5
20.9
1.7
11.4
13.2
29.8
6.7
6.9
26.4
13.3
15.9
8.0
0.0
2.7
0.9
12.2
6.3
22.5
13.7
3.1
9.6
36.5
17.0
28.2
19.5
22.4
20.1
1.9
5.1
34.3
2.3
46.7
23.6
11.6
30.9
6.4
5.0
3.9
4.0
10.8
0.2
14.5
14.7
33.3
0.9
5.9
19.9
11.3
27.4
7.7
0.5
6.7
4.5
10.4
7.6
17.8
11.3
3.4
27.2
72.3
30.0
39.8
10.7
40.0
27.0
23.1
26.3
57.1
30.8
63.5
41.8
54.1
28.5
23.9
7.3
15.0
20.8
17.4
5.1
27.7
24.3
41.8
27.4
41.7
22.3
17.2
18.6
29.3
34.0
20.3
24.3
33.1
23.0
41.3
24.5
20.8
43.3
23.6
12.8
20.5
11.2
45.6
10.6
16.6
26.1
22.1
51.6
1.9
0.2
38.7
1.6
47.7
29.2
1.0
48.4
40.8
42.3
61.3
24.4
0.7
7.3
27.5
31.8
2.5
17.5
35.8
29.1
33.3
30.6
18.6
58.2
3.6
38.9
29.5
27.2
15.7
1.8
21.2
17.6
13.9
7.5
22.6
20.5
12.2
18.3
1.6
0.6
2.7
0.0
12.0
3.4
0.1
14.3
6.6
4.7
7.6
18.9
1.6
4.6
9.3
0.8
4.9
0.2
8.3
12.8
27.1
21.0
8.1
18.0
2.2
19.8
11.2
28.7
11.6
0.9
14.7
4.5
10.6
9.6
8.7
19.7
1.8
4.6
6.8
1.2
8.0
11.2
15.2
2.0
0.1
9.0
2.2
4.8
7.5
9.0
5.1
4.8
12.2
1.7
1.1
3.3
8.7
22.3
6.1
9.6
19.4
18.0
6.4
18.7
12.7
1.8
8.1
9.1
7.6
1.5
2.3
23.2
12.4
57.6
8.3
11.3
27.4
4.4
4.2
25.0
0.1
8.3
5.3
6.9
7.8
0.1
0.3
8.5
14.2
8.0
12.3
17.9
7.8
15.8
8.8
12.7
3.9
11.3
5.3
3.7
4.6
7.6
7.7
A-3
-------
Table A-11. Continued
City Se/S04 NO3/EC NO3/OC EC/OC Mn/Ni Mn/V Ni/V K/Br Si/AI Si/Ti Si/Ca AI/Ti Al/Ca Ti/Ca Fe/Zn Fe/Cu Zn/Cu Na/CI
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
14.3
10.8
3.9
18.3
25.5
1.2
26.9
0.3
2.5
5.6
3.1
20.3
14.2
5.7
5.4
0.0
3.9
0.1
1.6
15.4
43.2
23.4
18.4
5.1
7.3
2.1
0.2
5.9
1.1
7.0
14.4
41.1
9.8
45.3
20.3
4.9
32.5
32.3
25.7
67.6
19.0
13.3
60.2
28.1
15.4
4.3
1.8
4.7
1.2
0.1
2.2
0.3
3.7
14.4
3.9
0.2
5.1
0.8
10.2
4.0
0.0
10.2
2.5
2.2
6.4
0.8
3.5
71.7
6.1
3.8
0.1
0.2
9.6
7.6
0.0
67.2
7.2
13.3
24.9
9.5
2.5
5.7
36.5
10.0
12.7
14.6
47.7
15.9
2.7
7.5
39.9
40.1
37.5
40.4
30.9
36.6
0.2
27.3
4.4
18.7
10.1
39.8
20.4
69.1
73.9
89.3
26.4
12.6
40.0
10.9
36.1
46.2
32.6
23.1
63.7
75.2
78.6
62.8
51.2
58.9
56.2
0.0
2.5
22.5
23.7
33.2
30.0
31.6
14.7
2.0
20.1
0.2
2.3
2.7
12.0
2.3
22.0
28.7
19.8
18.9
1.7
32.6
4.1
34.0
30.4
25.2
24.3
58.8
61.0
72.9
22.4
21.2
43.2
27.6
54.7
43.6
0.3
8.6
5.9
2.4
0.8
13.6
23.9
59.3
62.2
29.9
23.6
0.6
4.8
0.1
0.3
0.8
3.3
5.6
32.6
18.9
14.0
46.4
4.9
7.4
4.6
1.3
15.3
10.7
6.2
45.3
8.4
6.1
10.4
5.5
8.2
7.0
3.7
4.9
6.4
10.3
6.3
14.7
A-34
-------
Table A-12. Coefficients of Determination for Elemental Carbon and Selected Pollutants: 49 STN Monitors, 2001-2005
City
EC/Cu
EC/K
EC/Zn
EC/Ti
EC/AI
EC/CI
EC/S04
EC/Fe
EC/Ni
EC/Si
EC/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
12.9
54.7
5.8
20.9
17.2
29.1
19.9
16.3
31.4
12.4
32.9
7.4
8.2
16.4
0.1
11.0
4.5
5.3
26.8
3.4
8.7
16.2
3.5
0.9
6.7
16.0
4.7
0.3
11.0
13.4
20.4
18.6
13.2
5.1
10.7
4.7
7.8
17.1
40.8
15.4
38.8
60.4
20.8
29.7
52.9
32.4
47.9
21.4
26.7
0.0
12.8
21.8
36.3
33.5
45.2
8.4
39.4
10.6
39.7
28.3
21.6
11.7
20.7
21.4
37.5
11.8
29.1
