United States
Environmental Protection
Agency
National Air Quality and
Emissions Trends Report
2003 SPECIAL STUDIES EDITION
O
" 10.
S 5.
E 0
o
£w--
S40'
I »•
>* / *
-------
EPA454/R-03-005
National Air Quality and
Emissions Trends Report
2003 SPECIAL STUDIES EDITION
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Emissions Monitoring and Analysis Division
Air Quality Trends Analysis Group
Research Triangle Park, North Carolina 27711
September 2003
Printed on recycled paper.
-------
About the Cover
Cover graphics reflect the range of topics addressed in the series of
exploratory analyses studies included in this 2003 Special Studies Edition
of the National Air Quality and Emissions Trends Report. Subjects
addressed in these studies include new air quality reporting techniques,
chemical speciation of PM2 5, national spatial variation, ozone
exceedances, trends in CO concentrations, and transport of Asian dust.
Disclaimer
This report has been reviewed and approved for publication by the U.S.
Environmental Protection Agency's Office of Air Quality Planning and
Standards. Mention of trade names or commercial products is not
intended to constitute endorsement or recommendation for use.
Acknowledgments
The Trends Team would like to acknowledge the members of EPA's
Office of Research and Development, Office of Atmospheric Programs,
Office of Radiation and Indoor Air, and Office of Transportation and Air
Quality for their input for this report; Colorado State University for pro-
viding summary data from the IMPROVE monitoring network; support
for desktop publishing and Web site development provided under EPA
contract 68-D-02-065; and the Trends Workgroup in EPA's Office of Air
Quality Planning and Standards for providing comments throughout
report development.
-------
Preface
This is the 28th report on air pollution trends in the United States
issued by the U.S. Environmental Protection Agency. The report is
prepared by the Air Quality Trends Analysis Group (AQTAG) in
Research Triangle Park, North Carolina and is directed toward both
the technical air pollution audience and other interested parties
and individuals.
The report can be accessed via the Internet at http://www.epa.gov/
airfrends/. AQTAG solicits comments on this report and welcomes
suggestions regarding techniques, interpretations, conclusions, or
methods of presentation. Comments can be submitted via the web-
site or mailed to:
Attn: Trends Team
AQTAG (C304-01)
U.S. EPA
Research Triangle Park, NC 27711
Readers can access data from the Aerometric Information Retrieval
System (AIRS) at http://www.epa.gov/air/data/index/html and real
time air pollution data at http://www.epa.gov/airnow/.
-------
-------
Contents
Chapter 1
Executive Summary [[[ 1
Chapter 2
Criteria Pollutants — National Trends......................................... 7
Carbon Monoxide 9
Lead 13
Nitrogen Dioxide 17
Ozone 22
Particulate Matter 34
Sulfur Dioxide 43
References and Notes 48
Chapter 3
Criteria Pollutants — Metropolitan Area Trends ................................ 51
Status: 2000 51
Trends Analysis 52
The Air Quality Index 52
Summary of AQ1 Analyses 54
A New Display Technique 55
References and Notes 57
Chapter 4
Criteria Pollutants — Nonattainment Areas .................................... 59
Chapter 5
Air Toxics [[[ 63
Chapter 6
-------
Appendix A
Data Tables[[[ 71
Appendix B
Methodology [[[ 185
Special Studies
Impact of April 2001 Asian Dust Event on Particulate Matter Concentrations
in the United States SI
Chemical Speciation of PM2 5 in Urban, and Rural Areas S13
Trends in Monitored Concentrations of Carbon Monoxide S25
Cumulative Ozone Exceedances—A Measure of Current Year Ozone Levels Compared
-------
Figures
Figure 2-1. Number of people living in counties with air quality concentrations above the level of NAAQS in 2002 8
Figure 2-2. CO air quality, 1983-2002, based on annual second maximum 8-hour average 9
Figure 2-3. Trend in second maximum nonoverlapping 8-hour average CO concentrations by type of location, 1982-2001 10
Figure 2-4. Trend in CO second maximum nonoverlapping 8-hour concentrations by EPA Region, 1982-2001 10
Figure 2-5. CO emissions by source category, 2002 11
Figure 2-6. Density map of 2001 CO emissions, by county 11
Figure 2-7. CO emissions, 1983-2002 11
Figure 2-8. Highest second maximum nonoverlapping 8-hour average CO concentration by county, 2001 12
Figure 2-9. Pb air quality, 1983-2002, based on annual maximum quarterly average 14
Figure 2-10. Maximum quarterly mean Pb concentration trends by location (excluding sites designated as point-source oriented), 1982-2001. . 14
Figure 2-11. Pb emissions, 1982-2002 15
Figure 2-12. Pb emissions by source category, 2001 15
Figure 2-13. Trend in Pb maximum quarterly mean concentration by EPA Region, 1982-2001 16
Figure 2-14. Highest Pb maximum quarterly mean by county, 2001 16
Figure 2-15. NO2 air quality, 1982-2001, based on annual arithmetic average 18
Figure 2-16. Trend in annual mean NO, concentrations by type of location, 1982-2001 18
Figure 2-17. Trend in NO2 maximum quarterly mean concentration by EPA Region, 1982-2001 19
Figure 2-18. NOX emissions, 1983-2002 19
Figure 2-19. NOX emissions by source category, 2002 20
Figure 2-20. Density map of 2001 NO2 emissions, by county 21
Figure 2-21. Highest NO2 annual mean concentration by county, 2001 21
Figure 2-22. O3 air quality, 1983-2002, based on annual second maximum 1-hour average 23
Figure 2-23. O3 air quality, 1983-2002, based on annual fourth maximum 8-hour average 23
Figure 2-24. Trend in 1-hour O3 levels, 1983-2002, averaged across EPA Regions, based on annual second highest daily maximum 24
Figure 2-25. Trend in 8-hour O3 levels, 1983-2002, averaged across EPA Regions, based on annual fourth maximum 8-hour average 24
Figure 2-26. Trend in annual second-highest daily maximum 1-hour O3 concentrations by location, 1983-2002 25
Figure 2-27. Comparison of actual and meteorologically adjusted 8-hour O3 trends, 1993-2002 25
Figure 2-28. 1-Hour O3 trends for 1991-2000 and 1992-2001 26
Figure 2-29. 8-Hour O3 trends for 1991-2000 and 1992-2001 27
Figure 2-30. Median percent change for the period 1995-2001 at PAMS monitors for selected species 28
Figure 2-31. Annual 1-hour and 8-hour composite O3 design values in the Atlanta and Chicago-Gary lake county nonattainment areas 29
Figure 2-32. June-August weekday morning average NOX and TNMOC at PAMS Type 2 trend sites 30
Figure 2-33. Trends in fourth highest daily 8-hour O3 concentrations for 34 rural sites from CASTNet, 1990-2001 31
Figure 2-34. Trend in annual fourth-highest daily maximum 8-hour O3 concentrations in National Parks, 1992-2001 31
Figure 2-35. VOC emissions, 1983-2002 32
Figure 2-36. Anthropogenic VOC emissions by source category, 2002 32
Figure 2-37. Density map of 2001 anthropogenic VOC emissions, by county 33
Figure 2-38. PM10 air quality, 1993-2002, based on seasonally weighted annual average 35
Figure 2-39. PM10 annual mean concentration trends by location, 1992-2001 35
Figure 2-40. Trend in PM10 annual mean concentration by EPA Region, 1992-2001 36
Figure 2-41. Highest second maximum 24-hour PM10 concentration by county, 2001 37
Figure 2-42. National direct PM10 emissions, 1993-2002 (traditionally inventoried sources only) 38
Figure 2-43. National direct PM10 emissions by source category, 2002 38
Figure 2-44. Direct PM10 emissions density by county, 2001 39
Figure 2-45. National direct I'M, r, emissions, 1993-2002 (traditionally inventoried sources only) 39
-------
Figure 2-46. Annual average PM2 q concentrations (|ig/m3) and particle type in rural areas, 2002 40
Figure 2-47. Annual average PM2 ^ concentrations (|ig/rn3) and particle type in urban areas, 2002 40
Figure 2-48. Annual average PM2 r; concentrations in rural areas 41
Figure 2-49. Annual average PM2 c, concentrations by county, 2001 41
Figure 2-50. Annual average PM2 q concentrations (|ig/m3), 2002 (based on seasonally weighted annual average) 42
Figure 2-51. SO2 air quality, 1983-2002, based on annual arithmetic average 43
Figure 2-52. Annual mean SO2 concentration by trend location, 1982-2001 44
Figure 2-53. SO2 emissions, 1983-2002 44
Figtire 2-54. SO2 emissions by source category, 2002 44
Figure 2-55. Direct SO2 emissions density by county, 2001 45
Figure 2-56. National SO2 emissions trend for all Title IV affected units 45
Figure 2-57. Long-term ambient SO2 trend, 1982-2001 46
Figtire 2-58. Trend in SO2 annual arithmetic mean concentration by EPA Region, 1982-2001 47
Figtire 2-59. Highest SO2 annual mean concentration by county, 2001 47
Figure 3-1. Air quality index logo 54
Figure 3-2. Number of days with AQ1 values > 100, as a percentage of 1990 value 55
Figure 3-3. Percentage of days over 100 due to ozone 55
Figure 3-4. Sample from the new display technique 56
Figure 4-1. Location of nonattainment areas for criteria pollutants, September 2002 59
Figure 4-2. Classified ozone nonattainment areas 60
Figure 5-1. Map of 10 cities in monitoring pilot project 64
Figtire 5-2. National air toxics emissions, 1996 64
Figtire 5-3. National air toxics emissions 64
Figure 5-4. Ambient benzene, annual average urban concentrations, nationwide, 1994—2000 64
Figtire 6-1. Urban PM2 5 increments 65
Figure 6-2. Monitoring stations showing upward CO trends 67
Figure 6-3. Cumulative exceedances—5-year average (97-01) (Atlanta) compared to 2002 data and southeast region average 68
Figtire 6-4. Variance of the difference vs. distance 69
Figtire 6-5. CPA vs. distance (km) 69
Figure 6-6. Comparison of mean CPA vs. distance (km) 69
-------
Tables
Table 2-1. NAAQS in Effect as of December 2002 7
Table 2-2. Milestones in motor vehicle emission control 12
Table 2-3. Trends in TNMOC, NOX/ and Selected VOC Species 28
Table 2-4. Summary of 1991—2001 Trends in Ozone Design Values and 1995-2001 Trends in Summer Weekday Morning
Ozone Precursor Trends in Atlanta and Chicago 30
Table 2-5. Biogenic Sources of VOC Emissions by Region 32
Table 3-1. Summary of MSA Trend Analyses by Pollutant, 1990-1999 52
Table 3-2. AQ1 Categories, Colors, and Ranges 54
Table 4-1. Areas Redesignated to Attainment from September 2001 to September 2002 61
Table 6-1. Estimated PM2 ^ Concentrations Attributable to Asian Dust Cloud 66
-------
Acronyms
AIRS Aerometric Information Retrieval
System
AQI Air Quality Index
AQS Air Quality System
AQTAG Air Quality Trends Analysis Group
CA A Clean Air Act
CAAA Clean Air Act Amendments
CASTNet Clean Air Status and Trends Network
CEMs continuous emissions monitors
CFC chlorofluorocarbons
CFR Code of Federal Regulations
CMSA consolidated metropolitan statistical
area
CO carbon monoxide
CPA coefficient of perfect agreement
EGR emission gas recycle
EPA Environmental Protection Agency
GLM General Linear Model
HAPs hazardous air pollutants
I/M inspection and maintenance
IMPROVE Interagency Monitoring of Protected
Environments
MACT maximum achievable control
technology
MSA metropolitan statistical area
MDL minimum detectable level
NAAQS National Ambient Air Quality
Standards
NADP/NTN National Atmospheric Deposition
Program/National Trends Network
NAMS National Air Monitoring Stations
NEI National Emissions Inventory
NO2 nitrogen dioxide
NOX nitrogen oxides
NFS National Park Service
NTI National Toxics Inventory
O2 oxygen
O3 ozone
PAMS Photochemical Assessment
Monitoring Stations
PAN peroxyacetyl nitrate
Pb lead
PM participate matter
PM1() particulate matter of 10 micrometers
in diameter or less
PM0 5 particulate matter of 2.5 micrometers
in diameter or less
PMSA primary metropolitan statistical area
POC pollutant occurrence code
ppm parts per million
PSI Pollutant Standards Index
RVP Reid Vapor Pressure
SLAMS State and Local Air Monitoring
Stations
SO2 sulfur dioxide
SOX sulfur oxides
STN Speciation Trends Network
TNMOC total non-methane organic
compound
TSP total suspended particulate
VMT vehicle miles traveled
VOCs volatile organic compounds
pg/m3 micrograms per cubic meter
-------
CHAPTER
Executive Summary
This 28th National Air Quality and
Emission Trends Report documents air
pollution trends in the United States,
focusing on the 20-year period from
1983 to 2002 or 1982 to 2001 if that is
the most recent data available. This
document highlights the U.S.
Environmental Protection Agency's
(EPA's) most recent thorough assess-
ment of the nation's air quality, and,
http://www.epa.gov/oar/airtrends
for the first time, brings special
attention to a series of special studies
of policy-relevant air quality issues
(see Chapter 6 and the Special
Studies section).
In the future, the detailed infor-
mation traditionally contained in this
report will be provided on the Web
at http://www.epa.gov/airtrends to
facilitate timely updates. A summary of
that information will be published each
summer as it has for the past several
years in EPA's Latest Findings on National
Air Quality: Status and Trends. This
National Air Quality and Emissions Trends
Report will no longer appear annually in
hard copy. Expect future reports to focus
on special studies as this report does.
200%
150%
100%
50%
Comparison of Growth Areas and Emissions
• It
-50%
Gross Domestic Product
_
Energy Consumption
U.S. Population
Aggregate Emissions
(Six Principal Pollutants)
70 80 90 95 96 97 98 99 00 01 02
Between 1970 and 2002, gross domestic product increased 164 percent, vehicle miles traveled increased 155 percent, energy consumption
increased 42 percent, and U.S. population increased 38 percent. At the same time, total emissions of the six principal air pollutants
decreased 48 percent.
CHAPTER 1 • EXECUTIVE SUMMARY 1
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Highlights
• National air quality levels meas-
ured at thousands of monitoring
stations across the country have
shown improvements over the
past 20 years for all six principal
pollutants.
• Since 1970, aggregate emissions of
the six principal pollutants have
been cut 48 percent. During that
same time, U.S. gross domestic
product increased 164 percent,
energy consumption increased 42
percent, and vehicle miles trav-
eled increased 155 percent.
• Despite this progress, about 160
million tons of pollution are emit-
ted into the air each year in the
United States. Approximately 146
million people live in counties
where monitored air in 2002 was
unhealthy at times because of
high levels of at least one of the
six principal air pollutants.
• The vast majority of areas that
experienced unhealthy air did so
because of one or both of two pol-
lutants—ozone and particulate
matter (PM). Important efforts to
control these pollutants include
implementing more protective
National Ambient Air Quality
Standards (NAAQS) for ozone
and PM and issuing rules to
reduce emissions from onroad
transportation and stationary
combustion sources. These rules
will bring reductions in emissions
over the next several years.
• Additional reductions will be
needed to provide clean air in the
future. For example, the Clear
Skies legislation currently being
considered in Congress would, if
enacted, mandate reductions of
particle- and ozone-forming com-
pounds from power generators by
70 percent from current levels
Comparison of 1970 and 2002 Emissions
CO NOX VOC
(-48%) (-17%) (-51%)
through a nationwide cap and
trade program. This will also
reduce acid rain and improve visi-
bility. Also, in May 2003, EPA pro-
posed nonroad diesel engine regu-
lations that would help improve
PM and ozone air quality. By 2030,
this program would reduce annu-
al emissions of PM by 95 percent,
nitrogen oxides (NOX) by 90 per-
cent, and sulfur levels by 99 per-
cent from these engines.
Of the six tracked pollutants,
progress has been slowest for
ground-level ozone. Over the past
20 years, almost all geographic
areas experienced some progress
in lowering ozone concentrations.
The Northeast and Pacific South-
west exhibited the greatest
improvement. In particular, sub-
stantial progress seen in Los
Angeles has continued through
2002. However, the national aver-
age ozone (8-hour) levels have
been fairly constant in other met-
ropolitan areas. An analysis to
adjust 8-hour ozone levels in met-
ropolitan areas to account for the
influence of meteorological condi-
tions shows the 10-year trend to
be relatively unchanged. At the
same time, for many national
S02 PM10
(-52%) (-34%)a
parks, the 8-hour ozone levels
have increased somewhat.
Ground-level ozone is not emitted
directly into the air, but is formed
in the atmosphere by the reaction
of volatile organic compounds
(VOCs) and NOX in the presence
of heat and sunlight. Emissions of
VOCs have decreased about 40
percent over the past 20 years.
However, regional-scale NOX
reductions over the same period
are only 15 percent. More NOX
reductions will be necessary
before more substantial ozone air
quality improvements are real-
ized. Some of these additional
reductions will result from exist-
ing and recently enacted NOX
emission reduction programs and
also, potentially, from the Clear
Skies legislation, if enacted.
The improvement in overall emis-
sions since 1970 included in this
year's findings reflect more accu-
rate estimates of VOC, NOX, PM,
and carbon monoxide (CO) releases
from highway vehicles and non-
road engines. Previous years'
findings underreported emissions
for cars and trucks in the 1970s
and 1980s. This year's findings
incorporate improvements in
EXECUTIVE SUMMARY
CHAPTER 1
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
EPA's mobile source emission
models, which are based on actual
emissions measurements from
thousands of motor vehicles and
have been peer-reviewed. The new
mobile model better represents
average U.S. driving habits, such
as more rapid accelerations and
faster highway speeds.
Sulfates formed primarily from
sulfur dioxide (SO2) emissions
from coal-fired power plants are a
major component of fine particles
(known as PM2 5) in the eastern
United States. SO2 emissions
decreased approximately 33 per-
cent from 1983 to 2002. Nationally,
average SO2 ambient concen-
trations have been cut approxi-
mately 54 percent over the same
period. Reductions in SO2 concen-
trations and emissions since 1990
are primarily due to controls
implemented under EPA's Acid
Rain Program. Sulfate reductions
since 1999 are partly responsible
for some improvement in ambient
fine particle concentrations, partic-
ularly in the southeastern United
States.
In many locations, EPA now has
4 years of air quality monitoring
data for PM2 R. Areas across the
Southeast, Mid-Atlantic, Midwest
regions, and California have air
quality that is unhealthy due to
particle pollution. Region-wide
emissions from power plants and
motor vehicles are among the
largest contributors to the high
PM2 5 concentrations.
Since 1990, many actions have
been taken that will significantly
reduce air toxics across the coun-
try. Specifically, regulations for
facilities such as chemical plants,
dry cleaners, coke ovens, and
incinerators will reduce emissions
of toxic air pollution by 1.5 million
tons from 1990 levels. In addition,
recent actions to address emis-
sions of toxic air pollutants from
motor vehicles as well as stringent
standards for heavy-duty trucks,
buses, and diesel fuel will elimi-
nate 95 percent of emissions of
diesel particulate matter.
• Measurements have shown that
atmospheric concentrations of
methyl chloroform are falling,
indicating that emissions have
been greatly reduced. Concentra-
tions of other ozone-depleting
substances in the upper layers of
the atmosphere, like chlorofluoro-
carbons (CFCs), are also begin-
ning to decrease.
Air Pollution
The Concern
Exposure to air pollution is associat-
ed with numerous effects on human
health, including respiratory prob-
lems, hospitalization for heart or
lung diseases, and even premature
death. Children are at greater risk
because they are generally more
active outdoors and their lungs are
still developing. The elderly and
people with heart or lung diseases
are also more sensitive to some types
of air pollution.
Air pollution can also significant-
ly affect ecosystems. For example,
ground-level ozone has been associ-
ated with reductions of agricultural
and commercial forest yields, and
airborne releases of NOX are one of
the largest sources of nitrogen pollu-
tion in certain waterbodies, such as
the Chesapeake Bay.
The Causes
Air pollution comes from many
different sources. These include large
stationary sources such as factories,
power plants, and smelters; smaller
sources such as dry cleaners and
degreasing operations; mobile
sources such as cars, buses, planes,
trucks, and trains; and natural
sources such as windblown dust and
wildfires.
Under the Clean Air Act
EPA establishes air quality standards
to protect public health, including
the health of "sensitive" populations
such as children, older adults, and
people with asthma. EPA also sets
limits to protect public welfare. This
includes protecting ecosystems,
including plants and animals, from
harm, as well as protecting against
decreased visibility and damage to
crops, vegetation, and buildings.
EPA has set national air quality
standards for six principal air pollut-
ants (also called the criteria
pollutants): nitrogen dioxide (NO2),
ozone (O3), sulfur dioxide, particu-
late matter, carbon monoxide, and
lead (Pb). Four of these pollutants
(CO, Pb, NO2, and SO2) are emitted
directly from a variety of sources.
Ozone is not directly emitted, but is
formed when NOX and VOCs react
in the presence of sunlight. PM can
be directly emitted, or it can be
formed when emissions of nitrogen
oxides, sulfur oxides, ammonia,
organic compounds, and other gases
react in the atmosphere.
Each year EPA looks at the levels
of these pollutants in the air and the
amounts of emissions from various
sources to see how both have
changed over time and to summarize
the current status of air quality.
Reporting Air Quality and
Emissions Trends
Each year, air quality trends are cre-
ated using measurements from moni-
tors located across the country. The
following table shows that the air
quality based on concentrations
CHAPTER 1
EXECUTIVE SUMMARY
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Percent Change in Air Quality
1983-2002 1993-2002
NO2
O3l-h
8-h
SO2
PM10
PM2.5
CO
Pb
-21
-22
-14
-54
—
—
-65
-94
-11
-2a
+4a
-39
-13
-8b
-42
-57
Percent Change in Emissions
1983-2002 1993-2002
NOX
voc
SO2
PM10-
PM2.5C
CO
Pbe
-15
-40
-33
-34d
—
-41
-93
-12
-25
-31
-22
-17
-21
-5
—Trend data not available.
a Not statistically significant.
b Based on percentage change from 1999.
c Includes only directly emitted particles.
d Based on percentage change from 1985.
Emission estimates prior to 1985 are uncertain.
e Lead emissions are included in the toxic air
pollutant emissions inventory and are presented
for 1982-2001.
Negative numbers indicate improvements in
air quality or reductions in emissions. Positive
numbers show where emissions have increased.
of the principal pollutants has
improved nationally over the past
20 years (1983-2002).
EPA estimates nationwide emis-
sions of ambient air pollutants and
the pollutants they are formed from
(their precursors). These estimates
are based on actual monitored read-
ings or engineering calculations of
the amounts and types of pollutants
emitted by vehicles, factories, and
other sources. Emission estimates are
based on many factors, including
levels of industrial activity, techno-
logical developments, fuel consump-
tion, vehicle miles traveled, and
other activities that cause air
pollution.
Methods for estimating emissions
continue to improve. Today's esti-
mates are different from last year's
estimates. One reason is because this
year EPA used updated, peer-
reviewed models that estimate VOC,
NOX, CO, and PM emissions from
highway vehicles and nonroad
engines and and better represent
real-world conditions, such as more
rapid accelerations and faster high-
way speeds. The emissions estimates
generated by the new highway vehi-
cle model are derived from actual
tailpipe measurements from thou-
sands of vehicles. Another change in
the reporting of emissions trends is
that emissions from wildfires and
prescribed burnings are not consid-
ered in the estimates of emission
change. This is due to the large vari-
ability in the year-to-year levels of
these emissions and the relatively
small impact these distant emissions
have on most monitoring locations.
Because of the high degree of uncer-
tainty in predicting emissions for
these fires, their emissions have not
been projected for 2002 for PM, CO,
and VOCs. These emissions will be
estimated when 2002 acres-burned
data become available. However, fire
emissions are included in the emis-
sion graphics through 2001. As a
result of these reporting changes,
some emissions trends have changed
significantly. For example, rather
than describing no change in the
20-year emission trend for CO, EPA
now estimates a 41 percent decrease
in CO emissions from 1983 to 2002.
This estimated change in emissions is
supported by the trend in CO air
quality.
Emissions of air pollutants con-
tinue to play an important role in a
number of air quality issues. About
160 million tons of pollution are
emitted into the atmosphere each
year in the United States. These
emissions mostly contribute to the
formation of ozone and particles, the
deposition of acids, and visibility
impairment.
Despite great progress in air
quality improvement, approximately
146 million people nationwide lived
in counties with pollution levels
above the NAAQS in 2002. Out of
the 230 nonattainment areas identi-
fied during the 1990 Clean Air Act
Amendments designation process,
124 areas remain. In these nonattain-
ment areas, however, the severity of
air pollution episodes has decreased.
The Clean Air Act
The Clean Air Act provides the prin-
cipal framework for national, state,
tribal, and local efforts to protect air
quality. Improvements in air quality
are the result of effective implemen-
tation of clean air laws and regula-
tions, as well as efficient industrial
technologies. Under the Clean Air
Act, EPA has a number of responsi-
bilities, including
• Conducting periodic reviews of
the NAAQS for the six principal
pollutants that are considered
harmful to public health and the
environment.
• Ensuring that these air quality
standards are met (in cooperation
with the state, tribal, and local
governments) through national
standards and strategies to control
air pollutant emissions from vehi-
cles, factories, and other sources.
• Reducing emissions of SO2 and
NOX that cause acid rain.
• Reducing air pollutants such as
PM, SOX, and NOX, which can
reduce visibility across large
regional areas, including many of
the nation's most treasured parks
and wilderness areas.
EXECUTIVE SUMMARY
CHAPTER 1
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
» Ensuring that sources of toxic air
pollutants that may cause cancer
and other adverse human health
and environmental effects are well
controlled and that the risks to
public health and the environment
are substantially reduced.
« Limiting the use of chemicals that
damage the stratospheric ozone
layer in order to prevent increased
levels of harmful ultraviolet radia-
tion.
Criteria Pollutants —
Metropolitan Area Trends
Out of 263 metropolitan statistical
areas, 34 have significant upward
trends. Of these, only those trends
involving 8-hour ozone had values
over the level of the air quality
standard.
Of the five criteria pollutants used
to calculate the Air Quality Index
(AQI), only four (CO, O3/ PM10/ and
SO2) generally contribute to the AQI
value. Nitrogen dioxide is rarely the
highest pollutant measured.
Although five criteria pollutants can
contribute to the AQI, the index is
usually driven mostly by ozone.
Criteria Pollutants —
Official Nonattainment
Areas
As of September 2002, there were a
total of 124 classified nonattainment
areas on the condensed nonattain-
ment list (see Table A-19). The areas
on the condensed list are displayed
alphabetically by state. There were,
as of September 2002, approximately
126 million people living in classified
areas designated as nonattainment
for at least one of the criteria pollut-
ants.
Air Toxics
EPA has developed a National-Scale
Air Toxics Assessment, which is a
nationwide analysis of air toxics. The
assessment uses computer modeling
of the 1996 National Emissions
Inventory (NEI) air toxics data as the
basis for developing health risk
estimates for 33 toxic air pollutants
(a subset of the Clean Air Act's list of
188 air toxics plus diesel PM). The
highest ranking 20 percent of the
counties in terms of risk (622 coun-
ties) contain almost three-fourths of
the U.S. population. Three air toxics
(chromium, benzene, and formalde-
hyde) appear to pose the greatest
nationwide carcinogenic risk. One air
toxic, acrolein, is estimated to pose
the highest potential nationwide for
significant chronic adverse effects
other than cancer.
Special Studies
For the first time, a series of policy-
relevant studies and exploratory
analyses are summarized in this
report (see Chapter 6). These studies
address analysis of PM concentra-
tions, carbon monoxide trends, the
number of days above AQI levels of
100 for the ozone NAAQS, the spa-
tial variation of air pollutants, and a
proposed new reporting technique
for air quality data. The full reports
are also included in this Special
Studies edition.
CHAPTER 1
EXECUTIVE SUMMARY
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
EXECUTIVE SUMMARY « CHAPTER 1
-------
CHAPTER
Criteria Pollutants
National Trends
This chapter presents national and
regional trends for each of the six
criteria pollutants for which the U.S.
Environmental Protection Agency
(EPA) has established National
Ambient Air Quality Standards
(NAAQS): carbon monoxide (CO),
lead (Pb), nitrogen dioxide (NO2),
ozone (O3), particulate matter (PM),
and sulfur dioxide (SO2). Table 2-1
lists the NAAQS for each pollutant
in terms of the level and averaging
time of the standard used to evaluate
compliance.
There are two types of standards:
primary and secondary. Primary
standards protect against adverse
human health effects, whereas sec-
ondary standards protect against
welfare effects such as damage to
crops, ecosystems, vegetation, and
buildings, as well as decreased visi-
bility. There are primary standards
for all of the criteria pollutants. Some
pollutants (PM and SO2) have
primary standards for both long-
term (annual average) and short-
term (24 hours or less) averaging
times. Short-term standards most
directly protect people from adverse
health effects associated with peak
short-term exposures to air pollution,
whereas long-term standards can
protect people from adverse health
effects associated with short- and
long-term exposures to air pollution.
http://www.epa.gov/oar/airtrends
Table 2-1. NAAQS in Effect as of December 2002
Primary Standard
(Health-Related)
Standard Level
Pollutant Type of Average Concentration3
CO
Pb
NO2
03
PM10
PM25
S02
8-hourb
1-hourb
Maximum
Quarterly Average
Annual
Arithmetic Mean
Maximum Daily
1-hour Average0
4th Maximum Dailyd
8-hour Average
Annual
Arithmetic Mean
24-houre
Annual
Arithmetic Meanf
24-hour9
Annual
Arithmetic Mean
24-hourb
9 ppm
(10 mg/m3)
35 ppm
(40 mg/m3)
1.5 ug/m3
0.053 ppm
(100 ug/m3)
0.12 ppm
(235 ug/m3)
0.08 ppm
(157 ug/m3)
50 ug/m3
150 ug/m3
15 ug/m3
65 ug/m3
0.03 ppm
(80 ug/m3)
0.14 ppm
(365 ug/m3)
Secondary Standard
(Welfare-Related)
Standard Level
Type of Average Concentration3
No Secondary Standard
No Secondary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
Same as Primary Standard
3-hourb 0.50 ppm
(1,300 ug/m3)
a Parenthetical value is an approximately equivalent concentration. (See 40 CFR Part 50.)
b Not to be exceeded more than once per year.
c The standard is attained when the expected number of days per calendar year with
maximum hourly average concentrations above 0.12 ppm is equal to or less than 1,
as determined according to Appendix H of the Ozone NAAQS.
d Three-year average of the annual 4th highest daily maximum 8-hour average concentration.
e The short-term (24-hour) standard of 150 ug/m3 is not to be exceeded more than once per
year on average over 3 years.
f Spatially averaged over designated monitors.
9 The form is the 98th percentile.
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Secondary standards have been
established for each criteria pollutant
except CO. Secondary standards are
identical to the primary standards,
with the exception of the one for SO2.
As Figure 2-1 shows, approximately
146 million people in the United
States reside in counties that did not
meet the primary standard for at
least one of the criteria pollutants for
the single year 2002.
Figure 2-1. Number of people living
in counties with air quality
concentrations above the
level of NAAQS in 2002.
NO
136.4
(8-hour)
An'
NAAQS
146.2
50 100 150
Millions of People
The trends information presented
in this chapter is based on two types
of data: ambient concentrations and
emissions estimates. Ambient con-
centrations are measurements of pol-
lutant concentrations in the ambient
air from monitoring sites across the
country. This year's report contains
trends data accumulated between
1983 and 2002 on the criteria pollut-
ants at thousands of monitoring sta-
tions located throughout the United
States. For some pollutants, 2002
data are provided; for other pollut-
ants (e.g., lead), 2001 data are
reported. In each case, the most
recent, complete data are used, with
the relevant years clearly noted. The
trends presented here are derived
from the composite average of these
direct measurements. The averaging
times and air quality statistics used
in the trends calculations relate
directly to the NAAQS.
The second type of data presented
in this chapter are national emissions
estimates. These are based largely
on engineering calculations of the
amounts and kinds of pollutants
emitted by automobiles, factories,
and other sources over a given
period. In addition, some emissions
estimates are based on measure-
ments from continuous emissions
monitors (CEMs) that have been
installed at major electric utilities to
measure actual emissions. The emis-
sions data summarized in this chap-
ter and in Appendix A were obtained
from the National Emission
Inventory data located at
http://www.epa.gov/ttn/chief.
Methods for estimating emissions
continue to evolve. For example, the
emissions data presented here reflect
the use of new models for estimating
volatile organic compounds (VOCs),
nitrogen oxides (NOJ, and CO emis-
sions from highway vehicles and
nonroad engines. Also, emissions
from wildfires and prescribed burn-
ing have not been projected for 2002
for PM, CO, and VOCs, due to the
high degree of uncertainty in predict-
ing emissions for these fires. For a
complete description of the method-
ology changes for calculating emis-
sions, see Appendix B.
Changes in ambient concentra-
tions do not always match changes
in national emissions estimates, for
several reasons. First, because most
monitors are positioned in urban,
population-oriented locales, air
quality trends are more likely to
track changes in urban emissions
rather than changes in total national
emissions. Urban emissions are
generally dominated by mobile
sources, whereas total emissions in
rural areas may be dominated by
large stationary sources such as
power plants and smelters.
Second, emissions for some
pollutants are calculated or meas-
ured in a different form than the
primary air pollutant. For example,
concentrations of O3 are caused by
VOC emissions as well as NOX
emissions.
Third, the amount of some pollut-
ants measured at monitoring loca-
tions depends on what chemical
reactions, if any, occur in the atmos-
phere during the time it takes the
pollutant to travel from its source to
the monitoring station.
Fourth, meteorological conditions
often control the formation and
buildup of pollutants in the ambient
air. For example, peak ozone concen-
trations typically occur during hot,
dry, stagnant summertime conditions.
CO is predominantly a cold weather
problem. Also, the amount of rainfall
can affect particulate matter levels.
Fifth, emissions estimates have
uncertainties and may not reflect actu-
al emissions. In some cases, estimation
methods are not consistent across all
years presented in this report.
For a more detailed discussion of
the methodology used to compute
the trend statistics in this chapter,
please refer to Appendix B.
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Carbon Monoxide
Air Quality Concentrations
1983-02 65% decrease
1993-02
42% decrease
Emissions
1983-02 41%
1993-02
21%
decrease
decrease
Worth Noting
• Nationally, carbon monoxide
(CO) levels for 2002 are the low-
est recorded in the past 20 years
and improvement is consistent
across all regions of the country.
• All of the original 42 areas desig-
nated nonattainment for the 8-
hour CO NAAQS in 1991 met the
CO NAAQS in 2001-2002.
• However, three additional areas
failed to meet the CO NAAQS in
2001-2002.
Nature and Sources
Carbon monoxide is a colorless and
odorless gas, formed when carbon in
fuel is not burned completely. It is a
component of motor vehicle exhaust,
which contributes about 60 percent
of all CO emissions nationwide.
Nonroad vehicles account for the
remaining CO emissions from trans-
portation sources. High concentra-
tions of CO generally occur in areas
with heavy traffic congestion. In
cities, as much as 95 percent of all
CO emissions may come from auto-
mobile exhaust. Other sources of CO
emissions include industrial process-
es, nontransportation fuel combus-
tion, and natural sources such as
wildfires. Peak CO concentrations
typically occur during the colder
months of the year when CO auto-
motive emissions are greater and
nighttime inversion conditions
(where air pollutants are trapped
near the ground beneath a layer of
warm air) are more frequent.
Health Effects
CO enters the bloodstream through
the lungs and reduces oxygen deliv-
ery to the body's organs and tissues.
The health threat from levels of CO
sometimes found in the ambient air
is most serious for those who suffer
from cardiovascular disease such as
angina pectoris. At much higher lev-
els of exposure not commonly found
in ambient air, CO can be poisonous,
and even healthy individuals may be
affected. Visual impairment, reduced
work capacity, reduced manual dex-
terity, poor learning ability, and diffi-
culty in performing complex tasks
are all associated with exposure to
elevated CO levels.
Primary Standards
There are two primary NAAQS for
ambient CO: a 1-hour average of
35 ppm and an 8-hour average of
9 ppm. These concentrations are not
to be exceeded more than once per
year. There currently are no second-
ary standards for CO.
National Air Quality Trends
Nationally, CO concentrations have
consistently declined over the past
20 years. Figure 2-2 reveals a 65 per-
cent improvement in composite
average ambient CO concentrations
from 1983 to 2002 and a 42 percent
reduction over the past 10 years.1
Following an upturn in 1994, the
nation experienced year-to-year
reductions in peak 8-hour CO
concentrations through the remain-
der of the decade. In fact, the 2002
CO levels were the lowest recorded
during the past 20 years. Exceed-
ances of the 8-hour CO NAAQS
(which are simply a count of the
number of times the level of the
standard is exceeded) have declined.
In fact, all of the original 42 areas
designated nonattainment for the
8-hour CO NAAQS in 1991 met the
CO NAAQS in 2001-2002. However,
three additional areas failed to meet
the CO NAAQS in 2001-2002. This
improvement occurred despite a 23
percent increase in vehicle miles
traveled in the United States during
the past 10 years.
Figure 2-2. CO air quality, 1983-2002, based on annual second maximum
8-hour average.
16
205 Sites
90% of sites have concentrations below this line
NAAQS
0
10% of sites have concentrations below this line
83 84 85 86 87
! 89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 65% decrease
1993-02: 42% decrease
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Long-term reductions in ambient
CO concentrations have been meas-
ured across all monitoring environ-
ments—rural, suburban, and urban
sites. Figure 2-3 shows that, on aver-
age, urban monitoring sites record
higher CO concentrations than do
suburban sites, with the lowest levels
found at four rural sites. During the
past 20 years, the 8-hour CO
concentrations decreased 44 percent
at 4 rural monitoring sites, 60 percent
at 89 suburban sites, and
63 percent at 116 urban sites.
Regional Air Quality Trends
The map in Figure 2-4 shows region-
al trends in ambient CO concentra-
tions during the past 20 years, 1982
to 2001. All 10 EPA Regions recorded
20-year improvements in CO levels
as measured by the regional compos-
ite mean concentrations. Significant
20-year concentration reductions of
50 percent or more were evidenced
across the nation.
National Emissions Trends
Figure 2-5 shows that the transporta-
tion category, composed of onroad
and nonroad sources, accounted for
82 percent of the nation's total CO
Figure 2-3. Trend in second maximum nonoverlapping 8-hour average CO
concentrations by type of location, 1982-2001.
Q.
Q.
I
o
U
1982-2001
Rural Sites 4
Suburban Sites
Urban Sites
89
116
82 83 84 85 86 87
89 90 91 92 93 94 95 96 97 98 99 00 01
Year
Figure 2-4. Trend in CO second maximum nonoverlapping 8-hour concentrations by EPA Region, 1982-2001.
9.064
1982
11.500
8.684
12.170
f 68%
7.496
f 72%
7.720
f 67%
2.639
1982 2001
f-63%
7.097
The National Trend
8.420
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ppm.
10
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
emissions in 2002. Figure 2-6 pre-
sents the broad geographic distribu-
tions of 2001 CO emissions based on
the tonnage per square mile for each
county. This visualization clearly
shows that the eastern third of the
country and the West Coast emitted
more CO (on a density basis) than
did the western two-thirds of the
continental United States. As a result
of automotive emissions control pro-
grams, CO emission have decreased
41 percent the past 20 years (1983 to
2002) and 21 percent in the past 10
years (1993 to 2002) despite a 155
percent increase in VMT since 1970
(see Figure 2-7). However, emissions
from all transportation sources have
decreased only 10 percent over the
same period, primarily due to an
increase in offroad emissions that has
offset the gains realized in reductions
of onroad vehicle emissions.
Figure 2-5. CO emissions by source
category, 2002.
Figure 2-6. Density map of 2001 CO emissions, by county.
Transportation
82%
Figure 2-7. CO emissions, 1983-2002.
• Fuel Combustion
D Transportation
200,000
180,000
160,000
(/>
o 140,000
fe 120,000
100,000
80,000
60,000
40,000
20,000
0
D Industrial Processes
D Miscellaneous D Fires
Tons Per Year/Square Mile
0.0-9.2
9.3-17
18-31
| | 32 - 70
• 71-15000
In 1985, EPA refined its methods for estimating emissions.
Fire emissions not available for 2002
83 85 93 94 95 96 97 98 99 00 01 02
1983-02: 41% decrease3
1993-02: 21% decrease
Note: Emission estimation methods and
data sources have evolved over time,
resulting in some inconsistency in esti-
mates in different years. In the methods
used for this report, the significant
changes have occurred between 1984 and
1986 and between 1995 and 1996,
although not all source types were affect-
ed. More explanation is provided in
Appendix B.
s Emissions trends data are not available
for 1983; thus, the 20-year trend was
interpolated based on emissions data for
1980 and 1985.
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
11
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 2-2 lists some of the major
milestones in the control of emissions
from automobiles, starting with the
Clean Air Act (the Act) of 1970. At
the national level, these measures,
which have led to reductions in
emissions of CO as well as other
pollutants, include establishing
national standards for tailpipe emis-
sions, new vehicle technologies, and
clean fuels programs. State and local
emissions reduction measures
include inspection and maintenance
(I/M) programs and transportation
management programs.
In the area of clean fuels, the 1990
Clean Air Act Amendments (1990
Amendments) require oxygenated
gasoline programs in several regions
of the country during the winter
months. Under the program regula-
tions, a minimum oxygen content
(2.7 percent by weight) is required in
gasoline to ensure more complete
fuel combustion.2'3 Of the 36 CO
nonattainment areas that initially
Table 2-2. Milestones in motor vehicle emission control.
1970 New Clean Air Act sets auto
emissions standards.
1971 Charcoal canisters appear to
meet evaporative standards.
1973 Emission gas recycle (EGR)
valves appear to meet NOX
standards.
1974 Fuel economy standards are set.
1975 The first catalytic converters
appear for hydrocarbon, CO.
Unleaded gas appears for use in
catalyst-equipped cars.
1981 Three-way catalysts with
onboard computers and O2
sensors appear.
1983 Inspection and maintenance
programs (I/M) programs are
established in 64 cities.
1989 Fuel volatility limits are set for
Reid Vapor Pressure (RVP).
1990 The 1990 Amendments set new
tailpipe standards.
1992 Oxyfuel introduced in cities
with high CO levels.
1993 Limits set on sulfur content
of diesel fuel.
1994 Phase-in begins of new vehicle
standards and technologies.
1995 Onboard diagnostic systems
in 1996 model-year cars.
1995 Phase I Federal Reformulated
Gasoline sales begin in worst
ozone nonattainment areas.
1998 Sales of 1999 model-year
California emissions-equipped
vehicles begin in the Northeast.
implemented the program in 1992,
15 areas participated in the program
during 2000.4
2001 Air Quality Status
The map in Figure 2-8 shows the
variations in CO concentrations
across the country in 2001. The air
quality indicator is the largest annual
second maximum 8-hour CO concen-
tration measured at any site in each
county. The bar chart to the left of
the map displays the number of peo-
ple living in counties within each
concentration range. The colors on
the map and bar chart in Figure 2-8
correspond to the colors of the con-
centration ranges displayed in the
map legend. The only areas not
meeting the 8-hour CO NAAQS in
2001-2002 are Birmingham, AL,
Calexico, CA, and Weirton, WV.
Figure 2-8. Highest second maximum nonoverlapping 8-hour average CO concentration by county, 2001.
180-
160-
140-
Concentration, ppm
I I Insufficient Data
^m 12.4-25
9.4-12.4
4.5-9.4
<4.5
NAAQS = 9 ppm
12 CRITERIA POLLUTANTS —NATIONAL TRENDS • CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Lead
Air Quality Concentrations
1983-02 94% decrease
1993-02
57% decrease
Emissions
1982-02 93%
1993-02
5%
decrease
decrease
Worth Noting
• The lead (Pb) monitoring strategy
now focuses on emissions from
point sources since large reduc-
tions in long-term Pb emissions
from transportation sources have
occurred due to phase-out of
leaded gasoline.
Nature and Sources
In the past, automotive sources were
the major contributor of lead emis-
sions to the atmosphere. As a result
of EPA's regulatory efforts to reduce
the content of lead in gasoline, how-
ever, the contribution of air emis-
sions of lead from the transportation
sector, and particularly the automo-
tive sector, has greatly declined over
the past two decades. Today, indus-
trial processes, primarily metals pro-
cessing, are the major source of lead
emissions to the atmosphere. The
highest air concentrations of lead are
usually found in the vicinity of
smelters and battery manufacturers.
Health and Environmental Effects
Exposure to lead occurs through
ingestion of lead in food, water, soil,
or dust and through inhalation. It
accumulates in the blood, bones, and
soft tissues. Lead can also adversely
affect the kidneys, liver, nervous
system, and other organs. Excessive
exposure to lead may cause neurolog-
ical impairments such as seizures,
mental retardation, and/or behavioral
disorders. Even at low doses, Pb
exposure is associated with changes
in fundamental enzymatic, energy
transfer, and homeostatic mechanisms
in the human body. Additionally,
even low levels of Pb exposure may
cause central nervous system damage
in fetuses and children. Recent studies
show that neurobehavioral changes
may result from Pb exposure during
the child's first years of life and that
lead may be a factor in high blood
pressure and subsequent heart
disease.
Airborne lead can also have
adverse impacts on the environment.
Wild and domestic grazing animals
may ingest lead that has deposited on
plant or soil surfaces or that has been
absorbed by plants through leaves or
roots. Animals, however, do not
appear to be more susceptible or
more sensitive to adverse effects from
lead than are humans. Therefore, the
secondary standard for lead is identi-
cal to the primary standard.
At relatively low concentrations
(2-10 ug/m3), lead can inhibit plant
growth and result in a shift to more
tolerant plant species growing near
roadsides and stationary source emis-
sions. Although the majority of soil
lead becomes bound so that it is insol-
uble, immobile, and biologically
unavailable, elevated soil Pb concen-
trations have been observed to cause
shifts in the microbial community
(fungi and bacteria), reduced
numbers of invertebrates, and
reduced decomposition and nitrifica-
tion rates and has altered other soil
parameters. Because lead remains in
the soil, soil concentrations continue
to build over time, even when deposi-
tion rates are low. Thus, another con-
cern is that acid precipitation may be
increasing the mobility and bioavail-
ability of soil lead in some places.
Lead enters water systems mainly
through urban runoff, sewage efflu-
ents, and industrial waste streams.
Most of this lead is rapidly com-
plexed and bound in the sediment.
However, water Pb concentrations
can reach levels that are associated
with increased mortality and
impaired reproduction in aquatic
invertebrates and blood and neuro-
logical changes in fish. Because of
these effects, there continue to be
implications for the long-term impact
of lead on ecosystem function and
stability. (See also Chapter 5 in this
report as well as the December 1990
Office of Air Quality Planning and
Standards Staff Paper [EPA-450/2-89-
022].)
Primary and Secondary
Standards
The primary as well as secondary
NAAQS for lead is a quarterly aver-
age concentration not to exceed
1.5 ug/m3.
National Air Quality Trends
The statistic used to track ambient
lead air quality is the maximum quar-
terly mean concentration for each
year. From 1982 to 2001, a total of 39
ambient Pb monitors met the trends
data completeness criteria, and a total
of 96 ambient Pb monitors met the
trends data completeness criteria for
the 10-year period from 1992 to 2001.
Point-source-oriented monitoring
data were omitted from all ambient
trends analysis presented in this sec-
tion to avoid masking the underlying
urban trends.
Figure 2-9 indicates that between
1993 and 2002, maximum quarterly
average Pb concentrations decreased
57 percent at population-oriented
monitors. Between 1999 and 2002,
national average Pb concentrations
(approaching the minimum detect-
able level) remained unchanged.
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
13
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
The effect of the conversion to
unleaded gasoline usage in vehicles
on ambient Pb concentrations is most
evident when viewed over a longer
period, such as that illustrated in
Figure 2-9. Between 1983 and 2002,
ambient monitor data indicate that
concentrations of lead declined 94
percent. This large decline tracks
well with overall Pb emissions,
which also declined approximately
93 percent between 1983 and 2002.
Figure 2-10 examines urban, rural,
and suburban 20-year trends sepa-
rately. The overall downward trend
in Pb concentrations can be noted for
all locations from 1982 to 2001.
National Emission Trends
For stationary sources, Pb emissions
for past trends reports have been
estimated for fuel combustion and
industrial sources based on current
data for national activity, but with
emission factor and control efficiency
estimates that have not been updated
with any new information in many
years. When gasoline contained lead,
mobile sources were by far the largest
contributor to Pb emissions, and
approximations for stationary sources
did not introduce much uncertainty
into the understanding of the total
emissions trend. Now, most lead is
emitted by industrial facilities, partic-
ularly by primary and secondary
metals processing plants. Moreover,
many of these facilities have been the
focus of control and compliance
efforts in recent years. There are also
some issues of possible double count-
ing and inventory gaps.
For example, about 10 percent of
Pb emissions estimated in previous
reports were from miscellaneous fuel
combustion, the only element of
which is the combustion of used
motor oil containing lead picked up
from gasoline. This estimate should
be viewed with caution, as the reduc-
tion factor of 90 percent used for this
source category to reflect the end of
leaded gasoline for highway use
seems inconsistent with a much
greater reduction factor used for
exhaust emissions from vehicles.
Also, the emission estimates for the
sources that burn this fuel (e.g.,
cement kilns) may double count some
of the Pb emissions. Conversely, the
estimate of zero Pb emissions from
nonroad gasoline engines is inconsis-
tent with the assumption for highway
vehicles that cross-contamination
with leaded aviation gasoline causes
unleaded fuel to still have small
amounts of Pb content on average.
Aviation gasoline is not regulated for
Pb content and can use significant
amounts of lead to comply with
octane requirements.
EPA believes that the uncertainties
in the past top-down approach for
Figure 2-9. Pb air quality, 1983-2002, based on annual maximum quarterly average.
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0
- 90% of sites have concentrations below this line
42 Sites
NAAQS
83 84 85 86 87
10% of sites have concentrations below this line
89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 94% decrease
1993-02: 57% decrease
Figure 2-10. Maximum quarterly mean Pb concentration trends by location
(excluding sites designated as point-source oriented), 1982-2001.
Q.
Q.
C
o
c
o
U
.b
.7
.6
.5
.4
.3
.2
.1
_ \ Rural Sites
', Suburban Site
t ~ ~
^ \ Urban Sites
N ,
~~ — "\ *
t
\i
\\
\
\
NS% ^
\ %
x \%
S.^ X ~ •~"~"Z"*--.
1 1 I 1 1 I I 1 I 1 1 1 T^^^^*^^^^
1982-2001
2
s 18
18
-'"-' --*.- :
— i — i — i — H
82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01
Year
14
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
fuel combustion and industrial
sources are greater than the actual
year-to-year variation in emissions.
Consequently we have not repeated it
for this report. The Pb emission esti-
mates for these sources presented
here are the same as in the 2999
National Air Quality and Emissions
Trends Report, with the previous
estimates for 2000 repeated for 2001.
Lead emissions for transportation
sources have been adjusted for
activity changes.
The preferred approach for esti-
mating Pb emissions is to make facil-
ity-specific estimates for the source
types with significant emissions,
reflecting the best information on fuel
and ore Pb content, control equip-
ment, and throughput. Ideally, emis-
sion tests would be conducted. For
the single year of 1996, EPA collected
as many such estimates as possible
from state/local air agencies, the
Toxics Release Inventory, and from
EPA studies in preparation for the
promulgation of emission standards.
A comparison of these estimates to
the earlier top-down estimates sug-
gests that Pb emissions from
coal-fired utilities may have been
higher in 1996 than stated in this
report, depending on whether a few
states have correctly estimated such
emissions. Emissions of lead from
other industrial sources in 1996 were
somewhat lower than reported in this
document for that year.
Regardless of these uncertainties,
the long-term trend in Pb emissions is
very clear. Because of the phase-out of
leaded gasoline, Pb emissions (and
concentrations) decreased sharply
during the 1980s and early 1990s.
There was an approximate decrease in
Pb emissions of 93 percent from 1982
to 1991. Figure 2-11 indicates that total
Pb emissions have stayed about the
same from 1991 on. The large ambient
and emission reductions in lead going
from 1982 to 1991 can be largely
attributed to the phasing out of lead-
ed gasoline for automobiles. Relative
to levels in the 1970s, Pb emissions in
the past 10 years have been essential-
ly constant.
Figure 2-12 shows that industrial
processes were the major source of Pb
Figure 2-11. Pb emissions, 1982-2002.
emissions in 2001, accounting for 78
percent of the total. The transporta-
tion sector (which includes both
onroad and nonroad sources) now
accounts for only 12 percent of the
total 2001 Pb emissions, with most of
that coming from aircraft.
80,000
• Fuel Combustion D Industrial Processes
D Transportation
In 1985, EPA refined its methods for estimating emissions.
85
92 93 94 95 96 97 98 99 00 01 02
1982-02: 93% decrease3
1993-02: 5% decrease
Note: Emission estimation methods and data sources have evolved over time, resulting in
some inconsistency in estimates in different years. In the methods used for this report, the
significant changes have occurred between 1984 and 1986, and between 1995 and 1996,
although not all source types were affected. More explanation is provided in Appendix B.
a Emissions trends data are not available for 1982; thus, the 20-year trend was interpolated
based on emissions data for 1980 and 1985.
Figure 2-12. Pb emissions by source
category, 2001.
Transportation
12%
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
15
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Regional Trends
Figure 2-13 segregates the ambient
trend analysis by EPA Region.
Although most Regions showed large
concentration reductions between
1982 and 2001, there were some inter-
mittent upturns, including a rather
large upturn in the Region 1 trends
plot. Most of these "bumps" in the
trends graphs can be attributed to
the inherent variability and noise
associated with data reported near
minimum detectable levels.
2001/2002 Air Quality Status
The large reductions in long-term
Pb emissions from transportation
sources have changed the nature of
the ambient Pb problem in the United
States. Because industrial processes
are now responsible for all violations
of the Pb standard, the Pb monitoring
strategy currently focuses on emis-
sions from these point sources.
The map in Figure 2-14 shows the
highest quarterly mean Pb concentra-
tion by county in 2001. One area, with
a total population of 201,219, contain-
ing some of the point sources identi-
fied in Figure 2-14 did not meet the
Pb NAAQS in 2001.
Figure 2-13. Trend in Pb maximum quarterly mean concentration by EPA Region, 1982-2001.
.623
.388
1982
.728
•f-92%
.772
.283
^•96%
•f-90%
.576
.723
The National Trend
.634
.011
1982
2001
f-98%
Note: These trends
are influenced by the
distribution of monitoring
locations in a given
Region and, therefore,
can be driven largely by
urban concentrations.
They are thus not indica-
tive of background
regional concentrations.
No data were available for
Regions 1 and 8.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ug/m3.
Figure 2-14. Highest Pb maximum quarterly mean by county, 2001.
801
70-
60-
50-
.S 40-
& 30-
20-
10-
Concentration, u
^m a3.04
1.54-3.04
0.75-1.54
<0.75
NAAQS = 1.5
16 CRITERIA POLLUTANTS —NATIONAL TRENDS • CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Nitrogen Dioxide
Air Quality Concentrations
1983-02 21% decrease
1993-02
11% decrease
Emissions
1983-02 15%
1993-02
12%
decrease
decrease
Worth Noting
• Over the past 20 years, nitrogen
dioxide (NO2) concentrations
across the country have
decreased significantly.
• All areas of the country that once
violated the national air quality
standard for NO2 now meet that
standard.
Nature and Sources
Nitrogen dioxide is a reddish-brown,
highly reactive gas that is formed in
the ambient air through the oxida-
tion of nitric oxide (NO). Nitrogen
oxides (NOX), the term used to
describe the sum of NO, NO2, and
other oxides of nitrogen, play a
major role in the formation of ozone
in the atmosphere through a complex
series of reactions with VOCs. A vari-
ety of NOX compounds and their
transformation products occur both
naturally and as a result of human
activities. Anthropogenic (i.e., man-
made) emissions of NOX account for
a large majority of all nitrogen inputs
to the environment. The major
sources of anthropogenic NOX emis-
sions are high-temperature combus-
tion processes, such as those occur-
ring in automobiles and power
plants. Most NOX from combustion
sources (about 95 percent) are emit-
ted as NO; the remainder are largely
NO2. Because NO is readily convert-
ed to NO2 in the environment, the
emissions estimates reported here
assume nitrogen oxides are in the
NO2 form. Natural sources of NOX
are lightning, biological and abiologi-
cal processes in soil, and stratospher-
ic intrusion. Ammonia and other
nitrogen compounds produced natu-
rally are important in the cycling of
nitrogen through the ecosystem.
Home heaters and gas stoves also
produce substantial amounts of NO2
in indoor settings.
Health and Environmental
Effects
Nitrogen dioxide is the most wide-
spread and commonly found nitro-
gen oxide and is a matter of public
health concern. The most troubling
health effects associated with short-
term exposures (i.e., less than
3 hours) to NO2 at or near the ambi-
ent NO2 concentrations seen in the
United States include cough and
increased changes in airway respon-
siveness and pulmonary function in
individuals with preexisting respira-
tory illnesses, as well as increases in
respiratory illnesses in children 5 to
12 years old.5'6 Evidence suggests
that long-term exposures to NO2
may lead to increased susceptibility
to respiratory infection and may
cause structural alterations in the
lungs.
Atmospheric transformation of
NOX can lead to the formation of
ozone and nitrogen-bearing particles
(e.g., nitrates and nitric acid). As dis-
cussed in the ozone and particulate
matter sections of this chapter, expo-
sure to both PM and O3 is associated
with adverse health effects.
Nitrogen oxides contribute to a
wide range of effects on public wel-
fare and the environment, including
global warming and stratospheric
ozone depletion. Deposition of nitro-
gen can lead to fertilization, eutroph-
ication, or acidification of terrestrial,
wetland, and aquatic (e.g., fresh
water bodies, estuaries, and coastal
water) systems. These effects can
alter competition between existing
species, leading to changes in the
number and type of species (compo-
sition) within a community. For
example, eutrophic conditions in
aquatic systems can produce explo-
sive algae growth leading to a deple-
tion of oxygen in the water and/or
an increase in levels of toxins harm-
ful to fish and other aquatic life.
Primary and Secondary
Standards
The level for both the primary and
secondary NAAQS for NO2 is 0.053
ppm annual arithmetic average
(mean), not to be exceeded. In this
report, the annual arithmetic average
(mean) concentration is the metric
used to evaluate and track ambient
NO2 air quality trends.
National Air Quality Trends
Since 1983, monitored levels of NO2
have decreased 21 percent.7 These
downward trends in national NO2
levels are reflected in all regions of
the country. Nationally, average NO2
concentrations are well below the
NAAQS and are currently at the low-
est levels recorded in the past 20
years. All areas of the country that
once violated the NAAQS for NO2
now meet that standard. Over the
past 20 years, national emissions of
NOX have declined by almost 15 per-
cent. Annual mean NO2 concentra-
tions declined in the early 1980s,
were relatively unchanged during
the mid-to-late 1980s, and resumed
their decline in the 1990s. Figure 2-15
shows that the national composite
annual mean NO2 concentration in
2002 is 11 percent lower than that
recorded in 1993. Except for 1994 and
1999, NO2 concentrations have
decreased, or remained unchanged,
each year since 1989.
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
17
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-16 reveals how the trends Figure 2-15. NO2 air quality, 1982-2001, based on annual arithmetic average.
in annual mean NO2 concentrations
vary among rural, suburban, and
urban locations. The highest annual
mean NO2 concentrations are typi-
cally found in urban areas, with
significantly lower annual mean
concentrations recorded at rural sites.
Interestingly, as the nation has
experienced these significant
decreases in NO2 concentrations,
NOX emissions are increasing, as
described in more detail later in this
section of the chapter. One possible
explanation involves the location of
the majority of the nation's NO2
monitors. Most NO2 monitoring sites
are mobile-source-oriented sites in
urban areas, and the 20-year decline
in ambient NO2 levels closely tracks
the 19 percent reduction in emissions
from gasoline-powered vehicles over
the same time period.
Regional Air Quality Trends
The map in Figure 2-17 provides
regional trends in NO2 concentra-
tions during the past 20 years, 1982
to 2001 (except Region 10, which
does not have any NO2 trend sites).
The trends seen in the suburban and
urban sites track the declining trend
in NOX emissions, as compared with
the trend in rural sites. The trends
statistic is the regional composite
mean of the NO2 annual mean con-
centrations across all sites with at
least 8 years of ambient measure-
ments. The largest reductions in NO2
concentrations occurred in the south
coast of California and parts of the
Northeast and Mid-Atlantic states.
Slightly smaller reductions in mean
NO2 concentrations were recorded in
New England, the Southeast, and the
Southwest. Interestingly, NO2 con-
centrations were unchanged in the
Midwest states and have actually
increased in the North Central states.
Q.
Q.
c
0)
o
c
o
U
0.06
0.05
0.04
0.03
0.02
0.01
0.0
125 Sites
90% of sites have concentrations below this line
NAAQS
10% of sites have concentrations below this line
83 84 85 86 87
89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 21% decrease
1993-02: 11% decrease
Figure 2-16. Trend in annual mean NO2 concentrations by type of location, 1982-2001.
.030
.025
.020
C
O
to .015
c
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-17. Trend in NO2 maximum quarterly mean concentration by EPA Region, 1982-2001.
.030
.023
.023
1982
.023
2001
1982
2001
.026
.021
0%
.029
.021
1982
2001
The National Trend
.025 .019
1982 2001
f- 24%
f 23%
8%
.026
.025
.018
.015
.019
1982 2001
•f-17%
f 24%
.018
.015
.019
1982
.015
— —
2001
1982 2001
f-17%
21%
.024
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ppm.
This increase coincides with increas-
es in NOX emissions from transporta-
tion (both onroad and nonroad) as
well as power plants in selected
states with NO2 monitors in these
areas.
National Emissions Trends
The reduction in emissions for NOX
shown in Figure 2-18 differs from the
increase in NOX emissions reported
in previous editions of this report.
These emission trends reflect new
and improved emission estimates for
highway vehicles and nonroad
engines. While NOX emissions are
declining overall, emissions from
some sources such as nonroad
engines have actually increased since
1983. These increases are of concern
given the significant role NOX emis-
sions play in the formation of
ground-level ozone (smog) as well as
other environmental problems like
acid rain and nitrogen loadings to
Figure 2-18. NOX emissions, 1983-2002.
o
.c
GO
30,000
25,000
20,000
15,000
c
CO
Ift
o 10,000
5,000
83
• Fuel Combustion
D Transportation
D Industrial Processes
D Miscellaneous
In 1985, EPA refined its methods for estimating emissions.
85
93 94 95 96 97 98 99 00 01 02
1983-02: 15% decrease3
1993-02: 12% decrease
Note: Emission estimation methods and data sources have evolved over time, resulting in
some inconsistency in estimates in different years. In the methods used for this report, the
significant changes have occurred between 1984 and 1986, and between 1995 and 1996,
although not all source types were affected. More explanation is provided in Appendix B.
! Emissions trends data are not available for 1983; thus, the 20-year trend was interpolated
based on emissions data for 1980 and 1985.
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
19
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
waterbodies described above. In
response, EPA has proposed regula-
tions that will significantly control
NOX emissions from nonroad diesel
engines.
Figure 2-19 indicates that the two
primary sources of NOX emissions
are transportation and stationary
source fuel combustion. Together,
these two sources make up 93 per-
cent of 2002 total NOX emissions.
Emissions from transportation
sources have decreased 15 percent
over the past 20 years and decreased
5 percent during the past 10 years.
For both light-duty gasoline vehicles
and light-duty gasoline trucks, NOX
emissions peaked in 1994 and then
began a steady decrease through
2000. This decrease can be attributed
primarily to the implementation of
the Tier 1 emission standards that
lowered NOX emissions from new
cars and light-duty trucks. In con-
trast, NOX emissions from heavy-
duty vehicles, both gasoline and
diesel, decreased significantly over
the 10-year period (17 percent
Figure 2-19. NOX emissions by source
category, 2002.
Miscellaneous
2%
Transportation
56%
decrease for gasoline and 12 percent
increase for diesel). A portion of this
increase is due to the increase in
VMT for these categories for heavy-
duty gasoline vehicles and diesel
trucks. In addition, emissions from
heavy-duty diesel vehicles increased
over this period due to the identifica-
tion of "excess emissions" in many
diesel vehicles. These excess emis-
sions peaked in 1998, and emissions
of heavy-duty diesel vehicles are
now declining. New emission stan-
dards will lead to further reductions
in emissions from heavy duty vehi-
cles in the future. Further, emissions
from nonroad vehicles, particularly
those fueled with diesel, have steadi-
ly increased over the last 10 years.
EPA is developing new standards to
reduce these emissions.
Reductions in NOX emissions
from fuel combustion, particularly
those from electric power generator
units in the past 2 years, have par-
tially offset the impact of increases in
the transportation sector. Emissions
from these generator units in 2001
were 5 percent lower than they were
in 2000. The Acid Deposition Control
provisions of the Act (Title IV)
required EPA to establish NOX annu-
al emission limits for coal-fired elec-
tric utility units in two phases, result-
ing in NOX reductions of approxi-
mately 400,000 tons per year during
Phase I (1996-1999) and 2 million
tons per year in Phase II (year 2000
and subsequent years).8
Figure 2-20 shows the geographic
distribution of 2001 NOX emissions
based on the tonnage per square mile
for each county. This map illustrates
that the eastern half of the country
and the West Coast emit more NOX
(on a density basis) than does the
western half of the continental
United States.
2001 Air Quality Status
All monitoring locations across the
nation met the NO2 NAAQS in 2001.
This is reflected in Figure 2-21, which
displays the highest annual mean
NO2 concentration measured in each
county.
20
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-20. Density map of 2001 NO2 emissions, by county.
Tons Per Year/Square Mile
| | 0.0-1.5
I I 1.6-3.0
I I 3.1-5.6
I | 5.7-13
I I 14-2200
Figure 2-21. Highest NO2 maximum quarterly mean by county, 2001.
160-1
140-
120-
100-
° 80-
S. 60-
40-
20-
Concentration (ppm)
I I Insufficient Data
0.0275-0.041
0-0.0275
CHAPTER 2 • CRITERIA POLLUTANTS —NATIONAL TRENDS 21
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Ozone
Air Quality Concentrations
1983-02 22% decrease (1-hr)
14% decrease (8-hr)
1993-02 2% decrease (1-hr)
4% increase (8-hr)
Emissions (Anthropogenic VOCs)
1983-02 40% decrease
1993-02
25% decrease
Worth Noting
• Over the past 20 years, ozone (O3)
levels (1-hour and 8-hour) have
improved considerably nationwide.
• However, over the past 10 years,
ozone levels (1-hour and 8-hour)
have been relatively flat.
Nature and Sources
Ground-level O3 remains a pervasive
pollution problem in the United
States. Ozone is readily formed in the
atmosphere by the reaction of VOCs
and NOX in the presence of heat and
sunlight, which are most abundant in
the summer. VOCs are emitted from
a variety of sources, including motor
vehicles, chemical plants, refineries,
factories, consumer and commercial
products, other industries, and
natural (biogenic) sources. Nitrogen
oxides (a precursor to ozone) are
emitted from motor vehicles, power
plants, and other sources of combus-
tion, as well as natural sources
including lightning and biological
processes in soil. Changing weather
patterns contribute to yearly differ-
ences in O3 concentrations. Ozone
and the precursor pollutants that
cause O3 also can be transported into
an area from pollution sources locat-
ed hundreds of miles upwind.
Health and Environmental
Effects
Ozone occurs naturally in the
stratosphere and provides a protec-
tive layer high above the Earth.
However, at ground level, it is the
prime ingredient of smog. Short-term
(1- to 3-hour) and prolonged (6- to
8-hour) exposures to ambient O3
concentrations have been linked to a
number of health effects of concern.
For example, increased hospital
admissions and emergency room
visits for respiratory causes have
been associated with ambient O3
exposures.
Exposures to O3 result in lung
inflammation, aggravate preexisting
respiratory diseases such as asthma,
and may make people more suscepti-
ble to respiratory infection. Other
health effects attributed to short-term
and prolonged exposures to O3,
generally while individuals are
engaged in moderate or heavy exer-
tion, include significant decreases in
lung function and increased respira-
tory symptoms such as chest pain
and cough. Children active outdoors
during the summer when O3 levels
are at their highest are most at risk
of experiencing such effects. Other
at-risk groups include adults who
are active outdoors, such as outdoor
workers, and individuals with pre-
existing respiratory disorders such as
asthma and chronic obstructive lung
disease. Within each of these groups
are individuals who are unusually
sensitive to O3. In addition, repeated
long-term exposure to O3 presents
the possibility of irreversible changes
in the lungs, which could lead to
premature aging of the lungs and/or
chronic respiratory illnesses.
Ozone also affects sensitive vegeta-
tion and ecosystems. Specifically, O3
can lead to reductions in agricultural
and commercial forest yields, reduced
survivability of sensitive tree
seedlings, and increased plant suscep-
tibility to disease, pests, and other
environmental stresses such as harsh
weather. In long-lived species, these
effects may become evident only after
several years or even decades. As
these species are out-competed by
others, long-term effects on forest
ecosystems and habitat quality for
wildlife and endangered species
become evident. Furthermore, O3
injury to the foliage of trees and other
plants can decrease the aesthetic
value of ornamental species as well as
the natural beauty of our national
parks and recreation areas.
Primary and Secondary 1-hour
Ozone Standards
In 1979, EPA established 1-hour
primary and secondary standards for
O3. The level of the 1-hour primary
and secondary O3 NAAQS is 0.12
ppm daily maximum 1-hour concen-
tration that is not to be exceeded
more than once per year on average.
Primary and Secondary 8-hour
Ozone Standards
On July 18,1997, EPA strengthened
the O3 NAAQS based on the latest
scientific information showing
adverse effects from exposures
allowed by the then-existing stand-
ards. The standard was set in terms
of an 8-hour averaging time.9
Refer to http://www.epa.gov/
airlinks for up-to-date information
concerning actions surrounding the
revised standards.
Air Quality Trends
Because the 1-hour and 8-hour
NAAQS have different averaging
times and forms, two different statis-
tics are used in this report to track
22
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
ambient O3 air quality trends. For the
1-hour O3 NAAQS, this report uses
the composite mean of the annual
second-highest daily maximum 1-
hour O3 concentration as the statistic
to evaluate trends. For the 8-hour O3
NAAQS, this report relies on the
annual fourth-highest 8-hour daily
maximum O3 concentration as the
statistic of interest to assess trends.
National Air Quality Trends
Figure 2-22 clearly shows that, over
the past 20 years, peak 1-hour O3
concentrations have declined consid-
erably at monitoring sites across the
country. From 1983 to 2002, national
1-hour O3 levels improved 22 per-
cent, with 1983,1988, and 1995 repre-
senting peak years for this pollutant.
Figure 2-22 shows that 370 sites met
the data completeness criteria over
the past 20 years (1983-2002). It is
important to interpret such long-
term, quantitative ambient O3 trends
carefully given changes in network
design, siting criteria, spatial cover-
age, and monitoring instrument cali-
bration procedures during the past
two decades. More recently, national
1-hour O3 levels have continued to
improve, but the progress has been
less rapid, as evidenced by the
2 percent decrease from 1993 to 2002.
Figure 2-23 shows the national
trend in 8-hour O3 concentrations
across the same sites used to estimate
the national 1-hour O3 trends.
Nationally, 8-hour levels have
decreased 14 percent over the last
20 years. However, just as is true for
the 1-hour levels, the progress in
8-hour O3 levels over the last 10
years has slowed and actually shows
a 4 percent increase in national levels
between 1993 and 2002. Standard sta-
tistical tests applied to the 10-year
trends for both 1-hour and 8-hour
ozone shows that these trends are
not statistically significant. Ozone
Figure 2-22. O3 air quality, 1983-2002, based on annual second maximum
1-hour average.
0.20
0.15
Q.
Q.
g
'ra 0.10
0)
o
c
o
O
0.05
0.00
90% of sites have concentrations below this line
370 Sites
Average
f
NAAQS
10% of sites have concentrations below this line
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 22% decrease
1993-02: 2% decrease
Figure 2-23. O3 air quality, 1983-2002, based on annual fourth maximum 8-hour
average.
0.20
0.15
Q.
Q.
g
to 0.10
c
0)
u
c
o
O
0.05
0.00
370 Sites
90% of sites have concentrations below this line
Average f
NAAQS
10% of sites have concentrations below this line
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 14% decrease
1993-02: 4% increase
CHAPTER 2 • CRITERIA POLLUTANTS —NATIONAL TRENDS 23
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
concentrations varied over this 10-year
period from year to year but did not
change overall.The trend in the 8-
hour O3 statistic is similar to the
trend in the 1-hour values, although
the concentration range is smaller.
Regional Air Quality Trends
The map in Figure 2-24 examines
trends in 1-hour O3 concentrations
during the past 20 years by geo-
graphic region of the country. The
1-hour O3 levels in all areas of the
country have generally followed the
pattern of declining trends since
1982 similar to that of the national
observations. However, the magni-
tude of improvement has not been
consistent across all regions.
Figure 2-24. Trend in 1-hour O3 levels, 1983-2002, averaged across EPA Regions, based on annual second highest daily maximum.
.175
.083
10
.146
1983
.168
2002
0%
1983 2002
f- 39%
The National Trend
138 .108
1983 2002
f 22%
.119 .095
1983 2002
f- 20%
8 -109.
1983
6 -13°
1983
t
1OQQ onm i(
.128
ST
7
.092
2002
f- 1 2%
.108
v_
2002
17%
.107 ^N*.
^^^^^ — ^^
2002
+ 1 6%
5
.120
1983
4 *
f 1 8%
.135 •
1983
fl1%
.102
2002
15%
^
f 25%
2
/
.120 ;
2002 I'
C
t
C
[
[
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ppm.
Figure 2-25. Trend in 8-hour O3 levels, 1983-2002, averaged across EPA Regions, based on annual fourth maximum 8-hour average.
.058
.111
10
.059
2%
.079
29%
.124
.098
.083
.075
The National Trend
.100 .086
2002
1 4%
1983 2002
f 10%
8
.096
21%
.083
.080
1983 2002
*4%
.092
.082
1983 2002
f-11%
-1°5
-097
.092
.083
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ppm.
24
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Similarly, Figure 2-25 portrays
8-hour O3 trends by geographic
region of the country. Again, most
areas of the country show 20-year air
quality improvements (with respect
to 8-hour O3) consistent with the
national trend, with the most signifi-
cant improvements occurring in the
Northeast and Pacific Southwest.
The Pacific Northwest region showed
a slight increase in the 8-hour ozone
for the period 1983-2002.
In Figure 2-26, the national 1-hour
O3 trend is disaggregated to show
the 20-year change in ambient O3
concentrations among rural, subur-
ban, and urban monitoring sites. The
highest ambient O3 concentrations
are typically found at suburban sites,
consistent with the downwind trans-
port of emissions from the urban
center. During the past 20 years, O3
concentrations decreased by approxi-
mately 23 percent at suburban sites,
and 26 percent at urban sites. At
rural sites, 1-hour O3 levels for 2002
are approximately 16 percent lower
than they were in 1983 and, for the
sixth consecutive year, are greater
than the level observed for urban
sites.
Urban Area Air Quality Trends
It is important to note that year-to-
year changes in ambient ozone
trends are influenced by meteorolog-
ical conditions, population growth,
and changes in emission levels of
ozone precursors (i.e., VOCs and
NOX) resulting from ongoing control
measures. For example, to further
evaluate the 10-year 8-hour ozone
trends, EPA applied a model to the
annual rate of change in ozone based
on measurements in 53 metropolitan
areas (Figure 2-27). This model
adjusted the ozone data in these
areas to account for the influence of
local meteorological conditions,
including surface temperature and
windspeed. Figure 2-27 shows the
aggregated trend in 8-hour ozone for
these 53 areas adjusted for meteoro-
logical conditions for the 10-year
period 1993-2002. The figure also
shows the aggregated trend for these
areas unadjusted for meteorology
and the national average in 8-hour
ozone. From this figure, the
meteorologically adjusted trend for
this 10-year period can be seen as
relatively flat.
EPA's analysis of ambient ozone
concentration data indicates that
ozone concentrations are on the
increase in some urban areas. These
increases are evident based on both
1-hour and 8-hour trends, as shown
Figure 2-26. Trend in annual second-highest daily maximum 1-hour O3 concentrations
by location, 1983-2002.
0.16
0.14
E 0.12
°~ 0.10
0.08
§ 0.06
c
0 0.04
0.02
0.00
NAAQS
— Rural
- - - Suburban
Urban
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
Year
Figure 2-27. Comparison of actual and meteorologically adjusted 8-hour O3 trends,
1993-2002.
Concentration, ppm
o o o o c
b b b b P -
o rv3 j=» 01 co — > r\
A * — ~~A dr— *~ , ^- •*•
-H- Selected Area Trend in Average Daily Maximum 8-Hour Concentrations
— -^- Meteorologically Adjusted Trend in Average Daily Maximum 8-Hour Concentrations
-A- National Trend in Annual 4th Maximum 8-Hour Concentrations
I I I I I I I I I I
93 94 95 96 97 98 99 00 01 02
Year
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
25
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
in Figures 2-28 and 2-29. Ozone urban areas on the West Coast and in trends over two consecutive 10-year
concentrations are on the increase in New England generally show time frames. The 1-hour trends show
several cities in the southeastern and decreasing trends. Figures 2-28 and an increasing number of cities with
midwestern United States, while 2-29 show a comparison of ozone upward ozone trends in the western
Figure 2-28. 1 -Hour O3 trends for 1991-2000 and 1992-2001.
Increasing
Decreasing
Not Significant
26 CRITERIA POLLUTANTS —NATIONAL TRENDS • CHAPTER 2
-------
and mid-Atlantic urban areas and a
decreasing number of cities with
upward ozone trends in the South-
east. The 8-hour ozone trends also
show a decrease in the number of
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
cities with upward ozone trends in Trends at PAMS Sites
the Southeast, but an increasing Photochemical Assessment
number of cities with upward trends Monitoring Program Stations (PAMS)
in New England and around the are operated by states in areas that
Great Lakes. were originally classified as extreme,
Figure 2-29. 8-Hour O3 trends for 1991-2000 and 1992-2001.
Increasing
Decreasing
Not Significant
CHAPTER 2 • CRITERIA POLLUTANTS —NATIONAL TRENDS 27
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
severe, or serious nonattainment for
ozone. Ozone, ozone precursor, and
surface and upper air meteorological
conditions are monitored at PAMS
sites during the summer months
when meteorological conditions are
most conducive to ozone formation.
Some PAMS sites have been in oper-
ation since 1994 and there are now
sufficient data available to examine
long-term air quality trends. Trends
in total nonmethane organic com-
pounds (TNMOC), NOX, and select-
ed VOC species at PAMS locations
are tabulated in Table 2-3; median
percent changes are illustrated in
Figure 2-30. These trends are for
concentrations averaged over the
hours from 6 to 9 a.m. when ozone
precursor concentrations are typical-
ly at their maximum and best repre-
sent the influence of fresh, local
emissions. VOC species were select-
ed for inclusion in this analysis
based primarily on relative abun-
dance and status as a hazardous air
pollutant under the Clean Air Act.
Trends in other VOC species moni-
tored under the PAMS program can
generally be expected to be similar to
those shown here.
All species except isoprene and
NOX exhibited substantial median
percentage declines over the 1995
to 2001 trend period. Isoprene is
largely emitted by biogenic sources
(trees and other vegetation) and
would therefore not be expected to
show a significant trend. For
TNMOC and TNMOC species other
than isoprene, concentrations
decreased at all or nearly all sites,
although the decline was not statisti-
cally significant in every case. NOX
concentrations increased at roughly
one third of all sites, but none of
these increases were found to be sta-
tistically significant. Trends at PAMS
Type 2 sites, which are generally
located within areas of maximum
Table 2-3. Trends in TNMOC, NOX, and Selected VOC Species
TNMOC
NOX
Ethylene
Propylene
Isopentane
Isoprene
Benzene
Toluene
m/p-Xylene
o-Xylene
1,2,4-Trimethyl-
benzene
All Site Types
Total
28
63
21
17
22
22
22
22
22
20
20
All Sites8
UD Down
4
16
1
0
0
8
0
2
0
0
4
23
33
15
14
18
5
18
19
20
16
12
Stat.
Significant11
UD Down
0
0
0
0
0
0
0
0
0
0
0
14
7
7
5
8
0
12
7
9
8
4
Type 2 Sites
Total
14
25
12
10
11
12
12
12
12
12
11
All Sites8
UD Down
2
6
0
0
0
3
0
0
0
0
0
12
16
10
9
10
4
12
11
12
11
9
Stat.
Significant
UD Down
0
0
0
0
0
0
0
0
0
0
0
7
2
4
3
4
0
9
4
6
6
4
Median
% Change
All Type 2
Sites Sites
-32
-8
-42
-40
-30
0
-35
-29
-31
-36
-38
-36
-9
-40
-39
-36
0
-43
-38
-33
-36
-57
TNMOC = Total nonmethane organic compound.
alndicates sign of trend regardless of statistical significance.
Indicates sign of trend at sites where trend is statistically significant at the 95% confidence level.
Notes:
1 The number of sites listed in the up and down columns indicates the number of PAMS locations at which
the 1995-2001 trend in 6-9 a.m. average concentration is in the indicated direction. The number of
sites in the total column may not equal the total of the up and down columns-either because the non-
parametric trend estimate for some sites is identically zero or the trend at many sites is not statistically
significant.
2 Theil's two-sided nonparametric significance test for the slope was used to assess statistical significance
at the 95% confidence level consistent with the methodology used in previous National Air Quality and
Emissions Trends reports. Note that these results are not adjusted for multiple comparisons.
Figure 2-30. Median percent change for the period 1995-2001 at PAMS monitors for
selected species.
I All Sites • Type 2 Sites
TNMOC
NOX
Ethylene
Propylene
Isopentane
Isoprene
Benzene
Toluene
m/p-Xylene
o-Xylene
1,2,4-Trimethylbenzene
-60
-40 -20 0 20
Percent
40
60
28
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
precursor emissions, are similar to
trends over all site types although
the Type 2 sites exhibited somewhat
greater declines in isopentane, ben-
zene, toluene, and 1,2,4-trimethyl-
benzene.
Methodology
All data were obtained from EPA's
Air Quality System (AQS) database.
Trends are based on data from sites
meeting certain data completeness
criteria for the 1995-2001 period.
Data completeness requirements are
the same as those used in previous
National Air Quality and Emissions
Trends reports.10 Annual averages
computed from 1-hour samples of
TNMOC or NOX were considered
valid if data were available for 50
percent or more of all possible obser-
vations. Sites selected for trends
analysis must have valid annual
summary statistics available for 5 or
more years. Missing annual summa-
ry statistics were filled in via linear
interpolation from surrounding
years. If a missing value happened
to fall at the beginning or end year of
the period being investigated, the
value was set equal to the nearest
available valid year of data. Theil's
nonparametric trend-slope estimates
and two-sided significance test
results for the slope were used to
assess statistical significance consis-
tent with the methodology used in
previous National Air Quality and
Emissions Trends reports. Note that
these results are not adjusted for
multiple comparisons.
Ozone Ozone Precursor
Trends in Chicago and Atlanta
Despite much progress in the years
since passage of the 1990 Clean Air
Act Amendments, some metropoli-
tan areas are still classified as nonat-
tainment with respect to the NAAQS
for 1-hour ozone. Two notable
examples are Chicago and Atlanta.
Atlanta is currently classified as a
"serious" ozone nonattainmcnt area;
Chicago is currently classified as
"severe." In this section we take a
closer look at recent trends in ozone
and ozone precursors in these two
major metropolitan areas.
Composite ozone trends for
1-hour and 8-hour annual ozone
design values in Chicago and
Atlanta are depicted in Figure 2-31.1]
Trends in 1-hour design values are
shown for the period 1991 to 2001;
8-hour design values are shown for
the period 1996 to 2001 because 1996
is the first year for which EPA began
reporting 8-hour design values.
Design values vary from year to
year, largely in response to changes
in meteorological conditions that
make it difficult to identify any long-
term trend in either city.
Composite trends in summer
weekday morning ozone precursor
concentrations in Chicago and
Atlanta are illustrated in Figure 2-32.
Trends are shown for concentrations
on weekday mornings (6-9 a.m.), the
period when precursor concentra-
tions are typically at their maximum
and are most directly influenced by
fresh emissions from local sources.
To maintain consistency between
nonattainment areas and to retain
sites in the analysis from Chicago
that would otherwise not meet the
data completeness criteria for a time
period extending back to 1991, NOX
summary statistics were calculated
for the period 1995 to 2001 only.
TNMOC data are only available
starting in 1995 for both nonattain-
ment areas. An examination of
Figure 2 -34 indicates that TNMOC
concentrations declined in both cities
during this period, while NOX con-
centrations increased slightly.
Air quality trend statistics for
both cities are summarized in Table
2-4. Although none of the trends
were found to be statistically signifi-
cant, the results are generally consis-
tent with a slight decrease in ozone
accompanied by a more noticeable
decrease in morning TNMOC and a
slight increase in morning NOX.
Figure 2-31, Annual 1-hour and 8-hour composite O3 design values in the Atlanta and
Chicago-Gary lake county nonattainment areas.
1,600
1,400-
600-
400
Atlanta, 1 -hour - -
Chicago, 1-hour — •
Atlanta, 8-hour
Chicago, 8-hour
91 92 93 94 95 96 97
Year
99
00
01
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
29
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-32. June-August weekday morning average NOX and TNMOC at PAMS Type
2 trend sites (June 1-September 1, 6:00-9:00 a.m.).
2,500
2,000
Q.
Q.
500
Atlanta, TNMOC
Atlanta, NOX
•Chicago, TNMOC
•Chicago, NOX
95
96
97
98
Year
99
00
Table 2-4. Summary of 1991-2001 Trends in Ozone Design Values and
1995-2001 Trends in Summer Weekday Morning Ozone
Precursor Trends in Atlanta and Chicago
City
Atlanta
Chicago
Pollutant
O3 (1 -hour)
O3 (8-hour)
TNMOC
NOX
O3 (1 -hour)
O3 (8-hour)
TNMOC
NOV
Composite
Trend
(ppb/year)
1.4
-2.5
-11.4
2.1
-1.2
-1.3
-7.8
0.3
No. of Sites
with Trend
Increasing
2
1
0
2
1
2
0
1
No. of Sites
with Trend
Decreasing
1
3
1
0
7
9
2
1
TNMOC = Total nonmethane organic compound
01
Methodology
All data were obtained from EPA's
AirData Web site (for 1991-2000
data) and AQS database (for 2001
data). Trends are based on data
from sites meeting certain data com-
pleteness criteria for the 1991-2001
period. Data completeness require-
ments are the same as those used in
previous National Air Quality and
Emissions Trends reports.10 Annual
summary statistics for a year of
1-hour or 8-hour ozone data were
considered valid if data were avail-
able for at least 75 percent of all pos-
sible observations. Annual averages
computed from round-the-clock
1-hour samples of TNMOC or NOX
were considered valid if data were
available for 50 percent or more of all
possible observations. For monitors
with less frequent TNMOC sampling
schedules (1 day in 6, etc.), the annu-
al mean was considered valid if at
least 75 percent of scheduled sam-
ples were available. Sites selected
for trends analysis must have valid
annual summary statistics available
for 8 or more years for 1991-2001
trends; 5 or more years for 1995-2001
trends. Missing annual summary
statistics were filled in via linear
interpolation from surrounding
years. If a missing value happened
to fall at the beginning or end year of
the period being investigated, the
value was set equal to the nearest
available valid year of data.
Composite trends were calculated
for each pollutant in both nonattain-
ment areas by averaging the annual
summary statistic over all sites in a
region. Theil's nonparametric trend-
slope estimates and two-sided signif-
icance test results for the slope were
used to assess statistical significance
consistent with the methodology
used in previous National Air
Quality and Emissions Trends
reports. Note that these results are
not adjusted for multiple compar-
isons. Additional methodological
details are reported by Coulter-Burke
and Stoeckenius.12
Rural Area Air Quality Trends
Figure 2-33 presents the trend in
8-hour O3 concentrations for 34 rural
sites from the Clean Air Status and
Trends Network (CASTNet) for the
most recent 10-year period,
1990-2001.13 The 8-hour O3 concen-
trations at these eastern sites, which
were the highest during the hot and
dry summers of 1991 and 1998, have
decreased 8 percent over the last
10 years. This trend in 8-hour O3
levels at 34 selected sites is mirrored
at other rural sites nationwide.
Across the nation, rural 8-hour O3
30
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
levels improved 9 percent from 1981
to 2000, but improved by only
2 percent over the last 10 years.14
Figure 2-34 further examines
patterns in rural O3 levels by pre-
senting the 10-year trends in the
8-hour O3 concentrations at 11
selected National Park Service (NFS)
sites.15 These sites are located in
Class I areas, a special subset of rural
environments (all National Parks and
wilderness areas exceeding 5,000
Figure 2-33. Trends in fourth highest daily 8-hour O3 concentrations for 34 rural
sites from CASTNet, 1990-2001.
120
.a
a.
2 100
o
O
80
-- 90th Percentile
-- 75th Percentile
-- Median
-- Mean
-- 25th Percentile
-- 10th Percentile
34 Reference Sites
I
90
\
91
I
92
l
93
l
94
95 96
Year
i
97
l
98
l l l
99 00 01
acres) accorded a higher degree of
protection under the Clean Air
Act provisions for the prevention
of significant deterioration. There
are more than 33 NFS sites
nationally; however, this analysis
focuses on the specific sites with
sufficient data to evaluate 10-year
trends. Over the last 10 years,
8-hour O3 concentrations in 33 of
our National Parks increased
nearly 4 percent. Four monitoring
sites in 11 of these parks experi-
enced statistically significant
upward trends in 8-hour O3
levels-Great Smoky Mountains
(TN), Mammoth Cave (KY),
Yellowstone (WY), and Craters of
the Moon (ID). For the remaining
22 parks, 8-hour O3 levels at 18
increased only slightly between
1992 and 2001, five showed
decreasing levels, and three were
unchanged.
Figure 2-34. Trend in annual fourth highest daily maximum 8-hour O3 concentrations in National Parks, 1992-2001.
0.066
0.040
Craters of the Moon, ID
'0.050 °-065
Voyageurs, MN
0.055
Canyonlands, UT 0057 o.oes
Yellowstone, WY
0.057 0.064
Big Bend, TX
0.078 0.088 0079 0.096
Mammoth Cave, KY Great Smoky Mountains, TN
0.058
Everglades, FL
Level of the 8-hour standard.
Concentrations are ppm.
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
31
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
National Emissions Trends
Figure 2-35 shows that national total
VOC emissions (which contribute to
O3 formation) from anthropogenic
(man-made, excluding wildfires and
prescribed burnings) sources
decreased 40 percent between 1983
and 2002, and 25 percent over the
past 10 years. National total NOX
emissions (the other major precursor
to O3 formation) decreased approxi-
mately 15 percent and 12 percent,
respectively, over the same two
periods.
Nationally, the two major sources
of VOC emissions are industrial
processes (47 percent) and transpor-
tation sources (45 percent), as shown
in Figure 2-36. Solvent use makes up
63 percent of the industrial processes
emission category and 29 percent of
total VOC emissions. Industrial proc-
ess VOC emissions have decreased
26 percent since 1993, in part due to
the implementation of maximum
achievable control technology
(MACT) controls that affect specific
chemical and solvent industries. The
Figure 2-35. VOC emissions, 1983-2002.
• Fuel Combustion
D
30,000
D Transportation
D Industrial Processes
D Miscellaneous D Fires
,o
25,000
20,000
15,000
c
CO
Ift
o 10,000
5,000
0
In 1985 and 1996, EPA refined its methods
for estimating emissions.
Fire emissions not available for 2002. ,
83 85
93 94 95 96 97 98 99 00 01 02
1983-02: 40% decrease3
1993-02: 25% decrease
VOC emissions totals by source cate-
gory and year are presented in Table
A-5 in Appendix A. Recent control
measures to reduce transportation
sector emissions include regulations
to lower fuel volatility and to reduce
NOX and VOC emissions from
tailpipes.10 The effectiveness of these
control measures is reflected in a
decrease in VOC emissions from
highway vehicles. VOC emissions
from highway vehicles have declined
39 percent since 1993, whereas high-
way vehicle NOX emissions have
decreased 10 percent over the same
period.
In addition to anthropogenic
sources of VOC and NOX, there are
natural or biogenic sources of these
compounds as well. Table 2-5 shows
the different predominant plant
species responsible for VOC emis-
sions in different parts of the country
for two major biogenic species of
concern, isoprene and monoterpenes.
Although it is not possible to control
the level of these natural emissions,
Note: Emission estimation methods and
data sources have evolved over time, result-
ing in some inconsistency in estimates in dif-
ferent years. In the methods used for this
report, the significant changes have occurred
between 1984 and 1986 and between 1995
and 1996, although not all source types were
affected. More explanation is provided in
Appendix B.
s Emissions trends data are not available for
1983; thus, the 20-year trend was interpo-
lated based on emissions data for 1980
and 1985.
Figure 2-36. Anthropogenic VOC emissions
by source category, 2002.a
Transportation
45%
Miscellaneous
Table 2-5. Biogenic Sources of VOC Emissions by Region
Region VOC Source
Southwestern Isoprene Oak (mostly), citrus,
United States eucalyptus
Monoterpenes Pine, citrus,
eucalyptus
Northeastern Isoprene
Oak (mostly), spruce
United States
a Sums do not equal 100 due to rounding.
Monoterpenes Maple, hickory, pine,
spruce, fir, cottonwood
32
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
their presence is an important factor
to consider when developing O3
control strategies. Biogenic NOX
emissions are associated with light-
ning and biological processes in soil.
On a regional basis, biogenic VOC
emissions can be greater than anthro-
pogenic VOC emissions. Biogenic
NOX emissions, however, make up
less than 10 percent of total NOX
Figure 2-37 shows the geographic
distribution of 2001 anthropogenic
VOC emissions based on the tonnage
per square mile for each county. This
map illustrates that the eastern half
of the country and the West Coast
emit more VOC (on a density basis)
than does the western half of the
continental United States.
emissions.
17
Figure 2-37. Density map of 2001 anthropogenic VOC emissions, by county.
Tons Per Year/Square Mile
I | 0.0-1.6
I I 1.7-2.9
I I 3.0-5.0
I | 5.1-11
I 1 12-2300
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
33
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Participate Matter
PM10 Air Quality Concentrations
1993-02 13% decrease
PM10 Direct Emissions
1993-02 22% decrease
PM2 5 Air Quality Concentrations
1999-02 8% decrease
PM2 5 Direct Emissions
1993-02 17% decrease
Worth Noting
PM25
• Annual average PM2 5 concen-
trations decreased 8 percent
nationally from 1999 to 2002.
The Southeast was responsible
for most of that reduction, where
the monitored levels of PM25
decreased 18 percent from 1999
to 2002. Lower 2002 annual
average concentrations in the
Southeast are due, in part, to
decreases in sulfates, which
largely result from power plant
emissions of SO2.
Nature and Sources
Particulate matter is the general term
used for a mixture of solid particles
and liquid droplets found in the air.
Some particles are large enough to be
seen as dust or dirt. Others are so
small they can be detected only with
an electron microscope. PM2 5
describes the "fine" particles that are
less than or equal to 2.5 um in diam-
eter. "Coarse fraction" particles are
greater than 2.5 iim, but less than or
equal to 10 um in diameter. PM10
refers to all particles less than or
equal to 10 um in diameter. A parti-
cle 10 um in diameter is about one-
seventh the diameter of a human
hair. PM can be emitted directly or
form in the atmosphere. "Primary"
particles, such as dust from roads or
elemental carbon (soot) from wood
combustion, are emitted directly into
the atmosphere. "Secondary" parti-
cles are formed in the atmosphere
from primary gaseous emissions.
Examples include sulfates, formed
from SO2 emissions from power
plants and industrial facilities, and
nitrates, formed from NOX emissions
from power plants, automobiles, and
other types of combustion sources.
The chemical composition of parti-
cles depends on location, time of
year, and weather. Generally, coarse
PM is composed largely of primary
particles and fine PM contains many
more secondary particles.
Fine and coarse particles typically
exhibit different behavior in the
atmosphere. Coarse particles can set-
tle rapidly from the atmosphere
within hours, and their spatial
impact is typically limited because
they tend to fall out of the air in the
downwind area near their emission
point. Larger coarse particles are not
readily transported across urban or
broader areas because they are gen-
erally too large to follow air streams
and they tend to be removed easily
by impaction on surfaces. Smaller-
sized coarse particles can have
longer lives and longer travel dis-
tances, especially in extreme circum-
stances, such as dust storms.
Global meteorological conditions
play a role in transporting dust peri-
odically from Africa and Asia to
North America. A special study, sum-
marized in Chapter 6 and provided
in full in the Special Studies section
of this report, examines how a partic-
ularly large event in Asia in April
2001 affected PM concentrations in
the United States.
Health and Environmental
Effects
Scientific studies show a link
between inhalable PM (alone, or
combined with other pollutants in
the air), which includes both fine and
coarse particles, and a series of sig-
nificant health effects. Both coarse
and fine particles can accumulate in
the respiratory system and are asso-
ciated with numerous adverse health
effects. Exposure to coarse particles
is primarily associated with the
aggravation of respiratory conditions
such as asthma. Exposure to fine par-
ticles is most closely associated with
decreased lung function, increased
hospital admissions and emergency
room visits, increased respiratory
symptoms and disease, and prema-
ture death. Sensitive groups that
appear to be at greatest risk to such
PM effects include the elderly,
individuals with cardiopulmonary
disease such as asthma or congestive
heart disease, and children.
Particulate matter also can cause
adverse impacts to the environment.
Fine particles are the major cause of
reduced visibility in parts of the
United States, including many of our
National Parks. Other environmental
impacts occur when particles deposit
onto soils, plants, water, or materials.
For example, particles containing
nitrogen and sulfur that deposit onto
land or waterbodies may change the
nutrient balance and acidity of those
environments so that species compo-
sition and buffering capacity change.
Particles that are deposited directly
onto the leaves of plants can,
depending on their chemical compo-
sition, corrode leaf surfaces or inter-
fere with plant metabolism. Finally,
PM causes soiling and erosion dam-
age to materials, including culturally
important objects such as carved
monuments and statues.
34
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Primary and Secondary PM
Standards
The NAAQS for PM10 were estab-
lished in 1987. The primary (health-
based) and secondary (public
welfare-based) standards for PM10
include both short- and long-term
NAAQS. The short-term (24-hour)
standard of 150 ug/m3 is not to be
exceeded more than once per year,
on average, over 3 years. The long-
term standard specifies an expected
annual arithmetic mean not to
exceed 50 ug/m3 averaged over
3 years.
The NAAQS for PM2 5 were
established in 1997. The primary and
secondary standards for PM2 5 are
set at 15 ug/m3 and 65 ug/m3, respec-
tively, for the annual and 24-hour
NAAQS.18 Compliance with the
annual standard is determined by
the average of three consecutive
annual average values (e.g., for 1999,
2000, and 2001). Compliance with the
24-hour standard is determined by
the 3-year average of annual 98th
percentile concentrations.
coincided with major wildfires and
particularly dry conditions.
When the sites are grouped as
rural, suburban, and urban, as in
Figure 2-39, the individual trends are
similar to the national trend. The
highest values are generally found at
the urban sites, followed closely by
the values at suburban sites. The
annual mean is much lower at the
rural sites, which are generally locat-
ed away from local sources of PM10.
Several factors have played a role
in reducing PM10 concentrations.
Where appropriate, states required
emissions from industrial sources
Figure 2-38. PM10 air quality, 1993-2002, based on seasonally weighted
annual average.
60
50
40
c
o
t3 30
§ 20
U
10
804 Sites
NAAQS
90% of sites have concentrations below this line
10% of sites have concentrations below this line
93 94 95 96 97 98 99 00
1993-02: 13% decrease
01
02
National 10-Year PM10
Air Quality Trends
Because 1988 represents the first
complete year of PM10 data for most
monitored locations, a 20-year trend
is not available. However, as Figure
2-38 illustrates, the most recent
10-year period (1993 to 2002) shows
that the national average of annual
mean PM10 concentrations at 804
monitoring sites decreased 13 percent.
The downward trend is apparent
through 1998. However, between
1998 and 1999, the national average
increased 1 percent. This slight
increase was largely influenced by
higher concentrations in the West,
particularly in California. PM10
concentrations in California were
higher than normal from September
to December 1999, a period that
Figure 2-39. PM10 annual mean concentration trends by location, 1992-2001.
30
25
a. 20
Q.
13 15
I
o
U
10
1992-2001
Rural Sites 119
Suburban Sites
Urban Sites
297
316
92
93
94
95
96 97
Year
98
99
00
01
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
35
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
and construction activities to be
reduced to meet the PM10 standards.
Measures were also adopted to
reduce street dust emissions, includ-
ing the winter-time use of clean anti-
skid materials such as washed sand,
better control of the amount of mate-
rial used, and removal of the materi-
al from the street as soon as the ice
and snow melt. Additionally, cleaner
burning fuels such as natural gas and
fuel oil have replaced wood and coal
as fuels for residential heating,
industrial furnaces, and electric
utility and industrial boilers.
PM10 Regional Air Quality Trends have helped reduce emissions of
Figure 2-40 is a map of regional PM
trends for the PM10 annual mean
from 1992 to 2001. All 10 EPA
Regions show decreasing trends over
the 10-year period, with declines
ranging from 5 to 31 percent. The
largest 10-year decreases occurred
in the Northwest. This is significant
because PM10 concentrations gene-
rally have been higher in the western
regions.
In the western States, programs
such as those with residential wood
stoves and agricultural practices
10-
In the eastern United States, the
Clean Air Act's Acid Rain Program
has contributed to the decrease in
PM10 emissions. The program has
reduced SO2 and NOX emissions,
both of which are precursors of par-
ticulate matter in the atmosphere
(see the SO2 section in this chapter
for more information on the Acid
Rain Program).
Figure 2-40. Trend in PM10 annual mean concentration by EPA Region, 1992-2001.
20.529
18.287
30.797
25.275
1992
32.267
26.312
22.667
28.976
1992
2001
10%
30.046
•«*_^^
1992
27.360
~
2001
27.322
1992
2001
25.104
9%
23.850
28%
26.584
•
1992
23.119
1992
2001
13%
5%
The National Trend
27.745 23.907
1992
2001
14%
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ug/m3.
36
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
PM10 2001 Air Quality Status
The map in Figure 2-41 displays the
highest second maximum 24-hour
PM10 concentration in each county
for 2001. The highest of these was
recorded in Inyo County, California,
caused by wind-blown dust from a
dry lake bed.19 The bar chart that
accompanies the national map shows
the number of people living in coun-
ties within each concentration range.
The colors on the map and bar chart
correspond to the colors of the con-
centration ranges displayed in the
map legend. In 2001, approximately
8 million people lived in 13 counties
where the highest second maximum
24-hour PM10 concentration was
above the level of the 24-hour PM10
NAAQS. When both the annual and
24-hour PM10 standards are consid-
ered, there were 11 million people
living in 17 counties with PM10
concentrations above the NAAQS
levels in 2001. See Chapter 4 for
information concerning officially
designated PM10 nonattainment
areas.
The Franklin Smelter facility,
responsible for historically high
recorded PM10 concentrations in
Philadelphia, shut down in August
1997 and was dismantled in late
1999,20 resulting in 24-hour concen-
trations below the level of the stand-
ard at the nearby monitoring site.
National PM10 Emissions Trends
Direct PM10 emissions are generally
examined in two separate groups.
First, there are the emissions from
the more traditionally inventoried
sources, which decreased 22 percent
nationally between 1993 and 2002
(see Figure 2-42). These sources
include fuel combustion, industrial
processes, and transportation. Of
these, the fuel combustion category
saw the largest decrease over the
10-year period (27 percent).
The second group of direct PM10
emissions is a combination of miscel-
laneous and natural sources, includ-
ing agriculture and forestry, wildfires
and managed burning, and fugitive
dust from paved and unpaved roads.
Although fugitive dust emissions are
large and can adversely affect air
quality, they do not transport to
more distant areas readily as do
emissions from other source types. It
should be noted that fugitive dust
emissions from geogenic wind ero-
sion have been removed from the
emissions inventory for all years,
because the annual emission esti-
mates based on past methods for this
category are not believed to be repre-
sentative. As Figure 2-43 shows,
these miscellaneous and natural
sources actually account for a large
percentage of the total direct PM10
Figure 2-41. Highest second maximum 24-hour PM10 concentration by county, 2001.
180n
Concentration,
Insufficient Data
>424
355-424
255-354
155-254
55-154
<55
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
37
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-42. National direct PM10 emissions, 1993-2002 (traditionally inventoried
sources only).
,o
-a
c
CO
Ift
4,000
3,000
2,000
1,000
• Fuel Combustion D Industrial Processes
D Transportation
In 1996, EPA refined its methods
^ for estimating emissions.
94
95 96 97 98 99 00
1993-02: 22% decrease
01
02
Note: Emission estimation methods and data sources have evolved over time, resulting in
some inconsistency in estimates in different years. In the methods used for this report, the
significant changes have occurred between 1984 and 1986, and between 1995 and 1996,
although not all source types were affected. More explanation is provided in Appendix B.
Figure 2-43. National direct PM10
emissions by source
category, 2002.
Fugitive Dust
63%
emissions nationwide, although they
can be difficult to quantify compared
to the traditionally inventoried
sources. The trend of emissions in
the miscellaneous/natural group
may be more uncertain from one
year to the next or over several years
because of this difficulty and because
these emissions tend to fluctuate a
great deal from year to year.
Table A-6 lists PM10 emissions
estimates for the traditionally inven-
toried and miscellaneous and natural
sources.
Figure 2-44 shows the emission
density for PM10 in each U.S. county.
The PM10 emission density closely
follows patterns in population dens-
ity and thus is the highest in the
eastern half of the United States, in
large metropolitan areas, areas with a
high concentration of agriculture
(e.g., the San Joaquin Valley in
California), and along the Pacific
Coast. One exception is that open
biomass burning is an important
source category that is more preva-
lent in forested areas and in some
agricultural areas. Also, fugitive dust
is an important component in arid
and agricultural areas.
Trends in PM2 5 Levels
and Direct Emissions
Figure 2-45 shows that direct PM2 5
emissions from man-made sources
decreased 17 percent nationally
between 1993 and 2002. This chart
tracks only directly emitted particles
and does not account for secondary
particles formed when emissions of
NOX, SO2, ammonia, and other gases
react in the atmosphere. The princi-
pal types of secondary particles are
sulfates and nitrates, which are
formed when SO2 and NOX react
with ammonia.
Figures 2-46 and 2-47 show how
sulfates and nitrates, along with
other components, contribute to
PM2 5 concentrations. Figure 2-48
represents the most recent year of
data available from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE) network,
which was established in 1987 to
track trends in pollutants, such as
PM2 5, that contribute to visibility
impairment. Because the monitoring
sites are located in rural areas
38
CRITERIA POLLUTANTS — NATIONAL TRENDS
CHAPTER 2
-------
Figure 2-44. Direct PM10 emissions density by county, 2001.
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Tons Per Year/Square Mile
| | 0.0-4.1
I I 4.2-6.7
I | 6.8-10
| | 11 -16
• 17-460
Figure 2-45. National direct PM2 5 emissions, 1993-2002 (traditionally inventoried
sources only).
2,500
2,000
1,500
• Fuel Combustion D Industrial Processes
D Transportation
-a
™ 1,000
o
n
500
0
In 1996, EPA refined its methods
^- for estimating emissions.
93 94 95 96 97 98 99 00 01 02
1993-02: 17% decrease
Note: Emission estimation methods and data sources have evolved over time, resulting in
some inconsistency in estimates in different years. In the methods used for this report, the
significant changes have occurred between 1984 and 1986 and between 1995 and 1996,
although not all source types were affected. More explanation is provided in Appendix B.
throughout the country, the network
is a good source for assessing region-
al differences in PM2 5. Figure 2-47
represents the most recent year of
data from EPA's urban speciation
network, which was established in
1999. All of these sites are located in
urban areas.
The IMPROVE data show that
PM2 5 levels in rural areas are highest
in the eastern United States and
southern California, as shown by the
larger circles. Sulfates and associated
ammonium dominate the East, with
carbon as the next most prevalent
component. Sulfate concentrations in
the East largely result from SO2 emis-
sions from coal-fired power plants.
In California and other areas of the
West, carbon and nitrates make up
most of the PM2 5 measured.
The urban speciation data show
that sites in urban areas, as shown in
the circles in the map in Figure 2-47,
generally have higher annual
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
39
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-46. Annual average PM2 5 concentrations (ug/m3) and particle type in rural areas, 2002.
O
O
O
15
Sulfate
Ammonium
Nitrate
Total Carbon
Crustal
Source: Interagency
Monitoring of
Protected Visual
Environments
Network, 2002.
Figure 2-47. Annual average PM2 5 concentrations (ug/m3) and particle type in urban areas, 2002.
O
O
10 ug/
15 u,g/m3
30 ug/m3
Sulfate
Ammonium
Nitrate
Total Carbon
Crustal
Source: EPA
Speciation
Network, 2002.
Note: Direct comparisons of the information in Figures 2-46 and 2-47 should take into consideration
the fact that one is an urban network and the other is a rural network and that there are differences in
instruments and measurement methods.
40 CRITERIA POLLUTANTS —NATIONAL TRENDS • CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
average PM2 5 concentrations than
nearby rural areas. Urban sites in the
East include a large percentage of
carbon and sulfates (and ammoni-
um). Urban sites in the Midwest and
far West (and especially in
California) include a large percent-
age of carbon and nitrates.
Trends in rural PM2 5 concentra-
tions can be examined with data
from the IMPROVE network, as
shown in Figure 2-48. In the East,
where sulfates contribute most to
rural PM2 5, the annual average
PM2 5 concentrations decreased 16
percent from 1992 to 2001. This
decrease was largely due to a decline
in sulfate concentrations, which
decreased 17 percent. The other
major components remained rela-
tively unchanged over the same peri-
od. Average PM2 5 concentrations in
the West were less than one-half of
the average for the eastern sites dur-
ing this period.
In 1999, EPA and its state, tribal,
and local air pollution control part-
ners deployed a monitoring network
to begin measuring PM2 5 concentra-
tions nationwide. Figure 2-49 shows
annual average PM2 5 concentrations
by county. This map also indicates
that PM2 5 concentrations vary
regionally. Based on the monitoring
data, parts of California and much of
the eastern United States have annu-
al average PM2 5 concentrations
above the level of the annual PM2 5
standard, as indicated by the orange
and red on the map. With few excep-
tions, the rest of the country generally
2.5
has annual average concentrations
below the level of the annual PM
health standard.
Now that there are several years
of monitoring data available, EPA
has begun to examine trends at
the national level, as shown in
Figure 2-50. Annual average PM2 5
concentrations decreased 8 percent
nationally from 1999 to 2002. The
Southeast was responsible for most
Figure 2-48. Annual average PM2 5 concentrations in rural areas.
14
12
10
"S
1 6
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
of that reduction, where the moni-
tored levels of PM2 5 decreased 18
percent from 1999 to 2002. Lower
2002 annual average concentrations
in the Southeast are due, in part, to
decreases in sulfates, which largely
result from power plant emissions of
S02.
Figure 2-50. Annual average PM2 5 concentrations (ug/m3), 2002
(based on seasonally weighted annual average).
-5U
25
"E
o) 20
c
o
W 15
c
0)
§ 10
5
858 Sites
-
90% of sites have concentrations below this line
V
- Trends monitoring data for
PM, . not available.
NAAQS
* Average
1
10% of sites have concentrations below this line
93 94 95 96 97 98 99
1999-02: 8% decrease
oo
01
02
42 CRITERIA POLLUTANTS —NATIONAL TRENDS • CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Sulfur Dioxide
Air Quality Concentrations
1983-02 54% decrease
1993-02
39% decrease
Emissions
1983-02 33%
1993-02
decrease
31% decrease
Worth Noting
• Steady 20-year improvement has
reduced sulfur dioxide (SO2) ambient
concentrations by one-half and
emissions by more than one-third.
• Phase II of the Acid Rain Program
was implemented in 2000 and has
resulted in new reductions.
Nature and Sources
Sulfur dioxide (SO2) belongs to the
family of sulfur oxide (SOX) gases.
These gases are formed when fuel
containing sulfur (mainly coal and
oil) is burned and during metal
smelting and other industrial proc-
esses. The highest monitored concen-
trations of SO2 have been recorded in
the vicinity of large industrial facili-
ties.
Health and Environmental
Effects
High concentrations of SO2 can result
in temporary breathing impairment
for asthmatic children and adults
who are active outdoors. Short-term
exposures of asthmatic individuals to
elevated SO2 levels while at moder-
ate exertion may result in reduced
lung function that may be accompa-
nied by symptoms such as wheezing,
chest tightness, or shortness of
breath. Other effects that have been
associated with longer-term expo-
sures to high concentrations of SO2,
in conjunction with high levels of
PM, include respiratory illness, alter-
ations in the lungs' defenses, and
aggravation of existing cardiovas-
cular disease. The subgroups of the
population that may be affected
under these conditions include indi-
viduals with cardiovascular disease
or chronic lung disease, as well as
children and the elderly.
Additionally, there are a variety of
environmental concerns associated
with high concentrations of SO2.
