Air
Quality
Data
Analysis
Technical
Support
Document
for
the
Final
Nonroad
Diesel
Engine
and
Fuel
Rule
Prepared
For
Office
of
Air
and
Radiation
U.
S.
Environmental
Protection
Agency
Office
of
Air
and
Radiation
Office
of
Air
Quality
Planning
and
Standards
Emissions,
Monitoring
and
Analysis
Division
Air
Quality
Trends
Analysis
Group
Research
Triangle
Park,
North
Carolina
27711
April
2004
i
Table
of
Contents
I.
Introduction
and
Background
Regarding
Ambient
Air
Quality
Monitoring
Data
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1
A.
Ambient
Air
Monitoring
Networks
in
the
United
States
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1
B.
Air
Quality
System
Database
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3
C.
Indicators
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4
II.
Ambient
Air
Quality
Monitoring
Data
1999­
2001
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5
A.
1999­
2001
Data
Analysis
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5
B.
1999­
2001
Data
Summaries
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6
III.
Ambient
Air
Quality
Monitoring
Data
2000­
2002
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10
A.
2000­
2002
Data
Analysis
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10
B.
2000­
2002
Data
Summaries
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10
IV.
Description
of
Speciation
Data
and
Rural/
Urban
Comparisons
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12
A.
Data
Description,
Data
Acquisition
and
Pre­
Processing
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12
B.
Urban/
Rural
Comparisons
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14
C.
Spatial
and
Temporal
Observations
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15
V.
Use
of
Satellite
Data
and
Correlations
with
Ground­
Based
Data
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27
A.
Data
Description,
Data
Acquisition,
and
Pre­
Processing
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27
B.
Qualitative
Image
Analysis
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28
1.
Discussion
of
Qualitative
Image
Analysis
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28
2.
Midwest­
East
Haze
Event:
June
20­
28,
2002
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31
3.
Northeast
Fire
Event:
July
4­
9,
2002
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33
4.
Midwest­
South
East
Haze
Event:
September
8­
14,
2002
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35
C.
Quantitative
Data
Analysis
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37
D.
Application
of
Satellite
Data
to
Air
Quality
Policy
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40
VI.
Inter­
Site
Correlation
of
PM2.5
Mass
and
Component
Species
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41
A.
Background
and
Data
Description
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41
B.
Results
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42
VII.
Source
Apportionment/
Back
Trajectory
Analyses
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51
A.
Summary
of
Key
Source
Apportionment
Tools
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51
B.
Summary
of
Source
Apportionment
Research
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52
1.
Overview
of
the
Sources
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55
2.
Source
Locations
and
Time
Series
Analyses
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56
3.
Methodologies
and
Technical
Approaches
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57
C.
Eight
City
Report
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58
1.
Data
Sources
and
Study
Cities
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58
2.
Source
Apportionment
Analysis
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60
3.
Meteorological
Summaries
and
Back
Trajectory
Analysis
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65
ii
4.
Conclusions
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70
VIII.
References
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72
Appendix
A
Detailed
Listing
by
County
of
PM2.5
Air
Quality
Data
(
1999
­
2002)
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74
Appendix
B
Detailed
Listing
by
County
of
Ozone
Air
Quality
Data
(
1999
­
2002)
.
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99
Appendix
C
Nonattainment
Status.......................................................................................................
135
1
I.
Introduction
and
Background
Regarding
Ambient
Air
Quality
Monitoring
Data
There
are
several
components
associated
with
analyzing
data
measured
by
ambient
air
monitoring
networks
(
air
quality
data
analysis).
The
first
component
in
air
quality
data
analysis
is
the
monitoring
network.
The
Nation's
air
quality
is
measured
using
monitoring
networks
consisting
of
more
than
one
thousand
monitors
located
across
the
county.
The
monitors
are
sited
according
to
the
spatial
and
temporal
nature
of
the
pollutant
they
measure,
and
to
best
represent
the
actual
air
quality
in
the
United
States.
The
second
component
in
air
quality
data
analysis
is
the
database
that
accepts
the
data
from
the
monitoring
networks,
stores
it,
and
allows
the
analysts
to
retrieve
the
data.
The
final
component
is
the
job
of
the
analyst,
to
use
statistics
and
graphics
to
process
the
data
in
order
to
compare
the
results
to
meaningful
air
quality
indicators.

This
section
summarizes
key
aspects
of
these
components,
provides
references
for
further
details,
and
then
presents
the
results
of
air
quality
analyses
for
PM2.5
and
ozone.

A.
Ambient
Air
Monitoring
Networks
in
the
United
States
In
1970,
the
Clean
Air
Act
(
CAA)
was
signed
into
law.
The
CAA
and
its
amendments
provide
the
framework
for
the
Nation's
overall
protection
of
the
Nation's
air
quality.
EPA's
principal
responsibilities
under
the
CAA,
as
amended
in
1990
include:

°
setting
National
Ambient
Air
Quality
Standards
(
NAAQS)
for
pollutants
considered
harmful
to
the
public
health
and
environment,
°
ensuring,
in
cooperation
with
the
states,
that
the
air
quality
standards
are
met
or
attained
through
national
standards
and
strategies
to
control
emissions
from
sources,
and
°
ensuring
that
the
sources
of
toxic
air
pollutants
are
well
controlled.

To
assess
air
quality
and
to
ensure
compliance
with
air
quality
standards,
EPA
developed
an
ambient
air
quality
monitoring
program.
Air
quality
samples
are
generally
collected
for
one
or
more
of
the
following
purposes:

°
to
judge
compliance
with
and/
or
progress
made
towards
meeting
ambient
air
quality
standards,
°
to
activate
emergency
control
procedures
that
prevent
or
alleviate
air
pollution
episodes,
°
to
observe
pollution
trends
throughout
the
region,
including
non­
urban
areas,
°
to
provide
a
database
for
research
,
for
planning
(
urban,
land­
use,
and
transportation),
for
development
and
evaluation
of
abatement
strategies,
and
/
or
development
and
validation
of
diffusion
models,
and
°
to
provide
daily
air
quality
reporting
via
the
Air
Quality
Index
(
AQI).

With
the
end
use
of
the
air
quality
samples
as
a
prime
consideration,
the
network
should
be
designed
to
meet
one
of
these
four
basic
monitoring
objectives:
2
°
to
determine
the
highest
concentrations
expected
to
occur
in
the
area
covered
by
the
network,
°
to
determine
representative
concentrations
in
areas
of
high
population
density,
°
to
determine
the
impact
on
ambient
pollution
levels
of
significant
sources
or
source
categories,
°
to
determine
general
background
concentration
levels,
°
to
determine
the
extent
of
Regional
pollutant
transport
among
populated
areas;
and
in
support
of
secondary
standards,
and
°
to
determine
the
welfare­
related
impact
in
more
rural
and
remote
areas
(
such
as
visibility
impairment
and
effects
on
vegetation.

These
six
objectives
illustrate
the
nature
of
the
samples
that
a
monitoring
network
collects,
which
is
representative
of
the
spatial
area
being
studied.
With
respect
to
PM2.5
and
ground­
level
ozone,
the
monitoring
networks
are
primarily
designed
to
measure
in
major
metropolitan
areas.
Both
of
these
networks
include
some
monitors
to
measure
the
highest
concentrations
expected
to
occur
in
the
area
covered
by
the
network
and
to
determine
general
background
concentration
levels,
although
these
sites
are
relatively
few
in
number,
and
are
not
considered
in
the
analysis
of
air
quality
data
reported
in
this
section.

In
the
United
States,
monitoring
networks
are
operated
largely
by
State
and
local
agencies
and
tribal
nations.
The
networks
include
development,
review,
and
oversight
by
EPA
through
consultation
with
State,
local,
and
tribal
steering
committees
and
working
groups.
Principal
factors
that
have
expanded
and
challenged
the
capability
of
the
nation's
networks
include
the
following:
new
and
revised
NAAQS
and
other
regulatory
needs,
shifts
in
the
nation's
air
quality
issues
(
e.
g.,
general
trend
toward
reduced
concentrations
of
criteria
pollutants),
and
an
influx
of
scientific
findings
and
technological
advancements.
A
significant
recent
addition
to
the
networks
is
the
more
than
1,000
monitors
for
measuring
PM2.5.

To
measure
the
levels
of
NAAQS­
related
pollutants,
also
called
criteria
pollutants,
(
including
SO2,
NO2,
CO,
O3,
Pb,
PM2.5,
and
PM10),
the
State
and
Local
Air
Monitoring
Stations
(
SLAMS)
network,
and
a
subset
of
SLAMS
­
the
National
Air
Monitoring
Stations
(
NAMS),
were
started
in
the
1970s.
NAMS
are
designated
as
national
trend
sites.
SLAMS
and
NAMS
are
comprised
of
more
than
5,000
monitors
at
approximately
3,000
sites.
These
monitoring
sites
use
Federal
Reference
or
Equivalent
methods
(
FRM/
FEM),
when
making
measurements
so
that
direct
comparisons
to
the
NAAQS
can
be
made.
Design
and
measurement
requirements
for
these
networks
are
set
forth
in
the
Code
of
Federal
Regulations
(
CFR)
parts
58
(
design
and
quality
assurance),
53
(
equivalent
methods)
and
50
(
reference
methods).
The
SLAMS
and
NAMS
networks
experienced
accelerated
growth
throughout
the
1970s,
and
grew
again
with
a
major
addition
of
PM2.5
monitors
starting
in
1999
with
the
promulgation
of
the
1977
PM
NAAQS.
Most
recently,
the
number
of
monitors
has
declined,
with
the
exception
of
monitors
for
ozone
and
PM2.5.

The
SLAMS
network
consists
of
approximately
4,000
monitoring
sites
whose
distribution
is
largely
determined
by
the
needs
of
State
and
local
air
pollution
control
agencies
to
meet
their
3
respective
State
Implementation
Plan
(
SIP)
requirements.
The
NAMS
(
1,080
sites)
are
a
subset
of
the
SLAMS
network
with
emphasis
being
given
to
urban
and
multi­
source
areas.
In
effect,
they
are
key
sites
under
SLAMS,
with
emphasis
on
areas
of
maximum
concentrations
and
high
population
density.

The
SLAMS
and
NAMS
networks
are
subject
to
significant
quality
assurance
procedures.
All
ambient
monitoring
methods
or
analyzers
used
in
SLAMS
are
tested
periodically
to
quantitatively
assess
the
quality
of
the
SLAMS
data
being
produced.
Measurement
accuracy
and
precision
are
estimated
for
both
automated
and
manual
methods.
The
individual
results
of
these
tests
for
each
method
or
analyzer
are
reported
to
EPA,
and
EPA
calculates
quarterly
integrated
estimates
of
precision
and
accuracy
applicable
to
the
SLAMS
data.
Data
assessment
results
are
reported
to
EPA
only
for
methods
and
analyzers
approved
for
use
in
SLAMS
monitoring
under
Appendix
C
of
CFR
Part
50.
For
further
information,
see
http://
www.
epa.
gov/
airprogm/
oar/
oaqps/
qa/
index.
html.

In
summary,
State
and
local
agencies
and
tribes
implement
a
quality­
based
network
to
measure
air
quality
across
the
United
States.
EPA
provides
guidance
to
ensure
a
thorough
understanding
of
the
quality
of
the
data
produced
by
these
networks.
For
many
years,
the
monitoring
data
have
been
used
to
characterize
the
status
of
the
nation's
air
quality
and
the
trends
across
the
United
States
(
see
http://
www.
epa.
gov/
airtrends/).

B.
Air
Quality
System
Database
The
Air
Quality
System
(
AQS)
contains
ambient
air
pollution
data
collected
by
EPA,
State,
local,
and
tribal
air
pollution
control
agencies
from
thousands
of
monitoring
stations.
AQS
also
contains
meteorological
data,
descriptive
information
about
each
monitoring
station
(
including
its
geographic
location
and
its
operator),
and
data
quality
assurance/
quality
control
information.
State
and
local
agencies
are
required
to
submit
their
air
quality
monitoring
data
into
AQS
by
the
end
of
the
quarter
following
the
quarter
in
which
the
data
were
collected.
This
ensures
timely
submission
of
these
data
for
use
by
State,
local,
and
tribal
agencies,
EPA,
and
the
public.

EPA's
Office
of
Air
Quality
Planning
and
Standards
(
OAQPS)
and
other
AQS
users
rely
upon
the
data
in
AQS
to
assess
air
quality,
assist
in
attainment/
nonattainment
designations,
evaluate
State
Implementation
Plans
(
SIPs),
perform
modeling
for
permit
review
analysis,
and
other
air
quality
management
functions.
AQS
information
is
also
used
to
prepare
reports
for
Congress
as
mandated
by
the
Clean
Air
Act.

AQS
was
recently
re­
engineered
from
a
mainframe
application
(
referred
to
as
"
AIRS"
by
many)
to
a
UNIX
based
Oracle
database
accessed
by
a
PC­
based
application.
The
PC­
based
application
went
into
production
status
in
January
2002.
Today,
State,
local,
and
tribal
agencies
submit
their
data
directly
to
AQS
via
this
client/
server
application.
Registered
users
may
also
retrieve
data
through
the
AQS
application
and
through
the
use
of
third
party
software
such
as
the
Discoverer
tool
from
Oracle
Corporation.
The
mainframe
version
of
AQS
is
still
available
for
4
retrievals
of
data;
however,
no
updates
have
been
made
to
the
mainframe
AQS
database
since
December
2001.

Data
from
the
mainframe
AQS
database
was
imported
into
the
new
AQS
Oracle
database,
including
all
site
and
monitor
data
and
raw
data
for
the
years
1992­
2001.
All
summary
calculations
for
this
data
have
been
completed.
All
raw
precision
and
accuracy
data
values
have
been
loaded,
and
partial
summaries
have
been
calculated.
For
more
detailed
information
about
the
AQS
database,
see
http://
www.
epa.
gov/
ttn/
airs/
airsaqs/
index.
htm.

C.
Indicators
In
analyzing
the
levels
of
PM2.5
and
ozone
across
the
United
States,
the
raw
data
must
be
processed
into
a
form
pertinent
for
useful
interpretations.
For
this
study,
the
data
have
been
processed
consistent
with
the
formats
associated
with
the
NAAQS
for
these
air
pollutants
(
with
an
exception
discussed
below
for
the
PM2.5
data
during
the
1999­
2001
period).
The
resulting
estimates
are
used
to
indicate
the
level
of
air
quality
relative
to
the
NAAQS.
In
addition
to
air
quality
data,
we
also
present
information
about
areas
that
have
been
officially
designated
as
nonattainment
in
the
appendices
(
these
areas
may
or
may
not
be
currently
experiencing
air
quality
violations
as
there
are
additional
requirements
that
must
be
met
in
order
to
be
redesingated
as
attaining
the
standard).

For
PM2.5
air
quality
indicators,
we
developed
estimates
for
making
comparisons
with
the
annual
standard
for
PM2.5.
Compliance
with
this
standard
is
judged
on
the
basis
of
the
most
recent
three
years
of
ambient
air
quality
monitoring
data.
For
PM2.5,
the
annual
standard
is
met
when
the
3­
year
average
of
the
annual
mean
concentration
is
15.0
µ
g/
m3
or
less.
The
3­
year
average
annual
mean
concentration
is
computed
at
each
site
by
averaging
the
daily
Federal
Reference
Method
(
FRM)
samples
taken
each
quarter,
averaging
these
quarterly
averages
to
obtain
an
annual
average,
and
then
averaging
the
three
annual
averages.
The
3­
year
average
annual
mean
concentration
is
also
called
the
annual
standard
design
value.
For
details
see
40
CFR
Part
50,
Appendix
K
and
N.
The
PM2.5
design
values
are
based
on
1999
through
2001
and
2000
through
2002
data.

As
previously
mentioned,
the
PM2.5
monitoring
network
has
recently
been
installed.
In
general,
EPA
regulations
require
at
least
75%
data
capture
in
each
quarter
of
a
consecutive
3­
year
period
in
order
for
a
design
value
to
be
valid.
If
the
design
value
is
over
the
standard,
less
data
are
required.
For
the
annual
standard,
11
samples
a
quarter
are
sufficient.
In
addition,
EPA
regulations
and
guidance
permit
data
substitution
under
certain
circumstances
in
order
to
bolster
completeness,
(
see
40
CFR
Part
50,
Appendix
N
and
also
the
Guideline
on
Data
Handling
for
the
PM
NAAQS
EPA
number
or
web
location).
The
information
developed
for
this
analysis
is
based
on
data
after
applying
the
substitution
guidance.

The
only
exception
to
routine
data
handling
procedures
occurred
for
PM2.5
data
for
the
1999­
2001.
In
general,
the
data
completeness
criteria
for
monitors
exceeding
the
PM2.5
annual
standard
is
11
samples
per
quarter
for
all
12
quarters
for
the
three
year
period
(
1999­
2001).
5
Given
the
newness
of
this
network
(
where
many
monitors
had
not
been
installed
in
January
1999),
and
the
importance
of
having
as
broad
a
preliminary
understanding
of
PM2.5
levels
across
the
United
States
as
possible,
we
have
relaxed
this
criteria
somewhat
in
our
analysis
of
the
1999­
2001
data.
Thus,
in
an
attempt
to
understand
as
fully
as
possible
the
areas
where
PM2.5
levels
exceed
the
level
of
the
annual
standard,
we
estimated
air
quality
levels
for
monitors
that
had
at
least
one
sample
in
each
of
10
of
the
12
quarters
for
the
three
year
periods
(
1999­
2001).
This
added
20
counties
to
the
129
counties
where
monitors
exceeding
the
annual
PM2.5
standard
based
on
1999­
2001
data.

Two
qualifiers
are
worth
noting
here.
First,
it
is
possible
that
those
areas
with
the
somewhat
incomplete
data
could
show
overall
air
quality
to
be
better
than
the
standards
once
3
years
of
complete
data
are
available.
However,
for
the
areas
well
above
the
standard,
this
is
unlikely.
Second,
for
counties
that
do
not
have
monitors,
we
do
not
have
air
quality
estimates
and,
for
some
of
these
counties,
it
is
likely
that
the
PM2.5
levels
exceed
the
level
of
the
annual
standard.
Thus,
this
analysis
may
understate
the
number
of
people
that
experience
air
quality
that
exceeds
the
annual
standard
for
PM2.5.

The
analysis
for
2000­
2002
PM2.5
data
followed
the
data
completeness
criteria
specified
in
Appendix
N
using
certified
data.

For
ozone
air
quality
indicators,
we
developed
estimates
for
the
1­
hour
O3
standard
and
the
8­
hour
O3
standard.
The
EPA
set
the
1­
hour
O3
standard
at
0.12
parts
per
million
(
ppm)
daily
maximum
1­
hour
average
concentration
not
to
be
exceeded
more
than
once
per
year
on
average.
Compliance
with
the
1­
hour
ozone
standard
is
judged
on
the
basis
of
the
most
recent
three
years
of
ambient
air
quality
monitoring
data.
The
1­
hour
ozone
standard
is
not
met
at
a
monitoring
site
if
the
average
number
of
estimated
exceedances
of
the
ozone
standard
is
greater
than
1.0
(
1.05
rounds
up).
The
level
of
the
8­
hour
O3
NAAQS
is
0.08
ppm.
The
8­
hour
O3
standard
is
not
met
if
the
3­
year
average
of
the
annual
4th
highest
daily
maximum
8­
hour
O3
concentration
is
greater
than
0.08
ppm
(
0.085
rounds
up).
There
is
a
separate
process
for
determining
attainment
status
(
see
Federal
Register
68
FR
32802,
Proposed
Rule
to
Implement
the
8­
hour
Ozone
National
Ambient
Air
Quality
Standard,
Proposed
Rule,
June
2,
2003)
Accordingly,
further
analysis
will
occur
before
these
design
values
are
used
in
implementing
the
national
ambient
air
quality
standards
for
ozone.

II.
Ambient
Air
Quality
Monitoring
Data
1999­
2001
A.
1999­
2001
Data
Analysis
The
purpose
of
this
section
is
to
provide
summary
information
concerning
analyses
of
1999­
2001
air
quality
using
measured
ambient
air
quality
data.
The
analyses
will
be
used
as
input
data
for
general
characterizations
of
air
quality
in
the
United
States
and
for
use
in
Regulatory
Impact
Analysis
(
RIA)
accompanying
various
rulemakings.
As
described
further
below,
we
are
using
a
measured
air
quality
to
estimate
the
number
of
people
living
in
areas
with
the
potential
for
unhealthful
concentrations
of
air
pollution,
as
indicated
by
the
NAAQS
for
PM10,
PM2.5,
and
6
ozone.
We
estimated
air
quality
within
a
range
of
annual
average
concentrations
of
fine
particulate
matter
(
expressed
as
PM2.5),
and
8­
hour
peak
daily
average
concentrations
of
ozone.
These
analyses
will
be
used
to
provide
relevant
information
concerning
the
need
for
additional
reductions
in
emissions
to
attain
and
maintain
the
PM2.5
and
ozone
NAAQS,
to
reduce
exposures
to
harmful
levels
of
particulate
matter
and
ozone,
and
to
provide
input
to
estimate
the
effects
of
improvements
in
air
quality
on
public
health
and
welfare.
For
both
PM2.5
and
ozone,
air
quality
data
from
AQS
as
of
July
8,
2002
were
used
to
calculate
design
value
for
the
1999­
2001
period.

B.
1999­
2001
Data
Summaries
Table
II­
1
provides
a
summary
of
the
populations
living
in
counties
at
various
levels
of
air
quality,
based
on
1999
­
2001
data
(
as
represented
by
a
design
value).
Additional
results
are
presented
in
the
Appendix
A
for
PM2.5
and
Appendix
B
for
ozone.
Figures
II­
1,
II­
2,
and
II­
3
display
PM2.5
and
ozone
levels,
represented
by
design
values,
across
the
United
States.
7
Table
II­
1.
Population
Living
in
Counties
with
Measured
1999­
2001
PM2.5
and
Ozone
at
Various
Concentrations.

Air
Pollutant
AQ
Levels
Based
on
1999­
2001
Data
Population
2000
Co
#
Population
Categories
Result
PM2.5
Total
Population
>
NAAQS
(>
15
µ
g/
m3)
74,237,509
149
Total
Monitored
population
191,040,346
13
µ
g/
m3

Population
<
15
µ
g/
m3)
31,113,929
101
meeting
modified
completeness
criteria
Total
population
>
25
µ
g/
m3
12,774,159
3
Percent
of
monitored
population
>
25
µ
g/
m3
7%

Total
population
>
20
µ
g/
m3
21,985,250
13
Percent
of
monitored
population
>
20
µ
g/
m3
12%

Total
population
>
15
µ
g/
m3
74,237,509
149
Percent
of
monitored
population
>
15
µ
g/
m3
39%

Total
population
>
10
µ
g/
m3
170,009,669
433
Percent
of
monitored
population
>
10
µ
g/
m3
89%

Total
population
>
5
µ
g/
m3
189,796,377
544
Percent
of
monitored
population
>
5
µ
g/
m3
99%

Ozone
8hr
Total
Population
>
NAAQS
(

85
ppb)
110,747,890
291
Total
Monitored
population
201,084,504
meeting
completeness
criteria
Total
population

105
ppb
20,116,399
12
Percent
of
monitored
population

105
ppb
10%

Total
population

95
ppb
40,022,574
68
Percent
of
monitored
population

95
ppb
20%

Total
population

85
ppb
110,747,890
291
Percent
of
monitored
population

85
ppb
55%

Total
population

75
ppb
160,599,281
487
Percent
of
monitored
population

75
ppb
80%

Total
population

65
ppb
177,011,021
550
Percent
of
monitored
population

65
ppb
88%

Ozone
1hr
Total
Population
>
NAAQS
(

125
ppb)
55,049,038
81
Total
Monitored
population
201,084,504
Total
population

175
ppb
3,400,578
1
Percent
of
monitored
population

175
ppb
2%

Total
population

150
ppb
16,198,983
8
Percent
of
monitored
population

150
ppb
8%

Total
population

125
ppb
55,049,038
81
Percent
of
monitored
population

125
ppb
27%

Total
population

100
ppb
152,616,961
412
Percent
of
monitored
population

100
ppb
76%

Total
population

75
ppb
183,001,544
573
Percent
of
monitored
population

75
ppb
91%

113
ppb

Total
population
<
125
ppb
41,412,633
132
21%

Ozone
8hr
Total
Population
>
NAAQS
(

85
ppb)
but
not
exceeding
1­
hr
NAAQS
57,426,028
206
Total
Monitored
population
for
counties
not
exceeding
1­
hr
NAAQS
128,132,021
Total
Population

105
ppb
but
not
exceeding
1­
hr
NAAQS
0
0
Percent
of
monitored
population

105
ppb
but
not
exceeding
1­
hr
NAAQS
0%

Total
Population

95
ppb
but
not
exceeding
1­
hr
NAAQS
4,149,608
18
Percent
of
monitored
population

95
ppb
but
not
exceeding
1­
hr
NAAQS
3%

Total
Population

85
ppb
but
not
exceeding
1­
hr
NAAQS
57,426,028
206
Percent
of
monitored
population

85
ppb
but
not
exceeding
1­
hr
NAAQS
45%

1.
Population
data
are
from
the
2000
U.
S.
census.
2.
Total
population
in
the
U.
S.
in
2000
was
281.4
million
8
Figure
II­
1.
PM2.5
Levels
­
Annual
Design
Values
Figure
II­
2.
Ozone
Levels
1­
Hour
Design
Values
9
Figure
III­
3.
Ozone
Levels
8­
Hour
Design
Values
10
III.
Ambient
Air
Quality
Monitoring
Data
2000­
2002
A.
2000­
2002
Data
Analysis
The
purpose
of
this
section
is
to
provide
summary
information
concerning
analyses
of
2000­
2002
measured
air
quality
data.
Like
the
data
presented
in
Section
2
above,
the
2000­
2002
analyses
follow
the
data
completeness
criteria
established
in
the
relevant
NAAQS.
The
patterns
of
measured
air
quality
can
be
used
to
estimate
the
number
of
people
living
in
areas
with
the
potential
for
unhealthful
concentrations
of
air
pollution,
as
indicated
by
the
NAAQS
for
PM10,
PM2.5,
and
ozone.
These
analyses
will
be
used
to
provide
relevant
information
concerning
the
need
for
additional
reductions
in
emissions
to
attain
and
maintain
the
PM2.5
and
ozone
NAAQS,
to
reduce
exposures
to
harmful
levels
of
particulate
matter
and
ozone,
and
to
provide
input
to
estimate
the
effects
of
improvements
in
air
quality
on
public
health
and
welfare.
For
both
PM2.5
and
ozone,
air
quality
data
as
of
July
9,
2003
were
used
to
calculate
design
values
for
the
2000­
2002
period.

B.
2000­
2002
Data
Summaries
Table
III­
1
provides
a
summary
of
the
populations
living
in
counties
at
various
levels
of
air
quality,
based
on
2000­
2002
data
(
as
represented
by
a
design
value).
Additional
results
are
presented
in
the
Appendix
A
for
PM2.5
and
Appendix
B
for
ozone.
11
Table
III­
1.
Population
Living
in
Counties
with
Measured
2000­
2002
PM2.5
and
Ozone
at
Various
Concentrations
Air
Pollutant
AQ
Levels
Based
on
2000­
2002
Data
Population
2000
Co
#
Population
Categories
Result
PM2.5
Total
Population
>
NAAQS
(>
15
µ
g/
m3)
64,849,620
120
Total
Monitored
population
208,305,356
Total
population
>
25
µ
g/
m3
3,254,821
2
Percent
of
monitored
population
>
25
µ
g/
m3
2%

Total
population
>
20
µ
g/
m3
18,731,187
8
Percent
of
monitored
population
>
20
µ
g/
m3
9%

Total
population
>
15
µ
g/
m3
64,849,620
120
Percent
of
monitored
population
>
15
µ
g/
m3
31%

Total
population
>
10
µ
g/
m3
152,924,960
406
Percent
of
monitored
population
>
10
µ
g/
m3
73%

Total
population
>
5
µ
g/
m3
174,470,350
519
Percent
of
monitored
population
>
5
µ
g/
m3
84%

Ozone
8hr
Total
Population
>
NAAQS
(

85
ppb)
115,287,584
297
Total
Monitored
population
201,084,504
Total
population

105
ppb
18,671,025
9
Percent
of
monitored
population

105
ppb
9%

Total
population

95
ppb
48,172,440
66
Percent
of
monitored
population

95
ppb
24%

Total
population

85
ppb
115,287,584
297
Percent
of
monitored
population

85
ppb
57%

Total
population

75
ppb
159,509,592
503
Percent
of
monitored
population

75
ppb
79%

Total
population

65
ppb
180,586,014
575
Percent
of
monitored
population

65
ppb
90%

Ozone
1hr
Total
Population
>
NAAQS
(

125
ppb)
53,346,394
73
Total
Monitored
population
201,084,504
Total
population

175
ppb
3,400,578
1
Percent
of
monitored
population

175
ppb
2%

Total
population

150
ppb
15,428,757
4
Percent
of
monitored
population

150
ppb
8%

Total
population

125
ppb
53,346,394
73
Percent
of
monitored
population

125
ppb
27%

Total
population

100
ppb
151,720,506
412
Percent
of
monitored
population

100
ppb
75%

Total
population

75
ppb
187,699,461
602
Percent
of
monitored
population

75
ppb
93%

113
ppb

Total
population
<
125
ppb
44,923,912
132
45%

Ozone
8hr
Total
Population
>
NAAQS
(

85
ppb)
but
not
exceeding
1­
hr
NAAQS
67,073,815
221
Total
Monitored
population
for
counties
not
exceeding
1­
hr
NAAQS
134,539,671
Total
Population

105
ppb
but
not
exceeding
1­
hr
NAAQS
0
0
Percent
of
monitored
population

105
ppb
but
not
exceeding
1­
hr
NAAQS
0%

Total
Population

95
ppb
but
not
exceeding
1­
hr
NAAQS
10,433,485
20
Percent
of
monitored
population

95
ppb
but
not
exceeding
1­
hr
NAAQS
8%

Total
Population

85
ppb
but
not
exceeding
1­
hr
NAAQS
67,073,815
221
Percent
of
monitored
population

85
ppb
but
not
exceeding
1­
hr
NAAQS
50%

1.
Population
data
are
from
the
2000
U.
S.
census.
2.
Total
population
in
the
U.
S.
in
2000
was
281.4
million
12
IV.
Description
of
Speciation
Data
and
Rural/
Urban
Comparisons
A.
Data
Description,
Data
Acquisition
and
Pre­
Processing
With
the
promulgation
of
the
new
Particulate
Matter
National
Ambient
Air
Quality
Standards
(
PM2.5
NAAQS),
all
future
designated
nonattainment
areas
and
surrounding
regions
may
need
to
reduce
emission
of
fine
particles
and
their
precursors
to
permit
those
areas
to
attain
the
NAAQS.
Efficient
air
quality
management
required
knowing
which
sources
contribute
to
the
problem
and
by
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
(
Schichtel
&
Husar,
1992),
hiding
their
point
of
origin.
In
addition,
PM2.5
has
a
lifetime
on
the
order
of
several
days
(
Husar,
et
al.,
1978)
enabling
very
distant
sources
to
affect
a
region.

To
help
understand
levels
of
PM2.5
and
their
chemical
components
in
various
regions
and
to
arrive
at
a
first­
cut
approximation
of
how
much
of
those
levels
are
locally
generated
versus
transported,
EPA
has
analyzed
PM2.5
mass
and
speciation
data.
Graphical
displays
were
generated
to
show
the
chemical
makeup
of
PM2.5
across
the
country
and
by
season.
Then,
this
analysis
was
furthered
to
get
an
estimate
of
excess
levels
of
particulate
mass
and
chemical
species
in
urban
areas
over
background
levels
(
as
implied
using
nearby
rural
sites).

Two
sources
of
ambient
monitoring
data
were
used
in
all
the
analyses.
Data
from
EPA's
PM2.5
chemical
Speciation
Trends
Network
(
STN)
and
the
Interagency
Monitoring
of
Protected
Visual
Environmental
(
IMPROVE)
aerosol
monitoring
network
were
used
to
assess
the
urban
and
rural
PM2.5
mass
and
species
concentrations,
respectively,
across
the
United
States.
Both
these
networks
proved
speciated
PM2.5
data
using
a
1­
in­
3
day
sampling
protocol.
The
STN
began
operation
in
late
1999
and
routinely
quantifies
PM2.5
mass
and
constituent
urban
and
semi­
urban
concentrations,
including
numerous
trace
elements,
ions,
elemental
carbon,
and
organic
carbon.
There
are
a
total
of
52
STN
sites.
The
IMPROVE
network
quantifies
PM2.5
mass
concentrations
and
its
constituents
mostly
in
rural
areas.
Over
the
past
few
years,
the
IMPROVE
network
has
expanded
from
its
original
20
monitoring
sites
to
well
over
170
sites.
For
most
of
the
analyses
presented
here,
only
the
`
major'
components
of
PM2.5
mass
will
be
analyzed
as
part
of
the
chemical
constituent
analyses.
Major
components
include
sulfate,
ammonium,
nitrate,
total
carbonaceous
mass
(
based
on
organic
and
elemental
carbon),
and
crustal
material
(
which
is
based
on
the
weighted
average
of
5
trace
elements).
More
details
are
provided
below.

Rao
et.
al.
have
previously
examined
these
data
to
look
at
spatial
variation
of
the
chemical
species
in
rural
and
urban
areas
as
well
as
to
estimate
urban
increments
for
13
chosen
urban­
rural
paired
sites
(
Rao
et.
Al.,
2002).
All
the
analyses
in
this
work
were
based
on
one
year
of
data
(
and
sites
that
had
complete
data
for
that
time
frame)
that
spanned
March
2001­
February
2002.
The
reader
is
referred
to
the
Rao
et.
al.
paper
for
details,
but
some
of
the
methodology
and
the
most
salient
results
are
presented
below:

Since
slightly
different
measurement
protocols
are
used
at
STN
and
IMPROVE
sites,
the
13
following
adjustments
to
the
data
were
made
to
make
measurements
more
comparable
between
sites
from
the
two
different
networks:

°
Sulfates:
The
IMPROVE
program
estimates
sulfate
concentrations
as
three
times
the
sulfur
concentration,
whereas
with
the
STN
program,
sulfate
concentrations
are
used
as
measured.

°
Ammonium:
Although
directly
measured
ammonium
as
performed
by
STN
is
important
in
characterizing
the
composition
of
PM2.5,
network­
wide
IMPROVE
measurements
are
currently
lacking
in
this
area.
Thus,
to
make
comparisons
of
ammonium
concentrations
between
the
two
networks,
IMPROVE
ammonium
concentrations
are
estimated
from
sulfate
(
SO4)
and
nitrate
(
NO3)
measurements,
assuming:
(
1)
all
sulfates
are
ammonium
sulfate,
and
(
2)
all
nitrates
are
ammonium
nitrate.
When
ammonium
concentrations
are
compared
between
the
two
networks,
STN
ammonium
concentrations
are
also
estimated
the
same
way
(
and
the
measured
values
are
not
used
when
comparing
to
IMPROVE
ammonium
estimates).

Similarly,
in
several
instances
fully­
neutralized
(
AN)
ammonium
sulfate
and
AN
ammonium
nitrates
are
used
to
represent
the
ammonium­
nitrate­
sulfate
system
instead
of
the
individual
components.
In
all
cases
when
AN
values
are
used
for
sulfate
and
nitrate,
they
are
estimated
as:

AN
Ammonium
Sulfate
~
1.375
*
Sulfate
AN
Ammonium
Nitrate
~
1.290
*
Nitrate
`
Estimated'
Ammonium
~
AN
Ammonium
Sulfate
+
AN
Ammonium
Nitrate
­
[
Sulfate
+
Nitrate]

°
Carbon:
The
STN
and
IMPROVE
sites
vary
in
their
analytical
and
sampling
procedures
for
organic
and
elemental
carbon.
Consequently,
only
total
carbonaceous
mass
(
T.
M.)
is
considered
in
all
analyses.
T.
M.
is
estimated
as:
k*
OC
+
E.
C.
for
both
networks.
Here,
k
is
the
factor
for
converting
measured
organic
carbon
to
organic
carbon
mass
(
to
account
for
attached
hydrogen,
oxygen,
etc.).
Though
in
the
Rao
et
al.
paper,
different
factors
are
used
for
k
based
on
literature
values,
in
the
analyses
to
follow
k
was
set
equal
to
1.4
based
on
the
analytical
history
of
IMPROVE
and
EPA
programs.
OC
is
also
blank
corrected
in
both
networks;
in
the
STN,
network­
wide,
but
sampler­
specific
correction
factors
are
used.

°
For
all
analyses
of
urban
increments,
all
urban/
rural
pairings
were
elevation­
adjusted
to
account
for
the
effect
of
the
24­
hour
average
sample
volume
density
on
aerosol
concentrations.
For
the
most
part,
these
adjustments
resulted
only
in
minor
changes
to
the
reported
values
of
component
mass
concentrations.
The
reader
is
referred
to
the
Rao
et
al.
paper
for
further
details.

B.
Urban/
Rural
Comparisons
14
Urban
Location/
Site
Elevation
(
m)
Rural
Location/
Site
Elevation
(
m)
Distance
Apart
(
km)
Fresno,
CA
96
Pinnacles
National
Monument,
CA
317
28
Missoula,
MT
975
Monture,
MT
1293
72
Salt
Lake
City,
UT
1306
Great
Basin
National
Park,
NV
2068
277
Tulsa,
OK
198
Wichita
Mountains,
OK
487
298
St.
Louis,
MO
0
Cadiz,
KY
Hercules­
Glades,
MO
Bondville,
IL
188
423
211
296
322
220
Birmingham,
AL
174
Sipsy
Wilderness,
AL
279
100
Indianapolis,
IN
235
Livonia,
IN
298
142
Atlanta,
GA
308
Okefenokee
National
Wildlife
Refuge,
GA
Shining
Rock
Wilderness,
NC
49
1621
324
236
Cleveland,
OH
206
M.
K.
Goddard,
PA
383
129
Charlotte,
NC
232
Linville
Gorge,
NC
986
132
Richmond,
VA
59
James
River
Face,
VA
300
179
Baltimore,
MD
5
Dolly
Sods/
Otter
Creek
Wilderness,
WV
1158
256
Bronx,
NY
0
Brigantine
National
Wildlife
Refuge,
NJ
9
165
Once
the
data
were
adjusted
per
the
descriptions
above,
local
and
regional
contributions
to
the
urban
centers
were
estimated
by
computing
the
differences
between
the
annual
average
concentrations
of
the
urban
and
nearby
rural
monitoring
data.
For
reasons
outlined
in
the
Rao
paper,
it
is
important
when
comparing
urban/
rural
sites
to
make
sure
that
background
levels
are
estimated
separately
for
each
location
examined.
13
urban
sites
were
chosen
and
paired
with
nearby
rural
sites.
The
analysis
considered
five
urban
sites
in
the
Northeast
and
Mid­
Atlantic
States,
five
urban
sites
stretching
from
north
to
south
in
the
mid­
portion
of
the
USA,
and
three
urban
sites
in
the
West.
These
urban
locations
were
chosen
due
to:
(
1)
their
data
being
complete
for
the
year
in
question,
2)
their
ease
in
matching
up
with
nearby
IMPROVE
rural
sites,
and
3)
their
high
annual
values
of
PM2.5
mass.
Table
IV­
1
gives
the
13
urban/
rural
pairings
used
in
the
Rao
paper:

Table
IV­
1.
Urban/
Rural
Pairings
for
Urban
Excess
Calculations
Significant
findings
based
on
these
urban/
rural
pairings
included:

°
The
estimate
for
urban
excess
sulfate
(
and
associated
ammonium)
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.

°
Nitrates
(
and
associated
ammonium)
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.
In
the
North
and
West,
nitrates
are
in
excess
in
urban
areas
by
2­
6
 G/
m3.
15
°
Although
there
is
uncertainty
in
the
measured
mass
concentration
and
in
other
measurement
protocols,
it
is
clear
that
carbonaceous
mass
is
the
major
component
of
urban
excess
at
all
the
sites
investigated.
In
the
Eastern
sites,
T.
M.
urban
excess
is
in
the
range
4.5
­
10.5
µ
G/
m3
on
an
annual
basis.

In
an
attempt
to
update
the
analyses
described
in
the
Rao
paper
to
the
more
recent
air
quality
data,
the
exact
urban
excess
procedures
outlined
in
the
paper
were
applied
to
a
new
grouping
of
urban
and
rural
data
that
spanned
August
2001­
September
2002.
These
analyses
are
shown
in
Figures
IV­
1
through
IV­
5.
In
this
new
analyses,
the
following
minor
changes
were
made:

°
One
of
the
site
pairings
was
altered
slightly:
the
Missoula,
MT
urban
site
did
not
have
completed
data
for
the
year
in
question
so
it
was
displaced
with
an
urban
site
in
Reno,
NV
(
which
was
paired
with
the
Bliss
State
Park,
CA
IMPROVE
site
for
urban/
rural
comparisons).
°
The
urban
Atlanta
site
was
paired
with
a
ring
of
rural
monitors
to
better
represent
the
regional
contribution
of
the
chemical
species.
In
the
previous
analysis
(
Rao
paper),
the
Atlanta
urban
site
was
paired
with
2
rural
sites:
Okefenokee,
GA
and
Shining
Rock,
NC
using
an
inverse­
distance
weighting
scheme.
In
this
analysis,
the
7
different
rural
sites
were
used
to
generate
regional
concentrations:
Sispy,
AL:
St.
Marks,
FL;
Okefenokee,
GA;
Cape
Romain,
SC;
Linville
Gorge,
NC;
Shining
Rock,
NC;
and
Great
Smokies,
TN.
The
last
three
sites
were
averaged
to
form
one
set
of
concentrations
and
then
averaged
with
the
other
four
sites
using
a
inversedistance
weighting
scheme
to
estimated
regional
concentrations.
°
For
the
seasonal
analyses,
Winter
was
defined
as
Dec
2001­
Feb
2002;
Spring
was
defined
as
Mar
2002­
May
2002;
Summer
was
defined
as
Jun
2002­
Aug
2002;
and
Fall
was
defined
as
Sep
2001­
Nov
2001.

C.
Spatial
and
Temporal
Observations
As
part
of
this
revised
analysis,
spatial
variations
in
annual
averages
of
the
PM2.5
chemical
components
are
shown
in
Figures
IV­
1
and
IV­
2,
respectively,
for
all
urban
and
rural
data
that
were
complete
for
the
new
year
in
question,
respectively.
Similarly,
in
Figure
IV­
3,
seasonal
variations
in
the
urban
data
are
shown.
From
Figures
IV­
3,
the
following
observations
can
be
made:

°
In
both
urban
and
rural
areas,
more
sulfates
(
and
associated
ammonium)
exist
in
the
East
compared
to
other
regions
of
the
country.