31.9
11.3
9.6
27.4
27.0
15.2
15.0
35.6
30.1
19.6
56.5
45.5
57.4
32.2
66.1
42.6
45.5
57.7
16.0
49.5
36.9
24.2
59.2
25.7
31.6
39.2
22.6
40.4
29.0
46.9
48.1
24.7
22.7
30.9
39.0
53.6
15.2
46.9
30.2
35.7
43.2
56.2
37.0
22.0
27.4
48.5
49.8
23.1
10.6
6.5
4.5
11.0
34.6
2.9
17.3
28.7
12.9
18.1
3.3
0.0
23.4
1.2
12.3
10.3
1.9
11.7
11.1
1.0
19.3
10.5
0.9
10.2
14.3
0.6
2.9
19.3
15.8
7.6
16.2
5.0
3.8
28.1
1.8
1.0
8.8
4.9
0.5
1.0
0.0
1.6
10.9
0.0
6.7
6.4
2.7
2.4
0.4
0.2
6.3
1.5
1.4
4.8
0.2
3.9
0.4
0.7
6.5
0.4
0.3
0.7
2.2
1.4
0.5
5.0
3.9
0.3
1.7
2.7
0.9
7.6
0.3
0.0
1.6
6.5
2.0
8.0
20.1
1.0
4.0
4.4
0.0
0.0
0.4
4.8
6.7
1.6
2.9
9.5
0.9
1.6
0.3
8.3
0.1
9.9
16.8
3.6
1.3
2.5
1.9
11.8
1.5
6.7
1.1
4.5
4.6
4.3
1.2
1.8
2.0
0.2
4.6
7.4
8.0
4.7
3.6
2.1
0.2
6.6
4.7
0.0
9.2
9.6
0.3
4.1
3.2
17.4
16.3
13.1
0.6
6.8
15.6
22.8
0.2
1.2
2.5
2.6
12.9
19.5
1.1
0.4
19.5
9.8
20.3
24.3
0.0
17.3
0.5
8.7
19.7
35.7
26.0
26.9
30.9
34.7
65.2
24.9
48.4
56.5
28.1
56.2
21.9
5.1
65.3
12.6
47.2
56.1
26.6
39.1
35.4
33.5
51.7
22.2
2.6
39.9
18.8
23.9
10.3
33.8
29.9
38.1
56.4
25.8
33.1
34.1
19.5
19.1
48.6
0.0
0.8
0.3
0.3
4.1
3.1
0.0
3.5
2.4
0.8
13.5
0.2
0.0
0.2
0.0
1.5
0.0
6.7
9.6
14.7
20.6
0.7
0.5
0.0
0.2
0.5
0.0
0.7
0.7
23.7
17.7
22.8
43.9
0.1
0.9
0.5
11.7
27.7
30.6
0.1
1.0
0.0
4.2
25.8
0.1
17.4
17.6
10.5
10.7
0.5
0.1
11.3
2.6
10.0
13.0
1.8
9.6
11.1
5.6
17.4
7.5
0.2
7.8
6.5
2.3
2.6
10.2
11.3
7.9
22.0
9.1
3.4
21.2
2.3
1.5
12.5
1.6
19.8
0.0
1.9
5.4
7.4
0.8
2.5
6.8
5.0
19.9
0.7
1.2
6.6
0.1
12.3
0.0
3.9
12.2
14.6
20.4
1.4
0.9
0.4
0.0
3.7
1.7
0.2
0.9
27.4
16.6
25.2
36.9
1.3
2.3
0.0
7.7
28.8
A-35
-------
Table A-12. Continued
City EC/Cu EC/K EC/Zn EC/Ti EC/AI EC/CI EC/SO4 EC/Fe EC/Ni EC/Si EC/V
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
27.1
26.2
3.3
27.7
15.7
27.8
15.8
25.4
1.5
36.9
19.1
36.9
26.5
11.9
10.0
6.1
5.5
2.7
11.1
29.4
33.1
41.6
47.3
52.2
20.0
25.2
44.0
42.6
46.9
55.1
31.1
63.6
55.3
20.8
10.6
0.4
12.3
1.4
0.0
0.5
3.1
1.5
23.0
14.5
1.7
7.4
0.2
0.6
0.3
0.1
0.1
0.0
2.3
3.5
1.9
3.0
3.8
0.9
0.0
0.6
21.1
8.1
8.1
0.3
0.4
3.0
23.8
17.6
1.7
3.4
0.0
0.3
3.6
9.8
14.6
17.9
17.6
59.2
40.2
4.4
55.9
30.7
10.6
7.9
35.3
21.5
54.1
54.2
12.6
14.5
4.3
0.3
0.1
0.5
9.4
1.4
7.0
23.6
3.3
23.2
14.4
0.5
7.0
0.0
0.0
0.8
0.3
10.7
21.1
25.4
1.6
11.0
6.7
3.9
0.2
2.7
10.3
0.0
4.0
24.9
0.1
A-36
-------
Table A-13. Coefficients of Determination for Organic Carbon and Selected Pollutants: 49 STN Monitors, 2001-2005
City OC/Cu OC/K OC/Zn OC/Ti OC/AI OC/CI OC/SO4
OC/Fe
OC/Ni
OC/Si
OC/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
15.1
44.4
7.3
14.6
12.9
22.4
17.8
12.3
27.4
14.5
30.5
7.5
3.5
8.1
0.2
14.4
4.7
4.4
29.0
12.2
8.5
12.1
9.5
2.1
2.6
7.3
4.5
0.0
7.1
14.5
15.6
19.8
19.1
4.9
10.7
2.3
8.2
14.2
48.2
20.7
50.8
61.6
33.2
48.1
61.8
44.6
52.8
31.1
45.4
13.6
17.3
37.1
59.5
51.0
46.5
21.4
51.1
35.5
47.2
46.9
42.9
14.2