Because SO2, along with NOX, is a
major precursor to acidic deposition
(acid rain), it contributes to the acid-
ification of soils, lakes, and streams
and the associated adverse impacts
on ecosystems. Sulfur dioxide expo-
sure to vegetation can increase foliar
injury, decrease plant growth and
yield, and decrease the number and
variety of plant species in a given
community. Sulfur dioxide also is a
major precursor to PM2 5 (aerosols),
which is of significant concern to
human health (as discussed in the
particulate matter section of this
chapter), as well as a main pollutant
that impairs visibility. Finally, SO2
can accelerate the corrosion of natu-
ral and man-made materials (e.g.,
concrete and limestone) that are used
in buildings and monuments, as well
as paper, iron-containing metals,
zinc, and other protective coatings.
Primary and Secondary
Standards
There are both short- and long-term
primary NAAQS for SO2. The short-
term (24-hour) standard of 0.14 ppm
(365 ug/m3) is not to be exceeded
more than once per year. The long-
term standard specifies an annual
arithmetic mean not to exceed 0.030
ppm (80 ug/m3). The secondary
NAAQS (3-hour) of 0.50 ppm (1,300
ug/m3) is not to be exceeded more
than once per year. The standards for
SO2 have undergone periodic review,
but the science has not warranted a
change since they were established in
1972.
National 10-Year Air Quality
Trends
The national composite average of
SO2 annual mean concentrations
decreased 39 percent between 1993
and 2002 as shown in Figure 2-51,
with the largest single-year reduction
(16 percent) occurring between 1994
and 1995.21 The composite trend has
since leveled off, declining only
Figure 2-51. SO2 air quality, 1983-2002, based on annual arithmetic average.
0.04
0.03
Q.
Q.
03 0.02
c
0)
o
c
o
O
0.01
0.00
244 Sites
NAAQS
90% of sites have concentrations below this line
• 10% of sites have concentrations below this line
83 84 85 86 87
! 89 90 91 92 93 94 95 96 97 98 99 00 01 02
1983-02: 54% decrease
1993-02: 39% decrease
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
43
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
4.5 percent from 2001 to 2002. This
same general trend is seen in Figure
2-52, which plots the ambient
concentrations grouped by rural,
suburban, and urban sites. It shows
that the mean concentrations at the
urban and suburban sites have been
consistently higher than those at the
rural sites. However, the 1994 to 1995
reduction in the concentrations at
nonrural sites has narrowed the gap
between the trends. The greater
reduction seen in the nonrural sites
reflects the fact that the proportion of
nonrural sites is greater in the east-
ern United States, which is where
most of the 1994 to 1995 emissions
reductions at electric utilities
occurred.22 The national composite
second maximum 24-hour SO2
annual mean concentrations
decreased 35 percent between 1992
and 2001 with the largest single year
reduction (25 percent) also occurring
between 1994 and 1995.
National Emissions Trends
As shown in Figure 2-53, national
SO2 emissions decreased 31 percent
between 1993 and 2002, with an even
more impressive 33 percent decrease
in the past 20 years (1983 to 2002).
The dramatic reduction in 1995 was
caused by implementation of the
Acid Rain Program; subsequent
year-to-year variations are driven in
part by the yearly changes in emis-
sions from the electric utility indus-
try, which accounts for most of the
fuel combustion category in Figure
2-54. In particular, coal-burning
power plants have consistently been
the largest contributor to SO2 emis-
sions, as documented in Table A-9 in
Appendix A.
Figure 2-55 shows the emissions
density for SO2 in each U.S. county.
SO2 emissions density is highest in
the eastern United States, in large
metropolitan areas, and in areas with
coal-burning power plants.
Figure 2-52. Annual mean SO2 concentration by trend location, 1982-2001.
.015
o. .010
Q.
C
o
I
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-55. Direct SO2 emissions density by county, 2001.
Tons Per Year/Square Mile
I I 0.0-0.12
I I 0.13-0.29
I I 0.30-0.80
I I 0.81-4.3
The Acid Rain Program
The substantial national reductions
in SO2 emissions and ambient SO2
and sulfate concentrations from 1994
to 1995 were due mainly to Phase I
implementation of the Acid Rain
Program. Established by EPA under
Title IV of the 1990 Amendments, the
Acid Rain Program's principal goal
is to achieve significant reductions in
SO2 and NOX emissions from electric
utilities. Phase I compliance for SO2
began in 1995 and significantly
reduced emissions from the partici-
pating utilities.23 Phase II began in
2000 and sets restrictions on Phase I
plants as well as smaller coal-, gas-,
and oil-fired plants. Approximately
3,000 units are now affected by the
Acid Rain Program. Figure 2-56
shows the reduction in SO2 emis-
sions for all sources.
Between 1996 and 1998, total SO2
emissions from electric utilities had
increased slightly, compared to their
Figure 2-56. National SO2 emissions trend for all Title IV affected units.
18
16
14
12
00
|»
C 8
o
I 6
4
2
0
r- 17.30
1980 1985 1990 1995 1996 1997 1998 1999 2000 2001 2002
• Phase I Sources
CH Phase II Sources
CH All Sources
• • • • Allowances Allocated for that Year
levels in 1995. Since 2000, however,
total SO2 emissions have decreased,
falling slightly below 1995 levels.
Most Phase I plants overcomplied in
Phase I (1995 to 2000), banking their
SO2 allowances for use in Phase II,
resulting in significant early reduc-
tions. However, some Phase I units
did increase their emissions during
these years. Because Phase I units
CHAPTER 2 • CRITERIA POLLUTANTS — NATIONAL TRENDS
45
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
account for only 18 percent of the
total 1996 to 1998 increase, the
majority of the increase is attributed
to those units not yet participating in
the Acid Rain Program until Phase
II. By 2010, the Acid Rain Program
will reduce annual SO, emissions by
half from 1980 levels. The program
sets a permanent cap at 8.95 million
tons per year on the total amount of
SO2 that may be emitted from power
plants nationwide. For more infor-
mation on the Acid Rain Program,
visit http://www.epa.gov/airmarkets.
National 20-Year Air Quality
Trends
The progress in reducing ambient
SO2 concentrations during the past
20 years is shown in Figure 2-57. The
national 2001 composite average SO2
annual mean concentration is 50 per-
cent lower than it was in 1982. In
addition to the previously men-
tioned effects of the Acid Rain
Program, these steady reductions
over time were accomplished by
installing flue gas control equipment
at coal-fired generating plants,
reducing emissions from industrial
processing facilities such as smelters
and sulfuric acid manufacturing
plants, reducing the average sulfur
content of fuels burned, and using
cleaner fuels in residential and com-
mercial burners.
Regional Air Quality Trends
The map of regional trends in Figure
2-58 shows that ambient SO2 concen-
trations are generally higher in the
eastern United States. The effects of
Phase I of the Acid Rain Program are
seen most vividly in the northeast. In
particular, concentrations fell 20 to 25
percent between 1994 and 1995 in
EPA Regions 1, 2,3, and 5. These
broad regional trends are not surpris-
ing because most of the units affected
by Phase I of the Acid Rain Program
also are located in the East. This fig-
ure also shows that ambient concen-
trations have increased slightly
betwreen 1995 and 1997 in Regions 3
and 4 where many of the electric util-
ity units not yet affected by the Acid
Rain Program are located.
2001 Air Quality Status
The most recent year of ambient
data shows that all counties did
meet the primary SO2 short-term
standard, as shown by Figure 2-59.
Figure 2-57. Long-term ambient SO2 trend, 1982-2001.
0.04
a
U
E
O.
Q.
C
o
0.03
0.02
0.01
0.00
253 Sites
82 83 84 85 86 87
90 91 92 93 94 95 96 97 98 99 00 01
Year
46
CRITERIA POLLUTANTS —NATIONAL TRENDS « CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 2-58. Trend in SO2 annual arithmetic mean concentration by EPA Region, 1982-2001.
.013
.011
.006
1982
.004
.002
1982 2001
f 47%
The National Trend
.011
.011
.011
.007
.012
.004
1982 2001
f 67%
.003
1982 2001
•f-57%
1982 2001
•f'45%
.010
.005
Note: These trends
are influenced by the
distribution of monitoring
locations in a given Region
and, therefore, can be
driven largely by urban
concentrations. They are
thus not indicative of back-
ground regional
concentrations.
Data from Alaska, Hawaii,
and Puerto Rico are not
included in these regional
summaries.
Concentrations are ppm.
Figure 2-59. Highest SO2 annual mean concentration by county, 2001.
140-i
120-
Concentration (ppm)
I I Insufficient Data
aO.034
<0.034
NAAQS = 0.03 ppm
CHAPTER 2 • CRITERIA POLLUTANTS —NATIONAL TRENDS 47
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
References
1. Note that due to the annual loss
and replacement of ambient monitor-
ing sites (e.g., redevelopment, new
leases), too few sites possess a moni-
toring record sufficient to construct a
representative 20-year trend for the
nation. Therefore, this report assesses
long-term trends by piecing together
two separate 10-year trends databas-
es.
2. Oxygenated Gasoline Implementa-
tion Guidelines, EPA, Office of Mobile
Sources, Washington, DC, July 27,
1992.
3. Guidelines for Oxygenated Gasoline
Credit Programs and Guidelines on
Establishment of Control Periods Under
Section 2U(m) of the Clean Air Act as
Amended, 57 FR 47853 (October 20,
1992).
4. Table of winter oxygenated fuels
programs by state, EPA, Office of
Transportation and Air Quality,
Washington, DC, December 8,1999.
http://www.epa.gov/otaq/regs/fuels/
oxy-area.pdf
5. National Ambient Air Quality
Standards for Nitrogen Dioxide:
Final Decision, Federal Register, 61 PR
196, Washington, DC, October 8,
1996.
6. Review of the National Ambient Air
Quality Standards for Nitrogen Oxides:
Assessment of Scientific, and Technical
Information, EPA-452/R-95-005, U.S.
Environmental Protection Agency,
Research Triangle Park, NC,
September 1995.
7. Atmospheric concentrations of
NO2 are determined by indirect
photomultiplier measurement of the
luminescence produced by a critical
reaction of NO with ozone. The
measurement of NO2 is based first
on the conversion of NO2 to NO,
and then subsequent detection of NO
using this well-characterized chemi-
luminescence technique. This conver-
sion is not specific for NO2, hence
chemiluminescence analyzers are
subject to interferences produced by
response to other nitrogen-contain-
ing compounds (e.g., peroxyacetyl
nitrate [PAN]) that can be converted
to NO. The chemiluminescence tech-
nique has been reported to overesti-
mate NO2 due to these interferences.
This is not an issue for compliance
because there are no violations of the
NO2 NAAQS. In addition, the inter-
ferences are believed to be relatively
small in urban areas. The national
and regional air quality trends
depicted are based primarily on data
from monitoring sites in urban loca-
tions and are expected to be reason-
able representations of urban NO2
trends. That is not the case in rural
and remote areas, however, where air
mass aging could foster greater rela-
tive levels of PAN and nitric acid and
interfere significantly with the inter-
pretation of NO9 monitoring data.
8. 1998 Compliance Report, U.S.
Environmental Protection Agency,
Acid Rain Program, Washington, DC,
August 1999.
9. National Ambient Air Quality
Standards for Ozone; Final Rule,
Federal Register, 62 FR 38856,
Washington, DC, July 18,1997.
10. United States Environmental
Protection Agency. Office of Air
Quality Planning and Standards.
2000. "National Air Quality and
Emissions Trends Reports, 1998."
Appedix B.
11. The 1-hour annual ozone design
value is defined at an individual
monitoring location as the second
highest daily maximum 1-hour aver-
age concentration; the 8-hour annual
design value is definedas the fourth
highest daily maximum 8-hour aver-
age concentration.
12. Coulter-Burke, S. and T.
Stoeckenius, 2002. Analysis of Ambient
Air Quality Trends in the Chicago and
Atlanta Ozone Nonattainment Areas,
ENVIRON International Corp.,
September.
13. CASTNet is considered the
nation's primary source for atmos-
pheric data to estimate dry acidic
deposition and to provide data on
rural ozone levels. Used in conjunc-
tion with other national monitoring
networks, CASTNet helps to deter-
mine the effectiveness of national
emission control programs. Estab-
lished in 1987, CASTNet now com-
prises 79 monitoring stations across
the United States. The longest data
records are primarily at eastern sites.
The majority of the monitoring
stations are operated by EPA's Office
of Air and Radiation; however, 27
stations are operated by the National
Park Service (NFS) in cooperation
with EPA. The CASTNet data com-
plement the larger O3 data sets
gathered by the State and Local Air
Monitoring Stations (SLAMS) and
National Air Monitoring Stations
(NAMS) networks with additional
rural coverage.
14. Similarly, although registering
declines in 8-hour ozone levels of 16
and 12 percent, respectively, over the
last 20 years, urban and suburban
site progress slowed between 1991
and 2000 (to 8.5 and 8 percent
improvement).
15. This analysis utilizes a nonpara-
metric regression procedure to assess
statistical significance, a. description
of which is provided in Chapter 3:
Criteria Pollutants - Metropolitan
Area Trends.
16. "Volatility Regulations for
Gasoline and Alcohol Blends Sold in
Calendar Years 1989 and Beyond,"
Federal Register, 54 FR 11868,
48
CRITERIA POLLUTANTS —NATIONAL TRENDS « CHAPTER 2
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Washington, DC, March 22,1989.
17. Reformulated Gasoline: A Major
Step Toward Cleaner Air, EPA-420-B-
94-004, U.S. Environmental
Protection Agency, Office of Air and
Radiation, Washington, DC,
September 1994.
18. National Ambient Air Quality
Standards for Pa.rticula.te Matter:
Final Rule, Federal Register, 62 PR
38652, Washington, DC, July 18,1997.
http://www.epa.gov/ttn/oarp g/tl/fr__
notices/pmnaaqs.pdf.
1.9. Personal communication with
EPA Region 9.
20. Personal communication with
EPA Region 3.
21. Revised Requirements for Designa-
tion of Reference and Equivalent
Methods for PM2.5 and Ambient Air
Quality Surveillance for Particulate
Matter: Final Rule, Federal Register 62
July 18,1997.
22. IMPROVE, Cooperative Center
for Research in the Atmosphere,
Colorado State University, Ft. Col-
lins, CO, May 2000.
23. 1997 Compliance Report: Acid Rain
Program, EPA-430-R-98-012, U.S.
Environmental Protection Agency,
Office of Air and Radiation,
Washington, DC, August 1998.
CHAPTER 2
CRITERIA POLLUTANTS — NATIONAL TRENDS
49
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
50 CRITERIA POLLUTANTS —NATIONAL TRENDS « CHAPTER 2
-------
CHAPTER 3
Criteria Pollutants —
Metropolitan Area Trends
http://www.epa.gov/oar/airtrends/metro.html
Worth Noting
• Out of 296 metropolitan statistical areas, 36 have significant upward trends.
• Of these, only trends involving ozone had values over the level of air
quality standards.
This chapter presents status and
trends in criteria pollutants for
metropolitan statistical areas (MSAs)
in the United States. The MSA status
and trends give a local picture of air
pollution and can reveal regional
patterns of trends. Such information
can allow individuals to gauge the
air pollution situation where they
live. Not all areas in the country are
in MSAs, and not all MSAs are
included here. A complete list of
MSAs and their boundaries can be
found in the Statistical Abstract of the
United States.1 The status and trends
of MSAs are based on four tables
found in Appendix A (A-15 through
A-18). Table A-15 gives the 2000 peak
statistics for all MSAs, providing the
status of that year. It also shows
10-year trends for the 263 MSAs
having data that meet the trends
requirements explained in Appendix
B. Table A-16 lists these MSAs and
reports criteria pollutant trends as
"upward," "downward," or "not
significant." These categories are
based on a statistical test, known as
the Theil test, described later in this
chapter.
Another way to assess trends in
MSAs is to examine Air Quality
Index (AQI) values.2'3'4 The AQI is
used to present daily information to
the public on one or more criteria
pollutants in an easily understood
format and in a timely manner.
Tables A-17 and A-18 list the number
of days with AQI values greater than
100 for the nation's 94 largest metro-
politan areas (population greater
than 500,000). Table A-17 lists AQI
values based on all pollutants, and
Table A-18 lists AQI values based on
ozone alone. The tables listing
Pollutant Standards Index (PSI) data
from previous reports may not agree
with the tables in this report because
of the new way to calculate the AQI.
These changes are presented in more
detail later in this chapter.
A new technique for displaying
air quality information is also
described. This technique presents
visual clues as to the status of differ-
ent MSAs.
Not every MSA appears in these
tables. Some do not appear because
the population is so small or the air
quality is so good that AQI reporting
is not currently required. Ambient
monitoring for a particular pollutant
may not be conducted if there is no
problem, thus some MSAs have no
ongoing air quality monitoring for
one or more of the criteria pollutants.
In addition, there are also MSAs
with too little monitoring data for
trends analysis purposes (see
Appendix B).
Status: 2001
The air quality status for MSAs is
provided in Table A-15, which lists
peak statistics for all criteria pollut-
ants measured in an MSA. As dis-
cussed above, not all criteria
pollutants are measured in all MSAs,
hence the "ND" (no data) listings in
Table A-15. Examining Table A-15
shows that 140 areas had peak con-
centrations exceeding standard levels
for at least one criteria pollutant.
The number of these areas increased
by 4 the count from 2000 (136 areas).
These 140 areas are home to 56 per-
cent of the U.S. population. Similarly,
there were 60 areas (with 36 percent
of the population) that had peak
statistics that exceeded two or more
standards. Six areas—Bakersfield,
CA, Riverside-San Bernardino, CA,
Fresno, CA, Birmingham, AL,
St. Louis, MO, and Visalia-Tulare-
Porterville, CA (with 3 percent of the
U.S. population)—had peak statistics
CHAPTER 3
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
51
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
from three pollutants that exceeded
the respective standards. There was
one area that violated four or more
standards (St. Louis, MO).
Trends Analysis
Table A-16 displays air quality trends
for MSAs. The data in this table are
average statistics of pollutant
concentrations from the subset of
ambient monitoring sites that meet
the trends criteria explained in
Appendix B. A total of 246 MSAs
have at least one monitoring site that
meets these criteria. As stated previ-
ously, not all pollutants are measured
in every MSA. From 1992 to 2001,
statistics based on the standards
were calculated for each site and
pollutant with available data. Spatial
averages were obtained for each of
the 246 MSAs by averaging these
statistics across all sites in an MSA.
This process resulted in one value
per MSA per year for each pollutant.
Although there are seasonal patterns
of high values for some pollutants in
some locations, the averages for
every MSA and year provide a con-
sistent indicator with which to assess
trends.
Because air pollution levels are
affected by variations in meteorol-
ogy, emissions, and day-to-day activ-
ities of populations in MSAs, trends
in air pollution levels are not always
well defined. To assess upward or
downward trends, we applied a
statistical significance test to these
data. An advantage of using the
statistical test is the ability to test
whether or not the upward or
downward trend is real (significant)
or just a chance product of year-to-
year variation (not significant).
Because the underlying pollutant
distributions do not meet the usual
assumptions required for common
Table 3-1. Summary of MSA Trend Analyses by Pollutant, 1990-1999
CO
Pb
NO2
°3
°3
PM10
PM10
SO2
SO2
Trend Statistic
Second max. 8-hour
Max. quarterly mean
Arithmetic mean
Fourth max. 8-hour
Second daily max. 1-hour
Ninetieth percentile
Weighted annual mean
Arithmetic mean
Second max. 24-hour
Total #
MSAs
134
35
97
202
202
164
164
139
139
#MSAs
Up
0
1
3
17
12
4
7
4
2
#MSAs
Down
104
12
37
10
15
41
60
70
62
#MSAs
with No
Significant
Trend
30
22
57
175
175
119
97
65
75
significance tests, the test was based
on a nonparametric method
commonly referred to as the Theil
test.5'6'7'8 By using linear regression
to estimate the trend from changes
during the entire 10-year period, we
can detect an upward or downward
trend even when the concentration
level of the first year equals the
concentration level of the last year.
Table 3-1 summarizes the trend
analysis performed on the 246 MSAs.
It shows that there were no upward
trends in carbon monoxide (CO).
PM10 and sulfur dioxide had upward
trends in 7 MSAs over the past
decade, NO2 had upward trends in 3
MSAs, while SO2 had upward trends
in 4 MSAs. Lead had an upward
trend in 1 MSA. Further examination
of Table A-16 shows that, of the 246
MSAs, (1) 180 had downward trends
in at least one of the criteria pollu-
tants, (2) 36 had upward trends (of
these 36,25 also had downward
trends in other pollutants, leaving
9 MSAs with exclusively upward
trends), and (3) only 2 MSAs had no
significant trends. A closer look at
the 36 MSAs with upward trends
reveals that 13 were exceeding the
level of the 8-hour ozone standard,
and 3 were above the 1-hour stan-
dard. For all other pollutants with
upward trends in any MSA, the lev-
els observed were well below stan-
dard levels. Taken as a whole, these
results still demonstrate significant
improvements in urban air quality
over the past decade for the nation;
however, the number of MSAs with
upward trends is increasing when
compared to numbers in previous
reports.
The Air Quality Index
The AQI provides information on
pollutant concentrations for ground-
level ozone, particulate matter,
carbon monoxide, sulfur dioxide,
and nitrogen dioxide. Formerly
known as the PSI, this nationally
uniform air quality index is used by
state and local agencies for reporting
daily air quality to the public. In
1999, EPA updated the AQI to reflect
the latest science on air pollution
health effects and to make it more
appropriate for use in contemporary
news media, thereby enhancing the
public's understanding of air
52
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
CHAPTER 3
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
pollution across the nation. Currently,
the AQT may be found in national
media such as USA Today and on the
Weather Channel, as well as in local
newspapers and broadcasts across
the country. It also serves as a basis
for community-based programs that
encourage the public to take action to
reduce air pollution on days when
levels are projected to be of concern.
An Internet Web site, AIRNOW
(http://www.epa.gov/airnow), which
presents "real time" air quality data
and forecasts of summertime smog
levels for most states, uses the AQI to
communicate information about air
quality. The index has been adopted
by many other countries (e.g., Mexi-
co, Singapore, and Taiwan) and is
used around the world to provide the
public with information on air pollut-
ants.
AQT values for each of the pollut-
ants are derived from concentrations
of that pollutant. The index is
"normalized" across each pollutant
so that, generally, an index value of
100 is set at the level of the short-
term, health-based standard for that
pollutant. An index value of 500 is set
at the significant harm level, which
represents imminent and substantial
endangerment to public health.9 The
higher the index value, the greater
the level of air pollution and health
risk.
To make the AQI as easy to under-
stand as possible, EPA has divided
the AQI scale into six general cate-
gories that correspond to a different
level of health concern:
« Good (0-50): Air quality is consid-
ered satisfactory, and air pollution
poses little or no risk.
« Moderate (51-100): Air quality is
acceptable; however, for some
pollutants there may be a
moderate health concern for a very
small number of individuals. For
example, people who are unusu-
ally sensitive to ozone may expe-
rience respiratory symptoms.
• Unhealthy for Sensitive Groups
(101-150): Certain groups of
people may be particularly sensi-
tive to the harmful effects of
certain air pollutants. This means
they are likely to be affected at
lower levels than is the general
public. For example, people with
respiratory disease are at greater
risk from exposure to ozone,
while people with respiratory
disease or heart disease are at
greater risk from particulate
matter. When the AQI is in this
range, members of sensitive
groups may experience health
effects, but the general public is
not likely to be affected.
• Unhealthy (151-200): Everyone
may begin to experience health
effects. Members of sensitive
groups may experience more
serious health effects.
« Very Unhealthy (201-300): Air
quality in this range triggers a
health alert, meaning everyone
may experience more serious
health effects.
« Hazardous (over 300): Air quality
in this range triggers health warn-
ings of emergency conditions. The
entire population is more likely to
be affected.
Because different groups of peo-
ple are sensitive to different pollut-
ants, there are pollutant-specific
health effects and cautionary state-
ments for each category in the AQI.
An AQI report will contain an
index value, category name, and the
pollutant of concern and is often
featured on local television or radio
news programs and in newspapers,
especially when values are high. For
national consistency and ease of
understanding, if the AQI is reported
using color, there are specific,
required colors associated with each
category. Examples of the use of
color in AQI reporting include the
color bars that appear in many new'S-
papers and the color contours of the
ozone map. The six AQI categories,
their respective health effects des-
criptors, colors, index ranges, and
corresponding concentration ranges
are shown in Table 3-2. EPA has also
developed an AQI logo (Figure 3-1)
to increase the awareness of the AQI
in media reports and also to indicate
that the AQI is uniform throughout
the country.
The AQI integrates information on
pollutant concentrations across an
entire monitoring network into a
single number that represents the
worst daily air quality experienced in
an urban area. For each of the pollut-
ants, concentrations are converted
into index values between 0 and 500.
The level of the pollutant with the
highest index value is reported as the
AQI level for that day. There is a new
AQI requirement to report any
pollutant with an index value above
100. In addition, when the AQI is
above 100, a pollutant-specific state-
ment indicating what specific groups
are most at risk must be reported.
For example, when the index value is
above 100 for ozone, the AQI report
will state "children and people with
asthma are most at risk." The AQI
must be reported in all MSAs with
air quality problems and populations
greater than 350,000 according to the
2000 census. Previously, urbanized
areas with populations greater than
200,000 were required to report the
index.
CHAPTER 3
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
53
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 3-2. AQI Categories, Colors, and Ranges
Category
AQI
03 (ppm)
8-hour
03 (ppm)
1-hour
PM2.5
(ug/m3)
PM10
(ug/m3)
CO
(ppm)
S02
(ppm)
NO2
(ppm)
Moderate
Unhealthy for
Sensitive Groups
Jnhealthy
^^^—
sry unhealthy
Hazardou
0-50
51-100
101-150
151-200
201-300
0.000-0.
0.065-0.
0.085-0.
0.105-0.
0.125-0.
,064
,084
,104
,124
,374
(b) 0.0-15.4
(b)
0.125-0.
0.165-0.
0.205-0.
,164
,204
,404
15.
40.
65.
150.
,5-40.4
,5-65.4
,5-150.4
,5-250.4
0-54
55-154
155-254
255-354
355-424
0.0-4.4
4.5-9.4
9.5-12.4
12.5-15.4
15.5-30.4
0.000-0.
0.035-0.
0.145-0.
0.225-0.
0.305-0.
,034
,144
224
,304
,604
(c)
(c)
_
0.65-1.24
301-400
401-500
0.405-0.504
0.505-0.604
250.5-350.4
350.5-500.4
425-504
505-604
30.5-40.4
40.5-50.4
0.605-0.804 1
0.805-1.004 1
25-1.64
65-2.04
aNo health effects information for these levels—use 1-hour concentrations.
b1-hour concentrations provided for areas where the AQI is based on 1-hour values might be more cautionary.
CNO2 has no short-term standard but does have a short-term "alert" level.
Figure 3-1. Air quality index logo.
AIR QUALITY INDEX
Summary of AQI
Analyses
Of the five criteria pollutants used to
calculate the AQI, only four (CO, O3,
PM10, and SO2) generally contribute
to the AQI value. In recent years,
nitrogen dioxide has never been the
highest pollutant measured because
it does not have a short-term stand-
ard and can be included only when
the index reaches a value of 200 or
greater. Ten-year AQI trends are based
on daily maximum pollutant concen-
trations from the subset of ambient
monitoring sites that meet the trends
requirements in Appendix B.
Because an AQI value greater
than 100 indicates that at least one
criteria pollutant has reached levels
at which people in sensitive groups
are likely to suffer health effects, the
number of days with AQI values
greater than 100 provides an indica-
tor of air quality in urban areas.
Figure 3-2 shows the trend in the
number of days with AQI values
greater than 100 summed across the
nation's largest metropolitan areas.
This number is expressed as a per-
centage of the days in the first year
(1992). Because of their magnitude,
AQI totals for Los Angeles, CA,
Riverside, CA, Bakersfield, CA,
Ventura, CA, Orange County, CA,
and San Diego, CA, are shown
separately as California. Plotting
these values as a percentage of 1992
values allows trends of different
magnitudes to be compared on the
same graph. The long-term air qual-
ity improvement in California urban
areas is evident in this figure.
Between 1992 and 2001, the total
number of days with AQI values
greater than 100 decreased more
than 50 percent. The variability in
the remaining major cities across
the United States makes it difficult
to interpret the change over the
same period (labeled as "rest" in
Figure 3-2), though it does appear
to be rising. Other areas that had
serious, severe, or extreme ozone
problems (labeled as "pams" in
Figure 3-2) show almost no change.
Although five criteria pollutants
can contribute to the AQI, the index
is driven mostly by ozone. AQI
estimates depend on the number of
pollutants monitored as well as the
number of monitoring sites where
data are collected. The more pollut-
ants measured and the more sites
that are available in an area, the
better the estimate of the AQI for a
54
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
CHAPTER 3
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
given day. Historically, ozone
accounts for the majority of days,
with AQI values above 100. Soon,
PM2 - will also be monitored and
reported on a regular basis, which
will reduce the percentage of days
that ozone is the greatest AQI pollut-
ant. Table A-18 shows the number of
days with AQI values greater than
100 that are attributed to ozone
alone. Comparing Tables A-17 and
A-18, the number of days with an
AQI above 100 are increasingly due
to ozone. In fact, the percentage of
days with an AQI above 100 due to
ozone have increased from 94 per-
cent in 1992 to 98 percent in 2001
(Figure 3-3). This increase reveals
that ozone increasingly accounts for
those days above the 100 level and,
therefore, reflects the success in
achieving lower CO and PM]0 con-
centrations. However, the typical
l-in-6 day sampling schedule for
most PM10 sites limits the number of
days that PM]0 can factor into the
AQI determination, which may, in
some places, account for the pre-
dominance of ozone. In the future,
PM2 5 may challenge ozone as the
dominant pollutant.
A New Display
Technique
As more and more information
about air pollution and its effect on
our health is being presented to the
public through various media chan-
nels, a need has arisen to provide the
general public with a simple, visual
method for assessing the degree of
air pollution in their communities.
To meet this need, EPA is exploring a
new technique for displaying air
quality information that is designed
to allow the general public to quickly
and easily review the degree of air
pollution in the 319 MSAs across the
United States. This technique would
Figure 3-2. Number of days with AQI values >100, as a percentage of 1990 value.
California
t- parns - A - rest
LL.
15
0)
o
CD
Q.
--
1992 1993 1994 1995
1996
Year
1997 1998 1999 2000 2001
Figure 3-3. Percentage of days over 100 due to ozone.
100
99
97
96
95
94
1992 1993 1994 1995
1996 1997 1998 1999 2000 2001
Year
CHAPTER 3
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
55
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
use color-coded circles to show
levels of each criteria pollutant in
each MSA relative to its levels in the
other MSAs. A solid blue indicates
fewer days of unhealthy air (mean-
ing that MSA had fewer AQI days
over 100 for, say ozone than most of
the other MSAs had for ozone). On
the other end of the spectrum, a
black • indicates more days of
unhealthy air.
Figure 3-4 presents an example of
how this new display technique
might appear. The legend in Figure
3-4 explains how the color-coded
symbols could be used to quickly
and easily provide information about
air quality and air pollutants. The
new display technique would not
provide new or additional air quality
data, nor would it be used as a
rating system or show trends in air
quality over time. Rather, its purpose
would be to provide a simplified,
visual tool for interpreting air
quality information in selected MSAs
for a specific year for each of the
selected pollutants. EPA is continu-
ing to assess the feasibility of the
new technique and to explore addi-
tional capabilities that might be
added, such as a Web-based applica-
tion that would allow users to sort
and query information to generate
customized reports about health-
related air quality issues, as well as
components relating to multiyear
displays and visibility.
Additional information on this
new display technique is presented
in a discussion paper in the Special
Studies section of this report.
Figure 3-4. Sample from the new display technique.
Metropolitan Statistical Area (MSA)
Pollutants
location 1
location 2
location 4
location 5
o - ® • • •
• • e • • •
LEGEND
• e
Fewer days of
unhealthy air
(Days with AQI
>100) compared
to other MSAs
o e •
More days of
unhealthy air
(Days with AQI
>100) compared
to other MSAs
O Not Monitored
— Insufficient Data
56
CRITERIA POLLUTANTS —METROPOLITAN AREA TRENDS • CHAPTERS
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
References and Notes
1. Statistical Abstracts of the United
States, 2000, U.S. Department of
Commerce, U.S. Bureau of the
Census.
2. Air Quality Index, A Guide to Air
Quality and Your Health, EPA-454/
R-00-005, U.S. Environmental
Protection Agency, Office of Air
Quality Planning and Standards,
Research Triangle Park, NC, June
2000.
3. Code of Federal Regulations,
40 CFR Part 58, Appendix G.
4. Guideline for Reporting of Daily
Air Quality—Air Quality Index (AQI),
EPA-454/R-99-010, U.S. Environ-
mental Protection Agency, Office of
Air Quality Planning and Standards,
Research Triangle Park, NC, July
1999.
5. Note Although the results are
summarized in the report for com-
parison purposes, the intent of pub-
lishing Tables A-16 through A-18 is
to present information on a. localized
basis, to be used on a localized basis
(i.e., one MSA at a time). Therefore,
no attempt was made to adjust the
Type I error to a table-made basis. All
the tests for trends were conducted
at the 5 percent significance level.
No inference has been made from
the tables as a whole.
6. T. Fitz-Simons and D. Mintz,
Assessing Environmental Trends until
Nonparametric Regression in the SAS
Data Step, American Statistical
Association 1995 Winter Conference,
Raleigh, NC, January, 1995.
7. Freas, W.P. and E.A. Sieurin,
A Nonparametric Calibration Procedure
for Multi-Source Urban Air Pollution
Dispersion Models, presented at the
Fifth Conference on Probability and
Statistics in Atmospheric Sciences,
American Meteorological Society,
Las Vegas, NY, November 1977.
8. M. Hollander and D.A. Wolfe,
Nonparametric Statistical Methods,
John Wiley and Sons, Inc., New York,
NY, 1973.
9. Based on the short-term stand-
ards, federal episode criteria, and
significant harm levels, the AQI is
computed for PM (particulate
matter), SO2, CO, O3, and NO2. Lead
is the only criteria pollutant not
included in the index because it does
not have a short-term standard,
federal episode criteria, or significant
harm level.
CHAPTER 3
CRITERIA POLLUTANTS — METROPOLITAN AREA TRENDS
57
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
58 CRITERIA POLLUTANTS —METROPOLITAN AREA TRENDS • CHAPTERS
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
CHAPTER
Criteria Pollutants —
Nonattainment Areas
http://www.epa.gov/oar/airtrends/non.html
Worth Noting
• As of September 2002, there were 124 classified nonattainment areas on the
condensed nonattainment list.
This chapter provides general infor-
mation on geographical regions
known as nonattainment areas.
When an area does not meet the air
quality standard for one of the crite-
ria pollutants, the area may be sub-
ject to the formal rule-making
process that designates the area as
nonattainment. The 1990 Clean Air
Act Amendments (CAAA) further
classify ozone, carbon monoxide,
and some particulate matter non-
attainment areas based on the
magnitude of an area's problem.
Nonattainment classifications may
be used to specify what air pollution
reduction measures an area must
Figure 4-1. Location of nonattainment areas for criteria pollutants, September 2002.
AK • Eagle River
• Juneau
• North Star • Fairbanks
GUAM |
• Piti Power Plant
• Tanguisson
Power Plant
Note: Incomplete data, not classified, and Section 185(A) areas are not shown.
'Ozone nonattainment areas on map are based on the 1-hour ozone standard.
**PM10 nonattainment areas on map are based on the existing PM10 standards.
CHAPTER 4
CRITERIA POLLUTANTS — NONATTAINMENT AREAS
59
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 4-2. Classified ozone nonattainment areas.
1-nour Ozone Standard
September 30, 2001
Classifications
• Extreme (LA) & Severe
Serious
I Moderate
• Marginal
Note: San Francisco is classified Other/Sec 185(A) and nonattainment areas with incomplete data are not included.
adopt and when the area must reach
attainment. The technical details
underlying these classifications are
discussed in the Code of Federal
Regulations, Part 81 (40 CFR 81), see
http://www.epa.gov/docs/epacfr40/
chapt-I.info/subch-C.htm.
Figure 4-1 shows the location of
the classified nonattainment areas
for each criteria pollutant as of
September 2002. Figure 4-2 identifies
the 1-hour ozone nonattainment
areas classified by degree of severity.
A summary of classified nonattain-
ment areas can be found in Table
A-19 in Appendix A. An area is on
the condensed list if the area is
designated nonattainment for one or
more of the criteria pollutants. Note
that Section 185(A) nonattainment
classified areas (formerly known
as "transitional areas") and incom-
plete data nonattainment areas are
excluded from the counts in Table
A-19. Another source of information
for areas designated as nonattain-
ment, including Section 185(A) and
incomplete areas, is the Green Book.
The current Green Book is located at
http://www.epa.gov/oar/oaqps/
greenbk.
As of September 2002, there were
124 classified nonattainment areas
on the condensed nonattainment list.
The areas on the condensed list are
displayed alphabetically by state.
There were, as of September 2002,
approximately 126 million people
living in classified areas designated
as nonattainment for at least one
of the criteria pollutants. Areas
redesignated to attainment between
September 2001 and September 2002
are listed in Table 4-1 by pollutant.
60
CRITERIA POLLUTANTS — NONATTAINMENT AREAS
CHAPTER 4
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 4-1. Areas Redesignated to Attainment from September 2001 to September 2002
Pollutant Area
CO Denver-Boulder
CO Lowell
CO Springfield
CO Waltham
CO Worcester
CO Billings
CO Great Falls
CO New York-N. New Jersey-Long Island*
CO Klamath Falls
CO Medford
Ozone Louisville
Ozone Cincinnati-Hamilton
Ozone Louisville
Ozone Pittsburgh-Beaver Valley
PM10 Mohave County (part); Bullhead City
PM10 Final and Gila counties; Payson
PM10 Ramsey County; (part)
SO2 Central Steptoe Valley
SO2 AQCR 238: Marathon County: Rothschild
Sub-city area, Rib Mountain, Weston
Includes areas classified as nonattainment by the CAAA of 1990.
'The final approval of the NJ portion of the New York-N. New Jersey-Long
redesignation date was 10/22/2002.
State
CO
MA
MA
MA
MA
MT
MT
NY
OR
OR
IN
KY
KY
PA
AZ
AZ
MN
NV
Wl
Island CO
Classification
Serious
Not Classified
Not Classified
Not Classified
Not Classified
Not Classified
Not Classified
Moderate > 12.7ppm
Moderate < 12.7ppm
Moderate < 12.7ppm
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Moderate
Primary
Primary,
Secondary
area was published on 08/30/2002,
Redesignation
Effective Date
01/14/2002
04/22/2002
04/22/2002
04/22/2002
04/22/2002
04/22/2002
07/08/2002
05/20/2002
11/19/2001
09/23/2002
11/23/2001
08/30/2002
11/23/2001
11/19/2001
08/26/2002
08/26/2002
09/24/2002
06/11/2002
07/29/2002
and the effective
CHAPTER 4
CRITERIA POLLUTANTS — NONATTAINMENT AREAS
61
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
62 CRITERIA POLLUTANTS — NONATTAINMENT AREAS • CHAPTER 4
-------
CHAPTER
Air Toxics
http://www.epa.gov/oar/airtrends/toxicrnid.html
Nature and Sources of the
Problem
Toxic air pollutants, or air toxics, are
those pollutants that cause or may
cause cancer or other serious health
effects, such as reproductive effects
or birth defects. Air toxics may also
cause adverse environmental and
ecological effects. Examples of toxic
air pollutants include benzene,
found in gasoline; perchloroethylene,
emitted from some dry cleaning
facilities; and methylene chloride,
used as a solvent by a number of
industries. Most air toxics originate
from man-made sources, including
mobile sources (e.g., cars, trucks,
construction equipment) and station-
ary sources (e.g., factories, refineries,
power plants), as well as indoor
sources (e.g., some building materi-
als and cleaning solvents). Some air
toxics are also released from natural
sources such as volcanic eruptions
and forest fires. The Clean Air Act
identifies 188 air toxics from indus-
trial sources. EPA has identified 20 of
these pollutants that are associated
with mobile sources and one addi-
tional mobile source air toxic desig-
nated "diesel particulate matter and
diesel exhaust organic gases."
Health and Environmental
Effects
People exposed to toxic air pollut-
ants at sufficient concentrations may
experience various health effects,
including cancer, damage to the
immune system, as well as neurolog-
ical, reproductive (e.g., reduced fer-
tility), developmental, respiratory,
and other health problems. In addi-
tion to exposure from breathing air
toxics, risks also are associated with
the deposition of toxic pollutants
onto soils or surface waters, where
they are taken up by plants and
ingested by animals and eventually
magnified up through the food
chain. Like humans, animals may
experience health problems due to
air toxics exposure.
Trends in Toxic Air Pollutants
EPA and states do not maintain an
extensive nationwide monitoring
network for air toxics as they do for
many of the other pollutants dis-
cussed in this report. While EPA,
states, tribes, and local air regulatory
agencies collect monitoring data for
a number of toxic air pollutants,
both the chemicals monitored and
the geographic coverage of the moni-
tors vary from state to state. EPA is
working with these regulatory part-
ners to build upon the existing moni-
toring sites to create a national
monitoring network for a number of
toxic air pollutants. The goal is to
ensure that those compounds that
pose the greatest risk are measured.
The available monitoring data help
air pollution control agencies track
trends in toxic air pollutants in
various locations around the country.
EPA began a pilot city monitoring
project in 2001 and is scheduled to
include at least 12 months of sam-
pling in four urban areas and six
small city/rural areas (see Figure
5-1). This program is intended to
help answer several important
national network design questions
(e.g., sampling and analysis precision,
sources of variability, and minimal
detection levels). In addition, an initial
11-city trends network is being estab-
lished that will help develop nation-
al trends for several pollutants of
concern. For the latest information
on national air toxics monitoring, see
www.epa.gov/ttn/amtic/airtxfil.html.
EPA also compiles an air toxics
inventor}' as part of the National
Emissions Inventory (NEI, formerly
the National Toxics Inventory) to
estimate and track national emis-
sions trends for the 188 toxic air
pollutants regulated under the Clean
Air Act. In the NEI, EPA divides
emissions into four types of sectors:
(1) major (large industrial) sources;
(2) area and other sources, which
include smaller industrial sources
like small dry cleaners and gasoline
stations, as well as natural sources
like wildfires; (3) onroad mobile
sources, including highway
vehicles; and (4) nonroad mobile
sources like aircraft, locomotives,
and construction equipment.
CHAPTER 5
AIR TOXICS
63
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
As shown in Figure 5-2, based on
1996 estimates, the most recent year
of available data, the emissions of
toxic air pollutants are relatively
equally divided among the four
types of sources. However, this
distribution varies from city to city.
Based on the data in the NEI (Figure
5-3), estimates of nationwide air
toxics emissions have dropped
approximately 24 percent between
baseline (1990-1993) and 1996.
Thirty-three of these air toxics, which
pose the greatest threat to public
health in urban areas, have similarly
dropped 31 percent. Although
changes in how EPA compiled the
national inventory over time may
account for some differences, EPA
and state regulations, as well as vol-
untary reductions by industry, have
clearly achieved large reductions in
overall air toxic emissions. Trends for
individual air toxics vary from pollut-
ant to pollutant. Benzene, which is
the most widely monitored toxic air
pollutant, is emitted from cars,
trucks, oil refineries, and chemical
processes. Figure 5-4 shows meas-
urements of benzene taken from 95
urban monitoring sites around the
country. These urban areas generally
have higher levels of benzene than
other areas of the country. Measure-
ments taken at these sites show, on
average, a 47 percent drop in ben-
zene levels from 1994 to 2000.
During this period, EPA phased in
new (so-called "tier 1") car emission
standards; required many cities to
begin using cleaner-burning gasoline;
and set standards that required sig-
nificant reductions in benzene and
other pollutants emitted from oil
refineries and chemical processes.
EPA estimates that, nationwide,
benzene emissions from all sources
dropped 20 percent from 1990 to
1996.
Figure 5-1. Map of 10 cities in monitoring pilot project.
I Seattle
Detroit
Cedar Rapids ~ Providence
San Jacinto Rio Rancho
Figure 5-2. National air toxics emissions,
1996.
4.7MTons
San Juan
Tampa • \
Figure 5-3. National air toxics emissions.
Total for 188 Toxic Air Pollutants
Major
24%
Baseline 1996
(1990-1993)
Figure 5-4. Ambient benzene, annual average urban concentrations, nationwide,
1994-2000.
90% of sites have concentrations below this line
10% of sites have concentrations below this line
94 95 96 97 98 99
1994-00: 47% decrease
oo
64
AIR TOXICS
CHAPTER 5
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
CHAPTER
Special Studies Summary
http://www.epa.gov/oar/aqtrnd03/chapter6.pdf
Summary of Exploratory
Analyses
This chapter summarizes several
recent papers describing analyses
conducted on various policy-relevant
topics. Two of the papers analyze
aspects of particulate matter. The first
covers an event in which particulate
matter was transported from Asia
and its effect on parts of the United
States. The second discusses speciat-
ed PM2 5 in urban and rural areas.
Trends in CO in localized areas are
analyzed in a third article, providing
a better understanding of oxyfuel
programs. Current-year ozone levels
are compared to historical trends in a
fourth paper. New tools are discussed
in two additional papers. One tool is
the coefficient of perfect agreement,
or CPA, which is derived to assist in
characterizing the spatial variation of
pollutants. The final paper discusses
a new reporting and display tool that
could be used to present air quality
information in an innovative way.
The papers are presented in their
entirety in the Special Studies section
at the end of this report.
Impact of April 2001 Asian Dust
Event on PM Concentrations
in the United States
Jim Szykman, David Mintz, Jack Creilson,
Michelle Wayland
On April 6, 2001, the combination of
strong surface winds and an intense
area of low pressure over the Gobi
Desert produced a large dust cloud
that was lofted into the free tropo-
sphere and transported east. The
dust cloud, captured and tracked by
satellite imagery, made its way
across the Pacific Ocean and reached
the United States on April 12 and 13.
Examination of ridges and troughs,
rising or sinking air, and trajectories
showing origins and paths of air
masses were all used to understand
how and when the dust cloud
affected measurements of PM in the
United States.
Figure 6-1. Urban PM2 5 increments.
The position of the dust cloud and
vertical movement of air was found
to determine which regions experi-
enced elevated "soil" PM concen-
trations. U.S. regions from Utah to
Maine were impacted. Specific
regions impacted were the West (on
April 16th), the Southeast (on April
19th), and the Mid-Atlantic/North-
east (on April 22nd).
Quantities of soil-related particles
attributable to the dust storm were
calculated using historical trends to
develop a baseline of typical April
soil concentrations in particulate
*
nJL R^
J | Cleveland H Bronx
Indy rjL Fl
Baltimore
B Richmond
Charlotte
\
Concentrations are |jg/m3.
CHAPTER 6
VISIBILITY TRENDS
65
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
matter. Table 6-1 shows the quanti-
ties attributable to the dust storm by
region. This dust event is the first
time that East Coast soil particulate
matter peaks have been associated
with dust transport from Asia. Peak
concentrations were composed of
fine fraction (detected as PM2 5) in
some locations and coarse fraction
(detected as PM10) at other locations.
Composition of the dust-storm-
related particles was examined using
percentages of potassium, calcium,
and silicon as indicators of whether
the detected dust was Asian in
origin. These chemical speciation
data showed that the Asian dust
contributed, on average, 3.1 to
7.4 ug/in3 to the total PM2 5 mass
concentrations during the period
studied.
Potential health impacts of the
dust were also examined. On the
dates on which the dust cloud was
crossing the United States, there
were nine areas with an EPA Air
Quality Index (AQI) value above
100 for PM10 or PM2 5, indicating that
the air quality posed a health risk to
sensitive populations such as chil-
dren and the elderly. Unfortunately,
there are no speciation data in these
areas for estimating Asian dust con-
tributions. Further review and, in
some cases, additional data would
be needed to determine whether the
Asian dust event contributed to
these levels.
Chemical Speciation of PM2 5
in Rural and Urban Areas
Venkatesh Rao, Neil Frank, Alan Rush,
and Fred Dimmick
Existing ambient air quality monitor-
ing data from the predominantly
urban Speciation Trends Network
(STN) and the predominantly rural
Interagency Monitoring of Protected
Visual Environment (IMPROVE)
network were analyzed to identify
Table 6-1. Estimated PM2 5 Concentrations Attributable to Asian Dust Cloud
Date
4/1 6/01
4/1 9/01
4/22/01
Median
Typical April Soil
Number Site Concentration
of Sites Locations (ug/m3)
43 West 0.7
19 Midwest and 0.5
Southeast
16 Mid-Atlantic and 0.4
Northeast
Median Maximum
Asian Dust Asian Dust
Contribution Contribution
(ug/m3) (ug/m3)
7.4 21.2
3.6 12.9
3.1 7.4
first-order approximations of local
and regional contributions to urban
PM2 5 concentrations from March
2001 to February 2002. Urban sites
were paired with matched rural sites
to calculate the "urban increment" of
PM2 5 mass and increment of individ-
ual species. Data from the two moni-
toring networks were selected and
adjusted to create comparable
datasets. This work addressed the
problem that often half or more of
PM2 5 is composed of secondarily
formed species, thus hiding their
point of origin.
Figure 6-1 shows the urban incre-
ments by components. On average,
the urban excess for the site combi-
nations investigated was found to be
8 ug/m3. Carbonaceous mass was
found to be the major contributor to
urban excess at all sites studied. Such
an amount of PM2 5 implies that pro-
grams are likely needed to address
urban sources of PM2 5.
Carbonaceous mass appears to be
attributed to local emissions, with
mobile sources as a possible major
contributor. Nitrates are prevalent in
the urban excess estimates of the
North and West, but not in the East.
However, more work is needed to
assess the compatibility of nitrate
measurements and monitoring meth-
ods between networks. Some loca-
tions show a sizeable urban excess of
crustal materials, some of which may
be attributed to industrial sources.
Trends in Monitored
Concentrations of Carbon
Monoxide
Jo Ellen Brandmeyer, Peter Frechtel,
Margaret Z. Byron, Joe Elkins,
James Hemby, Venkatesh Rao
In 1999, numerous metropolitan
areas instituted oxygenated gasoline
(oxyfuel) programs during winter
months to reduce CO emissions from
motor vehicles. Some have since
discontinued these requirements.
This paper demonstrates a screening
method for determining CO trends at
specific monitoring stations. By con-
trast, we often examine trends for
regions based on metropolitan statis-
tical areas (MSAs). By eliminating
averaging across MSAs, this study
identified trends in more localized
areas. Uncovering localized trends is
important when one part of an MSA
experiences rapid population growth
accompanied by a rapid growth in
vehicular emissions.
This study used data from EPA's
Air Quality System (AQS), which
contains air quality data from the air
quality monitoring stations. Stations
with at least 8 years of relevant data
during the period 1990 through 2000
were screened for either an upward
linear trend or upward inflection.
The second maximum nonoverlap-
ping 8-hour average of CO for each
monitor over the 11-year period was
used.
66
VISIBILITY TRENDS
CHAPTER 6
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Because no single test will neces-
sarily detect trends at all relevant
sites, three separate statistical tests
were applied to data from each sta-
tion: Theil test, first-order linear
regression, and quadratic (second-
order) linear regression. The three
tests were used together to discern
patterns in the data. Of the 433 sites
analyzed, 34 showed a statistically
significant overall upward trend or
statistically significant upward
curvature. Figure 6-2 shows locations
of these sites and whether they have
discontinued their oxyfuel programs.
Of the sites listing dates ending the
oxyfuel program, all either are locat-
ed in a federal reformulated gasoline
area or have an oxyfuel requirement
in their contingency plan.
This analysis method can be used
to screen for sites with increasing CO
concentrations. The identified sites
should then be examined further to
determine the magnitude of the con-
centrations as compared to the exist-
ing standard. Because both vehicle
miles traveled and the vehicle mix in
fleets are changing with time, the
authors recommend repeating this
analysis annually to determine sites
that warrant further analysis.
Cumulative Ozone
Exceedances—A Measure of
Current Year Ozone Levels
Compared to Historical Trends
Dennis Doll, Terence Fitz-Simons
Policy makers at the state and federal
level are often asked how the current
year's ozone season compares to
previous years. In order to address
that question, the authors used data
measured in the Air Quality System
network of monitoring stations
maintained by EPA's Office of Air
Quality Planning and Standards. We
addressed data from the network of
Figure 6-2. Monitoring stations showing upward CO trends.
monitors assigned to cities for which
the air quality index (AQI) is fore-
casted during the ozone season (i.e.,
April-October), known as the "USA
Today list of cities." Data from 2002
(the most recent year) were com-
pared to a 5-year historical average
in these cities and the regions in
which they are located. Based on this
comparison, policy makers can
qualitatively assess the severity of
the most recent year's ozone meas-
urements with historical year meas-
urements.
To construct the measurements,
the authors used AQS data to ana-
lyze the number of days ozone
measurements exceeded the 8-hour
NAAQS for ozone (>0.085 ppm).
This indicates that air quality falls
into the category "Unhealthy for
Sensitive Groups." For the given set
of monitors assigned to a city, if one
or more monitors measured an
8-hour ozone level >0.085 ppm, the
researchers recorded an exceedance
for the day. This procedure was
repeated for each day of the year for
Upward Trend, Stopped Oxyfuel
• Upward Trend, Other
— State Boundary
the set of monitors assigned to each
city. In this way researchers counted
the number of days exceedances
were measured in a given city in
2002. For the historical 5-year period
1997 to 2001, the average number of
the cumulative count of days was
obtained over the 5-year period for
each set of monitors assigned to each
city to yield a 5-year trend.
We then divided the subject cities
into geographic regions and exam-
ined a 5-year cumulative regional
average as well as city-based aver-
ages. This measure helps illustrate
differences among and within
regions.
Analysis of the southeast region
showed that, in 2002, ozone trends in
Atlanta and Charlotte were similar to
5-year southeast regional trends,
while in Memphis, Nashville, and
New Orleans, the number of exceed-
ances was lower than the 5-year
regional trends. Figure 6-3 shows the
comparison of Atlanta and regional
trends. In contrast, for most of the
cities analyzed in this study in the
CHAPTER 6
VISIBILITY TRENDS
67
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
northeast region, the 2002 data
revealed a lower trend than the
5-year average through approxi-
mately early July, then a higher trend
than the 5-year average from mid-
July into mid-September.
Cities analyzed in the midwest
region analysis showed seasonal
variation for 2002 compared with
the 5-year average. For Chicago,
Cleveland, Cincinnati, Columbus,
Pittsburgh, Indianapolis, Detroit, and
St. Louis, the 2002 data trends were
lower than the 5-year average
through approximately mid- to late
June, then were progressively higher
than the 5-year average from late
June onward. Midwest cities outside
the core midwrest region (e.g., Kansas
City and Minneapolis) showed 2002
data trends similar to or lower than
the 5-year average data.
Characterization of National
Spatial Variation
Terence Fife-Simons
Spatial variability is an important
quality of air pollutants for many
areas of policy within EPA. Monitor-
ing regulations depend heavily on
knowledge of spatial variability.
Control strategies, "action day" pro-
grams, and public information pro-
grams also rely on this knowledge.
This paper explores a new way to
examine spatial variability on a
national scale that addresses the limi-
tations of existing spatial variability
methods.
Traditional Spatial Methods
and Their Limitations
Often spatial variability is exam-
ined by creating a map showing
ranges of pollutant levels by county.
Such a map shows which counties
have higher pollutant values, but
does not allow' easy visualization of
how close adjoining counties are to
others. Some analysts enhance
Figure 6-3. Cumulative exceedances—5-year average (97-01) (Atlanta) compared
to 2002 data and southeast region average.
50
45
40
35
30
25
20
15
10
5
0
Atlanta 5-year Average
.J
'I
ffj
2002
SE Region Average
I I I I I I I I I I I
1/1 2/1 3/1 4/1 5/1 6/1 7/1 8/1 9/1 10/1 11/1 12/1
Date
spatial maps with an estimated
surface of pollutant levels using a
spatial interpolation technique
known as kriging. Kriging removes
the blank areas on a map, making it
somewhat easier to see how pollut-
ants vary over space; however,
because the surface itself is smoothed
by the process, kriging actually hides
some of the spatial variation.
Kriging relies on variograms,
which represent the statistical vari-
ance of the difference between two
data points on a map as it relates to
the distance between the two points
on the map. The variogram, in turn,
relies on the variance, which is a
measure of the spread of a distribu-
tion or data representing measure-
ment differences between two loca-
tions paired by time. The authors use
a scatterplot of particulate matter
(PM2 5) data to examine how effec-
tively such kriged maps represent
the actual relationship between loca-
tions paired by time. The scatterplot
shown in Figure 6-4 makes clear that
there is no simple relationship
between the variance of the differ-
ence and distance. This brings into
question the assumption used in
kriging that the variance of the
difference over distance can be
described by a line.
The authors next investigated
correlation over distance, using
PM2 5 to calculate the correlation of
daily PM2 ^ values between two sites.
Latitude and longitude were used to
calculate the distance between two
sites, producing a correlation and a
distance for each pair of sites. Based
on that information, scatterplots
were generated that further question
the simplicity of the variogram used
in kriging.
Coefficient of Perfect Agreement Method
The coefficient of perfect agree-
ment (CPA) method addresses the
problems raised in the examination
of kriging. CPA provides a measure
of agreement with many of the
characteristics of the correlation
coefficient, thus allowing examina-
tion of the agreement between
pollutant values over distance.
The classical correlation coefficient
is a measure of how well paired
values track each other. The value 0
68
VISIBILITY TRENDS
CHAPTER 6
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
(zero) means they do not track each
other at all, while a value of 1 means
they track each other perfectly. The
correlation coefficient is defined as:
The authors discuss several issues
involved in constructing a CPA,
including sample size, and managing
units conversion so that the resulting
CPA is unitless. Within those restric-
tions, the authors apply the CPA to
construct a new scatterplot of PM2 5.
Figure 6-5 shows that the denser part
of the distribution dips quickly and
falls off gradually. This is a different
trend than that found in the earlier
scatterplot (shown in Figure 6-4)
based on variance of difference vs.
distance.
This scatterplot gives a national
picture of the spatial variation of
PM2 5. The mean CPA starts off at
around 0.6 and falls off rapidly out
to about 150 km, then falls off
gradually to about 0.2 at 500 km.
Quantitatively, interpretation of this
coefficient is difficult, but it is useful
in comparisons with other pollut-
ants. To compare pollutants, the
authors display the scatterplot as a
box and whisker plot. Pollutants can
then be compared by joining the
means by a line for several pollut-
ants.
Such comparisons between pollut-
ants could be used to guide policy.
For example, daily values of PM2 5,
daily values of PM10, hourly values
of CO (carbon monoxide), and
hourly values of ozone were used to
produce Figure 6-6. The plot of PM2 5
has a mean CPA that is above ozone
for most of the distances out to at
least 450 km. This might suggest
Figure 6-4. Variance of the difference
500
vs. distance.
- 4.rtai$"t*!«ttf?
t
50 100 150 200 250 300 350 400 450 500
Distance (km)
Figure 6-5. CPA vs. distance (km).
1.0
-0.8
0 50 100 150 200 250 300 350 400 450 500
Distance (km)
Figure 6-6. Comparison of mean CPA vs. distance (km).
o
1.0
0.8
0.6
0.4
0.2
0.0 \
Pollutant:
CO
— PM
2.5
1-hr ozone
50 100 150 200 250 300 350 400 450 500
Distance (km)
CHAPTERS • VISIBILITY TRENDS 69
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
that, if a regional control strategy is
being pursued for the ozone problem
in the United States, a regional strat-
egy also makes sense for PM2 -.
Development of a New Reporting
Technology for Air Quality
Prepared by RT1 International for the Office
of Air Quality Planning and Standards
This display technique would
provide the general public with a
new tool to review air quality in
MSAs around the United States. The
primary function of the display
would be to present location- and
pollutant-specific air quality data in
a graphical format that allows for
easy interpretation of air quality data
for MSAs. The display would not
provide new or additional air quality
data; rather, it would present existing
data in a new' format. The graphical
display of data would improve the
public's access to air quality informa-
tion and enhance their ability to use
this information in a meaningful
way Potential capabilities that may
be added include a Web-based appli-
cation that would allow users to sort
and query information to generate
customized reports, as well as visibil-
ity and multiyear components.
EPA recognizes that there are limi-
tations to this new display technique
and is continuing to assess the use-
fulness of such a reporting method
as well as additional capabilities that
might be added. Developing a
simple metric for displaying air
quality data on an urban basis across
the nation is a difficult and challeng-
ing endeavor. However, EPA feels
that this information is useful and
informative to the public, especially
to those who have potential health
concerns related to poor air quality.
A graphical display that is easily
understood is essential to communi-
cating this information, and EPA will
continue to refine the display to
ensure that it meets this objective
based on comments and input from
the air quality community and
potential users.
70
VISIBILITY TRENDS
CHAPTER 6
-------
2003 SPECIAL STUDIES
Chemical Speciation
of PM2.5 in Urban and
Rural Areas.. .. S13
-------
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Impact of April 2001 Asian Dust Event on Particulate Matter
Concentrations in the United States
Jim Szykman
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
(currently located at Atmospheric Science Competency, NASA Langley Research Center, Hampton, VA 23681)
David Mintz
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Jack Creilson
SAIC, NASA Langley Research Center, Hampton, VA 23681
Michelle Wayland
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Abstract
In April 2001, a large dust storm formed over the
Gobi desert in northern China. Satellite remote sensing
data and analyses of meteorological conditions were
used in this study to follow the dust cloud from China,
over the Pacific Ocean, and then coast to coast across
the United States over a period of several weeks.
Chemical speciation data were used to estimate the
PM2 5 mass increment associated with the Asian dust,
and peak concentrations were plotted to show the
progression of elevated concentrations across the
contiguous United States. Meteorological analyses,
including air parcel trajectories, were used to link the
dust cloud overhead to the concentrations below. Also,
the contribution of Asian dust to the total mass concen-
trations measured at the monitors was examined with
respect to the U.S. Environmental Protection Agency's
(EPA's) health standards and Air Quality Index (AQI)
for particulate matter. The findings suggest that this
transport event contributed to higher PM concentra-
tions in several areas across the United States, with
"average" estimated contributions ranging from
3.1 to 7.4 mg/m3. Because the event occurred in the
springtime when daily concentrations of other PM
components are generally low, there were relatively
few areas with "unhealthy" AQI days. Nevertheless,
this event possibly contributed to "unhealthy" AQI
days in three areas. In addition, it raised the 3-year
average related to the long-term PM2 5 health standard
by an estimated 0.1 mg/m3 in the affected regions.
For most sites, this is insignificant, but there are
implications for sites with 3-year averages just above
the level of the standard.
Introduction
In early April 2001, an unusually
large dust storm developed over the
Gobi desert in northern China
(Figure 1). The generation of dust
storms and their impact on islands in
the North Pacific have been the focus
of research dating back to the late
1960s.2 However, the focus on the
impacts of Asian dust storms did not
turn to the western United States
until 1998.3'4 In recent years, the
satellite remote sensing data from
such instruments as TOMS (Total
Ozone Mapping Spectrometer),
SeaWIFS (Sea-viewing Wide Field-
Figure 1. Map of Mongolia and northern China, highlighting the Gobi Desert region.1
Tanm Basin
40" North
Latitude
Taklimakan
Desert
SPECIAL STUDIES
ASIAN DUST EVENT
S1
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
of-view Sensor), MODIS (Moderate
Resolution Imaging Spectroradiom-
eter), and AVHRR (Advanced Very
High Resolution Radiometer) have
added a new dimension to studying
such episodic events. These satellite
sensors now allow the movement of
the dust plume to be captured. In the
case of the April 2001 dust storm, the
satellites provide an eye-catching
image of the dust cloud arriving at
the doorstep of the western United
States and beyond. But what does
such an event, and the compelling
satellite images resulting from the
event, mean with respect to air
quality in the United States and in
particular to the levels of health
concern for particulate matter?
The purpose of this paper is to
provide a meaningful analysis of the
impact of the April 2001 Asian dust
storm on ground-level particulate
matter concentrations within the
contiguous United States. In this
paper, we explore the formation of
the dust storm over the Gobi Desert,
the transport of the dust from its
origin to the east coast of the United
States, the mechanism for transport
of dust to the boundary layer, and
the ground-level impacts of the dust
storm.
Following the Asian
Dust Cloud
Formation over the Gobi Desert
Wind-blown dust in eastern Asia is a
locally well-known springtime
occurrence. The dust storms tend to
originate in the arid deserts of
Mongolia and China, particularly the
Gobi Desert, and spread eastward
with the prevailing winds. The dust
cloud itself forms when the friction
from high surface winds, with
speeds typically in excess of 5 m/s,5
lifts loose dust particles up into the
boundary layer and lofts them into
the free troposphere where they can
be transported eastward.3
An analysis of surface meteoro-
logical data for April 6 from the
National Center for Environmental
Prediction/National Center for
Atmospheric Research (NCEP/
NCAR) Reanalysis Project6 indicates
that a strong Siberian low-pressure
area (985 mb) was located in north-
eastern Mongolia (Figure 2). This
feature, coupled with relatively
higher pressure to the south,
produced strong surface winds in
excess of 24 m/s in eastern and
southern Mongolia. The windspeeds
shown are well above the threshold
for particle suspension of 5 to 6 m/s5
and are located over the Gobi Desert
region.
The deep low-pressure area
evident in Figure 2 continued to
propagate eastward on April 7 with
the center of maximum winds
mirroring the track of the cyclone
(low-pressure area). Averaged over a
24-hour period, the maximum sur-
face winds were greater than 20 m/s.
The sustained windspeed combined
with the upward vertical velocities
associated with the low-pressure
system were sufficient to elevate the
dust above the boundary layer for
transport. An analysis of the circula-
tions at 700 and 500 mb showed that
the flow was essentially zonal (along
the latitude) and toward the east-
northeast. The zonal flow allowed
the dust cloud a relatively direct
pathway to the Pacific Ocean.
Satellites also confirm the forma-
tion of the dust cloud. Figure 3 is a
composite AVHRR image from the
National Oceanographic and Atmos-
pheric Administration (NOAA)-16
satellite centered over Mongolia and
northern China on April 6. This
image clearly shows the wind-driven
dust over southeastern Mongolia
becoming entrained in the low-
pressure system to the north. The
low-pressure area is indicated in the
image by the cyclonic cloud forma-
tion. The location of the blowing
Figure 2. April 6, 2001, surface windspeeds (color-shaded regions in m/s) overlaid
with sea level pressure contours (mb) over the Mongolia and northern
China region.
60N
42N
39N-
36N-
33N-
30N
80E
85 E
90E
100E
80E
105E 110E
115E
120E
125E
130E
S2
ASIAN DUST EVENT
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
dust, highlighted by the red arrows,
correlates well with the center of
maximum surface winds shown in
Figure 2.
Transport across the Pacific
Ocean
Once the dust cloud reached the
Pacific Ocean on April 8, it was
carried by the northern midlatitude
westerly winds (30°-60°N) that are
typical during the springtime. Figure
4, created using data from both
TOMS and SeaWiFS,7 shows the daily
progression of the dust cloud.8 The
TOMS aerosol index (AI) has been
used in the past to show the daily
spatial distribution of dust clouds.9
As shown in Figure 4, the dust
cloud remained fairly compact, with
no large sections peeling off north-
ward and no evidence that longitudi-
nal stretching occurred. It is difficult
to determine the actual height at
which the cloud was transported. Its
rapid movement across the Pacific
Ocean (5-day average speeds in
excess of 20 m/s at 500 mb) and the
lack of strong removal processes
suggest that the cloud was in the free
troposphere and traveling with the
strong trans-Pacific westerly flow.
The transport speed and zonal flow
pattern during this period were veri-
fied by an analysis of the circulation
at 500 mb.
Transport across the United
States
As Figure 4 shows, the dust cloud
first passed over the west coast of
North America on April 12 and 13,
initially impacting Canada and then
the United States. An analysis of
meteorological data (Figure 5d-f)
shows that the transport of the dust
cloud in the free troposphere on April
12 and 13 was from the northwest
around the top of a high-pressure
ridge that was off the coast of the
Figure 3. NOAA-16 AVH RR image of the dust storm over Mongolia for April 6, 2001
(Image courtesy of NOAA).
Figure 4. Path of the dust cloud from Asia to the United States, April 6 through
April 14, 2001.
United States. The pattern then
became zonal, which lasted until
April 15, when a large high-pressure
ridge developed over the Rocky
Mountains. The strong ridge moved
slowly eastward, carrying the dust
cloud with it. Once the ridge moved
into the Southeast, it became stalled,
allowing the dome of high pressure
to increase in size and strengthen,
thus trapping the dust cloud within
it. The ridge over the Southeast
lasted from April 19 to 23, causing
southwesterly flow into the North-
east. This flow transported the dust
cloud from the Southeast into the
mid-Atlantic and Northeast regions
on April 22 and 23.
SPECIAL STUDIES
ASIAN DUST EVENT
S3
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
A review of TOMS AI and
SeaWIFS to assess the temporal and
spatial movement of the Asian dust
as it crossed over the United States
indicates that there were several days
that the TOMS AI showed a dust
cloud covering much of the United
States. An analysis of meteorological
conditions in conjunction with the
measurements taken at PM monitors
indicates that large-scale transport
from the free troposphere to the
boundary layer did not always occur.
In some instances, it appears that the
Asian dust was transported over the
entire United States with relatively
little effect on PM concentrations
below (Figure 5).
However, as the dust cloud
passed over the United States, moni-
tors in some locations did measure
elevated concentrations (>5 ug/m3)
of the soil component of PM2 5 at
some time during the month of April
2001, as discussed later in this paper.
A closer look at the meteorology,
including the location and move-
ment of ridges and troughs from
west to east, the rising or sinking of
large-scale areas of air (negative and
positive omega [CD], respectively) at
700 mb,10 and the calculation of
trajectories using the HYbrid Single-
Particle Lagrangian Integrated
Trajectories (HYSPLIT) program,11"13
helps to explain the timing and
location of the elevated particulate
concentrations with respect to the
cloud of Asian dust. Three dates are
described here, corresponding to
three areas of the country that were
affected by the dust cloud: the West,
the Southeast, and the Mid-Atlantic/
Northeast.
April 16, the West
As shown in Figure 5 (a and d), the
peak concentrations seen over the
West on this day can be attributed to
the synoptic-scale ridging that was in
place on April 15. The development
of this ridge, coupled with the
elevated terrain of the West, caused
descending air. This large-scale sink-
ing of air typically occurs under
domes of high pressure. Also influ-
encing the concentrations in the West
is the likelihood that the dust cloud
would have its greatest impact in
this region because its first opportu-
nity for measurable deposition was
here. The high concentrations in the
boundary layer were supported by
numerous reports of decreased
visibility at many of the national
parks (Figure 6) and major cities
located in that region, as well as with
laser radar (LIDAR) measurements
taken in Boulder, CO, on April 15.14
April 19, the Southeast
The peak concentrations seen in the
Southeast on April 19 (Figure 5b) can
be attributed to large-scale dynamic
forcing that is associated with epi-
sodes of strong sinking motion (posi-
tive CD). Figure 5e shows the 700-mb
height and omega patterns over the
Southeast for April 18. A large area
of sinking air is shown in red over
this region, suggesting that for the
Southeast there is a 1-day lag
Figure 5 (a-c). Peak PM2 5 estimated soil mass from IMPROVE and STN monitoring
networks.
(d-f) NCEP/NCAR reanalysis data for co (color-shaded regions in pascal/s),
overlaid with 700-mb heights.
S4
ASIAN DUST EVENT
SPECIAL STUDIES
-------
between the day of peak positive CD
(sinking motion) and the peak con-
centrations measured at the monitors
on April 19. The length of the lag
appears to depend on the meteorol-
ogy but may also be exaggerated by
the once-every-3-day monitoring
schedule, as well as the fact that 24-
hour PM concentrations are deter-
mined by averaging hourly measure-
ments from midnight to midnight.
Results of a 3-day backward
ensemble trajectory (Figure 7) pro-
vide insight into the origin of the air
mass coming into the Southeast on
April 19. The backward ensemble
trajectory starts from four separate
monitoring locations: Okefenokee,
FL, Cape Romain, SC, Great Smoky
Mountains, TN, and Gulfport, MS.
The results for the four ensemble
trajectories show consistent flow
fields with little divergence from the
general origin of the air mass, which
is the Midwest. The trajectory results
were not surprising when compared
to the NCEP/NCAR reanalysis data
over the same time period. The
NCEP/NCAR reanalysis data for the
700-mb heights (Figure 5e) show a
northerly flow from the Midwest
into the Southeast. A comparison of
the vertical motion of the trajectories
with CD (Figure 5e) shows good agree-
ment with a large area of sinking air
in the Southeast on April 18. When
compared with the April 17 TOMS
AI and SeaWIFS (Figure 8), this infor-
mation suggests that the large dust
cloud passing over the Great Lakes
region is the likely source of the ele-
vated levels of particulate matter.
April 22, Mid-Atlantic/Northeast
The peak concentrations seen in the
mid-Atlantic and Northeast on April
22 (Figure 5c) can be attributed to a
combination of ridging over the
Southeast and a pattern of generally
subsiding air over the region. The
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 6. Haze over Glen Canyon National Recreation Area (UT, AZ) on
April 16, 2001.4
Figure 7. Three-day backward ensemble
trajectories originating from Okefenokee,
FL (30.74 N 82.13 W), Cape Romain,
SC (32.94 N 79.66 W), Great Smoky I
Mountains, TN (35.63 N 83.94 W), and f
Gulfport, MS (30.39 N 89.05 W) and :
ending at 15 UTC (11:00 a.m. EOT) on I
April 19, 2001.
Figure 8. The SeaWiFS image taken on April 17, 2001, shows dust over the Great
Lakes region. The eclipsed area in the image is a result of areas not
covered during the SeaWiFS overpass on this day. The inset shows that
TOMS Aerosol Index for April 17 also captures the dust cloud over the
Great Lakes region, extending down into the southeastern United States.
Earth Probe TOMS AI
April 17,2001
NASA/Goddard Space Flight Center, The SeaWIFs Project, and ORBIMAGE Science Visualization Studio.
SPECIAL STUDIES • ASIAN DUST EVENT S5
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
ridging over the Southeast seen on
April 21 (Figure 5f) is associated with
a developing dome of high pressure
that generated southwesterly flow
toward the Northeast around the
periphery of the high. This return
flow would have transported any
boundary layer pollution (i.e., dust)
located over the region into the mid-
Atlantic and northeast regions. This
synoptic feature, coupled with any
sinking air that forced down the
remains of the dust cloud, is a likely
cause of the increased particulate
concentrations seen in the mid-
Atlantic/Northeast. A series of
backward trajectories from several
mid-Atlantic and northeastern moni-
toring sites with elevated particulate
matter concentrations on April 22
indicate that air originated from the
southeastern United States 2 days
prior to April 22. This result is con-
sistent with the results of the 700-mb
analysis.
Assessing the Impact of
the Asian Dust Cloud
Characteristics of Particulate
Matter
Monitoring data from the PM2 5
chemical Speciation Trends Network
(STN)15 and the Interagency Moni-
toring and Protected Visual Environ-
ment (IMPROVE) aerosol monitoring
network16 were used to examine the
elemental soil components. In addi-
tion, mass measurements from the
national PM10 and PM2 5 Federal
Reference Method (FRM) networks
were used to assess the health impact
of the April 2001 dust event across
the United States.
The STN and IMPROVE network
use similar sampling and analytical
methods to generate similar aerosol
composition data. The soil compo-
nent of PM2 5 can be determined from
the measurements made by these net-
works using the following formula:
PM2 5 dust = 2.2 [Al] + 2.49 [Si]
+ 1.63[Ca] + 2.42 [Fe]
+ 1.94[Ti].17
In the United States, dust (also
called crustal material or soil) in the
ambient air typically originates from
wind-blown dust, road surface
materials, construction activity, and
certain agricultural activities.18 Dust
particles are typically less than 10 um
in diameter. Those particles nomi-
nally less than 2.5 um in diameter are
typically measured as part of the fine
(PM2 5) mass. Those between 2.5 and
10 um are typically measured as part
of the coarse (PM10-PM2 5) mass.
Because monitors do not have a
perfectly sharp size separator at the
2.5-um cutpoint, some of the parti-
cles greater than 2.5 um can be cap-
tured as PM2 5 mass, and some of the
particles measuring less than 2.5 um
can be captured as coarse mass.19
The degree to which this occurs
varies, depending on the monitoring
device and particle separator. During
the April 1998 Asian dust event, the
mass mean diameter of the dust was
observed to be 2 to 3 um, overlap-
ping the 2.5-um cutpoint.3
Soil concentrations make up only
a small fraction of PM2 5 in the East
and most areas of the West. Other
components such as sulfates, nitrates,
and carbon make up the majority of
the PM2 5 mass. Concentrations of
these components are influenced by
meteorology and emission sources
and, therefore, vary by season and
region of the country.
Because very few speciation data
are available for the coarse mass, and
there is a growing network of PM2 5
speciation data, the analyses in this
paper focus on PM2 5 soil compo-
nents. Results relevant to EPA's
particulate matter health standards
are shown in terms of PM2 5 and
PM10 mass.
Examining Historical Trends
Although 24-hour PM2 5 soil con-
centrations are typically low
(<3 ug/m3),20 unusual events such
as dust storms can cause short-term
peaks. Local dust storms in the
desert Southwest are relatively com-
mon. However, long-term transport
of dust from Asia to North America
is not, although there is evidence
suggesting that Asian dust storms
have become more intense in the
past decade. Recent studies have
linked the increased intensity to
climate change, drought conditions,
and land use practices in China.
The dust transported from Asia in
April 2001 caused the soil compo-
nent of PM2 5 to rise dramatically at
certain locations in the United States,
with some monitoring sites seeing
record-high levels. The PM2 5 soil
concentration at Canyonlands
National Park in southeast Utah
(Figure 9), for example, measured
16.6 ug/m3, twice as high as any
previous measurement on record.
However, other sites have measured
higher levels in previous years.
Sula, MT (Figure 10), for example,
recorded a higher concentration
during the April 1998 Asian dust
event.3 At sites in the Southeast,
such as Okefenokee National
Wildlife Refuge in Georgia (Figure
11), the peaks in previous years are
consistent with seasonal Sahara dust
transport.
April 2001 is the first time that
East Coast soil peaks have been
associated with dust transport from
Asia. The site at Brigantine National
Wildlife Refuge in New Jersey
(Figure 12), for example, had a peak
soil concentration of 7.8 ug/m3 on
S6
ASIAN DUST EVENT
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 9. Historical PM2 5 soil concentrations at Canyonlands National Park.
20
18
16
14
10-
6
4
2
0
TO CO
c^
0) CD
"
o
o
April 16
16.6ug/m3
JlVJ^jtWJ^
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Figure 10
20
18-
16:
14:
I 12J
CO CO
a) "CD "
o ^
c
o
O
6-
4-
2:
0
Historical PM2 5 soil concentrations at Sula Wilderness Area.
.._..hL>A^
April 16
8.8 ug/m3
u
19891990199119921993199419951996199719981999200020012002
Figure 11. Historical PM2 5 soil concentrations at Okefenokee National Wildlife Refuge.
20 22.7 21.1 25.4
18-
14-
-
0) CD
10:
o 8
O
6-
April 19
13.5ug/m3
1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
SPECIAL STUDIES • ASIAN DUST EVENT S7
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 12. Historical PM25 soil concentrations at Brigantine National Wildlife Refuge.
20.
18-
16:
14:
12-
10:
8:
6:
4:
2-
0
April 22
7.8 ug/m3
Ji^wJU^uJfi^^
1988 1989 1990
1991
199219931994199519961997IE
1999200020012002
April 22. Other sites along the East
Coast, from Florida to Maine, had
modest increases in soil concentra-
tions from mid to late April.
Estimating Asian Dust
Contribution to PM2.5 Mass
A logical next step in assessing the
impact of this dust event on PM2 5
mass concentrations was to estimate
the soil increment associated with
Asian dust on days with peak soil
concentrations. The IMPROVE
network provided enough historical
data to develop a baseline of "typi-
cal" April soil concentrations. The
typical April soil concentration was
represented by the median of all
April observations from years other
than 2001. An estimate of Asian dust
contribution was obtained by sub-
tracting the typical April soil contri-
bution from the peak soil concentra-
tion on a site-by-site basis. In this
way, an estimate of Asian dust con-
tribution was obtained for every
IMPROVE site having adequate data.
A graphical illustration of this proce-
dure is provided in Figure 13.
Table 1 groups the sites by date of
peak soil concentration. Because it is
less resistant to extreme values, the
median among sites is used to repre-
sent typical values for each date. As
might be expected from the dust
cloud location shown earlier in this
paper, most sites in the West had
peak concentrations on April 16. The
median Asian dust contribution was
7.4 ug/m3, ten times as much as the
median of the typical April soil con-
centrations (0.7 ug/tn3). The highest
Asian dust contribution on this date
(21.2 ug/m3) occurred at a site in
Death Valley, CA. The PM2 5 and
PM10 mass values at this site were
30.7 ug/m3 and 59.9 ug/m3, respec-
tively.
On April 19, sites in the Midwest
and Southeast experienced peak soil
concentrations. The Asian dust con-
tribution on this date was 3.6 ug/m3,
compared to 0.5 ug/m3 for typical
April days. The site with the highest
contribution (12.9 ug/m3) was the
Okefenokee National Wildlife Refuge
in southeastern Georgia. The PM2 5
and PM10 mass values at this site
were 22.2 ug/m3 and 50.7 ug/m3,
respectively.
On April 22, sites in the mid-
Atlantic and Northeast experienced
peak soil concentrations. The Asian
dust contribution was 3.1 ug/m3,
compared to typical April soil
concentrations (0.4 ug/m3). The site
at Brigantine National Wildlife
Refuge had the highest Asian dust
contribution (7.4 ug/in3). The PM2 5
and PM10 mass values at this site
were 24.4 ug/m3 and 50.6 ug/m3,
respectively.
The dates of the peaks in soil con-
centrations correspond directly to the
meteorological and satellite informa-
tion presented in earlier sections. The
median Asian dust contribution
ranges from 3.1 to 7.4 ug/m3 during
the April 16-22 period, with double-
digit contributions in some locations.
Examining Soil Composition
on Peak Days
There is some uncertainty associated
with the composition of transported
dust, mainly because of the lack of
speciation data, especially for the
coarse fraction. However, some
insights can be gained by examining
the PM2 5 speciation data measured
during the April 2001 Asian dust
event.
We examined various elemental
concentrations and ratios in search
of potential indicators of Asian dust.
Specifically, we compared the
primary elemental soil components
on the April 2001 peak days with
typical April days (represented by
the median of April data from other
years). We then identified a subset of
20 sites with peak soil concentrations
S8
ASIAN DUST EVENT
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 13. PM2 5 soil concentrations, April 2001 vs. typical April days, at Brigantine National Wildlife Refuge.
20
o
O
15-
CO
I
I 10-1
5-
Baseline distribution: 77 obs from 1992 to 2000
1 Max
^^^ . Median = 0.4
Min
Peak Soil Concentration - Typical Soil Concentration = Asian dust increment
7.8 ug/m3 0.4 ug/m3 7.4 ug/m3
April 22
Table 1. Summary of Asian Dust Contribution by Date
Date
4/1 6/01
4/1 9/01
4/22/01
Number
of Sites
43
19
16
Site
Locations
West
(AZ, CA, CO, ID, MT,
NM, NV, OR, UT, WA,
WY)
Midwest and Southeast
(FL, GA, Ml, MN, NC,
ND, SC, SD)
Mid-Atlantic and Northeast
(DC, KY, ME, NJ, VA, VT, WV)
Median Typical
April Soil
Concentration
(ug/m3)
0.7
0.5
0.4
Median
Asian Dust
Contribution
(ug/m3)
7.4
3.6
3.1
Maximum
Asian Dust
Contribution
(ug/m3)
21.2
12.9
7.4
corresponding to the position of the
dust cloud. The most distinctive
contrast among the indicators was
potassium (K) as a percent of total
PM2 5 soil mass. The percent of
potassium (%K) was 3 to 4 on the
peak days. In eastern areas where
%K is typically much larger, this
appears to be a good indicator that
the soil composition is atypical.
However, in the desert Southwest
and Rocky Mountain regions,
where the %K is typically 4, the
ratio is of little help. Figure 14 is an
aggregation of the data at sites in
these regions.
In addition to %K, the percent
of calcium (%Ca) and the percent
of silicon (%Si) between 2001 peak
days and typical days are signifi-
cantly different in the eastern sites.
Because the peak day %Ca and
%Si are different in the western loca-
tions vs. the eastern locations, it is too
early to speculate whether they could
be potential indicators of Asian dust.
It is certainly possible that the dust
size and composition differ after sev-
eral days and several thousand miles
of transport. More speciation data,
especially in the coarse range, could
help explain differences in composi-
tion of transported dust.
Assessing Potential Health
Impact
As the satellite and meteorological
information suggests, only certain
regions (coinciding with the position
of the dust cloud and the vertical
movement of air) experienced
elevated soil concentrations and, con-
sequently, higher PM10 and PM2 5
concentrations. Sometimes the
increase was reflected evenly in the
coarse and fine fractions, but in most
cases the coarse fraction showed a
larger increase than the fine. Two
examples of the effect of this Asian
dust event on PM10 and PM2 5 mass
are shown in Figures 15 and 16. The
peak at the Salt Lake City site
occurred on April 16. In this example,
most of the increase is reflected in the
coarse fraction. On April 22, several
days later, concentrations peaked at
the Acadia National Park site in
Maine. Unlike the Salt Lake City
example, more of the increase here is
reflected in the fine fraction.
SPECIAL STUDIES
ASIAN DUST EVENT
S9
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 14. Summary of PM2 5 soil composition on April 2001 peak days vs. typical April days, by region.
Desert SW and Rocky Mountain Regions
(13 sites in CA, CO, MT, NM, NV, UT, WY)
Eastern Region
(7 sites in FL, GA, ME, NC, NJ, SC)
Ca
Fe
Si
Ti
Ca
Fe
Si
Ti
In the preceding examples, the
resulting PM mass concentrations
show an increase, but the peaks are
not above a significant level of health
concern for the general population.
EPA has designed an index, the Air
Quality Index, to communicate infor-
mation about daily air quality and
associated health concerns. Accord-
ing to the AQI, cautions for sensitive
populations (people with heart or
lung disease, older adults, and chil-
dren) are associated with daily PM2 5
and PM10 concentrations greater than
40.4 ug/m3 and 154 ug/m3, respec-
tively. These concentrations corre-
spond to an AQI value of 100. The
cautionary statement associated with
PM concentrations at this level of
concern says that "people with heart
or lung disease, older adults, and
children should limit prolonged or
heavy exertion." There are additional
health concerns associated with high-
er concentration ranges.22
There were nine areas (cities or
counties) corresponding to the gen-
eral location and movement of the
dust cloud that had at least 1 day
with an AQI value above 100 for
PM2 5 or PM10. Four of these areas
had no days above 100 during the
entire spring season in the surround-
ing years (1999, 2000, and 2002).
Unfortunately, there are no specia-
tion data in these areas for estimat-
ing Asian dust contribution. How-
ever, based on estimates computed
previously for nearby IMPROVE
sites, three of the nine areas might
have actually been below 100 were it
not for Asian dust contribution. Still,
further review and, in some cases,
additional data might be needed to
determine exact contributions from
Asian dust versus dust from other
sources.
Because this transport event
occurred in April, a temperate part of
the year, meteorological conditions
were not conducive to the formation
of sulfates, nitrates, or organic car-
bon (major components of PM2 5
mass). If higher levels of any of these
components were combined with the
increased dust concentrations, there
might have been more AQI values
above 100.
With respect to EPA's long-term
health standard for PM2 5, the 1- or
2-day increases from this dust event
had relatively little effect. For exam-
ple, when the "Median Asian Dust
Contribution" (3.1 to 7.4 ug/m3,
depending on region) from Table 1 is
excluded from the 3-year averages
for 1999 through 2001, the averages
are 0.1 ug/m3 lower. This small shift
could be important for any sites
bordering the level of the standard
of 15.0 ug/m3. For this particular
3-year period, there were three coun-
ties with averages of 15.1 ug/m3, just
above the standard. Further review
would be required to determine
whether or not the sites in these
counties were affected by the Asian
dust and to what extent.
Conclusions
On April 6, 2001, the combination of
strong surface winds and an intense
area of low pressure over the Gobi
Desert produced a large dust cloud
that was lofted into the free tropo-
sphere and transported eastward.
The dust cloud, captured and
tracked by satellite imagery, made its
way across the Pacific Ocean and
ultimately reached the United States
on April 12 and 13. Once the cloud
was over the United States, sinking
air associated with large areas of
subsidence and strong downward
vertical motion appeared to coincide
S10
ASIAN DUST EVENT
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 15. Daily PM10, PM2 5, and soil (PM2 5) concentrations at Salt Lake City, UT.
April 16
PMIOmass = 73
PM2.5 mass = 15.0
Soil (PM2.5) = 9.7
4/2 4/3 4/4 4/5 4/6 4/7 4/8 4/9 4/10 4/11 4/12 4/13 4/14 4/15 4/16 4/17 4/18 4/19 4/20 4/21 4/22 4/23 4/24 4/25 4/26 4/27 4/28 4/29 4/30 5/1
Figure 16. Daily PM10, PM2 5, and soil (PM2 5) concentrations at Acadia National Park, ME.
April 22
PMIOmass = 19.7
PM2.5 mass = 14.3
Soil (PM2.5) =
with increased soil concentrations in
certain areas of the country. In some
instances, there appeared to be a
lagged relationship (days with
increased concentrations lagging
days of strong downward vertical
motion). This lag could be exaggerat-
ed by the once-every-3-day monitor-
ing schedule as well as 24-hour aver-
aging technique employed at the
monitoring sites.
Although the TOMS imagery
showed days with a dust cloud over
much of the United States, an analy-
sis of meteorological conditions in
conjunction with IMPROVE and STN
monitors indicated that large-scale
transport to the boundary layer
(which would result in increased
particulate matter concentrations)
did not occur everywhere. Ridges
and troughs, rising or sinking air,
and trajectories showing the origins
and paths of air masses were all
examined to gain an increased
understanding of how and when the
Asian dust cloud affected the moni-
tors below.
In the areas identified by the satel-
lite and meteorological information,
chemical speciation data showed that
Asian dust contributed "on average"
3.1 to 7.4 ug/m3 to the total PM2 5
mass concentrations during the April
16-22 period. There were nine areas
(cities or counties) corresponding to
the general location and movement
of the dust cloud that had at least
1 day with an AQI value above 100
for PM2 5 or PM10. Values for three of
the nine areas might have actually
been below 100 were it not for Asian
dust contribution. Still, further
review and, in some cases, additional
data might be needed to determine
exact contributions from Asian dust
versus dust from other sources.
Because the event occurred in the
SPECIAL STUDIES • ASIAN DUST EVENT
S11
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
springtime when daily concentra-
tions of other PM components are
generally low, there were relatively
few areas with AQI days above 100.
If higher levels of any of these com-
ponents were combined with the
increased dust concentrations, there
might have been more AQI values
above 100.
With respect to EPA's long-term
health standard for PM2 5, this dust
event raised the 3-year average by an
estimated 0.1 ug/m3 in the affected
regions. For most sites, this is
insignificant, but there are implica-
tions for sites with 3-year averages
just above the level of the standard.
References
1. Asian Dust Clouds, (http://www.
lakepowell.net/asiandust.htm)
(accessed October 2002).
2. Rex, R. W.; Syers, J.W.; Jackson,
M.L.; Clayton, R.N. Eolian origin of
quartz in soils of Hawaiian Islands
and in Pacific pelagic sediments;
Science. 1969,163, 277-291.
3. Husar, R. B.; Tratt, D.M.; Schichtel,
B.A.; Falke, S.R.; Li, E; Jaffe, D.;
Gasso, S.; Gill, T.; Laulainen, N.S.;
Lu, E; Reheis, M.C.; Chun, Y.; West-
phal, D.; Holben, B.N.; Gueymard,
C; McKendry, I.; Kuring, N; Feld-
man, G.C.; McClain, C.; Frouin, R.J.;
Merrill, J.; DuBois, D.; Vignola, E;
Murayama, T.; Nickovic, S.; Wilson,
WE.; Sassen, K.; Sugimoto, N.;
Malm, WC. Asian dust events of
April 1998; /. Geophys. Res. 2001,106,
18,317-18,330.
4. Tratt, D. M.; Frouin, R.J.;
Westphal, D.L. April 1998 Asian dust
event: a southern California perspec-
tive; /. Geophys. Res. 2001,106,
18371-18379.
5. Gillette, D. A wind tunnel simula-
tion of the erosion of soil: Effect of
soil texture, sand-blasting, wind
speed, and soil consolidation on the
dust production; Atmos. Environ.
1978,12,1735-1743.
6. NCEP/NCAR Reanalysis, The
NCEP/NCAR 40-Year Reanalysis
Project, http://www.cdc.noaa.gov/,
NOAA-CIRES Climate Diagnostics
Center, Boulder, Colorado, USA,
2002.
7. McClain, C.R.; Cleave, M.L.; Feld-
man, G.C.; Gregg, WW; Hooker,
S.B.; Kuring, N. Science quality
SeaWiFS data for global biospheric
research; Sea Technol. 1998, 39,10-16.
8. Darmenova, K.; Sokolik, I.N.
Integrated Analysis of Satellite and
Ground-based Meteorological Obser-
vations of Asian Dust Outbreaks in
Spring of 2001. Presented at Eos
Trans. AGU, Fall Meeting, 2002.
9. Herman, J. R.; Bhartia, P.K.;
Torres, O.; Seftor, C.; Celarier, E.
Global distribution of UV-absorbing
aerosols from Nimbus 7/TOMS
data; /. Geophys. Res. 1997,102,
16,911-16,922.
10. Holton, J.R. An Introduction to
Dynamic Meteorology; Academic
Press: San Diego, CA, 1992; 511.
11. Draxler, R.R.; Hess, G.D. Descrip-
tion of the HYSPLIT_4 modeling
system, NOAA Technical Memoran-
dum ERL ARL-224; Dec.1997; 24.
12. Draxler, R.R.; Hess, G.D. An
overview of the HYSPLIT_4 model-
ing system for trajectories, dispersion
and deposition; Aust. Met. Mag. 1998,
47, 295-308.
13. HYSPLIT 4 modeling system;
http://www.arl.noaa.gov/ss/
models/hysplithtml, NOAA ARL
Transport Modeling and Assessment,
Silver Spring, MD (2002) (accessed
September 2002).
14. NOAA CMDL, Carbon Monox-
ide Measurements in the Mongolian
Desert Dust Cloud at Boulder,
http://www.cmdl.noaa.gov/
hotitems/asiandust.html, NOAA's
Climate Monitoring & Diagnostics
Laboratory, Boulder, CO, USA, 2001
(accessed October 2002).
15. Revised Requirements for Desig-
nation of Reference and Equivalent
Methods for PM2.5 and Ambient Air
Quality Surveillance for Particulate
Matter; Final Rule; Federal Register
1997, 62,38763.
16. Eldred, R. A.; Cahill, T.A.; Pitch-
ford, M.; Malm, WC. IMPROVE - a
new remote area particulate monitor-
ing system for visibility studies; Proc.
APCA (Air Pollution Control Assoc.)
Annual Meeting Pittsburgh, PA.
1988, 81,1-16.
17. Malm, W.C.; Sisler, J.F.; Huffman,
D.; Eldred, R.A.; Cahill, T.A. Spatial
and seasonal trends in particle
concentration and optical extinction
in the United States; /. Geophys. Res.
1994, 99,1347-1370.
18. National Air Quality and Emissions
Trends Report, 1999, EPA-454/
R-01-004, U.S. Environmental Pro-
tection Agency, Office of Air Quality
Planning and Standards, Research
Triangle Park, NC 27711, March 2001;
54.
19. Claiborn, C.S.; Finn, D.; Larson,
TV.; Koening, J.Q. Windblown dust
contributions to high PM2 5 concen-
trations; /. Air & Waste Manage. Assoc.
2000, 50,1440-1445.
20. Malm, W.C.; Sisler, J.F.; Pitchford,
M.L.; Scruggs, M.; Ames, R.; Cope-
land, S.; Gebhart, K.A.; Day, D.E.
Spatial and Seasonal Patterns and
Temporal Variability of Haze and its
Constituents in the United States:
Report III, Colorado State University,
Cooperative Institute for Research in
the Atmosphere, Fort Collins, CO,
May 2000.
21. Air Quality Index Brochure.
http://www.epa.gov/airnow/
aqibroch/ (accessed November 2002).
S12
ASIAN DUST EVENT
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Chemical Speciation of PM2 5 in Urban and Rural Areas
Venkatesh Rao
Office of Air and Radiation
U.S. Environmental Protection Agency, Ann Arbor, MI 48105
Neil Frank, Alan Rush, Fred Dimmick
Air Quality Trends Analysis Group
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Abstract
Data from the Interagency Monitor-
ing of PROtected Visual Environ-
ment (IMPROVE) and the Speciation
Trends Network (STN) are used to
analyze the chemical composition
of PM2 5 and to explore issues asso-
ciated with interpretation of their
measurements. The data from the
largely rural IMPROVE network
and urban STN are used to examine
spatial patterns and to develop
estimates of the local urban excess
over the regional background
concentrations. This work will give
some insights into which of the
chemical constituents are driving
urban excess of PM2 5 mass in differ-
ent regions of the United States.
Introduction
With the promulgation of the new
Particulate Matter National Ambient
Air Quality Standards (PM2 5
NAAQS), all future designated
nonattainment areas and surround-
ing regions may need to reduce
emission of fine particles and their
precursors to permit those areas to
attain the NAAQS. Efficient air qual-
ity management requires knowing
which sources contribute to the prob-
lem and how much. Determining
PM2 5 source contributions is
complicated due to the fact that often
half or more of the PM2 5 mass is
composed of secondarily formed
species,1 hiding their point of origin.
In addition, PM2 5 has a lifetime on
the order of several days,2 enabling
sources up to 1,500 miles away to
affect a source region.
This work examines a simple sub-
set of the source apportionment prob-
lem by providing evidence for local
and regional source contributions and
first-order approximations of their
respective contributions to the follow-
ing major urban areas: Fresno, CA,
Missoula, MT, Salt Lake City, UT,
Tulsa, OK, St. Louis, MO, Birmingham,
AL, Indianapolis, IN, Atlanta, GA,
Cleveland, OH, Charlotte, NC,
Richmond, VA, Baltimore, MD, and
New York, NY. This is accomplished
by computing urban excess concen-
trations—by comparing annual
concentrations of PM2 5 mass and its
most abundant chemical species at
the urban monitors with nearby rural
monitors. In the process of arriving at
the urban excess numbers, several
graphics are used to show the chemi-
cal species that make up PM2 5 mass
across the United States.
Data Sources
Ambient monitoring data from the
PM2 5 chemical Speciation Trends
Network (STN) and the Interagency
Monitoring of PROtected Visual
Environmental (IMPROVE) aerosol
monitoring network were the main
sources of data used to assess the
urban and rural PM2 5 species con-
centrations across the United States.
The PM2 5 STN was established
by regulation3 and is a companion
network to the mass-based Federal
Reference Method (FRM) network
implemented in support of the PM2 5
NAAQS. EPA established the STN
network to provide nationally consis-
tent speciated PM2 5 data for the
assessment of trends at representative
sites in urban areas across the coun-
try. As part of a routine monitoring
program, the STN quantifies mass
concentrations and PM2 5 constituents,
including numerous trace elements,
ions (sulfate, nitrate, sodium, potassi-
um, ammonium), elemental carbon,
and organic carbon. The STN began
operation in late 1999, and there are
currently a total of 54 STN sites.
In 1987 the IMPROVE aerosol
monitoring network was established
among federal and state agencies to
provide information for determining
the types of pollutants and sources
primarily responsible for visibility
impairment within federally desig-
nated Class I areas.4 Ambient aerosol
mass concentrations have been meas-
ured under the IMPROVE program
to characterize the visibility condi-
tions in these Class I areas since 1988.
Over the past few years, the
IMPROVE network has expanded
from its original 20 monitoring sites
to 110 sites in 2002. In addition, there
are currently over 50 supplemental
sites in regionally representative
rural areas that deploy the exact
same aerosol monitoring protocol. As
with the STN, the IMPROVE network
also quantifies mass concentrations
and PM2 5 constituents.
Both the STN and IMPROVE pro-
grams employ a l-in-3-day sampling
protocol.
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S13
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Data Work-Up
The time period chosen for this
analysis is the 1-year period from
March 2001 to February 2002. Any
references to an annual average will
refer to these 12 months. Out of the
possible 54 STN sites, 35 had "com-
plete" annual data. Similarly 98
IMPROVE sites had "complete"
annual data for this time period.
Complete data, for the purposes of
this analysis, refers to 50% or more
of the "relevant" species observa-
tions being present for the four quar-
ters that make up the 12 months
from March 2001 to February 2002.
To be consistent with previous EPA
characterizations5 of the composition
of ambient PM2 5, the following
"relevant" chemical species that
make up PM2 5 mass are considered
in this analysis. The relevant species
for the STN are nitrate, sulfate,
organic carbon, elemental carbon,
ammonium, and the trace elements
that go into the "crustal" calculation:
aluminum, silicon, calcium, iron, and
titanium. Similarly, for IMPROVE,
the relevant species are nitrate,
sulfate, organic carbon, elemental
carbon, and the same five trace
elements that go into the "crustal"
calculation. Because both networks
employ a l-in-3-day sampling pro-
tocol, the 50% completeness criterion
amounts to there being 15 or more
observations per quarter. No further
requirement was imposed for match-
ing days among sites or between net-
works. Quarters for the 12 months
analyzed are defined in Table 1.
Figures 1 and 2 show the 35 STN
and 98 IMPROVE locations that had
complete data, as defined by the
completeness criterion defined
above, for the time period analyzed.
Table 1. Quarter Definitions
Quarter Months Used in Analysis
1
January 2002, February 2002, March 2001
April 2001, May 2001, June 2001
July 2001, August 2001, September 2001
October 2001, November 2001, December 2001
Figure 1. 35 STN locations.
Figure 2. 98 IMPROVE locations.
S14
CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Data Handling Protocols
Even though the STN and IMPROVE
networks use similar sampling and
analytical methods, there are differ-
ences in the species they measure and
the operational protocols they
employ. To put aerosol composition
data derived from both these net-
works on an as-similar-as-possible
basis, the following data handling
protocols were employed:
• Ammonium: Although directly
measured ammonium as per-
formed by STN is important in
characterizing the composition of
PM2 5, network-wide IMPROVE
measurements are currently lack-
ing in this area. Ammonium con-
centrations are thus estimated for
IMPROVE (and for comparison
purposes, for STN as well) from
sulfate (SO4) and nitrate measure-
ments, assuming (1) all sulfates are
ammonium sulfate (NH4SO4), and
(2) all nitrates are ammonium
nitrate. For now, the inter-network
measure based on assumed ammo-
nium sulfate and assumed ammo-
nium nitrate compounds is more
comparable and will therefore be
used to define urban excess. These
"estimated" ammonium concen-
trations are the values shown on
all graphics that compare rural
and urban ammonium concentra-
tions.
• Sulfate: The IMPROVE program
estimates sulfate concentrations as
three times the sulfur concentra-
tion, whereas with the STN
program, sulfate concentrations
are used as measured. In this
analysis, the sulfate ion measure-
ment is used from both networks
to represent sulfates.
• Carbon: Carbon is monitored
somewhat differently by the
IMPROVE and STN programs.
The variances in their analytical
and sampling procedures effec-
tively result in two different oper-
ational definitions of organic and
elemental carbon.5'6 For this rea-
son, organic (OC) and elemental
carbon (EC) are not analyzed sep-
arately. Instead, total carbona-
ceous mass (TCM) is estimated as:
TCM = k * OC + EC for both
programs. Here k is the factor for
converting measured organic
carbon to organic carbon mass
(to account for hydrogen, oxygen,
etc.). Historically, EPA and
IMPROVE programs have used
k=1.4 to convert from carbon to
carbon mass. Most recent findings
by Turpin et al.7 suggest that a
higher factor to convert carbon to
carbon mass may be needed in
both urban and rural areas. In this
work, both k=1.4 and k=1.8 are
used to represent TCM. In some
cases, TCM (k=1.8) is used to
show total carbonaceous mass,
whereas in other cases, compari-
sons are made between use of
k=1.8 and k=1.4.7
The OC measurements reported
by STN are blank-corrected data
using network-wide estimates.5 This
is consistent with the approach used
by the IMPROVE program.6 The OC
values reported by the IMPROVE
program are automatically blank-
corrected using an appropriate blank
correction factor.6 Table 2 lists the OC
blank correction factors used for each
of the speciation samplers that are in
the STN network (also shown for
comparison purposes is the
IMPROVE blank correction factor).
It should be noted that only organic
carbon concentrations for the STN
are blank-corrected (none of the
other STN chemical constituents nor
the total gravimetric mass is blank-
corrected in this analysis).
Urban PM2 5 Excess
Local and regional contributions
to the urban centers were estimated
by computing the differences
between the concentrations of the
annual average urban and nearby
rural monitoring data. These esti-
mates are thus a first approximation
of local and regional contributions of
PM2 5 mass and its chemical con-
stituents to the urban areas investi-
gated. Although strong regional
similarity exists for each of the chem-
ical species on a large spatial scale,
there are still local gradients that
exist in the rural concentration
domain. See, for example, Figures 3
Table 2. Organic Carbon (OC) Blank Correction Factors
Speciation Sampler
MetOne SASS
Anderson RASS
R&P 2300
URG MASS
24-h Sample
Volume, m3
9.6
10.4
14.4
16.7
OC Blank Correction
Factor (ug/m3)
1.40
1.28
0.93
0.56
IMPROVE
0.4
Soil: The soil component of PM2 5 ("crustal" material) was computed using the
following formula, which is the same as that employed by the IMPROVE
program8:
PM2 5 Fine Soil = "Crustal" = 2.2[AI] + 2.49 [Si] + 1.63 [Ca] + 2.42 [Fe] + 1.94[Ti].
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S15
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
through 5, which show spatially
averaged concentrations of carbona-
ceous mass, sulfates, and nitrate for
the March 2001-February 2002 time
period (together with the annual
mean concentrations at each
IMPROVE monitoring location).
Thus, the location of a rural site (for
eventual pairing to an urban site to
determine urban increments) may
influence the amount of urban excess
seen for the specific chemical con-
stituents of PM2 5. One way to
remove this effect and standardize
the choice of rural background con-
centrations is to use spatial interpola-
tion to determine average concentra-
tions for any particular urban loca-
tion. Although doing this for all sites
is beyond the scope of this paper,
spatial averaging for rural concentra-
tions was applied, albeit in a simple
manner, at two urban locations. At
the St. Louis, MO, urban site, three
nearby IMPROVE sites were used to
determine an inverse-distance-
weighted annually averaged rural
concentration for each of the species.
Similarly at the Atlanta, GA, urban
site, two nearby IMPROVE sites
were used to determine an average
annual rural concentration for each
of the species. See the discussion in
the next section and Table 3 for more
information on the choice of pairing
of specific urban/rural sites. In
general, this approach assumes
that the PM2 5 at the rural sites
is generally representative of the
upwind regional concentrations
and is not significantly influenced
by nearby emissions and that the
regional sources (including upwind
urban areas) have the same impact
on the rural monitors and the partic-
ular urban monitors.
Figure 3. Spatial averaging of rural sulfate concentrations.
Figure 4. Spatial averaging of rural nitrate concentrations.
Figure 5. Spatial averaging of rural TCM (k=1,8) concentrations.
S16 CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Choice of Urban and Rural Sites
Figure 6 summarizes the urban and
rural locations chosen for this analy-
sis. There are five urban sites (Bronx,
NY, Baltimore, MD, Richmond, VA,
Charlotte, NC, and Atlanta, GA) in
the Northeast and Mid-Atlantic
States, five urban sites stretching
from north to south in the mid
portion of the United States (Cleve-
land, OH, Indianapolis, IN, St. Louis,
MO, Tulsa, OK, and Birmingham,
AL), and three urban sites in the
West (Fresno, CA, Salt Lake City, UT,
and Missoula, MT). These were
chosen due to their data being com-
plete for the year in question as well
as their ease in matching up with
nearby IMPROVE rural (discussed
further below) sites for the urban
excess study. Except for Tulsa, they
were also selected to represent states
with reported PM2 5 mass concen-
trations greater than 15 ug/m3, which
is the level of the annual PM2 5
NAAQS. IMPROVE sites with com-
plete data were chosen for assumed
representativeness of upwind back-
ground concentrations. In the case of
matching the urban Atlanta and St.
Louis sites to nearby rural sites, a
single available rural site with com-
plete data was not judged to be suffi-
ciently representative of the requisite
Figure 6. Thirteen urban/rural site paintings.
requirement, and therefore a multi-
ple site approach (as explained
above) was employed.
Table 3 summarizes all the STN
and IMPROVE sites for their eleva-
tion and separation distances. For
the analyses of urban excess, all
A 13 urban STN sites
O 16 rural IMPROVE sites
Table 3. STN and IMPROVE Site Particulars
Urban Location/Site
Fresno, CA
Missoula, MT
Salt Lake City, UT
Tulsa, OK
St. Louis, MO
Birmingham, AL
Indianapolis, IN
Atlanta, GA
Cleveland, OH
Charlotte, NC
Richmond, VA
Baltimore, MD
Bronx, NY
Elevation (m)
96
975
1,306
198
0
174
235
308
206
232
59
5
0
Rural Location/Site
Pinnacles National Monument, CA
Monture, MT
Great Basin National Park, NV
Wichita Mountains, OK
Cadiz, KY
Hercules-Glades, MO
Bondville, IL
Sipsy Wilderness, AL
Livonia, IN
Okefenokee National Wildlife Refuge, GA
Shining Rock Wilderness, NC
M.K. Goddard, PA
Linville Gorge, NC
James River Face, VA
Dolly Sods/Otter Creek Wilderness, WV
Brigantine National Wildlife Refuge, NJ
Elevation (m) Distance Apart (km)
317
1,293
2,068
487
188
423
211
279
298
49
1,621
383
986
300
1,158
9
28
72
277
298
296
322
220
100
142
324
236
129
132
179
256
165
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S17
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
urban/rural pairings were elevation-
adjusted to account for the effect of
24-h average sample volume density
on aerosol concentration. Both
IMPROVE- and STN-reported data
represent local conditions. This ele-
vation adjustment was done in two
steps: (1) all the concentrations from
the IMPROVE sites were adjusted to
sea-level conditions, and (2) all these
sea-level-adjusted concentrations
were adjusted once again to the ele-
vation corresponding to the matched
urban site. Except for the St. Louis
and Atlanta STN monitors and their
pairing with rural IMPROVE moni-
tors, all other STN sites were
matched one-on-one with the rural
monitors listed in Table 3. In the case
of St. Louis, the three IMPROVE
monitors shown in Table 3 as
matched sites were inverse-distance
weighted, and the urban Atlanta site
was compared to the averaged con-
centration^) derived from the two
IMPROVE sites shown in Table 3.
Elevation Effects on
PM2 5 Concentrations
As mentioned previously, all the
IMPROVE data were adjusted for
elevation (based on temperature and
barometric pressure correction fac-
tors) twice: once to adjust to sea level
and then again, as necessary, to
adjust to the elevation of the
matched urban site. Basically, this
elevation adjustment is a small tech-
nical correction to make the "urban
excess" calculation more meaningful.
Other than at the Dolly Sods/
Baltimore rural/urban pairing of
sites, however, the urban/rural
elevation differences were small, and
these adjustments are very minor as
can be seen in Figures 7 through 11,
which show the effects of elevation
adjustments for all the chemical
species of interest at the 13
urban/rural paired combinations.
Figure 7. Effect of evaluation on rural sulfate concentrations.
1.5
1
0.5
0
-0.5
-1
Avg Unadjusted: 0.447ng
Avg Adjusted: 0.334 \ng
<
CD
OL
~Q
in
o
>
o
Q
Unadjusted Sulfate Urban D Elevation-Adjusted Sulfate Urban
Figure 8. Effect of evaluation on rural ammonium concentration.
Avg Unadjusted: 0.831 fig
Avg Adjusted: 0.787ng
• Unadjusted Ammonium (estimated) Urban Increment
D Elevation-Adjusted Ammonium (estimated) Urban
Figure 9. Effect of evaluation on rural nitrate concentration.
Avg Unadjusted: 1.40 |
Avg Adjusted: 1.38 ng
o
>
Q_
O
o
Q
2
o
(D
OL
• Unadjusted Nitrate Urban Increment
D Elevation-Adjusted Nitrate Urban Increment
S18
CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Urban Increments of
PM2 5 Mass and the
Chemical Species
Urban sites were paired with
matched rural sites as listed in Table
3, and the annual average concentra-
tions were calculated for both the
urban sites and the companion rural
site(s). All rural values reflect eleva-
tion-adjusted values. These averaged
rural concentrations were subtracted
from the appropriate urban concen-
trations to arrive at the urban incre-
ments of mass and increments of the
individual chemical species.
Shown first in Figure 12 is the
comparison of urban concentrations
to estimated regional background for
total measured gravimetric mass.
The difference is the "urban incre-
ment." The height of each bar
represents the annually averaged
urban gravimetric mass. Overlaying
the nearby rural gravimetric mass on
top of the urban mass levels shows
how much of the total mass can be
attributed to rural vs. urban sources.