°
In
both
urban
and
rural
areas,
more
nitrates
(
and
associated
ammonium)
exist
in
the
upper
Midwest
compared
to
other
regions
of
the
country.

°
In
urban
areas,
carbon
is
prevalent
all
across
the
country.

°
In
eastern
urban
areas,
higher
mass
sites
are
represented
by
the
data
in
Birmingham,
16
AL
and
Cleveland,
OH.

°
Crustal
material
is
a
small
part
of
PM2.5
mass
everywhere
except
arid
regions
in
the
Southwest.

°
In
rural
areas,
PM2.5
mass
is
smaller
than
in
corresponding
urban
areas.

°
In
rural
areas,
carbon
is
less
prevalent
in
the
East
when
compared
to
the
South
and
the
West.

°
In
rural
areas,
the
crustal
component
is
small
in
the
East,
Midwest,
and
South.

°
In
the
summer,
approximately
equal
amounts
of
sulfates
exist
across
the
country.

°
In
the
summer,
carbon
is
more
prevalent
in
the
Southeast,
Midwest,
and
Northeast.

°
In
the
summer,
more
ammonium
and
nitrates
exist
in
the
Midwest.

°
In
the
fall,
carbon
and
sulfates
dominate
PM2.5
aerosol
throughout
the
East,
Southeast,
and
Midwest.

°
In
the
winter,
carbon
and
sulfates
dominate
southeast
aerosols.

°
In
the
winter,
nitrates
and
carbon
play
a
major
role
in
Northeast/
East
coast
aerosols.
Additionally,
there
are
elevated
levels
of
nitrates
in
Eastern
PM2.5
mass.

°
In
the
winter,
all
components
(
except
crustal)
are
equal
in
Midwest
aerosols.

°
In
the
spring,
patterns
are
similar
to
those
seen
in
the
fall
season,
except
that
more
nitrates
(
and
associated
ammonium)
is
present
in
the
Midwest.

Re­
doing
the
urban
increment
analyses
with
this
new
set
of
data
reveals
the
same
trends
as
those
based
on
the
Rao
et.
al.
paper
outlined
above.
These
trends
are
shown
in
Figures
IV­
4
and
IV­
5.
In
summary:

°
The
urban
excess
of
mass
is
consistently
between
3­
8
micrograms/
m3
in
the
East
based
on
an
annual
average
(
see
Figure
IV­
5).

°
Urban
excess
of
sulfates
are
very
low
(
or
non­
existent)
which
indicates
that
sulfates
are
a
regional
pollutant
which
is
transported
into
urban
areas.

°
Carbon
is
a
major
portion
of
urban
excess,
especially
in
the
Northeast,
East
Coast,
and
Southeast
corridors.
Annual
urban
excess
estimates
indicate
that
about
half
the
carbon
is
from
local
sources.
17
Sulfate
Ammonium
Nitrate
TCM
Crustal
6.20
18.69
31.18
Sulfate
Ammonium
Nitrate
TCM
Crustal
1.71
7.91
14.11
Figure
IV­
1.
Urban
Speciation
Patterns
Figure
IV­
2.
Rural
Speciation
Patterns
Figure
IV­
3.
18
Portland,
OR
Seattle,
WA
Fresno,
CA
Bakersfield,
CA
Riverside,
CA
Sacramento,
CA
San
Diego,
CA
Phoenix,
AZ
Boulder,
CO
Reno,
NV
SLC/
Ogden,
UT
El
Paso,
TX
Tulsa,
OK
Dallas,
TX
Houston,
TX
Birmingham,
AL
Charlotte,
NC
Charleston,
SC
Atlanta,
GA
Baton
Rouge,
LA
Biloxi,
MS
Memphis,
TN
Tampa,
FL
Washington,
DC
Essex,
MD
Camden,
NJ
Elizabeth,
NJ
NYC,
NY
(
Bronx)

Pittsburgh,
PA
Philadelphia,
PA
Richmond,
VA
IS
52,
NY
Queens
College,
NY
Burlington,
VT
Portsmouth,
NH
Chicago,
IL
Indianapolis,
IN
Davenport,
IA
Detroit,
MI
St.
Louis,
MO
St.
Louis,
MO
Milwaukee,
WI
Cleveland,
OH
Minneapolis,
MN
Fargo,
ND
0
5
10
15
ug/
m3
Sulfate
Ammonium
Nitrate
TCM
Crustal
Regionalized
Spring
Concentrations
North
West
California
Desert­
West
East
Texas­
South
South
East
East
Coast/
North
East
Far
North
East
Mid
West
North
Plains
Portland,
OR
Seattle,
WA
Fresno,
CA
Bakersfield,
CA
Riverside,
CA
Sacramento,
CA
San
Diego,
CA
Phoenix,
AZ
Boulder,
CO
Reno,
NV
SLC/
Ogden,
UT
El
Paso,
TX
Tulsa,
OK
Dallas,
TX
Houston,
TX
Birmingham,
AL
Charlotte,
NC
Charleston,
SC
Atlanta,
GA
Baton
Rouge,
LA
Biloxi,
MS
Memphis,
TN
Tampa,
FL
Washington,
DC
Essex,
MD
Camden,
NJ
Elizabeth,
NJ
NYC,
NY
(
Bronx)

Pittsburgh,
PA
Philadelphia,
PA
Richmond,
VA
IS
52,
NY
Queens
College,

Burlington,
VT
Portsmouth,
NH
Chicago,
IL
Indianapolis,
IN
Davenport,
IA
Detroit,
MI
St.
Louis,
MO
St.
Louis,
MO
Milwaukee,
WI
Cleveland,
OH
Minneapolis,
MN
Fargo,
ND
0
5
10
15
ug/
m3
Sulfate
Ammonium
Nitrate
TCM
Crustal
Regionalized
Summer
Concentrations
Seasonal
Patterns
in
Urban
Speciation
Data
19
Portland,
OR
Seattle,
WA
Fresno,
CA
Bakersfield,
CA
Riverside,
CA
Sacramento,
CA
San
Diego,
CA
Phoenix,
AZ
Boulder,
CO
(
Co
Reno,
NV
SLC/
Ogden,
UT
El
Paso,
TX
Tulsa,
OK
Dallas,
TX
Houston,
TX
Birmingham,
AL
Charlotte,
NC
Charleston,
SC
Atlanta,
GA
Baton
Rouge,
LA
Biloxi,
MS
Memphis,
TN
Tampa,
FL
Washington,
DC
Essex,
MD
Camden,
NJ
Elizabeth,
NJ
NYC,
NY
(
Bronx)

Pittsburgh,
PA
Philadelphia,
PA
Richmond,
VA
IS
52,
NY
Queens
College,

Burlington,
VT
Portsmouth,
NH
Chicago,
IL
Indianapolis,
IN
Davenport,
IA
Detroit,
MI
St.
Louis,
MO
St.
Louis,
MO
Milwaukee,
WI
Cleveland,
OH
Minneapolis,
MN
Fargo,
ND
0
5
10
15
ug/
m3
Sulfate
Ammonium
Nitrate
TCM
Crustal
Regionalized
Fall
Concentrations
Portland,
OR
Seattle,
WA
Fresno,
CA
Bakersfield,
CA
Riverside,
CA
Sacramento,
CA
San
Diego,
CA
Phoenix,
AZ
Boulder,
CO
Reno,
NV
SLC/
Ogden,
UT
El
Paso,
TX
Tulsa,
OK
Dallas,
TX
Houston,
TX
Birmingham,
AL
Charlotte,
NC
Charleston,
SC
Atlanta,
GA
Baton
Rouge,
LA
Biloxi,
MS
Memphis,
TN
Tampa,
FL
Washington,
DC
Essex,
MD
Camden,
NJ
Elizabeth,
NJ
NYC,
NY
(
Bronx)

Pittsburgh,
PA
Philadelphia,
PA
Richmond,
VA
IS
52,
NY
Queens
College,

Burlington,
VT
Portsmouth,
NH
Chicago,
IL
Indianapolis,
IN
Davenport,
IA
Detroit,
MI
St.
Louis,
MO
St.
Louis,
MO
Milwaukee,
WI
Cleveland,
OH
Minneapolis,
MN
Fargo,
ND
0
5
10
15
ug/
m3
Sulfate
Ammonium
Nitrate
TCM
Crustal
Regionalized
Winter
Concentrations
~
22
ug/
m3
Figure
IV­
3.
Seasonal
Patterns
in
Urban
Speciation
Data
(
Continued)
20
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
1
2
3
4
5
6
7
8
9
10
ug/
m3
Birmingham,
AL
/
SIPSI
Wilderness
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Atlanta,
GA
/
Ring
of
5
Rural
Locations
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
21
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Indianapolis,
IN
/
Livonia
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Baltimore,
MD
/
Dolly
Sods
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)
22
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
St.
Louis,
MO
/
HEGL­
BOND­
CADIZ
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Bronx,
NY
/
Brigantine
Wildlife
Refuge
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)
23
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Charlotte,
NC
/
Linville
Gorge
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Cleveland,
OH
/
Mt.
Goddard
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)
24
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Richmond,
VA
/
James
River
Face
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Salt
Lake
City,
UT
/
Great
Basin
National
Park
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)
25
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Tulsa,
OK
/
Wichita
Mountains
Bottom:
Regional
Contribution
Top:
Urban
Increment
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
ug/
m3
Reno,
NV
/
Bliss
State
Park
Bottom:
Regional
Contribution
Top:
Urban
Increment
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)
26
Sulfate
Est.
Ammonium
Nitrate
TCM
Crustal
0
2
4
6
8
10
12
14
ug/
m3
Fresno,
CA
/
Pinnacles
National
Park
Bottom:
Regional
Contribution
Top:
Urban
Increment
NYC,
NY
Baltimore,
MD
Richmond,
VA
Charlotte,
NC
Cleveland,
OH
Atlanta,
GA
Indianapolis,
IN
Birmingham,
AL
St.
Louis,
MO
Tulsa,
OK
Salt
Lake
City,
UT
Fresno,
CA
Reno,
NV
0
5
10
15
20
25
ug/
m3
Urban
Increment
of
PM2.5
Mass
Regional
PM2.5
Mass
(
Gravimetric)
PM2.5
Regional
Mass
and
Urban
Increments
Figure
IV­
4.
Urban
Excess
of
Chemical
Components
(
continued)

Figure
IV­
5.
Urban
Increment
of
Mass
(
based
on
gravimetric
mass)
27
V.
Use
of
Satellite
Data
and
Correlations
with
Ground­
Based
Data
Advances
in
satellite
sensors
have
provided
new
datasets
for
monitoring
air
quality,
including
the
ability
to
visually
observe
transport
events.
Satellite
sensors
do
not
measure
groundbased
particulate
matter
(
PM)
directly;
thus,
to
use
satellite
imagery
for
studying
PM
transport,
it
is
also
important
to
confirm
the
relationship
between
satellite­
based
data
and
PM2.5.
In
order
to
visualize
transport
of
particulate
matter
in
the
eastern
half
of
the
United
States,
qualitative
true
color
images
and
data
from
the
Moderate
Resolution
Imaging
Spectroradiometer
(
MODIS)
sensor
on
the
Terra
satellite
were
evaluated.
Additionally,
MODIS
sensor
aerosol
optical
depth
data
were
quantitatively
compared
with
ground­
based
particulate
matter
data
from
U.
S.
EPA
monitoring
networks
covering
the
period
from
April
1
to
September
30,
2002.

A.
Data
Description,
Data
Acquisition,
and
Pre­
Processing
The
ground­
based
data
(
PM10,
PM2.5,
and
speciation
of
these
particles)
used
in
this
analysis
were
from
three
air
quality
monitoring
networks:
Speciation
Trends
Network
and
PM2.5
Mass
network
(
collectively
referred
to
as
STN­
M
in
this
section);
and
Interagency
Monitoring
of
Protected
Visual
Environments
(
IMPROVE)
network
(
see
Section
II
for
a
description
of
the
datasets).

NASA
designs,
launches,
and
operates
a
set
of
Earth
Observing
System
(
EOS)
satellites,
each
with
several
sensors.
The
MODIS
sensor,
located
on
the
Terra
(
and
Aqua)
satellite
platforms,
has
36
spectral
channels
(
compared
to
4
to
8
for
most
sensors),
thus
was
designed
to
provide
a
wide
variety
of
information
for
land,
ocean,
and
atmosphere.
MODIS
has
good
spatial
(
1
km)
and
temporal
(
1­
2
days)
resolution.
With
the
large
number
of
spectral
bands,
the
MODIS
science
team
has
developed
44
products
(
processed
datasets)
for
a
range
of
observations.
The
data
products
relevant
to
this
study
are:

°
MOD021KM
 
Level
1B
Calibrated
Geolocated
Radiances,
1
km
resolution,
used
to
produce
red­
green­
blue
(
RGB)
"
true
color"
imagery
and
conduct
qualitative
analysis;
and
°
MOD04
 
Level
2
Aerosol
Products,
geospatial
information
with
aerosol
optical
depth
and
cloud
fraction,
for
both
qualitative
and
quantitative
analysis.

Aerosol
optical
depth
(
AOD
or
 
a)
is
a
dimensionless
measure
of
extinction,
the
amount
of
light
extinguished
or
scattered
by
particles
in
the
air.
Aerosol
optical
depth
derived
from
satellites
provides
a
measure
of
the
particles
through
the
entire
column
of
air,
from
surface
to
satellite.
Optical
depth
of
aerosols
typically
ranges
from
0
to
about
5,
with
values
over
1
generally
being
classified
as
heavy
haze.
It
is
easiest
to
calculate
aerosol
optical
depth
over
water
where
the
surface
is
dark
and
uniform,
but
optical
depths
over
land
have
been
derived.
The
key
steps
are:
removal
of
pixels
that
contain
clouds;
grouping
of
pixels
in
a
10
x
10
km
grid;
filtering
of
the
brightest
and
darkest
pixels
to
eliminate
any
remaining
clouds,
shadows,
or
other
contamination;
and
calculation
of
aerosol
optical
depth
using
one
of
four
models,
depending
on
location,
season,
and
the
ratio
of
scattering
in
the
red
and
blue
wavelengths.
The
derivation
of
aerosol
optical
depth
28
for
the
MODIS
sensor
is
described
in
Kaufman
and
Tanré
(
1998)
and
Remer,
et
al.
(
2001).

The
satellite
data
were
processed
from
the
NASA
hierarchal
data
format
to
images
for
qualitative
analysis
and
to
into
SAS
®
datasets
for
statistical
analysis.
Details
on
this
processing
can
be
found
in
Engel­
Cox,
et
al.
(
2003).
Linear
projection
was
used
for
all
images,
and
country
boundaries,
coastlines,
and
latitude
and
longitude
lines
were
added.

B.
Qualitative
Image
Analysis
Analysis
was
conducted
on
data
from
April
1
to
September
30,
2002,
the
most
recent
summer
season
with
both
EPA
and
satellite
data.
Three
specific
high
PM
events
were
selected
for
visualization
and
qualitative
analysis.
These
were
not
the
only
PM
events
during
this
time
frame,
but
represent
examples
of
types
of
transport
events.

RGB
images
created
from
the
L1B
data
for
three
events
examined
in
this
study
document
the
existence
and
transport
of
air
pollution,
specifically
smoke
and
haze.
These
are
further
illustrated
with
the
images
of
the
aerosol
optical
depth
data.
In
the
following
sections,
a
general
discussion
of
these
images
is
presented,
and
three
events
involving
haze
and
smoke
are
discussed
in
more
detail.

1.
Discussion
of
Qualitative
Image
Analysis
Figure
V­
1(
a)
is
the
RGB
image
from
July
6,
2002,
and
Figure
V­
1(
b)
is
the
aerosol
optical
depth
from
the
same
date.
The
atmosphere
above
the
U.
S.
on
that
day
was
a
mix
of
smoke,
haze,
clouds,
and
clear
skies.
Starting
with
Figure
V­
1(
a)
from
east
to
west,
there
is
a
large
plume
of
smoke
from
Eastern
Canada
dropping
south
into
the
Eastern
United
States.
This
is
the
smooth
slightly
yellowish­
white
plume
over
New
York
and
Pennsylvania.
There
is
also
moderately
dense
bluish­
white
haze
in
the
southeast
(
Louisiana
to
Georgia)
and
Midwest
(
Arkansas
to
Illinois).
Some
of
this
haze
appears
to
be
below
scattered
cirrus
clouds.
Bright
white
clouds
can
be
seen
scattered
throughout
as
well
as
snow
on
the
Canadian
Rockies.
The
two
bands
of
brightness
over
the
ocean
on
both
east
and
west
coasts
are
sunglint,
the
reflection
of
the
sun
off
the
surface
of
the
ocean.
The
vertical
discontinuities
splitting
the
country
into
thirds
mark
the
swaths
of
the
satellite
as
it
takes
sequential
images
orbiting
over
the
poles.
The
swaths
are
approximately
90
minutes
apart.

Even
without
looking
at
the
aerosol
optical
depth
or
ground
based
data,
this
image
provides
information
on
the
general
air
quality
on
this
day
as
well
as
its
potential
transport.
Both
the
northeast
and
southeast
are
likely
experiencing
decreased
visibility
and
increased
PM
levels.
A
review
of
the
STN­
M
data
shows,
for
example,
elevated
levels
of
sulfate
and
PM2.5
in
Birmingham
and
elevated
PM10
in
Pittsburgh
at
this
time.
The
satellite
data
represent
scattering
in
a
total
column
of
atmosphere,
so
there
remains
some
question
about
the
height
of
the
pollutants
(
e.
g.,
whether
the
smoke
from
the
fires
is
only
at
high
levels
in
the
troposphere
or
whether
it
is
reaching
ground
level).
This
emphasizes
the
importance
of
combining
the
satellite
image
with
ground
based
observations.
More
importantly,
images
such
as
these
document
the
source
of
certain
pollutants,
such
as
the
build
up
of
haze
in
the
Midwest
or
the
fires
in
Canada
potentially
29
causing
increases
in
PM
levels
in
the
northeast.
Even
though
MODIS
images
are
daily,
motion
can
still
be
observed
when
viewing
a
sequence
of
images
(
see
Sections
V.
B.
2
through
V.
B.
4).

Figure
V­
1.
(
a)
MODIS
Level
1b
RGB
composite
image,
July
6,
2002
(
upper);
(
b)
MODIS
Level
2
aerosol
optical
depth,
July
6,
2002
(
lower).

Figure
V­
1(
b)
is
the
aerosol
optical
depth
for
the
same
day.
Blue
represents
low
aerosol
optical
depth
(
clearer
air)
increasing
through
the
color
scale
to
dark
red
representing
higher
aerosol
optical
depth
(
the
numerical
correlation
between
aerosol
optical
depth
and
ground­
based
observations
is
discussed
in
Section
V.
C).
White
indicates
no
data
usually
because
of
cloud
cover;
also,
the
area
over
the
ocean
influenced
by
sunglint
is
a
section
of
no
data.
The
satellite
clearly
measures
both
the
smoke
plume
in
the
northeast
and
the
haze
in
the
Midwest.
However,
note
that
the
middle
of
the
smoke
plume
is
white
(
no
data).
Either
the
algorithm
used
to
calculate
areas
of
clouds
mistakes
the
dense
smoke
for
cloud,
masking
it
from
the
aerosol
optical
depth
calculation,
or
else
the
aerosol
optical
depth
algorithm
is
screening
out
these
values.
As
will
be
discussed
later,
very
high
aerosol
optical
depth
values
are
eliminated
from
the
dataset.
Note
also
that
the
pixel
size
changes
shape.
Each
pixel
represents
10
km
square
at
nadir;
however,
areas
near
the
edge
of
the
30
swath
experience
distortion.

Overall,
qualitatively,
the
two
images
in
Figure
V­
1
support
each
other
in
documenting
air
pollution,
particularly
in
the
eastern
portion
of
the
continent.

In
the
west
coast,
the
aerosol
optical
depth
data
are
more
erratic.
Data
seem
to
be
masked
in
cloud
free
areas
where
there
is
an
abrupt
change
in
terrain,
such
as
the
area
of
forest
in
Montana.
Other
times,
it
provides
no
data
over
large
areas
of
dry
terrain
(
high
surface
reflectance).
A
review
of
all
the
images
shows
that
the
dataset
west
of
about
90
to
100
°
W
appears
to
be
consistently
patchy.
Based
on
the
review
of
the
aerosol
optical
depth
algorithm,
the
causes
of
increased
masking
in
the
west
are
likely
a
combination
of
difficulties
in
the
cloud
masking
(
due
to
assumptions
of
surface
reflectance,
cloud
edges,
and
cloud­
surface
contrast),
overscreening
of
valid
pixels
in
regions
of
less
vegetation
and
high
reflectance,
and
use
of
the
smoke
model
in
areas
that
may
be
experiencing
other
types
of
pollution.

Another
way
to
view
these
data
is
to
overlay
the
Level
2
aerosol
optical
depth
data
on
the
Level
1b
RGB
image.
Figure
V­
2
is
such
an
image
from
September
10,
2002.
As
discussed
above,
several
areas
with
cloud
free
skies
do
not
appear
to
have
returned
aerosol
optical
depth
data,
notably
over
the
Blue
Ridge
Mountains
and
in
patchy
regions
in
the
West.

Figure
V­
2.
MODIS
Level
1b
RGB
and
MODIS
Level
2
aerosol
optical
depth,
September
10,
2002.
31
2.
Midwest­
East
Haze
Event:
June
20­
28,
2002
During
late
June
2002,
the
central
and
eastern
United
States
experienced
a
haze
event
from
a
combination
of
anthropogenic
air
pollutants
and
some
smoke.
Figure
V­
3
shows
the
series
of
L1B
images
from
June
20­
28,
2002.
As
discussed
in
Section
B.
1,
the
bright
white
is
clouds
and
the
bluish
tint
is
haze.
Both
the
Level
1B
(
Figure
V­
3)
and
Level
2
images
(
Figure
V­
4)
document
the
build
up
of
aerosols
in
the
Midwest
from
June
20­
22,
then
their
transport
across
the
northeast
from
June
23­
26.
The
final
two
images,
June
27
and
28,
appear
to
be
the
beginning
of
smoke
transported
from
fires
in
Canada
into
the
northern
Midwest
of
the
United
States.

This
series
from
June
20­
26
qualitatively
documents
a
haze
transport
event
from
the
Midwest
into
the
northeast.
The
imagery
also
documents
the
geographical
scale
of
the
smoke
transport
on
June
27­
28.

Close
examination
of
the
imagery
reveals
that
some
areas
with
apparent
high
haze
levels
and
no
clouds
do
not
have
aerosol
optical
depth
values.
For
example,
on
June
20,
southern
Michigan
and
northern
Indiana
appear
hazy
and
relatively
cloud
free
but
no
aerosol
optical
depth
was
provided.
This
can
be
seen
in
the
same
region
on
June
22
and
to
a
lesser
extent
on
June
23.
These
data
are
being
eliminated
in
either
the
cloud
or
aerosol
optical
depth
screening.
32
Figure
V­
3.
MODIS
Level
1b
RGB
composite
images,
June
20­
28,
2002.
33
Figure
V­
4.
MODIS
Level
2
aerosol
optical
depth
images,
June
20­
28,
2002.

3.
Northeast
Fire
Event:
July
4­
9,
2002
A
smoke
transport
event
is
documented
in
the
MODIS
imagery
in
early
July
2002.
Figure
V­
5
is
the
Level
1B
RGB
image
from
July
4­
9,
2002,
and
Figure
V­
6
is
the
corresponding
Level
2
aerosol
optical
depth
data.
The
first
two
images
in
each
figure,
July
4
and
5,
consist
primarily
of
urban
haze
in
the
east,
southeast,
and
Midwest.
This
haze
event
persists
in
the
southeast
and
southern
Midwest
throughout
the
remaining
days,
July
7
through
9.
However,
the
northeast
and
mid­
Atlantic
become
dominated
by
smoke
transported
into
the
region
from
July
6
through
July
8.
By
July
9,
the
smoke
(
and
the
southern
haze)
has
dissipated
toward
the
east
over
the
Atlantic.

This
series
from
July
6
through
8
qualitatively
documents
this
smoke
transport
event
from
major
fires
in
Canada.
The
imagery
also
documents
the
geographical
scale
of
haze,
particularly
from
July
4
through
8.

Close
examination
of
the
imagery
reveals
that
the
areas
with
very
dense
smoke
levels
do
not
have
aerosol
optical
depth
values.
This
can
be
seen
very
clearly
in
the
smoke
plume
on
July
6,
34
7,
and
8.
In
the
Level
1B
images,
the
plume
appears
very
white,
like
a
cloud,
but
with
a
slight
brown
tint
and
with
a
"
smooth"
appearance
(
as
opposed
to
the
rougher
texture
of
the
clouds).
In
the
Level
2
images,
the
centers
of
the
plumes
appear
blank
(
white)
with
no
data.

Due
to
the
dense
nature
of
these
high
smoke
plumes,
these
sections
are
being
eliminated
by
either
the
cloud
mask
or
aerosol
optical
depth
algorithm
or
a
combination
of
both.
Conversations
with
NASA
staff
and
a
brief
review
of
ground­
based
aerosol
optical
depth
data
have
indicated
that
the
aerosol
optical
depths
in
these
plumes
range
as
high
as
5.
Note
that
aerosol
optical
depth
data
are
returned
over
the
ocean
even
near
coastal
regions
where
no
data
are
being
returned
over
the
land.
This
is
indicative
of
the
difference
in
the
cloud
and/
or
aerosol
optical
depth
algorithms
over
the
land
versus
over
the
ocean.

Figure
V­
5.
MODIS
Level
1b
RGB
composite
images,
July
4­
9,
2002.
35
Figure
V­
6.
MODIS
Level
2
aerosol
optical
depth
images,
July
4­
9,
2002.

4.
Midwest­
South
East
Haze
Event:
September
8­
14,
2002
The
imagery
from
early
September
reveals
the
beginning
of
a
haze
event
that
becomes
influenced
by
a
strong
tropical
low
pressure
system.
Figure
V­
7
is
the
Level
1B
imagery
and
Figure
V­
8
is
the
Level
2
imagery
for
September
8­
14,
2002.
Haze
collects
in
the
Midwest
over
September
8
and
9.
A
tropical
storm
(
Gustav)
approaches
the
mid­
Atlantic,
coming
just
onshore
on
September
10.
The
haze
plume
divides
with
the
majority
traveling
south
and
west
toward
Texas
and
a
small
remnant
moving
northeast.
On
September
11
and
12,
the
Midwest
plume,
combined
with
additional
pollutants
from
Texas
and
the
southeast,
is
transported
to
the
east.
September
13
has
another
low
pressure
system
forcing
collection
of
pollutants
in
Texas
and
Louisiana,
which
are
obscured
by
cloud
cover
on
September
14.

This
series
reveals
the
geographic
extent
and
the
complex
transport
of
pollutants
during
this
event.
36
Close
examination
of
the
imagery
reveals
several
areas
without
aerosol
optical
depth
data
that
appear
to
be
cloud
free
but
also
haze­
free.
September
8
and
9
are
cloud
free
and
haze­
free
over
the
northeast
and
the
Blue
Ridge
Mountains,
respectively.
September
10
also
has
a
clean
area
between
the
tropical
storm
and
the
haze
plume
(
see
also
Figure
V­
2).
Yet,
there
is
no
aerosol
optical
depth
data
provided
for
these
regions
although
they
are
surrounded
by
very
low
aerosol
optical
depth
data
(
less
than
0.1).
September
12
has
a
very
large
area
of
apparently
clean
air
in
the
northeast,
particularly
obvious
in
the
Level
2
imagery
as
a
large
region
of
low
aerosol
optical
depths
0.2
(
blue),
an
outline
of
AOD
near
0
(
purple),
surrounding
a
center
of
blank
data.
It
is
not
understood
why
these
areas
are
not
assigned
values.

Figure
V­
7.
MODIS
Level
1b
RGB
composite
images,
September
8­
14,
2002.
37
Figure
V­
8.
MODIS
Level
2
aerosol
optical
depth
images,
September
8­
14,
2002.

C.
Quantitative
Data
Analysis
For
the
quantitative
analysis,
satellite
and
ground­
based
data
from
April
1,
2002,
to
September
30,
2002,
were
prepared;
then,
two
types
of
statistical
analyses
were
performed
to
determine
the
associations
between
satellite
readings
and
ground­
based
measurements.
First,
an
analysis
was
performed
using
all
of
the
data
from
both
STN­
M
and
IMPROVE
sites,
searching
for
overall
patterns
of
association
between
satellite
readings
and
ground­
based
readings.
The
second
analysis
focused
on
a
few
cities
and
parks
across
the
country.
For
these
cities
and
parks,
more
detailed
time­
series
and
correlation
analyses
were
performed
in
order
to
assess
the
ability
of
the
MODIS
satellite
to
detect
significant
air
quality
events.

The
first
analysis
examined
the
overall
associations
between
satellite
readings
and
groundbased
measurements
by
calculating
correlations
(
r).
Correlations
measure
the
strength
of
(
linear)
association
between
two
variables.
They
are
a
simple
measure
summarizing
how
well
one
variable
can
be
used
to
predict
another
variable.
Correlation
values
near
zero
indicate
that
the
two
quantities
examined
(
for
example,
satellite
readings
of
aerosol
optical
depth
and
ground
readings
of
aluminum
mass)
are
not
linearly
associated
with
each
other.
Values
close
to
1
indicate
that
an
increase
in
one
variable
is
strongly
associated
with
a
linear
increase
in
the
other
variable.
Values
38
close
to
­
1
indicate
a
strong
but
decreasing
relationship
(
an
increase
in
one
variable
is
associated
with
a
linear
decrease
in
the
other
variable).
In
all
analyses,
the
influence
of
each
observation
used
to
calculate
correlation
was
weighted
by
the
inverse
of
the
percentage
of
cloud
cover.
In
other
words,
satellite
observations
taken
under
conditions
with
more
cloud
cover
are
given
less
credence
than
those
observations
taken
under
clear
conditions.

To
examine
overall
correlations,
observations
from
1,157
STN­
M
sites
and
181
IMPROVE
sites
were
used.
Selected
top
overall
correlations
found
between
aerosol
optical
depth
and
the
STN­
M
and
IMPROVE
variables
at
all
of
these
sites
appear
in
Table
V­
1.
Overall,
both
aerosol
optical
depth
readings
and
mass
concentration
readings
are
most
strongly
correlated
with
daily
PM2.5
readings.

Table
V­
1.
Selected
top
correlations
across
all
sites
with
MODIS
Aerosol
Optical
Depth
Variable
Network
Correlation
Total
Number
of
Observations
PM2.5
­
Local
Conditions
(
LC)
(
daily)
STN­
M
0.428
35619
PM2.5
­
LC
(
hourly)
STN­
M
0.396
13967
Sulfate
PM2.5
LC
(
daily)
STN­
M
0.373
3292
Organic
Carbon
PM2.5
LC
(
daily)
STN­
M
0.361
3284
Sulfate:
Fine
IMPROVE
0.349
2891
The
aggregation
performed
over
all
sites
in
constructing
these
tables
tends
to
distort
the
geospatial
details
of
the
relationships.
For
instance,
correlations
between
aerosol
optical
depth
and
daily
PM2.5
levels
are
high
in
some
geographical
areas
and
low
in
others,
but
the
overall
correlation
gives
no
information
about
these
differences.
In
order
to
examine
these
differences
more
closely,
we
calculated
the
correlation
separately
for
each
STN­
M
and
IMPROVE
site
and
examined
the
smoothed
spatial
surface
of
correlations
across
the
entire
United
States.
Only
sites
with
more
than
five
valid
pairs
of
observations
were
used
to
calculate
the
correlation
surface
in
all
cases.
The
spatial
smoothing
was
performed
using
ordinary
kriging
techniques.

Figure
V­
9
shows
the
correlations
between
aerosol
optical
depth
and
hourly
PM2.5
readings
across
the
United
States.
The
non­
uniformity
of
the
correlations
across
space
is
clearly
illustrated
in
the
figure.
In
the
eastern
half
of
the
United
States,
the
correlations
are
strong,
demonstrating
that
aerosol
optical
depth
is
a
good
indicator
of
PM2.5
levels.
However,
in
the
western
United
States
aerosol
optical
depth
readings
and
PM2.5
readings
are
only
weakly
correlated.

These
variations
are
likely
due
to
the
difficulty
the
satellite
algorithm
has
in
determining
accurate
aerosol
optical
depth
readings
over
regions
of
low
reflectance
(
light
color
terrain),
specifically
arid
areas.
There
may
also
be
some
differences
in
the
model
used
to
calculate
aerosol
optical
depth
west
of
100
°
W.
More
detailed
discussion
of
these
differences
can
be
found
in
the
technical
report
(
Engel­
Cox,
et
al.,
2003).
39
The
key
finding
is
that
the
site­
specific
correlations
in
the
eastern
half
of
the
United
States
are
typically
0.6
to
0.8,
which
represents
a
good
correlation
for
evaluating
regional
PM.

Figure
V­
9.
Correlations
between
aerosol
optical
depth
and
hourly
PM2.5
readings
across
the
U.
S.

The
large
scale
spatial
analyses
of
correlations
give
some
indication
that
satellite
measurements
and
ground­
based
monitor
readings
are
not
related
in
the
same
way
in
all
areas
of
the
U.
S.
In
order
to
more
fully
understand
the
relationships
in
some
areas,
the
focus
was
on
a
few
cities
and
National
Parks
to
reflect
a
range
of
variables,
including
geographical
location,
coastal/
inland,
climate,
and
terrain.
For
each
of
the
cities,
an
analysis
was
first
performed
similar
to
the
one
performed
over
all
of
the
sites;
and
correlations
were
calculated
between
satellite
measurements
and
ground­
based
measurements
in
order
to
find
the
ground­
readings
most
strongly
associated
with
the
two
types
of
satellite
measurements.
Correlations
of
satellite
aerosol
optical
depth
with
PM2.5
readings
for
individual
monitors
are
shown
in
Table
V­
2.
More
results
can
be
found
in
the
technical
report
(
Engel­
Cox,
et
al.,
2003).
40
Table
V­
2.
Correlations
between
Aerosol
Optical
Depth
and
PM2.5
by
site
State
City
Site
Number
Correlation
of
AOD
with
PM2.5
­
Local
Conditions
(
daily)
ALABAMA
BIRMINGHAM
10730023
0.471
ALABAMA
BIRMINGHAM
10732003
0.671
COLORADO
DENVER
80310002
0.547
DISTRICT
OF
COLUMBIA
WASHINGTON
110010042
0.739
INDIANA
INDIANAPOLIS
180970042
0.583
INDIANA
INDIANAPOLIS
180970043
0.469
INDIANA
INDIANAPOLIS
180970066
0.318
INDIANA
INDIANAPOLIS
180970078
0.190
INDIANA
INDIANAPOLIS
180970079
0.560
INDIANA
INDIANAPOLIS
180970081
0.590
INDIANA
INDIANAPOLIS
180970083
0.548
MISSOURI
ST
LOUIS
295100007
0.558
MISSOURI
ST
LOUIS
295100085
0.529
MISSOURI
ST
LOUIS
295100086
0.452
MISSOURI
ST
LOUIS
295100087
0.489
NEW
YORK
BRONX
360050083
0.554
NORTH
CAROLINA
CHARLOTTE
371190010
0.733
NORTH
CAROLINA
CHARLOTTE
371190041
0.732
NORTH
CAROLINA
CHARLOTTE
371190042
0.726
PENNSYLVANIA
PITTSBURGH
420030008
0.447
PENNSYLVANIA
PITTSBURGH
420030021
0.485
TEXAS
DALLAS
481130035
0.632
TEXAS
DALLAS
481130050
0.655
TEXAS
DALLAS
481130057
0.687
TEXAS
DALLAS
481130069
0.497
TEXAS
DALLAS
481130087
0.413
TEXAS
HOUSTON
482010051
0.942
TEXAS
HOUSTON
482010055
0.928
TEXAS
HOUSTON
482010062
0.698
TEXAS
HOUSTON
482010075
0.887
TEXAS
HOUSTON
482011035
0.912
D.
Application
of
Satellite
Data
to
Air
Quality
Policy
EPA
established
its
ground­
based
air
monitoring
networks
to
meet
several
goals
including
monitoring
compliance
with
ambient
air
quality
requirements
and
evaluating
trends
and
abatement
strategies.
When
comparing
ground­
based
values
to
satellite
imagery
and
data,
the
key
question
is
how
satellite
data
can
be
used
to
support
and
enhance
EPA
air
quality
monitoring
and
modeling.

The
MODIS
imagery
and
aerosol
optical
depth
data
have
relevance
to
ambient
air
quality
monitoring.
The
ability
to
visualize
regional
scale
events
with
both
L1B
and
L2
data
can
be
used
effectively
to
understand
the
scope
of
regional
haze
and
smoke.
This
is
important
to
understanding
41
the
impact
of
large
events
on
pollutant
levels
at
the
local
level.
Visualization
can
validate
that
a
large
scale
event
(
as
opposed
to
an
urban
scale)
is
occurring
and
document
the
duration
and
geographic
scale
of
that
event.
Even
without
a
precise
correlation
with
a
ground­
based
concentration,
the
larger
scale
visualization
of
PM
improves
understanding
and
prediction
of
PM
concentrations,
especially
when
used
in
conjunction
with
the
point­
based
monitoring
of
individual
stations
in
a
number
of
states.
The
satellite
data
also
greatly
enhance
knowledge
of
PM
levels
in
areas
where
there
are
no
ground­
based
monitors.
Although
single
monitor
correlations
may
not
always
be
valid,
the
complete
geospatial
coverage
at
10
x
10
km
scale
of
the
MODIS
data
at
0.6
to
0.8
correlation
in
the
eastern
United
States
is
capable
of
producing
a
relative
index
of
severity
of
PM2.5
concentrations,
if
not
a
specific
ground­
level
index.

This
review
documented
that
the
MODIS
aerosol
algorithms
as
they
are
used
now
perform
best
east
of
about
100
°
W,
including
the
Midwest
and
east
coast.
Satellite
data
are
particularly
suited
to
monitoring
regional
and
synoptic
scale
air
pollution
events.
The
limitation
of
the
satellite
data
for
use
with
the
transport
rule
is
that
satellite
data
cannot
identify
a
specific
type
of
source
(
e.
g.,
mobile,
stationary,
biogenic).
However,
satellite
data
can
effectively
be
used
to
understand
the
geospatial
source
regions
and
document
the
occurrence
and
intensity
of
the
transport
of
PM2.5
across
state
boundaries.

VI.
Inter­
Site
Correlation
of
PM2.5
Mass
and
Component
Species
A.
Background
and
Data
Description
Average
PM2.5
concentrations
fluctuate
from
day­
to­
day
and
among
seasons
in
response
to
complex
atmospheric
interactions
that
occur
among
meteorological
conditions
and
source
emissions.
The
degree
of
spatial
homogeneity
of
PM2.5
concentrations
is
governed
in
large
part
by
the
spatial
homogeneity
of
the
component
species
of
PM2.5.
Generally,
concentrations
from
monitoring
stations
that
are
close
to
one
another
(
e.
g.
within
10
to
50
kilometers)
have
similar
temporal
patterns
with
a
strong
tendency
to
rise
and
fall
in
unison.
Stations
that
are
separated
by
large
distances
(
over
500
kilometers)
generally
do
not
track
as
well
since
the
air
mass
surrounding
the
stations
may
be
quite
different
with
respect
to
pollutant
loading.

The
correlation
coefficient
is
a
convenient
quantitative
measure
of
the
linear
association
between
two
variables,
and
the
square
of
the
correlation
coefficient
denoted
R2,
measures
how
much
of
the
total
variability
in
the
data
is
explained
by
a
simple
linear
model.
For
example,
a
correlation
coefficient
of
0.7
means
that
approximately
50
percent
of
the
variation
in
concentration
at
one
site
can
be
explained
by
variation
at
the
other
site.
It
should
be
noted
that
a
large
correlation
coefficient
does
not
mean
that
the
magnitude
of
the
concentrations
among
stations
are
the
same
 
only
that
the
concentrations
have
essentially
the
same
temporal
pattern.

Concentrations
of
PM2.5
and
component
species
from
the
IMPROVE
and
STN
monitoring
networks
operating
in
the
eastern
half
of
the
US
(
displayed
in
Figure
VI­
1)
were
used
to
calculate
the
correlation
coefficient
among
station
pairs
as
a
function
of
distance
separating
the
stations.
The
data
base
consisted
of
daily
average
concentrations
of
PM2.5
and
each
of
the
major
component
species
(
i.
e.,
sulfates,
nitrates,
organic
carbon,
elemental
carbon
and
42
crustal
mass).
For
analysis
purposes,
the
data
were
partitioned
into
four
calender
quarters
using
the
most
recently
available
and
quality
assured
data.
The
four
quarters
were
composed
of
daily
concentrations
taken
during
the
Fall
of
2001,
and
the
Winter,
Spring
and
Summer
of
2002
(
October
2001
through
September
2002).

The
nominal
sampling
schedule
for
both
networks
is
one
sample
every
third
day
which
results
in
approximately
30
sampled
days
per
quarter.
To
avoid
problems
with
missing
values,
a
thin
plate
spline
was
used
to
impute
values
for
stations
not
reporting
a
valid
measurement
for
a
given
day.
The
number
of
missing
values
varied
by
species
and
quarter
but
averaged
about
5
percent
overall.

The
number
of
pairs
of
monitoring
stations
involved
in
these
calculation
is
quite
large.
Since
there
are
approximately
50
IMPROVE
sites
and
150
STN
sites
monitoring
in
operation
during
this
time
period,
there
are
approximately
3500
correlation
coefficients
produced
for
each
species.
Since
it
is
impractical
to
examine
individual
pairs
of
stations,
the
data
were
grouped
into
distance
categories
and
aggregate
statistics
computed
using
the
data
within
each
category.
The
downside
with
aggregation
schemes,
is
that
unique
features
associated
with
a
particular
geographic
area
or
monitoring
site
(
e.
g
urban
site
located
near
a
particular
source)
cannot
be
accounted
for.

B.
Results
The
results
for
each
pollutant
species
are
displayed
in
the
form
of
box
plots
that
show
the
median,
the
inter­
quartile
range
and
data
span.
Figure
VI­
2
displays
box­
plot
for
the
correlation
coefficient
using
the
PM2.5
mass
concentration
data.
Results
are
only
shown
for
separation
distances
less
than
400
kilometers.
In
this
example,
the
data
are
grouped
in
bins
of
size
50
resulting
in
approximately
40
to
80
data
values
per
bin.