58.6
46.5
53.8
27.6
40.0
46.3
42.5
41.5
39.8
39.4
33.0
44.1
55.6
50.3
16.2
40.9
40.6
46.3
28.6
34.1
38.3
36.1
39.9
30.0
31.4
36.9
19.0
38.5
19.8
20.9
32.4
28.1
40.1
13.8
31.3
18.8
16.4
31.8
16.9
14.0
40.6
2.9
18.9
11.7
18.6
42.4
11.8
26.4
34.6
16.6
39.0
28.0
23.0
11.5
6.6
2.9
16.2
30.0
3.8
13.5
22.3
15.9
26.3
1.9
0.2
23.9
5.6
12.0
16.6
0.2
21.6
15.1
4.8
16.1
13.3
0.3
12.9
19.9
3.8
14.0
13.8
22.0
16.4
14.4
18.7
10.4
27.3
16.3
2.9
17.7
2.5
0.6
0.9
0.3
1.4
5.4
0.2
4.7
4.7
4.9
1.0
0.2
0.1
3.9
5.2
0.4
1.7
2.3
1.9
3.0
0.2
1.2
0.9
1.2
0.2
1.7
4.5
1.9
3.4
4.0
1.5
1.4
1.4
1.9
7.6
3.7
0.2
1.7
3.2
2.0
8.5
21.6
3.0
6.3
5.2
0.0
0.7
12.8
2.1
35.8
2.2
2.8
7.4
2.0
0.6
1.0
3.3
2.2
13.8
2.2
1.8
6.9
3.7
0.1
9.7
0.1
7.8
3.5
3.7
6.3
2.8
0.7
2.1
1.5
0.2
1.9
21.8
2.3
15.0
7.4
23.3
15.8
2.1
3.8
2.8
5.4
33.7
19.6
9.0
16.2
25.8
32.2
22.2
21.3
21.9
30.9
32.1
29.1
6.6
18.2
6.8
22.2
28.8
15.8
7.8
33.5
28.0
37.8
49.9
15.5
37.8
18.6
24.9
38.7
30.7
23.9
21.4
24.4
36.1
44.4
23.3
38.3
46.0
34.3
45.0
6.3
2.4
45.4
26.7
36.4
53.2
5.1
42.7
37.2
31.6
20.8
25.5
3.4
30.9
6.9
32.9
13.3
27.5
37.0
29.5
38.1
44.5
27.3
41.3
30.1
18.6
41.0
0.2
0.3
0.2
0.0
12.9
17.7
0.1
1.4
1.1
1.2
9.5
0.0
0.2
0.6
0.1
2.6
0.7
0.2
14.4
3.3
21.4
3.4
0.2
0.2
0.0
0.0
0.1
2.3
0.3
18.4
6.4
15.8
5.5
0.1
0.4
0.3
10.6
26.0
28.4
0.0
1.1
0.0
6.9
19.7
0.6
12.0
14.5
13.5
21.3
0.0
0.1
13.2
12.7
18.0
19.7
1.0
15.9
21.4
3.0
20.1
15.3
0.4
18.0
19.1
9.8
12.8
9.9
16.6
6.4
22.4
19.7
10.9
30.3
21.0
6.5
20.0
1.2
14.4
0.1
1.0
22.5
30.3
1.0
1.9
7.6
0.3
18.2
0.5
0.0
5.6
0.4
14.0
0.0
1.0
19.7
10.9
24.6
3.9
0.0
0.5
0.8
4.4
1.1
0.0
0.1
21.3
10.4
15.2
25.5
1.8
1.0
0.0
8.8
39.2
A-37
-------
Table A-13. Continued
City OC/Cu OC/K OC/Zn OC/Ti OC/AI OC/CI OC/SO4 OC/Fe OC/Ni OC/Si OC/V
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
22.1
19.6
3.0
16.7
10.2
23.0
23.5
21.2
1.9
32.4
19.3
44.5
38.6
28.6
14.4
23.0
20.6
6.4
28.0
41.0
53.6
51.0
32.8
21.4
12.3
16.0
38.2
29.4
52.6
33.3
16.6
57.7
31.4
24.3
10.8
0.8
17.2
2.4
4.1
0.7
5.9
7.6
24.5
15.4
0.3
1.3
0.2
2.7
0.0
2.0
0.3
0.3
2.3
5.6
2.2
2.0
0.0
11.4
0.0
0.8
22.9
8.4
14.2
0.4
0.6
1.4
34.6
34.5
19.1
13.3
11.2
3.2
11.4
25.2
24.2
32.5
23.3
39.3
25.3
4.3
44.2
18.8
19.5
4.8
24.3
28.3
46.7
31.4
12.9
1.9
0.7
0.0
0.2
0.1
13.1
1.7
5.4
25.5
2.0
23.3
19.0
0.7
15.0
3.2
4.6
1.2
2.6
18.1
27.9
19.3
2.1
4.5
0.8
5.2
0.1
4.8
7.3
0.0
4.6
31.0
0.5
A-38
-------
Table A-14. Coefficients of Determination for Copper and Potassium, and Selected Pollutants: 49 STN Monitors, 2001-2005
City Cu/K Cu/Ti Cu/AI Cu/CI Cu/NO3 Cu/SO4 Cu/Ni Cu/Si Cu/V K/Zn K/Ti K/AI K/CI K/NO3 K/SO4 K/Fe K/Ni K/Si K/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
16.2
17.2
9.7
14.0
12.9
16.6
24.0
6.4
26.6
10.1
27.8
0.3
2.1
5.1
0.2
13.6
2.7
1.1
27.2
5.4
11.3
11.6
8.7
0.4
2.2
4.4
6.4
0.5
5.0
11.0
11.3
13.2
15.9
4.8
25.8
1.3
20.8
13.3
17.6
22.6