It can be seen that Fresno, Cleveland,
and Birmingham are the urban sites
in this analysis with the largest
urban PM2 5 mass during the time
period investigated. The largest
urban increment in PM2 5 mass is
seen to be at the Fresno, CA, site,
with an average excess of about
18 ug/m3. The smallest urban incre-
ment for mass is seen to be at the St.
Louis site, which shows an average
urban excess of about 5 ug/m3 total
PM2 5 mass. Although this result
suggests that there are more local
sources influencing urban PM2 5
mass at the Fresno, CA, location than
at the St. Louis, MO, location, the
selected rural sites in the eastern
United States may be more reflective
of background concentrations. The
Fresno site may be influenced by
other PM2 5 sources throughout the
Figure 10. Effect of elevation on rural TCM (k=1.8) concentrations.
15
• Unadjusted TCM (k=1.8) Urban Increment
D Elevation-Adjusted TCM (k=1.8) Urban Increment
Figure 11. Effect of elevation on rural crustal concentrations.
1.5
1
E 0.5
1
0
-0.5
ILrn fT
—i-1 M_l
RIG
• Unadjusted Crustal Urban Increment
D Elevation-Adjusted Crustal Urban Increment
Figure 12. Urban excess for total PM2 5 gravimetric mass.
Urban
Rural
25
20
oo
E
-& 15
10
5
o
z
z
CL
0
la/MONT
_C/GRBA
O
ro
1/1
1
CO
7n
am/SIPS
§
_i
">!
•a
£=
1
t/i
r\i
75
id/MKGO
Dtte/LIGO
ond/JARI
OSOQ/3J
Dnx/BRIG
E
15
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S19
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
San Joaquin Valley. In general, the
total excess mass ranges from 4
to!6 ug/m3, with the West generally
showing more mass urban excess
than the East. On average, the urban
excess in PM2 5 mass for the investi-
gated 13 site combinations is seen to
be about 8 ug/m3.
Figures 13 through 16 show a
comparison of urban concentrations
with estimated regional background
for four example sites (urban sites:
Fresno, CA, St. Louis, MO, New
York, NY, and Charlotte, NC—see
Table 3 for the matched rural sites for
these urban locations) out of the total
13 urban/rural pairings investigated.
The height of each bar represents the
average urban concentration by
species. Overlaying the nearby rural
concentrations by chemical compo-
nent on the urban chemical compo-
nent concentrations, the example
stacked bar charts (Figures 13-16)
show that the estimated regional
background represents varying pro-
portions of the total urban concentra-
tions by component and location.
Specifically, TCM and nitrates domi-
nate Fresno particulate aerosol,
whereas carbon and sulfates are the
highest among the example eastern
sites. In terms of urban excess, all
four of these examples show TCM
and nitrate concentrations to be the
major components. Urban incre-
ments of TCM are seen to range from
13 ug/m3 at the Fresno, CA, location
to about 3 to 4 ug/m3 at the other
three locations. Similarly, nitrate
urban excess is seen to be 6.5 ug/m3
at the Fresno, CA, location and is in
the 0.5 to 1.3 ug/m3 range at the
other sites studied. As stated earlier,
the Fresno values are probably reflec-
tive of contributions from the San
Joaquin Valley.
Another interesting way to look
at urban excess at the 13 selected
urban/rural pairs is by examining
Figure 13. Urban excess at Fresno, CA.
20
15
E
"& 10
Urban • Rural S3
0
Sulfate Ammonium Nitrate
Figure 14. Urban excess at Charlotte, NC.
10
TCM (k=1.8) Crustal
'E
•& 6
Urban • Rural
Sulfate
Ammonium
Nitrate
TCM
Crustal
Figure 15. Urban excess at St. Louis, MO.
10
Urban • Rural
Sulfate Ammonium Nitrate
Figure 16. Urban excess at New York City, NY.
10
TCM (k=1.8) Crustal
Urban • Rural
Sulfate Ammonium Nitrate
TCM(k=1.8) Crustal
S20
CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
the urban increment of gravimetric
mass as it compares to the urban
increments of each of the chemical
species that drive that mass. This is
shown in Figure 17. The top line in
Figure 17 depicts the total PM2 5
mass urban excess for these 13
urban/rural site combination pairs.
The urban mass is derived from the
STN speciation samplers. The urban
sites are arranged to reflect a west-to-
east trend as you go from left to right
on the graph. At all locations, total
carbonaceous mass is seen to be the
major contributor to PM2 5 mass,
and, at the western sites, nitrates also
play a role in determining the total
PM2 5 mass increments for the time
period investigated. The average
excess urban mass seen in the eastern
sites is 5 to 8 ug/m3 with carbon
contributing between 3 and 5 ug/m3
to the mass increment. The exception
to this average is the Birmingham,
AL, urban site. This site is paired
with the Sipsy Wilderness rural site
(-100 km away) to estimate urban
excess. Birmingham shows a mass
increment of about 12 ug/m3, with
carbon contributing about 5.0 to 6.5
ug/m3 to the total mass increment.
Birmingham probably has local
(urban) emissions sources that are
contributing to the PM2 5 mass. To
understand why the mass is so much
higher in the urban Birmingham area
compared with the other eastern sites
studied, more work is needed to
investigate how these sources differ
from emissions sources in the other
eastern locations.
National Map of Urban
Excess
The estimated urban excess concen-
trations are displayed in the national
map shown in Figure 18 for the
selected 13 urban/rural combina-
tions. Table 4 presents these same
findings through summary statistics.
Figure 17. Comparison of mass urban increment to chemical species.
20
PM2.5 Mass
Nitrate
Sulfate
TCM(k=1.4)
Those urban excess numbers that
were less than zero were set equal to
zero in Table 4 (the "minimum"
values for sulfate and crustal concen-
trations in the "East" and "Overall"
columns). However, the actual num-
bers, both positive and negative,
were used to compute average con-
centrations (of urban excess concen-
trations).
The significant points and impor-
tant caveats are as follows:
• The estimate for urban excess
sulfate is invariably very small in
the eastern United States, which is
consistent with the notion that
most sulfates are transported from
regional sources of SO2. This small
estimated urban excess in the East
(0.0-0.5 ug/m3) is attributed at
least in part to sulfur emissions
associated with fuel combustion
from stationary and mobile
sources.
• Nitrates are seen to be in excess
in the more northern and western
locations, showing a larger local
contribution than sulfates or any
other species except carbon. This
is assumed to reflect local nitrogen
sources (e.g., mobile), nitric acid
TCM (k=1.
Crustal
Ammonium
from NOX/VOC reactions, and
preferential winter-time nitrate
formation compared to sulfates.
However, more work is needed to
assess the comparability of nitrate
measurements and monitoring
methods between networks. To
that end, a major study is planned
next year by the IMPROVE pro-
gram. This was initiated, in part,
because there is concern that the
IMPROVE protocol may produce
relatively lower concentrations of
nitrates, so some of the reported
difference may be measurement
related.
• Carbonaceous mass is shown to
have a substantial urban excess
(2.9 to 13.2 ug/m3 when k=1.8).
It is clearly the largest among
all reported chemical components
in this "urban excess" analysis.
It appears to be attributed to local
emissions, with mobile sources as
a possible major contributor.
• Some locations also show a size-
able urban excess of "crustal
material." The estimation proce-
dure used in the IMPROVE proto-
col includes the measurement of
iron and other trace elements.
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S21
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Therefore, this difference also
reflects oxidized particulate met-
als, some of which may be attrib-
uted to road dust or industrial
sources in urban areas.
Conclusions
In this work, the local and regional
source contributions of PM2 5 to
urban areas were investigated at
13 urban locations in the United
States. This was accomplished by
matching urban sites to nearby rural
sites and then comparing the appro-
priate concentrations of chemical
constituents and mass. Although
Figure 18. National map depicting urban excess by component for 13 example areas.
Sulfate:
_ a D
0.0 0.4 0.9
Ammonium:
_ m I
0.0 0.9 1.9
Nitrate:
^.A
J | Cleveland Bronx
Indy r^l L
o_d_L Baltimore
>t. Louis
Total Carbon Mass
(TCM)(k=1.8):
Concentrations are |jg/m3.
Table 4. Minimum, Maximum, and Average Urban Excess in ug/m3 for 13 STN/IMPROVE Combinations
Chemical Species
Sulfate
Estimated Ammonium
Nitrate
Total Carbonaceous
Mass(k=1.4)
Total Carbonaceous
Mass(k=1.8)
"Crustal"
Min
0.4
0.4
1.0
4.2
5.3
-0.1
West (3
Max
0.9
2.3
6.5
10.5
13.2
0.5
sites)
Average
0.6
1.4
3.7
6.6
8.3
0.2
East (10 sites)
Min
0
0.3
0.4
2.4
2.9
0
Max
0.8
1.1
1.5
5.4
6.7
0.8
Average
0.3
0.6
0.8
3.3
4.2
0.2
Overall (13 sites)
Min
0
0.3
0.4
2.4
2.9
0
Max
0.9
2.3
6.5
10.5
13.2
0.8
Average
0.3
0.8
1.5
4.1
5.1
0.2
S22 CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
there is uncertainty in the measured
mass and in other measurement pro-
tocols, it is clear that carbonaceous
mass is prevalent everywhere (aver-
age of 5.1 ug/m3 with k=1.8) and is
the major component of urban excess
at all the sites studied. In the western
sites, the TCM (based on k=1.4)
urban excess varies from 4.5 to 10.5
ug/m3, whereas in the eastern sites,
TCM urban excess is in the range of
2 to 5.4 ug/m3. TCM, based on k=1.8,
varies from a range of 5.3 to 13.2
ug/m3 in the West and to a range of
2.9 to 6.7 ug/m3 in the East. Similarly,
nitrates are prevalent in the urban
excess estimates for the North and
West (2 to 6 ug/m3). Consistent with
the theory that most sulfates are
transported from regional sources of
SO2, the urban excess of this chemi-
cal component is invariably very
small in the eastern United States.
These results may be viewed as a
first step in differentiating between
regional and local sources that con-
tribute to PM2 5 mass. More work is
needed in the areas of estimating
regional background associated with
specific urban areas using spatial
analysis, identifying specific emis-
sion sources with the estimated
urban excesses using source appor-
tionment techniques, more refined
data analysis that includes meteoro-
logical variables, and examination of
the data on finer time resolution to
get to the next and more refined level
of urban excess concentrations. These
will be the subjects of future papers
in this area.
Disclaimer
The views and opinions expressed in
this paper are solely those of the
authors and do not necessarily reflect
those of the U.S. Environmental
Protection Agency.
References
1. Schichtel, B.A.; Husar, R.B.
Aerosol types over the continental
U.S.: spatial and seasonal patterns.
Presented at the A&WMA Confer-
ence, Kansas City, MO, 1992; Paper
92-60.07.
2. Husar, R.B.; Lodge Jr., J.R;
Moore, J.D. Sulfur in the atmosphere.
In Proceedings of the International Sym-
posium, Dubrovnik, Yugoslavia, 7-14
September 1977; Pergamon Press:
Oxford, 1978.
3. U.S. Environmental Protection
Agency, Office of Air Quality Plan-
ning and Standards, Emissions,
Modeling, and Analysis Division,
Monitoring and Quality Assurance
Group. Particulate Matter (PM2.5)
Speciation Guidance, Final Draft,
Edition 1; October 17,1999.
4. Malm, W.C.; Sisler, J. E; Huff-
man, D.; Edred, R. A.; Cahill, T. A.
Spatial and seasonal trends in parti-
cle concentration and optical extinc-
tion in the United States. /. Geophys.
Res. 1994,99,1347-1370.
5. U.S. National Air Quality and
Emissions Trends Report, 1999; EPA-
454/R-01-004; Research Triangle
Park, NC, March 2001.
6. Dimmick, E Recent analysis
of PM2 5 Speciation Data with
Emphasis on Carbonaceous Mass.
EPA Memorandum, October 3, 2002.
7. Turpin, B.; Lim, H-J. Species
contributions to PM2 5 mass concen-
trations: revisiting common assump-
tions for estimating organic mass.
Aerosol Sci. Technology. 2001, 35, 602-
610.
8. IMPROVE Web Site, http://
vista.cira.colostate.edu/improve/.
Accessed October 2002.
SPECIAL STUDIES
CHEMICAL SPECIATION OF PM,
S23
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
S24 CHEMICAL SPECIATION OF PM,, • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Trends in Monitored Concentrations of Carbon Monoxide
Jo Ellen Brandmeyer, Peter Frechtel, and Margaret Z. Byron
RTI International, P.O. Box 12194, Research Triangle Park, NC 27709-2194
Joe Elkins and James Hemby
Air Quality Trends Analysis Group
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Venkatesh Rao
Office of Air and Radiation
U.S. Environmental Protection Agency, Ann Arbor, Ml 48105
Abstract
Carbon monoxide (CO) is one of the criteria pollutants
regulated under the Clean Air Act. Numerous metro-
politan areas instituted oxygenated gasoline (oxyfuel)
programs during winter months to reduce CO emis-
sions from motor vehicles, but some have since discon-
tinued these requirements. This paper demonstrates a
screening method for determining monitoring stations
of potential interest. Monitoring stations with at least 8
years of relevant data during the period from 1990
through 2000 were screened for either an upward linear
trend or upward inflection. Statistical tests assessed the
trend in the annual second maximum nonoverlapping
8-hour average of CO for each monitor over the 11-year
period. Of the 433 sites analyzed, 34 showed a statisti-
cally significant overall upward trend or statistically
significant upward curvature. This analysis method can
be used to screen for sites with increasing CO concen-
trations. The identified sites should then be examined
further to determine the magnitude of the concentra-
tions as compared to the existing standard. Because
some areas have changed their fuel requirements within
the last few years of the analysis, we recommend
repeating this test annually.
Introduction
Carbon monoxide (CO) is a colorless,
odorless, and poisonous gas pro-
duced by incomplete burning of
carbon in fuels. Approximately 75%
of nationwide CO emissions are from
transportation sources. The largest
emissions contribution comes from
highway motor vehicles. Thus, the
focus of CO controls as well as CO
monitoring has been on traffic-ori-
ented sites in urban areas where the
main source of CO is motor vehicle
exhaust. Other CO sources include
wood-burning stoves, incinerators,
and other heavy industrial sources.
The National Ambient Air Quality
Standard (NAAQS) for carbon
monoxide is 9 ppm for an 8-h aver-
age not to be exceeded more than
once per year. The EPA motor vehicle
program has achieved considerable
success in reducing CO emissions.
EPA standards in the early 1970s
prompted automakers to improve
basic engine design. By 1975, most
new cars were equipped with cat-
alytic converters designed to convert
CO to carbon dioxide. In the 1980s,
automakers introduced more sophis-
ticated converters plus on-board
computers and oxygen sensors to
help optimize the efficiency of the
catalytic converter.
CO emissions from automobiles
increase dramatically in cold weather
because cars need more fuel to start
at cold temperatures, and some
emission control devices operate less
efficiently when they are cold. Until
1994, vehicles were tested for CO
emissions only at 75°F. But, recogniz-
ing the effect of cold weather, the
1990 Clean Air Act (the Act) calls for
1994 and later cars and light trucks
to meet a carbon monoxide standard
at 20°F as well.
The Act also stipulates expanded
requirements for inspection and
maintenance programs. These rou-
tine emission system checks should
help identify malfunctioning vehicles
that emit excessive levels of CO and
other pollutants (the so-called "high
emitters"). The inspections will be
complemented by requirements for
onboard warning devices to alert
drivers when their emission control
systems are not working properly.
Yet another strategy to reduce
CO emissions from vehicles is to add
oxygen-containing compounds to
gasoline. This has the effect of "lean-
ing-out" the air-to-fuel ratio, thereby
promoting more complete fuel com-
bustion. The most common oxygen
additives are ethers and alcohols.
Several western and northern U.S.
cities have employed wintertime
oxygenated gasolines for many
years. The Act expands this concept
SPECIAL STUDIES • CARBON MONOXIDE
S25
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
and requires that oxygenated gaso-
lines be used during the winter
months in certain metropolitan areas
with high CO levels.
With these control programs and
technology improvements, today's
passenger cars and light-duty trucks
are capable of emitting 90% to 95%
less CO over their lifetimes than
their uncontrolled counterparts of
the 1960s. As a result, ambient CO
levels have dropped, despite large
increases in the number of vehicles
on the road and the number of miles
they travel. However, in recent
months, with continued heavy
increase in vehicle travel, there have
been indications that CO levels are
climbing again in certain parts of the
country. The objective of this work is
to examine those areas of the country
where mobile-source activity is
heavy (in CO nonattainment and
problem areas) and/or where CO
air quality has been a persistent
problem and determine whether
CO levels are increasing.
Experimental Methods
CO concentration data were
extracted for 858 monitoring sites
from EPA's Aerometric Information
Retrieval System (AIRS) on March
14, 2002. To meet the completeness
requirement for this analysis, at least
8 years of data must have been
available for the years 1990 to 2000,
inclusive. Statistical analyses were
performed for the 433 sites that met
this requirement.
The Metropolitan Statistical Area
(MSA) code was also downloaded
for each site. The codes were linked
to the most recent list of areas that
employ or have discontinued oxy-
fuel requirements.1 This information
was used to group the sites (oxyfuel
ended vs. no change in oxyfuel
requirements) and to interpret the
results of the analyses.
The effects of meteorology on
ambient CO concentrations were not
examined in this study. For example,
certain meteorological parameters
(e.g., mixing height and windspeed)
need to be considered when compar-
ing emissions to ambient concentra-
tion measurements.3'4 However, the
Glen et al. study3 concluded that
seasonal fluctuations in CO concen-
trations are explained by the
variations in these meteorological
parameters, whereas the long-term
trend is primarily due to the trend in
emissions. Although the current
analysis did not account for inter-
annual meteorological changes, the
same overall downward trend was
identified.
The analysis used the second
maximum nonoverlapping 8-h aver-
age CO concentration (SECMX) for
each year. This statistic was selected
for analysis because it coincides with
the 8-h NAAQS for CO. Missing
values (i.e., years without a SECMX
value for a monitor) were not filled
in; that is, linear interpolation or
some other method was not
employed to fill in missing data.
The data for each site were then
analyzed independently of all other
sites; that is, no spatial averaging
was performed to obtain annual
average values for each MSA.
Although the SECMX values form
the basis of the annual CO trends
published by EPA's Air Quality
Trends Analysis Group in the Trends
Report,2 the methodology employed
in this study differed in three basic
ways:
• The Trends Report fills in missing
data, whereas this study used
only the data that were available
from AIRS.
• The Trends Report aggregates
data and analyzes results for
each MSA, whereas this study
performed the data analysis
separately for each monitor.
• The analysis for the Trends Report
used only the nonparametric Theil
test, whereas this study also used
two linear regression models.
The three analyses that were
performed for each site were the
Theil test, first-order linear regres-
sion, and quadratic (second-order)
linear regression. Each of these
analyses included a statistical
hypothesis test that computes a
p-value for each monitor. If the
p-value is less than a critical value
n between 0 and 1, then the test has
a result that is "significant at a = n."
A smaller value for a indicates a
greater likelihood that the data truly
possess the detected trend.
Every test was two-sided, mean-
ing that the a-level used to detect an
increasing or a decreasing trend was
a/2. Therefore, if a monitor exhib-
ited an increasing trend, then the p-
value for the test would have to be
less than a/2 for the increasing trend
to be significant. For example, if a
monitor exhibited an upward trend
that was significant at a = 0.01, then
the probability of seeing as extreme
an upward trend as this monitor
under the null hypothesis of no
trend is less than 0.005 (0.5%).
The Theil test and both regression
models are discussed below.
Theil Test
The Theil test5 is a nonparametric
statistical test that can be used
instead of regression-based methods
for discerning a monotonic trend. It
examines whether the concentration
from year to year tends to increase or
decrease consistently, making it a test
of monotonicity This test is not con-
cerned with the magnitude of the
year-to-year differences. The null
hypothesis is that there is no mono-
tonic trend in the data.
The first step in the test is to
examine all possible [n(n-l)/2] pairs
S26 CARBON MONOXIDE • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
of data points from a given monitor,
where n = 8, 9,10, or 11. Next, a
count is taken of all the pairs that
show an increasing or decreasing
trend. The null hypothesis will be
rejected and the test results will
indicate a significant monotonic
increasing (or decreasing) trend if
this count of the data point pairs is
greater than (or less than) a certain
critical value. A large positive value
indicates a positive trend, and a large
negative value indicates a negative
trend.
The Theil test was applied for two
reasons. First, it is appropriate when
the errors from a linear regression are
not normally, or close to normally,
distributed. The data here may not
meet the normality assumption.
Second, this test was recommended
to EPA for determining whether an
area has a significant trend.6 There-
fore, this test is used in EPA's annual
Trends Reports.
Choice of Urban and Rural Sites
Unlike the Theil test, linear regression
is a parametric test. All linear regres-
sion models incorporate three basic
assumptions: (1) the data are nor-
mally distributed, (2) the variance is
constant at each time, and (3) no
autocorrelation exists between time
periods.
A first-order linear regression was
performed using PROC REG in SAS.7
The linear regression model used
SECMX as the dependent variable.
To make the results less dependent
on the magnitude of the year, a trans-
formation was performed on the
value of the year by subtracting 1989
(i.e., 1 less than the minimum year in
the dataset):
PROC REG includes a hypothesis
test for a nonzero slope. The p-value
from this hypothesis test is presented
in the results tables.
Quadratic Regression
A second linear regression was also
performed using PROC REG. This
test was a quadratic (second-order)
linear regression that used both (YRX)
and (YRX)2 as independent variables.
The p-value from the test for a
nonzero coefficient on the squared
term is presented in the results
tables. A significant p-value for this
test indicates significant curvature in
the regression line. That is, an
upward trend suggests that the slope
has increased from the early years to
the recent years.
Interpretation of Statistical Results
These three statistical tests are com-
plementary in that each examines
the data differently. The Theil test
looks for a monotonic trend, first-
order linear regression applies nor-
mality theory for a linear trend, and
Figure 1. Examples of trends A through E.
quadratic regression applies normali-
ty theory for a nonlinear trend. All
three spotlight sites that may be of
interest to policy makers, but no
single test will detect all interesting
sites. They can be used together,
however, to discern patterns in the
data. Consider the following five
trends, as illustrated in Figure 1.
Trend A
This site has a consistent, upward
trend that is not dramatic. However,
1996 was a very "clean" year at the
site, with a SECMX value lower than
the rest of the years.
The Theil test undoubtedly will
detect a significant upward trend at
site A. The first-order regression
model may not find a significant
trend at site A for two reasons. First,
the anomalous point in 1996 inflates
the variance. Second, the slope esti-
mate will not be much greater than
zero because the increasing trend is
only slight. The quadratic regression
model may or may not be significant
for this site.
YR' = YEAR -1989
YR' was the only independent
variable in the regression model.
(1)
SPECIAL STUDIES • CARBON MONOXIDE
S27
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Site A may be of interest to policy
makers. For example, upon examina-
tion of associated data such as tem-
perature, they may find a meteoro-
logical reason that 1996 was such a
clean year (e.g., warm winter) and
decide that the true pattern is a con-
sistent increase in CO concentration.
Trend B
From 1990 to 1996, the concentra-
tions at site B decreased slightly.
The concentrations then increased
dramatically from 1997 to 2000.
At site B the Theil test may not
detect a trend because of a lack of a
consistent pattern in the early years.
It also will not be influenced by the
explosive pattern in the recent years.
However, the first-order regression
model will certainly detect an
increasing trend. The high concentra-
tions in the later years will increase
the slope of the regression line. If the
increase is more dramatic in the very
recent years, the quadratic regression
model may also detect a significant
upward inflection.
Site B also would likely be of
interest to policy makers, because the
most recent years show a dramatic
increase in concentration.
Trend C
The concentrations at site C
increased dramatically from 1990 to
1995. The rate of increase then
slowed from 1996 to 2000, although
the concentrations continued to
increase.
At site C, both the Theil test and
the first-order regression model will
detect an increasing trend. However,
the quadratic regression model
might detect a downward curvature.
This may be a site where popula-
tion growth is explosive, but the
state or local government has taken
drastic steps to reduce emissions
per capita. This pattern is likely to
interest policy makers because the
site is showing improvement via
slower concentration growth,
although the concentration at the site
is still increasing.
Trend D
The concentrations at site D
decreased from 1990 to 1995 but
increased from 1996 to 2000. The
concentrations in 1990 and 2000
were similar to each other.
At site D, both the Theil test and
the first-order regression model
likely will fail to detect a trend. The
Theil test will have about the same
number of increasing and decreasing
pairs. The slope of the first-order
linear regression line likely will be
nearly zero. The quadratic regression
model, however, will detect a signifi-
cant upward curvature.
This site may be of interest to
policy makers because the pattern
suggests that the concentrations will
continue to increase. This pattern
may be prevalent where the oxyfuels
program was discontinued.
Trend E
The concentrations at site E increased
from 1990 to 1995. The increase
became more pronounced from 1996
to 2000.
At site E, all three tests will
produce significant results. This site
exhibits a consistent increase in
concentrations, and it merits special
vigilance.
Results and Discussion
This study analyzed data for the
433 sites that met the completeness
test. One or more statistical tests
revealed significance at 79% of the
sites at the a = 0.10 level. This result
was expected due to the effects of
fleet turnover.
Of greater interest to this study,
however, was that a statistically
significant upward trend or curva-
ture was revealed at 34 sites. Table 1
lists the results of the three statistical
models for all sites where at least one
model revealed a significant upward
trend or positive quadratic compo-
nent. Seven pieces of information are
included for each site: (1) MSA con-
taining the site, (2) ending date for
the oxyfuel program (if applicable),
(3) monitor ID in AIRS, (4) number
of years of data used in the analysis,
(5) results of the Theil test, (6) results
of a hypothesis test that the slope of
the line from the first-order linear
regression model is nonzero, and
(7) results of a hypothesis test that
the coefficient associated with the
squared term is nonzero for the
quadratic regression model. Of the
sites listing dates ending the oxyfuel
program, all either are located in a
federal reformulated gasoline area or
have an oxyfuel requirement in their
contingency plan.
Figure 2 shows the locations of the
monitoring sites with at least one
statistical model showing a statisti-
cally significant upward trend or
positive quadratic component. Only
those sites located within the coter-
minous United States are included in
this map.
A plot of the SECMX vs. year was
generated for each of the 433 sites in
this analysis. For each plot the con-
centration values are shown as stars.
The solid line represents the quad-
ratic regression line, and the dashed
lines represent the 95% confidence
bands around the regression line.
That is, there is a 95% probability
that the true trend lies within the
area bounded by the dashed lines
and only a 5% probability that the
true trend lies outside this area.
Examples of patterns found in these
plots are included as Figures 3
through 7.
S28
CARBON MONOXIDE • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Carbon Monoxide Monitoring Sites Where at Least One Statistical Test Shows Increasing Concentration
MSA
—
—
Charlotte, NC
Charlotte, NC
Kansas City, MO
Los Angeles, CA
Los Angeles, CA
Louisville, KY
Minneapolis-St. Paul, MN
Modesto, CA
Oakland, CA
Oakland, CA
Oakland, CA
Vancouver, WA
Provo, UT
Reno, NV
Sacramento, CA
Sacramento, CA
Sacramento, CA
Sacramento, CA
San Diego, CA
San Diego, CA
San Francisco, CA
San Jose, CA
San Jose, CA
San Juan, PR
San Luis Obispo, CA
Santa Rosa, CA
Seattle, WA
Stockton, CA
Stockton, CA
Tampa, FL
Vallejo, CA
Ending Date
Oxyfuel
Requirement
—
—
—
—
—
—
—
—
—
6/1/1998*
—
—
—
10/21/1996*
—
—
6/1/1998*f
6/1/1998*f
6/1/1998*f
6/1/1 998 *T
6/1/1998*f
6/1/1998*f
6/1/1998*
—
—
—
—
—
10/11/1996*
6/1/1998*
6/1/1998*
—
—
Monitor ID
370770001421011
410350006421011
371190038421011
371191009421011
290470009421011
060371201421011
060379002421011
211110046421011
271230865421011
060990005421011
060010003421011
060130002421011
060133001421011
530110010421011
490490002421011
320311005421011
060170010421011
060170011421011
060670006421011
060670007421011
060730003421011
060731007421011
060811001421011
060850004421011
060850004421012
721270002421011
060792002421011
060970003421011
530610012421011
060770008421011
060771002421011
120571045421011
060950004421011
Yuba City, CA 061 01 0003421 01 1
*Oxyfuel program retained as contingency measure.
t Federal reformulated gasoline program area.
The following notation was used for the statistical results:
DOWN01 = downward trend, significant at a level 0.01 UP01 =
DOWN05 = downward trend, significant at a level 0.05 UP05 =
DOWN 10 = downward trend, significant at a level 0.10 UP10 =
NS = no significant trend
Years of
Data
8
11
11
8
10
11
11
11
8
11
10
11
11
11
11
11
9
8
11
11
10
11
11
11
11
11
11
11
11
11
11
8
11
10
upward trend,
upward trend,
upward trend,
Theil Test
NS
DOWN01
DOWN01
UP05
DOWN05
DOWN01
DOWN01
DOWN05
DOWN05
DOWN05
DOWN05
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN05
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN05
DOWN05
DOWN05
DOWN01
DOWN05
DOWN01
DOWN05
DOWN05
DOWN05
DOWN01
DOWN01
significant at
significant at
significant at
1st Order
Regression
Model
UP10
DOWN01
DOWN01
UP05
DOWN05
DOWN01
DOWN01
DOWN01
DOWN05
DOWN05
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN01
DOWN05
DOWN01
DOWN05
DOWN01
DOWN05
DOWN01
DOWN01
DOWN01
DOWN01
a level 0.01
a level 0.05
a level 0.10
2nd Order
Regression
Model
NS
UP01
UP05
NS
UP10
UP10
UP05
UP05
UP05
UP01
UP10
UP05
UP05
UP01
UP05
UP05
UP01
UP10
UP10
UP01
UP05
UP01
UP10
UP01
UP01
UP10
UP10
UP10
UP05
UP05
UP05
UP10
UP05
UP05
SPECIAL STUDIES • CARBON MONOXIDE
S29
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 3 illustrates a site that was
screened out by this analysis; none
of the three tests revealed an upward
trend. The statistical results were
DOWN01, DOWN01, and NS for the
Theil test, first-order linear regres-
sion, and quadratic regression,
respectively.
The Theil test revealed a statisti-
cally significant upward trend at
only one site. Its data and quadratic
regression results are shown in
Figure 4. The first-order linear
regression model also revealed an
upward trend at this site. Both these
tests were significant at the a = 0.05
level. The second-order linear regres-
sion found no significant trend at
this site. This pattern is similar to
Trend C, described above.
Figure 4 also demonstrates how
this analysis method should be used
to screen monitoring sites. Although
two statistical tests revealed an
upward trend, this site is not of
immediate concern because the
concentrations are far below the
NAAQS value of 9 ppm. If this site is
located in an area of high population
growth, then it should be reeval-
uated in the future.
Figures 5 through 7 illustrate
patterns that are similar to Trend D,
described above. The site in Figure 5
apparently experienced minimum
CO concentrations during the period
1995 to 1997. The concentrations
increased after that period. For this
site, the Theil test revealed a down-
ward pattern at the a = 0.05 level,
and the first-order linear regression
model revealed a downward pattern
at the a = 0.01 level. However, the
quadratic regression model revealed
an upward pattern at the a = 0.01
level. Also, the lower bound of the
95% confidence limit is increasing,
and concentrations are not low like
those shown in Figure 4.
Figure 2. Locations of monitoring sites in the coterminous United States with at least
one statistical model showing a significant upward trend. Circles represent
sites that have stopped an oxygenated gasoline requirement. Diamonds
represent other sites.
Upward Trend, Stopped Oxyfuel
Upward Trend, Other
State Boundary
Figure 3. Example of a site screened out by the combined statistical models.
T
6-
o
O
O , j
o 4H
3-
90 91 92 93 94 95 96 97 98
Year
>(c Concentration value
2nd order regression line
95% confidence bands around regression line
99 00
S30 CARBON MONOXIDE • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 4. Example of a site with increasing trend. This site did not have data for
the years 1990 through 1992.
0.7-1
90 91 92 93 94 95 96
Year
>(c Concentration value
2nd order regression line
95% confidence bands around regression line
Figure 5. Example of a site with increasing trend in recent years.
98 99 00
111
10-
9-
o
o
o
o
7-
6-
V *
90
91
92
93
94
95
Year
96
97
98
99
00
5^c Concentration value
2nd order regression line
95% confidence bands around regression line
The site in Figure 6 discontinued
its oxyfuel requirements as of
October 21,1996; the vertical line at
Year = 1996 indicates the year that
this requirement ended. However,
the data do not include whether the
second highest concentration for
1996 occurred during or after the
oxyfuel program. For this site, both
the Theil test and the first-order lin-
ear regression model revealed a
downward pattern at the a = 0.01
level. However, the second-order
linear regression model revealed an
upward pattern at the a = 0.01 level.
The pattern of the 95% confidence
limits of the second-order linear
regression line indicates a high
probability of nearly stable to
rapidly increasing concentration.
The site in Figure 7 discontinued
its oxyfuel requirements as of June 1,
1998, more recently than the site in
Figure 6. Because of the increased
scatter of the data around the regres-
sion line, the 95% confidence region
is larger and the patterns not as
statistically significant as those for
the site in Figure 6. For this site, both
the Theil test and the first-order
linear regression model revealed a
downward pattern at the a = 0.05
level, whereas the quadratic regres-
sion model revealed an upward
pattern at the a = 0.05 level.
This study demonstrates the
utility of using more than one statis-
tical test to determine patterns in
ambient concentration data. The
Theil test is a nonparametric, mono-
tonic test that measures numbers of
pairs of data that increase vs.
decrease. First-order linear regres-
sion examines the significance of
the slope of the least-squares line
through all the available data.
Quadratic regression examines the
significance of the coefficient of
the second-order term in the least-
squares regression. Although
SPECIAL STUDIES • CARBON MONOXIDE
S31
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
interpolation cannot be used to
extrapolate beyond the range of the
data, the significance of the second-
order term provides a measure of the
curvature (i.e., change in the trend)
of the regression line. This additional
information is useful in locating sites
with recent increasing concentra-
tions, even when the overall trend is
downward or not significant.
Unlike the Trends Report,2 which
examines trends for regions based on
MSA, this study looked for trends
associated with individual monitors.
Trends in more localized areas, there-
fore, could be discovered because
areal averaging was not performed.
Uncovering localized trends is
important when one part of an MSA
experiences rapid population growth
with the associated rapid growth in
vehicular emissions.
Conclusions
This analysis revealed relatively few
sites with statistically significant
upward trends or inflection in CO
concentrations during the period
1990 to 2000. By combining regres-
sion models with the Theil test, 34 of
433 sites were identified for further
analysis. Because this study demon-
strated that the simpler Theil test
performed nearly as well as the first-
order linear regression in identifying
upward linear trends, we do not
recommend performing first-order
linear regression on these relatively
short data sets in the future. How-
ever, this study showed that the
quadratic regression model success-
fully identifies sites where the con-
centration has increased in recent
years, thereby identifying potential
problem areas earlier than the Theil
test. Because this method is to be
used to identify sites of potential
interest, we further recommend
using a = 0.10 and a one-sided
Figure 6. Example of a site with increasing trend in recent years. The vertical line
indicates the year that the oxygenated gasoline requirement ended.
11H
10-
9-
o
O
o
O
91 92 93 94 95 96 97 98 99 00
Year
5^c Concentration value
2nd order regression line
95% confidence bands around regression line
Figure 7. Example of a site with increasing trend that discontinued oxygenated
gasoline requirements more recently. The trends for this site are not as
significant as those shown in Figure 5.
11H
10-
9-
§ 8-
7-
o
O
91
92
93
94 95 96
Year
>(c Concentration value
2nd order regression line
95% confidence bands around regression line
97
98
99
00
S32 CARBON MONOXIDE • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
hypothesis test to reduce the number
of false negative results.
This method was designed to be
an automated screening method for
potential problem areas. Because
both vehicle-miles traveled and the
vehicle mix in fleets are changing
with time, we recommend repeating
this analysis annually to determine
sites that warrant further analysis.
Acknowledgments
Support for this project was pro-
vided by the U.S. Environmental
Protection Agency, Office of Air
Quality Planning and Standards,
under contract number GS-10F-
0283K(SIN 899-2). We are grateful to
Michael Riggs, senior statistician at
RTI, for his insightful comments and
technical review of this manuscript.
References
1. U.S. Environmental Protection
Agency, Air and Radiation, Office
of Transportation and Air Quality.
State Winter Oxygenated Fuel Program
Requirements for Attainment or Mainte-
nance of CO NAAQS; October 2001.
Available from http://www.epa.
gov/otaq/regs/fuels/oxy-area.pdf
(accessed March 2002).
2. U.S. Environmental Protection
Agency, Office of Air Quality Plan-
ning and Standards, Emissions
Monitoring and Analysis Division,
Air Quality Trends Analysis Group.
National Air Quality and Emissions
Trends Report, 1999; EPA 454/R-01-
004; Research Triangle Park, NC,
2001.
3. Glen, W. Graham; Zelenka,
Michael P.; Graham, Richard C.
Relating Meteorological Variables
and Trends in Motor Vehicle Emis-
sions to Monthly Urban Carbon
Monoxide Concentrations. Atmos-
pheric Environment 1996, 30,4225-
4232.
4. Flaum, Jennifer B.; Rao, S. Trivi-
krama; Zurbenko, Igor G. Moder-
ating the Influence of Meteorological
Conditions on Ambient Ozone Con-
centrations. JAWMA 1996,46, 35-46.
5. Hollander, M.; Wolfe, D.A.
Nonparametric Statistical Methods;
John Wiley & Sons: New York, 1973;
pp 200-204.
6. U.S. Environmental Protection
Agency, Office of Air Quality Plan-
ning and Standards. IIS. Environ-
mental Protection Agency Intra-Agency
Task Force Report on Air Quality
Indicators; EPA-450/4-81-015;
Research Triangle Park, NC, Feb-
ruary 1981.
7. SAS/STAT Users' Guide Version 8;
SAS Institute: Gary NC, 1999.
SPECIAL STUDIES • CARBON MONOXIDE
S33
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
S34 CARBON MONOXIDE • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Cumulative Ozone Exceedances—A Measure of Current Year
Ozone Levels Compared to Historical Trends
Dennis Doll
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Terence Fitz-Simons
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Introduction
The U.S. Environmental Protection
Agency (EPA) maintains a historical
record of air pollutant data in the
EPA Air Quality System (AQS),
which is overseen by the Office of
Air Quality Planning and Standards.
This database provides quality-
assured pollutant measurement data
from a network of monitoring sta-
tions in metropolitan areas and
regions throughout the United
States. The AQS usually contains the
most recent 10-year period of moni-
tored data. Pollutant measurement
data are entered into the AQS by
state and local agencies maintaining
the network of monitoring stations.
These data are entered on a continu-
ous basis throughout the year but are
usually complete within about
3 months after the end of the calen-
dar year.
Ozone is one of the principal
pollutants measured at a network of
monitoring stations throughout the
United States. The historical ozone
database maintained in the AQS
provides a unique opportunity to
conduct analyses to investigate and
characterize the ozone levels in these
metropolitan areas and regions.
Comparisons of historical data with
the most recent year of data in the
AQS can provide an indication of the
current magnitude of ozone pollut-
ant levels in metropolitan areas and
regions throughout the United States
compared to historical levels and can
show whether ozone levels are
worse, better, or about the same in
the most recent year compared to
recent historical trends.
Origin of Data
The ozone monitoring "season"
occurs in the period from April
through October in most major
metropolitan areas throughout the
United States. Frequently states, EPA
Regional Offices, and EPA Head-
quarter Offices are asked how this
year's ozone season compared to
that of previous years. These queries
occur particularly when there may
have been several ozone "episodes"
during the year or if there were
periods of especially high ozone
measurements prompting air quality
alerts that may have been widely
reported in the media.
One potentially useful way
to compare ozone seasons is to
depict the seasonal trend in ozone
by counting the number of days
in which ozone exceedances are
measured in selected metropolitan
areas and/or regions. The measure
of ozone exceedances that is most
widely reported in the media is
the EPA Air Quality Index (AQI).
The AQI contains categories of
ozone levels based on health
effects and includes (1) Moderate,
(2) Unhealthy for Sensitive Groups,
(3) Unhealthy, (4) Very Unhealthy,
and (5) Hazardous.
The Unhealthy for Sensitive
Groups category is based on the
8-hour National Ambient Air
Quality Standards (NAAQS) for
ozone (>0.085 ppm). Other categories
(Unhealthy, Very Unhealthy, and
Hazardous) are based on ozone
levels of increasing severity. By
tracking the number of days ozone
measurements exceed the NAAQS
(e.g., Unhealthy for Sensitive
Groups) during the ozone season as
reported in the AQS, a comparison
can be made of the most recent
year's ozone measurements with
previous or historical year measure-
ments. Based on this comparison, a
qualitative assessment of the "sever-
ity" of the most recent year's ozone
measurements with historical year
measurements can be made.
In this analysis, we use ozone
data measured from the network of
monitors assigned to the LISA Today
newspaper cities, for which the AQI
is forecasted during the ozone
season. Monitoring data from addi-
tional cities could be used as well,
but we chose the LISA Today cities as
an illustration of the type of compar-
isons that can be done and because it
the most widely reported measure of
ozone levels in the media.
EPA maintains a list of monitors
that are assigned to these LISA Today
cities (see Table I).1 Using these
same monitors, the historical ozone
data can be obtained for each of the
LISA Today cities from previous
years' data reported in the AQS. In
this analysis, we use the 2002 data
reported in the AQS as the most
recent year data and the previous
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S35
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities
City
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Atlanta
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Baltimore
Charlotte
Charlotte
Charlotte
AIRSJD
130570001
130670003
130770002
130890002
130893001
130970002
130970004
131130001
131210034
131210053
131210055
131215001
131215002
131350002
131510002
132230001
132230002
132230003
132470001
132558001
240030001
240030014
240030019
240031003
240032002
240050003
240050010
240051007
240053001
240054002
240056001
240130001
240250080
240251001
240259001
240270005
245100004
245100011
245100018
245100019
245100036
245100040
245100050
371790003
450910002
450910004
Site
130570001
130670003
130770002
130890002
130893001
130970002
130970004
131130001
131210034
131210053
131210055
131215001
131215002
131350002
131510002
132230001
132230002
132230003
132470001
132558001
240030001
240030014
240030019
240031003
240032002
240050003
240050010
240051007
240053001
240054002
240056001
240130001
240250080
240251001
240259001
240270005
245100004
245100011
245100018
245100019
245100036
245100040
245100050
371790003
450910002
450910004
City
Baltimore
Baltimore
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Boston
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Charlotte
Chicago
Chicago
Chicago
AIRSJD
245100051
245100053
250091002
250091201
250092006
250093001
250093102
250094001
250094003
250094004
250170004
250171001
250171002
250171005
250171102
250173003
250176001
250211001
250212002
250213003
250232001
250250002
250250015
250250021
250250041
250250042
250250081
250251003
371090004
371090099
371190011
371190018
371190019
371190026
371190028
371190030
371190033
371190034
371190041
371191005
371191009
371590021
371590022
170314006
170314007
170314201
Site
245100051
245100053
250091002
250091201
250092006
250093001
250093102
250094001
250094003
250094004
250170004
250171001
250171002
250171005
250171102
250173003
250176001
250211001
250212002
250213003
250232001
250250002
250250015
250250021
250250041
250250042
250250081
250251003
371090004
371090099
371190011
371190018
371190019
371190026
371190028
371190030
371190033
371190034
371190041
371191005
371191009
371590021
371590022
170314006
170314007
170314201
S36 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Charlotte
Charlotte
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
AIRSJD
450910006
450911004
170310001
170310002
170310003
170310004
170310006
170310007
170310009
170310025
170310026
170310027
170310032
170310033
170310034
170310036
170310037
170310038
170310039
170310040
170310042
170310044
170310045
170310050
170310053
170310062
170310063
170310064
170310072
170310075
170311002
170311003
170311501
170311601
170312002
170312301
170313001
170313005
170314002
170314003
390610003
390610006
390610010
390610019
390610020
390610034
Site
450910006
450911004
170310001
170310002
170310003
170310004
170310006
170310007
170310009
170310025
170310026
170310027
170310032
170310033
170310034
170310036
170310037
170310038
170310039
170310040
170310042
170310044
170310045
170310050
170310053
170310062
170310063
170310064
170310072
170310075
170311002
170311003
170311501
170311601
170312002
170312301
170313001
170313005
170314002
170314003
390610003
390610006
390610010
390610019
390610020
390610034
City
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Chicago
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Columbus
Columbus
Columbus
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
AIRSJD
170315001
170315002
170316002
170317002
170318001
170318003
170370002
170430003
170431002
170436001
170438002
170890003
170890005
170890006
170970001
170970006
170970007
170970008
170970009
170971002
170971003
170971007
170973001
171110001
171111001
171970005
171971007
171971008
171971011
180290003
210150003
210151002
210370003
210371001
210374001
211170007
211910002
390250002
390250020
390250022
390970006
390970007
391298001
480850004
480850005
480850010
Site
170315001
170315002
170316002
170317002
170318001
170318003
170370002
170430003
170431002
170436001
170438002
170890003
170890005
170890006
170970001
170970006
170970007
170970008
170970009
170971002
170971003
170971007
170973001
171110001
171111001
171970005
171971007
171971008
171971011
180290003
210150003
210151002
210370003
210371001
210374001
211170007
211910002
390250002
390250020
390250022
390970006
390970007
391298001
480850004
480850005
480850010
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S37
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cincinnati
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Cleveland
Columbus
Columbus
Columbus
Columbus
Columbus
Columbus
Columbus
Columbus
Columbus
Denver
Denver
Denver
Denver
Denver
Denver
Detroit
Detroit
Detroit
AIRSJD
390610035
390610037
390610040
390616002
391650006
391651002
390071001
390350002
390350033
390350034
390350035
390350064
390350081
390352001
390353003
390354003
390355002
390550004
390850001
390850003
390853002
390930013
390930017
390931002
390931003
391030002
391030003
391032001
390410002
390490004
390490009
390490015
390490028
390490029
390490037
390490081
390890005
80590004
80590005
80590006
80590011
80590600
80590601
260990009
260991003
261150037
Site
390610035
390610037
390610040
390616002
391650006
391651002
390071001
390350002
390350033
390350034
390350035
390350064
390350081
390352001
390353003
390354003
390355002
390550004
390850001
390850003
390853002
390930013
390930017
390931002
390931003
391030002
391030003
391032001
390410002
390490004
390490009
390490015
390490028
390490029
390490037
390490081
390890005
080590004
080590005
080590006
080590011
080590600
080590601
260990009
260991003
261150037
City
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Dallas-Fort Worth
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Denver
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
AIRSJD
480850085
481130039
481130044
481130045
481130047
481130052
481130055
481130069
481130075
481130086
481130087
481131047
481133003
481210002
481210033
481210034
481210054
481390015
481390082
482570001
482570005
483970001
483970081
80010600
80013001
80017015
80050002
80050003
80051002
80310002
80310009
80310010
80310011
80310014
80350002
80350603
80590002
482010047
482010051
482010055
482010059
482010062
482010066
482010070
482010075
482010099
Site
480850085
481130039
481130044
481130045
481130047
481130052
481130055
481130069
481130075
481130086
481130087
481131047
481133003
481210002
481210033
481210034
481210054
481390015
481390082
482570001
482570005
483970001
483970081
080010600
080013001
080017015
080050002
080050003
080051002
080310002
080310009
080310010
080310011
080310014
080350002
080350603
080590002
482010047
482010051
482010055
482010059
482010062
482010066
482010070
482010075
482010099
S38 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Detroit
Honolulu
Honolulu
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
AIRSJD
261150745
261250001
261250902
261251002
261470003
261470005
261470030
261630001
261630009
261630014
261630016
261630018
261630019
261630020
261630025
261630062
261632002
261632003
150031001
150031004
480710900
480710901
480710902
480710903
481570004
482010007
482010024
482010026
482010027
482010028
482010029
482010038
482010039
482010046
180970050
180970057
180970070
180970073
180970082
180970901
180970902
180970903
180970904
180970905
180970906
180972001
Site
261150745
261250001
261250902
261251002
261470003
261470005
261470030
261630001
261630009
261630014
261630016
261630018
261630019
261630020
261630025
261630062
261632002
261632003
150031001
150031004
480710900
480710901
480710902
480710903
481570004
482010007
482010024
482010026
482010027
482010028
482010029
482010038
482010039
482010046
180970050
180970057
180970070
180970073
180970082
180970901
180970902
180970903
180970904
180970905
180970906
180972001
City
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Houston
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
Los Angeles
AIRSJD
482011003
482011034
482011035
482011036
482011037
482011039
482011050
482017001
482910089
483390078
483390088
483390089
484730001
180110001
180570004
180571001
180590001
180590002
180590003
180590004
180591001
180630004
180810001
180810002
180950009
180950010
180970004
180970021
180970025
180970030
180970031
180970033
180970037
180970042
320030043
320030071
320030072
320030073
320030538
320030601
320031001
320031005
320031007
320031019
60370001
60370002
Site
482011003
482011034
482011035
482011036
482011037
482011039
482011050
482017001
482910089
483390078
483390088
483390089
484730001
180110001
180570004
180571001
180590001
180590002
180590003
180590004
180591001
180630004
180810001
180810002
180950009
180950010
180970004
180970021
180970025
180970030
180970031
180970033
180970037
180970042
320030043
320030071
320030072
320030073
320030538
320030601
320031001
320031005
320031007
320031019
060370001
060370002
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S39
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Indianapolis
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Kansas City
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Las Vegas
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Memphis
Memphis
Memphis
Memphis
Memphis
Memphis
Memphis
Memphis
Miami
AIRSJD
181090001
181090003
181090004
181090005
181450001
200910005
201030002
201210001
202090001
20209001 1
202090017
202090021
290370002
290370003
290470003
290470004
290470005
290470018
290470025
290472004
290950022
290950036
291650003
291650023
320030005
320030007
320030009
320030016
320030020
320030021
320030022
60376012
60377001
60378001
60379002
60379006
60379033
50350005
280330002
470470103
471570012
471570021
471570024
471570032
471571004
120250008
Site
181090001
181090003
181090004
181090005
181450001
200910005
201030002
201210001
202090001
20209001 1
202090017
202090021
290370002
290370003
290470003
290470004
290470005
290470018
290470025
290472004
290950022
290950036
291650003
291650023
320030005
320030007
320030009
320030016
320030020
320030021
320030022
060376012
060377001
060378001
060379002
060379006
060379033
050350005
280330002
470470103
471570012
471570021
471570024
471570032
471571004
120250008
City
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
Nashville
AIRSJD
60370004
60370016
60370018
60370019
60370030
60370031
60370113
60370206
60371002
60371004
60371102
60371103
60371104
60371105
60371106
60371201
60371301
60371401
60371601
60371701
60371902
60372002
60372005
60372101
60372301
60372401
60374001
60374002
60374101
60375001
60376002
271636015
271710009
551090001
551091002
470370011
470370012
470370026
470430007
470430009
471490101
471650007
471650101
471870103
471870105
471870106
Site
060370004
060370016
060370018
060370019
060370030
060370031
060370113
060370206
060371002
060371004
060371102
060371103
060371104
060371105
060371106
060371201
060371301
060371401
060371601
060371701
060371902
060372002
060372005
060372101
060372301
060372401
060374001
060374002
060374101
060375001
060376002
271636015
271710009
551090001
551091002
470370011
470370012
470370026
470430007
470430009
471490101
471650007
471650101
471870103
471870105
471870106
S40 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Miami
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
Minneapolis-St. Paul
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Orlando
Orlando
Orlando
AIRSJD
120250021
120250023
120250026
120250027
120250029
120250030
120251006
120251008
120251009
120254002
270030002
270031001
270031002
270032002
270370006
270371007
270376018
270530022
270530027
270530047
271230001
271230003
271230030
271230031
271410001
271410002
271410008
271630027
360610010
360610050
360610056
360610061
360610063
360790005
360810004
360810070
360810097
360810098
360810124
360850067
361191002
361192004
361195003
120690002
120950008
120952002
Site
120250021
120250023
120250026
120250027
120250029
120250030
120251006
120251008
120251009
120254002
270030002
270031001
270031002
270032002
270370006
270371007
270376018
270530022
270530027
270530047
271230001
271230003
271230030
271230031
271410001
271410002
271410008
271630027
360610010
360610050
360610056
360610061
360610063
360790005
360810004
360810070
360810097
360810098
360810124
360850067
361191002
361192004
361195003
120690002
120950008
120952002
City
Nashville
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New Orleans
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
AIRSJD
471890103
220510003
220511001
220512001
220710005
220710011
220710012
220710082
220710083
220711001
220870002
220890001
220890003
220890100
220930001
220930002
220950002
360050003
360050006
360050073
360050080
360050083
360050110
360470007
360470011
360470018
360470076
360610005
421010023
421010024
421010025
421010026
421010027
421010029
421010136
40130009
40130013
40130014
40130015
40130016
40130018
40130019
40131003
40131004
40131006
40131010
Site
471890103
220510003
220511001
220512001
220710005
220710011
220710012
220710082
220710083
220711001
220870002
220890001
220890003
220890100
220930001
220930002
220950002
360050003
360050006
360050073
360050080
360050083
360050110
360470007
360470011
360470018
360470076
360610005
421010023
421010024
421010025
421010026
421010027
421010029
421010136
040130009
040130013
040130014
040130015
040130016
040130018
040130019
040131003
040131004
040131006
040131010
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S41
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Orlando
Orlando
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Philadelphia
Phoenix
Phoenix
Phoenix
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
AIRSJD
120972002
121171002
340050007
340053001
340070003
340071001
340150002
340333001
420170012
420290050
420290070
420290100
420450002
420450102
420450103
420910013
420910069
420910101
421010002
421010004
421010014
421010019
421010020
421010021
421010022
40139997
40139998
40218001
420030008
420030010
420030067
420030080
420030081
420030088
420031001
420031005
420070002
420070003
420070004
420070005
420070014
420070501
420190501
421250005
421250200
421250501
Site
120972002
121171002
340050007
340053001
340070003
340071001
340150002
340333001
420170012
420290050
420290070
420290100
420450002
420450102
420450103
420910013
420910069
420910101
421010002
421010004
421010014
421010019
421010020
421010021
421010022
040139997
040139998
040218001
420030008
420030010
420030067
420030080
420030081
420030088
420031001
420031005
420070002
420070003
420070004
420070005
420070014
420070501
420190501
421250005
421250200
421250501
City
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Phoenix
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
AIRSJD
40132001
40132004
40132005
40133002
40133003
40133004
40133006
40133009
40133010
40134003
40134004
40134005
40134006
40134007
40139508
40139604
40139701
40139702
40139704
40139706
40139707
40139805
40139993
40139994
40139995
60171002
60172002
60610002
60610004
60610006
60610810
60611003
60613001
60670001
60670002
60670003
60670005
60670006
60670010
6067001 1
60670012
60670013
60671001
60675001
60675002
60675003
Site
040132001
040132004
040132005
040133002
040133003
040133004
040133006
040133009
040133010
040134003
040134004
040134005
040134006
040134007
040139508
040139604
040139701
040139702
040139704
040139706
040139707
040139805
040139993
040139994
040139995
060171002
060172002
060610002
060610004
060610006
060610810
060611003
060613001
060670001
060670002
060670003
060670005
060670006
060670010
06067001 1
060670012
060670013
060671001
060675001
060675002
060675003
S42 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Pittsburgh
Pittsburgh
Pittsburgh
Pittsburgh
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Portland
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
Sacramento
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Diego
San Francisco
San Francisco
San Francisco
San Francisco
San Francisco
San Francisco
San Francisco
Seattle
Seattle
AIRSJD
421255001
421290006
421290008
421290101
410050004
410051006
410052001
410052002
410053001
410054001
410090004
410511002
530110007
530110009
530110011
530111001
60170006
60170009
60170010
60170011
60170012
60170020
60730003
60730005
60730006
60731001
60731002
60731003
60731004
60731005
60731006
60731007
60731008
60731009
60732007
60734001
60737001
60410001
60410002
60750003
60750004
60750005
60810002
60811001
530330010
530330017
Site
421255001
421290006
421290008
421290101
410050004
410051006
410052001
410052002
410053001
410054001
410090004
410511002
530110007
530110009
530110011
530111001
060170006
060170009
060170010
060170011
060170012
060170020
060730003
060730005
060730006
060731001
060731002
060731003
060731004
060731005
060731006
060731007
060731008
060731009
060732007
060734001
060737001
060410001
060410002
060750003
060750004
060750005
060810002
060811001
530330010
530330017
City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
Salt Lake City
San Diego
San Diego
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
AIRSJD
490110001
490110002
490350002
490350003
490350004
490350009
490351001
490351002
490351005
490352004
490353001
490353003
490353006
490353007
490570001
490570003
490570007
490571001
490571002
490571003
60730001
60730002
171192005
171192006
171192007
171192008
171193007
171198001
171331001
171332001
171630008
171630009
171630010
171631001
171631006
171631007
171631008
171631009
290990012
291830002
291830005
291830008
291831002
291831004
291890001
291890002
Site
490110001
490110002
490350002
490350003
490350004
490350009
490351001
490351002
490351005
490352004
490353001
490353003
490353006
490353007
490570001
490570003
490570007
490571001
490571002
490571003
060730001
060730002
171192005
171192006
171192007
171192008
171193007
171198001
171331001
171332001
171630008
171630009
171630010
171631001
171631006
171631007
171631008
171631009
290990012
291830002
291830005
291830008
291831002
291831004
291890001
291890002
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S43
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
Seattle
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
AIRSJD
530330018
530330023
530330058
530330059
530330080
530330088
530332001
530337001
530337002
530610007
530612001
170830001
170831001
171190005
171190006
171190008
171190012
171191004
171191009
295100066
295100067
295100068
295100069
295100070
295100071
295100072
295100080
295100086
120570025
120570074
120570081
120570110
120571021
120571022
120571035
120571042
120571052
120571055
120571065
120571068
120574004
121010005
121012001
121030003
121030004
121030012
Site
530330018
530330023
530330058
530330059
530330080
530330088
530332001
530337001
530337002
530610007
530612001
170830001
170831001
171190005
171190006
171190008
171190012
171191004
171191009
295100066
295100067
295100068
295100069
295100070
295100071
295100072
295100080
295100086
120570025
120570074
120570081
120570110
120571021
120571022
120571035
120571042
120571052
120571055
120571065
120571068
120574004
121010005
121012001
121030003
121030004
121030012
City
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
St. Louis
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
AIRSJD
291890004
291890006
291890007
291890008
291890009
291890010
291892002
291893001
291894001
291895001
291897001
291897002
291897003
295100002
295100007
295100061
295100062
295100063
295100064
110010043
110011000
240090010
240170010
240210034
240210037
240310005
240310006
240311001
240311004
240313001
240330002
240330003
240330004
240338001
240338002
510130008
510130020
510590005
510590014
510590018
510590030
510591004
510595001
510610002
511071005
511530008
Site
291890004
291890006
291890007
291890008
291890009
291890010
291892002
291893001
291894001
291895001
291897001
291897002
291897003
295100002
295100007
295100061
295100062
295100063
295100064
110010043
110011000
240090010
240170010
240210034
240210037
240310005
240310006
240311001
240311004
240313001
240330002
240330003
240330004
240338001
240338002
510130008
510130020
510590005
510590014
510590018
510590030
510591004
510595001
510610002
511071005
511530008
S44
CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table 1. Monitoring Sites for USA Today Cities (continued)
City
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Tampa
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
AIRSJD
121030018
121030020
121030021
121030023
121033001
121035002
121037001
110010003
110010008
110010011
110010013
110010014
110010017
110010018
110010025
110010041
Site
121030018
121030020
121030021
121030023
121033001
121035002
121037001
City
Washington
Washington
Washington
Washington
Washington
Washington
Washington
110010003
110010008
110010011
110010013
110010014
110010017
110010018
110010025
110010041
AIRSJD
511530009
511790001
511870002
515100009
516000005
516300003
540030003
Site
511530009
511790001
511870002
515100009
516000005
516300003
540030003
5 years (1997 through 2001) of data
from the AQS for developing the
historical data.
Procedures
Using the data described above, we
observed the following procedure for
determining an ozone exceedance
day for a particular USA Today city.
For each day of the year, if one of the
monitors assigned to a particular city
measured an 8-hour ozone level
>0.085 ppm, that one measurement
resulted in one exceedance day for
the city. Even if more than one of the
city's assigned monitors recorded an
8-hour ozone level >0.085 ppm on a
given day, the exceedance count for
that day and city remained one. The
number of days exceedances are
measured are then accumulated over
the year to obtain a count of days (or
cumulative count) of exceedance
measurements.
For 2002, the cumulative count of
days was obtained from the AQS
database described above for each
city. For the historical 5-year period
(i.e., 1997 through 2001), the average
number of the cumulative count of
days was obtained over the 5-year
period for each set of monitors
assigned to each city to yield a
5-year trend. We decided to use
an average value as a comparison
instead of a year-to-year comparison
because the year-to-year cumulative
count of days will vary, making
comparisons with the most recent
year less meaningful.
Using these data, we generated
graphs showing the 5-year average
cumulative count of days with the
2002 cumulative count of days for
selected cities. Figure 1 provides
the graph for Atlanta, which shows
that the cumulative count of days
in 2002 for the Atlanta area closely
matches the 5-year average trend
in the cumulative count of days
through approximately the middle
of August. After the middle of
August, the 2002 count of days was
less than the 5-year average, and,
by the end of the ozone season, the
cumulative count of days for 2002
was 37 compared to the 5-year aver-
age trend of 46.
Figure 1. Cumulative exceedances—5-year average (97-01) (Atlanta)
compared to 2002 data and SE region average.
in
0)
co
-a
0)
0)
o
x
CD
_a
50
40
30
20
10
0
Atlanta
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S45
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
We also added a regional aspect
for comparison to the individual city
data. We grouped the LISA Today
cities into geographic regions and
then calculated a 5-year regional
average cumulative count of days
based on the individual city data
within the region. This regional aver-
age was also depicted on the individ-
ual city graphics to offer a compari-
son of the city data to regional data.
As shown in Table 2, the LISA
Today cities were grouped into south-
east, northeast, midwest, and south-
west regions. Dallas, Houston, and
Los Angeles were treated as individ-
ual cities because of their unique
geographic locations and—especially
in the case of Los Angeles—unique
emission density characteristics
compared to other LISA Today cities.
The combination of cities included in
the regional average cumulative
count of days was somewhat subjec-
tive for this illustration, and other
combinations could be done for
different comparative purposes.
Discussion of Graphical
Depictions of
Cumulative Count
of Days
The following sections discuss the
graphical depictions of the cumula-
tive count of days for 30 of the 36
LISA Today cities used in this analy-
sis. The LISA Today cities of Portland
(OR), Seattle, Denver, Honolulu, Salt
Lake City, and San Francisco were
not included because ozone exceed-
ances are typically minimal in these
locations.
Southeast U.S. Region
We have included the following
cities in the Southeast (SE) U.S.
Region: Atlanta, Charlotte, Memphis,
Nashville, New Orleans, Miami,
Orlando, and Tampa. The graph for
Table 2. Regional Groupings of USA Today Cities
Southeast U.S. Cities
Northeast U.S. Cities
Midwest U.S. Cities
Southwest U.S. Cities
Individual U.S. Cities
Atlanta
Charlotte
Memphis
Nashville
Boston
New York
Philadelphia
Chicago
Cleveland
Cincinnati
Columbus
Detroit
Las Vegas
Phoenix
Dallas
Houston
New Orleans
Miami
Orlando
Tampa
Baltimore
Washington, D.C.
Indianapolis
Kansas City
Minneapolis
Pittsburgh
St. Louis
Sacramento
San Diego
Los Angeles
each SE city depicts the city 5-year
average cumulative count of days,
the combined 5-year average for
all SE Region cities, and the 2002
cumulative count of days for
the city.
The Atlanta graph (see Figure 1)
shows that the 2002 count of days
was tracking the Atlanta 5-year
average rather closely through
approximately the middle of August
then trended less than the 5-year
average for the remainder of the
year. An ozone episode of several
days is depicted on the graph in
early August, when the count of
days increased from 22 days to 30
days. For comparative purposes, the
Atlanta data are higher than those
for the combined SE Region average;
that is, the Atlanta 5-year average
cumulative count of days is about 46
days per year, whereas the SE Region
average is approximately 18 days per
year.
For Charlotte (Figure 2), the 2002
count of days trended slightly less
than the 5-year average through
early June but then trended slightly
greater than the 5-year average from
early July onward. Ozone episodes
are noted in early July and early
August. Also, the Charlotte data are
comparatively higher than those for
Figure 2. Cumulative exceedances—5-year average (97-01) (Charlotte)
compared to 2002 data and SE region average.
in
0)
o
c
co
T3
0)
0)
CD
_a
40
35
30
25
20
15
10
5
0
2002
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
S46 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
the combined SE Region average.
The Charlotte data show that the
city's 5-year average cumulative
count of days is about 35 days per
year, whereas the combined SE
Region average is about 18 days per
year.
The graph for Memphis (Figure 3)
shows that the 2002 data were trend-
ing less than the Memphis 5-year
average count of days throughout
the year. As a result, the total cumu-
lative count of days for 2002 was 16,
whereas the 5-year average total is
approximately 23 days. Again, an
ozone episode is noted in early
August for Memphis, similar to
those noted in Atlanta and Charlotte.
As with the graph for Memphis,
the graph for Nashville (Figure 4)
also shows the 2002 data trending
slightly less than the 5-year average
throughout the year. The total count
of days for 2002 was 21 days, where-
as the 5-year average count of days is
approximately 25 days. Notable
ozone episodes are shown in early
August and early September.
The graph for New Orleans
(Figure 5) shows the count of days
for 2002 trended less than the 5-year
average throughout the year. The
2002 total was 2 days, whereas the
5-year is 8 days.
Figure 3. Cumulative exceedances—5-year average (97-01) (Memphis)
compared to 2002 data and SE region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 4. Cumulative exceedances—5-year average (97-01) (Nashville)
compared to 2002 data and SE region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 5. Cumulative exceedances—5-year average (97-01) (New Orleans)
compared to 2002 data and SE region average.
co
-a
0)
0)
o
x
o
0)
E
=3
10 -
5 -
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S47
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 6. Cumulative exceedances—5-year average (97-01) (Miami)
compared to 2002 data and SE region average.
20
0)
U -i(-
c 15
co
T3
0)
0)
o
x -in
uj 'u
"6
0)
E 5
=j
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 7. Cumulative exceedances—5-year average (97-01) (Orlando)
compared to 2002 data and SE region average.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Miami (Figure 6), Orlando (Figure
7), and Tampa (Figure 8) all show
2002 cumulative counts of days
throughout the year less than the
5-year average. Miami and Tampa
show no exceedances counted for
2002. In comparison, Miami aver-
aged 5 days for the 5-year period,
and Tampa averaged 7 days.
Northeast U.S. Region
The following cities were included
for the Northeast (NE) U.S. Region:
Boston, New York, Philadelphia,
Baltimore, and Washington, DC. The
graph for each NE city depicts the
city 5-year average count of days, the
combined 5-year average count of
days for all NE cities, and the city's
2002 count of days.
The graphical depiction for the
Boston area (Figure 9) shows that the
2002 data trended greater than the
5-year average from approximately
late June onward. A notable ozone
episode of high ozone with several
days of measured exceedances
occurred during early to mid-
August. The total count of days in
the Boston area for 2002 was 18,
whereas the 5-year average count of
days is approximately 8 days.
Figure 8. Cumulative exceedances—5-year average (97-01) (Tampa)
compared to 2002 data and SE region average.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
S48 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
Figure 9. Cumulative exceedances—5-year average (97-01) (Boston)
compared to 2002 data and NE region average.
30
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 10. Cumulative exceedances—5-year average (97-01) (New York)
compared to 2002 data and NE region average.
35
./> 30
CD
u
ro 25
-a
CD
20
CD 1n
_Q I 0
I 5
0
2002
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
The graph for the New York area
(Figure 10) shows a trend similar to
the one in Boston, with the 2002 data
trending greater than the 5-year
average from approximately the
beginning of July onward. The New
York data also show an ozone
episode in early to mid-August. The
total count of days for 2002 was 30,
compared to the 5-year average of 19
days.
For Philadelphia (Figure 11), the
graph shows the 2002 data trending
similar to the 5-year data until the
beginning of August. After that, the
2002 data trend greater, with a 2002
total count of days of 37, whereas the
5-year average is approximately
29 days. As with Boston and New
York, the ozone episode is evident in
early to mid-August.
NE Region
New York
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 11. Cumulative exceedances—5-year average (97-01) (Philadelphia)
compared to 2002 data and NE region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S49
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
The graph for Baltimore (Figure
12) shows a pattern nearly identical
to that of Philadelphia. The 2002 total
count of days was 39, whereas the
5-year average is approximately
33 days.
The Washington, DC, graph
(Figure 13) shows a pattern similar to
that of Philadelphia and Baltimore,
with the 2002 data showing a greater
trend than the 5-year average from
approximately the beginning of
August onward. The total 2002 count
of days for Washington was 37, as
compared to the 5-year average of
31 days.
Midwest U.S. Region
The following cities were included in
the Midwest U.S. Region: Chicago,
Cleveland, Cincinnati, Columbus,
Detroit, Indianapolis, Kansas City,
Minneapolis, Pittsburgh, and St.
Louis.
The graph for Chicago (Figure 14)
shows a similar trend for 2002 count
of days compared to the 5-year aver-
age trend through approximately the
middle of June. Thereafter, the 2002
data show a notably greater trend
than the 5-year average. A notable
ozone episode of several days is
evident in the middle of July. Other
episodes are shown in early August
and early September. The total count
of days for 2002 in the Chicago area
was 20, as compared to the 5-year
average of approximately 9.
Figure 12. Cumulative exceedances—5-year average (97-01) (Baltimore)
compared to 2002 data and NE region average.
50
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 13. Cumulative exceedances—5-year average (97-01) (Washington, DC)
compared to 2002 data and NE region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 14. Cumulative exceedances—5-year average (97-01) (Chicago)
compared to 2002 data and MW region average.
25
20
co
T3
0)
0)
o
x
15
o 10
0)
1 5
2002
MW Region
Chicago
—I 1 1— -"T" —P 1 1 1 1 1 T
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
S50 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
The graph for Cleveland (Figure
15) shows a pattern similar to the
one for Chicago. There is a similar
trend in the 2002 data and 5-year
average data through the end of
June, then a notably greater trend in
the count of days from the middle of
June onward. The total 2002 count of
days was 31 compared to the 5-year
average of approximately 18 days.
Cincinnati (Figure 16), Columbus
(Figure 17), Detroit (Figure 18),
Indianapolis (Figure 19), Pittsburgh
(Figure 20), and St. Louis (Figure 21)
all show a similar pattern, with the
2002 data trending less than the
5-year average until the middle or
end of June, then trending notably
greater than the 5-year average
onward. All show ozone episodes
around the beginning of August and
in early September. Another episode
common to all cities is seen in the
middle of June. For Cincinnati, the
2002 total count of days was 28,
compared to a 5-year average of
approximately 17 days. For Colum-
bus, the 2002 total was 27 days,
compared to a 5-year average of
approximately 16 days.
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 15. Cumulative exceedances—5-year average (97-01) (Cleveland)
compared to 2002 data and MW region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 16. Cumulative exceedances—5-year average (97-01) (Cincinnati)
compared to 2002 data and MW region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 17. Cumulative exceedances—5-year average (97-01) (Columbus)
compared to 2002 data and MW region average.
0)
o
c
co
T3
0)
0)
CD
_a
E
15 -
10 -
5 -
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S51
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
For Detroit, the 2002 total was 22
days, compared to approximately 12
days for the 5-year average. For
Indianapolis, the 2002 total was 24
days, compared to approximately 15
days for the 5-year average.
For Pittsburgh, the 2002 total was
33 days, compared to a 5-year aver-
age of approximately 23 days.
Figure 18. Cumulative exceedances—5-year average (97-01) (Detroit)
compared to 2002 data and MW region average.
in
0)
o
c
co
-a
0)
0)
CD
_a
25
20
15
10
2002
MW Region
Detroit
—i 1 1— p~ —r—i 1 1 1 1 r
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 19. Cumulative exceedances—5-year average (97-01) (Indianapolis)
compared to 2002 data and MW region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 20. Cumulative exceedances—5-year average (97-01) (Pittsburgh)
compared to 2002 data and MW region average.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
S52 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
For St. Louis, the 2002 total was 32
days, compared to a 5-year average
of approximately 19 days.
The graph for Kansas City (Figure
22) showed no exceedances until
early July. Ozone exceedances
trended similar to the 5-year average
for July and into August, then
trended less than the 5-year average
onward. The 2002 cumulative count
of days was 7, whereas the 5-year
average for Kansas City is approxi-
mately 11 days.
Minneapolis (Figure 23) histori-
cally has few exceedance days,
averaging about 1 day over the
5-year period. The 2002 data show
there were 2 exceedance days.
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 21. Cumulative exceedances—5-year average (97-01) (St. Louis)
compared to 2002 data and MW region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 22. Cumulative exceedances—5-year average (97-01) (Kansas City)
compared to 2002 data and MW region average.
o
CD
E
=3
16
14
12
MW Region
Kansas City
2002
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 23. Cumulative exceedances—5-year average (97-01) (Minneapolis)
compared to 2002 data and MW region average.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S53
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Southwest U.S. Region
The following cities were included in
the Southwest (SW) U.S. Region: Las
Vegas, Phoenix, Sacramento, and San
Diego. Los Angeles was viewed
separately for the SW Region. Also,
any comparisons of the SW Region
to individual cities may be less
meaningful than comparisons in
other regions because of the larger
distances and more unique geo-
graphic and emission characteristics
among the SW region cities.
For Las Vegas (Figure 24), the
trend in the cumulative count of
days for 2002 was similar to the
5-year average trend. The total num-
ber of days for 2002 was 6, whereas
the 5-year average count of days is 3.
The 2002 cumulative count of
days for San Diego (Figure 25)
trended persistently less than the
5-year average throughout the year.
The total count of days for 2002 was
13, as compared to the 5-year aver-
age of approximately 20 days.
The graph for Sacramento (Figure
26) showed a similar trend for 2002
as compared to the 5-year average
through the beginning of July.
Thereafter, the 2002 count of days
trended greater than the 5-year
average from early July onward. The
total 2002 cumulative count of days
was 45 days, whereas the 5-year
average is approximately 35 days.
Figure 24. Cumulative exceedances—5-year average (97-01) (Las Vegas)
compared to 2002 data and SW region average.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 25. Cumulative exceedances—5-year average (97-01) (San Diego)
compared to 2002 data and SW region average.
25
S 20
c
co
1 15
O
o 10
i_
CD
_a
I 5
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 26. Cumulative exceedances—5-year average (97-01) (Sacramento)
compared to 2002 data and SW region average.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
S54
CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
The data for Phoenix (Figure 27)
showed distinct ozone episodes in
early June and early July. The result-
ing pattern for 2002 trended less than
the 5-year average through early
June but greater than the 5-year aver-
age for early July onward. After late
July there were no additional exceed-
ances reported in the AQS for the
Phoenix area. For 2002, the total
cumulative count of days was 14,
whereas the 5-year average count of
days is approximately 19.
Other Areas
Dallas, Houston, and Los Angeles
were treated separately in this analy-
sis due to their unique geographic
locations and emission densities as
compared to nearby locations.
For Dallas (Figure 28), the 2002
data trended close to the 5-year aver-
age data through early August then
trended somewhat less than the
5-year average from early August
onward. The 2002 count of days was
20 days, whereas the 5-year average
count of days is approximately
33 days.
The 2002 data for Houston (Figure
29) was similar to that for Dallas in
that it also trended lower than the
5-year average, especially after early
August. For 2002, the total cumula-
tive count of days was 22, whereas
the 5-year average is approximately
36 days.
Figure 27. Cumulative exceedances—5-year average (97-01) (Phoenix)
compared to 2002 data and SW region average.
0)
o
c
co
T3
0)
0)
CD
_a
E
10 -
5 -
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 28. Cumulative exceedances—5-year average (97-01) (Dallas)
compared to 2002 data.
0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
Figure 29. Cumulative exceedances—5-year average (97-01) (Houston)
compared to 2002 data.
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
SPECIAL STUDIES
CUMULATIVE OZONE EXCEEDANCES
S55
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
For Los Angeles (Figure 30), the
2002 data showed a similar trend to
the 5-year average data through the
beginning of June, then trended
progressively greater than the 5-year
average from early June onward. A
notable episode occurred in early to
mid-August. For 2002, the total count
of days was 68, whereas the 5-year
average is approximately 40 days.
Summary
This analysis provided a comparative
illustration of accumulated ozone
exceedance days among LISA Today
cities throughout the United States.
These comparisons were illustrated
for distinct geographic regions due
to the regional nature of ground-
level ozone formation and transport.
The illustrations show distinctive
differences among regions and also
within regions when 2002 data are
compared to historical 5-year aver-
age trends. For example, in the SE
region, the 2002 accumulated count
of days trended in a similar pattern
to the 5-year average trend for some
cities (e.g., Atlanta, Charlotte),
whereas the 2002 data trended lower
than the 5-year average for some
other cities (e.g., Memphis, Nash-
ville, New Orleans). In contrast, for
most of the cities analyzed in this
study in the NE region, the 2002 data
trended lower than the 5-year aver-
age through approximately early
July, then trended higher than the
5-year average from mid-July into
mid-September.
The MW Region comparison
presented different results than did
the comparisons for the SE and NE
regions. For example, for all cities in
the core area of the MW region
(Chicago, Cleveland, Cincinnati,
Columbus, Pittsburgh, Indianapolis,
Detroit, and St. Louis), the 2002 data
trended less than the 5-year average
Figure 30. Cumulative exceedances—5-year average (97-01) (Los Angeles)
compared to 2002 data.
0)
o
c
co
T3
0)
0)
80
70
60
50
40
30
20
10
0
2002
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
5-Year Average
through approximately mid- to late
June, then trended progressively
higher than the 5-year average from
late June onward. Other cities
outside the core MW Region (e.g.,
Kansas City, Minneapolis) showed
2002 data trending similar to or less
than the 5-year average data.
Reference
1. John E. White. Information
Transfer Group, Information Transfer
and Program Integration Division,
Office of Air Quality Planning and
Standards, Research Triangle Park,
NC. Personal communication,
September, 2002.
S56 CUMULATIVE OZONE EXCEEDANCES • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Characterization of National Spatial Variation
Terence Fitz-Simons
U.S. Environmental Protection Agency, Research Triangle Park, NC 27711
Abstract
Spatial variability is an important quality of air
pollutants for many areas of policy within the U.S.
Environmental Protection Agency (EPA). Obviously
monitoring regulations depend heavily on knowledge
of spatial variability. In addition, control strategies
depend on this knowledge, which helps determine
whether a local or regional program would be more
effective. Action day programs and public information
programs also benefit from this knowledge.
Traditionally, spatial variation has been depicted by
isopleth maps, concentration maps, and box plots of
various sites. Does this really give us useful knowledge
about spatial variation? This paper explores a new way
to examine spatial variability on a national scale and
also presents an extension of this method in an attempt
to characterize spatial variability in a useful way. The
new methodology is presented along with its applica-
tion using PM2 5 and ozone data.
Introduction
Spatial variability is a very impor-
tant quality of air pollutants for
many areas of EPA policy. Obviously,
monitoring regulations and network
design depend heavily on knowl-
edge of spatial variability, as do
implementation strategies and poli-
cies. Control strategies also depend
heavily on this knowledge, which
helps state and local agencies decide
whether a local or regional program
may be more effective. Action day
programs and public information
programs also depend on this infor-
mation to facilitate decisions regard-
ing how large of an area should be
included in various alerts or infor-
mation publications. Traditionally,
spatial variation has been depicted
by isopleth maps, concentration
maps, and box plots of various sites.
Each of these methods gives a crude
idea of spatial variability. This paper
explores a new way to visualize
large-scale spatial variability and
also presents an extension of this
method in an attempt to characterize
spatial variability in a useful way.
The new methodology is presented
along with its application using data
from several pollutants nationwide.
Characterizing
Spatial Variation
One of the first questions arising
from almost any investigation of an
air pollutant is, "What is the spatial
and temporal variability or varia-
tion?" Very often, the spatial part of
the question is answered with a map
showing ranges of pollutant levels
by county. These maps show where
pollutant levels are higher and lower
and, in general, where information is
available or where monitoring sites
are located (see Figure 1).
After the work of producing the
map is done, the question is usually
considered answered. However, this
is a crude view of spatial variability.
Looking at such a map, counties with
Figure 1. PM10 annual averages (county maximum).
Alaska
Concentration ugm3
O s X s 2O
30 < X s 50
20 < X s 30
50 < X
SPECIAL STUDIES
NATIONAL SPATIAL VARIATION
S57
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
higher values are easily spotted but
it is hard to visualize how close
adjoining counties are to others.
Some analysts go a step farther and
show a map of an estimated surface
of pollutant levels. The latest and
most popular way to do this is called
kriging.1 Rriging is a spatial interpo-
lation technique developed for the
mining industry in South Africa to
predict ore reserves. With an interpo-
lated surface, all the blank areas on
the map are gone, and it is some-
what easier to see how pollutants
may vary over space. Figure 2
provides an example of a kriged
surface. Because the surface itself is
smoothed by the process, kriging
actually hides some of the spatial
variation, which may or may not be
a good result depending on the
purpose of the analysis.
At the heart of kriging is a con-
cept called a variogram, which is a
representation of the statistical vari-
ance of the difference between two
data points on a map as it relates to
the distance between the two points
on the map. Much like the mean,
which is a measure of the center
of a distribution of data, the variance
is a measure of the spread of a
distribution of data. In this case, the
Figure 2. Example of a kriged surface.
data are a series of measurements
representing differences between two
locations paired by time. Thus if d; is
the difference between two readings
at two monitors at a given time i,
then d; = x1;-x2i. If Xj and x2 are both
random variables from two locations,
then the variance of the difference is
V(x1-x2), or V(d). In fact, the vari-
ance of the difference is V(d) =
V(x1)+V(x2)-2COV(x1,x2). This is the
sum of the variances of the two
random variables minus twice the
covariance (a measure of how much
the two random variables vary
together). Basically, this says that the
more the two random variables
change together (they go up or down
together but they do not necessarily
change the same amount), the
smaller the variance of the difference
will be because the values at two
different sites would be expected to
vary together more if they are close
together and vary more independ-
ently if they are far apart. This leads
to the concept of the variogram,
which, in this case, is the relationship
between the variance of the differ-
ences and the distance between two
sites (Figure 3). The dotted line in
Figure 3 shows how the variance
changes with the distance. At a
W '
distance of zero (0), there is still
variation left that does not go away
even if the sites are at the same
location. This is called the nugget.
Similarly, there is a point, called the
sill, at which the variance levels out.
The area between 0 and the sill is
called the range. The range can be
thought of as the region where there
is a correlation between two sites.
The region after the sill can be
thought of as the distances at which
sites appear to be independent of
each other.
Figure 3. Schematic of a variogram.
l Nugget
Sill
distance
range
Figure 4 shows how PM2 5 data
can be used to plot the variance of
the difference against distance. The
difference in daily PM2 5 values was
calculated for various sites across the
country. The variance of the differ-
ences was calculated, and the
latitude and longitude of each site
were used to calculate the distance
between two sites. Each pair of sites
then had a variance of the difference
and a distance, which were plotted
for all possible pairs of sites across
the country.
Looking at the scatterplot, it is
clear that there is no simple relation-
ship between the variance of the dif-
ference and distance. A very dense
cluster of points seems to center over
25 at 0 distance and then slowly
increases as the distance increases.
However, from a casual examination
S58
NATIONAL SPATIAL VARIATION • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
of the plot, enough points fall outside
the dense cloud (in fact, many were
cut off to actually see any trend at all
by setting the maximum variance
displayed to 500) to bring into ques-
tion the assumption used in kriging,
as shown in Figure 3, that the vari-
ance of the difference over distance
can be described by a line.
The point of defining all these
terms is to show that the variance of
the differences between two measure-
ments taken at the same time but at
different locations is generally
increasing because the covariance is
decreasing over the distance. Because
the correlation is covariance normal-
ized by the variances, we can charac-
terize the spatial dependence of data
from two locations through the corre-
lation. Because the variance of the
difference generally increased, the
covariance and, therefore, the correla-
tion should decrease over distance.
This raises the question, how does the
correlation vary over distance? To
answer this question, PM2 5 data were
used to calculate the correlation of
daily PM2 5 values between two sites,
and the latitude and longitude were
used to calculate the distance between
two sites. Thus for each pair of sites,
we have correlation and a distance.
Looking at all the possible pairs of
sites, scatterplots may be generated,
such as the one in Figure 5. The values
of the correlations are restricted to all
values between -1 and 1, but the vari-
ance of the distance must be positive.
These restrictions help provide a
much more coherent picture. There
is, again, a dense cloud that trends
downward as the distance increases.
Also, there are many points not in the
dense cloud that fall beneath the
trend. Again, these points are numer-
ous enough to question the simplicity
of the variogram used in kriging.
To simplify what is seen in this
scatter plot, the data could be
Figure 4. Variance of the difference vs. distance.
500
50
100 150
200 250 300
Distance (km)
350 400 450
500
Figure 5. Correlation (r) vs. distance for PM2 5.
1.0
0 50 100 150 200 250 300 350 400 450 500
Distance (km)
summarized by box plots of the data
over 20-km intervals. This would
result in Figure 6, which shows a
much less confusing picture. The
whiskers represent the maxima and
minima of the intervals. The box
represents the 75th and 25th per-
centiles, the plus sign (+) represents
the mean, and the single line in the
box represents the median or 50th
percentile. Now a trend is much
more apparent in the correlation than
in the scatterplot. However, this
display shows only how well the
data "track" or follow a pattern. It
does not show how well the data
from different sites actually agree. In
other words, the data from one site
might track the data from another
site very well but still have very
different concentrations on average
than data from the other site. Here
we present a solution to this prob-
lem, a coefficient of perfect agree-
ment, or CPA.
SPECIAL STUDIES
NATIONAL SPATIAL VARIATION
S59
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 6. Box plot of correlation vs. distance.
1.0
0.7
0.4
c£ 0.1
o
-0.2
-0.5
-0.8
50 100 150 200 250 300
Distance (km)
350 400 450 500
The Coefficient of
Perfect Agreement
The goal of formulating a CPA is to
give a measure of agreement with
many of the characteristics of the
correlation coefficient.
The classical correlation coefficient
is a measure of how well paired
values track each other. The value
0 (zero) means they do not track each
other at all, whereas a value of 1
means they track each other perfectly
(all the points in a scatterplot would
be on a straight line). A value of -1
also means perfect tracking, but the
scatterplot line would have a down-
ward or negative slope. The correla-
tion coefficient is defined as shown
in Equation A.
As stated earlier, the correlation
coefficient has a nice feature in that,
when the data from two sites agree
in a perfectly linear fashion, then r is
1 (or -1). However, if the data agreed
perfectly, the only line that mattered
would be a line with a slope of 1 and
an intercept of 0 (the line y = x).
Therefore, the first characteristic we
desire in a CPA is that the CPA = 1
when all points in a scatterplot fall
on the line y = x, and the CPA = 0 if
there is no systematic agreement.
One way to create this would be to
include a term in the denominator of
the correlation coefficient as shown
in Equation B.
If there were no agreement, this
term would become large and the
CPA would become small (or close to
0). If there were perfect agreement,
the term would be 0, and, because all
the points would fall on a straight
line, the rest of the equation (the
correlation coefficient) would be 1,
allowing the CPA to be 1. However,
if the two data streams fell on a
straight line that did not have a slope
of 1 and an intercept of 0, then the
Equation A
, O
Equation B
CPA =
Equation C
CPA =
Equation D
CPA =
S60 NATIONAL SPATIAL VARIATION • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
CPA would certainly not be 1 but
less than 1 because y would not
equal x everywhere. This seems to
have all the characteristics desired in
a CPA.
However, note that the 2(x ~ y)2
term will get larger and larger as the
number of data points gets larger
and larger, making the CPA get
smaller and smaller. Unless there
were a situation of perfect agree-
ment, then such a CPA could be
made to be arbitrarily small by
taking larger and larger numbers of
data points to compute the CPA. A
further refinement would then be
defined as shown in Equation C.
This solves the sample size prob-
lem, but there is one problem left.
The correlation coefficient is a unit-
less or unit invariant quantity. This
CPA is not, but it should be. Units
have been reintroduced into the
formula. Because a units conversion
could result in a different CPA value,
this is not a desirable trait for a coef-
ficient. The added term is divided by
the same divisor used to normalize
the covariance to get the correlation
resulting in Equation D.
Figure 7. CPA vs distance (km)
1.0
Now the CPA is unitless.
Monte Carlo studies of the CPA
were performed by generating
values from a straight line. In linear
regression, Y = a + bX + e, where e
has a normal distribution with a
mean of 0 and a variance of a2. This
last term is also called the variation
about the line. Five hundred sets of
values were generated with different
slopes, intercepts, and variations
about the line. Slopes ranged from 0
to 5, intercepts ranged from -10 to 10,
and the variance about the line, a2,
ranged from 0 to 100. In this case,
whenever o2 is 0, then r is 1 (a per-
fect linear relationship). However,
the CPA is equal to 1 only if a is 0,
b is 1, and o2 is 0. The studies found
the CPA to be relatively sensitive to
the lack of perfect agreement when
there was only a perfect linear rela-
tionship (when r is 1 and the CPA
should be less than 1).
Application
Using the CPA instead of r, a new
scatterplot can be constructed (Figure
7). Now the denser part of the distri-
bution of points has a different trend.
The trend dips quickly and then falls
off gradually. If, as before, the data
are displayed as box and whisker
plots, the more pronounced trend in
Figure 8 is revealed. This gives a
national picture of the spatial varia-
tion of PM2 5. The mean CPA starts
off at around 0.6 and falls off rapidly
out to about 150 km, then falls off
gradually from there to about 0.2 at
500 km. The maximum and mini-
mum of the coefficient (the whiskers
on the box and whiskers plot) still
vary almost across all possible values
of the coefficient (perfect agreement,
or 1, to no agreement at all, or 0) at
any distance. Quantitatively, inter-
pretation of this coefficient is difficult
at best. Where it might be of most
use is in comparisons with other
pollutants.
Comparison
of Pollutants
Pollutants can be compared by
following the previous steps used to
produce Figure 8. The means in
Figure 8 (the pluses [+]) can be
joined by a line for several pollut-
ants. This is where the usefulness
of a CPA can be demonstrated. A
comparison between pollutants
could be made to help guide policy.
For example, daily values of PM2 5,
daily values of PM10, hourly values
of CO (carbon monoxide), and
hourly values of ozone were used to
produce Figure 9. As can be seen
from the plot, PM2 5 has a mean CPA
that is above ozone for most of the
distances out to 500 km (at least until
450 km). This might suggest that if a
regional control strategy is being
pursued for the ozone problem in the
United States, a regional strategy
also makes sense for PM2 5.
50
100 150
200 250 300
Distance (km)
350 400 450
500
SPECIAL STUDIES
NATIONAL SPATIAL VARIATION
S61
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 8. Coefficient of perfect agreement vs distance (km).
o
1.0
0.7
0.4
0.1
-0.2
-0.5
-0.8
50 100 150 200 250 300 350 400 450 500
Distance (km)
Figure 9. Comparison of mean CPA vs distance (km).
1.0
o
0.6
0.4
0.2
0.0 LT
Pollutant: CO
— PM,
50 100 150 200 250 300 350 400 450 500
Distance (km)
Conclusions
A CPA can be formulated that can be
of some use in assessing spatial vari-
ation on a national scale. The statisti-
cal properties of the CPA used here
are not known, and the CPA cannot
be used to quantify this variability.
However, it can be a useful compara-
tive tool to visualize differences in
national scale spatial variation
among pollutants.
References
1. Matheron, G. Principles of
Geostatistics. Economic Geology. 1963,
58,1246-1266.
S62
NATIONAL SPATIAL VARIATION • SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Development of a New Reporting Technique for Air Quality
Prepared by
RTI International
Research Triangle Park, NC 27709
Prepared for
The Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27709
Reporting Air Quality
Information
The U.S. Environmental Protection
Agency (EPA) has long taken the
lead in reporting air quality informa-
tion to the general public. EPA
routinely presents status and trends
for the outdoor concentrations of
different kinds of air pollutants in
documents that provide clear and
informative text, graphics, and data
tables for general and technical audi-
ences. These documents include the
National Air Quality and Emissions
Trends Report (the Trends Report) and
a related booklet, Latest Findings on
National Air Quality: Status and
Trends. In addition, EPA maintains
the Air Trends Web site (http://
www.epa.gov/airtrends/index.html),
which presents current and past
air trends information and data,
highlights of EPA's air pollution
programs, and detailed information
about air quality in the United
States.
Air quality information is often
complex and not always easily inter-
preted by the general public. As
more and more information about
air pollution and its effect on our
health is being presented to the
public through common channels
such as television and radio news
programs, daily newspapers, and
Web postings, a need has arisen to
provide the general public with a
simple, visual method for assessing
the degree of air pollution in their
communities. As one approach to
meeting this need, EPA is exploring a
method of displaying air quality
information that is designed to allow
the general public to quickly and
easily review the degree of air pollu-
tion in locations across the United
States. Although this simplified
display offers obvious benefits to
users, there are limitations to this
reporting technique as well. This
paper describes the new reporting
technique in detail and discusses its
advantages and disadvantages.
A New Reporting Tool
EPA is evaluating the use of a new
tool for displaying air quality infor-
mation using data from EPA's Air
Quality Index (AQI), which monitors
air quality in selected city groupings
known as metropolitan statistical
areas (MSAs). Information for 319
MSAs would be included in the
display. MSAs are defined by the
Office of Management and Budget
and generally include one or more
entire counties, except in New
England where cities and towns are
the basic geographic units. MSAs
have been selected as the reporting
unit because they are the basis for
AQI reports and for listings of
attainment and nonattainment status
for National Ambient Air Quality
Standards (NAAQS).
The new display technique would
present air quality information by
MSA for the following pollutants:
• Carbon monoxide (CO)
• Nitrogen dioxide (NO2)
• Ozone (O3)
• Particulate matter (PM10 and
PM2.5)
• Sulfur dioxide (SO2).
Information would be displayed
using color-coded circles to indicate
air quality for each of these pollut-
ants in the selected MSAs. Users
would be able to view the air quality
status for different locations and
pollutants by scrolling up and down
an alphabetical list of MSAs.
The purpose of this new reporting
technique would be to provide a
simplified, visual tool for interpret-
ing air quality information in select-
ed MSAs for a specific year for each
of the selected pollutants. It would
not be used as a rating system, nor
would it show trends in air quality
over time. Future versions of this
method could allow users to sort the
information based on the relative
rankings for each pollutant of inter-
est and generate a report based on
their relative degree of suitability for
someone with asthma, angina, or
other health conditions.
SPECIAL STUDIES • NEW REPORTING TECHNIQUE
S63
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 1. Interpreting the symbols in the new display technique
Example MSA Report
Metropolitan Statistical Area (MSA)
Pollutants
location 1
location 2
location 3
location 4
location 5
O
Legend
LEGEND
• e
Fewer days of
unhealthy air
(Days with AQI
>100) compared to
other MSAs
O ® •
More days of
unhealthy air
(Days with AQI
>100) compared to
other MSAs
0 Not Monitored
— Insufficient Data
Outpoint Table for 2001
Pollutant
O
Ozone
Carbon
monoxide
PM2.5
PM-io
Sulfur
dioxide
Nitrogen
dioxide
1 or fewer
days with AQI
above 100
Odays
with AQI
above 100
1 or fewer
days with AQI
above 100
1 day
with AQI
above 100
Odays
with AQI
above 100
Odays
with AQI
above 100
2 or 3 days
with AQI
above 100
1 days
with AQI
above 100
2 or 3 days
with AQI
above 100
2 days
with AQI
above 100
1 day
with AQI
above 100
1 day
with AQI
above 100
4-1 2 days
with AQI
above 100
2 days
with AQI
above 100
4-1 2 days
with AQI
above 100
3-1 1 days
with AQI
above 100
2 days
with AQI
above 100
2 days
with AQI
above 100
1 3-25 days
with AQI
above 100
3 days
with AQI
above 100
1 3-28 days
with AQI
above 100
1 2-36 days
with AQI
above 100
3 days
with AQI
above 100
3 days
with AQI
above 100
more than 25
days with AQI
above 100
more than 3
days with AQI
above 100
more than 28
days with AQI
above 100
more than 36
days with AQI
above 100
more than 3
days with AQI
above 100
more than 3
days with AQI
above 100
Developing the Tool
Selecting Pollutants
The pollutants to be included in this
display are CO, NO2/ O3, particulate
matter (PM10 and PM2 5), and SO2.
These pollutants are five of the six
"criteria" pollutants for which EPA
has set National Ambient Air Quality
Standards (NAAQS) as required by
the Clean Air Act. The NAAQS for
each pollutant indicate an outdoor
(or ambient) concentration not to be
exceeded on average over a 3-year
period; concentrations below the
NAAQS are preferable and would be
expected to cause fewer adverse
health effects. EPA tracks air quality
based on measurements of pollutant
concentrations in outdoor air at
monitoring sites across the country
and then compiles and processes
these data to generate the Air
Quality Index or AQI.
Designing the Display
Figure 1 shows one potential display
method for a sample of several
MSAs. In this sample, a solid black
circle indicates poorer air quality
than most MSAs and a solid blue
circle indicates better air quality than
most MSAs, with indications for
three degrees of quality in between
(half blue circle, empty circle, and
half black circle). Again, this display
would be pollutant-specific and
limited to a specific year. It would
not suggest air quality trends for
these locations over time.
The colored circle symbols would
be derived in different ways for
different pollutants. For pollutants
with a lot of data available, EPA
would use percentiles to set ranges
for the symbols. For those pollutants
with few data, EPA would set the
ranges to facilitate presentation.
S64 NEW REPORTING TECHNIQUE
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Figure 1 presents the basis for the
suggested symbols for each of the
pollutants. The following section
describes the methodology for assign-
ing the symbols to data ranges in
more detail.
Looking at sample MSAs in
Figure 1, we can determine that loca-
tion 3, for example, has fewer days of
unhealthy air than most of the MSAs
monitored for CO, particulate matter,
SO2, and NO2 (indicated by the solid
blue circles). For ozone, location 3 has
about the median number of days of
unhealthy air; in other words, roughly
equal numbers of MSAs have more
days and fewer days of unhealthy air
than location 3 for ozone. Thus, loca-
tion 3 would appear to be a relatively
good location for someone with asth-
ma, since particulate matter, sulfur
dioxide, and ozone are pollutants of
concern for people with asthma.
Where the "Not monitored"
symbol (•) appears, no monitoring is
performed for that pollutant in that
particular MSA, and the MSA is pre-
sumed to have healthy air for that
pollutant. The "Insufficient data"
symbol (—) means that the area is
monitored but not enough data were
available to be included.
Methodology
The new reporting method would be
developed from outdoor air quality
data collected at monitoring stations
operated by state, tribal, and local
government agencies as well as some
federal agencies, including EPA. The
monitoring data are used to calculate
the AQI, which reports daily air
quality for a given location. The AQI
values, in turn, would be the basis for
this reporting tool. To generate the
new display, three steps would be
required, as described in the follow-
ing sections: analyze outdoor air
quality monitoring data, calculate the
AQI, and assign the symbols shown
in Figure 1 for each pollutant indi-
vidually.
Analyze Outdoor Air Quality Data
As currently conceived, the display
would be generated based on meas-
urements of pollutant concentrations
in the outdoor air at monitoring
stations across the country. The
air quality data consist of daily
(24-hour) measurements for PM10
and PM 2 5 and continuous (1-hour)
measurements for CO, NO2, O3, and
SOj.1 The daily measurements for
particulate matter are taken from
monitoring instruments that produce
one 24-hour measurement and
typically operate on a systematic
sampling schedule of once every
6 days, or 61 samples per year. In
other words, these instruments
generate one 24-hour sample every
6 days. EPA has determined that
these 61 daily samples adequately
represent outdoor air quality
throughout the year. Monitoring
instruments for CO, NO2,03, and
SO2 operate continuously and
produce a measurement every hour
for a possible total of 8,760 hourly
measurements in a year.
Calculate Air Quality Index
EPA compiles and processes outdoor
air quality data to generate the AQI.
The AQI is an index for reporting
daily air quality for a given location
and is a key tool in EPA's efforts to
make air quality data accessible and
useful to the general public. It indi-
cates how clean or how polluted the
outside air is. Based on monitoring
data, the AQI gives a daily score of
1 to 500 for each pollutant monitored
in each MSA. An AQI of 100 means
the outdoor air concentration is gen-
erally no higher than the respective
NAAQS. For example, an AQI of 50
means good air quality, whereas an
AQI of 300 means poor air quality.
The AQI for particulate matter is a
special case, in that day counts are
derived slightly differently. AQI
levels for particulate matter are best
estimated from daily particulate
matter monitors, and, therefore, the
nation's air programs are installing
more continuous particulate matter
monitors. However, when using
EPA's Federal Reference Method
(FRM) data, the nondaily sampling
schedules for particulate matter (e.g.,
one sample per 3 days) can affect the
observed day counts. Therefore, EPA
is evaluating methods for adjusting
the counts for particulate matter
days with an AQI over 100. The
easiest method to adjust particulate
matter counts, and that currently
being used, is based on a simple ratio
of the number of days in a quarter to
the number of days with at least one
sample in an MSA. The ratio is
multiplied times the actual number
of days in the quarter with the AQI
above 100 for particulate matter to
get an adjusted quarterly count,
which can then be used to calculate
an annual number. For example, if
there are 90 days in a quarter and
15 sampling days in that quarter, the
ratio of 90:15, or 6, is used to adjust
the count of days with an AQI over
100 for particulate matter. Thus, if
there are 2 days with sample values
resulting in an AQI greater than 100,
the count is adjusted to 12 days with
an AQI greater than 100.
EPA maintains a Web site that
fully explains the derivation of the
AQI and its interpretation and use
at http://www.epa.gov/airnow/
aqibroch/aqi.html#l. This Web site
includes information linking particu-
lar health effects such as asthma and
angina to the different principal
pollutants. Users can determine
SPECIAL STUDIES • NEW REPORTING TECHNIQUE
S65
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
which of the pollutants are particu-
larly problematic for different health
conditions. For example, asthma is
related to concentrations of O3, PM10,
PM2 5, and SO2/ and angina is exacer-
bated by elevated concentrations of
CO.
Assign Pollutant-Specific
Symbols
To generate the new display EPA
would compile the AQI values for all
MSAs (for a given time period, say
calendar year 2001) and assign the
symbols for each pollutant sepa-
rately as shown in Figure 1. For each
pollutant, EPA would first count the
number of days for each MSA when
the AQI was above 100. The data for
the MSAs would then be listed in
order from the fewest days with AQI
above 100 to the most days with AQI
above 100. The data display tech-
nique is designed to indicate the
MSA's relative rank by percentile.
An MSA's percentile rank tells what
portion of the sampled MSAs is
above it (fewer days of unhealthy
air) and what portion is below (more
days of unhealthy air). For example,
if an MSA is at the 90th percentile,
10% of the MSAs have fewer days of
unhealthy air and 90% have more
days of unhealthy air.
This approach works when there
is sufficient variability, or range, in
the data. In the 2001 data for O3,
PM10, and PM2 5, the range is rela-
tively wide from the MSA with the
fewest days with the AQI above 100
to the MSA with the most days, and
the percentile method would be used
for these pollutants. However, the
2001 data for CO, NO2, and SO2 do
not vary enough among MSAs for
percentiles to be derived. For these
pollutants, the 2001 data show three
or four MSAs having 1 day with
the AQI greater than 100 and the
remaining MSAs having no days
with the AQI above 100. Therefore,
the symbols would simply be
assigned to 0,1, 2, 3,4, and greater
than 4 days. While two different
methods are used to set the bound-
aries, or "cutpoints," for the symbols,
MSAs can be interpreted in the same
manner for all pollutants.
The cutpoint table in Figure 1
presents the cutpoints, or ranges of
day counts, indicated by each sym-
bol for each pollutant. For pollutants
with sufficient data variability to use
the percentile method (i.e., O3, PM10,
and PM2 5), the top 5% would be
considered to have the best air quali-
ty for that particular pollutant. Thus,
MSAs within the top 5% would be
given a blue circle. For example, as
shown in Figure 1, location 4 has a
blue circle for O3, which means that
location 4 is in the 5% of MSAs
reporting the lowest number of days
with the AQI above 100 for O3. The
remaining 95% of the MSAs sampled
have more unhealthy days than loca-
tion 4 with respect to O3 levels (i.e.,
they had more days with the AQI for
O3 greater than 100). If there were
300 MSAs for which O3 was sam-
pled, location 4 would be one of 15
MSAs assigned a blue circle for O3.
Note that the blue circle does not
indicate the actual number of days
when the AQI was greater than 100;
it simply tells whether location 4
experienced fewer or more
unhealthy days than other sampled
MSAs.
The remaining symbols for O3,
PM10, and PM2 5 would be assigned
similarly, based on percentiles, as
shown in the cutpoint table in Figure
1. A half blue circle would be
assigned to MSAs above the 5th per-
centile and below the 25th percentile.
An MSA with this symbol would
have had more unhealthy days than
those with a full blue circle (the top
5%), but fewer unhealthy days than
the remaining 75% of the MSAs
sampled. Likewise, the white circle
would be assigned to MSAs from the
25th to 75th percentiles; they experi-
ence more unhealthy days than the
MSAs with the full or half blue cir-
cles, but they have fewer unhealthy
days than the remaining 25% of the
MSAs sampled. The half black and
full black circles would be assigned
to the MSAs with more unhealthy
days. The half black circle indicates
that the MSA has more unhealthy
days than 95% of the MSAs sampled
and that only 5% of the MSAs have
as many or more unhealthy days.
The full black circle would be
assigned to the MSAs with the most
unhealthy days.
Assumptions and Limitations
The new reporting technique that
EPA is evaluating includes several
assumptions and limitations, as
described below. These issues
indicate areas where discussion and
further development may be appro-
priate.
• The new display technique is
based on the AQI, which, in turn,
is based on short-term (daily) con-
centrations. However, for NO2,
PM, and SO2, long-term standards
also apply. Some MSAs may have
no problem complying with short-
term standards (thus being
assigned a blue circle) while fail-
ing to meet the annual standard.
An additional component that
incorporates annual concentration
data into the display technique
may be desirable.
• At this time, the new display tech-
nique is designed to address CO,
NO2, O3, particulate matter (PM10
and PM2 5), and SO2; it does not
566 NEW REPORTING TECHNIQUE
SPECIAL STUDIES
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
address any hazardous air pollut-
ants (HAPs). Addition of a com-
ponent addressing HAPs could be
considered. Benzene may provide
a reasonable test case for report-
ing on HAPs, because it common-
ly occurs in ambient air and is
monitored in the most locations.
• EPA acknowledges that the gen-
eral public is not always familiar
with MSAs. For example, users
living in small towns may not
realize they are part of an MSA
named for a nearby larger town.
Furthermore, not all areas in the
country are in MSAs, and not all
MSAs would be included in this
display. Those MSAs with small
populations, those with air quality
that is so good that AQI reporting
is not currently required, and
those with too little monitoring
data would not be included.
• Information would be presented
for those air quality data that meet
EPA's data quality requirements.2
However, all pollutants are not
monitored in all MSAs, and some
MSAs are not monitored at all. For
example, certain MSAs with small
populations and those where the
air quality is not considered a
problem would not have data in
the display. Thus, the "Not moni-
tored" symbol can mean that there
is no perceived air quality prob-
lem for that pollutant in that
MSA, and the "Insufficient data"
symbol means that there is not
enough data available to be
included. The latter case does not
necessarily mean that there is no
cause for concern.
• Different MSAs have different
numbers of monitors. This display
technique would not account for
the fact that MSAs with more
monitors will tend to have more
days with AQIs above 100. The
display technique might be modi-
fied to normalize the day counts
based on number of monitors.
• Air quality may vary across a
single MSA. In assigning a single
symbol for each pollutant in each
MSA, the display would not
reflect this potential variation.
• The methods used to set the cut-
points for the data display are
designed to give an intuitive
visual display of air quality in
MSAs. The new method would be
based on percentiles to provide
consistency in setting cutpoints
from one year to the next; how-
ever, there are other approaches
that might also work to meet the
objectives.
• The color-coded symbols sug-
gested for the new display tech-
nique would indicate an MSA's
air quality relative to the air qual-
ity in the other MSAs reported.
As such, the symbols would not
be an indication of a particular
level of health protection. Because
the symbols would indicate rela-
tive air quality, a black circle, for
example, could be assigned for
few days or for many days of
unhealthy air, depending on the
number of unhealthy days for
most MSAs. For example, a black
circle would be assigned for
20 days of unhealthy air if most
MSAs had fewer than 20 unheal-
thy days, or for 120 days of
unhealthy air if most MSAs had
up to 120 unhealthy days. It will
be important to ensure that users
are aware of the relative nature of
the information.
• The color-coded symbols would
be based on counts of days with
the AQI exceeding 100, but, as
currently conceived, there is no
indication of the degree of
exceedance. For example, a day
with an AQI of 103 counts the
same as a day with an AQI of 350.
To reflect increased concern for
days with higher AQI values,
alternatives such as weighting
days with an AQI above, say 200,
could be considered.
• The display would present air
quality for the current year. The
percentile-based symbols would
indicate an MSA's status relative
to the other sampled MSAs. The
percentiles reflect a given year's
data; therefore, the number of
unhealthy days implied by each
symbol would change with each
subsequent year's data. In its ini-
tial format, the display would not
indicate trends in air quality or
whether air quality in a particular
MSA is improving or declining.
Furthermore, users should be
made aware that a single year's
report may or may not indicate an
MSA's general air quality or
whether it is a "good" place to
live, since any given year can
reflect anomalies in air quality
trends.
• The display would not provide
any indication or distinction of
source contribution.
Potential Uses for the
New Display Technique
The new display technique is a work
in progress. The preceding section
described the report's current itera-
tion, but EPA is exploring additional
capabilities and features to enhance
the technique. For example, EPA is
determining how to add this display
to the Air Trends Web site to allow
users to sort and query the list to
SPECIAL STUDIES • NEW REPORTING TECHNIQUE
S67
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
focus on particular health effects.
Capabilities currently being dis-
cussed for this new technique are
described in the following sections.
Particular Health Effect
Perspective
Allowing users to evaluate air qual-
ity with respect to particular health
concerns is perhaps the most signifi-
cant capability that is being consid-
ered for the new display technique.
The AQI Web site (http://www.epa.
gov/airnow/aqibroch/aqi.html#l)
provides information linking health
concerns and sensitive populations
to particular pollutants and outdoor
concentrations. For example, the AQI
is used as the basis for advisories to
people with asthma; these individ-
uals are advised to limit outdoor
exertion when AQI values for O3,
PM10/ PM2 5, or SO2 are over 100.
Similarly, people with angina are
cautioned when the AQI for CO is
over 100. EPA is looking into ways in
which the MSA report could allow
users to sort the data based on spe-
cific health-based concerns for any of
these pollutants and generate a
report focusing on health concerns
for someone with asthma, angina, or
other health conditions.
Visibility and Regional Haze
Degradation in visibility is related to
several criteria pollutants and is an
important environmental issue for
the public, particularly in National
Parks and wilderness areas (Class I
areas). For example, the annual
Trends Report presents useful infor-
mation on the impacts of air pollu-
tion on visibility. Without the effects
of pollution, a natural visual range in
the United States is approximately
75 to 150 km (45 to 90 miles) in the
East and 200 to 300 km (120 to 180
miles) in the West. However, data
collected by EPA show that, in 1999,
mean visual range in the East was
only 24 km (14.4 miles) for the worst
days and only 84 km (50.4 miles) for
the best days. In the West, the mean
visual range for 1999 was 80 km
(48 miles). EPA is considering meth-
ods for including similar graphical
information of this type of data in
the display.
Multiyear Reports
EPA is considering adding a multi-
year dimension to the display. In
addition to presenting the annual
reports described above, EPA would
also provide graphically similar
reports that would reflect a 5- or 10-
year average for the number of days
that the AQI was above 100 for each
pollutant in each MSA. Using these
averaged day counts, percentiles
would be derived and symbols
assigned as described above for the
annual data. Users could see the
report for a 5-year average as well as
for any individual year for the past
5 years. Reports for individual years
could be compared to the average as
well as to each other.
Summary and
Conclusions
This display technique would
provide the general public with a
new tool to review air quality in
MSAs around the United States. The
primary function of the display
would be to present location- and
pollutant-specific air quality data in
a graphical format that allows for
easy interpretation of air quality data
for MSAs. The display would not
provide new or additional air quality
data; rather, it would present existing
data in a new format. The graphical
display of data would improve the
public's access to air quality informa-
tion and enhance their ability to use
this information in a meaningful
way. Potential capabilities that may
be added include a Web-based appli-
cation that would allow users to sort
and query information to generate
customized reports, as well as visibil-
ity and multiyear components.
EPA recognizes that there are limi-
tations to this new display technique
and is continuing to assess the use-
fulness of such a reporting method
as well as additional capabilities that
might be added. Developing a
simple metric for displaying air
quality data on an urban basis across
the nation is a difficult and challeng-
ing endeavor. However, EPA feels
that this information is useful and
informative to the public, especially
to those who have potential health
concerns related to poor air quality.