PM2.5
inter­
station
correlations
are
quite
high
at
distances
within
100
kilometers.
Median
concentrations
range
from
0.8
to
above
0.9
among
the
four
quarters.
The
correlation
coefficients
decrease
slowly
with
increasing
distance
with
median
values
dropping
into
the
0.6
to
0.7
range
at
distances
of
approximately
400
kilometers.
The
inter­
quartile
range,
which
is
a
measure
of
the
spread
in
the
correlation
coefficient,
tends
to
increase
with
distance.
The
spread
in
values
indicates
that
individual
station
pairs,
even
within
the
same
distance
bin,
can
have
quite
different
degrees
of
correlation.
Part
of
the
data
spread
can
be
attributed
to
expected
stochastic
variability
but
much
of
this
variation
is
likely
the
result
of
unique
aspects
of
individual
pairings,
for
example,
geographic
orientation
and
location
with
respect
to
meteorological
conditions
and
source
impact.

Figure
VI­
3
displays
similar
plots
for
sulfate
concentrations.
Generally,
the
correlation
pattern
for
sulfates
is
quite
similar
to
that
for
PM2.5.
Correlation
coefficients
are
generally
highest
during
the
fall
quarter
and
decrease
slowly
to
a
median
of
about
0.8
near
300
to
400
kilometers.
Summer
season
correlation
coefficients
are
also
very
high
but
tend
to
decrease
slightly
faster
than
during
the
fall
season.

For
nitrates
(
FigureVI­
4)
correlation
coefficients
are
smaller
than
those
for
PM2.5
or
sulfates
and
vary
somewhat
among
the
four
quarters.
Warm
season
correlations,
when
nitrates
are
43
lowest,
tend
to
be
low
(
about
0.4)
for
stations
separated
by
300
kilometers
or
more.
Cool
season
correlations
for
nitrates
are
larger
than
warm
season
correlations
and
range
from
about
0.5
to
0.6
for
stations
separated
by
300
kilometers
or
more.

For
organic
carbon
(
Figure
VI­
5)
correlation
coefficients
range
from
about
0.4
to
0.6
for
separation
distances
above
300
kilometers.
Like
sulfates,
the
organic
carbon
correlations
appear
to
decrease
more
rapidly
during
the
summer
season
compared
with
the
other
three
seasons.
For
both
organic
carbon
and
nitrates,
inter­
quartile
ranges
appear
to
be
somewhat
larger
than
for
PM2.5
and
sulfates
indicating
greater
variation
among
station
pairs
for
these
species.

For
elemental
carbon
(
Figure
VI­
6)
correlation
coefficients
are
relatively
small
and
show
little
tendency
to
decrease
with
increasing
separation
distance.
Correlation
values
during
the
fall
season
are
highest
(
median
about
0.6)
but
are
frequently
below
0.4
to
0.3
for
the
other
three
quarters.

For
crustal
matter
(
Figure
VI­
7),
correlation
coefficients
tend
to
decrease
slowly
with
increasing
separation
distance.
Also,
they
tend
to
be
somewhat
higher
than
might
be
expected
given
that
crustal
matter
is
assumed
to
be
locally
generated.
Values
during
the
fall
months
(
lowest
crustal
values)
range
from
about
0.8
for
the
most
closely
paired
stations
to
about
0.6
for
stations
separated
by
300
kilometers
or
more.
Correlations
for
the
summer
season
(
highest
crustal
values)
are
generally
the
lowest
ranging
from
about
0.6
to
about
0.5
at
300
kilometers
and
more.

The
formation
rate
and
relative
stability
for
the
major
PM2.5
species
help
explain
the
observed
correlation
patterns.
For
sulfate,
which
is
one
of
the
major
contributors
to
PM2.5
mass,
the
conversion
of
SO2
to
sulfate
occurs
slowly
over
relatively
large
distances
downwind
of
major
emission
sources
of
SO2.
Slow
conversion
of
SO2
to
sulfate
over
large
travel
distances
promotes
greater
spatial
homogeneity
and
thus
can
lead
to
large
correlation
among
distant
monitoring
stations.
For
nitrates,
evidence
suggests
that
higher
inter­
station
correlations
in
winter
are
associated
with
increased
stability
of
nitrate
(
longer
travel
distances)
when
conditions
are
cool
compared
with
warm
seasons
when
nitrates
are
much
less
stable.

The
formation
of
secondary
organic
carbon
from
natural
sources
helps
maintain
a
relatively
homogeneous
regional
component
(
higher
correlation)
that
is
offset
somewhat
by
higher
organic
carbon
in
urban
areas
associated
with
local
carbon
sources.
For
elemental
carbon,
it
is
believed
that
most
of
the
contributions
come
from
nearby
sources
(
e.
g,
agricultural
burning,
mobile
sources)
and
hence
the
relatively
low
correlation
among
stations
that
are
separated
by
modest
distances.

The
correlation
pattern
for
crustal
is
less
easily
understood.
Daily
and
quarterly
average
spatial
plots
of
crustal
matter
exhibit
sharp
gradients,
a
pattern
which
is
usually
associated
with
a
less
homogeneous
air
mass.
Preliminary
analysis
prior
to
this
study
showed
that
crustal
concentrations
near
urban
areas
varied
significantly
which
suggests
that
local
sources
(
wind
blown
dust
and
soil
re­
entrainment
,
local
industrial
activity)
may
be
responsible.
EPA
plans
to
further
study
these
correlation
patterns
for
crustal
matter,
to
determine
if
they
may
be
artifacts
of
common
meteorological
patterns
(
e.
g.,
windy
vs
calm
days).
44
Figure
VI­
1.
Ambient
Monitoring
Stations
45
Figure
VI­
2.
Correlation
of
PM2.5
vs
Distance
Separating
Monitoring
Stations
46
Figure
VI­
3.
Correlation
of
Sulfate
vs
Distance
Separating
Monitoring
Stations
47
Figure
VI­
4.
Correlation
of
Nitrate
vs
Distance
Separating
Monitoring
Stations
48
Figure
VI­
5.
Correlation
of
Organic
Carbon
vs
Distance
Separating
Monitoring
Stations
49
50
Figure
VI­
6.

Correlation
of
Elemental
Carbon
vs
Distance
Separating
Monitoring
Stations
51
Figure
VI­
7.
Correlation
of
Crustal
vs
Distance
Separating
Monitoring
Stations
52
VII.
Source
Apportionment/
Back
Trajectory
Analyses
Source
apportionment
is
a
modeling
technique
that
uses
the
constituent
species
of
PM2.5
to
determine
the
major
sources
of
particulate
air
pollution
in
a
region
over
time.
A
wide
variety
of
studies
have
been
conducted
using
source
apportionment
models.
This
section
explains
source
apportionment
tools,
presents
a
summary
of
recent
research,
and
discusses
a
detailed
source
apportionment
study
on
eight
cities
in
the
eastern
half
of
the
United
States.

A.
Summary
of
Key
Source
Apportionment
Tools
The
main
goal
for
source
apportionment
is
to
describe
and
quantify
the
major
source
categories
contributing
to
the
observed
concentrations
of
fine
particulate
matter
in
the
atmosphere.
Note
that
the
intention
is
not
to
find
every
source
contributing
to
a
site,
just
the
larger
ones.
This
is
done
by
modeling
the
PM2.5
mass
concentration
and
10
to
30
constituent
species
as
a
mixture
from
the
major
sources
that
varies
from
day­
to­
day.
One
of
the
key
assumptions
of
source
apportionment
analysis
is
that
individual
sources
contribute
to
the
species
mass
concentrations
at
the
receptor
with
fixed
proportions
between
the
various
species.
This
assumption
should
be
at
least
approximately
true
for
most
species
and
sources
considered
in
the
referenced
study.
Source
apportionment
decomposes
data
into
a
matrix
of
pollutant
profiles
and
a
matrix
of
relative
contributions.
The
matrix
of
pollutant
profiles
identifies,
for
each
source,
the
relative
mass
of
the
various
PM2.5
species
detected
at
the
monitor
and
identified
as
originating
at
the
source.
The
matrix
of
relative
contributions
identifies
the
relative
strength
of
each
of
the
identified
sources
on
each
monitored
day.
Because
of
measurement
error,
the
tools
used
for
source
apportionment
can
detect
only
sources
with
a
significant
contribution
to
one
or
more
of
the
fitting
species.

The
primary
tools
that
are
used
to
apportion
the
mass
concentrations
include
the
following:

°
Positive
Matrix
Factorization
(
PMF
or
PMF2)
uses
constrained,
weighted
least
squares
estimation
to
apportion
the
species
masses.
The
input
data
include
the
species
masses
and
the
uncertainties
associated
with
each
measurement.
The
main
outputs
are
the
source
profiles
and
the
associated
time
series
(
the
day­
by­
day
apportioning
of
species
mass).
Secondary
output
includes
various
model
diagnostics.

°
Multilinear
Engine
(
ME)
and
Positive
Matrix
Factorization
(
3­
dimensional)
(
PMF3)
generalize
the
standard
PMF
model.
The
ME
model
also
allows
for
known
constraints
and
an
even
broader
range
of
models.
The
output
for
both
is
similar
to
the
PMF
output.

°
UNMIX
apportions
the
data
based
on
the
"
edges"
produced
in
the
data
when
one
or
more
of
the
sources
do
not
significantly
contribute
to
the
total
mass
of
any
species
being
modeled.

°
Chemical
Mass
Balance
(
CMB)
apportions
the
mass
using
historical
emission
source
profiles
that
are
assumed
known
and
weighted
regression
methods.
The
53
output
does
not
include
the
source
profiles,
since
they
are
required
inputs.

There
are
a
variety
of
secondary
tools
and
methods
used
in
conjunction
with
the
source
apportionment
tools
to
investigate
and
possibly
refine
the
source
apportionment.
The
most
common
pairing
is
source
apportionment
data
with
meteorological
data
based
on
back
trajectory
methods,
such
as
Hybrid
Single­
Particle
Lagrangian
Integrated
Trajectory
(
HYSPLIT)
model.
In
this
case,
the
source
apportionment
output
is
paired
with
the
output
from
a
meteorological
model
that
indicates
a
likely
path
back
in
time
for
a
packet
of
air
arriving
at
the
receptor
location
during
the
sampling
period.
Inferences
on
the
source
location(
s)
are
made
by
comparing
the
paths
that
correspond
to
high
source
strengths
with
all
paths
generated
from
the
modeled
period
and/
or
the
paths
that
correspond
to
low
source
strength
periods.
Since
the
inference
is
generally
made
through
a
probabilistic
framework,
the
output
is
sometimes
referred
to
as
a
probability
field.
These
methods
are
also
referred
to
as
(
conditional)
ensemble
back
trajectory
methods.

B.
Summary
of
Source
Apportionment
Research
In
support
of
the
IAQR
proposal,
a
literature
compilation
was
completed
to
summarize
where
some
of
the
source
apportionment
research
has
been
conducted
and
its
general
findings.
The
literature
included
in
the
compilation
was
not
exhaustive
but
was
selected
as
representative
of
recent
source
apportionment
research,
focusing
primarily
(
but
not
exclusively)
on
the
PMF
and
UNMIX
source
apportionment
models
applied
to
data
in
the
eastern
United
States.
Figure
VII­
1
shows
the
locations
of
the
various
source
apportionment
studies
and
Table
VII­
1
lists
the
location
names.
Detailed
summaries
of
the
articles
can
be
found
in
Coutant,
et
al.
(
2003a).
54
Figure
VII­
1.
Map
of
source
apportionment
studies
involving
PM2.5
55
Table
VII­
1.
Location
of
source
apportionment
studies
involving
PM2.5
Label
Location
or
Nearest
City
Label
Location
or
Nearest
City
Label
Location
or
Nearest
City
1
Acadia
National
Park,
ME
15
Quaker
City,
OH
29
Spokane,
WA
2
Lye
Brook
Wilderness,
VT
16
Livonia,
IN
30a­
c
Seattle,
WA
3a­
d
Underhill,
VT
17
Mammoth
Cave
National
Park,
KY
31
Potsdam,
NY
4
Bronx,
NY
18
Great
Smoky
Mountains
National
Park,
TN
32
Stockton,
NY
5
Connecticut
Hill,
NY
19
Indianapolis,
IN
33
Crater
Lake
National
Park,
OR
6a­
c
Brigantine
National
Wildlife
Refuge,
NJ
20
Bondville,
IL
34
Lassen
Volcano
National
Park,
CA
7
Arendtsville,
PA
21
St.
Louis,
MO
35
Salt
lake
City,
UT
8
M.
K.
Goddard,
PA
22
Milwaukee,
WI
36
Bountiful,
UT
9
Fort
Meade,
MD
23
Boundary
Waters
Canoe
Area,
MN
37
Narragansett,
RI
10a­
d
Washington,
DC
24
Charlotte,
NC
38
Gulfport,
MS
11
Jefferson/
James
River
Face
Wilderness,
VA
25a­
c
Atlanta,
GA
39
Pensacola,
FL
12
Shenandoah
National
Park,
VA
26
Birmingham,
AL
40
Centreville,
AL
13
Dolly
Sods/
Otter
Creek
Wilderness,
WV
27
Houston,
TX
41
Oak
Grove,
MS
14
Toronto,
ON
28
Phoenix,
AZ
42
Yorkville,
GA
56
1.
Overview
of
the
Sources
The
authors
of
the
studies
identified
and
named
sources
using
different
nomenclature,
but
these
sources
can
be
grouped
into
categories.
Each
of
these
source
categories
is
discussed
below.

Sulfate
Dominated
Source
A
sulfate
dominated
source
was
identified
as
the
largest
or
one
of
the
largest
sources
in
nearly
every
study,
often
consisting
of
over
50
percent
of
the
source
of
PM2.5
at
some
locations
during
some
seasons.
In
a
few
cases,
there
was
a
known
local
source
of
sulfate,
but
most
of
the
eastern
studies
(
in
conjunction
with
back
trajectory
analysis)
pointed
to
coal­
fired
power
plants
in
the
Midwest.
The
studies
with
multiple
years
of
data
also
tended
to
identify
a
winter
and
summer
signature
to
the
sulfate
source,
with
the
summer
version
apportioning
more
mass.
The
studies
speculate
that
the
two
profiles
represent
two
extremes
in
the
atmospheric
chemistry
between
the
source
regions
and
the
receptor.
Note
that
the
source
category
is
often
referred
to
by
its
dominant
species,
sulfate,
but
the
"
sulfate
source"
is
often
associated
with
significant
amounts
of
organic
carbon
and
is
usually
the
single
largest
source
of
selenium
and
other
trace
elements.

Secondary
Organic
Matter
Secondary
organic
matter
was
also
a
major
source
for
nearly
all
sites.
As
with
sulfate,
the
source
is
sometimes
named
after
the
dominant
species
since
it
is
often
formed
through
a
secondary
process
in
the
atmosphere
rather
than
being
emitted
directly.
This
case
is
even
further
complicated
by
the
fact
that
the
particulate
organic
carbon
is
itself
a
mix
of
many
species
that
are
not
usually
measured
separately.
Some
studies
associated
the
secondary
organic
matter
with
mobile
sources.
Only
a
few
studies
are
able
to
separate
the
mobile
source
into
gasoline
sources
and
diesel
sources.

Nitrate
Dominated
Source
Among
the
eastern
sites,
a
nitrate
dominated
source
is
also
found
to
be
a
major
source,
often
the
second
largest
source.
The
back
trajectory
analyses
sometimes
show
an
association
with
agricultural
areas
that
would
have
high
ammonia
emissions.
However,
the
interpretation
of
this
nitrate
dominated
sources
is
not
consistent
from
study
to
study.
Some
authors
associate
this
source
type
with
NOx
point
sources
and
motor
vehicles
from
major
cities
that
are
sufficiently
far
from
the
receptor
for
the
NOx
to
oxidize
and
react
with
ammonia.
Other
authors
associate
this
source
type
with
motor
vehicles
from
nearby
highways.

Biomass
Burning
The
biomass
burning
category
includes
the
wood
smoke
and
forest
fire
categories
identified
at
several
sites.
The
size
of
the
source
varies
considerably
from
site
to
site,
but
usually
as
expected
(
e.
g.,
larger
in
rural
areas
and
in
the
northwest).

Sometimes,
this
category
also
includes
fireworks.
This
is
because
the
source
is
characterized
by
organic
carbon
and
potassium.
Usually
an
explicit
reference
to
fireworks
is
based
57
on
a
4th
of
July
spike
in
the
source
strength,
but
may
also
be
supported
by
trace
metals,
particularly
copper,
found
in
the
profile.
The
source
profiles
are
similar
enough
and
the
source
strength
small
enough
that
the
models
do
not
generally
separate
biomass
burning
from
fireworks.

Industrial
This
category
includes
a
variety
of
small
sources
characterized
by
elemental
carbon
and
trace
metals,
such
as
smelters
and
incinerators
that
may
or
may
not
have
been
found
at
the
various
sites.
Frequently,
the
industrial
sources
are
associated
with
known
local
sources
or,
in
the
case
of
the
northeast,
known
smelters
in
Canada.
These
sources
also
tend
to
be
distinctive
enough
for
the
models
to
separate
them
into
several
small
sources
within
a
site.

Crustal
and
Salt
The
crustal
source
category
is
identified
for
all
sites,
but
is
usually
small,
0.1
to
1.5
µ
g/
m3.
There
are
three
notable
rural
exceptions:
M.
K.
Goddard,
Pennsylvania;
Quaker
City,
Ohio;
and
Livonia,
Indiana,
each
with
7.8
µ
g/
m3
or
more.
The
Phoenix
site
is
also
apportioned
a
larger
crustal
source,
2.8
µ
g/
m3.

Other/
Not
Identified
Four
of
the
six
CASTNET
sites
(
Arrentsville,
Pennsylvania;
Connecticut
Hill,
New
York;
Quaker
City,
Ohio;
and
Bondville,
Illinois)
have
large
(>
3.0
µ
g/
m3)
unidentified
sources.
The
particular
study
was
concerned
with
light
extinction;
since
these
were
not
significantly
associated
with
light
extinction,
it
was
not
felt
necessary
to
identify
those
sources.
Otherwise,
sources
greater
than
1.0
µ
g/
m3
are
identified.
The
remaining
miscellaneous
sources
are
generally
under
1.0
µ
g/
m3
also.

2.
Source
Locations
and
Time
Series
Analyses
The
compilation
concentrated
on
the
source
apportionment
models
of
Positive
Matrix
Factorization
(
PMF),
its
variations,
and
UNMIX.
In
each
study,
PMF
or
UNMIX
was
used
as
the
sole
source
apportionment
tool
and/
or
as
a
check
on
the
results
of
the
other
model.
More
importantly,
nearly
all
studies
agreed
that
source
apportionment
models
cannot
stand
alone
for
many
of
the
desired
uses.
In
fact,
additional
supporting
evidence
is
frequently
needed
to
complete
the
source
identification
process.
Thus,
the
models
are
usually
used
in
conjunction
with
other
tools,
commonly
back
trajectory
analyses
via
a
meteorological
model
such
as
HYSPLIT.

Back
trajectory
analyses
for
the
eastern
sites
associate
the
sulfate
with
the
Ohio
River
Valley
area.
Industrial
sources
are
also
frequently
associated
with
known
source
areas.
Several
studies
noted
transport
across
the
Canadian
border,
specifically
sulfates
from
the
Midwestern
United
States
into
Canada,
and
smelter
emissions
from
Canada
into
the
northeastern
United
States.

All
of
the
studies
looked
at
long­
term
average
source
contributions
and
most
looked
at
seasonal
(
3­
month)
average
contributions.
There
was
very
little
analysis
of
daily
or
weekly
events,
58
with
a
few
exceptions,
such
as
Saharan
dust,
fireworks,
or
local
events
that
changed
emissions
patterns
temporarily.
In
several
cases
where
datasets
covering
very
long
time
periods
were
evaluated,
reductions
in
emissions
could
be
seen
for
power
plants,
fuel
oil,
and
smelters.
These
were
attributed
to
increased
emission
controls,
fuel
switching
(
e.
g.,
from
oil
to
natural
gas),
and
meteorological
conditions
(
e.
g.,
warmer
winters
in
the
late
1990s).
More
detail
about
these
can
be
found
in
Coutant,
et
al.
(
2003a).

3.
Methodologies
and
Technical
Approaches
The
technical
approach
varied
significantly
among
the
various
studies.
Some
studies,
through
preplanned
additional
data
collection,
have
also
used
tools
such
as
scanning
electron
microscope
analysis
of
the
particulate
matter
or
specialized
tracers
to
gain
a
greater
understanding
of
specific
PM2.5
sources.
Typically,
however,
the
data
used
are
very
similar
to
data
from
IMPROVE
or
IMPROVE
protocol
sites
or
more
recently
from
EPA's
Speciation
Trends
Network,
with
a
few
super
sites
having
specialized
data.

PMF,
its
variations,
and
UNMIX
represent
very
different
approaches
to
source
apportionment.
Data
preprocessing
for
missing
data
and
identification
of
outliers
is
not
standardized.
Profile
interpretation
is
essentially
a
matter
of
"
expert
opinion."
Even
the
derivation
and
processing
of
the
back
trajectories
varies
significantly
among
the
studies
surveyed.

Where
both
models
have
been
used,
PMF
has
been
used
to
model
more
sources
than
UNMIX.
However,
PMF
is
typically
used
to
model
more
species,
so
it
should
be
able
to
identify
more
sources.
This
is
probably
driven
by
the
fact
that
multiple
modeling
steps
are
sometimes
required
to
model
a
large
number
of
species
with
UNMIX.
The
results
then
need
to
be
merged
into
a
single
solution.
PMF
is
generally
not
used
in
this
manner,
except
for
apportioning
the
total
mass.

The
preprocessing
of
the
data
for
use
in
the
models
is
dependent
on
the
amount
of
data
available
and
the
particular
study
goals.
For
example,
if
long­
term
trends
are
a
part
of
the
study
goals,
then
isolated
events
are
sometimes
screened.
Missing
data,
or
rather
incomplete
data,
are
sometimes
handled
by
data
imputation
and
sometimes
deletion
of
the
data.
Data
that
are
below
minimum
detection
are
fairly
consistently
handled
by
MDL/
2
substitution.

Analyses
of
the
time
series
output,
particularly
back
trajectory
methods,
are
frequently
being
used
to
aid
interpretation.
However,
this
adds
an
additional
layer
of
divergent
methods
and
models.
ATAD
and
HYSPLIT
are
the
two
most
common
models
used
to
generate
the
individual
back
trajectories.
The
methods
for
implementing
these
models
vary
in
the
choices
of
starting
times
and
heights
and
in
other
technical
aspects.
The
processing
of
the
back
trajectories
also
varies
considerably
in
the
definition
of
high
and
low
day
source
strength,
the
base
unit
used
from
the
trajectories
(
hour
or
number
of
end
points),
the
metric
used
to
measure
the
relative
likeliness
of
the
source
location,
and
the
contouring
methods.

C.
Eight
City
Report
59
In
order
to
assess
the
contributions
of
pollutant
sources,
both
local
and
distant,
to
local
pollutant
levels,
a
source
apportionment
and
back
trajectory
study
was
conducted
at
eight
cities
across
the
eastern
United
States
(
Coutant,
et
al.,
2003b).
The
purpose
of
the
study
was
to
determine
the
types
and
locations
of
sources
of
PM2.5
detected
in
the
eight
cities.
Each
of
the
eight
cities
was
analyzed
separately.
The
first
analysis
performed
in
each
city,
the
source
apportionment
analysis,
used
PMF
to
identify
the
pollutant
profiles
(
chemical
makeup
or
signature)
of
the
major
sources
of
pollution
at
the
receptor
site
and
determine
the
signal
strength
from
each
source
on
each
monitored
day.
After
identifying
the
chemical
makeup
and
daily
strength
of
each
source,
several
data­
analytic
techniques
were
employed
to
determine
the
categories
that
each
source
represented.
The
second
analysis
performed
in
each
city,
the
back
trajectory
analysis,
combined
the
information
on
source
strength
obtained
from
the
back
trajectory
analysis
with
information
on
air
packet
transport
patterns
to
determine
likely
locations
from
which
the
pollution
came.
This
analysis
allowed
identification
of
likely
source
regions
of
different
classes
of
PM2.5.

The
following
sections
give
a
more
detailed
description
of
the
two
analyses
performed
in
the
study.
Section
VII.
C.
1
describes
the
data
on
which
the
source
apportionment
analysis
and
back
trajectory
analysis
were
performed
and
describes
the
locations
at
which
data
were
collected.
Section
VII.
C.
2
describes
the
source
apportionment
part
of
the
study
while
Section
VII.
C.
3
describes
the
back
trajectory
analysis.
Finally,
Section
VII.
C.
4
presents
conclusions.

1.
Data
Sources
and
Study
Cities
The
source
apportionment
and
back
trajectory
study
analyzed
speciated
PM2.5
data
from
eight
of
EPA's
Trends
Sites
located
in
Birmingham,
Alabama;
Bronx,
New
York;
Charlotte,
North
Carolina;
Houston,
Texas;
Indianapolis,
Indiana;
Milwaukee,
Wisconsin;
St.
Louis,
Missouri;
and
Washington,
D.
C.
These
sites
are
all
in
urban
areas.
The
results
of
the
study
indicate
that
these
sites
are
influenced
strongly
by
both
local
sources
of
PM2.5
as
well
as
long­
range
transport
of
PM2.5.

The
source
apportionment
results
presented
in
this
report
are
based
on
speciated
PM2.5
measurements.
Speciated
PM2.5
measurements
are
measurements
detailing
the
mass
of
each
constituent
of
PM2.5.
In
other
words,
rather
than
only
containing
the
total
PM2.5
mass
(
total
weight
of
particles
less
than
2.5
µ
m
in
diameter),
the
data
contain
the
amount
of
the
total
mass
composed
of
sulfate,
nitrate,
ammonium,
and
several
other
elements
and
compounds.
The
measurements
are
from
integrated
24­
hour
collection
periods
typically
collected
every
three
days
using
filter­
based
methods.
Specifically,
the
PM2.5
speciation
sites
use
X­
Ray
Fluorescence
(
XRF),
Ion
Chromatography
(
IC),
and
Thermal­
Optical
Analysis
(
TOR)
analyses
done
on
Teflon,
nylon,
and
quartz
filters,
respectively.
The
species
used
were
PM2.5
total
mass
(
both
from
the
speciation
monitor
and
a
co­
located
FRM
when
available),
sulfate,
nitrate,
ammonium,
Al,
As,
Ba,
Br,
Ca,
Cl,
Cr,
Cu,
Elemental
Carbon
(
E.
C.),
Fe,
Pb,
Mn,
Ni,
Organic
Carbon
(
OC),
K,
K+,
Se,
Si,
Na,
S,
Sn,
Ta,
Ti,
V,
and
Zn.
The
inclusion
of
both
the
mass
measurements
and
both
the
sulfur
and
sulfate
measurements
effectively
doubles
the
weight
given
to
these
species
and
provides
a
means
for
evaluating
the
error
in
the
apportionment.

The
initial
data
for
the
project
were
for
Bronx,
St.
Louis,
and
Houston
and
came
from
the
1
http://
www.
epa.
gov/
ttn/
airs/
airsaqs/
sysoverview.
htm
2
http://
www.
dec.
state.
ny.
us/
website/
dar/
baqs/
pm25mon.
html
60
AQS
database1
in
January
2002.
This
was
supplemented
with
data
from
the
New
York
Department
of
Environmental
Conservation
website2.
AQS
data
for
Milwaukee
and
Washington,
D.
C.,
were
obtained
in
September
2002
and
the
AQS
data
for
Birmingham,
Charlotte,
and
Indianapolis
were
added
in
January
2003.
The
uncertainty
estimates
for
measurements
at
all
the
sites
are
based
in
part
on
the
co­
located
data
within
the
original
AIRS
database
(
commonly
referred
to
as
the
Mini­
Trends
sites).
Table
V­
2
summarizes
the
time
periods
over
which
monitor
readings
were
recorded
at
each
of
the
eight
sites.

Table
VII­
2.
Dates
modeled
for
each
of
the
eight
sites
Site
Start
Date
End
Date
Days
Modeled
Sampling
Frequency
Birmingham,
AL
1/
13/
2001
8/
9/
2002
186
1­
in­
3
day
Bronx,
NY
9/
3/
2000
1/
29/
2002
160
1­
in­
3
day
Charlotte,
NC
1/
13/
2001
8/
6/
2002
143
1­
in­
3
day
Houston,
TX
8/
17/
2000
7/
7/
2001
121
1­
in­
3
day
/
daily
Indianapolis,
IN
12/
20/
2000
8/
6/
2002
155
1­
in­
3
day
Milwaukee,
WI
12/
14/
2000
9/
8/
2002
172
1­
in­
3
day
St.
Louis,
MO
8/
4/
2000
7/
12/
2001
112
1­
in­
3
day
Washington,
D.
C.
4/
7/
2001
8/
6/
2002
124
1­
in­
3
day
In
addition
to
speciated
PM2.5
data,
local
meteorological
data
were
obtained
for
characterization
of
sources
and
verification
of
source
category
identifications.
Local
meteorological
data
were
obtained
for
each
site
from
the
NOAA
archives.
Table
VII­
3
indicates
the
site
location
and
the
distance
to
the
nearest
NOAA
MET
station
with
sufficient
data
to
use
in
the
analysis.
61
Table
VII­
3.
Nearest
NOAA
meteorological
station*

Site
Site
Lat.
Site
Long.
Nearest
Available
Meteorological
Station
WBAN
Number
MET
Station
Name
MET
Station
Location
Distance
(
miles)
Birmingham,
AL
33.55
­
86.82
13876
Birmingham,
AL
International
Airport
25.6
Bronx,
NY
40.87
­
73.88
94741
Teterboro,
NJ
Teterboro
Airport
25.9
Charlotte,
NC
35.24
­
80.79
13881
Charlotte,
NC
Douglas
International
Airport
14.8
Houston,
TX
29.90
­
95.33
53910
Houston,
TX
Hooks
Memorial
Airport
9.6
Indianapolis,
IN
39.81
­
86.11
53842
Indianapolis,
IN
Eagle
Creek
Airpark
21.8
Milwaukee,
WI
43.06
­
87.91
4840
Fond
Du
Lac,
WI
Fond
Du
Lac
County
Airport
33.6
St.
Louis,
MO
38.66
­
90.20
53904
St.
Charles,
MO
St.
Charles
Smart
Airport
7.4
Washington,
D.
C.
38.92
­
77.01
13743
Washington,
D.
C.
Ronald
Reagan
Nat'l
Airport**
27.5
*
Subsequent
to
the
study,
errors
have
been
found
in
the
Lat/
Long
data
within
the
NOAA
data.
The
sites
used
may
not
be
the
nearest
stations.
**
Second
nearest
used
because
of
MET
station
data
problems.

Finally,
back
trajectory
data
were
collected
for
use
in
the
back
trajectory
analysis.
This
type
of
data
is
discussed
in
more
detail
in
Section
VII.
C.
3.

2.
Source
Apportionment
Analysis
The
first
analysis
performed
on
the
data
at
each
site
was
a
source
apportionment
analysis,
described
in
Section
VII.
A.
The
source
apportionment
analysis
was
performed
in
three
steps.
First,
some
preliminary
procedures
were
performed
to
identify
possible
patterns
in
the
data.
Next,
PMF
was
applied
to
decompose
the
data
into
pollutant
profile
and
relative
contribution
matrices.
Finally,
the
pollutant
profiles
identified
in
the
second
step
were
compared
against
known
pollutant
profiles
in
the
speciate
database
to
determine
source
categories.

Preliminary
Procedures
The
first
step
in
source
apportionment
is
to
examine
plots
of
the
speciated
PM2.5
data.
Scatter
plots
of
concentrations
of
one
species
versus
another
were
examined
as
a
part
of
the
site
selection,
but
they
are
also
useful
after
the
sites
have
been
selected
as
a
first
analysis
tool.
There
are
a
few
patterns
that
can
be
observed
in
these
plots
that
give
insight
into
the
data.
Plots
that
are
nearly
linear
indicate
that
the
significant
sources
produce
these
species
in
the
same
ratio,
and
it
is
likely
that
there
is
only
one
major
source
of
the
pair.
Wedge­
shaped
plots
indicate
at
least
two
major
sources
of
the
pair
of
species.
The
edges
of
the
plots
are
produced
from
the
two
major
sources
of
the
species
pair
with
the
most
disparate
ratios
between
the
two
species.
Considerations
such
as
these
give
the
first
indication
of
which
species
will
be
useful
in
the
source
apportionment
fitting
and
a
lower
bound
for
the
number
of
sources
that
affect
the
receptor.
62
Source
Apportionment
The
next
step
in
analyzing
the
data
is
to
use
source
apportionment
techniques
to
identify
the
number
of
sources
at
each
site,
the
pollutant
profiles
of
those
sources,
and
the
relative
contributions
of
those
sources
on
the
monitored
days.
For
this
purpose,
two
source
apportionment
tools
were
used:
UNMIX
and
PMF.

The
main
source
apportionment
tool
used
in
the
analysis
was
PMF.
PMF
starts
with
the
matrix
of
speciated
PM2.5
data
by
date
and
decomposes
it
into
two
other
matrices
with
all
positive
entries.
One
of
these
matrices,
the
pollutant
profile
matrix,
has
a
row
for
each
source
and
a
column
for
each
species.
Each
row
represents
the
average
apportioned
mass
of
each
species
of
PM2.5
at
a
given
source.
The
other
matrix,
the
relative
contribution
matrix,
has
a
row
for
each
day
of
data
analyzed
and
a
column
for
each
source.
Each
row
represents
the
relative
strengths
of
each
source
on
a
given
day.
Essentially,
these
two
matrices
provide
two
pieces
of
information
for
each
source:
a
source
profile
and
a
time
series
of
each
source's
strength
at
the
receptor.
In
this
report,
a
source
profile
is
a
list
of
the
mean
species
concentrations
from
the
source
at
the
receptor.

PMF
was
set
to
search
for
5
to
10
source
solutions
at
all
sites.
The
program
was
run
from
at
least
six
different
random
starting
points
and
the
best
fitting
solution
was
used.
Analysis
of
the
solutions
led
to
the
use
of
between
6
and
8
sources
depending
on
the
site.
A
statistical
algorithm
was
implemented
for
the
selection
of
the
number
of
sources
for
Birmingham,
Charlotte,
Indianapolis,
Milwaukee,
and
Washington,
D.
C.
This
algorithm
is
based
on
the
Bayesian
Information
Criterion
(
BIC)
that
is
frequently
used
for
time
series
model
selection
(
Wei,
1990).

In
addition
to
model
fitting,
a
residual
analysis
and
goodness­
of­
fit
tests
were
performed
on
the
PMF
decompositions.
Modeling
error
was
assessed
by
examining
the
difference
between
the
apportioned
values
for
the
FRM
mass
and
the
mass
from
the
speciation
monitor
(
except
in
Houston,
which
did
not
have
a
co­
located
FRM)
and
the
difference
between
three
times
the
sulfur
concentration
(
the
apportioned
XRF
sulfur
mass)
and
the
sulfate
concentration
(
the
apportioned
IC
sulfate
mass).
The
two
mass
values
should
differ
only
by
measurement
error
as
should
the
sulfursulfate
pair
under
the
assumption
that
all
of
the
sulfur
is
present
in
the
form
of
sulfate.
The
differences
give
a
direct
means
of
estimating
the
errors
in
the
apportioned
masses
of
the
species
(
assuming
that
the
other
species
are
similar).
For
each
site,
Table
VII­
4
shows
an
estimate
of
the
relative
error
of
the
mean
of
the
apportioned
FRM
mass
and
the
speciation
mass.
3
http://
www.
epa.
gov/
ttnchie1/
software/
speciate/
index.
html
63
Table
VII­
4.
Model
error
estimates
Site
Mass
CV
Bronx,
NY
45%
Birmingham,
Al
24%
Charlotte,
NC
43%
Houston,
TX
84%
St.
Louis,
MO
21%
Milwaukee,
WI
47%
Washington,
D.
C.
47%
Indianapolis,
IN
32%

Source
Characterization
and
Identification
The
source
apportionment
output
yields
a
chemical
profile
for
each
source
(
or
source
category)
and
a
time
series
for
the
mass.
While
the
profile
is
unique
for
the
source,
it
does
not
explicitly
identify
the
source.
Two
main
methods
were
employed
to
identify
the
sources
from
the
PMF
output.
Both
of
these
methods
were
applied
to
each
source
identified
at
each
of
the
eight
sites.
First,
an
automated
method
was
used
to
match
the
output
with
source
profiles
in
the
SPECIATE
database.
3
The
matching
algorithm
produces
up
to
ten
possible
source
matches
with
specific
sources
from
the
speciate
database.
The
second
"
method"
is
informed
opinion.
Using
the
automated
matching,
past
experience,
and
discussions
with
local
individuals,
most
of
the
profiles
can
be
identified
with
specific
source
categories.

The
final
identifications
are
a
merging
of
all
the
various
analyses
and
review
by
source
apportionment
experts
and
local
representatives,
and
represent
the
best
current
understanding
of
the
sources.
This
section
discusses
the
primary
characteristics
of
the
sources
identified.

Ammonium
nitrate
 
As
the
name
implies,
the
"
source
profiles"
for
this
category
are
dominated
by
ammonium
and
nitrate.
Ammonium
nitrate
is
formed
from
a
combination
of
ammonia
(
with
a
large
portion
coming
from
agricultural
sources)
and
NO
x
(
with
substantial
portions
from
both
utilities
and
mobile
sources).
Some
of
the
profiles
contain
coal
burning
tracers
and
some
of
the
preliminary
transport
analyses
seem
to
indicate
a
relationship
to
coal
burning,
but
these
only
reveal
that
coal
burning
is
part
of
the
source.
Apportionment
of
these
species
may
be
possible
by
restricting
the
analyses
to
periods
with
cooler
temperatures.

Canadian
fires
 
In
July
2002,
there
were
major
fires
in
Canada.
The
plume
from
these
fires
can
be
seen
in
satellite
photos
and
the
source
is
clearly
tied
to
this
event.
It
would
be
expected
that
any
wood
smoke
during
the
rest
of
the
year
would
also
be
apportioned
to
this
source,
but
the
source
is
so
strongly
dominated
by
the
single
event
that
it
is
difficult
to
tell.

Coal
combustion­
This
is
the
major
source
of
sulfate
for
all
sites
and
the
major
source.
64
Differences
in
fuel
sources
and
distances
to
the
source
contribute
to
the
site­
to­
site
variations
in
the
profiles.
The
coal
combustion
source
is
also
a
major
source
of
Se,
a
coal
burning
tracer.

Crustal
 
All
sites
are
apportioned
a
crustal
source.
The
profiles
match
the
profiles
found
in
the
SPECIATE
database
quite
well.

Industrial
sources
 
These
are
expected
to
vary
considerably
from
site
to
site.
Most
of
the
time
they
probably
represent
a
mix
of
a
strong
local
set
of
industrial
emissions
and
small
amounts
of
any
similar
sources/
mixes
that
happen
to
be
in
the
region.
In
Houston,
the
wind
data
suggest
a
relationship
with
the
industries
in
the
ship
channel.
In
Bronx,
the
back
trajectory
analyses
suggest
a
regional
mixture
of
sources
from
along
the
east
coast.

Marine
and
industrial
salts­
These
sources
have
sea
spray
components
(
including
trace
metals)
and
source
regions
that
extend
into
the
ocean.
There
appear
to
be
inland
sources
also.
This
leads
to
the
industrial
salt
characterization.
It
is
likely
that
neither
category
is
large
enough
or
distinctive
enough
for
the
tools
to
separate.

Mobile
sources­
These
include
both
gas
and
diesel
mobile
sources.
The
sources
in
this
study
are
the
dominant
sources
of
organic
carbon
and,
hence,
are
expected
to
be
mostly
associated
with
gasoline
combustion.
Local
mobile
sources
would
generally
be
expected
to
be
stronger
during
the
week
compared
with
weekends.
However,
the
delays
in
transport
would
obscure
that
relationship
if
a
significant
portion
is
not
local.

Oil
combustion­
Two
oil
combustion
sources
were
identified.
They
are
carbon
and
sulfate
sources,
which
are
also
the
major
sources
of
Ba,
Ni,
and
V.

Road
construction­
This
was
identified
for
the
Washington,
D.
C.,
site.
The
source
profile
is
a
mix
of
crustal
components
and
diesel
mobile
(
EC
dominant).
The
source
is
stronger
during
weekdays
and
lasts
for
several
months.

Smelting
and
steel
production
 
These
are
characterized
by
their
metal
content
and
distinguished
from
incinerators
by
the
lack
of
carbon.
The
profiles
may
also
show
power
production
components
either
due
to
direct
coal
burning
or
coal
burning
by
the
electrical
source
that
varies
with
production.
In
St.
Louis
the
local
wind
pattern
associates
the
source
strengths
with
known
local
sources.

Vegetative
burning
and
fireworks­
The
July
4th
source
events
clearly
dominate
the
source
strength
pattern.
Both
source
categories
are
high
in
organic
carbon
and
are
major
sources
of
potassium.
The
fireworks
are
probably
responsible
for
the
copper
and
other
trace
metal
components.
However,
the
other
similarities
in
the
profiles
and
indications
of
small
amounts
of
source
activity
during
other
times
of
the
year
suggest
that
vegetative
burning
is
included
in
this
source
category.

Zinc
and
other
sources
identified
by
species­
These
are
each
characterized
by
being
a
major
contributor
of
a
specific
species
or
containing
an
unusual
amount
of
the
species.
In
65
St.
Louis,
there
is
a
zinc
refinery
in
a
direction
indicated
by
the
local
wind
data
and,
hence,
this
zinc
source
is
identified.
However,
zinc
is
also
found
in
incinerator
and
recycling
emissions
and
these
may
be
included
in
that
profile
and
in
the
zinc
source
found
in
Birmingham.
The
other
sources
only
identified
by
species
are
a
lead
source
for
Birmingham
and
a
chlorine
source
for
Milwaukee.