11.1
6.0
9.4
22.4
8.9
5.3
20.9
7.2
19.9
1.6
2.5
5.7
0.2
10.6
1.8
2.6
14.7
4.4
8.3
8.9
3.6
0.4
2.2
4.4
0.9
1.0
5.0
11.5
13.8
11.8
7.6
2.8
8.2
2.0
6.3
7.6
3.0
5.3
5.3
0.9
1.1
7.2
1.9
3.8
11.3
1.9
3.3
0.0
1.5
1.8
0.1
0.4
2.7
1.4
1.6
2.1
1.6
3.3
1.8
0.1
0.4
0.6
0.0
0.1
0.2
0.9
0.5
2.1
1.5
1.4
2.2
0.2
4.4
0.7
3.8
0.5
0.0
0.2
0.7
0.0
0.5
0.9
0.1
0.0
0.4
6.4
3.6
0.0
1.9
0.2
1.2
9.5
2.1
0.0
1.4
0.2
0.1
3.8
0.0
0.2
0.7
1.7
0.5
1.7
0.0
0.2
0.1
1.5
2.8
0.0
0.5
0.1
0.4
11.4
0.2
9.4
0.9
5.3
2.5
1.1
15.0
0.0
5.9
3.4
1.5
0.0
0.1
3.3
0.0
3.8
5.0
2.7
9.4
0.0
0.9
1.6
1.0
0.9
3.2
0.5
7.2
5.5
10.1
7.9
10.9
0.1
3.4
0.3
2.8
9.4
4.7
1.9
3.7
4.1
0.7
1.8
1.4
1.0
0.2
4.7
8.0
0.2
0.7
2.1
0.0
2.5
2.2
0.6
3.4
4.8
3.6
0.5
2.3
0.6
0.0
5.2
2.5
0.8
0.0
5.4
5.1
7.1
10.9
0.1
10.1
0.0
8.4
4.2
2.5
0.9
1.6
0.0
2.3
5.3
0.6
1.0
1.2
5.4
5.2
1.7
0.5
0.8
8.3
8.7
0.8
0.4
5.8
0.9
6.4
1.6
1.1
3.0
0.7
6.3
2.6
3.4
6.3
4.9
9.7
20.4
2.5
2.6
21.3
3.0
3.5
9.0
20.1
3.0
8.5
2.0
5.8
14.1
2.2
4.3
13.4
3.9
11.2
0.0
0.1
5.1
0.3
8.6
0.6
6.4
9.2
1.3
2.1
9.0
2.1
0.1
0.0
0.7
0.1
0.0
1.0
5.9
6.4
12.9
10.3
1.4
10.0
0.2
4.6
6.4
2.5
26.1
0.8
10.5
3.9
10.6
0.9
0.7
8.7
10.6
12.4
0.1
1.2
5.4
16.4
5.0
10.3
0.7
6.3
3.3
4.0
11.1
6.8
1.2
12.2
3.8
15.8
15.0
9.2
2.8
7.1
15.5
9.6
5.3
12.5
6.7
19.7
5.5
25.0
25.5
35.6
49.7
22.4
20.1
46.1
25.7
40.0
34.1
44.1
6.3
17.1
26.8
19.0
23.2
24.8
10.5
47.0
21.3
50.9
14.0
25.7
12.8
8.5
15.8
50.2
5.3
21.0
26.2
29.1
35.9
29.3
32.0
49.6
15.3
51.1
30.6
34.0
53.9
17.4
8.9
14.1
35.8
6.5
8.8
28.5
15.2
20.8
15.6
8.3
21.4
22.4
17.1
14.9
1.8
17.0
13.8
6.7
47.9
22.2
11.4
15.4
25.7
3.9
24.9
22.2
23.9
15.6
11.2
14.5
5.8
16.1
14.8
7.8
18.8
10.5
34.1
8.4
1.5
4.3
15.6
1.8
7.8
10.9
5.9
9.0
11.0
6.8
15.0
19.0
3.7
7.7
0.1
5.9
10.7
1.9
36.5
11.8
6.1
2.6
8.2
3.0
12.1
19.9
7.9
9.4
6.7
7.3
5.9
13.0
9.5
4.9
5.6
9.3
21.6
7.4
23.8
6.1
3.2
15.2
6.7
4.7
3.0
7.6
1.1
0.0
3.3
6.1
1.4
2.6
3.6
11.3
5.8
11.4
7.4
3.5
1.1
3.0
0.5
10.8
1.6
6.7
0.0
5.6
7.8
7.6
2.1
8.2
4.3
1.1
6.7
4.2
5.6
7.5
39.5
7.0
6.6
25.4
2.9
36.6
1.2
14.3
39.6
16.4
8.3
6.5
6.8
0.1
12.6
17.3
17.2
45.7
10.0
2.0
15.6
0.2
0.1
30.6
0.0
17.9
37.9
19.7
28.9
28.2
15.3
2.1
2.3
26.6
19.4
8.8
5.1
6.0
3.5
7.9
4.8
1.9
5.6
3.1
21.1
15.6
14.8
3.6
1.7
22.7
26.8
12.8
3.4
6.8
22.2
19.9
2.3
3.4
6.1
1.8
17.1
36.9
2.8
12.2
31.9
22.3
26.0
31.0
2.9
25.0
17.0
23.0
26.8
45.3
55.4
34.8
31.6
33.1
42.4
29.6
21.1
50.2
28.0
45.3
11.4
21.1
32.6
52.3
47.0
45.9
9.7
46.0
26.9
23.3
74.1
34.5
22.1
26.0
6.2
33.1
27.2
45.7
36.5
31.8
27.4
37.9
27.4
64.8
26.1
29.1
41.5
0.0
0.0
0.1
0.0
4.3
3.9
0.1
0.4
5.7
1.8
8.0
0.0
0.2
0.6
0.0
0.4
0.0
0.7
13.1
5.4
26.3
0.1
0.3
0.5
0.1
0.2
0.0
4.2
0.6
23.5
4.4
10.8
9.8
0.1
6.1
0.6
6.0
22.5
50.0
45.7
13.0
1.7
21.1
32.0
2.4
10.9
23.9
24.2
23.6
17.3
11.6
20.9
36.6
25.6
20.2
7.2
8.7
30.3
7.8
70.8
23.0
17.1
17.9
28.8
8.0
35.1
37.9
21.7
16.1
22.3
17.9
10.2
35.4
23.0