A graphical display that is easily
understood is essential to communi-
cating this information, and EPA will
continue to refine the display to
ensure that it meets this objective
based on comments and input from
the air quality community and
potential users.
References
1. Although continuous PM moni-
tors are being installed and some
continuous monitoring data are
available, these data would not be
included in this display. Only
Federal Reference Method (FRM)
data would be incorporated into the
data display as currently conceived,
and the PM continuous monitoring
data are not based on EPA's FRM.
2. For more information on EPA's
data quality requirements, see
Appendix B-Metropolitan Area
Trends of the Trends Report at http://
www.epa.gov/airtrends/metro.html.
S68 NEW REPORTING TECHNIQUE
SPECIAL STUDIES
-------
-------
-------
TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
l. REPORT NO.
EPA-454/R-03-008
3. RECIPIENTS ACCESSION NO.
4. TITLE AND SUBTITLE
National Air Quality and Emission Trends Report 2003 Special
Studies Edition
5. REPORT DATE
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S) J.Elkins, J.Hemby, V. Rao, J. Szykman, T. Fitz-Simons,
D. Doll, D. Mintz, A. Rush, N. Frank, M. Schmidt, M. Wayland, J.
Creilson, F. Dimmick, J. Brandmeyer, P. Frechtel, M. Byron, D
Solomon
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
14. SPONSORING AGENCY CODE
EPA/200/04
15. SUPPLEMENTARY NOTES
16. ABSTRACT
THIS REPORT PRESENTS NATIONAL TRENDS IN AIR QUALITY FOR OZONE, PARTICULATE MATTER, NITROGEN
DIOXIDE, CARBON MONOXIDE, LEAD AND SULFUR DIOXIDE FOR 10 AND 20 YEAR PERIODS. IN ADDITION TO
AIR QUALITY TRENDS FROM DATA COLLECTED AT MONITORING STATIONS ACROSS THE COUNTRY, TRENDS
IN NATIONWIDE EMISSIONS ARE ALSO PRESENTED. THIS REPORT ALSO INCLUDES A SPECIAL STUDIES
SECTION INCLUDING REPORTS ON CHEMICAL SPECIATION INCLUDING THE URBAN INCREMENT, IMPACT OF
THE ASIAN DUST STORM, CUMULATIVE OZONE EXCEEDANCES, CHARACTERIZATION OF NATIONAL SPATIAL
VARIATION, AND A NEW REPORTING TECHNIQUE.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
AIR POLLUTION TRENDS, AIR TOXICS, ACID
DEPOSITION, EMISSION TRENDS, PARTICULATE
MATTER
Air Pollution control
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77)
PREVIOUS EDITION IS OBSOLETE
-------
-------
APPENDIX A
Data Tables
http://www.epa.gov/oar/aqtrnd03/appenda.pdf
APPENDIX A • DATA TABLES 71
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-1a. National Air Quality Trends Statistics for Criteria Pollutants, 1981-1990
Statistic
# of Sites Units Percentile 1981 1982 1983 1984 1985 1986 1987
1988 1989 1990
Carbon Monoxide
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
Lead
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Nitrogen Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Ozone
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
321
321
321
321
321
321
321
321
228
228
228
228
228
228
228
228
169
169
169
169
169
169
169
169
471
471
471
471
471
471
471
471
468
468
468
468
468
468
468
468
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
15.2
12.9
10.6
7.7
5.6
4.2
3.7
8.4
1.39
1.02
0.61
0.41
0.28
0.20
0.15
0.58
0.051
0.041
0.028
0.021
0.016
0.009
0.006
0.024
0.220
0.167
0.140
0.116
0.100
0.090
0.080
0.126
0.133
0.116
0.101
0.088
0.077
0.065
0.057
0.091
15.3
12.8
10.0
7.4
5.5
4.3
3.6
8.1
1.31
0.96
0.69
0.43
0.28
0.18
0.14
0.58
0.050
0.039
0.029
0.021
0.016
0.009
0.004
0.023
0.210
0.161
0.136
0.115
0.100
0.087
0.080
0.125
0.131
0.115
0.098
0.088
0.076
0.065
0.058
0.090
15.3
12.4
9.8
7.3
5.2
4.0
3.4
7.9
1.04
0.77
0.55
0.37
0.24
0.16
0.13
0.47
0.046
0.038
0.028
0.022
0.016
0.008
0.004
0.023
0.224
0.186
0.150
0.130
0.110
0.095
0.086
0.137
0.145
0.126
0.110
0.097
0.083
0.070
0.064
0.099
13.8
11.9
9.9
7.3
5.2
4.2
3.5
7.8
1.03
0.72
0.50
0.33
0.23
0.15
0.12
0.45
0.046
0.040
0.029
0.023
0.016
0.009
0.004
0.023
0.204
0.165
0.139
0.114
0.100
0.090
0.081
0.125
0.132
0.113
0.100
0.088
0.077
0.067
0.061
0.091
12.7
11.0
8.9
6.3
4.9
3.8
3.4
7.1
0.70
0.56
0.32
0.21
0.14
0.10
0.07
0.28
0.048
0.039
0.029
0.022
0.017
0.009
0.005
0.023
0.190
0.160
0.133
0.112
0.098
0.088
0.078
0.123
0.134
0.113
0.097
0.087
0.078
0.068
0.062
0.091
12.2
11.0
8.9
6.7
5.0
3.9
3.3
7.2
0.41
0.30
0.19
0.12
0.08
0.06
0.05
0.18
0.050
0.036
0.029
0.022
0.016
0.009
0.004
0.023
0.170
0.150
0.130
0.112
0.099
0.086
0.080
0.118
0.123
0.107
0.095
0.085
0.076
0.068
0.061
0.088
11.6
9.7
8.3
6.3
4.7
3.7
3.3
6.7
0.31
0.21
0.13
0.09
0.06
0.04
0.03
0.13
0.043
0.038
0.028
0.022
0.017
0.010
0.004
0.023
0.183
0.164
0.140
0.118
0.104
0.090
0.087
0.125
0.128
0.116
0.102
0.091
0.080
0.071
0.067
0.093
11.3
9.9
7.8
6.0
4.5
3.5
3.1
6.4
0.29
0.20
0.11
0.07
0.04
0.02
0.02
0.12
0.048
0.038
0.029
0.023
0.017
0.009
0.003
0.023
0.203
0.181
0.155
0.130
0.110
0.097
0.088
0.136
0.141
0.129
0.116
0.102
0.087
0.076
0.067
0.102
10.9
9.6
7.8
6.0
4.5
3.6
2.9
6.4
0.23
0.15
0.10
0.06
0.04
0.03
0.02
0.10
0.045
0.038
0.029
0.022
0.016
0.009
0.004
0.023
0.180
0.147
0.124
0.108
0.098
0.086
0.080
0.116
0.122
0.106
0.093
0.084
0.076
0.068
0.063
0.087
10.2
8.8
7.2
5.5
4.3
3.3
2.9
5.9
0.17
0.13
0.08
0.05
0.03
0.02
0.01
0.08
0.042
0.035
0.028
0.020
0.015
0.009
0.004
0.022
0.170
0.146
0.122
0.109
0.096
0.084
0.077
0.114
0.116
0.106
0.094
0.083
0.075
0.066
0.059
0.085
72
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-1a. National Air Quality Trends Statistics for Criteria Pollutants, 1981-1990 (continued)
Statistic
# of Sites Units Percentile 1981 1982 1983
1984
1985 1986
1987
1988 1989 1990
PM..
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Sulfur Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
—
456
456
456
456
456
456
456
456
—
—
—
—
—
—
—
—
jig/m3
jig/m3
jig/m3
jig/m3
ng/m3
jig/m3
jig/m3
jig/m3
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
—
0.0223
0.0186
0.0134
0.0091
0.0061
0.0028
0.0018
0.0102
—
—
—
—
—
—
—
—
—
0.0199
0.0165
0.0123
0.0087
0.0058
0.0030
0.0015
0.0095
—
—
—
—
—
—
—
—
—
0.0184
0.0152
0.0121
0.0086
0.0058
0.0028
0.0016
0.0093
—
—
—
—
—
—
—
—
—
0.0193
0.0164
0.0126
0.0089
0.0055
0.0028
0.0017
0.0095
—
—
—
—
—
—
—
—
—
0.0186
0.0160
0.0117
0.0087
0.0053
0.0026
0.0018
0.0090
—
—
—
—
—
—
—
—
—
0.0180
0.0147
0.0118
0.0083
0.0052
0.0024
0.0016
0.0088
—
—
—
—
—
—
—
—
—
0.0169
0.0142
0.0114
0.0082
0.0051
0.0024
0.0016
0.0086
—
—
—
—
—
—
—
—
—
0.0182
0.0150
0.0113
0.0082
0.0050
0.0025
0.0019
0.0087
—
—
—
—
—
—
—
—
—
0.0176
0.0148
0.0114
0.0080
0.0047
0.0023
0.0017
0.0085
—
—
—
—
—
—
—
—
—
0.0160
0.0137
0.0103
0.0074
0.0045
0.0022
0.0016
0.0079
—
—
—
—
—
—
—
—
APPENDIX A • DATA TABLES 73
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-1b. National Air Quality Trends Statistics for Criteria Pollutants, 1991-2000
Statistic
# of Sites Units Percentile 1991 1992 1993
1994
1995 1996
1997
1998 1999 2000
Carbon Monoxide
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
Lead
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Max. Qtr. AM
Nitrogen Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Ozone
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
327
327
327
327
327
327
327
327
130
130
130
130
130
130
130
130
234
234
234
234
234
234
234
234
738
738
738
738
738
738
738
738
741
741
741
741
741
741
741
741
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
9.8
8.9
7.1
5.3
4.0
2.8
2.1
5.6
0.38
0.19
0.08
0.04
0.03
0.01
0.01
0.08
0.043
0.032
0.025
0.018
0.012
0.008
0.005
0.019
0.161
0.145
0.121
0.106
0.093
0.081
0.075
0.111
0.115
0.107
0.095
0.084
0.073
0.063
0.057
0.085
8.9
8.0
6.6
5.0
3.8
2.8
2.2
5.3
0.23
0.15
0.07
0.04
0.02
0.01
0.01
0.07
0.038
0.032
0.024
0.018
0.013
0.008
0.005
0.019
0.152
0.130
0.112
0.100
0.090
0.081
0.075
0.105
0.106
0.096
0.087
0.079
0.072
0.065
0.059
0.081
8.5
7.4
6.2
4.8
3.6
2.8
2.1
5.0
0.18
0.12
0.07
0.04
0.02
0.01
0.01
0.06
0.037
0.031
0.024
0.018
0.013
0.008
0.005
0.019
0.150
0.135
0.120
0.104
0.091
0.080
0.074
0.107
0.108
0.100
0.090
0.081
0.073
0.063
0.058
0.082
8.3
7.7
6.3
5.0
3.9
2.7
2.1
5.1
0.16
0.12
0.06
0.03
0.02
0.01
0.01
0.05
0.040
0.032
0.024
0.019
0.013
0.008
0.005
0.020
0.146
0.128
0.116
0.104
0.092
0.082
0.077
0.106
0.105
0.097
0.090
0.082
0.074
0.067
0.061
0.083
7.9
7.0
5.7
4.4
3.3
2.5
2.2
4.6
0.18
0.10
0.05
0.03
0.02
0.01
0.01
0.05
0.039
0.031
0.023
0.018
0.012
0.007
0.005
0.019
0.149
0.138
0.122
0.110
0.097
0.085
0.078
0.112
0.111
0.106
0.095
0.088
0.077
0.068
0.062
0.087
7.7
6.7
5.2
4.0
3.0
2.3
1.9
4.3
0.15
0.10
0.05
0.03
0.01
0.01
0.00
0.05
0.037
0.031
0.023
0.018
0.012
0.007
0.005
0.019
0.140
0.125
0.114
0.103
0.093
0.083
0.079
0.105
0.102
0.097
0.090
0.082
0.075
0.068
0.062
0.083
6.9
6.2
5.0
3.8
2.9
2.1
1.7
4.1
0.12
0.09
0.05
0.03
0.01
0.01
0.01
0.04
0.034
0.029
0.022
0.017
0.012
0.008
0.004
0.018
0.140
0.129
0.115
0.103
0.091
0.080
0.074
0.104
0.105
0.099
0.091
0.082
0.074
0.065
0.059
0.082
7.0
5.8
4.7
3.6
2.8
2.1
1.8
3.9
0.14
0.10
0.05
0.03
0.01
0.01
0.01
0.04
0.035
0.030
0.023
0.017
0.012
0.007
0.005
0.018
0.147
0.132
0.119
0.109
0.097
0.086
0.076
0.110
0.109
0.102
0.095
0.087
0.078
0.069
0.062
0.086
6.5
5.6
4.5
3.6
2.6
1.9
1.6
3.7
0.10
0.09
0.05
0.02
0.01
0.01
0.01
0.04
0.035
0.029
0.023
0.017
0.013
0.008
0.005
0.018
0.138
0.130
0.117
0.107
0.096
0.085
0.076
0.107
0.105
0.101
0.094
0.087
0.077
0.068
0.061
0.086
6.1
5.1
4.1
3.2
2.4
1.8
1.4
3.4
0.11
0.09
0.05
0.02
0.01
0.01
0.01
0.04
0.033
0.028
0.021
0.017
0.012
0.008
0.005
0.017
0.134
0.124
0.111
0.098
0.088
0.079
0.073
0.100
0.100
0.095
0.087
0.080
0.072
0.064
0.057
0.079
74
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-1 b. National Air Quality Trends Statistics for Criteria Pollutants, 1991-2000 (continued)
Statistic
# of Sites Units Percentile 1991 1992 1993
1994
1995 1996
1997
1998 1999 2000
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Annual Avg.
Sulfur Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
Arith. Mean
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
2nd Max. 24-hr.
886
886
886
886
886
886
886
886
457
457
457
457
457
457
457
457
457
457
457
457
457
457
457
457
jig/mS
f^g/m3
f^g/m3
f^g/m3
f^g/m3
(Kj/m3
(Kj/m3
(Kj/m3
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
95th
90th
75th
50th
25th
10th
5th
Arith. Mean
46.4
40.1
33.8
28.2
23.6
18.5
16.1
29.4
0.0167
0.0145
0.0101
0.0076
0.0046
0.0023
0.0017
0.0081
0.0800
0.0640
0.0440
0.0320
0.0210
0.0110
0.0080
0.0364
41.8
36.7
31.3
26.1
22.2
18.0
15.2
27.3
0.0167
0.0130
0.0096
0.0070
0.0044
0.0023
0.0015
0.0076
0.0800
0.0630
0.0450
0.0310
0.0200
0.0110
0.0070
0.0353
41.5
36.6
30.5
25.9
21.1
17.4
14.3
26.6
0.0159
0.0130
0.0095
0.0068
0.0041
0.0023
0.0016
0.0074
0.0730
0.0600
0.0420
0.0290
0.0190
0.0110
0.0070
0.0340
40.0
36.4
30.7
25.6
21.1
16.9
14.1
26.4
0.0151
0.0125
0.0094
0.0067
0.0039
0.0022
0.0016
0.0072
0.0760
0.0640
0.0460
0.0330
0.0200
0.0100
0.0060
0.0358
38.9
34.9
29.1
24.1
19.9
15.9
13.3
25.1
0.0118
0.0104
0.0077
0.0051
0.0033
0.0019
0.0014
0.0057
0.0590
0.0490
0.0340
0.0230
0.0160
0.0080
0.0060
0.0267
37.9
33.6
27.7
23.1
19.4
16.1
13.8
24.2
0.0113
0.0100
0.0075
0.0054
0.0033
0.0019
0.0015
0.0057
0.0610
0.0480
0.0330
0.0230
0.0150
0.0090
0.0060
0.0267
38.1
33.0
27.2
23.1
19.5
16.1
13.4
24.1
0.0111
0.0094
0.0073
0.0052
0.0032
0.0019
0.0014
0.0056
0.0530
0.0470
0.0330
0.0230
0.0150
0.0080
0.0050
0.0257
35.8
31.9
27.5
23.4
19.7
15.3
13.4
23.8
0.0107
0.0096
0.0074
0.0050
0.0033
0.0020
0.0014
0.0055
0.0540
0.0450
0.0320
0.0220
0.0150
0.0080
0.0050
0.0247
39.7
33.2
27.6
23.2
19.1
15.4
13.5
24.1
0.0105
0.0091
0.0070
0.0049
0.0032
0.0020
0.0015
0.0053
0.0530
0.0430
0.0290
0.0210
0.0140
0.0080
0.0060
0.0238
39.0
32.9
27.5
23.1
19.1
15.2
12.7
23.8
0.0106
0.0090
0.0065
0.0048
0.0030
0.0019
0.0015
0.0051
0.0470
0.0410
0.0300
0.0210
0.0140
0.0080
0.0060
0.0233
APPENDIX A • DATA TABLES 75
-------
0>
1
5!
DO
[—
m
co
•
>
TJ
m
D
X
fr
Source Category
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
Oil
Gas
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
Oil
Gas
Other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
fireplaces
woodstoves
other
Residential Other
Industrial Processes
CHEMICAL & ALLIED PRODUCT MFG
Organic Chemical Mfg
ethylene dichloride
maleic anhydride
cyclohexanol
other
Inorganic Chemical Mfg
1980
7,302
322
188
48
85
NA
NA
750
58
35
418
239
NA
6,230
13
21
26
NA
5,992
5,992
NA
NA
178
9,250
2,151
543
17
103
37
386
191
pigments; Ti02 chloride process: reactor 34
other
Polymer & Resin Mfg
Agricultural Chemical Mfg
Paint, Varnish, Lacquer, Enamel Mfg
Pharmaceutical Mfg
Other Chemical Mfg
carbon black mfg
carbon black furnace: fugitives
other
METALS PROCESSING
Nonferrous Metals Processing
aluminum anode baking
prebake aluminum cell
other
157
NA
NA
NA
NA
1,417
1,417
NA
NA
2,246
842
421
421
NA
1985
8,485
291
207
18
56
NA
10
670
86
47
257
167
113
7,525
14
18
42
57
7,232
7,232
NA
NA
162
7,215
1,845
251
0
16
5
230
89
77
12
19
16
NA
0
1,471
1,078
155
238
2,223
694
41
257
396
1989
7,443
321
233
26
51
NA
11
672
87
46
271
173
96
6,450
15
17
49
55
6,161
6,161
NA
NA
153
7,013
1,925
285
0
16
6
264
95
84
12
18
17
NA
0
1,510
1,112
180
219
2,132
677
41
254
382
1990
5,510
363
234
20
51
NA
57
879
105
74
226
279
195
4,269
14
18
44
149
3,781
3,781
NA
NA
262
5,852
1,183
149
0
3
0
146
133
119
14
3
44
0
0
854
798
17
39
2,640
436
41
260
135
1991
5,856
349
234
19
51
NA
45
920
101
60
284
267
208
4,587
14
17
44
141
4,090
4,090
NA
NA
281
5,740
1,127
128
0
3
0
125
129
119
11
6
19
0
0
844
756
54
35
2,571
438
47
260
131
1992
6,155
350
236
15
51
NA
47
955
102
64
300
264
227
4,849
15
18
51
141
4,332
4,332
NA
NA
292
5,683
1,112
131
0
4
0
127
130
119
12
5
19
0
0
827
736
57
34
2,496
432
41
260
131
1993
5,586
363
246
16
49
NA
51
1,043
101
66
322
286
268
4,181
15
18
53
143
3,679
3,679
NA
NA
274
5,898
1,093
132
0
4
0
128
131
119
13
5
18
0
0
805
715
60
30
2,536
423
41
260
122
1994
5,519
370
247
15
53
NA
55
1,041
100
66
337
287
251
4,108
15
18
54
147
3,607
3,607
NA
NA
268
5,839
1,171
130
0
4
1
125
135
119
16
5
17
0
0
885
793
63
30
2,475
421
41
260
120
1995
5,934
372
250
10
55
NA
58
1,056
98
71
345
297
245
4,506
15
19
54
145
3,999
3,999
NA
NA
273
5,790
1,223
127
0
4
1
123
134
119
15
5
17
0
0
939
845
65
29
2,380
424
41
260
123
1996
4,349
409
251
12
79
8
58
1,191
110
54
340
349
337
2,749
14
19
64
46
2,351
1,043
1,308
NA
255
7,187
1,053
90
0
0
0
89
120
117
3
5
12
0
0
826
796
4
26
1,604
459
22
277
160
1997
4,336
423
257
14
84
9
60
1,163
109
52
339
333
330
2,750
14
20
65
48
2,351
1,043
1,308
NA
252
7,348
1,071
91
0
0
0
90
121
118
3
5
13
0
0
841
811
4
26
1,709
475
23
288
164
1998
4,337
450
242
19
97
33
60
1,151
106
51
336
334
324
2,736
15
16
63
49
2,351
1,043
1,308
NA
242
7,362
1,081
92
0
0
0
92
123
120
3
5
13
0
0
847
818
4
26
1,702
465
23
281
160
1999
4,348
424
229
19
96
19
61
1,175
109
52
340
341
334
2,749
15
16
68
50
2,351
1,043
1,308
NA
249
7,343
1,081
93
0
0
0
92
125
122
3
5
13
0
1
845
815
4
26
1,673
451
23
271
157
2000
4,590
445
234
18
105
26
62
1,221
110
55
361
355
340
2,924
15
16
69
51
2,526
1,118
1,408
NA
246
7,521
1,112
96
0
0
0
95
128
125
3
5
13
0
1
869
839
4
26
1,735
461
23
278
160
s1
5"
>
10
jf
o'
CD
O
CD
O"
O
^
O
o
x
5;
CD
m
3
en
o'
en
m
3
CD
t-t-
CD
CD
00
O
_^
CD
00
_cn
CD
oo
f
o
o
o
^
o
CD
Q.
(/)
_)
O
t-t-
O
^L.
•z.
H
0
I—
ii
D
D
m
co
co
0
"Z.
co
H
m
D
CO
m
ID
O
H
N)
o
o
oo
-------
>
•o
z
0
X
^
D
H
DO
1
m
cn
3
bource Category 1
Ferrous Metals Processing 1,
basic oxygen furnace
carbon steel electric arc furnace
coke oven charging
gray iron cupola
iron ore sinter plant windbox
other
Metals Processing NEC
PETROLEUMS RELATED INDUSTRIES 1
Oil & Gas Production
Petroleum Refineries & Related Industries'
fluid catalytic cracking units 1
other
Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
Agriculture, Food, & Kindred Products
Textiles, Leather, & Apparel Products
Wood, Pulp & Paper, & Publishing Prod.
sulfate pulping: rec. furnace/evaporator
sulfate (kraft) pulping: lime kiln
other
Rubber & Miscellaneous Plastic Products
Mineral Products
Machinery Products
Electronic Equipment
Transportation Equipment
Miscellaneous Industrial Processes
SOLVENT UTILIZATION
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
Nonindustrial
Solvent Utilization NEC
STORAGES TRANSPORT
Bulk Terminals & Plants
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Service Stations: Stage I
Service Stations: Stage II
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Bulk Materials Storage
i)80
404
80
280
43
340
600
61
NA
723
NA
,723
680
44
NA
830
NA
NA
798
NA
798
NA
NA
32
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1985
1,523
694
19
9
302
304
194
6
462
11
449
403
46
2
694
0
0
627
475
140
12
0
43
0
18
0
6
2
1
0
NA
0
0
NA
NA
49
0
0
0
NA
NA
42
NA
0
6
1989
1,449
662
18
9
280
293
187
6
436
8
427
390
37
2
716
0
0
655
497
146
13
0
43
0
12
0
5
2
1
0
NA
1
0
NA
NA
55
0
0
0
NA
NA
49
NA
0
5
1990
2,163
594
45
14
124
211
1,174
40
333
38
291
284
7
3
537
3
0
473
370
87
16
0
54
0
2
0
5
5
0
0
0
0
4
0
NA
76
0
0
0
NA
NA
74
0
0
1
1991
2,108
731
54
16
118
211
979
25
345
18
324
315
9
4
548
3
0
461
360
81
21
0
77
0
2
0
5
5
0
0
0
1
4
0
NA
28
2
12
0
NA
NA
13
0
0
1
1992
2,038
767
49
17
114
211
880
26
371
21
345
333
13
5
544
3
0
449
348
75
25
0
85
0
2
0
6
5
0
0
0
1
4
0
NA
17
0
0
0
NA
NA
13
0
0
3
1993
2,089
768
58
7
121
211
924
25
371
22
344
328
17
5
594
3
0
453
350
78
24
0
131
0
2
0
4
5
0
0
0
1
4
0
NA
51
4
32
0
NA
NA
13
0
0
2
1994
2,029
677
61
7
128
211
945
25
338
35
299
286
13
5
600
2
0
461
355
76
30
0
131
0
2
0
4
5
0
0
1
1
4
0
NA
24
4
4
0
NA
0
13
0
0
3
1995
1,930
561
65
8
120
211
966
25
348
34
309
299
10
5
624
6
0
484
370
82
32
0
127
0
2
0
4
6
0
0
1
1
4
0
NA
25
4
4
0
NA
0
13
0
0
3
1996
1,101
268
60
4
111
46
612
44
354
27
319
308
11
8
561
4
0
356
274
50
32
0
180
1
0
0
19
1
0
0
0
1
0
0
0
70
0
0
0
0
0
68
0
0
1
1997
1,189
296
65
4
115
50
659
46
367
27
332
320
12
8
582
4
0
370
285
52
33
0
186
1
0
0
19
2
0
0
0
1
0
0
0
71
0
0
0
0
0
69
0
0
1
1998
1,193
301
66
4
111
50
661
44
366
27
331
319
12
8
590
4
0
378
291
53
34
0
186
1
0
0
20
2
0
0
0
1
0
0
0
72
0
0
0
0
0
70
0
0
1
1999
1,181
301
66
4
106
50
654
41
366
27
332
320
12
7
599
4
0
388
299
55
34
0
185
1
0
0
20
2
0
0
0
1
0
0
0
72
0
0
0
0
0
70
0
0
1
2000
1,233
316
69
4
108
52
683
41
369
28
333
321
12
7
620
4
0
401
309
57
36
0
192
1
0
0
20
2
0
0
0
1
0
0
0
74
0
0
0
0
0
72
0
0
1
H
Si
IT
5"
>
N>
CD
t—t-
o'
CD
O
CD
3-
O
3
O
o
X
o!
CD
m
3
o'
CO
m
ft-
CD
t-t-
CD
-
CD
00
O
~_j.
CD
00
cn
CD
oo
CD
K3
O
O
o
t-t-
o
CD
^
Q.
=T
O
t-t-
O
"o"
o
^
^_^_
1
— 1
o
z
[—
71
O
•5
^
^
D
m
cn
cn
O
cn
H
m
z
cn
71
m
O
71
NJ
8
CO
-------
08
§
§
£
DO
m
w
•o
m
z
D
X
^
Source Category
WASTE DISPOSAL & RECYCLING
Incineration
conical wood burner
municipal incinerator
industrial
commmercial/institutional
residential
other
Open Burning
industrial
commmercial/institutional
residential
land clearing debris
other
POTW
Industrial Waste Water
TSDF
Landfills
Other
Transportation
ON-ROAD VEHICLES
1980
2,300
1,246
228
13
NA
60
945
NA
1,054
1,007
47
NA
NA
NA
NA
NA
NA
NA
NA
92,538
78,049
Light-Duty Gas Vehicles & Motorcycles 53,561
light-duty gas vehicles
motorcycles
Light-Duty Gas Trucks
light-duty gas trucks 1
light-duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
heavy-duty diesel vehicles
light-duty diesel trucks
light-duty diesel vehicles
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
53,342
219
16,137
10,395
5,742
7,189
1,161
1,139
4
19
14,489
12,760
299
527
709
6,764
338
2,095
28
9
NA
1,990
1985
1,941
958
17
34
9
32
865
2
982
20
4
958
NA
NA
NA
NA
NA
0
0
93,386
77,387
49,451
49,273
178
18,960
11,834
7,126
7,716
1,261
1,235
4
22
15,999
13,659
312
603
807
7,166
372
2,263
31
10
5
2,090
1989
1,747
876
19
35
9
39
773
2
870
21
5
845
NA
NA
NA
NA
NA
0
0
83,829
66,050
42,234
42,047
187
15,940
9,034
6,906
6,506
1,369
1,336
6
28
17,779
15,021
321
603
740
8,023
407
2,754
47
10
6
2,112
1990
1,079
372
6
16
9
19
294
27
706
14
46
509
NA
137
0
0
0
1
0
76,635
58,444
34,996
34,806
190
17,118
9,672
7,446
5,029
1,301
1,233
46
22
18,191
15,394
355
603
723
8,237
416
2,877
50
10
6
2,117
1991
1,116
392
7
17
10
20
312
26
722
14
48
516
NA
144
0
0
0
1
0
81,583
62,999
35,680
35,503
177
20,622
11,606
9,016
5,369
1,327
1,292
8
27
18,585
15,738
361
602
707
8,451
424
3,000
54
10
6
2,122
1992
1,138
404
6
15
10
21
324
28
731
15
50
523
NA
144
0
0
0
2
0
80,235
61,236
33,761
33,582
179
21,536
12,065
9,471
4,586
1,353
1,317
9
27
18,999
16,081
366
602
690
8,665
433
3,123
58
9
6
2,128
1993
1,248
497
6
14
87
21
340
29
749
15
52
529
NA
153
0
0
0
2
1
81,224
61,833
33,185
32,995
190
22,795
12,647
10,148
4,483
1,370
1,333
10
28
19,391
16,424
371
602
674
8,880
442
3,246
62
9
6
2,133
1994
1,225
467
6
14
48
21
347
30
755
15
54
533
NA
153
0
0
0
2
1
82,699
62,903
33,317
33,122
195
22,614
12,428
10,186
5,523
1,449
1,411
10
29
19,796
16,765
374
602
657
9,094
450
3,369
66
9
6
2,138
1995
1,185
432
6
15
10
21
351
29
750
15
52
536
NA
147
0
0
0
2
1
75,035
54,811
29,787
29,601
187
19,434
11,029
8,405
4,103
1,487
1,447
10
29
20,224
17,112
382
602
640
9,308
459
3,491
69
9
7
2,144
1996
3,544
72
2
7
9
22
0
32
3,466
0
0
425
2,998
43
0
0
0
6
0
82,631
54,388
29,163
28,974
189
16,873
11,221
5,652
6,260
2,093
2,074
7
12
28,243
25,432
4,796
723
864
11,330
340
3,992
1,160
9
7
2,211
1997
3,546
74
2
7
10
23
0
32
3,466
0
0
425
2,998
43
0
0
0
6
0
81,353
53,315
28,639
28,449
191
16,949
11,296
5,652
5,549
2,178
2,162
6
10
28,038
25,210
4,796
688
823
11,243
343
4,061
1,012
9
7
2,228
1998
3,549
77
2
8
10
24
0
33
3,466
0
0
425
2,998
43
0
0
0
6
0
80,288
52,360
28,420
28,225
195
16,948
11,315
5,634
4,782
2,210
2,197
5
8
27,928
25,098
4,796
674
793
11,073
346
4,138
1,016
9
7
2,244
1999
3,550
76
2
8
10
24
0
33
3,467
0
0
425
2,998
43
0
0
0
6
0
77,821
49,740
26,685
26,502
183
16,532
11,111
5,421
4,264
2,260
2,249
4
7
28,081
25,087
4,796
671
826
11,148
359
4,062
1,067
9
7
2,144
2000
3,609
78
2
8
10
25
0
33
3,524
0
0
436
3,044
44
0
0
0
6
0
76,426
48,469
26,718
26,519
199
15,837
10,732
5,105
3,680
2,234
2,223
4
6
27,957
24,980
4,792
668
796
11,057
360
4,051
1,105
9
6
2,137
•z.
H
H O
IT >
5" >
> *
l\> §
>
QJ- j
o >
=! -z.
O 5
Q) 5
3- 91
o y±
=! 0
o |
•)/ m
— • -z.
£• D
<"> W
3 rn
tn' 3
co O
5' '"
=j -H
CO M
o
m o
CO CO
I
t-t-
CD
CO
CD
oo
o
CD
oo
en
CD
oo
CD
O
o
o
t-t-
o
f—
CO
Q)
Q.
CO
—t
o
t-t-
o
CO
"o"
o
-------
£
•o
m
z
D
X
D
-H
DO
I
m
bource Category
Non-Road Diesel
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Aircraft
Marine Vessels
coal
diesel
residual oil
gasoline
other
Railroads
Non-Road Other
liquified petroleum gas
compressed natural gas
MISCELLANEOUS
Agriculture & Forestry
Other Combustion
structural fires
agricultural fires
slash/prescribed burning
forest wildfires
other
Health Services
Cooling Towers
Fugitive Dust
TOTAL ALL SOURCES
1980
829
2
479
83
13
174
28
49
1
NA
NA
743
62
4
57
1
NA
NA
96
NA
NA
NA
8,344
NA
8,344
217
501
2,226
5,396
4
NA
NA
NA
117,434
1985
900
3
534
105
14
142
34
61
2
1
3
831
73
5
67
1
NA
NA
106
430
288
142
7,927
NA
7,927
242
396
4,332
2,957
NA
NA
NA
NA
117,013
1989
1,062
3
637
121
26
163
44
58
3
2
4
955
98
7
90
2
NA
NA
121
522
376
146
8,153
NA
8,153
242
571
4,332
3,009
NA
NA
NA
NA
106,439
Note.
1990
1,098
3
662
124
29
166
46
58
4
2
4
904
129
4
80
11
2
31
121
545
398
147
11,122
NA
11,122
78
415
4,668
5,928
32
0
NA
0
99,119
Some
1991
1,134
3
688
127
32
168
48
58
4
2
4
888
136
4
83
11
2
36
120
568
420
148
8,618
NA
8,618
80
413
4,666
3,430
28
NA
0
0
101,797
columns
1992
1,169
3
714
130
34
170
49
57
5
2
4
901
132
4
79
12
2
35
125
591
442
149
6,934
NA
6,934
81
421
4,729
1,674
30
NA
0
0
99,007
may not
1993
1,204
3
739
134
37
172
51
57
5
2
4
905
126
4
75
12
2
33
120
614
464
150
7,082
NA
7,082
82
415
4,966
1,586
34
NA
NA
0
99,791
sum to
1994
1,238
3
763
138
39
174
52
56
5
3
4
915
127
5
76
12
2
33
114
637
486
151
9,656
NA
9,656
83
441
4,990
4,114
28
NA
0
0
103,713
totals due
1995
1,269
3
785
142
42
175
54
55
6
3
5
942
127
4
77
10
2
34
114
660
508
152
7,298
NA
7,298
84
465
5,252
1,469
28
NA
0
0
94,058
1996
1,386
5
878
149
47
165
62
63
7
3
7
360
138
NA
131
8
NA
0
117
810
704
106
10,472
0
10,472
18
454
5,402
4,574
22
0
0
0
104,639
1997
1,377
5
869
151
50
163
64
58
7
3
7
360
139
NA
131
8
NA
0
121
831
724
108
12,474
0
12,474
18
464
5,769
6,200
23
0
0
0
105,511
1998
1,352
5
846
151
53
161
67
52
8
3
7
360
140
NA
131
8
NA
0
120
858
749
109
9,303
0
9,303
18
471
6,152
2,638
23
0
0
0
101,290
1999
1,300
5
802
151
53
157
72
46
8
3
4
360
140
NA
131
8
NA
0
119
1,075
950
125
12,886
0
12,885
18
479
3,967
8,398
24
0
0
0
102,398
2000
1,242
4
754
151
49
153
77
40
8
3
4
365
141
NA
133
8
NA
0
119
1,110
983
127
20,806
0
20,806
18
489
2,397
17,878
24
0
0
0
109,343
to rounding.
H
ar
IT
5"
>
N>
if
o'
CD
O
CD
3-
O
^
O
o
X
o!
CD
m
3
o'
en
m
;±
CD
CD"
en
CD
oo -z.
-"• o
oo 1
01 £
CD 71
00 O
? §
10 r-
O ^
o 5
0 >
3= 0
0 m
en —
§ en
°- O
en Z
0 H
rt- 5
o z
=! D
cn en
~~^ 73
I 3
•O ,H
hJ
8
CO
-------
08
O
1
£
DO
[—
m
w
•
>
TJ
m
D
X
>
Source Category
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
bituminous
subbituminous
anthracite & lignite
Oil
residual
distillate
FUEL COMB. INDUSTRIAL
Coal
bituminous
subbituminous
anthracite & lignite
Oil
residual
distillate
FUEL COMB. OTHER
Commercial/Institutional Coal
bituminous
subbituminous
anthracite, lignite
Commercial/Institutional Oil
residual
distillate
other
Misc. Fuel Comb. (Except Residential)
Residential Other
Industrial Processes
CHEMICAL & ALLIED PRODUCT MFC
Inorganic Chemical Mfg
lead oxide and pigments
METALS PROCESSING
Nonferrous Metals Processing
primary lead production
primary copper production
primary zinc production
secondary lead production
secondary copper production
lead battery manufacture
lead cable coating
other
1980
4,299
129
95
57
28
9
34
34
0
60
45
31
10
4
14
14
1
4,111
12
6
2
4
10
9
1
NA
4,080
9
5,148
104
104
104
3,026
1,826
1,075
20
24
481
116
50
37
24
1985
515
64
51
31
15
5
13
13
0
30
22
15
5
2
8
7
1
421
6
4
1
1
4
3
1
NA
400
11
3,402
118
118
118
2,097
1,376
874
19
16
288
70
65
43
3
1989
505
67
46
28
14
4
21
21
0
18
14
10
3
1
4
3
1
420
4
3
1
1
4
3
1
NA
400
12
3,161
136
136
136
2,088
1,337
715
19
9
433
37
74
50
1
1990
500
64
46
28
14
4
18
18
0
18
14
10
3
1
3
3
1
418
4
3
1
0
4
3
1
NA
400
10
3,278
136
136
136
2,170
1,409
728
19
9
449
75
78
50
1
1991
495
61
46
28
14
4
15
15
0
18
15
10
3
1
3
2
1
416
3
2
1
0
4
3
1
NA
400
9
3,081
132
132
132
1,974
1,258
623
19
11
414
65
77
48
1
1992
491
59
47
28
14
4
12
12
0
18
14
10
3
1
4
3
1
414
4
2
1
0
4
3
1
NA
400
7
2,736
93
93
93
1,774
1,112
550
20
11
336
73
77
44
1
1993
497
62
50
30
15
5
12
12
0
19
14
10
3
1
5
4
1
416
4
2
1
1
4
3
1
NA
400
8
2,872
92
92
92
1,900
1,210
637
21
13
341
70
81
47
1
1994
496
62
50
30
15
5
12
12
0
19
14
10
3
1
5
4
1
415
3
2
1
0
4
3
1
NA
400
8
3,007
96
96
96
2,027
1,287
633
22
12
405
76
94
44
1
1995
490
57
50
30
15
5
7
7
0
18
14
10
3
1
4
3
1
415
4
2
1
1
3
2
1
NA
400
8
2,875
163
163
163
2,049
1,337
674
21
12
432
79
102
16
1
1996
492
61
53
32
16
5
8
8
0
16
13
9
3
1
3
2
1
415
5
3
1
1
3
2
1
NA
400
7
3,061
167
167
167
2,055
1,333
588
22
13
514
76
103
16
1
1997
493
64
54
33
16
5
10
10
0
16
14
9
3
1
2
2
1
413
5
3
1
1
2
2
1
0
400
6
3,121
188
188
188
2,081
1,342
619
24
13
484
82
107
14
1
1998
494
69
55
33
16
5
14
14
0
15
13
9
3
1
2
1
1
410
4
2
1
1
2
1
1
0
400
5
3,045
194
194
194
1,991
1,259
608
25
12
413
78
110
13
1
1999
501
72
56
34
17
5
16
16
0
17
13
9
3
1
3
3
1
412
4
2
1
1
3
3
1
0
400
5
3,162
218
218
218
2,078
1,329
623
25
12
465
81
117
4
1
2000
501
72
56
34
17
5
16
16
0
17
13
9
3
1
3
3
1
412
4
2
1
1
3
3
1
0
400
5
3,162
218
218
218
2,078
1,329
623
25
12
465
81
117
4
1
a
IT
5"
If
Q
-^
CD
t-t-
o'
Q)
1
CD
Q)
Q.
m
3
(/)
o'
m
3
Q)
t-t-
CD
-<"
CD
00
O
~_j.
CD
00
cn
"_>.
CD
00
CD
O
O
O
O
3"
J^
•z.
H
0
£
ii
D
D
m
w
w
0
w
H
m
D
w
m
0
H
N)
O
O
oo
-------
^
•o
m
z
D
X
>
*
D
H
H
DO
1
m
00
bource Category
Ferrous Metals Processing
coke manufacturing
ferroalloy production
iron production
steel production
gray iron production
Metals Processing NEC
metal mining
other
OTHER INDUSTRIAL PROCESSES
Mineral Products
cement manufacturing
Miscellaneous Industrial Processes
WASTE DISPOSAL & RECYCLING
Incineration
municipal waste
other
Transportation
ON-ROAD VEHICLES
1980
911
6
13
38
481
373
289
207
82
808
93
93
715
1,210
1,210
161
1,049
64,706
60,501
Light-Duty Gas Vehicles & Motorcycles 47,184
Light-Duty Gas Trucks
Heavy-Duty Gas Vehicles
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
Aircraft
TOTAL ALL SOURCES
11,671
1,646
4,205
3,320
885
74,153
1985
577
3
7
21
209
336
144
141
3
316
43
43
273
871
871
79
792
18,973
18,052
13,637
4,061
354
921
229
692
22,890
1989
582
4
20
19
138
401
170
169
1
173
23
23
150
765
765
45
720
1,802
982
733
232
16
820
166
655
5,468
Note:
1990
576
4
18
18
138
397
185
184
1
169
26
26
143
804
804
67
738
1,197
421
314
100
7
776
158
619
4,975
Some
1991
517
3
14
16
145
339
199
198
1
167
24
24
143
808
808
70
738
592
18
13
4
0
574
0
574
4,169
columns
1992
461
3
14
17
139
288
202
201
1
56
26
26
30
812
812
68
744
584
18
14
4
0
565
0
565
3,810
may not
1993
496
2
12
18
145
319
194
193
1
55
27
27
28
825
825
69
756
547
19
14
5
0
529
0
528
3,916
sum to
1994
540
0
13
18
160
349
200
199
1
54
28
28
26
830
830
68
762
544
19
14
5
0
525
0
525
4,047
totals due
1995
528
0
8
19
159
342
184
183
1
59
29
29
30
604
604
70
534
564
19
14
5
0
544
0
544
3,929 4
to rounding.
996
529
0
8
18
160
343
193
192
1
51
29
29
22
788
788
76
712
525
19
12
7
0
505
0
505
,077
1997
538
0
8
18
165
348
201
200
1
54
30
30
25
798
798
76
722
523
20
13
7
0
503
0
503
4,137
1998
536
0
7
18
168
343
196
195
1
54
30
30
23
806
806
76
729
518
21
14
7
1
497
0
497
4,057
1999
555
0
6
18
173
357
195
194
1
53
31
31
22
813
813
77
736
536
22
14
7
1
515
0
515
4,199
2000
555
0
6
18
173
357
195
194
1
53
31
31
22
813
813
77
736
565
20
14
5
1
545
0
545
4,228
9?
Z
9
^
U
•^
S-
o'
CD
|—
CD
CD
Q.
m
3
o'
CO
m
5'
J
Q)
CD"
~
CD
co
o
co 5
en o
cS 1
co >
CD £
o >
O r-
o" g
-------
08
10
1
£
DO
[—
m
w
•
TJ
TJ
m
D
*
Source Category
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
other
Gas
natural
process
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
other
Gas
natural
process
other
Other
wood/bark waste
liquid waste
other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
Residential Other
distillate oil
natural gas
other
Industrial Processes
1980
11,320
7,024
6,123
3,439
1,694
542
447
901
39
862
NA
NA
NA
NA
NA
NA
3,555
444
306
94
44
NA
286
179
63
44
2,619
2,469
5
145
205
138
NA
67
NA
741
25
155
131
NA
74
356
85
238
33
666
1985
10,048
6,127
5,240
4,378
668
194
NA
193
178
15
NA
646
646
NA
NA
48
3,209
608
430
14
33
131
309
191
89
29
1,520
1,282
227
11
118
89
12
17
655
712
37
106
145
11
88
326
75
248
3
891
1989
10,537
6,593
5,676
4,595
837
245
NA
285
268
17
NA
582
582
NA
NA
49
3,209
615
446
14
30
124
294
176
88
29
1,625
1,405
209
10
120
92
12
16
556
736
38
106
159
11
75
347
78
267
3
852
1990
10,895
6,663
5,642
4,532
857
254
NA
221
207
14
0
565
565
NA
NA
235
3,035
585
399
18
26
141
265
180
71
14
1,182
967
211
3
131
89
8
34
874
1,196
40
97
200
34
46
780
209
449
121
892
1991
10,779
6,519
5,559
4,435
874
250
NA
212
198
14
NA
580
580
NA
NA
168
2,979
570
387
20
26
137
237
146
73
18
1,250
1,025
222
3
129
82
11
36
793
1,281
36
88
210
32
50
865
211
469
185
816
1992
10,928
6,504
5,579
4,456
868
255
NA
170
158
13
NA
579
579
NA
NA
175
3,071
574
405
21
26
122
244
154
73
17
1,301
1,068
230
3
126
82
10
34
825
1,353
38
93
225
28
53
916
210
489
218
857
1993
11,111
6,651
5,744
4,403
1,087
255
NA
180
166
14
NA
551
551
NA
NA
176
3,151
589
413
28
26
122
245
153
75
17
1,330
1,095
233
2
124
83
11
30
863
1,308
40
93
232
31
45
867
210
513
144
861
1994
11,015
6,565
5,636
4,207
1,167
262
NA
163
149
14
NA
591
591
NA
NA
175
3,147
602
420
38
27
117
241
149
76
17
1,333
1,103
228
2
124
83
11
30
846
1,303
40
95
237
31
44
857
210
516
131
878
1995
10,827
6,384
5,579
3,830
1,475
273
NA
96
94
2
NA
562
562
NA
NA
148
3,144
597
412
46
26
112
247
156
73
17
1,324
1,102
220
2
123
84
11
28
854
1,298
38
103
231
30
49
847
210
519
118
873
1996
10,502
6,141
5,574
3,776
1,570
229
0
118
116
2
0
285
273
12
6
158
3,157
543
369
46
19
109
225
141
73
11
1,205
993
210
3
120
83
9
29
1,064
1,204
34
96
247
27
30
770
193
470
108
888
1997
10,563
6,279
5,644
3,828
1,591
225
0
145
142
2
0
319
306
13
7
165
3,102
537
364
46
19
108
216
130
74
12
1,189
970
216
3
115
79
8
28
1,045
1,182
35
97
252
28
30
740
188
437
114
923
1998
10,389
6,231
5,436
3,635
1,575
226
0
223
220
3
0
381
363
19
27
164
3,051
524
357
44
18
105
209
126
72
11
1,175
958
215
3
115
80
8
27
1,028
1,107
37
80
243
29
30
688
172
400
117
933
1999
9,964
5,672
4,929
3,176
1,551
201
0
188
184
3
0
370
368
2
19
166
3,130
539
367
46
18
108
214
129
73
11
1,200
984
214
3
118
83
8
27
1,059
1,162
37
79
265
28
30
723
175
433
116
933
2000
9,649
5,266
4,573
2,910
1,462
201
0
154
149
4
0
353
351
1
18
170
3,222
543
370
46
18
109
228
139
75
13
1,253
1,010
240
3
123
86
9
28
1,076
1,161
37
80
269
29
33
713
169
431
114
967
H
ai
tr
^
^
"
z
o'
CD
•^
^
O
CQ
CD
0
X
CD
m
3
U)
o'
c/)
m
3
B3_
CD
_>.
CD
00
o
CD
00
cn
_^
CD
00
CD
ro
o
o
o
•^f
o
(/)
03
Q_
o
3-
o"
m
•z.
H
0
£
1
D
a
I—
H
-<
•z.
0
m
w
0
•z.
w
H
m
D
CO
m
-g
0
H
N)
O
O
00
-------
„
V
•a
m
o
*
•
D
— |
5
DO
m
cn
00
u
bource Category 1
CHEMICAL & ALLIED PRODUCT MFG
Organic Chemical Mfg
Inorganic Chemical Mfg
Polymer & Resin Mfg
Agricultural Chemical Mfg
Paint, Varnish, Lacquer, Enamel Mfg
Pharmaceutical Mfg
Other Chemical Mfg
METALS PROCESSING
Nonferrous Metals Processing
Ferrous Metals Processing
Metals Processing NEC
PETROLEUM & RELATED INDUSTRIES
Oil & Gas Production
Petroleum Refineries & Related Industries
Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
Agriculture, Food, & Kindred Products
Textiles, Leather, & Apparel Products
980
213
54
159
NA
NA
NA
NA
NA
65
NA
65
NA
72
NA
72
NA
205
NA
NA
Wood, Pulp & Paper, & Publishing Prods 24
Rubber & Miscellaneous Plastic Prods
Mineral Products
cement mfg
glass mfg
other
Machinery Products
Electronic Equipment
Transportation Equipment
Miscellaneous Industrial Processes
SOLVENT UTILIZATION
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
Nonindustrial
Solvent Utilization NEC
STORAGES TRANSPORT
Bulk Terminals & Plants
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Service Stations: Stage 1
Service Stations: Stage II
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Bulk Materials Storage
NA
181
98
60
23
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1985
262
37
22
22
143
0
0
38
87
16
58
13
124
69
55
1
327
5
0
73
0
239
137
48
54
2
NA
0
8
2
0
0
NA
2
0
NA
NA
2
NA
1
0
NA
NA
1
NA
0
0
1989
273
42
18
23
152
0
0
39
83
15
54
14
97
47
49
1
311
5
0
77
0
220
124
45
51
2
NA
0
7
3
0
0
NA
2
0
NA
NA
2
NA
1
0
NA
NA
1
NA
0
1
1990
168
18
12
6
80
0
0
52
97
14
78
6
153
104
47
3
378
3
0
91
0
270
151
59
61
3
0
0
10
1
0
0
0
1
0
0
NA
3
0
2
0
NA
NA
0
0
0
0
1991
165
22
12
6
77
0
0
48
76
15
56
5
121
65
52
4
352
3
0
88
0
249
131
59
59
2
0
0
10
2
0
1
0
2
0
0
NA
6
1
2
0
NA
NA
2
0
0
0
1992
163
22
10
6
76
0
0
50
81
13
62
6
148
68
76
4
361
3
0
86
0
259
139
61
60
2
0
0
10
3
0
1
0
2
0
0
NA
5
1
0
0
NA
NA
3
0
0
0
1993
155
19
5
5
74
0
0
51
83
12
67
4
123
70
49
5
370
4
0
86
0
267
143
64
60
3
0
0
9
3
0
1
0
2
0
0
NA
5
1
0
0
NA
NA
3
0
0
0
1994
160
20
6
5
76
0
0
54
91
12
75
4
117
63
49
5
389
3
0
89
0
281
150
66
64
6
0
0
9
3
0
1
0
2
0
0
0
5
1
0
0
NA
0
3
0
0
0
1995
158
20
7
4
74
0
0
54
98
12
83
4
110
58
48
5
399
6
0
89
0
287
153
67
66
7
0
0
10
3
0
1
0
2
0
0
0
6
1
0
0
NA
0
4
0
0
1
1996
125
21
6
3
50
0
0
45
83
11
66
6
139
86
47
7
438
5
1
86
0
331
200
69
62
2
0
0
12
2
0
1
0
2
0
0
0
15
2
7
0
0
0
4
0
0
2
1997
127
21
6
3
51
0
0
46
88
12
71
6
143
88
48
7
460
5
1
89
0
350
212
74
64
3
0
0
12
3
0
1
0
2
0
0
0
16
2
8
0
0
0
4
0
0
2
1998
129
21
6
3
52
0
0
47
88
12
71
6
143
88
48
7
467
5
1
91
0
355
214
76
65
3
0
0
12
3
0
1
0
2
0
0
0
16
2
8
0
0
0
4
0
0
2
1999
131
21
6
3
53
0
0
47
88
12
70
6
143
88
48
7
465
5
1
92
0
351
208
77
65
3
0
0
12
3
0
1
0
2
0
0
0
16
2
8
0
0
0
4
0
0
2
2000
134
22
6
3
55
0
0
48
91
12
73
7
146
90
49
7
487
5
1
96
0
369
220
81
67
3
0
0
12
3
0
1
0
2
0
0
0
17
2
8
0
0
0
4
0
0
2
s1
7
^
^
•
03
t-t-
O
03
i
O
CQ
CD
O
X
o'
CD
m
3
$
o'
(/)
m
3
CD"
en
x
CD
oo
o
CD
00
cn
x
CD
00
CD
|
ro
o
o
o
•^-
o
ui
03
Q.
o
o"
3
•£•
o
5'
CD
Q.
Z
O
"Z.
£
71
D
r^
^
~z.
D
m
cn
cn
O
cn
H
m
z
cn
m
TJ
O
71
NJ
8
CO
-------
08
1
2
DO
m
w
•o
m
D
X
Source Category
WASTE DISPOSAL & RECYCLING
Incineration
Open Burning
POTW
Industrial Waste Water
TSDF
Landfills
Other
Transportation
ON-ROAD VEHICLES
Light-Duty Gas Vehicles & Motorcycles
light-duty gas vehicles
motorcycles
Light-Duty Gas Trucks
light-duty gas trucks 1
light-duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
heavy-duty diesel vehicles
light-duty diesel trucks
light-duty diesel vehicles
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Non-Road Diesel
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
1980
111
37
74
NA
NA
NA
NA
NA
12,150
8,621
4,421
4,416
5
1,408
864
544
300
2,493
2,463
5
25
3,529
101
1
4
13
29
5
11
0
0
NA
38
2,125
2
843
193
19
926
44
94
2
NA
NA
1985
87
27
59
NA
NA
NA
0
0
11,948
8,089
3,806
3,797
9
1,530
926
603
330
2,423
2,389
6
28
3,859
108
1
4
14
31
5
12
0
0
0
40
2,155
2
943
244
22
755
54
118
3
2
13
1989
84
31
52
NA
NA
NA
0
0
12,210
7,682
3,494
3,483
11
1,386
803
584
343
2,458
2,416
7
35
4,528
114
1
4
13
35
5
14
0
0
0
41
2,472
3
1,083
270
40
877
72
101
6
3
16
1990
91
49
42
0
0
0
0
0
12,014
7,210
3,013
3,002
11
1,552
901
651
306
2,340
2,248
63
28
4,804
120
6
4
12
36
6
15
0
0
0
41
2,513
3
1,102
268
45
898
77
94
7
3
17
1991
95
51
43
0
0
0
0
1
12,457
7,557
3,069
3,058
11
1,839
1,074
766
321
2,328
2,284
11
33
4,900
121
6
4
12
37
6
16
0
0
0
41
2,552
3
1,120
265
50
917
82
88
7
4
17
1992
96
51
43
0
0
0
1
1
12,692
7,759
3,098
3,086
12
2,004
1,171
833
309
2,347
2,302
11
33
4,934
123
6
4
12
38
6
16
0
0
0
41
2,595
3
1,138
265
54
936
87
82
8
4
18
1993
123
74
44
0
0
0
1
4
12,902
7,960
3,117
3,105
12
2,131
1,242
888
316
2,397
2,351
12
33
4,942
124
6
4
11
39
6
17
0
0
0
41
2,640
3
1,156
268
59
953
91
79
8
4
19
1994
114
65
44
0
0
0
1
3
13,191
8,176
3,173
3,161
13
2,160
1,251
909
351
2,492
2,446
12
34
5,015
126
6
4
11
40
6
18
0
0
0
41
2,687
3
1,174
270
64
970
96
77
9
4
19
1995
99
53
44
0
0
0
1
1
13,085
7,956
3,043
3,031
12
1,991
1,183
809
330
2,591
2,544
13
34
5,128
127
6
4
11
41
6
18
0
0
0
41
2,739
3
1,198
274
69
987
101
75
9
4
20
1996
86
53
30
0
0
0
2
1
14,260
8,793
3,006
2,994
12
1,709
1,166
543
518
3,560
3,538
8
14
5,467
164
29
4
14
51
4
22
3
0
0
37
2,746
5
1,267
240
70
935
109
79
10
4
28
1997
86
53
30
0
0
0
2
1
14,470
8,924
2,996
2,983
12
1,742
1,185
557
505
3,680
3,662
7
11
5,546
181
29
5
14
61
4
27
4
0
0
37
2,760
5
1,273
242
76
934
114
73
10
4
29
1998
87
54
30
0
0
0
2
1
14,371
8,816
2,933
2,920
12
1,703
1,157
546
467
3,713
3,698
6
9
5,555
197
29
6
14
71
4
31
5
0
0
37
2,751
5
1,267
241
81
926
119
67
10
4
30
1999
87
54
30
0
0
0
2
1
13,731
8,612
2,825
2,813
12
1,676
1,141
535
455
3,655
3,644
5
7
5,558
203
29
6
15
79
4
32
5
0
0
33
2,707
5
1,247
237
84
910
123
61
10
7
23
2000
89
55
31
0
0
0
2
1
13,251
8,150
2,790
2,777
13
1,608
1,099
509
439
3,312
3,300
4
7
5,558
212
30
6
14
84
4
34
5
0
0
34
2,660
5
1,222
234
83
894
126
56
10
7
24
s1
jr
^
^
•
~7
03
O
Q)
~z_
O
CO
CD
=>
O
X
cL
CD
m
3
in'
o'
en
m
ft-
3
Q)
t-t-
CD
y
CD
CO
O
~_j.
CD
CO
cn
CD
co
CD
rb
o
o
o
t-t-
o
Q)
Q.
=T
o
t-t-
O
^
"o"
o
t-t-
CD
Q.
•z.
H
0
1 —
5
D
H
>
D
m
w
w
0
"Z.
w
H
m
D
w
m
§
H
N)
O
o
oo
-------
_
^
•o
m
z
D
X
•
D
-H
CD
m
c/>
8
bource Category
Aircraft
Marine Vessels
coal
diesel
residual oil
gasoline
other
Railroads
Non-Road Other
liquified petroleum gas
compressed natural gas
MISCELLANEOUS
Agriculture and Forestry
agricultural livestock
Other Combustion
Health Services
Cooling Towers
Fugitive Dust
TOTAL ALL SOURCES
1980
106
467
0
396
71
NA
NA
731
NA
NA
NA
248
NA
NA
248
NA
NA
NA
24,384
1985
119
557
0
469
87
NA
NA
808
112
75
37
310
NA
NA
310
NA
NA
NA
23,198
1989
138
747
0
628
118
NA
NA
923
135
98
38
293
NA
NA
293
NA
NA
NA
23,893
Note:
1990
158
943
0
630
114
10
190
929
141
103
38
369
NA
NA
368
NA
NA
1
24,170
Some
1991
155
995
0
649
115
10
221
929
147
109
38
286
NA
NA
285
NA
NA
1
24,338
columns
1992
156
961
0
621
116
9
214
946
153
115
39
255
NA
NA
253
0
0
1
24,732
may not
1993
156
917
0
593
114
9
201
945
159
120
39
241
NA
NA
240
0
NA
1
25,116
sum to
1994
161
929
0
604
115
9
201
947
165
126
39
390
NA
NA
388
0
0
1
25,474
totals due
1995
165
936 1
0
615
105
10
206
990 1
171
132
39
267
NA
NA
265
0
0
1
1996
81
,083
NA
996
87
NA
0
,183
210
183
27
415
0
0
415
0
0
0
25,051 26,065
to rounding.
1997
81
1,084
NA
996
87
NA
0
1,222
218
190
28
401
0
0
401
0
0
0
26,357
1998
81
1,084
NA
996
87
NA
0
1,215
227
199
28
318
0
0
318
0
0
0
26,011
1999
81
1,083
NA
996
87
NA
0
1,242
271
240
31
343
0
0
343
0
0
0
25,439
2000
84
1,090
NA
1,006
84
NA
0
1,230
281
249
32
576
0
0
576
0
0
0
24,899
s1
£
^
•
•
^•
!-*
O
CD
^
O
CO
CD
0
X
CD
m
3
y,
o'
en
m
3
CD
CD"
en
CD
co
o
co =!
en O
CD <~
co >
CD Jj
rb D
0 >
o c
•^ >
w D
0) m
°- v>
(/) CO
o ^
3- en
f-t- _|
^ D
0 M
O ZJ
1 O
CD H
0. 'M
8
CO
-------
08
0>
§
DO
[—
m
w
•
>
TJ
•o
m
D
X
>
Source Category
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
Oil
Gas
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
Oil
Gas
Other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential]
Residential Wood
fireplaces
woodstoves
other
Residential Other
Industrial Processes
CHEMICAL & ALLIED PRODUCT MFG
Organic Chemical Mfg
ethylene oxide mfg
phenol mfg
terephthalic acid mfg
ethylene mfg
charcoal mfg
socmi reactor
socmi distillation
socmi air oxidation processes
socmi fugitives
other
Inorganic Chemical Mfg
Polymer & Resin Mfg
polypropylene mfg
polyethylene mfg
polystyrene resins
Polymer & Resin Mfg (continued)
synthetic fiber
styrene/butadiene rubber
other
Agricultural Chemical Mfg
1980
1,050
45
31
9
5
NA
NA
157
3
3
62
89
NA
848
1
3
7
NA
809
809
NA
NA
28
12,861
1,595
884
10
NA
60
111
40
118
NA
NA
254
291
93
384
1
22
15
199
70
77
NA
1985
1,570
32
24
5
2
NA
1
134
7
17
57
35
18
1,403
1
4
6
4
1,372
1,372
NA
NA
16
10,474
881
349
2
0
24
28
37
43
7
0
179
27
3
343
12
51
6
217
45
12
11
1989
1,372
37
27
7
2
NA
1
134
7
16
61
36
15
1,200
1
4
7
4
1,169
1,169
NA
NA
15
10,755
980
387
2
0
27
33
45
49
7
1
193
30
3
389
13
57
7
250
50
13
12
1990
1,005
47
27
6
2
NA
12
182
7
12
58
51
54
776
1
3
8
8
718
718
NA
NA
38
10,000
634
192
0
4
20
9
33
26
8
2
61
29
2
242
2
39
4
144
15
37
6
1991
1,075
44
27
5
2
NA
10
196
6
11
60
51
68
835
1
3
8
8
776
776
NA
NA
39
10,178
710
216
1
4
23
11
33
30
9
2
67
38
3
268
2
44
5
161
15
41
7
1992
1,114
44
27
4
2
NA
10
187
7
12
52
49
66
884
1
3
10
8
822
822
NA
NA
40
10,380
715
211
1
4
17
10
33
30
8
2
69
37
3
283
2
45
5
173
16
42
8
1993
993
45
29
4
2
NA
10
186
6
12
51
51
66
762
1
3
11
9
698
698
NA
NA
40
10,578
701
215
1
4
19
10
33
32
8
2
70
36
2
269
2
46
5
157
17
42
7
1994
989
45
29
4
2
NA
10
196
8
12
63
50
64
748
1
3
11
9
684
684
NA
NA
40
10,738
691
217
1
4
21
9
34
33
8
2
70
35
2
257
2
46
5
143
18
43
6
1995
1,073
44
29
3
2
NA
10
206
6
12
73
50
65
823
1
3
11
8
759
759
NA
NA
41
10,780
660
210
1
2
17
10
33
33
8
2
70
34
3
222
2
35
5
142
16
22
5
1996
1,125
50
28
3
8
0
10
179
7
9
59
35
69
896
1
3
14
9
833
541
292
NA
36
8,682
387
131
0
2
11
5
30
27
4
1
40
12
3
128
2
16
5
78
11
16
8
1997
1,122
52
29
4
8
0
11
175
7
8
59
34
68
895
1
3
14
9
833
541
292
NA
35
8,900
388
133
0
2
11
5
31
28
4
1
41
12
3
124
2
17
3
80
7
16
8
1998
1,122
56
29
5
10
1
11
174
7
8
59
34
67
892
1
3
13
9
833
541
292
NA
33
8,442
394
136
0
2
11
5
31
28
4
1
42
12
3
126
2
17
3
82
7
16
8
1999
1,136
62
29
5
10
7
11
179
7
8
60
35
69
895
1
3
15
10
833
541
292
NA
34
8,003
396
139
0
2
11
5
32
29
4
1
42
13
3
124
2
17
3
83
7
13
8
2000
1,206
64
30
4
11
8
11
185
7
9
63
37
70
957
1
3
15
10
895
580
315
NA
34
8,033
407
143
0
2
12
5
33
30
4
1
43
13
3
128
2
17
3
86
7
13
8
I
5"
>
01
z
Q)
o'
Q)
Q"
CD"
O
CQ
03
o'
O
O
T3
O
(~
Q.
CO
m
3
CO
o'
CO
m
CO
3
CD
CO
CD
co
o
CD
co
en
_>.
CD
CO
CD
O
O
O
^
O
r~
CO
Q)
Q.
CO
O
3-
3"
CO
•z.
H
0
•z.
I—
ii
D
c:
rz
—\
•z.
0
m
w
w
0
"Z.
w
H
m
D
^
m
~u
0
H
N)
O
O
CO
-------
Source Category
1980
1985
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Paint, Varnish, Lacquer, Enamel Mfg 65
paint & varnish mfg 65
other NA
Pharmaceutical Mfg 77
Other Chemical Mfg 92
carbon black mfg 92
printing ink mfg NA
fugitives unclassified NA
carbon black furnace: fugitives NA
other NA
METALS PROCESSING 273
Nonferrous Metals Processing NA
Ferrous Metals Processing 273
coke oven door & topside leaks 1 52
coke oven by-product plants NA
other 121
Metals Processing NEC NA
PETROLEUM & RELATED INDUSTRIES 1,440
Oil & Gas Production 379
Petroleum Refineries & Related Industries1,045
vaccuum distillation 32
fluid catalytic cracking units 21
process unit turnarounds NA
petroleum refinery fugitives NA
other 992
Asphalt Manufacturing 16
OTHER INDUSTRIAL PROCESSES 237
Agriculture, Food, & Kindred Products 191
vegetable oil mfg 81
whiskey fermentation: aging 64
bakeries 46
other NA
Textiles, Leather, & Apparel Products NA
Wood, Pulp & Paper, & Publishing ProductsNA
Rubber & Miscellaneous Plastic Products 44
rubber tire mfg 44
green tire spray NA
other NA
Mineral Products 2
Machinery Products NA
Electronic Equipment NA
Transportation Equipment NA
Construction NA
Miscellaneous Industrial Processes NA
8
8
0
43
125
26
2
12
4
81
76
18
57
12
3
41
1
703
107
592
15
34
15
76
454
3
390
169
46
24
51
49
10
42
41
10
5
26
15
4
0
1
NA
108
8
8
0
48
132
26
3
12
5
87
74
19
54
12
3
39
1
639
68
568
13
31
13
65
446
3
403
175
49
23
51
52
10
44
46
11
6
29
14
4
0
0
NA
109
14
13
1
20
158
9
1
23
0
125
122
18
98
19
7
71
7
612
301
308
7
15
11
99
177
3
401
138
16
24
43
55
20
96
58
5
3
50
18
7
2
2
0
59
16
15
1
21
179
17
1
23
1
136
123
19
99
22
9
68
6
640
301
337
7
17
11
105
196
3
391
130
18
16
44
52
18
92
59
5
4
50
17
8
2
2
0
62
17
16
1
24
169
16
1
21
1
129
124
17
100
27
9
63
8
632
297
332
7
16
11
103
195
3
414
127
19
12
44
51
19
101
64
5
3
55
27
10
3
2
0
62
18
16
1
23
166
16
1
20
1
127
124
18
98
27
9
62
8
649
310
336
7
15
11
109
194
3
442
146
19
24
46
58
19
112
62
5
3
53
28
8
3
3
0
62
17
16
1
24
168
21
2
27
1
117
126
20
97
26
9
62
8
647
305
339
7
16
10
109
198
3
438
145
16
24
46
58
19
105
61
6
3
52
30
11
3
3
0
62
18
16
2
38
164
24
2
30
1
107
125
21
96
26
9
61
8
642
299
339
6
16
12
111
194
4
450
147
16
25
47
60
19
122
60
6
3
51
31
11
2
2
0
57
7
6
2
7
104
27
1
13
0
63
73
19
44
5
5
35
10
477
271
201
3
16
2
84
97
5
422
104
1
15
41
47
10
154
49
6
2
41
31
11
1
3
0
58
8
6
2
7
105
28
1
13
0
64
78
20
47
6
5
37
11
487
274
208
3
16
2
87
101
5
438
108
1
16
42
49
10
160
51
6
2
43
32
12
1
4
0
60
8
6
2
7
106
28
1
13
0
64
78
20
47
6
5
36
11
485
272
208
3
16
2
86
101
5
443
109
1
16
42
50
10
164
52
6
2
44
32
12
1
4
0
60
8
6
2
8
107
28
1
13
0
65
76
20
46
6
5
35
70
424
271
149
3
16
2
27
101
4
463
110
1
16
43
50
10
167
52
6
2
44
32
12
13
4
0
64
8
6
2
8
109
29
1
13
0
66
79
21
48
6
5
37
77
433
279
150
3
16
2
27
101
4
480
114
1
17
44
52
10
173
54
6
2
46
33
12
13
4
0
66
i
jr
&
Ul
Q)
5'
Q)
<
O
Q)
t-t-
CD"
O
CQ
Q)
o'
O
O
3
T3
O
Q.
m
.~. 3
9 in'
§ .
CD
00
O
CD
00
cn
"_^
CD
00
CD
O
O
O
t-t-
O
c
CD
Q_
^-
O
,— i-
o
>
o
z
[—
71
O
£
^
^
D
m
en
cn
O
z
cn
H
m
z
71
m
O
71
m
cn
-------
08
08
1
2
DO
m
w
fr
•o
m
Z
D
X
>
Source Category
SOLVENT UTILIZATION
Degreasing
open top
conveyorized
cold cleaning
other
Graphic Arts
letterpress
flexographic
lithographic
gravure
other
Dry Cleaning
perchloroethylene
petroleum solvent
other
Surface Coating
industrial adhesives
fabrics
paper
large appliances
magnet wire
autos & light trucks
metal cans
metal coil
wood furniture
metal furniture
flatwood products
plastic parts
large ships
aircraft
misc. metal parts
steel drums
architectural
traffic markings
maintenance coatings
railroad
auto refinishing
machinery
electronic & other electrical
general
miscellaneous
thinning solvents
other
1980
6,584
513
NA
NA
NA
513
373
NA
NA
NA
NA
373
320
NA
NA
320
3,685
55
186
626
36
5
165
73
21
231
52
82
25
20
2
NA
NA
477
NA
106
9
186
62
NA
52
799
NA
415
1985
5,699
756
28
5
31
691
317
2
18
4
131
162
169
85
84
0
2,549
381
34
106
22
0
85
97
50
132
41
4
11
15
27
14
NA
473
100
79
4
111
37
79
146
104
90
306
1989
5,964
757
29
4
35
689
363
2
20
4
150
187
212
107
105
0
2,635
375
35
114
18
0
87
95
50
140
44
4
11
15
34
14
NA
500
106
80
3
132
28
79
154
103
96
317
1990
5,750
744
18
5
30
691
274
4
20
14
75
162
215
110
104
0
2,523
390
14
75
21
1
92
94
45
158
48
9
27
15
7
59
3
495
105
79
3
130
28
78
121
32
96
297
1991
5,782
718
25
6
23
664
301
8
24
17
82
171
218
112
106
0
2,521
374
14
64
20
1
90
91
49
154
47
10
22
14
7
87
3
500
106
76
3
132
26
75
127
37
97
295
1992
5,901
737
26
6
24
680
308
8
26
18
81
175
224
115
109
0
2,577
386
16
61
20
1
93
93
47
159
49
10
23
15
7
90
3
505
107
78
3
137
26
77
129
42
100
302
1993
6,016
753
26
6
24
697
322
8
26
21
87
180
225
116
110
0
2,632
400
16
59
21
1
92
96
49
171
52
11
22
15
7
92
3
510
108
81
3
140
27
80
133
39
94
310
1994
6,162
775
27
6
22
719
333
8
25
22
93
185
228
117
111
0
2,716
419
15
59
22
1
96
98
48
185
56
12
22
15
7
93
4
515
109
85
4
144
27
85
140
38
96
321
1995
6,183
789
24
5
23
737
339
8
24
20
91
196
230
118
112
1
2,681
410
15
52
21
1
96
102
47
179
53
13
18
13
6
92
4
522
111
84
4
142
25
85
138
35
99
314
1996
5,474
602
8
4
22
567
287
6
19
12
50
200
154
58
89
7
2,373
351
10
48
23
2
94
99
45
175
52
16
15
17
11
38
4
480
93
80
3
161
25
78
100
30
51
273
1997
5,621
624
8
5
23
588
293
6
19
12
51
205
163
61
94
8
2,456
366
10
49
24
2
100
106
47
185
54
17
16
18
11
40
4
485
94
83
3
163
25
82
105
31
53
280
1998
5,149
372
4
2
10
356
300
6
20
13
52
210
166
63
96
8
2,193
147
10
50
23
2
102
109
48
127
56
17
16
18
12
40
4
487
94
84
3
163
22
82
106
32
54
282
1999
4,828
371
4
2
11
354
295
6
16
13
45
214
168
63
97
8
2,138
148
11
51
22
2
105
113
49
130
58
18
16
19
5
41
4
483
93
85
4
104
20
82
107
33
54
282
2000
4,827
382
4
2
11
365
304
6
16
13
46
222
169
64
98
8
2,087
154
11
53
23
2
109
117
51
143
59
19
17
16
6
41
4
406
77
71
4
102
19
87
113
35
56
293
IT
5"
^
Ol
"
~z.
CD
t—t-
o'
Q)
O
Q)
t-t-
CD
O
CO
Q)
o'
O
o
3
T3
O
§
Q.
CO
m
.~. 3
o ;;;•
§ 2.
3- 0
13' =i
c co
<& m
3
CD
CO
CD
00
o
CD
oo
_cn
_>.
CD
oo
CD
O
O
O
t-t-
O
f—
CO
Q)
Q.
CO
O
o"
CO
•z.
H
0
1 —
5
D
f£
H
>
D
m
w
w
0
C/5
H
m
D
w
m
s
H
N)
O
O
00
-------
>
?
z
0
X
D
-H
— |
5
DO
m
CO
§
bource Category 1
Other Industrial
miscellaneous
rubber & plastics mfg
other
Nonindustrial 1
cutback asphalt
other asphalt
pesticide application
adhesives
consumer solvents
other
Solvent Utilization NEC
STORAGES TRANSPORT 1
Bulk Terminals & Plants
fixed roof
floating roof
variable vapor space
efrwith seals
ifrwith seals
underground tanks
area source: gasoline
other
Petroleum & Petroleum Product Storage
fixed roof gasoline
fixed roof crude
floating roof gasoline
floating roof crude
efr/seal gasoline
efr / seal crude
ifr/seal gasoline
ifr / seal crude
variable vapor space gasoline
area source: crude
other
Petroleum & Petroleum Product Transport
gasoline loading: normal / splash
gasoline loading: balanced / submerged
gasoline loading: normal / submerged
gasoline loading: clean / submerged
marine vessel loading: gasoline & crude
other
Service Stations: Stage I
Service Stations: Stage II
Service Stations: Breathing & Emptying
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Inorganic Chemical Transport
Bulk Materials Storage
Bulk Materials Transport
980
690
44
327
319
002
323
NA
241
NA
NA
437
NA
975
517
12
39
1
NA
NA
0
440
26
306
43
148
45
36
3
2
1
2
3
NA
23
61
0
2
3
0
50
6
461
583
NA
46
NA
NA
NA
NA
NA
1985
125
NA
25
100
1,783
191
NA
212
345
1,035
NA
NA
1,747
606
14
46
1
NA
NA
0
512
32
223
26
26
27
5
2
0
1
0
1
NA
133
126
3
21
41
2
24
35
207
485
49
34
17
0
0
0
NA
1989
131
NA
29
102
1,867
199
NA
260
353
1,056
NA
NA
1,753
651
15
50
1
NA
NA
0
553
33
210
23
21
24
5
2
0
1
0
2
NA
132
125
3
22
42
2
22
35
223
441
52
36
15
0
0
0
NA
1990
94
NA
28
66
1,900
199
NA
258
361
1,083
NA
0
1,495
359
9
26
2
2
2
1
282
36
157
13
21
15
2
7
3
1
0
1
0
92
151
3
15
26
0
31
76
300
433
52
30
10
0
0
2
NA
1991
98
NA
28
71
1,925
202
NA
264
365
1,095
NA
NA
1,532
369
11
29
2
3
2
2
281
40
195
17
25
25
7
11
3
2
0
2
0
102
146
2
17
25
0
30
73
295
430
51
35
8
1
0
2
NA
1992
102
NA
28
74
1,952
207
NA
272
368
1,105
NA
NA
1,583
384
12
30
1
3
3
2
292
42
204
17
26
24
7
13
3
2
0
5
0
106
149
2
15
26
0
30
75
303
442
52
38
8
1
0
2
NA
1993
102
NA
29
73
1,982
214
NA
280
372
1,116
NA
0
1,600
395
13
34
1
4
5
2
292
44
205
16
28
24
8
14
3
2
0
6
0
103
142
2
13
24
0
29
73
309
449
53
39
7
1
0
1
NA
1994
99
NA
31
68
2,011
221
NA
289
375
1,126
NA
0
1,629
403
16
29
1
4
3
2
305
43
194
16
24
22
6
14
3
2
0
3
0
103
139
3
11
25
0
28
72
322
467
55
39
7
1
0
1
NA
1995
96
NA
31
64
2,048
227
NA
299
380
1,142
NA
0
1,652
406
16
19
0
3
3
2
322
41
191
16
21
22
6
15
2
2
0
0
0
106
134
2
10
23
0
29
70
334
484
57
37
7
1
0
1
NA
1996
106
NA
38
68
1,949
135
43
388
301
1,076
6
3
1,289
208
6
11
0
2
3
2
163
21
181
14
25
16
5
9
3
3
1
0
0
104
115
3
7
13
0
31
61
310
399
43
26
5
1
0
1
0
1997
110
NA
40
70
1,973
140
44
393
304
1,085
6
3
1,327
215
6
11
0
2
3
2
167
22
187
14
26
16
6
9
3
3
1
0
0
108
119
3
7
14
0
32
62
318
410
45
26
5
1
0
1
0
1998
111
NA
40
71
2,004
144
45
408
307
1,095
6
3
1,327
214
6
11
0
2
3
2
167
22
187
14
25
16
6
9
4
3
1
0
0
108
119
3
7
13
0
33
62
318
410
45
27
5
1
0
1
0
1999
113
NA
40
72
1,743
147
46
412
250
883
5
2
1,245
206
6
12
0
2
3
2
157
24
108
1
10
11
2
9
3
3
1
0
0
68
121
3
7
14
1
34
63
320
412
45
25
5
1
0
1
0
2000
118
NA
42
76
1,765
150
48
421
252
890
5
2
1,225
208
7
12
0
2
3
2
157
25
109
1
11
11
2
9
3
3
1
0
0
69
97
3
7
14
1
12
60
321
414
45
26
3
1
0
1
0
s1
IT
5"
Ul
Q)
t-t-
o'
Q)
-
O
Q)
t-t-
CD"
0
CQ
Q)
o'
O
O
3
T3
O
^
Q.
CO
m
.~. 3
§ 1
3- 0
13' =J
C C/)
0. m
3
S-
CD
CO
->.
CO
oo
o
CD
oo
en
~_j.
CD
00
CD
O
O
O
t-t-
O
CO
Q)
Q.
CO
O
t-t-
0
m
>
o
"Z.
1
71
D
£
^
^
D
m
CO
CO
O
CO
H
m
z
CO
71
m
O
71
NJ
8
CO
-------
(O
o
1
2
DO
m
w
•o
m
z
D
X
>
Source Category
WASTE DISPOSAL & RECYCLING
Incineration
Open Burning
industrial
commmercial/institutional
residential
land clearing debris
other
POTW
Industrial Waste Water
TSDF
Landfills
Other
Transportation
ON-ROAD VEHICLES
Light-Duty Gas Vehicles & Motorcycles
light-duty gas vehicles
motorcycles
Light-Duty Gas Trucks
light-duty gas trucks 1
light-duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
heavy-duty diesel vehicles
light-duty diesel trucks
light-duty diesel vehicles
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Non-Road Diesel
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
1980
758
366
372
NA
NA
NA
NA
372
NA
NA
NA
NA
20
11,291
8,979
5,907
5,843
64
2,059
1,229
830
611
402
392
2
8
2,312
1,787
151
39
33
583
17
127
5
1
NA
830
327
1
135
28
4
138
8
11
0
NA
NA
1985
979
64
309
6
1
302
NA
NA
10
1
594
0
0
11,818
9,376
5,864
5,810
54
2,425
1,437
988
716
370
360
2
8
2,442
1,886
156
45
37
616
19
137
5
1
0
869
332
1
151
36
5
113
10
14
1
1
2
1989
941
59
274
6
2
266
NA
NA
11
2
595
0
0
9,744
7,192
4,462
4,412
50
1,867
1,018
849
517
346
332
3
11
2,552
1,907
160
44
33
682
20
164
8
1
0
793
384
1
176
40
9
127
13
14
1
1
3
1990
986
48
196
4
9
165
NA
19
49
14
589
64
26
8,988
6,443
3,692
3,635
56
2,016
1,103
912
405
331
298
24
9
2,545
1,889
128
44
33
700
20
171
9
1
0
784
390
1
181
40
10
126
13
14
1
1
3
1991
999
50
200
4
9
167
NA
20
47
18
591
66
28
9,240
6,660
3,608
3,571
36
2,318
1,245
1,073
416
318
303
4
11
2,581
1,920
130
44
32
718
21
179
9
1
0
787
397
1
185
41
11
126
14
15
1
1
3
1992
1,010
51
203
4
10
169
NA
20
48
19
589
69
31
8,882
6,289
3,288
3,256
33
2,347
1,255
1,092
335
318
302
5
11
2,594
1,925
132
44
31
734
21
185
10
1
0
768
403
1
190
41
12
125
14
15
2
1
3
1993
1,046
76
207
5
10
171
NA
21
50
19
588
74
33
8,973
6,348
3,232
3,198
34
2,471
1,313
1,157
327
318
302
5
11
2,624
1,957
133
44
30
752
21
192
11
1
0
772
408
1
194
42
13
124
15
15
2
1
3
1994
1,046
65
208
5
10
172
NA
21
52
19
587
80
35
9,235
6,563
3,332
3,295
37
2,488
1,307
1,181
414
330
313
5
12
2,672
1,991
135
44
29
771
22
200
11
1
0
778
414
1
199
42
14
123
16
14
2
1
3
1995
1,067
54
208
5
10
173
NA
20
51
16
628
75
36
8,515
5,816
3,029
2,991
38
2,135
1,172
963
325
326
309
5
12
2,699
2,021
138
44
28
789
22
207
12
1
0
779
420
1
204
43
14
121
16
14
2
1
3
1996
560
24
364
0
0
150
206
8
48
19
41
35
29
9,336
5,541
2,911
2,875
36
1,786
1,157
629
488
356
348
4
5
3,834
3,303
604
68
42
1,047
17
233
372
0
0
917
412
1
207
41
15
107
18
15
2
1
4
1997
561
24
364
0
0
150
206
8
48
20
41
35
29
9,082
5,438
2,878
2,842
36
1,789
1,164
624
439
332
325
3
4
3,684
3,156
604
59
34
971
17
204
344
0
0
924
406
1
205
41
16
104
19
13
2
1
4
1998
566
25
364
0
0
150
206
8
49
20
42
36
30
8,972
5,439
2,935
2,895
39
1,788
1,171
617
400
316
311
3
3
3,573
3,056
604
54
32
888
16
182
351
0
0
929
395
1
198
41
17
101
20
10
2
1
5
1999
571
26
364
0
0
150
206
8
50
21
42
36
32
8,754
5,332
2,907
2,865
42
1,759
1,166
593
375
290
286
2
3
3,461
2,973
604
51
29
852
15
163
369
0
0
890
369
1
185
39
18
95
20
8
2
2
1
2000
582
26
371
0
0
154
209
8
51
21
43
37
33
8,396
5,035
2,798
2,756
42
1,655
1,108
546
323
260
256
2
2
3,404
2,942
605
50
28
830
14
155
382
0
0
878
342
1
169
37
15
89
21
6
2
2
1
IT
5"
>
Ul
^
o'
03
O
Q)
t-t-
CD
O
CO
Q)
o'
o
o
T3
O
Q.
CO
m
"8 1
a 5-
i 3
0. m
"-" ^
3
Q)
i— i-
CD
<"
CD
00
O
_>.
CD
00
cn
CD
oo
rb
o
o
o
^
o
CO
Q)
Q.
CO
o
^
CO
•z.
H
0
1 —
5
D
I—
H
^
•z.
0
m
w
w
0
C/5
H
m
D
w
m
~u
0
H
N)
o
o
00
-------
*f
J
m
z
D
X
>
•
D
H
-H
DO
I
m
cn
to
bource Category
Aircraft
Marine Vessels
coal
diesel
residual oil
gasoline
other
Railroads
Non-Road Other
liquified petroleum gas
compressed natural gas
MISCELLANEOUS
Agriculture & Forestry
Other Combustion
structural fires
agricultural fires
slash/prescribed burning
forest wildfires
other
Catastrophic/Accidental Releases
Health Services
Cooling Towers
Fugitive Dust
TOTAL ALL SOURCES
1980
146
19
0
17
1
NA
NA
33
NA
NA
NA
1,134
NA
1,134
40
70
285
739
1
NA
NA
NA
NA
26,336
1985
165
22
1
20
1
NA
NA
37
0
0
0
566
NA
565
44
55
182
283
NA
NA
0
NA
NA
24,428
1989
190
30
1
27
2
NA
NA
42
0
0
0
642
NA
641
44
79
182
335
NA
NA
1
NA
NA
22,513
Note:
1990
180
32
0
21
3
1
7
52
0
0
0
1,059
5
1,049
14
48
234
749
3
4
1
0
0
21,053
Some
1991
177
34
0
22
3
1
8
52
0
0
0
756
6
743
14
48
239
439
3
4
0
2
0
21,249
columns
1992
179
33
0
21
3
1
8
54
0
0
0
486
6
474
15
49
243
164
3
4
1
2
0
20,862
may not
1993
176
32
0
20
3
1
8
52
0
0
0
556
6
544
15
48
266
212
3
4
1
1
0
21,099
sum to
1994
176
43
1
27
4
1
11
49
0
0
0
720
6
707
15
51
259
379
3
4
1
2
0
21,683
totals due
1995
178
32
0
20
3
1
8
49
0
0
0
551
7
537
15
54
293
171
3
4
1
2
0
996
32
39
NA
31
8
NA
0
48
0
0
0
742
7
729
3
51
277
395
3
4
0
1
0
20,918 19,924
to rounding.
1997
32
39
NA
31
8
NA
0
50
0
0
0
1,181
7
1,168
3
52
293
817
3
5
1
1
0
20,325
1998
32
40
NA
31
8
NA
0
50
0
0
0
702
7
688
3
52
311
319
3
5
1
1
0
19,278
1999
32
39
NA
31
8
NA
0
48
0
0
0
1,506
8
1,493
3
53
281
1,152
3
5
1
1
0
19,439
2000
29
39
NA
32
7
NA
0
48
0
0
0
2,710
8
2,696
3
54
183
2,452
3
5
1
1
0
20,384
O1
5"
^
01
Q)
5'
Q)
-
O
Q)
CD"
O
CQ
Q)
O
O
O
3
T3
O
^
Q.
m
.~. 3
§ <".
3- 0
13' =J
c (/)
0. m
3
Q)
CD"
CD
oo
o
_^
CD
00
cn
CD
oo
CD
Jj
o
o
o
•^-
o
Q)
Q.
cn
o
(~l"
o"
^L.
^
H
o
^
^
=g
D
c
^
r^
J
z
D
m
s
cn
cn
O
•z.
cn
H
m
D
cn
m
TJ
O
.H
NJ
8
CO
-------
(O
10
§
>!
£
DO
[—
m
w
•
TJ
TJ
m
D
X
Source Category
Fue/ Combustion
FUEL COMB. ELEC. UTIL.
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
Gas
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
other
Gas
natural
process
other
Other
wood/bark waste
liquid waste
other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
fireplaces
woodstoves
other
Residential Other
1980
2,445
879
796
483
238
75
NA
76
74
2
7
NA
NA
679
18
12
4
2
NA
67
63
4
0
23
20
3
NA
571
566
NA
5
NA
887
8
30
4
NA
818
818
NA
NA
27
1985
1,536
280
268
217
35
16
0
8
8
0
1
NA
3
247
71
48
1
7
15
52
43
5
4
47
24
22
1
75
67
1
6
3
1,009
13
12
4
3
959
959
NA
NA
18
1989
1,382
271
255
193
39
22
0
12
11
0
1
NA
3
243
70
49
1
6
14
48
39
5
4
44
24
20
1
78
71
1
6
3
869
13
13
5
3
817
817
NA
NA
18
1990
1,196
295
265
188
37
41
NA
9
9
0
1
NA
20
270
84
59
5
2
19
52
44
6
2
41
30
11
0
87
80
1
6
6
631
15
13
5
79
501
501
NA
NA
18
1991
1,147
257
232
169
39
23
NA
10
10
0
1
NA
15
233
72
48
3
1
19
44
36
6
2
34
24
10
0
72
67
1
5
10
657
14
11
6
73
535
535
NA
NA
18
1992
1,183
257
234
167
43
23
NA
7
7
0
0
NA
16
243
74
53
3
1
17
45
37
6
1
40
26
13
0
74
67
1
6
11
683
15
12
6
73
558
558
NA
NA
18
1993
1,124
279
253
185
46
22
NA
9
9
0
1
NA
17
257
71
51
3
1
16
45
38
6
1
43
29
13
0
86
71
1
14
12
588
15
11
6
72
464
464
NA
NA
18
1994
1,113
273
246
181
44
21
NA
8
8
0
1
NA
17
270
70
49
5
1
16
44
37
6
1
43
30
14
0
74
68
1
6
38
570
15
12
7
73
446
446
NA
NA
18
1995
1,179
268
244
174
48
21
NA
5
5
0
1
NA
18
302
70
49
5
1
15
49
42
6
1
45
30
15
0
73
68
1
5
64
610
16
12
6
73
484
484
NA
NA
18
1996
978
289
264
195
51
19
0
6
6
0
1
1
17
239
73
43
5
1
24
46
38
7
1
42
28
14
0
61
54
1
7
17
450
16
12
8
72
319
144
175
NA
23
1997
980
294
268
196
51
21
0
7
7
0
1
1
18
233
73
43
5
1
23
43
35
7
1
42
27
15
0
58
51
1
6
17
453
16
12
8
76
319
144
175
NA
22
1998
912
229
197
134
47
17
0
5
5
0
1
7
18
230
71
42
5
1
23
42
34
7
1
42
27
15
0
59
52
1
6
16
453
17
10
7
79
319
144
175
NA
21
1999
950
259
231
125
57
49
0
3
3
0
0
6
19
235
74
44
5
1
23
43
35
7
1
42
28
14
0
59
53
1
6
17
456
17
9
8
81
319
144
175
NA
22
2000
997
270
242
129
57
56
0
3
3
0
0
6
19
244
74
44
5
1
24
46
38
7
1
45
29
16
0
62
55
1
6
17
483
17
10
8
84
342
154
188
NA
21
e>
CD
t^t-
o'
5L
Tl
m
3
m
CD
CO
CD
00
O
CD
00
cn
CD
oo
I
o
o
o
CO
CD
Q.
CO
o
o"
CO
D
>
D
m
w
w
D
w
o
o
00
-------
•o
•o
m
O
X
m
CO
Source Category 1980
Industrial Processes 3,026
CHEMICAL & ALLIED PRODUCT MFC 148
Organic Chemical Mfg 19
I norganic Chemical Mfg 25
Polymer & Resin Mfg NA
Agricultural Chemical Mfg 61
Paint, Varnish, Lacquer, Enamel Mfg NA
Pharmaceutical Mfg NA
Other Chemical Mfg 42
METALS PROCESSING 622
Nonferrous Metals Processing 130
copper 32
lead 18
zinc 3
other 77
Ferrous Metals Processing 322
primary 271
secondary 51
other NA
Metals Processing NEC 1 70
PETROLEUMS RELATED INDUSTRIES 138
Oil & Gas Production NA
Petroleum Refineries & Related Industries 41
fluid catalytic cracking units 41
other NA
Asphalt Manufacturing 97
OTHER INDUSTRIAL PROCESSES 1,846
Agriculture, Food, & Kindred Products 402
country elevators 258
terminal elevators 86
feed mills 3
soybean mills 22
wheat mills 1
other grain mills 6
other 26
Textiles, Leather, & Apparel Products NA
Wood, Pulp & Paper, & Publishing Products183
sulfate (kraft) pulping 142
other 41
Rubber & Miscellaneous Plastic Products NA
Mineral Products 1,261
cement mfg 417
surface mining 127
stone quarrying/processing 421
other 296
Machinery Products NA
Electronic Equipment NA
Transportation Equipment NA
Construction NA
Miscellaneous Industrial Processes NA
1985
1,339
58
19
7
4
9
0
0
18
220
46
3
4
3
36
164
136
26
2
10
63
0
28
24
4
35
611
68
7
6
6
13
3
7
25
0
101
71
30
3
401
213
20
52
116
8
0
2
NA
28
1989
1,276
63
22
8
5
10
0
0
18
211
45
3
3
3
36
156
129
26
2
10
58
0
24
21
3
34
591
72
9
6
7
14
3
8
25
0
106
74
33
4
374
193
15
54
111
9
0
2
NA
23
1990
1,306
77
26
19
5
11
1
1
14
214
50
14
3
6
27
155
128
25
2
9
55
2
20
17
3
33
583
73
9
6
7
14
3
8
25
0
105
73
32
4
367
190
15
54
108
9
0
2
0
23
1991
1,264
68
28
4
4
11
1
0
20
251
46
14
2
6
23
123
99
24
0
82
43
2
20
17
3
21
520
80
10
7
4
15
4
6
34
0
81
53
27
4
320
147
14
59
99
8
0
2
0
25
1992
1,269
71
28
5
5
11
1
0
20
250
47
15
2
6
23
115
92
23
0
88
43
2
21
18
3
20
506
69
10
8
5
11
4
5
26
0
79
50
29
4
318
145
15
60
98
9
0
2
0
24
1993
1,240
66
28
5
4
11
1
0
18
181
40
12
2
1
25
121
97
24
0
20
38
2
20
17
3
17
501
73
10
8
5
12
4
6
28
0
78
49
29
3
316
140
17
60
99
7
0
0
0
22
1994
1,219
76
29
5
4
10
1
0
27
184
39
11
2
2
25
125
100
25
0
20
38
2
19
16
3
17
495
73
9
7
5
12
4
6
30
0
76
50
26
3
313
139
17
58
100
7
0
0
0
22
1995
1,231
67
29
5
4
10
1
0
18
212
41
12
3
2
25
149
123
26
0
22
40
2
20
18
3
18
511
80
9
7
5
12
4
7
37
0
81
53
28
3
317
140
17
58
102
7
0
0
0
23
1996
1,180
63
29
4
3
8
1
0
19
144
34
6
2
2
24
91
64
27
0
19
29
1
17
12
5
12
325
59
5
2
3
7
2
5
36
1
75
38
37
4
160
23
16
23
97
5
1
0
0
21
1997
1,203
64
29
4
3
9
1
0
19
151
35
6
2
2
25
96
68
28
0
20
30
1
17
12
5
12
336
61
5
2
3
7
2
5
37
1
77
40
38
4
166
24
17
24
101
5
1
0
0
21
1998
1,207
65
30
4
3
9
1
0
19
150
35
6
2
2
25
95
68
27
0
20
30
1
17
12
5
11
338
59
5
2
3
7
2
5
34
1
79
40
39
4
167
25
17
24
102
5
1
0
0
21
1999
1,209
65
30
4
3
9
1
0
19
148
35
6
2
2
25
93
67
26
0
21
29
1
17
12
5
11
343
61
5
2
3
7
2
5
36
1
80
41
39
4
168
24
17
24
103
5
1
0
0
22
2000
1,242
67
31
4
3
9
1
0
19
152
35
6
2
2
25
96
70
26
0
21
30
1
17
12
5
11
355
63
6
2
4
8
2
6
37
1
84
43
41
4
174
26
17
24
107
6
1
0
0
22
ar
IT
5"
>
o>
m
3
o
3
CO
m
CO
o
co
CD
00
O
CD
00
cn
CD =!
oo O
O >
o 5;
° O
E? i
CO
CD
Q.
CO
^
O
o"
3
CO
o
o
CD
Q.
D
m
CO
CO
o
CO
71
m
-
o
71
-------
(O
»
1
2
DO
I —
m
w
•
>
TJ
TJ
m
z
D
X
>
Source Category 1980
SOLVENT UTILIZATION
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
Nonindustrial
Solvent Utilization NEC
STORAGES TRANSPORT
Bulk Terminals & Plants
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Service Stations: Stage II
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Inorganic Chemical Transport
Bulk Materials Storage
storage
transfer
combined
other
Bulk Materials Transport
WASTE DISPOSAL & RECYCLING
Incineration
residential
other
Open Burning
residential
land clearing debris
other
POTW
Industrial Waste Water
TSDF
Landfills
Other
Transportation
ON-ROAD VEHICLES
Light-Duty Gas Vehicles & Motorcycles
light-duty gas vehicles
motorcycles
Light-Duty Gas Trucks
light -duty gas trucks 1
light -duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
heavy-duty diesel vehicles
light -duty diesel trucks
light -duty diesel vehicles
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
273
75
42
32
198
198
NA
NA
NA
NA
NA
NA
NA
795
397
120
119
1
55
25
29
15
208
194
2
12
1985
2
0
0
0
2
0
NA
NA
107
0
0
0
NA
1
0
0
NA
105
33
72
1
NA
0
278
52
39
13
225
221
NA
4
NA
0
NA
0
0
786
363
77
77
0
43
19
24
14
229
219
1
8
1989
2
0
0
0
2
0
NA
NA
101
0
0
0
NA
1
0
0
NA
99
31
67
1
NA
0
251
50
35
15
200
195
NA
5
NA
0
NA
0
0
844
367
65
64
0
34
16
19
11
257
247
2
9
1990
4
0
0
0
3
1
NA
NA
102
0
0
0
NA
1
0
1
0
100
31
69
1
NA
1
271
65
39
26
206
195
NA
11
0
NA
0
0
0
838
349
57
57
0
37
18
19
10
245
225
13
7
1991
5
0
0
0
4
1
NA
NA
101
0
1
0
NA
1
0
1
0
99
27
71
0
0
0
276
66
41
25
209
797
NA
12
0
0
0
0
0
842
353
56
55
0
44
21
23
10
243
233
2
8
1992
5
0
0
0
4
1
NA
NA
117
0
1
0
NA
1
0
1
0
115
30
85
0
0
0
278
65
43
23
211
199
NA
12
0
0
0
1
0
839
349
55
54
0
47
22
25
9
238
228
3
8
1993
6
0
0
0
5
1
NA
NA
114
0
1
0
NA
1
0
1
0
111
32
79
0
NA
0
334
119
44
74
214
202
NA
13
0
0
0
1
0
810
327
55
55
0
46
22
24
10
215
206
2
7
1994
6
0
0
0
5
1
NA
NA
106
0
0
0
0
1
0
1
0
104
31
73
0
0
0
313
96
45
52
216
203
NA
13
0
0
0
1
1
804
324
55
54
0
46
22
24
10
213
204
2
7
1995
6
0
0
0
5
1
NA
NA
109
0
0
0
0
1
0
1
0
107
30
76
0
0
0
287
69
45
25
217
204
NA
13
0
0
0
0
1
756
300
55
55
0
41
23
19
9
194
185
2
7
1996
6
0
1
0
4
0
0
0
81
0
1
0
0
1
0
0
0
78
26
51
0
0
0
532
26
0
26
502
190
302
10
0
0
0
3
1
809
345
56
56
0
35
23
12
14
239
235
2
3
1997
6
0
1
0
5
0
0
0
83
0
1
0
0
1
0
0
0
80
26
53
0
0
0
533
27
0
27
502
190
302
10
0
0
0
3
1
791
331
57
56
0
36
24
12
13
225
221
1
2
1998
6
0
1
0
5
0
0
0
84
0
1
0
0
1
0
1
0
81
27
54
0
0
0
534
28
0
28
502
190
302
10
0
0
0
3
1
769
312
58
58
0
36
24
12
12
206
203
1
2
1999
6
0
1
0
5
0
0
0
85
0
1
0
0
1
0
1
0
82
27
54
0
0
0
533
28
0
28
502
190
302
10
0
0
0
3
1
741
296
59
58
0
36
25
11
11
190
188
1
1
2000
7
0
1
0
5
0
0
0
87
0
1
0
0
1
0
1
0
84
28
56
0
0
0
544
29
0
29
511
195
306
10
0
0
0
3
1
708
273
59
58
0
36
25
11
11
168
166
1
1
J
31
£
«T
>
o>
-z.
CD
r+
O
3
CD
Tl
g
O
m
3
c/>
c/>
Q
3
CO
m
<5-
3
CD
t-t-
CD
CO
CD
00
O
x
CD
00
_cn
CD
oo
f
M
O
O
O
t-t-
^-
O
c
CO
CD
3
Q.
CO
^
O
3-
o"
CO
'o~
O
^
5'
c
CD
Q.
•z.
>
H
0
•^
>
|
>
70
O
a
>
H
-<
>
•z.
0
m
^
w
w
O
•z.
w
— 1
;o
m
•z.
Q
W
;o
m
s
-X
jH
N)
Q
O
CO
-------
>
^
•o
m
D
X
^
•
D
£
H
DO
1
m
CO
8
bource Category
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Non-Road Diesel
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Aircraft
Marine Vessels
coal
diesel
residual oil
gasoline
Railroads
Non-Road Other
liquified petroleum gas
compressed natural gas
TOTAL ALL SOURCES
1980
398
42
3
1
0
9
0
1
0
0
NA
28
263
0
123
27
4
85
7
16
0
NA
NA
33
23
2
15
7
NA
37
NA
NA
NA
6,267
1985
424
44
3
1
0
9
0
1
0
0
0
29
272
1
134
35
4
70
9
19
0
0
1
37
28
2
17
9
NA
41
1
1
0
3,662
1989
477
46
3
1
0
10
0
2
0
0
0
30
302
1
149
38
8
78
11
15
1
1
1
43
38
3
23
12
NA
47
1
1
0
3,502
Note:
1990
489
47
3
1
0
11
0
2
0
0
0
30
301
1
149
38
8
78
12
13
1
1
1
44
44
3
27
14
1
53
1
1
0
3,340
Some
1991
489
47
3
1
0
11
0
2
0
0
0
30
299
1
148
37
9
77
12
11
1
1
1
44
46
3
28
14
1
53
1
1
0
3,253
columns
1992
490
48
3
1
0
11
0
2
0
0
0
30
297
1
147
37
10
76
12
10
1
1
1
45
45
3
27
14
1
54
1
1
0
3,292
may not
1993 1
483
48
3
1
0
12
0
2
0
0
0
30
296
1
147
38
11
75
13
9
1
1
1
43
43
3
26
14
1
52
1
1
0
3,174 3
sum to totals
994
480
48
3
1
0
12
0
2
0
0
0
30
296
1
146
38
11
74
13
9
1
1
2
41
44
3
26
14
1
50
1
1
0
136
due to
1995
456
49
3
1
0
12
0
2
0
0
0
30
296
1
146
38
12
73
14
8
1
1
2
40
43
3
26
13
1
27
1
1
0
1996
464
89
6
2
0
21
0
2
19
0
0
38
273
1
142
33
11
62
13
8
1
1
2
5
66
NA
42
24
NA
29
2
1
0
3,165 2,967
rounding.
1997
460
90
6
2
0
21
0
2
20
0
0
38
268
1
139
33
11
59
14
7
1
1
2
5
65
NA
42
24
NA
30
2
1
0
2,974
1998
457
91
6
2
0
20
0
2
22
0
0
39
263
1
135
33
12
57
14
7
1
1
2
5
66
NA
42
24
NA
30
2
1
0
2,888
1999
445
92
6
2
0
20
0
2
23
0
0
39
251
1
128
33
12
54
15
6
1
1
1
5
66
NA
42
24
NA
30
1
1
0
2,900
2000
435
93
6
2
0
21
0
2
23
0
0
39
241
1
121
33
12
52
15
5
1
1
1
5
65
NA
42
23
NA
30
1
1
0
2,947
s1
V
5"
>
o>
-z_
S-
o'
S^.
Tl
5
o
m
3
CO
CO
o'
CO
m
3
CD
CO
_»
CD
00
O
CD
00
cn
x
CD
00
I
O
o
o
-3
o
CO
Q)
Q.
CO
O
3"
CO
^ ^
0
o
;3:
c
CD
Q.
Z
H
O
Z
J
5
D
r^
^
"^
Z
D
m
CO
CO
O
Z
CO
H
m
D
CO
m
Tl
O
71
hj
8
CO
-------
(O
0>
1
2
DO
m
w
fr
•o
W
V
m
z
D
X
>
Source Category
NATURAL SOURCES
Geogenic
Wind Erosion
MISCELLANEOUS
Agriculture & Forestry
agricultural crops
agricultural livestock
Other Combustion
structural fires
agricultural fires
slash/prescribed burning
forest wildfires
other
Cooling Towers
Fugitive Dust
unpaved roads
paved roads
construction
other
TOTAL ALL SOURCES
1980
NA
NA
NA
852
NA
NA
NA
852
23
NA
315
514
0
NA
NA
NA
NA
NA
NA
852
1985
NA
NA
NA
37,736
7,108
6,833
275
894
59
59
468
308
0
NA
29,734
11,644
5,080
12,670
339
37,736
1989
NA
NA
NA
37,461
7,320
6,923
396
912
59
85
468
300
0
NA
29,229
11,798
5,769
11,269
392
37,461
1990
NA
NA
NA
24,540
5,292
4,745
547
1,181
22
88
470
601
0
0
18,068
11,234
2,248
4,249
336
24,540
1991
NA
NA
NA
24,233
5,234
4,684
550
924
22
88
481
332
0
0
18,075
11,206
2,399
4,092
377
24,233
1992
NA
NA
NA
23,958
5,017
4,464
553
770
23
89
487
171
0
0
18,170
10,918
2,423
4,460
369
23,958
1993
NA
NA
NA
24,328
4,575
4,016
558
800
23
86
539
152
0
0
18,953
11,430
2,462
4,651
409
24,328
1994
NA
NA
NA
25,619
4,845
4,281
564
1,053
23
92
514
424
0
0
19,722
11,370
2,538
5,245
569
25,619
1995
NA
NA
NA
22,765
4,902
4,334
569
849
24
97
583
145
0
1
17,012
10,362
2,409
3,654
586
22,765
1996
NA
NA
NA
20,283
4,911
4,330
581
1,136
3
99
532
502
0
3
14,233
9,071
2,400
2,117
645
20,283
1997
NA
NA
NA
21,124
4,952
4,373
579
1,283
3
101
579
599
0
3
14,886
9,461
2,595
2,117
713
21,124
1998
NA
NA
NA
20,836
4,951
4,366
585
987
3
103
620
261
0
3
14,895
9,327
2,663
2,117
788
20,836
1999
NA
NA
NA
21,138
4,998
4,408
590
1,332
3
104
444
780
0
3
14,805
9,158
2,769
2,117
760
21,138
2000
NA
NA
NA
21,926
5,045
4,449
596
2,018
3
106
248
1,660
0
3
14,860
9,154
2,741
2,187
111
21,926
H
a?
IT
^
^1
"
s.
o
CD
oT
CD
O
CO
Q)
Q.
C?
2"
Q)
Tl
o
m
3
CO
CO
o'
CO
m
3
Q)
CD"
CO
CD
oo
o
CD
oo
_cn
CD
oo
CD
I
o
o
o
o
CO
Q)
Q.
CO
o
1— t-
l-t-
o
CO
•z.
H
0
1 —
5
D
c
I—
H
•p,
0
m
w
w
0
w
H
m
Q
w
m
s
H
N)
o
o
CO
-------
^
•o
m
0
x
^
D
5
— 1
5
DO
m
CO
3
MJUKCLCAILGOKY 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
bituminous
subbituminous
anthracite & lignite
Oil
Gas
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
other
Gas
natural
process
other
Other
wood/bark waste
liquid waste
other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential)
Residential Wood
fireplaces
woodstoves
Residential Other
Industrial Processes
CHEMICAL & ALLIED PRODUCT MFG
Organic Chemical Mfg
Inorganic Chemical Mfg
Polymer & Resin Mfg
Agricultural Chemical Mfg
Paint, Varnish, Lacquer, Enamel Mfg
Pharmaceutical Mfg
Other Chemical Mfg
909
121
97
59
14
23
5
NA
NA
20
177
29
23
2
1
3
31
26
4
1
39
29
11
0
73
68
1
4
5
611
6
5
5
78
501
501
NA
15
794
47
10
12
4
8
0
0
13
893
105
85
53
16
16
5
NA
NA
15
151
23
18
1
1
3
26
22
3
1
34
23
10
0
58
55
0
3
10
638
6
5
5
73
535
535
NA
15
812
43
10
3
3
8
0
0
17
927
106
87
53
18
16
4
NA
NA
16
159
25
20
1
0
3
26
22
3
1
39
26
13
0
59
54
0
4
10
662
6
5
6
72
558
558
NA
15
819
45
11
4
4
8
0
0
17
852
112
90
57
18
15
5
NA
NA
17
172
24
20
2
0
3
27
23
4
1
41
28
13
0
69
58
1
10
11
568
6
5
6
72
464
464
NA
15
788
41
10
4
3
8
0
0
15
841
108
86
54
17
15
5
NA
NA
17
183
25
19
3
0
2
26
22
4
1
42
29
14
0
60
55
0
4
29
550
6
5
6
72
446
446
NA
15
771
49
11
4
3
8
0
0
23
898
107
86
52
20
15
3
NA
NA
18
203
25
19
3
1
2
28
24
4
1
44
29
15
0
59
55
0
3
48
589
6
5
6
73
484
484
NA
15
749
42
11
3
3
8
0
0
16
735
157
133
88
32
13
5
1
0
17
153
23
18
3
0
2
26
22
4
0
39
25
13
0
50
44
0
6
15
425
7
5
7
72
319
144
175
15
874
39
12
3
2
5
0
0
16
737
161
135
89
31
15
6
1
0
18
149
23
18
3
0
2
24
20
4
1
39
25
14
0
48
42
0
5
15
427
7
5
7
75
319
144
175
14
886
39
12
3
2
6
0
0
16
705
130
103
62
30
11
4
1
3
18
147
23
18
3
0
2
24
19
4
0
38
25
14
0
48
42
0
5
15
428
7
4
7
78
319
144
175
13
891
40
12
3
2
6
0
0
17
719
137
113
57
35
20
3
0
3
19
150
24
18
3
0
2
24
20
4
0
39
25
14
0
48
43
0
5
15
432
7
4
7
81
319
144
175
14
893
40
12
3
2
6
0
0
17
756
141
116
59
35
22
2
0
4
19
157
24
18
3
0
2
26
22
4
1
41
26
15
0
51
45
0
5
16
458
7
4
7
83
342
154
188
14
915
41
13
3
2
6
0
0
17
ai
o-
5"
>
09
Z
CD
2^-
o'
m
3
m
co
CD
CD
O
O
O
O
co
CD
3
Q.
CO
^
O
l-t-
O
co
3
O
>
D
D
m
co
CO
O
CO
71
m
-
O
71
-------
(O
08
1
2
DO
m
w
^
•o
m
z
D
X
>
SOURCE CATEGORY
METALS PROCESSING
Non-Ferrous Metals Processing
copper
lead
zinc
other
Ferrous Metals Processing
primary
secondary
other
Metals Processing NEC
PETROLEUM & RELATED INDUSTRIES
Oil & Gas Production
1990
157
31
9
2
5
14
121
103
17
1
5
27
2
Petroleum Refineries & Related Industries 1 3
fluid catalytic cracking units
other
Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
Agriculture, Food, & Kindred Products
country elevators
terminal elevators
feed mills
soybean mills
wheat mills
other grain mills
other
Textiles, Leather, & Apparel Products
11
2
12
284
39
6
3
2
5
1
4
17
0
Wood, Pulp & Paper, & Publishing Products77
sulfate (kraft) pulping
other
57
21
Rubber & Miscellaneous Plastic Products 3
Mineral Products
cement mfg
surface mining
stone quarrying/processing
other
Machinery Products
Electronic Equipment
Transportation Equipment
Construction
Miscellaneous Industrial Processes
144
54
6
24
61
3
0
1
0
16
1991
197
29
9
2
5
13
89
72
16
0
80
24
2
14
12
2
9
264
46
6
3
2
4
1
3
26
0
61
40
21
3
134
40
6
28
60
3
0
1
0
16
1992
198
29
9
2
5
13
83
66
16
0
85
24
2
14
12
2
8
259
40
7
4
2
4
1
3
19
0
59
38
21
3
135
39
7
28
61
3
0
1
0
17
1993
125
25
8
2
1
14
86
68
17
0
14
22
2
13
11
2
7
260
44
6
5
2
5
1
3
21
0
59
38
21
3
136
38
7
28
62
3
0
0
0
15
1994
125
25
8
2
1
14
86
68
18
0
14
22
2
13
11
2
7
256
43
6
4
2
5
1
3
22
0
57
38
19
3
133
38
7
26
63
3
0
0
0
16
1995
134
25
8
2
1
14
92
74
19
0
16
22
2
13
11
2
8
256
40
6
4
2
5
1
3
20
0
60
40
20
3
134
38
6
26
63
3
0
0
0
16
1996
100
22
4
2
1
15
65
47
18
0
13
17
1
12
7
4
4
180
20
1
0
1
3
1
2
14
0
52
31
21
2
88
11
7
9
61
2
1
0
0
14
1997
105
23
5
2
1
15
69
50
18
0
14
17
1
12
8
4
4
186
21
1
0
1
3
1
3
14
1
53
32
22
2
92
11
7
9
64
2
1
0
0
14
1998
105
23
4
2
1
15
68
50
18
0
14
17
1
12
8
4
4
189
21
1
0
1
3
1
3
14
0
55
32
22
2
93
11
7
9
65
2
1
0
0
15
1999
104
23
4
2
1
15
66
50
17
0
15
17
1
12
8
4
4
191
22
1
0
1
3
1
3
14
0
56
33
23
2
93
11
7
9
66
2
1
0
0
15
2000
107
23
5
2
2
15
68
52
17
0
15
17
1
12
8
4
4
198
22
1
0
1
3
1
3
15
1
58
34
24
2
97
12
8
9
68
2
1
0
0
15
•z.