Source
Apportionment
Results
Source
identifications,
along
with
apportioned
mass,
are
presented
in
Table
VII­
5.
Any
mention
of
explicit
sources
within
the
source
identifications
is
included
only
as
an
example
of
a
local
source
with
the
characteristics
similar
to
what
the
study
has
found.
Additional
analysis
would
be
needed
to
relate
an
effect
at
the
receptor
to
an
explicit
source.
Only
boxes
containing
numbers
represent
sources
identified
at
their
respective
sites.
66
Table
VII­
5.
Summary
of
the
mean
apportioned
mass
concentration
across
sites
Major
Source
Categories
Mean
Apportioned
Mass
Concentration:
µ
g/
m3
(%
total)
Birmingham
Bronx
Charlotte
Houston
Ammonium
Nitrate
1.84
(
9.4%)
4.09
(
25.4%)
1.21
(
7.5%)
Canadian
Fires
Coal
Combustion
7.27
(
37.2%)
5.29
(
32.9%)
5.71
(
35.4%)
5.54
(
39.1%)
Crustal
1.27
(
6.5%)
0.97
(
6.0%)
0.57
(
3.5%)
0.77
(
5.4%)
Industrial
1.50
(
7.7%)
1.82
(
11.3%)
0.87
(
6.1%)
Marine
0.30
(
1.9%)
0.08
(
0.5%)
0.29
(
2.0%)
Metal
production
0.67
(
4.2%)
Mobile
Source
or
Grain
dust
1.04
(
7.3%)
Mobile
sources
6.51
(
33.4%)
2.49
(
15.5%)
3.87
(
24.0%)
5.19
(
36.7%)
Oil
combustion
1.22
(
7.6%)
1.87
(
11.6%)
Vegetative
Burning
and
Fireworks
1.15
(
5.9%)
0.48
(
3.0%)
0.49
(
3.5%)

Total
mass
conc.
being
apportioned
(
µ
g/
m3)
19.53
16.08
16.15
14.16
Major
Source
Categories
Mean
Apportioned
Mass
Concentration:
µ
g/
m3
(%
total)
Indianapolis
Milwaukee
St.
Louis
Washington
Ammonium
Nitrate
3.58
(
20.7%)
4.07
(
28.1%)
5.02
(
29.2%)
1.23
(
7.4%)
Canadian
Fires
0.25
(
1.5%)
1.11
(
6.7%)
Coal
Combustion
8.67
(
50.1%)
4.54
(
31.3%)
5.74
(
33.4%)
7.70
(
46.2%)
Crustal
0.51
(
3.0%)
0.31
(
2.1%)
1.43
(
8.3%)
1.47
(
8.8%)
Industrial
2.66
(
18.4%)
Marine
0.47
(
2.7%)
Metal
production
2.20
(
12.8%)
Mobile
Source
or
Grain
dust
Mobile
sources
3.21
(
18.5%)
2.46
(
17.0%)
2.92
(
17.0%)
4.72
(
28.3%)
Oil
combustion
Vegetative
Burning
and
Fireworks
0.69
(
4.0%)
0.35
(
2.5%)
0.53
(
3.2%)

Total
mass
conc.
being
apportioned
(
µ
g/
m3)
17.29
14.47
17.19
16.67
3.
Meteorological
Summaries
and
Back
Trajectory
Analysis
Once
the
source
categories
are
identified,
it
is
possible
to
combine
the
source
apportionment
output
with
meteorological
information
to
gain
more
insight
into
the
exact
nature
of
each
source.
Two
different
types
of
meteorological
data
are
used
in
this
analysis:
local
meteorological
data
and
back
trajectory
information.
Using
local
meteorological
data,
it
is
possible
to
make
simple
summary
statistics
that
can
reveal
patterns
in
the
source's
strength
related
to
wind
direction,
temperature,
and
pressure.
It
is
also
simple
to
examine
seasonal
patterns
and
weekday/
weekend
effects
using
simple
summary
statistics.
Back
trajectory
information
giving
the
likely
spatial
path
followed
by
air
particles
in
the
days
before
arriving
at
the
receptor
can
be
used
to
search
more
globally
for
source
regions
for
each
of
the
identified
sources.
As
a
final
check
on
two
67
of
the
types
of
sources
identified,
sulfate
and
nitrate
sources,
source
regions
identified
by
the
back
trajectory
analysis
are
compared
to
emissions
inventories.

Local
Meteorological
Data
At
each
site
and
for
each
source,
the
source
strength
on
each
day
was
paired
with
local
meteorological
variables
from
the
closest
national
weather
station
(
see
Table
VII­
3).
In
addition,
variables
for
the
day
of
the
week
and
the
season
of
the
year
were
added
to
the
analysis.

Pollution
roses
were
created
for
each
source
at
each
site.
Pollution
roses
show
the
mean
source
strength
relative
to
the
overall
source
strength
by
direction
and
wind
category:
1
to
5
mi/
hr,
5
to
10
mi/
hr,
and
10+
mi/
hr.
Figure
VII­
2
shows
a
pollution
rose
for
a
zinc
source
identified
at
the
St.
Louis
site
(
there
is
a
zinc
smelter
in
the
vicinity).
The
rose
clearly
indicates
that
the
zinc
signature
is
strongest
when
the
wind
is
blowing
from
the
east.

Figure
VII­
2.
Pollution
rose
for
St.
Louis,
Missouri,
zinc
source.

The
local
meteorological
data
were
also
used
to
compare
the
source
strength
with
temperature
and
pressure.
The
temperature
comparison
was
made
seasonally,
and
the
pressure
comparison
is
over
the
entire
modeling
period.
While
the
source
strengths
are
rarely
related
to
the
pressure,
it
was
felt
to
be
a
good
check
because
high
pressure
systems
tend
to
concentrate
the
pollution.
Hence,
a
strong
correlation
would
indicate
that
the
source
strength
is
being
driven
by
the
meteorological
conditions
rather
than
increased
source
activity
and/
or
favorable
wind
directions,
which
would
violate
the
assumptions
made
in
the
back
trajectory
and
pollution
rose
analyses.
A
summary
of
the
relationship
between
source
strength
and
temperature
and
pressure
68
may
be
found
in
Coutant,
et
al.
(
2003b).

In
addition
to
comparing
source
strength
to
meteorological
conditions,
an
investigation
was
conducted
into
the
differences
in
source
strength
between
weekdays
and
weekends.
This
analysis
was
performed
by
calculating
the
mean
source
strength
on
all
weekday
days
and
calculating
the
mean
source
strength
on
all
weekend
days.
A
table
summarizing
the
results
for
each
source
in
each
city
and
a
similar
analysis
performed
to
compare
source
strength
across
seasons
can
be
found
in
Coutant,
et
al.
(
2003b).

Back
Trajectory
Analysis
Back
trajectory
analysis
is
a
technique
used
to
find
probable
source
regions
for
each
source
identified
in
the
source
apportionment
analysis.
The
back
trajectory
analysis
uses
the
source
strengths
reported
by
the
PMF
model
along
with
computer
simulations
of
air
flow
patterns
to
determine
likely
source
locations
from
which
air
packets
may
have
traveled
to
the
receptor.
The
simulations
are
performed
using
NOAA's
HYSPLIT
model,
which
can
track
packets
of
air
backwards
in
time
over
long
distances.
By
examining
the
locations
from
which
packets
of
air
came
on
days
when
the
receptor
registers
a
high
source
strength,
likely
source
locations
can
be
identified.

For
each
source
at
a
site,
four
72­
hour
back
trajectories
were
calculated
for
each
day
on
which
a
speciated
PM2.5
reading
was
taken.
These
back
trajectories
were
then
divided
into
three
groups:
days
when
the
source's
strength
was
high
(
the
days
with
the
largest
20
percent
source
strength),
low
(
the
lowest
20
percent),
and
medium
(
all
other
trajectories).
The
conceptual
model
is
based
on
the
assumption
that
on
high
source
strength
days
the
air
must
pass
over
the
source.
Likewise,
on
the
majority
of
the
low
source
strength
days,
the
path
most
likely
did
not
pass
over
the
source
location.
The
analysis
tries
to
find
areas
that
are
associated
with
sources
by
considering
where
the
various
back
trajectories
from
the
high
strength
days
cross.

Two
methods
were
used
to
present
the
information
from
the
back
trajectory
analysis:
incremental
probability
fields
and
source
contribution
functions.
In
each
case,
a
fine
grid
(
approximately
80km
×
80km
grid
cells)
is
created
covering
the
region
spanned
by
the
majority
of
trajectories.
To
create
an
incremental
probability
field
each
grid
cell
is
considered
separately.
In
each
grid
cell,
the
number
of
hours
that
"
high
strength"
back
trajectories
spent
crossing
the
cell
is
divided
by
the
total
number
of
hours
in
back
trajectories
classified
as
high.
Next,
the
ratio
of
the
total
number
of
hours
that
back
trajectories
spent
crossing
the
cell
to
the
total
number
of
hours
in
any
back
trajectory
is
subtracted
from
this
number.
The
larger
(
more
positive)
this
number,
the
more
likely
the
existence
of
a
source
in
that
cell.
Figure
VII­
3
illustrates
an
incremental
probability
field
for
a
source
identified
in
Birmingham,
Alabama.
To
create
a
source
contribution
function,
each
cell
is
again
considered
separately.
In
each
cell,
the
number
of
hours
high
back
trajectories
spent
in
the
cell
is
divided
by
the
total
number
of
hours
any
back
trajectory
spent
in
the
cell.
The
larger
this
ratio
is,
the
more
likely
the
existence
of
a
source
in
that
cell.
Figure
VII­
4
illustrates
a
source
contribution
function
for
a
source
identified
in
Birmingham,
AL.
Note
that
both
the
incremental
probability
plot
and
source
contribution
plot
have
been
rescaled
so
that
the
results
may
be
compared
across
sites.
Details
of
the
rescaling
may
be
found
in
Coutant,
et
al.
(
2003b).
69
Figure
VII­
3.
Incremental
probability
contour
plot
for
Birmingham,
Alabama,
Source
1
­
Ammonium
Nitrate
Figure
VII­
4.
Source
contribution
contour
plot
for
Birmingham,
Alabama,
Source
1
­
Ammonium
Nitrate.
70
Pollution
roses
and
back
trajectory
analyses
are
not
well
suited
for
certain
source
categories.
Consider
a
source
like
crustal
dust.
The
source
is
"
located"
virtually
everywhere
on
land,
but
may
require
particular
winds
to
create
a
strong
source­
day
at
the
receptor.
Inland
areas
may
seem
not
to
be
associated
with
a
high
source
day
because
air
from
an
inland
area
may
be
associated
with
winds
that
are
too
low.
At
the
same
time,
a
grid
cell
over
the
ocean
could
be
associated
with
the
source,
because
air
passing
over
the
grid
cell
is
associated
with
strong
winds.
Another
problem
occurs
if
the
major
source
within
the
source
category
is
located
within
the
receptor
grid
(
or
even
within
a
few
grid
cells)
since
the
source
contribution
function
could
appear
to
be
less
than
20
percent
everywhere.
Finally,
since
the
analysis
is
based
on
80
km
grid
cells,
local
sources
may
not
be
indicated.

4.
Conclusions
This
source
apportionment
and
back
trajectory
study
analyzed
speciated
PM2.5
data
from
eight
of
EPA's
Trends
Sites.
For
each
site,
the
PM2.5
was
apportioned
into
six
to
eight
sources.
While
the
species
were
chosen
to
be
consistent
across
the
sites,
the
number
of
sources
used
in
the
modeling
was
allowed
to
vary
between
sites.
Eight
sources
may
be
the
limit
of
the
model
for
the
amount
of
data
that
were
available.
There
were
several
commonly
identified
sources,
each
of
which
was
expected
to
affect
the
receptor.

°
For
each
site,
a
coal
combustion
source
was
identified
with
a
mean
mass
concentration
of
between
4.5
and
7.7
µ
g/
m3.
The
back
trajectory
analyses
for
these
sources
are
somewhat
mixed.
The
back
trajectory
analysis
corresponds
well
to
the
utility
plants
in
the
Midwest,
Southeast,
and
eastern
seashore.
To
some
extent
in
St.
Louis
and
to
a
greater
extent
in
Houston,
the
high
concentrations
of
sulfate
are
partially
related
to
the
effects
of
high
pressure
systems.

°
For
each
site,
a
mobile
source
was
identified
with
a
mean
mass
concentration
of
2.5
to
6.5
µ
g/
m3.

°
Each
site
also
had
a
small
crustal
dirt
source
with
a
mean
mass
concentration
between
0.3
µ
g/
m3
and
1.5
µ
g/
m3.
The
1.5
µ
g/
m3
source
is
for
Washington,
D.
C.;
it
also
contains
diesel
components
and
is
probably
tied
to
a
large
road
construction
project
under
way
during
the
period
modeled.

°
Houston
had
a
very
small
nitrate
source
that
was
associated
with
a
marine
profile.
The
other
sites
had
nitrate
sources
that
ranged
from
1.2
to
5.0
µ
g/
m3.

°
Bronx,
Charlotte,
Houston,
and
Indianapolis
each
had
small
marine
and
industrial
salt
sources.
The
largest
is
for
Indianapolis,
but
the
source
profile
shows
signs
of
nitrate
substitution
for
the
chlorine
during
transport.

°
A
source
clearly
dominated
by
fireworks
was
found
for
Birmingham,
Charlotte,
Houston,
Indianapolis,
Milwaukee,
and
Washington,
D.
C.

°
Sources
that
appear
to
be
related
to
industrial
activity
were
found
in
Birmingham,
71
Bronx,
and
Houston.

°
Both
Bronx
and
Charlotte
had
oil
combustion
sources
with
mass
concentrations
of
1.2
and
1.9
µ
g/
m3,
respectively.

°
Charlotte
and
St.
Louis
had
zinc
sources
with
each
having
mass
concentrations
of
0.9
µ
g/
m3.
The
pollution
rose
for
the
St.
Louis
source
is
consistent
with
a
local
zinc
refinery.
In
addition,
St.
Louis
had
a
copper
smelting
(
0.6
µ
g/
m3)
and
steel
production
(
0.8
µ
g/
m3)
source.

°
Finally,
there
was
a
huge
spike
in
the
PM2.5
mass
on
July
7,
2002,
in
Washington,
D.
C.,
that
is
associated
with
Canadian
forest
fires.
This
source
is
apportioned
over
1.0
µ
g/
m3
of
the
16.6
µ
g/
m3
of
mass
observed
during
the
modeled
period.
The
Indianapolis
site
was
also
affected
by
these
fires,
but
to
a
much
lesser
extent.

The
various
analyses
are
generally
self­
consistent,
consistent
among
analysis
types,
consistent
with
expectations
for
the
sites,
and
consistent
from
site­
to­
site.
Taken
together,
they
show
that
a
monitoring
and
modeling
combination
provides
an
effective
means
of
understanding
the
source
categories
affecting
urban
areas.
The
coal
combustion
sources
account
for
about
onethird
of
the
PM2.5.
The
next
largest
portion
is
either
from
nitrate
or
mobile
sources.
All
three
of
these
source
categories
show
transport
components.
72
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74
Appendix
A
Detailed
Listing
by
County
of
PM2.5
Air
Quality
Data
Analysis
of
1999­
2001
data
and
2000­
2002
data
and
associated
2000
populations
75
Table
A­
1.
Counties
with
1999­
2001
PM2.5
Annual
Design
Values
Greater
Than
15
ug/
m3
and
Associated
Populations.

FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
Data
Completeness
Status*

01027
AL
Clay
County
15.5
14,254
NA
01033
AL
Colbert
County
15.3
54,984
NA
01049
AL
DeKalb
County
16.8
64,452
NA
01069
AL
Houston
County
16.3
88,787
NA
01073
AL
Jefferson
County
21.6
662,047
NA
01089
AL
Madison
County
15.5
276,700
NA
01097
AL
Mobile
County
15.3
399,843
NA
01101
AL
Montgomery
County
16.8
223,510
NA
01103
AL
Morgan
County
19.1
111,064
NA
01113
AL
Russell
County
18.4
49,756
NA
01117
AL
Shelby
County
17.2
143,293
NA
01121
AL
Talladega
County
17.8
80,321
NA
05035
AR
Crittenden
County
15.3
50,866
NAL
05119
AR
Pulaski
County
15.9
361,474
NAL
06007
CA
Butte
County
15.4
203,171
NA
06019
CA
Fresno
County
24
799,407
NA
06025
CA
Imperial
County
15.7
142,361
NA
06029
CA
Kern
County
23.7
661,645
NA
06031
CA
Kings
County
16.6
129,461
NA
06037
CA
Los
Angeles
County
25.9
9,519,338
NA
06047
CA
Merced
County
18.9
210,554
NA
06059
CA
Orange
County
22.4
2,846,289
NA
06065
CA
Riverside
County
29.8
1,545,387
NA
06071
CA
San
Bernardino
County
25.8
1,709,434
NA
06073
CA
San
Diego
County
17.1
2,813,833
NA
06077
CA
San
Joaquin
County
16.4
563,598
NA
06099
CA
Stanislaus
County
19.7
446,997
NA
06107
CA
Tulare
County
24.7
368,021
NA
09009
CT
New
Haven
County
16.8
824,008
NA
10003
DE
New
Castle
County
16.6
500,265
NA
11001
DC
District
of
Columbia
16.6
572,059
NA
13021
GA
Bibb
County
17.6
153,887
NA
13051
GA
Chatham
County
16.5
232,048
NA
13059
GA
Clarke
County
18.6
101,489
NA
13063
GA
Clayton
County
19.2
236,517
NA
13067
GA
Cobb
County
18.6
607,751
NA
13089
GA
DeKalb
County
19.6
665,865
NA
13095
GA
Dougherty
County
16.6
96,065
NA
13115
GA
Floyd
County
18.5
90,565
NA
13121
GA
Fulton
County
21.2
816,006
NA
13139
GA
Hall
County
17.2
139,277
NA
13215
GA
Muscogee
County
18
186,291
NA
13223
GA
Paulding
County
16.8
81,678
NA
13245
GA
Richmond
County
17.4
199,775
NA
13303
GA
Washington
County
16.5
21,176
NA
13319
GA
Wilkinson
County
18.1
10,220
NA
17031
IL
Cook
County
18.8
5,376,741
NA
17043
IL
DuPage
County
15.4
904,161
NA
17115
IL
Macon
County
15.4
114,706
NAL
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
Data
Completeness
Status*

76
17119
IL
Madison
County
17.3
258,941
NA
17163
IL
St.
Clair
County
17.4
256,082
NA
17197
IL
Will
County
15.9
502,266
NA
18019
IN
Clark
County
17.3
96,472
NA
18039
IN
Elkhart
County
15.1
182,791
NAL
18043
IN
Floyd
County
15.6
70,823
NA
18067
IN
Howard
County
15.4
84,964
NAL
18089
IN
Lake
County
16.3
484,564
NA
18097
IN
Marion
County
17
860,454
NA
18157
IN
Tippecanoe
County
15.4
148,955
NAL
18163
IN
Vanderburgh
County
16.9
171,922
NAL
18167
IN
Vigo
County
15.4
105,848
NAL
21013
KY
Bell
County
16.8
30,060
NAL
21019
KY
Boyd
County
15.5
49,752
NA
21029
KY
Bullitt
County
16
61,236
NA
21037
KY
Campbell
County
15.5
88,616
NA
21059
KY
Daviess
County
15.8
91,545
NAL
21067
KY
Fayette
County
16.8
260,512
NA
21111
KY
Jefferson
County
17.1
693,604
NA
21117
KY
Kenton
County
15.9
151,464
NA
21145
KY
McCracken
County
15.1
65,514
NA
21195
KY
Pike
County
16.1
68,736
NA
21227
KY
Warren
County
15.4
92,522
NA
24005
MD
Baltimore
County
16
754,292
NAL
24033
MD
Prince
George's
County
17.3
801,515
NAL
24510
MD
Baltimore
city
17.8
651,154
NA
25025
MA
Suffolk
County
16.1
689,807
NAL
26163
MI
Wayne
County
18.9
2,061,162
NA
28035
MS
Forrest
County
15.2
72,604
NAL
28049
MS
Hinds
County
15.1
250,800
NA
28067
MS
Jones
County
16.6
64,958
NA
28075
MS
Lauderdale
County
15.3
78,161
NAL
28087
MS
Lowndes
County
15.1
61,586
NAL
29510
MO
St.
Louis
city
16.3
348,189
NA
30053
MT
Lincoln
County
16.4
18,837
NA
34017
NJ
Hudson
County
17.5
608,975
NA
34039
NJ
Union
County
16.3
522,541
NA
36005
NY
Bronx
County
16.4
1,332,650
NAL
36061
NY
New
York
County
17.8
1,537,195
NA
37001
NC
Alamance
County
15.3
130,800
NA
37025
NC
Cabarrus
County
15.7
131,063
NA
37035
NC
Catawba
County
17.1
141,685
NA
37051
NC
Cumberland
County
15.4
302,963
NA
37057
NC
Davidson
County
17.3
147,246
NA
37063
NC
Durham
County
15.3
223,314
NA
37067
NC
Forsyth
County
16.2
306,067
NA
37071
NC
Gaston
County
15.3
190,365
NA
37081
NC
Guilford
County
16.3
421,048
NA
37087
NC
Haywood
County
15.4
54,033
NA
37111
NC
McDowell
County
16.2
42,151
NA
37119
NC
Mecklenburg
County
16.8
695,454
NA
37121
NC
Mitchell
County
15.5
15,687
NA
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
Data
Completeness
Status*

77
37183
NC
Wake
County
15.3
627,846
NA
37191
NC
Wayne
County
15.3
113,329
NA
39017
OH
Butler
County
17.4
332,807
NA
39035
OH
Cuyahoga
County
20.3
1,393,978
NA
39049
OH
Franklin
County
18.1
1,068,978
NA
39061
OH
Hamilton
County
19.3
845,303
NA
39081
OH
Jefferson
County
18.9
73,894
NA
39087
OH
Lawrence
County
17.4
62,319
NAL
39093
OH
Lorain
County
15.1
284,664
NA
39095
OH
Lucas
County
16.7
455,054
NAL
39099
OH
Mahoning
County
16.4
257,555
NA
39113
OH
Montgomery
County
17.6
559,062
NA
39133
OH
Portage
County
15.3
152,061
NA
39145
OH
Scioto
County
20
79,195
NA
39151
OH
Stark
County
18.3
378,098
NA
39153
OH
Summit
County
17.3
542,899
NA
39155
OH
Trumbull
County
16.2
225,116
NA
42003
PA
Allegheny
County
21
1,281,666
NA
42011
PA
Berks
County
15.6
373,638
NA
42021
PA
Cambria
County
15.3
152,598
NA
42043
PA
Dauphin
County
15.5
251,798
NA
42071
PA
Lancaster
County
16.9
470,658
NA
42101
PA
Philadelphia
County
16.6
1,517,550
NA
42125
PA
Washington
County
15.5
202,897
NA
42129
PA
Westmoreland
County
15.6
369,993
NA
42133
PA
York
County
16.3
381,751
NA
45045
SC
Greenville
County
17
379,616
NA
45063
SC
Lexington
County
15.6
216,014
NA
45079
SC
Richland
County
15.4
320,677
NA
45083
SC
Spartanburg
County
15.4
253,791
NA
47037
TN
Davidson
County
17
569,891
NA
47065
TN
Hamilton
County
18.9
307,896
NA
47093
TN
Knox
County
20.4
382,032
NA
47145
TN
Roane
County
17
51,910
NA
47157
TN
Shelby
County
15.6
897,472
NA
47163
TN
Sullivan
County
17
153,048
NA
47165
TN
Sumner
County
15.7
130,449
NA
48201
TX
Harris
County
15.1
3,400,578
NAL
51520
VA
Bristol
city
16
17,367
NA
51770
VA
Roanoke
city
15.2
94,911
NA
54003
WV
Berkeley
County
16
75,905
NA
54009
WV
Brooke
County
17.4
25,447
NA
54011
WV
Cabell
County
17.8
96,784
NA
54029
WV
Hancock
County
17.4
32,667
NA
54039
WV
Kanawha
County
18.4
200,073
NA
54051
WV
Marshall
County
16.5
35,519
NA
54069
WV
Ohio
County
15.7
47,427
NA
54107
WV
Wood
County
17.6
87,986
NA
Counties:
149
Total
74,237,509
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
Data
Completeness
Status*

78
*
Where
NA
=
Meets
the
Appendix
N
completeness
criteria
or
an
approved
substitution
technique;
NAL
=
does
not
meet
criteria
Table
A.
2.
Summary
of
PM2.5
Counties
with
1999­
2001
Data
Not
Meeting
the
Completeness
Criteria:
20
Counties
with
at
least
one
sample
in
each
of
10
of
the
12
quarters
for
the
3­
year
period
(
1999­
2001)