10.0
26.8
3.9
29.2
0.6
3.2
11.0
15.8
0.0
1.3
8.0
9.4
20.6
1.3
4.0
12.9
1.0
18.4
1.7
2.2
22.4
13.8
32.0
2.4
0.9
6.3
2.8
9.1
4.4
1.0
0.3
22.5
13.1
11.1
29.7
7.9
4.4
2.7
22.8
37.0
A-39
-------
Table A-14. Continued
City Cu/K Cu/Ti Cu/AI Cu/CI Cu/NO3 Cu/SO4 Cu/Ni Cu/Si Cu/V K/Zn K/Ti K/AI K/CI K/NO3 K/SO4 K/Fe K/Ni K/Si K/V
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
23.9
16.6
1.5
3.6
0.6
5.3
0.5
5.8
2.8
25.3
18.7
19.9
17.7
0.3
7.6
2.8
0.6
1.4
2.8
4.4
31.1
9.4
4.7
2.0
0.0
0.0
0.1
0.2
1.7
0.3
5.0
17.4
3.0
0.2
1.7
5.6
1.2
3.2
5.2
12.9
1.4
2.3
1.0
0.2
3.8
10.4
2.5
0.2
3.0
13.3
0.3
18.5
0.7
27.6
6.5
11.1
8.7
0.8
0.6
0.7
0.9
3.4
7.0
1.5
23.1
11.3
14.1
7.3
5.0
1.1
0.6
1.3
1.7
5.1
4.9
21.8
8.1
20.1
14.7
0.0
1.3
1.2
1.3
5.5
0.1
1.0
30.4
8.5
9.0
8.4
2.4
4.0
2.3
0.9
0.1
4.9
6.1
24.2
3.4
29.5
36.1
8.5
4.6
12.1
6.8
4.6
15.4
49.3
42.8
47.1
19.5
13.7
6.4
7.5
10.1
54.5
10.0
19.8
6.4
17.3
16.8
8.1
9.1
7.5
2.5
4.9
23.5
5.1
10.8
4.8
9.2
3.9
4.8
7.4
0.8
0.1
2.9
23.2
4.7
9.3
1.9
2.5
4.0
7.0
20.1
22.6
0.5
4.5
2.5
8.1
1.8
29.3
49.7
14.8
13.5
23.0
9.9
0.5
12.2
1.5
8.7
16.6
25.3
28.6
25.3
34.2
29.2
21.4
13.8
21.4
74.1
27.6
36.6
36.0
26.6
43.2
7.2
10.7
1.3
0.0
0.5
0.0
0.0
0.2
7.7
8.9
2.1
21.7
20.8
11.1
8.7
20.6
71.2
26.4
27.8
19.1
22.4
27.2
5.4
15.5
3.7
2.8
11.5
6.0
6.8
0.1
10.3
16.6
2.8
A-40
-------
Table A-15. Coefficients of Determination for Zinc and Titanium, and Selected Pollutants: 49 STN Monitors, 2001-2005
City
Zn/Ti
Zn/AI
Zn/CI
Zn/N03
Zn/SO4
Zn/Ni
Zn/Si
Zn/V
Ti/CI
Ti/N03
Ti/S04 Ti/Fe
Ti/Ni
Ti/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
22.0
25.5
0.8
0.1
12.7
30.1
2.3
14.2
25.2
7.1
17.3
0.8
1.4
11.6
0.6
8.7
3.6
0.0
12.2
11.4
4.7
9.9
4.5
1.4
2.4
8.0
0.3
1.1
9.3
6.1
6.4
12.3
2.5
2.2
15.8
1.6
6.6
5.8
3.5
5.8
0.4
2.8
0.2
12.0
0.3
1.3
3.1
3.0
2.5
2.5
1.3
3.6
0.5
1.4
0.3
0.2
2.3
1.1
0.3
1.6
0.5
3.5
2.2
0.7
0.9
1.0
0.1
0.0
0.6
3.1
1.8
0.0
9.8
0.0
1.2
0.1
10.9
3.3
11.0
20.3
0.0
3.6
2.3
0.2
0.7
6.4
4.2
21.3
4.5
1.1
0.3
1.3
1.1
0.4
9.7
3.8
3.6
27.7
1.1
7.4
2.2
8.0
11.9
0.0
0.5
0.8
3.7
6.6
2.5
0.0
11.1
0.1
0.0
5.3
0.3
17.8
35.5
56.1
9.0
5.1
24.7
18.1
27.3
10.9
28.8
19.2
13.2
9.2
26.1
18.8
12.2
8.9
21.0
14.2
49.7
4.3
17.8
8.1
10.5
13.7
61.3
12.0
31.1
26.0
39.0
37.0
31.6
17.1
7.1
6.5
24.4
40.1
3.2
1.7
2.8
1.8
4.1
0.0
1.3
0.5
0.6
10.1
10.9
7.8
6.1
1.2
16.7
10.0
13.5
5.6
10.0
8.7
23.7
0.0
3.3
4.0
1.0
10.3
30.0
0.0
0.1
10.0
16.5
22.6
10.0
1.0
23.0
1.1
18.0
11.8
0.3
0.7
0.1
0.7
3.5
2.6
0.0
8.8
2.1
3.4
15.3
0.1
0.0
0.9
0.1
1.5
0.9
3.2
22.0
16.0
25.9
0.0
0.1
0.0
0.0
1.8
0.7
0.0
0.0
20.1
21.7
32.7
58.7
0.4
2.1
0.4
9.7
34.0
32.8
5.7
0.1
2.3
6.7
26.7
0.2
13.1
16.4
13.2
10.9
0.0
0.2
5.3
0.1
5.0
4.4
0.0
6.0
16.6
10.7
5.7
1.1
0.0
0.2
4.0
0.4
0.4
4.7
3.4
8.4
21.6
1.6
2.1
22.3
0.7
8.0
10.5
2.0
21.9
1.7
1.6
11.1
6.8
1.3
14.5
10.4
10.3
24.1
0.8
2.0
5.3
5.1
6.7
0.4
1.8
24.2
13.8