H
0
1 —
5
H e
a? g
IT !Z
5" 3
b >
T "z.
09 D
m
CO w
f 52
zj O
Q) Z
— w
~D — |
^ m
01 Z
m °
3
S m
5' 3
CO H
m ~K>
CO O
nf. o
3 u
Q)
0
y
CD
CD
-------
^
•o
m
z
0
X
>
•
§
DO
m
CO
8
MJUKCLCAILGOKY 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
SOLVENT UTILIZATION 4
Degreasing 0
Graphic Arts 0
Dry Cleaning 0
Surface Coating 3
Other Industrial 1
Nonindustrial NA
Solvent Utilization NEC NA
STORAGES TRANSPORT 42
Bulk Terminals & Plants 0
Petroleum & Petroleum Product Storage 0
Petroleum & Petroleum Product Transport 0
Service Stations: Stage II 0
Organic Chemical Storage 0
Organic Chemical Transport 0
Inorganic Chemical Storage 0
Inorganic Chemical Transport 0
Bulk Materials Storage 41
storage 1 3
transfer 28
combined 0
other NA
Bulk Materials Transport 0
WASTE DISPOSALS RECYCLING 234
Incineration 46
residential 27
other 19
Open Burning 187
residential 177
land clearing debris NA
other 10
POTW 0
Industrial Waste Water 0
TSDF 0
Landfills 0
Other 0
Transportation 719
ON-ROAD VEHICLES 286
Light-Duty Gas Vehicles & Motorcycles 34
Idgv 34
motorcycles 0
Light-Duty Gas Trucks 24
Idgt! 12
Idgt2 13
4
0
0
0
3
1
NA
NA
42
0
1
0
0
0
0
0
0
41
11
29
0
0
0
238
47
28
18
190
179
NA
11
0
0
0
0
0
720
288
33
33
0
28
13
15
5
0
0
0
4
1
NA
NA
50
0
1
0
0
0
0
0
0
48
12
36
0
0
0
239
46
30
16
192
181
NA
11
0
0
0
1
0
717
284
32
32
0
30
14
16
6
0
0
0
4
1
NA
NA
46
0
1
0
0
0
0
0
0
44
13
31
0
NA
0
288
93
31
62
195
183
NA
11
0
0
0
1
0
688
261
32
32
0
30
14
16
6
0
0
0
4
1
NA
NA
43
0
0
0
0
0
0
0
0
41
13
28
0
0
0
271
73
31
42
196
184
NA
12
0
0
0
1
1
682
258
32
32
0
29
14
15
5
0
0
0
4
1
NA
NA
42
0
0
0
0
0
0
0
0
41
12
29
0
0
0
247
50
31
19
197
185
NA
11
0
0
0
0
0
640
237
32
32
0
26
14
12
5
0
1
0
4
0
0
0
30
0
0
0
0
1
0
0
0
28
11
17
0
0
0
503
15
0
15
486
174
302
10
0
0
0
2
0
702
276
32
32
0
22
14
8
5
0
1
0
4
0
0
0
31
0
0
0
0
1
0
0
0
29
11
18
0
0
0
503
15
0
15
486
174
302
10
0
0
0
2
0
686
263
33
33
0
22
15
8
5
0
1
0
4
0
0
0
31
0
0
0
0
1
0
0
0
29
11
18
0
0
0
504
16
0
16
486
174
302
10
0
0
0
2
0
666
246
34
33
0
22
15
7
6
0
1
0
4
0
0
0
31
0
0
0
0
1
0
0
0
30
11
18
0
0
0
504
16
0
16
486
174
302
10
0
0
0
2
0
638
230
34
34
0
22
15
7
6
0
1
0
4
0
0
0
32
0
0
0
0
1
0
0
0
31
12
19
0
0
0
514
16
0
16
495
178
306
10
0
0
0
2
0
608
209
33
33
0
22
15
7
ar
o-
5"
>
09
5'
3
5L
Tl
m
3
m
~
CD
t-t-
CD
CO
CD
CD
O
O
O
O
CO
CD
Q.
CO
O
3
CO
o
o
CD
Q.
O
D
m
co
co
O
CO
H
71
m
71
m
-
O
71
-------
.k
§
52
>
DO
m
w
•
>
•o
m
D
X
SOURCE CATEGORY
Heavy-Duty Gas Vehicles
Diesels
hddv
Iddt
Iddv
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Non-Road Diesel
recreational
construction
industrial
lawn & garden
farm
light commercial
logging
airport service
railway maintenance
recreational marine vessels
Aircraft
Marine Vessels
coal
diesel
residual oil
gasoline
Railroads
Non-Road Other
liquified petroleum gas
compressed natural gas
NATURAL SOURCES
Geogenic
Wind Erosion
MISCELLANEOUS
Agriculture & Forestry
agricultural crops
agricultural livestock
1990
6
221
204
12
6
432
43
2
1
0
10
0
1
0
0
0
27
277
1
137
35
8
71
11
12
1
1
1
31
32
1
25
6
0
49
1
1
0
NA
NA
NA
5,234
1,031
949
82
1991
6
220
211
2
7
432
43
3
1
0
10
0
2
0
0
0
27
275
1
136
34
8
71
11
10
1
1
1
31
34
1
26
6
0
48
1
1
0
NA
NA
NA
5,003
1,019
937
83
1992
6
216
207
2
7
433
43
3
1
0
10
0
2
0
0
0
27
273
1
136
34
9
70
11
9
1
1
1
32
33
1
25
6
0
50
1
1
0
NA
NA
NA
4,854
976
893
83
1993
7
192
184
2
6
427
44
3
1
0
11
0
2
0
0
0
28
273
1
135
35
10
69
12
8
1
1
1
30
31
1
24
6
0
48
1
1
0
NA
NA
NA
4,926
887
803
84
1994
7
190
182
2
6
424
44
3
1
0
11
0
2
0
0
0
28
272
1
134
35
11
68
12
8
1
1
1
29
32
1
24
6
0
46
1
1
0
NA
NA
NA
5,359
941
856
85
1995
6
173
165
2
6
403
45
3
1
0
11
0
2
0
0
0
28
272
1
134
35
11
67
13
8
1
1
1
28
31
1
24
6
0
25
1
1
0
NA
NA
NA
4,726
952
867
85
1996
9
212
208
1
2
426
81
5
2
0
19
0
2
17
0
0
35
251
1
130
30
10
57
12
8
1
1
2
4
61
NA
38
22
NA
27
2
1
0
NA
NA
NA
4,411
953
866
87
1997
9
199
196
1
2
423
82
5
2
0
19
0
2
19
0
0
35
247
1
128
30
10
55
13
7
1
1
2
4
60
NA
38
22
NA
28
2
1
0
NA
NA
NA
4,735
961
875
87
1998
8
181
179
1
1
420
83
5
2
0
19
0
2
20
0
0
36
242
1
124
30
11
53
13
6
1
1
2
4
61
NA
38
22
NA
28
2
1
0
NA
NA
NA
4,479
961
873
88
1999
8
166
165
1
1
408
84
5
2
0
19
0
2
21
0
0
36
231
1
118
30
11
50
13
5
1
0
1
4
61
NA
38
22
NA
27
1
1
0
NA
NA
NA
4,829
970
882
89
2000
7
147
145
1
1
399
85
5
2
0
19
0
2
22
0
0
35
222
1
111
30
11
48
14
5
1
0
1
4
60
NA
39
21
NA
27
1
1
0
NA
NA
NA
5,466
979
890
89
ai
o-
5"
>
09
O
D
m
S- M
i I
Q) Z
— W
3 H
<. ^
[•o m
D
W
m
s
m
3
CO
CO
o'
in H
m M
CO O
=± O
-J CO
CD
CD
O
O
O
CO
CD
Q.
CO
O
3
CO
o
o
CD
Q.
-------
^
•o
m
Z
0
X
•
§
DO
m
CO
O
bUUKUL UAILliUKY 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Other Combustion
structural fires
agricultural fires
slash/prescribed burning
forest wildfires
other
Cooling Towers
Fugitive Dust
unpaved roads
paved roads
construction
other
TOTAL ALL SOURCES
1,037 807 666 693 912 735 931 1,123 855 1,200 1,817
20 20 21 21 21 22 3 33 3 3
80 80 81 78 83 89 89 91 93 94 96
399 408 413 457 436 494 453 491 526 400 223
538 299 151 137 372 130 386 538 233 702 1,494
000 00000000
00000122333
3,166 3,177 3,212 3,346 3,506 3,037 2,525 2,649 2,660 2,657 2,667
1,687 1,684 1,642 1,718 1,709 1,559 1366 1427 1406 1,381 1,380
562 600 606 616 634 585 600 649 666 693 686
850 818 892 930 1,049 777 423 423 423 423 437
67 75 73 81 113 117 136 150 165 159 163
7,655 7,429 7,318 7,254 7,653 7,013 6,722 7,044 6,741 7,079 7,745
Atofe; Some columns may not sum to totals due to rounding.
ai
IT
09
5'
3
5L
Tl
m
3
co
co
o'
3
co
m
CD
CD
M >
O =!
§ i
S.
o
.
•-i- D
§ m
co S
"— ' co
•o- 2
o O
=j -z.
nf. co
O
71
-------
o
10
^
^
DO
m
co
TJ
•o
m
D
X
>
Source Category
Fuel Combustion
FUEL COMB. ELEC. UTIL.
Coal
bituminous
subbituminous
anthracite & lignite
Oil
residual
distillate
Gas
Other
Internal Combustion
FUEL COMB. INDUSTRIAL
Coal
bituminous
subbituminous
anthracite & lignite
other
Oil
residual
distillate
other
Gas
Other
Internal Combustion
FUEL COMB. OTHER
Commercial/Institutional Coal
Commercial/Institutional Oil
Commercial/Institutional Gas
Misc. Fuel Comb. (Except Residential]
Residential Wood
Residential Other
distillate oil
bituminous/subbituminous coal
other
Industrial Processes
CHEMICAL & ALLIED PRODUCT MFG
Organic Chemical Mfg
Inorganic Chemical Mfg
sulfur compounds
other
Polymer & Resin Mfg
Agricultural Chemical Mfg
Paint, Varnish, Lacquer, Enamel Mfg
Pharmaceutical Mfg
Other Chemical Mfg
1980
21,391
17,469
16,073
NA
NA
NA
1,395
NA
NA
1
NA
NA
2,951
1,527
1,058
326
144
NA
1,065
851
85
129
299
60
NA
971
110
637
1
NA
13
211
757
43
11
3,807
280
NA
271
271
NA
NA
NA
NA
NA
10
1985
20,021
16,272
15,630
14,029
1,292
309
612
604
8
1
NA
30
3,169
1,818
1,347
28
90
353
862
671
111
80
397
86
7
579
158
239
2
1
13
167
728
29
70
2,467
456
16
354
346
8
7
4
NA
0
76
1989
19,924
16,215
15,404
13,579
1,422
404
779
765
14
1
NA
30
3,086
1,840
1,384
29
79
348
812
625
107
80
346
82
6
624
169
274
2
1
11
167
732
27
8
2,010
440
17
334
326
8
7
4
NA
0
77
1990
20,290
15,909
15,220
13,371
1,415
434
639
629
10
1
NA
49
3,550
1,914
1,050
50
67
746
927
687
198
42
543
158
9
831
212
425
7
6
7
175
737
30
9
1,900
297
10
214
211
2
1
5
NA
0
67
1991
19,796
15,784
15,087
73,275
1,381
491
652
642
10
1
NA
45
3,256
1,805
949
53
68
735
779
550
190
39
516
142
14
755
184
376
7
6
7
176
141
26
8
1,720
280
9
208
205
3
1
4
NA
0
57
1992
19,493
15,416
14,824
12,914
1,455
455
546
537
9
1
NA
46
3,292
1,783
1,005
60
67
650
801
591
191
20
552
140
16
784
190
396
7
6
8
177
744
26
8
1,758
278
9
203
199
4
1
4
NA
0
60
1993
19,245
15,189
14,527
72,272
1,796
579
612
607
10
1
NA
49
3,284
1,763
991
67
68
636
809
597
193
20
555
140
17
772
193
381
8
6
6
178
745
25
8
1,723
269
9
191
187
4
1
4
0
0
64
1994
18,887
14,889
14,313
77,847
1,988
484
522
572
10
1
NA
53
3,218
1,740
988
77
68
606
111
564
193
20
542
141
19
780
192
391
8
6
6
177
745
25
8
1,675
275
8
194
189
4
1
4
0
0
68
1995
16,230
12,080
11,603
8,609
2,345
649
413
408
5
9
NA
55
3,357
1,728
1,003
81
68
576
912
701
191
20
548
147
23
793
200
397
8
5
7
176
744
24
8
1,638
286
8
199
195
4
0
5
0
0
74
1996
16,232
12,730
12,206
8,998
2,632
576
460
454
6
7
4
53
2,863
1,321
885
63
61
312
807
626
158
23
575
140
20
639
179
308
10
6
5
131
108
17
6
1,408
255
4
173
171
2
1
1
0
0
76
1997
16,649
13,195
12,615
9,577
2,490
608
514
509
5
6
4
56
2,805
1,306
877
63
60
306
764
578
161
25
582
134
19
649
184
314
10
6
5
130
706
78
6
1,458
259
4
176
174
2
1
1
0
0
76
1998
16,746
13,416
12,470
9,357
2,486
627
762
756
6
6
121
57
2,742
1,274
858
61
57
298
738
559
156
23
578
133
19
588
196
250
10
6
5
121
97
78
6
1,463
261
4
178
176
2
1
1
0
0
77
1999
16,027
12,653
11,826
8,596
2,609
627
639
637
7
7
123
58
2,788
1,305
878
64
57
306
754
571
159
24
575
135
20
586
196
245
11
6
5
123
98
78
6
1,457
262
4
179
177
2
1
1
0
0
76
2000
14,876
11,389
10,723
7,866
2,367
489
511
502
8
9
88
59
2,894
1,320
889
64
58
309
806
618
161
27
609
140
20
593
200
252
11
6
5
119
95
78
6
1,498
268
5
183
181
2
1
1
0
0
78
i
5"
>
-------
?
m
z
D
X
>
•
§
-1
^
S
DO
m
cn
O
u
bource Category 1
METALS PROCESSING 1
Nonferrous Metals Processing 1
copper 1
lead
aluminum
other
Ferrous Metals Processing
Metals Processing NEC
PETROLEUM & RELATED INDUSTRIES
Oil & Gas Production
natural gas
other
Petroleum Refineries & Related Industries
fluid catalytic cracking units
other
Asphalt Manufacturing
OTHER INDUSTRIAL PROCESSES
Agriculture, Food, & Kindred Products
Textiles, Leather, & Apparel Products
Wood, Pulp & Paper, & Publishing Prods
Rubber & Miscellaneous Plastic Prods
Mineral Products
cement mfg
other
Machinery Products
Electronic Equipment
Transportation Equipment
Miscellaneous Industrial Processes
SOLVENT UTILIZATION
Degreasing
Graphic Arts
Dry Cleaning
Surface Coating
Other Industrial
STORAGES TRANSPORT
Bulk Terminals & Plants
Petroleum & Petroleum Product Storage
Petroleum & Petroleum Product Transport
Service Stations: Stage II
Organic Chemical Storage
Organic Chemical Transport
Inorganic Chemical Storage
Inorganic Chemical Transport
Bulk Materials Storage
980
,842
279
,080
34
95
71
562
NA
734
157
157
NA
577
330
247
NA
918
NA
NA
223
NA
694
630
64
NA
NA
0
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
1985
1,042
853
655
121
62
14
172
18
505
204
202
2
300
212
88
1
425
3
0
131
1
286
192
95
0
0
0
3
1
0
0
NA
1
0
4
NA
0
1
NA
1
NA
0
0
1
1989
695
513
327
113
60
13
165
17
429
156
155
1
272
195
77
1
405
3
0
136
1
261
172
89
0
0
0
3
1
0
0
NA
1
0
5
NA
0
1
NA
1
NA
0
0
2
1990
726
517
323
129
60
4
186
22
430
122
120
2
304
183
121
4
399
3
0
116
0
275
181
94
0
0
0
5
0
0
0
NA
0
0
7
0
5
0
NA
0
0
0
0
1
1991
612
435
234
135
61
5
159
18
378
98
96
2
274
182
92
7
396
3
0
123
0
267
165
102
0
0
0
3
0
0
0
NA
0
0
10
1
7
0
NA
0
0
0
0
1
1992
615
438
247
131
55
5
158
18
416
93
92
2
315
185
130
7
396
3
0
119
0
270
168
102
1
0
0
3
1
0
0
0
0
0
9
1
0
0
NA
0
0
0
0
7
1993
603
431
250
122
53
6
153
19
383
98
96
2
278
183
95
7
392
3
0
113
0
272
170
102
0
0
0
3
1
0
0
NA
0
0
5
0
0
0
NA
0
0
0
0
4
1994
562
391
206
128
51
6
153
19
379
95
93
2
276
188
88
8
398
3
0
109
0
282
167
114
1
0
0
3
1
0
0
0
0
0
2
0
0
0
0
0
0
0
0
1
1995
530
361
177
126
53
6
151
18
369
89
88
1
271
188
83
9
403
3
0
114
0
282
171
111
1
0
0
4
1
0
0
0
0
0
2
0
0
0
0
0
0
0
0
1
1996
390
267
93
112
57
5
107
17
335
90
89
1
238
157
81
8
390
4
0
101
1
270
171
99
0
0
0
13
1
0
0
0
0
1
5
1
0
1
0
0
0
0
0
2
1997
407
276
99
113
59
5
114
17
344
90
90
1
246
163
83
8
409
4
0
105
1
285
181
103
0
0
0
13
1
0
0
0
0
1
5
1
0
1
0
0
0
0
0
2
1998
405
274
98
114
57
5
114
17
342
90
89
1
245
162
83
8
415
4
0
107
1
288
183
105
0
0
0
14
1
0
0
0
0
1
5
1
0
2
0
0
0
0
0
2
1999
400
271
97
114
55
5
112
16
341
90
89
1
244
162
82
7
414
5
0
109
1
284
179
105
0
0
0
14
1
0
0
0
0
1
5
1
0
2
0
0
0
0
0
2
2000
411
278
100
116
56
5
116
17
346
92
92
1
246
163
83
7
432
5
0
113
1
299
189
109
0
0
0
14
1
0
0
0
0
1
5
1
0
2
0
0
0
0
0
2
S1
IT
5"
^
jb
-^
S-
o'
Q)
CO
^
-i
D
o'
X
cL
CD
m
3
en'
en
o
en
m
S5-
3
Q)
t-t-
CD
y
CD
00
O
~_j.
CD
00
cn
CD
oo
ro
o
o
o
^
o
en
Q)
Q.
=T
o
o
m
0
o
3
~
c
CD
Q.
>
o
"Z.
r~
71
o
r^
^
>
D
m
cn
cn
O
cn
H
m
z
cn
71
m
O
71
hj
8
CO
-------
.k
2
$.
>
DO
m
•
>
•o
m
D
X
Source Category
WASTE DISPOSAL & RECYCLING
Incineration
industrial
other
Open Burning
industrial
land clearing debris
other
POTW
Industrial Waste Water
TSDF
Landfills
industrial
other
Other
Transportation
ON-ROAD VEHICLES
Light-Duty Gas Vehicles & Motorcycles
light -duty gas vehicles
motorcycles
Light-Duty Gas Trucks
light-duty gas trucks 1
light-duty gas trucks 2
Heavy-Duty Gas Vehicles
Diesels
NON-ROAD ENGINES AND VEHICLES
Non-Road Gasoline
Non-Road Diesel
Aircraft
Marine Vessels
Railroads
Non-Road Other
MISCELLANEOUS
Agriculture & Forestry
Other Combustion
Fugitive Dust
1980
33
21
NA
21
12
NA
NA
12
NA
NA
NA
NA
NA
NA
NA
697
521
159
158
0
50
33
16
10
303
175
NA
NA
6
117
53
NA
11
NA
11
NA
TOTAL ALL SOURCES 25,905
1985
34
25
10
15
9
0
NA
8
NA
NA
NA
0
0
0
0
1,159
522
146
145
0
55
36
19
11
311
637
20
407
6
143
59
1
11
NA
11
NA
23,658
1989
36
28
10
18
8
0
NA
7
NA
NA
NA
0
0
0
0
1,349
570
145
145
0
58
38
21
11
356
779
22
488
7
193
67
2
11
NA
11
NA
23,293
Note:
1990
43
32
5
26
11
0
NA
10
0
0
0
0
0
0
0
1,476
560
129
128
0
69
45
24
10
352
916
22
509
11
251
122
2
12
NA
12
0
23,679
Some
1991
44
32
4
28
11
0
NA
10
0
0
0
0
0
0
1
1,517
573
126
126
0
81
52
29
10
356
944
22
529
11
259
120
2
11
NA
11
0
23,044
columns
1992
44
32
5
27
11
0
NA
11
0
0
0
0
0
0
1
1,553
586
125
125
0
87
56
31
10
364
968
22
549
11
258
125
2
10
NA
9
0
22,813
may not
1993
71
51
25
26
11
0
NA
11
0
0
0
0
0
0
8
1,497
526
124
124
0
90
58
32
11
300
972
23
570
11
249
117
2
10
NA
9
1
22,474
sum to
1994
59
42
17
26
11
0
NA
11
0
0
0
0
0
0
6
1,297
307
125
124
0
92
59
32
12
79
990
23
590
11
252
113
2
15
NA
15
0
21,875
totals due
1995 1996
47
35
8
27
11
0
NA
11
0
0
0
0
0
0
0
1,311 1
311
126
126
0
93
60
32
11
82
999 1
23
610
11
239
113
2
10
NA
10
0
19,189 19
to rounding.
32
26
6
20
5
0
NA
5
0
0
0
1
0
0
0
791
343
128
128
0
85
62
22
18
112
,448
35
459
8
887
56
3
16
0
16
0
447
1997
33
27
6
21
5
0
NA
5
0
0
0
1
0
0
0
1,816
353
131
130
0
89
65
23
18
117
1,463
35
474
8
887
56
3
15
0
15
0
19,939
1998
34
28
7
21
5
0
NA
5
0
0
0
1
0
0
0
1,837
358
134
134
0
90
66
24
17
117
1,479
35
490
8
887
56
3
12
0
12
0
20,059
1999
34
28
7
21
5
0
NA
5
0
0
0
1
0
0
0
1,853
366
136
136
0
94
69
24
17
119
1,487
35
497
8
887
56
4
12
0
12
0
19,349
2000
35
29
7
22
5
0
NA
5
0
0
0
1
0
0
0
1,805
314
108
107
0
75
55
20
13
118
1,491
35
516
8
872
56
4
21
0
21
0
18,201
H
ai
«r
'
o
m
~
03
CD"
en
CD
co
o
x
CD
CO
cn
CD
co
CD
ro
o
o
o
o
03
Q.
=y
o
t-t-
o
3
TZ.
H
0
>
1
70
O
I—
H
^
•z.
0
m
w
w
•z.
w
— 1
70
m
•z.
D
W
70
m
§
H
ho
o
o
oo
-------
>
^
m
z
0
X
fr
•
§
5!
j!
DO
m
CO
O
Ul
Source Category 1990
Fuel Combustion 25
FUEL COMB. ELEC. UTIL. 0
Coal NA
Oil NA
Gas NA
Other NA
Internal Combustion 0
FUEL COMB. INDUSTRIAL 17
Coal 0
Oil 4
Gas 13
Other 0
Internal Combustion 0
FUEL COMB. OTHER 8
Commercial/Institutional Coal 0
Commercial/Institutional Oil 2
Commercial/Institutional Gas 1
Misc. Fuel Comb. (Except Residential) NA
Residential Other 5
Industrial Processes 351
CHEMICAL & ALLIED PRODUCT MFG 183
Organic Chemical Mfg NA
Inorganic Chemical Mfg NA
Polymer & Resin Mfg NA
Agricultural Chemicals 183
ammonium nitrate/urea mfg. 111
other 71
Other Chemical Mfg NA
METALS PROCESSING 6
Non-Ferrous Metals Processing 0
Ferrous Metals Processing 6
Metals Processing NEC 0
PETROLEUMS RELATED INDUSTRIES 43
Oil & Gas Production 0
Petroleum Refineries & Related Industries 43
catalytic cracking 43
other 0
OTHER INDUSTRIAL PROCESSES 38
Agriculture, Food, & Kindred Products 2
Textiles, Leather, & Apparel Products NA
Wood, Pulp & Paper, & Publishing ProductsNA
Rubber & Miscellaneous Plastic Products NA
Mineral Products 0
Machinery Products NA
Electronic Equipment NA
Miscellaneous Industrial Processes 35
1991
25
0
NA
NA
NA
NA
0
17
0
4
13
0
0
8
0
2
1
NA
5
355
183
NA
NA
NA
183
111
71
NA
6
0
6
0
43
0
43
43
0
38
2
NA
NA
NA
0
NA
NA
35
1992
25
0
NA
NA
NA
NA
0
17
0
4
13
0
0
8
0
2
1
NA
5
359
183
NA
NA
NA
183
111
71
NA
6
0
6
0
43
0
43
43
0
39
3
NA
NA
NA
0
NA
NA
36
1993
26
0
NA
NA
NA
NA
0
18
0
4
14
0
0
8
0
2
1
NA
5
364
183
NA
NA
NA
183
111
71
NA
6
0
6
0
43
0
43
43
0
39
3
NA
NA
NA
0
NA
NA
37
1994
26
0
NA
NA
NA
NA
0
18
0
4
14
0
0
8
0
2
1
NA
5
364
183
NA
NA
NA
183
111
71
NA
6
0
6
0
43
0
43
43
0
40
2
NA
NA
NA
0
NA
NA
38
1995
26
0
NA
NA
NA
NA
0
18
0
4
13
0
0
8
0
2
1
NA
5
365
183
NA
NA
NA
183
111
71
NA
6
0
6
0
43
0
43
43
0
40
2
NA
NA
NA
0
NA
NA
38
1996
47
6
0
2
4
0
0
34
0
4
25
0
5
7
0
2
1
0
5
271
123
0
0
0
109
41
68
13
4
0
4
0
16
0
76
16
0
43
4
0
1
0
0
0
0
39
1997
47
6
0
2
4
0
0
34
0
4
25
0
5
7
0
2
1
0
5
277
125
0
0
0
111
42
70
14
5
0
5
0
17
0
77
17
0
45
4
0
1
0
0
0
0
40
1998
47
8
0
3
4
0
0
33
0
4
25
0
5
6
0
2
1
0
4
284
130
0
0
0
115
43
72
14
5
0
5
0
17
0
77
17
0
45
4
0
1
0
0
0
0
40
1999
49
8
0
3
5
0
0
34
0
4
25
0
5
7
0
2
1
0
4
289
133
0
0
0
118
44
73
15
5
0
5
0
17
0
77
17
0
45
4
0
1
0
0
0
0
40
2000
50
8
0
3
5
0
0
35
0
4
27
0
5
7
0
2
1
0
4
296
137
0
0
0
121
46
76
15
5
0
5
0
17
0
77
17
0
47
4
0
1
0
0
0
0
42
ar
IT
5"
3
o
2
Q)'
m
3
(/>'
co
o'
3
CO
m
co
o
co
CD
CD
O
O
o
o
CO
Q)
Q.
CO
O
3
CO
3
o
>
O
D
m
co
co
O
CO
71
m
-
O
71
-------
.k
s
o
1
— 1
5
DO
W
•
>
•o
m
D
X
Source Category 1990
SOLVENT UTILIZATION NA
Degreasing NA
Graphic Arts NA
Dry Cleaning NA
Surface Coating NA
Other Industrial NA
STORAGES TRANSPORT 0
Bulk Terminals & Plants NA
Petroleum & Petroleum Product Storage NA
Petroleum & Petroleum Product Transport NA
Organic Chemical Storage NA
Inorganic Chemical Storage NA
Bulk Materials Storage 0
WASTE DISPOSALS RECYCLING 82
Incineration NA
Open Burning NA
POTW 82
wastewater treatment 82
other NA
Industrial Waste Water NA
TSDF NA
Landfills NA
Other NA
Transportation 194
ON-ROAD VEHICLES 188
Light-Duty Gas Vehicles & Motorcycles 1 49
Light-Duty Gas Trucks 38
Heavy-Duty Gas Vehicles 0
Diesels 0
NON-ROAD ENGINES AND VEHICLES 6
Non-Road Gasoline 1
Non-Road Diesel 2
Aircraft NA
Marine Vessels 1
Railroads 2
NATURAL SOURCES 0
Biogenic 0
MISCELLANEOUS 3,757
Agriculture & Forestry 3,757
agricultural crops 420
agricultural livestock 3,337
Fugitive Dust 0
TOTAL ALL SOURCES 4,327
1991
NA
NA
NA
NA
NA
NA
0
NA
NA
NA
NA
NA
0
86
NA
NA
86
86
NA
NA
NA
NA
NA
205
198
151
46
0
0
7
1
3
NA
1
2
0
0
3,799
3,799
446
3,353
0
4,383
1992
NA
NA
NA
NA
NA
NA
0
NA
NA
NA
NA
NA
0
89
NA
NA
89
89
NA
NA
NA
NA
NA
214
208
155
52
1
0
7
1
3
NA
1
2
0
0
3,841
3,841
473
3,368
0
4,440
Note:
1993
NA
NA
NA
NA
NA
NA
0
NA
NA
NA
NA
NA
0
93
NA
NA
93
93
NA
NA
NA
NA
NA
224
218
159
58
1
0
7
1
3
NA
1
2
0
0
3,897
3,897
499
3,398
0
4,512
Some
1994
NA
NA
NA
NA
NA
NA
0
NA
NA
NA
NA
NA
0
93
NA
NA
93
93
NA
NA
NA
NA
NA
239
233
168
63
1
0
7
1
3
NA
1
2
0
0
3,953
3,953
525
3,428
0
4,583
columns
1995
NA
NA
NA
NA
NA
NA
0
NA
NA
NA
NA
NA
0
93
NA
NA
93
93
NA
NA
NA
NA
NA
258
252
180
70
1
0
7
1
3
NA
1
2
0
0
4,009
4,009
551
3,458
0
4,658
may not
1996
0
0
0
0
0
0
1
0
1
0
0
0
0
84
0
0
84
84
0
0
0
0
0
238
229
157
63
4
6
9
1
3
3
1
1
0
0
4,138
4,138
649
3,489
0
4,694
sum to
1997
0
0
0
0
0
0
1
0
1
0
0
0
0
84
0
0
84
84
0
0
0
0
0
267
258
168
80
4
6
9
1
3
3
1
1
0
0
4,196
4,196
678
3,518
0
4,787
totals due
1998
0
0
0
0
0
0
1
0
1
0
0
0
0
86
0
0
86
86
0
0
0
0
0
262
252
169
72
4
6
10
1
3
4
1
1
0
0
4,293
4,293
739
3,554
0
4,886
1999
0
0
0
0
0
0
1
0
1
0
0
0
0
88
0
0
87
87
0
0
0
0
0
265
261
173
78
4
6
4
1
3
NA
NA
NA
0
0
4,311
4,311
724
3,587
0
4,914
2000
0
0
0
0
0
0
1
0
1
0
0
0
0
89
0
0
89
89
0
0
0
0
0
268
264
174
80
4
6
4
1
3
NA
NA
NA
0
0
4,349
4,349
724
3,625
0
4,963
to rounding.
ar
IT
5"
3
o
O
D
m
2 8
Q)
m
3
CO
o'
m
co
CD
CD"
co
CD
CD
O
O
O
CO
CD
3
Q.
CO
^
O
l-t-
o
3
CO
o
o
CD
Q.
D
W
m
s
o
o
oo
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-11. National Long-Term Air Quality Trends, 1981-2000
Year
7987—90
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991-00
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
CO
2nd Max. 8-hr
ppm
321 sites
8.4
8.1
7.9
7.8
7.1
7.2
6.7
6.4
6.4
5.9
327 sites
5.6
5.3
5.0
5.1
4.6
4.3
4.1
3.9
3.7
3.4
Pb
Max. Qtr.
|xg/m3
228 sites
0.58
0.58
0.47
0.45
0.28
0.18
0.13
0.12
0.10
0.08
130 sites
0.08
0.07
0.06
0.05
0.05
0.05
0.04
0.04
0.04
0.04
NO2
Arith. Mean
ppm
169 sites
0.024
0.023
0.023
0.023
0.023
0.023
0.023
0.023
0.023
0.022
234 sites
0.019
0.019
0.019
0.020
0.019
0.019
0.018
0.018
0.018
0.017
Ozone
2nd Max. 1-hr
ppm
471 sites
0.126
0.125
0.137
0.125
0.123
0.118
0.125
0.136
0.116
0.114
738 sites
0.111
0.105
0.107
0.106
0.112
0.105
0.104
0.110
0.107
0.100
PM10
Wtd. Arith. Mean
|xg/m3
—
—
—
—
—
—
—
—
—
—
886 sites
29.4
27.3
26.6
26.4
25.1
24.2
24.1
23.8
24.1
23.8
SO2
Arith. Mean
ppm
456 sites
0.0102
0.0095
0.0093
0.0095
0.0090
0.0088
0.0086
0.0087
0.0085
0.0079
457 sites
0.0081
0.0076
0.0074
0.0072
0.0057
0.0057
0.0056
0.0055
0.0053
0.0051
APPENDIX A • DATA TABLES
107
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-12. National Air Quality Trends by Monitoring Location, 1981-2000
Statistic # of Sites Units Location 1981 1982 1983 1984 1985
1986
1987
1988 1989 1990
Carbon Monoxide
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
Lead
Max. Qtr.
Max. Qtr.
Max. Qtr.
Nitrogen Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Ozone
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Wtd. Arith. Mean
Wtd. Arith. Mean
Sulfur Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
4
136
178
10
107
106
22
81
64
127
229
105
127
227
104
_
—
—
120
187
142
ppm
ppm
ppm
jig/m3
jig/m3
Wj/m3
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
jig/m3
jig/m3
jig/m3
ppm
ppm
ppm
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
4.7
8.0
9.1
1.12
0.57
0.54
0.009
0.025
0.028
0.117
0.131
0.128
0.089
0.093
0.090
_
—
—
0.0083
0.0102
0.0118
4.9
7.8
8.8
0.98
0.52
0.60
0.008
0.024
0.027
0.114
0.130
0.126
0.087
0.093
0.086
_
—
—
0.0076
0.0096
0.0111
3.8
7.5
8.6
0.94
0.45
0.44
0.008
0.024
0.027
0.126
0.142
0.140
0.096
0.102
0.098
_
—
—
0.0075
0.0094
0.0106
3.3
7.5
8.3
1.03
0.43
0.41
0.008
0.024
0.028
0.117
0.128
0.127
0.089
0.092
0.091
_
—
—
0.0079
0.0098
0.0106
4.1
7.3
8.2
0.37
0.30
0.26
0.008
0.024
0.028
0.115
0.127
0.123
0.089
0.093
0.090
_
—
—
0.0076
0.0094
0.0098
3.8
6.6
7.5
0.48
0.19
0.15
0.009
0.024
0.028
0.112
0.122
0.119
0.087
0.090
0.086
_
—
—
0.0075
0.0090
0.0098
4.5
6.6
7.6
0.29
0.14
0.11
0.009
0.024
0.028
0.117
0.129
0.126
0.091
0.095
0.091
_
—
—
0.0075
0.0086
0.0095
3.8
6.4
7.0
0.25
0.12
0.09
0.008
0.024
0.028
0.129
0.141
0.134
0.102
0.105
0.098
_
—
—
0.0075
0.0088
0.0098
3.5
6.1
6.8
0.24
0.10
0.08
0.008
0.024
0.027
0.110
0.119
0.116
0.086
0.088
0.086
_
—
—
0.0073
0.0084
0.0096
3.2
6.1
6.6
0.17
0.09
0.06
0.008
0.023
0.026
0.111
0.116
0.112
0.087
0.086
0.082
_
—
—
0.0070
0.0079
0.0088
' PM10 trend data is not available for this 10-year period.
108
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-12. National Air Quality Trends by Monitoring Location, 1991-2000
Statistic # of Sites Units Location 1991
1992 1993 1994 1995
1996
1997 1998 1999 2000
Carbon Monoxide
2nd Max. 8-hr.
2nd Max. 8-hr.
2nd Max. 8-hr.
Lead
Max. Qtr.
Max. Qtr.
Max. Qtr.
Nitrogen Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
Ozone
2nd Max. 1-hr.
2nd Max. 1-hr.
2nd Max. 1-hr.
4th Max. 8-hr.
4th Max. 8-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Wtd. Arith. Mean
Wtd. Arith. Mean
Sulfur Dioxide
Arith. Mean
Arith. Mean
Arith. Mean
13
153
217
4
58
63
39
105
87
259
332
127
263
332
126
140
353
373
119
197
131
ppm
ppm
ppm
f^g/m3
f^g/m3
Wj/m3
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
ppm
jig/m3
f^g/m3
f^g/m3
ppm
ppm
ppm
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
Rural
Suburban
Urban
2.3
5.4
6.0
0.03
0.07
0.09
0.009
0.020
0.023
0.105
0.116
0.110
0.082
0.088
0.082
24.3
30.0
30.9
0.0068
0.0087
0.0087
2.3
5.1
5.6
0.03
0.06
0.07
0.009
0.020
0.023
0.101
0.108
0.106
0.080
0.082
0.079
22.8
28.0
28.5
0.0065
0.0081
0.0079
2.0
4.9
5.2
0.04
0.05
0.07
0.009
0.019
0.023
0.103
0.111
0.105
0.081
0.084
0.079
22.0
27.2
27.8
0.0067
0.0079
0.0076
2.2
5.1
5.4
0.04
0.05
0.06
0.009
0.020
0.024
0.102
0.110
0.106
0.081
0.085
0.080
21.9
27.1
27.6
0.0063
0.0076
0.0076
2.2
4.4
4.9
0.11
0.05
0.05
0.009
0.020
0.023
0.107
0.115
0.110
0.085
0.090
0.084
20.3
26.0
26.1
0.0053
0.0059
0.0060
1.9
4.1
4.6
0.03
0.04
0.05
0.009
0.019
0.022
0.102
0.107
0.106
0.082
0.084
0.081
20.3
24.8
25.4
0.0050
0.0060
0.0058
1.7
4.0
4.3
0.02
0.04
0.05
0.009
0.018
0.022
0.101
0.108
0.102
0.082
0.084
0.079
20.1
24.8
25.1
0.0048
0.0059
0.0057
1.7
3.8
4.0
0.04
0.04
0.04
0.008
0.018
0.022
0.108
0.113
0.105
0.086
0.089
0.082
19.7
24.5
24.9
0.0047
0.0059
0.0056
1.6
3.7
3.9
0.03
0.04
0.04
0.009
0.019
0.022
0.105
0.110
0.104
0.086
0.087
0.081
20.4
24.9
24.9
0.0045
0.0058
0.0055
1.6
3.4
3.5
0.04
0.04
0.04
0.008
0.018
0.021
0.099
0.103
0.097
0.080
0.081
0.075
20.3
24.4
24.7
0.0044
0.0056
0.0052
APPENDIX A • DATA TABLES
109
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-13. National Air Quality Trends Statistics by EPA Region, 1981-1990
Statistic
# of Sites
Units 1981 1982 1983
1984
1985 1986
1987
1988 1989 1990
Region 1
CO
Pb
N02
03
03
PM10*
S02
Region 2
CO
Pb
N02
°3
°3
PM10*
S02
Region 3
CO
Pb
N02
°3
°3
PM10*
S02
Region 4
CO
Pb
N02
°3
°3
PM10*
S02
Region 5
CO
Pb
N02
03
03
PM10*
S02
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
11
15
4
23
23
—
49
22
12
10
28
28
—
37
41
30
36
64
64
—
62
49
38
10
70
70
—
59
40
44
17
97
97
—
124
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
ng/m3
ppm
ppm
ppm
jig/m3
ppm
9.1
0.51
0.030
0.142
0.101
—
0.0096
9.4
0.73
0.031
0.134
0.100
—
0.0142
7.0
0.40
0.023
0.122
0.092
—
0.0141
7.8
0.60
0.019
0.108
0.084
—
0.0088
7.8
0.47
0.021
0.116
0.087
—
0.0113
9.6
0.56
0.028
0.150
0.109
—
0.0095
8.5
0.73
0.032
0.136
0.098
—
0.0132
7.0
0.44
0.023
0.124
0.095
—
0.0134
7.3
0.70
0.019
0.106
0.081
—
0.0078
7.1
0.57
0.021
0.113
0.086
—
0.0106
9.2
0.44
0.026
0.166
0.119
—
0.0089
7.8
0.65
0.033
0.153
0.112
—
0.0124
6.8
0.34
0.024
0.138
0.107
—
0.0133
7.4
0.61
0.019
0.120
0.092
—
0.0073
7.1
0.38
0.022
0.129
0.097
—
0.0105
8.9
0.38
0.032
0.153
0.105
—
0.0097
8.3
0.67
0.032
0.131
0.096
—
0.0130
7.6
0.35
0.025
0.119
0.092
—
0.0141
7.7
0.58
0.019
0.108
0.084
—
0.0072
7.5
0.33
0.022
0.110
0.083
—
0.0107
7.0
0.32
0.031
0.140
0.102
—
0.0093
6.7
0.50
0.031
0.131
0.099
—
0.0115
5.7
0.22
0.024
0.118
0.093
—
0.0131
6.2
0.28
0.019
0.105
0.082
—
0.0071
5.8
0.21
0.021
0.106
0.082
—
0.0102
7.5
0.12
0.029
0.123
0.090
—
0.0100
7.4
0.16
0.030
0.123
0.095
—
0.0112
6.3
0.15
0.024
0.113
0.089
—
0.0136
6.2
0.22
0.019
0.114
0.087
—
0.0072
6.0
0.13
0.021
0.108
0.082
—
0.0096
6.7
0.08
0.030
0.132
0.095
—
0.0098
6.4
0.11
0.031
0.141
0.106
—
0.0107
5.9
0.12
0.025
0.128
0.100
—
0.0132
5.9
0.15
0.019
0.113
0.089
—
0.0074
6.2
0.10
0.022
0.119
0.090
—
0.0093
5.7
0.06
0.030
0.161
0.120
—
0.0100
6.2
0.08
0.031
0.160
0.121
—
0.0115
5.5
0.14
0.024
0.150
0.116
—
0.0138
5.6
0.13
0.019
0.124
0.097
—
0.0077
5.4
0.10
0.021
0.131
0.104
—
0.0092
5.8
0.05
0.028
0.129
0.094
—
0.0093
6.1
0.05
0.030
0.118
0.092
—
0.0109
5.3
0.10
0.023
0.111
0.088
—
0.0136
6.0
0.12
0.019
0.103
0.080
—
0.0071
5.6
0.08
0.022
0.107
0.085
—
0.0092
6.1
0.04
0.027
0.124
0.093
—
0.0084
5.6
0.05
0.029
0.126
0.096
—
0.0097
5.2
0.07
0.023
0.112
0.089
—
0.0124
5.3
0.09
0.017
0.110
0.086
—
0.0068
5.0
0.08
0.019
0.100
0.079
—
0.0089
' PM10 trend data is not available for this 10-year period.
110
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-13. National Air Quality Trends Statistics by EPA Region, 1981-1990 (continued)
Statistic
# of Sites
Units 1981 1982 1983
1984
1985 1986
1987
1988 1989 1990
Region 6
CO
Pb
N02
03
03
PM10*
S02
Region 7
CO
Pb
N02
°3
°3
PM10*
S02
Region 8
CO
Pb
N02
°3
°3
PM10*
S02
Region 9
CO
Pb
N02
°3
°3
PM10*
S02
Region 10
CO
Pb
N02
03
03
PM10*
S02
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
25
22
14
41
41
—
32
15
19
9
24
24
—
19
16
6
15
13
13
—
20
77
36
54
105
105
—
48
25
6
—
6
6
—
5
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
ng/m3
ppm
ppm
ppm
jig/m3
ppm
7.9
0.71
0.017
0.129
0.091
—
0.0076
7.0
0.21
0.015
0.104
0.068
—
0.0087
10.9
1.18
0.624
0.101
0.073
—
0.0064
8.1
0.62
0.030
0.153
0.101
—
0.0056
11.6
1.69
—
0.121
0.084
—
0.0101
7.8
0.62
0.017
0.124
0.087
—
0.0072
6.9
0.17
0.017
0.100
0.069
—
0.0093
10.6
1.23
0.586
0.103
0.074
—
0.0060
8.0
0.59
0.028
0.149
0.097
—
0.0043
11.5
0.65
—
0.108
0.075
—
0.0096
7.3
0.58
0.017
0.124
0.089
—
0.0079
5.6
0.17
0.016
0.116
0.088
—
0.0092
11.9
1.13
0.449
0.110
0.078
—
0.0055
7.9
0.45
0.027
0.161
0.106
—
0.0039
11.2
0.54
—
0.093
0.063
—
0.0088
7.2
0.55
0.017
0.124
0.090
—
0.0070
6.4
0.17
0.016
0.113
0.085
—
0.0088
10.8
1.31
0.416
0.104
0.075
—
0.0048
7.0
0.42
0.027
0.152
0.103
—
0.0044
10.3
0.53
—
0.098
0.066
—
0.0096
7.3
0.35
0.017
0.121
0.090
—
0.0074
5.2
0.13
0.015
0.104
0.075
—
0.0081
9.7
0.98
0.246
0.102
0.076
—
0.0050
7.8
0.25
0.028
0.156
0.105
—
0.0041
10.5
0.42
—
0.105
0.074
—
0.0091
7.1
0.19
0.017
0.115
0.084
—
0.0065
6.3
0.09
0.016
0.103
0.074
—
0.0079
10.9
0.79
0.189
0.109
0.076
—
0.0045
7.5
0.19
0.028
0.138
0.097
—
0.0035
9.4
0.23
—
0.107
0.078
—
0.0100
7.4
0.16
0.017
0.119
0.088
—
0.0062
6.0
0.05
0.017
0.110
0.079
—
0.0074
9.3
0.68
0.135
0.097
0.075
—
0.0043
6.5
0.13
0.027
0.141
0.098
—
0.0031
9.3
0.15
—
0.098
0.073
—
0.0097
6.4
0.13
0.017
0.123
0.091
—
0.0059
5.3
0.04
0.016
0.114
0.088
—
0.0072
8.7
0.65
0.104
0.104
0.078
—
0.0040
7.1
0.10
0.029
0.144
0.100
—
0.0033
9.1
0.10
—
0.110
0.072
—
0.0071
6.3
0.12
0.016
0.120
0.085
—
0.0058
5.5
0.04
0.015
0.095
0.074
—
0.0074
7.4
0.51
0.091
0.103
0.077
—
0.0043
7.0
0.09
0.029
0.138
0.096
—
0.0032
8.4
0.07
—
0.089
0.064
—
0.0067
6.3
0.09
0.015
0.122
0.088
—
0.0055
5.3
0.02
0.014
0.092
0.070
—
0.0068
6.8
0.46
0.079
0.096
0.073
—
0.0041
6.6
0.08
0.027
0.130
0.089
—
0.0030
7.7
0.07
—
0.114
0.082
—
0.0070
' PM10 trend data is not available for this 10-year period.
APPENDIX A • DATA TABLES
111
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-13. National Air Quality Trends Statistics by EPA Region, 1991-2000
Statistic
# of Sites
Units 1991 1992 1993
1994
1995 1996
1997
1998 1999 2000
Region 1
CO
Pb
N02
03
03
PM10
S02
Region 2
CO
Pb
N02
°3
°3
PM10
S02
Region 3
CO
Pb
N02
°3
°3
PM10
S02
Region 4
CO
Pb
N02
°3
°3
PM10
S02
Region 5
CO
Pb
N02
03
03
PM10
S02
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
18
1
15
41
41
62
40
28
4
11
37
37
65
42
39
16
35
79
79
55
79
62
21
32
144
144
159
80
45
36
12
142
142
154
102
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
ng/m3
ppm
ppm
ppm
jig/m3
ppm
5.5
0.69
0.022
0.118
0.096
23.2
0.0077
6.0
0.05
0.029
0.121
0.098
26.4
0.0088
4.9
0.09
0.021
0.117
0.095
32.6
0.0126
4.8
0.04
0.014
0.097
0.075
28.0
0.0057
4.6
0.10
0.021
0.109
0.087
29.5
0.0092
5.6
0.19
0.020
0.127
0.086
20.5
0.0072
5.4
0.05
0.028
0.109
0.085
23.8
0.0081
4.6
0.06
0.021
0.103
0.083
29.5
0.0117
4.8
0.04
0.014
0.096
0.077
26.4
0.0054
4.4
0.09
0.022
0.098
0.078
27.7
0.0081
4.8
0.02
0.021
0.110
0.088
20.2
0.0069
4.9
0.07
0.028
0.109
0.088
23.8
0.0075
4.6
0.06
0.021
0.116
0.093
29.2
0.0117
4.9
0.03
0.014
0.104
0.082
25.9
0.0055
4.3
0.09
0.022
0.097
0.077
26.6
0.0082
5.9
0.02
0.022
0.119
0.086
20.6
0.0068
5.7
0.07
0.029
0.104
0.084
24.3
0.0077
5.2
0.06
0.022
0.111
0.088
29.2
0.0117
4.6
0.03
0.014
0.100
0.081
25.2
0.0051
5.0
0.09
0.023
0.104
0.083
28.3
0.0078
5.3
0.04
0.019
0.114
0.090
18.7
0.0053
5.0
0.06
0.027
0.115
0.094
21.6
0.0059
4.2
0.04
0.020
0.117
0.094
27.1
0.0085
4.3
0.03
0.014
0.104
0.083
24.8
0.0043
4.0
0.07
0.023
0.111
0.090
27.5
0.0062
4.8
0.03
0.020
0.116
0.081
19.3
0.0051
4.3
0.06
0.028
0.102
0.081
22.5
0.0060
3.8
0.04
0.021
0.105
0.084
26.9
0.0086
3.7
0.02
0.014
0.101
0.081
23.8
0.0044
3.4
0.06
0.023
0.103
0.085
24.8
0.0062
4.1
0.03
0.019
0.102
0.090
19.5
0.0052
3.9
0.06
0.027
0.111
0.091
23.0
0.0055
3.6
0.04
0.020
0.116
0.093
25.8
0.0090
4.0
0.02
0.014
0.102
0.082
23.9
0.0045
3.3
0.06
0.022
0.102
0.083
24.9
0.0060
3.7
0.02
0.020
0.116
0.084
19.3
0.0052
3.5
0.06
0.027
0.107
0.087
22.1
0.0054
3.4
0.04
0.020
0.115
0.095
24.6
0.0086
3.6
0.02
0.014
0.112
0.090
24.7
0.0046
3.3
0.06
0.023
0.106
0.085
26.5
0.0060
3.7
0.01
0.019
0.106
0.087
18.9
0.0048
3.7
0.05
0.027
0.114
0.093
21.8
0.0053
3.2
0.04
0.019
0.120
0.095
23.6
0.0083
3.7
0.03
0.014
0.109
0.089
24.0
0.0045
3.0
0.05
0.022
0.105
0.088
25.2
0.0059
3.2
0.02
0.018
0.113
0.075
17.9
0.0045
3.3
0.05
0.027
0.102
0.081
22.1
0.0054
3.0
0.04
0.018
0.104
0.084
23.8
0.0082
3.2
0.04
0.014
0.103
0.084
24.0
0.0044
2.8
0.05
0.022
0.094
0.077
25.5
0.0056
112
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-13. National Air Quality Trends Statistics by EPA Region, 1991-2000 (continued)
Statistic
# of Sites
Units 1990 1991 1992
1993
1994 1995 1996 1997
1998 1999
Region 6
CO
Pb
N02
03
03
PM10
S02
Region 7
CO
Pb
N02
°3
°3
PM10
S02
Region 8
CO
Pb
N02
°3
°3
PM10
S02
Region 9
CO
Pb
N02
°3
°3
PM10
S02
Region 10
CO
Pb
N02
03
03
PM10
S02
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
2nd Max. 8-hr.
Max. Qtr.
Arith. Mean
2nd Max. 1-hr.
4th Max. 8-hr.
Wtd. Arith. Mean
Arith. Mean
31
11
28
76
76
50
27
20
4
11
28
28
46
25
20
8
12
18
18
99
24
97
24
78
157
157
127
30
27
5
—
16
16
69
8
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
ng/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
jig/m3
ppm
ppm
ppm
jig/m3
ppm
ppm
ng/m3
ppm
ppm
ppm
jig/m3
ppm
5.6
0.15
0.013
0.112
0.079
25.5
0.0062
5.2
0.04
0.015
0.092
0.075
29.2
0.0073
6.9
0.06
0.013
0.089
0.067
26.6
0.0074
5.9
0.05
0.023
0.125
0.090
36.7
0.0021
8.4
0.06
—
0.086
0.062
32.0
0.0063
5.5
0.11
0.014
0.109
0.078
25.2
0.0064
4.6
0.02
0.016
0.090
0.074
28.6
0.0067
7.0
0.06
0.013
0.087
0.066
25.3
0.0082
5.1
0.04
0.022
0.123
0.090
32.2
0.0021
7.7
0.04
—
0.087
0.067
30.5
0.0068
5.5
0.10
0.014
0.110
0.080
24.4
0.0054
4.4
0.01
0.015
0.086
0.066
27.5
0.0065
5.9
0.06
0.014
0.084
0.065
24.3
0.0080
4.7
0.04
0.021
0.119
0.088
31.2
0.0018
7.1
0.05
—
0.080
0.058
30.0
0.0065
4.6
0.08
0.015
0.109
0.082
24.7
0.0048
4.3
0.01
0.016
0.098
0.078
28.0
0.0068
5.5
0.04
0.015
0.087
0.068
23.6
0.0070
5.1
0.03
0.022
0.116
0.087
30.4
0.0019
6.8
0.05
—
0.087
0.063
26.6
0.0061
4.5
0.12
0.015
0.120
0.089
25.9
0.0046
4.1
0.01
0.016
0.102
0.081
27.5
0.0054
5.0
0.04
0.014
0.087
0.067
20.8
0.0060
4.4
0.03
0.021
0.119
0.088
29.9
0.0019
6.6
0.05
—
0.085
0.063
23.0
0.0053
4.9
0.12
0.015
0.109
0.082
24.9
0.0048
4.2
0.01
0.016
0.094
0.076
28.2
0.0052
5.0
0.03
0.014
0.090
0.070
20.9
0.0048
4.3
0.02
0.020
0.114
0.087
28.0
0.0019
6.5
0.04
—
0.095
0.074
22.9
0.0049
4.4
0.06
0.014
0.113
0.083
23.1
0.0044
3.8
0.01
0.015
0.094
0.076
26.2
0.0047
4.7
0.03
0.013
0.084
0.067
20.2
0.0037
4.0
0.03
0.019
0.102
0.078
28.5
0.0019
6.1
0.05
—
0.074
0.057
23.2
0.0048
4.0
0.08
0.014
0.115
0.086
24.0
0.0042
4.4
0.01
0.016
0.099
0.078
26.4
0.0045
4.0
0.04
0.014
0.096
0.076
20.1
0.0035
4.0
0.02
0.019
0.114
0.085
25.9
0.0019
5.4
0.06
—
0.094
0.067
20.5
0.0048
3.6
0.06
0.014
0.111
0.086
26.1
0.0037
3.5
0.01
0.017
0.100
0.080
26.2
0.0047
4.0
0.04
0.013
0.089
0.070
19.8
0.0034
3.9
0.03
0.020
0.102
0.079
30.4
0.0020
5.6
0.04
—
0.073
0.059
21.0
0.0051
3.4
0.06
0.013
0.116
0.086
25.1
0.0034
2.9
0.01
0.016
0.097
0.077
25.2
0.0042
3.5
0.04
0.013
0.087
0.069
20.6
0.0034
3.5
0.03
0.019
0.100
0.077
28.4
0.0021
4.9
0.04
—
0.074
0.057
20.8
0.0051
APPENDIX A • DATA TABLES 113
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000
State County
AL Baldwin County
AL Clay County
AL Colbert County
AL DeKalb County
AL Elmore County
AL Escambia County
AL Etowah County
AL Franklin County
AL Houston County
AL Jackson County
AL Jefferson County
AL Lawrence County
AL Madison County
AL Marengo County
AL Mobile County
AL Montgomery County
AL Morgan County
AL Pike County
AL Russell County
AL Shelby County
AL Sumter County
AL Talladega County
AL Tuscaloosa County
AL Walker County
AK Anchorage Municipality
AK Fairbanks North Star Borough
AK Juneau City and Borough
AK Ketchikan Gateway Borough
AK Matanuska-Susitna Borough
AK Yukon-Koyukuk Census Area
AZ Cochise County
AZ Coconino County
AZ Gila County
AZ Graham County
AZ Maricopa County
AZ Mohave County
AZ Navajo County
AZ Pima County
AZ Santa Cruz County
AZ Yavapai County
AZ Yuma County
AR Arkansas County
AR Ashley County
AR Craighead County
AR Crittenden County
AR Faulkner County
AR Garland County
AR Jefferson County
AR Marion County
AR Miller County
AR Mississippi County
AR Montgomery County
AR Newton County
AR Ouachita County
AR Phillips County
AR Polk County
AR Pope County
\S\J
2000
Population
140,415
14,254
54,984
64,452
65,874
38,440
103,459
31,223
88,787
53,926
662,047
34,803
276,700
22,539
399,843
223,510
111,064
29,605
49,756
143,293
14,798
80,321
164,875
70,713
260,283
82,840
30,711
14,070
59,322
6,551
117,755
116,320
51,335
33,489
3,072,149
155,032
97,470
843,746
38,381
167,517
160,026
20,749
24,209
82,148
50,866
86,014
88,068
84,278
16,140
40,443
51,979
9,245
8,608
28,790
26,445
20,229
54,469
r u
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
5
ND
2
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
6
9
ND
ND
ND
ND
ND
ND
ND
ND
7
ND
ND
5
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.57
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.011
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.036
ND
ND
0.017
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.12
0.09
ND
ND
0.10
ND
ND
ND
ND
ND
0.12
0.10
0.11
ND
0.12
0.11
0.11
ND
ND
0.13
0.09
ND
ND
ND
ND
ND
ND
ND
ND
0.05
0.08
0.08
ND
ND
0.11
ND
ND
0.09
ND
0.09
0.08
ND
ND
ND
0.11
ND
ND
ND
ND
ND
ND
0.08
0.08
ND
ND
ND
ND
rm10
8-hr
(ppm)
0.10
0.08
ND
ND
0.08
ND
ND
ND
ND
ND
0.09
0.08
0.09
ND
0.09
0.09
0.09
ND
ND
0.10
0.08
ND
ND
ND
ND
ND
ND
ND
ND
0.04
0.07
0.07
ND
ND
0.09
ND
ND
0.08
ND
0.08
0.06
ND
ND
ND
0.09
ND
ND
ND
ND
ND
ND
0.07
0.08
ND
ND
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
ND
23
ND
26
26
ND
24
ND
IN
ND
24
23
24
25
23
24
26
27
ND
26
IN
IN
IN
IN
IN
ND
IN
ND
38
16
25
IN
70
15
IN
39
49
16
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IN
44
ND
60
64
IN
70
ND
125
ND
80
46
150
61
53
48
52
60
ND
68
68
IN
108
IN
27
ND
58
ND
90
33
65
IN
232
29
34
123
120
34
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IN
IN
IN
IN
ND
IN
IN
ND
IN
ND
22.3
ND
IN
ND
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
6.1
12.2
IN
IN
IN
IN
IN
IN
IN
ND
IN
ND
ND
IN
IN
ND
ND
IN
IN
15.2
15.7
IN
IN
15.0
IN
IN
IN
ND
ND
ND
14.7
12.3
14.4
IN
IN
IN
IN
ND
IN
IN
ND
IN
ND
53
ND
IN
ND
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
20
42
IN
IN
IN
IN
IN
IN
IN
ND
IN
ND
ND
IN
IN
ND
ND
IN
IN
IN
IN
IN
IN
27
IN
IN
IN
ND
ND
ND
30
26
29
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.003 0.017
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.005 0.041
IN 0.057
0.002 0.005
ND ND
ND ND
0.002 0.008
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.003 0.016
ND ND
ND ND
0.002 0.007
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
114
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
AR Pulaski County
AR Sebastian County
AR Union County
AR Washington County
AR White County
CA Alameda County
CA Amador County
CA Butte County
CA Calaveras County
CA Colusa County
CA Contra Costa County
CA Del Norte County
CA El Dorado County
CA Fresno County
CA Glenn County
CA Humboldt County
CA Imperial County
CA Inyo County
CA Kern County
CA Kings County
CA Lake County
CA Lassen County
CA Los Angeles County
CA Madera County
CA Marin County
CA Mariposa County
CA Mendocino County
CA Merced County
CA Modoc County
CA Mono County
CA Monterey County
CA Napa County
CA Nevada County
CA Orange County
CA Placer County
CA Plumas County
CA Riverside County
CA Sacramento County
CA San Benito County
CA San Bernardino County
CA San Diego County
CA San Francisco County
CA San Joaquin County
CA San Luis Obispo County
CA San Mateo County
CA Santa Barbara County
CA Santa Clara County
CA Santa Cruz County
CA Shasta County
CA Sierra County
CA Siskiyou County
CA Solano County
CA Sonoma County
CA Stanislaus County
CA Sutler County
CA Tehama County
CA Trinity County
\S\J
2000
Population
361,474
115,071
45,629
157,715
67,165
1,443,741
35,100
203,171
40,554
18,804
948,816
27,507
156,299
799,407
26,453
126,518
142,361
17,945
661,645
129,461
58,309
33,828
9,519,338
123,109
247,289
17,130
86,265
210,554
9,449
12,853
401,762
124,279
92,033
2,846,289
248,399
20,824
1,545,387
1,223,499
53,234
1,709,434
2,813,833
776,733
563,598
246,681
707,161
399,347
1,682,585
255,602
163,256
3,555
44,301
394,542
458,614
446,997
78,930
56,039
13,022
r u
8-hr
(ppm)
3
ND
ND
ND
ND
3
1
4
1
ND
3
ND
2
6
ND
ND
10
ND
5
ND
ND
ND
10
ND
2
ND
2
ND
ND
IN
1
3
ND
6
2
ND
4
6
ND
4
5
3
4
2
4
3
7
1
ND
ND
ND
5
3
4
4
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
0.00
ND
0.00
ND
ND
0.00
ND
ND
0.00
ND
ND
0.02
ND
0.00
ND
ND
ND
0.06
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.00
ND
0.05
ND
ND
0.05
0.02
0.00
0.00
ND
ND
0.00
0.00
ND
ND
ND
ND
ND
ND
0.00
ND
ND
ND
"3
AM
(ppm)
0.010
ND
ND
ND
ND
0.020
ND
0.012
ND
ND
0.016
ND
0.011
0.020
ND
ND
IN
ND
0.023
0.014
ND
ND
0.044
0.013
0.016
ND
0.011
0.012
ND
ND
0.007
0.012
ND
0.029
0.017
ND
0.022
0.019
ND
0.038
0.024
0.020
0.020
0.012
0.018
0.018
0.025
0.005
ND
ND
ND
0.013
0.013
0.018
0.013
ND
ND
"3
1-hr
(ppm)
0.11
ND
ND
ND
ND
0.13
0.12
0.10
0.12
0.09
0.10
ND
0.13
0.15
0.09
ND
0.16
0.09
0.14
0.12
0.08
ND
0.17
0.10
0.07
0.11
0.07
0.12
ND
ND
0.08
0.08
0.12
0.12
0.12
0.08
0.15
0.13
0.10
0.17
0.12
0.06
0.11
0.08
0.08
0.10
0.10
0.09
0.11
ND
0.10
0.10
0.08
0.11
0.10
0.10
ND
rm10
8-hr
(ppm)
0.09
ND
ND
ND
ND
0.08
0.09
0.09
0.10
0.07
0.08
ND
0.10
0.11
0.07
ND
0.09
0.08
0.11
0.11
0.06
ND
0.11
0.09
0.05
0.09
0.05
0.10
ND
ND
0.06
0.06
0.10
0.08
0.10
0.07
0.11
0.10
0.08
0.12
0.10
0.04
0.08
0.07
0.05
0.08
0.07
0.06
0.08
ND
0.06
0.07
0.06
0.09
0.08
0.08
ND
rm,0 rm25
WtdAM 24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
25 48
ND ND
ND ND
ND ND
ND ND
22 63
ND ND
27 77
18 33
25 88
20 50
IN 36
20 50
41 122
22 75
21 46
212 545
140 6230
46 136
50 129
11 21
IN IN
46 93
ND ND
20 39
25 56
23 47
35 89
23 59
13 1642
30 70
16 43
17 49
40 119
24 50
20 61
59 190
27 82
16 31
53 108
31 86
24 53
32 79
21 102
21 50
26 62
27 68
26 50
24 47
IN IN
IN 33
18 46
18 40
35 100
28 66
IN 43
19 48
15.7
13.5
IN
IN
IN
11.2
ND
16.3
9.0
8.0
10.9
ND
7.8
25.4
ND
9.2
16.8
IN
21.7
16.2
IN
ND
23.9
ND
ND
ND
IN
17.3
8.3
IN
8.0
ND
IN
20.4
12.2
IN
28.4
12.3
ND
26.0
15.9
IN
17.3
10.5
10.9
9.7
13.5
7.9
IN
ND
ND
11.6
10.3
18.9
11.5
ND
ND
34
27
IN
IN
IN
50
ND
70
30
26
46
ND
22
89
ND
22
IN
67
100
IN
IN
ND
83
ND
ND
ND
IN
47
37
IN
22
ND
IN
37
43
IN
81
81
ND
70
IN
IN
IN
41
43
19
57
18
IN
ND
ND
60
40
71
38
ND
ND
ovi
AM
(ppm)
0.002
ND
0.005
ND
ND
ND
ND
ND
ND
ND
0.003
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
0.003
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.002
ND
ND
0.002
IN
ND
0.003
0.004
0.002
ND
0.005
ND
0.002
ND
0.001
ND
ND
ND
0.002
ND
ND
ND
ND
ND
24-hr
(ppm)
0.007
ND
0.030
ND
ND
ND
ND
ND
ND
ND
0.021
ND
ND
ND
ND
ND
0.007
ND
ND
ND
ND
ND
0.010
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.005
ND
ND
0.026
0.015
ND
0.010
0.011
0.007
ND
0.028
ND
0.003
ND
0.003
ND
ND
ND
0.005
ND
ND
ND
ND
ND
APPENDIX A • DATA TABLES 115
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
CA Tulare County
CA Tuolumne County
CA Ventura County
CA Yolo County
CO Adams County
CO Alamosa County
CO Arapahoe County
CO Archuleta County
CO Boulder County
CO Delta County
CO Denver County
CO Douglas County
CO Eagle County
CO Elbert County
CO El Paso County
CO Fremont County
CO Garfield County
CO Gunnison County
CO Jefferson County
CO Lake County
CO La Plata County
CO Larimer County
CO Mesa County
CO Montezuma County
CO Montrose County
CO Pitkin County
CO Prowers County
CO Pueblo County
CO Routt County
CO San Miguel County
CO Summit County
CO Teller County
CO Weld County
CT Fairfield County
CT Hartford County
CT Litchfield County
CT Middlesex County
CT New Haven County
CT New London County
CT Tolland County
DE Kent County
DE New Castle County
DE Sussex County
DC District of Columbia
FL Alachua County
FL Baker County
FL Bay County
FL Brevard County
FL Broward County
FL Citrus County
FL Collier County
FL Duval County
FL Escambia County
FL Hamilton County
FL Hillsborough County
FL Holmes County
FL Lake County
\S\J
2000
Population
368,021
54,501
753,197
168,660
363,857
14,966
487,967
9,898
291,288
27,834
554,636
175,766
41,659
19,872
516,929
46,145
43,791
13,956
527,056
7,812
43,941
251,494
116,255
23,830
33,432
14,872
14,483
141,472
19,690
6,594
23,548
20,555
180,936
882,567
857,183
182,193
155,071
824,008
259,088
136,364
126,697
500,265
156,638
572,059
217,955
22,259
148,217
476,230
1,623,018
118,085
251,377
778,879
294,410
13,327
998,948
18,564
210,528
r u
8-hr
(ppm)
3
2
3
1
3
ND
ND
ND
4
ND
5
ND
ND
ND
4
ND
ND
ND
4
ND
ND
4
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
4
3
7
ND
ND
3
ND
ND
ND
3
ND
5
ND
ND
ND
ND
4
ND
ND
4
ND
ND
3
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
0.00
ND
0.15
ND
ND
ND
ND
ND
0.02
ND
ND
ND
0.01
ND
ND
ND
ND
0.03
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.02
ND
ND
ND
ND
ND
0.00
ND
ND
ND
ND
0.05
ND
ND
0.03
ND
ND
2.01
ND
ND
"3
AM
(ppm)
0.018
ND
0.020
0.011
0.016
ND
ND
ND
ND
ND
IN
ND
ND
ND
0.035
ND
ND
ND
0.011
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
0.017
ND
ND
0.025
ND
IN
ND
IN
ND
0.023
ND
ND
ND
ND
0.010
ND
ND
0.015
0.010
ND
0.011
ND
ND
"3
1-hr
(ppm)
0.12
0.11
0.12
0.10
0.08
ND
0.10
ND
0.09
ND
0.10
0.10
ND
ND
0.09
ND
ND
ND
0.11
ND
ND
0.10
ND
0.09
ND
ND
ND
ND
ND
ND
ND
ND
0.09
0.12
0.10
0.11
0.12
0.14
0.14
0.10
0.13
0.12
0.11
0.12
0.10
0.09
0.12
0.09
0.09
ND
ND
0.11
0.12
ND
0.11
0.10
IN
rm10
8-hr
(ppm)
0.11
0.10
0.10
0.08
0.06
ND
0.08
ND
0.07
ND
0.07
0.08
ND
ND
0.07
ND
ND
ND
0.08
ND
ND
0.08
ND
0.07
ND
ND
ND
ND
ND
ND
ND
ND
0.07
0.09
0.08
0.09
0.09
0.09
0.08
0.08
0.09
0.10
0.10
0.09
0.08
0.08
0.09
0.08
0.07
ND
ND
0.08
0.10
ND
0.08
0.08
IN
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
53
ND
31
26
43
IN
ND
28
23
24
29
15
IN
ND
25
17
23
28
16
ND
36
IN
20
ND
IN
22
22
24
25
IN
22
27
21
31
18
15
ND
32
16
ND
ND
26
ND
ND
20
ND
25
IN
19
ND
IN
26
22
24
33
ND
20
127
ND
80
66
134
88
ND
87
74
62
80
31
23
ND
87
36
53
88
32
ND
121
66
53
ND
87
71
136
64
96
62
71
113
58
67
39
31
ND
86
40
ND
ND
46
ND
ND
36
ND
46
34
31
ND
IN
46
38
46
73
ND
53
23.7
ND
IN
10.3
11.6
ND
8.7
IN
9.5
IN
10.8
IN
ND
4.1
7.5
ND
ND
IN
ND
ND
IN
8.3
7.4
ND
ND
ND
ND
7.9
IN
IN
ND
ND
8.9
IN
IN
ND
ND
16.2
IN
ND
12.9
16.8
14.6
18.9
11.9
ND
ND
IN
9.6
10.5
ND
IN
13.9
ND
13.5
ND
ND
103
ND
IN
38
41
ND
22
IN
25
IN
30
IN
ND
12
16
ND
ND
IN
ND
ND
IN
20
26
ND
ND
ND
ND
22
IN
IN
ND
ND
28
IN
IN
ND
ND
40
IN
ND
23
29
28
50
27
ND
ND
IN
36
31
ND
IN
32
ND
33
ND
ND
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.002 0.007
ND ND
0.003 0.009
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.017
ND ND
ND ND
ND ND
0.004 0.014
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.026
0.004 0.021
ND ND
ND ND
0.006 0.031
ND ND
ND ND
ND ND
0.007 0.047
ND ND
0.008 0.023
ND ND
ND ND
ND ND
ND ND
0.003 0.026
ND ND
ND ND
0.003 0.055
0.005 0.032
0.004 0.013
0.006 0.025
ND ND
ND ND
116
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
FL Lee County
FL Leon County
FL Manatee County
FL Marion County
FL Monroe County
FL Nassau County
FL Orange County
FL Osceola County
FL Palm Beach County
FL Pasco County
FL Pinellas County
FL Polk County
FL Putnam County
FL St. Lucie County
FL Santa Rosa County
FL Sarasota County
FL Seminole County
FL Volusia County
GA Baldwin County
GA Bartow County
GA Bibb County
GA Chatham County
GA Chattooga County
GA Cherokee County
GA Clarke County
GA Clayton County
GA Cobb County
GA Coweta County
GA Dawson County
GA DeKalb County
GA Dougherty County
GA Douglas County
GA Fannin County
GA Fayette County
GA Floyd County
GA Fulton County
GA Glynn County
GA Gwinnett County
GA Hall County
GA Henry County
GA Houston County
GA Lowndes County
GA Murray County
GA Muscogee County
GA Paulding County
GA Richmond County
GA Rockdale County
GA Spalding County
GA Sumter County
GA Walker County
GA Washington County
GA Wilkinson County
HI Hawaii County
HI Honolulu County
HI Kauai County
HI Maui County
\S\J
2000
Population
440,888
239,452
264,002
258,916
79,589
57,663
896,344
172,493
1,131,184
344,765
921,482
483,924
70,423
192,695
117,743
325,957
365,196
443,343
44,700
76,019
153,887
232,048
25,470
141,903
101,489
236,517
607,751
89,215
15,999
665,865
96,065
92,174
19,798
91,263
90,565
816,006
67,568
588,448
139,277
119,341
110,765
92,115
36,506
186,291
81,678
199,775
70,111
58,417
33,200
61,053
21,176
10,220
148,677
876,156
58,463
128,094
r u
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
3
ND
3
ND
2
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.04
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.11
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
0.009
ND
ND
ND
0.012
ND
0.016
ND
0.013
ND
ND
0.010
ND
0.004
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
ND
ND
ND
ND
ND
0.023
ND
ND
ND
ND
ND
ND
ND
ND
0.005
ND
0.008
ND
ND
ND
ND
ND
ND
0.005
ND
ND
"3
1-hr
(ppm)
0.09
0.09
0.11
0.09
ND
ND
0.11
0.10
0.09
0.09
0.10
0.10
ND
0.08
0.11
0.11
0.10
0.09
ND
ND
0.13
0.10
ND
0.08
ND
ND
0.12
0.11
0.10
0.15
ND
0.12
ND
0.15
ND
0.16
0.09
0.13
ND
0.16
ND
ND
0.11
0.11
0.10
0.12
0.13
ND
0.11
ND
ND
ND
0.05
0.05
ND
ND
rm10
8-hr
(ppm)
0.08
0.08
0.09
0.08
ND
ND
0.08
0.08
0.08
0.08
0.08
0.08
ND
0.07
0.10
0.09
0.08
0.08
ND
ND
0.10
0.08
ND
0.07
ND
ND
0.11
0.10
0.08
0.11
ND
0.10
ND
0.10
ND
0.11
0.07
0.10
ND
0.11
ND
ND
0.09
0.09
0.09
0.09
0.10
ND
0.09
ND
ND
ND
0.04
0.04
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
19
18
23
ND
18
IN
26
ND
IN
ND
26
23
27
18
ND
26
IN
21
ND
ND
IN
26
IN
ND
ND
ND
ND
ND
ND
IN
IN
28
ND
ND
24
36
IN
ND
ND
ND
ND
ND
ND
IN
ND
IN
ND
26
ND
IN
IN
ND
ND
16
IN
24
43
46
40
ND
36
65
50
ND
38
ND
45
121
49
35
ND
48
32
53
ND
ND
48
66
IN
ND
ND
ND
ND
ND
ND
64
IN
56
ND
ND
50
85
41
ND
ND
ND
ND
ND
ND
59
ND
48
ND
56
ND
IN
54
ND
ND
52
IN
76
9.6
IN
IN
11.0
ND
ND
12.1
ND
9.4
ND
12.4
12.2
ND
10.1
ND
11.0
11.0
10.5
ND
ND
18.6
15.1
ND
ND
19.0
19.2
18.7
ND
ND
18.9
17.4
ND
ND
ND
18.4
21.4
IN
19.4
18.3
ND
IN
15.6
ND
19.2
16.9
17.5
ND
ND
ND
IN
IN
17.6
ND
4.9
ND
IN
25
IN
IN
24
ND
ND
31
ND
27
ND
43
28
ND
23
ND
30
27
26
ND
ND
37
IN
ND
ND
IN
IN
50
ND
ND
IN
IN
ND
ND
ND
IN
IN
IN
IN
IN
ND
IN
IN
ND
71
46
IN
ND
ND
ND
IN
IN
IN
ND
10
ND
IN
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.002 0.014
ND ND
ND ND
0.007 0.053
0.003 0.009
ND ND
0.002 0.008
ND ND
0.005 0.031
0.005 0.018
0.003 0.014
ND ND
ND ND
0.002 0.019
ND ND
ND ND
0.003 0.016
0.003 0.016
0.003 0.015
0.003 0.024
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.003 0.018
ND ND
0.003 0.013
0.005 0.019
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.007
ND ND
ND ND
APPENDIX A • DATA TABLES 117
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
ID
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IL
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
County
Ada County
Bannock County
Benewah County
Bingham County
Bonner County
Bonneville County
Boundary County
Butte County
Canyon County
Caribou County
Kootenai County
Lemhi County
Lewis County
Minidoka County
Nez Perce County
Power County
Shoshone County
Twin Falls County
Adams County
Champaign County
Cook County
DuPage County
Effingham County
Hamilton County
Jackson County
Jersey County
Kane County
Lake County
La Salle County
McHenry County
McLean County
Macon County
Macoupin County
Madison County
Peoria County
Randolph County
Rock Island County
St. Clair County
Sangamon County
Tazewell County
Wabash County
Will County
Winnebago County
Allen County
Bartholomew County
Boone County
Clark County
Daviess County
Dearborn County
DeKalb County
Delaware County
Dubois County
Elkhart County
Floyd County
Fountain County
Gibson County
\S\J
2000
Population
300,904
75,565
9,171
41,735
36,835
82,522
9,871
2,899
131,441
7,304
108,685
7,806
3,747
20,174
37,410
7,538
13,771
64,284
68,277
179,669
5,376,741
904,161
34,264
8,621
59,612
21,668
404,119
644,356
111,509
260,077
150,433
114,706
49,019
258,941
183,433
33,893
149,374
256,082
188,951
128,485
12,937
502,266
278,418
331,849
71,435
46,107
96,472
29,820
46,109
40,285
118,769
39,674
182,791
70,823
17,954
32,500
r u
8-hr
(ppm)
3
ND
ND
ND
ND
ND
ND
ND
5
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
3
ND
ND
ND
2
ND
ND
1
3
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.08
ND
ND
ND
0.15
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
1.76
0.02
ND
ND
0.07
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.58
ND
ND
ND
ND
ND
"3
AM
(ppm)
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.032
ND
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
ND
ND
ND
0.009
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.010
"3
1-hr
(ppm)
ND
ND
ND
ND
ND
ND
ND
0.07
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.08
0.08
0.10
0.08
0.09
0.10
ND
0.10
0.08
0.09
ND
0.09
ND
0.09
0.10
0.11
0.08
0.09
0.07
0.11
0.10
ND
ND
0.09
0.08
0.10
ND
0.10
0.10
ND
ND
ND
ND
ND
0.08
0.09
ND
0.08
rm10
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
ND
0.07
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.07
0.07
0.08
0.06
0.07
0.08
ND
0.08
0.07
0.07
ND
0.08
ND
0.08
0.08
0.08
0.07
0.08
0.06
0.08
0.08
ND
ND
0.08
0.07
0.09
ND
0.08
0.09
ND
ND
ND
ND
ND
0.06
0.08
ND
0.07
rm10
WtdAM
rm25
24-hr
rm25 o«2
WtdAM 24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
34
31
IN
IN
22
21
IN
ND
30
IN
21
44
31
25
23
IN
21
25
ND
ND
35
ND
ND
ND
23
ND
IN
ND
26
ND
ND
ND
23
45
24
ND
ND
32
26
ND
ND
IN
ND
IN
IN
ND
28
23
ND
24
ND
26
ND
ND
ND
ND
88
94
63
IN
56
54
42
ND
82
IN
70
255
58
58
53
221
64
47
ND
ND
123
ND
ND
ND
55
ND
IN
ND
135
ND
ND
ND
40
116
54
ND
ND
62
54
ND
ND
59
ND
43
70
ND
65
60
ND
60
ND
62
ND
ND
ND
ND
9.2 38
10.5 57
ND ND
ND ND
9.8 37
IN IN
ND ND
ND ND
9.7 38
ND ND
9.9 33
ND ND
ND ND
ND ND
10.1 30
ND ND
12.2 30
3.2 19
13.1 30
14.8 28
20.2 43
15.3 34
ND ND
ND ND
ND ND
ND ND
IN IN
12.2 31
15.2 35
14.7 35
14.9 33
15.0 31
IN IN
20.6 37
14.8 32
15.2 33
13.6 28
17.4 36
13.4 32
ND ND
ND ND
16.0 31
15.0 36
15.7 47
ND ND
ND ND
18.6 IN
ND ND
ND ND
ND ND
16.1 49
17.1 48
15.7 IN
16.0 IN
ND ND
ND ND
ovi
AM 24-hr
(ppm) (ppm)
ND ND
0.008 0.036
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.034
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.025
0.002 0.016
0.012 0.075
0.003 0.018
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.005 0.025
0.003 0.012
0.008 0.041
0.006 0.036
0.003 0.017
0.003 0.012
0.007 0.030
0.005 0.035
0.005 0.063
0.006 0.035
0.005 0.023
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.015
0.009 0.053
ND ND
ND ND
ND ND
ND ND
0.015 0.037
0.007 0.031
0.006 0.070
118
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
IN Greene County
IN Hamilton County
IN Hancock County
IN Hendricks County
IN Henry County
IN Howard County
IN Huntington County
IN Jackson County
IN Jasper County
IN Jefferson County
IN Johnson County
IN Knox County
IN Lake County
IN La Porte County
IN Madison County
IN Marion County
IN Morgan County
IN Perry County
IN Pike County
IN Porter County
IN Posey County
IN Putnam County
IN St. Joseph County
IN Shelby County
IN Spencer County
IN Sullivan County
IN Tippecanoe County
IN Vanderburgh County
IN Vigo County
IN Warrick County
IN Wayne County
IA Black Hawk County
IA Bremer County
IA Cerro Gordo County
IA Clinton County
IA Delaware County
IA Emmet County
IA Harrison County
IA Johnson County
IA Lee County
IA Linn County
IA Muscatine County
IA Palo Alto County
IA Polk County
I A Pottawattamie County
IA Scott County
IA Story County
IA Van Buren County
IA Warren County
IA Woodbury County
KS Ford County
KS Johnson County
KS Linn County
KS Montgomery County
KS Neosho County
KS Sedgwick County
\S\J
2000
Population
33,157
182,740
55,391
104,093
48,508
84,964
38,075
41,335
30,043
31,705
115,209
39,256
484,564
110,106
133,358
860,454
66,689
18,899
12,837
146,798
27,061
36,019
265,559
43,445
20,391
21,751
148,955
171,922
105,848
52,383
71,097
128,012
23,325
46,447
50,149
18,404
11,027
15,666
111,006
38,052
191,701
41,722
10,147
374,601
87,704
158,668
79,981
7,809
40,671
103,877
32,458
451,086
9,570
36,252
16,997
452,869
r u
8-hr
(ppm)
ND
ND
ND
2
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
ND
ND
5
ND
ND
ND
ND
ND
ND
ND
ND
2
ND
ND
6
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.11
ND
ND
0.12
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
0.020
ND
ND
0.017
ND
ND
ND
ND
ND
ND
0.016
ND
0.007
ND
ND
0.014
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.005
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
ND
0.004
ND
ND
ND
"3
1-hr
(ppm)
0.10
0.10
0.10
0.10
ND
ND
0.09
0.10
ND
ND
0.10
ND
0.10
0.10
0.09
0.10
0.10
0.10
ND
0.10
0.10
ND
0.10
0.10
ND
ND
ND
0.09
0.09
0.09
ND
ND
0.09
ND
0.09
ND
ND
0.09
ND
ND
0.08
ND
0.08
0.07
ND
0.09
0.08
0.08
0.08
ND
ND
ND
0.11
ND
ND
0.09
rm10
8-hr
(ppm)
0.09
0.09
0.09
0.09
ND
ND
0.09
0.08
ND
ND
0.08
ND
0.09
0.08
0.08
0.08
0.09
0.09
ND
0.09
0.09
ND
0.08
0.09
ND
ND
ND
0.08
0.08
0.08
ND
ND
0.08
ND
0.08
ND
ND
0.08
ND
ND
0.08
ND
0.07
0.06
ND
0.08
0.07
0.07
0.07
ND
ND
ND
0.08
ND
ND
0.08
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
ND
IN
ND
ND
ND
ND
18
ND
ND
ND
31
ND
21
27
ND
30
ND
18
ND
25
19
ND
25
ND
ND
28
25
ND
24
31
ND
35
24
IN
17
ND
ND
ND
IN
25
IN
31
23
41
ND
ND
ND
25
22
ND
ND
24
26
26
ND
ND
ND
67
ND
ND
ND
ND
34
ND
ND
ND
123
ND
40
55
ND
75
ND
54
ND
57
35
ND
51
ND
ND
68
54
ND
47
71
ND
138
70
46
39
ND
ND
ND
60
119
IN
134
39
141
ND
ND
ND
76
49
ND
ND
75
63
87
ND
ND
ND
ND
IN
15.6
ND
ND
ND
ND
ND
IN
17.1
IN
16.9
17.8
ND
ND
ND
13.4
ND
ND
13.7
ND
IN
ND
15.6
16.1
15.7
ND
ND
11.6
ND
10.6
12.0
ND
IN
ND
10.9
ND
10.7
IN
ND
10.8
9.9
12.7
9.8
9.7
ND
9.5
ND
11.2
11.3
ND
ND
12.7
ND
ND
ND
ND
IN
35
ND
ND
ND
ND
ND
IN
38
IN
IN
36
ND
ND
ND
30
ND
ND
36
ND
IN
ND
35
39
37
ND
ND
29
ND
28
29
ND
IN
ND
28
ND
29
IN
ND
28
27
30
27
27
ND
31
ND
26
29
ND
ND
29
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
ND ND
IN 0.108
ND ND
ND ND
ND ND
ND ND
0.003 0.014
0.007 0.027
ND ND
ND ND
0.006 0.046
0.004 0.016
ND ND
0.007 0.025
ND ND
0.007 0.030
0.008 0.029
0.006 0.027
ND ND
ND ND
ND ND
ND ND
0.008 0.028
0.008 0.040
ND ND
0.004 0.020
0.012 0.055
0.015 0.084
0.006 0.031
ND ND
ND ND
0.003 0.053
0.005 0.028
ND ND
ND ND
ND ND
ND ND
0.002 0.011
0.003 0.037
0.009 0.084
ND ND
ND ND
ND ND
0.003 0.014
ND ND
0.001 0.005
ND ND
ND ND
ND ND
ND ND
0.001 0.004
0.006 0.044
ND ND
ND ND
APPENDIX A • DATA TABLES
119
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
KS Shawnee County
KS Sherman County
KS Sumner County
KS Trego County
KS Wyandotte County
KY Bell County
KY Boone County
KY Boyd County
KY Bullitt County
KY Campbell County
KY Carter County
KY Christian County
KY Daviess County
KY Edmonson County
KY Fayette County
KY Franklin County
KY Graves County
KY Greenup County
KY Hancock County
KY Hardin County
KY Harlan County
KY Henderson County
KY Jefferson County
KY Jessamine County
KY Kenton County
KY Livingston County
KY McCracken County
KY McLean County
KY Madison County
KY Marshall County
KY Oldham County
KY Perry County
KY Pike County
KY Pulaski County
KY Scott County
KY Simpson County
KY Trigg County
KY Warren County
KY Whitley County
LA Ascension Parish
LA Beauregard Parish
LA Bossier Parish
LA Caddo Parish
LA Calcasieu Parish
LA Concordia Parish
LA East Baton Rouge Parish
LA Grant Parish
LA Iberville Parish
LA Jefferson Parish
LA Lafayette Parish
LA Lafourche Parish
LA Livingston Parish
LA Orleans Parish
LA Ouachita Parish
LA Pointe Coupee Parish
LA Rapides Parish
LA St. Bernard Parish
\S\J
2000
Population
169,871
6,760
25,946
3,319
157,882
30,060
85,991
49,752
61,236
88,616
26,889
72,265
91,545
11,644
260,512
47,687
37,028
36,891
8,392
94,174
33,202
44,829
693,604
39,041
151,464
9,804
65,514
9,938
70,872
30,125
46,178
29,390
68,736
56,217
33,061
16,405
12,597
92,522
35,865
76,627
32,986
98,310
252,161
183,577
20,247
412,852
18,698
33,320
455,466
190,503
89,974
91,814
484,674
147,250
22,763
126,337
67,229
r u
8-hr
(ppm)
ND
ND
2
ND
5
3
ND
1
ND
ND
ND
ND
1
ND
2
ND
ND
ND
ND
ND
ND
2
4
ND
2
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
IN
ND
0.017
ND
ND
0.015
0.013
0.015
ND
ND
0.011
ND
0.013
ND
ND
ND
ND
ND
ND
0.016
0.013
ND
0.018
ND
0.010
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.010
ND
ND
IN
ND
ND
0.005
ND
0.017
ND
0.010
0.011
ND
ND
0.005
0.019
ND
IN
ND
ND
"3
1-hr
(ppm)
ND
ND
0.09
0.09
0.11
0.11
0.11
0.09
0.10
0.11
0.09
0.10
0.08
0.10
0.09
ND
0.10
0.09
0.09
0.09
ND
0.09
0.11
0.08
0.11
0.10
0.10
0.09
ND
ND
0.11
0.09
0.09
0.10
0.08
0.10
0.09
0.10
ND
0.13
0.13
0.13
0.11
0.13
ND
0.14
0.10
0.13
0.12
0.12
0.12
0.13
0.11
0.10
0.11
ND
0.11
rm10
8-hr
(ppm)
ND
ND
0.08
0.08
0.09
0.09
0.08
0.08
0.08
0.09
0.08
0.08
0.07
0.09
0.08
ND
0.08
0.08
0.08
0.08
ND
0.08
0.09
0.08
0.09
0.08
0.08
0.08
ND
ND
0.09
0.07
0.08
0.09
0.07
0.09
0.08
0.09
ND
0.10
0.08
0.09
0.09
0.09
ND
0.10
0.08
0.10
0.10
0.09
0.09
0.10
0.08
0.08
0.08
ND
0.09
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
20
25
ND
ND
37
IN
ND
32
IN
IN
ND
ND
20
ND
21
ND
ND
ND
ND
IN
24
IN
31
ND
19
IN
21
ND
IN
IN
ND
IN
IN
25
ND
ND
ND
19
25
ND
ND
ND
24
ND
ND
IN
ND
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
49
60
ND
ND
64
54
ND
80
68
IN
ND
ND
64
ND
49
ND
ND
ND
ND
IN
48
48
84
ND
50
IN
74
ND
43
IN
ND
IN
43
50
ND
ND
ND
47
57
ND
ND
ND
51
ND
ND
53
ND
ND
ND
ND
ND
ND
44
ND
ND
ND
ND
10.8
ND
10.6
ND
13.3
IN
ND
IN
IN
IN
IN
IN
IN
ND
IN
IN
ND
ND
ND
IN
ND
IN
17.9
ND
IN
ND
IN
ND
IN
ND
ND
IN
IN
ND
ND
ND
ND
IN
ND
ND
ND
ND
13.8
13.1
12.3
15.0
ND
IN
13.5
13.0
ND
ND
14.1
13.3
ND
13.3
13.1
23
ND
23
ND
32
IN
ND
IN
IN
IN
IN
IN
IN
ND
IN
IN
ND
ND
ND
IN
ND
IN
IN
ND
IN
ND
IN
ND
IN
ND
ND
IN
IN
ND
ND
ND
ND
IN
ND
ND
ND
ND
31
34
27
35
ND
IN
35
33
ND
ND
37
27
ND
30
35
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.001 0.002
ND ND
0.002 0.012
ND ND
ND ND
0.007 0.020
ND ND
0.007 0.040
ND ND
ND ND
0.005 0.018
ND ND
0.005 0.020
ND ND
ND ND
0.007 0.024
0.005 0.018
ND ND
ND ND
0.006 0.034
0.008 0.036
ND ND
ND ND
0.005 0.017
0.002 0.014
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.006
ND ND
0.004 0.013
ND ND
0.004 0.015
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.003
ND ND
ND ND
0.005 0.020
120
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
LA St. Charles Parish
LA St. James Parish
LA St. John the Baptist Parish
LA St. Mary Parish
LA Tangipahoa Parish
LA Terrebonne Parish
LA West Baton Rouge Parish
ME Androscoggin County
ME Aroostook County
ME Cumberland County
ME Franklin County
ME Hancock County
ME Kennebec County
ME Knox County
ME Oxford County
ME Penobscot County
ME Piscataquis County
ME Sagadahoc County
ME York County
MD Anne Arundel County
MD Baltimore County
MD Calvert County
MD Carroll County
MD Cecil County
MD Charles County
MD Frederick County
MD Harford County
MD Kent County
MD Montgomery County
MD Prince George's County
MD Washington County
MD Wicomico County
MD Baltimore city
MA Barnstable County
MA Berkshire County
MA Bristol County
MA Essex County
MA Hampden County
MA Hampshire County
MA Middlesex County
MA Norfolk County
MA Plymouth County
MA Suffolk County
MA Worcester County
Ml Allegan County
Ml Alpena County
Ml Bay County
Ml Benzie County
Ml Berrien County
Ml Calhoun County
Ml Cass County
Ml Clinton County
Ml Delta County
Ml Genesee County
Ml Grand Traverse County
Ml Huron County
\S\J
2000
Population
48,072
21,216
43,044
53,500
100,588
104,503
21,601
103,793
73,938
265,612
29,467
51,791
117,114
39,618
54,755
144,919
17,235
35,214
186,742
489,656
754,292
74,563
150,897
85,951
120,546
195,277
218,590
19,197
873,341
801,515
131,923
84,644
651,154
222,230
134,953
534,678
723,419
456,228
152,251
1,465,396
650,308
472,822
689,807
750,963
105,665
31,314
110,157
15,998
162,453
137,985
51,104
64,753
38,520
436,141
77,654
36,079
r u
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
4
ND
3
ND
ND
2
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
0.12
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.02
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
ND
"3
AM
(ppm)
ND
IN
ND
ND
ND
ND
0.017
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
ND
0.010
IN
0.017
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
0.024
IN
ND
0.007
0.011
0.026
0.006
ND
ND
ND
0.029
0.018
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.12
0.12
0.12
0.12
ND
ND
0.12
ND
ND
0.08
ND
0.10
0.08
0.09
0.06
IN
0.07
0.09
0.09
0.12
0.11
0.11
0.11
0.13
0.11
0.11
0.11
0.13
0.09
0.13
0.10
ND
ND
0.11
IN
0.10
0.09
0.10
0.10
0.09
ND
ND
0.09
0.10
0.12
ND
ND
0.09
0.11
ND
0.10
0.09
ND
0.09
ND
0.09
rm10
8-hr
(ppm)
0.09
0.09
0.09
0.09
ND
ND
0.09
ND
ND
0.07
ND
0.08
0.06
0.07
0.05
IN
0.06
0.08
0.07
0.10
0.08
0.09
0.09
0.11
0.09
0.09
0.09
0.11
0.08
0.09
0.08
ND
ND
0.08
IN
0.08
0.07
0.08
0.08
0.08
ND
ND
0.07
0.08
0.08
ND
ND
0.08
0.08
ND
0.08
0.07
ND
0.07
ND
0.07
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
IN
ND
ND
ND
ND
ND
IN
IN
24
27
IN
ND
IN
IN
IN
17
ND
ND
ND
25
15
ND
ND
IN
ND
ND
ND
ND
ND
24
ND
13
29
ND
ND
ND
ND
28
11
ND
ND
ND
29
19
ND
ND
ND
ND
ND
IN
ND
ND
ND
19
ND
ND
57
ND
ND
ND
ND
ND
68
36
87
74
29
ND
IN
32
31
37
ND
ND
ND
48
33
ND
ND
27
ND
ND
ND
ND
ND
56
ND
29
75
ND
ND
ND
ND
57
25
ND
ND
ND
59
54
ND
ND
ND
ND
ND
IN
ND
ND
ND
36
ND
ND
ND
ND
ND
ND
14.0
12.4
14.2
9.6
10.4
11.0
ND
5.6
9.6
IN
IN
9.0
ND
ND
9.4
16.1
IN
ND
ND
14.1
ND
ND
15.5
ND
14.3
17.1
15.6
ND
19.7
ND
IN
11.7
IN
15.9
IN
IN
IN
IN
15.8
12.1
11.7
IN
IN
ND
12.1
ND
ND
ND
ND
12.9
IN
ND
ND
ND
ND
ND
35
29
36
26
24
35
ND
14
31
IN
IN
24
ND
ND
24
IN
IN
ND
ND
25
ND
ND
IN
ND
25
IN
29
ND
IN
ND
IN
29
IN
37
IN
IN
IN
IN
IN
33
32
IN
IN
ND
30
ND
ND
ND
ND
32
IN
ND
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.031
0.004 0.018
ND ND
0.005 0.018
ND ND
ND ND
ND ND
ND ND
0.003 0.013
ND ND
ND ND
ND ND
ND ND
0.006 0.024
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.005 0.042
0.004 0.020
0.005 0.023
0.002 0.015
IN 0.034
ND ND
ND ND
0.006 0.035
0.006 0.019
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.010
0.004 0.015
ND ND
ND ND
APPENDIX A • DATA TABLES
121
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
Ml Ingham County
Ml Kalamazoo County
Ml Kent County
Ml Lenawee County
Ml Macomb County
Ml Mason County
Ml Missaukee County
Ml Monroe County
Ml Muskegon County
Ml Oakland County
Ml Ottawa County
Ml Saginaw County
Ml St. Clair County
Ml Washtenaw County
Ml Wayne County
MN Anoka County
MN Crow Wing County
MN Dakota County
MN Douglas County
MN Freeborn County
MN Hennepin County
MN Itasca County
MN Kandiyohi County
MN Koochiching County
MN Lake County
MN McLeod County
MN Mills Lacs County
MN Nicollet County
MN Olmsted County
MN Otter Tail County
MN Pine County
MN Ramsey County
MN St. Louis County
MN Scott County
MN Stearns County
MN Washington County
MN Wright County
MS Adams County
MS Bolivar County
MS DeSoto County
MS Forrest County
MS Hancock County
MS Harrison County
MS Hinds County
MS Jackson County
MS Jones County
MS Lauderdale County
MS Lee County
MS Lowndes County
MS Madison County
MS Pearl River County
MS Rankin County
MS Scott County
MS Warren County
MO Buchanan County
MO Cass County
\S\J
2000
Population
279,320
238,603
574,335
98,890
788,149
28,274
14,478
145,945
170,200
1,194,156
238,314
210,039
164,235
322,895
2,061,162
298,084
55,099
355,904
32,821
32,584
1,116,200
43,992
41,203
14,355
11,058
34,898
22,330
29,771
124,277
57,159
26,530
511,035
200,528
89,498
133,166
201,130
89,986
34,340
40,633
107,199
72,604
42,967
189,601
250,800
131,420
64,958
78,161
75,755
61,586
74,674
48,621
115,327
28,423
49,644
85,998
82,092
r u
8-hr
(ppm)
ND
ND
3
ND
1
ND
ND
ND
ND
3
ND
ND
ND
ND
5
2
ND
2
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
5
2
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
0.00
ND
ND
ND
0.00
ND
ND
ND
ND
ND
ND
0.00
0.04
ND
ND
0.40
ND
ND
0.01
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
IN
ND
ND
ND
ND
ND
0.004
ND
ND
ND
ND
ND
ND
ND
0.024
ND
ND
0.012
ND
ND
0.022
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.017
ND
ND
ND
ND
ND
ND
ND
0.010
ND
0.005
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.09
0.09
0.11
0.09
0.09
0.12
0.08
ND
0.12
0.09
0.11
ND
0.10
0.09
0.10
0.09
ND
0.08
ND
ND
ND
ND
ND
ND
0.07
ND
0.07
ND
ND
ND
ND
ND
0.07
ND
ND
0.09
ND
0.10
0.09
0.12
ND
0.14
0.12
0.10
0.11
ND
0.10
0.10
ND
0.09
ND
ND
ND
0.10
ND
0.12
rm10
8-hr
(ppm)
0.08
0.07
0.07
0.08
0.08
0.08
0.07
ND
0.08
0.08
0.08
ND
0.08
0.08
0.08
0.07
ND
0.07
ND
ND
ND
ND
ND
ND
0.06
ND
0.07
ND
ND
ND
ND
ND
0.07
ND
ND
0.07
ND
0.09
0.08
0.09
ND
0.09
0.09
0.08
0.09
ND
0.08
0.08
ND
0.08
ND
ND
ND
0.08
ND
0.08
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
21
ND
ND
ND
ND
ND
ND
ND
IN
ND
ND
ND
43
ND
IN
IN
IN
IN
31
IN
IN
ND
IN
IN
12
IN
ND
IN
ND
36
29
ND
ND
21
IN
ND
ND
ND
ND
ND
ND
24
16
ND
ND
17
ND
ND
ND
ND
ND
ND
31
ND
ND
ND
49
ND
ND
ND
ND
ND
ND
ND
40
ND
ND
ND
113
ND
IN
IN
IN
IN
103
IN
IN
ND
IN
IN
26
IN
ND
IN
ND
74
69
ND
ND
42
IN
ND
ND
ND
ND
ND
ND
64
35
ND
ND
34
ND
ND
ND
ND
ND
ND
80
ND
13.6
15.1
13.8
ND
13.4
ND
ND
15.2
11.9
15.4
13.2
IN
IN
IN
20.1
ND
IN
IN
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
15.6
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
11.8
10.9
38
37
35
ND
33
ND
ND
37
35
IN
34
IN
IN
IN
45
ND
IN
IN
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
35
IN
IN
IN
IN
IN
ND
IN
IN
IN
IN
27
25
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.002 0.010
ND ND
0.003 0.014
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.039
ND ND
0.008 0.043
ND ND
ND ND
0.003 0.016
ND ND
ND ND
0.003 0.023
ND ND
ND ND
IN 0.001
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.009
ND ND
ND ND
ND ND
0.002 0.011
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.003 0.033
0.002 0.006
0.002 0.010
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.021
ND ND
122
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
MO Cedar County
MO Clay County
MO Greene County
MO Holt County
MO Howell County
MO Iron County
MO Jackson County
MO Jasper County
MO Jefferson County
MO Lincoln County
MO Mercer County
MO Monroe County
MO Platte County
MO St. Charles County
MO Ste. Genevieve County
MO St. Louis County
MO St. Louis city
MT Big Horn County
MT Cascade County
MT Flathead County
MT Gallatin County
MT Glacier County
MT Jefferson County
MT Lake County
MT Lewis and Clark County
MT Lincoln County
MT Missoula County
MT Park County
MT Ravalli County
MT Roosevelt County
MT Rosebud County
MT Sanders County
MT Silver Bow County
MT Yellowstone County
NE Cass County
NE Cedar County
NE Cherry County
NE Dawson County
NE Deuel County
NE Douglas County
NE Hall County
NE Lancaster County
NE Lincoln County
NE Sarpy County
NE Scotts Bluff County
NE Washington County
NV Clark County
NV Douglas County
NV Elko County
NV Lander County
NV Washoe County
NV White Pine County
NV Carson City
NH Carroll County
NH Cheshire County
NH Coos County
\S\J
2000
Population
13,733
184,006
240,391
5,351
37,238
10,697
654,880
104,686
198,099
38,944
3,757
9,311
73,781
283,883
17,842
1,016,315
348,189
12,671
80,357
74,471
67,831
13,247
10,049
26,507
55,716
18,837
95,802
15,694
36,070
10,620
9,383
10,227
34,606
129,352
24,334
9,615
6,148
24,365
2,098
463,585
53,534
250,291
34,632
122,595
36,951
18,780
1,375,765
41,259
45,291
5,794
339,486
9,181
52,457
43,666
73,825
33,111
r u
8-hr
(ppm)
ND
4
3
ND
ND
ND
5
ND
ND
ND
ND
ND
ND
ND
ND
3
4
ND
4
4
5
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
5
5
ND
ND
ND
ND
ND
3
ND
3
ND
ND
ND
ND
7
4
ND
ND
5
ND
4
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
0.00
ND
1.00
0.01
ND
6.86
ND
ND
ND
ND
ND
ND
0.01
ND
ND
ND
ND
ND
ND
ND
ND
0.98
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.08
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
IN
0.014
0.012
ND
ND
ND
ND
ND
ND
ND
0.004
ND
0.009
0.009
IN
0.021
0.026
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.008
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.11
0.12
0.09
ND
ND
ND
ND
ND
0.10
ND
ND
0.09
0.12
0.12
0.12
0.12
0.11
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.08
ND
0.07
ND
ND
ND
ND
0.09
0.09
ND
ND
0.09
0.08
0.08
0.07
0.08
IN
rm10
8-hr
(ppm)
0.09
0.09
0.08
ND
ND
ND
ND
ND
0.08
ND
ND
0.08
0.09
0.09
0.09
0.09
0.09
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.07
ND
0.06
ND
ND
ND
ND
0.08
0.07
ND
ND
0.07
0.08
0.07
0.06
0.06
IN
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
18
ND
ND
ND
29
IN
ND
17
ND
12
ND
ND
ND
19
39
IN
ND
24
IN
20
ND
21
20
26
18
IN
19
IN
29
IN
20
18
IN
ND
ND
IN
ND
48
ND
ND
ND
ND
ND
ND
48
9
IN
22
42
ND
ND
ND
19
28
ND
ND
35
ND
ND
ND
56
126
ND
51
ND
37
ND
ND
ND
50
92
106*
ND
98
65
101
ND
86
58
69
58
17*
60
IN
124
41
66
43
118
ND
ND
125
ND
124
ND
ND
ND
ND
ND
ND
188
19
91
91
96
ND
ND
ND
41
72
IN
13.1
12.3
ND
13.4
ND
13.4
13.2
IN
ND
ND
10.9
ND
14.9
15.1
14.8
16.4
ND
IN
IN
IN
ND
ND
12.1
IN
17.1
IN
ND
IN
ND
IN
6.9
IN
8.1
IN
IN
IN
ND
IN
11.5
IN
IN
IN
IN
IN
IN
10.8
IN
ND
ND
9.0
ND
ND
ND
IN
ND
IN
29
27
ND
28
ND
30
26
IN
ND
ND
30
ND
34
33
33
43
ND
IN
IN
IN
ND
ND
33
IN
IN
IN
ND
IN
ND
IN
18
IN
25
IN
IN
IN
ND
IN
28
IN
IN
IN
IN
IN
IN
32
IN
ND
ND
31
ND
ND
ND
IN
ND
ovi
AM 24-hr
(ppm) (ppm)
ND ND
0.002 0.007
0.005 0.077
ND ND
ND ND
0.008 0.099
0.004 0.039
ND ND
0.005 0.042
ND ND
ND ND
0.003 0.013
0.002 0.008
0.004 0.017
ND ND
0.005 0.026
0.007 0.043
ND ND
IN 0.008
ND ND
ND ND
ND ND
0.005 0.035
ND ND
0.006 0.028
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.002
ND ND
ND ND
0.006 0.026
ND ND
ND ND
ND ND
ND ND
ND ND
0.001 0.016
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.022
0.005 0.030
APPENDIX A • DATA TABLES
123
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
NH Grafton County
NH Hillsborough County
NH Merrimack County
NH Rockingham County
NH Strafford County
NH Sullivan County
NJ Atlantic County
NJ Bergen County
NJ Burlington County
NJ Camden County
NJ Cumberland County
NJ Essex County
NJ Gloucester County
NJ Hudson County
NJ Hunterdon County
NJ Mercer County
NJ Middlesex County
NJ Monmouth County
NJ Morris County
NJ Ocean County
NJ Passaic County
NJ Union County
NJ Warren County
NM Bernalillo County
NM Chaves County
NM Dona Ana County
NM Eddy County
NM Grant County
NM Hidalgo County
NM Lea County
NM Luna County
NM Otero County
NM Sandoval County
NM San Juan County
NM Santa Fe County
NM Taos County
NM Valencia County
NY Albany County
NY Bronx County
NY Broome County
NY Chautauqua County
NY Chemung County
NY Columbia County
NY Dutchess County
NY Erie County
NY Essex County
NY Hamilton County
NY Herkimer County
NY Jefferson County
NY Kings County
NY Madison County
NY Monroe County
NY Nassau County
NY New York County
NY Niagara County
NY Oneida County
\S\J
2000
Population
81,743
380,841
136,225
277,359
112,233
40,458
252,552
884,118
423,394
508,932
146,438
793,633
254,673
608,975
121,989
350,761
750,162
615,301
470,212
510,916
489,049
522,541
102,437
556,678
61,382
174,682
51,658
31,002
5,932
55,511
25,016
62,298
89,908
113,801
129,292
29,979
66,152
294,565
1,332,650
200,536
139,750
91,070
63,094
280,150
950,265
38,851
5,379
64,427
111,738
2,465,326
69,441
735,343
1,334,544
1,537,195
219,846
235,469
r u
8-hr
(ppm)
ND
4
ND
ND
ND
ND
ND
3
4
4
ND
ND
ND
5
ND
ND
3
3
3
ND
ND
5
ND
4
ND
4
ND
ND
ND
ND
ND
ND
1
2
2
ND
ND
1
4
ND
ND
ND
ND
ND
2
ND
ND
ND
ND
4
ND
3
3
4
2
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
ND
ND
ND
ND
ND
0.15
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.02
ND
"3
AM
(ppm)
ND
0.011
ND
0.006
ND
ND
ND
ND
ND
0.021
ND
0.029
ND
0.026
ND
0.016
0.019
ND
0.011
ND
ND
0.041
ND
0.017
ND
0.012
0.006
ND
ND
ND
ND
ND
0.010
0.011
ND
ND
ND
ND
0.032
ND
ND
ND
ND
ND
0.022
ND
ND
ND
ND
ND
ND
ND
0.024
0.038
ND
ND
"3
1-hr
(ppm)
0.08
0.09
0.08
0.08
0.08
0.08
0.11
0.10
ND
0.13
0.12
ND
0.12
0.10
0.11
0.11
0.11
0.13
0.11
0.14
0.10
ND
ND
0.09
ND
0.12
0.08
ND
ND
ND
ND
ND
0.09
0.09
ND
ND
0.08
0.08
0.10
ND
0.11
0.09
ND
0.11
0.11
0.09
0.09
0.08
0.08
ND
0.08
0.08
ND
0.07
0.10
0.08
rm10
8-hr
(ppm)
0.06
0.07
0.07
0.07
0.07
0.07
0.09
0.08
ND
0.10
0.09
ND
0.10
0.08
0.09
0.10
0.09
0.10
0.09
0.11
0.08
ND
ND
0.08
ND
0.08
0.07
ND
ND
ND
ND
ND
0.08
0.08
ND
ND
0.07
0.07
0.07
ND
0.09
0.07
ND
0.08
0.09
0.08
0.07
0.07
0.07
ND
0.07
0.07
ND
0.06
0.08
0.07
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
15
IN
IN
13
IN
23
37
ND
29
ND
ND
ND
IN
ND
26
ND
ND
ND
ND
ND
35
ND
25
20
42
ND
20
IN
21
IN
20
17
16
11
10
ND
ND
23
ND
14
ND
IN
ND
ND
IN
ND
9
ND
IN
ND
ND
17
22
IN
ND
ND
39
26
33
29
24
42
86
ND
76
ND
ND
ND
63
ND
55
ND
ND
ND
ND
ND
108
ND
122
41
96
ND
43
38
40
35
57
36
27
28
36
ND
ND
57
ND
32
ND
29
ND
ND
21
ND
23
ND
IN
ND
ND
38
49
31
ND
ND
IN
ND
IN
ND
ND
ND
14.6
ND
15.5
ND
15.6
15.1
17.5
ND
14.7
IN
ND
12.9
IN
IN
18.7
13.9
7.9
6.8
10.5
ND
5.5
ND
6.8
ND
ND
6.3
6.1
5.2
ND
ND
12.3
16.6
IN
IN
ND
ND
11.3
16.1
5.5
ND
ND
ND
16.2
ND
11.8
12.2
18.4
IN
11.8
ND
IN
ND
IN
ND
ND
ND
36
ND
IN
ND
IN
34
69
ND
43
IN
ND
30
IN
IN
47
38
19
15
31
ND
11
ND
14
ND
ND
10
13
10
ND
ND
30
44
IN
IN
ND
ND
33
33
18
ND
ND
ND
44
ND
28
36
48
IN
34
ovi
AM 24-hr
(ppm) (ppm)
ND ND
0.005 0.022
0.005 0.044
0.003 0.013
ND ND
0.004 0.015
0.003 0.013
0.005 0.020
0.004 0.016
0.006 0.020
0.004 0.017
ND ND
0.005 0.021
0.008 0.025
ND ND
ND ND
0.005 0.018
ND ND
0.004 0.021
ND ND
ND ND
0.009 0.025
ND ND
ND ND
ND ND
0.001 0.003
0.001 0.007
0.004 0.024
0.001 0.002
ND ND
ND ND
ND ND
ND ND
0.008 0.032
ND ND
ND ND
ND ND
0.004 0.020
0.011 0.042
ND ND
0.008 0.065
0.003 0.012
ND ND
ND ND
0.010 0.051
0.002 0.006
0.002 0.008
0.001 0.007
ND ND
IN 0.000
0.002 0.012
0.006 0.021
0.006 0.025
0.013 0.046
0.005 0.017
ND ND
124