1999­
2001
Design
Population
FIPS
Code
State
County
Value
2000
05035
AR
Crittenden
County
15.3
50,866
05119
AR
Pulaski
County
15.9
361,474
17115
IL
Macon
County
15.4
114,706
18039
IN
Elkhart
County
15.1
182,791
18067
IN
Howard
County
15.4
84,964
18157
IN
Tippecanoe
County
15.4
148,955
18163
IN
Vanderburgh
County
16.9
171,922
18167
IN
Vigo
County
15.4
105,848
21013
KY
Bell
County
16.8
30,060
21059
KY
Daviess
County
15.8
91,545
24005
MD
Baltimore
County
16
754,292
24033
MD
Prince
George's
County
17.3
801,515
25025
MA
Suffolk
County
16.1
689,807
28035
MS
Forrest
County
15.2
72,604
28075
MS
Lauderdale
County
15.3
78,161
28087
MS
Lowndes
County
15.1
61,586
36005
NY
Bronx
County
16.4
1,332,650
39087
OH
Lawrence
County
17.4
62,319
39095
OH
Lucas
County
16.7
455,054
48201
TX
Harris
County
15.1
3,400,578
Note:
These
counties
did
not
meet
strict
application
of
Appendix
N
completeness
criterua
nor
were
they
able
to
utilize
an
approved
data
substitution
technique;
however,
the
20
counties
listed
were
deemed
sufficiently
complete
to
be
included
in
the
IAQR
analyses.
Additional
counties
did
not
meet
completeness
criteria
(
strict
Appendix
N
interpretation,
or
an
approved
data
substitution
technique)
but
were
deemed
unusable
for
the
analyses.
79
Table
A­
3.
Counties
with
2000­
2002
PM2.5
Annual
Design
Values
Greater
Than
15
µ
G/
m3
and
Associated
Populations
2000­
2002
Design
Population
2000
FIPS
Code
State
County
Value
01049
ALABAMA
DE
KALB
15.4
64,452
01055
ALABAMA
ETOWAH
16.5
103,459
01073
ALABAMA
JEFFERSON
19
662,047
01101
ALABAMA
MONTGOMERY
15.2
223,510
01113
ALABAMA
RUSSELL
16.4
49,756
01121
ALABAMA
TALLADEGA
15.7
80,321
06019
CALIFORNIA
FRESNO
21.9
799,407
06025
CALIFORNIA
IMPERIAL
15.6
142,361
06029
CALIFORNIA
KERN
22.8
661,645
06031
CALIFORNIA
KINGS
19
129,461
06037
CALIFORNIA
LOS
ANGELES
24.4
9,519,338
06047
CALIFORNIA
MERCED
17.6
210,554
06059
CALIFORNIA
ORANGE
20.3
2,846,289
06065
CALIFORNIA
RIVERSIDE
28.9
1,545,387
06071
CALIFORNIA
SAN
BERNARDINO
25.9
1,709,434
06073
CALIFORNIA
SAN
DIEGO
16.4
2,813,833
06077
CALIFORNIA
SAN
JOAQUIN
15.3
563,598
06099
CALIFORNIA
STANISLAUS
17.7
446,997
06107
CALIFORNIA
TULARE
23.2
368,021
09009
CONNECTICUT
NEW
HAVEN
16.5
824,008
10003
DELAWARE
NEW
CASTLE
16.5
500,265
11001
DISTRICT
OF
COLUMBIA
WASHINGTON
16.4
572,059
13021
GEORGIA
BIBB
16.4
153,887
13059
GEORGIA
CLARKE
17
101,489
13063
GEORGIA
CLAYTON
17.3
236,517
13067
GEORGIA
COBB
17.1
607,751
13089
GEORGIA
DE
KALB
17.3
665,865
13115
GEORGIA
FLOYD
16.2
90,565
13121
GEORGIA
FULTON
19.3
816,006
13135
GEORGIA
GWINNETT
16.7
588,448
13139
GEORGIA
HALL
16.1
139,277
13215
GEORGIA
MUSCOGEE
16.3
186,291
13223
GEORGIA
PAULDING
15.2
81,678
13245
GEORGIA
RICHMOND
16
199,775
13295
GEORGIA
WALKER
16.4
61,053
13319
GEORGIA
WILKINSON
16.1
10,220
17031
ILLINOIS
COOK
18.1
5,376,741
17043
ILLINOIS
DU
PAGE
15.2
904,161
17119
ILLINOIS
MADISON
17.5
258,941
17163
ILLINOIS
ST
CLAIR
17
256,082
17197
ILLINOIS
WILL
15.5
502,266
18019
INDIANA
CLARK
17.2
96,472
18035
INDIANA
DELAWARE
15.1
118,769
18037
INDIANA
DUBOIS
16.7
39,674
18039
INDIANA
ELKHART
15.5
182,791
2000­
2002
Design
Population
2000
FIPS
Code
State
County
Value
80
18043
INDIANA
FLOYD
15.5
70,823
18067
INDIANA
HOWARD
15.1
84,964
18089
INDIANA
LAKE
17.7
484,564
18097
INDIANA
MARION
17
860,454
18163
INDIANA
VANDERBURGH
15.7
171,922
18167
INDIANA
VIGO
15.2
105,848
21013
KENTUCKY
BELL
16.2
30,060
21019
KENTUCKY
BOYD
15.7
49,752
21029
KENTUCKY
BULLITT
15.8
61,236
21037
KENTUCKY
CAMPBELL
15.3
88,616
21067
KENTUCKY
FAYETTE
16.5
260,512
21093
KENTUCKY
HARDIN
15.1
94,174
21111
KENTUCKY
JEFFERSON
17.3
693,604
21117
KENTUCKY
KENTON
15.7
151,464
24003
MARYLAND
ANNE
ARUNDEL
15.8
489,656
24005
MARYLAND
BALTIMORE
15.1
754,292
24033
MARYLAND
PRINCE
GEORGES
17.4
801,515
24510
MARYLAND
BALTIMORE
(
CITY)
17
651,154
26115
MICHIGAN
MONROE
15.6
145,945
26163
MICHIGAN
WAYNE
19.9
2,061,162
29510
MISSOURI
ST
LOUIS
(
CITY)
15.7
348,189
30053
MONTANA
LINCOLN
16.4
18,837
34017
NEW
JERSEY
HUDSON
15.5
608,975
34039
NEW
JERSEY
UNION
15.9
522,541
36005
NEW
YORK
BRONX
16.1
1,332,650
36061
NEW
YORK
NEW
YORK
17.6
1,537,195
37025
NORTH
CAROLINA
CABARRUS
15.1
131,063
37035
NORTH
CAROLINA
CATAWBA
16.4
141,685
37057
NORTH
CAROLINA
DAVIDSON
16.7
147,246
37067
NORTH
CAROLINA
FORSYTH
15.6
306,067
37111
NORTH
CAROLINA
MC
DOWELL
15.6
42,151
37119
NORTH
CAROLINA
MECKLENBURG
15.8
695,454
39017
OHIO
BUTLER
16.7
332,807
39035
OHIO
CUYAHOGA
19.1
1,393,978
39049
OHIO
FRANKLIN
17.1
1,068,978
39061
OHIO
HAMILTON
18.6
845,303
39081
OHIO
JEFFERSON
18.2
73,894
39087
OHIO
LAWRENCE
16.7
62,319
39099
OHIO
MAHONING
15.7
257,555
39113
OHIO
MONTGOMERY
15.6
559,062
39133
OHIO
PORTAGE
15.1
152,061
39145
OHIO
SCIOTO
17.5
79,195
39151
OHIO
STARK
17.9
378,098
39153
OHIO
SUMMIT
16.9
542,899
39155
OHIO
TRUMBULL
15.6
225,116
42003
PENNSYLVANIA
ALLEGHENY
21.4
1,281,666
42007
PENNSYLVANIA
BEAVER
16
181,412
42011
PENNSYLVANIA
BERKS
16.7
373,638
42021
PENNSYLVANIA
CAMBRIA
15.8
152,598
2000­
2002
Design
Population
2000
FIPS
Code
State
County
Value
81
42043
PENNSYLVANIA
DAUPHIN
15.6
251,798
42045
PENNSYLVANIA
DELAWARE
15.7
550,864
42071
PENNSYLVANIA
LANCASTER
17.1
470,658
42101
PENNSYLVANIA
PHILADELPHIA
16.8
1,517,550
42125
PENNSYLVANIA
WASHINGTON
15.7
202,897
42129
PENNSYLVANIA
WESTMORELAND
15.6
369,993
42133
PENNSYLVANIA
YORK
17.1
381,751
45045
SOUTH
CAROLINA
GREENVILLE
15.3
379,616
47037
TENNESSEE
DAVIDSON
15.3
569,891
47065
TENNESSEE
HAMILTON
16.9
307,896
47093
TENNESSEE
KNOX
18.4
382,032
47107
TENNESSEE
MC
MINN
16.1
49,015
47145
TENNESSEE
ROANE
15.4
51,910
47163
TENNESSEE
SULLIVAN
15.7
153,048
51520
VIRGINIA
BRISTOL
15.3
17,367
51770
VIRGINIA
ROANOKE
(
CITY)
15.1
94,911
51775
VIRGINIA
SALEM
15.3
24,747
54003
WEST
VIRGINIA
BERKELEY
16.2
75,905
54009
WEST
VIRGINIA
BROOKE
16.8
25,447
54011
WEST
VIRGINIA
CABELL
17.3
96,784
54029
WEST
VIRGINIA
HANCOCK
17.5
32,667
54039
WEST
VIRGINIA
KANAWHA
17.8
200,073
54049
WEST
VIRGINIA
MARION
15.7
56,598
54051
WEST
VIRGINIA
MARSHALL
16
35,519
54069
WEST
VIRGINIA
OHIO
15.3
47,427
54107
WEST
VIRGINIA
WOOD
17
87,986
Counties:
120
Total
population
64,849,620
82
Table
A­
4.
Summary
of
Counties
Potentially
Violating
the
PM2.5
NAAQS
for
2000­
2002
with
Incomplete
Data
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
01103
ALABAMA
MORGAN
18
111,064
05001
ARKANSAS
ARKANSAS
15.3
20,749
05003
ARKANSAS
ASHLEY
16.7
24,209
13153
GEORGIA
HOUSTON
15.5
110,765
18157
INDIANA
TIPPECANOE
15.4
148,955
21059
KENTUCKY
DAVIESS
15.8
91,545
21193
KENTUCKY
PERRY
15.6
29,390
25025
MASSACHUSETTS
SUFFOLK
15.1
689,807
29125
MISSOURI
MARIES
19.6
8,903
37081
NORTH
CAROLINA
GUILFORD
15.1
421,048
39023
OHIO
CLARK
15.4
144,742
39093
OHIO
LORAIN
15.1
284,664
42041
PENNSYLVANIA
CUMBERLAND
15.8
213,674
47009
TENNESSEE
BLOUNT
15.9
105,823
Counties:
14
Total
population
2,405,338
Note:
These
counties
did
not
meet
Appendix
N
completeness
criteria
nor
were
they
able
to
utilize
an
approved
data
substitution
technique.
Several
of
the
incomplete
counties
listed
have
subsequently
demonstrated
attainment
of
the
annual
standard
via
merging
of
sites.
Data
listed
may
not
be
representative
due
to
data
capture
limitations.
83
Table
A­
5.
Counties
Within
10%
of
the
Annual
PM2.5
NAAQS
for
2000­
2002
with
Complete
Data
Design
Population
FIPS
Code
State
County
Value
2000
01027
ALABAMA
CLAY
14.1
14,254
01053
ALABAMA
ESCAMBIA
13.7
38,440
01089
ALABAMA
MADISON
14.9
276,700
01117
ALABAMA
SHELBY
15
143,293
02090
ALASKA
FAIRBANKS
NORTH
STAR
13.6
82,840
05091
ARKANSAS
MILLER
13.6
40,443
05119
ARKANSAS
PULASKI
14.6
361,474
06007
CALIFORNIA
BUTTE
14.6
203,171
06111
CALIFORNIA
VENTURA
14.8
753,197
09001
CONNECTICUT
FAIRFIELD
13.7
882,567
10005
DELAWARE
SUSSEX
14.2
156,638
17115
ILLINOIS
MACON
14.5
114,706
17111
ILLINOIS
MC
HENRY
13.6
260,077
17113
ILLINOIS
MC
LEAN
14.2
150,433
17143
ILLINOIS
PEORIA
14.2
183,433
18003
INDIANA
ALLEN
14.8
331,849
18091
INDIANA
LA
PORTE
13.6
110,106
18127
INDIANA
PORTER
14.3
146,798
18141
INDIANA
ST
JOSEPH
14.1
265,559
20209
KANSAS
WYANDOTTE
13.5
157,882
21047
KENTUCKY
CHRISTIAN
14.1
72,265
21073
KENTUCKY
FRANKLIN
14.4
47,687
21101
KENTUCKY
HENDERSON
14.8
44,829
21151
KENTUCKY
MADISON
14.4
70,872
21195
KENTUCKY
PIKE
14.6
68,736
21227
KENTUCKY
WARREN
14.5
92,522
22033
LOUISIANA
EAST
BATON
ROUGE
13.6
412,852
24043
MARYLAND
WASHINGTON
14.8
131,923
25013
MASSACHUSETTS
HAMPDEN
13.8
456,228
26065
MICHIGAN
INGHAM
13.5
279,320
26077
MICHIGAN
KALAMAZOO
15
238,603
26081
MICHIGAN
KENT
13.9
574,335
26099
MICHIGAN
MACOMB
13.5
788,149
26139
MICHIGAN
OTTAWA
13.5
238,314
26147
MICHIGAN
ST
CLAIR
14
164,235
28035
MISSISSIPPI
FORREST
13.8
72,604
28049
MISSISSIPPI
HINDS
13.8
250,800
28067
MISSISSIPPI
JONES
15
64,958
28075
MISSISSIPPI
LAUDERDALE
13.7
78,161
28121
MISSISSIPPI
RANKIN
13.6
115,327
29095
MISSOURI
JACKSON
13.9
654,880
29097
MISSOURI
JASPER
14
104,686
29099
MISSOURI
JEFFERSON
14.9
198,099
29183
MISSOURI
ST
CHARLES
14.6
283,883
29189
MISSOURI
ST
LOUIS
14.5
1,016,315
29186
MISSOURI
STE
GENEVIEVE
14.1
17,842
34015
NEW
JERSEY
GLOUCESTER
14.2
254,673
Design
Population
FIPS
Code
State
County
Value
2000
84
34021
NEW
JERSEY
MERCER
14.5
350,761
34041
NEW
JERSEY
WARREN
13.6
102,437
36029
NEW
YORK
ERIE
15
950,265
36047
NEW
YORK
KINGS
14.6
2,465,326
36085
NEW
YORK
RICHMOND
14.4
443,728
37001
NORTH
CAROLINA
ALAMANCE
14.4
130,800
37021
NORTH
CAROLINA
BUNCOMBE
14.2
206,330
37033
NORTH
CAROLINA
CASWELL
14
23,501
37051
NORTH
CAROLINA
CUMBERLAND
14.7
302,963
37063
NORTH
CAROLINA
DURHAM
14.7
223,314
37071
NORTH
CAROLINA
GASTON
14.7
190,365
37087
NORTH
CAROLINA
HAYWOOD
14.6
54,033
37121
NORTH
CAROLINA
MITCHELL
14.8
15,687
37135
NORTH
CAROLINA
ORANGE
13.6
118,227
37183
NORTH
CAROLINA
WAKE
14.6
627,846
37191
NORTH
CAROLINA
WAYNE
14.6
113,329
39085
OHIO
LAKE
13.8
227,511
39095
OHIO
LUCAS
14.9
455,054
41039
OREGON
LANE
13.7
322,959
42077
PENNSYLVANIA
LEHIGH
14.3
312,090
42091
PENNSYLVANIA
MONTGOMERY
14.2
750,097
45043
SOUTH
CAROLINA
GEORGETOWN
13.5
55,797
45047
SOUTH
CAROLINA
GREENWOOD
14.1
66,271
45063
SOUTH
CAROLINA
LEXINGTON
14.6
216,014
45079
SOUTH
CAROLINA
RICHLAND
13.8
320,677
45083
SOUTH
CAROLINA
SPARTANBURG
14.5
253,791
47113
TENNESSEE
MADISON
13.5
91,837
47119
TENNESSEE
MAURY
13.6
69,498
47141
TENNESSEE
PUTNAM
14.4
62,315
47157
TENNESSEE
SHELBY
14.9
897,472
47165
TENNESSEE
SUMNER
14.3
130,449
48037
TEXAS
BOWIE
14.3
89,306
48113
TEXAS
DALLAS
13.6
2,218,899
48201
TEXAS
HARRIS
14.1
3,400,578
49035
UTAH
SALT
LAKE
14.6
898,387
51013
VIRGINIA
ARLINGTON
14.9
189,453
51041
VIRGINIA
CHESTERFIELD
14.2
259,903
51059
VIRGINIA
FAIRFAX
13.9
969,749
51087
VIRGINIA
HENRICO
14
262,300
51107
VIRGINIA
LOUDOUN
13.8
169,599
54033
WEST
VIRGINIA
HARRISON
14.5
68,652
54061
WEST
VIRGINIA
MONONGALIA
15
81,866
54081
WEST
VIRGINIA
RALEIGH
13.5
79,220
55079
WISCONSIN
MILWAUKEE
13.7
940,164
Counties:
91
Total
population
31,645,778
85
Table
A­
6.
Counties
Attaining
the
PM2.5
NAAQS
for
2000­
2002
with
Complete
Data
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
1003
ALABAMA
BALDWIN
11.8
140,415
1027
ALABAMA
CLAY
14.1
14,254
1053
ALABAMA
ESCAMBIA
13.7
38,440
1089
ALABAMA
MADISON
14.9
276,700
1097
ALABAMA
MOBILE
13.2
399,843
1117
ALABAMA
SHELBY
15
143,293
1119
ALABAMA
SUMTER
13.1
14,798
2020
ALASKA
ANCHORAGE
6.4
260,283
2090
ALASKA
FAIRBANKS
NORTH
STAR
13.6
82,840
2110
ALASKA
JUNEAU
6.1
30,711
2170
ALASKA
MATANUSKA­
SUSITNA
6
59,322
4013
ARIZONA
MARICOPA
10
3,072,149
4023
ARIZONA
SANTA
CRUZ
12
38,381
5031
ARKANSAS
CRAIGHEAD
12.8
82,148
5051
ARKANSAS
GARLAND
12
88,068
5089
ARKANSAS
MARION
9.4
16,140
5091
ARKANSAS
MILLER
13.6
40,443
5107
ARKANSAS
PHILLIPS
12.9
26,445
5113
ARKANSAS
POLK
11.7
20,229
5115
ARKANSAS
POPE
12.9
54,469
5119
ARKANSAS
PULASKI
14.6
361,474
5131
ARKANSAS
SEBASTIAN
13
115,071
5143
ARKANSAS
WASHINGTON
11.6
157,715
6001
CALIFORNIA
ALAMEDA
12.3
1,443,741
6007
CALIFORNIA
BUTTE
14.6
203,171
6009
CALIFORNIA
CALAVERAS
9
40,554
6011
CALIFORNIA
COLUSA
9.7
18,804
6013
CALIFORNIA
CONTRA
COSTA
11.3
948,816
6017
CALIFORNIA
EL
DORADO
7.8
156,299
6023
CALIFORNIA
HUMBOLDT
8.8
126,518
6053
CALIFORNIA
MONTEREY
8.6
401,762
6057
CALIFORNIA
NEVADA
8.6
92,033
6061
CALIFORNIA
PLACER
12.4
248,399
6067
CALIFORNIA
SACRAMENTO
12.7
1,223,499
6075
CALIFORNIA
SAN
FRANCISCO
12
776,733
6079
CALIFORNIA
SAN
LUIS
OBISPO
9.9
246,681
6081
CALIFORNIA
SAN
MATEO
11.2
707,161
6083
CALIFORNIA
SANTA
BARBARA
9.9
399,347
6085
CALIFORNIA
SANTA
CLARA
11.8
1,682,585
6087
CALIFORNIA
SANTA
CRUZ
8.5
255,602
6089
CALIFORNIA
SHASTA
9.6
163,256
6095
CALIFORNIA
SOLANO
12.6
394,542
6097
CALIFORNIA
SONOMA
10.5
458,614
6101
CALIFORNIA
SUTTER
11.8
78,930
6111
CALIFORNIA
VENTURA
14.8
753,197
6113
CALIFORNIA
YOLO
10.5
168,660
8005
COLORADO
ARAPAHOE
8.9
487,967
8013
COLORADO
BOULDER
9.5
291,288
Design
Population
FIPS
Code
State
County
Value
2000
86
8031
COLORADO
DENVER
10.9
554,636
8041
COLORADO
EL
PASO
7.7
516,929
8039
COLORADO
ELBERT
4.3
19,872
8051
COLORADO
GUNNISON
6.5
13,956
8067
COLORADO
LA
PLATA
5.3
43,941
8069
COLORADO
LARIMER
8.2
251,494
8077
COLORADO
MESA
7.7
116,255
8101
COLORADO
PUEBLO
8
141,472
8123
COLORADO
WELD
9.6
180,936
9001
CONNECTICUT
FAIRFIELD
13.7
882,567
9003
CONNECTICUT
HARTFORD
12.7
857,183
9011
CONNECTICUT
NEW
LONDON
11.8
259,088
10001
DELAWARE
KENT
13.4
126,697
10005
DELAWARE
SUSSEX
14.2
156,638
12001
FLORIDA
ALACHUA
10.7
217,955
12011
FLORIDA
BROWARD
8.7
1,623,018
12017
FLORIDA
CITRUS
9.6
118,085
12031
FLORIDA
DUVAL
10.8
778,879
12033
FLORIDA
ESCAMBIA
12.1
294,410
12057
FLORIDA
HILLSBOROUGH
12
998,948
12071
FLORIDA
LEE
8.9
440,888
12073
FLORIDA
LEON
13
239,452
12083
FLORIDA
MARION
10.4
258,916
12086
FLORIDA
Miami­
Dade
10.1
2,253,362
12095
FLORIDA
ORANGE
10.9
896,344
12099
FLORIDA
PALM
BEACH
8
1,131,184
12103
FLORIDA
PINELLAS
11.3
921,482
12105
FLORIDA
POLK
11.1
483,924
12115
FLORIDA
SARASOTA
9.9
325,957
12117
FLORIDA
SEMINOLE
9.8
365,196
12111
FLORIDA
ST
LUCIE
9
192,695
12127
FLORIDA
VOLUSIA
9.7
443,343
15003
HAWAII
HONOLULU
4.9
876,156
15009
HAWAII
MAUI
4.8
128,094
16001
IDAHO
ADA
9.7
300,904
16005
IDAHO
BANNOCK
9.7
75,565
16017
IDAHO
BONNER
8.7
36,835
16019
IDAHO
BONNEVILLE
7.6
82,522
16027
IDAHO
CANYON
10.2
131,441
16055
IDAHO
KOOTENAI
9.6
108,685
16069
IDAHO
NEZ
PERCE
9.4
37,410
16079
IDAHO
SHOSHONE
12.9
13,771
16083
IDAHO
TWIN
FALLS
7.2
64,284
17001
ILLINOIS
ADAMS
13
68,277
17019
ILLINOIS
CHAMPAIGN
13.2
179,669
17097
ILLINOIS
LAKE
13.1
644,356
17115
ILLINOIS
MACON
14.5
114,706
17111
ILLINOIS
MC
HENRY
13.6
260,077
17113
ILLINOIS
MC
LEAN
14.2
150,433
17143
ILLINOIS
PEORIA
14.2
183,433
17157
ILLINOIS
RANDOLPH
12.9
33,893
Design
Population
FIPS
Code
State
County
Value
2000
87
17167
ILLINOIS
SANGAMON
13.4
188,951
18003
INDIANA
ALLEN
14.8
331,849
18091
INDIANA
LA
PORTE
13.6
110,106
18127
INDIANA
PORTER
14.3
146,798
18141
INDIANA
ST
JOSEPH
14.1
265,559
19013
IOWA
BLACK
HAWK
11.4
128,012
19033
IOWA
CERRO
GORDO
10.4
46,447
19045
IOWA
CLINTON
12.1
50,149
19063
IOWA
EMMET
8.7
11,027
19103
IOWA
JOHNSON
11.3
111,006
19113
IOWA
LINN
11.2
191,701
19153
IOWA
POLK
10.6
374,601
19155
IOWA
POTTAWATTAMIE
10.3
87,704
19163
IOWA
SCOTT
12.7
158,668
19169
IOWA
STORY
10
79,981
19177
IOWA
VAN
BUREN
10.3
7,809
19193
IOWA
WOODBURY
9.9
103,877
20091
KANSAS
JOHNSON
11.9
451,086
20107
KANSAS
LINN
10.7
9,570
20173
KANSAS
SEDGWICK
11.3
452,869
20177
KANSAS
SHAWNEE
10.9
169,871
20191
KANSAS
SUMNER
10.4
25,946
20209
KANSAS
WYANDOTTE
13.5
157,882
21043
KENTUCKY
CARTER
13.1
26,889
21047
KENTUCKY
CHRISTIAN
14.1
72,265
21073
KENTUCKY
FRANKLIN
14.4
47,687
21101
KENTUCKY
HENDERSON
14.8
44,829
21151
KENTUCKY
MADISON
14.4
70,872
21195
KENTUCKY
PIKE
14.6
68,736
21227
KENTUCKY
WARREN
14.5
92,522
22017
LOUISIANA
CADDO
13.1
252,161
22019
LOUISIANA
CALCASIEU
12
183,577
22033
LOUISIANA
EAST
BATON
ROUGE
13.6
412,852
22047
LOUISIANA
IBERVILLE
12.9
33,320
22051
LOUISIANA
JEFFERSON
12.5
455,466
22055
LOUISIANA
LAFAYETTE
11.5
190,503
22071
LOUISIANA
ORLEANS
12.8
484,674
22073
LOUISIANA
OUACHITA
12
147,250
22079
LOUISIANA
RAPIDES
12
126,337
22087
LOUISIANA
ST
BERNARD
11.3
67,229
22105
LOUISIANA
TANGIPAHOA
12
100,588
22109
LOUISIANA
TERREBONNE
10.9
104,503
22121
LOUISIANA
WEST
BATON
ROUGE
13.1
21,601
23001
MAINE
ANDROSCOGGIN
10.5
103,793
23003
MAINE
AROOSTOOK
11.2
73,938
23005
MAINE
CUMBERLAND
11.3
265,612
23009
MAINE
HANCOCK
6.1
51,791
23011
MAINE
KENNEBEC
10.2
117,114
23017
MAINE
OXFORD
10
54,755
23019
MAINE
PENOBSCOT
9.8
144,919
23031
MAINE
YORK
9.5
186,742
Design
Population
FIPS
Code
State
County
Value
2000
88
24015
MARYLAND
CECIL
13.4
85,951
24031
MARYLAND
MONTGOMERY
13.4
873,341
24043
MARYLAND
WASHINGTON
14.8
131,923
25013
MASSACHUSETTS
HAMPDEN
13.8
456,228
25015
MASSACHUSETTS
HAMPSHIRE
8.8
152,251
25027
MASSACHUSETTS
WORCESTER
12.2
750,963
26005
MICHIGAN
ALLEGAN
12.3
105,665
26021
MICHIGAN
BERRIEN
12.6
162,453
26049
MICHIGAN
GENESEE
12.9
436,141
26065
MICHIGAN
INGHAM
13.5
279,320
26077
MICHIGAN
KALAMAZOO
15
238,603
26081
MICHIGAN
KENT
13.9
574,335
26099
MICHIGAN
MACOMB
13.5
788,149
26121
MICHIGAN
MUSKEGON
12.2
170,200
26139
MICHIGAN
OTTAWA
13.5
238,314
26145
MICHIGAN
SAGINAW
10.8
210,039
26147
MICHIGAN
ST
CLAIR
14
164,235
26161
MICHIGAN
WASHTENAW
13.4
322,895
27037
MINNESOTA
DAKOTA
10.9
355,904
27053
MINNESOTA
HENNEPIN
11.1
1,116,200
27123
MINNESOTA
RAMSEY
12.6
511,035
27139
MINNESOTA
SCOTT
10.9
89,498
27137
MINNESOTA
ST
LOUIS
8.5
200,528
28001
MISSISSIPPI
ADAMS
11.6
34,340
28011
MISSISSIPPI
BOLIVAR
13.2
40,633
28033
MISSISSIPPI
DE
SOTO
13.1
107,199
28035
MISSISSIPPI
FORREST
13.8
72,604
28045
MISSISSIPPI
HANCOCK
10.7
42,967
28047
MISSISSIPPI
HARRISON
11.7
189,601
28049
MISSISSIPPI
HINDS
13.8
250,800
28059
MISSISSIPPI
JACKSON
12.2
131,420
28067
MISSISSIPPI
JONES
15
64,958
28075
MISSISSIPPI
LAUDERDALE
13.7
78,161
28081
MISSISSIPPI
LEE
13.1
75,755
28121
MISSISSIPPI
RANKIN
13.6
115,327
28123
MISSISSIPPI
SCOTT
12.1
28,423
28149
MISSISSIPPI
WARREN
12.8
49,644
29021
MISSOURI
BUCHANAN
12.6
85,998
29037
MISSOURI
CASS
11.4
82,092
29039
MISSOURI
CEDAR
11.7
13,733
29047
MISSOURI
CLAY
13
184,006
29077
MISSOURI
GREENE
12.4
240,391
29095
MISSOURI
JACKSON
13.9
654,880
29097
MISSOURI
JASPER
14
104,686
29099
MISSOURI
JEFFERSON
14.9
198,099
29137
MISSOURI
MONROE
11.2
9,311
29183
MISSOURI
ST
CHARLES
14.6
283,883
29189
MISSOURI
ST
LOUIS
14.5
1,016,315
29186
MISSOURI
STE
GENEVIEVE
14.1
17,842
30013
MONTANA
CASCADE
6
80,357
30029
MONTANA
FLATHEAD
8.3
74,471
Design
Population
FIPS
Code
State
County
Value
2000
89
30031
MONTANA
GALLATIN
9.2
67,831
30047
MONTANA
LAKE
10
26,507
30049
MONTANA
LEWIS
AND
CLARK
8.6
55,716
30063
MONTANA
MISSOULA
11.4
95,802
30081
MONTANA
RAVALLI
10.7
36,070
30087
MONTANA
ROSEBUD
7.1
9,383
30089
MONTANA
SANDERS
6.5
10,227
30111
MONTANA
YELLOWSTONE
7.5
129,352
31025
NEBRASKA
CASS
10.3
24,334
31055
NEBRASKA
DOUGLAS
11
463,585
31079
NEBRASKA
HALL
8.6
53,534
31109
NEBRASKA
LANCASTER
9.9
250,291
31111
NEBRASKA
LINCOLN
7
34,632
31157
NEBRASKA
SCOTTS
BLUFF
6.1
36,951
32003
NEVADA
CLARK
10.9
1,375,765
32031
NEVADA
WASHOE
9.5
339,486
34015
NEW
JERSEY
GLOUCESTER
14.2
254,673
34021
NEW
JERSEY
MERCER
14.5
350,761
34023
NEW
JERSEY
MIDDLESEX
12.7
750,162
34041
NEW
JERSEY
WARREN
13.6
102,437
35001
NEW
MEXICO
BERNALILLO
6.4
556,678
35005
NEW
MEXICO
CHAVES
6.7
61,382
35013
NEW
MEXICO
DONA
ANA
11.2
174,682
35017
NEW
MEXICO
GRANT
6
31,002
35025
NEW
MEXICO
LEA
6.7
55,511
35045
NEW
MEXICO
SAN
JUAN
6.4
113,801
35043
NEW
MEXICO
SANDOVAL
4.9
89,908
35049
NEW
MEXICO
SANTA
FE
4.9
129,292
36001
NEW
YORK
ALBANY
10.8
294,565
36007
NEW
YORK
BROOME
11.5
200,536
36013
NEW
YORK
CHAUTAUQUA
11.3
139,750
36027
NEW
YORK
DUTCHESS
11.3
280,150
36029
NEW
YORK
ERIE
15
950,265
36031
NEW
YORK
ESSEX
6.4
38,851
36047
NEW
YORK
KINGS
14.6
2,465,326
36055
NEW
YORK
MONROE
11.6
735,343
36059
NEW
YORK
NASSAU
12.3
1,334,544
36063
NEW
YORK
NIAGARA
12.6
219,846
36065
NEW
YORK
ONEIDA
12
235,469
36067
NEW
YORK
ONONDAGA
11.8
458,336
36071
NEW
YORK
ORANGE
11.7
341,367
36085
NEW
YORK
RICHMOND
14.4
443,728
36093
NEW
YORK
SCHENECTADY
11
146,555
36089
NEW
YORK
ST.
LAWRENCE
8.6
111,931
36101
NEW
YORK
STEUBEN
9.9
98,726
36103
NEW
YORK
SUFFOLK
12.5
1,419,369
37001
NORTH
CAROLINA
ALAMANCE
14.4
130,800
37021
NORTH
CAROLINA
BUNCOMBE
14.2
206,330
37033
NORTH
CAROLINA
CASWELL
14
23,501
37037
NORTH
CAROLINA
CHATHAM
12.8
49,329
37051
NORTH
CAROLINA
CUMBERLAND
14.7
302,963
37061
NORTH
CAROLINA
DUPLIN
12.6
49,063
Design
Population
FIPS
Code
State
County
Value
2000
90
37063
NORTH
CAROLINA
DURHAM
14.7
223,314
37071
NORTH
CAROLINA
GASTON
14.7
190,365
37087
NORTH
CAROLINA
HAYWOOD
14.6
54,033
37107
NORTH
CAROLINA
LENOIR
12
59,648
37121
NORTH
CAROLINA
MITCHELL
14.8
15,687
37123
NORTH
CAROLINA
MONTGOMERY
13
26,822
37129
NORTH
CAROLINA
NEW
HANOVER
11.4
160,307
37133
NORTH
CAROLINA
ONSLOW
11.6
150,355
37135
NORTH
CAROLINA
ORANGE
13.6
118,227
37139
NORTH
CAROLINA
PASQUOTANK
12
34,897
37147
NORTH
CAROLINA
PITT
12.9
133,798
37173
NORTH
CAROLINA
SWAIN
13.4
12,968
37183
NORTH
CAROLINA
WAKE
14.6
627,846
37191
NORTH
CAROLINA
WAYNE
14.6
113,329
38013
NORTH
DAKOTA
BURKE
5.6
2,242
38015
NORTH
DAKOTA
BURLEIGH
6.6
69,416
38017
NORTH
DAKOTA
CASS
7.9
123,138
38057
NORTH
DAKOTA
MERCER
6
8,644
39085
OHIO
LAKE
13.8
227,511
39095
OHIO
LUCAS
14.9
455,054
40019
OKLAHOMA
CARTER
10.3
45,621
40031
OKLAHOMA
COMANCHE
9.4
114,996
40039
OKLAHOMA
CUSTER
9
26,142
40047
OKLAHOMA
GARFIELD
10.2
57,813
40071
OKLAHOMA
KAY
10.6
48,080
40097
OKLAHOMA
MAYES
11.9
38,369
40101
OKLAHOMA
MUSKOGEE
12.1
69,451
40109
OKLAHOMA
OKLAHOMA
10.7
660,448
40115
OKLAHOMA
OTTAWA
11.9
33,194
40119
OKLAHOMA
PAYNE
10.2
68,190
40121
OKLAHOMA
PITTSBURG
11.4
43,953
40143
OKLAHOMA
TULSA
12.6
563,299
41003
OREGON
BENTON
7.6
78,153
41009
OREGON
COLUMBIA
6.5
43,560
41025
OREGON
HARNEY
9.4
7,609
41029
OREGON
JACKSON
12
181,269
41035
OREGON
KLAMATH
11.8
63,775
41037
OREGON
LAKE
7.7
7,422
41039
OREGON
LANE
13.7
322,959
41043
OREGON
LINN
8.5
103,069
41047
OREGON
MARION
8.4
284,834
41051
OREGON
MULTNOMAH
9
660,486
41059
OREGON
UMATILLA
9
70,548
41061
OREGON
UNION
6.8
24,530
41065
OREGON
WASCO
8.3
23,791
41067
OREGON
WASHINGTON
9.8
445,342
42001
PENNSYLVANIA
ADAMS
13.3
91,292
42069
PENNSYLVANIA
LACKAWANNA
12.4
213,295
42077
PENNSYLVANIA
LEHIGH
14.3
312,090
42079
PENNSYLVANIA
LUZERNE
12.9
319,250
42091
PENNSYLVANIA
MONTGOMERY
14.2
750,097
42099
PENNSYLVANIA
PERRY
12.7
43,602
Design
Population
FIPS
Code
State
County
Value
2000
91
72021
PUERTO
RICO
BAYAMON
7
224,044
72053
PUERTO
RICO
FAJARDO
5.2
40,712
72057
PUERTO
RICO
GUAYAMA
6.9
44,301
72059
PUERTO
RICO
GUAYANILLA
7.2
23,072
72061
PUERTO
RICO
GUAYNABO
9.6
100,053
72097
PUERTO
RICO
MAYAGUEZ
8.1
98,434
72113
PUERTO
RICO
PONCE
7.6
186,475
72127
PUERTO
RICO
SAN
JUAN
9.4
434,374
44003
RHODE
ISLAND
KENT
9
167,090
44007
RHODE
ISLAND
PROVIDENCE
11.3
621,602
44009
RHODE
ISLAND
WASHINGTON
8.8
123,546
45013
SOUTH
CAROLINA
BEAUFORT
11.4
120,937
45019
SOUTH
CAROLINA
CHARLESTON
12.4
309,969
45025
SOUTH
CAROLINA
CHESTERFIELD
12.7
42,768
45037
SOUTH
CAROLINA
EDGEFIELD
13.3
24,595
45041
SOUTH
CAROLINA
FLORENCE
13.3
125,761
45043
SOUTH
CAROLINA
GEORGETOWN
13.5
55,797
45047
SOUTH
CAROLINA
GREENWOOD
14.1
66,271
45063
SOUTH
CAROLINA
LEXINGTON
14.6
216,014
45073
SOUTH
CAROLINA
OCONEE
11.6
66,215
45079
SOUTH
CAROLINA
RICHLAND
13.8
320,677
45083
SOUTH
CAROLINA
SPARTANBURG
14.5
253,791
46011
SOUTH
DAKOTA
BROOKINGS
9.1
28,220
46099
SOUTH
DAKOTA
MINNEHAHA
9.6
148,281
46103
SOUTH
DAKOTA
PENNINGTON
7.9
88,565
47045
TENNESSEE
DYER
12.7
37,279
47099
TENNESSEE
LAWRENCE
12.6
39,926
47113
TENNESSEE
MADISON
13.5
91,837
47119
TENNESSEE
MAURY
13.6
69,498
47125
TENNESSEE
MONTGOMERY
13.3
134,768
47141
TENNESSEE
PUTNAM
14.4
62,315
47157
TENNESSEE
SHELBY
14.9
897,472
47165
TENNESSEE
SUMNER
14.3
130,449
48037
TEXAS
BOWIE
14.3
89,306
48061
TEXAS
CAMERON
9.7
335,227
48085
TEXAS
COLLIN
11.6
491,675
48113
TEXAS
DALLAS
13.6
2,218,899
48141
TEXAS
EL
PASO
10.1
679,622
48167
TEXAS
GALVESTON
11.1
250,158
48183
TEXAS
GREGG
12.6
111,379
48201
TEXAS
HARRIS
14.1
3,400,578
48303
TEXAS
LUBBOCK
7.5
242,628
48439
TEXAS
TARRANT
12.3
1,446,219
48453
TEXAS
TRAVIS
11.5
812,280
48479
TEXAS
WEBB
10.8
193,117
49011
UTAH
DAVIS
10
238,994
49035
UTAH
SALT
LAKE
14.6
898,387
49045
UTAH
TOOELE
8.1
40,735
49049
UTAH
UTAH
11.2
368,536
49057
UTAH
WEBER
10.3
196,533
50003
VERMONT
BENNINGTON
10.2
36,994
50007
VERMONT
CHITTENDEN
9.3
146,571
Design
Population
FIPS
Code
State
County
Value
2000
92
50021
VERMONT
RUTLAND
11.6
63,400
50023
VERMONT
WASHINGTON
10.6
58,039
51013
VIRGINIA
ARLINGTON
14.9
189,453
51036
VIRGINIA
CHARLES
CITY
13.3
6,926
51550
VIRGINIA
CHESAPEAKE
13
199,184
51041
VIRGINIA
CHESTERFIELD
14.2
259,903
51059
VIRGINIA
FAIRFAX
13.9
969,749
51650
VIRGINIA
HAMPTON
12.9
146,437
51087
VIRGINIA
HENRICO
14
262,300
51107
VIRGINIA
LOUDOUN
13.8
169,599
51700
VIRGINIA
NEWPORT
NEWS
12.4
180,150
51710
VIRGINIA
NORFOLK
13.3
234,403
51139
VIRGINIA
PAGE
13.4
23,177
51810
VIRGINIA
VIRGINIA
BEACH
12.8
425,257
53005
WASHINGTON
BENTON
7.2
142,475
53011
WASHINGTON
CLARK
10.1
345,238
53033
WASHINGTON
KING
11.8
1,737,034
53053
WASHINGTON
PIERCE
11.7
700,820
53061
WASHINGTON
SNOHOMISH
11.8
606,024
53063
WASHINGTON
SPOKANE
10.4
417,939
53067
WASHINGTON
THURSTON
9.8
207,355
53073
WASHINGTON
WHATCOM
7.8
166,814
54033
WEST
VIRGINIA
HARRISON
14.5
68,652
54055
WEST
VIRGINIA
MERCER
13.4
62,980
54061
WEST
VIRGINIA
MONONGALIA
15
81,866
54081
WEST
VIRGINIA
RALEIGH
13.5
79,220
54089
WEST
VIRGINIA
SUMMERS
10.4
12,999
55009
WISCONSIN
BROWN
11.6
226,778
55025
WISCONSIN
DANE
12.8
426,526
55027
WISCONSIN
DODGE
11.4
85,897
55029
WISCONSIN
DOOR
7.6
27,961
55031
WISCONSIN
DOUGLAS
8
43,287
55043
WISCONSIN
GRANT
11.7
49,597
55055
WISCONSIN
JEFFERSON
11.8
74,021
55059
WISCONSIN
KENOSHA
11.9
149,577
55071
WISCONSIN
MANITOWOC
10.1
82,887
55079
WISCONSIN
MILWAUKEE
13.7
940,164
55087
WISCONSIN
OUTAGAMIE
11.1
160,971
55089
WISCONSIN
OZAUKEE
11.3
82,317
55109
WISCONSIN
ST
CROIX
9.9
63,155
55125
WISCONSIN
VILAS
5.8
21,033
55133
WISCONSIN
WAUKESHA
13.4
360,767
55139
WISCONSIN
WINNEBAGO
10.8
156,763
55141
WISCONSIN
WOOD
10.4
75,555
56005
WYOMING
CAMPBELL
6.3
33,698
56021
WYOMING
LARAMIE
5.1
81,607
56033
WYOMING
SHERIDAN
11.1
26,560
Counties:
404
Total
population
110,864,052
93
94
Table
A­
7.
Counties
with
Design
Values
Below
the
PM2.5
NAAQS
for
2000­
2002
with
Incomplete
Data
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
01033
ALABAMA
COLBERT
13.7
54,984
01069
ALABAMA
HOUSTON
14.1
88,787
01125
ALABAMA
TUSCALOOSA
13.9
164,875
01127
ALABAMA
WALKER
14.9
70,713
02130
ALASKA
KETCHIKAN
GATEWAY
5.3
14,070
02290
ALASKA
YUKON­
KOYUKUK
2
6,551
04003
ARIZONA
COCHISE
7.8
117,755
04005
ARIZONA
COCONINO
7.1
116,320
04007
ARIZONA
GILA
9.7
51,335
04019
ARIZONA
PIMA
7.2
843,746
04021
ARIZONA
PINAL
8.1
179,727
05035
ARKANSAS
CRITTENDEN
14
50,866
05045
ARKANSAS
FAULKNER
13.1
86,014
05069
ARKANSAS
JEFFERSON
15
84,278
05093
ARKANSAS
MISSISSIPPI
13.2
51,979
05139
ARKANSAS
UNION
13.5
45,629
05145
ARKANSAS
WHITE
12.9
67,165
06027
CALIFORNIA
INYO
7.8
17,945
06033
CALIFORNIA
LAKE
4.9
58,309
06045
CALIFORNIA
MENDOCINO
7
86,265
06049
CALIFORNIA
MODOC
7
9,449
06051
CALIFORNIA
MONO
14.1
12,853
06063
CALIFORNIA
PLUMAS
14.2
20,824
08001
COLORADO
ADAMS
11.7
363,857
08007
COLORADO
ARCHULETA
6.8
9,898
08029
COLORADO
DELTA
7.7
27,834
08035
COLORADO
DOUGLAS
5.7
175,766
08107
COLORADO
ROUTT
7.5
19,690
08113
COLORADO
SAN
MIGUEL
5.7
6,594
12005
FLORIDA
BAY
11.2
148,217
12009
FLORIDA
BREVARD
8.7
476,230
12081
FLORIDA
MANATEE
9.9
264,002
12113
FLORIDA
SANTA
ROSA
9.3
117,743
13051
GEORGIA
CHATHAM
14.7
232,048
13095
GEORGIA
DOUGHERTY
15
96,065
13127
GEORGIA
GLYNN
12.5
67,568
13185
GEORGIA
LOWNDES
13.2
92,115
13303
GEORGIA
WASHINGTON
15
21,176
16015
IDAHO
BOISE
10.5
6,670
16021
IDAHO
BOUNDARY
9.4
9,871
16029
IDAHO
CARIBOU
4.7
7,304
16057
IDAHO
LATAH
6.6
34,935
16077
IDAHO
POWER
14.7
7,538
16085
IDAHO
VALLEY
10.2
7,651
17089
ILLINOIS
KANE
14.6
404,119
17099
ILLINOIS
LA
SALLE
14.8
111,509
17161
ILLINOIS
ROCK
ISLAND
13.6
149,374
17201
ILLINOIS
WINNEBAGO
14.6
278,418
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
95
18065
INDIANA
HENRY
13.4
48,508
18083
INDIANA
KNOX
13.8
39,256
18095
INDIANA
MADISON
15
133,358
18147
INDIANA
SPENCER
15
20,391
19137
IOWA
MONTGOMERY
10
11,771
19139
IOWA
MUSCATINE
13.3
41,722
21125
KENTUCKY
LAUREL
13
52,715
21145
KENTUCKY
MC
CRACKEN
14.1
65,514
22029
LOUISIANA
CONCORDIA
13.1
20,247
23013
MAINE
KNOX
6.6
39,618
24025
MARYLAND
HARFORD
14
218,590
25003
MASSACHUSETTS
BERKSHIRE
12.4
134,953
25005
MASSACHUSETTS
BRISTOL
12.2
534,678
25009
MASSACHUSETTS
ESSEX
11.3
723,419
25017
MASSACHUSETTS
MIDDLESEX
10.8
1,465,396
25021
MASSACHUSETTS
NORFOLK
11.5
650,308
25023
MASSACHUSETTS
PLYMOUTH
11.6
472,822
26007
MICHIGAN
ALPENA
8.9
31,314
26017
MICHIGAN
BAY
11
110,157
26033
MICHIGAN
CHIPPEWA
8.1
38,543
26055
MICHIGAN
GRAND
TRAVERSE
8.7
77,654
26125
MICHIGAN
OAKLAND
15
1,194,156
27035
MINNESOTA
CROW
WING
11.8
55,099
27041
MINNESOTA
DOUGLAS
7.2
32,821
27047
MINNESOTA
FREEBORN
13.1
32,584
27061
MINNESOTA
ITASCA
9
43,992
27067
MINNESOTA
KANDIYOHI
10.2
41,203
27075
MINNESOTA
LAKE
6.6
11,058
27085
MINNESOTA
MC
LEOD
11
34,898
27095
MINNESOTA
MILLE
LACS
7.7
22,330
27103
MINNESOTA
NICOLLET
10.8
29,771
27109
MINNESOTA
OLMSTED
11.4
124,277
27111
MINNESOTA
OTTER
TAIL
9.5
57,159
27145
MINNESOTA
STEARNS
10.4
133,166
27163
MINNESOTA
WASHINGTON
11.8
201,130
27171
MINNESOTA
WRIGHT
11.5
89,986
28087
MISSISSIPPI
LOWNDES
14.1
61,586
28109
MISSISSIPPI
PEARL
RIVER
12.4
48,621
29019
MISSOURI
BOONE
12.4
135,454
29091
MISSOURI
HOWELL
14.3
37,238
29129
MISSOURI
MERCER
11.7
3,757
30093
MONTANA
SILVER
BOW
9.1
34,606
31027
NEBRASKA
CEDAR
8.5
9,615
31031
NEBRASKA
CHERRY
4.5
6,148
31049
NEBRASKA
DEUEL
5.7
2,098
31153
NEBRASKA
SARPY
10.5
122,595
31177
NEBRASKA
WASHINGTON
10
18,780
32005
NEVADA
DOUGLAS
3.5
41,259
33001
NEW
HAMPSHIRE
BELKNAP
10.8
56,325
33005
NEW
HAMPSHIRE
CHESHIRE
12
73,825
33007
NEW
HAMPSHIRE
COOS
9.7
33,111
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
96
33009
NEW
HAMPSHIRE
GRAFTON
8.3
81,743
33011
NEW
HAMPSHIRE
HILLSBOROUGH
11.3
380,841
33013
NEW
HAMPSHIRE
MERRIMACK
10
136,225
33015
NEW
HAMPSHIRE
ROCKINGHAM
11.4
277,359
33019
NEW
HAMPSHIRE
SULLIVAN
9.7
40,458
34001
NEW
JERSEY
ATLANTIC
11.4
252,552
34003
NEW
JERSEY
BERGEN
14.2
884,118
34007
NEW
JERSEY
CAMDEN
14.8
508,932
34013
NEW
JERSEY
ESSEX
15
793,633
34027
NEW
JERSEY
MORRIS
12.8
470,212
34029
NEW
JERSEY
OCEAN
11.6
510,916
34031
NEW
JERSEY
PASSAIC
13.4
489,049
36081
NEW
YORK
QUEENS
13.8
2,229,379
36119
NEW
YORK
WESTCHESTER
12.7
923,459
37065
NORTH
CAROLINA
EDGECOMBE
13.1
55,606
37099
NORTH
CAROLINA
JACKSON
13.7
33,121
37155
NORTH
CAROLINA
ROBESON
13.7
123,339
37189
NORTH
CAROLINA
WATAUGA
10.7
42,695
38007
NORTH
DAKOTA
BILLINGS
4.9
888
38035
NORTH
DAKOTA
GRAND
FORKS
8.2
66,109
38053
NORTH
DAKOTA
MC
KENZIE
5.3
5,737
38089
NORTH
DAKOTA
STARK
5.7
22,636
38091
NORTH
DAKOTA
STEELE
6.5
2,258
39009
OHIO
ATHENS
13
62,223
39135
OHIO
PREBLE
13.8
42,337
40015
OKLAHOMA
CADDO
8.8
30,150
40017
OKLAHOMA
CANADIAN
9.3
87,697
40021
OKLAHOMA
CHEROKEE
12.1
42,521
40081
OKLAHOMA
LINCOLN
10
32,080
40117
OKLAHOMA
PAWNEE
9.1
16,612
40125
OKLAHOMA
POTTAWATOMIE
10.8
65,521
40133
OKLAHOMA
SEMINOLE
9.9
24,894
41017
OREGON
DESCHUTES
8.5
115,367
41033
OREGON
JOSEPHINE
13.5
75,726
42017
PENNSYLVANIA
BUCKS
14.3
597,635
42027
PENNSYLVANIA
CENTRE
12.4
135,758
42029
PENNSYLVANIA
CHESTER
14.6
433,501
42049
PENNSYLVANIA
ERIE
13.7
280,843
42085
PENNSYLVANIA
MERCER
14.6
120,293
42095
PENNSYLVANIA
NORTHAMPTON
14.6
267,066
72069
PUERTO
RICO
HUMACAO
5.6
59,035
72081
PUERTO
RICO
LARES
6
34,415
45015
SOUTH
CAROLINA
BERKELEY
10.2
142,651
45029
SOUTH
CAROLINA
COLLETON
11.1
38,264
45051
SOUTH
CAROLINA
HORRY
10.6
196,629
45075
SOUTH
CAROLINA
ORANGEBURG
11.9
91,582
45091
SOUTH
CAROLINA
YORK
14.3
164,614
46013
SOUTH
DAKOTA
BROWN
8.5
35,460
46071
SOUTH
DAKOTA
JACKSON
5.4
2,930
46093
SOUTH
DAKOTA
MEADE
6.2
24,253
48029
TEXAS
BEXAR
10.1
1,392,931
2000­
2002
Design
Population
FIPS
Code
State
County
Value
2000
97
48039
TEXAS
BRAZORIA
10.1
241,767
48043
TEXAS
BREWSTER
6.2
8,866
48055
TEXAS
CALDWELL
9.6
32,194
48135
TEXAS
ECTOR
7.4
121,123
48139
TEXAS
ELLIS
11.8
111,360
48203
TEXAS
HARRISON
12.4
62,110
48215
TEXAS
HIDALGO
10.7
569,463
48243
TEXAS
JEFF
DAVIS
3.9
2,207
48245
TEXAS
JEFFERSON
11.4
252,051
48257
TEXAS
KAUFMAN
12.6
71,313
48315
TEXAS
MARION
11.4
10,941
48309
TEXAS
MC
LENNAN
10.2
213,517
48339
TEXAS
MONTGOMERY
12
293,768
48355
TEXAS
NUECES
10.3
313,645
48361
TEXAS
ORANGE
11.7
84,966
48375
TEXAS
POTTER
6.7
113,546
49003
UTAH
BOX
ELDER
9.4
42,745
49005
UTAH
CACHE
13
91,391
78001
VIRGIN
ISLANDS
ST
CROIX
6.9
49,725
78005
VIRGIN
ISLANDS
ST
THOMAS
7.5
44,372
51680
VIRGINIA
LYNCHBURG
14.7
65,269
51760
VIRGINIA
RICHMOND
(
CITY)
14.5
197,790
53001
WASHINGTON
ADAMS
7.5
16,428
53009
WASHINGTON
CLALLAM
11.8
64,525
53015
WASHINGTON
COWLITZ
8.8
92,948
53027
WASHINGTON
GRAYS
HARBOR
8.3
67,194
53031
WASHINGTON
JEFFERSON
9
25,953
53041
WASHINGTON
LEWIS
10.3
68,600
53045
WASHINGTON
MASON
6.2
49,405
53057
WASHINGTON
SKAGIT
6.8
102,979
53059
WASHINGTON
SKAMANIA
6.8
9,872
53065
WASHINGTON
STEVENS
9.7
40,066
53071
WASHINGTON
WALLA
WALLA
6.8
55,180
53075
WASHINGTON
WHITMAN
6.3
40,740
53077
WASHINGTON
YAKIMA
10.2
222,581
55003
WISCONSIN
ASHLAND
6.2
16,866
55105
WISCONSIN
ROCK
13.4
152,307
56009
WYOMING
CONVERSE
3.5
12,052
56013
WYOMING
FREMONT
13.4
35,804
56039
WYOMING
TETON
8
18,251
Counties:
190
Total
population
18,624,625
98
Appendix
B
Detailed
Listing
by
County
of
Ozone
Air
Quality
Data
Analysis
of
1999­
2001
data
and
2000­
2002
data
and
associated
2000
populations
99
Table
B­
1.
Counties
with
Design
Values
above
the
level
of
the
8­
hour
Ozone
Standard
(
1999­
2001)

FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
1073
ALABAMA
JEFFERSON
0.091
662,047
01089
ALABAMA
MADISON
0.087
276,700
01101
ALABAMA
MONTGOMERY
0.085
223,510
01117
ALABAMA
SHELBY
0.096
143,293
04013
ARIZONA
MARICOPA
0.085
3,072,149
05035
ARKANSAS
CRITTENDEN
0.092
50,866
05119
ARKANSAS
PULASKI
0.087
361,474
06005
CALIFORNIA
AMADOR
0.091
35,100
06009
CALIFORNIA
CALAVERAS
0.094
40,554
06017
CALIFORNIA
EL
DORADO
0.104
156,299
06019
CALIFORNIA
FRESNO
0.108
799,407
06025
CALIFORNIA
IMPERIAL
0.092
142,361
06029
CALIFORNIA
KERN
0.109
661,645
06031
CALIFORNIA
KINGS
0.098
129,461
06037
CALIFORNIA
LOS
ANGELES
0.105
9,519,338
06039
CALIFORNIA
MADERA
0.088
123,109
06043
CALIFORNIA
MARIPOSA
0.091
17,130
06047
CALIFORNIA
MERCED
0.101
210,554
06057
CALIFORNIA
NEVADA
0.096
92,033
06061
CALIFORNIA
PLACER
0.101
248,399
06065
CALIFORNIA
RIVERSIDE
0.111
1,545,387
6067
CALIFORNIA
SACRAMENTO
0.099
1,223,499
06071
CALIFORNIA
SAN
BERNARDINO
0.129
1,709,434
06073
CALIFORNIA
SAN
DIEGO
0.094
2,813,833
06099
CALIFORNIA
STANISLAUS
0.091
446,997
06103
CALIFORNIA
TEHAMA
0.086
56,039
06107
CALIFORNIA
TULARE
0.104
368,021
06109
CALIFORNIA
TUOLUMNE
0.092
54,501
06111
CALIFORNIA
VENTURA
0.101
753,197
09001
CONNECTICUT
FAIRFIELD
0.097
882,567
09003
CONNECTICUT
HARTFORD
0.088
857,183
09007
CONNECTICUT
MIDDLESEX
0.099
155,071
09009
CONNECTICUT
NEW
HAVEN
0.097
824,008
09011
CONNECTICUT
NEW
LONDON
0.090
259,088
09013
CONNECTICUT
TOLLAND
0.090
136,364
10001
DELAWARE
KENT
0.093
126,697
10003
DELAWARE
NEW
CASTLE
0.097
500,265
10005
DELAWARE
SUSSEX
0.095
156,638
11001
DISTRICT
OF
COLUMBIA
WASHINGTON
0.094
572,059
12033
FLORIDA
ESCAMBIA
0.088
294,410
13021
GEORGIA
BIBB
0.098
153,887
13067
GEORGIA
COBB
0.096
607,751
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
100
13077
GEORGIA
COWETA
0.096
89,215
13089
GEORGIA
DE
KALB
0.102
665,865
13097
GEORGIA
DOUGLAS
0.098
92,174
13113
GEORGIA
FAYETTE
0.099
91,263
13121
GEORGIA
FULTON
0.107
816,006
13135
GEORGIA
GWINNETT
0.094
588,448
13151
GEORGIA
HENRY
0.107
119,341
13215
GEORGIA
MUSCOGEE
0.090
186,291
13223
GEORGIA
PAULDING
0.092
81,678
13245
GEORGIA
RICHMOND
0.087
199,775
13247
GEORGIA
ROCKDALE
0.104
70,111
13261
GEORGIA
SUMTER
0.086
33,200
17031
ILLINOIS
COOK
0.088
5,376,741
17083
ILLINOIS
JERSEY
0.089
21,668
18003
INDIANA
ALLEN
0.087
331,849
18019
INDIANA
CLARK
0.086
96,472
18057
INDIANA
HAMILTON
0.091
182,740
18059
INDIANA
HANCOCK
0.089
55,391
18081
INDIANA
JOHNSON
0.087
115,209
18089
INDIANA
LAKE
0.090
484,564
18091
INDIANA
LA
PORTE
0.085
110,106
18095
INDIANA
MADISON
0.087
133,358
18097
INDIANA
MARION
0.088
860,454
18109
INDIANA
MORGAN
0.087
66,689
18123
INDIANA
PERRY
0.090
18,899
18127
INDIANA
PORTER
0.090
146,798
18129
INDIANA
POSEY
0.086
27,061
21015
KENTUCKY
BOONE
0.085
85,991
21019
KENTUCKY
BOYD
0.086
49,752
21029
KENTUCKY
BULLITT
0.085
61,236
21047
KENTUCKY
CHRISTIAN
0.085
72,265
21061
KENTUCKY
EDMONSON
0.088
11,644
21089
KENTUCKY
GREENUP
0.086
36,891
21111
KENTUCKY
JEFFERSON
0.089
693,604
21117
KENTUCKY
KENTON
0.086
151,464
21139
KENTUCKY
LIVINGSTON
0.087
9,804
21149
KENTUCKY
MC
LEAN
0.086
9,938
21185
KENTUCKY
OLDHAM
0.091
46,178
21199
KENTUCKY
PULASKI
0.086
56,217
21213
KENTUCKY
SIMPSON
0.088
16,405
22005
LOUISIANA
ASCENSION
0.086
76,627
22015
LOUISIANA
BOSSIER
0.090
98,310
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
101
22019
LOUISIANA
CALCASIEU
0.086
183,577
22033
LOUISIANA
EAST
BATON
ROUGE
0.091
412,852
22047
LOUISIANA
IBERVILLE
0.086
33,320
22051
LOUISIANA
JEFFERSON
0.089
455,466
22063
LOUISIANA
LIVINGSTON
0.088
91,814
22089
LOUISIANA
ST
CHARLES
0.086
48,072
22095
LOUISIANA
ST
JOHN
THE
BAPTIST
PAR
0.086
43,044
22121
LOUISIANA
WEST
BATON
ROUGE
0.088
21,601
23009
MAINE
HANCOCK
0.089
51,791
23031
MAINE
YORK
0.086
186,742
24003
MARYLAND
ANNE
ARUNDEL
0.103
489,656
24005
MARYLAND
BALTIMORE
0.093
754,292
24009
MARYLAND
CALVERT
0.089
74,563
24013
MARYLAND
CARROLL
0.093
150,897
24015
MARYLAND
CECIL
0.106
85,951
24017
MARYLAND
CHARLES
0.096
120,546
24021
MARYLAND
FREDERICK
0.091
195,277
24025
MARYLAND
HARFORD
0.104
218,590
24029
MARYLAND
KENT
0.100
19,197
24031
MARYLAND
MONTGOMERY
0.089
873,341
24033
MARYLAND
PRINCE
GEORGES
0.097
801,515
24043
MARYLAND
WASHINGTON
0.085
131,923
25001
MASSACHUSETTS
BARNSTABLE
0.096
222,230
25005
MASSACHUSETTS
BRISTOL
0.093
534,678
25009
MASSACHUSETTS
ESSEX
0.086
723,419
25013
MASSACHUSETTS
HAMPDEN
0.085
456,228
25015
MASSACHUSETTS
HAMPSHIRE
0.087
152,251
25017
MASSACHUSETTS
MIDDLESEX
0.088
1,465,396
25027
MASSACHUSETTS
WORCESTER
0.085
750,963
26005
MICHIGAN
ALLEGAN
0.087
105,665
26019
MICHIGAN
BENZIE
0.089
15,998
26021
MICHIGAN
BERRIEN
0.087
162,453
26027
MICHIGAN
CASS
0.087
51,104
26049
MICHIGAN
GENESEE
0.086
436,141
26099
MICHIGAN
MACOMB
0.088
788,149
26105
MICHIGAN
MASON
0.091
28,274
26121
MICHIGAN
MUSKEGON
0.092
170,200
26147
MICHIGAN
ST
CLAIR
0.085
164,235
26163
MICHIGAN
WAYNE
0.088
2,061,162
28033
MISSISSIPPI
DE
SOTO
0.086
107,199
28045
MISSISSIPPI
HANCOCK
0.087
42,967
28047
MISSISSIPPI
HARRISON
0.089
189,601
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
102
28059
MISSISSIPPI
JACKSON
0.087
131,420
28081
MISSISSIPPI
LEE
0.086
75,755
29099
MISSOURI
JEFFERSON
0.089
198,099
29183
MISSOURI
ST
CHARLES
0.090
283,883
29186
MISSOURI
STE
GENEVIEVE
0.085
17,842
29189
MISSOURI
ST
LOUIS
0.088
1,016,315
34001
NEW
JERSEY
ATLANTIC
0.091
252,552
34007
NEW
JERSEY
CAMDEN
0.103
508,932
34011
NEW
JERSEY
CUMBERLAND
0.097
146,438
34015
NEW
JERSEY
GLOUCESTER
0.101
254,673
34017
NEW
JERSEY
HUDSON
0.093
608,975
34019
NEW
JERSEY
HUNTERDON
0.100
121,989
34021
NEW
JERSEY
MERCER
0.105
350,761
34023
NEW
JERSEY
MIDDLESEX
0.103
750,162
34025
NEW
JERSEY
MONMOUTH
0.094
615,301
34027
NEW
JERSEY
MORRIS
0.097
470,212
34029
NEW
JERSEY
OCEAN
0.109
510,916
34031
NEW
JERSEY
PASSAIC
0.089
489,049
36013
NEW
YORK
CHAUTAUQUA
0.089
139,750
36027
NEW
YORK
DUTCHESS
0.087
280,150
36029
NEW
YORK
ERIE
0.092
950,265
36045
NEW
YORK
JEFFERSON
0.087
111,738
36063
NEW
YORK
NIAGARA
0.087
219,846
36071
NEW
YORK
ORANGE
0.087
341,367
36079
NEW
YORK
PUTNAM
0.089
95,745
36081
NEW
YORK
QUEENS
0.086
2,229,379
36085
NEW
YORK
RICHMOND
0.098
443,728
36103
NEW
YORK
SUFFOLK
0.091
1,419,369
36119
NEW
YORK
WESTCHESTER
0.092
923,459
37003
NORTH
CAROLINA
ALEXANDER
0.087
33,603
37027
NORTH
CAROLINA
CALDWELL
0.087
77,415
37033
NORTH
CAROLINA
CASWELL
0.090
23,501
37051
NORTH
CAROLINA
CUMBERLAND
0.088
302,963
37059
NORTH
CAROLINA
DAVIE
0.096
34,835
37063
NORTH
CAROLINA
DURHAM
0.087
223,314
37065
NORTH
CAROLINA
EDGECOMBE
0.087
55,606
37067
NORTH
CAROLINA
FORSYTH
0.094
306,067
37069
NORTH
CAROLINA
FRANKLIN
0.086
47,260
37077
NORTH
CAROLINA
GRANVILLE
0.088
48,498
37081
NORTH
CAROLINA
GUILFORD
0.090
421,048
37087
NORTH
CAROLINA
HAYWOOD
0.087
54,033
37099
NORTH
CAROLINA
JACKSON
0.085
33,121
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
103
37101
NORTH
CAROLINA
JOHNSTON
0.087
121,965
37109
NORTH
CAROLINA
LINCOLN
0.091
63,780
37119
NORTH
CAROLINA
MECKLENBURG
0.101
695,454
37145
NORTH
CAROLINA
PERSON
0.089
35,623
37157
NORTH
CAROLINA
ROCKINGHAM
0.085
91,928
37159
NORTH
CAROLINA
ROWAN
0.099
130,340
37179
NORTH
CAROLINA
UNION
0.087
123,677
37183
NORTH
CAROLINA
WAKE
0.094
627,846
37199
NORTH
CAROLINA
YANCEY
0.089
17,774
39003
OHIO
ALLEN
0.086
108,473
39007
OHIO
ASHTABULA
0.089
102,728
39017
OHIO
BUTLER
0.089
332,807
39023
OHIO
CLARK
0.087
144,742
39025
OHIO
CLERMONT
0.089
177,977
39027
OHIO
CLINTON
0.095
40,543
39041
OHIO
DELAWARE
0.091
109,989
39055
OHIO
GEAUGA
0.093
90,895
39057
OHIO
GREENE
0.085
147,886
39061
OHIO
HAMILTON
0.086
845,303
39083
OHIO
KNOX
0.090
54,500
39085
OHIO
LAKE
0.091
227,511
39087
OHIO
LAWRENCE
0.086
62,319
39089
OHIO
LICKING
0.088
145,491
39095
OHIO
LUCAS
0.085
455,054
39097
OHIO
MADISON
0.088
40,213
39103
OHIO
MEDINA
0.086
151,095
39113
OHIO
MONTGOMERY
0.087
559,062
39133
OHIO
PORTAGE
0.092
152,061
39151
OHIO
STARK
0.088
378,098
39153
OHIO
SUMMIT
0.092
542,899
39155
OHIO
TRUMBULL
0.088
225,116
39165
OHIO
WARREN
0.088
158,383
39167
OHIO
WASHINGTON
0.088
63,251
39173
OHIO
WOOD
0.085
121,065
40143
OKLAHOMA
TULSA
0.087
563,299
42003
PENNSYLVANIA
ALLEGHENY
0.092
1,281,666
42005
PENNSYLVANIA
ARMSTRONG
0.092
72,392
42007
PENNSYLVANIA
BEAVER
0.089
181,412
42011
PENNSYLVANIA
BERKS
0.095
373,638
42017
PENNSYLVANIA
BUCKS
0.105
597,635
42021
PENNSYLVANIA
CAMBRIA
0.088
152,598
42043
PENNSYLVANIA
DAUPHIN
0.094
251,798
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
104
42045
PENNSYLVANIA
DELAWARE
0.094
550,864
42049
PENNSYLVANIA
ERIE
0.087
280,843
42055
PENNSYLVANIA
FRANKLIN
0.092
129,313
42059
PENNSYLVANIA
GREENE
0.092
40,672
42069
PENNSYLVANIA
LACKAWANNA
0.086
213,295
42071
PENNSYLVANIA
LANCASTER
0.096
470,658
42077
PENNSYLVANIA
LEHIGH
0.096
312,090
42085
PENNSYLVANIA
MERCER
0.088
120,293
42091
PENNSYLVANIA
MONTGOMERY
0.100
750,097
42095
PENNSYLVANIA
NORTHAMPTON
0.097
267,066
42101
PENNSYLVANIA
PHILADELPHIA
0.088
1,517,550
42125
PENNSYLVANIA
WASHINGTON
0.088
202,897
42129
PENNSYLVANIA
WESTMORELAND
0.086
369,993
42133
PENNSYLVANIA
YORK
0.090
381,751
44003
RHODE
ISLAND
KENT
0.094
167,090
44007
RHODE
ISLAND
PROVIDENCE
0.087
621,602
44009
RHODE
ISLAND
WASHINGTON
0.092
123,546
45001
SOUTH
CAROLINA
ABBEVILLE
0.085
26,167
45003
SOUTH
CAROLINA
AIKEN
0.086
142,552
45007
SOUTH
CAROLINA
ANDERSON
0.090
165,740
45021
SOUTH
CAROLINA
CHEROKEE
0.087
52,537
45023
SOUTH
CAROLINA
CHESTER
0.085
34,068
45031
SOUTH
CAROLINA
DARLINGTON
0.086
67,394
45077
SOUTH
CAROLINA
PICKENS
0.087
110,757
45079
SOUTH
CAROLINA
RICHLAND
0.093
320,677
45083
SOUTH
CAROLINA
SPARTANBURG
0.093
253,791
47001
TENNESSEE
ANDERSON
0.090
71,330
47009
TENNESSEE
BLOUNT
0.096
105,823
47037
TENNESSEE
DAVIDSON
0.087
569,891
47065
TENNESSEE
HAMILTON
0.092
307,896
47075
TENNESSEE
HAYWOOD
0.089
19,797
47089
TENNESSEE
JEFFERSON
0.096
44,294
47093
TENNESSEE
KNOX
0.096
382,032
47141
TENNESSEE
PUTNAM
0.087
62,315
47149
TENNESSEE
RUTHERFORD
0.086
182,023
47155
TENNESSEE
SEVIER
0.098
71,170
47157
TENNESSEE
SHELBY
0.093
897,472
47163
TENNESSEE
SULLIVAN
0.090
153,048
47165
TENNESSEE
SUMNER
0.093
130,449
47187
TENNESSEE
WILLIAMSON
0.088
126,638
47189
TENNESSEE
WILSON
0.087
88,809
48039
TEXAS
BRAZORIA
0.091
241,767
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
105
48085
TEXAS
COLLIN
0.099
491,675
48113
TEXAS
DALLAS
0.093
2,218,899
48121
TEXAS
DENTON
0.101
432,976
48139
TEXAS
ELLIS
0.088
111,360
48167
TEXAS
GALVESTON
0.098
250,158
48183
TEXAS
GREGG
0.095
111,379
48201
TEXAS
HARRIS
0.110
3,400,578
48245
TEXAS
JEFFERSON
0.085
252,051
48339
TEXAS
MONTGOMERY
0.092
293,768
48439
TEXAS
TARRANT
0.097
1,446,219
48453
TEXAS
TRAVIS
0.088
812,280
51013
VIRGINIA
ARLINGTON
0.092
189,453
51033
VIRGINIA
CAROLINE
0.085
22,121
51036
VIRGINIA
CHARLES
CITY
0.087
6,926
51041
VIRGINIA
CHESTERFIELD
0.086
259,903
51059
VIRGINIA
FAIRFAX
0.095
969,749
51087
VIRGINIA
HENRICO
0.090
262,300
51107
VIRGINIA
LOUDOUN
0.086
169,599
51113
VIRGINIA
MADISON
0.087
12,520
51153
VIRGINIA
PRINCE
WILLIAM
0.085
280,813
51161
VIRGINIA
ROANOKE
0.086
85,778
51179
VIRGINIA
STAFFORD
0.085
92,446
51510
VIRGINIA
ALEXANDRIA
0.088
128,283
51650
VIRGINIA
HAMPTON
0.087
146,437
51800
VIRGINIA
SUFFOLK
0.086
63,677
54011
WEST
VIRGINIA
CABELL
0.088
96,784
54039
WEST
VIRGINIA
KANAWHA
0.090
200,073
54107
WEST
VIRGINIA
WOOD
0.088
87,986
55029
WISCONSIN
DOOR
0.093
27,961
55055
WISCONSIN
JEFFERSON
0.086
74,021
55059
WISCONSIN
KENOSHA
0.095
149,577
55061
WISCONSIN
KEWAUNEE
0.089
20,187
55071
WISCONSIN
MANITOWOC
0.092
82,887
55079
WISCONSIN
MILWAUKEE
0.089
940,164
55089
WISCONSIN
OZAUKEE
0.095
82,317
55101
WISCONSIN
RACINE
0.087
188,831
55105
WISCONSIN
ROCK
0.086
152,307
55117
WISCONSIN
SHEBOYGAN
0.095
112,646
55133
WISCONSIN
WAUKESHA
0.086
360,767
Counties:
291
Total
Population:
110,747,890
106
107
Table
B­
2.
Counties
with
Design
Values
at
or
below
the
level
of
the
8­
hour
Ozone
Standard
(
1999­
2001)

FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
01027
ALABAMA
CLAY
0.084
14,254
01051
ALABAMA
ELMORE
0.079
65,874
01079
ALABAMA
LAWRENCE
0.082
34,803
01119
ALABAMA
SUMTER
0.075
14,798
02290
ALASKA
YUKON­
KOYUKUK
0.051
6,551
04003
ARIZONA
COCHISE
0.070
117,755
04005
ARIZONA
COCONINO
0.072
116,320
04019
ARIZONA
PIMA
0.072
843,746
04025
ARIZONA
YAVAPAI
0.081
167,517
05097
ARKANSAS
MONTGOMERY
0.069
9,245
05101
ARKANSAS
NEWTON
0.078
8,608
06001
CALIFORNIA
ALAMEDA
0.066
1,443,741
06007
CALIFORNIA
BUTTE
0.081
203,171
06011
CALIFORNIA
COLUSA
0.077
18,804
06013
CALIFORNIA
CONTRA
COSTA
0.082
948,816
06021
CALIFORNIA
GLENN
0.077
26,453
06027
CALIFORNIA
INYO
0.079
17,945
06033
CALIFORNIA
LAKE
0.063
58,309
06041
CALIFORNIA
MARIN
0.051
247,289
06045
CALIFORNIA
MENDOCINO
0.055
86,265
06053
CALIFORNIA
MONTEREY
0.063
401,762
6055
CALIFORNIA
NAPA
0.066
124,279
06059
CALIFORNIA
ORANGE
0.077
2,846,289
06069
CALIFORNIA
SAN
BENITO
0.072
53,234
06075
CALIFORNIA
SAN
FRANCISCO
0.046
776,733
06077
CALIFORNIA
SAN
JOAQUIN
0.084
563,598
06079
CALIFORNIA
SAN
LUIS
OBISPO
0.072
246,681
06081
CALIFORNIA
SAN
MATEO
0.049
707,161
06083
CALIFORNIA
SANTA
BARBARA
0.080
399,347
06085
CALIFORNIA
SANTA
CLARA
0.076
1,682,585
06087
CALIFORNIA
SANTA
CRUZ
0.065
255,602
06089
CALIFORNIA
SHASTA
0.077
163,256
06095
CALIFORNIA
SOLANO
0.077
394,542
06097
CALIFORNIA
SONOMA
0.069
458,614
06101
CALIFORNIA
SUTTER
0.083
78,930
06113
CALIFORNIA
YOLO
0.082
168,660
08001
COLORADO
ADAMS
0.065
363,857
08005
COLORADO
ARAPAHOE
0.076
487,967
08013
COLORADO
BOULDER
0.072
291,288
08031
COLORADO
DENVER
0.070
554,636
8041
COLORADO
EL
PASO
0.068
516,929
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
108
08059
COLORADO
JEFFERSON
0.081
527,056
08067
COLORADO
LA
PLATA
0.062
43,941
08069
COLORADO
LARIMER
0.074
251,494
08083
COLORADO
MONTEZUMA
0.069
23,830
08123
COLORADO
WELD
0.070
180,936
12001
FLORIDA
ALACHUA
0.078
217,955
12003
FLORIDA
BAKER
0.075
22,259
12009
FLORIDA
BREVARD
0.076
476,230
12011
FLORIDA
BROWARD
0.075
1,623,018
12031
FLORIDA
DUVAL
0.074
778,879
12057
FLORIDA
HILLSBOROUGH
0.083
998,948
12059
FLORIDA
HOLMES
0.074
18,564
12071
FLORIDA
LEE
0.075
440,888
12073
FLORIDA
LEON
0.077
239,452
12081
FLORIDA
MANATEE
0.082
264,002
12083
FLORIDA
MARION
0.078
258,916
12086
FLORIDA
Miami­
Dade
0.074
2,253,362
12095
FLORIDA
ORANGE
0.081
896,344
12097
FLORIDA
OSCEOLA
0.077
172,493
12099
FLORIDA
PALM
BEACH
0.075
1,131,184
12101
FLORIDA
PASCO
0.079
344,765
12103
FLORIDA
PINELLAS
0.082
921,482
12105
FLORIDA
POLK
0.079
483,924
12111
FLORIDA
ST
LUCIE
0.072
192,695
12115
FLORIDA
SARASOTA
0.084
325,957
12117
FLORIDA
SEMINOLE
0.078
365,196
12127
FLORIDA
VOLUSIA
0.074
443,343
13051
GEORGIA
CHATHAM
0.076
232,048
13057
GEORGIA
CHEROKEE
0.076
141,903
13085
GEORGIA
DAWSON
0.083
15,999
13127
GEORGIA
GLYNN
0.073
67,568
15003
HAWAII
HONOLULU
0.044
876,156
17001
ILLINOIS
ADAMS
0.074
68,277
17019
ILLINOIS
CHAMPAIGN
0.080
179,669
17043
ILLINOIS
DU
PAGE
0.068
904,161
17049
ILLINOIS
EFFINGHAM
0.081
34,264
17065
ILLINOIS
HAMILTON
0.077
8,621
17089
ILLINOIS
KANE
0.077
404,119
17097
ILLINOIS
LAKE
0.080
644,356
17111
ILLINOIS
MC
HENRY
0.083
260,077
17115
ILLINOIS
MACON
0.078
114,706
17117
ILLINOIS
MACOUPIN
0.080
49,019
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
109
17119
ILLINOIS
MADISON
0.082
258,941
17143
ILLINOIS
PEORIA
0.078
183,433
17157
ILLINOIS
RANDOLPH
0.078
33,893
17163
ILLINOIS
ST
CLAIR
0.082
256,082
17167
ILLINOIS
SANGAMON
0.075
188,951
17197
ILLINOIS
WILL
0.079
502,266
17201
ILLINOIS
WINNEBAGO
0.076
278,418
18043
INDIANA
FLOYD
0.082
70,823
18051
INDIANA
GIBSON
0.071
32,500
18141
INDIANA
ST
JOSEPH
0.084
265,559
18163
INDIANA
VANDERBURGH
0.084
171,922
18167
INDIANA
VIGO
0.079
105,848
18173
INDIANA
WARRICK
0.081
52,383
19045
IOWA
CLINTON
0.079
50,149
19085
IOWA
HARRISON
0.074
15,666
19113
IOWA
LINN
0.073
191,701
19147
IOWA
PALO
ALTO
0.069
10,147
19153
IOWA
POLK
0.060
374,601
19163
IOWA
SCOTT
0.079
158,668
19169
IOWA
STORY
0.066
79,981
19181
IOWA
WARREN
0.067
40,671
20107
KANSAS
LINN
0.079
9,570
20173
KANSAS
SEDGWICK
0.081
452,869
20209
KANSAS
WYANDOTTE
0.080
157,882
21013
KENTUCKY
BELL
0.082
30,060
21043
KENTUCKY
CARTER
0.083
26,889
21059
KENTUCKY
DAVIESS
0.079
91,545
21067
KENTUCKY
FAYETTE
0.081
260,512
21083
KENTUCKY
GRAVES
0.083
37,028
21091
KENTUCKY
HANCOCK
0.083
8,392
21101
KENTUCKY
HENDERSON
0.077
44,829
21113
KENTUCKY
JESSAMINE
0.078
39,041
21145
KENTUCKY
MC
CRACKEN
0.084
65,514
21195
KENTUCKY
PIKE
0.078
68,736
21209
KENTUCKY
SCOTT
0.072
33,061
21221
KENTUCKY
TRIGG
0.082
12,597
22011
LOUISIANA
BEAUREGARD
0.078
32,986
22017
LOUISIANA
CADDO
0.083
252,161
22043
LOUISIANA
GRANT
0.081
18,698
22055
LOUISIANA
LAFAYETTE
0.083
190,503
22071
LOUISIANA
ORLEANS
0.076
484,674
22073
LOUISIANA
OUACHITA
0.080
147,250
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
110
22077
LOUISIANA
POINTE
COUPEE
0.075
22,763
22087
LOUISIANA
ST
BERNARD
0.081
67,229
22093
LOUISIANA
ST
JAMES
0.083
21,216
22101
LOUISIANA
ST
MARY
0.083
53,500
23005
MAINE
CUMBERLAND
0.080
265,612
23011
MAINE
KENNEBEC
0.075
117,114
23013
MAINE
KNOX
0.080
39,618
23017
MAINE
OXFORD
0.061
54,755
23021
MAINE
PISCATAQUIS
0.065
17,235
25025
MASSACHUSETTS
SUFFOLK
0.084
689,807
26037
MICHIGAN
CLINTON
0.082
64,753
26063
MICHIGAN
HURON
0.083
36,079
26065
MICHIGAN
INGHAM
0.083
279,320
26077
MICHIGAN
KALAMAZOO
0.082
238,603
26081
MICHIGAN
KENT
0.084
574,335
26091
MICHIGAN
LENAWEE
0.083
98,890
26113
MICHIGAN
MISSAUKEE
0.082
14,478
26125
MICHIGAN
OAKLAND
0.084
1,194,156
26139
MICHIGAN
OTTAWA
0.084
238,314
27003
MINNESOTA
ANOKA
0.071
298,084
27137
MINNESOTA
ST
LOUIS
0.067
200,528
27163
MINNESOTA
WASHINGTON
0.075
201,130
28001
MISSISSIPPI
ADAMS
0.082
34,340
28011
MISSISSIPPI
BOLIVAR
0.082
40,633
28049
MISSISSIPPI
HINDS
0.080
250,800
28075
MISSISSIPPI
LAUDERDALE
0.079
78,161
28089
MISSISSIPPI
MADISON
0.079
74,674
28149
MISSISSIPPI
WARREN
0.078
49,644
29039
MISSOURI
CEDAR
0.084
13,733
29047
MISSOURI
CLAY
0.084
184,006
29077
MISSOURI
GREENE
0.075
240,391
29137
MISSOURI
MONROE
0.081
9,311
29165
MISSOURI
PLATTE
0.081
73,781
29510
MISSOURI
ST
LOUIS
(
CITY)
0.081
348,189
30029
MONTANA
FLATHEAD
0.054
74,471
31055
NEBRASKA
DOUGLAS
0.062
463,585
31109
NEBRASKA
LANCASTER
0.053
250,291
32003
NEVADA
CLARK
0.080
1,375,765
32005
NEVADA
DOUGLAS
0.072
41,259
32031
NEVADA
WASHOE
0.073
339,486
32033
NEVADA
WHITE
PINE
0.072
9,181
32510
NEVADA
CARSON
CITY
0.068
52,457
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
111
33003
NEW
HAMPSHIRE
CARROLL
0.066
43,666
33005
NEW
HAMPSHIRE
CHESHIRE
0.072
73,825
33009
NEW
HAMPSHIRE
GRAFTON
0.068
81,743
33011
NEW
HAMPSHIRE
HILLSBOROUGH
0.083
380,841
33013
NEW
HAMPSHIRE
MERRIMACK
0.070
136,225
33015
NEW
HAMPSHIRE
ROCKINGHAM
0.081
277,359
33017
NEW
HAMPSHIRE
STRAFFORD
0.075
112,233
33019
NEW
HAMPSHIRE
SULLIVAN
0.072
40,458
35001
NEW
MEXICO
BERNALILLO
0.075
556,678
35013
NEW
MEXICO
DONA
ANA
0.080
174,682
35015
NEW
MEXICO
EDDY
0.068
51,658
35043
NEW
MEXICO
SANDOVAL
0.072
89,908
35045
NEW
MEXICO
SAN
JUAN
0.073
113,801
35061
NEW
MEXICO
VALENCIA
0.069
66,152
36001
NEW
YORK
ALBANY
0.080
294,565
36005
NEW
YORK
BRONX
0.083
1,332,650
36015
NEW
YORK
CHEMUNG
0.079
91,070
36031
NEW
YORK
ESSEX
0.078
38,851
36041
NEW
YORK
HAMILTON
0.077
5,379
36043
NEW
YORK
HERKIMER
0.072
64,427
36053
NEW
YORK
MADISON
0.078
69,441
36065
NEW
YORK
ONEIDA
0.076
235,469
36067
NEW
YORK
ONONDAGA
0.081
458,336
36091
NEW
YORK
SARATOGA
0.084
200,635
36093
NEW
YORK
SCHENECTADY
0.075
146,555
36111
NEW
YORK
ULSTER
0.081
177,749
36117
NEW
YORK
WAYNE
0.081
93,765
37011
NORTH
CAROLINA
AVERY
0.075
17,167
37021
NORTH
CAROLINA
BUNCOMBE
0.083
206,330
37029
NORTH
CAROLINA
CAMDEN
0.080
6,885
37037
NORTH
CAROLINA
CHATHAM
0.081
49,329
37061
NORTH
CAROLINA
DUPLIN
0.082
49,063
37107
NORTH
CAROLINA
LENOIR
0.082
59,648
37117
NORTH
CAROLINA
MARTIN
0.079
25,593
37129
NORTH
CAROLINA
NEW
HANOVER
0.075
160,307
37131
NORTH
CAROLINA
NORTHAMPTON
0.082
22,086
37147
NORTH
CAROLINA
PITT
0.084
133,798
37173
NORTH
CAROLINA
SWAIN
0.073
12,968
38007
NORTH
DAKOTA
BILLINGS
0.058
888
38017
NORTH
DAKOTA
CASS
0.063
123,138
38057
NORTH
DAKOTA
MERCER
0.056
8,644
39035
OHIO
CUYAHOGA
0.083
1,393,978
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
112
39049
OHIO
FRANKLIN
0.084
1,068,978
39081
OHIO
JEFFERSON
0.084
73,894
39093
OHIO
LORAIN
0.081
284,664
39109
OHIO
MIAMI
0.084
98,868
39135
OHIO
PREBLE
0.078
42,337
40027
OKLAHOMA
CLEVELAND
0.079
208,016
40031
OKLAHOMA
COMANCHE
0.081
114,996
40109
OKLAHOMA
OKLAHOMA
0.080
660,448
41005
OREGON
CLACKAMAS
0.068
338,391
41009
OREGON
COLUMBIA
0.053
43,560
41039
OREGON
LANE
0.054
322,959
41047
OREGON
MARION
0.060
284,834
42013
PENNSYLVANIA
BLAIR
0.084
129,144
42027
PENNSYLVANIA
CENTRE
0.080
135,758
42033
PENNSYLVANIA
CLEARFIELD
0.083
83,382
42073
PENNSYLVANIA
LAWRENCE
0.078
94,643
42079
PENNSYLVANIA
LUZERNE
0.084
319,250
42081
PENNSYLVANIA
LYCOMING
0.076
120,044
42099
PENNSYLVANIA
PERRY
0.084
43,602
42117
PENNSYLVANIA
TIOGA
0.081
41,373
45011
SOUTH
CAROLINA
BARNWELL
0.083
23,478
45019
SOUTH
CAROLINA
CHARLESTON
0.078
309,969
45029
SOUTH
CAROLINA
COLLETON
0.079
38,264
45037
SOUTH
CAROLINA
EDGEFIELD
0.080
24,595
45087
SOUTH
CAROLINA
UNION
0.081
29,881
45089
SOUTH
CAROLINA
WILLIAMSBURG
0.073
37,217
45091
SOUTH
CAROLINA
YORK
0.082
164,614
47099
TENNESSEE
LAWRENCE
0.083
39,926
48029
TEXAS
BEXAR
0.082
1,392,931
48061
TEXAS
CAMERON
0.064
335,227
48141
TEXAS
EL
PASO
0.075
679,622
48215
TEXAS
HIDALGO
0.075
569,463
48355
TEXAS
NUECES
0.081
313,645
48361
TEXAS
ORANGE
0.074
84,966
48469
TEXAS
VICTORIA
0.079
84,088
48479
TEXAS
WEBB
0.066
193,117
49011
UTAH
DAVIS
0.079
238,994
49035
UTAH
SALT
LAKE
0.079
898,387
49049
UTAH
UTAH
0.078
368,536
49057
UTAH
WEBER
0.075
196,533
50003
VERMONT
BENNINGTON
0.079
36,994
50007
VERMONT
CHITTENDEN
0.075
146,571
FIPS
Code
State
County
1999­
2001
Design
Value
Population
2000
113
51061
VIRGINIA
FAUQUIER
0.082
55,139
51069
VIRGINIA
FREDERICK
0.083
59,209
51139
VIRGINIA
PAGE
0.082
23,177
51163
VIRGINIA
ROCKBRIDGE
0.080
20,808
51197
VIRGINIA
WYTHE
0.081
27,599
53009
WASHINGTON
CLALLAM
0.045
64,525
53011
WASHINGTON
CLARK
0.059
345,238
53033
WASHINGTON
KING
0.069
1,737,034
53039
WASHINGTON
KLICKITAT
0.065
19,161
53053
WASHINGTON
PIERCE
0.067
700,820
53057
WASHINGTON
SKAGIT
0.048
102,979
53063
WASHINGTON
SPOKANE
0.068
417,939
53067
WASHINGTON
THURSTON
0.057
207,355
53073
WASHINGTON
WHATCOM
0.050
166,814
54025
WEST
VIRGINIA
GREENBRIER
0.083
34,453
54029
WEST
VIRGINIA
HANCOCK
0.082
32,667
54069
WEST
VIRGINIA
OHIO
0.082
47,427
55009
WISCONSIN
BROWN
0.081
226,778
55021
WISCONSIN
COLUMBIA
0.078
52,468
55025
WISCONSIN
DANE
0.078
426,526
55027
WISCONSIN
DODGE
0.082
85,897
55037
WISCONSIN
FLORENCE
0.075
5,088
55039
WISCONSIN
FOND
DU
LAC
0.080
97,296
55073
WISCONSIN
MARATHON
0.076
125,834
55085
WISCONSIN
ONEIDA
0.073
36,776
55087
WISCONSIN
OUTAGAMIE
0.079
160,971
55109
WISCONSIN
ST
CROIX
0.073
63,155
55111
WISCONSIN
SAUK
0.077
55,225
55123
WISCONSIN
VERNON
0.072
28,056
55125
WISCONSIN
VILAS
0.072
21,033
55127
WISCONSIN
WALWORTH
0.084
93,759
55131
WISCONSIN
WASHINGTON
0.084
117,493
55139
WISCONSIN
WINNEBAGO
0.080
156,763
56039
WYOMING
TETON
0.067
18,251
78003
VIRGIN
ISLANDS
ST
JOHN
0.047
4,197
Counties:
286
Total
Population:
72,695,359
114
Table
B­
3.
Counties
with
incomplete
data
for
calculating
the
8­
hour
Ozone
Design
Value
(
1999­
2001)

FIPS
Code
State
County
Population
2000
01003
ALABAMA
BALDWIN
140,415
01033
ALABAMA
COLBERT
54,984
01055
ALABAMA
ETOWAH
103,459
01061
ALABAMA
GENEVA
25,764
01097
ALABAMA
MOBILE
399,843
01103
ALABAMA
MORGAN
111,064
01125
ALABAMA
TUSCALOOSA
164,875
01127
ALABAMA
WALKER
70,713
04007
ARIZONA
GILA
51,335
04017
ARIZONA
NAVAJO
97,470
04021
ARIZONA
PINAL
179,727
04027
ARIZONA
YUMA
160,026
06051
CALIFORNIA
MONO
12,853
06063
CALIFORNIA
PLUMAS
20,824
06093
CALIFORNIA
SISKIYOU
44,301
08035
COLORADO
DOUGLAS
175,766
09005
CONNECTICUT
LITCHFIELD
182,193
12005
FLORIDA
BAY
148,217
12021
FLORIDA
COLLIER
251,377
12023
FLORIDA
COLUMBIA
56,513
12055
FLORIDA
HIGHLANDS
87,366
12069
FLORIDA
LAKE
210,528
12109
FLORIDA
ST
JOHNS
123,135
12113
FLORIDA
SANTA
ROSA
117,743
12129
FLORIDA
WAKULLA
22,863
13059
GEORGIA
CLARKE
101,489
13111
GEORGIA
FANNIN
19,798
13213
GEORGIA
MURRAY
36,506
15001
HAWAII
HAWAII
148,677
16001
IDAHO
ADA
300,904
16023
IDAHO
BUTTE
2,899
16027
IDAHO
CANYON
131,441
16039
IDAHO
ELMORE
29,130
17023
ILLINOIS
CLARK
17,008
17113
ILLINOIS
MC
LEAN
150,433
17161
ILLINOIS
ROCK
ISLAND
149,374
18011
INDIANA
BOONE
46,107
18015
INDIANA
CARROLL
20,165
18035
INDIANA
DELAWARE
118,769
18039
INDIANA
ELKHART
182,791
18055
INDIANA
GREENE
33,157
18063
INDIANA
HENDRICKS
104,093
FIPS
Code
State
County
Population
2000
115
18069
INDIANA
HUNTINGTON
38,075
18071
INDIANA
JACKSON
41,335
18145
INDIANA
SHELBY
43,445
19017
IOWA
BREMER
23,325
19137
IOWA
MONTGOMERY
11,771
19177
IOWA
VAN
BUREN
7,809
20087
KANSAS
JEFFERSON
18,426
20191
KANSAS
SUMNER
25,946
20195
KANSAS
TREGO
3,319
21037
KENTUCKY
CAMPBELL
88,616
21093
KENTUCKY
HARDIN
94,174
21127
KENTUCKY
LAWRENCE
15,569
21177
KENTUCKY
MUHLENBERG
31,839
21193
KENTUCKY
PERRY
29,390
21227
KENTUCKY
WARREN
92,522
22057
LOUISIANA
LAFOURCHE
89,974
23019
MAINE
PENOBSCOT
144,919
23023
MAINE
SAGADAHOC
35,214
24510
MARYLAND
BALTIMORE
(
CITY)
651,154
25003
MASSACHUSETTS
BERKSHIRE
134,953
25021
MASSACHUSETTS
NORFOLK
650,308
26055
MICHIGAN
GRAND
TRAVERSE
77,654
26153
MICHIGAN
SCHOOLCRAFT
8,903
26161
MICHIGAN
WASHTENAW
322,895
27017
MINNESOTA
CARLTON
31,671
27037
MINNESOTA
DAKOTA
355,904
27075
MINNESOTA
LAKE
11,058
27095
MINNESOTA
MILLE
LACS
22,330
27139
MINNESOTA
SCOTT
89,498
28003
MISSISSIPPI
ALCORN
34,558
28107
MISSISSIPPI
PANOLA
34,274
29037
MISSOURI
CASS
82,092
29095
MISSOURI
JACKSON
654,880
30063
MONTANA
MISSOULA
95,802
33001
NEW
HAMPSHIRE
BELKNAP
56,325
33007
NEW
HAMPSHIRE
COOS
33,111
34003
NEW
JERSEY
BERGEN
884,118
34013
NEW
JERSEY
ESSEX
793,633
36055
NEW
YORK
MONROE
735,343
36061
NEW
YORK
NEW
YORK
1,537,195
36075
NEW
YORK
OSWEGO
122,377
36083
NEW
YORK
RENSSELAER
152,538
37151
NORTH
CAROLINA
RANDOLPH
130,454
38025
NORTH
DAKOTA
DUNN
3,600
38065
NORTH
DAKOTA
OLIVER
2,065
FIPS
Code
State
County
Population
2000
116
38091
NORTH
DAKOTA
STEELE
2,258
39091
OHIO
LOGAN
46,005
39099
OHIO
MAHONING
257,555
39159
OHIO
UNION
40,909
40001
OKLAHOMA
ADAIR
21,038
40017
OKLAHOMA
CANADIAN
87,697
40019
OKLAHOMA
CARTER
45,621
40021
OKLAHOMA
CHEROKEE
42,521
40043
OKLAHOMA
DEWEY
4,743
40067
OKLAHOMA
JEFFERSON
6,818
40071
OKLAHOMA
KAY
48,080
40077
OKLAHOMA
LATIMER
10,692
40085
OKLAHOMA
LOVE
8,831
40087
OKLAHOMA
MC
CLAIN
27,740
40095
OKLAHOMA
MARSHALL
13,184
40097
OKLAHOMA
MAYES
38,369
40101
OKLAHOMA
MUSKOGEE
69,451
40111
OKLAHOMA
OKMULGEE
39,685
40115
OKLAHOMA
OTTAWA
33,194
40121
OKLAHOMA
PITTSBURG
43,953
41029
OREGON
JACKSON
181,269
41043
OREGON
LINN
103,069
42001
PENNSYLVANIA
ADAMS
91,292
42029
PENNSYLVANIA
CHESTER
433,501
42089
PENNSYLVANIA
MONROE
138,687
45015
SOUTH
CAROLINA
BERKELEY
142,651
45025
SOUTH
CAROLINA
CHESTERFIELD
42,768
45045
SOUTH
CAROLINA
GREENVILLE
379,616
45073
SOUTH
CAROLINA
OCONEE
66,215
46099
SOUTH
DAKOTA
MINNEHAHA
148,281
46103
SOUTH
DAKOTA
PENNINGTON
88,565
47031
TENNESSEE
COFFEE
48,014
47043
TENNESSEE
DICKSON
43,156
47045
TENNESSEE
DYER
37,279
47063
TENNESSEE
HAMBLEN
58,128
47121
TENNESSEE
MEIGS
11,086
47125
TENNESSEE
MONTGOMERY
134,768
47131
TENNESSEE
OBION
32,450
47145
TENNESSEE
ROANE
51,910
48043
TEXAS
BREWSTER
8,866
48203
TEXAS
HARRISON
62,110
48221
TEXAS
HOOD
41,100
48251
TEXAS
JOHNSON
126,811
48257
TEXAS
KAUFMAN
71,313
48315
TEXAS
MARION
10,941
FIPS
Code
State
County
Population
2000
117
48367
TEXAS
PARKER
88,495
48397
TEXAS
ROCKWALL
43,080
48423
TEXAS
SMITH
174,706
49003
UTAH
BOX
ELDER
42,745
49005
UTAH
CACHE
91,391
49037
UTAH
SAN
JUAN
14,413
51085
VIRGINIA
HANOVER
86,320
53015
WASHINGTON
COWLITZ
92,948
53041
WASHINGTON
LEWIS
68,600
53045
WASHINGTON
MASON
49,405
54003
WEST
VIRGINIA
BERKELEY
75,905
54061
WEST
VIRGINIA
MONONGALIA
81,866
55045
WISCONSIN
GREEN
33,647
55095
WISCONSIN
POLK
41,319
56005
WYOMING
CAMPBELL
33,698
72033
PUERTO
RICO
CATANO
30,071
Counties:
148
Total
Population:
17,641,255
118
Table
B­
4.
Counties
with
Design
Values
above
the
level
of
the
8­
hour
Ozone
Standard
(
2000­
2002).

FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
01073
ALABAMA
JEFFERSON
0.088
662,047
01103
ALABAMA
MORGAN
0.085
111,064
01117
ALABAMA
SHELBY
0.092
143,293
04013
ARIZONA
MARICOPA
0.085
3,072,149
05035
ARKANSAS
CRITTENDEN
0.094
50,866
05119
ARKANSAS
PULASKI
0.086
361,474
06005
CALIFORNIA
AMADOR
0.088
35,100
06007
CALIFORNIA
BUTTE
0.089
203,171
06009
CALIFORNIA
CALAVERAS
0.092
40,554
06017
CALIFORNIA
EL
DORADO
0.106
156,299
06019
CALIFORNIA
FRESNO
0.115
799,407
06025
CALIFORNIA
IMPERIAL
0.087
142,361
06029
CALIFORNIA
KERN
0.112
661,645
06031
CALIFORNIA
KINGS
0.099
129,461
06037
CALIFORNIA
LOS
ANGELES
0.113
9,519,338
06039
CALIFORNIA
MADERA
0.091
123,109
06043
CALIFORNIA
MARIPOSA
0.089
17,130
06047
CALIFORNIA
MERCED
0.101
210,554
06057
CALIFORNIA
NEVADA
0.098
92,033
06061
CALIFORNIA
PLACER
0.101
248,399
06065
CALIFORNIA
RIVERSIDE
0.113
1,545,387
6067
CALIFORNIA
SACRAMENTO
0.100
1,223,499
06071
CALIFORNIA
SAN
BERNARDINO
0.128
1,709,434
06073
CALIFORNIA
SAN
DIEGO
0.095
2,813,833
06099
CALIFORNIA
STANISLAUS
0.095
446,997
06107
CALIFORNIA
TULARE
0.105
368,021
06109
CALIFORNIA
TUOLUMNE
0.091
54,501
06111
CALIFORNIA
VENTURA
0.097
753,197
09001
CONNECTICUT
FAIRFIELD
0.098
882,567
09003
CONNECTICUT
HARTFORD
0.090
857,183
09007
CONNECTICUT
MIDDLESEX
0.097
155,071
09009
CONNECTICUT
NEW
HAVEN
0.098
824,008
09011
CONNECTICUT
NEW
LONDON
0.089
259,088
09013
CONNECTICUT
TOLLAND
0.094
136,364
10001
DELAWARE
KENT
0.092
126,697
10003
DELAWARE
NEW
CASTLE
0.096
500,265
10005
DELAWARE
SUSSEX
0.094
156,638
11001
DISTRICT
OF
COLUMBIA
WASHINGTON
0.095
572,059
13021
GEORGIA
BIBB
0.092
153,887
13067
GEORGIA
COBB
0.098
607,751
13077
GEORGIA
COWETA
0.093
89,215
13089
GEORGIA
DE
KALB
0.095
665,865
13097
GEORGIA
DOUGLAS
0.095
92,174
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
119
13113
GEORGIA
FAYETTE
0.090
91,263
13121
GEORGIA
FULTON
0.099
816,006
13135
GEORGIA
GWINNETT
0.089
588,448
13151
GEORGIA
HENRY
0.098
119,341
13213
GEORGIA
MURRAY
0.087
36,506
13223
GEORGIA
PAULDING
0.090
81,678
13245
GEORGIA
RICHMOND
0.087
199,775
13247
GEORGIA
ROCKDALE
0.096
70,111
17031
ILLINOIS
COOK
0.088
5,376,741
17083
ILLINOIS
JERSEY
0.089
21,668
17163
ILLINOIS
ST
CLAIR
0.085
256,082
18003
INDIANA
ALLEN
0.088
331,849
18011
INDIANA
BOONE
0.088
46,107
18019
INDIANA
CLARK
0.090
96,472
18055
INDIANA
GREENE
0.089
33,157
18057
INDIANA
HAMILTON
0.093
182,740
18059
INDIANA
HANCOCK
0.092
55,391
18063
INDIANA
HENDRICKS
0.088
104,093
18069
INDIANA
HUNTINGTON
0.086
38,075
18071
INDIANA
JACKSON
0.085
41,335
18081
INDIANA
JOHNSON
0.087
115,209
18089
INDIANA
LAKE
0.092
484,564
18091
INDIANA
LA
PORTE
0.092
110,106
18095
INDIANA
MADISON
0.091
133,358
18097
INDIANA
MARION
0.090
860,454
18109
INDIANA
MORGAN
0.088
66,689
18127
INDIANA
PORTER
0.090
146,798
18129
INDIANA
POSEY
0.087
27,061
18141
INDIANA
ST
JOSEPH
0.090
265,559
18145
INDIANA
SHELBY
0.093
43,445
21013
KENTUCKY
BELL
0.086
30,060
21015
KENTUCKY
BOONE
0.086
85,991
21019
KENTUCKY
BOYD
0.088
49,752
21029
KENTUCKY
BULLITT
0.085
61,236
21037
KENTUCKY
CAMPBELL
0.094
88,616
21047
KENTUCKY
CHRISTIAN
0.085
72,265
21111
KENTUCKY
JEFFERSON
0.085
693,604
21117
KENTUCKY
KENTON
0.088
151,464
21185
KENTUCKY
OLDHAM
0.087
46,178
21227
KENTUCKY
WARREN
0.086
92,522
22033
LOUISIANA
EAST
BATON
ROUGE
0.086
412,852
22047
LOUISIANA
IBERVILLE
0.086
33,320
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
120
22051
LOUISIANA
JEFFERSON
0.085
455,466
22121
LOUISIANA
WEST
BATON
ROUGE
0.085
21,601
23005
MAINE
CUMBERLAND
0.086
265,612
23009
MAINE
HANCOCK
0.093
51,791
23031
MAINE
YORK
0.090
186,742
24003
MARYLAND
ANNE
ARUNDEL
0.102
489,656
24005
MARYLAND
BALTIMORE
0.093
754,292
24013
MARYLAND
CARROLL
0.092
150,897
24015
MARYLAND
CECIL
0.104
85,951
24017
MARYLAND
CHARLES
0.094
120,546
24021
MARYLAND
FREDERICK
0.091
195,277
24025
MARYLAND
HARFORD
0.104
218,590
24029
MARYLAND
KENT
0.102
19,197
24031
MARYLAND
MONTGOMERY
0.089
873,341
24033
MARYLAND
PRINCE
GEORGES
0.095
801,515
24043
MARYLAND
WASHINGTON
0.087
131,923
25001
MASSACHUSETTS
BARNSTABLE
0.093
222,230
25005
MASSACHUSETTS
BRISTOL
0.090
534,678
25009
MASSACHUSETTS
ESSEX
0.090
723,419
25013
MASSACHUSETTS
HAMPDEN
0.092
456,228
25015
MASSACHUSETTS
HAMPSHIRE
0.088
152,251
25017
MASSACHUSETTS
MIDDLESEX
0.089
1,465,396
25025
MASSACHUSETTS
SUFFOLK
0.089
689,807
25027
MASSACHUSETTS
WORCESTER
0.085
750,963
26005
MICHIGAN
ALLEGAN
0.092
105,665
26019
MICHIGAN
BENZIE
0.086
15,998
26021
MICHIGAN
BERRIEN
0.087
162,453
26027
MICHIGAN
CASS
0.090
51,104
26091
MICHIGAN
LENAWEE
0.085
98,890
26099
MICHIGAN
MACOMB
0.088
788,149
26105
MICHIGAN
MASON
0.087
28,274
26121
MICHIGAN
MUSKEGON
0.089
170,200
26125
MICHIGAN
OAKLAND
0.086
1,194,156
26139
MICHIGAN
OTTAWA
0.085
238,314
26147
MICHIGAN
ST
CLAIR
0.088
164,235
26161
MICHIGAN
WASHTENAW
0.087
322,895
26163
MICHIGAN
WAYNE
0.085
2,061,162
28033
MISSISSIPPI
DE
SOTO
0.086
107,199
29047
MISSOURI
CLAY
0.085
184,006
29099
MISSOURI
JEFFERSON
0.086
198,099
29183
MISSOURI
ST
CHARLES
0.090
283,883
29189
MISSOURI
ST
LOUIS
0.089
1,016,315
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
121
29510
MISSOURI
ST
LOUIS
(
CITY)
0.088
348,189
33011
NEW
HAMPSHIRE
HILLSBOROUGH
0.085
380,841
34001
NEW
JERSEY
ATLANTIC
0.091
252,552
34003
NEW
JERSEY
BERGEN
0.091
884,118
34007
NEW
JERSEY
CAMDEN
0.103
508,932
34011
NEW
JERSEY
CUMBERLAND
0.098
146,438
34015
NEW
JERSEY
GLOUCESTER
0.104
254,673
34017
NEW
JERSEY
HUDSON
0.087
608,975
34019
NEW
JERSEY
HUNTERDON
0.096
121,989
34021
NEW
JERSEY
MERCER
0.104
350,761
34023
NEW
JERSEY
MIDDLESEX
0.101
750,162
34025
NEW
JERSEY
MONMOUTH
0.097
615,301
34027
NEW
JERSEY
MORRIS
0.098
470,212
34029
NEW
JERSEY
OCEAN
0.115
510,916
34031
NEW
JERSEY
PASSAIC
0.088
489,049
36013
NEW
YORK
CHAUTAUQUA
0.092
139,750
36027
NEW
YORK
DUTCHESS
0.093
280,150
36029
NEW
YORK
ERIE
0.097
950,265
36031
NEW
YORK
ESSEX
0.086
38,851
36045
NEW
YORK
JEFFERSON
0.091
111,738
36055
NEW
YORK
MONROE
0.085
735,343
36063
NEW
YORK
NIAGARA
0.091
219,846
36079
NEW
YORK
PUTNAM
0.092
95,745
36085
NEW
YORK
RICHMOND
0.096
443,728
36103
NEW
YORK
SUFFOLK
0.097
1,419,369
36119
NEW
YORK
WESTCHESTER
0.090
923,459
37003
NORTH
CAROLINA
ALEXANDER
0.091
33,603
37021
NORTH
CAROLINA
BUNCOMBE
0.085
206,330
37027
NORTH
CAROLINA
CALDWELL
0.086
77,415
37033
NORTH
CAROLINA
CASWELL
0.091
23,501
37051
NORTH
CAROLINA
CUMBERLAND
0.087
302,963
37059
NORTH
CAROLINA
DAVIE
0.095
34,835
37063
NORTH
CAROLINA
DURHAM
0.091
223,314
37065
NORTH
CAROLINA
EDGECOMBE
0.088
55,606
37067
NORTH
CAROLINA
FORSYTH
0.094
306,067
37069
NORTH
CAROLINA
FRANKLIN
0.091
47,260
37077
NORTH
CAROLINA
GRANVILLE
0.094
48,498
37081
NORTH
CAROLINA
GUILFORD
0.093
421,048
37087
NORTH
CAROLINA
HAYWOOD
0.087
54,033
37099
NORTH
CAROLINA
JACKSON
0.086
33,121
37101
NORTH
CAROLINA
JOHNSTON
0.085
121,965
37109
NORTH
CAROLINA
LINCOLN
0.094
63,780
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
122
37119
NORTH
CAROLINA
MECKLENBURG
0.102
695,454
37145
NORTH
CAROLINA
PERSON
0.090
35,623
37157
NORTH
CAROLINA
ROCKINGHAM
0.090
91,928
37159
NORTH
CAROLINA
ROWAN
0.101
130,340
37179
NORTH
CAROLINA
UNION
0.088
123,677
37183
NORTH
CAROLINA
WAKE
0.094
627,846
37199
NORTH
CAROLINA
YANCEY
0.087
17,774
39003
OHIO
ALLEN
0.088
108,473
39007
OHIO
ASHTABULA
0.094
102,728
39017
OHIO
BUTLER
0.089
332,807
39023
OHIO
CLARK
0.090
144,742
39025
OHIO
CLERMONT
0.090
177,977
39027
OHIO
CLINTON
0.096
40,543
39035
OHIO
CUYAHOGA
0.086
1,393,978
39041
OHIO
DELAWARE
0.089
109,989
39055
OHIO
GEAUGA
0.099
90,895
39057
OHIO
GREENE
0.086
147,886
39061
OHIO
HAMILTON
0.089
845,303
39081
OHIO
JEFFERSON
0.086
73,894
39083
OHIO
KNOX
0.090
54,500
39085
OHIO
LAKE
0.092
227,511
39087
OHIO
LAWRENCE
0.086
62,319
39089
OHIO
LICKING
0.090
145,491
39093
OHIO
LORAIN
0.085
284,664
39095
OHIO
LUCAS
0.089
455,054
39097
OHIO
MADISON
0.089
40,213
39099
OHIO
MAHONING
0.087
257,555
39103
OHIO
MEDINA
0.087
151,095
39109
OHIO
MIAMI
0.087
98,868
39113
OHIO
MONTGOMERY
0.086
559,062
39133
OHIO
PORTAGE
0.091
152,061
39151
OHIO
STARK
0.089
378,098
39153
OHIO
SUMMIT
0.095
542,899
39155
OHIO
TRUMBULL
0.090
225,116
39165
OHIO
WARREN
0.089
158,383
39167
OHIO
WASHINGTON
0.087
63,251
39173
OHIO
WOOD
0.086
121,065
40143
OKLAHOMA
TULSA
0.085
563,299
42003
PENNSYLVANIA
ALLEGHENY
0.095
1,281,666
42005
PENNSYLVANIA
ARMSTRONG
0.091
72,392
42007
PENNSYLVANIA
BEAVER
0.090
181,412
42011
PENNSYLVANIA
BERKS
0.092
373,638
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
123
42017
PENNSYLVANIA
BUCKS
0.104
597,635
42021
PENNSYLVANIA
CAMBRIA
0.088
152,598
42027
PENNSYLVANIA
CENTRE
0.085
135,758
42029
PENNSYLVANIA
CHESTER
0.095
433,501
42033
PENNSYLVANIA
CLEARFIELD
0.087
83,382
42043
PENNSYLVANIA
DAUPHIN
0.091
251,798
42045
PENNSYLVANIA
DELAWARE
0.095
550,864
42049
PENNSYLVANIA
ERIE
0.088
280,843
42055
PENNSYLVANIA
FRANKLIN
0.094
129,313
42059
PENNSYLVANIA
GREENE
0.090
40,672
42069
PENNSYLVANIA
LACKAWANNA
0.085
213,295
42071
PENNSYLVANIA
LANCASTER
0.094
470,658
42077
PENNSYLVANIA
LEHIGH
0.093
312,090
42085
PENNSYLVANIA
MERCER
0.092
120,293
42091
PENNSYLVANIA
MONTGOMERY
0.097
750,097
42095
PENNSYLVANIA
NORTHAMPTON
0.092
267,066
42101
PENNSYLVANIA
PHILADELPHIA
0.098
1,517,550
42125
PENNSYLVANIA
WASHINGTON
0.088
202,897
42129
PENNSYLVANIA
WESTMORELAND
0.086
369,993
42133
PENNSYLVANIA
YORK
0.092
381,751
44003
RHODE
ISLAND
KENT
0.097
167,090
44007
RHODE
ISLAND
PROVIDENCE
0.091
621,602
44009
RHODE
ISLAND
WASHINGTON
0.093
123,546
45001
SOUTH
CAROLINA
ABBEVILLE
0.085
26,167
45003
SOUTH
CAROLINA
AIKEN
0.088
142,552
45007
SOUTH
CAROLINA
ANDERSON
0.088
165,740
45021
SOUTH
CAROLINA
CHEROKEE
0.087
52,537
45031
SOUTH
CAROLINA
DARLINGTON
0.086
67,394
45077
SOUTH
CAROLINA
PICKENS
0.085
110,757
45079
SOUTH
CAROLINA
RICHLAND
0.093
320,677
45083
SOUTH
CAROLINA
SPARTANBURG
0.090
253,791
47001
TENNESSEE
ANDERSON
0.092
71,330
47009
TENNESSEE
BLOUNT
0.094
105,823
47065
TENNESSEE
HAMILTON
0.093
307,896
47075
TENNESSEE
HAYWOOD
0.086
19,797
47089
TENNESSEE
JEFFERSON
0.095
44,294
47093
TENNESSEE
KNOX
0.096
382,032
47121
TENNESSEE
MEIGS
0.093
11,086
47141
TENNESSEE
PUTNAM
0.086
62,315
47155
TENNESSEE
SEVIER
0.098
71,170
47157
TENNESSEE
SHELBY
0.090
897,472
47163
TENNESSEE
SULLIVAN
0.092
153,048
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
124
47165
TENNESSEE
SUMNER
0.088
130,449
47187
TENNESSEE
WILLIAMSON
0.087
126,638
47189
TENNESSEE
WILSON
0.085
88,809
48029
TEXAS
BEXAR
0.086
1,392,931
48039
TEXAS
BRAZORIA
0.086
241,767
48085
TEXAS
COLLIN
0.093
491,675
48113
TEXAS
DALLAS
0.091
2,218,899
48121
TEXAS
DENTON
0.099
432,976
48139
TEXAS
ELLIS
0.086
111,360
48167
TEXAS
GALVESTON
0.089
250,158
48183
TEXAS
GREGG
0.088
111,379
48201
TEXAS
HARRIS
0.107
3,400,578
48251
TEXAS
JOHNSON
0.089
126,811
48339
TEXAS
MONTGOMERY
0.091
293,768
48367
TEXAS
PARKER
0.086
88,495
48439
TEXAS
TARRANT
0.098
1,446,219
48453
TEXAS
TRAVIS
0.085
812,280
51013
VIRGINIA
ARLINGTON
0.096
189,453
51036
VIRGINIA
CHARLES
CITY
0.090
6,926
51041
VIRGINIA
CHESTERFIELD
0.086
259,903
51059
VIRGINIA
FAIRFAX
0.097
969,749
51069
VIRGINIA
FREDERICK
0.085
59,209
51087
VIRGINIA
HENRICO
0.090
262,300
51107
VIRGINIA
LOUDOUN
0.090
169,599
51113
VIRGINIA
MADISON
0.085
12,520
51153
VIRGINIA
PRINCE
WILLIAM
0.085
280,813
51161
VIRGINIA
ROANOKE
0.087
85,778
51179
VIRGINIA
STAFFORD
0.086
92,446
51510
VIRGINIA
ALEXANDRIA
0.090
128,283
51650
VIRGINIA
HAMPTON
0.089
146,437
51800
VIRGINIA
SUFFOLK
0.088
63,677
54011
WEST
VIRGINIA
CABELL
0.088
96,784
54029
WEST
VIRGINIA
HANCOCK
0.085
32,667
54039
WEST
VIRGINIA
KANAWHA
0.085
200,073
54069
WEST
VIRGINIA
OHIO
0.085
47,427
54107
WEST
VIRGINIA
WOOD
0.088
87,986
55029
WISCONSIN
DOOR
0.091
27,961
55059
WISCONSIN
KENOSHA
0.100
149,577
55061
WISCONSIN
KEWAUNEE
0.088
20,187
55071
WISCONSIN
MANITOWOC
0.088
82,887
55079
WISCONSIN
MILWAUKEE
0.091
940,164
55089
WISCONSIN
OZAUKEE
0.093
82,317
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
125
55101
WISCONSIN
RACINE
0.093
188,831
55117
WISCONSIN
SHEBOYGAN
0.099
112,646
Counties:
297
Total
Population:
115,287,584
126
Table
B­
5.
Counties
with
Design
Values
at
or
below
the
level
of
the
8­
hour
Ozone
Standard
(
2000­
2002).

FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
01003
ALABAMA
BALDWIN
0.082
140,415
01027
ALABAMA
CLAY
0.082
14,254
01051
ALABAMA
ELMORE
0.080
65,874
01079
ALABAMA
LAWRENCE
0.078
34,803
01089
ALABAMA
MADISON
0.082
276,700
01097
ALABAMA
MOBILE
0.081
399,843
01101
ALABAMA
MONTGOMERY
0.081
223,510
01119
ALABAMA
SUMTER
0.076
14,798
02290
ALASKA
YUKON­
KOYUKUK
0.051
6,551
04003
ARIZONA
COCHISE
0.069
117,755
04005
ARIZONA
COCONINO
0.073
116,320
04019
ARIZONA
PIMA
0.073
843,746
04025
ARIZONA
YAVAPAI
0.082
167,517
05097
ARKANSAS
MONTGOMERY
0.069
9,245
05101
ARKANSAS
NEWTON
0.078
8,608
06001
CALIFORNIA
ALAMEDA
0.081
1,443,741
06011
CALIFORNIA
COLUSA
0.076
18,804
06013
CALIFORNIA
CONTRA
COSTA
0.078
948,816
06021
CALIFORNIA
GLENN
0.074
26,453
06027
CALIFORNIA
INYO
0.081
17,945
06033
CALIFORNIA
LAKE
0.064
58,309
06041
CALIFORNIA
MARIN
0.047
247,289
06045
CALIFORNIA
MENDOCINO
0.055
86,265
06053
CALIFORNIA
MONTEREY
0.064
401,762
06055
CALIFORNIA
NAPA
0.063
124,279
06059
CALIFORNIA
ORANGE
0.075
2,846,289
06069
CALIFORNIA
SAN
BENITO
0.081
53,234
06075
CALIFORNIA
SAN
FRANCISCO
0.044
776,733
06077
CALIFORNIA
SAN
JOAQUIN
0.081
563,598
06079
CALIFORNIA
SAN
LUIS
OBISPO
0.073
246,681
06081
CALIFORNIA
SAN
MATEO
0.052
707,161
06083
CALIFORNIA
SANTA
BARBARA
0.082
399,347
06085
CALIFORNIA
SANTA
CLARA
0.082
1,682,585
06087
CALIFORNIA
SANTA
CRUZ
0.064
255,602
06089
CALIFORNIA
SHASTA
0.074
163,256
06095
CALIFORNIA
SOLANO
0.072
394,542
06097
CALIFORNIA
SONOMA
0.063
458,614
06101
CALIFORNIA
SUTTER
0.084
78,930
06103
CALIFORNIA
TEHAMA
0.083
56,039
06113
CALIFORNIA
YOLO
0.083
168,660
08001
COLORADO
ADAMS
0.064
363,857
08005
COLORADO
ARAPAHOE
0.076
487,967
08013
COLORADO
BOULDER
0.073
291,288
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
127
08031
COLORADO
DENVER
0.072
554,636
08035
COLORADO
DOUGLAS
0.080
175,766
08041
COLORADO
EL
PASO
0.070
516,929
08059
COLORADO
JEFFERSON
0.083
527,056
08067
COLORADO
LA
PLATA
0.058
43,941
08069
COLORADO
LARIMER
0.078
251,494
08083
COLORADO
MONTEZUMA
0.069
23,830
08123
COLORADO
WELD
0.066
180,936
12001
FLORIDA
ALACHUA
0.075
217,955
12003
FLORIDA
BAKER
0.072
22,259
12005
FLORIDA
BAY
0.081
148,217
12009
FLORIDA
BREVARD
0.076
476,230
12011
FLORIDA
BROWARD
0.071
1,623,018
12031
FLORIDA
DUVAL
0.069
778,879
12033
FLORIDA
ESCAMBIA
0.084
294,410
12057
FLORIDA
HILLSBOROUGH
0.079
998,948
12059
FLORIDA
HOLMES
0.072
18,564
12071
FLORIDA
LEE
0.069
440,888
12073
FLORIDA
LEON
0.072
239,452
12081
FLORIDA
MANATEE
0.076
264,002
12083
FLORIDA
MARION
0.075
258,916
12086
FLORIDA
Miami­
Dade
0.069
2,253,362
12095
FLORIDA
ORANGE
0.078
896,344
12097
FLORIDA
OSCEOLA
0.073
172,493
12099
FLORIDA
PALM
BEACH
0.068
1,131,184
12101
FLORIDA
PASCO
0.077
344,765
12103
FLORIDA
PINELLAS
0.076
921,482
12105
FLORIDA
POLK
0.077
483,924
12111
FLORIDA
ST
LUCIE
0.068
192,695
12113
FLORIDA
SANTA
ROSA
0.084
117,743
12115
FLORIDA
SARASOTA
0.081
325,957
12117
FLORIDA
SEMINOLE
0.078
365,196
12127
FLORIDA
VOLUSIA
0.072
443,343
13051
GEORGIA
CHATHAM
0.070
232,048
13057
GEORGIA
CHEROKEE
0.078
141,903
13085
GEORGIA
DAWSON
0.083
15,999
13127
GEORGIA
GLYNN
0.073
67,568
13215
GEORGIA
MUSCOGEE
0.083
186,291
13261
GEORGIA
SUMTER
0.081
33,200
15003
HAWAII
HONOLULU
0.043
876,156
17001
ILLINOIS
ADAMS
0.077
68,277
17019
ILLINOIS
CHAMPAIGN
0.076
179,669
17043
ILLINOIS
DU
PAGE
0.071
904,161
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
128
17049
ILLINOIS
EFFINGHAM
0.077
34,264
17065
ILLINOIS
HAMILTON
0.080
8,621
17089
ILLINOIS
KANE
0.077
404,119
17097
ILLINOIS
LAKE
0.084
644,356
17111
ILLINOIS
MC
HENRY
0.083
260,077
17115
ILLINOIS
MACON
0.077
114,706
17117
ILLINOIS
MACOUPIN
0.080
49,019
17119
ILLINOIS
MADISON
0.084
258,941
17143
ILLINOIS
PEORIA
0.079
183,433
17157
ILLINOIS
RANDOLPH
0.079
33,893
17167
ILLINOIS
SANGAMON
0.077
188,951
17197
ILLINOIS
WILL
0.080
502,266
17201
ILLINOIS
WINNEBAGO
0.075
278,418
18043
INDIANA
FLOYD
0.083
70,823
18051
INDIANA
GIBSON
0.071
32,500
18163
INDIANA
VANDERBURGH
0.083
171,922
18167
INDIANA
VIGO
0.079
105,848
18173
INDIANA
WARRICK
0.084
52,383
19017
IOWA
BREMER
0.072
23,325
19045
IOWA
CLINTON
0.078
50,149
19085
IOWA
HARRISON
0.077
15,666
19113
IOWA
LINN
0.071
191,701
19147
IOWA
PALO
ALTO
0.066
10,147
19153
IOWA
POLK
0.060
374,601
19163
IOWA
SCOTT
0.079
158,668
19169
IOWA
STORY
0.064
79,981
19177
IOWA
VAN
BUREN
0.074
7,809
19181
IOWA
WARREN
0.063
40,671
20107
KANSAS
LINN
0.076
9,570
20173
KANSAS
SEDGWICK
0.081
452,869
20191
KANSAS
SUMNER
0.080
25,946
20195
KANSAS
TREGO
0.066
3,319
20209
KANSAS
WYANDOTTE
0.081
157,882
21043
KENTUCKY
CARTER
0.080
26,889
21059
KENTUCKY
DAVIESS
0.077
91,545
21061
KENTUCKY
EDMONSON
0.084
11,644
21067
KENTUCKY
FAYETTE
0.078
260,512
21083
KENTUCKY
GRAVES
0.081
37,028
21089
KENTUCKY
GREENUP
0.083
36,891
21091
KENTUCKY
HANCOCK
0.083
8,392
21093
KENTUCKY
HARDIN
0.081
94,174
21101
KENTUCKY
HENDERSON
0.079
44,829
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
129
21113
KENTUCKY
JESSAMINE
0.079
39,041
21139
KENTUCKY
LIVINGSTON
0.084
9,804
21145
KENTUCKY
MC
CRACKEN
0.082
65,514
21149
KENTUCKY
MC
LEAN
0.084
9,938
21193
KENTUCKY
PERRY
0.075
29,390
21195
KENTUCKY
PIKE
0.078
68,736
21199
KENTUCKY
PULASKI
0.081
56,217
21209
KENTUCKY
SCOTT
0.070
33,061
21213
KENTUCKY
SIMPSON
0.083
16,405
21221
KENTUCKY
TRIGG
0.075
12,597
22005
LOUISIANA
ASCENSION
0.082
76,627
22011
LOUISIANA
BEAUREGARD
0.074
32,986
22015
LOUISIANA
BOSSIER
0.084
98,310
22017
LOUISIANA
CADDO
0.079
252,161
22019
LOUISIANA
CALCASIEU
0.081
183,577
22043
LOUISIANA
GRANT
0.078
18,698
22055
LOUISIANA
LAFAYETTE
0.081
190,503
22057
LOUISIANA
LAFOURCHE
0.080
89,974
22063
LOUISIANA
LIVINGSTON
0.084
91,814
22071
LOUISIANA
ORLEANS
0.071
484,674
22073
LOUISIANA
OUACHITA
0.078
147,250
22077
LOUISIANA
POINTE
COUPEE
0.071
22,763
22087
LOUISIANA
ST
BERNARD
0.079
67,229
22089
LOUISIANA
ST
CHARLES
0.081
48,072
22093
LOUISIANA
ST
JAMES
0.076
21,216
22095
LOUISIANA
ST
JOHN
THE
BAPTIST
PAR
0.081
43,044
22101
LOUISIANA
ST
MARY
0.077
53,500
23011
MAINE
KENNEBEC
0.078
117,114
23013
MAINE
KNOX
0.083
39,618
23017
MAINE
OXFORD
0.060
54,755
26037
MICHIGAN
CLINTON
0.082
64,753
26049
MICHIGAN
GENESEE
0.084
436,141
26063
MICHIGAN
HURON
0.082
36,079
26065
MICHIGAN
INGHAM
0.082
279,320
26077
MICHIGAN
KALAMAZOO
0.081
238,603
26081
MICHIGAN
KENT
0.082
574,335
26113
MICHIGAN
MISSAUKEE
0.078
14,478
28001
MISSISSIPPI
ADAMS
0.080
34,340
28011
MISSISSIPPI
BOLIVAR
0.077
40,633
28045
MISSISSIPPI
HANCOCK
0.082
42,967
28047
MISSISSIPPI
HARRISON
0.081
189,601
28049
MISSISSIPPI
HINDS
0.076
250,800
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
130
28059
MISSISSIPPI
JACKSON
0.082
131,420
28075
MISSISSIPPI
LAUDERDALE
0.076
78,161
28081
MISSISSIPPI
LEE
0.081
75,755
28089
MISSISSIPPI
MADISON
0.076
74,674
28149
MISSISSIPPI
WARREN
0.078
49,644
29037
MISSOURI
CASS
0.079
82,092
29039
MISSOURI
CEDAR
0.083
13,733
29077
MISSOURI
GREENE
0.076
240,391
29137
MISSOURI
MONROE
0.079
9,311
29165
MISSOURI
PLATTE
0.084
73,781
29186
MISSOURI
STE
GENEVIEVE
0.084
17,842
30029
MONTANA
FLATHEAD
0.052
74,471
31055
NEBRASKA
DOUGLAS
0.068
463,585
31109
NEBRASKA
LANCASTER
0.054
250,291
32003
NEVADA
CLARK
0.082
1,375,765
32005
NEVADA
DOUGLAS
0.072
41,259
32031
NEVADA
WASHOE
0.073
339,486
32033
NEVADA
WHITE
PINE
0.072
9,181
33003
NEW
HAMPSHIRE
CARROLL
0.067
43,666
33005
NEW
HAMPSHIRE
CHESHIRE
0.073
73,825
33009
NEW
HAMPSHIRE
GRAFTON
0.068
81,743
33013
NEW
HAMPSHIRE
MERRIMACK
0.074
136,225
33015
NEW
HAMPSHIRE
ROCKINGHAM
0.083
277,359
33017
NEW
HAMPSHIRE
STRAFFORD
0.077
112,233
33019
NEW
HAMPSHIRE
SULLIVAN
0.073
40,458
35001
NEW
MEXICO
BERNALILLO
0.075
556,678
35013
NEW
MEXICO
DONA
ANA
0.080
174,682
35015
NEW
MEXICO
EDDY
0.070
51,658
35043
NEW
MEXICO
SANDOVAL
0.072
89,908
35045
NEW
MEXICO
SAN
JUAN
0.076
113,801
35061
NEW
MEXICO
VALENCIA
0.069
66,152
36001
NEW
YORK
ALBANY
0.083
294,565
36005
NEW
YORK
BRONX
0.081
1,332,650
36015
NEW
YORK
CHEMUNG
0.081
91,070
36041
NEW
YORK
HAMILTON
0.079
5,379
36043
NEW
YORK
HERKIMER
0.074
64,427
36053
NEW
YORK
MADISON
0.080
69,441
36065
NEW
YORK
ONEIDA
0.078
235,469
36067
NEW
YORK
ONONDAGA
0.083
458,336
36071
NEW
YORK
ORANGE
0.084
341,367
36081
NEW
YORK
QUEENS
0.074
2,229,379
36093
NEW
YORK
SCHENECTADY
0.076
146,555
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
131
36111
NEW
YORK
ULSTER
0.081
177,749
36117
NEW
YORK
WAYNE
0.083
93,765
37011
NORTH
CAROLINA
AVERY
0.079
17,167
37037
NORTH
CAROLINA
CHATHAM
0.083
49,329
37061
NORTH
CAROLINA
DUPLIN
0.081
49,063
37107
NORTH
CAROLINA
LENOIR
0.081
59,648
37117
NORTH
CAROLINA
MARTIN
0.081
25,593
37129
NORTH
CAROLINA
NEW
HANOVER
0.079
160,307
37131
NORTH
CAROLINA
NORTHAMPTON
0.084
22,086
37147
NORTH
CAROLINA
PITT
0.083
133,798
37173
NORTH
CAROLINA
SWAIN
0.074
12,968
38007
NORTH
DAKOTA
BILLINGS
0.059
888
38017
NORTH
DAKOTA
CASS
0.062
123,138
38057
NORTH
DAKOTA
MERCER
0.058
8,644
39049
OHIO
FRANKLIN
0.084
1,068,978
39135
OHIO
PREBLE
0.082
42,337
40027
OKLAHOMA
CLEVELAND
0.077
208,016
40031
OKLAHOMA
COMANCHE
0.079
114,996
40087
OKLAHOMA
MC
CLAIN
0.079
27,740
40109
OKLAHOMA
OKLAHOMA
0.082
660,448
41005
OREGON
CLACKAMAS
0.065
338,391
41009
OREGON
COLUMBIA
0.057
43,560
41029
OREGON
JACKSON
0.069
181,269
41039
OREGON
LANE
0.058
322,959
41047
OREGON
MARION
0.059
284,834
42013
PENNSYLVANIA
BLAIR
0.084
129,144
42073
PENNSYLVANIA
LAWRENCE
0.078
94,643
42079
PENNSYLVANIA
LUZERNE
0.084
319,250
42081
PENNSYLVANIA
LYCOMING
0.079
120,044
42099
PENNSYLVANIA
PERRY
0.083
43,602
42117
PENNSYLVANIA
TIOGA
0.084
41,373
45011
SOUTH
CAROLINA
BARNWELL
0.083
23,478
45019
SOUTH
CAROLINA
CHARLESTON
0.074
309,969
45023
SOUTH
CAROLINA
CHESTER
0.084
34,068
45029
SOUTH
CAROLINA
COLLETON
0.080
38,264
45037
SOUTH
CAROLINA
EDGEFIELD
0.083
24,595
45087
SOUTH
CAROLINA
UNION
0.081
29,881
45089
SOUTH
CAROLINA
WILLIAMSBURG
0.073
37,217
45091
SOUTH
CAROLINA
YORK
0.084
164,614
47037
TENNESSEE
DAVIDSON
0.080
569,891
47099
TENNESSEE
LAWRENCE
0.078
39,926
47149
TENNESSEE
RUTHERFORD
0.084
182,023
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
132
48061
TEXAS
CAMERON
0.064
335,227
48141
TEXAS
EL
PASO
0.081
679,622
48215
TEXAS
HIDALGO
0.075
569,463
48221
TEXAS
HOOD
0.084
41,100
48245
TEXAS
JEFFERSON
0.084
252,051
48257
TEXAS
KAUFMAN
0.070
71,313
48355
TEXAS
NUECES
0.081
313,645
48361
TEXAS
ORANGE
0.081
84,966
48397
TEXAS
ROCKWALL
0.083
43,080
48423
TEXAS
SMITH
0.084
174,706
48469
TEXAS
VICTORIA
0.076
84,088
48479
TEXAS
WEBB
0.066
193,117
49005
UTAH
CACHE
0.069
91,391
49011
UTAH
DAVIS
0.082
238,994
49035
UTAH
SALT
LAKE
0.081
898,387
49049
UTAH
UTAH
0.078
368,536
49057
UTAH
WEBER
0.076
196,533
50003
VERMONT
BENNINGTON
0.080
36,994
50007
VERMONT
CHITTENDEN
0.077
146,571
51033
VIRGINIA
CAROLINE
0.083
22,121
51061
VIRGINIA
FAUQUIER
0.081
55,139
51139
VIRGINIA
PAGE
0.080
23,177
51163
VIRGINIA
ROCKBRIDGE
0.079
20,808
51197
VIRGINIA
WYTHE
0.081
27,599
53009
WASHINGTON
CLALLAM
0.043
64,525
53011
WASHINGTON
CLARK
0.059
345,238
53033
WASHINGTON
KING
0.068
1,737,034
53039
WASHINGTON
KLICKITAT
0.065
19,161
53053
WASHINGTON
PIERCE
0.067
700,820
53057
WASHINGTON
SKAGIT
0.047
102,979
53063
WASHINGTON
SPOKANE
0.070
417,939
53067
WASHINGTON
THURSTON
0.058
207,355
53073
WASHINGTON
WHATCOM
0.051
166,814
54025
WEST
VIRGINIA
GREENBRIER
0.082
34,453
54061
WEST
VIRGINIA
MONONGALIA
0.081
81,866
55009
WISCONSIN
BROWN
0.081
226,778
55021
WISCONSIN
COLUMBIA
0.076
52,468
55025
WISCONSIN
DANE
0.076
426,526
55027
WISCONSIN
DODGE
0.079
85,897
55037
WISCONSIN
FLORENCE
0.069
5,088
55039
WISCONSIN
FOND
DU
LAC
0.077
97,296
55045
WISCONSIN
GREEN
0.074
33,647
FIPS
Code
State
County
2000­
2002
Design
Value
Population
2000
133
55073
WISCONSIN
MARATHON
0.072
125,834
55085
WISCONSIN
ONEIDA
0.068
36,776
55087
WISCONSIN
OUTAGAMIE
0.075
160,971
55105
WISCONSIN
ROCK
0.084
152,307
55109
WISCONSIN
ST
CROIX
0.072
63,155
55111
WISCONSIN
SAUK
0.073
55,225
55123
WISCONSIN
VERNON
0.071
28,056
55125
WISCONSIN
VILAS
0.068
21,033
55127
WISCONSIN
WALWORTH
0.082
93,759
55131
WISCONSIN
WASHINGTON
0.081
117,493
55133
WISCONSIN
WAUKESHA
0.081
360,767
55139
WISCONSIN
WINNEBAGO
0.078
156,763
56039
WYOMING
TETON
0.065
18,251
Counties:
309
Total
population:
72,585,880
134
Table
B­
6.
Counties
with
incomplete
data
for
calculating
the
8­
hour
Ozone
Design
Value
(
2000­
2002).