36.9
2.4
1.5
0.1
0.8
3.5
4.3
1.9
5.6
20.2
30.0
25.5
43.8
7.1
1.5
0.4
22.9
28.3
1.1
9.6
6.4
2.4
0.0
0.6
0.7
1.8
0.1
0.1
1.6
0.0
0.0
0.0
0.0
5.0
1.1
0.7
1.5
0.9
0.2
2.4
0.3
0.0
0.8
5.4
0.7
2.0
2.2
5.2
0.0
0.0
0.5
0.3
0.1
0.3
0.0
0.0
0.1
0.2
4.2
0.0
3.9
3.5
1.4
3.8
17.2
0.2
0.5
3.3
0.0
2.0
3.2
0.2
2.3
0.0
0.0
1.2
1.2
4.0
0.1
0.2
2.4
1.5
0.2
4.9
3.2
5.9
1.4
2.3
2.6
1.1
0.4
3.9
0.2
1.8
9.7
2.4
3.6
6.8
9.4
3.1
1.9
1.9
2.8
11.2
14.9
0.1
1.6
6.3
0.5
6.5
11.4
0.3
10.8
3.2
3.7
0.4
4.3
1.0
4.4
9.1
1.9
2.8
5.3
16.4
9.5
10.7
8.4
5.0
12.8
6.7
7.7
9.6
42.6
82.3
57.7
55.8
32.6
67.2
36.6
45.1
56.4
20.1
39.4
42.5
33.0
55.2
41.6
25.7
24.7
27.8
35.5
38.2
10.5
53.2
33.6
38.5
22.8
18.9
26.9
21.1
46.4
44.6
26.0
24.3
26.8
23.2
24.7
31.5
24.7
24.6
0.1
0.6
0.1
4.2
4.9
5.7
1.6
6.5
17.8
2.3
3.9
1.1
2.5
0.7
0.1
0.2
2.1
1.0
9.4
0.1
5.1
2.8
0.0
0.9
0.9
0.5
1.3
4.5
0.8
10.7
1.5
6.1
0.6
0.2
0.7
0.7
2.6
5.7
8.1
30.2
0.2
2.8
11.0
14.2
0.8
5.3
5.6
6.7
5.2
4.3
9.0
5.8
1.4
9.3
0.2
0.3
10.1
1.2
5.3
0.2
0.0
3.9
1.7
3.1
0.1
1.7
6.3
8.2
6.1
11.5
6.1
2.2
0.3
0.5
6.9
9.9
A-41
-------
Table A-15. Continued
City Zn/Ti Zn/AI Zn/CI Zn/NO3 Zn/SO4 Zn/Ni Zn/Si Zn/V Ti/CI Ti/NO3 Ti/SO4 Ti/Fe Ti/Ni Ti/V
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
10.6
12.7
1.8
1.8
1.7
1.4
0.3
1.9
3.7
27.8
12.5
2.9
2.9
1.6
0.6
0.5
1.7
0.1
0.3
0.8
9.1
1.5
4.5
2.1
6.8
0.5
0.5
18.9
14.5
6.8
0.2
0.0
2.8
12.1
37.3
17.7
14.6
11.5
7.5
10.4
46.2
46.2
51.4
22.1
6.6
20.4
5.9
4.7
1.6
1.1
6.1
11.1
20.1
24.4
21.3
10.8
26.7
5.0
0.3
2.1
0.0
6.8
0.5
8.7
23.7
2.2
8.6
8.4
2.4
0.0
0.3
1.6
1.9
0.2
9.2
25.0
20.9
1.8
23.6
14.4
0.8
0.1
6.1
6.1
0.7
9.6
26.6
1.6
0.1
2.4
0.4
9.8
1.7
3.8
0.0
0.8
1.7
3.5
0.1
0.1
0.9
0.8
2.1
2.1
0.4
0.5
0.5
0.0
15.2
0.9
21.0
6.2
1.1
5.6
1.6
1.7
0.1
3.6
3.8
30.9
8.8
30.8
35.5
18.8
32.8
37.3
60.6
42.4
34.1
18.7
50.8
18.0
7.9
2.5
0.0
2.6
0.2
0.1
0.9
5.6
2.1
25.3
2.0
1.9
2.4
0.0
0.4
1.6
1.8
0.1
0.5
3.8
25.5
0.4
A-42
-------
Table A-16. Coefficients of Determination for Aluminum, Chlorine, and Nitrate, and Selected Pollutants: 49 STN Monitors, 2001-2005
City
AI/CI AI/N03 AI/S04 AI/Fe
AI/Ni
AI/V
CI/N03 CI/S04 CI/Fe CI/Ni Cl/Si CI/V NO3/SO4 NOs/Fe NO3/Ni NOs/Si NO3/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
0.0
9.7
2.6
8.0
0.1
1.1
1.2
0.0
2.2
0.6
1.4
1.3
0.6
0.1
2.9
3.7
1.8
1.3
3.7
0.1
7.6
2.6
4.1
7.5
3.0
0.0
1.6
2.8
3.9
0.0
1.3
0.4
2.4
2.6
1.8
4.2
0.3
1.6
2.6
1.2
6.9
3.6
0.2
0.8
3.4
0.5
3.3
0.0
0.5
0.4
0.5
3.0
2.3
0.5
0.1
0.6
0.1
0.0
1.2
6.4
0.0
0.0
0.3
1.8
0.1
1.8
0.4
1.3
0.1
1.6
0.7
0.9
0.0
1.3
0.1
0.1
1.5
1.9
2.6
4.7
1.4
0.1
1.2
0.3
0.5
1.0
0.1
0.0
0.0
0.6
1.0
0.1
2.3
0.3
0.0
0.1
0.1
0.3
0.0
0.0
0.2
1.0
0.8
0.0
5.3
2.7
0.7
1.1
0.9
0.0
2.5
1.2
0.6
0.3
14.8
42.8
36.7
33.6
11.2
30.4
18.0
19.5
21.2
7.6
8.4
21.2
20.2
21.1
33.4
6.1
8.4
5.4
7.6
13.8
5.4
35.8
17.7
17.2
5.9
2.7
25.3
14.9
35.8
20.3
6.8
5.1