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
NY Onondaga County
NY Orange County
NY Putnam County
NY Queens County
NY Rensselaer County
NY Richmond County
NY St. Lawrence County
NY Saratoga County
NY Schenectady County
NY Steuben County
NY Suffolk County
NY Ulster County
NY Wayne County
NY Westchester County
NC Alamance County
NC Alexander County
NC Avery County
NC Beaufort County
NC Buncombe County
NC Cabarrus County
NC Caldwell County
NC Camden County
NC Caswell County
NC Catawba County
NC Chatham County
NC Cumberland County
NC Davidson County
NC Davie County
NC Duplin County
NC Durham County
NC Edgecombe County
NC Forsyth County
NC Franklin County
NC Gaston County
NC Granville County
NC Guilford County
NC Harnett County
NC Haywood County
NC Henderson County
NC Jackson County
NC Johnston County
NC Lenoir County
NC Lincoln County
NC McDowell County
NC Martin County
NC Mecklenburg County
NC Mitchell County
NC Montgomery County
NC New Hanover County
NC Northampton County
NC Onslow County
NC Orange County
NC Pasquotank County
NC Person County
NC Pitt County
NC Robeson County
\S\J
2000
Population
458,336
341,367
95,745
2,229,379
152,538
443,728
111,931
200,635
146,555
98,726
1,419,369
177,749
93,765
923,459
130,800
33,603
17,167
44,958
206,330
131,063
77,415
6,885
23,501
141,685
49,329
302,963
147,246
34,835
49,063
223,314
55,606
306,067
47,260
190,365
48,498
421,048
91,025
54,033
89,173
33,121
121,965
59,648
63,780
42,151
25,593
695,454
15,687
26,822
160,307
22,086
150,355
118,227
34,897
35,623
133,798
123,339
r u
8-hr
(ppm)
2
ND
ND
3
ND
ND
ND
ND
3
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
1
ND
4
ND
ND
1
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
5
ND
ND
4
ND
ND
IN
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
0.18
ND
ND
ND
0.02
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
0.030
ND
ND
ND
ND
ND
ND
0.017
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.08
0.10
0.10
0.11
ND
0.12
ND
0.09
0.08
ND
0.13
0.09
0.09
0.11
ND
0.11
0.09
ND
0.11
ND
0.10
0.08
0.12
ND
0.10
0.11
ND
0.11
0.10
0.12
0.11
0.11
0.11
ND
0.11
0.12
ND
0.10
ND
0.10
0.12
0.10
0.11
ND
0.10
0.14
ND
ND
0.10
0.10
ND
ND
ND
0.10
0.11
ND
rm10
8-hr
(ppm)
0.07
0.08
0.08
0.08
ND
0.09
ND
0.07
0.06
ND
0.09
0.08
0.07
0.08
ND
0.09
0.08
ND
0.09
ND
0.09
0.07
0.09
ND
0.08
0.09
ND
0.10
0.08
0.09
0.09
0.09
0.09
ND
0.09
0.09
ND
0.09
ND
0.09
0.08
0.08
0.09
ND
0.08
0.10
ND
ND
0.08
0.08
ND
ND
ND
0.08
0.08
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
ND
ND
ND
IN
ND
ND
ND
ND
ND
10
ND
ND
ND
ND
ND
ND
18
21
ND
ND
ND
22
ND
IN
21
ND
ND
23
20
22
ND
21
ND
24
28
26
23
ND
ND
ND
ND
22
ND
31
27
ND
17
ND
17
ND
17
ND
19
ND
ND
ND
ND
ND
ND
46
ND
ND
ND
ND
ND
29
ND
ND
ND
ND
ND
ND
38
40
ND
ND
ND
42
ND
52
41
ND
ND
43
41
51
ND
37
ND
44
52
47
44
ND
ND
ND
ND
45
ND
62
50
ND
36
ND
32
ND
34
ND
36
ND
IN
IN
ND
14.1
ND
14.3
7.3
ND
10.8
9.1
IN
ND
ND
IN
15.4
ND
ND
ND
15.1
16.5
ND
ND
14.9
17.4
13.3
16.2
17.8
ND
13.1
15.8
14.7
16.5
ND
16.0
ND
16.8
ND
14.8
ND
IN
ND
12.7
ND
16.4
ND
17.2
16.3
IN
12.5
ND
12.3
14.4
IN
ND
13.9
IN
IN
IN
ND
43
ND
42
22
ND
26
31
IN
ND
ND
IN
IN
ND
ND
ND
IN
IN
ND
ND
46
38
32
67
38
ND
32
40
35
35
ND
37
ND
37
ND
33
ND
IN
ND
32
ND
39
ND
34
37
IN
32
ND
34
30
IN
ND
41
IN
ovi
AM 24-hr
(ppm) (ppm)
0.003 0.022
ND ND
0.003 0.015
0.007 0.025
0.002 0.010
IN 0.028
ND ND
ND ND
0.004 0.016
ND ND
0.007 0.023
0.002 0.009
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.020
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.018
ND ND
ND ND
ND ND
0.005 0.019
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.018
ND ND
ND ND
0.004 0.017
ND ND
ND ND
0.006 0.030
0.004 0.012
ND ND
ND ND
ND ND
ND ND
0.003 0.007
ND ND
APPENDIX A • DATA TABLES 125
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
NC Rockingham County
NC Rowan County
NC Swain County
NC Union County
NC Wake County
NC Wayne County
NC Yancey County
ND Billings County
ND Burke County
ND Burleigh County
ND Cass County
ND Dunn County
ND Grand Forks County
ND McKenzie County
ND McLean County
ND Mercer County
ND Morton County
ND Oliver County
ND Stark County
ND Steele County
ND Williams County
OH Adams County
OH Allen County
OH Ashtabula County
OH Athens County
OH Belmont County
OH Butler County
OH Clark County
OH Clermont County
OH Clinton County
OH Columbiana County
OH Cuyahoga County
OH Delaware County
OH Franklin County
OH Fulton County
OH Geauga County
OH Greene County
OH Hamilton County
OH Hancock County
OH Jefferson County
OH Knox County
OH Lake County
OH Lawrence County
OH Licking County
OH Logan County
OH Lorain County
OH Lucas County
OH Madison County
OH Mahoning County
OH Medina County
OH Meigs County
OH Miami County
OH Monroe County
OH Montgomery County
OH Morgan County
OH Ottawa County
\S\J
2000
Population
91,928
130,340
12,968
123,677
627,846
113,329
17,774
888
2,242
69,416
123,138
3,600
66,109
5,737
9,311
8,644
25,303
2,065
22,636
2,258
19,761
27,330
108,473
102,728
62,223
70,226
332,807
144,742
177,977
40,543
112,075
1,393,978
109,989
1,068,978
42,084
90,895
147,886
845,303
71,295
73,894
54,500
227,511
62,319
145,491
46,005
284,664
455,054
40,213
257,555
151,095
23,072
98,868
15,180
559,062
14,897
40,985
r u
8-hr
(ppm)
ND
1
ND
ND
5
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
8
ND
3
ND
ND
ND
2
ND
5
ND
1
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
ND
ND
ND
0.20
ND
0.03
0.33
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.24
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
0.003
ND
0.007
0.003
ND
ND
ND
0.004
ND
0.003
ND
0.003
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.023
ND
ND
ND
ND
ND
0.022
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.10
0.12
0.08
0.10
0.12
ND
0.11
0.07
ND
ND
0.07
IN
ND
ND
ND
0.06
ND
0.06
ND
0.07
ND
ND
0.10
0.11
ND
ND
0.10
0.11
0.11
0.11
ND
0.10
0.10
0.11
ND
0.11
0.11
0.11
ND
0.10
0.10
0.11
0.09
0.11
ND
IN
0.10
0.12
0.10
0.10
ND
0.10
ND
0.09
ND
ND
rm10
8-hr
(ppm)
0.08
0.10
0.07
0.09
0.09
ND
0.09
0.06
ND
ND
0.06
IN
ND
ND
ND
0.05
ND
0.06
ND
0.06
ND
ND
0.09
0.08
ND
ND
0.08
0.09
0.09
0.10
ND
0.08
0.08
0.08
ND
0.09
0.08
0.09
ND
0.08
0.09
0.08
0.08
0.09
ND
IN
0.08
0.09
0.08
0.08
ND
0.08
ND
0.08
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
19
ND
23
21
ND
ND
IN
ND
17
ND
ND
6
8
ND
ND
ND
ND
ND
ND
ND
IN
ND
IN
28
32
ND
ND
ND
IN
43
ND
34
ND
ND
21
32
IN
31
ND
21
23
IN
ND
29
23
ND
27
ND
ND
ND
25
32
ND
24
ND
ND
33
ND
51
40
ND
ND
49
ND
39
ND
ND
17
20
ND
ND
ND
ND
ND
ND
ND
42
ND
39
62
69
ND
ND
ND
128
122
ND
73
ND
ND
46
70
41
70
ND
46
41
IN
ND
52
60
ND
55
ND
ND
ND
48
64
ND
43
ND
ND
14.1
ND
16.5
15.8
ND
IN
5.9
6.6
8.2
ND
8.2
ND
ND
6.2
ND
ND
5.4
6.8
ND
ND
ND
ND
IN
ND
17.0
IN
ND
ND
ND
19.8
ND
18.5
ND
ND
ND
19.7
ND
19.1
ND
13.8
17.0
ND
ND
15.1
IN
ND
15.9
ND
ND
ND
ND
18.0
ND
ND
ND
ND
38
ND
52
40
ND
IN
12
14
29
ND
25
ND
ND
12
ND
ND
10
21
ND
ND
ND
ND
IN
ND
38
IN
ND
ND
ND
46
ND
IN
ND
ND
ND
44
ND
47
ND
40
IN
ND
ND
IN
IN
ND
35
ND
ND
ND
ND
43
ND
ND
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.001 0.004
0.002 0.011
ND ND
0.001 0.003
0.001 0.008
ND ND
0.002 0.011
0.002 0.007
0.003 0.016
0.006 0.053
0.002 0.011
ND ND
0.001 0.002
0.003 0.020
0.007 0.029
0.003 0.015
0.005 0.021
ND ND
0.010 0.043
0.006 0.023
0.004 0.018
0.005 0.029
ND ND
IN 0.037
0.007 0.035
ND ND
0.004 0.019
ND ND
ND ND
ND ND
0.007 0.031
ND ND
0.010 0.045
ND ND
0.009 0.040
0.005 0.025
ND ND
ND ND
0.003 0.021
0.005 0.017
ND ND
0.007 0.024
ND ND
0.006 0.034
ND ND
ND ND
0.004 0.016
0.006 0.040
ND ND
126
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
OH Portage County
OH Preble County
OH Richland County
OH Sandusky County
OH Scioto County
OH Seneca County
OH Stark County
OH Summit County
OH Trumbull County
OH Tuscarawas County
OH Warren County
OH Washington County
OH Wood County
OH Wyandot County
OK Caddo County
OK Canadian County
OK Carter County
OK Cherokee County
OK Cleveland County
OK Comanche County
OK Custer County
OK Garfield County
OK Jefferson County
OK Kay County
OK Latimer County
OK Lincoln County
OK Love County
OK McClain County
OK Marshall County
OK Ma County
OK Muskogee County
OK Oklahoma County
OK Ottawa County
OK Pawnee County
OK Payne County
OK Pittsburg County
OK Pottawatomie County
OK Seminole County
OK Tulsa County
OR Benton County
OR Clackamas County
OR Columbia County
OR Deschutes County
OR Harney County
OR Jackson County
OR Josephine County
OR Klamath County
OR Lake County
OR Lane County
OR Linn County
OR Marion County
OR Multnomah County
OR Umatilla County
OR Union County
OR Wasco County
OR Washington County
\S\J
2000
Population
152,061
42,337
128,852
61,792
79,195
58,683
378,098
542,899
225,116
90,914
158,383
63,251
121,065
22,908
30,150
87,697
45,621
42,521
208,016
114,996
26,142
57,813
6,818
48,080
10,692
32,080
8,831
27,740
13,184
38,369
69,451
660,448
33,194
16,612
68,190
43,953
65,521
24,894
563,299
78,153
338,391
43,560
115,367
7,609
181,269
75,726
63,775
7,422
322,959
103,069
284,834
660,486
70,548
24,530
23,791
445,342
r u
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
3
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
1
2
1
ND
ND
ND
1
ND
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
4
ND
5
ND
IN
ND
4
ND
IN
4
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.008
0.011
ND
ND
0.007
ND
0.007
IN
ND
ND
ND
ND
0.007
0.008
0.013
ND
ND
ND
ND
ND
ND
0.015
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.012
ND
ND
ND
ND
"3
1-hr
(ppm)
0.11
0.09
ND
ND
ND
ND
0.10
0.11
0.09
ND
0.11
0.10
0.09
ND
ND
ND
ND
0.10
0.09
0.09
ND
ND
0.10
0.10
0.08
ND
0.12
0.10
0.10
ND
ND
0.10
ND
ND
ND
ND
ND
ND
0.12
ND
0.08
0.08
ND
ND
0.08
ND
ND
ND
IN
ND
0.07
ND
ND
ND
ND
ND
rm10
8-hr
(ppm)
0.09
0.07
ND
ND
ND
ND
0.09
0.08
0.08
ND
0.09
0.08
0.08
ND
ND
ND
ND
0.09
0.08
0.09
ND
ND
0.09
0.08
0.06
ND
0.10
0.08
0.09
ND
ND
0.09
ND
ND
ND
ND
ND
ND
0.09
ND
0.07
0.05
ND
ND
0.07
ND
ND
ND
IN
ND
0.06
ND
ND
ND
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
IN
25
29
22
24
22
24
ND
ND
IN
ND
29
ND
ND
ND
ND
ND
ND
23
ND
ND
IN
ND
ND
ND
ND
ND
ND
IN
26
ND
ND
ND
IN
ND
ND
25
ND
IN
ND
IN
ND
IN
IN
IN
IN
IN
ND
ND
IN
IN
IN
ND
ND
ND
ND
53
46
59
100
49
53
50
ND
ND
75
ND
63
ND
ND
ND
ND
ND
ND
50
ND
ND
48
ND
ND
ND
ND
ND
ND
99
62
ND
ND
ND
43
ND
ND
58
ND
IN
ND
109
ND
68
40
93
78
69
ND
ND
45
45
71
ND
ND
15.6
IN
ND
ND
15.6
ND
18.6
16.8
15.5
ND
ND
ND
ND
ND
IN
10.8
10.2
IN
ND
9.1
9.7
10.3
ND
10.3
ND
IN
ND
ND
ND
11.1
IN
11.5
IN
IN
IN
IN
10.8
IN
12.1
8.1
ND
7.0
7.3
IN
11.4
8.9
9.6
7.0
IN
9.1
8.9
9.6
8.9
IN
9.7
9.9
36
IN
ND
ND
IN
ND
40
36
IN
ND
ND
ND
ND
ND
IN
26
24
IN
ND
19
30
25
ND
23
ND
IN
ND
ND
ND
30
IN
29
IN
IN
IN
IN
24
IN
30
30
ND
18
27
IN
49
33
48
46
IN
42
31
31
37
IN
30
34
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
ND ND
ND ND
0.007 0.024
ND ND
0.008 0.028
0.009 0.044
ND ND
0.006 0.031
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.001 0.004
ND ND
ND ND
ND ND
ND ND
ND ND
0.005 0.020
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.003 0.019
0.003 0.007
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.006 0.027
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
APPENDIX A • DATA TABLES 127
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
OR Yamhill County
PA Adams County
PA Allegheny County
PA Armstrong County
PA Beaver County
PA Berks County
PA Blair County
PA Bucks County
PA Cambria County
PA Carbon County
PA Centre County
PA Chester County
PA Clearfield County
PA Cumberland County
PA Dauphin County
PA Delaware County
PA Erie County
PA Franklin County
PA Greene County
PA Lackawanna County
PA Lancaster County
PA Lawrence County
PA Lehigh County
PA Luzerne County
PA Lycoming County
PA Mercer County
PA Montgomery County
PA Northampton County
PA Perry County
PA Philadelphia County
PA Schuylkill County
PA Tioga County
PA Warren County
PA Washington County
PA Westmoreland County
PA York County
Rl Kent County
Rl Providence County
Rl Washington County
SC Abbeville County
SC Aiken County
SC Anderson County
SC Barnwell County
SC Beaufort County
SC Berkeley County
SC Charleston County
SC Cherokee County
SC Chester County
SC Chesterfield County
SC Colleton County
SC Darlington County
SC Dillon County
SC Edgefield County
SC Fairfield County
SC Florence County
SC Georgetown County
\S\J
2000
Population
84,992
91,292
1,281,666
72,392
181,412
373,638
129,144
597,635
152,598
58,802
135,758
433,501
83,382
213,674
251,798
550,864
280,843
129,313
40,672
213,295
470,658
94,643
312,090
319,250
120,044
120,293
750,097
267,066
43,602
1,517,550
150,336
41,373
43,863
202,897
369,993
381,751
167,090
621,602
123,546
26,167
142,552
165,740
23,478
120,937
142,651
309,969
52,537
34,068
42,768
38,264
67,394
30,722
24,595
23,454
125,761
55,797
r u
8-hr
(ppm)
ND
1
3
ND
1
2
1
4
2
ND
ND
ND
ND
ND
2
ND
6
ND
0
2
2
2
3
2
ND
ND
2
2
ND
4
1
ND
ND
1
2
2
ND
4
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
0.11
ND
0.03
ND
0.07
0.33
ND
ND
0.05
0.11
ND
ND
ND
ND
ND
0.05
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.05
ND
ND
ND
ND
0.04
ND
ND
ND
ND
ND
0.01
ND
ND
0.00
ND
0.02
ND
ND
ND
ND
ND
0.00
ND
ND
0.01
0.02
"3
AM
(ppm)
ND
0.004
0.025
ND
0.017
0.020
0.014
0.017
0.015
ND
ND
ND
ND
ND
0.017
0.019
0.012
ND
ND
0.015
0.014
0.019
0.013
0.014
ND
ND
0.018
0.017
0.007
0.028
ND
ND
ND
0.015
0.017
0.018
IN
0.020
ND
ND
0.005
ND
0.004
ND
ND
0.011
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
ND
ND
0.11
0.10
0.10
0.11
0.10
0.12
0.10
ND
0.11
IN
0.11
ND
0.11
0.12
0.10
0.10
0.11
0.09
0.11
0.09
0.11
0.09
0.09
0.10
0.13
0.11
0.10
0.11
ND
0.10
ND
0.11
0.10
0.11
0.12
0.12
0.12
0.10
0.11
0.10
0.11
ND
0.09
0.11
0.12
0.09
ND
0.10
0.11
ND
0.09
ND
ND
ND
rm10
8-hr
(ppm)
ND
ND
0.09
0.08
0.08
0.08
0.08
0.10
0.09
ND
0.08
IN
0.08
ND
0.09
0.09
0.08
0.09
0.09
0.08
0.09
0.07
0.09
0.08
0.07
0.08
0.10
0.09
0.07
0.09
ND
0.08
ND
0.08
0.08
0.09
0.09
0.08
0.09
0.09
0.09
0.08
0.09
ND
0.08
0.08
0.09
0.08
ND
0.08
0.09
ND
0.08
ND
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
ND
39
ND
IN
IN
IN
IN
IN
ND
ND
ND
ND
ND
IN
IN
IN
ND
ND
IN
IN
IN
IN
IN
IN
ND
IN
IN
ND
IN
ND
ND
ND
IN
IN
IN
12
29
ND
ND
21
ND
21
ND
ND
23
ND
ND
ND
ND
ND
ND
ND
23
ND
33
ND
ND
124
ND
52
45
51
39
51
ND
ND
ND
ND
ND
53
45
41
ND
ND
40
56
62
79
46
IN
ND
41
85
ND
IN
ND
ND
ND
78
45
53
26
91
ND
ND
34
ND
42
ND
ND
52
ND
ND
ND
ND
ND
ND
ND
40
ND
72
ND
IN
20.0
ND
16.3
16.9
ND
IN
15.9
ND
IN
ND
ND
IN
15.8
16.0
IN
ND
ND
11.7
18.4
ND
14.5
12.7
ND
IN
IN
IN
12.2
IN
ND
ND
ND
15.4
16.0
16.6
8.8
14.9
8.8
ND
ND
ND
ND
12.6
ND
14.8
ND
ND
IN
IN
ND
ND
14.8
ND
14.4
15.6
ND
IN
84
ND
IN
34
ND
IN
IN
ND
IN
ND
ND
IN
IN
30
IN
ND
ND
31
IN
ND
37
33
ND
IN
IN
IN
23
IN
ND
ND
ND
30
IN
31
26
36
21
ND
ND
ND
ND
23
ND
31
ND
ND
IN
IN
ND
ND
27
ND
25
28
ovi
AM 24-hr
(ppm) (ppm)
ND ND
ND ND
0.011 0.054
ND ND
0.013 0.086
0.008 0.028
0.006 0.045
0.007 0.027
0.007 0.026
ND ND
ND ND
ND ND
ND ND
ND ND
0.005 0.024
0.010 0.026
0.008 0.041
ND ND
0.007 0.022
0.004 0.021
0.005 0.024
0.008 0.031
0.007 0.027
0.006 0.026
0.005 0.019
0.007 0.024
0.005 0.022
0.008 0.023
0.003 0.015
0.006 0.027
0.006 0.025
ND ND
0.013 0.092
0.009 0.031
0.010 0.029
0.006 0.020
ND ND
0.007 0.026
ND ND
ND ND
ND ND
ND ND
0.002 0.007
ND ND
ND ND
0.003 0.013
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.010
128
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
SC Greenville County
SC Greenwood County
SC Hampton County
SC Horry County
SC Laurens County
SC Lexington County
SC Oconee County
SC Pickens County
SC Richland County
SC Spartanburg County
SC Sumter County
SC Union County
SC Williamsburg County
SC York County
SD Brookings County
SD Brown County
SD Jackson County
SD Minnehaha County
SD Pennington County
TN Anderson County
TN Blount County
TN Bradley County
TN Davidson County
TN Dickson County
TN Dyer County
TN Greene County
TN Hamilton County
TN Hawkins County
TN Haywood County
TN Humphreys County
TN Jefferson County
TN Knox County
TN Lawrence County
TN McMinn County
TN Madison County
TN Maury County
TN Meigs County
TN Montgomery County
TN Polk County
TN Putnam County
TN Roane County
TN Rutherford County
TN Sevier County
TN Shelby County
TN Stewart County
TN Sullivan County
TN Sumner County
TN Union County
TN Williamson County
TN Wilson County
TX Bexar County
TX Bowie County
TX Brazoria County
TX Brewster County
TX Caldwell County
TX Cameron County
\S\J
2000
Population
379,616
66,271
21,386
196,629
69,567
216,014
66,215
110,757
320,677
253,791
104,646
29,881
37,217
164,614
28,220
35,460
2,930
148,281
88,565
71,330
105,823
87,965
569,891
43,156
37,279
62,909
307,896
53,563
19,797
17,929
44,294
382,032
39,926
49,015
91,837
69,498
11,086
134,768
16,050
62,315
51,910
182,023
71,170
897,472
12,370
153,048
130,449
17,808
126,638
88,809
1,392,931
89,306
241,767
8,866
32,194
335,227
r u
8-hr
(ppm)
4
ND
ND
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
6
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
4
ND
2
ND
ND
ND
ND
3
ND
ND
ND
ND
2
IHWj
QMax
(jig/m3)
0.02
0.02
0.00
0.01
0.01
0.02
ND
ND
0.07
0.01
0.01
ND
ND
0.04
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.00
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.59
ND
0.20
ND
ND
1.50
ND
ND
ND
ND
ND
ND
0.01
"3
AM
(ppm)
0.016
ND
ND
ND
ND
ND
ND
ND
0.014
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IN
0.014
0.019
IN
ND
ND
ND
ND
ND
ND
ND
0.013
ND
0.015
ND
ND
ND
IN
ND
ND
0.008
ND
ND
0.025
ND
0.015
ND
ND
ND
ND
0.018
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
IN
ND
ND
ND
ND
ND
0.10
0.10
0.12
0.11
ND
0.09
0.09
0.09
ND
ND
ND
IN
IN
0.11
0.11
ND
0.11
IN
ND
ND
0.12
ND
0.12
ND
0.12
0.13
0.09
ND
ND
ND
0.11
0.11
ND
0.10
0.12
0.10
0.12
0.12
ND
0.13
0.12
ND
0.12
0.10
0.10
ND
0.14
0.07
ND
0.08
rm10
8-hr
(ppm)
IN
ND
ND
ND
ND
ND
0.08
0.08
0.10
0.09
ND
0.08
0.08
0.08
ND
ND
ND
IN
IN
0.09
0.10
ND
0.08
IN
ND
ND
0.10
ND
0.09
ND
0.10
0.10
0.08
ND
ND
ND
0.10
0.09
ND
0.09
0.09
0.09
0.10
0.09
ND
0.10
0.09
ND
0.09
0.09
0.08
ND
0.08
0.06
ND
0.06
rm10
WtdAM
rm25
24-hr
rm25 o«2
WtdAM 24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
IN
ND
ND
ND
ND
46
ND
ND
26
24
ND
ND
ND
28
23
19
12
20
38
ND
ND
33
34
ND
ND
IN
30
ND
ND
ND
ND
30
ND
40
23
ND
ND
23
ND
ND
27
ND
ND
28
ND
ND
ND
34
ND
ND
IN
ND
ND
ND
ND
25
54
ND
ND
ND
ND
132
ND
ND
109
44
ND
ND
ND
46
71
50
35
53
139
ND
ND
105
65
ND
ND
66
67
ND
ND
ND
ND
73
ND
96
44
ND
ND
51
ND
ND
77
ND
ND
71
ND
ND
ND
125
ND
ND
IN
ND
ND
ND
ND
58
16.5 32
15.3 27
ND ND
IN IN
ND ND
16.3 26
IN IN
ND ND
16.3 28
15.4 31
ND ND
ND ND
ND ND
IN IN
IN IN
IN IN
IN IN
IN IN
IN IN
ND ND
IN IN
ND ND
IN IN
ND ND
IN IN
ND ND
IN IN
ND ND
ND ND
ND ND
ND ND
IN IN
IN IN
IN IN
IN IN
IN IN
ND ND
IN IN
ND ND
IN IN
IN IN
ND ND
ND ND
IN IN
ND ND
IN IN
IN IN
ND ND
ND ND
ND ND
IN IN
14.7 31
IN IN
ND ND
IN IN
IN IN
ovi
AM
(ppm)
0.003
ND
ND
ND
ND
0.003
0.002
ND
0.003
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.004
0.010
0.008
0.004
IN
ND
ND
ND
0.007
ND
0.004
ND
0.002
ND
0.006
ND
ND
ND
0.006
0.009
ND
0.003
ND
ND
0.006
0.002
0.011
0.004
ND
ND
ND
ND
ND
ND
IN
ND
0.001
24-hr
(ppm)
0.011
ND
ND
ND
ND
0.014
0.009
ND
0.010
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
0.060
0.026
0.017
0.012
ND
ND
ND
0.043
ND
0.025
ND
0.012
ND
0.022
ND
ND
ND
0.018
0.023
ND
0.018
ND
ND
0.038
0.010
0.043
0.040
ND
ND
ND
ND
ND
ND
0.002
ND
0.002
APPENDIX A • DATA TABLES
129
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
TX Collin County
TX Dallas County
TX Denton County
TX Ector County
TX Ellis County
TX El Paso County
TX Galveston County
TX Gregg County
TX Harris County
TX Hidalgo County
TX Hood County
TX Jefferson County
TX Johnson County
TX Kaufman County
TX Lubbock County
TX McLennan County
TX Marion County
TX Montgomery County
TX Nueces County
TX Orange County
TX Parker County
TX Potter County
TX Rockwall County
TX Smith County
TX Tarrant County
TX Travis County
TX Victoria County
TX Webb County
UT Box Elder County
UT Cache County
UT Davis County
UT Grand County
UT Salt Lake County
UT San Juan County
UT Tooele County
UT Utah County
UT Weber County
VT Bennington County
VT Chittenden County
VT Rutland County
VT Washington County
VA Arlington County
VA Caroline County
VA Carroll County
VA Charles City County
VA Chesterfield County
VA Culpeper County
VA Fairfax County
VA Fauquier County
VA Frederick County
VA Henrico County
VA King William County
VA Loudoun County
VA Madison County
VA Northumberland County
VA Page County
\S\J
2000
Population
491,675
2,218,899
432,976
121,123
111,360
679,622
250,158
111,379
3,400,578
569,463
41,100
252,051
126,811
71,313
242,628
213,517
10,941
293,768
313,645
84,966
88,495
113,546
43,080
174,706
1,446,219
812,280
84,088
193,117
42,745
91,391
238,994
8,485
898,387
14,413
40,735
368,536
196,533
36,994
146,571
63,400
58,039
189,453
22,121
29,245
6,926
259,903
34,262
969,749
55,139
59,209
262,300
13,146
169,599
12,520
12,259
23,177
r u
8-hr
(ppm)
ND
2
ND
ND
ND
9
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
1
ND
6
ND
3
3
ND
5
ND
ND
6
6
ND
2
3
ND
3
ND
ND
ND
ND
ND
4
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
0.54
0.13
ND
ND
ND
0.10
ND
ND
0.01
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.04
ND
ND
ND
ND
0.07
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
0.014
0.009
ND
0.009
0.029
0.005
0.006
0.021
ND
ND
0.008
ND
0.007
ND
ND
0.005
0.006
ND
0.008
ND
ND
ND
0.006
0.015
0.005
ND
ND
ND
ND
0.019
ND
0.026
ND
ND
0.024
IN
ND
IN
0.011
ND
0.023
IN
ND
0.011
ND
ND
0.021
ND
ND
ND
ND
0.013
ND
ND
ND
"3
1-hr
(ppm)
0.12
0.13
0.12
ND
0.12
0.12
0.14
0.13
0.19
0.09
0.10
0.16
0.11
IN
ND
ND
0.12
0.14
0.10
0.12
IN
ND
0.12
0.10
0.12
0.11
0.09
0.09
ND
0.08
0.10
ND
0.10
IN
ND
0.10
0.09
0.09
0.08
ND
ND
0.11
0.10
ND
0.09
0.10
ND
0.11
0.09
0.09
0.11
ND
0.09
0.09
ND
0.09
rm10
8-hr
(ppm)
0.10
0.10
0.10
ND
0.10
0.08
0.09
0.10
0.12
0.08
0.08
0.10
0.08
IN
ND
ND
0.10
0.10
0.08
0.09
IN
ND
0.09
0.09
0.10
0.09
0.08
0.07
ND
0.07
0.08
ND
0.08
IN
ND
0.08
0.07
0.07
0.07
ND
ND
0.08
0.08
ND
0.08
0.08
ND
0.09
0.08
0.08
0.08
ND
0.08
0.08
ND
0.08
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
ND
29
ND
ND
28
46
27
ND
46
IN
ND
ND
ND
ND
IN
ND
ND
ND
36
ND
ND
ND
ND
ND
23
23
ND
31
ND
25
ND
20
46
ND
ND
32
IN
15
12
18
17
ND
ND
20
ND
ND
18
20
ND
ND
ND
18
ND
ND
18
ND
ND
55
ND
ND
58
124
53
ND
102
53
ND
ND
ND
ND
38
ND
ND
ND
71
ND
ND
ND
ND
ND
42
50
ND
56
ND
79
ND
44
117
ND
ND
89
IN
28
28
42
43
ND
ND
52
ND
ND
39
45
ND
ND
ND
40
ND
ND
38
ND
11.6
13.2
ND
IN
ND
9.8
IN
13.4
IN
11.0
ND
IN
ND
ND
7.4
IN
12.3
IN
IN
IN
ND
IN
ND
ND
12.7
12.1
ND
12.1
IN
IN
9.0
ND
14.2
ND
7.1
10.1
7.6
9.5
8.3
11.1
10.1
14.6
ND
ND
IN
15.1
ND
14.0
ND
ND
14.6
ND
13.5
ND
ND
13.2
26
32
ND
IN
ND
23
IN
29
IN
23
ND
122
ND
ND
19
IN
29
IN
IN
IN
ND
IN
ND
ND
29
27
ND
23
IN
IN
40
ND
57
ND
30
34
25
20
17
24
20
28
ND
ND
IN
29
ND
34
ND
ND
30
ND
28
ND
ND
25
ovi
AM 24-hr
(ppm) (ppm)
ND ND
0.002 0.005
ND ND
ND ND
0.006 0.047
0.002 0.006
0.004 0.037
0.002 0.011
0.006 0.031
ND ND
ND ND
0.006 0.046
ND ND
0.002 0.005
ND ND
ND ND
ND ND
ND ND
0.003 0.017
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.013
ND ND
0.004 0.013
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.007
0.005 0.033
ND ND
ND ND
ND ND
ND ND
0.006 0.017
ND ND
ND ND
0.011 0.030
ND ND
ND ND
ND ND
ND ND
ND ND
0.003 0.011
ND ND
ND ND
130
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
VA Prince William County
VA Roanoke County
VA Rockbridge County
VA Rockingham County
VA Stafford County
VA Warren County
VA Wise County
VA Wythe County
VA Alexandria city
VA Bristol city
VA Charlottesville city
VA Chesapeake city
VA Fredericksburg city
VA Hampton city
VA Lynchburg city
VA Newport News city
VA Norfolk city
VA Richmond city
VA Roanoke city
VA Salem city
VA Suffolk city
VA Virginia Beach city
VA Winchester city
WA Adams County
WA Asotin County
WA Benton County
WA Chelan County
WA Clallam County
WA Clark County
WA Cowlitz County
WA Jefferson County
WA King County
WA Kittitas County
WA Klickitat County
WA Lewis County
WA Pierce County
WA Skagit County
WA Snohomish County
WA Spokane County
WA Stevens County
WA Thurston County
WA Walla Walla County
WA Whatcom County
WA Whitman County
WA Yakima County
WV Berkeley County
WV Brooke County
WV Cabell County
WV Greenbrier County
WV Hancock County
WV Harrison County
WV Kanawha County
WV Marion County
WV Marshall County
WV Mercer County
WV Monongalia County
\S\J
2000
Population
280,813
85,778
20,808
67,725
92,446
31,584
40,123
27,599
128,283
17,367
45,049
199,184
19,279
146,437
65,269
180,150
234,403
197,790
94,911
24,747
63,677
425,257
23,585
16,428
20,551
142,475
66,616
64,525
345,238
92,948
25,953
1,737,034
33,362
19,161
68,600
700,820
102,979
606,024
417,939
40,066
207,355
55,180
166,814
40,740
222,581
75,905
25,447
96,784
34,453
32,667
68,652
200,073
56,598
35,519
62,980
81,866
r u
8-hr
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
3
ND
ND
ND
ND
2
ND
ND
4
3
3
ND
ND
ND
ND
ND
ND
ND
ND
ND
6
ND
ND
6
ND
ND
ND
6
ND
6
6
ND
5
ND
ND
ND
3
ND
ND
ND
ND
8
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
0.009
0.011
ND
ND
ND
ND
ND
ND
0.023
ND
ND
ND
ND
ND
ND
ND
0.016
0.017
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.021
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.09
0.10
0.09
ND
0.10
ND
ND
0.09
0.10
ND
ND
ND
ND
0.09
ND
ND
ND
ND
ND
ND
0.10
ND
ND
ND
ND
ND
ND
0.06
0.07
ND
ND
0.10
ND
0.07
IN
0.08
0.06
ND
0.08
ND
0.08
ND
0.06
ND
ND
ND
ND
0.09
0.09
0.09
ND
0.09
ND
ND
ND
0.10
rm10
8-hr
(ppm)
0.08
0.08
0.08
ND
0.08
ND
ND
0.08
0.08
ND
ND
ND
ND
0.08
ND
ND
ND
ND
ND
ND
0.08
ND
ND
ND
ND
ND
ND
0.05
0.06
ND
ND
0.07
ND
0.07
IN
0.06
0.05
ND
0.07
ND
0.06
ND
0.05
ND
ND
ND
ND
0.08
0.08
0.07
ND
0.09
ND
ND
ND
0.08
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
IN
ND
ND
26
ND
20
IN
ND
ND
ND
23
IN
18
20
ND
ND
22
IN
32
ND
ND
ND
20
ND
27
IN
20
ND
16
21
ND
23
IN
ND
ND
28
ND
IN
28
30
15
29
15
ND
27
24
26
24
ND
31
20
27
23
IN
22
23
47
ND
ND
59
ND
43
IN
ND
ND
ND
70
40
36
41
ND
ND
39
42
66
ND
ND
ND
43
ND
59
140
49
ND
41
49
ND
66
104
ND
ND
58
ND
47
87
137
36
108
29
ND
58
68
54
60
ND
95
43
50
54
43
48
47
ND
ND
ND
ND
ND
ND
ND
ND
ND
16.4
ND
IN
ND
IN
IN
13.0
13.6
IN
15.9
15.5
ND
13.0
ND
IN
ND
IN
ND
10.8
10.8
ND
9.1
12.7
ND
ND
IN
13.0
8.2
12.6
11.0
ND
10.3
ND
8.4
6.8
IN
16.1
16.6
17.6
ND
16.5
14.9
18.1
15.9
16.3
13.5
15.0
ND
ND
ND
ND
ND
ND
ND
ND
ND
29
ND
IN
ND
IN
IN
24
26
IN
31
33
ND
25
ND
IN
ND
IN
ND
26
40
ND
25
36
ND
ND
IN
49
18
43
38
ND
41
ND
21
19
IN
46
35
40
ND
45
31
37
IN
33
33
33
ovi
AM 24-hr
(ppm) (ppm)
ND ND
0.003 0.014
ND ND
0.003 0.008
ND ND
ND ND
ND ND
ND ND
0.006 0.020
ND ND
ND ND
ND ND
ND ND
0.005 0.017
ND ND
ND ND
0.007 0.023
0.005 0.015
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.002 0.005
ND ND
ND ND
ND ND
0.003 0.011
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.013 0.060
0.006 0.028
ND ND
0.014 0.069
ND ND
0.012 0.046
ND ND
0.013 0.044
ND ND
0.010 0.040
APPENDIX A • DATA TABLES
131
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
State County
WV Ohio County
WV Raleigh County
WV Summers County
WV Wayne County
WV Wood County
Wl Brown County
Wl Columbia County
Wl Dane County
Wl Dodge County
Wl Door County
Wl Douglas County
Wl Florence County
Wl Fond du Lac County
Wl Grant County
Wl Green County
Wl Jefferson County
Wl Kenosha County
Wl Kewaunee County
Wl Manitowoc County
Wl Marathon County
Wl Milwaukee County
Wl Oneida County
Wl Outagamie County
Wl Ozaukee County
Wl Racine County
Wl Rock County
Wl St. Croix County
Wl Sauk County
Wl Sheboygan County
Wl Vernon County
Wl Vilas County
Wl Walworth County
Wl Washington County
Wl Waukesha County
Wl Winnebago County
Wl Wood County
WY Albany County
WY Campbell County
WY Converse County
WY Fremont County
WY Laramie County
WY Natrona County
WY Park County
WY Sheridan County
WY Sweetwater County
WY Teton County
PR Barceloneta Municipio
PR Bayamon Municipio
PR Carolina Municipio
PR Catano Municipio
PR Fajardo Municipio
PR Guayama Municipio
PR Guayanilla Municipio
PR Guaynabo Municipio
PR Humacao Municipio
PR Lares Municipio
PR Manati Municipio
\S\J
2000
Population
47,427
79,220
12,999
42,903
87,986
226,778
52,468
426,526
85,897
27,961
43,287
5,088
97,296
49,597
33,647
74,021
149,577
20,187
82,887
125,834
940,164
36,776
160,971
82,317
188,831
152,307
63,155
55,225
112,646
28,056
21,033
93,759
117,493
360,767
156,763
75,555
32,014
33,698
12,052
35,804
81,607
66,533
25,786
26,560
37,613
18,251
22,322
224,044
186,076
30,071
40,712
44,301
23,072
100,053
59,035
34,415
45,409
r u
8-hr
(ppm)
2
ND
ND
ND
ND
ND
ND
2
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
2
ND
ND
ND
2
ND
ND
ND
ND
ND
ND
ND
ND
2
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
IHWj
QMax
(jig/m3)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
"3
AM
(ppm)
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.021
ND
ND
IN
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.018
ND
ND
ND
ND
ND
ND
ND
"3
1-hr
(ppm)
0.09
ND
ND
ND
0.11
0.09
0.09
0.09
0.09
0.10
ND
0.08
0.08
ND
IN
IN
0.10
0.10
0.09
0.08
0.10
0.07
0.08
0.10
0.10
0.10
0.09
0.08
0.11
0.08
0.07
0.09
0.09
0.09
0.09
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.07
ND
ND
ND
0.10
ND
ND
ND
ND
ND
ND
ND
rm10
8-hr
(ppm)
0.07
ND
ND
ND
0.09
0.07
0.07
0.07
0.07
0.08
ND
0.07
0.07
ND
IN
IN
0.09
0.08
0.08
0.07
0.08
0.07
0.07
0.09
0.08
0.08
0.07
0.07
0.09
0.07
0.07
0.08
0.08
0.08
0.07
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.07
ND
ND
ND
0.05
ND
ND
ND
ND
ND
ND
ND
rm10
WtdAM
rm25
24-hr
rm25
WtdAM
ovi
24-hr
(ng/m3) (ng/m3) (ng/m3) (ng/m3)
23
19
16
ND
21
ND
ND
22
ND
ND
19
ND
ND
ND
ND
ND
ND
ND
ND
IN
20
ND
ND
ND
ND
ND
ND
ND
ND
ND
7
ND
ND
21
ND
ND
IN
47
26
22
16
17
20
IN
26
IN
IN
25
IN
30
IN
26
ND
37
IN
ND
IN
43
43
41
ND
42
ND
ND
57
ND
ND
35
ND
ND
ND
ND
ND
ND
ND
ND
IN
59
ND
ND
ND
ND
ND
ND
ND
ND
ND
20
ND
ND
45
ND
ND
64
143
62
53
30
38
62
67
124
IN
74
77
74
89
84
77
ND
102
IN
ND
73
15.5
13.8
10.4
ND
17.5
11.3
ND
13.2
11.7
7.2
8.2
ND
ND
12.3
ND
12.1
11.4
ND
10.1
ND
14.2
ND
11.5
11.5
ND
13.3
IN
ND
ND
ND
5.4
ND
ND
13.4
11.4
10.9
ND
ND
ND
IN
5.6
ND
ND
11.6
ND
ND
ND
7.3
ND
ND
IN
IN
IN
IN
IN
IN
ND
35
32
30
ND
36
32
ND
34
28
26
24
ND
ND
27
ND
33
27
ND
30
ND
35
ND
32
27
ND
29
IN
ND
ND
ND
17
ND
ND
31
32
35
ND
ND
ND
IN
13
ND
ND
36
ND
ND
ND
18
ND
ND
IN
IN
IN
IN
IN
IN
ND
ovi
AM 24-hr
(ppm) (ppm)
0.009 0.041
ND ND
ND ND
0.012 0.046
0.011 0.036
0.004 0.016
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
0.004 0.026
0.006 0.075
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.019
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
IN 0.016
0.004 0.058
ND ND
0.006 0.027
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
ND ND
132
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-14. Maximum Air Quality Concentrations by County, 2000 (continued)
CO Pb N02 03 03 PM10 PM10 PM25 PM25 SO2 SO2
State County 2000 8-hr QMax AM 1-hr 8-hr Wtd AM 24-hr Wtd AM 24-hr AM 24-hr
Population (ppm) (ng/m3) (ppm) (ppm) (ppm) (ng/m3) (ng/m3) (ng/m3) (ng/m3) (ppm) (ppm)
PR Mayaguez Municipio 98,434 ND ND ND ND ND ND ND IN IN ND ND
PR Ponce Municipio 186,475 ND ND ND ND ND 40 77 IN IN ND ND
PR Rio Grande Municipio 52,362 ND ND ND ND ND IN 71 ND ND ND ND
PR San Juan Municipio 434,374 6 0.02 IN ND ND IN 60 ND ND ND ND
PR Vieques Municipio 9,106 ND ND ND ND ND IN IN ND ND ND ND
CO - Highest second maximum non-overlapping 8-hour concentration (Applicable NAAQS is 9 ppm)
Pb - Highest quarterly maximum concentration (Applicable NAAQS is 1.5 fig/m3)
NO2 - Highest arithmetic mean concentration (Applicable NAAQS is 0.053 ppm)
O3 (1-hr) - Highest second daily maximum 1-hour concentration (Applicable NAAQS is 0.12 ppm)
O3 (8-hr) - Highest fourth daily maximum 8-hour concentration (Applicable NAAQS is 0.08 ppm)
PM10 - Highest weighted annual mean concentration (Applicable NAAQS is 50 fig/m3)
- Highest second maximum 24-hour concentration (Applicable NAAQS is 150 ng/m3)
SO2 - Highest annual mean concentration (Applicable NAAQS is 0.03 ppm)
- Highest second maximum 24-hour concentration (Applicable NAAQS is 0.14 ppm)
ND - Indicates data not available
IN - Indicates insufficient data to calculate summary statistic
Wtd - Weighted
AM - Annual mean
ng/m3 - Units are micrograms per cubic meter
PPM - Units are parts per million
Data from exceptional events not included.
(*) - These PM10 statistics were converted from local temperature and pressure to standard temperature and pressure to ensure all PM10 data
in this table reflect standard conditions.
Note: The reader is cautioned that this summary is not adequate in itself to numerically rank MSAs according to their air quality. The monitoring
data represent the quality of air in the vicinity of the monitoring site but may not necessarily represent urban-wide air quality.
APPENDIX A • DATA TABLES 133
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000
Metropolitan Statistical Area
Population
Akron, OH PMSA
Albany, GA MSA
Albany— Schenectady— Troy, NY MSA
Albuquerque, NM MSA
Alexandria, LA MSA
Allentown— Bethlehem— Easton, PA MSA
Altoona, PA MSA
Amarillo, TX MSA
Anchorage, AK MSA
Ann Arbor, Ml PMSA
Appleton — Oshkosh — Neenah, Wl
Asheville, NC MSA
Athens, GA MSA
Atlanta, GA MSA
Atlantic— Cape May, NJ PMSA
Augusta— Aiken, GA— SC MSA
Austin — San Marcos, TX MSA
Bakersfield, CA MSA
Baltimore, MD PMSA
Bangor, ME MSA
Baton Rouge, LA MSA
Beaumont— Port Arthur, TX MSA
Bellingham, WA MSA
Benton Harbor, Ml MSA
Bergen— Passaic, NJ PMSA
Billings, MT MSA
Biloxi— Gulfport— Pascagoula, MS MSA
Binghamton, NY MSA
Birmingham, AL MSA
Bismarck, ND MSA
Bloomington — Normal, IL MSA
Boise City, ID MSA
Boston, MA— NH PMSA
Boulder— Longmont, CO PMSA
Brazoria, TX PMSA
Bridgeport, CT PMSA
Brockton, MA PMSA
Brownsville— Harlingen— San Benito, T
Buffalo— Niagara Falls, NY MSA
Burlington, VT MSA
Canton— Massillon, OH MSA
Casper, WY MSA
Cedar Rapids, IA MSA
Champaign— Urbana, IL MSA
Charleston— North Charleston, SC MSA
Charleston, WV MSA
Charlotte— Gastonia— Rock Hill, NC— S
Charlottesville, VA MSA
Chattanooga, TN— GA MSA
Cheyenne, WY MSA
Chicago, IL PMSA
Chico— Paradise, CA MSA
Cincinnati, OH— KY— IN PMSA
Clarksville— Hopkinsville, TN— KY MSA
Cleveland— Lorain— Elyria, OH PMSA
CO
2000
(ppm)
694,960
120,822
875,583
712,738
126,337
637,958
129,144
217,858
260,283
578,736
358,365
225,965
153,444
4,112,198
354,878
477,441
1,249,763
661,645
2,552,994
90,864
602,894
385,090
166,814
162,453
1,373,167
129,352
363,988
252,320
921,106
94,719
150,433
432,345
3,406,829
291,288
241,767
459,479
255,459
335,227
1,170,111
169,391
406,934
66,533
191,701
179,669
549,033
251,662
1,499,293
159,576
465,161
81,607
8,272,768
203,171
1,646,395
207,033
2,250,871
Pb
8-hr
(jig/m3)
3
ND
3
4
ND
3
1
ND
6
ND
ND
ND
ND
3
ND
ND
1
5
3
ND
4
ND
ND
ND
3
5
ND
ND
5
ND
ND
5
2
4
ND
2
ND
2
2
2
3
ND
2
ND
3
ND
5
ND
ND
ND
4
4
2
ND
8
N02
QMax
(ppm)
ND
ND
ND
ND
ND
0.11
ND
ND
ND
0.00
ND
ND
ND
0.04
ND
0.01
ND
0.00
0.01
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.02
ND
ND
ND
ND
0.01
0.02
ND
ND
ND
ND
ND
0.02
ND
0.04
ND
ND
ND
0.15
0.00
ND
ND
0.20a
°3
AM
(ppm)
ND
ND
ND
0.017
ND
0.017
0.014
ND
ND
ND
ND
ND
ND
0.023
ND
0.005
0.005
0.023
0.024
ND
0.017
0.008
ND
ND
ND
ND
0.005
ND
0.011
ND
ND
IN
0.029
ND
ND
0.018
0.007
ND
0.022
IN
ND
ND
0.005
ND
0.011
ND
0.018
ND
ND
ND
0.032
0.012
0.022
IN
0.023
°3
1-hr
(ppm)
0.11
ND
0.09
0.09
ND
0.11
0.10
ND
ND
0.09
0.09
0.11
ND
0.16
0.11
0.12
0.11
0.14
0.12
IN
0.14
0.16
0.06
0.11
0.10
ND
0.14
ND
0.13
ND
ND
ND
0.09
0.09
0.14
0.12
0.09
0.08
0.11
ND
0.10
ND
0.08
0.08
0.11
0.09
0.14
ND
0.12
ND
0.10
0.10
0.11
0.11
0.11
PM10
8-hr
(^g/m3)
0.09
ND
0.07
0.08
ND
0.09
0.08
ND
ND
0.08
0.07
0.09
ND
0.11
0.09
0.09
0.09
0.11
0.10
IN
0.10
0.10
0.05
0.08
0.08
ND
0.09
ND
0.10
ND
ND
ND
0.08
0.07
0.08
0.09
0.07
0.06
0.09
ND
0.09
ND
0.08
0.07
0.08
0.09
0.10
ND
0.10
ND
0.08
0.09
0.09
0.09
0.09
PM10
WtdAM
I (ng/m3) |
22
IN
ND
25
ND
IN
IN
ND
IN
ND
ND
18
ND
36
23
21
23
46
29
17
IN
ND
15
ND
37
18
16
ND
27
ND
ND
34
29
23
ND
20
ND
25
IN
12
24
17
IN
ND
23
27
31
23
30
16
35
27
32
23
43
PM,5
24-hr
;^g/m3;
53
IN
ND
122
ND
85
51
ND
108
ND
ND
38
ND
85
42
48
50
136
75
37
68
ND
29
ND
86
43
35
ND
125
ND
ND
88
59
74
ND
51
ND
58
31
28
49
38
60
ND
52
50
62
70
67
30
123
77
70
51
122
PM,5
WtdAM
I (ng/m3)
16.8
17.4
12.3
7.9
13.3
14.5
ND
IN
6.1
IN
11.5
15.1
19.0
21.4
ND
17.5
12.1
21.7
19.7
9.0
15.0
IN
8.4
12.1
14.6
8.1
IN
IN
22.3
6.6
14.9
9.7
15.8
9.5
IN
IN
IN
IN
16.1
8.3
18.6
ND
10.7
14.8
14.8
18.1
17.2
ND
IN
5.6
20.2
16.3
19.7
IN
19.8
S02
24-hr
(ppm)
36
IN
30
19
30
37
ND
IN
20
IN
32
IN
IN
50
ND
27
27
100
IN
24
36
122
21
30
36
25
IN
IN
53
14
33
38
IN
25
IN
IN
IN
IN
33
17
40
ND
29
28
31
37
37
ND
IN
13
43
70
44
IN
46
S02
AM
(ppm)
0.009
ND
0.004
ND
ND
0.008
0.006
ND
ND
ND
ND
ND
ND
0.005
0.003
ND
ND
ND
0.006
ND
0.006
0.006
ND
ND
0.005
0.006
0.003
ND
IN
0.006
ND
ND
0.006
ND
ND
0.006
ND
0.001
0.010
IN
0.008
ND
0.003
0.002
0.003
0.012
0.004
ND
ND
ND
0.012
ND
0.009
0.006
0.009
24-hr
0.044
ND
0.020
ND
ND
0.027
0.045
ND
ND
ND
ND
ND
ND
0.019
0.013
ND
ND
ND
0.024
ND
0.031
0.046
ND
ND
0.020
0.026
0.033
ND
IN
0.053
ND
ND
0.030
ND
ND
0.024
ND
0.002
0.051
IN
0.028
ND
0.037
0.016
0.013
0.046
0.018
ND
ND
ND
0.075
ND
0.053
0.018
0.040
134
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000 (continued)
Metropolitan Statistical Area
Population
Colorado Springs, CO MSA
Columbia, SC MSA
Columbus, GA— AL MSA
Columbus, OH MSA
Corpus Christi, TX MSA
Dallas, TX PMSA
Danbury, CT PMSA
Davenport— Moline— Rock Island, IA— 1
Dayton— Springfield, OH MSA
Daytona Beach, FL MSA
Decatur, AL MSA
Decatur, IL MSA
Denver, CO PMSA
Des Moines, IA MSA
Detroit, Ml PMSA
Dothan, AL MSA
Dover, DE MSA
Duluth— Superior, MN— Wl MSA
Dutchess County, NY PMSA
El Paso, TX MSA
Elkhart— Goshen, IN MSA
Elmira, NY MSA
Enid, OK MSA
Erie, PA MSA
Eugene— Springfield, OR MSA
Evansville— Henderson, IN— KY MSA
Fargo— Moorhead, ND— MN MSA
Fayetteville, NC MSA
Fayetteville— Springdale— Rogers, AR
Fitchburg — Leominster, MA PMSA
Flagstaff, AZ— UT MSA
Flint, Ml PMSA
Florence, AL MSA
Florence, SC MSA
Fort Collins— Loveland, CO MSA
Fort Lauderdale, FL PMSA
Fort Myers— Cape Coral, FL MSA
Fort Pierce— Port St. Lucie, FL MSA
Fort Smith, AR— OK MSA
Fort Wayne, IN MSA
Fort Worth— Arlington, TX PMSA
Fresno, CA MSA
Gadsden, AL MSA
Gainesville, FL MSA
Galveston— Texas City, TX PMSA
Gary, IN PMSA
Goldsboro, NC MSA
Grand Forks, ND— MN MSA
Grand Junction, CO MSA
Grand Rapids — Muskegon — Holland, Ml M
Great Falls, MT MSA
Greeley, CO PMSA
Green Bay, Wl MSA
Greensboro— Winston-Salem— High Point
CO
2000
(ppm)
516,929
536,691
274,624
1,540,157
380,783
3,519,176
217,980
359,062
950,558
493,175
145,867
114,706
2,109,282
456,022
4,441,551
137,916
126,697
243,815
280,150
679,622
182,791
91,070
57,813
280,843
322,959
296,195
174,367
302,963
311,121
142,284
122,366
436,141
142,950
125,761
251,494
1,623,018
440,888
319,426
207,290
502,141
1,702,625
922,516
103,459
217,955
250,158
631,362
113,329
97,478
116,255
1,088,514
80,357
180,936
226,778
1,251,509
Pb
8-hr
(jig/m3)
4
4
ND
3
ND
2
ND
ND
3
ND
ND
ND
5
5
5
ND
ND
2
ND
9
ND
ND
ND
6
4
3
ND
4
ND
ND
ND
ND
ND
ND
4
4
ND
ND
ND
4
2
6
ND
ND
ND
3
ND
ND
4
3
4
4
ND
4
N02
QMax
(ppm)
0.01
0.07
0.11b
O.OSc
ND
0.54d
ND
ND
ND
ND
ND
ND
0.15
ND
0.04
ND
ND
ND
ND
0.10
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.01
ND
0.01
ND
0.05
ND
ND
ND
ND
ND
0.00
ND
ND
ND
0.11
ND
ND
ND
0.00
ND
ND
ND
ND
°3
AM
(ppm)
0.035
0.014
ND
ND
ND
0.014
ND
IN
ND
ND
ND
ND
0.016
ND
0.024
ND
ND
ND
ND
0.029
ND
ND
0.007
0.012
ND
0.016
0.007
ND
ND
ND
ND
ND
ND
ND
ND
0.010
ND
0.010
ND
ND
0.015
0.020
ND
ND
0.005
0.020
ND
ND
ND
ND
ND
ND
ND
0.018
°3
1-hr
(ppm)
0.09
0.12
0.11
0.12
0.10
0.13
0.12
0.09
0.11
0.09
0.11
0.09
0.11
0.08
0.10
ND
0.13
0.07
0.11
0.12
0.08
0.09
ND
0.10
IN
0.10
0.07
0.11
ND
ND
0.08
0.09
ND
ND
0.10
0.09
0.09
0.08
ND
0.10
0.12
0.15
ND
0.10
0.14
0.10
ND
ND
ND
0.12
ND
0.09
0.09
0.12
PM10
8-hr
(^g/m3)
0.07
0.10
0.09
0.09
0.08
0.10
0.09
0.08
0.09
0.08
0.09
0.08
0.08
0.07
0.08
ND
0.09
0.07
0.08
0.08
0.06
0.07
ND
0.08
IN
0.09
0.06
0.09
ND
ND
0.07
0.07
ND
ND
0.08
0.07
0.08
0.07
ND
0.09
0.10
0.11
ND
0.08
0.09
0.09
ND
ND
ND
0.08
ND
0.07
0.07
0.10
PM10
WtdAM
I (ng/m3) |
25
46
26
34
36
29
ND
41
32
21
23
ND
43
31
43
24
ND
29
ND
46
ND
ND
ND
IN
IN
28
17
IN
ND
ND
16
19
ND
ND
IN
19
19
18
ND
24
23
41
26
20
27
31
21
ND
20
21
ND
21
ND
24
PM,5
24-hr
;^g/m3;
87
132
59
73
71
58
ND
141
64
53
53
ND
134
134
113
70
ND
69
ND
124
ND
ND
ND
41
69
68
39
52
ND
ND
33
36
ND
ND
66
31
43
35
ND
60
42
122
64
36
53
123
40
ND
53
49
ND
58
ND
51
PM,5
WtdAM
I (ng/m3)
7.5
16.3
19.2
18.5
IN
13.2
IN
13.6
18.0
10.5
IN
15.0
11.6
10.8
20.1
IN
12.9
8.2
11.3
9.8
15.7
ND
10.3
IN
IN
16.1
8.2
16.2
IN
IN
IN
12.9
IN
14.4
83
9.6
9.6
10.1
13.5
15.7
12.7
25.4
IN
11.9
IN
17.1
15-8
8.2
7.4
13.8
IN
8.9
11.3
17.8
S02
24-hr
(ppm)
16
28
71
IN
IN
32
IN
30
43
26
IN
31
41
28
45
IN
23
24
33
23
IN
ND
25
IN
IN
39
29
67
IN
IN
IN
32
IN
25
20
36
25
23
27
47
29
89
IN
27
IN
38
40
25
26
35
IN
28
32
38
S02
AM
(ppm)
0.004
0.003
ND
0.004
0.003
0.006
0.003
0.003
0.004
ND
0.002
0.005
0.003
ND
0.008
ND
ND
ND
ND
0.002
ND
0.003
ND
0.008
ND
0.015
0.001
ND
ND
ND
ND
0.004
0.003
ND
ND
0.003
ND
ND
ND
ND
ND
ND
ND
ND
0.004
0.006
ND
ND
ND
0.002
IN
ND
0.004
0.005
24-hr
0.014
0.014
ND
0.019
0.017
0.047
0.017
0.014
0.018
ND
0.005
0.025
0.009
ND
0.043
ND
ND
ND
ND
0.006
ND
0.012
ND
0.041
ND
0.084
0.003
ND
ND
ND
ND
0.015
0.017
ND
ND
0.026
ND
ND
ND
ND
ND
ND
ND
ND
0.037
0.046
ND
ND
ND
0.010
IN
ND
0.016
0.019
APPENDIX A • DATA TABLES 135
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000 (continued)
Metropolitan Statistical Area
Population
Greenville, NC MSA
Greenville — Spartanburg — Anderson, SC
Hagerstown, MD PMSA
Hamilton— Middletown, OH PMSA
Harrisburg— Lebanon— Carlisle, PA MSA
Hartford, CT MSA
Hattiesburg, MS MSA
Hickory— Morganton— Lenoir, NC MSA
Honolulu, HI MSA
Houma, LA MSA
Houston, TX PMSA
Huntington— Ashland, WV— KY— OH MSA
Huntsville, AL MSA
Indianapolis, IN MSA
Iowa City, IA MSA
Jackson, MS MSA
Jackson, TN MSA
Jacksonville, FL MSA
Jacksonville, NC MSA
Jamestown, NY MSA
Janesville— Beloit, Wl MSA
Jersey City, NJ PMSA
Johnson City — Kingsport — Bristol, TN-
Johnstown, PA MSA
Jonesboro, AR MSA
Joplin, MO MSA
Kalamazoo— Battle Creek, Ml MSA
Kansas City, MO— KS MSA
Kenosha, Wl PMSA
KnoxvilleJNMSA
Kokomo, IN MSA
Lafayette, LA MSA
Lafayette, IN MSA
Lake Charles, LA MSA
Lakeland— Winter Haven, FL MSA
Lancaster, PA MSA
Lansing — East Lansing, Ml MSA
Laredo, TX MSA
Las Cruces, NM MSA
Las Vegas, NV— AZ MSA
Lawrence, MA— NH PMSA
Lawton, OK MSA
Lewiston— Auburn, ME MSA
Lexington, KY MSA
Lima, OH MSA
Lincoln, NE MSA
Little Rock— North Little Rock, AR MS
Longview— Marshall, TX MSA
Los Angeles— Long Beach, CA PMSA
Louisville, KY— IN MSA
Lowell, MA— NH PMSA
Lubbock, TX MSA
Lynchburg, VA MSA
Macon, GA MSA
Madison, Wl MSA
CO
2000
(ppm)
133,798
962,441
131,923
332,807
629,401
1,183,110
111,674
341,851
876,156
194,477
4,177,646
315,538
342,376
1,607,486
111,006
440,801
107,377
1,100,491
150,355
139,750
152,307
608,975
480,091
232, 621
82,148
157,322
452,851
1,776,062
149,577
687,249
101,541
385,647
182,821
183,577
483,924
470,658
447,728
193,117
174,682
1,563,282
396,230
114,996
90,830
479,198
155,084
250,291
583,845
208,780
9,519,338
1,025,598
301,686
242,628
214,911
322,549
426,526
Pb
8-hr
(jig/m3)
ND
4
ND
ND
2
7
ND
ND
2
ND
4
1
2
4
ND
3
ND
4
ND
ND
ND
5
2
2
ND
ND
ND
5
ND
3
ND
ND
ND
ND
ND
2
ND
6
4
7
ND
1
ND
2
ND
3
3
ND
10
4
3
ND
ND
ND
2
N02
QMax
(ppm)
ND
0.02
ND
0.01
ND
ND
ND
ND
ND
ND
0.01
ND
ND
0.1 2e
ND
ND
ND
0.03
ND
ND
ND
ND
0.20
0.05
ND
ND
ND
0.01
ND
0.00
ND
ND
ND
ND
ND
ND
ND
0.04
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.06
ND
ND
ND
ND
ND
ND
°3
AM
(ppm)
ND
0.016
ND
ND
0.017
0.017
ND
ND
0.005
ND
0.021
0.015
ND
0.017
ND
ND
ND
0.015
ND
ND
ND
0.026
0.015
0.015
ND
ND
ND
0.017
ND
0.013
ND
ND
ND
0.005
ND
0.014
IN
ND
0.012
ND
ND
ND
ND
0.013
ND
ND
0.010
0.006
0.044
0.013
ND
ND
ND
ND
ND
°3
1-hr
(ppm)
0.11
0.12
0.10
0.10
0.11
0.12
ND
0.11
0.05
0.12
0.19
0.09
0.11
0.10
ND
0.10
ND
0.11
ND
0.11
0.10
0.10
0.13
0.10
ND
ND
0.09
0.12
0.10
0.13
ND
0.12
ND
0.13
0.10
0.11
0.09
0.09
0.12
0.09
0.07
0.09
ND
0.09
0.10
0.07
0.11
0.13
0.17
0.11
ND
ND
ND
0.13
0.09
PM10
8-hr
(^g/m3)
0.08
0.09
0.08
0.08
0.09
0.09
ND
0.09
0.04
0.09
0.12
0.08
0.09
0.09
ND
0.08
ND
0.08
ND
0.09
0.08
0.08
0.10
0.09
ND
ND
0.07
0.09
0.09
0.10
ND
0.09
ND
0.09
0.08
0.09
0.08
0.07
0.08
0.08
0.06
0.09
ND
0.08
0.09
0.06
0.09
0.10
0.11
0.09
ND
ND
ND
0.10
0.07
PM10
WtdAM
I (ng/m3) |
19
24
ND
32
IN
18
ND
22
16
ND
46
32
24
27
ND
24
23
26
17
14
ND
IN
ND
IN
ND
IN
IN
37
ND
34
ND
ND
ND
ND
23
IN
ND
31
42
48
ND
ND
IN
21
IN
ND
25
ND
46
31
ND
IN
ND
IN
22
PM,5
24-hr
;^g/m3;
36
54
ND
69
53
39
ND
42
52
ND
102
80
80
67
ND
64
44
65
32
32
ND
63
ND
51
ND
126
IN
64
ND
125
ND
ND
ND
ND
121
56
ND
56
96
188
ND
ND
36
49
42
ND
48
ND
93
84
ND
38
ND
48
57
PM,5
WtdAM
I (ng/m3)
13.9
16.5
15.6
17.0
15.8
IN
IN
17.4
4.9
12.4
IN
17.6
IN
17.8
10.9
15.6
IN
IN
12.3
IN
13.3
17.5
16.4
15.9
15.2
13.2
15.1
13.4
11.4
IN
15.6
13.0
15.6
13.1
12.2
18.4
13.6
12.1
10.5
10.8
IN
9.1
9.6
IN
ND
IN
15.7
13.4
23.9
18.6
IN
7.4
IN
18.6
13.2
S02
24-hr
(ppm)
41
32
29
38
23
IN
IN
38
10
29
IN
40
IN
36
28
35
IN
IN
34
IN
29
69
29
IN
IN
26
37
32
27
IN
35
33
35
34
28
IN
38
23
31
32
IN
19
26
IN
ND
IN
34
29
83
IN
IN
19
IN
37
34
S02
AM
(ppm)
0.003
0.003
ND
0.006
0.005
0.004
ND
ND
0.002
ND
0.006
0.012
ND
0.007
ND
0.002
ND
0.007
ND
0.008
ND
0.008
0.011
0.007
ND
ND
ND
0.004
ND
0.010
ND
ND
ND
0.004
0.005
0.005
ND
ND
0.001
ND
0.004
ND
0.004
0.005
0.003
ND
0.002
0.002
0.003
0.015
ND
ND
ND
0.003
ND
24-hr
0.007
0.011
ND
0.023
0.024
0.021
ND
ND
0.007
ND
0.031
0.046
ND
0.025
ND
0.006
ND
0.055
ND
0.065
ND
0.025
0.043
0.026
ND
ND
ND
0.039
ND
0.060
ND
ND
ND
0.013
0.018
0.024
ND
ND
0.003
ND
0.020
ND
0.018
0.020
0.015
ND
0.007
0.011
0.010
0.037
ND
ND
ND
0.015
ND
136
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000 (continued)
Metropolitan Statistical Area
Population
Manchester, NH PMSA
Mansfield, OH MSA
Mayaguez, PR MSA
McAllen— Edinburg— Mission, TX MSA
Medford— Ashland, OR MSA
Melbourne — Titusville — Palm Bay, FL M
Memphis, TN— AR— MS MSA
Merced, CA MSA
Miami, FLPMSA
Middlesex — Somerset — Hunterdon, NJ PM
Milwaukee— Waukesha, Wl PMSA
Minneapolis— St. Paul, MN— Wl MSA
Mobile, AL MSA
Modesto, CA MSA
Monmouth— Ocean, NJ PMSA
Monroe, LA MSA
Montgomery, AL MSA
Muncie, IN MSA
Myrtle Beach, SC MSA
Naples, FL MSA
Nashua, NH PMSA
Nashville-TNMSA
Nassau— Suffolk, NY PMSA
New Bedford, MA PMSA
New Haven— Meriden, CT PMSA
New London— Norwich, CT— Rl MSA
New Orleans, LA MSA
New York, NY PMSA
Newark, NJ PMSA
Newburgh, NY— PA PMSA
Norfolk — Virginia Beach — Newport News
Oakland, CA PMSA
Ocala, FL MSA
Odessa— Midland, TX MSA
Oklahoma City, OK MSA
Olympia, WA PMSA
Omaha, NE— IA MSA
Orange County, CA PMSA
Orlando, FL MSA
Owensboro, KY MSA
Panama City, FL MSA
Parkersburg— Marietta, WV— OH MSA
Pensacola, FL MSA
Peoria— Pekin, IL MSA
Philadelphia, PA— NJ PMSA
Phoenix— Mesa, AZ MSA
Pine Bluff, AR MSA
Pittsburgh, PA MSA
Pittsfield, MA MSA
Pocatello, ID MSA
Ponce, PR MSA
Portland, ME MSA
Portland— Vancouver, OR— WA PMSA
Portsmouth— Rochester, NH— ME PMSA
Providence — Fall River — Warwick, Rl —
CO
2000
(ppm)
198,378
175,818
253,347
569,463
181,269
476,230
1,135,614
210,554
2,253,362
1,169,641
1,500,741
2,968,806
540,258
446,997
1,126,217
147,250
333,055
118,769
196,629
251,377
190,949
1,231,311
2,753,913
175,198
542,149
293,566
1,337,726
9,314,235
2,032,989
387,669
1,569,541
2,392,557
258,916
237,132
1,083,346
207,355
716,998
2,846,289
1,644,561
91,545
148,217
151,237
412,153
347,387
5,100,931
3,251,876
84,278
2,358,695
84,699
75,565
361,094
243,537
1,918,009
240,698
1,188,613
Pb
8-hr
(jig/m3)
ND
ND
ND
ND
5
ND
4
ND
3
3
2
5
ND
4
3
ND
ND
ND
ND
ND
4
6
3
ND
3
ND
4
4
5
ND
4
3
ND
ND
4
5
3
6
3
1
ND
ND
ND
3
4
7
ND
3
ND
ND
ND
ND
6
ND
4
N02
QMax
(ppm)
ND
ND
ND
ND
ND
ND
0.59f
ND
ND
0.1 5g
ND
0.40h
ND
0.00
ND
ND
ND
0.58I
0.01
ND
ND
1.50J
ND
ND
ND
ND
0.12
0.02
ND
0.18k
ND
0.00
ND
ND
ND
ND
0.08I
ND
ND
ND
ND
ND
ND
0.02
0.05
ND
ND
0.07
ND
ND
ND
ND
0.11
ND
ND
°3
AM
(ppm)
0.011
ND
ND
ND
ND
ND
0.025
0.012
0.016
0.019
0.021
0.022
ND
0.018
ND
ND
ND
ND
ND
ND
ND
0.019
0.024
ND
0.025
ND
0.019
0.038
0.041
ND
0.016
0.020
ND
ND
0.013
ND
ND
0.029
0.012
0.011
ND
ND
0.010
ND
0.028
0.036
ND
0.025
ND
ND
ND
ND
0.012
0.010
0.020
°3
1-hr
(ppm)
0.09
ND
ND
0.09
0.08
0.09
0.12
0.12
0.09
0.11
0.10
0.09
0.12
0.11
0.14
0.10
0.11
ND
ND
ND
0.09
0.12
0.13
0.10
0.14
0.14
0.12
0.12
0.11
0.10
0.10
0.13
0.09
ND
0.10
0.08
0.08
0.12
0.11
0.08
0.12
0.11
0.12
0.08
0.13
0.11
ND
0.11
IN
ND
ND
0.08
0.08
0.09
0.12
PM10
8-hr
(^g/m3)
0.06
ND
ND
0.08
0.07
0.08
0.09
0.10
0.08
0.09
0.09
0.07
0.10
0.09
0.11
0.08
0.09
ND
ND
ND
0.07
0.09
0.09
0.08
0.09
0.08
0.10
0.09
0.09
0.08
0.08
0.08
0.08
ND
0.09
0.06
0.07
0.08
0.08
0.07
0.09
0.09
0.10
0.07
0.10
0.09
ND
0.09
IN
ND
ND
0.07
0.07
0.07
0.09
PM10
Wtd AM
I (ng/m3) |
IN
IN
ND
IN
IN
IN
28
35
26
ND
21
36
24
35
ND
ND
25
ND
ND
IN
15
34
17
ND
32
16
IN
23
35
ND
22
22
ND
ND
26
15
48
40
26
20
25
21
22
24
29
70
ND
39
ND
31
40
27
16
13
29
PM,5
24-hr
;^g/m3;
39
53
ND
53
68
34
71
89
51
ND
59
103
150
100
ND
ND
61
ND
ND
IN
33
65
38
ND
86
40
57
57
108
ND
41
63
ND
ND
62
36
124
119
53
64
46
75
38
54
76
232
ND
124
ND
94
77
74
45
33
91
PM,5
Wtd AM
I (ng/m3)
IN
ND
IN
11.0
11.4
IN
15.7
17.3
11.3
IN
14.2
IN
IN
18.9
IN
13.3
IN
16.1
IN
ND
ND
IN
12.2
IN
16.2
IN
14.1
18.4
18.7
IN
13.6
11.2
11.0
IN
11.5
10.3
11.5
20.4
12.1
IN
ND
17.5
13.9
14.8
16.0
IN
15.0
20.0
IN
10.5
IN
11.0
10.8
IN
14.9
S02
24-hr
(ppm)
IN
ND
IN
23
49
IN
IN
47
24
IN
35
IN
IN
71
IN
27
IN
49
IN
ND
ND
IN
36
IN
40
IN
37
48
47
IN
26
50
24
IN
29
41
28
37
31
IN
ND
36
32
32
34
IN
27
84
IN
57
IN
35
40
IN
36
S02
AM
(ppm)
0.005
ND
ND
ND
ND
ND
0.006
ND
0.002
0.005
0.004
0.003
0.002
ND
ND
0.002
ND
ND
ND
ND
0.004
0.004
0.007
ND
0.006
ND
0.005
0.013
0.009
ND
0.007
0.003
ND
ND
0.003
ND
0.001
0.002
0.003
0.005
ND
0.011
0.005
0.006
0.010
0.003
ND
0.013
ND
0.008
ND
0.005
ND
0.003
0.007
24-hr
0.022
ND
ND
ND
ND
ND
0.038
ND
0.003
0.018
0.026
0.023
0.008
ND
ND
0.003
ND
ND
ND
ND
0.020
0.040
0.025
ND
0.031
ND
0.020
0.046
0.025
ND
0.023
0.021
ND
ND
0.007
ND
0.016
0.005
0.009
0.018
ND
0.036
0.032
0.063
0.027
0.016
ND
0.086
ND
0.036
ND
0.018
ND
0.013
0.042
APPENDIX A • DATA TABLES 137
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000 (continued)
Metropolitan Statistical Area
Population
Provo— Orem, UT MSA
Pueblo, CO MSA
Racine, Wl PMSA
Raleigh— Durham— Chapel Hill, NC MSA
Rapid City, SD MSA
Reading, PA MSA
Redding, CA MSA
Reno, NV MSA
Richland— Kennewick— Pasco, WA MSA
Richmond— Petersburg, VA MSA
Riverside — San Bernardino, CA PMSA
Roanoke, VA MSA
Rochester, MN MSA
Rochester, NY MSA
Rockford, IL MSA
Rocky Mount, NC MSA
Sacramento, CA PMSA
Saginaw— Bay City— Midland, Ml MSA
St. Cloud, MN MSA
St. Joseph, MO MSA
St. Louis, MO— IL MSA
Salem, OR PMSA
Salinas, CA MSA
Salt Lake City— Ogden, UT MSA
San Antonio, TX MSA
San Diego, CA MSA
San Francisco, CA PMSA
San Jose, CA PMSA
San Juan— Bayamon, PR PMSA
San Luis Obispo— Atascadero— Paso Rob
Santa Barbara — Santa Maria — Lompoc, C
Santa Cruz— Watsonville, CA PMSA
Santa Fe, NM MSA
Santa Rosa, CA PMSA
Sarasota— Bradenton, FL MSA
Savannah, GA MSA
Scranton — Wilkes-Barre — Hazleton, PA
Seattle— Bellevue— Everett, WA PMSA
Sharon, PA MSA
Sheboygan, Wl MSA
Shreveport— Bossier City, LA MSA
Sioux City, IA— NE MSA
Sioux Falls, SD MSA
South Bend, IN MSA
Spokane, WA MSA
Springfield, IL MSA
Springfield, MO MSA
Springfield, MA MSA
Stamford— Norwalk, CT PMSA
State College, PA MSA
Steubenville— Weirton, OH— WV MSA
Stockton— Lodi, CA MSA
Sumter, SC MSA
Syracuse, NY MSA
CO
2000
(ppm)
368,536
141,472
188,831
1,187,941
88,565
373,638
163,256
339,486
191,822
996,512
3,254,821
235,932
124,277
1,098,201
371,236
143,026
1,628,197
403,070
167,392
102,490
2,603,607
347,214
401,762
1,333,914
1,592,383
2,813,833
1,731,183
1,682,585
1,967,627
246,681
399,347
255,602
147,635
458,614
589,959
293,000
624,776
2,414,616
120,293
112,646
392,302
124,130
172,412
265,559
417,939
201,437
325,721
591,932
353,556
135,758
132,008
563,598
104,646
732,117
Pb
8-hr
(jig/m3)
6
ND
2
5
ND
2
ND
5
ND
3
4
3
ND
3
3
ND
6
ND
3
ND
4
IN
1
6
3
5
4
7
6
2
3
1
2
3
4
ND
2
6
ND
ND
ND
ND
ND
ND
6
2
3
4
3
ND
8
4
ND
2
N02
QMax
(ppm)
ND
ND
ND
ND
ND
0.33m
ND
ND
ND
ND
0.05
ND
ND
ND
ND
ND
0.00
ND
ND
ND
6.86n
ND
ND
0.07
ND
0.02
0.00
0.00
0.02
ND
0.00
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.00
0.01
ND
°3
AM
(ppm)
0.024
ND
ND
ND
ND
0.020
ND
0.008
ND
0.017
0.038
0.011
ND
ND
ND
ND
0.019
ND
ND
ND
0.026
ND
0.007
0.026
0.018
0.024
0.020
0.025
0.018
0.012
0.018
0.005
ND
0.013
0.009
ND
0.015
0.021
ND
ND
ND
ND
ND
0.016
ND
ND
0.012
0.026
ND
ND
ND
0.020
ND
ND
°3
1-hr
(ppm)
0.10
ND
0.10
0.12
IN
0.11
0.11
0.09
ND
0.11
0.17
0.10
ND
0.09
0.08
0.11
0.13
ND
ND
ND
0.12
0.07
0.08
0.10
0.10
0.12
0.08
0.10
0.10
0.08
0.10
0.09
ND
0.08
0.11
0.10
0.09
0.10
0.10
0.11
0.13
ND
IN
0.10
0.08
0.10
0.09
0.10
0.12
0.11
0.10
0.11
ND
0.08
PM10
8-hr
(^g/m3)
0.08
ND
0.08
0.09
IN
0.08
0.08
0.07
ND
0.08
0.12
0.08
ND
0.07
0.07
0.09
0.10
ND
ND
ND
0.09
0.06
0.06
0.08
0.08
0.10
0.05
0.07
0.05
0.07
0.08
0.06
ND
0.06
0.09
0.08
0.08
0.07
0.08
0.09
0.09
ND
IN
0.08
0.07
0.08
0.08
0.08
0.08
0.08
0.08
0.08
ND
0.07
PM10
WtdAM
I (ng/m3) |
32
24
ND
23
38
IN
24
42
IN
IN
59
32
ND
ND
ND
20
27
ND
ND
31
45
ND
30
46
IN
31
24
27
37
21
26
26
11
18
26
26
IN
23
ND
ND
24
25
20
19
28
26
18
28
31
ND
31
32
ND
ND
PM,5
24-hr
;^g/m3;
89
64
ND
51
139
45
47
96
140
42
190
66
ND
ND
ND
41
82
ND
ND
80
116
ND
70
117
IN
86
53
68
102
102
62
50
28
40
48
66
46
66
ND
ND
51
76
53
35
87
54
35
57
67
ND
95
79
ND
ND
PM,5
WtdAM
I (ng/m3)
10.1
7.9
ND
16.5
IN
16.9
IN
9.0
IN
15.1
28.4
15.9
IN
11.8
15.0
14.7
12.3
IN
IN
11.8
20.6
8.9
8.0
14.2
IN
15.9
10.9
13.5
7.3
10.5
9.7
7.9
5.2
10.3
11.0
15.1
12.7
12.7
IN
ND
13.8
9.5
IN
13.7
11.0
13.4
12.3
15.9
IN
IN
19.1
17.3
ND
IN
S02
24-hr
(ppm)
34
22
ND
52
IN
34
IN
31
IN
30
81
33
IN
28
36
35
81
IN
IN
27
43
31
22
57
IN
IN
43
57
18
41
19
18
10
40
30
IN
33
43
IN
ND
31
31
IN
36
38
32
27
37
IN
IN
47
IN
ND
IN
S02
AM
(ppm)
ND
ND
ND
ND
ND
0.008
ND
ND
ND
0.006
0.003
0.003
ND
0.006
ND
ND
IN
ND
ND
IN
0.008
ND
ND
0.004
ND
0.004
0.002
ND
0.006
0.005
0.002
0.001
ND
ND
0.002
0.003
0.006
0.003
0.007
ND
0.002
ND
ND
ND
ND
0.005
0.005
0.005
0.005
ND
0.014
ND
ND
0.003
24-hr
ND
ND
ND
ND
ND
0.028
ND
ND
ND
0.017
0.026
0.014
ND
0.021
ND
ND
IN
ND
ND
IN
0.043
ND
ND
0.013
ND
0.011
0.007
ND
0.058
0.028
0.003
0.003
ND
ND
0.019
0.024
0.026
0.011
0.024
ND
0.006
ND
ND
ND
ND
0.035
0.077
0.023
0.026
ND
0.069
ND
ND
0.022
138
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-15. Maximum Air Quality Concentrations by Metropolitan Statistical Area, 2000 (continued)
CO Pb N02 03 03 PM10 PM10
Metropolitan Statistical Area 2000 8-hr QMax AM 1-hr 8-hr Wtd AM
PM25
24-hr
PM25
Wtd AM
Population (ppm) (jig/m3) (ppm) (ppm) (ppm) (ng/m3) (ng/m3) (ng/m3) (ng/m3)
Tacoma, WA PMSA 700,820 6 ND ND 0.08 0.06 28
Tallahassee, FL MSA 284,539 ND ND ND 0.09 0.08 18
Tampa— St. Petersburg— Clearwater, FL 2,395,997 3 2.01o 0.013 0.11 0.08 33
Terre Haute, IN MSA 149,192 ND ND ND 0.09 0.08 25
Texarkana, TX— Texarkana, AR MSA 129,749 ND ND ND ND ND ND
Toledo, OH MSA 618,203 ND 0.33 ND 0.10 0.08 23
Topeka, KS MSA 169,871 ND ND ND ND ND 20
Trenton, NJ PMSA 350,761 ND ND 0.016 0.11 0.10 26
Tucson, AZ MSA 843,746 5 ND 0.017 0.09 0.08 39
Tulsa, OKMSA 803,235 4 ND 0.015 0.12 0.09 25
Tuscaloosa, AL MSA 164,875 ND ND ND ND ND IN
Tyler, TX MSA 174,706 ND ND 0.006 0.10 0.09 ND
Utica— Rome, NY MSA 299,896 ND ND ND 0.08 0.07 9
Vallejo— Fairfield— Napa, CA PMSA 518,821 5 ND 0.013 0.10 0.07 18
Ventura, CA PMSA 753,197 3 0.00 0.020 0.12 0.10 31
Victoria, TX MSA 84,088 ND ND ND 0.09 0.08 ND
Vineland— Millville— Bridgeton, NJ PM 146,438 ND ND ND 0.12 0.09 ND
Visalia— Tulare— Porterville, CA MSA 368,021 3 ND 0.018 0.12 0.11 53
Waco, TX MSA 213,517 ND ND ND ND ND ND
Washington, DC— MD—VA—WV PMSA 4,923,153 5 0.00 0.023 0.13 0.09 24
Waterbury, CT PMSA 228,984 ND 0.02 ND ND ND 21
Waterloo— Cedar Falls, IA MSA 128,012 ND ND ND ND ND 31
Wausau, WIMSA 125,834 ND ND ND 0.08 0.07 IN
West Palm Beach— Boca Raton, FL MSA 1,131,184 3 ND 0.016 0.09 0.08 IN
Wheeling, WV— OH MSA 153,172 2 ND ND 0.09 0.07 28
Wichita, KS MSA 545,220 6 ND ND 0.09 0.08 26
Williamsport, PA MSA 120,044 ND ND ND 0.09 0.07 IN
Wilmington— Newark, DE—MD PMSA 586,216 3 ND IN 0.13 0.11 26
Wilmington, NC MSA 233,450 4 ND ND 0.10 0.08 17
Worcester, MA— CT PMSA 511,389 3 ND 0.018 0.10 0.08 19
Yakima, WA MSA 222,581 3 ND ND ND ND 27
Yolo, CAPMSA 168,660 1 ND 0.011 0.10 0.08 26
York, PA MSA 381,751 2 ND 0.018 0.11 0.09 IN
Youngstown— Warren, OH MSA 594,746 ND ND ND 0.10 0.08 27
Yuba City, CA MSA 139,149 4 ND 0.013 0.10 0.08 28
Yuma, AZ MSA 160,026 ND ND ND 0.08 0.06 IN
CO - Highest second maximum non-overlapping 8-hour concentration (Applicable NAAQS is 9 ppm)
Pb - Highest quarterly maximum concentration (Applicable NAAQS is 1 .5 fig/m3)
NO2 - Highest arithmetic mean concentration (Applicable NAAQS is 0.053 ppm)
O3 (1-hr) - Highest second daily maximum 1-hour concentration (Applicable NAAQS is 0.1 2 ppm)
O3 (8-hr) - Highest fourth daily maximum 8-hour concentration (Applicable NAAQS is 0.08 ppm)
PM10 - Highest weighted annual mean concentration (Applicable NAAQS is 50 fig/m3)
- Highest second maximum 24-hour concentration (Applicable NAAQS is 150 /ig/m3)
SO2 - Highest annual mean concentration (Applicable NAAQS is 0.03 ppm)
- Highest second maximum 24-hour concentration (Applicable NAAQS is 0. 14 ppm)
ND - Indicates data not available
IN - Indicates insufficient data to calculate summary statistic
Wtd - Weighted
AM - Annual mean
ng/m3 - Units are micrograms per cubic meter
PPM - Units are parts per million
58
46
73
54
ND
60
49
55
123
58
68
ND
23
46
80
ND
ND
127
ND
68
41
71
IN
38
62
87
IN
46
36
54
58
66
53
128
66
IN
13.0
IN
13.5
15.7
14.7
IN
10.8
14.7
IN
12.1
IN
ND
11.8
11.6
IN
ND
ND
23.7
IN
18.9
IN
11.6
ND
9.4
16.3
12.7
ND
16.8
12.5
12.1
IN
10.3
16.6
15.9
11.5
ND
S02
24-hr
(ppm)
49
IN
43
37
31
IN
23
43
IN
30
IN
ND
34
60
IN
ND
ND
103
IN
50
IN
29
ND
27
35
29
ND
29
32
33
IN
38
31
35
38
ND
S02
AM
(ppm)
ND
ND
0.006
0.012
ND
0.005
ND
ND
0.002
0.006
ND
ND
0.001
0.002
0.002
ND
0.004
ND
ND
0.011
0.004
ND
ND
0.002
0.013
ND
0.005
0.007
0.006
0.006
ND
ND
0.006
0.007
ND
ND
24-hr
ND
ND
0.031
0.055
ND
0.017
ND
ND
0.007
0.027
ND
ND
0.007
0.005
0.007
ND
0.017
ND
ND
0.030
0.017
ND
ND
0.008
0.044
ND
0.019
0.047
0.030
0.019
ND
ND
0.020
0.024
ND
ND
APPENDIX A • DATA TABLES
139
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002
Metropolitan Statistical Area
AKRON, OH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ALBANY, GA
PM26* 98th percentile
Weighted annual mean
ALBANY-SCHENECTADY-TROY, NY
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ALBUQUERQUE, NM
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ALEXANDRIA, LA
PM25* 98th percentile
Weighted annual mean
ALLENTOWN-BETHLEHEM-EASTON, PA
Lead Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ALTOONA, PA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
ANCHORAGE, AK
PM25* 98th percentile
Weighted annual mean
ANN ARBOR, Ml
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ASHEVILLE, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
Trend
down
ns
ns
ns
ns
NA
NA
NA
NA
down
down
ns
ns
ns
NA
NA
down
ns
ns
up
down
down
NA
NA
NA
NA
ns
down
down
ns
down
up
ns
NA
NA
down
ns
ns
ns
ns
ns
NA
NA
ns
ns
NA
NA
up
up
ns
down
NA
NA
#Trend
Sites
1
1
1
2
2
2
2
1
1
1
1
1
2
2
2
2
2
1
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1993
3.1
0.056
0.015
0.108
0.093
ND
ND
ND
ND
3.8
0.028
0.006
0.102
0.083
ND
ND
6.2
0.024
0.086
0.065
43
26.85
ND
ND
ND
ND
0.181
3.5
0.034
0.007
0.02
0.104
0.082
ND
ND
2
0.052
0.009
0.015
0.1
0.086
ND
ND
0.09
0.074
ND
ND
0.079
0.066
43
22.3
ND
ND
1994
5.3
0.042
0.012
0.1
0.086
ND
ND
ND
ND
5.2
0.037
0.007
0.103
0.078
ND
ND
5
0.023
0.078
0.063
36
22.6
ND
ND
ND
ND
0.131
4.7
0.053
0.008
0.021
0.105
0.084
ND
ND
2.4
0.058
0.01
0.015
0.106
0.092
ND
ND
0.094
0.084
ND
ND
0.084
0.069
30
19
ND
ND
1995
3.3
0.046
0.009
0.117
0.092
ND
ND
ND
ND
4.3
0.023
0.003
0.101
0.08
ND
ND
4.35
0.018
0.082
0.061
36.5
22.4
ND
ND
ND
ND
0.074
4.8
0.028
0.006
0.018
0.109
0.091
ND
ND
1.7
0.037
0.008
0.013
0.112
0.091
ND
ND
0.11
0.089
ND
ND
0.085
0.076
28
18.4
ND
ND
1996
3.4
0.042
0.01
0.105
0.091
ND
ND
ND
ND
3.7
0.025
0.004
0.095
0.077
ND
ND
4.3
0.022
0.089
0.071
30.5
20.5
ND
ND
ND
ND
0.083
3.2
0.035
0.006
0.018
0.114
0.094
ND
ND
1.9
0.033
0.008
0.013
0.101
0.083
ND
ND
0.104
0.085
ND
ND
0.084
0.074
29
18.8
ND
ND
1997
3.2
0.072
0.012
0.103
0.087
ND
ND
ND
ND
4.5
0.02
0.003
0.094
0.077
ND
ND
4.05
0.019
0.088
0.071
30.5
19.7
ND
ND
ND
ND
0.093
2.7
0.03
0.008
0.016
0.116
0.101
ND
ND
1.5
0.046
0.01
0.014
0.114
0.096
ND
ND
0.089
0.076
ND
ND
0.09
0.075
38
20.7
ND
ND
1998
2.6
0.044
0.01
0.112
0.097
ND
ND
ND
ND
4.4
0.016
0.004
0.096
0.075
ND
ND
3.85
0.016
0.089
0.07
28.5
19.2
ND
ND
ND
ND
0.12
2.9
0.03
0.008
0.016
0.106
0.095
ND
ND
1.2
0.032
0.008
0.013
0.114
0.098
ND
ND
0.097
0.086
ND
ND
0.114
0.09
36
20.1
ND
ND
1999
2.5
0.065
0.011
0.115
0.097
41.45
17.215
ND
ND
4.2
0.016
0.003
0.106
0.082
ND
ND
4.05
0.016
0.091
0.071
28.5
19.3
22.1
6.54
30.7
14.29
0.071
3.2
0.03
0.006
0.015
0.125
0.105
31.4
12.37
1.6
0.03
0.007
0.013
0.111
0.091
ND
ND
0.09
0.083
ND
ND
0.099
0.084
36
20.5
31.6
15.61
2000 2001
2.4 2.7
0.044 0.044
0.009 0.01
0.106 0.113
0.085 0.096
37.15 44.1
16.435 16.75
37.7 36.1
16.61 14.64
2.9 2.4
0.02 0.024
0.004 0.005
0.08 0.104
0.066 0.086
25.3 32.1
10.54 10.685
3.45 3.25
0.017 0.017
0.088 0.085
0.07 0.07
29.5 27.5
18.65 18.7
17.5 19.7
6.39 6.39
30.1 29.4
13.35 12.15
0.111 0.071
2.6 3.3
0.027 0.028
0.007 0.007
0.013 0.017
0.112 0.126
0.091 0.094
37.75 42.85
13.975 15.215
1 1.1
0.045 0.042
0.006 0.009
0.014 0.014
0.104 0.107
0.08 0.083
20.2 16.3
6.05 6.17
0.094 0.103
0.082 0.086
31.7 39.1
13.73 13.995
0.107 0.091
0.09 0.076
33 26
18.3 17.5
30.5 29.4
14.195 12.78
2002
1.8
0.06
0.01
0.12
0.1
41.9
16.745
30.5
13.82
3.4
0.019
0.004
0.113
0.088
33.65
10.91
2.8
0.019
0.087
0.075
39.5
24.55
18.5
6.31
24.7
10.55
0.088
2.3
0.028
0.008
0.014
0.114
0.094
39.9
13.62
0.7
0.032
0.007
0.013
0.102
0.089
18.2
6.92
0.1
0.089
31.1
14.215
0.106
0.09
28
7.6
30.5
13.77
140
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
ATHENS, GA
ATLANTA
Lead
CO
SO,
98th percentile
Weighted annual mean
GA
Maximum quarterly value
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ATLANTIC-CAPE MAY, NJ
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
AUGUSTA-AIKEN, GA-SC
Lead Maximum quarterly value
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
AUSTIN-SAN MARCOS, TX
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
BAKERSFIELD, CA
Lead Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BALTIMORE, MD
CO
S02
N02
Ozone
PM10*
PM25*
BANGOR,
PM10*
PM *
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
ME
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
NA
NA
ns
down
down
down
ns
ns
ns
ns
ns
NA
NA
down
down
ns
ns
down
ns
ns
ns
ns
ns
NA
NA
ns
ns
NA
NA
down
ns
down
ns
ns
ns
ns
NA
NA
down
ns
down
down
ns
ns
down
down
NA
NA
ns
down
NA
NA
1 ND
1 ND
1 0.02
1 4.9
2 0.026
2 0.006
1 0.025
1 0.158
1 0.122
1 57
1 35.1
3 ND
3 ND
1 0.014
1 0.003
1 0.115
1 0.093
1 0.012
1 0.005
3 0.1
3 0.084
1 35
1 22.1
2 ND
2 ND
1 0.091
1 0.08
2 ND
2 ND
1 0.013
1 3.6
2 0.02
2 0.136
2 0.103
1 103
1 58.2
1 ND
1 ND
1 4.9
1 0.024
1 0.007
1 0.033
3 0.135
3 0.106
4 49.5
4 29.375
5 ND
5 ND
1 34
1 22.2
1 ND
1 ND
ND
ND
0.02
5.3
0.026
0.005
0.023
0.125
0.089
53
32.2
ND
ND
0.019
0.003
0.099
0.083
0.011
0.005
0.093
0.08
35
21.3
ND
ND
0.102
0.085
ND
ND
0.013
3.6
0.02
0.133
0.104
103
58.2
ND
ND
5.7
0.029
0.008
0.032
0.121
0.09
48
29.225
ND
ND
35
21.9
ND
ND
ND
ND
0.027
4.5
0.019
0.004
0.019
0.145
0.118
56
33.3
ND
ND
0.011
0.003
0.116
0.1
0.006
0.005
0.1
0.079
29
18.7
ND
ND
0.105
0.089
ND
ND
0.013
3.6
0.018
0.133
0.106
103
58.2
ND
ND
4.2
0.022
0.006
0.026
0.135
0.104
46
27.475
ND
ND
32
20
ND
ND
ND
ND
0.02
3.7
0.021
0.004
0.027
0.137
0.11
48
31.2
ND
ND
0.014
0.003
0.108
0.095
0.004
0.005
0.098
0.083
29
18.7
ND
ND
0.098
0.08
ND
ND
0.012
3.6
0.019
0.144
0.117
87
53.6
ND
ND
3.9
0.028
0.008
0.027
0.112
0.086
40.75
25.9
ND
ND
27
18.8
ND
ND
ND
ND
0.017
4.3
0.023
0.004
0.025
0.133
0.104
61
32.2
ND
ND
0.011
0.003
0.131
0.106
0.009
0.005
0.104
0.083
31
21.4
ND
ND
0.089
0.075
ND
ND
0.011
2.7
0.016
0.122
0.098
69
46.5
ND
ND
4.8
0.025
0.007
0.026
0.134
0.1
43.75
27.058
ND
ND
33
21.1
ND
ND
ND
ND
0.013
4.1
0.018
0.004
0.024
0.157
0.126
53
31.1
ND
ND
0.01
0.003
0.118
0.091
0.02
0.005
0.116
0.096
38
22.4
ND
ND
0.115
0.088
ND
ND
0.015
2.8
0.016
0.132
0.11
103
47
ND
ND
3.3
0.021
0.007
0.026
0.12
0.098
45
26.542
ND
ND
34
17.5
ND
ND
48.2
19.84
0.053
4.1
0.019
0.004
0.024
0.156
0.124
56
34.9
46.05
22.035
0.009
0.003
0.118
0.095
0.003
0.005
0.106
0.087
35
21.1
42.3
19.89
0.102
0.087
ND
ND
0.01
5
0.018
0.122
0.103
109
59.3
95.3
26.36
4.6
0.02
0.006
0.024
0.135
0.107
39.5
24.975
ND
ND
24
16.7
25.7
8.98
39.7
18.48
0.04
3.2
0.019
0.004
0.023
0.158
0.113
52
36
51
19.987
0.013
0.003
0.108
0.085
0.006
0.005
0.106
0.087
30
20.5
34.9
16.005
0.107
0.088
24.5
10.925
0.011
5.2
0.016
0.128
0.105
87
53.6
93.9
22.63
3.4
0.024
0.006
0.024
0.111
0.089
44
26.225
37.24
16.58
31
17.3
22.8
9.08
50.9
17.53
0.05
4.1
0.015
0.003
0.023
0.114
0.084
85
37.6
37.867
17.79
0.01
0.003
0.105
0.095
0.004
0.004
0.096
0.08
27
16.7
28.45
13.78
0.091
0.078
20.85
9.625
0.008
3.2
0.012
0.124
0.102
111
59.8
95.9
21.83
3.3
0.026
0.006
0.023
0.122
0.094
41.75
24.95
40.3
16.052
32
17.1
31.1
10.09
27.8
14.96
0.037
3.6
0.018
0.003
0.019
0.123
0.1
45
26.4
33.167
15.93
0.009
0.003
0.107
0.093
0.002
0.004
0.107
0.092
28
17.2
28.4
13.295
0.103
0.091
27.35
10.565
0.008
2.5
0.017
0.133
0.11
87
59.1
80.4
24.08
3
0.021
0.006
0.025
0.138
0.105
35.75
20.825
39.08
14.966
33
16.6
27
10.44
APPENDIX A • DATA TABLES
141
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
BATON ROUGE, LA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
BEAUMONT-PORT ARTHUR, TX
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
BELLINGHAM, WA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
BENTON HARBOR, Ml
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
BERGEN-PASSAIC, NJ
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BILLINGS, MT
S02 2nd daily max
Annual mean
PM25* 98th percentile
Weighted annual mean
BILOXI-GULFPORT-PASCAGOULA, MS
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BINGHAMTON, NY
PM26* 98th percentile
Weighted annual mean
BIRMINGHAM, AL
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
BISMARCK, ND
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
ns
ns
ns
up
up
NA
NA
ns
down
down
ns
ns
down
down
NA
NA
ns
ns
NA
NA
down
ns
down
ns
ns
NA
NA
down
down
NA
NA
ns
down
ns
ns
ns
down
NA
NA
NA
NA
down
ns
ns
ns
ns
NA
NA
NA
NA
1
2
2
2
3
3
1
1
2
2
1
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
1
1
2
2
2
2
1
1
2
2
1
1
1
1
1
2
2
2
2
1
1
9
0.023
0.006
0.015
0.117
0.084
29
18.2
ND
ND
0.059
0.008
0.009
0.11
0.085
0.08
0.058
ND
ND
0.093
0.079
ND
ND
4.5
0.023
0.007
59
36.5
ND
ND
0.099
0.023
ND
ND
0.026
0.005
0.094
0.076
31
20.4
ND
ND
ND
ND
7.1
0.05
0.009
0.118
0.091
ND
ND
ND
ND
4.6
0.021
0.006
0.017
0.121
0.084
40
26.8
ND
ND
0.05
0.007
0.01
0.118
0.082
0.082
0.059
ND
ND
0.116
0.086
ND
ND
5.3
0.028
0.006
71
40.9
ND
ND
0.073
0.017
ND
ND
0.018
0.003
0.105
0.085
32
20.9
ND
ND
ND
ND
6.9
0.037
0.007
0.103
0.081
ND
ND
ND
ND
3.4
0.026
0.005
0.017
0.12
0.091
40
25.9
ND
ND
0.031
0.006
0.01
0.12
0.088
0.079
0.054
ND
ND
0.115
0.098
ND
ND
5
0.023
0.005
53
34.6
ND
ND
0.066
0.013
ND
ND
0.018
0.003
0.104
0.085
26
18.7
ND
ND
ND
ND
6.4
0.016
0.006
0.124
0.1
ND
ND
ND
ND
4.7
0.022
0.006
0.018
0.118
0.089
40
26.4
ND
ND
0.044
0.006
0.01
0.119
0.08
0.078
0.062
ND
ND
0.125
0.098
ND
ND
3.6
0.018
0.005
58
37.4
ND
ND
0.048
0.008
ND
ND
0.03
0.003
0.103
0.079
28
17.7
ND
ND
ND
ND
4.9
0.015
0.004
0.122
0.094
ND
ND
ND
ND
5.4
0.023
0.006
0.017
0.122
0.09
45
27
ND
ND
0.038
0.006
0.01
0.156
0.09
0.07
0.052
ND
ND
0.118
0.099
ND
ND
3.65
0.018
0.004
58.5
38.25
ND
ND
0.033
0.006
ND
ND
0.021
0.003
0.103
0.087
40
21.4
ND
ND
ND
ND
5.9
0.018
0.006
0.113
0.085
ND
ND
ND
ND
3.9
0.027
0.006
0.017
0.123
0.087
48.5
30.35
ND
ND
0.028
0.004
0.008
0.11
0.073
0.07
0.056
ND
ND
0.136
0.093
ND
ND
3.7
0.018
0.004
59
39.1
ND
ND
0.024
0.006
ND
ND
0.019
0.003
0.113
0.093
36
20.3
ND
ND
ND
ND
4.4
0.032
0.007
0.132
0.104
ND
ND
ND
ND
4.5
0.022
0.005
0.017
0.117
0.087
52
33.7
32.05
15.005
0.023
0.003
0.01
0.092
0.064
0.062
0.05
24.5
8.08
0.107
0.096
35.4
12.27
3.8
0.02
0.005
53
34.3
39.65
12.985
0.02
0.005
16.6
7.98
0.02
0.003
0.107
0.09
28
14.7
32.65
14.93
ND
ND
4.4
0.057
0.009
0.121
0.096
52.7
23.41
23
7.61
3.6 4.8
0.023 0.021
0.005 0.005
0.017 0.017
0.127 0.106
0.093 0.08
52 55
31.8 32.6
35.55 30.2
14.42 13.39
0.046 0.039
0.005 0.005
0.008 0.009
0.124 0.093
0.087 0.073
0.063 0.061
0.052 0.05
20.7 18.3
8.38 7.17
0.107 0.117
0.077 0.088
29.7 32.3
12.11 13.16
3.4 2.6
0.02 0.018
0.005 0.005
61 63
36.5 36.3
35.9 34.775
14.155 13.918
0.02 0.025
0.005 0.006
24.7 23.4
8.07 7.55
0.022 0.011
0.003 0.002
0.121 0.097
0.09 0.083
29 26
16.2 17.7
29.65 24.15
12.825 11.215
25.3 29.6
11.6 11.1
3.7 6.3
0.057 0.019
0.01 0.004
0.119 0.107
0.093 0.084
45.5 36.25
19.51 16.275
14.3 17.1
6.62 6.68
3.7
0.027
0.006
0.017
0.112
0.078
37
26.4
23.8
12.19
0.03
0.004
0.008
0.111
0.085
0.067
0.053
23.3
7.8
0.118
0.098
30.6
12.53
2.6
0.018
0.004
49
28.8
33.95
12.94
0.024
0.006
14.2
6.56
0.021
0.002
0.09
0.075
24
15.2
21.2
10.24
38.7
11.51
3.7
0.015
0.004
0.11
0.087
35.9
14.895
15.5
6.38
142
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
BLOOMINGTON-NORMAL, IL
PM25* 98th percentile
Weighted annual mean
BOISE CITY, ID
CO 2nd max (daily-non-overlapping 8-h)
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BOSTON, MA-NH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BOULDER-LONGMONT, CO
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BRAZORIA, TX
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
BRIDGEPORT, CT
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
BROCKTON, MA
PM25* 98th percentile
Weighted annual mean
BROWNSVILLE-HARLINGEN-SAN BENITO, TX M
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
BUFFALO-NIAGARA FALLS, NY
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
NA
NA
down
ns
down
NA
NA
down
down
down
down
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
up
NA
NA
ns
ns
NA
NA
down
ns
down
ns
ns
ns
down
NA
NA
NA
NA
down
ns
down
ns
ns
NA
NA
down
ns
ns
ns
up
up
NA
NA
1
1
1
1
1
2
2
2
3
3
4
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
3
3
ND
ND
6.4
62
32.9
ND
ND
3.6
0.035
0.01
0.026
0.102
0.078
44
30.2
ND
ND
6.4
0.092
0.072
35
19.5
ND
ND
0.132
0.092
ND
ND
3.7
0.035
0.01
0.165
0.111
43
20.8
ND
ND
ND
ND
3.8
0.034
0.072
45
22.4
ND
ND
4.4
0.04
0.01
0.017
0.088
0.072
ND
ND
ND
ND
5.4
57
31.5
ND
ND
4.3
0.035
0.009
0.027
0.121
0.09
45
28.2
ND
ND
6.2
0.092
0.072
35
19.5
ND
ND
0.112
0.085
ND
ND
5.8
0.049
0.01
0.174
0.093
46
25.7
ND
ND
ND
ND
3.8
0.085
0.072
36
22.5
ND
ND
4.2
0.043
0.011
0.019
0.088
0.079
ND
ND
ND
ND
6.4
47
24.8
ND
ND
3.55
0.025
0.007
0.024
0.119
0.094
41
26.2
ND
ND
4.7
0.09
0.074
35
19.5
ND
ND
0.148
0.113
ND
ND
4.9
0.028
0.007
0.14
0.115
37
21.8
ND
ND
ND
ND
2.6
0.084
0.069
35
21.4
ND
ND
3.1
0.039
0.008
0.019
0.099
0.082
ND
ND
ND
ND
5
40
23.7
ND
ND
3.25
0.023
0.007
0.024
0.105
0.083
33
24.4
ND
ND
5.5
0.087
0.075
30
19.6
ND
ND
0.11
0.079
ND
ND
3
0.023
0.006
0.123
0.096
32
20.6
ND
ND
ND
ND
2.2
0.077
0.065
28
18.9
ND
ND
3.7
0.033
0.008
0.019
0.091
0.074
ND
ND
ND
ND
6.2
40
24.1
ND
ND
3.3
0.032
0.008
0.024
0.105
0.091
37
24.7
ND
ND
5.4
0.092
0.072
31
20.9
ND
ND
0.137
0.085
ND
ND
4
0.031
0.007
0.135
0.103
34
21.4
ND
ND
ND
ND
3.2
0.08
0.065
36
20.6
ND
ND
3.3
0.057
0.009
0.018
0.088
0.073
ND
ND
ND
ND
3.9
30
17.7
ND
ND
2.9
0.028
0.008
0.024
0.113
0.1
42
26.4
ND
ND
4.7
0.111
0.089
36
24.1
ND
ND
0.111
0.09
ND
ND
2.8
0.024
0.007
0.134
0.097
33
20.8
ND
ND
ND
ND
3.2
0.081
0.069
45
24.6
ND
ND
3.1
0.034
0.008
0.018
0.111
0.094
ND
ND
ND
ND
4.6
47
23.7
31.35
9.045
3.9
0.024
0.007
0.023
0.115
0.088
43
29.6
33
11.31
3.7
0.099
0.075
35
22.5
21.4
7.53
0.161
0.112
ND
ND
3.2
0.023
0.006
0.14
0.096
30
19.4
31.1
13.06
26
11.08
2.6
0.075
0.066
32
21.5
ND
ND
2.2
0.037
0.008
0.019
0.102
0.09
ND
ND
32.5
14.86
3.1
41
23
36.3
9.24
2.35
0.025
0.005
0.02
0.085
0.07
36
24.5
27.2
11.35
3.1
0.09
0.072
32
22.4
20.1
8.82
0.136
0.079
25.3
10.48
2.4
0.024
0.006
0.122
0.09
37
20.4
41.5
13.89
28.95
11.63
1.6
0.08
0.064
47
25.4
18.3
9.59
2
0.038
0.008
0.018
0.105
0.085
30.033
13.45
32.4
14.79
3.2
34
22.9
44.7
10.37
2.45
0.019
0.005
0.021
0.122
0.1
40
26.8
31.5
12.13
3.5
0.088
0.071
36
24.2
22.85
9.145
0.113
0.084
24.9
10.21
2.7
0.029
0.007
0.144
0.102
36
19.3
40.1
13.73
31.9
12.18
1.5
0.074
0.063
31
19.3
18
9.75
1.9
0.037
0.008
0.018
0.116
0.102
39.767
13.29
25.7
12.85
3.1
49
25.7
32.65
9.69
1.6
0.018
0.005
0.02
0.145
0.1
41
24.6
29.3
10.06
3.2
0.094
0.078
37
23.4
22.95
8.635
0.136
0.095
22.7
9.47
2.5
0.029
0.005
0.145
0.103
34
17.4
32.9
12.7
35.9
11.64
1.9
0.077
0.065
33
20
22.7
9.79
1.8
0.046
0.008
0.017
0.116
0.105
38.333
12.363
APPENDIX A • DATA TABLES
143
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
BURLINGTON, VT
PM26* 98th percentile
Weighted annual mean
CANTON-MASSILLON, OH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CASPER, WY
PM10* 90th percentile
Weighted annual mean
CEDAR RAPIDS, IA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CHAMPAIGN-URBANA, IL
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
CHARLESTON-NORTH CHARLESTON, SC
Lead Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
CHARLESTON, WV
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CHARLOTTE-GASTONIA-ROCK HILL, NC-SC M
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
CHARLOTTESVILLE, VA
PM10* 90th percentile
Weighted annual mean
NA
NA
ns
down
ns
ns
ns
ns
down
NA
NA
ns
ns
down
ns
ns
up
up
NA
NA
ns
down
ns
ns
NA
NA
ns
down
down
down
ns
ns
ns
ns
down
NA
NA
ns
ns
NA
NA
ns
down
ns
ns
ns
ns
NA
NA
ns
down
1
1
1
1
1
2
2
3
3
2
2
1
1
1
2
2
1
1
2
2
1
1
1
1
1
1
2
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
2
2
3
3
2
2
1
1
ND
ND
3.2
0.046
0.01
0.104
0.09
41
24.633
ND
ND
27
17.7
3.2
0.017
0.003
32
20.7
ND
ND
0.015
0.004
0.074
0.066
ND
ND
0.012
5.8
0.025
0.004
0.012
0.109
0.076
40.5
25.65
ND
ND
52
29.2
ND
ND
0.016
5.8
0.126
0.098
41.333
28.467
ND
ND
40
23.7
ND
ND
5.2
0.052
0.009
0.098
0.084
47.667
27.233
ND
ND
34
17.3
4.2
0.016
0.003
33
21.5
ND
ND
0.024
0.004
0.094
0.083
ND
ND
0.015
4
0.038
0.004
0.011
0.097
0.074
38.5
24.9
ND
ND
49
28.1
ND
ND
0.032
5.8
0.114
0.089
43.667
29.1
ND
ND
33
21.5
ND
ND
3
0.033
0.006
0.111
0.093
48.667
27.567
ND
ND
32
19.4
2.6
0.013
0.003
34
21.4
ND
ND
0.011
0.003
0.095
0.084
ND
ND
0.01
6.4
0.02
0.003
0.011
0.087
0.066
30.5
20.7
ND
ND
40
26
ND
ND
0.013
4.7
0.114
0.094
41.667
27.767
ND
ND
41
22.5
ND
ND
2.5
0.032
0.006
0.096
0.085
35.667
23.967
ND
ND
33
19.1
7.8
0.011
0.002
33
20.9
ND
ND
0.013
0.003
0.094
0.085
ND
ND
0.016
4.7
0.021
0.003
0.01
0.099
0.076
32.5
21.65
ND
ND
41
24
ND
ND
0.01
4.4
0.127
0.099
44
30.4
ND
ND
35
21.3
ND
ND
2.5
0.025
0.007
0.096
0.083
41.333
24.067
ND
ND
29
15.7
2.4
0.012
0.003
41
25.7
ND
ND
0.018
0.004
0.088
0.076
ND
ND
0.011
3.9
0.022
0.003
0.011
0.09
0.072
31
21
ND
ND
32
21.1
ND
ND
0.007
6
0.115
0.098
43.667
28.433
ND
ND
36
20.9
ND
ND
3.5
0.029
0.007
0.114
0.096
43.333
24.433
ND
ND
31
17.2
2.5
0.01
0.003
42
26.4
ND
ND
0.019
0.003
0.105
0.083
ND
ND
0.021
2.9
0.013
0.003
0.01
0.106
0.083
40
22.8
ND
ND
35
21.4
ND
ND
0.021
5
0.13
0.107
49.667
29.767
ND
ND
33
22.7
ND
ND
2.3
0.028
0.007
0.105
0.09
35.333
23.133
41.7
17.735
29
19.7
2
0.016
0.003
34
23.3
32.15
11.7
0.01
0.002
0.108
0.094
ND
ND
0.01
4
0.011
0.002
0.01
0.099
0.081
31.5
20.8
ND
ND
37
21.9
38.7
17.89
0.018
4.3
0.126
0.104
43.667
27.8
37.5
17.58
32
19.9
22.7
8.32
2.6
0.028
0.008
0.099
0.084
37.667
23
38.8
17.785
30
17
1.8
0.008
0.002
47
31.6
27.95
10.78
0.016
0.002
0.084
0.073
27.8
14.82
0.02
2.7
0.013
0.003
0.011
0.093
0.08
35.5
22.6
30.3
13.44
44
26.5
37
18.2
0.042
4.7
0.123
0.094
47.333
29.267
32.95
16.54
43
22.9
29.9 38
9.74 9.89
2.6 2.8
0.025 0.021
0.007 0.007
0.105 0.108
0.089 0.096
33 39
21.733 21.367
44.6 40.4
17.225 16.515
31 30
18.7 17.1
1.9 1.4
0.015 0.018
0.002 0.002
39 35
26.7 24
33.9 26.1
11.635 11.045
0.016 0.016
0.002 0.002
0.08 0.091
0.073 0.082
29.3 23.4
12.59 12.2
0.008 0.005
3 2.8
0.011 0.01
0.003 0.003
0.011 0.01
0.085 0.095
0.071 0.074
32 26.5
20.45 17.8
25.8 27.2
11.97 11.64
41 36
24.8 23.4
44.5 38.6
18.1 17.2
0.008 0.006
4.3 3.2
0.118 0.125
0.093 0.101
38.667 41
25.9 24.933
31.85 31
15.16 14.28
32 30
17.8 19.4
144
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
CHATTANOOGA, TN-GA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
CHEYENNE, WY
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CHICAGO, IL
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CHICO-PARADISE, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CINCINNATI, OH-KY-IN
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CLARKSVILLE-HOPKINSVILLE, TN-KY
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CLEVELAND-LORAIN-ELYRIA, OH
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
down
down
ns
ns
NA
NA
down
down
ns
ns
ns
ns
ns
ns
ns
NA
NA
ns
ns
down
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
down
down
NA
NA
down
ns
ns
ns
down
down
NA
NA
down
down
down
down
down
ns
ns
down
down
NA
NA
2