FIPS
Code
State
County
Population
2000
01033
ALABAMA
COLBERT
54,984
01055
ALABAMA
ETOWAH
103,459
01061
ALABAMA
GENEVA
25,764
01125
ALABAMA
TUSCALOOSA
164,875
01127
ALABAMA
WALKER
70,713
04007
ARIZONA
GILA
51,335
04017
ARIZONA
NAVAJO
97,470
04021
ARIZONA
PINAL
179,727
04027
ARIZONA
YUMA
160,026
06051
CALIFORNIA
MONO
12,853
06063
CALIFORNIA
PLUMAS
20,824
06093
CALIFORNIA
SISKIYOU
44,301
09005
CONNECTICUT
LITCHFIELD
182,193
12021
FLORIDA
COLLIER
251,377
12023
FLORIDA
COLUMBIA
56,513
12055
FLORIDA
HIGHLANDS
87,366
12069
FLORIDA
LAKE
210,528
12109
FLORIDA
ST
JOHNS
123,135
12129
FLORIDA
WAKULLA
22,863
13059
GEORGIA
CLARKE
101,489
13111
GEORGIA
FANNIN
19,798
15001
HAWAII
HAWAII
148,677
16001
IDAHO
ADA
300,904
16023
IDAHO
BUTTE
2,899
16027
IDAHO
CANYON
131,441
16039
IDAHO
ELMORE
29,130
17023
ILLINOIS
CLARK
17,008
17113
ILLINOIS
MC
LEAN
150,433
17161
ILLINOIS
ROCK
ISLAND
149,374
18015
INDIANA
CARROLL
20,165
18035
INDIANA
DELAWARE
118,769
18039
INDIANA
ELKHART
182,791
18123
INDIANA
PERRY
18,899
19137
IOWA
MONTGOMERY
11,771
20087
KANSAS
JEFFERSON
18,426
21127
KENTUCKY
LAWRENCE
15,569
21177
KENTUCKY
MUHLENBERG
31,839
23019
MAINE
PENOBSCOT
144,919
23021
MAINE
PISCATAQUIS
17,235
23023
MAINE
SAGADAHOC
35,214
24009
MARYLAND
CALVERT
74,563
24510
MARYLAND
BALTIMORE
(
CITY)
651,154
25003
MASSACHUSETTS
BERKSHIRE
134,953
FIPS
Code
State
County
Population
2000
135
25021
MASSACHUSETTS
NORFOLK
650,308
26055
MICHIGAN
GRAND
TRAVERSE
77,654
26153
MICHIGAN
SCHOOLCRAFT
8,903
27003
MINNESOTA
ANOKA
298,084
27017
MINNESOTA
CARLTON
31,671
27037
MINNESOTA
DAKOTA
355,904
27075
MINNESOTA
LAKE
11,058
27095
MINNESOTA
MILLE
LACS
22,330
27137
MINNESOTA
ST
LOUIS
200,528
27139
MINNESOTA
SCOTT
89,498
27163
MINNESOTA
WASHINGTON
201,130
28003
MISSISSIPPI
ALCORN
34,558
28107
MISSISSIPPI
PANOLA
34,274
29095
MISSOURI
JACKSON
654,880
30063
MONTANA
MISSOULA
95,802
32510
NEVADA
CARSON
CITY
52,457
33001
NEW
HAMPSHIRE
BELKNAP
56,325
33007
NEW
HAMPSHIRE
COOS
33,111
34013
NEW
JERSEY
ESSEX
793,633
36061
NEW
YORK
NEW
YORK
1,537,195
36075
NEW
YORK
OSWEGO
122,377
36083
NEW
YORK
RENSSELAER
152,538
36091
NEW
YORK
SARATOGA
200,635
37029
NORTH
CAROLINA
CAMDEN
6,885
37151
NORTH
CAROLINA
RANDOLPH
130,454
38025
NORTH
DAKOTA
DUNN
3,600
38065
NORTH
DAKOTA
OLIVER
2,065
38091
NORTH
DAKOTA
STEELE
2,258
39091
OHIO
LOGAN
46,005
39159
OHIO
UNION
40,909
40001
OKLAHOMA
ADAIR
21,038
40017
OKLAHOMA
CANADIAN
87,697
40019
OKLAHOMA
CARTER
45,621
40021
OKLAHOMA
CHEROKEE
42,521
40043
OKLAHOMA
DEWEY
4,743
40067
OKLAHOMA
JEFFERSON
6,818
40071
OKLAHOMA
KAY
48,080
40077
OKLAHOMA
LATIMER
10,692
40085
OKLAHOMA
LOVE
8,831
40095
OKLAHOMA
MARSHALL
13,184
40097
OKLAHOMA
MAYES
38,369
40101
OKLAHOMA
MUSKOGEE
69,451
40111
OKLAHOMA
OKMULGEE
39,685
40115
OKLAHOMA
OTTAWA
33,194
40121
OKLAHOMA
PITTSBURG
43,953
FIPS
Code
State
County
Population
2000
136
41043
OREGON
LINN
103,069
42001
PENNSYLVANIA
ADAMS
91,292
42089
PENNSYLVANIA
MONROE
138,687
45015
SOUTH
CAROLINA
BERKELEY
142,651
45025
SOUTH
CAROLINA
CHESTERFIELD
42,768
45045
SOUTH
CAROLINA
GREENVILLE
379,616
45073
SOUTH
CAROLINA
OCONEE
66,215
46099
SOUTH
DAKOTA
MINNEHAHA
148,281
46103
SOUTH
DAKOTA
PENNINGTON
88,565
47031
TENNESSEE
COFFEE
48,014
47043
TENNESSEE
DICKSON
43,156
47045
TENNESSEE
DYER
37,279
47063
TENNESSEE
HAMBLEN
58,128
47125
TENNESSEE
MONTGOMERY
134,768
47131
TENNESSEE
OBION
32,450
47145
TENNESSEE
ROANE
51,910
48043
TEXAS
BREWSTER
8,866
48203
TEXAS
HARRISON
62,110
48315
TEXAS
MARION
10,941
49003
UTAH
BOX
ELDER
42,745
49037
UTAH
SAN
JUAN
14,413
51085
VIRGINIA
HANOVER
86,320
53015
WASHINGTON
COWLITZ
92,948
53041
WASHINGTON
LEWIS
68,600
53045
WASHINGTON
MASON
49,405
54003
WEST
VIRGINIA
BERKELEY
75,905
55055
WISCONSIN
JEFFERSON
74,021
55095
WISCONSIN
POLK
41,319
56005
WYOMING
CAMPBELL
33,698
72033
PUERTO
RICO
CATANO
30,071
78003
VIRGIN
ISLANDS
ST
JOHN
4,197
Counties:
119
Total
Population:
13,211,040
137
Table
C­
1.
PM10
Nonattainment
Areas
Number
of
2000
PM10
Nonattainment
Counties
Population
EPA
Areas
Listed
Alphabetically
Classification
NAA
(
thousands)
Region
State
Ajo
(
Pima
County),
AZ
Moderate
1
8
9
AZ
Anthony,
NM
Moderate
1
3
6
NM
Bonner
Co
(
Sandpoint),
ID
Moderate
1
37
10
ID
Butte,
MT
Moderate
1
35
8
MT
Clark
Co,
NV
Serious
1
1,376
9
NV
Coachella
Valley,
CA
Serious
1
182
9
CA
Columbia
Falls,
MT
Moderate
1
4
8
MT
Coso
Junction,
CA
Moderate
1
7
9
CA
Douglas
(
Cochise
County),
AZ
Moderate
1
16
9
AZ
Eagle
River,
AK
Moderate
1
195
10
AK
El
Paso
Co,
TX
Moderate
1
564
6
TX
Eugene­
Springfield,
OR
Moderate
1
179
10
OR
Flathead
County;
Whitefish
and
vicinity,
MT
Moderate
1
5
8
MT
Fort
Hall
Reservation,
ID
Moderate
2
1
10
ID
Hayden/
Miami,
AZ
Moderate
2
4
9
AZ
Imperial
Valley,
CA
Moderate
1
120
9
CA
Juneau,
AK
Moderate
1
14
10
AK
Kalispell,
MT
Moderate
1
15
8
MT
LaGrande,
OR
Moderate
1
12
10
OR
Lake
Co,
OR
Moderate
1
3
10
OR
Lamar,
CO
Moderate
1
9
8
CO
Lame
Deer,
MT
Moderate
1
1
8
MT
Lane
Co,
OR
Moderate
1
3
10
OR
Libby,
MT
Moderate
1
3
8
MT
Los
Angeles
South
Coast
Air
Basin,
CA
Serious
4
14,594
9
CA
Lyons
Twsp.,
IL
Moderate
1
109
5
IL
Medford­
Ashland,
OR
Moderate
1
78
10
OR
Missoula,
MT
Moderate
1
52
8
MT
Mono
Basin,
CA
Moderate
1
0
9
CA
Mun.
of
Guaynabo,
PR
Moderate
1
92
2
PR
New
Haven
Co,
CT
Moderate
1
124
1
CT
New
York
Co,
NY
Moderate
1
1,537
2
NY
Nogales,
AZ
Moderate
1
25
9
AZ
Ogden,
UT
Moderate
1
77
8
UT
Owens
Valley,
CA
Serious
1
7
9
CA
Paul
Spur,
AZ
Moderate
1
1
9
AZ
Phoenix,
AZ
Serious
2
3,112
9
AZ
Pinehurst,
ID
Moderate
1
2
10
ID
Polson,
MT
Moderate
1
4
8
MT
Portneuf
Valley,
ID
Moderate
2
66
10
ID
Rillito,
AZ
Moderate
1
1
9
AZ
Ronan,
MT
Moderate
1
3
8
MT
Sacramento
Co,
CA
Moderate
1
1,223
9
CA
Salt
Lake
Co,
UT
Moderate
1
898
8
UT
San
Bernardino
Co,
CA
Moderate
1
199
9
CA
San
Joaquin
Valley,
CA
Serious
7
3,080
9
CA
Sanders
County
(
part);
Thompson
Falls
and
Moderate
1
1
8
MT
138
vicinity,
MT
Sheridan,
WY
Moderate
1
16
8
WY
Shoshone
Co,
ID
Moderate
1
10
10
ID
Southeast
Chicago,
IL
Moderate
1
3
5
IL
Spokane
Co,
WA
Moderate
1
205
10
WA
Steamboat
Springs
Moderate
1
10
8
CO
Trona,
CA
Moderate
1
4
9
CA
Utah
Co,
UT
Moderate
1
369
8
UT
Wallula,
WA
Serious
1
0
10
WA
Washoe
Co,
NV
Serious
1
339
9
NV
Weirton,
WV
Moderate
2
15
3
WV
Yakima
Co,
WA
Moderate
1
64
10
WA
Yuma,
AZ
Moderate
1
82
9
AZ
59
Total
Areas
58
29,198
139
Table
C­
2.
1­
Hour
Ozone
Nonattainment
Areas
Design
Average
Number
of
2000
1­
Hour
Ozone
Nonattainment
Value
Expected
Counties
Population
Areas
Listed
Alphabetically
(
ppm)
Exceedance
Classification
NAA
(
thousand
s)
State
Albany­
Schenectady­
Troy,
NY
0.128
2.7
Marginal
6
892
NY
Allentown­
Bethlehem­
Easton,
PA­
NJ
0.137
3.1
Marginal
4
740
PA­
NJ
Altoona,
PA
0.129
2
Marginal
1
129
PA
Atlanta,
GA
0.162
9.3
Severe­
15
13
3,699
GA
Atlantic
City,
NJ
0.145
4
Moderate
2
355
NJ
Baltimore,
MD
0.194
10.7
Severe­
15
6
2,512
MD
Baton
Rouge,
LA
0.164
4.5
Severe­
15
5
636
LA
Beaumont­
Port
Arthur,
TX
0.158
3.7
Moderate
3
385
TX
Birmingham,
AL
0.133
3
Marginal
2
805
AL
Boston­
Lawrence­
Worcester
(
E.
MA),
MA­
NH
0.165
10
Serious
12
5,883
MA­
NH
Buffalo­
Niagara
Falls,
NY
0.131
3.8
Marginal
2
1,170
NY
Chicago­
Gary­
Lake
County,
IL­
IN
0.19
13
Severe­
17
10
8,758
IL­
IN
Cincinnati­
Hamilton,
OH­
KY
(
OH
Portion)
0.157
5.4
Moderate
4
1,514
OH­
KY
Dallas­
Fort
Worth,
TX
0.14
3.5
Serious
4
4,590
TX
East
Kern
Co,
CA
0.17
44.2
Serious
1
111
CA
El
Paso,
TX
0.17
7.9
Serious
1
680
TX
Erie,
PA
0.129
3
Marginal
1
281
PA
Essex
Co,
NY
0.127
1.8
Marginal
RT
1
0
NY
Greater
Connecticut,
CT
0.172
7.9
Serious
8
2,532
CT
Harrisburg­
Lebanon­
Carlisle,
PA
0.136
2.2
Marginal
4
629
PA
Houston­
Galveston­
Brazoria,
TX
0.22
12.2
Severe­
17
8
4,670
TX
Jefferson
Co,
NY
0.143
3.4
Marginal*
1
112
NY
Johnstown,
PA
0.133
2.5
Marginal
2
233
PA
Kent
&
Queen
Anne's
Co.
s,
MD
0.131
1.6
Marginal
2
60
MD
Knox
&
Lincoln
Co.
s,
ME
0.158
11.1
Moderate*
2
73
ME
Lancaster,
PA
0.125
1.3
Marginal
1
471
PA
Lewiston­
Auburn,
ME
0.137
1.5
Moderate*
2
221
ME
Los
Angeles
South
Coast
Air
Basin,
CA
0.33
137.5
Extreme
4
14,594
CA
Manchester,
NH
0.128
1.4
Marginal
3
365
NH
Milwaukee­
Racine,
WI
0.183
9.8
Severe­
17
6
1,839
WI
140
New
York­
N.
New
Jersey­
Long
Island,
NY­

NJCT
0.201
17.4
Severe­
17
24
19,171
NY­
NJCT
Philadelphia­
Wilmington­
Trenton,
PA­
NJ­

DEMD
0.187
8.8
Severe­
15
14
6,311
PA­

NJDE
MD
Phoenix,
AZ
0.141
6
Serious
1
3,029
AZ
Portland,
ME
0.156
6.1
Moderate
3
488
ME
Portsmouth­
Dover­
Rochester,
NH
0.165
5.3
Serious
2
192
NH
Poughkeepsie,
NY
0.134
1.3
Moderate
3
600
NY
Providence
(
All
RI),
RI
0.162
6.4
Serious
5
1,048
RI
Reno,
NV
0.131
2
Marginal
1
339
NV
Sacramento
Metro,
CA
0.16
15.8
Severe­
15
6
1,978
CA
San
Francisco
Bay
Area,
CA
0.14
3.4
Other
9
6,542
CA
San
Joaquin
Valley,
CA
0.17
44.2
Severe­
15
8
3,191
CA
Scranton­
Wilkes­
Barre,
PA
0.129
3
Marginal
5
763
PA
Smyth
Co,
VA
(
White
Top
Mtn)
0.125
1.9
Marginal
RT
1
0
VA
Southeast
Desert
Modified
AQMA,
CA
0.24
59.6
Severe­
17
3
981
CA
Springfield
(
Western
MA),
MA
0.167
6.7
Serious
4
815
MA
Sunland
Park,
NM
(
New
Area
1995)
0.127
1.7
Marginal
1
10
NM
Sussex
Co,
DE
0.13
3.6
Marginal
1
157
DE
Ventura
Co,
CA
0.17
38.8
Severe­
15*
1
753
CA
Washington,
DC­
MD­
VA
0.165
5
Severe­
15
16
4,545
DC­
MDVA
York,
PA
0.129
1.5
Marginal
2
473
PA
Youngstown­
Warren­
Sharon,
PA
portion
0.134
2.1
Marginal
1
120
OH
51
Total
Areas
221
110,445
141
Table
C­
3.
Carbon
Monoxide
Nonattainment
Areas
CO
Nonattainment
CountiesPopulation
EPA
Areas
Listed
Alphabetically
Classification
NAA
Thousands
Region
State
Anchorage,
AK
Serious
1
255
10
AK
El
Paso,
TX
Moderate
<=
12.7pp
1
62
6
TX
Fairbanks,
AK
Serious
1
39
10
AK
Las
Vegas,
NV
Serious
1
479
9
NV
Los
Angeles
South
Coast
Air
Basin,
CA
Serious
4
14,594
9
CA
Missoula,
MT
Moderate
<=
12.7pp
1
52
8
MT
Phoenix,
AZ
Serious
1
3,029
9
AZ
Provo,
UT
Moderate
>
12.7ppm
1
119
8
UT
Reno,
NV
Moderate
<=
12.7pp
1
179
9
NV
Spokane,
WA
Serious
1
323
10
WA
10
Total
Areas
13
19,131
Regions
Design
Value
in
ppb
Page
142
Area
8­
hr
1­
hr
Category/
Classification
Table
C­
4.
Area
Design
Values
and
Classifications
142
2
Albany­
Schenectady­
Troy,
NY
87
115
Subpart
1
5
Allegan
Co,
MI
97
115
Subpart
1
3
Allentown­
Bethlehem­
Easton,
PA
91
114
Subpart
1
3
Altoona,
PA
85
107
Subpart
1
9
Amador
and
Calaveras
Cos,
CA(
Central
Mtn
Co)
91
117
Subpart
1
4
Atlanta,
GA
91
125
Subpart
2
Marginal
3
Baltimore,
MD
103
143
Subpart
2
Moderate
6
Baton
Rouge,
LA
86
131
Subpart
2
Marginal
6
Beaumont­
Port
Arthur,
TX
91
129
Subpart
2
Marginal
5
Benton
Harbor,
MI
91
117
Subpart
1
5
Benzie
Co,
MI
88
116
Subpart
1
3
Berkeley
and
Jefferson
Counties,
WV
86
105
EAC
Subpart
1
4
Birmingham,
AL
87
113
Subpart
1
1
Boston­
Lawrence­
Worcester
(
E.
MA),
MA
95
124
Subpart
2
Moderate
1
Boston­
Manchester­
Portsmouth(
SE),
NH*
95
124
Subpart
2
Moderate
2
Buffalo­
Niagara
Falls,
NY
99
116
Subpart
1
5
Canton­
Massillon,
OH
90
109
Subpart
1
5
Cass
Co,
MI
93
124
Subpart
2
Moderate
3
Charleston,
WV
86
107
Subpart
1
4
Charlotte­
Gastonia­
Rock
Hill,
NC­
SC
100
129
Subpart
2
Moderate
4
Chattanooga,
TN­
GA
88
113
Subpart
1
5
Chicago­
Gary­
Lake
County,
IL­
IN
101
134
Subpart
2
Moderate
9
Chico,
CA
89
102
Subpart
1
5,4
Cincinnati­
Hamilton,
OH­
KY­
IN
96
118
Subpart
1
4
Clarksville­
Hopkinsville,
TN­
KY
85
99
Subpart
1
3
Clearfield
and
Indiana
Cos,
PA
90
106
Subpart
1
5
Cleveland­
Akron­
Lorain,
OH
103
128
Subpart
2
Moderate
4
Columbia,
SC
89
108
EAC
Subpart
1
5
Columbus,
OH
95
117
Subpart
1
6
Dallas­
Fort
Worth,
TX
100
135
Subpart
2
Moderate
Regions
Design
Value
in
ppb
Page
143
Area
8­
hr
1­
hr
Category/
Classification
143
5
Dayton­
Springfield,
OH
90
117
Subpart
1
8
Denver­
Boulder­
Greeley­
Ft
Collins­
Love.,
CO
87
114
EAC
Subpart
1
5
Detroit­
Ann
Arbor,
MI
97
127
Subpart
2
Moderate
5
Door
Co,
WI
94
113
Subpart
1
3
Erie,
PA
92
114
Subpart
1
2
Essex
Co
(
Whiteface
Mtn)
NY
91
113
Subpart
1
5
Evansville,
IN
85
106
Subpart
1
4
Fayetteville,
NC
87
108
EAC
Subpart
1
5
Flint,
MI
90
103
Subpart
1
5
Fort
Wayne,
IN
88
106
Subpart
1
3
Franklin
Co,
PA
93
114
Subpart
1
3
Frederick
Co,
VA
85
106
EAC
Subpart
1
3
Fredericksburg,
VA*
99
140
Subpart
2
Moderate
5
Grand
Rapids,
MI
89
110
Subpart
1
1
Greater
Connecticut,
CT
95
139
Subpart
2
Moderate
5
Greene
Co,
IN
88
102
Subpart
1
3
Greene
Co,
PA
89
107
Subpart
1
4
Greensboro­
Winston
Salem­
High
Point,
NC
93
121
EAC
Subpart
2
Moderate
4
Greenville­
Spartanburg­
Anderson,
SC
87
114
EAC
Subpart
1
1
Hancock,
Knox,
Lincoln
and
Waldo
Cos,
ME
94
120
Subpart
1
3
Harrisburg­
Lebanon­
Carlisle,
PA
88
111
Subpart
1
4
Haywood
and
Swain
Cos
(
Great
Smoky
NP),
NC
85
104
Subpart
1
4
Hickory­
Morganton­
Lenoir,
NC
88
105
EAC
Subpart
1
6
Houston­
Galveston­
Brazoria,
TX
102
175
Subpart
2
Moderate
3,4
Huntington­
Ashland,
WV­
KY
91
115
Subpart
1
5
Huron
Co,
MI
87
109
Subpart
1
9
Imperial
Co,
CA
87
142
Subpart
2
Marginal
5
Indianapolis,
IN
96
119
Subpart
1
5
Jackson
Co,
IN
85
100
Subpart
1
2
Jamestown,
NY
94
115
Subpart
1
2
Jefferson
Co,
NY
97
121
Subpart
2
Moderate
4
Johnson
City­
Kingsport­
Bristol,
TN
86
110
EAC
Subpart
1
Regions
Design
Value
in
ppb
Page
144
Area
8­
hr
1­
hr
Category/
Classification
144
3
Johnstown,
PA
87
106
Subpart
1
5
Kalamazoo­
Battle
Creek,
MI
86
102
Subpart
1
3
Kent
and
Queen
Anne's
Co,
MD
95
122
Subpart
2
Moderate
9
Kern
Co
(
Eastern
Kern),
CA
98
118
Subpart
1
5
Kewaunee
Co,
WI
93
110
Subpart
1
4
Knoxville,
TN
92
114
Subpart
1
5
La
Porte
Co,
IN
93
135
Subpart
2
Moderate
3
Lancaster,
PA
92
124
Subpart
2
Moderate
5
Lansing­
East
Lansing,
MI
86
102
Subpart
1
9
Las
Vegas,
NV
86
107
Subpart
1
5
Lima,
OH
89
108
Subpart
1
9
Los
Angeles
South
Coast
Air
Basin,
CA
131
180
Subpart
2
Severe
17
9
Los
Angeles­
San
Bernardino
Cos(
W
Mojave),
CA
106
138
Subpart
2
Moderate
4,5
Louisville,
KY­
IN
92
120
Subpart
1
4
Macon,
GA
86
113
Subpart
1
3
Madison
and
Page
Cos
(
Shenandoah
NP),
VA
87
104
Subpart
1
5
Manitowoc
Co,
WI
90
110
Subpart
1
9
Mariposa
and
Tuolumne
Cos,
CA
(
S.
Mtn
Cos)
91
113
Subpart
1
5
Mason
Co,
MI
89
114
Subpart
1
4,6
Memphis,
TN­
AR
92
126
Subpart
2
Moderate
5
Milwaukee­
Racine,
WI
101
134
Subpart
2
Moderate
5
Muncie,
IN
88
104
Subpart
1
4
Murray
Co
(
Chattahoochee
Nat
Forest),
GA
85
103
Subpart
1
5
Muskegon,
MI
95
121
Subpart
2
Moderate
4
Nashville,
TN
86
107
EAC
Subpart
1
9
Nevada
Co,
CA
(
Western
Portion)
98
116
Subpart
1
2,1
New
York­
N.
New
Jersey­
Long
Island,
NY­
NJ­
CT
106
146
Subpart
2
Moderate
3
Norfolk­
Virginia
Beach­
Newport
News
(
HR),
VA
90
121
Subpart
2
Marginal
3,5
Parkersburg­
Marietta,
WV­
OH
87
113
Subpart
1
3,2
Philadelphia­
Wilmin­
Atlantic
Ci,
PA­
NJ­
MD­
DE
101
133
Subpart
2
Moderate
9
Phoenix­
Mesa,
AZ
87
111
Subpart
1
3
Pittsburgh­
Beaver
Valley,
PA
94
120
Subpart
1
Regions
Design
Value
in
ppb
Page
145
Area
8­
hr
1­
hr
Category/
Classification
145
1
Portland,
ME
91
126
Subpart
2
Marginal
2
Poughkeepsie,
NY
94
126
Subpart
2
Moderate
1
Providence
(
All
RI),
RI
95
130
Subpart
2
Moderate
4
Raleigh­
Durham­
Chapel
Hill,
NC
94
118
Subpart
1
3
Reading,
PA
91
116
Subpart
1
3
Richmond­
Petersburg,
VA
94
131
Subpart
2
Moderate
9
Riverside
Co,
(
Coachella
Valley),
CA
108
133
Subpart
2
Serious
3
Roanoke,
VA
85
107
EAC
Subpart
1
2
Rochester,
NY
88
110
Subpart
1
4
Rocky
Mount,
NC
89
106
Subpart
1
9
Sacramento
Metro,
CA
107
143
Subpart
2
Serious
6
San
Antonio,
TX
89
119
EAC
Subpart
1
9
San
Diego,
CA
93
118
Subpart
1
9
San
Francisco
Bay
Area,
CA
86
123
Subpart
2
Marginal
9
San
Joaquin
Valley,
CA
115
151
Subpart
2
Serious
3
Scranton­
Wilkes­
Barre,
PA
86
108
Subpart
1
5
Sheboygan,
WI
100
124
Subpart
2
Moderate
5
South
Bend­
Elkhart,
IN
93
116
Subpart
1
1
Springfield
(
Western
MA),
MA
94
132
Subpart
2
Moderate
7,5
St
Louis,
MO­
IL
92
122
Subpart
2
Moderate
3
State
College,
PA
88
109
Subpart
1
5,3
Steubenville­
Weirton,
OH­
WV
86
113
Subpart
1
9
Sutter
Co,
CA
(
Sutter
Buttes)
88
113
Subpart
1
5
Terre
Haute,
IN
87
108
Subpart
1
3
Tioga
Co,
PA
86
102
Subpart
1
5
Toledo,
OH
93
112
Subpart
1
9
Ventura
Co,
CA
95
124
Subpart
2
Moderate
3
Washington
Co
(
Hagerstown),
MD
86
109
EAC
Subpart
1
3
Washington,
DC­
MD­
VA
99
140
Subpart
2
Moderate
3,5
Wheeling,
WV­
OH
87
111
Subpart
1
3
York,
PA
89
114
Subpart
1
5,3
Youngstown­
Warren­
Sharon,
OH­
PA
95
118
Subpart
1
Regions
Design
Value
in
ppb
Area
8­
hr
1­
hr
Category/
Class
146
Boston­
Manchester­
Portsmouth(
SE),
NH
has
the
same
classification
as
Boston­
Lawrence­
Worcester(
E.
MA),
MA
Fredericksburg,
VA
has
the
same
classification
as
Washington,
DC­
MD­
VA
The
level
of
the
8­
hour
ozone
(
O3)
National
Ambient
Air
Quality
Standards
(
NAAQS)
is
0.08
parts
per
million
(
ppm).
The
air
quality
design
value
for
the
8­
hour
O3
NAAQS
is
the
3­
year
average
of
the
annual
4th
highest
daily
maximum
8­
hour
average
O3
concentration.
The
8­
hour
O3
NAAQS
is
not
met
when
the
8­
hour
ozone
design
value
is
greater
than
0.08
ppm
(
85
parts
per
billion
[
ppb]
rounds
up).
Therefore,
an
area
with
a
design
value
of
85
ppb
does
not
meet
the
NAAQS.
An
area
with
a
1­
hour
design
value
of
120
ppb
or
lower
is
in
a
Subpart
1
category.
147
Table
C­
5.
County
8­
hour
Ozone
Air
Quality
Data
2001­
2003
By
State
297
fail
to
meet
the
standard
(
V)
654
total
counties
with
3
years
of
data
2
year
average
data
not
included
in
counts
and
are
not
considered
a
violation.

The
level
of
the
8­
hour
ozone
(
O3)
National
Ambient
Air
Quality
Standards
(
NAAQS)
is
0.08
parts
per
million
(
ppm).
The
air
quality
design
value
for
the
8­
hour
O3
NAAQS
is
the
3­
year
average
of
the
annual
4th
highest
daily
maximum
8­
hour
average
O3
concentration.
The
8­
hour
O3
NAAQS
is
not
met
when
the
8­
hour
ozone
design
value
is
greater
than
0.08
ppm
(
85
ppb
rounds
up).
Therefore,
a
county
with
a
design
value
of
85
ppb
does
not
meet
the
NAAQS.

State
County
Design
Value
in
ppb
Alabama
Baldwin
Co
76
Clay
Co
80
Elmore
Co
76
Jefferson
Co
83
Lawrence
Co
76
Madison
Co
79
Mobile
Co
77
Montgomery
Co
74
Morgan
Co
81
Shelby
Co
87
V
Sumter
Co
71
Tuscaloosa
Co
78
Alaska
Yukon­
Koyukuk
CA
54
Arizona
Cochise
Co
71
Coconino
Co
74
Maricopa
Co
87
V
Pima
Co
74
Pinal
Co
83
Yavapai
Co
77
Arkansas
Crittenden
Co
92
V
Montgomery
Co
66
Newton
Co
78
Pulaski
Co
81
California
Alameda
Co
84
Amador
Co
85
V
Butte
Co
89
V
Calaveras
Co
91
V
Colusa
Co
75
Contra
Costa
Co
81
El
Dorado
Co
107
V
Fresno
Co
111
V
Glenn
Co
73
State
County
Design
Value
in
ppb
Page
148
148
Imperial
Co
87
V
Inyo
Co
81
Kern
Co
115
V
Kings
Co
95
V
Lake
Co
63
Los
Angeles
Co
126
V
Madera
Co
93
V
Marin
Co
48
Mariposa
Co
91
V
Mendocino
Co
57
Merced
Co
102
V
Monterey
Co
66
Napa
Co
65
Nevada
Co
98
V
Orange
Co
86
V
Placer
Co
99
V
Plumas
Co
69
Riverside
Co
118
V
Sacramento
Co
100
V
San
Benito
Co
81
San
Bernardino
Co
131
V
San
Diego
Co
93
V
San
Francisco
Co
47
San
Joaquin
Co
81
San
Luis
Obispo
Co
74
San
Mateo
Co
58
Santa
Barbara
Co
84
Santa
Clara
Co
86
V
Santa
Cruz
Co
65
Shasta
Co
80
Siskiyou
Co
57
Solano
Co
71
Sonoma
Co
62
Stanislaus
Co
96
V
Sutter
Co
88
V
Tehama
Co
84
Tulare
Co
107
V
Tuolumne
Co
85
V
Ventura
Co
95
V
Yolo
Co
76
Colorado
Adams
Co
66
Arapahoe
Co
81
Boulder
Co
77
Denver
Co
76
Douglas
Co
85
V
El
Paso
Co
73
Jefferson
Co
87
V
La
Plata
Co
58
Larimer
Co
81
Montezuma
Co
67
Weld
Co
79
Connecticut
Fairfield
Co
102
V
State
County
Design
Value
in
ppb
Page
149
149
Hartford
Co
90
V
Litchfield
Co
92
2
year
average
Middlesex
Co
98
V
New
Haven
Co
102
V
New
London
Co
93
V
Tolland
Co
95
V
Delaware
Kent
Co
89
V
New
Castle
Co
93
V
Sussex
Co
91
V
Dist.
Columbia
Washington
94
V
Florida
Alachua
Co
72
Baker
Co
71
Bay
Co
79
Brevard
Co
74
Broward
Co
68
Collier
Co
62
Columbia
Co
71
Duval
Co
68
Escambia
Co
79
Highlands
Co
64
Hillsborough
Co
78
Holmes
Co
71
Lake
Co
76
Lee
Co
68
Leon
Co
71
Manatee
Co
75
Marion
Co
74
Miami­
Dade
Co
66
Orange
Co
76
Osceola
Co
71
Palm
Beach
Co
67
Pasco
Co
77
Pinellas
Co
74
Polk
Co
77
St.
Lucie
Co
68
Santa
Rosa
Co
80
Sarasota
Co
79
Seminole
Co
77
Volusia
Co
70
Wakulla
Co
76
Georgia
Bibb
Co
86
V
Chatham
Co
67
Cobb
Co
90
V
Coweta
Co
87
V
Dawson
Co
80
De
Kalb
Co
89
V
Douglas
Co
91
V
Fayette
Co
83
State
County
Design
Value
in
ppb
Page
150
150
Fulton
Co
91
V
Glynn
Co
72
Gwinnett
Co
85
V
Henry
Co
89
V
Murray
Co
85
V
Muscogee
Co
74
Paulding
Co
89
V
Richmond
Co
83
Rockdale
Co
89
V
Sumter
Co
74
Hawaii
Hawaii
Co
42
Honolulu
Co
41
Idaho
Ada
Co
76
Butte
Co
65
Elmore
Co
68
Illinois
Adams
Co
77
Champaign
Co
76
Clark
Co
75
Cook
Co
87
V
Du
Page
Co
73
Effingham
Co
75
Hamilton
Co
79
Jersey
Co
89
V
Kane
Co
79
Lake
Co
87
V
McHenry
Co
84
McLean
Co
77
Macon
Co
75
Macoupin
Co
78
Madison
Co
88
V
Peoria
Co
80
Randolph
Co
79
Rock
Island
Co
71
St.
Clair
Co
83
Sangamon
Co
76
Will
Co
79
Winnebago
Co
77
Indiana
Allen
Co
88
V
Boone
Co
90
V
Carroll
Co
84
Clark
Co
92
V
Delaware
Co
88
V
Elkhart
Co
93
2
year
average
Floyd
Co
86
V
Gibson
Co
73
Greene
Co
88
V
Hamilton
Co
96
V
Hancock
Co
94
V
State
County
Design
Value
in
ppb
Page
151
151
Hendricks
Co
85
V
Huntington
Co
84
Jackson
Co
85
V
Johnson
Co
86
V
Lake
Co
90
V
La
Porte
Co
93
V
Madison
Co
95
V
Marion
Co
92
V
Morgan
Co
85
V
Porter
Co
87
V
Posey
Co
84
St.
Joseph
Co
93
V
Shelby
Co
94
V
Vanderburgh
Co
83
Vigo
Co
87
V
Warrick
Co
85
V
Iowa
Bremer
Co
69
Clinton
Co
78
Harrison
Co
76
Linn
Co
69
Palo
Alto
Co
63
Polk
Co
56
Scott
Co
79
Story
Co
60
Van
Buren
Co
74
Warren
Co
60
Kansas
Linn
Co
75
Sedgwick
Co
81
Sumner
Co
78
Trego
Co
62
Wyandotte
Co
80
Kentucky
Bell
Co
82
Boone
Co
85
V
Boyd
Co
91
V
Bullitt
Co
81
Campbell
Co
91
V
Carter
Co
78
Christian
Co
85
V
Daviess
Co
76
Edmonson
Co
80
Fayette
Co
76
Graves
Co
79
Greenup
Co
83
Hancock
Co
82
Hardin
Co
79
Henderson
Co
80
Jefferson
Co
84
Jessamine
Co
77
Kenton
Co
85
V
Livingston
Co
84
State
County
Design
Value
in
ppb
Page
152
152
McCracken
Co
79
McLean
Co
82
Oldham
Co
86
V
Perry
Co
76
Pike
Co
73
Pulaski
Co
77
Scott
Co
69
Simpson
Co
81
Trigg
Co
73
Warren
Co
82
Louisiana
Ascension
Par
77
Beauregard
Par
73
Bossier
Par
80
Caddo
Par
77
Calcasieu
Par
78
East
Baton
Rouge
Par
86
V
Grant
Par
74
Iberville
Par
84
Jefferson
Par
82
Lafayette
Par
78
Lafourche
Par
78
Livingston
Par
78
Orleans
Par
69
Ouachita
Par
78
Pointe
Coupee
Par
73
St.
Bernard
Par
78
St.
Charles
Par
78
St.
James
Par
73
St.
John
The
Baptist
Pa
78
St.
Mary
Par
74
West
Baton
Rouge
Par
84
Maine
Cumberland
Co
88
V
Hancock
Co
94
V
Kennebec
Co
80
Knox
Co
87
V
Oxford
Co
62
Penobscot
Co
83
York
Co
91
V
Maryland
Anne
Arundel
Co
98
V
Baltimore
Co
93
V
Carroll
Co
89
V
Cecil
Co
98
V
Charles
Co
94
V
Frederick
Co
88
V
Harford
Co
103
V
Kent
Co
95
V
Montgomery
Co
88
V
Prince
George's
Co
93
V
Washington
Co
86
V
Baltimore
city
82
State
County
Design
Value
in
ppb
Page
153
153
Massachusetts
Barnstable
Co
95
V
Berkshire
Co
87
V
Bristol
Co
95
V
Essex
Co
93
V
Hampden
Co
94
V
Hampshire
Co
87
V
Middlesex
Co
89
V
Suffolk
Co
91
V
Worcester
Co
86
V
Michigan
Allegan
Co
97
V
Benzie
Co
88
V
Berrien
Co
91
V
Cass
Co
93
V
Clinton
Co
86
V
Genesee
Co
90
V
Huron
Co
87
V
Ingham
Co
85
V
Kalamazoo
Co
86
V
Kent
Co
88
V
Lenawee
Co
87
V
Macomb
Co
97
V
Mason
Co
89
V
Missaukee
Co
81
Muskegon
Co
95
V
Oakland
Co
91
V
Ottawa
Co
89
V
St.
Clair
Co
90
V
Washtenaw
Co
91
V
Wayne
Co
91
V
Minnesota
Anoka
Co
74
Carlton
Co
60
Dakota
Co
68
Lake
Co
61
Mille
Lacs
Co
72
St.
Louis
Co
65
Washington
Co
74
Mississippi
Adams
Co
77
Bolivar
Co
75
De
Soto
Co
81
Hancock
Co
82
Harrison
Co
80
Hinds
Co
73
Jackson
Co
80
Lauderdale
Co
73
Lee
Co
79
Madison
Co
74
Warren
Co
74
Missouri
State
County
Design
Value
in
ppb
Page
154
154
Cass
Co
79
Cedar
Co
79
Clay
Co
84
Greene
Co
73
Jefferson
Co
87
V
Monroe
Co
78
Platte
Co
80
St.
Charles
Co
92
V
Ste.
Genevieve
Co
83
St.
Louis
Co
91
V
St.
Louis
city
89
V
Montana
Flathead
Co
54
Nebraska
Douglas
Co
67
Lancaster
Co
55
Nevada
Clark
Co
86
V
Douglas
Co
71
Washoe
Co
74
White
Pine
Co
71
Carson
City
69
New
Hampshire
Belknap
Co
78
Cheshire
Co
76
Coos
Co
80
Grafton
Co
72
Hillsborough
Co
87
V
Merrimack
Co
75
Rockingham
Co
84
Strafford
Co
80
Sullivan
Co
75
New
Jersey
Atlantic
Co
91
V
Bergen
Co
94
V
Camden
Co
101
V
Cumberland
Co
95
V
Essex
Co
68
Gloucester
Co
99
V
Hudson
Co
87
V
Hunterdon
Co
97
V
Mercer
Co
100
V
Middlesex
Co
98
V
Monmouth
Co
97
V
Morris
Co
98
V
Ocean
Co
106
V
Passaic
Co
88
V
New
Mexico
Bernalillo
Co
77
Dona
Ana
Co
79
State
County
Design
Value
in
ppb
Page
155
155
Eddy
Co
69
Sandoval
Co
73
San
Juan
Co
74
Valencia
Co
66
New
York
Albany
Co
86
V
Bronx
Co
84
Chautauqua
Co
94
V
Chemung
Co
83
Dutchess
Co
94
V
Erie
Co
99
V
Essex
Co
91
V
Hamilton
Co
81
Herkimer
Co
76
Jefferson
Co
97
V
Madison
Co
82
Monroe
Co
88
V
Niagara
Co
95
V
Oneida
Co
83
Onondaga
Co
85
V
Orange
Co
87
V
Putnam
Co
93
V
Queens
Co
85
V
Rensselaer
Co
91
2
year
average
Richmond
Co
94
V
Saratoga
Co
87
V
Schenectady
Co
81
Suffolk
Co
100
V
Ulster
Co
83
Wayne
Co
88
V
Westchester
Co
94
V
North
Carolina
Alexander
Co
88
V
Avery
Co
78
Buncombe
Co
78
Caldwell
Co
84
Caswell
Co
88
V
Chatham
Co
82
Cumberland
Co
87
V
Davie
Co
93
V
Duplin
Co
79
Durham
Co
89
V
Edgecombe
Co
89
V
Forsyth
Co
93
V
Franklin
Co
90
V
Granville
Co
94
V
Guilford
Co
89
V
Haywood
Co
85
V
Jackson
Co
84
Johnston
Co
85
V
Lenoir
Co
81
Lincoln
Co
92
V
Martin
Co
81
Mecklenburg
Co
98
V
State
County
Design
Value
in
ppb
Page
156
156
New
Hanover
Co
78
Northampton
Co
84
Person
Co
91
V
Pitt
Co
82
Randolph
Co
85
V
Rockingham
Co
91
V
Rowan
Co
100
V
Swain
Co
74
Union
Co
88
V
Wake
Co
92
V
Yancey
Co
83
North
Dakota
Billings
Co
61
Cass
Co
63
Dunn
Co
60
McKenzie
Co
62
Mercer
Co
62
Oliver
Co
58
Ohio
Allen
Co
89
V
Ashtabula
Co
99
V
Butler
Co
92
V
Clark
Co
88
V
Clermont
Co
90
V
Clinton
Co
96
V
Cuyahoga
Co
90
V
Delaware
Co
91
V
Franklin
Co
95
V
Geauga
Co
103
V
Greene
Co
90
V
Hamilton
Co
93
V
Jefferson
Co
86
V
Knox
Co
88
V
Lake
Co
95
V
Lawrence
Co
83
Licking
Co
89
V
Lorain
Co
90
V
Lucas
Co
93
V
Madison
Co
90
V
Mahoning
Co
89
V
Medina
Co
90
V
Miami
Co
88
V
Montgomery
Co
87
V
Portage
Co
93
V
Preble
Co
81
Stark
Co
90
V
Summit
Co
96
V
Trumbull
Co
95
V
Warren
Co
92
V
Washington
Co
86
V
Wood
Co
90
V
Oklahoma
Cherokee
Co
76
State
County
Design
Value
in
ppb
Page
157
157
Cleveland
Co
76
Comanche
Co
77
Kay
Co
75
McClain
Co
78
Oklahoma
Co
80
Ottawa
Co
79
Tulsa
Co
83
Oregon
Clackamas
Co
69
Columbia
Co
62
Jackson
Co
71
Lane
Co
70
Marion
Co
64
Pennsylvania
Allegheny
Co
93
V
Armstrong
Co
93
V
Beaver
Co
94
V
Berks
Co
91
V
Blair
Co
85
V
Bucks
Co
100
V
Cambria
Co
87
V
Centre
Co
88
V
Chester
Co
98
V
Clearfield
Co
90
V
Dauphin
Co
88
V
Delaware
Co
92
V
Erie
Co
92
V
Franklin
Co
93
V
Greene
Co
89
V
Lackawanna
Co
85
V
Lancaster
Co
92
V
Lawrence
Co
80
Lehigh
Co
91
V
Luzerne
Co
86
V
Lycoming
Co
80
another
monitor
87
2
year
average
Mercer
Co
94
V
Montgomery
Co
92
V
Northampton
Co
90
V
Perry
Co
87
V
Philadelphia
Co
97
V
Tioga
Co
86
V
Washington
Co
89
V
Westmoreland
Co
91
V
York
Co
89
V
Rhode
Island
Kent
Co
95
V
Providence
Co
93
V
Washington
Co
95
V
South
Carolina
Abbeville
Co
82
Aiken
Co
80
State
County
Design
Value
in
ppb
Page
158
158
Anderson
Co
86
V
Barnwell
Co
78
Berkeley
Co
71
Charleston
Co
72
Cherokee
Co
84
Chester
Co
84
Colleton
Co
77
Darlington
Co
82
Edgefield
Co
79
Oconee
Co
84
Pickens
Co
84
Richland
Co
89
V
Spartanburg
Co
87
V
Union
Co
80
Williamsburg
Co
71
York
Co
84
South
Dakota
Minnehaha
Co
65
Tennessee
Anderson
Co
87
V
Blount
Co
92
V
Davidson
Co
77
Hamilton
Co
88
V
Haywood
Co
81
Jefferson
Co
91
V
Knox
Co
92
V
Lawrence
Co
77
Meigs
Co
88
V
Putnam
Co
82
Rutherford
Co
80
Sevier
Co
92
V
Shelby
Co
89
V
Sullivan
Co
86
V
Sumner
Co
86
V
Williamson
Co
84
Wilson
Co
82
Texas
Bexar
Co
89
V
Brazoria
Co
91
V
Brewster
Co
62
Cameron
Co
65
Collin
Co
88
V
Dallas
Co
90
V
Denton
Co
97
V
Ellis
Co
82
El
Paso
Co
79
Galveston
Co
89
V
Gregg
Co
82
Harris
Co
102
V
Harrison
Co
76
Hidalgo
Co
73
Hood
Co
84
Jefferson
Co
91
V
State
County
Design
Value
in
ppb
Page
159
159
Johnson
Co
90
V
Kaufman
Co
73
Montgomery
Co
89
V
Nueces
Co
80
Orange
Co
80
Parker
Co
89
V
Rockwall
Co
81
Smith
Co
81
Tarrant
Co
100
V
Travis
Co
84
Victoria
Co
78
Webb
Co
64
Utah
Box
Elder
Co
79
Cache
Co
70
Davis
Co
83
Salt
Lake
Co
80
San
Juan
Co
71
Utah
Co
79
Weber
Co
81
Vermont
Bennington
Co
80
Chittenden
Co
78
Virginia
Arlington
Co
99
V
Caroline
Co
84
Charles
City
Co
91
V
Chesterfield
Co
86
V
Fairfax
Co
97
V
Fauquier
Co
80
Frederick
Co
85
V
Hanover
Co
94
V
Henrico
Co
90
V
Loudoun
Co
92
V
Madison
Co
87
V
Page
Co
82
Prince
William
Co
87
V
Roanoke
Co
85
V
Rockbridge
Co
78
Stafford
Co
88
V
Wythe
Co
80
Alexandria
city
92
V
Hampton
city
90
V
Suffolk
city
88
V
Washington
Clallam
Co
43
Clark
Co
63
King
Co
71
Klickitat
Co
66
Pierce
Co
72
Skagit
Co
50
Spokane
Co
73
State
County
Design
Value
in
ppb
Page
160
160
Thurston
Co
64
Whatcom
Co
53
West
Virginia
Berkeley
Co
86
V
Cabell
Co
88
V
Greenbrier
Co
80
Hancock
Co
86
V
Kanawha
Co
86
V
Monongalia
Co
79
Ohio
Co
87
V
Wood
Co
87
V
Wisconsin
Brown
Co
83
Columbia
Co
79
Dane
Co
78
Dodge
Co
82
Door
Co
94
V
Florence
Co
70
Fond
du
Lac
Co
80
Green
Co
75
Jefferson
Co
83
Kenosha
Co
101
V
Kewaunee
Co
93
V
Manitowoc
Co
90
V
Marathon
Co
73
Milwaukee
Co
94
V
Oneida
Co
69
Outagamie
Co
78
Ozaukee
Co
98
V
Racine
Co
95
V
Rock
Co
83
St.
Croix
Co
73
Sauk
Co
73
Sheboygan
Co
100
V
Vernon
Co
72
Vilas
Co
69
Walworth
Co
84
Washington
Co
83
Waukesha
Co
81
Winnebago
Co
82
Wyoming
Teton
Co
65
Virgin
Islands
St.
John
44