7.2
10.9
13.2
15.9
7.3
4.8
0.9
0.2
0.4
1.2
2.8
0.1
4.1
1.2
9.5
0.2
0.0
0.0
0.2
1.0
0.6
3.2
0.1
0.1
1.4
0.4
1.4
3.6
1.7
0.6
0.6
0.6
1.4
14.3
3.1
1.2
0.0
0.3
0.5
0.4
0.0
0.7
0.1
1.1
5.8
20.5
1.4
3.3
1.4
1.3
0.2
0.0
1.8
6.4
0.2
0.6
0.7
4.8
1.4
6.5
1.8
0.1
3.8
0.0
0.1
2.0
2.8
0.9
0.0
1.9
1.3
0.9
1.6
1.3
0.8
0.1
1.0
1.7
0.0
0.1
3.0
3.2
4.3
0.0
14.7
25.7
3.4
14.1
5.2
0.1
1.8
6.2
8.9
17.4
0.0
2.8
6.3
11.0
2.6
0.3
20.7
3.3
13.3
2.2
4.5
1.4
5.4
15.7
13.3
2.0
6.4
0.1
6.6
10.4
8.3
0.7
19.7
1.7
0.0
7.3
1.6
0.1
0.0
0.1
0.3
1.8
0.8
4.4
0.8
0.4
0.4
25.1
10.3
1.2
5.5
0.7
0.1
0.0
1.7
0.3
2.6
0.0
0.7
2.3
0.0
1.4
11.7
0.1
0.0
6.3
0.7
0.7
1.6
0.1
0.0
0.8
0.3
0.1
8.9
11.2
0.4
0.1
0.7
0.0
1.8
1.0
0.6
2.3
2.5
0.1
1.0
2.3
0.7
0.0
0.9
0.1
1.3
0.0
4.4
11.4
3.5
0.4
1.0
0.4
2.9
0.1
3.2
4.1
1.5
3.5
3.1
1.3
6.6
2.4
0.1
3.2
7.9
2.1
0.0
0.2
3.4
0.6
0.6
0.1
2.6
3.0
7.0
1.1
0.7
1.4
0.1
4.3
0.2
0.3
6.4
3.2
10.4
1.1
0.6
2.0
0.6
0.8
0.4
0.3
0.4
1.4
1.5
9.3
4.0
0.2
3.1
0.7
0.2
6.7
5.7
9.5
6.9
9.2
0.8
0.7
0.0
0.1
2.0
0.0
0.8
0.9
0.1
0.1
0.2
2.5
0.6
0.0
1.1
0.1
0.2
2.1
2.3
1.0
1.0
2.3
0.1
1.1
1.5
0.1
0.5
0.7
2.2
0.4
3.7
0.5
0.0
1.3
0.0
10.1
0.1
0.3
0.6
0.5
0.2
0.3
1.2
0.0
10.4
2.2
0.1
2.4
2.5
0.7
0.2
8.3
9.6
3.7
12.6
0.4
0.2
0.8
0.2
0.9
1.2
2.2
1.3
0.5
6.3
5.5
7.3
1.6
0.6
0.2
0.0
5.8
0.1
4.2
6.1
10.2
51.8
61.5
2.4
33.6
33.9
20.1
0.1
29.9
11.7
6.0
31.7
10.2
0.3
1.7
2.8
43.9
36.6
14.6
23.4
0.6
3.3
1.2
40.1
5.4
0.0
38.1
18.0
29.6
38.6
0.4
5.4
0.3
38.1
19.8
0.2
2.3
0.0
9.4
3.2
3.6
0.3
10.8
30.2
1.3
9.3
2.6
2.8
0.2
1.0
5.7
0.0
0.0
2.8
6.9
26.1
2.0
0.4
0.8
1.8
1.3
4.6
4.3
6.7
14.9
16.3
14.5
15.9
0.1
1.1
1.3
6.8
16.2
2.3
3.2
0.5
0.0
25.2
31.8
0.6
12.4
1.9
3.9
13.7
1.0
2.7
1.7
0.3
3.6
3.1
4.1
11.3
34.5
32.2
0.0
2.9
2.1
1.2
1.1
0.2
0.4
0.7
30.1
21.9
25.0
27.9
1.4
1.9
0.9
11.6
28.1
0.1
4.6
15.4
3.0
0.1
0.1
7.8
4.7
12.3
1.9
0.0
1.1
0.0
6.6
5.6
0.3
0.8
0.8
0.1
2.1
7.6
10.9
2.5
0.0
8.2
4.7
0.2
13.0
0.4
7.5
5.0
9.1
8.1
1.2
0.9
5.3
1.4
4.5
0.9
2.5
0.0
0.8
32.6
42.1
0.4
21.9
14.3
1.6
12.1
2.3
2.8
0.0
0.0
0.4
0.4
0.5
9.8
33.5
29.2
0.2
0.2
1.9
1.0
0.5
0.0
0.2
0.2
27.1
16.8
15.2
40.9
0.7
0.2
0.1
4.3
17.2
A-43
-------
Table A-16. Continued
City AI/CI AI/N03 AI/SO4 AI/Fe AI/Ni AI/V CI/NO3 CI/SO4 CI/Fe CI/Ni Cl/Si CI/V NO3/SO4 NOs/Fe NO3/Ni NOs/Si NO3/V
Pittsburgh, PA
Providence, Rl
Charleston, SC
Memphis, TN
Dallas, TX
El Paso, TX
Houston, TX
Salt Lake City, UT
Burlington, VT
Seattle, WA
Milwaukee, Wl
0.2
2.3
0.4
0.5
0.0
8.0
1.2
1.5
1.8
0.1
1.5
0.0
4.0
0.0
1.6
4.1
0.1
2.7
0.7
0.8
5.8
0.7
0.2
0.5
0.7
0.1
0.0
2.7
0.4
1.4
0.9
10.7
0.1
7.5
8.1
19.8
7.5
8.7
22.7
20.3
15.0
6.7
17.0
2.8
0.2
0.6
0.1
0.0
1.5
0.2
1.9
1.4
0.1
7.0
0.5
5.5
0.8
0.1
0.0
1.9
0.1
0.1
1.7
5.7
11.5
2.0
7.7
5.5
1.1
3.9
0.7
4.7
0.6
11.0
3.6
0.0
8.1
0.1
1.0
12.9
2.7
0.3
0.8
1.2
8.6
0.8
4.5
0.3
2.0
0.0
0.0
0.1
0.6
20.3
0.0
6.3
0.1
1.1
2.1
8.0
2.2
0.0