2
1
1
1
1
1
1
2
2
3
3
1
3
3
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
4
4
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
3
3
3
3
0.104
0.088
49
32.1
24
15.5
ND
ND
0.076
5.45
0.044
0.007
0.031
0.085
0.068
67
37.4
ND
ND
0.01
3.9
0.016
0.09
0.076
60
27.2
ND
ND
4.8
0.037
0.011
0.104
0.08
54.5
28.75
ND
ND
0.058
0.01
0.103
0.082
40
25.8
ND
ND
0.11
4.6
0.053
0.014
0.028
0.107
0.084
81
38.867
ND
ND
0.114
0.088
50
33.9
28
17.8
ND
ND
0.077
5.8
0.042
0.007
0.032
0.098
0.076
67
37.4
ND
ND
0.008
4.1
0.015
0.097
0.082
55
33.3
ND
ND
4.1
0.051
0.009
0.118
0.099
47.5
28.25
ND
ND
0.037
0.007
0.103
0.092
40
25.8
ND
ND
0.06
7.7
0.047
0.012
0.028
0.093
0.074
76.667
46.933
ND
ND
0.108
0.09
51
32.1
26
14.6
ND
ND
0.061
4.1
0.032
0.006
0.032
0.114
0.085
67
37.4
ND
ND
0.005
3.5
0.014
0.091
0.076
52
26.3
ND
ND
3.1
0.025
0.007
0.114
0.098
53
29.9
ND
ND
0.019
0.006
0.102
0.086
40
25.8
ND
ND
0.053
8.2
0.039
0.01
0.027
0.108
0.088
78
45.667
ND
ND
0.113
0.088
49
32.5
25
15.1
ND
ND
0.059
4.05
0.028
0.006
0.031
0.102
0.077
61
35.8
ND
ND
0.005
3.4
0.013
0.096
0.074
40
25
ND
ND
2.7
0.045
0.011
0.112
0.088
41
25.6
ND
ND
0.023
0.006
0.1
0.079
41
24.9
ND
ND
0.037
4.9
0.037
0.01
0.026
0.103
0.089
65
40.433
ND
ND
0.107
0.088
43
26.4
20
12.9
ND
ND
0.059
4.15
0.033
0.007
0.034
0.105
0.082
59
33.9
ND
ND
0.006
3.5
0.013
0.074
0.066
40
25.9
ND
ND
2.4
0.045
0.01
0.11
0.084
44
25.65
ND
ND
0.026
0.005
0.099
0.082
35
21.4
ND
ND
0.05
4.5
0.044
0.01
0.028
0.096
0.084
67.667
40.1
ND
ND
0.129
0.1
43
27
22
13.9
ND
ND
0.063
4.6
0.039
0.007
0.032
0.096
0.078
59
35.2
ND
ND
0.006
3.8
0.013
0.103
0.078
37
22.3
ND
ND
2.5
0.036
0.01
0.121
0.091
43.75
25.675
ND
ND
0.02
0.006
0.111
0.086
39
23.1
ND
ND
0.043
3.9
0.046
0.01
0.027
0.12
0.098
66.667
42.5
ND
ND
0.117 0.119
0.096 0.097
42 45
26.9 28.9
23 24
14.9 15.7
12.4 13.2
5.57 5.58
0.042 0.085
3.9 3.2
0.036 0.043
0.007 0.007
0.032 0.032
0.104 0.083
0.089 0.067
66 68
36.1 35.1
54.1 40.65
21.85 19.265
0.004 0.005
4 3.5
0.015 0.012
0.11 0.091
0.087 0.078
50 56
28.6 27.4
ND 70
ND 16.26
2.5 2.4
0.03 0.053
0.008 0.009
0.105 0.11
0.091 0.087
42 43
24.7 25
35.433 37.2
17 16.83
0.016 0.018
0.005 0.006
0.115 0.099
0.092 0.081
36 40
22.9 23.3
30.1 38.3
15.12 15.45
0.03 0.023
3.9 3.2
0.044 0.031
0.01 0.008
0.025 0.023
0.118 0.101
0.092 0.081
67.333 69
41.4 40.4
43.967 44.267
19.16 19.573
0.106
0.085
42
26.9
26
15.7
12.2
5.03
0.038
2.9
0.023
0.005
0.032
0.093
0.073
72
38
45.95
20.12
0.005
3.8
0.012
0.094
0.08
47
29.2
56
13.01
2.2
0.042
0.011
0.106
0.082
40.5
23.3
40.3
15.695
0.017
0.005
0.096
0.082
31
20.4
27.2
13.51
0.03
3.5
0.029
0.008
0.024
0.108
0.086
65
38.7
44.4
18.33
0.111
0.097
33
21.8
29
16.4
13.8
4.66
0.037
2.85
0.022
0.005
0.032
0.104
0.082
69
36.1
40.35
17.11
0.005
3.4
0.012
0.092
0.081
49
28.1
53
15.13
2.6
0.043
0.01
0.115
0.096
37.5
21.95
40.375
15.763
0.017
0.007
0.1
0.093
30
19.3
29.3
13.09
0.027
2.2
0.029
0.008
0.022
0.108
0.088
55.333
33.033
41.467
17.287
APPENDIX A • DATA TABLES
145
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
COLORADO SPRINGS, CO
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
COLUMBIA, SC
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
COLUMBUS, GA-AL
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
COLUMBUS, OH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
CORPUS CHRISTI, TX
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
CORVALLIS, OR
PM25* 98th percentile
Weighted annual mean
DALLAS, TX
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
DANBURY, CT
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
ns
down
down
down
ns
ns
ns
NA
NA
ns
ns
ns
ns
ns
ns
ns
NA
NA
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
NA
NA
ns
ns
ns
ns
NA
NA
down
ns
down
down
ns
ns
NA
NA
ns
down
ns
ns
NA
NA
1
1
1
1
1
3
3
1
1
2
2
1
1
1
2
2
2
2
1
1
1
1
2
2
1
1
1
1
1
1
1
3
3
1
1
1
1
1
1
1
2
2
1
2
2
1
1
1
1
1
1
1
1
0.013
5.7
0.011
0.003
0.008
40
23.067
ND
ND
0.013
0.003
0.013
0.112
0.089
73.5
41
ND
ND
0.097
0.075
37
25.4
ND
ND
5.3
0.032
0.007
0.101
0.083
50
27.1
ND
ND
0.11
0.08
57
30.6
ND
ND
4.4
0.012
0.003
0.02
42.5
25.1
ND
ND
0.024
0.006
0.14
0.096
ND
ND
0.014
4.9
0.01
0.004
0.008
36
21.5
ND
ND
0.013
0.002
0.011
0.103
0.082
71
40.65
ND
ND
0.097
0.075
44
26.5
ND
ND
4.1
0.04
0.006
0.102
0.088
46
26.7
ND
ND
0.103
0.079
48
31.3
ND
ND
4.4
0.012
0.003
0.02
44
25.6
ND
ND
0.037
0.006
0.125
0.093
ND
ND
0.01
5.5
0.01
0.003
0.008
35
19.7
ND
ND
0.011
0.002
0.013
0.104
0.079
72
40.6
ND
ND
0.113
0.089
46
28.2
ND
ND
4.9
0.016
0.003
0.106
0.088
53
30.6
ND
ND
0.109
0.089
47
31.1
ND
ND
4.4
0.012
0.003
0.02
45
26.65
ND
ND
0.02
0.004
0.134
0.093
ND
ND
0.004
5
0.008
0.002
0.007
31.333
19.333
ND
ND
0.015
0.003
0.013
0.088
0.074
69
38.75
ND
ND
0.095
0.08
33
22.2
ND
ND
2.7
0.015
0.004
0.106
0.087
39
24.8
ND
ND
0.099
0.083
37
25.1
ND
ND
5.3
0.012
0.002
0.02
42
25.7
ND
ND
0.02
0.005
0.11
0.081
ND
ND
0.004
4.9
0.007
0.002
0.008
28.333
18.7
ND
ND
0.015
0.003
0.011
0.108
0.086
75.5
42.75
ND
ND
0.094
0.08
39
26.4
ND
ND
2.9
0.021
0.005
0.095
0.083
63
30.9
ND
ND
0.094
0.077
50
30.5
ND
ND
4.6
0.013
0.002
0.018
39.5
24.8
ND
ND
0.024
0.005
0.138
0.105
ND
ND
0.012
3.8
0.007
0.002
0.007
30.667
18.833
ND
ND
0.016
0.003
0.014
0.116
0.098
89.5
48.6
ND
ND
0.108
0.091
45
30.1
ND
ND
3.7
0.018
0.005
0.113
0.094
70
34.2
ND
ND
0.102
0.082
57
34.5
ND
ND
4.4
0.007
0.002
0.02
45.5
27.7
ND
ND
0.02
0.004
0.115
0.092
ND
ND
0.009
4.2
0.008
0.002
0.007
27
18.233
ND
ND
0.013
0.003
0.014
0.117
0.094
83.5
47.55
36.6
15.9
0.107
0.089
40
26.5
44.1
19.885
2.5
0.015
0.004
0.111
0.095
62
32.6
38.633
17.603
0.103
0.084
62
34.9
31.6
7.05
3.2
0.01
0.002
0.021
42.5
26.7
ND
ND
0.024
0.004
0.151
0.106
ND
ND
0.011
3
0.006
0.002
0.009
30.333
18.6
14.6
7.58
0.012
0.003
0.014
0.113
0.096
70.5
40.6
29.5
15.795
0.105
0.087
44
25.6
46.5
18.975
2.8
0.019
0.004
0.105
0.079
54
34.1
39.167
17.597
0.099
0.083
54
35.7
30.1
7.88
2.2
0.01
0.002
0.019
41.5
27.05
33
13.45
0.017
0.004
0.124
0.09
32.9
12.73
0.009 0.006
2.8 5.2
0.006 0.006
0.002 0.002
0.008 0.008
32 34.333
20.2 21.533
15.5 19.6
7.72 7.77
0.014 0.014
0.003 0.003
0.014 0.012
0.104 0.101
0.082 0.084
65.5 60.5
39.75 34.6
25 28.1
13.58 12.99
0.088 0.095
0.073 0.079
39 33
22.4 22.6
40.1 33.1
15.695 14.45
2.6 2.5
0.017 0.017
0.004 0.004
0.097 0.112
0.08 0.095
52 44
30.5 29.2
40.433 39.567
17.083 16.003
0.092 0.104
0.077 0.084
41 48
27.2 32.9
27.5 27.3
7.26 7.64
2.4 2.1
0.01 0.01
0.002 0.001
0.019 0.018
41.5 45.5
27.2 26.1
31.3 37.6
13.91 13.57
0.022 0.023
0.004 0.004
0.133 0.141
0.096 0.109
35.2 30.7
13.2 12.59
146
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
DAVENPORT-MOLINE-ROCK ISLAND, IA-IL M
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
DAYTON-SPRINGFIELD, OH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
DAYTONA BEACH, FL
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
DECATUR, IL
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
DENVER, CO
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
DES MOINES, IA
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
DETROIT, Ml
Maximum Quarterly Value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
DOTHAN, AL
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
down
down
down
down
NA
NA
down
ns
ns
ns
ns
ns
ns
ns
ns
NA
NA
ns
down
ns
ns
NA
NA
ns
down
ns
down
up
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
NA
NA
ns
down
up
ns
ns
ns
ns
down
ns
NA
NA
down
ns
NA
NA
1
1
1
1
1
1
1
1
1
3
3
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
2
2
1
1
2
2
2
2
1
2
2
3
3
5
5
1
1
1
1
0.022
0.005
79
46.5
ND
ND
4
0.028
0.006
0.109
0.087
50
24.9
0.094
0.074
ND
ND
0.025
0.006
0.077
0.065
ND
ND
0.108
10.4
0.035
0.009
0.034
0.103
0.079
23
14.3
ND
ND
5.4
0.08
0.04
52
31.7
ND
ND
0.038
4.15
0.031
0.008
0.022
0.102
0.076
63.667
37.567
ND
ND
52
26.4
ND
ND
0.034
0.006
92
59.9
ND
ND
4.4
0.034
0.007
0.114
0.091
44
25.5
0.084
0.072
ND
ND
0.03
0.007
0.095
0.079
ND
ND
0.067
8.2
0.034
0.007
0.035
0.098
0.076
20
12.7
ND
ND
4.9
0.073
0.052
57.5
32.8
ND
ND
0.047
5.8
0.038
0.008
0.025
0.128
0.095
71.667
43.5
ND
ND
47
27.8
ND
ND
0.02
0.006
108
66.8
ND
ND
3.7
0.017
0.004
0.116
0.091
48
27.3
0.083
0.068
ND
ND
0.024
0.005
0.097
0.08
ND
ND
0.054
9.5
0.019
0.005
0.035
0.098
0.077
19
9.7
ND
ND
5.7
0.081
0.071
53
30.1
ND
ND
0.047
5
0.039
0.007
0.022
0.114
0.081
65.667
38.7
ND
ND
46
28.1
ND
ND
0.014
0.004
89
50
ND
ND
3
0.031
0.005
0.113
0.097
38
22.7
0.079
0.066
ND
ND
0.022
0.005
0.1
0.094
ND
ND
0.05
7.3
0.024
0.006
0.033
0.103
0.081
22
11.6
ND
ND
3.6
0.082
0.064
56
32.8
ND
ND
0.034
3.55
0.032
0.007
0.02
0.099
0.085
53
33.6
ND
ND
36
22.3
ND
ND
0.02
0.005
90
49.3
ND
ND
4
0.022
0.005
0.107
0.089
40
24.5
0.086
0.072
ND
ND
0.021
0.006
0.087
0.077
ND
ND
0.03
5.5
0.026
0.006
0.034
0.095
0.076
18
9.4
ND
ND
3
0.075
0.063
65
34
ND
ND
0.063
3.15
0.037
0.007
0.026
0.115
0.085
55.667
33.2
ND
ND
45
24.9
ND
ND
0.018
0.004
68
37.9
ND
ND
3.4
0.016
0.004
0.117
0.096
45
24.5
0.094
0.079
ND
ND
0.02
0.005
0.094
0.078
ND
ND
0.106
4.7
0.023
0.004
0.035
0.115
0.087
23
12.6
ND
ND
4.1
0.065
0.056
55.5
30.3
ND
ND
0.043
3.15
0.036
0.008
0.023
0.115
0.092
63.667
33.833
ND
ND
41
27.3
ND
ND
0.014
0.004
80
43.5
29.7
13.14
2.8
0.017
0.004
0.116
0.093
45
23.6
0.087
0.075
25.2
11.36
0.027
0.006
0.102
0.087
ND
ND
0.078
5
0.024
0.005
0.036
0.099
0.079
20
11.8
ND
ND
3.5
0.069
0.059
50.75
28.15
28.3
11.39
0.056
3.85
0.044
0.009
0.024
0.112
0.09
61.667
35.733
31.9
12.72
43
28.8
39.7
19.58
0.014
0.003
70
40.2
30.3
12.75
3.1
0.018
0.004
0.1
0.086
44
26.7
0.087
0.075
26
10.48
0.025
0.005
0.092
0.077
30.9
15.04
0.149
5.4
0.025
0.005
0.036
0.101
0.081
24
13.1
27.9
10.78
2.7
0.071
0.061
48
28.4
32.3
10.56
0.03
3.9
0.038
0.007
0.024
0.091
0.076
59
35.333
40.34
16.54
48
24.4
34.6
15.42
0.01 0.013
0.002 0.002
63 70
36.2 42
33 29.5
13.21 12.25
2.6 1.8
0.017 0.023
0.004 0.005
0.097 0.11
0.083 0.096
42 37
25.2 23.8
0.085 0.085
0.072 0.068
21.7 21.6
10 8.75
0.025 0.021
0.005 0.004
0.078 0.094
0.071 0.085
34.7 33.9
14.27 14.1
0.103 0.114
4.1 3.7
0.026 0.023
0.005 0.005
0.037 0.035
0.102 0.105
0.08 0.086
24 24
13.1 13.1
37.2 24.5
11.81 10.1
2.3 2.7
0.067 0.071
0.06 0.059
57 40.5
33.2 24.3
29.9 31.9
10.61 10.55
0.031 0.031
2.5 3
0.04 0.042
0.007 0.007
0.023 0.021
0.112 0.11
0.091 0.094
57 52.333
34.867 30.733
42.64 40.36
16.924 16.428
37 31
22.5 21
26.6 26.7
14 13.03
APPENDIX A • DATA TABLES
147
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
DOVER, DE
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
DULUTH-SUPERIOR, MN-WI
CO 2nd max (daily-non-overlapping 8-h)
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
DUTCHESS COUNTY, NY
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
EL PASO, TX
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ELKHART-GOSHEN, IN
PM25* 98th percentile
Weighted annual mean
ELMIRA, NY
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
ENID, OK
PM25* 98th percentile
Weighted annual mean
ERIE, PA
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
EUGENE-SPRINGFIELD, OR
CO 2nd max (daily-nonoverlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
EVANSVILLE-HENDERSON, IN-KY
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
Trend
down
ns
NA
NA
down
ns
ns
NA
NA
ns
ns
NA
NA
down
down
ns
ns
NA
NA
NA
NA
ns
ns
up
ns
NA
NA
down
ns
down
ns
ns
NA
NA
down
ns
ns
down
down
NA
NA
down
ns
ns
ns
ns
ns
down
down
NA
NA
#Trend
Sites
1
1
1
1
1
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
3
3
1
1
1
3
3
1
1
1993
0.137
0.097
ND
ND
4.1
36
21.4
ND
ND
0.139
0.099
ND
ND
0.229
10.6
0.098
0.059
ND
ND
ND
ND
0.019
0.005
0.09
0.08
ND
ND
0.072
0.011
0.014
0.107
0.081
ND
ND
5.9
0.072
0.054
72
27.9
ND
ND
4.35
0.06
0.014
0.017
0.094
0.071
50
29.733
ND
ND
1994
0.137
0.097
ND
ND
4.3
33.5
20.65
ND
ND
0.117
0.087
ND
ND
0.14
7.6
0.115
0.075
ND
ND
ND
ND
0.023
0.004
0.084
0.074
ND
ND
0.076
0.01
0.015
0.101
0.09
ND
ND
6.4
0.082
0.068
53
23.65
ND
ND
4.05
0.055
0.012
0.018
0.096
0.079
51
31.333
ND
ND
1995
0.137
0.097
ND
ND
4.5
35
20.5
ND
ND
0.115
0.093
ND
ND
0.192
7.5
0.126
0.084
ND
ND
ND
ND
0.014
0.004
0.088
0.076
ND
ND
0.05
0.009
0.015
0.105
0.088
ND
ND
5.7
0.077
0.06
49
21.5
ND
ND
3.2
0.051
0.01
0.017
0.108
0.089
51.333
29.833
ND
ND
1996
0.11
0.088
ND
ND
4.5
31
20.05
ND
ND
0.109
0.089
ND
ND
0.153
9.1
0.123
0.078
ND
ND
ND
ND
0.016
0.004
0.088
0.072
ND
ND
0.066
0.011
0.015
0.1
0.083
ND
ND
5.7
0.111
0.084
37
18.6
ND
ND
3.05
0.064
0.01
0.017
0.092
0.081
39.333
24.833
ND
ND
1997
0.124
0.099
ND
ND
3.2
32.5
19.85
ND
ND
0.111
0.089
ND
ND
0.108
7.2
0.114
0.071
ND
ND
ND
ND
0.015
0.003
0.081
0.073
ND
ND
0.035
0.009
0.015
0.103
0.087
ND
ND
5.2
0.073
0.056
41.5
20.1
ND
ND
3.65
0.062
0.01
0.016
0.086
0.075
43
25.667
ND
ND
1998
0.131
0.102
ND
ND
3.7
31
21.3
ND
ND
0.108
0.089
ND
ND
0.144
8.3
0.122
0.088
ND
ND
ND
ND
0.011
0.003
0.094
0.082
ND
ND
0.068
0.01
0.014
0.122
0.098
ND
ND
4.6
0.089
0.073
36
17
ND
ND
3.05
0.057
0.012
0.018
0.103
0.078
44.333
27.367
ND
ND
1999
0.12
0.097
28.5
12.47
2.9
37
21.75
25.3
8.64
0.12
0.093
ND
ND
0.145
5.7
0.108
0.071
20.7
9.24
ND
ND
0.015
0.003
0.092
0.082
ND
ND
0.043
0.01
0.015
0.112
0.096
ND
ND
5
0.068
0.056
36.5
17.9
46.65
10.65
2.95
0.074
0.012
0.016
0.109
0.081
43.333
25.167
ND
ND
2000 2001
0.126 0.117
0.093 0.091
34.4 34.4
13.2 13.05
2.1 2.5
44 34.5
23.9 22.25
25.2 23.45
8.385 8.48
0.105 0.109
0.079 0.091
30.8 27.6
11.55 11.17
0.099 0.099
7.3 5.8
0.114 0.116
0.08 0.075
23 23.8
9.18 9.34
38.6 37.5
15.67 15.7
0.012 0.015
0.003 0.004
0.089 0.094
0.073 0.082
24.8 28.7
10.24 10.73
0.041 0.043
0.008 0.01
0.012 0.012
0.095 0.104
0.078 0.089
28.2 37.5
13.99 13.83
4.3 4.1
0.056 0.077
0.047 0.061
36.5 40
18.4 18.35
45.75 46.9
11.28 11.605
2.3 2.4
0.062 0.05
0.011 0.01
0.016 0.016
0.092 0.09
0.078 0.072
41.333 39.667
24.5 24.733
37.3 36.4
16.08 15.57
2002
0.112
0.094
37.1
12.38
2.1
36.5
21.05
23.25
7.855
0.152
0.111
31.2
10.74
0.099
4.8
0.127
0.089
29.5
10.61
35.2
14.98
0.013
0.004
0.098
0.089
27.8
9.7
0.037
0.011
0.012
0.114
0.098
42.9
13.21
4.2
0.08
0.067
36.5
19.4
50.8
11.88
2.3
0.051
0.01
0.016
0.09
0.072
38.333
24.267
46.7
15.36
148
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend #Trend 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Sites
FARGO-MOORHEAD, ND-MN
PM26* 98th percentile
Weighted annual mean
FAYETTEVILLE, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
FAYETTEVILLE-SPRINGDALE-ROGERS, AR
PM25* 98th percentile
Weighted annual mean
FITCHBURG-LEOMINSTER, MA
PM25* 98th percentile
Weighted annual mean
FLAGSTAFF, AZ-UT
Ozone 2nd highest daily max
4th highest daily max 8-h average
FLINT, Ml
Maximum quarterly value
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
FLORENCE, AL
S02 2nd daily max
Annual mean
PM25* 98th percentile
Weighted annual mean
FLORENCE, SC
PM26* 98th percentile
Weighted annual mean
FORT COLLINS-LOVELAND, CO
CO 2nd max (daily-nonoverlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
FORT LAUDERDALE, FL
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
FORT MYERS-CAPE CORAL, FL
PM25* 98th percentile
Weighted annual mean
FORT PIERCE-PORT ST. LUCIE, FL
PM25* 98th percentile
Weighted annual mean
FORT SMITH, AR-OK
PM26* 98th percentile
Weighted annual mean
NA
NA
ns
ns
ns
ns
NA
NA
NA
NA
NA
NA
ns
ns
ns
down
down
ns
ns
ns
down
NA
NA
down
down
NA
NA
NA
NA
down
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
ns
NA
NA
NA
NA
NA
NA
NA
NA
1 ND
1 ND
1 0.115
1 0.093
1 41
1 27.3
1 ND
1 ND
1 ND
1 ND
1 ND
1 ND
1 0.07
1 0.066
1 0.016
1 0.017
1 0.005
1 0.106
1 0.068
1 40
1 23.9
1 ND
1 ND
1 0.022
1 0.004
1 ND
1 ND
1 ND
1 ND
1 6.6
2 0.091
2 0.068
1 ND
1 ND
2 4.45
1 0.011
1 0.002
1 0.01
2 0.102
2 0.081
3 28.667
3 18.967
2 ND
2 ND
1 ND
1 ND
1 ND
1 ND
1 ND
1 ND
ND
ND
0.098
0.084
40
25.1
ND
ND
ND
ND
ND
ND
0.081
0.073
0.011
0.017
0.004
0.09
0.077
36
20.1
ND
ND
0.022
0.003
ND
ND
ND
ND
6
0.095
0.072
ND
ND
4.65
0.013
0.002
0.009
0.097
0.071
23.333
17.2
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.1
0.081
35
23.3
ND
ND
ND
ND
ND
ND
0.075
0.069
0.014
0.016
0.003
0.097
0.082
37
21.1
ND
ND
0.018
0.003
ND
ND
ND
ND
5.2
0.089
0.072
ND
ND
5.15
0.008
0.002
0.011
0.097
0.066
23.667
16.767
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.099
0.086
39
25.3
ND
ND
ND
ND
ND
ND
0.082
0.073
0.012
0.012
0.002
0.113
0.089
31
20.2
ND
ND
0.019
0.003
ND
ND
ND
ND
5.1
0.092
0.069
ND
ND
3.65
0.008
0.002
0.01
0.102
0.066
27.667
17.967
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.098
0.085
41
24.8
ND
ND
ND
ND
ND
ND
0.076
0.072
0.011
0.012
0.002
0.094
0.081
33
20.2
ND
ND
0.02
0.003
ND
ND
ND
ND
5.2
0.088
0.07
ND
ND
3.7
0.011
0.002
0.01
0.091
0.071
25.333
17.733
ND
ND
ND
ND
ND
ND
ND
ND
ND
ND
0.112
0.093
41
26.5
ND
ND
ND
ND
ND
ND
0.076
0.072
0.015
0.014
0.002
0.104
0.089
37
20.6
ND
ND
0.019
0.003
ND
ND
ND
ND
4.1
0.092
0.076
ND
ND
2.8
0.017
0.003
0.01
0.1
0.077
30.333
19.967
ND
ND
ND
ND
ND
ND
ND
ND
26.7
9.39
0.12
0.1
39
24.4
33.5
16.19
ND
ND
34.5
9.39
0.086
0.076
0.014
0.011
0.003
0.108
0.089
33
19
32.8
12.02
0.017
0.003
35.6
17.32
31.7
14.37
5.1
0.085
0.069
ND
ND
4.05
0.015
0.003
0.011
0.102
0.073
23
17.3
25.2
9.23
21.2
10.12
18.7
9.63
ND
ND
26.4
7.71
0.101
0.086
39
28
33
15.86
31.4
12.5
21.1
9.79
0.082
0.071
0.011
0.015
0.004
0.086
0.072
32
18.6
32.2
12.95
0.017
0.003
32.4
15.62
31.3
14.4
3.8
0.093
0.074
19.7
8.3
3.2
0.026
0.003
0.01
0.091
0.073
24
17.1
24.55
9.41
24.5
9.55
23.4
10.06
27.3
13.54
23.9
8.43
0.108
0.08
39
28
27
14.28
25
11.56
23.35
9.595
0.074
0.07
0.012
0.014
0.002
0.108
0.091
42
20
38
13.12
0.016
0.003
28.7
12.82
24.3
13.11
3
0.086
0.069
24.7
8.63
3.55
0.016
0.002
0.009
0.1
0.074
29.333
18.033
21.6
8.485
21.9
9.21
21
8.99
29.5
13.74
21
7.35
0.113
0.094
39
28
30.6
13.64
25.8
10.76
25.6
9.4
0.085
0.079
0.015
0.006
0.002
0.102
0.088
32
17.4
30.8
12.54
0.013
0.002
33.5
12.81
30.5
12.11
2.9
0.097
0.08
18
7.73
3.2
0.011
0.002
0.008
0.091
0.063
22
15.5
18.25
7.915
16.4
7.81
16.9
8.01
26.2
11.75
APPENDIX A • DATA TABLES
149
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
FORT WAYNE, IN
CO 2nd max (daily-nonoverlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
FORT WORTH-ARLINGTON, TX
2nd max (daily-nonoverlapping 8-h)
Annual mean
2nd highest daily max
4th highest daily max 8-h average
98th percentile
Weighted annual mean
CO
N02
Ozone
PM25*
FRESNO,CA
Maximum quarterly value
CO 2nd max (daily-nonoverlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
GAINESVILLE, FL
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
GALVESTON-TEXAS CITY, TX
S02
Ozone
2nd daily max
Annual mean
2nd highest daily max
4th highest daily max 8-h average
GARY, IN
Maximum quarterly value
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
GOLDSBORO, NC
PM25* 98th percentile
Weighted annual mean
GRAND FORKS, ND-MN
PM25* 98th percentile
Weighted annual mean
GRAND JUNCTION, CO
CO 2nd max (daily-nonoverlapping 8-h)
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
NA
NA
down
down
down
ns
ns
ns
ns
NA
NA
ns
ns
NA
NA
down
ns
ns
ns
ns
down
down
down
ns
ns
NA
NA
NA
NA
NA
NA
down
ns
ns
NA
NA
1 4.7
2 0.093
2 0.081
1 36
1 22.9
1 ND
1 ND
1 3.5
1 0.013
2 0.113
2 0.093
2 ND
2 ND
1 0.025
4 4.175
4 0.021
4 0.14
4 0.107
3 91.667
3 46.9
2 ND
2 ND
1 30
1 19.5
2 ND
2 ND
1 0.056
1 0.005
1 0.176
1 0.114
1 0.044
1 5
1 0.044
1 0.008
2 0.1
2 0.083
1 ND
1 ND
1 ND
1 ND
1 ND
1 ND
1 6.1
1 31
1 21.5
1 ND
1 ND
4.7
0.113
0.094
43
23.5
ND
ND
2.7
0.017
0.133
0.101
ND
ND
0.02
4.925
0.02
0.127
0.098
66
42.567
ND
ND
33
18.5
ND
ND
0.052
0.006
0.125
0.088
0.052
4.6
0.055
0.008
0.11
0.088
ND
ND
ND
ND
ND
ND
6
32
21.4
ND
ND
4.7
0.109
0.094
44
23.9
ND
ND
3.3
0.017
0.141
0.104
ND
ND
0.015
4.225
0.02
0.134
0.102
83.333
44.567
ND
ND
27
17.5
ND
ND
0.089
0.006
0.198
0.14
0.044
3.7
0.039
0.008
0.118
0.097
ND
ND
ND
ND
ND
ND
5.4
31
21.7
ND
ND
2.7
0.1
0.091
28
17.2
ND
ND
2.8
0.015
0.129
0.094
ND
ND
0.008
4.15
0.019
0.14
0.107
63.333
37.333
ND
ND
23
17.1
ND
ND
0.067
0.014
0.107
0.08
0.064
2.8
0.031
0.007
0.112
0.094
ND
ND
ND
ND
ND
ND
5.8
30
20.6
ND
ND
6.3
0.095
0.087
28
19.6
ND
ND
2.8
0.016
0.123
0.092
ND
ND
0.011
3.5
0.018
0.126
0.101
81
42.767
ND
ND
32
20.7
ND
ND
0.053
0.006
0.175
0.097
0.043
3.8
0.032
0.008
0.113
0.093
ND
ND
ND
ND
ND
ND
5.4
28
19.6
ND
ND
3
0.103
0.089
39
23.7
ND
ND
2.5
0.013
0.126
0.099
ND
ND
0.013
3.5
0.018
0.155
0.118
62
34.833
ND
ND
29
19.9
ND
ND
0.039
0.004
0.146
0.095
0.04
3.2
0.055
0.009
0.109
0.085
ND
ND
ND
ND
ND
ND
5.3
29
19.8
ND
ND
3.3
0.1
0.089
31
17
35.5
13.33
2.6
0.017
0.145
0.102
22.7
12.58
0.008
3.4
0.021
0.129
0.102
96.333
48.1
89.6
23.73
29
19
25.9
11.28
0.04
0.007
0.172
0.108
0.077
3.1
0.028
0.007
0.11
0.095
43.8
15.87
40.6
15.42
26.3
10.23
4.7
31
20
18.3
6.93
3.9
0.093
0.086
32
20.2
33.6
15.65
2.1
0.012
0.118
0.094
27.8
12.36
0.01
3.35
0.018
0.134
0.105
76
41.933
85.75
20.115
31
19.9
26.5
11.505
0.037
0.004
0.127
0.09
0.108
3.2
0.025
0.006
0.094
0.081
43.6
17.38
34.4
15.77
24.6
8.18
4.1
37
23.6
18.4
7.21
2.6
0.091
0.078
33
18
32
14.16
2
0.012
0.125
0.098
26.75
12.18
0.01
3.1
0.018
0.134
0.106
78.333
46.733
74.75
19.1
29
19.7
23.25
10.215
0.045
0.005
0.113
0.076
0.017
3.2
0.03
0.006
0.106
0.087
50.2
18.11
29.2
14.65
22.5
8.28
3.7
35
23.6
20.7
7.86
3.3
0.11
0.095
34
17.9
32.1
14.88
2.1
0.013
0.13
0.101
34.7
12.275
0.009
2.8
0.018
0.142
0.111
76.333
44.633
64.1
18.905
29
19.7
24.55
9.88
0.025
0.004
0.109
0.083
0.032
2.6
0.013
0.004
0.122
0.098
39.5
16.43
28.8
13.18
22.5
8.28
3.6
39
26.5
18.2
8.1
150
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
GRAND RAPIDS-MUSKEGON-HOLLAND, Ml
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
GREAT FALLS, MT
PM25* 98th percentile
Weighted annual mean
GREELEY, CO
CO 2nd max (daily-nonoverlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
GREEN BAY, Wl
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
GREENSBORO-WINSTON-SALEM-HIGH POINT, NC
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
GREENVILLE, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
GREENVILLE-SPARTANBURG-ANDERSON, SC MS
Maximum quarterly value
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
HAGERSTOWN, MD
PM25* 98th percentile
Weighted annual mean
ns
down
down
ns
ns
down
down
NA
NA
NA
NA
down
ns
ns
ns
ns
NA
NA
ns
down
ns
ns
NA
NA
ns
down
down
ns
up
ns
down
NA
NA
ns
ns
NA
NA
ns
down
ns
up
down
ns
up
down
down
NA
NA
1 3.2
1 0.012
1 0.003
4 0.103
4 0.083
2 39
2 21.85
3 ND
3 ND
1 ND
1 ND
1 5.8
1 0.087
1 0.063
1 40
1 22.6
1 ND
1 ND
1 0.018
1 0.003
1 0.085
1 0.069
2 ND
2 ND
1 0.022
1 0.006
1 0.017
2 0.112
2 0.089
2 40.5
2 24.8
3 ND
3 ND
1 0.108
1 0.091
1 ND
1 ND
1 0.02
1 5.4
1 0.012
1 0.003
1 0.018
2 0.116
2 0.085
2 41
2 25.95
1 ND
1 ND
4
0.013
0.003
0.113
0.088
46
26.9
ND
ND
ND
ND
5.2
0.087
0.071
37
23.1
ND
ND
0.015
0.003
0.085
0.069
ND
ND
0.021
0.007
0.017
0.104
0.084
35.5
23.95
ND
ND
0.086
0.074
ND
ND
0.018
5.5
0.016
0.003
0.018
0.101
0.085
42.5
26.4
ND
ND
4.6
0.011
0.002
0.129
0.101
40
20.95
ND
ND
ND
ND
5.3
0.093
0.072
34
19.9
ND
ND
0.017
0.004
0.112
0.083
ND
ND
0.025
0.007
0.016
0.114
0.09
37.5
25.25
ND
ND
0.098
0.082
ND
ND
0.012
5.3
0.007
0.001
0.017
3.3
0.011
0.002
0.122
0.09
35.5
20.25
ND
ND
ND
ND
7
0.097
0.07
30
17.7
ND
ND
0.011
0.003
0.105
0.091
ND
ND
0.026
0.007
0.016
0.106
0.082
37.5
24.65
ND
ND
0.097
0.086
ND
ND
0.011
4.6
0.012
0.002
0.016
2.4
0.008
0.002
0.107
0.086
32
18.65
ND
ND
ND
ND
4.8
0.095
0.069
30
17.8
ND
ND
0.017
0.003
0.091
0.073
ND
ND
0.023
0.007
0.017
0.11
0.089
38
24.2
ND
ND
0.122
0.097
ND
ND
0.01
5.6
0.014
0.003
0.017
0.1170.1030.1030.118
0.09
45.5
30.6
ND
ND
0.086
46.5
31.3
ND
ND
0.087
38.5
23.5
ND
ND
2.9
0.008
0.002
0.109
0.088
38.5
21.25
ND
ND
ND
ND
4.4
0.102
0.075
30
16.4
ND
ND
0.011
0.003
0.098
0.077
ND
ND
0.023
0.006
0.017
0.117
0.099
41
25.2
ND
ND
0.109
0.089
ND
ND
0.011
4.3
0.015
0.003
0.017
0.12
0.099
39.5
25
ND
ND
3.5
0.006
0.001
0.111
0.093
36
18.9
36.733
12.977
ND
ND
3.4
0.092
0.069
29
17.5
ND
ND
0.011
0.003
0.097
0.085
33.4
10.81
0.02
0.005
0.016
0.112
0.098
38.5
23.9
36.8
16.897
0.109
0.093
ND
ND
0.012
4.8
0.009
0.003
0.017
0.107
0.1
40.5
25.5
ND
ND
2.6
0.01
0.002
0.112
0.076
31
18.65
33.567
12.917
23
6.13
3.8
0.093
0.069
34
20.5
20.4
8.93
0.016
0.004
0.09
0.071
32.1
10.96
0.019
0.005
0.016
0.11
0.09
36.5
22.3
35.633
17.04
0.109
0.082
30.5
13.92
0.021
3.7
0.011
0.003
0.016
0.104
0.087
38.5
23.95
39.9
15.55
3.1
0.007
0.002
0.112
0.089
36.5
20.4
37
13.693
17.3
5.39
3.7
0.105
0.074
33
20.8
35.7
10.61
0.013
0.003
0.107
0.088
33.85
11.35
0.016
0.005
0.016
0.109
0.09
36.5
22.7
35.267
15.667
0.091
0.077
27.8
12.52
0.01
3.4
0.013
0.003
0.015
0.112
0.089
35.5
22.15
41.6
14.17
2.8
0.007
0.002
0.116
0.095
34
18.45
36.2
13.113
17.6
5.25
3.7
0.064
0.057
34
21
25.9
9.22
0.013
0.002
0.094
0.084
28.45
10.75
0.024
0.005
0.014
0.124
0.102
34.5
21.95
32.533
14.88
0.106
0.091
30.6
12.28
0.01
3.3
0.014
0.003
0.016
0.093
35
21.15
42.7
14.9
APPENDIX A • DATA TABLES
151
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
HAMILTON-MIDDLETOWN, OH
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
HARRISBURG-LEBANON-CARLISLE, PA
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
HARTFORD, CT
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
HATTIESBURG, MS
PM25* 98th percentile
Weighted annual mean
HICKORY-MORGANTON-LENOIR, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
HONOLULU, HI
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
HOUMA, LA
PM25* 98th percentile
Weighted annual mean
HOUSTON, TX
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
ns
down
down
NA
NA
down
down
down
ns
ns
down
down
NA
NA
down
down
down
ns
ns
ns
ns
down
NA
NA
NA
NA
up
up
ns
down
NA
NA
down
down
ns
ns
ns
down
ns
down
NA
NA
down
down
down
ns
ns
ns
ns
ns
NA
NA
1
1
1
1
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
3
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
2
2
1
1
2
2
2
2
2
2
2
2
2
2
0.042
0.008
0.121
0.086
62.667
31.567
ND
ND
0.025
0.006
0.015
0.118
0.095
45
27.5
ND
ND
7.2
0.023
0.006
0.018
0.146
0.1
22
12.9
ND
ND
ND
ND
0.092
0.075
44
26.4
ND
ND
2
0.011
0.002
0.004
0.055
0.049
24.5
17.7
ND
ND
4.75
0.023
0.005
0.016
0.166
0.09
56
34.6
ND
ND
0.046
0.008
0.103
0.087
54
30.667
ND
ND
0.04
0.007
0.022
0.118
0.091
40
23.6
ND
ND
7.9
0.031
0.007
0.02
0.133
0.099
25
14.3
ND
ND
ND
ND
0.092
0.075
39
26.3
ND
ND
1.8
0.007
0.001
0.004
0.055
0.052
22.5
16.25
ND
ND
4.15
0.02
0.005
0.017
0.154
0.099
62
37.6
ND
ND
0.02
0.005
0.121
0.089
57.333
33.433
ND
ND
0.02
0.005
0.02
0.099
0.084
37
21.7
ND
ND
7
0.023
0.005
0.017
0.134
0.097
19
12.1
ND
ND
ND
ND
0.093
0.077
36
23.2
ND
ND
1.85
0.004
0.001
0.003
0.056
0.051
21
16.1
ND
ND
3.8
0.02
0.004
0.019
0.173
0.114
59.5
33.85
ND
ND
0.026
0.006
0.107
0.092
43
29.133
ND
ND
0.022
0.006
0.021
0.096
0.078
38
23.4
ND
ND
6.45
0.022
0.006
0.016
0.098
0.082
23
12.4
ND
ND
ND
ND
0.094
0.078
37
24.1
ND
ND
1.9
0.008
0.002
0.003
0.047
0.041
23
17.05
ND
ND
4.85
0.024
0.004
0.019
0.154
0.113
46.5
30.5
ND
ND
0.035
0.006
0.104
0.088
55
30.933
ND
ND
0.022
0.007
0.019
0.112
0.084
37
22.2
ND
ND
5.9
0.025
0.005
0.018
0.143
0.099
27
13.8
ND
ND
ND
ND
0.099
0.08
37
23.7
ND
ND
1.7
0.004
0.002
0.003
0.053
0.047
21.5
16.1
ND
ND
3.45
0.018
0.004
0.018
0.203
0.113
59.5
34.65
ND
ND
0.022
0.006
0.109
0.089
53.667
30.733
ND
ND
0.021
0.006
0.019
0.116
0.097
35
20.4
ND
ND
7.1
0.019
0.005
0.02
0.12
0.09
22
13.7
ND
ND
ND
ND
0.133
0.096
37
23.1
ND
ND
1.45
0.008
0.002
0.003
0.056
0.049
23
16
ND
ND
3.45
0.019
0.004
0.016
0.185
0.119
75
40.25
ND
ND
0.021
0.007
0.117
0.096
48
28.1
37
18.82
0.021
0.005
0.018
0.114
0.095
34
20.3
39.7
14.39
5.5
0.019
0.004
0.018
0.138
0.097
23
11.9
29.5
10.79
ND
ND
0.106
0.082
43
25
34
17.43
1.25
0.003
0.001
0.003
0.054
0.048
18.5
13.95
ND
ND
3.35
0.016
0.004
0.018
0.144
0.102
57.5
35.6
ND
ND
0.023
0.006
0.095
0.082
51
29.867
38.1
16.96
0.024
0.005
0.017
0.101
0.079
33
20.2
45.8
15.69
7.3
0.021
0.004
0.017
0.106
0.082
22
11.3
32.3
10.67
29.6
14.93
0.107
0.091
33
22
34.2
17.35
1.3
0.005
0.001
0.004
0.048
0.044
23
15.6
28.7
12.38
3.2
0.021
0.004
0.016
0.161
0.106
57
35.4
25.6
12.745
0.027 0.034
0.006 0.006
0.107 0.115
0.083 0.1
46 39.333
26.567 24.233
41.7 40.7
16.43 16.83
0.015 0.013
0.005 0.005
0.018 0.016
0.099 0.126
0.086 0.098
39 35
21.9 19.6
47.7 42.7
16.5 14.5
4.5 5.1
0.023 0.018
0.005 0.004
0.02 0.017
0.137 0.14
0.099 0.104
20 20
10.8 10.5
32.8 31.5
12.27 11.28
30 31.5
13.56 12.78
0.099 0.111
0.088 0.095
33 37
21 22
32 33.5
15.98 15.16
1.15 1
0.004 0.003
0.001 0.001
0.004 0.004
0.051 0.053
0.042 0.043
24 24
16.05 15.8
26.2 17.8
10.89 9.33
3.35 2.8
0.017 0.016
0.004 0.003
0.017 0.015
0.139 0.137
0.097 0.096
48 47
30.65 28.5
32.2 31.35
12.4 12.925
152
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
PM,,
HUNTINGTON-ASHLAND, WV-KY-OH
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
HUNTSVILLE, AL
CO 2nd max (daily-nonoverlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
INDIANAPOLIS, IN
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
IOWA CITY, IA
PM25* 98th percentile
Weighted annual mean
JACKSON, MS
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
JACKSON,TN
PM26* 98th percentile
Weighted annual mean
JACKSONVILLE, FL
Maximum quarterly value
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
JACKSONVILLE, NC
PM25* 98th percentile
Weighted annual mean
down
down
ns
ns
down
ns
NA
NA
down
ns
ns
ns
ns
NA
NA
down
ns
down
ns
ns
ns
down
down
NA
NA
NA
NA
down
ns
ns
ns
ns
ns
ns
NA
NA
NA
NA
ns
down
ns
ns
ns
ns
ns
NA
NA
NA
NA
3
3
1
1
1
1
2
2
1
1
1
3
3
1
1
2
3
3
1
2
2
2
2
2
2
1
1
1
1
1
2
2
1
1
3
3
1
1
1
1
2
2
1
1
1
1
1
1
1
0.05
0.012
0.119
0.099
61
33.1
ND
ND
4
0.112
0.087
38.667
23.267
ND
ND
4
0.04
0.009
0.018
0.094
0.079
54
31.4
ND
ND
ND
ND
6.2
0.01
0.003
0.089
0.073
42
22.8
ND
ND
ND
ND
0.022
4.8
0.032
0.004
0.015
0.103
0.08
ND
ND
ND
ND
0.048
0.01
0.12
0.097
65
39
ND
ND
3.5
0.107
0.075
34.333
23.233
ND
ND
3.45
0.041
0.008
0.019
0.107
0.09
57.5
32
ND
ND
ND
ND
5.1
0.008
0.002
0.086
0.073
35
22.1
ND
ND
ND
ND
0.017
3.4
0.041
0.004
0.014
0.087
0.069
ND
ND
ND
ND
0.04
0.01
0.122
0.092
64
38.4
ND
ND
3.6
0.102
0.08
34.333
22.1
ND
ND
3.85
0.022
0.006
0.02
0.108
0.091
49.5
29.6
ND
ND
ND
ND
4.4
0.007
0.002
0.09
0.076
39
21.9
ND
ND
ND
ND
0.027
3.7
0.033
0.004
0.016
0.1
0.068
ND
ND
ND
ND
0.031
0.01
0.113
0.086
52
37
ND
ND
3
0.096
0.081
32.667
20.7
ND
ND
2.75
0.027
0.006
0.018
0.118
0.093
34
22
ND
ND
ND
ND
4.8
0.008
0.002
0.093
0.078
35
21.8
ND
ND
ND
ND
0.023
3.1
0.024
0.004
0.015
0.086
0.073
ND
ND
ND
ND
0.034
0.009
0.124
0.086
62
39
ND
ND
3.1
0.096
0.086
37
20.867
ND
ND
3.15
0.025
0.006
0.015
0.101
0.086
40.5
24.05
ND
ND
ND
ND
3.8
0.007
0.002
0.095
0.077
44
25.6
ND
ND
ND
ND
0.015
2.8
0.025
0.003
0.014
0.085
0.073
ND
ND
ND
ND
0.031
0.009
0.136
0.105
53
35.2
ND
ND
3.3
0.118
0.092
36.667
22.633
ND
ND
2.65
0.022
0.005
0.019
0.105
0.09
43.5
25.45
ND
ND
ND
ND
3.7
0.008
0.002
0.105
0.084
48
28
ND
ND
ND
ND
0.017
2.8
0.03
0.004
0.015
0.1
0.08
ND
ND
ND
ND
0.029
0.009
0.115
0.096
68
39.1
35.25
16.175
4.3
0.106
0.093
36.667
23.4
30.9
15.61
2.4
0.021
0.006
0.018
0.106
0.095
37
21
39.2
17.32
32.4
12.32
5
0.007
0.002
0.103
0.083
38
24.9
33.65
16.195
37.5
16.22
0.017
3.9
0.028
0.004
0.016
0.103
0.08
ND
ND
35.7
12.7
0.038
0.01
0.092
0.081
50
32.7
36.8
16.63
2.3
0.111
0.088
37.333
24
41.5
16.28
3.3
0.023
0.006
0.017
0.097
0.08
38.5
22.3
38.725
17.618
28.4
10.93
3.2
0.006
0.002
0.096
0.08
36
23.5
35.633
15.233
30.4
14.99
0.029
2.6
0.032
0.005
0.015
0.09
0.071
30.1
12.1
27.7
12.28
0.028
0.008
0.11
0.087
50
30
41.15
16.385
2.3
0.088
0.08
35.333
21.033
29.7
14.6
2.35
0.022
0.005
0.017
0.092
0.08
32.5
20.7
41.8
17.855
34.5
11.67
4.2
0.006
0.002
0.091
0.076
33
20.6
29.2
13.45
27.4
13.56
0.017
2.7
0.027
0.004
0.013
0.092
0.072
26.2
10.94
26
11.45
0.028
0.008
0.123
0.097
47
27.9
42.55
16.135
2.3
0.098
0.078
29.667
18.9
34.1
13.8
3.3
0.023
0.005
0.018
0.126
0.103
27.5
18.1
40.75
17.56
25.6
11.38
3
0.008
0.002
0.09
0.074
33
20.6
29.433
12.233
32.2
12.23
0.008
2.9
0.032
0.004
0.015
0.087
0.066
22.3
9.29
23.8
10.88
APPENDIX A • DATA TABLES 153
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
JAMESTOWN, NY
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
JANESVILLE-BELOIT, Wl
Ozone 2nd highest daily max
4th highest daily max 8-h average
JERSEY CITY, NJ
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
JOHNSON CITY-KINGSPORT-BRISTOL, TN-VA
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
JOHNSTOWN, PA
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
JONESBORO, AR
PM26* 98th percentile
Weighted annual mean
JOPLIN, MO
PM26* 98th percentile
Weighted annual mean
KALAMAZOO-BATTLE CREEK, Ml
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
KANSAS CITY, MO-KS
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
down
down
ns
ns
ns
down
NA
NA
down
ns
down
down
down
ns
ns
ns
ns
down
NA
NA
down
ns
ns
down
ns
ns
NA
NA
down
down
down
down
up
ns
NA
NA
NA
NA
NA
NA
ns
ns
NA
NA
ns
ns
ns
ns
ns
ns
ns
NA
NA
1 0.032
1 0.007
1 0.104
1 0.081
1 26
1 15.4
1 ND
1 ND
1 0.108
1 0.077
1 5.6
2 0.03
2 0.009
1 0.027
1 0.131
1 0.103
1 54
1 34.3
1 ND
1 ND
1 6.5
1 0.045
1 0.01
1 0.017
1 0.125
1 0.088
1 ND
1 ND
1 4.2
1 0.049
1 0.015
1 0.017
1 0.099
1 0.083
1 ND
1 ND
1 ND
1 ND
1 ND
1 ND
1 40
1 24
1 ND
1 ND
1 0.025
1 0.002
1 0.009
1 0.114
1 0.082
1 43
1 30.7
3 ND
3 ND
0.033
0.006
0.094
0.08
32
14.4
ND
ND
0.108
0.077
5.9
0.036
0.009
0.026
0.118
0.095
62
38.8
ND
ND
3.4
0.05
0.011
0.017
0.103
0.083
ND
ND
4.1
0.08
0.014
0.018
0.094
0.083
ND
ND
ND
ND
ND
ND
44
25.9
ND
ND
0.033
0.002
0.008
0.112
0.09
47
33.8
ND
ND
0.023
0.005
0.104
0.089
32
15.7
ND
ND
0.103
0.087
6.2
0.026
0.007
0.026
0.125
0.104
48
30.8
ND
ND
3.1
0.038
0.01
0.018
0.114
0.091
ND
ND
3.5
0.042
0.012
0.015
0.101
0.09
ND
ND
ND
ND
ND
ND
50
26
ND
ND
0.023
0.002
0.009
0.131
0.099
41
19.1
ND
ND
0.027
0.005
0.097
0.081
28
15.1
ND
ND
0.103
0.085
4.9
0.027
0.008
0.027
0.12
0.087
51
32.8
ND
ND
3
0.05
0.012
0.018
0.099
0.082
ND
ND
4.8
0.034
0.011
0.018
0.098
0.083
ND
ND
ND
ND
ND
ND
33
22
ND
ND
0.033
0.003
0.009
0.114
0.087
58
32.6
ND
ND
0.019
0.005
0.101
0.085
32
15.4
ND
ND
0.097
0.085
4.3
0.025
0.008
0.026
0.119
0.105
50
30.6
ND
ND
3.5
0.042
0.011
0.018
0.111
0.082
ND
ND
2.7
0.03
0.009
0.016
0.104
0.092
ND
ND
ND
ND
ND
ND
38
22.6
ND
ND
0.021
0.003
0.009
0.121
0.098
38
26.2
ND
ND
0.019
0.005
0.112
0.095
35
16.9
ND
ND
0.1
0.084
4.1
0.022
0.007
0.027
0.118
0.089
42
26.9
ND
ND
3.4
0.039
0.011
0.017
0.115
0.096
ND
ND
3.1
0.027
0.008
0.015
0.124
0.098
ND
ND
ND
ND
ND
ND
47
26.7
ND
ND
0.01
0.002
0.009
0.133
0.095
47
29.7
ND
ND
0.022
0.005
0.101
0.087
32
14.1
ND
ND
0.105
0.093
3.9
0.024
0.007
0.026
0.139
0.106
43
27.8
46
16.13
2.8
0.038
0.01
0.016
0.106
0.086
ND
ND
2.8
0.025
0.009
0.015
0.107
0.09
31
14.78
ND
ND
26.7
13.11
44
22.5
38
14.89
0.009
0.002
0.009
0.111
0.082
41
27.8
28.1
12.68
0.023
0.005
0.101
0.083
29
13.7
30.6
11.38
0.098
0.083
3.8
0.024
0.007
0.026
0.103
0.082
50
30.6
39.5
16.83
2.2
0.043
0.011
0.015
0.109
0.092
42.2
17.17
2
0.026
0.007
0.015
0.104
0.086
34.1
15.34
27.9
14.64
29.5
13.49
49
26.3
35.5
15.1
0.039
0.004
0.009
0.115
0.091
47
29.1
27.333
12.647
0.02
0.005
0.097
0.085
25
11.7
34.2
11.06
0.093
0.084
3
0.027
0.008
0.026
0.132
0.091
53
29.3
34.1
14.1
2.1
0.037
0.01
0.015
0.11
0.085
37.1
15.4
2.1
0.031
0.008
0.014
0.106
0.09
40.1
15.85
28.6
12.69
28.7
14.48
49
26.3
40
15.63
0.009
0.002
0.008
0.106
0.079
47
31.6
29.567
13.333
0.016
0.004
0.109
0.094
21
12.4
37.8
11.25
0.098
0.087
2.8
0.022
0.006
0.023
0.109
0.09
50
28.3
34.3
14.35
1.9
0.044
0.008
0.014
0.109
0.093
34
14.3
2.6
0.025
0.007
0.012
0.106
0.088
46.6
16.09
31.5
11.16
31.5
13.9
49
26.3
32.3
14.78
0.015
0.002
0.008
0.105
0.087
53
36.2
29.867
13.213
154
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
KENOSHA, Wl
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
KNOXVILLE, TN
CO 2nd max (daily-nonoverlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
KOKOMO, IN
PM25* 98th percentile
Weighted annual mean
Lafayette, LA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LAKE CHARLES, LA
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LAKELAND-WINTER HAVEN, FL
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LANCASTER, PA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LANSING-EAST LANSING, Ml
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LAREDO, TX
PM25* 98th percentile
Weighted annual mean
LAS CRUCES, NM
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
Trend
ns
ns
NA
NA
down
ns
ns
ns
ns
down
down
NA
NA
NA
NA
ns
ns
NA
NA
ns
ns
ns
ns
ns
NA
NA
ns
ns
ns
ns
NA
NA
ns
ns
down
down
ns
ns
NA
NA
ns
ns
NA
NA
NA
NA
down
down
down
down
ns
up
ns
NA
NA
#Trend
Sites
2
2
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
2
2
1
1
1
3
3
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
2
2
3
3
1
1
1
1
1993
0.114
0.085
ND
ND
4.6
0.063
0.009
0.11
0.088
64
39.6
ND
ND
ND
ND
0.101
0.083
ND
ND
0.019
0.006
0.004
0.11
0.081
ND
ND
0.019
0.004
0.103
0.082
ND
ND
3
0.026
0.007
0.015
0.118
0.095
ND
ND
0.096
0.079
ND
ND
ND
ND
8.7
0.055
0.006
0.107
0.073
51
31.3
ND
ND
1994
0.119
0.088
ND
ND
4.3
0.057
0.01
0.109
0.09
58
38.1
ND
ND
ND
ND
0.101
0.083
ND
ND
0.017
0.004
0.006
0.094
0.074
ND
ND
0.016
0.004
0.088
0.072
ND
ND
3.8
0.03
0.006
0.019
0.111
0.093
ND
ND
0.093
0.079
ND
ND
ND
ND
5
0.023
0.004
0.104
0.074
62
36
ND
ND
1995
0.119
0.103
ND
ND
4.1
0.053
0.01
0.117
0.098
55
37.1
ND
ND
ND
ND
0.109
0.09
ND
ND
0.018
0.005
0.005
0.103
0.078
ND
ND
0.013
0.004
0.089
0.073
ND
ND
2.4
0.018
0.006
0.016
0.124
0.102
ND
ND
0.096
0.082
ND
ND
ND
ND
4.4
0.021
0.004
0.105
0.074
65
38.4
ND
ND
1996
0.13
0.084
ND
ND
3.3
0.058
0.009
0.102
0.086
54
35.3
ND
ND
ND
ND
0.098
0.084
ND
ND
0.018
0.003
0.005
0.096
0.074
ND
ND
0.019
0.005
0.089
0.07
ND
ND
2.6
0.021
0.005
0.017
0.101
0.085
ND
ND
0.087
0.077
ND
ND
ND
ND
4.3
0.03
0.004
0.104
0.075
60
37.2
ND
ND
1997
0.111
0.087
ND
ND
4.8
0.048
0.008
0.111
0.091
56
33.1
ND
ND
ND
ND
0.105
0.078
ND
ND
0.012
0.003
0.005
0.119
0.084
ND
ND
0.016
0.005
0.101
0.078
ND
ND
3.3
0.023
0.007
0.016
0.133
0.102
ND
ND
0.087
0.077
ND
ND
ND
ND
4.8
0.014
0.003
0.09
0.067
56
31.6
ND
ND
1998
0.121
0.09
ND
ND
3.9
0.038
0.007
0.114
0.099
47
29.9
ND
ND
ND
ND
0.1
0.084
ND
ND
0.012
0.003
0.005
0.119
0.085
ND
ND
0.022
0.006
0.104
0.087
ND
ND
1.9
0.02
0.006
0.015
0.119
0.101
ND
ND
0.1
0.08
ND
ND
ND
ND
4.2
0.012
0.003
0.1
0.072
58
32.3
ND
ND
1999
0.121
0.097
34.2
12.35
3.8
0.056
0.009
0.123
0.1
43
30.1
42.8
22.72
ND
ND
0.094
0.081
26.9
12.85
0.015
0.004
0.005
0.103
0.079
35.4
12.99
0.016
0.005
0.097
0.078
23.4
11.03
2.1
0.021
0.005
0.015
0.127
0.102
38.2
15.64
0.1
0.088
34.6
12.6
ND
ND
3.8
0.005
0.001
0.092
0.074
80
44.6
26.8
11.2
2000
0.097
0.084
27.2
11.38
3.1
0.06
0.01
0.11
0.095
46
28.9
45.7
20.08
34.3
15.59
0.123
0.092
32
13.07
0.013
0.004
0.005
0.117
0.085
33.75
12.795
0.017
0.005
0.101
0.078
28.1
12.21
1.9
0.024
0.005
0.014
0.107
0.09
47.4
18.22
0.091
0.076
37.2
13.07
23.2
12.1
3.7
0.003
0.001
0.1
0.073
73
41.6
30.5
10.54
2001
0.12
0.098
33
12.7
3
0.089
0.01
0.101
0.086
44
26.3
36.8
17.45
38.1
15.01
0.09
0.077
29.75
11.445
0.012
0.003
0.005
0.097
0.078
30.55
11.235
0.014
0.004
0.108
0.084
25.9
11.14
2.2
0.018
0.004
0.014
0.127
0.097
42.1
17.11
0.105
0.085
37.2
14.04
26.4
10.29
3.3
0.004
0.001
0.087
0.068
74
37.3
30.3
10.91
2002
0.14
0.113
31.7
11.57
3
0.07
0.011
0.117
0.101
36
23.2
34.3
16.48
29.7
14.72
0.095
0.074
22.6
10.05
0.017
0.004
0.004
0.089
0.072
30.35
10.005
0.01
0.004
0.09
0.072
24.4
10.09
2.2
0.014
0.005
0.013
0.115
0.096
40.2
16.15
0.096
0.087
32.8
13.52
25.5
10.06
3
0.003
0.001
0.089
0.072
80
39.5
38.7
12.22
APPENDIX A • DATA TABLES 155
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
LAS VEGAS, NV-AZ
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
LAWRENCE, MA-NH
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
LAWTON, OK
CO 2nd max (daily-non-overlapping 8-h)
PM25* 98th percentile
Weighted annual mean
LEWISTON-AUBURN, ME
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
LEXINGTON, KY
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
LIMA, OH
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
LINCOLN, NE
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LITTLE ROCK-NORTH LITTLE ROCK, AR
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
LONGVIEW-MARSHALL, TX
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ns
ns
down
ns
NA
NA
down
down
ns
ns
ns
NA
NA
down
down
ns
ns
NA
NA
ns
down
down
ns
ns
down
down
NA
NA
down
ns
ns
ns
down
down
ns
ns
ns
NA
NA
down
down
ns
ns
up
NA
NA
ns
ns
NA
NA
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
0.099
0.082
81.5
43.4
ND
ND
0.027
0.007
0.1
0.076
2.6
ND
ND
0.025
0.007
50
24.3
ND
ND
0.026
0.007
0.017
0.102
0.081
42
23.85
ND
ND
0.023
0.005
0.099
0.09
40
27.9
5.1
0.057
0.049
ND
ND
0.017
0.006
0.009
0.096
0.076
ND
ND
0.114
0.093
ND
ND
0.099
0.077
77.5
45.55
ND
ND
0.032
0.007
0.101
0.082
1.9
ND
ND
0.025
0.006
35
20.2
ND
ND
0.037
0.008
0.016
0.102
0.086
46
27.6
ND
ND
0.036
0.005
0.102
0.089
42
30.6
5.3
0.075
0.062
ND
ND
0.009
0.003
0.011
0.09
0.076
ND
ND
0.104
0.081
ND
ND
0.086
0.074
82.5
45.2
ND
ND
0.033
0.007
0.081
0.069
3.1
ND
ND
0.02
0.004
37
19.8
ND
ND
0.016
0.006
0.017
0.103
0.088
39.5
22.8
ND
ND
0.015
0.003
0.106
0.092
38
27.2
6.2
0.07
0.06
ND
ND
0.008
0.002
0.011
0.106
0.086
ND
ND
0.145
0.102
ND
ND
0.096
0.082
83
51.7
ND
ND
0.023
0.005
0.092
0.079
2.667
ND
ND
0.018
0.004
31
20
ND
ND
0.02
0.006
0.014
0.089
0.081
37.5
23.1
ND
ND
0.015
0.003
0.11
0.092
38
24.9
4.7
0.06
0.054
ND
ND
0.009
0.002
0.011
0.096
0.078
ND
ND
0.106
0.082
ND
ND
0.09
0.075
76
47.5
ND
ND
0.027
0.006
0.097
0.078
2.233
ND
ND
0.017
0.004
35
20.6
ND
ND
0.016
0.006
0.014
0.098
0.081
37
21.85
ND
ND
0.016
0.003
0.091
0.083
43
24
6.9
0.061
0.054
ND
ND
0.006
0.002
0.01
0.099
0.077
ND
ND
0.124
0.091
ND
ND
0.103
0.084
69.75
41.95
ND
ND
0.031
0.008
0.096
0.076
1.8
ND
ND
0.019
0.004
31
18.2
ND
ND
0.023
0.006
0.011
0.104
0.089
40
23
ND
ND
0.017
0.003
0.102
0.089
37
24.3
6
0.068
0.058
ND
ND
0.006
0.002
0.011
0.096
0.078
ND
ND
0.129
0.104
ND
ND
0.09
0.074
70
40.8
32.6
11.71
0.021
0.005
0.09
0.068
1.7
ND
ND
0.016
0.004
31
18.6
35.7
9.99
0.02
0.008
0.013
0.108
0.087
40
22.55
35.2
15.475
0.013
0.003
0.107
0.093
26
16.6
5.7
0.062
0.053
25.2
10.57
0.005
0.002
0.011
0.103
0.083
ND
ND
0.134
0.105
ND
ND
0.086
0.074
63
38.4
31.6
10.53
0.02
0.004
0.072
0.06
1.4
19.2
9.08
0.018
0.003
28
17.5
25.8
9.6
0.02
0.005
0.013
0.085
0.077
37.5
22.95
37.45
16.59
0.015
0.003
0.1
0.085
36
24.6
2.9
0.072
0.057
25.1
10.25
0.007
0.002
0.01
0.113
0.09
33.5
15.47
0.131
0.099
28.8
13.41
0.092
0.07
65.5
40.4
33.3
10.62
0.021
0.004
0.081
0.062
2.2
26.2
9.91
0.015
0.004
37
20.7
32.5
11.31
0.029
0.005
0.013
0.088
0.077
35
22.2
34.2
15.955
0.013
0.003
0.096
0.081
29
20.8
4
0.061
0.051
23.4
10.08
0.005
0.002
0.01
0.102
0.079
32
14.72
0.111
0.082
28
12.18
0.096
0.078
68
45.7
28.8
11.68
0.015
0.004
0.124
0.088
2.1
25.2
9.35
0.016
0.004
37
18.8
30.3
10.45
0.016
0.004
0.012
0.095
0.083
36.5
21.45
41.3
15.32
0.01
0.003
0.109
0.098
36
24.4
3.7
0.063
0.054
26
9.55
0.005
0.002
0.01
0.101
0.085
31.9
13.24
0.11
0.084
39.2
12.36
156
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
LOS ANGELES-LONG BEACH, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
LOUISVILLE, KY-IN
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
LOWELL, MA-NH
CO 2nd max (daily-non-overlapping 8-h)
LUBBOCK, TX
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
MACON, GA
PM25* 98th percentile
Weighted annual mean
MADISON, Wl
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
MANSFIELD, OH
PM10* 90th percentile
Weighted annual mean
MAYAGUEZ, PR
PM25* 98th percentile
Weighted annual mean
MCALLEN-EDINBURG-MISSION, TX
PM25* 98th percentile
Weighted annual mean
MEDFORD-ASHLAND, OR
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
ns
down
ns
ns
down
down
down
ns
ns
NA
NA
ns
down
down
ns
ns
ns
ns
ns
NA
NA
2
4
2
2
3
4
4
2
2
4
4
1
2
2
1
2
2
2
2
3
3
0.088
8.725
0.008
0.002
0.039
0.15
0.098
74.5
44
ND
ND
4.5
0.038
0.011
0.026
0.123
0.096
54.5
29.65
ND
ND
0.072
10.75
0.006
0.002
0.046
0.165
0.101
62.5
41.1
ND
ND
4.6
0.038
0.011
0.026
0.124
0.099
45.5
30.5
ND
ND
0.058
9.525
0.005
0.002
0.045
0.14
0.089
73.5
45.15
ND
ND
3.6
0.036
0.01
0.022
0.124
0.097
49
29
ND
ND
0.053
9.25
0.006
0.002
0.043
0.126
0.085
71
43.35
ND
ND
3.4
0.033
0.008
0.02
0.109
0.087
49
28.25
ND
ND
0.067
8.55
0.006
0.002
0.039
0.112
0.081
70
45.55
ND
ND
3.8
0.029
0.006
0.02
0.126
0.091
49.5
30.45
ND
ND
0.045
7.575
0.007
0.002
0.039
0.138
0.088
62.5
38.3
ND
ND
3.8
0.027
0.005
0.023
0.136
0.102
43
26.35
ND
ND
0.094
7.475
0.006
0.002
0.041
0.101
0.07
75
50
53.775
23.955
3.3
0.027
0.009
0.022
0.114
0.092
44.5
26.35
39.267
16.443
0.059
6.5
0.006
0.002
0.039
0.118
0.08
64.5
42.7
66.475
23.211
3.8
0.033
0.007
0.022
0.097
0.081
57.5
31.1
37.9
16.673
0.08
5.025
0.006
0.002
0.037
0.104
0.074
67
43.1
65.025
24.325
3.9
0.031
0.005
0.023
0.101
0.081
48.75
28.7
41.383
16.908
0.036
5.125
0.007
0.002
0.035
0.103
0.073
58
41.75
55.225
23.355
4.8
0.024
0.005
0.02
0.12
0.099
42
25.9
44.233
16.41
down
5.1
6.5
7.8 4.5
3.6 3.4
4.2
3.2 2.7
2.4
ns
ns
NA
NA
NA
NA
ns
ns
ns
ns
NA
NA
down
down
NA
NA
NA
NA
ns
ns
down
down
NA
NA
1 30
1 19.9
1 ND
1 ND
2 ND
2 ND
1 0.079
1 0.066
1 37
1 21
1 ND
1 ND
1 44
1 27.7
1 ND
1 ND
2 ND
2 ND
1 0.081
1 0.066
3 50.667
3 28.767
3 ND
3 ND
33
23
ND
ND
ND
ND
0.082
0.071
33
22.4
ND
ND
49
29.2
ND
ND
ND
ND
0.087
0.068
45.667
27.6
ND
ND
34
20.8
ND
ND
ND
ND
0.1
0.08
43
22.8
ND
ND
42
24.7
ND
ND
ND
ND
0.091
0.071
37.333
22.067
ND
ND
34
21.7
ND
ND
ND
ND
0.094
0.079
30
19.6
ND
ND
40
24.3
ND
ND
ND
ND
0.101
0.075
37
21.167
ND
ND
27
16.7
ND
ND
ND
ND
0.088
0.079
34
20.3
ND
ND
40
23.3
ND
ND
ND
ND
0.074
0.063
36.333
22.2
ND
ND
37
20.5
ND
ND
ND
ND
0.089
0.076
43
26.6
ND
ND
41
23.8
ND
ND
ND
ND
0.117
0.085
33
21
ND
ND
26
18.1
ND
ND
49.2
18.21
0.098
0.085
38
20.8
33.4
13.43
39
22.6
18.8
8.79
ND
ND
0.077
0.065
42
24.1
33.95
9.08
32
19
18.5
7.42
36.45
17.505
0.087
0.071
34
22
34.2
12.75
37
23.7
16.4
7.91
22.4
10.835
0.079
0.067
38.333
20.933
34.6
9.447
29
19.7
17.2
7.66
31
14.935
0.088
0.078
32
22
36.6
13.31
37
23.7
15.7
8.08
21.45
10.52
0.081
0.064
34.667
19.8
26.1
8.673
29
19.7
21.3
7.55
31.75
14.635
0.09
0.08
31
19
32.7
12.31
37
23.7
16.7
7.8
28.55
10.48
0.099
0.078
36.667
21.033
33.6
10.37
APPENDIX A • DATA TABLES 157
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MEMPHIS, TN-AR-MS
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
MERCED, CA
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
MIAMI, FL
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
MIDDLESEX-SOMERSET-HUNTERDON, NJ PMS
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
MILWAUKEE-WAUKESHA, Wl
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
MINNEAPOLIS-ST. PAUL, MN-WI
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
MISSOULA, MT
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
down
ns
ns
ns
down
down
NA
NA
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
down
NA
NA
down
ns
ns
ns
ns
ns
NA
NA
down
ns
ns
down
ns
ns
ns
ns
NA
NA
down
ns
down
down
ns
ns
NA
NA
down
down
NA
NA
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
2
2
2
3
3
2
2
1
1
1
1
2
2
1
1
1
1
1
1
2
2
2
2
3
3
3
2
2
1
2
2
1
1
1
1
1
1
8.5
0.026
0.102
0.077
49.5
29.55
ND
ND
0.015
0.12
0.096
ND
ND
5.5
0.004
0.001
0.012
0.105
0.081
39
27.533
ND
ND
3.7
0.018
0.005
0.019
0.088
0.069
ND
ND
2.9
0.018
0.003
0.017
0.103
0.082
44
23.95
ND
ND
3.933
0.021
0.003
0.019
0.074
0.058
ND
ND
76
45
ND
ND
7.8
0.027
0.109
0.084
45.5
28.05
ND
ND
0.013
0.119
0.097
ND
ND
4.9
0.004
0.001
0.01
0.092
0.072
33.667
25.067
ND
ND
4.3
0.028
0.005
0.019
0.085
0.065
ND
ND
3
0.032
0.004
0.017
0.133
0.087
37.5
24.25
ND
ND
4.833
0.025
0.004
0.019
0.081
0.069
ND
ND
63
33
ND
ND
6.2
0.027
0.14
0.099
47
28.7
ND
ND
0.012
0.13
0.107
ND
ND
5.1
0.004
0.002
0.011
0.098
0.072
35.667
26.067
ND
ND
5.4
0.018
0.004
0.019
0.133
0.106
ND
ND
2.4
0.025
0.004
0.017
0.123
0.103
51
25.65
ND
ND
3.867
0.018
0.003
0.019
0.101
0.077
ND
ND
45
24.2
ND
ND
5
0.024
0.114
0.096
39.5
26.15
ND
ND
0.012
0.124
0.102
ND
ND
4.6
0.005
0.002
0.011
0.092
0.069
41.667
26.767
ND
ND
3.3
0.024
0.005
0.02
0.117
0.092
ND
ND
1.9
0.028
0.004
0.017
0.112
0.086
34.5
23.35
ND
ND
3.067
0.019
0.003
0.015
0.092
0.071
ND
ND
45
24
ND
ND
4.2
0.028
0.122
0.091
45.5
27.55
ND
ND
0.013
0.09
0.074
ND
ND
4.1
0.004
0.001
0.012
0.101
0.073
32
23.467
ND
ND
3.8
0.019
0.005
0.018
0.13
0.105
ND
ND
1.8
0.028
0.004
0.016
0.118
0.083
33.5
22.1
ND
ND
3.233
0.024
0.004
0.014
0.088
0.076
ND
ND
40
21.3
ND
ND
5.1
0.029
0.1
0.085
41.5
25.8
ND
ND
0.011
0.14
0.112
ND
ND
3.4
0.004
0.001
0.011
0.103
0.083
35.667
26
ND
ND
3
0.018
0.005
0.019
0.118
0.098
ND
ND
1.9
0.022
0.004
0.016
0.118
0.084
37.5
24.65
ND
ND
4
0.019
0.003
0.013
0.092
0.071
ND
ND
37
20.2
ND
ND
4.6
0.025
0.13
0.095
42
26.1
34.9
15.85
0.012
0.125
0.105
ND
ND
3.9
0.003
0.001
0.012
0.107
0.077
32.667
23.067
21.75
10.33
3.2
0.016
0.005
0.019
0.144
0.11
31.4
11.49
1.9
0.024
0.004
0.016
0.116
0.091
36
22.3
37.733
14.427
3.033
0.022
0.003
0.014
0.085
0.074
ND
ND
29
17.7
29.3
9.83
4.4
0.025
0.112
0.091
37.5
26.2
36
16.3
0.012
0.12
0.103
68.4
17.28
3.4
0.003
0.002
0.011
0.088
0.074
33.667
23.967
22.65
10.075
3.2
0.018
0.005
0.019
0.111
0.093
34.5
13.14
1.5
0.026
0.004
0.016
0.096
0.08
32.5
20.55
31.233
13.253
3.067
0.02
0.003
0.012
0.088
0.068
42.7
13.12
30
18.3
33.8
12.41
4.1 3.5
0.025 0.022
0.121 0.126
0.092 0.1
36 30.5
23.9 19.4
31.9 36.3
14.53 13.78
0.012 0.012
0.113 0.137
0.096 0.105
70.1 55.1
16.75 18.74
4.2 3
0.004 0.004
0.002 0.002
0.011 0.01
0.098 0.089
0.067 0.063
38 33.333
24.133 21.4
19.45 19.6
8.97 8.27
3.3 2.6
0.024 0.016
0.005 0.005
0.018 0.016
0.132 0.121
0.103 0.101
34.1 26
13.23 11.13
1.5 1.5
0.018 0.018
0.003 0.003
0.016 0.016
0.113 0.118
0.093 0.091
33.5 38.5
22.05 22.5
37.3 35.033
13.67 12.873
3 2.633
0.015 0.015
0.003 0.002
0.012 0.01
0.097 0.088
0.075 0.074
34.7 24.3
13.02 11.33
34 31
19.9 16.4
43.7 24.8
10.43 8.47
158
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
MOBILE, AL
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
MODESTO, CA
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
MONMOUTH-OCEAN, NJ
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
MONROE, LA
PM25* 98th percentile
Weighted annual mean
MONTGOMERY, AL
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
MUNCIE, IN
PM25* 98th percentile
Weighted annual mean
MYRTLE BEACH, SC
Maximum quarterly value
NASHUA, NH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NASHVILLE, TN
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NASSAU-SUFFOLK, NY
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
NA
NA
ns
down
ns
ns
ns
ns
NA
NA
down
ns
ns
NA
NA
NA
NA
ns
ns
ns
ns
NA
NA
NA
NA
ns
down
down
down
ns
ns
ns
ns
NA
NA
down
down
down
ns
ns
ns
ns
down
NA
NA
down
ns
ns
ns
down
NA
NA
1 0.098
1 0.074
1 ND
1 ND
2 4.65
2 0.02
2 0.12
2 0.093
2 52.5
2 34.45
1 ND
1 ND
1 6.4
1 0.123
1 0.103
1 ND
1 ND
1 ND
1 ND
2 0.116
2 0.086
2 37
2 24.35
1 ND
1 ND
1 ND
1 ND
1 0.006
1 5.8
2 0.019
2 0.005
1 0.125
1 0.086
2 28.5
2 16.55
1 ND
1 ND
1 7.3
2 0.063
2 0.01
1 0.012
2 0.098
2 0.074
2 41.5
2 27.25
1 ND
1 ND
1 0.026
1 0.134
1 0.097
1 30
1 19.4
1 ND
1 ND
0.085
0.072
ND
ND
5.1
0.02
0.112
0.09
52.5
34.45
ND
ND
5
0.119
0.099
ND
ND
ND
ND
0.098
0.078
38
25.45
ND
ND
ND
ND
0.006
8
0.023
0.006
0.105
0.083
32.5
14.8
ND
ND
7.1
0.041
0.007
0.02
0.093
0.076
45.5
26.1
ND
ND
0.028
0.126
0.092
41
23.9
ND
ND
0.108
0.079
ND
ND
4.2
0.019
0.125
0.099
69.5
33.7
ND
ND
3.6
0.149
0.117
ND
ND
ND
ND
0.097
0.082
42
25
ND
ND
ND
ND
0.004
7.6
0.019
0.004
0.111
0.088
26
13.85
ND
ND
7.3
0.025
0.005
0.014
0.095
0.078
44
27.2
ND
ND
0.025
0.146
0.11
37
20.1
ND
ND
0.104
0.081
ND
ND
4.3
0.019
0.124
0.096
48
28.6
ND
ND
4.6
0.118
0.095
ND
ND
ND
ND
0.097
0.072
36
21.85
ND
ND
ND
ND
0.004
7.8
0.019
0.004
0.098
0.081
28.5
16.9
ND
ND
5
0.049
0.006
0.012
0.096
0.078
39
25.55
ND
ND
0.026
0.12
0.091
29
18
ND
ND
0.117
0.081
ND
ND
3.7
0.019
0.11
0.086
51.5
31.55
ND
ND
3.2
0.15
0.113
ND
ND
ND
ND
0.085
0.069
39.5
23.4
ND
ND
ND
ND
0.003
5.3
0.02
0.005
0.115
0.094
30
18.25
ND
ND
6.3
0.059
0.006
0.013
0.113
0.092
40.5
24.45
ND
ND
0.025
0.137
0.106
35
21.3
ND
ND
0.114
0.098
ND
ND
4.3
0.019
0.14
0.103
54.5
28.15
ND
ND
2.9
0.135
0.104
ND
ND
ND
ND
0.119
0.092
40
27.25
ND
ND
ND
ND
0.009
5.3
0.016
0.004
0.1
0.084
29
16.65
ND
ND
5.6
0.035
0.005
0.011
0.105
0.088
43
25.4
ND
ND
0.022
0.143
0.096
29
18.1
ND
ND
0.118
0.085
36.1
16.81
4.85
0.02
0.109
0.089
71
38.55
100
24.88
3.4
0.135
0.105
36.8
10.37
28.2
13.93
0.103
0.085
40
24.65
44.5
18.94
ND
ND
0.01
5.3
0.015
0.004
0.1
0.089
28
16.5
50.9
13.5
5.4
0.029
0.004
0.019
0.116
0.092
40.5
24.2
43
18.83
0.025
0.126
0.091
25
15.9
ND
ND
0.115
0.089
39.7
15.27
4.25
0.017
0.108
0.089
53
30
71
18.92
3.2
0.136
0.114
36.6
11.52
27.2
13.33
0.105
0.085
42.5
25.25
42.2
17.2
34.8
16.24
0.005
4.1
0.016
0.003
0.089
0.07
25
15.05
20.8
10.29
5.6
0.029
0.004
0.019
0.096
0.079
44.5
26.95
36.9
16.97
0.024
0.112
0.086
29
17
32.1
12.22
0.095
0.076
26.7
12.35
3.95
0.018
0.11
0.093
54.5
32.4
69
15.58
3.8
0.13
0.108
32.55
11.165
27.2
11.85
0.093
0.077
40
22.1
29
14.4
35.7
14.49
0.008
4
0.014
0.004
0.108
0.091
32.5
16.85
28.2
10.83
5.8
0.026
0.004
0.018
0.086
0.073
39.5
24.15
34.7
15.23
0.024
0.126
0.084
26
17.4
31.3
12.86
0.094
0.075
22.8
10.57
3.2
0.017
0.115
0.095
53.5
31
69
18.67
1.9
0.146
0.125
28.5
10.81
32.9
10.77
0.099
0.081
33
21.2
28.4
14.56
30
14.51
0.002
3.7
0.013
0.003
0.12
0.094
30.5
15.5
28.2
10.83
5.1
0.015
0.003
0.016
0.098
0.082
41
22.4
33.3
14.3
0.022
0.141
0.108
31
17.5
31.9
11.35
APPENDIX A • DATA TABLES 159
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
NEW BEDFORD, MA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
NEW HAVEN-MERIDEN, CT
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NEW LONDON-NORWICH, CT-RI
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NEW ORLEANS, LA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NEW YORK, NY
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NEWARK, NJ
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
NEWBURGH, NY-PA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
down
NA
NA
ns
ns
down
down
NA
NA
ns
ns
ns
down
ns
ns
ns
ns
ns
NA
NA
ns
down
down
down
down
ns
ns
down
down
NA
NA
down
down
ns
ns
ns
ns
ns
ns
NA
NA
ns
down
NA
NA
1 0.088
1 0.073
1 ND
1 ND
1 3.7
1 0.044
1 0.009
1 0.027
1 0.147
1 0.105
2 48
2 28.05
3 ND
3 ND
1 0.126
1 0.099
1 32
1 18.8
1 ND
1 ND
1 0.074
1 5.2
1 0.025
1 0.006
1 0.019
3 0.108
3 0.079
1 42
1 26.7
2 ND
2 ND
1 0.031
3 4.7
1 0.052
1 0.018
2 0.037
2 0.116
2 0.094
1 35
1 19.7
4 ND
4 ND
1 6
2 0.025
2 0.007
2 0.024
1 0.121
1 0.104
1 59
1 33.7
2 ND
2 ND
1 0.115
1 0.095
1 ND
1 ND
0.096
0.077
ND
ND
3.7
0.056
0.01
0.03
0.148
0.093
61.5
34
ND
ND
0.118
0.093
40
22.7
ND
ND
0.121
4.3
0.027
0.008
0.02
0.11
0.084
42
26.7
ND
ND
0.031
4.967
0.064
0.017
0.038
0.121
0.099
34
20.7
ND
ND
11.3
0.033
0.007
0.027
0.119
0.094
62
36.7
ND
ND
0.115
0.095
ND
ND
0.138
0.107
ND
ND
3.7
0.038
0.008
0.025
0.165
0.117
48.5
26.7
ND
ND
0.14
0.101
31
17.6
ND
ND
0.411
3.1
0.022
0.007
0.021
0.11
0.086
35
24.6
ND
ND
0.024
5.633
0.047
0.015
0.036
0.123
0.1
30
19.1
ND
ND
7.7
0.026
0.005
0.025
0.125
0.11
48
28.9
ND
ND
0.115
0.095
ND
ND
0.118
0.092
ND
ND
2.9
0.031
0.008
0.026
0.12
0.095
40
24.35
ND
ND
0.121
0.095
31
19.4
ND
ND
0.093
4
0.035
0.006
0.018
0.106
0.084
33
23.1
ND
ND
0.024
4.467
0.047
0.015
0.037
0.12
0.089
31
20
ND
ND
6
0.027
0.006
0.026
0.114
0.093
52
35.6
ND
ND
0.12
0.091
ND
ND
0.123
0.092
ND
ND
3.9
0.032
0.006
0.024
0.145
0.109
38.5
24.95
ND
ND
0.15
0.104
30
18.9
ND
ND
0.055
3.2
0.017
0.005
0.018
0.098
0.078
39
25.8
ND
ND
0.022
3.667
0.04
0.012
0.035
0.14
0.109
30
19.6
ND
ND
5.1
0.025
0.006
0.026
0.111
0.097
51
32
ND
ND
0.102
0.088
ND
ND
0.101
0.083
ND
ND
2.7
0.031
0.006
0.027
0.13
0.097
35.5
23.95
ND
ND
0.116
0.083
29
18
ND
ND
0.115
3
0.026
0.004
0.02
0.11
0.083
43
26.45
ND
ND
0.021
3.767
0.038
0.012
0.035
0.104
0.078
29
17.5
ND
ND
5.1
0.021
0.006
0.027
0.119
0.097
49
31.2
ND
ND
0.104
0.088
ND
ND
0.125
0.098
30
12.12
3.1
0.027
0.007
0.026
0.143
0.104
38
23.5
36.05
15.525
0.127
0.096
25
16.5
ND
ND
0.078
3.1
0.023
0.005
0.022
0.108
0.087
47
27.1
36
15.04
0.022
4.167
0.045
0.013
0.035
0.142
0.104
35
16.2
ND
ND
6.6
0.022
0.006
0.026
0.119
0.102
55
32.7
35.25
13.365
0.119
0.094
ND
ND
0.101
0.082
34.65
12.395
2.6
0.031
0.006
0.025
0.136
0.087
38.5
24.15
37.133
13.94
0.135
0.084
26
16.2
27.6
11.05
0.115
4
0.02
0.005
0.019
0.115
0.089
44
26.2
33.45
14.02
0.023
3.533
0.046
0.013
0.034
0.106
0.083
31
18.8
38.525
15.108
4.7
0.023
0.006
0.026
0.11
0.09
54
35.3
37.2
14.03
0.096
0.078
29.8
11.87
0.136
0.101
39.3
12.67
2.5
0.037
0.007
0.027
0.146
0.1
43.5
24.85
37.4
14.377
0.11
0.09
32
17.1
34.4
12.74
0.103
3.6
0.026
0.005
0.02
0.098
0.078
49
29.6
29
13.135
0.024
2.833
0.038
0.013
0.034
0.111
0.087
28
15.9
36.425
15.135
4.8
0.023
0.006
0.026
0.121
0.101
50
32.4
36.6
13.78
0.108
0.09
27.8
11.58
0.113
0.087
23.1
10.25
2.3
0.032
0.007
0.025
0.146
0.11
39
22.2
34.067
13.403
0.134
0.095
28
14.6
25.7
11.13
0.125
3.6
0.016
0.004
0.017
0.102
0.073
37
23.3
22.2
11.13
0.024
2.667
0.036
0.012
0.033
0.125
0.098
27
18.3
34.075
13.783
4.4
0.02
0.006
0.025
0.142
0.105
51
29.8
36.7
12.58
0.099
0.085
30.5
11.04
160
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
NORFOLK-VIRGINIA BEACH-NEWPORT NEWS, V
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
OAKLAND, CA
Maximum quarterly value
PM25* 98th percentile
Weighted annual mean
OCALA, FL
PM25* 98th percentile
Weighted annual mean
OKLAHOMA CITY, OK
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
OLYMPIA, WA
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
OMAHA, NE-IA
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ORANGE COUNTY, CA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ORLANDO, FL
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
down
ns
ns
ns
ns
ns
NA
NA
down
NA
NA
NA
NA
down
ns
down
ns
ns
ns
NA
NA
down
down
NA
NA
ns
ns
ns
ns
up
NA
NA
down
ns
ns
down
down
down
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
ns
NA
NA
2
1
1
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
2
2
2
2
2
1
1
2
2
2
1
1
2
2
2
1
1
1
2
2
3
3
2
2
5.55
0.027
0.007
0.021
0.123
0.095
ND
ND
0.015
ND
ND
ND
ND
6.2
0.013
0.103
0.077
39
23.9
ND
ND
49
23.8
ND
ND
7.3
0.058
0.048
50
31.95
ND
ND
6.65
0.006
0.002
0.03
0.14
0.084
63
38.3
ND
ND
3.8
0.011
0.002
0.012
0.097
0.081
32.333
22.333
ND
ND
6.3
0.025
0.008
0.019
0.101
0.085
ND
ND
0.012
ND
ND
ND
ND
5.3
0.015
0.1
0.079
35
23.3
ND
ND
30
17.7
ND
ND
4.2
0.078
0.065
55
35.05
ND
ND
7.95
0.005
0.002
0.032
0.131
0.085
54
37.5
ND
ND
3.6
0.012
0.002
0.011
0.101
0.082
30
21.6
ND
ND
4.7
0.028
0.007
0.018
0.099
0.082
ND
ND
0.009
ND
ND
ND
ND
4.8
0.014
0.103
0.085
42
22.8
ND
ND
35
16.8
ND
ND
7.5
0.088
0.075
49
30.1
ND
ND
6.3
0.005
0.003
0.031
0.117
0.078
74
43.5
ND
ND
3.3
0.006
0.002
0.01
0.099
0.075
30.333
20.5
ND
ND
5.05
0.025
0.007
0.018
0.097
0.083
ND
ND
0.009
ND
ND
ND
ND
5.2
0.014
0.102
0.081
49
27.4
ND
ND
30
15.4
ND
ND
6.9
0.074
0.063
53
36.2
ND
ND
6.35
0.004
0.001
0.027
0.105
0.075
57
35.2
ND
ND
3.25
0.008
0.002
0.013
0.1
0.077
34.333
22.267
ND
ND
3.7
0.023
0.007
0.019
0.113
0.097
ND
ND
0.005
ND
ND
ND
ND
5.4
0.015
0.103
0.084
42
23.8
ND
ND
36
16
ND
ND
5.4
0.074
0.063
59.5
35.4
ND
ND
5.2
0.006
0.001
0.026
0.097
0.071
58
38.8
ND
ND
3.55
0.006
0.002
0.013
0.103
0.079
30
21.8
ND
ND
5.55
0.021
0.006
0.019
0.104
0.09
ND
ND
0.006
ND
ND
ND
ND
4.1
0.015
0.109
0.089
42.667
24.4
ND
ND
22
14.1
ND
ND
7.7
0.075
0.065
69.5
36.15
ND
ND
5.7
0.005
0.002
0.026
0.126
0.08
53
35.8
ND
ND
2.95
0.007
0.002
0.011
0.109
0.089
34.667
23.9
ND
ND
4.25
0.022
0.007
0.017
0.135
0.097
35.4
13.33
0.007
ND
ND
21.3
11.4
4.3
0.014
0.097
0.084
43.333
25
ND
ND
26
14.4
29.4
9.21
8.8
0.088
0.068
83.5
44.55
ND
ND
5.675
0.005
0.002
0.027
0.108
0.073
89
44.3
ND
ND
2.75
0.007
0.002
0.012
0.101
0.082
33.333
23.2
24.6
11.275
3.65 3.85
0.023 0.023
0.007 0.006
0.016 0.018
0.094 0.1
0.081 0.085
29.75 30.4
13.585 13.515
0.012 0.005
38.3 54.4
11.21 11.93
23.9 22.8
10.95 10.37
4.2 4
0.013 0.013
0.091 0.093
0.08 0.078
44 38
25.6 22.9
25.9 26
10.66 10.895
31 26
15.4 15.4
41.2 36.4
10.33 9.64
3 3.8
0.077 0.07
0.063 0.056
60 57
39.3 36.7
25.8 23.75
10.775 10.52
5.45 4.1
0.005 0.004
0.002 0.002
0.025 0.022
0.1 0.094
0.071 0.068
59 55
39.5 36
51.4 52.05
17.53 18.91
2.5 2.05
0.009 0.008
0.003 0.002
0.012 0.012
0.104 0.094
0.08 0.078
33.333 30
22.8 22.8
29.7 27
11.94 10.795
3.6
0.031
0.006
0.018
0.128
0.102
28.05
12.155
0.005
50.5
13.83
24.8
9.82
3
0.014
0.091
0.08
38
22.5
29.5
10.445
25
13.9
30.1
9.04
3.9
0.08
0.07
59
36.85
27.1
10.615
4.15
0.009
0.002
0.021
0.092
0.068
49
33.5
46.95
17.055
2.5
0.005
0.001
0.011
0.101
0.075
27.667
18.967
21.85
9.605
APPENDIX A • DATA TABLES
161
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
OWENSBORO, KY
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
PANAMA CITY, FL
PM10* 90th percentile
Weighted annual mean
PARKERSBURG-MARIETTA, WV-OH
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PENSACOLA, FL
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PEORIA-PEKIN, IL
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PHILADELPHIA, PA-NJ
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PHOENIX-MESA, AZ
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
down
down
down
ns
ns
down
down
NA
NA
ns
ns
ns
ns
ns
ns
ns
down
NA
NA
ns
down
ns
ns
ns
down
NA
NA
down
down
ns
down
ns
ns
ns
ns
NA
NA
down
down
down
down
down
ns
ns
down
down
NA
NA
down
ns
ns
ns
ns
ns
ns
ns
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
2
2
2
2
2
2
1
1
1
1
2
2
2
2
1
1
1
1
2
5
4
4
4
5
5
2
2
4
4
2
1
1
1
2
2
2
2
0.05
0.009
0.012
0.106
0.081
43
24.9
ND
ND
46
29.3
0.065
0.014
0.114
0.092
51
29.2
ND
ND
0.047
0.006
0.102
0.08
39
25.8
ND
ND
0.032
7.3
0.039
0.007
0.079
0.064
35
19.6
ND
ND
0.076
5.4
0.029
0.008
0.024
0.121
0.096
48
28.25
ND
ND
6.5
0.008
0.002
0.029
0.116
0.081
56
38.55
0.035
0.009
0.012
0.107
0.092
42
25.6
ND
ND
34
22.6
0.084
0.017
0.113
0.095
51
27.3
ND
ND
0.045
0.005
0.108
0.085
34.5
23
ND
ND
0.019
5.7
0.05
0.007
0.089
0.076
39
20.6
ND
ND
0.06
6.16
0.04
0.009
0.027
0.115
0.088
59
32.75
ND
ND
7.25
0.008
0.002
0.029
0.108
0.077
62
38.85
0.028
0.007
0.013
0.109
0.088
42
24.9
ND
ND
37
23.4
0.041
0.01
0.117
0.097
40
25.3
ND
ND
0.023
0.003
0.117
0.083
31.5
21.7
ND
ND
0.026
5.6
0.084
0.007
0.094
0.082
38
20.1
ND
ND
0.058
4.36
0.028
0.007
0.024
0.131
0.106
48.5
29.25
ND
ND
6.1
0.008
0.002
0.029
0.123
0.088
64.5
39.9
0.02
0.007
0.011
0.107
0.086
40
23.4
ND
ND
31
21.9
0.046
0.01
0.107
0.088
34
22.7
ND
ND
0.024
0.004
0.098
0.079
31
20
ND
ND
0.024
4.6
0.045
0.007
0.089
0.081
31
20.6
ND
ND
0.05
4.72
0.026
0.007
0.025
0.12
0.091
49
29.9
ND
ND
5.95
0.017
0.003
0.029
0.111
0.088
64
39.9
0.027
0.007
0.012
0.108
0.087
39
22.8
ND
ND
38
25.1
0.052
0.01
0.106
0.085
39
23.1
ND
ND
0.031
0.004
0.11
0.085
41.5
23.7
ND
ND
0.019
4.7
0.042
0.007
0.086
0.072
41
26.2
ND
ND
0.045
4
0.026
0.007
0.023
0.117
0.095
49
28.25
ND
ND
5.2
0.009
0.004
0.028
0.105
0.085
67
43.75
0.023
0.007
0.013
0.11
0.086
40
23.1
ND
ND
41
25.4
0.089
0.013
0.113
0.093
44
23.1
ND
ND
0.023
0.004
0.121
0.095
37
21.9
ND
ND
0.017
5.8
0.041
0.007
0.085
0.076
42
25.5
ND
ND
0.037
3.6
0.022
0.007
0.023
0.115
0.093
42
24.5
ND
ND
6.35
0.011
0.004
0.028
0.113
0.088
59.5
34.15
0.024 0.017
0.006 0.005
0.011 0.011
0.102 0.082
0.09 0.074
38 32
22 20
33.1 32.3
15.22 15.2
35 37
25.2 24.8
0.058 0.036
0.013 0.011
0.121 0.104
0.096 0.085
36 39
20.5 21.4
42.8 38
17.27 17.68
0.024 0.027
0.004 0.004
0.102 0.113
0.084 0.09
38 32.5
23.25 21.8
29.8 31.8
14.82 13.93
0.017 0.018
4.6 3.4
0.036 0.05
0.006 0.006
0.098 0.083
0.082 0.072
40 43
23.1 24.3
38 32.2
16.04 14.85
0.039 0.049
3.86 3.58
0.022 0.024
0.006 0.007
0.022 0.022
0.126 0.113
0.099 0.09
37.5 42
20.35 23.825
34.8 36.975
13.353 14.895
5.5 5.15
0.012 0.012
0.003 0.003
0.031 0.029
0.109 0.103
0.088 0.084
74 70
42.95 44.9
0.019
0.004
0.01
0.086
0.073
34
20.6
31.5
15.18
31
22.4
0.035
0.009
0.106
0.085
37
22.1
42.1
17.4
0.025
0.003
0.093
0.079
30
20.9
22.2
11.39
0.019
3.5
0.054
0.006
0.081
0.074
36
22.3
36.4
13.94
0.031
3.34
0.025
0.006
0.022
0.116
0.094
42
24.1
37.975
15.244
4.55
0.009
0.003
0.026
0.098
0.081
54
36.1
0.02
0.004
0.01
0.109
0.086
33
19.9
29.5
14.64
31
20.8
0.038
0.01
0.114
0.095
37
23.5
37
15.76
0.021
0.003
0.09
0.073
27.5
17.7
22.4
10.95
0.013
3.1
0.043
0.005
0.098
0.083
36
21.2
33.6
13.88
0.03
2.36
0.023
0.006
0.021
0.126
0.104
38.5
23.25
36.025
14.148
4.1
0.01
0.003
0.029
0.11
0.085
62.5
45.15
162
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
PITTSBURGH, PA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PITTSFIELD, MA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
POCATELLO, ID
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PONCE, PR
PM25* 98th percentile
Weighted annual mean
PORTLAND, ME
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PORTLAND-VANCOUVER, OR-WA
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PORTSMOUTH-ROCHESTER, NH-ME
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PROVIDENCE-FALL RIVER-WARWICK, RI-MA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
ns
down
down
down
down
ns
ns
down
down
NA
NA
ns
ns
NA
NA
ns
ns
ns
ns
NA
NA
NA
NA
ns
ns
ns
down
NA
NA
down
ns
ns
down
down
NA
NA
down
down
down
ns
ns
down
down
down
ns
down
ns
ns
ns
down
down
NA
NA
1
1
2
2
1
3
3
2
2
2
2
1
1
1
1
1
1
1
1
2
2
2
2
1
1
1
1
2
2
1
1
1
3
3
4
4
1
1
1
2
2
1
1
1
3
3
1
1
1
1
1
3
3
0.134
5.4
0.077
0.016
0.024
0.116
0.094
79
38.45
ND
ND
0.112
0.083
ND
ND
0.037
0.007
56
39.4
ND
ND
ND
ND
0.112
0.089
51
29
ND
ND
6.8
0.082
0.062
47.667
26.867
ND
ND
0.019
0.006
0.014
0.117
0.085
31
19
5.4
0.034
0.008
0.022
0.12
0.089
46
28.6
ND
ND
0.171
7
0.087
0.016
0.027
0.114
0.097
82
43.3
ND
ND
0.085
0.074
ND
ND
0.037
0.007
50
30.5
ND
ND
ND
ND
0.122
0.088
46
26.5
ND
ND
7.8
0.106
0.078
41.333
25.2
ND
ND
0.022
0.006
0.013
0.118
0.087
29
15.3
6.7
0.035
0.008
0.022
0.12
0.089
46
28.6
ND
ND
0.115
5.9
0.073
0.013
0.023
0.124
0.104
71
37.1
ND
ND
0.086
0.072
ND
ND
0.037
0.007
40
23.2
ND
ND
ND
ND
0.116
0.096
69
34.3
ND
ND
6.3
0.092
0.073
35
21.433
ND
ND
0.017
0.004
0.012
0.122
0.087
27
15.3
7
0.024
0.006
0.022
0.131
0.096
36
21.5
ND
ND
0.058
4.3
0.053
0.013
0.024
0.107
0.09
68
35.55
ND
ND
0.108
0.081
ND
ND
0.03
0.006
46
24.4
ND
ND
ND
ND
0.1
0.083
43
27.1
ND
ND
6.4
0.124
0.099
33
21.267
ND
ND
0.015
0.004
0.013
0.097
0.079
30
17.8
4.4
0.03
0.007
0.025
0.112
0.083
40
24.5
ND
ND
0.075
3.8
0.068
0.013
0.022
0.114
0.094
67.5
34.3
ND
ND
0.087
0.078
ND
ND
0.034
0.005
39
22.9
ND
ND
ND
ND
0.13
0.103
51
29.3
ND
ND
6
0.079
0.062
34.333
22.667
ND
ND
0.018
0.004
0.013
0.121
0.091
29
17.9
5.6
0.031
0.007
0.025
0.108
0.084
35
24.1
ND
ND
0.061
3.8
0.073
0.013
0.026
0.114
0.095
70
35.75
ND
ND
0.078
0.069
ND
ND
0.034
0.006
37
22.4
ND
ND
ND
ND
0.12
0.089
46
26.7
ND
ND
5.5
0.136
0.081
31.667
20.533
ND
ND
0.016
0.004
0.012
0.11
0.087
27
16.4
4.7
0.025
0.006
0.025
0.098
0.077
32
22.5
ND
ND
0.081
3.9
0.065
0.013
0.024
0.128
0.096
61.5
32.2
49.3
18.8
0.092
0.075
47.7
12.78
0.046
0.007
48
25.3
72.25
9.64
17.35
8.19
0.105
0.076
33
21.4
34.2
10.01
6.7
0.094
0.072
31.333
19.3
30.55
9.115
0.019
0.004
0.01
0.107
0.087
30
16.2
3.9
0.024
0.006
0.024
0.108
0.08
35
23.1
36
11.75
0.07 0.057
3.2 3.4
0.064 0.063
0.011 0.012
0.022 0.021
0.098 0.105
0.082 0.089
65.5 67
34.05 35.85
49.15 52.8
18.275 19.815
0.088 0.112
0.072 0.092
28.8 33.8
11.8 13.35
0.036 0.037
0.008 0.007
45 48
24.9 26
51.1 36.2
10.46 9.32
17.95 14.25
7.27 7.24
0.077 0.116
0.067 0.097
46 46
23.7 25.6
27.1 30.5
9.565 10.28
6.2 4.7
0.082 0.093
0.065 0.069
31.667 26.333
18.667 17.1
32.325 27.325
10.028 8.998
0.013 0.013
0.003 0.003
0.011 0.011
0.087 0.102
0.069 0.079
26 26
14.5 14.5
3.5 3.8
0.031 0.028
0.006 0.006
0.02 0.02
0.115 0.128
0.08 0.102
31 38
21.3 21.7
28.867 33.267
10.99 12.383
0.111
2.7
0.057
0.013
0.02
0.117
0.103
63
31.6
50.65
17.815
0.103
0.086
31.5
11.44
0.027
0.005
45
25.4
36.85
8.66
13.1
7.23
0.122
0.096
53
24.6
28.3
9.58
5.7
0.099
0.063
27.667
17.1
34.525
9.323
0.013
0.003
0.011
0.106
0.081
26
14.5
2.7
0.022
0.005
0.018
0.124
0.092
30
18.3
29.067
10.837
APPENDIX A • DATA TABLES
163
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
PROVO-OREM, UT
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
PUEBLO, CO
PM25* 98th percentile
Weighted annual mean
RACINE, Wl
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
RALEIGH-DURHAM-CHAPEL HILL, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
RAPID CITY, SD
PM26* 98th percentile
Weighted annual mean
READING, PA
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
REDDING, CA
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
RENO, NV
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
RICHLAND-KENNEWICK-PASCO, WA
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
RICHMOND-PETERSBURG, VA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
ns
ns
NA
NA
NA
NA
down
ns
ns
ns
ns
ns
ns
NA
NA
NA
NA
down
down
down
ns
ns
NA
NA
ns
ns
NA
NA
down
ns
ns
ns
down
NA
NA
up
up
NA
NA
down
down
down
ns
ns
ns
down
down
NA
NA
1
1
1
2
2
2
2
1
1
1
1
1
4
4
2
2
4
4
3
3
1
1
1
2
2
1
1
1
1
1
1
3
2
2
3
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
4
4
0.026
0.084
0.068
71.5
37.5
ND
ND
ND
ND
4.1
0.103
0.08
0.103
0.085
39
24.75
ND
ND
ND
ND
0.027
0.009
0.021
0.108
0.088
ND
ND
47
29.9
ND
ND
6.067
0.087
0.063
81.333
45.767
ND
ND
27
15.1
ND
ND
3
0.032
0.007
0.01
0.132
0.1
45
24.2
ND
ND
0.024
0.084
0.069
55
34.25
ND
ND
ND
ND
4.3
0.114
0.088
0.101
0.081
31
21.8
ND
ND
ND
ND
0.037
0.01
0.023
0.104
0.084
ND
ND
47
29.9
ND
ND
7.633
0.088
0.07
74
41.767
ND
ND
27
15.1
ND
ND
2.7
0.024
0.006
0.012
0.101
0.082
36
22.1
ND
ND
0.023
0.083
0.068
48.5
28.8
ND
ND
ND
ND
4.3
0.113
0.096
0.102
0.084
33.5
23.3
ND
ND
ND
ND
0.032
0.009
0.021
0.112
0.093
ND
ND
47
25.2
ND
ND
5.533
0.083
0.069
58
36.567
ND
ND
34
17.8
ND
ND
2.3
0.023
0.005
0.011
0.106
0.088
43
24.3
ND
ND
0.024
0.097
0.078
56.5
33.7
ND
ND
ND
ND
3
0.129
0.083
0.095
0.08
39
25.1
ND
ND
ND
ND
0.037
0.009
0.022
0.105
0.086
ND
ND
39
24.1
ND
ND
6.467
0.096
0.074
61
34.167
ND
ND
38
20.3
ND
ND
2.5
0.022
0.006
0.01
0.104
0.084
44
23.8
ND
ND
0.023
0.08
0.07
49.5
30
ND
ND
ND
ND
3.1
0.117
0.098
0.106
0.09
39.5
24.6
ND
ND
ND
ND
0.028
0.008
0.021
0.115
0.092
ND
ND
37
22.2
ND
ND
6.533
0.084
0.068
67
37.133
ND
ND
33
19.4
ND
ND
2.5
0.017
0.006
0.012
0.123
0.1
39
22.7
ND
ND
0.024
0.102
0.083
44
26.25
ND
ND
ND
ND
3
0.124
0.084
0.115
0.095
40
24.4
ND
ND
ND
ND
0.022
0.009
0.021
0.105
0.091
ND
ND
46
23.4
ND
ND
6.033
0.093
0.075
64.333
35.467
ND
ND
30
19.9
ND
ND
1.7
0.019
0.006
0.012
0.116
0.092
39
23.4
ND
ND
0.024
0.096
0.073
51.5
29.6
31.15
9.355
ND
ND
2.7
0.114
0.093
0.122
0.097
36.5
22.15
35.375
15.258
25
9.09
0.027
0.008
0.021
0.126
0.101
35.7
13.51
40
28.5
55
11.53
7
0.094
0.075
62
40.233
32.8
9.93
42
20.8
ND
ND
1.9
0.017
0.005
0.011
0.133
0.097
28
18.6
35.467
14.117
0.024 0.024
0.085 0.086
0.071 0.067
52 53
29.1 31.4
33.75 55.15
9.925 11.685
19.9 19.4
7.81 8.52
2.3 2.1
0.096 0.115
0.078 0.092
0.11 0.103
0.086 0.085
35.5 37
23.05 23.15
31.125 30.85
14.843 13.958
23.133 19.533
7.833 7.917
0.028 0.025
0.008 0.007
0.02 0.02
0.103 0.122
0.08 0.095
37.5 43
16.87 16.49
37 41
23.6 23.6
42 29
10.355 9.18
4.633 4.5
0.083 0.087
0.067 0.07
64.333 64.667
33.4 34.367
31.4 36.4
8.92 9.82
40 38
24 22
31.2 18.2
8.4 6.76
2 2.3
0.017 0.019
0.006 0.005
0.011 0.012
0.094 0.119
0.076 0.089
38 35
22.2 20.4
32.875 33.1
14.515 13.74
0.025
0.096
0.077
48.5
30.15
41.4
11.26
16.9
7.76
2
0.141
0.111
0.116
0.1
34.5
21.55
30.025
13.12
22.6
7.37
0.019
0.007
0.019
0.11
0.093
48.5
16.66
42
25.4
38
10.68
4.3
0.095
0.076
56
35.1
25.9
9.12
42
22.8
22.5
6.39
2
0.021
0.005
0.012
0.137
0.105
29
18.1
30.35
13.093
164
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
RIVERSIDE-SAN BERNARDINO, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
ROANOKE, VA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
ROCHESTER, NY
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
ROCKFORD, IL
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
ROCKY MOUNT, NC
Ozone 2nd highest daily max
4th highest daily max 8-h average
SACRAMENTO, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ST. CLOUD, MN
CO 2nd max (daily-non-overlapping 8-h)
ST. JOSEPH, MO
PM25* 98th percentile
Weighted annual mean
ST. LOUIS, MO-IL
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
ns
down
down
down
down
down
ns
NA
NA
down
ns
down
ns
ns
ns
down
down
NA
NA
down
down
down
ns
ns
NA
NA
down
ns
ns
ns
ns
down
down
up
up
ns
ns
ns
ns
ns
NA
NA
down
NA
NA
ns
ns
down
ns
ns
ns
NA
NA
1
2
2
3
3
2
2
3
3
1
1
1
1
1
1
1
1
1
1
2
2
2
1
1
1
1
1
1
1
1
1
1
3
2
2
3
3
3
3
3
1
1
1
1
1
1
3
3
1
3
3
2
2
0.036
5.55
0.036
0.223
0.162
81.5
51.35
ND
ND
4.5
0.018
0.004
0.015
0.103
0.084
63
40
ND
ND
3.15
0.041
0.01
0.092
0.074
ND
ND
4
0.079
0.062
0.11
0.092
0.01
6.5
0.004
0.001
0.018
0.117
0.085
57.667
30.6
ND
ND
5
ND
ND
3.6
0.048
0.011
0.024
0.118
0.085
ND
ND
0.026
5.8
0.036
0.218
0.153
71.5
45.8
ND
ND
5.7
0.011
0.004
0.013
0.102
0.084
63
40
ND
ND
4.5
0.043
0.011
0.099
0.079
ND
ND
4
0.101
0.079
0.104
0.088
0.007
6.633
0.005
0.001
0.016
0.105
0.086
44.333
28.367
ND
ND
6.4
ND
ND
3.8
0.051
0.012
0.028
0.126
0.095
ND
ND
0.033
5.5
0.038
0.216
0.151
77
44.45
ND
ND
5.2
0.01
0.003
0.013
0.093
0.079
64
40.3
ND
ND
3.15
0.038
0.01
0.103
0.09
ND
ND
4.5
0.104
0.084
0.097
0.084
0.005
5
0.005
0.002
0.017
0.131
0.093
53.333
26.7
ND
ND
4.4
ND
ND
3.2
0.047
0.01
0.026
0.124
0.095
ND
ND
0.031
4.8
0.033
0.195
0.138
67
43.3
ND
ND
5.9
0.014
0.003
0.013
0.084
0.073
71
37.9
ND
ND
3.7
0.033
0.009
0.083
0.068
ND
ND
3.2
0.089
0.077
0.091
0.08
0.006
5
0.004
0.002
0.017
0.12
0.093
39.667
24.2
ND
ND
4
ND
ND
3.9
0.059
0.011
0.025
0.111
0.089
ND
ND
0.045
4.95
0.03
0.163
0.118
68.5
43.35
ND
ND
4.3
0.013
0.003
0.013
0.102
0.084
64
34.6
ND
ND
1.9
0.038
0.009
0.097
0.085
ND
ND
3.7
0.08
0.071
0.106
0.089
0.005
4.8
0.005
0.002
0.015
0.095
0.078
37
23.033
ND
ND
4
ND
ND
3.7
0.042
0.009
0.025
0.106
0.083
ND
ND
0.046
4.4
0.029
0.205
0.152
66
41.65
ND
ND
3.9
0.009
0.003
0.014
0.126
0.099
54
33.3
ND
ND
2.7
0.053
0.009
0.088
0.077
ND
ND
3.6
0.085
0.073
0.107
0.09
0.046
4.933
0.01
0.002
0.016
0.14
0.093
45
23.833
ND
ND
3.8
ND
ND
4
0.042
0.009
0.026
0.115
0.091
ND
ND
0.038
4
0.032
0.143
0.112
73
49.55
70.15
28.875
3.7
0.01
0.003
0.012
0.105
0.089
54
34.7
31.8
13.82
2.5
0.03
0.006
0.096
0.088
ND
ND
3.8
0.093
0.082
0.104
0.092
0.005
4.9
0.008
0.003
0.017
0.113
0.088
54.333
29.333
67
16.58
3.3
28.2
12.48
2.3
0.042
0.009
0.027
0.126
0.1
35.85
16.165
0.032 0.029
4 3.45
0.03 0.031
0.16 0.158
0.115 0.121
64.5 71.5
41.6 46.2
55.633 53.7
21.85 23.547
3.1 3.4
0.014 0.009
0.003 0.003
0.011 0.014
0.095 0.101
0.081 0.089
57 42
31.5 26.6
35.5 34.2
15.52 15.1
2.2 1.75
0.021 0.025
0.006 0.007
0.08 0.099
0.073 0.084
28.4 37.5
11.76 11.66
2.9 2.9
0.084 0.086
0.069 0.078
0.106 0.099
0.085 0.085
0.005 0.005
3.767 4.167
0.01 0.01
0.003 0.002
0.016 0.016
0.113 0.113
0.086 0.087
43 46.667
24.733 27.167
49 53
12.37 11.63
2.7 2.6
26.8 29
11.89 12.9
2.2 2.6
0.038 0.043
0.007 0.006
0.026 0.025
0.108 0.103
0.083 0.086
33.3 33.55
16.295 15.895
0.027
3.25
0.03
0.148
0.115
66
44.3
53.333
22.79
3
0.009
0.003
0.013
0.107
0.091
47
28.2
36
15.09
2.1
0.016
0.005
0.114
0.098
31.9
11.22
2.4
0.091
0.079
0.109
0.095
0.005
3.3
0.007
0.002
0.016
0.117
0.094
43
26.767
63
14.33
2.9
30.9
13
6.9
0.045
0.006
0.023
0.118
0.097
44.7
16.38
APPENDIX A • DATA TABLES 165
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
SALEM, OR
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
SALINAS, CA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
SALT LAKE CITY-OGDEN, UT
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
SAN ANTON 10, TX
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
SAN DIEGO, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SAN FRANCISCO, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SAN JOSE, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
San Juan-Bayamon, PR
PM25* 98th percentile
Weighted annual mean
ns
ns
NA
NA
down
ns
ns
ns
ns
down
down
down
ns
ns
ns
ns
ns
NA
NA
down
ns
ns
NA
NA
ns
down
ns
ns
ns
down
down
ns
ns
NA
NA
down
down
ns
ns
down
ns
ns
ns
down
NA
NA
down
ns
down
ns
down
ns
ns
NA
NA
1
1
1
1
1
1
1
1
1
2
1
1
2
2
2
2
2
4
4
1
1
1
2
2
2
5
3
3
5
5
5
3
3
3
3
1
3
1
1
3
3
3
3
3
1
1
1
1
1
2
2
1
1
1
1
0.1
0.064
ND
ND
0.09
0.077
25
15.7
0.096
5.95
0.052
0.009
0.022
0.104
0.079
80
43.9
ND
ND
5.3
0.111
0.084
ND
ND
0.032
5.14
0.009
0.002
0.019
0.122
0.089
57.667
37.1
ND
ND
0.026
4.3
0.01
0.002
0.022
0.083
0.048
40.333
25.967
ND
ND
0.03
6.7
0.027
0.1
0.074
48
27.6
ND
ND
0.1
0.064
ND
ND
0.09
0.068
24
15.3
0.054
5.7
0.014
0.005
0.021
0.109
0.081
66
38.65
ND
ND
3.3
0.101
0.083
ND
ND
0.017
5.42
0.013
0.003
0.02
0.111
0.084
60.667
39.9
ND
ND
0.016
3.967
0.005
0.001
0.021
0.072
0.049
42.667
25.267
ND
ND
0.019
7.5
0.028
0.102
0.073
57
30.4
ND
ND
0.1
0.064
ND
ND
0.08
0.064
23
13.4
0.066
5
0.012
0.004
0.021
0.115
0.083
65
36.75
ND
ND
4.3
0.121
0.095
ND
ND
0.026
4.74
0.012
0.003
0.021
0.119
0.084
70.667
37.967
ND
ND
0.027
3.2
0.005
0.002
0.019
0.094
0.061
35
21.733
ND
ND
0.018
5.8
0.027
0.12
0.084
48
25.3
ND
ND
0.117
0.092
ND
ND
0.089
0.07
23
14.2
0.032
6.55
0.021
0.004
0.023
0.114
0.085
79
41.35
ND
ND
4.5
0.11
0.082
ND
ND
0.023
4.96
0.015
0.004
0.019
0.106
0.084
49.667
32.067
ND
ND
0.014
3.5
0.007
0.002
0.02
0.082
0.055
35
21.867
ND
ND
0.013
5.8
0.025
0.109
0.08
36
24.5
ND
ND
0.081
0.061
ND
ND
0.078
0.061
21
14.3
0.105
5.95
0.011
0.004
0.022
0.102
0.077
63
36.65
ND