0.0
0.0
0.4
0.3
3.1
0.0
3.7
0.6
0.6
0.9
0.1
0.0
0.6
8.3
0.0
1.2
0.1
7.2
2.6
1.0
5.0
0.6
3.7
6.2
1.5
0.6
1.4
0.0
4.8
0.3
0.4
31.8
6.0
0.1
0.1
1.5
6.2
10.6
24.6
27.8
27.0
1.8
15.2
2.8
0.5
0.0
3.0
0.7
7.2
6.9
30.3
9.7
2.8
21.3
4.7
0.2
6.8
3.1
8.4
3.4
8.8
13.5
2.5
0.2
4.7
0.4
6.1
5.8
0.5
0.0
7.8
2.0
19.4
3.7
0.6
17.9
10.9
0.3
0.2
0.3
13.6
0.0
8.2
21.7
0.3
A-44
-------
Table A-17. Coefficients of Determination for Sulfate, Iron, Nickel, Silicon, and Vanadium: 49 STN Monitors, 2001-2005
City
SOVFe
SO4/Si
S04/V
Fe/Ni
Fe/Si
Fe/V
Ni/Si
Ni/V
Si/V
Birmingham, AL
Phoenix, AZ
Bakersfield, CA
Fresno, CA
Oxnard, CA
Riverside, CA
Sacramento, CA
San Diego, CA
San Jose, CA
Denver, CO
Washington, DC
Miami, FL
Tampa, FL
Atlanta, GA
Boise City, ID
Chicago, IL
Indianapolis, IN
Baton Rouge, LA
Baltimore, MD
Boston, MA
Springfield, MA
Detroit, Ml
Minneapolis, MN
Gulfport, MS
Kansas City, MO
St. Louis, MO
Missoula, MT
Omaha, NE
Reno, NV
Rockingham, NH
Edison, NJ
Newark, NJ
New York City, NY
Charlotte, NC
Cleveland, OH
Tulsa, OK
Portland, OR
Philadelphia, PA
8.6
0.2
5.4
14.1
7.3
0.4
0.3
0.2
3.1
0.0
15.0
0.1
4.9
3.6
4.2
12.0
24.0
0.8
17.4
14.2
30.0
9.5
1.6
3.8
2.7
3.7
11.2
0.0
8.6
29.4
14.8
18.6
28.6
3.6
26.6
2.7
22.3
20.4
0.1
0.7
0.4
2.5
32.2
38.7
0.9
14.6
2.5
1.5
4.4
1.6
3.2
0.0
0.1
0.8
2.2
4.3
6.0
8.9
19.4
6.9
5.6
1.4
0.6
0.5
0.4
0.1
0.4
27.8
5.4
10.2
4.7
0.2
4.3
0.3
5.4
15.3
13.6
10.6
3.3
11.8
3.8
0.7
5.2
0.6
6.4
0.4
12.6
0.0
2.5
13.7
1.2
21.9
9.6
4.8
13.4
9.4
9.9
23.4
3.0
2.8
3.3
9.8
1.8
0.0
14.5
14.6
12.8
19.6
11.4
3.9
19.6
5.8
16.5
10.1
1.6
0.0
0.0
3.5
51.9
55.5
3.4
30.1
14.4
1.9
9.6
3.9
5.9
4.3
0.0
6.2
0.5
4.3
10.9
17.4
20.2
6.8
2.5
2.5
0.6
6.5
0.2
0.0
0.6
24.8
13.6
14.4
23.7
0.6
2.4
2.4
16.8
20.8
1.1
0.7
0.5
2.9
7.8
7.6
1.7
12.9
21.3
4.3
11.4
1.7
6.9
0.1
0.2
1.4
3.9
1.9
14.3
3.2
27.1
1.2
0.2
6.8
2.5
6.7
1.5
16.0
1.9
22.6
21.0
32.6
4.2
3.1
2.8
4.1
8.9
33.0
60.8
56.2
64.8
52.4
43.9
69.7
41.1
48.4
55.8
23.6
27.6
32.9
46.1
36.2
73.6
25.9
26.5
40.3
23.5
45.9
18.8
72.2
50.6
49.0
24.3
10.4
57.6
37.3
67.3
48.5
23.1
29.9
28.8
25.7
32.8
35.0
35.1
30.1
4.8
38.8
1.2
7.9
14.6
14.2
0.8
10.9
9.5
14.7
22.9
4.7
7.2
8.4
0.4
17.6
0.1
4.0
21.4
7.6
16.2
3.5
0.0
1.0
1.7
0.0
1.5
2.3
0.2
17.6
19.7
34.0
17.8
3.7
4.4
0.5
21.6
29.6
0.2
0.1
0.1
5.3
2.6
3.8
0.7
5.7
22.9
1.8
2.6
0.0
3.6
0.2
0.0
0.0
0.6
1.4
3.1
0.3
4.3
0.8
0.0
1.0
0.0
0.0
1.0
8.2
0.8
7.5
4.0
7.8
4.6
0.9
1.6
1.0
1.3
10.1
2.3
0.0
1.0
0.4
24.5
39.1
3.0
18.8
0.0
2.1
40.6
23.0
6.6
0.1
0.3
0.7
2.4
36.4
49.7
52.6
31.5
4.6
0.5
0.7
0.3
1.5
3.3
0.3
2.5
58.8
27.4
50.1
42.1
0.6
1.5
0.0
1.4
38.3
3.4
15.9
0.5
3.6
2.5
8.5
0.1
3.0
2.1
7.4
2.5
0.9
5.2
9.9
0.8
10.9
1.4
2.0
2.9
1.3
4.4
0.4
2.1
1.1
0.2
0.4
2.8
0.3
0.5
5.7
4.5
11.5
5.2
0.0
0.6
0.0
3.1
12.0
A-45
-------
Table A-17. Continued
City S0
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