ND
4.4
0.103
0.084
ND
ND
0.024
4.26
0.012
0.003
0.019
0.115
0.082
54.667
35.5
ND
ND
0.02
3.167
0.006
0.002
0.018
0.074
0.048
32.333
23.1
ND
ND
0.012
5.6
0.025
0.087
0.064
36
25.4
ND
ND
0.112
0.077
ND
ND
0.073
0.057
17
12.2
0.094
5.3
0.01
0.004
0.021
0.122
0.094
56
32.9
ND
ND
4.6
0.107
0.089
ND
ND
0.018
4.02
0.011
0.003
0.019
0.106
0.082
49.667
30.733
ND
ND
0.013
3.5
0.006
0.002
0.018
0.063
0.045
34.333
21.4
ND
ND
0.016
6.3
0.025
0.121
0.078
41
25.1
ND
ND
0.082
0.065
26.3
7.51
0.074
0.063
26
15.4
0.082
5.1
0.01
0.004
0.023
0.107
0.08
69
36.95
42.925
10.315
4.2
0.109
0.091
ND
ND
0.028
4.28
0.012
0.003
0.021
0.098
0.072
61
38.267
40.067
16.91
0.012
3.333
0.006
0.002
0.019
0.082
0.052
43.667
24.333
53.4
12.13
0.012
6.2
0.026
0.107
0.071
47
28.7
16.8
7.51
0.074
0.059
28.7
8.94
0.084
0.063
19
12.7
0.068
4.55
0.013
0.004
0.022
0.096
0.075
66
37.65
50.125
11.575
2.7
0.094
0.077
22
9.365
0.035
4.18
0.01
0.003
0.019
0.097
0.074
54
36.733
43.133
14.85
0.011
2.767
0.007
0.002
0.018
0.067
0.045
36.333
21.633
36.9
10.9
0.014
6.9
0.025
0.092
0.06
52
26.8
18.1
7.26
0.081 0.096
0.057 0.063
32.7 34.8
8.15 8.15
0.078 0.077
0.063 0.066
23 23
14.6 14.4
0.042 0.055
3.95 3.4
0.013 0.01
0.004 0.004
0.022 0.021
0.105 0.107
0.079 0.085
64 66.5
37.95 36.65
61.55 56.725
12.438 13.295
2.7 2.6
0.089 0.126
0.078 0.104
17.7 26.15
8.2 9.005
0.045 0.024
4.22 3.32
0.01 0.009
0.003 0.004
0.018 0.019
0.099 0.096
0.074 0.074
62 59
36.933 38.933
37.267 36.967
16.523 15.157
0.01 0.014
2.867 2.233
0.007 0.005
0.002 0.002
0.018 0.018
0.074 0.069
0.05 0.049
40.333 35.667
23 21.2
46.1 36.3
11.31 12.6
0.011 0.011
5 5
0.024 0.024
0.106 0.09
0.07 0.064
46 46
28.9 28.9
14.9 11.4
6.83 6.43
166
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
SAN LUIS OBISPO-ATASCADERO-PASO ROBLES
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
SANTA BARBARA-SANTA MARIA-LOMPOC, CA M
CO
SO,
N02
Ozone
PM *
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
SANTA CRUZ-WATSONVILLE, CA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SANTA FE, NM
CO 2nd max (daily-non-overlapping 8-h)
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SANTA ROSA, CA
CO
N02
Ozone
2nd max (daily-non-overlapping 8-h)
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
SARASOTA-BRADENTON, FL
PM,,
CO
S02
Ozone
PM *
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
down
ns
down
down
ns
ns
ns
down
NA
NA
down
down
ns
down
down
down
ns
ns
NA
NA
ns
ns
ns
down
ns
ns
ns
ns
NA
NA
down
ns
ns
NA
NA
down
down
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
NA
NA
1
1
1
2
3
3
2
2
1
1
3
4
4
5
5
5
3
3
1
1
1
1
1
1
2
2
1
1
1
1
1
2
2
1
1
1
1
2
2
3
3
1
1
1
1
1
2
2
2
2
2
2
3.1
0.028
0.006
0.014
0.088
0.067
45.5
23.2
ND
ND
1.867
0.004
0.001
0.007
0.103
0.079
35.333
21.1
ND
ND
1
0.006
0.002
0.006
0.075
0.06
49
31.1
ND
ND
3.4
21.5
14.4
ND
ND
3.8
0.016
0.085
0.061
33
19.133
ND
ND
6.5
0.012
0.003
0.102
0.078
37
25.25
ND
ND
3.1
0.028
0.006
0.014
0.087
0.07
37
21.15
ND
ND
1.933
0.004
0.001
0.007
0.101
0.077
33
20.733
ND
ND
1.2
0.006
0.002
0.006
0.074
0.056
49
31.1
ND
ND
2.7
21
13.25
ND
ND
3.2
0.015
0.085
0.06
28.667
18.1
ND
ND
5.3
0.012
0.003
0.095
0.079
34.5
21.5
ND
ND
2.4
0.028
0.006
0.012
0.091
0.071
39
22.25
ND
ND
1.333
0.003
0.001
0.007
0.118
0.081
28.667
18.4
ND
ND
0.8
0.008
0.001
0.005
0.073
0.058
65
36.4
ND
ND
2.3
17.5
12.25
ND
ND
2.4
0.015
0.089
0.065
25.333
15.033
ND
ND
5.9
0.012
0.002
0.097
0.076
30.5
19.75
ND
ND
2.3
0.029
0.006
0.012
0.1
0.079
31.5
19.4
ND
ND
1.267
0.003
0.001
0.007
0.114
0.084
29.667
17.267
ND
ND
0.7
0.003
0.002
0.005
0.086
0.062
61
32.8
ND
ND
2.2
20
13.45
ND
ND
3
0.014
0.08
0.062
26
15.633
ND
ND
5.1
0.015
0.002
0.094
0.075
27
19.05
ND
ND
2.3
0.026
0.005
0.012
0.079
0.066
29
20.75
ND
ND
1.267
0.002
0.001
0.007
0.089
0.073
31.333
20.067
ND
ND
0.7
0.002
0.001
0.004
0.071
0.057
65
36.9
ND
ND
2.1
19
13
ND
ND
3.1
0.013
0.089
0.064
23.667
15.567
ND
ND
5.3
0.012
0.002
0.104
0.08
32
21.1
ND
ND
2
0.03
0.005
0.012
0.093
0.076
28
17.35
ND
ND
1.267
0.002
0.001
0.006
0.093
0.07
29.667
17.633
ND
ND
0.8
0.003
0.001
0.004
0.074
0.059
47
28.5
ND
ND
2
20
13.6
ND
ND
3
0.015
0.084
0.063
24.667
14.7
ND
ND
5.6
0.014
0.003
0.12
0.089
33
21.25
ND
ND
2.9
0.027
0.005
0.013
0.085
0.069
34
20.75
26.9
9.32
1.267
0.002
0.001
0.007
0.083
0.068
29.667
18.9
ND
ND
0.7
0.002
0.001
0.005
0.078
0.064
53
30.9
21.9
9.2
1.7
18.5
12.95
11
4.89
3.3
0.014
0.096
0.073
32.333
18.567
44.5
12.11
4.95
0.011
0.002
0.111
0.084
34
21.55
30.75
11.095
2.2
0.028
0.005
0.012
0.078
0.065
36.5
19.95
41
10.31
1.2
0.002
0.001
0.007
0.088
0.069
33.333
20.433
19.3
9.77
0.7
0.003
0.001
0.005
0.074
0.057
41
26.2
17.9
7.93
1.7
19.5
12.05
9.5
4.9
2.7
0.013
0.07
0.056
27
14.8
36.8
10.31
4.3
0.019
0.002
0.106
0.084
33
22.2
26.9
10.64
1.7
0.028
0.005
0.011
0.085
0.068
30.5
19.65
50.7
10.12
1.333
0.002
0.001
0.006
0.085
0.07
28.667
18.067
23.4
10.4
0.9
0.006
0.001
0.005
0.075
0.059
50
28.7
23.1
9.13
2.1
17
12.15
10.1
4.73
2.3
0.013
0.083
0.059
28.333
16.833
41.4
10.8
3.4
0.013
0.002
0.109
0.084
30.5
21.6
28.6
10.205
1.6
0.021
0.004
0.01
0.083
0.07
30.5
19.8
25.7
9.23
1.1
0.002
0.001
0.006
0.082
0.067
29
17.933
19.4
9.52
0.8
0.007
0.001
0.005
0.074
0.058
45
26.8
22
8.6
1.5
21
13.75
13.9
4.94
2
0.013
0.075
0.058
24.667
15.867
42.4
10.54
3.4
0.013
0.002
0.088
0.072
29
18.2
21.7
8.885
APPENDIX A • DATA TABLES 167
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
SAVANNAH, GA
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
SCRANTON-WILKES-BARRE-HAZLETON, PA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
SEATTLE-BELLEVUE-EVERETT, WA
CO
N02
Ozone
PM10*
PM25*
SHARON
SO,
2nd max (daily-non-overlapping 8-h)
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
PA
2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
SHREVEPORT-BOSSIER CITY, LA
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SIOUX CITY, IA-NE
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
SIOUX FALLS, SD
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
SOUTH BEND, IN
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
down
ns
ns
NA
NA
down
ns
ns
down
ns
ns
down
ns
NA
NA
down
ns
ns
down
down
down
NA
NA
ns
down
ns
ns
ns
ns
ns
ns
down
down
NA
NA
ns
ns
NA
NA
ns
ns
NA
NA
ns
ns
ns
down
NA
NA
1 0.023
1 0.006
1 0.089
1 0.073
1 ND
1 ND
2 2.9
2 0.026
2 0.007
2 0.018
3 0.111
3 0.091
1 35
1 16
2 ND
2 ND
1 6.7
1 0.019
1 0.097
1 0.06
2 61.5
2 30.7
4 ND
4 ND
1 0.029
1 0.008
1 0.105
1 0.083
1 0.011
1 0.004
1 0.122
1 0.092
1 45
1 25.3
1 ND
1 ND
1 40
1 22.2
1 ND
1 ND
1 27
1 18.2
2 ND
2 ND
2 0.087
2 0.076
1 36
1 23.4
2 ND
2 ND
0.023
0.006
0.089
0.073
ND
ND
3.55
0.035
0.007
0.018
0.103
0.086
35
16
ND
ND
7
0.019
0.106
0.06
42.5
24.35
ND
ND
0.047
0.008
0.111
0.09
0.008
0.002
0.094
0.08
41
25.5
ND
ND
42
22.9
ND
ND
42
23.5
ND
ND
0.096
0.084
43
28.6
ND
ND
0.023
0.006
0.089
0.073
ND
ND
2.8
0.036
0.005
0.016
0.107
0.09
42
23.3
ND
ND
6.1
0.019
0.087
0.062
46
25.4
ND
ND
0.032
0.008
0.113
0.095
0.004
0.001
0.092
0.078
41
24.1
ND
ND
55
26
ND
ND
40
23.1
ND
ND
0.112
0.091
45
22.9
ND
ND
0.03
0.005
0.085
0.072
ND
ND
3.8
0.028
0.006
0.018
0.109
0.083
36
21
ND
ND
6.8
0.02
0.098
0.073
36
22.75
ND
ND
0.029
0.007
0.103
0.09
0.004
0.002
0.096
0.078
31
22.1
ND
ND
72
32.1
ND
ND
32
22.2
ND
ND
0.107
0.089
35
20.2
ND
ND
0.024
0.004
0.08
0.071
ND
ND
3.05
0.029
0.007
0.016
0.104
0.089
35
20.3
ND
ND
6.5
0.019
0.072
0.058
43.5
24.85
ND
ND
0.032
0.007
0.111
0.092
0.007
0.002
0.103
0.083
37
23.3
ND
ND
53
27.9
ND
ND
39
22.6
ND
ND
0.114
0.091
30
17
ND
ND
0.027
0.003
0.097
0.075
ND
ND
2.5
0.024
0.005
0.015
0.105
0.088
35
20
ND
ND
5.5
0.02
0.111
0.063
34.5
19.7
ND
ND
0.029
0.007
0.121
0.106
0.01
0.003
0.111
0.088
37
22.85
ND
ND
45
27.9
ND
ND
36
22.2
ND
ND
0.115
0.092
44
23.9
ND
ND
0.018
0.003
0.107
0.083
ND
ND
2.15
0.022
0.006
0.015
0.111
0.094
32.5
18.6
31.25
11.77
5.9
0.019
0.067
0.054
33.5
20.6
27.5
9.233
0.039
0.007
0.108
0.091
0.006
0.002
0.108
0.094
37
22.4
30.9
14.16
48
28
24.9
9.92
37
22.1
32.6
12.21
0.103
0.089
39
23.2
ND
ND
0.024
0.003
0.102
0.079
32.1
15.38
2.15
0.024
0.005
0.014
0.086
0.074
30
17.2
32.2
12.16
5.2
0.02
0.08
0.056
41
23.5
29.65
10.023
0.024
0.007
0.098
0.081
0.006
0.002
0.129
0.093
37
23.9
30.7
13.77
43
25.4
31.4
9.54
33
19.8
28.35
9.305
0.093
0.08
30
19.4
33.3
13.885
0.02
0.003
0.085
0.067
30.5
14.71
2.05
0.029
0.006
0.015
0.099
0.087
33
19.5
37.05
13.22
6.5
0.02
0.069
0.051
30.5
19.55
26.9
9.208
0.033
0.007
0.113
0.094
0.004
0.002
0.105
0.084
34
21.7
28.1
13.15
51
28.6
24.5
10.55
42
24.3
21.15
10.08
0.107
0.086
29
17.2
37.2
14.635
0.022
0.003
0.083
0.065
27.3
13.09
2.1
0.024
0.006
0.014
0.121
0.092
34
18.4
35.45
12.22
5
0.019
0.071
0.054
28.5
19.1
28.225
9.15
0.024
0.006
0.118
0.103
0.005
0.002
0.091
0.076
35
21.3
31.8
12.37
46
27.1
24.7
9.63
31
20.8
22.3
9.09
0.123
0.102
30
16.7
32.05
14.165
168
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
SPOKANE, WA
CO
Ozone
PM,,
2nd max (daily-non-overlapping 8-h)
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
SPRINGFIELD, IL
CO
SO,
Ozone
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
2nd highest daily max
4th highest daily max 8-h average
98th percentile
Weighted annual mean
SPRINGFIELD, MO
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SPRINGFIELD, MA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
STAMFORD-NORWALK, CT
CO
SO,
Ozone
PM,,
2nd max (daily-non-overlapping 8-h)
2nd daily max
Annual mean
2nd highest daily max
4th highest daily max 8-h average
90th percentile
Weighted annual mean
98th percentile
Weighted annual mean
STEUBENVILLE-WEIRTON, OH-WV
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
down
ns
up
down
down
NA
NA
down
ns
ns
ns
ns
NA
NA
down
down
ns
ns
ns
ns
ns
ns
NA
NA
down
ns
ns
ns
ns
ns
ns
ns
NA
NA
down
ns
down
ns
ns
ns
down
NA
NA
1
1
1
2
2
1
1
1
1
1
1
1
1
1
1
2
2
1
2
2
1
1
1
1
2
1
1
2
2
2
2
2
2
2
1
1
1
1
1
1
1
2
2
9.8
0.069
0.06
71
39.25
ND
ND
3.9
0.04
0.006
0.106
0.081
ND
ND
5.3
0.04
0.006
0.011
0.075
0.069
30
17.5
ND
ND
6.1
0.026
0.007
0.02
0.132
0.097
47.5
24.75
ND
ND
5.2
0.032
0.008
0.145
0.101
44
29.7
ND
ND
8.1
0.085
0.068
65
36.1
ND
ND
3.1
0.05
0.006
0.101
0.081
ND
ND
5.9
0.067
0.008
0.013
0.093
0.072
28
17.6
ND
ND
7.5
0.041
0.008
0.023
0.125
0.092
44
27.25
ND
ND
6.2
0.057
0.01
0.155
0.107
58
36.4
ND
ND
8.4
0.08
0.065
55.5
29.6
ND
ND
3.2
0.062
0.006
0.1
0.08
ND
ND
4.1
0.021
0.003
0.012
0.098
0.079
28
17.3
ND
ND
7.9
0.031
0.006
0.019
0.128
0.093
38.5
22.65
ND
ND
5.4
0.032
0.011
0.136
0.102
56
32.1
ND
ND
9
0.079
0.067
52
30.95
ND
ND
3
0.061
0.006
0.098
0.079
ND
ND
3.3
0.044
0.005
0.011
0.086
0.074
26
17.9
ND
ND
7.1
0.027
0.006
0.02
0.105
0.082
41
25
ND
ND
4.1
0.026
0.005
0.121
0.093
50
32.3
ND
ND
6.3
0.083
0.068
48
28.05
ND
ND
2.1
0.043
0.006
0.085
0.071
ND
ND
4.6
0.022
0.002
0.011
0.08
0.066
24
15.4
ND
ND
5.1
0.02
0.005
0.017
0.12
0.092
38
25.15
ND
ND
5.1
0.03
0.006
0.142
0.101
48
31.3
ND
ND
5.6
0.082
0.07
50
28.3
ND
ND
1.9
0.061
0.007
0.093
0.078
ND
ND
4
0.021
0.004
0.012
0.09
0.071
29
17.5
ND
ND
4.1
0.019
0.004
0.016
0.105
0.087
42.5
23.35
ND
ND
3.8
0.025
0.006
0.113
0.089
42
28.1
ND
ND
5.7
0.073
0.065
47
26.35
30
10.26
2.4
0.059
0.006
0.099
0.075
38.8
15.88
3.1
0.021
0.004
0.013
0.094
0.078
28
17.5
30.4
12.22
4.8
0.019
0.004
0.017
0.105
0.085
43.5
26.6
41.1
14.66
3.8
0.026
0.006
0.143
0.107
44
28.7
ND
ND
5.6
0.082
0.068
47.5
27.8
35.5
10.95
1.7
0.035
0.005
0.1
0.079
32.2
13.36
2.6
0.02
0.004
0.012
0.088
0.076
30
18.4
26.7
12.26
3.8
0.023
0.005
0.019
0.098
0.075
40.5
24.4
33.05
11.985
3
0.026
0.005
0.123
0.084
45
30.5
34.85
12.975
5.2
0.084
0.071
45.5
27.7
28.4
10.12
2.8
0.028
0.004
0.095
0.073
33.3
13.25
2.9
0.024
0.004
0.013
0.089
0.072
30
19.8
28.5
12.23
2.95
0.022
0.006
0.019
0.113
0.086
45
25.75
37.6
12.485
3.1
0.035
0.006
0.13
0.098
48
28.3
35.95
12.54
4.9
0.086
0.071
52
29.55
37.9
10.2
1.5
0.017
0.004
0.095
0.08
31.5
13.55
3.3
0.018
0.003
0.011
0.087
0.076
29
17.9
27.8
12.66
3.45
0.025
0.005
0.019
0.137
0.103
40.5
23.8
42.9
12.2
3.2
0.035
0.005
0.15
0.103
51
27.8
33.8
12.115
7.55
8.9 5.95 4.85
6.1 8.95 3.45 6.4 6.25 9.2
down
ns
ns
ns
down
down
NA
NA
4
4
1
1
2
2
2
2
0.12
0.024
0.093
0.081
75.5
39.95
ND
ND
0.125
0.021
0.096
0.082
77.5
40.75
ND
ND
0.063
0.011
0.108
0.091
66.5
37.9
ND
ND
0.056
0.011
0.099
0.082
69
36.6
ND
ND
0.054
0.013
0.097
0.083
59
31.9
ND
ND
0.045
0.012
0.099
0.088
65
33.1
ND
ND
0.056
0.013
0.108
0.091
54
29.5
42.75
18.145
0.047
0.012
0.088
0.072
55
30.15
46.4
18.39
0.043
0.012
0.093
0.083
55
30.85
45.85
17.79
0.046
0.011
0.113
0.1
63
30.65
49.65
17.4
APPENDIX A • DATA TABLES
169
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
STOCKTON-LODI, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
SYRACUSE, NY
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
TACOMA, WA
CO 2nd max (daily-non-overlapping 8-h)
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
TALLAHASSEE, FL
PM25* 98th percentile
Weighted annual mean
TAMPA-ST. PETERSBURG-CLEARWATER, FL MS
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
TERRE HAUTE, IN
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
TEXARKANA, TX-TEXARKANA, AR
PM26* 98th percentile
Weighted annual mean
TOLEDO, OH
Maximum quarterly value
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
TOPEKA, KS
PM25* 98th percentile
Weighted annual mean
down
down
down
ns
down
ns
ns
NA
NA
down
down
down
ns
ns
NA
NA
ns
ns
ns
NA
NA
NA
NA
ns
down
down
ns
ns
ns
ns
ns
NA
NA
ns
ns
ns
ns
ns
down
NA
NA
NA
NA
down
ns
ns
ns
ns
NA
NA
NA
NA
1
1
1
2
2
1
1
1
1
1
2
2
2
2
2
2
1
1
1
1
1
1
1
1
2
2
1
3
3
2
2
1
1
1
1
1
1
2
2
1
1
1
1
1
1
1
2
2
2
2
1
1
0.024
5.1
0.024
0.11
0.083
84
39.1
ND
ND
5.6
0.018
0.003
0.097
0.083
ND
ND
6
52
28.4
ND
ND
ND
ND
3.9
0.032
0.007
0.01
0.091
0.072
39
28.35
ND
ND
0.035
0.011
0.088
0.074
48.5
28.15
ND
ND
ND
ND
0.63
0.025
0.006
0.117
0.089
ND
ND
ND
ND
0.015
6.4
0.024
0.12
0.086
63
36.9
ND
ND
6.5
0.02
0.003
0.095
0.077
ND
ND
6
41
23.1
ND
ND
ND
ND
3.5
0.043
0.007
0.01
0.097
0.076
40.5
27.8
ND
ND
0.033
0.012
0.106
0.094
42.5
27.7
ND
ND
ND
ND
0.7
0.056
0.007
0.115
0.09
ND
ND
ND
ND
0.019
4.4
0.022
0.125
0.087
49
31.4
ND
ND
3.3
0.016
0.003
0.1
0.086
ND
ND
6.3
43
26
ND
ND
ND
ND
5
0.032
0.006
0.011
0.107
0.08
46
28.3
ND
ND
0.035
0.01
0.099
0.085
53
29.5
ND
ND
ND
ND
0.43
0.024
0.004
0.108
0.09
ND
ND
ND
ND
0.023
5.3
0.023
0.101
0.079
40
27.4
ND
ND
3.9
0.014
0.003
0.085
0.073
ND
ND
6.3
43
23.1
ND
ND
ND
ND
3.9
0.025
0.005
0.01
0.111
0.081
49
29.85
ND
ND
0.039
0.012
0.112
0.098
39
24.95
ND
ND
ND
ND
0.437
0.014
0.003
0.111
0.092
ND
ND
ND
ND
0.014
3.4
0.022
0.094
0.073
47
29.7
ND
ND
4
0.017
0.002
0.096
0.078
ND
ND
6.8
50
27.4
ND
ND
ND
ND
3.7
0.034
0.006
0.01
0.109
0.084
48.5
30.95
ND
ND
0.025
0.006
0.096
0.083
40.5
24.8
ND
ND
ND
ND
0.417
0.021
0.003
0.105
0.085
ND
ND
ND
ND
0.013
5.3
0.023
0.108
0.085
53
29.1
ND
ND
3
0.01
0.002
0.093
0.082
ND
ND
5.8
35
21.1
ND
ND
ND
ND
4.1
0.027
0.006
0.011
0.122
0.089
45.5
29.35
ND
ND
0.032
0.01
0.099
0.084
43
26.1
ND
ND
ND
ND
0.35
0.021
0.004
0.106
0.086
ND
ND
ND
ND
0.009 0.012
4.5 3.7
0.024 0.021
0.12 0.103
0.083 0.078
69 60
36.4 31.5
79 55
19.56 15.62
3.1 2.4
0.014 0.017
0.002 0.003
0.092 0.083
0.084 0.074
ND 28.65
ND 11.545
6.6 5.5
44 48
23.1 28.4
ND 49
ND 13
31.3 29.5
13.92 13.64
3.3 3.1
0.028 0.024
0.006 0.005
0.01 0.011
0.111 0.106
0.085 0.082
50.5 44.5
30 29.6
24.6 30.6
12.92 12.39
0.024 0.055
0.007 0.012
0.093 0.088
0.082 0.075
45 44
24.75 24.35
ND 34.2
ND 15.72
ND 31
ND 14.68
0.263 0.33
0.052 0.017
0.009 0.005
0.119 0.094
0.085 0.08
41 38.575
15.56 15.388
26.1 23.5
12.32 10.73
0.008
3.6
0.019
0.102
0.078
55
35.8
58
13.85
2.2
0.011
0.003
0.096
0.084
35.3
11.07
5
38
20.5
41.5
11.39
31.4
12.51
3
0.026
0.005
0.011
0.113
0.083
44
27.6
27.9
11.7
0.058
0.01
0.096
0.082
39.5
21.85
38.4
15.18
29.6
15.09
0.273
0.02
0.006
0.109
0.092
35.85
14.745
22.8
10.71
0.01
3.2
0.021
0.099
0.077
56
34.9
50
16.68
2.1
0.012
0.003
0.1
0.088
38.8
11.205
4.5
36
20.8
42.9
10.56
28.4
12.92
3.8
0.022
0.005
0.011
0.091
0.07
37
24.75
22.3
10.75
0.027
0.007
0.096
0.082
36
21.15
40.2
14.55
35.7
13.21
0.13
0.026
0.007
0.114
0.095
38.15
15.115
29.1
11.14
170
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
TRENTON, NJ
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
TUCSON, AZ
CO 2nd max (daily-non-overlapping 8 h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
TULSA, OK
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
TUSCALOOSA, AL
PM10* 90th percentile
Weighted annual mean
UTICA-ROME, NY
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
VALLEJO-FAIRFIELD-NAPA, CA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
VENTURA, CA
Maximum quarterly value
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
ns
down
NA
NA
down
ns
ns
down
down
down
ns
ns
NA
NA
ns
ns
ns
ns
ns
NA
NA
up
up
ns
down
ns
up
ns
ns
NA
NA
down
down
ns
down
ns
ns
ns
ns
NA
NA
ns
down
ns
ns
down
down
down
ns
ns
NA
NA
1
1
1
1
1
1
1
2
1
1
1
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
2
2
1
1
1
1
2
1
1
2
2
2
3
3
1
1
1
2
1
1
2
2
2
2
2
2
2
0.016
0.135
0.102
46
26.6
ND
ND
4.55
0.005
0.002
0.018
0.097
0.079
45
28.05
ND
ND
4.5
0.026
0.006
0.117
0.077
ND
ND
43
26
0.012
0.002
0.085
0.067
24
11.7
ND
ND
5.55
0.007
0.002
0.015
0.1
0.07
33
21.3
ND
ND
0.01
2.45
0.004
0.001
0.018
0.13
0.098
45.5
28.2
ND
ND
0.016
0.14
0.103
52
29.1
ND
ND
4.35
0.004
0.002
0.019
0.098
0.078
36.5
25.7
ND
ND
4.7
0.025
0.004
0.112
0.091
ND
ND
41
25.9
0.012
0.002
0.085
0.072
23
11.6
ND
ND
5.2
0.007
0.002
0.015
0.095
0.066
31.667
20.867
ND
ND
0.01
2.75
0.004
0.001
0.02
0.136
0.101
47
29.85
ND
ND
0.016
0.132
0.107
38
23.9
ND
ND
4.25
0.004
0.002
0.019
0.103
0.082
56.5
34.4
ND
ND
4.5
0.034
0.008
0.121
0.096
ND
ND
48
27.4
0.008
0.002
0.092
0.077
19
11.2
ND
ND
4.2
0.005
0.002
0.015
0.106
0.076
30.333
19
ND
ND
0.01
3.15
0.003
0.001
0.02
0.137
0.104
49.5
27.15
ND
ND
0.017
0.121
0.09
40
26.7
ND
ND
3.95
0.004
0.001
0.018
0.091
0.077
45
32.85
ND
ND
6.8
0.042
0.008
0.115
0.088
ND
ND
41
26.2
0.009
0.002
0.075
0.063
24
12.3
ND
ND
4.15
0.006
0.002
0.014
0.1
0.071
29
17.933
ND
ND
0.008
2.35
0.003
0.001
0.019
0.131
0.103
42
26.45
ND
ND
0.017
0.126
0.106
40
27
ND
ND
3.5
0.004
0.002
0.018
0.093
0.078
48.5
33.45
ND
ND
6.3
0.028
0.008
0.114
0.081
ND
ND
44
25.2
0.007
0.002
0.085
0.073
21
11.3
ND
ND
4.4
0.005
0.002
0.013
0.08
0.054
26.667
17.433
ND
ND
0.008
2.35
0.011
0.003
0.017
0.114
0.09
45
29.8
ND
ND
0.015
0.113
0.095
35
23.9
ND
ND
3.15
0.004
0.002
0.017
0.094
0.075
55.5
37.35
ND
ND
4.7
0.034
0.01
0.11
0.092
ND
ND
44
28.3
0.005
0.001
0.089
0.074
24
12.5
ND
ND
4.2
0.005
0.002
0.013
0.104
0.064
31.333
17.133
ND
ND
0.006
2.25
0.011
0.003
0.016
0.119
0.093
40
22.8
ND
ND
0.017
0.149
0.113
36
20.6
28.3
11.14
2.9
0.005
0.002
0.018
0.09
0.07
65.5
44.55
21.75
9.22
3.5
0.051
0.008
0.114
0.091
ND
ND
51
28.1
0.007
0.001
0.087
0.076
24
12
ND
ND
4.15
0.006
0.002
0.014
0.102
0.075
34.333
19.2
ND
ND
0.013
1.9
0.005
0.002
0.018
0.108
0.084
46
28.8
32.6
12.975
0.016
0.113
0.099
41
25.6
31.5
12.06
3.55
0.007
0.002
0.017
0.084
0.075
61
37.95
11.95
7.3
3.7
0.027
0.006
0.122
0.088
28
12.53
59
28.7
0.007
0.001
0.081
0.067
15
8.7
26.9
11.8
3.75
0.005
0.002
0.013
0.073
0.056
28.667
16.533
44
11.57
0.011
2.05
0.007
0.002
0.017
0.1
0.084
41.5
27.75
37.1
13.94
0.017
0.134
0.104
41
23.3
31.85
11.765
2.3
0.003
0.001
0.015
0.08
0.068
48
31.65
17.75
7.205
4.1
0.028
0.008
0.107
0.084
29.5
12.96
59
28.7
0.007
0.002
0.095
0.08
19
9.4
34.6
11.69
3.35
0.004
0.001
0.013
0.081
0.062
32
21.267
56
12.48
0.009
2
0.009
0.004
0.015
0.102
0.084
47
29.8
36.2
14
0.016
0.133
0.109
35
21.1
32.2
11.47
2.2
0.004
0.001
0.017
0.089
0.076
52
36.25
20.85
6.49
3
0.032
0.006
0.108
0.083
29.5
12.26
59
28.7
0.008
0.001
0.098
0.083
23
11.3
38.4
12.06
3
0.004
0.002
0.013
0.086
0.065
33.667
20.967
54
13.61
0.007
1.6
0.004
0.001
0.013
0.1
0.078
45
28.4
31.55
13.76
APPENDIX A • DATA TABLES
171
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
VICTORIA, TX
Ozone 2nd highest daily max
4th highest daily max 8-h average
VINELAND-MILLVILLE-BRIDGETON, NJ PMS
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
VISALIA-TULARE-PORTERVILLE, CA
CO 2nd max (daily-non-overlapping 8-h)
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
WASHINGTON, DC-MD-VA-WV
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
WATERBURY, CT
Maximum quarterly value
S02 2nd daily max
Annual mean
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
WATERLOO-CEDAR FALLS, IA
PM25* 98th percentile
Weighted annual mean
WAUSAU
Ozone
Wl
2nd highest daily max
4th highest daily max 8-h average
WEST PALM BEACH-BOCA RATON, FL
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
PM10* 90th percentile
Weighted annual mean
PM26* 98th percentile
Weighted annual mean
WHEELING, WV-OH
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
ns
ns
ns
down
ns
ns
down
ns
down
ns
NA
NA
down
ns
down
ns
ns
ns
NA
NA
ns
ns
down
down
down
NA
NA
NA
NA
ns
ns
down
down
down
up
ns
ns
NA
NA
down
down
down
ns
ns
down
down
NA
NA
1 0.098
1 0.081
1 0.019
1 0.006
1 0.121
1 0.103
1 3.5
1 0.023
2 0.138
2 0.107
1 ND
1 ND
2 5.6
2 0.023
2 0.008
4 0.027
3 0.127
3 0.099
4 ND
4 ND
1 0.02
1 0.021
1 0.006
1 43
1 22.6
1 ND
1 ND
1 ND
1 ND
1 0.081
1 0.066
1 3.1
1 0.028
1 0.004
1 0.013
1 30
1 18.6
1 ND
1 ND
1 4.1
2 0.064
2 0.018
1 0.11
1 0.077
1 49
1 27.5
2 ND
2 ND
0.094
0.075
0.032
0.005
0.102
0.086
4
0.023
0.137
0.108
ND
ND
5.3
0.03
0.009
0.027
0.127
0.09
ND
ND
0.017
0.03
0.007
41
25.1
ND
ND
ND
ND
0.077
0.064
2.8
0.016
0.003
0.012
30
18.6
ND
ND
4.6
0.067
0.016
0.095
0.078
46
27
ND
ND
0.104
0.087
0.016
0.004
0.126
0.091
4.2
0.023
0.118
0.1
ND
ND
4.95
0.021
0.008
0.023
0.12
0.097
ND
ND
0.037
0.019
0.005
37
23.6
ND
ND
ND
ND
0.088
0.075
2.8
0.019
0.002
0.012
30
18.6
ND
ND
5
0.061
0.013
0.104
0.089
45
27.7
ND
ND
0.087
0.071
0.016
0.005
0.105
0.086
3.9
0.018
0.131
0.104
ND
ND
4.1
0.036
0.007
0.024
0.109
0.083
ND
ND
0.033
0.022
0.005
45
25.4
ND
ND
ND
ND
0.079
0.07
2.5
0.014
0.002
0.013
42
22.6
ND
ND
3.5
0.059
0.012
0.105
0.087
38
27
ND
ND
0.092
0.078
0.018
0.004
0.115
0.104
3.5
0.019
0.114
0.096
ND
ND
4.4
0.023
0.007
0.023
0.127
0.093
ND
ND
0.025
0.02
0.005
36
23.3
ND
ND
ND
ND
0.08
0.068
3.5
0.013
0.002
0.013
34
20.4
ND
ND
3.1
0.048
0.012
0.11
0.082
40
23.2
ND
ND
0.093
0.073
0.012
0.004
0.117
0.098
3.6
0.017
0.13
0.102
ND
ND
3.45
0.021
0.007
0.024
0.113
0.097
ND
ND
0.017
0.021
0.006
32
21.6
ND
ND
ND
ND
0.098
0.077
2.5
0.004
0.001
0.013
34
25.7
ND
ND
3.5
0.051
0.013
0.104
0.087
45
24.8
ND
ND
0.102
0.086
0.012
0.003
0.117
0.096
3.9
0.021
0.116
0.099
114
27.6
4.7
0.022
0.007
0.023
0.126
0.099
37.4
15.145
0.01
0.02
0.005
32
19.2
38.4
13.22
30.3
12.05
0.095
0.084
2.8
0.013
0.002
0.014
30
19
ND
ND
3
0.047
0.012
0.1
0.088
43
25.1
36.15
16.51
0.094
0.079
0.017
0.004
0.117
0.094
3.3
0.018
0.111
0.095
103
23.92
3.85
0.022
0.007
0.022
0.11
0.081
39.725
15.523
0.017
0.017
0.004
30
19.9
34.4
13.56
28.7
11.37
0.081
0.073
2.7
0.008
0.002
0.016
30
19.4
26.9
9.37
2.3
0.043
0.011
0.093
0.071
39
23.2
34.45
15.88
0.085
0.073
0.021
0.004
0.129
0.101
3.2
0.018
0.117
0.098
96
22.49
3.6
0.024
0.006
0.024
0.121
0.096
42.25
15.905
0.013
0.018
0.004
35
19.8
35.4
13.97
30.2
11.8
0.078
0.072
2.2
0.003
0.001
0.017
30
19.7
18
7.69
1.9
0.04
0.01
0.104
0.088
41
24
37.75
15.8
0.096
0.078
0.016
0.004
0.12
0.101
2.8
0.019
0.124
0.105
70
23.22
3.55
0.02
0.007
0.024
0.144
0.107
41.425
15.628
0.017
0.02
0.004
34
19.1
32.6
13.13
24.1
10.95
0.08
0.073
2.3
0.002
0.001
0.017
24
15.4
16.1
7.04
1.6
0.036
0.011
0.111
0.097
39
23.3
40.3
15.32
172 DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
WICHITA, KS
CO 2nd max (daily-non-overlapping 8-h)
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
WILMINGTON-NEWARK, DE-MD
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
WILMINGTON, NC
S02 2nd daily max
Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
WORCESTER, MA-CT
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
YOLO, CA
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM26* 98th percentile
Weighted annual mean
YORK, PA
CO 2nd max (daily-non-overlapping 8-h)
S02 2nd daily max
Annual mean
N02 Annual mean
Ozone 2nd highest daily max
4th highest daily max 8-h average
PM25* 98th percentile
Weighted annual mean
YOUNGSTOWN-WARREN, OH
PM10* 90th percentile
Weighted annual mean
PM25* 98th percentile
Weighted annual mean
down
ns
up
down
down
NA
NA
ns
down
down
ns
ns
NA
NA
ns
down
down
ns
NA
NA
down
down
ns
down
ns
ns
down
down
NA
NA
ns
ns
NA
NA
down
down
ns
down
ns
ns
NA
NA
down
down
NA
NA
1 5.6
1 0.08
1 0.059
1 54
1 32.6
2 ND
2 ND
1 1.7
1 0.06
1 0.012
2 0.118
2 0.086
3 ND
3 ND
1 0.063
1 0.009
1 0.104
1 0.081
1 ND
1 ND
1 6.1
1 0.025
1 0.007
1 0.028
1 0.155
1 0.092
1 37
1 20.3
2 ND
2 ND
1 0.09
1 0.076
1 ND
1 ND
1 3.3
1 0.032
1 0.008
1 0.022
1 0.112
1 0.09
1 ND
1 ND
1 48
1 25.9
1 ND
1 ND
6.5
0.08
0.067
42
24.6
ND
ND
1.7
0.056
0.011
0.108
0.082
ND
ND
0.063
0.009
0.104
0.081
ND
ND
5.9
0.024
0.008
0.025
0.125
0.097
35
20.3
ND
ND
0.097
0.076
ND
ND
3.9
0.041
0.009
0.024
0.115
0.082
ND
ND
46
29.3
ND
ND
5.4
0.09
0.069
50
26
ND
ND
1.5
0.098
0.013
0.136
0.105
ND
ND
0.063
0.009
0.097
0.079
ND
ND
4.2
0.023
0.006
0.021
0.118
0.096
32
20.1
ND
ND
0.108
0.083
ND
ND
2.7
0.02
0.006
0.021
0.097
0.086
ND
ND
53
32.5
ND
ND
6.1
0.095
0.074
43
25.8
ND
ND
1.6
0.067
0.011
0.108
0.085
ND
ND
0.036
0.007
0.09
0.076
ND
ND
5.3
0.021
0.005
0.019
0.091
0.074
29
19.1
ND
ND
0.113
0.087
ND
ND
2.8
0.022
0.007
0.021
0.098
0.081
ND
ND
37
26.2
ND
ND
5.3
0.092
0.079
39
21.8
ND
ND
1.1
0.057
0.01
0.124
0.093
ND
ND
0.028
0.007
0.102
0.083
ND
ND
3.4
0.021
0.004
0.019
0.106
0.092
34
20.3
ND
ND
0.092
0.068
ND
ND
3.4
0.026
0.009
0.019
0.109
0.094
ND
ND
41
24.8
ND
ND
5.3
0.1
0.083
46
25.2
ND
ND
1.3
0.044
0.008
0.118
0.093
ND
ND
0.026
0.007
0.102
0.086
ND
ND
3.5
0.017
0.005
0.019
0.124
0.097
27
18.2
ND
ND
0.109
0.087
ND
ND
2.4
0.023
0.008
0.019
0.112
0.095
ND
ND
45
26.5
ND
ND
4.5
0.095
0.079
38
23.2
25.75
12.205
1.4
0.049
0.008
0.128
0.1
35.35
15.135
0.027
0.007
0.081
0.067
37.4
12.81
3.3
0.013
0.004
0.02
0.113
0.093
34
20.8
35.5
13.34
0.115
0.088
56
16.29
2.4
0.019
0.007
0.019
0.121
0.094
34.9
15.4
40
24.7
38.6
16.94
3.7
0.093
0.08
38
21.7
25.85
11.695
1.6
0.047
0.006
0.115
0.09
39.1
15.783
0.03
0.006
0.097
0.08
28
12.48
2.6
0.019
0.006
0.018
0.098
0.076
31
19
29.55
11.955
0.101
0.08
38
10.25
1.8
0.02
0.006
0.018
0.112
0.09
41.1
16.55
40
25.5
34.6
15.97
4.1
0.096
0.084
36
22.2
25
11.18
1.6
0.043
0.006
0.112
0.092
41.133
16.29
0.039
0.006
0.089
0.078
25.4
11.49
2.6
0.022
0.005
0.02
0.118
0.088
30
17.7
34.75
13.01
0.099
0.075
35
10.39
2.2
0.019
0.006
0.02
0.104
0.087
41.3
16.62
33
22.7
44.8
16.36
3.7
0.092
0.079
37
21.6
27.9
10.76
1.2
0.054
0.006
0.132
0.101
36.633
14.297
0.04
0.007
0.091
0.08
22.9
10.36
2.9
0.018
0.005
0.017
0.127
0.091
30
15.3
37.5
11.23
0.104
0.076
31
10.72
2.2
0.014
0.005
0.017
0.124
0.101
47.3
17.09
39
22.1
38.3
14.75
APPENDIX A • DATA TABLES 173
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-16. Metropolitan Statistical Area Air Quality Trends, 1993-2002 (continued)
Metropolitan Statistical Area
Trend
#Trend
Sites
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
YUBA CITY, CA
CO
N02
Ozone
PM10*
PM25*
YUMA, AZ
PM10*
CO
Pb
N02
PM10 =
S02 =
ppm
jig/m3 =
2nd max (daily-non-overlapping 8-h) down
Annual mean ns
2nd highest daily max ns
4th highest daily max 8-h average ns
90th percentile ns
Weighted annual mean ns
98th percentile NA
Weighted annual mean NA
90th percentile up
Weighted annual mean up
1 5
1 0.018
1 0.09
1 0.078
1 59
1 30.4
1 ND
1 ND
1 50
1 31.8
5.6
0.016
0.107
0.089
51
34.1
ND
ND
51
31.1
4.1
0.014
0.102
0.085
68
32.2
ND
ND
67
35.1
4.1
0.013
0.108
0.085
50
29.2
ND
ND
52
37.1
3.9
0.014
0.09
0.072
48
28.6
ND
ND
62
36.6
3.9
0.013
0.102
0.088
44
23.1
ND
ND
75
40.1
4.2
0.014
0.103
0.083
68
38.4
53
15.85
59
35.2
3.6
0.013
0.097
0.079
40
27.9
37
11.46
68
42.3
3.4
0.014
0.099
0.081
52
29
54
11.79
84.5
45.1
3.2
0.015
0.101
0.08
49
30.4
35
12.64
101
47.9
Highest second maximum non-overlapping 8-hour concentration (Applicable NAAQS is 9 ppm)
Highest quarterly maximum concentration (Applicable NAAQS is 1.5 iig/m3)
Highest arithmetic mean concentration (Applicable NAAQS is 0.053 ppm)
Highest second maximum 24-hour concentration
Highest second maximum 24-hour concentration
Units are parts per million
Units are micrograms per cubic meter
(Applicable NAAQS is 150 ng/m3)
(Applicable NAAQS is 0. 14 ppm)
*PM25 does not have enough years to assess trends.
174
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-17. Number of Days with AQI Values Greater Than 100 at Trend Sites, 1993-2002,
and All Sites in 2002
Metropolitan Statistical Area
Akron, OH
Albany-Schenectady-Troy, NY
Albuquerque, NM
Allentown-Bethlehem-Easton, PA
Atlanta, GA
Austin-San Marcos, TX
Bakersfield, CA
Baltimore, MD
Baton Rouge, LA
Bergen-Passaic, NJ
Birmingham, AL
Boston, MA-NH
Buffalo-Niagara Falls, NY
Charleston-North Charleston, SC
Charlotte-Gastonia-Rock Hill, NC-SC
Chicago, IL
Cincinnati, OH-KY-IN
Cleveland-Lorain-Elyria, OH
Columbus, OH
Dallas, TX
Dayton-Springfield, OH
Denver, CO
Detroit, Ml
El Paso, TX
Fort Lauderdale, FL
Fort Worth-Arlington, TX
Fresno, CA
Gary, IN
Grand Rapids-Muskegon-Holland, Ml
Greensboro-Winston Salem-High Point, NC
Greenville-Spartanburg-Anderson, SC
Harrisburg-Lebanon-Carlisle, PA
Hartford, CN
Honolulu, HI
Houston, TX
Indianapolis, IN
Jacksonville, FL
Jersey City, NJ
Kansas City, MO-KS
Knoxville, TN
Las Vegas, NV-AZ
Little Rock-North Little Rock, AR
Los Angeles-Long Beach, CA
Louisville, KY-IN
Memphis, TN-AR-MS
Miami, FL
Middlesex-Somerset-Hunterdon, NJ
Milwaukee-Waukesha, Wl
Minneapolis-St. Paul, MN-WI
Monmouth-Ocean, NJ
Nashville, TN
Nassau-Suffolk, NY
New Haven-Meriden, CT
New Orleans, LA
#of
Trend
Sites
7
6
23
4
21
1
27
20
18
5
18
21
8
12
15
51
16
42
9
21
12
32
33
19
16
5
19
19
9
15
9
9
9
19
29
25
12
7
18
16
15
6
56
35
15
16
5
20
27
3
18
7
8
12
1993
10
5
0
3
36
2
97
48
13
0
10
2
1
2
29
4
5
17
8
12
11
6
5
7
4
9
59
0
3
22
8
15
14
0
27
9
0
19
4
25
3
2
134
23
15
6
13
4
0
24
19
17
12
6
1994
8
6
1
3
15
4
105
40
10
0
6
6
4
2
15
13
16
25
12
24
14
3
11
6
1
31
55
6
14
7
5
12
18
0
41
22
0
12
10
16
3
2
139
28
10
1
9
12
2
13
21
15
13
8
1995
12
3
0
7
36
10
107
36
22
0
32
7
6
1
18
24
19
27
18
29
11
5
14
3
1
28
61
18
18
13
7
13
14
0
66
21
0
16
21
26
3
7
113
26
21
2
20
14
5
20
26
10
14
20
1996
11
4
0
6
28
0
110
28
12
0
15
4
3
3
21
7
10
19
19
10
18
2
13
6
1
14
70
12
9
7
7
3
5
0
28
16
0
5
7
21
14
1
94
17
19
1
15
5
0
17
23
8
8
8
1997
6
3
0
12
33
0
58
30
16
0
8
7
1
3
29
10
11
13
13
27
10
0
11
2
0
14
75
12
10
14
9
9
16
0
47
12
0
9
16
37
4
1
60
18
17
3
19
5
0
21
20
12
19
7
1998
14
3
0
18
52
5
78
51
21
1
23
8
13
3
50
12
13
22
21
33
19
9
17
6
1
17
67
9
19
26
28
22
10
0
38
19
3
7
14
54
5
3
56
29
27
8
22
12
1
31
30
11
9
7
1999
20
6
1
19
67
8
144
40
26
0
51
10
8
5
42
19
16
40
26
25
21
5
20
5
3
19
133
16
22
24
19
19
18
2
52
24
2
20
3
66
8
5
56
47
35
7
26
19
1
27
36
18
19
18
2000*
4
1
0
5
34
6
132
19
33
0
49
1
5
4
28
2
14
22
10
22
14
3
15
4
2
16
131
10
6
14
11
16
7
2
42
5
0
4
11
41
2
16
87
18
24
2
11
5
2
11
19
5
9
17
2001*
12
11
1
9
18
0
125
32
5
0
35
12
13
0
27
22
14
32
13
16
7
8
27
9
3
17
138
19
17
14
13
22
16
2
29
10
0
7
4
23
1
4
88
19
13
1
21
15
2
21
7
3
15
5
2002*
22
8
4
18
24
5
152
42
6
0
16
16
21
1
37
21
30
31
21
15
28
8
26
13
3
23
152
20
21
24
28
21
21
2
23
25
1
8
7
45
6
9
80
29
16
1
29
12
1
31
16
13
25
2
Total #
of Sites
2002
9
12
31
12
37
8
29
33
22
9
30
35
15
13
26
70
33
48
15
40
15
29
35
40
21
19
25
30
14
22
11
11
13
26
60
34
17
9
34
21
56
14
69
36
20
16
7
28
49
4
21
13
11
19
2002
Count*
24
18
4
27
37
5
153
44
7
21
23
26
22
3
40
26
32
33
30
22
30
8
28
18
3
33
156
24
24
32
29
24
23
2
30
26
1
8
12
45
14
11
108
29
17
1
30
12
2
32
21
19
29
2
APPENDIX A • DATA TABLES 175
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-17. Number of Days with AQI Values Greater Than 100 at Trend Sites, 1993-2002,
and All Sites in 2002 (continued)
Metropolitan Statistical Area
New York, NY
Newark, NJ
Norfolk-Virginia Beach-Newport News,VA-NC
Oakland, CA
Oklahoma City, OK
Omaha, NE-IA
Orange County, CA
Orlando, FL
Philadelphia, PA-NJ
Phoenix-Mesa, AZ
Pittsburgh, PA
Portland-Vancouver, OR-WA
Providence-Fall River-Warwick, RI-MA
Raleigh-Durham-Chapel Hill, NC
Richmond-Petersburg, VA
Riverside-San Bernardino, CA
Rochester, NY
Sacramento, CA
St. Louis, MO-IL
Salt Lake City-Ogden, UT
San Antonio, TX
San Diego, CA
San Francisco, CA
San Jose, CA
SanJuan-Bayamon, PR
Sera nton-Wi Ikes Barre-Hazleton, PA
Seattle-Bellevue-Everett, WA
Springfield, MA
Syracuse, NY
Tacoma, WA
Tampa-St. Petersburg-Clearwater, FL
Toledo, OH
Tucson, AZ
Tulsa, OK
Ventura, CA
Washington, DC-MD-VA-WV
West Palm Beach-Boca Raton, FL
Wilmington-Newark, DE-MD
Youngstown-Warren, OH
#of
Trend
Sites
19
11
10
30
9
11
15
14
44
25
57
13
9
11
8
47
6
39
55
24
2
36
16
11
17
14
13
16
5
8
36
3
23
11
21
46
8
8
9
1993
11
13
19
4
2
1
25
4
62
14
14
0
0
17
22
168
0
20
9
5
3
59
0
4
0
10
0
13
4
0
1
7
1
4
43
52
3
29
9
1994
16
12
6
3
5
1
15
3
37
10
22
2
5
15
9
150
1
37
33
17
3
46
0
2
0
7
3
12
1
2
3
8
0
12
63
22
0
24
5
1995
21
20
6
12
13
1
9
1
38
22
27
2
7
12
14
125
6
41
38
5
17
48
2
14
0
12
2
9
5
0
2
9
3
21
66
32
0
27
11
1996
14
11
4
11
2
1
9
1
38
15
12
6
2
14
5
118
0
44
23
14
2
31
0
8
1
4
6
5
0
1
3
11
0
14
62
18
0
13
8
1997
23
13
17
0
4
0
3
5
38
12
21
0
3
22
19
107
6
17
15
2
3
14
0
0
1
11
1
10
2
0
4
4
1
7
45
30
0
22
10
1998
18
22
15
12
7
5
6
14
37
14
39
3
2
40
22
96
4
29
24
19
6
33
0
8
0
7
3
7
3
4
11
5
0
9
29
47
2
28
20
1999
25
24
17
17
4
5
14
4
32
10
40
4
3
29
21
123
9
69
31
8
9
33
10
23
2
12
6
15
4
4
10
4
7
14
24
39
1
21
16
2000*
19
10
5
12
6
1
31
3
22
10
29
5
3
13
6
145
1
45
18
15
0
31
4
24
0
3
7
3
1
5
8
2
0
10
31
11
0
18
5
2001*
19
16
7
9
2
1
31
6
29
8
52
4
10
8
15
155
5
49
17
15
0
31
12
14
0
12
3
13
4
4
4
9
0
6
25
22
1
19
22
2002*
31
30
15
19
2
0
19
1
33
8
53
6
9
29
22
145
13
69
34
18
17
20
17
11
0
23
6
12
9
0
0
13
3
5
11
34
0
21
18
Total #
of Sites
2002
44
23
17
45
19
20
16
16
60
68
66
21
20
19
16
68
8
52
68
37
12
36
16
13
31
12
30
19
9
9
47
10
27
17
25
65
10
18
15
2002
Count*
34
30
15
21
4
0
21
1
39
22
55
6
15
30
25
147
13
77
36
36
17
20
17
13
0
23
7
17
10
7
0
18
3
6
16
39
0
23
25
•Includes PM,
176
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-18. Number of Days with Air Quality Index Values Greater Than 100 at Trend Sites, 1993-2002,
and All Sites in 2002, Ozone Only
#of
Trend
Metropolitan Statistical Area
AKRON, OH
ALBANY-SCHENECTADY-TROY, NY
ALBUQUERQUE, NM
ALLENTOWN-BETHLEHEM-EASTON, PA
ATLANTA, GA
AUSTIN-SAN MARCOS, TX
BAKERSFIELD, CA
BALTIMORE, MD
BATON ROUGE, LA
BERGEN-PASSAIC, NJ
BIRMINGHAM, AL
BOSTON, MA-NH
BUFFALO-NIAGARA FALLS, NY
CHARLESTON-NORTH CHARLESTON, SC
CHARLOTTE-GASTONIA-ROCK HILL, NC-SC
CHICAGO, IL
CINCINNATI, OH-KY-IN
CLEVELAND-LORAIN-ELYRIA, OH
COLUMBUS, OH
DALLAS, TX
DAYTON-SPRINGFIELD, OH
DENVER, CO
DETROIT, Ml
EL PASO, TX
FORT LAUDERDALE, FL
FORT WORTH-ARLINGTON, TX
FRESNO, CA
GARY, IN
GRAND RAPIDS-MUSKEGON-HOLLAND, Ml
GREENSBORO-WINSTON SALEM-HIGH POINT, NC
GREENVILLE-SPARTANBURG-ANDERSON.SC
HARRISBURG-LEBANON-CARLISLE, PA
HARTFORD, CN
HONOLULU, HI
HOUSTON, TX
INDIANAPOLIS, IN
Sites
2
3
8
1
5
1
8
7
7
6
2
2
3
6
22
4
8
4
3
4
8
7
2
2
2
5
3
4
4
4
3
3
1
9
7
1993
10
5
0
3
36
2
97
48
12
0
10
2
1
2
29
3
5
16
8
12
11
3
5
3
4
9
59
0
3
22
8
15
14
0
27
9
1994
8
6
1
3
15
4
105
40
10
0
6
6
4
2
15
8
16
23
12
24
14
2
11
2
1
31
55
6
14
7
5
12
18
0
41
22
1995
12
3
0
7
36
10
106
36
22
0
32
7
6
1
18
24
19
24
18
29
11
3
12
3
1
28
61
18
18
13
7
13
13
0
66
21
1996
11
4
0
6
28
0
110
28
12
0
15
4
3
3
21
7
10
18
19
10
18
2
12
1
1
14
70
12
9
7
7
3
5
0
28
16
1997 1998
6
3
0
12
33
0
58
30
16
0
8
7
1
3
29
10
11
13
13
27
10
0
11
0
0
14
75
11
10
14
9
9
16
0
47
12
14
3
0
18
52
5
76
51
21
0
23
8
13
3
50
12
13
21
21
33
19
9
17
6
1
17
67
9
19
26
28
22
10
0
38
19
1999
20
6
1
19
61
8
93
40
26
0
30
8
8
5
42
14
11
20
22
25
19
3
14
0
1
19
81
10
21
24
19
17
18
0
51
24
2000 2001 2002
4
1
0
5
27
6
82
16
30
0
21
1
5
4
24
1
4
4
6
22
6
2
3
3
1
16
78
5
3
12
11
5
7
0
41
4
12
11
1
9
10
0
85
26
5
0
11
12
13
0
26
16
6
17
7
16
4
2
16
1
2
17
92
10
11
12
13
17
16
0
28
8
22
8
0
18
24
5
91
39
6
0
13
13
21
1
36
20
26
29
19
15
28
7
21
4
1
23
91
20
20
24
28
17
21
0
22
23
Total
#of
Sites
2002
2
4
11
3
12
2
8
8
7
2
10
6
2
3
8
21
8
9
7
10
5
8
7
6
3
8
9
6
5
7
4
3
3
1
17
12
2002
Count
22
16
0
21
37
5
91
39
6
20
15
19
21
1
38
21
29
31
28
22
28
7
21
6
1
33
95
23
20
30
28
17
21
0
29
24
APPENDIX A • DATA TABLES 177
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-18. Number of Days with Air Quality Index Values Greater Than 100 at Trend Sites, 1993-2002,
and All Sites in 2002, Ozone Only (continued)
Metropolitan Statistical Area
JACKSONVILLE, FL
JERSEY CITY, NJ
KANSAS CITY, MO- KS
KNOXVILLE, TN
LAS VEGAS, NV-AZ
LITTLE ROCK-NORTH LITTLE ROCK, AR
LOS ANGELES-LONG BEACH, CA
LOUISVILLE, KY-IN
MEMPHIS, TN-AR-MS
MIAMI, FL
MIDDLESEX-SOMERSET-HUNTERDON, NJ
MILWAUKEE-WAUKESHA.WI
MINNEAPOLIS-ST. PAUL, MN-WI
MONMOUTH-OCEAN, NJ
NASHVILLE, TN
NASSAU-SUFFOLK, NY
NEWHAVEN-MERIDEN, CT
NEW ORLEANS, LA
NEW YORK, NY
NEWARK, NJ
NORFOLK-VIRGINIA BEACH-NEWPORT
NEWS, VA-NC
OAKLAND, CA
OKLAHOMA CITY, OK
OMAHA, NE-IA
ORANGE COUNTY, CA
ORLANDO, FL
PHILADELPHIA, PA-NJ
PHOENIX-MESA, AZ
PITTSBURGH, PA
PORTLAND-VANCOUVER, OR-WA
PROVIDENCE-FALL RIVER-WARWICK, RI-MA
RALEIGH-DURHAM-CHAPEL HILL, NC
RICHMOND-PETERSBURG, VA
RIVERSIDE-SAN BERNARDINO, CA
ROCHESTER, NY
#of
Trend
Sites
1
1
4
7
4
2
14
7
4
4
2
9
4
2
6
2
1
6
5
1
3
8
3
3
4
4
10
7
11
3
1
7
3
15
2
1993 1994
0
19
3
25
3
2
112
22
13
6
13
4
0
24
18
17
12
6
11
13
19
4
2
0
25
4
51
14
13
0
0
17
22
167
0
0
12
10
16
3
2
117
28
10
1
9
12
0
13
21
15
13
8
16
11
6
3
5
0
15
3
25
7
20
1
5
15
9
149
1
1995
0
16
21
26
0
7
97
26
21
2
20
14
3
20
26
10
14
20
20
20
6
12
13
0
8
1
30
19
25
2
7
12
14
119
6
1996
0
5
6
21
4
1
74
17
18
1
15
5
0
17
22
8
8
8
14
11
4
11
2
0
9
1
22
15
12
6
2
14
5
115
0
1997 1998 1999 2000 2001 2002
0
9
16
37
0
1
45
18
17
3
19
5
0
21
20
12
19
7
23
13
17
0
4
0
3
5
32
10
20
0
3
22
19
104
6
3
7
14
54
3
2
46
29
27
8
22
12
1
31
30
11
9
7
18
22
15
12
7
0
6
14
37
14
39
3
2
40
22
95
4
2
17
3
62
0
5
19
44
35
5
26
17
0
27
33
18
16
18
25
21
16
8
4
2
1
4
32
10
23
0
2
29
21
96
9
0
3
10
36
0
16
45
10
24
0
11
4
0
11
16
5
6
17
11
6
5
3
6
0
4
3
17
9
4
0
2
12
5
98
1
0
6
4
17
1
4
37
10
13
1
21
12
2
21
7
3
11
5
16
13
6
3
2
0
2
3
27
6
19
0
10
8
12
92
5
0
6
7
45
2
9
35
26
16
0
29
12
1
31
16
13
20
2
30
27
15
5
2
0
0
1
33
6
28
1
9
29
21
96
13
Total
#of
Sites
2002
3
1
6
7
15
3
16
7
4
4
2
9
6
2
7
3
2
6
7
2
3
11
6
3
4
5
12
21
12
4
2
8
4
18
2
2002
Count
0
6
12
45
6
9
68
26
16
0
29
12
2
31
21
18
24
2
30
27
15
6
3
0
1
1
37
14
32
1
14
29
25
97
13
178
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-18. Number of Days with Air Quality Index Values Greater Than 100 at Trend Sites, 1993-2002,
and All Sites in 2002, Ozone Only (continued)
Metropolitan Statistical Area
SACRAMENTO, CA
ST. LOUIS, MO-IL
SALT LAKE CITY-OGDEN, UT
SAN ANTONIO, TX
SAN DIEGO, CA
SAN FRANCISCO, CA
SAN JOSE, CA
SANJUAN-BAYAMON, PR
SCRANTON-WILKES BARRE-HAZLETON, PA
SEATTLE-BELLEVUE-EVERETT, WA
SPRINGFIELD, MA
SYRACUSE, NY
TACOMA, WA
TAMPA-ST. PETERSBURG-CLEARWATER, FL
TOLEDO, OH
TUCSON, AZ
TULSA, OK
VENTURA, CA
WASHINGTON, DC-MD-VA-WV
WEST PALM BEACH-BOCA RATON, FL
WILMINGTON-NEWARK, DE-MD
YOUNGSTOWN-WARREN, OH
#of
Trend
Sites
10
15
6
1
9
3
5
4
2
4
2
2
7
2
5
3
6
16
1
4
2
1993 1994
20
9
2
3
58
0
4
0
10
0
13
4
0
1
7
1
4
43
52
3
29
9
37
31
9
3
46
0
2
0
7
3
12
1
2
3
8
0
12
63
22
0
24
5
1995
41
38
5
17
48
2
14
0
12
0
9
5
0
2
9
3
21
66
32
0
27
11
1996
44
23
12
2
31
0
8
0
4
6
4
0
1
3
11
0
14
62
18
0
13
8
1997 1998
17
14
2
3
14
0
0
0
11
1
10
2
0
4
4
1
7
44
30
0
22
10
29
24
19
6
33
0
8
0
7
3
7
3
4
11
5
0
9
29
47
2
28
20
1999
39
29
4
9
16
0
3
0
12
1
10
4
0
9
4
1
14
22
39
1
21
12
2000 2001 2002
29
16
7
0
14
0
1
0
1
1
2
1
0
6
2
0
10
27
11
0
18
2
34
14
4
0
17
0
3
0
10
0
13
4
0
4
9
0
4
19
22
1
19
12
39
32
7
17
13
0
6
0
16
0
12
9
0
0
13
1
5
10
34
0
21
16
Total
#of
Sites
2002
15
17
8
3
9
3
6
1
4
4
4
3
4
10
5
6
5
7
20
2
5
3
2002
Count
47
32
9
17
13
0
6
0
16
0
12
9
0
0
16
1
6
15
38
0
21
24
APPENDIX A • DATA TABLES 179
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-19. Condensed Nonattainment Areas List3
State
Area Name>>
1-h Pollutant
CO SO2 PM10
Pb
NO2
O3
1-h Population'' (1000s)
CO SO2 PM10 Pb All
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
AK
AK
AK
AL
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CT
DC-MD-VA
DE
GA
GU
GU
ID
ID
ID
ID
IL-IN
LA
MA
MA
MD
MD
ME
ME
ME
MO
MO-IL
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NH
NH
NJ
NM
Anchorage . 1
Fairbanks . 1
Juneau
Birmingham 1
Ajo
Douglas
Miami-Hayden
Morenci
Nogales
Paul Spur
Phoenix 1 1
Rillito
San Manuel
Yuma
Imperial Valley
Los Angeles-South Coast 1 1
Mono Basin (in Mono Co.)
Owens Valley
Sacramento Metro 1
San Diego 1
San Francisco-Oakland-San Jose 1
San Joaquin Valley 2
Santa Barbara-Santa Maria-Lompoc 1
Searles Valley
Southeast Desert Modified AQMA 1
Ventura Co. 1
Aspen
Denver-Boulder
Fort Collins . 1
Lamar
Steamboat Springs
Greater Connecticut 1
Washington 1
Sussex County 1
Atlanta 1
Piti Power Plant
Tanguisson Power Plant
Boise . 1
Bonner Co.(Sandpoint )
Pocatello Area
Shoshone Co.
Chicago-Gary-Lake County 1
Baton Rouge 1
Bostton-Lawerence 1
Springfield (W. Mass) 1
Baltimore 1
Kent and Queen Anne Cos. 1
Knox/Lincoln County 1
Lewiston-Auburn 1
Portland 1
Liberty-Arcadia
St. Louis 1
Billings/Laural
Butte
Columbia Falls
East Helena
Kalispell
Lame Deer
Libby
Missoula . 1
Poison
Ronan
Thompson Falls
Whitefish
Manchester 1
Portsmouth-Bover-Rocherster 1
Atlantic City 1
Anthony
1
1
1 1
1 1
2 1
1
1
1
1
1
1
1
1
1
1
1
1
1
3
2
1
1
1
1
1
1
1
1
2
2
1 3
1
1
1
1
! 1
1
1
1
1
1
! 1
805
! ! 3028
14550
1 978
2813
6541
3302
399
1024
753
! . 2532
4544
156
3698
8757
636
5883
814
2512
59
73
220
487
1
1 e . 2482
1
1
1
! ! 364
192
354
255 . 195
39
13
! 7 7
15 15
4 4
8
24
1
3028 . 3111
0
7
82
119
14550 . 14550
0
7
. 1223
. 3080
22
424
5
. 2389
143
8
9
123
1
1
197
36
66
12
484 322
6 !
34
3
2
15
0
3
52 . 52
3
2
1
5
! ! 2
255
39
13
805
7
15
4
8
24
1
. 3111
0
7
82
119
. 14550
0
7
. 1978
. 2813
. 6541
. 3302
399
22
. 1024
753
5
. 2389
143
8
9
. 2532
. 4544
156
. 3698
1
1
197
36
66
12
. 8757
636
. 5883
814
. 2512
59
73
220
487
6 6
2 2482
6
34
3
2 2
15
0
3
52
3
2
1
5
364
192
354
2
180
DATA TABLES • APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-19. Condensed Nonattainment Areas List3 (continued)
State
Area Nameb
1-h Pollutant
CO S02 PM10
Pb NO,
03
1-h Population'1 (1000s)
CO S02 PM10 Pb All
69 NM
70 NM
71 NV
72 NV
73 NV
74 NY
75 NY
76 NY
77 NY
78 NY
79 NY-NJ-CT
80 OH
81 OH
82 OH-KY
83 OH-PA
84 OR
85 OR
86 OR
87 OR
88 OR
89 OR
90 OR
91 OR
92 PA
93 PA
94 PA
95 PA
96 PA
97 PA
98 PA
99 PA
100 PA
Grant Co. . . 1 . .
Sunland Park 1'
Lake Tahoe Nevada . 1 . . .
Las Vegas .1 . 1 .
Reno 11.1.
Abany-Schenectedy 1 ....
Buffalo-Niagara Falls 1 ....
"Essex Cy, Whiteface" 1 ....
Jefferson County 1 ....
Poughkeepsie 1 ....
New York-N. New Jersey-Long Island 1 . . 1 .
Cleveland-Akron-Lorain . . 1 . .
Lucas Co. (Toledo) . . 1 . .
Cinncinnati-Hamilton 1 ....
Youngstown-Warren 1 ....
Grants Pass . . . 1 .
Klamath Falls . . . 1 .
LaGrande . . . 1 .
Lakeview . . . 1 .
Medford . . . 1 .
Oakridge . . . 1 .
Springfield-Eugene . . . 1 .
Salem . 1 . . .
Altoona 1 ....
Erie 1 ....
Harrisburg-Lebanon 1 ....
Johnstown 1 ....
Lancaster 1 ....
Pittsburgh-Beaver Valley .121
Scranton-Wilkes_Barre 1 ....
Warren Co . . 2 . .
York 1 ....
101 PA-DE-NJ-MDPhiladelphia-Wilmington-Trenton 1 ....
102 PA-NJ
103 PR
104 Rl
105TX
106 TX
107 TX
108TX
109 UT
110 UT
111 UT
112 UT
113 VA
114 WA
115 WA
116 WA
117 Wl
118 Wl
119 Wl
120 WV
121 WV
122 WV
123WV-KY
124 WY
Allentown-Bethlehem 1 . 1 . .
Guaynabo Co. . . . 1 .
Providence (all of Rl) 1 ....
Beaumont-Port Arthur 1 ....
Dallas-Fort Worth 1 ....
El Paso 11.1.
Houston-Galveston-Brazoria 1 ....
Ogden . . . 1 .
Salt Lake City ..11.
Tooele Co. . . 1 . .
Utah Co. (Provo) .1.1.
"Smyth Cy, White Top" 1 ....
Spokane .1 . 1 .
Wallula . . . 1 .
Yakima .1.1.
Door County 1 ....
Manitowoc Co. 1 ....
Milwaukee-Racine 1 ....
Follansbee . . . 1 .
New Manchester Gr. (in Hancock Co) . . 1 . .
Wier.-Butler-Clay (in Hancock Co) ..11.
Huntington-Ashland . . 1 . .
Sheridan . . . 1 .
! 10
! 339
892
1170
0
111
600
19171
! 1514
120
129
280
629
232
470
763
! 473
6311
740
1048
385
4589
679
4669
27
82
1839
31
29
478
178
. 1095
455
135 !
335 410
20
102
62
898
40
118
0
322
! 9
16
49
1375
339
1537
20
19
12
3
78
3
179
21
92
563
77
898
368
204
0
63
2
15
15
31
10
29
. 1375
339
892
. 1170
0
111
600
. 19171
. 1095
455
. 1514
120
20
19
12
3
78
3
179
135
129
280
629
232
470
410
763
20
473
. 6311
740
92
. 1048
385
. 4589
679
. 4669
77
898
40
368
0
322
0
63
27
82
. 1839
2
9
16
49
15
56
16
24
67
116228
19921 3660 31850 10125730
APPENDIX A • DATA TABLES
181
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-19. Condensed Nonattainment Areas List3 (continued)
Notes:
This is a simplified listing of Classified Nonattainment areas. Unclassified and Section 185(A) nonattainment areas are not included. In certain cases,
footnotes are used to clarify the areas involved. For example, the lead Readers interested in more detailed information should use the official Federal
Register Citation (40CFR81).
Names of nonattainment areas are listed alphabetically within each state. The largest city determines which state is listed first in the case of multiple-city
nonattainment areas. When a larger nonattainment area, such as ozone, contains one or more smaller nonattainment areas, such as PMio or lead, the
common name for the larger nonattainment area is used. Note that several smaller nonattainment areas may be inside one larger nonattainment area, as
illustrated in Figure 1. For the purpose of this table, these are considered one nonattainment area and are listed on one line. Occasionally, two
nonattainment areas may only partially overlap, as illustrated in Figure 2. These are counted as two distinct nonattainment areas and are listed on
separate lines.
The number of nonattainment areas for each of the criteria pollutants is listed.
Population figures were obtained from 2000 census data. For nonattainment areas defined as only partial counties, population figures for just the
nonattainment area were used when these were available. Otherwise, whole county population figures were used. When a larger nonattainment area
encompasses a smaller one, double-counting the population in the "AN" column is avoided by only counting the population of the larger nonattainment
area.
Lead nonattainment area is Herculaneum, Missouri, in Jefferson County.
Ozone nonattainment area is a portion of Dona Ana County, New Mexico.
Figure A-1. (Multiple NA areas within a larger NA
area) Two SO2 areas inside the Pittsburgh-Beaver
Valley ozone NA. Counted as one NA area.
Figure A-2. (Overlapping NA areas) Searles Valley
PM-io NA partially overlaps the San Joaquin Valley
ozone NA. Counted as two NA areas.
I NA for O3
I NA for SO2
NA for O3
NAforPM10
182
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-2O. Trend in 8-hr ozone concentrations (ppm) exceedances at National Park and National Monument sites,
1991-2000
Trend
1991
1992 1993 1994 1995 1996 1997 1998 1999
2000
National Park
Acadia NP
Big Bend NP
Brigantine
Canyonlands NP
Cape Cod NS
Cape Remain
Chamizal
Chiricahua NM
Congaree Swamp
Cowpens NB
Craters of the Moon
Denali NP
Everglades NP
Glacier NP
Grand Canyon NP
Great Smoky Mtn
Great Smoky Mtn
Great Smoky Mtn
Lassen Volcanic
Mammoth Cave NP
Mount Rainier
Olympic NP
Pinnacles NM
Rocky Mountain
Saguaro NM
Sequoia/Kings C
Shenandoah NP
Theodore Roosevelt
Voyageurs NP
Yellowstone
Yosemite NP
/Votes/
1. The trends statistic is the annual fourth highest daily maximum 8-hour ozone concentration (ppm). The number of exceedances of the level of the
8-hour ozone NAAQS is shown below the concentration value.
2. "nd" indicates no data available for that year.
3. "inc" indicates less than 90 days of monitoring data available for that year.
4. "NS" indicates no statistically significant trend (at the 0.05 level).
5. "UP" indicates a statistically significant upward trend in ozone concentrations.
NS
NS
NS
UP
NS
UP
NS
NS
UP
UP
UP
NS
UP
NS
NS
UP
UP
UP
NS
UP
NS
NS
NS
NS
NS
NS
NS
NS
UP
UP
UP
0.095
7
0.057
0
0.111
34
nd
nd
0.111
16
0.060
0
nd
nd
0.071
0
0.059
0
0.078
1
nd
nd
0.049
0
0.060
0
0.051
0
0.073
0
0.079
2
0.082
1
nd
nd
0.066
0
0.078
0
nd
nd
0.041
0
0.084
3
0.076
0
0.073
0
0.097
34
0.083
3
0.060
0
0.050
0
0.057
0
0.098
31
0.080
1
0.061
0
0.094
8
0.055
0
0.096
6
0.072
0
0.072
2
0.065
0
0.067
0
0.086
4
0.040
0
0.050
0
0.061
0
0.051
0
0.074
0
0.088
5
0.075
3
nd
nd
0.069
0
0.073
0
nd
nd
0.046
0
0.084
3
0.071
0
0.074
1
0.102
50
0.077
1
0.057
0
0.054
0
0.063
0
0.091
7
0.080
3
0.063
0
0.093
13
0.063
0
0.088
4
0.069
0
0.059
0
0.068
0
0.063
0
0.082
3
0.056
0
0.048
0
0.064
0
0.044
0
0.066
0
0.088
4
0.089
7
0.074
0
0.064
0
0.072
0
0.055
0
0.042
0
0.060
2
0.071
1
0.082
1
0.106
48
0.083
2
0.055
0
0.058
0
0.053
0
0.063
0
0.075
0
0.069
0
0.083
2
0.068
0
0.088
4
0.067
0
0.075
2
0.071
0
0.064
0
0.083
2
0.063
0
0.049
0
0.064
0
0.055
0
0.073
0
0.093
10
0.088
6
0.076
3
0.078
1
0.075
1
0.067
2
0.041
0
0.078
0
0.076
0
0.080
0
0.106
58
0.083
2
0.057
0
0.062
0
0.061
0
0.094
12
0.092
5
0.065
0
0.100
10
0.063
0
0.105
9
0.075
1
0.084
3
0.069
0
0.076
1
0.084
3
0.057
0
0.053
0
0.058
0
nd
nd
0.069
0
0.099
13
0.093
12
0.089
9
0.074
0
0.088
6
0.065
0
0.044
0
0.083
3
0.076
0
0.083
2
0.095
18
0.087
7
0.058
0
0.064
0
0.060
0
0.091
11
0.073
2
0.073
0
0.095
13
0.074
0
0.096
8
0.071
1
0.078
1
0.072
0
0.074
0
0.080
2
0.064
0
0.053
0
0.063
0
0.057
0
0.073
0
0.088
8
0.092
12
0.087
7
0.073
1
0.082
2
0.065
0
0.046
0
0.094
9
0.072
0
0.076
0
0.105
50
0.081
1
0.059
0
0.067
0
0.061
0
0.090
10
0.077
1
0.063
0
0.106
18
0.067
0
0.100
17
0.082
3
0.071
0
0.065
0
0.065
0
0.091
5
0.060
0
0.051
0
0.066
0
0.040
0
0.072
0
0.098
19
0.095
20
0.089
6
0.067
0
0.078
4
0.040
0
0.045
0
0.076
1
0.070
0
0.079
0
0.097
26
0.089
6
0.071
0
0.071
0
0.061
0
0.081
3
0.088
4
0.070
0
0.091
22
0.071
0
0.084
2
0.076
0
0.088
6
0.067
0
0.081
0
0.096
15
0.065
0
0.054
0
0.072
0
0.053
0
0.072
0
0.110
35
0.106
34
0.106
33
0.078
1
0.092
12
0.051
0
0.041
0
0.088
5
0.080
1
0.077
0
0.094
27
0.107
22
0.056
0
0.067
0
0.066
0
0.094
9
0.092
5
0.064
0
0.095
13
0.073
0
0.101
12
0.080
2
0.071
0
0.072
0
0.080
0
0.094
9
0.068
0
0.054
0
0.067
2
0.048
0
0.076
0
0.106
37
0.101
36
0.101
29
0.084
2
0.098
19
0.064
0
0.043
0
0.082
1
0.074
1
0.069
1
0.097
39
0.093
15
0.058
0
0.074
0
0.069
0
0.085
4
0.070
0
0.064
0
0.085
4
0.076
0
0.083
3
0.076
2
0.080
2
0.071
0
0.073
0
0.088
4
0.066
0
0.038
0
0.066
0
0.050
0
0.071
0
0.096
12
0.096
18
0.100
21
0.074
0
0.088
4
0.057
0
0.047
0
0.078
0
0.078
2
0.074
0
0.090
8
0.080
1
0.059
0
0.065
0
0.065
0
0.087
6
APPENDIX A • DATA TABLES
183
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
Table A-21. Onroad and Nonroad Emissions of 21 Mobile Source Air Toxics, 1996
Compound
1 ,3-Butadiene*
Acetaldehyde*
Acrolein*
Arsenic Compounds*
Benzene*
Chromium Compounds*
Dioxins/Furans* 1
Ethylbenzene
Formaldehyde*
Lead Compounds*
Manganese Compounds*
Mercury Compounds*
MTBE
n-Hexane
Naphthalene2
Nickel Compounds*
POM (as sum of 7 PAH)*
Styrene
Toluene
Xylene
Diesel Particulate Matter
Onroad
Tons
23,500
28,700
5,000
0.25
168,200
14
NA
80,800
83,000
19
5.8
0.2
65,100
63,300
NA
10.7
42.0
16,300
549,900
311,000
182,000
Percent of
Total
National
Emissions
42%
29%
16%
0.06%
48%
1 .2%
NA
47%
24%
0.8%
0.2%
0.1%
47%
26%
NA
0.9%
4%
33%
51%
43%
34%
Nonroad
Tons
9,900
40,800
7,400
2.01
98,700
35
NA
62,200
86,400
546
35.5
6.6
53,900
43,600
NA
92.8
19.3
3,500
252,200
258,400
341 ,000
Percent of
Total
National
Emissions
18%
41%
23%
0.51%
28%
3%
NA
37%
25%
21.8%
1 .3%
4.1%
39%
18%
NA
7.6%
2%
7%
23%
36%
65%
Mobile Sources
Tons
33,400
69,500
12,400
2.26
266,900
49
NA
143,000
169,400
565
41.3
6.8
119,000
106,600
NA
103.5
61.3
19,800
802,100
569,400
523,000
Percent of
Total
National
Emissions
60%
70%
39%
0.57%
76%
4.2%
NA
84%
49%
22.6%
1 .5%
4.2%
86%
44%
NA
8.5%
6%
40%
74%
79%
99%
*On the urban HAPs list for the Integrated Urban Air Toxics Strategy
1Dioxin/Furans emission estimates are still under review
2Naphthalene emission estimates are currently included in POM. This will be corrected in the 1999 NTI.
184
DATA TABLES
APPENDIX A
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
APPENDIX A • DATA TABLES 185
-------
APPENDIX B
Methodology
http://www.epa.gov/oar/aqtrnd03/appendb.pdf
AQS Methodology
The ambient air quality data present-
ed in Chapters 2 and 3 of this report
are based on data retrieved from the
Air Quality System (AQS) on July
2003. These are direct measurements
of pollutant concentrations at moni-
toring stations operated by tribes
and state and local governments
throughout the nation. The monitor-
ing stations are generally located in
larger urban areas. EPA and other
federal agencies also operate some
air quality monitoring sites on a tem-
porary basis as a part of air pollution
research studies. The national moni-
toring network conforms to uniform
criteria for monitor siting, instrumen-
tation, and quality assurance.1'2
Emission estimation methods
used for historical years prior to 1985
are considered "top-down approach-
es," e.g., pollutant emissions were
estimated by using national average
emission characterization techniques
(for NOX, VOC, CO, Pb, and PM10).
Emission estimates for the years 1985
to present represent an evolution in
methods for significant categories,
resulting in a "bottom-up approach"
including data submitted directly by
state/local agencies (for all criteria
pollutants, PM2 5, and NH3).
In 2002, thousands of monitoring
sites reported air quality data for one
or more of the six National Ambient
Air Quality Standards (NAAQS)
Table B-1. Number of Ambient Monitors with Valid Annual Summary Statistics
Pollutant
No. of Sites with Valid
Annual Summary
Statistics in 2002
No. of Trend Sites
1992-2002
CO
Pb
NO2
°3
PM10
S02
331
77
217
718
629
361
387
96
250
785
770
449
pollutants to AQS, as shown in Table
B-1. The sites consist of National Air
Monitoring Stations (NAMS), State
and Local Air Monitoring Stations
(SLAMS), and other special-purpose
monitors. NAMS were established to
ensure a long-term national network
for urban area-oriented ambient
monitoring and to provide a system-
atic, consistent database for air quali-
ty comparisons and trends analysis.
SLAMS allow state or local govern-
ments to develop networks tailored
for their immediate monitoring
needs.
Air quality monitoring sites are
selected as national trends sites if
they have complete data for at least 8
of the 10 years. The annual data
completeness criteria are specific to
each pollutant and measurement
methodology. Table B-1 displays the
number of sites meeting the 10-year
trend completeness criteria. Because
of the annual turnover of monitoring
sites, the use of a moving 10-year
window maximizes the number of
sites available for trends and yields a
database that is consistent with the
current monitoring network.
The air quality data are divided
into two major groupings: daily
(24-hour) measurements and contin-
uous (1-hour) measurements. The
daily measurements are obtained
from monitoring instruments that
produce one measurement per
24-hour period and typically operate
on a systematic sampling schedule of
once every 6 days, or 61 samples per
year. Such instruments are used to
measure PM10 and lead. More fre-
quent sampling of PM10 (every other
day or every day) also is common.
Only PM10-weighted (for each quar-
ter to account for seasonality) annual
arithmetic means that meet the AQS
annual summary criteria are selected
as valid means for trends purposes.3
Only lead sites with at least six
APPENDIX B
AQS METHODOLOGY 185
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
samples per quarter in three of the
four calendar quarters qualify as
trends sites. Monthly composite lead
data are used if at least two monthly
samples are available for at least
three of the four calendar quarters.
Monitoring instruments that
operate continuously produce a
measurement every hour for a possi-
ble total of 8,760 hourly measure-
ments in a year. For hourly data,
only annual averages based on at
least 4,380 hourly observations are
considered as trends statistics. The
SO9 standard-related daily statistics
require at least 183 daily values to be
included in the analysis. Ozone sites
meet the annual trends data com-
pleteness requirement if they have at
least 50 percent of the daily data
available for the ozone season, which
varies by state, but typically runs
from May through September.4
Air Quality Trend Statistics
The air quality statistics presented in
this report relate to the pollutant-
specific NAAQS and comply with
the recommendations of the Intra-
Agency Task Force on Air Quality
Indicators.5 A composite average of
each trend statistic is used in the
graphical presentations throughout
this report. All sites were weighted
equally in calculating the composite
average trend statistic. Missing
annual summary statistics for the
second through ninth years for a site
are estimated by linear interpolation
from the surrounding years. Missing
end points are replaced with the
nearest valid year of data. The result-
ing data sets are statistically bal-
anced, allowing simple statistical
procedures and graphics to be easily
applied. This procedure is conserva-
tive since endpoint rates of change
are dampened by the interpolated
estimates.
Emissions Estimate
Methodology
Trends are presented for annual
nationwide emissions of CO, lead,
NOX, VOC, PM10, SO2, and NH3.
These trends are estimates of the
amount and kinds of pollution being
emitted by automobiles, factories,
and other sources based on best
available engineering calculations.
Methodologies for estimating emis-
sions are constantly evolving and
resources do not always allow for
them to be recalculated for all years.
Thus, some apparent changes in the
emission trends are actually caused
by a methods change rather than an
actual change in emissions. Compari-
son of the estimates for a given year
in this report to the same year in
previous reports is not appropriate.
The emission estimates presented
in this report reflect several major
changes in methodologies. For
stationary sources, state-derived
emission estimates were included
primarily for nonutility point and
area sources beginning in 1996. Also,
1985-1994 source NOX emission rates
derived from test data from EPA's
Acid Rain Division were used.
For mobile sources, the MOBILE6
model and 2002 draft of the NON-
ROAD model were run for several
base years and interpolated between
modeled years, making mobile
source trends and emission method-
ology consistent across the entire
period of years shown. This change
in mobile source estimation methods
makes for significant changes in the
trends, in particular raising esti-
mated emission levels for earlier
years over previous reports. New
methods have also been developed
for estimating emissions from
locomotives, aircraft, and commercial
marine vessels. Improved methods
for these three categories are based
on year-specific activity data and are
superior to the previous estimates
that were projected from year to
year. However, they leave a few data
gaps. For instance, the emission
estimates erroneously show no PM
emissions for commercial aircraft
due to problems in confirming a
valid emission factor.
In addition to the changes in
methodology affecting most source
categories and pollutants, other
changes were made to the emissions
for specific pollutants, source cate-
gories, and/or individual sources.
Activity data and correction param-
eters for agricultural crops and
paved roads were included. A
change in methodology occurred
starting in 1996 for calculating PM10
emissions from unpaved roads and
in 1999 for calculating emissions
from construction. This has led to
lower PM10 emissions than would
have been predicted using the
previous methods. The development
of new emission estimation method-
ologies has added emissions for open
burning of residential yard waste
and land-clearing debris burning.
Starting in 1999, these estimates con-
tributed to a significant increase in
industrial category emissions for CO,
PM10, and PM25 between 1998 and
1999. Rule effectiveness from pre-
1990 chemical and allied product
emissions was removed. Alaska and
Hawaii nonutility point and area
source emissions from several
sources were added. Also, this report
incorporates data from continuous
emissions monitors (CEMs) collected
between 1994 and 1999 for NOX and
SO2 emissions at major electric
utilities.
Another change is the addition of
PM condensible emissions. Previous
reports included only the filterable
186 AQS METHODOLOGY » APPENDIX B
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
portion of PM for stationary sources.
Onroad and nonroad mobile source
estimates included condensibles due
to the test methodology on which the
estimates are based. In this latest
report, we have tried to address this
by augmenting our estimates to
include the condensible portion for
point source and selected area source
emissions. This primarily affects
combustion sources.
All of these changes are part of a
broad effort to update and improve
emission estimates. Additional emis-
sion estimates and a more detailed
description of the estimation method-
ology are available from EPA's Emis-
sion Factor and Inventory Group (go
to www.epa.gov/ttn/chief and click
on "Emission Inventories," then click
on "National Emissions Inventory
Data," then click on the documenta-
tion and data for the latest year avail-
able).
IMPROVE
Methodology
Data collected from the Interagency
Monitoring of Protected Visual
Environments (IMPROVE) network
is summarized in Chapter 2 (PM2 5
section) of this report. The complete-
ness criteria and averaging method
used to summarize the IMPROVE
data are slightly different from those
used for the criteria pollutants.
(Data handling guidance is currently
being developed for the IMPROVE
network. Future summaries will be
based on this guidance.) The source
data sets were obtained from
Dr. James Sisler of Colorado State
University.
The annual average statistics in
these files were used to assess trends
in this report. The IMPROVE data
are not reported in terms of a calen-
dar year. The IMPROVE year runs
from March to February of the fol-
lowing year. It follows that the four
seasons are: March to May (spring),
June to August (summer), September
to November (autumn), and
December to the following February
(winter). The network samplers mon-
itor on Wednesdays and Saturdays
throughout the year, yielding 104
samples per year and 26 samples per
season. To be included in this analy-
sis, sites were required to have data
for at least 50 percent of the sched-
uled samples (13 days) for every cal-
endar quarter.
IMPROVE monitoring sites are
selected as trends sites if they have
complete data for at least 8 of the 10
years between 1990 and 1999 (or 6 of
8 years for those who began monitor-
ing in 1992). A year is valid only if
there are at least 13 samples (50 per-
cent complete) per season for both
measured and reconstructed PM2 5.
Figure B-1. Class I Areas in the IMPROVE Network meeting data completeness criteria.
• Glacier
Mount Rainier
Three Sisters
• Crater Lake
• Redwood
Lassen Volcanic
. Yellowstone
Bridger Badlands
Point Reyes
Great
.Yosemite
Moosehorn
•
Acadia •
Lye Brook •
Brigantine
t-
'Rocky Mountain
Pinnacles • BrVce Cannon • Canyonlands
Sequoia •• "Great Sand Dunes
Mesa Verde Weminuche
• . • Bandelier
San Gorgomo Petrified Forest
Q Tonto
Chiricahua
Upper Buffalo
Denali
1 Guadalupe Mntns
Big Bend
Dolly Sods,
Shenandoah
Mammoth Cave 'Jefferson
• Great Smoky Mntn
Sipsey • Cape Remain
n Okefenokee
Chassahowitza
• Complete for Both
D Complete for Trends Only
• Complete for 1999 Only
APPENDIX B
AQS METHODOLOGY 187
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
The same linear interpolation
applied to the criteria pollutants is
applied here. The IMPROVE sites
meeting the data completeness crite-
ria are shown in Figure B-l.
For consistency, the same sites are
used in both the PM2 - section. The
exceptions are Washington, DC, and
South Lake Tahoe, which are not
included in the visibility trends
analysis because they are urban sites.
Air Toxics Methodology
Database
The 1990-1999 ambient air quality
data presented in Chapter 5 of this
report are based on air toxics data
retrieved from AIRS in July 2000,
data retrieved from the IMPROVE
network in June 2000, and data vol-
untarily submitted to EPA by state
and local monitoring agencies and
received by June 30, 2000. For more
details about the database, see
Rosenbaum et al., 1999.6 All statisti-
cal summaries are based on annual
average concentrations. Measure-
ments for hazardous air pollutants
(HAPs) are frequently reported as
nondetectable concentrations. To
calculate annual average concentra-
tions, one-half of the actual or plausi-
ble detection limit is used to substi-
tute values for nondetects (or if the
reported value is zero). The plausible
detection limit, used for cases where
the minimum detectable limit
(MDL) is missing, is the lowest of the
measured concentrations and MDLs
for the given monitor and HAP.
Separate summaries are presented
for sites in a metropolitan statistical
area (MSA)/PMSA (primary MSA),
excluding the (primarily rural) sites
from the IMPROVE network, and
for other sites. Areas (one or more
counties) are assigned to either an
MSA or a CMSA (consolidated MSA)
consisting of two or more PMSAs or
are just assigned to a county. Each
non-IMPROVE site in an MSA or
CMSA was assigned either to its MSA
or PMSA. Some analyses allocated
MSA/PMSAs to states. If the
MS A/PMSA crosses state bound-
aries, the state containing the largest
portion of that MSA/PMSA was
used.
Completeness
All calculations are based on the
average of calculated or measured
24-hour values. For each HAP, a
series of completeness rules are
applied sequentially starting with
using the raw hourly data to deter-
mine daily completeness. Multiple
records for the same HAP, monitor-
ing site, day, and time period are
averaged together. A day is complete
if the total number of hours moni-
tored for that day is 18 or more (i.e.,
75 percent of 24 hours). For example,
18 hourly averages, three 6-hour
averages, or three 8-hour averages
will satisfy the daily completeness
criteria. Once daily completeness is
satisfied, quarterly completeness is
determined. Calendar quarters are
• (Late winter) January-March
« (Early summer) April-June
« (Late summer) July-September
• (Early winter) October-December.
A calendar quarter is complete if it
has 75 percent or more complete
days out of the expected number of
daily samples for that quarter and if
there are at least five complete days
in the quarter. To determine the
expected number of daily samples,
the most frequently occurring sam-
pling interval (days from one sample
to the next sample) w'as used; in
cases of ties, the minimum sampling
interval was applied. A calendar year
is complete if both the summer and
winter 6-month seasons have at least
one complete quarter, that is, if (1)
quarter 1 or 4 or both quarters 1 and
4 are complete, and (2) quarter 2 or 3
or both quarters 2 and 3 are
complete.
In some cases, collocated samples
for the same HAP and location were
collected. For AQS data, collocated
monitors are identified by having the
same 9-digit AQS ID number but a
different pollutant occurrence code
(POC) number. The higher POC
numbers are generally used for qual-
ity assurance monitoring data that
are not as complete as the primary
sampling data. Therefore, if multiple
AIRS monitors at the same location
meet the above completeness
requirements, then only the data
from the monitor with the lowest
POC number were used for these
analyses. For data not reported to
AIRS, collocated monitors can have
very different monitor identifiers. If
multiple monitors at the same latitude
and longitude location for a given
sampling program and HAP meet
the completeness requirements, then
only the data from the monitor with
the highest monitoring frequency
were used for these analyses. In case
of tied highest monitoring frequen-
cies, the monitor with the most daily
average records (from complete
quarters in the trend period) was
used.
National
Based on the available years of moni-
toring data across the nation, the
national analyses were restricted to
the 6-year period 1994 to 1999. A site
w'as included for a particular HAP if,
and only if, there w^ere four or more
complete years for that period.
California Analyses
A similar, but longer term trend anal-
ysis was performed on metropolitan
sites located only in California using
1990 to 1999 data. A site was includ-
ed for a given HAP if there was at
least one period of 5 years or longer
188 AQS METHODOLOGY » APPENDIX B
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
so that at least 75 percent of those
years are complete and the period
ends in 1997 or later. Only the data
from the most recent of the longest
such periods were used.
Trend Analysis
Annual averages for years with four
complete quarters were computed by
averaging the four quarterly aver-
ages. If a year had one or more miss-
ing or incomplete quarters, then
those missing or incomplete quarter-
ly averages were filled in (if possible)
using the General Linear Model
(GLM) fill-in methodology described
below, and the annual average was
computed by first averaging the
quarterly averages (actual or filled-
in) for a season and then averaging
across the two seasons.7 Filled-in
quarterly averages were used for
incomplete quarters even if there
were some data for that quarter. Data
from incomplete quarters were not
used in the analyses. The filled-in
quarterly average can be negative
sometimes, and occasionally this
leads to a negative annual average.
To deal with this case, negative or
zero filled-in quarterly averages
were used to compute the annual
average (this avoids biasing the
results), but any resulting negative
annual averages were reset to zero.
In the summary analyses, averages
across multiple sites were computed
as trimmed means rather than simple
arithmetic means in order to reduce
the influence of the most extreme
monitor averages on the trend line.
If there were nine sites or less, then
no trimming was performed, so the
trimmed mean is the arithmetic
mean of all the site averages. If
there wrere between 10 and 40 sites,
inclusive, the trimmed mean is the
arithmetic mean of all the site aver-
ages except for the highest and low-
est averages. If there were 41 sites
or more, the trimmed mean is the
arithmetic mean of all the site aver-
ages except for the highest 2.5 per-
cent and the lowest 2.5 percent of the
averages. The reported numbers of
sites and percentiles are based on all
sites meeting the completeness crite-
ria, that is, including the sites that
were excluded for the trimmed mean
calculation.
The overall slope (trend) was esti-
mated nonparametrically as the
median of the ratios of the difference
in the annual average to the differ-
ence in calendar year, for all pairs of
calendar years. The significance level
of the trend was computed using the
associated nonparametric Theil test,
based on the number of pairs of
years where the annual averages
increased. The p-values are calculat-
ed for a two-sided test for whether or
not the annual averages have a trend
(which may be increasing or decreas-
ing). The trend is reported as "Signif-
icant Up Trend" or "Significant Down
Trend" if the corresponding one-
sided test is significant at the 5 per-
cent significance level; otherwise the
result is reported as "Non-significant
Up Trend," "No Trend," or "Non-sig-
nificant Down Trend."
For the tables summarizing the
annual average trends by monitor,
the GLM fill-in method was not
used. Instead, those monitor annual
averages were computed by averag-
ing all complete daily averages for
each complete quarter, then averag-
ing the complete quarterly averages
for each season, and then averaging
over the two seasons. All other
analyses used the filled-in quarterly
averages as described above.
GLM Fill-in Methodology
The GLM fill-in methodology and
software used to fill in missing
quarterly averages were based on the
report by Cohen and Pollack (1990),8
which can be consulted for more
details. The method was modified to
apply to the sequence of quarterly
averages (24 values for the 6-year
1994-1999 period) instead of five
annual means. The method was also
modified to use a fitted statistical
model with six year effects and four
quarterly adjustments, instead of
having 24 independent year/quarter
effects. In other words, the fitted
model assumes that the seasonal
(quarterly) variation is the same for
every site and year. Initially, each site
is allocated to a region, which for
these analyses was the MSA/PMSA
for sites within an MSA or PMSA or
was the county. Suppose that for
each of the four quarters there is at
least one site in the region with com-
plete data for that quarter in at least
1 year. Suppose also that for each of
the 6 years there is at least one site in
the region with complete data for at
least one quarter in that year. If these
two conditions apply, then the miss-
ing quarterly averages for all sites in
that region are computed by fitting a
GLM so that the expected value for a
given site and quarter q is the sum of
the site average, a yearly adjustment
term, and a quarterly adjustment
term. The yearly adjustment term
is the fixed effect of the y'th year,
1 < y < 6, assumed to be the same
value for all sites in the region.
The quarterly adjustment term is
the fixed effect of the q'th quarter,
1 < q < 4, assumed to be the same
value for all sites in the region and
all years. If a region does not meet
these two conditions, then the region
is expanded to become a larger, aug-
mented region with some site data
for every quarter and some site data
for every year, and the GLM
approach is applied to the augment-
ed region. Candidates for the aug-
mented region are selected by find-
ing the nearest site(s) in the same
APPENDIX B « AQS METHODOLOGY 189
-------
Co
m
5
> m c
(O 3 3
m < 5;
3 =;' »
O O Q.
*< 3 ••
3 2
® St
2. ®
Si *>
•o
!
o
3
> o
-• 3
O g
tl m m
*D
-------
NATIONAL AIR QUALITY AND EMISSIONS TRENDS REPORT, 2003
state that have complete data for the
missing quarter(s) and year(s). The
selected augmented region is the
region giving the lowest mean
square error for the GLM.
Although the GLM methodology
filled in most missing quarters, there
were some states, HAPs, and years
that had no complete quarters for
any site in the state. In those cases,
the missing quarters were not filled
in by the GLM approach (which
restricts the augmented regions to
sites in the same state). For the
national analyses of distributions
across sites in different states, the
missing site-years were then filled in
using the same EPA extrapolation
and interpolation method used else-
where in this report: If the site annu-
al average for 1994 was missing, it
was filled in with the 1995 annual
average; if the 1995 annual average
was also missing, then the 1994 and
1995 annual averages were filled in
with the 1996 annual average. If the
site annual average for 1999 was
missing, it was filled in with the 1998
annual average; if the 1998 annual
average was also missing, then the
1999 and 1998 annual averages were
filled in with the 1997 annual aver-
age. Otherwise, any missing annual
averages were filled in using simple
linear interpolation from the two sur-
rounding annual averages.
References
1. Clean Air Act Amendments of
1990, U.S. Code, volume 42, section
7403 (c) (2), 1990.
2. Ambient Air Quality Surveil-
lance, 44 CFR 27558, May 10,1979.
3. Aerometric Information
Retrieval System (AIRS), Volume 2,
U.S. Environmental Protection
Agency, Office of Air Quality Plan-
ning and Standards, Research
Triangle Park, NC, October, 1993.
4. Ambient Air Quality Surveil-
lance, 51 FR 9597, March 19,1986.
5. U.S. Environmental Protection
Agency Intm-Agency Task Force Report
on Air Quality Indicators, EPA-450/4-
81-015, U.S. Environmental
Protection Agency, Office of Air
Quality Planning and Standards,
Research Triangle Park, NC, Febru-
ary 1981.
6. Rosenbaum, A. S., Stiefer, M. P.,
and Iwamiya, R. K. November, 1999.
Air Toxics Data Archive and AIRS
Combined Dataset: Contents Summary
Report. SYSAPP-99/26d. Systems
Applications International, San
Rafael, CA.
7. In all cases analyzed, four non-
missing quarterly means were avail-
able after applying the GLM method,
so that the resulting annual mean is
the arithmetic mean of the four quar-
terly averages.
8. Cohen, J.P. and A. K. Pollack.
1990. General Linear Models Approach
to Estimating National Air Quality
Trends Assuming Different Regional
Trends. SYSAPP-90/102. Systems
Applications International, San
Rafael, CA.
190
AQS METHODOLOGY
APPENDIX B
-------
|